text: UFDC Home | Help | RSS TABLE OF CONTENTS HIDE Front Cover Foreword Acknowledgement Table of Contents Main Group Title: Technical report - U.S. Geological Survey, Biological Resources Division. Florida Cooperative Fish and Wildlife Research Unit ; 56 Title: Demography and movements of snail kites in Florida CITATION THUMBNAILS PAGE IMAGE ZOOMABLE Full Citation STANDARD VIEW MARC VIEW Permanent Link: http://ufdc.ufl.edu/UF00000125/00001 Material Information Title: Demography and movements of snail kites in Florida Series Title: Technical report - U.S. Geological Survey, Biological Resources Division. Florida Cooperative Fish and Wildlife Research Unit ; 56 Physical Description: Book Creator: Bennetts, Robert E.Kitchens, Wiley M. Publisher: University of FloridaUnited States Geological Survey Place of Publication: Gainesville, Fla. Publication Date: 1997 Record Information Bibliographic ID: UF00000125 Volume ID: VID00001 Source Institution: University of Florida Rights Management: All rights reserved by the source institution and holding location. Resource Identifier: aleph - 003507177ltqf - AAA0280ltuf - AME1632 Related Items Other version: Alternate version (PALMM)PALMM Version Table of Contents Front Cover Front Cover 1 Front Cover 2 Front Cover 3 Front Cover 4 Foreword Foreword 1 Foreword 2 Foreword 3 Acknowledgement Acknowledgement Table of Contents Table of Contents 1 Table of Contents 2 Table of Contents 3 Table of Contents 4 Main Page 1 Page 2 Page 3 Page 4 Page 5 Page 6 Page 7 Page 8 Page 9 Page 10 Page 11 Page 12 Page 13 Page 14 Page 15 Page 16 Page 17 Page 18 Page 19 Page 20 Page 21 Page 22 Page 23 Page 24 Page 25 Page 26 Page 27 Page 28 Page 29 Page 30 Page 31 Page 32 Page 33 Page 34 Page 35 Page 36 Page 37 Page 38 Page 39 Page 40 Page 41 Page 42 Page 43 Page 44 Page 45 Page 46 Page 47 Page 48 Page 49 Page 50 Page 51 Page 52 Page 53 Page 54 Page 55 Page 56 Page 57 Page 58 Page 59 Page 60 Page 61 Page 62 Page 63 Page 64 Page 65 Page 66 Page 67 Page 68 Page 69 Page 70 Page 71 Page 72 Page 73 Page 74 Page 75 Page 76 Page 77 Page 78 Page 79 Page 80 Page 81 Page 82 Page 83 Page 84 Page 85 Page 86 Page 87 Page 88 Page 89 Page 90 Page 91 Page 92 Page 93 Page 94 Page 95 Page 96 Page 97 Page 98 Page 99 Page 100 Page 101 Page 102 Page 103 Page 104 Page 105 Page 106 Page 107 Page 108 Page 109 Page 110 Page 111 Page 112 Page 113 Page 114 Page 115 Page 116 Page 117 Page 118 Page 119 Page 120 Page 121 Page 122 Page 123 Page 124 Page 125 Page 126 Page 127 Page 128 Page 129 Page 130 Page 131 Page 132 Page 133 Page 134 Page 135 Page 136 Page 137 Page 138 Page 139 Page 140 Page 141 Page 142 Page 143 Page 144 Page 145 Page 146 Page 147 Page 148 Page 149 Page 150 Page 151 Page 152 Page 153 Page 154 Page 155 Page 156 Page 157 Page 158 Page 159 Page 160 Page 161 Page 162 Page 163 Page 164 Page 165 Page 166 Page 167 Page 168 Page 169 Page 170 Page 171 Full Text of Sni KienFoida I~./I EN.*' x RECOMMENDED CITATION Bennetts, RE and WM. Kitchens. 1997 The Demography and Movements of Snail Kites in Florida. US G.S. Biological Resources Division, Florida Cooperative Fish & Wildlife Research Unit Tech. Rep. No. 56. 169 pp ~:: -. THE DEMOGRAPHY AND MOVEMENTS OF SNAIL KITES IN FLORIDA Robert E. Bennetts and Wiley M. Kitchens U.S. Geological Survey/Biological Resources Division Florida Cooperative Fish & Wildlife Research Unit Technical Report No. 56 1997 Prepared In Cooperation with: U.S. Fish & Wildlife Service Jacksonville Ecological Services Field Office and South Florida Ecosystem Field Office National Park Service Everglades National Park and Big Cypress National Preserve U.S Army Corps of Engineers U S Geological Survey Soulh Florida Waler Managemeni Dislrcl SI. Johns River Waler Managemenl DIslirci QX~ Foreword In preparing this report, we recognized that our audience consisted of both scientists and managers. We also recognized that there often is a significant gap in the information exchange between these disciplines. Consequently, the format and style of this report represent an attempt to bridge that gap. To this end, we have tried to provide sufficient detail of our methodology and results to enable the scientific community to evaluate the validity of our research or to incorporate our findings into a wide variety of potential models. At the same time we have also tried to provide "down to earth" explanations, and to frame our research in such a way as to enable managers to incorporate our findings into realistic management scenarios and planning. For those with a strong quantitative science background, we hope that our explanations do not border on being offensive. For those with a background of more applied biology, we hope that we have retained the essence of the biology within a potential myriad of statistics. Our goal is to make this report usable to all and that requires a balance in presentation. We hope that we have succeeded in our attempt. We also realize that there often is some reluctance (and rightly so) on the part of scientists to make conclusions when they know that the temporal or spatial scale of their research precludes valid inferences beyond the scope of the conditions in which their research was conducted. At the same time, management agencies urgently need answers to questions that may be a long way from being answered. Hence, we are often forced to balance the need for answers with the validity of inferences derived from research conducted within a limited set of conditions. Again, we have attempted to bridge this gap through our interpretation and conclusions of the patterns we have observed. This occurs primarily in the Management and Conservation section of this report and we have tried to be very clear about what is speculative and what we have actually observed. We anticipate and welcome challenges to our ideas. That is part of the scientific process and how we advance in our knowledge. UNIVERSITY OF FLORIDA Executive Summary Florida's wetlands have undergone extensive anthropogenic change over the past century including drainage, impoundment, changes in water flow regimes, increased nutrient loadings, and invasion of exotic plants and animals. The Snail Kite (Rostrhamus sociabilis), like many other species, is potentially influenced by these environmental changes. Snail Kite populations during this century have changed considerably in number and distribution and several authors have suggested that changes in kite populations correspond with changes in environmental conditions, particularly hydrology. Our knowledge, however, of demographic processes and their influences has been far from complete. In addition to demographic parameters, movements of Snail Kites are poorly understood and have been the subject of recent controversy during the planning of wetland restoration (e.g., the Everglades) within Central and South Florida. The underlying purpose of this study was to better understand snail kite population and spatial dynamics and how they are influenced by environmental conditions. This understanding combined with reliable estimates of demographic parameters would enable a wide range of demographic modeling (e.g., viability analyses and risk assessments) with a higher degree of confidence. It also increases our predictive capability regarding the response of snail kites to changes in water management. The primary objectives of this study were to (1) estimate demographic parameters with an emphasis on survival, (2) evaluate the influences of environmental conditions (e.g., hydrology) on survival, (3) evaluate the movement patterns of snail kites in Florida including rates, locations, and what environmental conditions are correlated with these movements, and (4) develop a protocol for future monitoring of snail kites. Radio telemetry and mark-resighting (banding) were the two primary field methods we used to obtain estimates of survival and movement probabilities. The combination of these two methods provided a comprehensive assessment of these parameters and was complimentary to each other. Radio telemetry enabled assessment of "within-year" patterns of both survival and movement This allowed us to determine such things as the cause of death of an animal or what the environmental conditions were at the time an animal moved. Mark-resighting is intended to assess "between- year" patterns of these parameters. Our goal was to annually (for three consecutive years) capture and radio tag 100 snail kites of which 60% were adults and 40% juveniles. Our targeted ratio of adults to juveniles was intended to emphasize adult survival because demography of long-lived avian species (e.g., snail kites) tends to be more sensitive to adult rather than juvenile survival. Additionally, we targeted a 50:50 sex ratio of adults to keep our sample balanced. Our annual sample size of 100 was based on estimates of the statistical power to distinguish survival differences among groups (e.g., age or sex) or time periods. We attached 282 radio transmitters on 271 individual Snail Kites. Eleven birds were recaptured and re-tagged in a subsequent year. We were short (82%) of our targeted sample size of 100 birds during 1992, but fully attained our targeted sample sizes in 1993 and 1994. We were very close to our targeted age and sex ratios for all years. Our total sample of individual banded birds for our mark-resighting analyses of survival was 913. Of this sample, 191 were adults at the time of addition to our sample and 722 were juveniles. We estimated adult survival to be an average of approximately 0.90 using radio telemetry and 0.92 mark- resighting. Estimates of juvenile survival were not as consistent between methods and averaged 0.71 using radio telemetry and 0.50 using mark-resighting. We present evidence that the radio telemetry estimates may be biased high. Both sources of data indicated that survival was age-dependent and that survival differed among years for juveniles but not adults. Survival was high enough that within-year patterns would have been difficult to detect without enormous effort (i.e., too few individuals died to enable quantitative comparisons with those that survived) and probably not have been very insightful. Based on our results, we believe that between-year differences in survival would be more detectable and more insightful. From the outset of this study we realized that our scope of inference would be limited to those conditions encountered during our study. We encountered relatively high water conditions throughout this study. Consequently, we were unable to derive inferences about drought conditions. Fortunately, the mark-resighting program should enable a long-term evaluation of environmental correlates (e.g., hydrologic conditions) as variable conditions occur in the future. Our data on movement indicated that Snail Kites frequently move among wetlands throughout their range. Overall probabilities of movement (per month) averaged approximately 0.25 for adults and 0.20 for juveniles. Our results also indicated that the probability of movement is influenced by age, time (yearly and seasonal differences), and location. Although relatively high water conditions persisted throughout this study, water levels (independently of location) did not appear to influence monthly movement probabilities. This does not imply that such an influence would not occur under low- water conditions. Some shifting of birds among regions within the state also may have been a result of a drought that preceded our study. Dispersal is generally thought to be favored when local resources (e.g., food) are low or better conditions exist elsewhere. In contrast, our results from both within-year and between year comparisons suggest that higher probabilities of movement occur when food resources are high. We also found that natal dispersal of juveniles was lower in areas where food resources were likely depressed. We suggest the hypothesis that this may be a reasonable strategy given the dynamic and unpredictable nature of a kite's environment. In years that food is not limiting, which for kites may be most years, high food resources may enable kites to "explore" their potential habitats with little risk. The resulting experience may then help kites to locate food resources faster during times (e.g., droughts) when food is limiting. Prior to 1969 the statewide Snail Kite population was monitored only through sporadic and haphazard surveys. Numbers of Snail Kites in Florida since 1969 have been monitored via a quasi-systematic annual survey. Since these surveys began there have been numerous biological interpretations, often with little or no regard for the inherent sources of variation in these data that could influence the validity of subsequent interpretations. We examined several sources of variation inherent in the annual survey and discuss how this variation could influence the validity of data interpretations. Based on our results and on logic of valid scientific inference, we suggest the annual survey is not a valid estimator of population size; nor should year to year variation in the count be used to estimate demographic parameters (i.e., survival or recruitment). We do, however, believe that the annual count has some value for examining long term population trends provided that the sources of variation be incorporated into any analysis. We explore alternatives to the annual count and provide recommendations that are dependent on what parameters are being estimated. There has been considerable discussion in the literature about the influence of drought on Snail Kite populations; however, few authors have even defined a drought sufficiently to enable an independent observer to designate a given year as a "drought year" based on objective criteria. We suggest that there are three essential characteristics of droughts (i.e., intensity, spatial extent, and temporal extent) that should be operationally defined for effective evaluation of droughts. We further suggest ways that these components can be measured such that they may be incorporated into demographic analyses. Our data are consistent with previous views that the habitats used by Snail Kites in Florida are considerably more extensive than the currently- designated-critical habitat. We also believe that the protection of only the currently designated critical habitat would be insufficient to maintain viable populations of Snail Kites over the long term. We suggest that the use of habitats can be characterized as an extensive network and present a hypothesis of how the spatial and temporal patterns of this network might influence viability of Snail Kites in Florida. We believe that the general directions and goals of the South Florida Ecosystem restoration process are not in conflict with maintaining a viable population of Snail Kites; however, a broader spatial extent of habitat protection (i.e., outside of the Everglades and Okeechobee watersheds) probably is necessary for long-term viability of the Florida population. ACKNOWLEDGMENTS We are very grateful to the many people who helped us during this study. Financial support was provided by the U.S. Fish and Wildlife Service (USFWS), National Park Service (NPS), U.S. Army Corps of Engineers (USACOE), South Florida Water Management District (SFWMD), St. Johns River Water Management District (SJRWMD), and the Biological Resources Division (BRD) of the U.S. Geological Service. John Ogden (SFWMD) and David Wesley (USFWS) were largely responsible for getting this project started and continued to provide strong support throughout its duration. We are also especially grateful to Reid Goforth (BRD), Steve Miller (SJRWMD), Ed Lowe (SJRWMD), Mary Ann Lee (SJRWMD), Jon Mouldling (USACOE), Lewis Hornung (USACOE), Peter David (SFWMD), Dale Gawlik (SFWMD), Paul Warner (SFWMD), and Jim Brown (USFWS). We greatly appreciate the help of our field biologists Phil Darby, Patty Valentine-Darby, Katie Golden, Steve McGehee, Scott Severs, Hilary Maier, David Boyd, James Conner, and Lynn Bjork. Their ability to work independently for long hours, and get the job done made our job much easier. We also appreciate the volunteer assistance from Bob Dill and Theresa Johnson. This project has been a cooperative effort among biologists and agencies from the outset. For their help in the field and/or logistic support we are grateful to Brian Toland (USFWS), Tim Towles (GFC), Laura Brandt (UF), Peter Frederick (UF), Marilyn Spaulding (UF), Mary Beth Mihalik (West Palm Beach Solid Waste Authority), Al Vasquez (West Palm Beach Solid Waste Authority), Deborah Jansen (NPS), Mike Wilson (NPS), Sue McDonald (NPS), Vivie Thue (NPS), Fred Broerman (Arthur R. Marshall Loxahatchee National Wildlife Refuge), Angela Chong (SFWMD), Vicky Dreitz (Univ. Miami), F.K. Jones (Miccosukee Tribe of Indians), and Steve Terry (Miccosukee Tribe of Indians). The banding of Snail Kites was conducted in cooperation with the GFC. In this effort, we appreciate the cooperation of James Rodgers Jr. (GFC), Jon Buntz (GFC), and Brian Toland (USFWS). We greatly appreciate the effort of Charlie Shaiffer (Mingo National Wildlife Refuge) who took the time to travel to Florida to share his knowledge of trapping and handling birds. We are grateful to Patuxent Wildlife Research Center, particularly Jim Nichols and Jim Hines, for housing and assistance during the analysis phase of this project. We appreciate the helpful comments on drafts of this report by Jim Nichols (BRD), Steve Miller (SJRWMD), Don DeAngelis (BRD), Dale Gawlik (SFWMD), Jim Rodgers Jr. (GFC), and Vicky Dreitz (UM). For allowing us access to areas used by kites, which were closed to public access, we are grateful to the Miccosukee Tribe of Indians, the City of West Palm Beach, and the Arthur R. Marshall Loxahatchee National Wildlife Refuge. We are grateful to our pilots Karen Dunne and Morton Sund of Wyatt Aviation. We appreciate their patience, competence, and many hours of safe flying. We are grateful to the employees of the Florida Cooperative Fish and Wildlife Research Unit, particularly Barbara Fesler and Debra Hughes, for their help with administration of this study. Table of Contents FORWARD ........................................................................................................................ i EXECUTIVE SUMMARY ............................................................... .................................... ii ACKNOW LEDGMENTS........................................................................................................... iv Chapter 1. INTRODUCTION ................................................................................................. 1 Objectives .............. ..................................................................................... ................... 2 Chapter 2. STUDY AREA ........................................................................................................ 5 Study Population ......................................................................... ................................... 5 Spatial Scales ............................................ .............................................. ................ 5 Regions ............................................................................................................... 5 Habitat Types .................................... .......... ........................... 6 Chapter 3. METHODS ............................................................................................................. 9 Overview of Field Methods .................................... ......................................... 9 Capture and Marking of Animals ...................... ........................................ 10 Sampling Protocols ....................................................... .............................................. 10 Monitoring Protocols ..................................................................... ............................. 11 Estimation of Survival ................................................................. ............ ................... 11 The Kaplan-Meier Estimator ............................................................. ........................ 11 The Cormack-Jolly-Seber Model ............................................................ 12 Mean Life Span .................................................................................. 13 Estimation of Natal Dispersal of Juveniles ................................................ 14 Estimation of Movement Probabilities ........................ .................... ................ 14 Model Selection as a Basis for Data Analysis ..................... .................. .................. 14 Analysis Philosophy ..................... ..................................... 14 The Likelihood Ratio Test ................................................ ........................................ 15 Akaike's Information Criteria ..................... .......................................... 15 Estimating Power .................................................. .............................................. 16 Logistic Regression and Log-linear Models ........................................... ................... 17 The Analysis of Residuals from Cross-classification Models ................................ ........ 17 Multiple Comparisons ................................... ......................................... 17 Chapter 4. SURVIVAL AND MORTALITY ................................................................................. 18 Longevity ........................................... ... .. ....... .......................... ..................... 18 Mean Life Span ................................................................................................................... 19 Estimation of Survival from Radio Telemetry ............................................................................ 19 Effects of Age on Survival ............................. ............................. ........................ 21 Effects of Sex on Survival ......................... .......................................... 22 Temporal Effects of Survival ........................... ...................................... 22 Spatial Effects of Survival ........................ .......................................... 24 Model Selection for Effects on Survival .................................... ............................... 30 Effects of Hydrology ................................................. ........................................ 30 Estimation of Survival from Banding Data .................................................... ........................ 31 Effects of Age and Time ............................................ .................................. 31 Effects of Sex ............................................................ ............................................. 34 Regional Effects ........................................................................ 34 Conclusions About the Effects of Survival from Banding Data ................................ ...... 38 A Synthesis of the Effects of Survival .................................... .................................... 38 Effects of Age ............................................................ ............................................ 38 Effects of sex ........................................ .................................. .. ..................... 38 Temporal Effects ............................................... .................................................. 38 Assumptions, Bias, and Sources of Error ....... ............ ......... ..................... 38 Assumptions Inherent in the Study Design for Valid Inferences From Survival Analyses ............... 38 Assumptions of the Kaplan-Meier Estimator ..................................... .................. 39 Assumptions of the Cormack-Jolly-Seber Models ...................... ................ ......... .......... 43 Causes of Mortality ........................................ ........................................ ................... 44 Previous Estimates of Survival ............................................... ............................................ 46 Chapter 5. REPRODUCTION ........................................................... ........... ...................... 48 Semantics .......................................................... ............................................... 48 The Breeding Season .................................................... .............................................. 50 The Breeding Population .................................................. ............................................... 51 Age of First Reproduction .............. .......................... .. ............................. 51 Proportion of Birds Attempting to Breed ........................ ........ ........ ..................... 51 Nest Success ................................................................................. .................................. 52 Areas of Disagreement Regarding Estimates of Nest Success ................................. ....... 52 Estimates of Nest Success and its Process Variance ............................................................ 54 Influences of Nest Success ............................... ........................................ ... 55 Number of Young Per Successful Nest ........................................ ... ................... 58 Number of Nesting Attempts Per Year ........................ ..... ......... ................................. 58 Chapter 6. MOVEMENTS ................................................................................................. 64 Natal D ispersal ......................................................................... ...................................... 64 Temporal Patterns of Natal Dispersal ............................................................ 65 Differences in Natal Dispersal Between Northern and Southern Regions ................................... 65 Differences in Natal Dispersal Between Lake and Marsh Habitats ......................................... 66 Discussion of Natal Dispersal ......................... ............................ 66 Movement Probabilities ..................................................................................................... 67 The Effect of Age and Sex on Movement Probabilities ............................................... 67 Temporal Effects on Movement Probabilities ............................... ..................... .... 68 Spatial Effects on the Probability of Movement .............................................. 69 Hydrologic Effects on the Probability of Movement .................................. ............... 72 The Effect of Food Resources on Movement ....................................... ................... 74 Model Selection and Synthesis of Effects on the Probability of Movement ................................ 75 Spatial Patterns of Movement ................................... ..................................................... 78 Effect of Distance ............................................................... ............. .................. 78 The Effect of Age, Sex, and Time on Movements between Specific Locations ........................... 80 Seasonal Shifts in Latitude ...................................................... 80 Shifts in Regional Use ....................................................................................... 81 Seasonal Shifts in Habitat Use .................................... ...................................... 82 Natal Philopatry and Site Fidelity ..................... .................................. 84 Natal Philopatry ............................................................................. ................... 84 Site Fidelity ........................................................... 85 Assumptions, Bias, and Sources of Error ..................................... .......................... 89 Effects of Radio Transmitters on Movement of Snail Kites ........................................... 89 Conditional Independence ................................................................. ................... 91 M monthly Time Steps ............................................................ .............. ................ 91 Chapter 7. MONITORING THE FLORIDA SNAIL KITE POPULATION ...................................... 92 The Annual Survey ......................................................... ........................................ .... 92 Sources of Variation ........................................................... .............. ................. 93 vii Some Alternative Field Methods to the Annual Survey ................................... ........................ 97 Radio Telemetry ................................................................................ 97 Capture-Recapture (Mark-Resighting) Data From Banding .................................................. 98 Distance Sampling ............................................................... .....98 Discussion and Recommendations ................................................................ .. 100 Population Size .......................................................................... .................... 100 Population Indices .......................................................... ............ ................... 101 Population Change and Viability ..................................................... .......................... 101 Estimates of Demographic Parameters .............................................................................. 103 External Influences on the Population ............................ ................... ........ ............... .... 105 Conclusions ................................................................................ .......... ............ 105 Chapter 8. MANAGEMENT AND CONSERVATION .................................................................... 106 W ater Management and Snail Kites ................................................... 106 Drought Semantics ............................................. .................................. .......... 106 Hydrologic Regimes of Snail Kite Habitat ........................................ ................ 110 The Hydrologic Window ............................................... ........................................... 113 Critical Habitat .......................................................................................... ........... .......... 116 Current Designation ............................................... ........................................... 116 The Habitat Network .............................................................................................. 116 Meta-habitats: a Hypothesis about the Relationship Between the Habitat Network and Meta- population Structure ........................................ ......... .............................. 118 Protection of Habitat ........................................... ......... 123 South Florida Ecosystem Restoration and Snail Kites ...................................... ................ 125 LITERATURE CITED .................................................................... .................... ............. 126 APPENDICES ................................................................................ ................................. 135 Chapter 1. INTRODUCTION Florida' weldands have undergone extensive anthropogemr change over the past century Icludmg drainage, impoundmen, changes I water flow re- gmes, increased nutrient loadlngs, and inasion by exoic plants d and ams TheSnadlKile(Roprrhamus ouabtasa), Itke many other species, is poaenlially in- fluenced by Ihese environmental changes. Snail Kite population dunng th century have changed consid- erably in number and distribution and several authors (e g, Sykes 1984, Belssinger 1988; Bennetts et al knowledge however of demographic processes and their sllduencss s far rom complete (Bennetts and Kitchens 1994) Changes the size of all populations are sum of births and immigration mlnu deaths and em- grallon The Florda populatan ofSnal Kaes, how- ion and emigration Snai Kates n lorida have long ng to our previous suggestion that the Florida popu- lation is not compr sed of d\etesubpopulatlons, but instead, is one population tha frequently shifts n dis change between population of the United Staes and pothess hasemerged Thus, fom a demographic per- spcctive, we are concerned priaanly with birth and death process and the nencesonthose process (Fig 1-1) The bth and death process can be concept wilh a higher degree of confidence It also would increase our predictive capability regarding the response of Snail Kites to changes in water management. Given the scope of projects currently being planned or implemented (e.g., the Central and South Florida Project, the South Florida Ecosystem Restoration Initiative, Kissimmee River Restoration, Upper St. Johns River Basin Project, Kissimmee chain of Lakes Fishery Restoration) an improved predictive capability would be highly beneficial and would greatly reduce controversies. The goal of this study was to better understand Snail Kite population and spatial dynamics and how they are affected by both natural and anthropogenic processes. We believe that demographic models play an important role in the refinement of our understanding of these dynamics. However, it is also our belief that reliable parameter estimates, particularly if a model is sensitive to those parameters, are an essential basis for reliable model outputs. Lastly, we believe that our models, as well as our knowledge, should be an iterative and adaptive process (Walters 1986). As we acquire new information or better parameter estimates, or if our predictions are falsified, we need to adjust our models, as well as our thinking, to adapt to new information (Fig. 1-3). Conservation Strategy Parameter Population Estimation Modelg Projection Risk Assessment Figure 1-3. Conceptual framework for this study. Reliable estimation ofparameters is the first step toward the development of a wide variety of demographic models. Objectives Most previous research on the demography of Snail Kites has focused on reproduction. Nesting success, in particular, has received considerable attention in recent years (e.g., Sykes 1987b, 1987c, Bennetts et al. 1988, 1994; Snyder et al. 1989a). There remains considerable debate about the what factors influences nesting success (Bennetts et al. 1994, Sykes et al. 1995); however, compared to other species, the relationship between nesting success of Snail Kites and environmental conditions is relatively well understood. Other reproductive parameters are less well known. Unsubstantiated estimates or speculation have been made regarding the proportion of birds attempting to breed each year and the number of nesting attempts per year (e.g., Snyder et al. 1989a, Beissinger 1995); however, reliable estimates for these parameters have been lacking. In as much as there appears to be general agreement that changes in Snail Kite populations are more sensitive to survival than to reproduction (Nichols et al. 1980, Beissinger 1995, Sykes et al. 1995), data to estimate survival are very limited (Snyder et al. 1989a) and as a result, reliable estimates of survival are sorely lacking (Beissinger 1995). Consequently, the first objective of this study was to estimate adult and juvenile Figure 1-2. The demographic cycle of Snail Kites showing three age classes (Juveniles =J, Subadults=S, and Adults=A). Parameters for survival (0) and fecundity () are shown for each age class. Adapted from Caswell (1989), Beissinger (1995) and Legendre and Clobert (1995). survival and to evaluate the influences of environmental conditions (e.g., hydrology) on survival. In addition to this primary goal, we also recorded supplementary information on reproductive parameters to the extent that it did not conflict with accomplishing our primary goals and in areas where such information was not already being collected. In addition to demographic parameters, movements of Snail Kites also are poorly understood and have been the subject of recent controversy during the planning of marsh restoration within Central and South Florida. While long term changes in Snail Kite distribution tend to coincide with changes in hydrologic regimes, shorter term (e.g., annual and seasonal) shifts do not always coincide with local hydrologic conditions (Bennetts et al. 1994). It has been hypothesized that dispersal of kites may be in response to hydrologic conditions (Takekawa and Beissinger 1989), localized food depletion (Bennetts et al. 1988), or localized environmental conditions (e.g., dissolved oxygen in the water) that may influence apple snail (Pomacea paludosa) availability (Bennetts et al. 1994). To what extent movements reflect long-term changes in habitat quality versus short-term environmental dynamics is poorly understood, as is their ability to locate and re- colonize wetlands that have been, or will be, restored. Thus, movements are critical to understanding Snail Kite population dynamics leading to our second primary objective to evaluate the movement patterns of Snail Kites in Florida including rates, locations, and what environmental conditions are correlated with movements. Because of the kites' endangered status and because there are a multitude of projects than have and continue to alter hydrologic regimes of southern Florida, there has been considerable interest in monitoring the Florida population of Snail Kites. Since 1969, Snail Kites in Florida have been monitored using an annual statewide survey. Unfortunately, there are serious questions regarding the inferences that can be reliably made from this data source. Consequently, our third primary objective was to evaluate the validity of existing monitoring and to provide recommendations for future monitoring of Snail Kites in Florida. ' * Chapter 2. STUDY AREA Study Population Snail Kites within the United States occur only in Florida (Sykes 1984). It has been suggested (Bennetts and Kitchens 1992, 1993, 1994, Beissinger 1995) that Snail Kites comprise one population that shifts in distribution throughout the state, rather than there being separate subpopulations within the state. Data from studies on movements (this study) and genetics (Rodgers and Stangel 1996) support that there is considerable interchange of birds among wetlands in Florida. Consequently, it was deemed essential for the scope of this study to include the entire population of Snail Kites in Florida and our study area comprised a network of wetlands throughout Central and South Florida within the entire documented range of Snail Kites (Fig. 2-1). Spatial Scales Because the scale of our study is statewide, we did not focus on movements within individual wetlands. For the purpose of this study, we considered wetlands to be distinct if they were separated by a physical barrier (e.g., ridge or levee) and/or were under a different hydrologic regime either through natural or managed control. Thus, adjacent wetlands, which were once hydrologically continuous (e.g., WCA-2A and WCA- 2B), were considered separate units if they were under different water regulation schedules. Although we recorded locations of animals by specific wetland, for many analyses we had insufficient data to consider wetlands individually. For example, in the highly fragmented agricultural areas, there were more than 50 wetlands used by kites during this study. Consequently, some pooling of locations was required. For most analyses agricultural areas were pooled into a single class of wetlands. It was not uncommon for kites to frequent several such wetlands in immediate proximity, and we seldom (if ever) would have had sufficient data to support estimating parameters (i.e., survival or movement probabilities) for each of these wetlands. Other cases of pooling are described below, or are reported on a case specific basis based on model selection criteria (see methods). T ToH0 ET'OO Atlantic ss Ocean WPB. - 'Lox HOLEY_ 2A Gulf of I, :;2- Mexico BIC Figure 2-1. Major wetlands of South Florida referred to in this report. Wetlands are Everglades National Park (ENP), Big Cypress National Preserve (BICY), Water Conservation Areas 3A, 3B, 2B, 2A, Loxahatchee National Wldlife Refuge (LOX), Holey Land Wildlife Management Area (HOLEY), West Palm Beach Water Catchment Area (WPB), Lake Okeechobee (OKEE), Upper St. Johns [Blue Cypress] Marsh (SJM), Lake Kissimmee (KISS), Lake Tohopekaliga (TOHO), and East Lake Tohopekaliga (ETOHO). REGIONS For some analyses (e.g., survival) we treated location at a regional scale because it was infeasible to estimate separate parameters for all wetlands. Based primarily on watersheds, climatic factors, physiography, and management regimes we assigned each location to one of five primary regions (Fig. 2-2). Locations not included in these five regions (e.g., agricultural areas and isolated peripheral wetlands) were assigned to a sixth region we call the peripheral region. Undoubtedly, there are differences in the quantity and quality of habitats within this sixth "catch all" region (and within the 5 primary regions as well); however, the amount of data required to partition the effects of this within-region variability would be enormous and require significantly more effort that the scope of this study. However, whenever the data supported partitioning beyond a regional scale we did so. The Everglades and Big Cypress Region is comprised of Water Conservation Areas 1,2, and 3, Everglades National Park, and Big Cypress National Preserve. The Loxahatchee Slough Region is comprised of wetlands in the drainage system of the Loxahatchee Slough and vicinity including the Corbitt Wildlife Management Area, Pal-Mar Water Control District, private wetlands owned by Pratt-Whitney Corp., and wetlands within the Loxahatchee Slough owned by the City of West Palm Beach (i.e., the West Palm Beach Water Catchment Area and vicinity). The Okeechobee Region is comprised of Lake Okeechobee within the Herbert Hoover Dike. The Kissimmee Chain-of-Lakes Region was comprised of all lakes within this chain including Lakes Kissimmee, Tohopekaliga, East Tohopekaliga, Marion, Marian, Tiger, Pierce, Jackson, and Hatchineha. The Upper St Johns Region includes wetlands within the Upper St. Johns River Basin, but most Snail Kites used the Blue Cypress Marsh Water Conservation Area, Blue Cypress Water Management Area, and surrounding wetlands in private ownership. Agricultural areas (e.g., citrus groves, canals, agricultural fields, or agricultural retention ponds) within each of these regions, as well as all other areas not included in one of the above regions, were assigned to the peripheral region. HABITAT TYPES Snail Kites inhabit freshwater wetlands throughout central and south Florida. There is considerable variation in the physiographic characteristics and specific plant communities that comprise Snail Kite habitat (reviewed by Sykes et al. 1995). Our objectives did not warrant documentation of micro-habitat use by kites, nor was our sampling (often by aircraft) conducive to recording such data. However, for some analyses we wanted to incorporate the effects of at least a broad classification of habitats being used by kites. This classification had to be broad enough to enable assignment of locations obtained from aircraft to a given habitat type and sufficiently broad such that micro-habitat variation did not confound the assignment given normal daily movements of foraging birds. Consequently we assigned each location to one of five habitat types: (1) graminoid marsh, (2) cypress prairie, (3) Okeechobee, (4) northern lakes, and (5) miscellaneous peripheral. Graminoid marshes (Fig. 2-3) were generally slough and wet prairie communities (Loveless 1959). We distinguished cypress prairies (Fig. 2-4) in that a dominant feature of the landscape profile was comprised of cypress. This habitat occurred primarily in western WCA3A, and portions of the Big Cypress National Preserve and Loxahatchee Slough. The littoral zone of Lake Okeechobee is an extensive system of diverse marsh habitats, and consequently had elements of at least three of our other habitat types (i.e., graminoid marsh, northern lake, and highly disturbed). Because of this high local diversity we were unable to assign locations to a particular type without extensive ground verification. Even then, birds often used more than one of these habitat types within a given day. Thus, we assigned locations at Lake Okeechobee (Fig. 2-5) to its own habitat type. The northern lake habitat type (Fig. 2-6) consisted primarily of lakes within the Kissimmee Chain-of-Lakes, but also included a few lakes along the Lake Wales Ridge. In contrast to Lake Okeechobee, this habitat type generally was comprised of a narrow littoral zone (usually < 200 m) on the periphery of these lakes. This littoral zone had a relatively steep elevation gradient compared to other habitat types; the zone used by foraging kites often was a band of < 100 m usually dominated by maidencane (Panicum spp) interspersed with patches of bulrush (Scirpus spp) or cattail (Typha spp). Primary nesting areas were often a zone of cattail and/or willow (Sahx spp) in the shallower zone adja- cent to foraging areas. The peripheral habitat type (Fig. 2-7) was comprised primarily of agricultural areas. These included retention ponds for citrus groves, ag- ricultural ditches, and other miscellaneous, usually highly disturbed, habitats. Larger canals, not neces- sarily associated with agriculture, were also included in this habitat type. For some analyses we had insufficient data to Figure 2-3 Graminoid marsh habitat type. Figure 2-4. Cypress prairie habitat type. partition locations into each of these habitat types. Con- sequently, for some analyses we assigned locations to an even broader category of lakes (i.e., Lake Okeechobee, the northern lake habitat type, and per- manently flooded canals [<0.01% of our locations]) and marshes (any non-lake habitat). This was intended to distinguish habitats that had a permanent water source component available (even if it was not used) with those that dried periodically. Fgure 2-5 Lake Okeechobee habitat pe. Figure 2-6. Not themlkake habitua type. Figue 2-7. Peripheral habi/,tat txe. ~. ~ .1r Chapter3. METHODS To provide sufficient detail of our methods and still keep it readable for general audiences we have split up some of the information between the text and appendices. In the text, we have tried to provide a more general description of our methods including the appropriate citations for analytical procedures. How- ever, for some analyses we have provided a more de- tailed description, including the corresponding fmnnu- lae in appendices. Because we also make use of exten- sive notation, which for readers not familiar with such notation, can be confusing, we have also provided a summary (Appendix 3-1) of notation used through- out this report. Overview of Field Methods Radio telemetry and mark-resighting (band- ing) were two primary field methods used to estimate survival and movement probabilities. The combina- tion of these two methods provided a comprehensive assessment of these parameters and were complemen- tary to each other. Radio telemetry enabled assess- ment of "within-year" patterns of both survival and movement. This allowed us to determine such things as the cause of death of an animal or what the environ- mental conditions were at the time an animal moved. Mark-resighting is intended to assess "between-year" patterns of these parameters. This approach was a much more cost efficient method for estimating an- nual survival. We are also exploring its potential to estimate population size of Snail Kites. Within-year patterns could not be determined using the mark- resighting approach; however, we had hypothesized that the primary factor that regulates Snail Kite populations is periodic drought (Takekawa and Beissinger 1989, Belssinger 1995), which is a between- year phenomenon. Thus, the ability to detect differ- ences and to estimate survival for drought phenomena can therefore be assessed using mark-resightmg methodology. 9 Capture and Marking of Animals We captured adult snail kites primarily using a net gun (Mechlin and Shaiffer 1979). We initially tried to capture kites using a variety of noose carpets (Snyder et al. 1989b) and traps. These methods proved ineffi- cient and indicated a relatively strong heterogeneity (catchability) among individuals that could have biased our sample. Some individuals would actively avoid perches or food that were associated with traps, while others showed no apparent avoidance. We decided to use the net gun after careful evaluation of the potential risks (i.e., injury, death, or nest abandonment) and ap- parent poor success and potential for bias in other tech- niques. The net gun propels a 10-foot triangular nylon net using 22 caliber blank cartridges. The projectiles of the net gun were encased in foam rubber to reduce the chance of injury. Most captures were of adults while flying close during nest defense. We also captured some foraging birds although this was less effective. To fur- ther reduce the potential for injury, we did not shoot at birds that were: (1) closer than 7-8 m, (2) in a defense dive toward us, or (3) in a position where the vegetation below the bird posed a risk (e.g, too high to retrieve the bird). We almost always were able to retrieve the bird within 60 s of capture. In order to reduce the risk of nest abandonment, we did not attempt to capture birds in the early stages of nesting (e.g., courtship or egg lay- ing). We attempted to capture birds primarily after eggs group defense of an adjacent nest. All adults captured and most nestlings encoun- tered at fledging were handed with U.S. Fish & Wild- lile Service bands as well as a uniquely-numbered-an- odized aluminum band. The anodized-aluminum bands enabled an individual to be identified from up to 40 m (a distance not difficult to obtain with snail kites) using a spotting scope The banding of nestlngs was con- ducted in a cooperative effort with the Florida Game and Fresh Water Fish Commission (GFC) Biologists with the GFC were banding Snail Kites as part of other ongoing studies using colors to designate natal wetlands Consequently, we provided GFC biologists our anod- i/ed numbered bands which enabled us to identify indi- viduals in the field using colors needed for their respec- tive studies. In addition to leg bands, all adults and some juveniles (below) were equipped with radio transmit- ters Radio tagging more than one juvenile per nest would have allowed us to explore some questions about the tendency for siblings to disperse together; however, it would have violated the assumption of independence required for our survival analysis. Consequently, a maximum of one juvenile per nest was equipped with a radio transmitter. All radio transmitters were equipped with a mortality switch that upon prolonged lack of motion (= 6 h) altered the pulse rate such that we could remotely determine if a bird were dead or had dropped its radio. Radio transmitters were attached using a backpack har- ness Harnesses were made of four separate pieces of i'" Teflon ribbon which were securely fastened at the transmitter, but were sewn together at the breast with a single cotton stnng. This cotton string attachment was intended as a "weak link" which would allow the trans- mitter to fall off cleanly as the string weakened and broke over time The 15 g transmitters were approximately 3.5% of the body weight of adult snail kites (X, =427 g) and 3.9% of the body weight of juveniles at the time of fledging (x,-v =384 g) (Darby et al., in press), which were within the 5% required by our permits. Sampling Protocols For our sample to be representative of the state- wide population we targeted the sample from each lo- cation to be proportional to the preceding annual count made approximately 3 months prior to trapping. Our goal was to annually (flor three consecutive years) cap- ture and radio tag 100 snail kites of which 60% were adults and 40% juveniles. Our targeted ratio of adults to juveniles was intended to emphasize adult survival because demography of long-lived avian species (e.g., snail kites) tends to be more sensitive to adult rather than juvenile survival (Young 1968, Grier 1980, Beissinger 1995). Additionally, we targeted a 50:50 sex ratio of adults to keep our sample balanced. Our annual sample size of 100 was based on estimates of the statistical power to distinguish survival differences among groups (e.g., age or sex) or time periods (Fig. 3-1). We estimated power assuming a binomial distribution because we intended to use a binomial estimator for movement probabilities (below) and the Kaplan-Meier estimator for survival analyses (below) which is an extension of a binomial estimator that enables censoring and staggered entry of animals (White and Garrott 1990). Consequently, a binomial estimator was our best starting point from which to estimate power prior to actual collection of data. We used an initial survival estimate of 0.90 as a null hypothesis (H.) based on previous speculation (Nichols et al. 1980, Snyder et al. 1989a). We then computed power for the alternative hypotheses (Ha) of 0.80, 0.70 and 0.60 using a range of potential sample sizes. This analysis indicated that substantially smaller sample sizes than those we targeted would have had little power to detect even large differences in annual survival. Sample sizes needed for capture-recapture data are highly dependent on estimates of re-sighting probability for which we had no preliminary indication. Consequently, our goal was to band as many birds as logistically possible to evaluate whether this method had sufficient power given the sample sizes of banded birds and resightings we were able to obtain. Monitoring Protocols We attempted to locate all radio-tagged kites bi- weekly. At this frequency we were likely to detect most major movements and had a reasonable probability of finding most dead birds before they were too decomposed to determine cause of death. Given the size of our study area, more frequent locations would have required considerably more effort and expense. Also because of the size of our study area, searches were conducted most efficiently by aircraft. Even by aircraft, only a small portion of the potential habitat could be adequately covered during any given flight. Consequently, we flew approximately two times per week (for = 4-5 h each flight) to obtain our bi-weekly locations. We also verified the status (i.e. alive or dead) and location of birds on the ground (i.e., by airboat or from levees) whenever we visited each area. Mark-resighting was conducted from March through June of each year. Using this period enabled simultaneous estimates of survival along with recruitment through nest monitoring and created additional advantages. For example, the tendency for a bird to remain in proximity of its nest increased our ability to read band numbers (i.e., a resighting) of an individual. Nest visits were also required for the marking of individuals (i.e., fledglings), making for an efficient use of our effort by simultaneously monitoring nesting success. During nest monitoring we checked the adults at each nest for leg bands, in addition to other non- nesting birds observed, and attempted to read the bands of all birds encountered. Estimation of Survival THE KAPLAN-MEIER ESTIMATOR We estimated survival (0) of radio-tagged kites using a staggered entry design (Pollock et al. 1989) of the Kaplan-Meier product limit estimator (Kaplan and Meier 1958). Detailed descriptions of the estimator and its properties can be found in Kaplan and Meier (1958), Cox and Oakes (1984), Pollock et al. (1989a), and White and Garrott (1990); formulae are provided in Appendix 3-2. This estimator is preferred to other estimators because (1) no assumptions are required about the hazard function (e.g., constant survival over intervals)(White and Garrott 1990), (2) the estimator allows for staggered entry of radios (i.e., radios can be added to the population at any time)(Pollock et al. 1989), and (3) the 0.4 -d .... Difference in Surviva of 0.1 --- Diference n Survival of0.2 -- ierence h Surviv of 0.3 20 40 60 80 100 120 140 160 180 Sample Size Figure 3-1. The probability (power) of detecting differences in survival based on using different sample sizes. We assumed a binomial distribution and an initial survival estimate of 0.9. ------------ ... ... ... ... ... ... ... ... .. ..... .... ..... estimator allows censoring (i.e., radio failure or loss)(White and Garrott 1990). We used an arbitrary starting date of 15 April for our survival estimates. At this time we had a reasonable sample (n = 16 during our 1st year) to allow estimation of survival. Birds captured after 15 April were included in the analysis in accordance with the staggered entry procedures described by Pollock et al. (1989) and annual survival each year was estimated from April 15 to April 14. All calculations were conducted in SAS (SAS Inc. 1988) using variations (for our specific data) of the program provided in White and Garrott (1990). Comparison among groups- For comparison among survivorship curves generated by the Kaplan- Meier estimator we used the log-rank tests (Savage 1956, Cox and Oakes 1984). For a log-rank test, each time step is treated as a 2 x 2 contingency table. Detailed descriptions of the test and corresponding formulae are provided in Appendix 3-3. While alternative tests are possible, the log-rank test is easily generalized to the staggered entry design (Pollock et al. 1989). Cox and Oakes (1984) describe three variations of the log-rank test based on slightly different procedures for estimating the variance for the number of deaths (dy). The tests differ slightly in their Type I error rate and corresponding power. Unless otherwise stated, we have reported only the results from the most conservative test (i.e., less likely to make a Type I error, but having lower power). However, when the significance levels were questionable (i.e., at or near a=0.05) we reported all three test results for comparison. All of the variations assume conditional independence and asymptotic normality of dy (Cox and Oakes 1984, Pollock et al. 1989, White and Garrott 1990). THE CORMACK-JOLLY-SEBER MODEL We estimated annual survival from banding data using the capture-recapture (resighting) models originally developed by Cormack (1964), Jolly (1965) and Seber (1965). The basic Cormack-Jolly-Seber (CJS) approach has undergone extensive advancement in recent years to become an extremely flexible unified framework capable of handling a variety of models ranging from simple to complex models of survival including effects of individual characteristics (e.g., age and sex), environmental variables (e.g., weather), and the ability to incorporate transition probabilities and multiple states (e.g., exchange among geographically stratified populations)(Lebreton et al. 1992, Nichols 1992, Brownie et al. 1993, Nichols et al. 1993). This increased flexibility has shifted the emphasis of CJS models from primarily parameter estimation to a powerful tool for testing biological hypothesis about the life history parameters being estimated (Lebreton et al. 1992, Nichols 1992). Unlike radio telemetry data, where survival is estimated on a relatively continuous-time basis (estimation is discrete at the interval of obtaining radio locations), survival from banding data is estimated for discrete time intervals, which are usually yearly. Our capture and resighting period corresponded with the peak time of fledging (March-June). Thus, survival estimates are not strictly annual, but rather can be roughly interpreted as survival from one breeding season to the next, regardless of whether a given animal was breeding. The basic probabilistic framework for estimating survival lies in the estimation of two parameters. Let < denote the probability that an animal is alive and in the population at time t + 1 given that the animal was alive at time t, and let p denote the probability that an animal alive and in the population at time t is seen (i.e., recaptured or resighted). As such, the probabilities of survival (0) for each sampling occasion (for this study each occasion equals 1 year) can be described (Fig. 3-2). Unfortunately, without radio telemetry, we usually do not know if an animal survived over an interval; we only know whether or not it was observed. We summarize this knowledge in the form of capture (resighting) histories for each individual marked during the study (Fig. 3-3). For example, a capture history of {1 1 1} indicates that an animal was seen on each of 3 occasions for a 3-occasion study. Similarly, a capture history of {1 0 1} represents an animal that was seen on occasions 1 and 3, but not seen on occasion 2. We can then represent each capture history in the form of a probablistic model using the parameters 0P and p (Table 3-1). From this probabilistic framework, parameters were estimated using maximum likelihood estimation. Maximum likelihood estimates (MLE) in the Time 4 Time 3 Time 2 Time 1 Arinal AlAve ANove<3An" Not Akive Ajkrdn WlWi y Alve cas^ mykeo and rIasedA101 Animal Not Alve Figure 3-2. The probabilistic framework for estimating survival over 4 time periods. Adapted from Nichols (1992). Capture Time 1 Time 2 Time 3 History Animal 111 Seen Animalnimal Animal Initially Not Seen caught marked, and released Animal Se /^101 < Animal Seen 10 Not Seen Animal Animal NotSeen 100 Figure 3-3. Diagram of events that result in each of 4 capture historiesfor animals released at time 1 in a 3- occasion Cormack-Jolly-Seber (CJS) model. Adapted from Nichols (1992). context of survival analyses are described in considerable detail elsewhere (e.g., White 1983, Brownie et al. 1985, Pollock et al. 1990, Lebreton et al 1992); however, for our purposes, it is important to recognize that an MLE is asymptotically unbiased, normally distributed, and has minimum variance (White 1982, Brownie et al. 1985, Lebreton et al. 1992). These properties make MLE well suited for our purposes compared to other estimators. An additional advantage of MLE is that it enables the resulting models to be evaluated in the context of likelihood-ratio framework for hypothesis testing (White 1983, Lebreton et al. 1992)(see Likelihood Ratio Tests below). All parameter estimation of CJS models was conducted in either Program SURVIV (White 1983, White and Garrott 1990) or MSSURVIV (Hines 1994). Table 3-1. Possible capture historiesfor animals released at time 1 and corresponding probabilities for a Cormack-Jolly-Seber model with 4 occasions. Capture histories and probabilities for animals released at times 2 and 3 of this model are not shown.. Capture Capture Probability History 1111 12 P, 1P3 1110 01p,2 1101 1P2 02 (1 p3)IP 1100 01p, [l- p ( 1-p3,)3pl 1011 1, (1 -p)02p, A 1010 (1 p2) p, (1 ApO) 1001 (1 p2)2 (1 -p) Op. 1000 1 (all terms above) MEAN LIFE SPAN We estimated mean life span (MLS) for adults using the estimator introduced by Cormack (1964): MLS. = - This approach assumes constant survival among years (Cormack 1964); however, when the above estimator is considered as an approximation, Brownie et al. (1985) suggest that this estimate is useful when computed from mean annual survival (6.). Our data also do not indicate a violation of the assumption of constancy for adults. Our data do indicate that juvenile survival is lower than adults for approximately the first 100 days post fledging after which it is similar to adult survival. Thus, our estimate of adult MLS, is applicable to birds conditional upon their survival of the first 100 days. It has also been suggested by numerous authors (e.g., Sykes 1979, Beissinger 1986, Rodgers et al. 1988, Snyder et al. 1989a) that there is increased mortality associated with droughts. Beissinger (1995) estimated adult survival during droughts to be 0.60. This was based on the average difference in the annual count for drought years after 1973 (Beissinger 1995)(but see discussion in Monitoring Snail Kite Populations in Florida). Based on ancillary evidence (e.g., band sightings from known-age birds), we believe Beissinger's survival estimate for drought years is substantially low. To our knowledge, no reliable data exist for estimating survival during droughts (although we have a mark-resighting program in place to derive such estimates when future droughts occur). Consequently, we estimated MLS using a range of hypothesized estimates for drought-year survival ranging from Beissinger's (1995) estimate of 0.60 to estimates that we believe are more reasonable. Beissinger (1995) also suggested that survival in lag years (i.e., one-year after a drought) was 0.90 based on radio telemetry of eight adult Snail Kites that were radioed during the 1981 drought (7 of 8 survived and the fate of the other was unknown)(Snyder et al. 1989a, 1989b). We are unclear as to what extent this estimate applies to lag years versus drought years (the radios were attached in May and June when water levels were lowest) and the actual survival of these birds was 0.885 < < 1.00. However, for lack of a better estimate, we also included this estimate as a hypothesized value for lag years. Brownie et al. (1985) derived an analogous estimator for the expected life time E(T) of juveniles as: 1 i -ln(:) -In(o ) + JU Thus, for our application this estimates the expected life time of a juvenile conditional upon it surviving to fledging (i.e., when birds are banded). It includes the first 100 days post fledging, but does not include the pre- fledging nestling period. Estimation of Natal Dispersal of Juveniles Natal dispersal of birds has been defined as the dispersal of juveniles from their natal site to the site of their first reproduction or potential reproduction (Greenwood and Harvey 1982). Unlike most species for which natal dispersal has been described, Snail Kites exhibit nomadic tendencies and may breed colonially or solitarily throughout their range (Sykes et al. 1995). Consequently, the assessment of whether or not a site is a potential breeding site is rather ambiguous. Thus, for the purposes of this study, we have defined natal dispersal as the initial dispersal of a bird from its natal wetland regardless of whether or not there was potential for breeding at the site to which it dispersed. We estimated the cumulative probability of natal dispersal of radio-transmitted birds using the Kaplan- Meier product-limit estimator (see above). For this analysis, a juvenile was considered to have dispersed when it was located alive outside its natal wetland. The time of dispersal was estimated as the midpoint between its previous location (in its natal wetland) and the first location outside of its natal wetland. This analysis only relates to the initial dispersal from its natal wetland. Any movements subsequent to initial dispersal were ignored for this analysis, but were included in our estimation of movement probabilities (below). Birds were censored if either we were unable to locate their radio signal or if they were known to have died prior to dispersal from their natal area. For clarity, we report these results as the complement of our estimate (the actual estimate is the probability of not dispersing). Log-rank tests were used for comparisons among dispersal functions. Estimation of Movement Probabilities We estimated the conditional probability of radio-transmittered kites moving (0f) or staying (1 i,) at a given location over a finite time period t to t + 1 (one month) as a simple binomial parameter, conditional upon the animal being alive and its location known at t and t + 1 (Appendix 3-4). A one-month time interval was based on our sampling frequencies. Although the average time between consecutive locations was 13.5 days (our targeted interval was 14 d), there was considerable variability in this parameter. We estimated the upper limit of a 95% confidence interval for the time between locations as 29 days. Thus, we were reasonably certain to have located all birds within our study area every 29 days. For the ease of interpreting our results we used calendar months as the time step for these analyses. There were alternative approaches to estimating movement probabilities that did not rely upon a fixed time interval (i.e., variations of the Kaplan Meier); however we chose a fixed time interval because it allows more straightforward interpretations of our results. The estimates derived from a variable-time estimator would have been confusing because each estimate would be associated with a different length of time. One problem with using a fixed time interval is that some birds may have moved more than once during our interval. However, this problem is inherent in our data regardless of what analysis approach was used because our sampling of radio locations was not continuous. Model Selection as a Basis for Data Analysis ANALYSIS PHILOSOPHY Although the philosophy and statistical methods we used for much of our data analysis have become increasingly more common in ecological literature and are described in considerable detail elsewhere (e.g., Burnham and Anderson 1992, Lebreton et al. 1992, Brownie et al. 1993, Nichols et al. 1993, Anderson et al. 1994, Spendelow et al. 1995), some of the statistical tools we used may not be familiar to some readers. Consequently, we present a brief general discussion of our approach. A critical concept to our approach to data analysis is that a given amount of data will support only limited inference (Burnham and Anderson 1992). Given that premise, we treat data analysis as a problem of optimization, rather than strict elimination of alternative hypotheses based on arbitrary criteria (e.g., c level). We also recognize that data often will suggest more than one model as being appropriate (McCullagh and Nelder 1989), and that we must integrate biological common sense into our statistical inference. The Principle of Parsimony- We use the principle of parsimony as a basis for model selection. We begin with the recognition that all models are wrong (McCullagh and Nelder 1989). Given this recognition, our goal is to be able to make generalizations about the system we are trying to model that are supportable by the data. If we incorporate all possible effects (including their interactions) into our model, we will have the best fit of the data. The problem is that we will not have learned anything. As the number of parameters in a model is increased, the fit of the model to data will improve. The cost, however, for increasing the number of parameters in a model is that precision of the parameter estimates decreases (i.e., the confidence intervals increase)(Burnham and Anderson 1992, Lebreton et al. 1992)(Fig. 3-4). Thus, the goal of our analysis is to find a good balance between having enough parameters in the model to adequately describe the underlying patterns, but not so many so as to defeat the purpose of making generalizations. Thus, a parsimonious model is one which retains only those parameters that are warranted (judged using the tools such as likelihood ratio tests and Akaike's Information Criterion [AIC] described below) and eliminates excessive parameters (Lebreton et al. 1992). Few Number of Parameters Many Figure 3-4. Conceptual tradeoff between precision (low variance)(doned line) and bias (solid line) as function of the number ofparameters in a model (After Burnham and Anderson 1992). Starting Point-- Whenever possible, we tried to evaluate a full spectrum of models. However, in some cases the number of parameters in more saturated models (i.e., with many main effects and interactions) was prohibitively large (e.g., >200). These more complex models also usually contained some parameters that were not identifiable or estimable (see discussion in parameter identifiability in Lebreton et al. 1992). Consequently, when we were unable to evaluate a full suite of models, we used the starting procedures described by Hosmer and Lemeshow (1989). Their approach begins with a univariate assessment of each main effect. Effects that are significant at a liberal a =0.25 are retained for further evaluation. This liberal rejection criterion retains main effects that may have an influence in an interaction that might have been masked during initial tests. The next step was to evaluate a multivariate model with all of the retained main effects, but lacking interactions. Effects that were retained at this stage were further evaluated including their interaction effects. This approach enabled us to reduce the parameter space for more complex models down to a more manageable level, while retaining the integrity of the analysis. THE LIKELIHOOD RATIO TEST One of the advantages of maximum likelihood estimation (MLE) is that it enables a likelihood ratio testing framework. Likelihood ratio tests (LRT) allow testing hypotheses about whether the addition of parameters to a model significantly improves the fit of that model. The test statistic is: -21n = -2 [ln(,) ln(2)] where 3 is the likelihood for a given model evaluated at its maximum, and is distributed as X2 with np, np2 degrees of freedom, where np is the number of estimable parameters. The null hypothesis (Ho) is that the reduced model (i.e., the model with fewer parameters) fits equally well as the more general model (i.e., the model with more parameters). Thus, a failure to reject H, indicates that the additional parameters of the more general model are not warranted. A limitation of LRTs is that they are limited to pairwise comparisons and that the models being compared must be nested (i.e., one is a reduced subset of the other). Consequently, for non-nested models and for multiple comparisons we used Akaike's Information Criteria (AIC)(below) to compare models. AKAIKE'S INFORMATION CRITERION In contrast to using a LRT for hypothesis testing, where the intent (in the context used for our analysis) is to test whether the addition of a particular parameters) improves the fit of a model, the use of Akaike's Information Criterion (AIC)(Akaike 1973, Sakamoto et al. 1986, Shibata 1989, Anderson et al. 1994) provides a systematic approach to the analysis of complex multidimensional data, and it removes the limitation of nested models required by LRTs (Burnham and Anderson 1992, Lebreton et al. 1992, Anderson et al. 1994). Akaike's Information Criterion is defined as: -2 In(Ef) + 2np where In(2) is the log-likelihood function evaluated at the maximum likelihood estimates (Akaike 1973). The first term of AIC (i.e., -2 In(2), called the relative deviance (Lebreton et al. 1992), is a measure of goodness-of-fit of the model and the second term (i.e., 2np) can be viewed as a cost for adding excessive parameters (Lebreton et al. 1992). The properties and benefits of using AIC as a model selection tool have been extensively documented (Shibata 1989, Burnham and Anderson 1992, Anderson et al. 1994, Burnham et al. 1994). We emphasize that the notion of finding one "true" model for complex biological data is unlikely, and often more than one model may be indicated as being appropriate for a given data set (Burnham and Anderson 1992). Anderson et al. (1994) suggest viewing model selection in the analogous context of confidence intervals, rather than point estimation, such that the selection process often indicates a range of models that are appropriate, rather than a single model. Thus, as the differences in AIC become smaller, so does the statistical basis for distinguishing among alternative models (Burnham and Anderson 1992). Sakamoto et al. (1986) suggests that AIC differences of > 1-2 should be considered as statistically significant. Models within this range were consequently judged not to be more or less suitable based on AIC criteria alone. ESTIMATING POWER In the context of hypothesis testing, the results of a given statistical test can have only one of four possible outcomes with respect to "truth"; two that are correct (i.e., are consistent with truth) and two that are erroneous (Table 3-2). The Type I error rate (i.e., a) is reported as a standard protocol for reporting the outcome of statistical tests. Unfortunately, the Type II error rate (i.e., /) is often overlooked. Power is defined as 1 f, or the probability of not committing a Type I error. Estimation of power Table 3-2. Four outcomes with respect to truth of the null hypothesis H, and their corresponding errors (After White et al. 1982). Decision Based on Test Statistic Reject H, Type IError No Error (a) Type II Error Fail to Reject H, No Error Err requires specifying the Type I error rate (a) and an alternative hypothesis (HJ to the null hypothesis (Ij ). We have attempted to report power for tests where we fail to reject a specified alternative hypothesis. If otherwise unspecified, all Type I error rates for power estimation were a = 0.05. In most cases we have used more than one alternative hypothesis H, and express these hypotheses in the form of a systematic departure (A) from the null hypothesis H,, so that we may evaluate power over a range of hypothesized differences. We usually express these alternative hypotheses as: H(0) =A*0 where 0 is a given parameter (e.g., survival or movement probability), and A is the departure from H, imposed on that parameter. For example, if 6=0.8 under our null hypothesis and A = 0.95, then 6 under our alternative hypothesis would be 0.95*0.8 = 0.76 We evaluated the power of a given test by simulating an effect on the actual data (Lebreton et al. 1992). We generated expected count data under an alternative model H. (as above) that included the differences (A) we intended to evaluate. These expected values were then analyzed as if they were real data (Drost et al. 1989, Lebreton et al. 1992). The resulting test statistic asymptotically has a non-central chi-square distribution and can be used to approximate the non- centrality parameter for an alternative hypothesis K to the null hypothesis H of no effect. The probchi function (SAS Inc. 1988) enables us to then estimate f (i.e., the Type II error rate) using this non-centrality parameter for a specified a (in this case a=0.05). Power is then estimated as 1 8. LOGISTIC REGRESSION AND LOG-LINEAR MODELS For some analyses of the influences of survival and movement probabilities, we used a conditional logistic regression or log-linear models based on one month time intervals (see discussion of time interval under Estimation of Movement Probabilities). We used logistic regression in cases where there was a binary response variable (i.e., survival or movement)(Cox 1970, Cox and Oakes 1984, Hosmer and Lemeshow 1989). Because individual animals can move more than once, we are assuming conditional independence (e.g., that the probability of moving between times t and t + 1 is not dependent on whether that animal moved during the previous interval). We used log-linear models for cases where there was no designated response variable, but rather, for exploration of interactions in multi-way cross tabulation (e.g., the probabilities of movements between specific locations)(Agresti 1990, Everitt 1992). We used MLE for the parameter estimation from both logistic and log- linear models (SAS inc. 1988) and consequently, could apply all of the model selection tools described above. THE ANALYSIS OF RESIDUALS FROM CROSS- CLASSIFICATION MODELS In some cases in our analysis of cross-classified models (e.g., contingency tables or log linear analyses) we make use of the residuals for exploring the contributions of individual cells to the overall test significance. We do not use this approach in a hypothesis testing or model selection capacity as described for LRTs or AIC; but rather, to further describe patterns already detected in our analysis. This is often done informally by inspection of deviations from expected values. A problem, however, with this approach is that a fixed deviation does not have the same importance across all sample sizes (Everitt 1992). Consequently we used standardized residuals (Agresti 1990, Everitt 1992) for evaluating the contribution of individual cells to the overall model significance from models in which the expected values were derived from an a priori hypothesis. Where expected values were derived from the marginal totals, we evaluated cell contributions using adjusted residuals (Haberman 1973). Adjusted residuals are normally distributed with a mean of zero and a standard deviation of one such that a residual of 1.96 corresponds with an a of 0.05. MULTIPLE COMPARISONS Throughout this report we have conducted numerous statistical analyses, often involving multiple comparisons of the same parameter. Multiple comparisons can result in an inflated alpha (a) level (Day and Quinn 1989, Fowler 1990); however, the complexity of many of our analyses would enable several approaches to correct for this inflation. Consequently, unless otherwise stated, we did not attempt to adjust the a level. Thus, the reader is cautioned to consider the number of multiple comparisons when interpreting our results. ('Impur -4. IR 1I IX)IN I/ LomtI iitr' .. ... .. .. I . . .. -h... .. h r, i I I I I. , I ,,, I, Ih ,I I I ,,. 1 I h .... .. h I I ,, I. I I .. II ,, , ., ,, 1 I I ... ... 1 I ,. 1 I, I L_ Mean Life Span (MLS) Beissinger (1986) suggested that the average adult life span of Snail Kites in the wild is 5-8 years. This suggestion was based on presumed mortality associated with droughts, based on differences in consecutive counts from the annual Snail Kite survey. Our results, based on Cormack's (1964) estimator, suggests that, given a bird has survived 100 days post-fledging, mean life span (MLS) for adults ranges from 6.0 to 10.3 years (depending on the assumptions made about drought intervals and survival during droughts), and the expected life time E(T), based on the estimator of Brownie et al. (1985), for a newly- fledged juvenile ranges from 4.2 to 6.7 years (Table 4- 1). Improvements on these estimates should be possible as data on drought-year and lag-year survival become available (a monitoring program is in place to obtain these data, provided that funding is available to maintain this effort). These estimates also do not take into account the potential for senescence (i.e., declines in survival rates as age increases). Estimation of Survival from Radio Telemetry We attached a total of 282 radio transmitters, representing 271 individual Snail Kites with 11 birds having been recaptured in a subsequent year and their radios replaced. We were slightly short (82%) of our targeted sample size of 100 birds during 1992 (Table 4- 2); but fully attained our targeted sample sizes in 1993 (Table 4-3) and 1994 (Table 44). We were very close to our targets of age and sex ratios of our sample. The overall age distribution of our sample was 59% (165 of 282) adults and 39% (117 of 282) juveniles; our target was 60% adults and 40% juveniles. We had a total of 82 males (49.7%) and 83 females (50.3%); our target was 50% of each. We had a few minor discrepancies between the distribution of our samples each year and the preceding annual counts; however, most of these resulted from shifts in the distribution of birds, rather than an inability to sample from particular areas. Table 4-1. Mean life span (MLS)for adult Snail Kites and expected life time E(T)forjuvenile Snail Kites; given estimated parameter values for adult (ad) and juvenile (juv) survival (0) during high water and hypothesized parameter values for the drought interval (years), Pad (drought), and 4a (lag year). Hypothesized Hypothesized Hypothesized Drought -d ~d ad 4 MLSad E(T)j, Interval (High Water) (Drought) (Lag Year) 5 0.91 0.6 0.9 0.58 6.0 4.2 5 0.91 0.7 0.9 0.58 7.0 4.8 5 0.91 0.8 0.9 0.58 8.3 5.6 5 0.91 0.9 0.9 0.58 10.1 6.6 6 0.91 0.6 0.9 0.58 6.5 4.5 6 0.91 0.7 0.9 0.58 7.4 5.1 6 0.91 0.8 0.9 0.58 8.6 5.7 6 0.91 0.9 0.9 0.58 10.2 6.7 7 0.91 0.6 0.9 0.58 6.9 4.7 7 0.91 0.7 0.9 0.58 7.7 5.3 7 0.91 0.8 0.9 0.58 8.8 5.9 7 0.91 0.9 0.9 0.58 10.3 6.7 Table 4-2. The number of snail kites captured during 1992 and equipped with radio transmitters at each location of each age class and sex. Total 1992 % of % of 1991 Location Adult Juvenile Male' Female' Sample Sample Count' WCA-2A 2 7 0 2 9 12 4 WCA-2B 1 0 1 0 1 1 3 WCA-3A 2 0 1 1 2 2 1 Holey Land W.M.A. 1 1 1 0 2 2 0 Loxahatchee Slough 2 2 1 1 4 5 7 Lake Okeechobee 19 14 12 8 34 41 40 Lake Kissimmee 7 0 5 2 7 9 13 Lake Tohopekaliga 3 7 1 2 10 12 1 St Johns Marsh 7 6 2 5 13 16 22 Total 45 37 24 21 82 100 913 'Applies to adults only, juveniles cannot readily be sexed in the field 2 Percentage of kites in each area based on the 1992 annual survey 'Some peripheral areas included in the annual count were not sampled due to absence of birds at time of sampling. Table 4-3. The number of snail kites captured during 1993 and equipped with radio transmitters at each location of each age class and sex. Total 1993 % of % of 1992 Location Adult Juvenile Male' Female' Sample Sample Count2 Everglades N.P.' 0 2 0 0 2 2 9 WCA-2A 5 2 3 2 7 7 6 WCA-2B 1 1 0 1 2 2 0 WCA-3A 6 9 4 2 15 15 15 WCA-3B 2 2 1 1 4 4 2 Holey Land W.M.A. 2 1 0 2 3 3 2 Loxahatchee Slough 7 2 1 6 9 9 2 Lake Okeechobee 20 11 10 10 31 31 29 Lake Kissimmee 10 3 7 3 13 13 5 Lake Tohopekaliga 2 0 0 2 2 2 3 E. Lake Tohopekaliga 1 2 0 1 3 3 1 St. Johns Marsh 4 5 4 0 9 9 11 Total 60 40 30 30 100 100 854 'Applies to adults only, juveniles cannot readily be sexed in the field SPercentage of kites in each area based on the 1992 annual survey 'Including the North East Shark River Slough addition lands. 'Some peripheral areas included in the annual count were not sampled due to absence of birds at time of sampling. EFFECTS OF AGE ON SURVIVAL Our estimates of survival for adults were generally higher than for juveniles in each year of this study (Fig. 4-1)(Appendix 4-1). Based on log-rank statistics, adult and juvenile survival estimates differed for study years (SY) 1992 and 1994, but not in SY 1993 (Table 4-5). In both years where they differed, estimates of adult survival were higher than estimates of juvenile survival (Table 4-6). However, in 1993 estimates of juvenile survival were slightly (but not significantly, P = 0.869) higher than adult survival. Ancillary evidence suggests that our estimates for juvenile survival during 1992 and 1993 were biased high (see discussion of Assumptions, Bias, and Sources of Error). Our data from banding (i.e., mark-resighting) also indicates an age effect on survival (below). Consequently, we believe that real differences exist between adult and juvenile survival- and that differences were underestimated for 1992 and 1993. Table 4-4. The number of snail kites captured during 1994 and equipped with radio transmitters at each location of each age class and sex. Total 1994 % of % of 1993 Location Adult Juvenile Male' Female' Sample Sample Count2 Everglades N.P.3 0 1 0 0 1 1 3 Loxahatchee N.W.R. 0 1 0 0 1 1 0 WCA-2A 0 0 0 0 0 0 0 WCA-2B 18 10 8 10 28 28 4 WCA-3A 11 11 4 7 22 22 41 WCA-3B 2 0 1 1 2 2 12 Lake Jackson 1 1 0 1 2 2 0 Loxahatchee Slough 0 0 0 0 0 0 4 Lake Okeechobee 10 4 5 5 14 14 13 Lake Kissimmee 9 6 5 4 15 15 5 Lake Tohopekaliga 4 1 1 3 5 5 0 E. Lake Tohopekaliga 2 4 2 0 6 6 3 St. Johns Marsh 3 1 2 1 4 4 1 Total 60 40 28 32 100 100 86' 'Applies to adults only, juveniles cannot readily be sexed in the field 2Percentage of kites in each area based on the 1992 annual survey 'Including the North East Shark River Slough addition lands. 4 Some peripheral areas included in the annual count were not sampled due to absence of birds at time of sampling. Table 4-5. Results of log-rank tests between survivorship functions of adult and juvenile Snail Kites during each study year (April 15 April 14). Year X2 df Prob 1992 4.611 1 0.032 1993 0.027 1 0.869 1994 29.520 1 <0.001 1992 1993 1.0 ---1.0 0.8 ......................... ...... ....... 0.8 a0.6 0.6 0.4 30.4 0.2 l 0.2 I ene 0.0 0.0 0 50 100 150 200 250 300 350 0 50 100 150 200 250 300 350 Day Since 15 April Day Since 15 April 1.0------- 0.8 I0.6 0.2 0.0 0 50 100 150 200 250 300 350 Day Since 15 April Figure 4-1. Estimates of survivorship functions of radio-transmittered adult and juvenile Snail Kites during study year (SY) 1992, 1993, and 1994. Estimates were derived using a Kaplan-Meier estimator. Confidence intervals for estimates are not shown to minimize cluttering, but are provided in detail in Appendix 4-1. EFFECTS OF SEX ON SURVIVAL In contrast to age, survivorship functions did not differ between adult male and female kites at a = 0.05 (Table 4-7); although there was a slight divergence (significant at a = 0.1) in these functions during SY 1994 (Fig. 4-2)(Appendix 4-2). We were unable to determine the sex of juveniles in the field and consequently have no information regarding survival differences between sexes for this age class. TEMPORAL EFFECTS OF SURVIVAL Annual Effects- Our estimates of survivorship functions of adult Snail Kites did not differ among years at a = 0.05; although, SY 1992 differed from SY 1993 at a = 0.10 (Fig. 4-3)(Table 4-8). In contrast, our estimates of survivorship of juveniles during SY 1994 differed from both SY 1992 and SY 1993 (Table 4-9). Table 4-6. Annual survival estimates () for adult and juvenile Snail Kitesfor 1992, 1993, and 1994. Survival is estimated from 15 April to 14 April (e.g., Q for 1992 is estimated from 15 April, 1992 to 14 April, 1993). Estimates are derived from Kaplan Meier estimator. Age Year 4 SE(<) 95% CI(~ Class Adult 1992 0.962 0.038 0.888 1.000 Adult 1993 0.858 0.063 0.734 0.982 Adult 1994 0.883 0.042 0.801 0.965 Juvenile 1992 0.825 0.080 0.668 -0.981 Juvenile 1993 0.867 0.088 0.695 1.000 Juvenile 1994 0.439 0.090 0.263 0.615 1992 1993 1994 1.0 1.0.... --- 1.0.......... ...... 0.8 0.8 0.8 ........... S0.6 0.6 0.6 S04 0.4 0.4 tn -F (0 --- Fema: e c -- F-emale 0.2 ae 0.2 Male 0.2 e 0.0 0.0 0.0 0 50 100 150 200 250 300 350 0 50 0 1 150 200 250 300 350 0 50 100 150 200 250 300 350 Day Since 15 April Day Since 15 April Day Since 15 April Figure 4-2. Estimates ofsurvivorship functions ofradio-transmittered adult male and female Snail Kites during study years (SY) 1992, 1993, and 1994. Estimates were derived using a Kaplan-Meier estimator. Confidence intervals for estimates are not shown to minimize cluttering, but are provided in detail in Appendix 4-2. Table 4-7. Results of log-rank tests between survivorshipfunctions of adult female and male Snail Kites during each study year (April 15 April 14). Year X df Prob 1992 1.667 1 0.280 1993 0.243 1 0.622 1994 2.753 1 0.097' SUsing the alternative tests described by Cox and Oakes (1984)(Appendix 3-3) that are slightly less conservative (i.e., have greater power, but higher risk of Type I error) we estimated Z=2.77, P=0.095 and f=2.75, P=0.097for alternative tests I and 2, respectively. 1.0" 1.0- -.--- ..... .. '- ---.. 0.8- S0.6- O8 0.4 119943 0.2- 0 50 100 150 200 250 300 350 Day Since 15 April Figure 4-3. Kaplan-Meier estimates ofsurvivorship functions of radio-transmittered adult Snail Kites during each study year (SY). Confidence intervals for estimates are not shown to minimize cluttering, but are provided in detail in Appendix 4-1. Table 4-8. Results of log-rank tests between survivorshipfunctions of adult Snail Kites during each study year (SY)(April 15-April 14). Comparison X2 df Prob 1992 vs. 1993 2.836 1 0.092' 1992 vs. 1994 1.762 1 0.184 1993 vs. 1994 0.480 1 0.486 SUsing the alternative tests described by Cox and Oakes (1984) (Appendix 3-3) that are slightly less conservative (i.e., have greater power, but higher risk of Type I error) we estimated x2=2.93, P=0.087for both tests. Survival estimates for juveniles during SYs 1992 and 1993 were very similar, but were markedly higher than the estimate for 1994 (Fig. 4-4) (but see discussion of censoring below in section on Assumptions, Bias, and Sources of Error). Table 4-9. Results of log-rank tests between survivorship functions ofjuvenile Snail Kites during each study year (SY)(April 15-April 14). Comparison X2 df Prob 1992 vs. 1993 1.432 1 0.231 1992 vs. 1994 6.156 1993 vs. 1994 12.412 1 0.013 1 <0.001 1.0 0.8- S0.6- o 0.4- -1 0.2 0.0 ..... .. ... ... ........ ....... ....... ., 0 50 100 150 200 250 300 350 Day Since 15 April Figure 4-4. Kaplan-Meier estimates ofsurvivorshp functions of radio-transmittered juvenile Snail Kites during each study year (SY). Confidence intervals for estimates are not shown to minimize cluttering, but are provided in detail in Appendix 4-1. Seasonal Effects-- It was apparent from survivorship functions that the risk of mortality (i.e., hazard function) was not constant over time. Compared to an expected value for the number of deaths being equal in each month, mortality of adults tended to be highest during the winter months (x= 18.00, df=ll, P=0.08), and juveniles during late spring and summer (X2=39.54, df= 11, P<0.001)(Fig. 4-5). The seasonal pattern of juveniles corresponds with the first few months post fledging (Fig. 4-6). Juveniles are becoming independent of their parents, beginning to forage on their own, and disperse into unfamiliar areas. Juveniles that survived the first few months post fledging appeared to be most vulnerable at the same time as peak mortality for adults (i.e., January). It is less clear why adult 10 Adults S8- 0 06 E 4 - S APR MAY JUN JUL AUG SEP OCT NOV DEC JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC JAN FEB MAR Month Figure 4-5. The number of radio-transmittered adult and juvenile Snail Kites found dead during each month. 10 Adults 8- 06- 4- 2 l 0 30 90 120 150180 210240 270 300 330 30 420 450 480 mortality was highest in winter; however, during this time cold temperatures tend to lower the availability of existing food resources (Cary 1983, 1985), and leaves are absent from willows, which is the most commonly used species for communal roosting (Sykes et al. 1995, Darby et al. 1996a). We offer this latter suggestion because predation was the most common cause of death of adults. Ancillary evidence suggests that Great-horned Owls (Bubo virginianus), which forage at night, were the most common predator. We emphasize, however, that our suggestions for why adult mortality tended to occur during winter is purely speculative. Other anecdotal evidence (i.e., approximately 6 to 10 birds without radios found dead at nests) suggested that adults can also be vulnerable to owl predation during nesting while incubating at night. SPATIAL EFFECTS OF SURVIVAL Regional Effects (By Region of Initial Capture)- We approached regional differences in survival two ways. First we tested the hypothesis that differences in survival were attributable to differences in the region of initial capture. For this analysis, a bird was assigned to the region of its initial capture, regardless of whether it moved subsequent to capture. For juveniles, the region of capture represents their natal region; however, in most cases we do not know the natal origin of adults or their history of locations prior to capture. Consequently, this hypothesis is biologically more intuitive for juveniles than adults. Differences in survival could reflect several aspects of regional quality (e.g., food abundance). Overall there were few differences in survival among regions of capture. Survival of adult birds captured in different regions did not differ among any regions during 1992 or 1993 (Table 4-10)(Fig. 4-7). During 1994, adult survival differed between the Everglades and Okeechobee regions and between the Everglades and Kissimmee regions but not among any other pairwise combinations. Juvenile survival differed between the Everglades and Okeechobee regions during 1992, but no other differences were detected in any year (Table 4-1 )(Fig. 4-8). We urge caution in interpreting these results. Partitioning survival into two age classes and five different regions (no animals were captured in the peripheral region) often resulted in the number of animals at risk for a given group (r5) being small. Although this does not impose bias, it can present misleading appearances (see discussion of small sample size in Assumptions, Bias, and Sources ofError). 0 30 60 90 120150180210 240270320 0003 604 204 50 4 Day Since Fledging Figure 4-6. The number of radio-transmittered adult and juvenile Snail Kites found dead during each 30-day interval after fledging (uveniles) or capture (adults). 10 Adults 0 a 6- AT Z2 APR MAY JUN JUL AUG SEP OCT NOV DEC JAN FE MAR Month Figure 4-5. The number of radio-transmittered adult and juvenile Snail Kites found dead during each month. 10 Adults 8- 04 2 Z 2 0 30 60 9 0 1 150 180210240270 300 0360390420450 48 10 3 111 240 27-00 330 300 3003420-450 40 Figure 4-6. The number of radio-transmittered adult and juvenile Snail Kites found dead during each 30-day interval after fledging uveniles) or capture (ults). j28- 0 S0 0 120W15 I 180210240 270 303W0360 340450440 Day Since Fledging Figure 4-6. he number of radio-transmittered adult andjuvenile Snail Kites found dead during each 30-day interval afterfledging (juveniles) or capture (adults). mortality was highest in winter; however, during this time cold temperatures tend to lower the availability of existing food resources (Cary 1983, 1985), and leaves are absent from willows, which is the most commonly used species for communal roosting (Sykes et al. 1995, Darby et al. 1996a). We offer this latter suggestion because predation was the most common cause of death of adults. Ancillary evidence suggests that Great-horned Owls (Bubo virginianus), which forage at night, were the most common predator. We emphasize, however, that our suggestions for why adult mortality tended to occur during winter is purely speculative. Other anecdotal evidence (i.e., approximately 6 to 10 birds without radios found dead at nests) suggested that adults can also be vulnerable to owl predation during nesting while incubating at night. SPATIAL EFFECTS OF SURVIVAL Regional Effects (By Region of Initial Capture)- We approached regional differences in survival two ways. First we tested the hypothesis that differences in survival were attributable to differences in the region of initial capture. For this analysis, a bird was assigned to the region of its initial capture, regardless of whether it moved subsequent to capture. For juveniles, the region of capture represents their natal region; however, in most cases we do not know the natal origin of adults or their history of locations prior to capture. Consequently, this hypothesis is biologically more intuitive for juveniles than adults. Differences in survival could reflect several aspects of regional quality (e.g., food abundance). Overall there were few differences in survival among regions of capture. Survival of adult birds captured in different regions did not differ among any regions during 1992 or 1993 (Table 4-10)(Fig. 4-7). During 1994, adult survival differed between the Everglades and Okeechobee regions and between the Everglades and Kissimmee regions but not among any other pairwise combinations. Juvenile survival differed between the Everglades and Okeechobee regions during 1992, but no other differences were detected in any year (Table 4-11)(Fig. 4-8). We urge caution in interpreting these results. Partitioning survival into two age classes and five different regions (no animals were captured in the peripheral region) often resulted in the number of animals at risk for a given group (r,) being small. Although this does not impose bias, it can present misleading appearances (see discussion of small sample size in Assumptions, Bias, and Sources of Error). Regional Effects (By Region of Current Location)- The second approach we used for regional differences in survival reflected actual time spent in each region, rather than just focusing on the region of capture. Thus, we test the hypothesis that the survival of a bird is affected by its current location (e.g., predation risk). For this analysis, a bird that moved from a given region to another was censored from the number of animals at risk for that region (rj) at the midpoint of the interval, and added to the number of animals at risk in the region to which it moved. All deaths were assigned to the 1.0 0.8 > -Evr.gld. |04 --.-.:S S -- Upper StJohn 02 0.2. =-JPoP m 0.0 0 50 100 150 200 250 300 350 Date region where the dead bird was actually found. Similar to region of initial capture, there was little indication of regional differences in adult survival. We found no differences during 1992 or 1993 although, as above, the number of birds at risk (rj) was often low (Fig. 4-9)(Table 4-12). Survival did differ between the Everglades and Okeechobee regions during 1994 at a = 0.05, and between the Everglades and both the Upper St. Johns and Peripheral regions at a = 0.10. We found no significant regional differences for juveniles among any regions during any year (Fig. 4-10)(Table 4-13). 1993 1994 1.0 ---------- 1.0 0.4a D0.4 o.s ........... o. ^' -- -4 *.. 22 0. 2 =.*fl0. 0.0 0.0 0 50 100 150 200 250 300 350 0 50 100 150 200 250 300 350 Date Date Figure 4-9. Estimates of survivorship functions of radio-transmittered adult Snail Kites while they were present in each region during each study year (SY). Estimates were derived using a Kaplan-Meier estimator. Confidence intervals for estimates are not shown to minimize cluttering, but can be easily estimated from information in Appendix 4-5. Table 4-11. Results ofpairwise log-rank tests between survivorship functions of radio-transmittered juvenile Snail Kites fledged from each region during each study year (SY). SY1992 SY1993 SY 1994 Comparisoni X2 df Prob 72 df Prob X2 df Prob EVER vs. OKEE 4.582 1 0.032 0.300 1 0.584 0.095 1 0.758 EVER vs. KISS 1.138 1 0.286 2 1.121 1 0.290 EVER vs. USJ 0.873 1 0.350 2 _2 -- EVER vs. LOXSL 3 _3 .3 _ OKEE vs. KISS 1.333 1 0.248 0.655 1 0.418 OKEE vs. USJ 1.750 1 0.186 2 -3 .3 OKEE vs. LOXSL 3 3 3 KISS vs. USJ 0.050 1 0.824 2 3 KISS vs. LOXSL -- ---3 USJ vs. LOXSL --3 3 ' Regions ofcomparison are Everglades (EVER), Okeechobee (OKEE), Kissimmee (KISS), Upper St. Johns (USJ), and Loxahatchee Slough (LOXSL). SNo deaths occurred of birds from this region during this year; however some sample sizes were quite small (Appendix 4-4). s There were insufficient data to estimate survival and to conduct corresponding log-rank test. Table 4-12. Results ofpairwise log-rank tests between survivorship functions of radio-transmittered adult Snail Kites while they were present in each region during each study year (SY). SY1992 SY1993 SY 1994 Comparison' Y2 df Prob k2 df Prob 92 df Prob EVER vs. OKEE 0.545 1 0.460 -2 4.061 1 0.044' EVER vs. KISS 0.377 1 0.539 1.358 1 0.244 EVER vs. USJ 2 -. 2.842 1 0.092' EVER vs. LOXSL 4 4 _ EVER vs. PERI 2.768 1 0.096' OKEE vs. KISS 0.364 1 0.546 0.1787 1 0.673 0.055 1 0.814 OKEE vs. USJ 0.136 1 0.712 0.035 1 0.851 OKEE vs. LOXSL _4 -. OKEE vs. PERI 0.045 1 0.831 0.099 1 0.753 KISS vs. USJ 2 -4 0.042 1 0.838 KISS vs. LOXSL 4 -- - KISS vs. PERI- -4 -2 _ USJ vs. LOXSL 4 -. 4 - UJS vs. PERI 2 0.020 1 0.887 LOXSL vs. PERI __4 _4 __4 SRegions of comparison are Everglades (EVER), Okeechobee (OKEE), Kissimmee (KISS), Upper St. Johns (USJ), Loxahatchee Slough (LOXSL), and Peripheral Wetlands periI). SNo deaths occurred while in this region during this year; however some sample sizes were quite small (Appendix 4-5). SUsing the alternative tests described by Cax and Oakes (1984)(Appendix 3-3) that are slightly less conservative (i.e., have greater power, but higher risk of Type I error) we estimated f =4.210, P=0.040 and eJ=4.149, P=0.042for alternative tests 1 and 2, respectively. There were insufficient data to estimate survival for one or both groups and to conduct corresponding log-rank test. s Using the alternative tests described by Cox and Oakes (1984)(Appendix 3-3) that are slightly less conservative (i.e., have greater power, but higher risk of Type I error) we estimated j=2.847, P=0.092for both alternative tests I and 2. 6 Using the altenative tests described by Cox and Oakes (1984)(Appendix 3-3) that are slightly less conservative (i.e., have greater power, but higher risk of Type I error) we estimated =2.829, P=0.093 for both alternative tests 1 and 2. 1992 1993 1994 1.0 --------------------- 1.0 ---------------- 1.0 0.8 0.8 0.8 -- ---------- g ., 0.6 > .B. 0.6 0.6 -i 0 --i0.4 | 0.2 UppSWtJoSto 02 P- 0.2 ier 0.0 -- 0.0 0.0 0 50 100 150 250 300 350 0 50 100 150 200 250 300 350 50 100 150 200 250 300 350 Date Date Date Figure 4-10. Estimates of survivorshipfunctions of radio-transmittered juvenile Snail Kites while they were present in each region during each study year (SY). Estimates were derived using a Kaplan-Meier estimator. Confidence intervals for estimates are not shown to minimize cluttering, but can be easily estimated from information in Appendix 4-6. Habitat Effects-Because most of our radio locations were obtained from aircraft, we were sometimes unable to record the habitat type of a given animal's location. This, in combination with the intermittent use of some habitat types (e.g., cypress and disturbed habitats) precluded a meaningful analysis using a Kaplan-Meier estimator (i.e., the number of animals at risk [rJ for these habitats was often too small, or zero). However, we did conduct a more cursory examination by testing whether the number of deaths that occurred in each habitat was proportional to the overall use of that habitat. This test gave us no indication of disparity between mortality.and habitat type (x2=4.68, 4 df, P=0.68)(Fig. 4-11). We were unable to detect any difference in survival of adults between lake and marsh habitats for any year (Table 4-14)(Fig. 4-12). In contrast, juvenile survival differed between these habitats during 1992 (Table 4-15), but not during 1993 or 1994 (Fig. 4-13). Although differences were not statistically significant when regions were compared, this is consistent with the survival estimates from different regions (i.e., estimates from the Everglades and Upper St. Johns regions [marsh habitats] were lower than from Okeechobee or Kissimmee regions [lakes]. This may, at least partially, reflect the conditions in the Everglades following an extended dry period from 1989 to 1991. % Habitat Use % Deaths 36.7% 30 35,0% 15.205 .Sa ia -150% SOmiao M-n 0 Olkhob. 0 N~m ,ort5,m e LW Cypn Pr*l Figure 4-11. Percentage of total use by adult Snail Kites of each habitat type and percentage of the total number of birds that died in each habitat type. Table 4-13. Results ofpairwise log-rank tests between survivorship functions of radio-transmittered juvenile Snail Kites while they were present in each region during each study year (SY). SY 1992 SY 1993 SY 1994 Comparison' X2 df Prob Y2 df Prob Z2 df Prob EVER vs. OKEE 1.750 1 0.186 0.400 1 0.527 2 EVER vs. KISS 1.000 1 0.317 1.452 1 0.228 EVER vs. USJ 0.002 1 0.968 -2 -2 -- EVERvs.LOXSL --2 2 _ EVER vs. PERI 2 0.200 1 0.655 -2 OKEE vs. KISS -j 2 _ OKEE vs. USJ 2.333 1 0.127 2 OKEE vs. LOXSL -2 -- 2 OKEE vs. PERI 2 2 KISS vs. USJ 1.333 1 0.2482 2 KISS vs. LOXSL -2 --2 2_ .2 KISS vs. PERI 2 _ USJ vs. LOXSL _2 2 _2 UJS vs. PERI _2 2 2 LOXSL vs. PERI _2 -2 .2 - 'Regions of comparison are Everglades (EVER), Okeechobee (OKEE), Kissimmee (KISS), Upper St. Johns (USJ), Loxahatchee Slough (LOXSL), and Peripheral Wetlands periI). 2 There were insufficient data to estimate survival for one or both groups and to conduct corresponding log-rank test. SNo deaths occurred while in one or both of these regions during this year; however some sample sizes were quite small (Appendix 4-6). 1992 1993 1994 1.0 1.0 771--\ 1.0 0.8 0.8 ........ 0.8 00.6 0.6 0.6 S0.4 0.4 04 )) U) 0.2 0.2 0.2 i Ii 0.0 0.0 0.0 00 50 100 150 200 250 300 350 0 50 100 150 200 250 300 350 0 50 100 150 200 250 300 350 Date Date Date Figure 4-12. Estimates ofsurvivorshipfunctions of radio-transmittered adult Snail Kites while they were present in lake and marsh habitats during each study year (SY). Estimates were derived using a Kaplan-Meier estimator. Confidence intervalsfor estimates are not shown to minimize cluttering, but are provided in detail in Appendix 4-7. Table 4-14. Results of log-rank tests between survivorship functions of adult Snail Kites while they were in lake and marsh habitats during each study year (SY). Year 2 df Prob 1992 0.633 1 0.426 1993 1.367 1 0.242 1994' 2.028 1 0.154 SUsing the altenative tests described by Cox and Oakes (1984)(Appendix 3-3) that are slightly less conservative (i.e., have greater power, but higher risk of Type I error) we estimated X'=2.075, P=0.150 and e=2.046, P=O.153 for alternative tests 1 and 2, respectively. Table 4-15. Results of log-rank tests between survivorship functions ofjuvenile Snail Kites while they were in lake and marsh habitats during each study year (SY). Year X2 df Prob 1992' 4.353 1 0.037 1993 0.903 1 0.342 1994 0.559 1 0.455 SUsing the alternative tests described by Cox and Oakes (1984)(Appendix 3-3) that are slightly less conservative (i.e., have greater power, but higher risk of Type I error) we estimated J=4.383, P=0.036for alternative tests I and 2, respectively. 1992 1993 1994 0 ------- ------------- 1.0 ------------- 1.0 0.8 0. 0.8 0 6 0.6 -0.6- 0.4 =0.4 0.4 0.2 L02 2------- 0.0 0.0 l .l l i ii l 11 111. .1..1. 0.0 0 50 100 150 200 250 300 350 0 50 100 150 200 250 300 350 0 50 100 150 200 250 300 350 Date Date Date Figure 4-13. Estimates of survivorship functions of radio-transmittered juvenile Snail Kites while they were present in lake and marsh habitats during each study year (SY). Estimates were derived using a Kaplan-Meier estimator. Confidence intervals for estimates are not shown to minimize cluttering, but are provided in detail in Appendix 4-8. MODEL SELECTION FOR EFFECTS ON SURVIVAL Above we provided evidence for individual effects on survival based on relatively continuous time (data were discrete at the interval of telemetry locations) using a Kaplan-Meier estimator. While this approach has many advantages (e.g., no assumptions about the hazard function), it is not a convenient method for exploration of more complex models including the relative effects of multiple factors. Here we use a discrete-time approach (conditional logistic regression) to evaluate the probability that a bird survives to time t + 1, conditional on it being alive at time t. This approach enables us to better evaluate effects on survival in combination to determine effects most appropriately supported by our data in an overall survival model. We began our analysis with a univariate examination of the sources of variation (Table 4-16). Because the potential for interaction effects to be masked at this preliminary stage, we followed the recommendation of Hosmer and Lemeshow (1989) and used 0.25 as a rejection criteria (a) for inclusion in further models. The effects due to region were the only source eliminated at this phase of our analysis. We also found little evidence for regional effects on survival using a Kaplan-Meier estimator. We were unable to estimate the effects of month because no deaths occurred during some months. Consequently, we retained month Table 4-16. Summary statisticsfor the univariate analyses (using conditional logistic regression) of effects on the conditional probability of an animal surviving to time t + 1, given that it was alive at time t. Z' is based on a Wald statistic (SAS Inc. 1988). Source df X2 P Age 1 18.29 <0.001 Sex 2 18.32 <0.001 Season 2 5.17 0.075 Month' -- -- Year 2 8.59 <0.001 Region 5 3.06 0.691 'Because deaths did not occur in some months, estimation of these statistics were not reliable. We evaluated this source in a more comprehensive analysis (below) using model selection procedures. as a potential source of variation at this stage of the analysis. Some of the parameters at this stage of the analysis were subsets of other parameters (e.g., season represents combinations of months). Next we evaluated which of these potentially redundant parameters to use in further models. For this analysis, we used 3 classes of sex (male, female, and unknown). Because we were unable to determine the sex of juveniles in the field, sex represents a combination of age and sex (i.e., all cases where the sex class is unknown are juveniles). Based on AIC (Table 4-17) sex provided no substantial gain in model fit at the cost of an additional parameter. Consequently, we used age, but not sex, in all subsequent models. Similarly, individual months provided no substantial gain in model fit over season at the cost of 9 additional parameters. Thus, we used season, but not month, in all subsequent models. The remainder of our model selection invloved the comparison of models using different combinations of age, season, and year effects. Based on AIC, any of the models which did not include some combination of all three of these effects generally were less adequate than models which included all three effects. Although model selection often indicates that more than one model is suitable (McCullagh and Nelder 1989) for a given data set, our analysis indicated (by a substantially lower AIC) that a model including the main effects of age, season, year, and the interaction effects of age*year, and age*season was the most appropriate model for our data. This conclusion is further supported by the goodness-of- fit (P=0.806) of this model compared to the fully saturated model. EFFECTS OF HYDROLOGY One objective of this study was to determine the environmental correlates of survival, particularly hydrology. However, as previously suggested (Bennetts and Kitchens 1993, 1994), survival was sufficiently high that within-year patterns would have been difficult to detect without enormous effort (i.e., too few individuals died to enable quantitative comparisons with those that survived) and probably would not have been very insightful. Rather, we now believe that between-year differences in survival will be more appropriate. Even though we encountered relatively high water conditions throughout this study, the mark-resighting phase of our study (below) was intended to detect these differences and will enable a long-term evaluation of environmental correlates (e.g., hydrologic conditions) as variable conditions occur. Table 4-17. Summary statistics for conditional logistic regression modelfor the factors effecting the probability ofsurvival to time t + 1 (at monthly time steps), given that an animal was alive at time t. Shown are the model description, number of estamable parameters (np), relative deviance (-21n[]), Akiake's Information Criteria (AIC), and a measure ofgoodness-of-fit (GOF)for models with relatively low AIC values. GOF was derived using the probability of a LRT comparing a given model with afully saturated model. The null hypotheses (HI) of the GOF statistic is that the reduced model (with fewer parameters) fits tha data as well as the more general model (with more parameters). Thus, failure to reject H, indicates a fit of the reduced model. The model with the lowest AIC.(bold) would be selected if based only on this criteria. Model' np -21n(f) AIC GOF Age 2 369.333 373.333 Sex 3 368.243 374.243 Seas 3 382.272 388.272 Mon 12 366.496 390.496 Yr 3 379.712 385.712 Reg 6 385.931 397.931 Age Seas 4 363.063 371.063 Age Seas Age*Seas 6 346.849 358.802 Age Yr 4 359.446 367.446 Age Yr Age*Yr 6 349.633 361.633 Seas Yr 5 372.513 382.513 Seas Yr Seas*Yr 9 363.101 381.101 Age Seas Yr Age*Seas Age*Yr 10 324.142 344.142 0.806 Age Seas Yr Age*Seas Seas*Yr 12 327.212 351.212 0.240 Age Seas Yr Age*Yr Seas*Yr 12 333.876 357.876 0.045 Age Seas Yr Age*Seas Age*Yr Seas*Yr 14 322.120 350.120 0.665 Age Seas Yr Age*Seas Age*Yr Seas*Yr Age*Seas*Yr 18 318.039 354.039 1.000 ' Term abbreviations: Season (Seas), Month (Mon), Year (Yr), and Region (Reg). Estimation of Survival from Banding Data Our sample of banded birds for survival analyses was obtained through a cooperative banding effort with the Florida Game and Fresh Water Fish Commission (GFC). This sample was also supplemented by resighting of individually marked birds banded on previous studies that were observed during this study (REB, J. Rodgers unpubl. data). Our total sample of individual banded birds for analyses of survival was 913 (Table 4-18); although birds banded in 1995 will only contribute to estimates following subsequent resighting periods (i.e., in future years). Of this sample, 191 were adults at the time of addition to our sample and 722 were juveniles; however, resightings of juveniles after their fledging year then re-enter our sample as adults (although resightings were not included in our sample size reported above). EFFECTS OF AGE AND TIME Estimates of survival from banding data differ from those obtained via radio telemetry in that they are discrete at the interval of sampling periods. In this case we measured survival from one breeding season to the next (i.e., approximately annually, see methods). This precludes the ability for testing some of the effects that we tested using radio telemetry data. For example, we are unable to test seasonal differences in survival without having had a sampling (i.e., capture and/or resighting) period in each season of interest. We began our assessment using the testing sequence of models described by Pollock et al. (1990) as Models A, B, and D. Model A assumes that both survival and resighting probabilities are time dependent (i.e., that separate estimates for each year are warranted). Model B assumes that survival is constant over time (i.e., that a single estimate can be applied to all years), but that resighting probability is time dependent. Model D assumes that both survival and resighting probabilities are constant over time. Each of these models was generated with and without separate parameters for each age class. In this set of models juveniles are assumed to become adults at the beginning of their first resighting period after their fledging year (this assumption is tested below). Juveniles are capable of breeding at nine months of age (Snyder et al. 1989a) and our radio telemetry results suggest that, for our data, survival of juveniles was similar to adults following an initial period of about 3-4 months of high post-fledging mortality. Thus, in these models, one resighting probability is used for both juveniles and adults (because juveniles become adults at the beginning of their first resighting period); but, a separate estimate of juvenile survival is initially generated (because juvenile survival is measured from the time of fledging to their first resighting period), but may be later deemed unnecessary through model selection procedures. The results from this initial sequence (Table 4- 19) indicated that model A with 2 age classes was the most suitable. The AIC (114.811) from this model was substantially lower than the alternative models. Likelihood ratio tests (LRTs) reinforced the selection of Model A as being the most appropriate with regard to time dependency. LRTs between models B and A with Table 4-18. Number of banded birds from each age class, location, and year obtained through a cooperative banding effort with the Florida Game and Fresh Water Fish Commission or through resighting of birds banded on previous studies. Individual birds are only shown at the time and location that they entered our sample. 1992 1993 1994 1995' Location AD JUV AD JUV AD JUV AD JUV Everglades N.P.2 2 -- 1 -- 8 Big Cypress N.P. -- -- -- 18 Loxahatchee N.W.R. 1 2 2 - WCA-2A 3 13 6 15 4 WCA-2B 1 1 2 16 45 -- 87 WCA-3A 4 1 8 24 11 23 2 40 WCA-3B 1 -- 1 6 3 -- -- Holey Land W.M.A. 2 2 2 6 -- Loxahatchee Slough 3 3 6 10 1 -- Lake Okeechobee 27 61 20 96 9 6 -- 15 Lake Kissimmee' 8 14 9 31 11 19 Lake Tohopekaliga 4 21 1 7 4 8 1 15 East Lake Tohopekaliga 1 1 1 18 1 14 1 - Upper St. Johns Marsh 12 43 6 33 3 1 5 Total 66 159 62 252 59 119 4 192 ' Birds newly marked in 1995 do not contribute to survival analyses until subsequent resighting periods are completed. ' Includes the Northeast Shark River Slough addition lands ' Includes nearby Lake Jackson and without age effects both strongly rejected the more reduced model (Table 4-20), as did the comparisons between models A and D with and without age effects. This indicates that constraining survival (0) and/or resighting probability (p) to be constant among years was not justified (i.e., these data support a year effect). Similarly, a LRT between models A with and without an age effect (Models A and A.) strongly rejected (x2 = 44.846, 3 df, P < 0.001) suggesting that constraining parameter estimates to be equal for adults and juveniles also was not justified (i.e., these data also support an age effect). The goodness-of-fit (GOF) test also failed to reject the null hypothesis (Ho) that this model adequately fits the data (GOF tests for all of the alternative models strongly rejected Ho. In order to test the assumption (above) that a separate estimate for juvenile resighting probability was not warranted, we generated an alternative model Table 4-20. Resulting statistics from Likelihood Ratio Tests (LRT) between Cormack-Jolly-Seber (CJS) models "A ", "B and 'D"with and without separate estimates for each age class. General Reduced > 2 Model Model df P > A B 10.687 2 0.005 A, B, 19.350 4 <0.001 A D 12.756 4 0.013 A, D,. 21.783 6 0.001 structure in which we assumed that juveniles became adults at the end (rather than the beginning) of their first resighting period in the post fledging year. This enabled separate estimation for juvenile resighting, as well as survival, probabilities. We then tested the assumption using an analogous model to Model A, from above by comparison of models where resighting probability is constrained and unconstrained to be equal for juveniles and adults. The AIC of the model in which resighting probability was constrained to be equal (which is equivalent to Model A, from above) for the two age classes was lower than for the unconstrained model (Table 4-21). A LRT between these models also failed to reject the more reduced model (x2 = 1.808, 3 df, P =0.613) indicating that the additional parameters for a separate resighting probability for each age class were not warranted for these data. Based on the results from our radio telemetry, which indicated greater differences in survival among years for juveniles than adults, we generated one additional model to test this hypothesis (i.e., that survival was time dependent for juveniles, but not for adults). This model (Model Aj,.) had a lower AIC than model A,, (Table 4-22). A LRT between these models also failed to reject the null hypothesis (Ho) that the reduced model (Model A,,. ) fit the data equally well as the more general model (Model Ay) (X2 = 0.290, 2 df, P =0.865). The goodness-of-fit for this model also failed to reject the null hypothesis (Ho) that this model adequately fits the data. Thus, these data support a model that survival differs among years for juveniles, but not adults. Parameter estimates for the most parsimonious model (Model Aju.) are provided in Table 4-23. Table 4-19. Model selection statistics for initial set of Cormack-Jolly-Seber (CIS) models with and without age and time dependency. The model with the lowest AIC (bold) would be selected if based only on this criteria. Model No. Age Model Description Classes -21n(S) np AIC GOF A A Pi 1 141.657 6 153.657 <0.001 A., p, 2 96.811 9 114.811 0.167 B 9. p, 1 152.344 4 160.344 <0.001 B,. 0. p, 2 116.161 5 126.161 0.004 D P. p. 1 154.412 2 158.412 <0.001 D,, 0. p. 2 118.595 3 124.595 0.004 EFFECTS OF SEX Most banded birds in this study were banded as juveniles, which cannot be sexed in the field. Thus, a large segment of our sample was of unknown sex. Consequently, we did not attempt any analysis from banding data on survival differences attributable to sex. REGIONAL EFFECTS To test for regional effects of survival and resighting probabilities, we generated a suite of multi- state models (Brownie et al. 1993, Nichols et al. 1993) analogous to the base models described above as models A, B, and D by Pollock et al. (1990) except that they include a multi-state component that enables parameters to be estimated for multiple strata (Brownie et al. 1993, Nichols et al. 1993)(in this case strata= 5 of the 6 regions of capture no captures occurred in the peripheral region). Regional effects were tested based only on the region of capture since banding data do not provide information regarding where a given bird has Table 4-21. Model selection statistics for model A4g in which the assumption of equal resighting probabilities between adults and juveniles is relaxed or constrained. The model with the lowest AIC (bold) would be selected if based only on this criteria. Model Resighting probability Model Description ) e lfor age -21n(E) np AIC GOF classes A., p, No 95.004 12 119.004 0.106 A,, A p, Yes 96.811 9 114.811 0.167 Table 4-22. Model selection statistics for variations of models in which survival is time dependent for both adults and juveniles (Model A,), and is constrained to be constant for adults, but not juveniles (Model A,,,. The model with the lowest AIC (bold) would be selected if based only on this criteria. S Model Model Description -21n(S) np AIC GOF A. p, 96.811 9 114.811 0.167 Aj.. 4,,, pA 97.101 7 111.101 0.253 Table 4-23. Parameter estimates for the selected Cormack-Jolly-Seber (CIS) Model (ModelIA4), in which adults and juveniles differ in survival, survival is constant among years for adults, and survival is variable among years for juveniles. Resighting probability in this model differs among years, but is the same for adults and juveniles (i.e., juveniles are considered adults at the beginning of their first resighting period after their fledging year). Age Year i 95% C.I.(O) pi 95% C.I.(p) Adult 1992 0.840 0.637 1.000 0.142 0.073 0.210 Adult 1993 0.840 0.637 1.000 0.255 0.150 0.359 Adult 1994 0.840 0.637 1.000 0.217 0.095 0.336 Juvenile 1992 0.451 0.281 0.620 0.142 0.073 0.210 Juvenile 1993 0.163 0.071 0.256 0.255 0.150 0.359 Juvenile 1994 0.620 0.177 1.000 0.217 0.095 0.336 been between resightings. These models are also the default models generated by program MSSURVIV (Hines 1994) used for this analysis. As above, we generated models with and without age dependency, enabling us to test hypotheses that survival ( ) and/or resighting probabilities (p) were affected by age, time, and region, simultaneously. For this set of models, the AIC was lowest for Model B with no age effect; however, Model B. with an age effect was sufficiently similar (a difference of 1.36- less than our criteria for exclusion) to be retained as a reasonable alternative (Table 4-24). LRTs reinforced the selection of Model B as being the most appropriate with regard to time dependency. LRTs between models B and A with and without age effects both failed to reject (Table 4-25), indicating that separate estimates of survival (0) for each year were not warranted. Similarly, LRTs between models B and D were highly significant, indicating that constraining resighting probability (pi) to be constant among years also was not justified. Because AIC was similar for models B and B,, we used a LRT to specifically test the null hypothesis that an age effect for Model B was not warranted (i.e., that the fit of the 1-age model was equal to the fit of 2-age model). This test (Model B vs. Model B,.) strongly rejected the null hypothesis (x2 =48.638, 25 df, P=0.003) indicating that separate parameter estimates for each age class were warranted (i.e., there was an age effect). Thus, from this set of models we selected Model B with 2 age classes (Model B.) as the most appropriate model for our data. Using Model B, as a base model, we generated another set of models in which (1) survival was constrained to be constant for all regions (Model Ba,_,.), (2) resighting probability was constrained to be constant for all regions (Model B ,,P), and (3) both survival and resighting probability were constrained to be constant for all regions (Model B_,,.p.). Based on AIC, these results indicated that Model B ,, was an improvement (i.e., had a lower AIC) over the base model but the other two regional models were worse than the base model (Table 4-26). Thus, our data do not support regional differences in survival but do support differences in resighting probability among regions. As above we also wished to test the hypothesis that juvenile survival was time dependent but adult survival was not. To test this hypothesis, we used model Bm, _.p. as our base model and relaxed juvenile survival to be time specific. The resulting model (Model B-.,r p) had a lower AIC than alternative models (Table 4-27) indicating a time effect for juvenile, but not adult, survival. A LRT between Model B.,_,.p (the reduced model) and Model B. ra, (the more general model) also strongly rejected (2 = 10.204, 2 df, P=0.006), indicating that the additional parameters for year-specific survival of juveniles were warranted. Parameter estimates using the model selected from above (Model Ba.,,,) are provided in Table 4-28. Table 4-24. Model selection statisticsfor initial set of multi-state (regional) models with and without age and time dependency. The models) in bold text were selected as being the most appropriate (parsimonious) for these data. Model No. Age Model Description Classes -21n(g) np AIC A p, 1 420.679 89 598.679 A.. 2 338.447 164 666.447 B 4. p; 1 448.420 42 532.420 B.. 0. p, 2 399.781 67 533.781 D 0. p. 1 473.808 32 537.808 D, 0. p. 2 429.037 57 543.037 Table 4-25. Resulting statistics from Likelihood Ratio Tests (LRT) between multi-state (regional) models "A", '"B and D" with and without separate estimatesfor each age class. General Model Reduced Model X2 df P > x2 A B 27.740 47 0.989 A. B,, 61.335 97 0.998 B D 25.389 10 0.005 B. D, 29.256 10 0.001 Table 4-26. Model selection statistics for set of models which constrained survival and/or resighting probability to be constant for all regions. The base modelfor comparison was Model B,,. Model(s) in bold text were selected as being the most appropriate (parsimonious) for these data. Model Parameters constrained Model Description to be constant across np AIC for time dependency regions, B. 4. p, 399.781 67 533.781 B0_. 0. p; 0 409.749 59 527.749 B ,0 p, p 490.202 55 600.202 B. ,0. p, O& p 455.231 47 549.231 ' One parameter estimate is used for all regions. For example Model B,_. has one estimate ofsurvivalfor each age class that is used for all regions, but a separate estimate of resighting probability for each age class and each region. SIncludes parameters for transition probabilities (i. e., the probability of being in a different strata [region] between times t and t + 1. These probabilities are better estimated using radio telemetry and consequently not reported [see methods]). Table 4-27. Model selection statistics for set of models which constrained survival to be constant for adults, variable forjuveniles. The base model for comparison was Model B,. Model(s) in bold text were selected as being the most appropriate (parsimonious) for these data. Model Parameters constrained Model Description to be constant across -21n() n AIC for time dependency regions, B 0. pP -- 399.781 67 533.781 B 0 p, 409.749 59 527.749 B _,,p .. p, 4& p 455.231 47 549.231 Bd. rO 0.(< pi O&p 389.717 61 511.717 1 Oneparameter estimate is usedfor all regions. For example Model B._,. has one estimate of survivalfor each age class that is used for all regions, but a separate estimate of resightingprobabilityfor each age class and each region. 'Includesparametersfor transition probabilities (i.e., the probability of being in a different strata [region] between times t and t + 1. These probabilities are better estimated using radio telemetry and consequently not reported [see methods]). Table 4-28. Parameter estimatesfor the selected multi-state model (Model B~,,), in which adults and juveniles differ in survival, survival is constant among yearsfor adults, and survival is variable among years for juveniles. Resighting probability in this model differs among years and regions, but is the same for adults and juveniles (i.e., juveniles are considered adults at the beginning of theirfirst resighting period after their fledging year). Age Year Region A 95% C.I.() Pi 95% C.I.(p) Adult Adult Adult Adult Adult Adult Adult Adult Adult Adult Adult Adult Adult Adult Adult Juvenile Juvenile Juvenile Juvenile Juvenile Juvenile Juvenile Juvenile Juvenile Juvenile Juvenile Juvenile Juvenile Juvenile Juvenile EVER OKEE KISS USJ LOXSL EVER OKEE KISS USJ LOXSL EVER OKEE KISS USJ LOXSL EVER OKEE KISS USJ LOXSL EVER OKEE KISS USJ LOXSL EVER OKEE KISS USJ LOXSL 0.996 0.996 0.996 0.996 0.996 0.996 0.996 0.996 0.996 0.996 0.996 0.996 0.996 0.996 0.996 0.709 0.709 0.709 0.709 0.709 0.302 0.302 0.302 0.302 0.302 0.731 0.731 0.731 0.731 0.731 0.715 1.000 0.715 1.000 0.715 1.000 0.715 1.000 0.715 1.000 0.715 1.000 0.715 1.000 0.715 1.000 0.715 1.000 0.715 1.000 0.715 1.000 0.715 1.000 0.715 1.000 0.715 1.000 0.715 1.000 0.228 1.000 0.228 1.000 0.228 1.000 0.228 1.000 0.228 1.000 0.036 0.568 0.036 0.568 0.036 0.568 0.036 0.568 0.036 0.568 0.000 1.000 0.000- 1.000 0.000 1.000 0.000 1.000 0.000- 1.000 0.126 0.099 0.293 0.766 <0.001 1.000 0.119 0.416 0.298 0.005 0.967 0.082 0.170 1.000 <0.001 0.126 0.099 0.293 0.766 <0.001 1.000 0.119 0.416 0.298 0.005 0.967 0.082 0.170 1.000 <0.001 0.000 0.377 0.000 0.213 0.076 0.510 0.000 1.000 0.000 0.321 0.999 1.000 0.000 0.250 0.156 0.678 0.000 1.000 0.000 0.018 0.378 1.000 0.000 0.199 0.026 0.314 0.000 1.000 0.000 0.237 0.000 0.377 0.000 -0.213 0.076 0.510 0.000 1.000 0.000 0.321 0.999 1.000 0.000 0.250 0.156 -0.678 0.000 1.000 0.000 0.018 0.378 1.000 0.000 0.199 0.026 0.314 0.000 1.000 0.000 0.237 'Region abbreviations: Everglades (EVER), Okeechobee (OKEE), Loxahatchee Slough (LOXSL). Kissimmee (KISS), Upper St. Johns (USJ), and CONCLUSIONS ABOUT THE EFFECTS OF SURVIVAL FROM BANDING DATA Our overall conclusion from both the CJS models and the extension to multi-state models support (1) an age effect on survival, (2) annual differences in survival of juveniles, but not adults, and (3) regional differences in resighting probability, but not survival. Both the CJS models and the multi-state models ended up with the same model with respect to age and time dependency; although they arrived there in different ways. The CJS model indicated Model Ag, (i.e., age and time dependency for both survival and resighting probabilities) of the initial set of models was most parsimonious. However, when survival was constrained to be constant among years for adults (but not juveniles), it improved the model. In contrast, the multi-state models initially resulted in the selection of Model B,. (i.e., survival was constant among years but resighting probabilities differed among years). However, when juvenile (but not adult) survival was relaxed to vary among years, it likewise improved the model. Thus, both the CJS and multi-state models ended up with selection of models in which (1) adult and juvenile survival differed, (2) adult survival was constant among years, (3) juvenile survival differed among years, and (4) resighting probability differed among years. The multi- state models also supported the hypothesis that resighting probabilities differed among regions but survival probabilities did not. A Synthesis of the Effects of Survival EFFECTS OF AGE The results from radio telemetry and banding data were generally consistent. Both sources of data indicated that survival was dependent on age. Log-rank tests between survivorship functions of radio- transmittered adults and juveniles were significant for 2 of 3 years. Model selection from a conditional logistic model of survival of radio-transmittered birds also indicated an age effect on survival; as did LRTs of banding from both (CJS) and multi-state models. In all cases, where significant differences occurred adult survival was higher than juvenile survival. The exception to that pattern was the radio telemetry data for 1993 in which juvenile survival (0.87) was estimated to be slightly (and not significantly, X2=0.243, 1 df, P = 0.622) higher than adult survival (0.86). EFFECTS OF SEX As explained above, we did not attempt any analysis from banding data to evaluate differences in survival that were attributable to the sex of the bird. Our Kaplan-Meier estimates and our logistic regression analyses of radio-telemetry data generally did not support differences in survival attributable to sex of the bird. However; survivorship functions from Kaplan-Meier estimates did indicate a difference between males and females for one of three years (1994) at a = 0.10. TEMPORAL EFFECTS Survival estimates from both radio telemetry and banding data generally indicated annual differences in survival among juveniles but not adults. However, survivorship functions of adults did differ between 1992 and 1993 at a = 0.10. Although both sources of data indicated differences among years for juvenile survival, the parameter estimates from these two data sources were not consistent in their relative ranking among years. The Kaplan-Meier estimates for juveniles were 0.83, 0.87, and 0.44 for 1992, 1993, and 1994, respectively. In contrast the CJS estimates were 0.45, 0.16, and 0.62 for 1992, 1993 and 1994, respectively. Assumptions, Bias, and Sources of Error for Survival Estimators Although it is often not explicitly stated, virtually all estimators require making assumptions. Estimators are often robust to violation of some assumptions (i.e., violation of the assumption does not strongly affect its performance) but not to others. Some of the readers of this report may not be familiar with particular estimators we have used or their corresponding assumptions. We have attempted to summarize here what we believe are the major factors that could produce spurious or misleading results regarding our estimates of survival and discuss what influence these factors may or may not have on interpreting our results. ASSUMPTIONS INHERENT IN THE STUDY DESIGN FOR VALID INFERENCES FROM SURVIVAL ANALYSES Study Animals are Representative of Population-- Random sampling within a population is usually intended to assure that the scope of inference from a study can validly be applied to the population of interest. Our design was intended to systematically accomplish that same goal. Our study area encompassed the entire range of Snail Kites in Florida. We then stratified our sample to be proportional to the annual count and balanced our sample with respect to age and sex. Thus, we believe that we have a representative sample from the entire Florida population. We cannot be sure that there was an equal probability of capturing any given individual; however, we believe that heterogeneity of catchability played only a minor role in determining our final sample. Study Conditions are Representative of the Conditions of Interest- There is considerable variability in the environmental conditions that may influence Snail Kite populations. We recognized from the outset of this project that our inference would be limited to the conditions encountered during our study (Bennett and Kitchens 1992, 1993, 1994). Most notably, drought has been reported as a major influence on kite populations (Sykes 1983b, Takekawa and Beissinger 1989, Beissinger 1995), and we did not encounter drought conditions during this study. Consequently, valid inference from our study cannot be extended to drought conditions. Although speculation has often been made concerning survival during drought conditions (e.g., Snyder et al. 1989a, Beissinger 1995), we know of no valid estimation of survival during such conditions. Survival Is Independent of Other Animals-- An assumption shared by both radio telemetry and banding studies is that the fate of one marked animal is not influenced by the fate of another. This assumption is likely to be violated when being in a group of animals results in exposure to some common risk (e.g., predation at a roost site). Although we were unable to explicitly test for independence, violation of this assumption does not cause bias in the estimates of survival. Rather, it will artificially reduce the variance of the resulting estimates (Burnham et al. 1987, Pollock et al. 1989). We do not believe that any severe violation of this assumption occurred. Only once during this study did we find mortality of more than one individual at the same time and place. Two banded siblings, one of which was radio transmittered, were found dead at the same site in Loxahatchee National Wildlife Refuge at what was believed to be the feeding site of a Great-horned Owl. ASSUMPTIONS OF THE KAPLAN-MEIER ESTIMATOR Carrying a Radio Transmitter Does Not Affect Survival-- A critical assumption of studies using radio telemetry to estimate survival is that the radio transmitter does not affect survival. There has been substantial evidence in recent years to suggest that, for some species, radio transmitters may influence the behavior, weight gain, reproduction, or survival of study animals (e.g., Marks and Marks 1987, Hooge 1991, Paton et al. 1991, Foster et al. 1992). We tested for the effect of carrying a radio transmitter on survival using both CJS and multi-state CJS models from our banding data. These data consisted of animals both with and without radio transmitters, thus allowing us to specifically test for radio effects. Because our previous analyses using CJS models had indicated Model Aj. (see above) as the most parsimonious, we used it as a base model for testing radio effects. We maintained the constraints of this model for constant adult survival among years but variable juvenile survival; however, we derived separate parameter estimates (for survival, resighting probability, or both) for birds with and without radio transmitters. The resulting models all had a higher AIC than the previously selected model (Table 4-29). This indicates that using separate parameters for birds with and without radio transmitters was not supported by our model selection procedures (i.e., there is no radio effect). This conclusion was further supported by LRTs between models with separate estimates for birds with and without radio transmitters and our base model in which parameters among these groups were equal (Table 4-29). In addition to model selection and LRTs, the actual parameter estimates from these models were higher for birds with radio transmitters than for birds without radio transmitters. Of course, we do not mean to imply that radio transmitters somehow improved the chances for survival (our procedures above suggest survival of birds with and without radio transmitters does not differ); but rather, that our parameter estimates also support the conclusion that radio transmitters did not lower the probability of survival. We also explored the effects of radio transmitters on survival using a link function (Lebreton et al. 1992) in our multi-state models. Rather than use a separate parameter for survival of birds with and without radios for each group of animals (e.g., age class), a link function (t) links the parameters to an external variable (e.g., radios) via a linear formula. In this case we modeled survival of birds with and without radios as: 0 d- 0*fdit*y(o) and P-ad Pdi** APdio) Thus, the values of the link functionsAj~ and ftp) are the only two additional parameters estimated and a test of a radio effect is whether or not the confidence intervals of these link functions cover 1 (i.e., a value of 1 shows no effect from the parameter). As above, we used the most parsimonious model from our previous analysis of multi-state CJS models (Model B, ,,y) as a base model for testing radio effects. For both survival and resighting probability the confidence intervals of the link functions covered 1 (Table 4-30). In both cases the estimated link-function values were < 1, which would imply that our survival estimate for birds with radio transmitters was higher than for those without radios. We concluded this assessment by simulating a radio effect (A[4]) on the actual data and estimating the power to detect differences in survival attributable to that effect. This analysis showed that we had relatively low power to detect small differences attributable to radio transmitters, but had reasonable power to detect substantial radio effects (Fig. 4-14). Although our tests for an effect of radios on survival were not completely conclusive, all evidence that we had indicated that radios had no negative effect on survival during this study. Table 4-30. Parameter estimates, standard error (SE), and 95% confidence intervals (C..) for link functions f(O) of radio effects on survival and resighting probability from the most parsimonious multi-state CJS model (Model B ,a). Parameter Estimate SE 95% C.I. f() 0.977 0.053 0.873 1.081 ftp) 0.848 0.240 0.377 1.319 _0.8- S0.6 b 0.4- 0.2 0.0 0.5 0.6 0.7 0.8 0.9 1.0 A Figure 4-14. Estimated powerfor detecting differences (A) in survival of birds with and without radio transmitters. Differences in survival were simulated using Model B, as a base modelfrom which differences were imposed. Table 4-29. Variations of base (most parsimonious) model (Model Aj, in which separate estimatesfor birds with and without radio transmitters were derivedfor survival (4), resighting probability (p), or both. Parameter(s)' -21n(?g) np AIC LRT2 df P> X2 none 97.101 7 111.101 -- - 0 92.697 11 114.697 4.404 4 0.354 p 89.755 13 115.755 7.345 6 0.290 0and p 87.478 14 115.478 9.623 7 0.211 Parameter(s) for which separate estimates are derivedfor birds with and without radio transmitters. 2 Compared to base model where parameter estimates are equalfor birds with and without radio transmitters. Censoring Is Random- Censoring is the removal of radio-transmittered animals from a sample when the radio transmitter signal can no longer be detected. An important assumption of the Kaplan-Meier estimator is that the censoring mechanism is random (Pollock et al. 1989). This means that the probability of a bird being censored is not related to its fate (i.e., censored and uncensored animals have the same survival probability). In the case of simple radio failure (a common reason for censoring) this assumption probably is valid. In cases where a radio is destroyed in the process of an animal dying (e.g., a radio is destroyed during predation or scavenging), or a radio-transmittered animal leaves the study area (e.g., through migration, or having been hunted or poached) this assumption may not be valid. When the probability of a censored animal dying is higher than an uncensored animal the resulting survival estimate will be biased high (i.e., survival will be over estimated). We believe that there are 3 likely reasons animals were censored on this study: (1) simple radio failure, (2) when Snail Kites die they likely end up in water which reduces the life of the radio and/or decreases its range (i.e., they were harder to detect), and (3) some birds left the study area. We defined simple radio failure as the failure of a radio transmitter that resulted from manufacturer defects, exhausted batteries, or electronic deterioration resulting from normal exposure to environmental elements. We did not include in this category, radios that had been damaged as a result of traumatic encounters (e.g., predation or vehicle collisions) or radios that had been exposed to conditions not normally encountered by living birds (e.g., prolonged submersion in water). Given the above definition, we had no reason to suspect that the rate of simple radio failure should differ between adults and juveniles. Consequently, the expected rate of censoring of radio-transmittered adult and juvenile Snail Kites due to simple radio failure also should not have differed. An examination of the distribution of "times to censoring" revealed a departure from this expectation (t=3.77, df=179, P<0.001)(Fig. 4-15). Juveniles had a substantial surge in censored animals that occurred within the first 60 days after radio attachment that was not apparent for adults. This surge also coincided with the period of high mortality of juveniles. Some of this censoring was likely the result of post-fledging dispersal into peripheral areas where our searches were less intensive. Banded juveniles have been occasionally recovered (usually dying or dead) outside of the usual range of adults. Our radio telemetry also revealed that juveniles will sometimes wander throughout peripheral areas and later return to more Adult 0 60 120 180 240 300 360 420 480 540 600 660 720 780 Day From Attachement 0 60 120 180 240 300 360 420 480 540 600 660 720 780 Day From Attachement Figure 4-15. The percentage of radio-transmittered adult and juvenile Snail Kites that were censored in each 60- day time interval from the time of attachment. typical habitats (i.e., temporary emigration). However, some of the censoring probably was undetected mortality. Birds that wander into atypical habitats are likely to have encountered food shortages if apple snails were not abundant. In addition, the risk of predation by more terrestrial predators (e.g., Great-horned Owls) also probably increased in these habitats compared to the contiguous marshes usually inhabited. Search efficiency for the radio signal would also decrease if either the birds were in habitats not normally used by adults (i.e., we spent less time in areas where birds were usually not present), or if mortality resulted in the radio being on the ground (as opposed to up on a perch or flying) or submerged in water. Because we suspected that we may have been failing to detect some mortality, we increased our search effort during SY 1994 by hiring an additional field biologist whose primary responsibility was aerial searches for missing birds. Additional evidence that some of the censoring of juveniles was actually undetected mortality resulted from this increased effort. We examined the proportions of censored and dead birds during the first few months after attachment (i.e., < 1 September of each year) before radio batteries were likely to have been exhausted; consequently, simple radio failure from that cause was less likely. For adults, the proportions of censored birds and birds confirmed dead remained relatively constant for each year including 1994 when effort was increased (Fig. 4-16). In contrast, the proportions for juveniles remained relatively constant for SY 1992 and 1993, but showed a dramatic departure from this pattern during SY 1994. During SY 1994 the proportion of birds confirmed dead substantially increased and the proportion of censored birds substantially decreased. The proportion of censored juveniles during 1994 also closely matched the proportion of censored adults, which it had not during 1992 or 1993. This suggests that we were finding dead birds during 1994; whereas a substantial number of dead birds may have gone undetected during 1992 and 1993. 50 40 E i, so- 2 20- 1o- n 1992 1993 1994 50 ]----------| Juveniles Censo Birds 4 Dead Birds so -- so 20- I- 10 -- 1992 1993 1994 Year Figure 4-16. The percentage of adult and juvenile Snail Kites from each sampling cohort (i.e., the year that they fledged or were captured) which were censored or which died between the time of capture and 1 September in each year. Since all birds remaining in the sample will ultimately be censored due to radio battery failure, we used 1 September as a cutoff point which enabled comparability among years, without the confounding of battery failure. Based on this evidence, we believe that we may have overestimated survival of radio-transmittered juveniles during SY 1992 and 1993. Although some undetected mortality of both adults and juveniles was certainly possible, even during 1994, the risk of biased estimates probability was less during 1994. Our estimates of juvenile survival from an independent data source (i.e., banding data using either CJS or multi-state CJS models) also were lower than the Kaplan-Meier estimates for 1992 and 1993, but not during 1994. In contrast estimates of adult survival from banding data did not differ among years and the average estimates from CIS and multi-state CIS models (0, = 0.92) were very similar to the average of the annual estimates derived from the Kaplan-Meier estimator (<0 = 0.90). Thus, while we believe that survival of radio-transmittered juveniles was overestimated for 1992 and 1993, we have no evidence to support a similar conclusion for adults. Small Sample Effect on Staggered Entry Design- Small sample sizes can produce misleading, but unbiased, estimates of survival. The Kaplan-Meier product-limit estimator is a simple extension of a binomial estimator (Kaplan and Meier 1958, White and Garrott 1990). One of the characteristics of this estimator is that the resulting product (i.e., the cumulative estimate) is equivalent to a binomial estimate over the interval of study (White and Garrott 1990). For example, if we start with a total of 10 animals and during the course of the study 2 animals die at different times, the final estimate derived from the Kaplan-Meier estimate will (9/10=0.9) x (8/9= 0.88) = 0.80 (Fig. 4-17); exactly the same as 8/10 from a binomial estimator for the overall interval. However, when using a staggered entry design the estimates for any given time interval will be equivalent to a binomial estimate, but the cumulative estimate (i.e., the product) will not necessarily be the equivalent to a binomial estimate for the overall interval (Fig. 4-17). For example, if only 2 animals are at risk over an interval, estimates of survival can have only 3 possible outcomes: 1.0 (both lived), 0.5 (1 died and 1 lived), or 0.0 (both died). The expected value (i.e., the mean of 6 from a very large number of repeated experiments) of the estimate may still reflect the "true" estimate, but the individual outcomes for intervals with a low number of animals at risk may be an inadequate reflection of the expected value. For an estimate to be biased, the expected value of the estimate, not the outcome of any particular trial, must differ from the true parameter value. In this study, we tried to circumvent this problem by beginning our estimation at a time when a sufficient sample size was established (i.e., April 15 of each year). There is always a balance that must be established between having sufficient sample size to "trust" the results and not missing potentially "real" Adults | Censored Birds B DeadBirds U-L * effects that may exist early in the sample interval. This was less of a problem for adults after the first year because our sample from the previous year is retained; and newly captured animals merely supplement the existing sample. However, juveniles from the previous year could not be retained for our sample of juveniles because they are no longer juveniles (they were added to the adult sample after their first year). However, when animals are partitioned into multiple groups for comparison (e.g., region effects), the number of animals at risk at any given time (even for adults) may have been very low. Thus, we urge caution in interpreting our Kaplan-Meier estimates without taking into account the confidence intervals or number of animals at risk (r-) at a given time. We have provided the details from each Kaplan-Meier analysis (including r,) in appendices. ASSUMPTIONS OF CORMACK-JOLLY-SEBER MODELS Capture and Release Occurs Over Brief Time Interval- When this assumption is met all animals in the study should have been exposed to the risks of mortality for the same time (Burnham et al. 1987). This assumption also enables a clear definition of the interval over which survival is measured. The life history of Snail Kites makes this assumption difficult to meet. Snail Kites have a relatively long breeding season and are not particularly synchronous in their breeding attempts. Consequently, the time span over which fledging, and therefore banding, occurs can be relatively long. We have tried to minimize the violation of this assumption by limiting our resighting period to the peak four months of fledging (March-June). We do not believe that violation of this assumption caused substantial bias to our estimates. For adults, the highest risk of mortality appears to be during the fall and winter. Thus, animals within a given study year all experience the same period of high risk. For juveniles, the highest risk of mortality occurs over the first few months post fledging and again all juveniles within a given cohort were exposed to that period of high risk. Violation of this assumption does, however, present some ambiguity about the period of time over which survival is estimated. As described in our methods section, we have defined our estimates of survival from banding data roughly as survival from one breeding season to the next. There is No Band Loss or Misreading of Bands- The loss of bands in studies of marked birds can produce a serious negative bias in survival estimates (Pollock 1981, Nichols and Hines 1993). We believe 9/10 1 8/9 =8/10 1 0.8 0.8- 1/2 S0.6- 0.6- x *,,2/3 / 8/10 0 0.4- ( 0.4- 0.2 0.2 A B t-- 0 5 10 15 20 25 30 35 40 45 50 0 5 10 15 20 25 30 35 40 45 50 a,--- 1 0 0 0 o 0 0 0 0 0 0 1 1 1 1 2 2 2 0 0 0 0 di- 0 1 0 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 r--10 10 9 9 8 8 8 8 8 8 8 1 2 2 3 4 6 8 8 8 8 8 Figure 4-17. Hypothetical estimates of survival derived from Kaplan-Meier estimator where animals are entered into the sample all at once at the beginning of the study (A) and with staggered entry over the first 30 days of the study (B). In each case the total sample size was 10 animals and in each case 2 of the 10 animals died; however, the cumulative survival estimates were not equal. To illustrate how the estimates were derived we have also shown the number of animals added to the sample (a ) at time t, the number of animals that died (di), and the number of animals at risk (r ). that the problem of band loss was negligible on our study because almost all (859 of 913= 94.1%) birds were marked with riveted aluminum bands that were virtually assured of remaining on the bird. The other 5.9% were made of PVC and anecdotal evidence suggests that band loss from these bands also was extremely low. There have been bands used on previous studies of Snail Kites that were made from other materials (e.g., plastic and Lynnply) more likely to have been lost (e.g., Bennetts et al. 1988, Snyder et al. 1989a); however, we had no birds with bands of these types in our sample. Thus, we believe the potential for band loss effects on our results was minimal. We probably had greater potential for misreading bands than for loss of bands. The misreading of bands does not necessarily result in bias (unless there is some systematic pattern to the misreading) but may have an effect on the variance (G.C. White, Pers. Comm.). We tried to minimize this potential effect two ways. First, the numbering sequence of bands was controlled by the manufacturer (ACRAFT Sign and Nameplate Co. LTD) to minimize duplication errors. For example, numbers that can be read as a different number if the band were accidentally put on upside down (e.g., 6S and 9S) were not used in a combination that would allow accidental duplication of number combinations. For example the number "19" could be misread as "61" if the band were upside down. Thus, only one of these two numbers would have been used. Secondly, we did not record any band resighting until the observer was virtually certain that the number had been read correctly. Whenever possible, we also had a second observer verify the number. This carefulness in recording probably resulted in a lower overall resighting probability because some bands that the observer wasn't reasonably sure of went unrecorded; however, we were reasonably assured that errors from misreading bands were minimized. Statistical Analyses are Based on Correct Model- This assumption is the essence of all statistical inference and its violation can seriously affect parameter estimates (Burnham et al. 1987). In the strictest sense, this assumption is always violated; however, our model selection procedures and subsequent goodness-of-fit tests help to ensure that violations are within acceptable limits; however, when the reported model does not fit the data then concern about bias of the estimator is warranted (Lebreton et al. 1992). Capture and Release Does Not Influence the Subsequent Resighting of Animals- This is a common and long-recognized problem for studies requiring recapturing or resighting of animals (Pollock 1981, White et al. 1982). We had some evidence of minor violation of this assumption from our radio telemetry. A few radio-transmittered birds (i.e., <5%) would depart the immediate vicinity upon our approach after having been captured. However, these birds usually just move to a new perch a short distance away. We were usually able to still read the bands through a slow careful approach. In addition, many birds were nesting and would consequently return to the nest vicinity within a few minutes. There were a few (= 1%) non-nesting individuals, however, that upon our approach, would leave the vicinity and we would be unable to read their bands. There were also occasionally unmarked individuals that would depart the vicinity upon our approach, suggesting that some birds are inherently more wary, regardless of previous capture history. Because radio-transmittered birds often had a more traumatic capture experience (i.e., many were captured with a net gun) than birds that were merely banded as nestlings, we believe that our observations of radio-transmittered birds overestimates the extent of avoidance as a result of having been banded. Consequently, although our ability to relocate birds was probably influenced by birds having been captured, our observations of radio-transmittered birds suggests that this influence is not substantial. Causes of Mortality Although our sample size of dead birds was not large, it was sufficient to provide an indication of the relative frequencies of different causes of death. Predation appears to have been the most frequent cause of death for both adults and juveniles (Fig. 4-18). We caution, however, that without finding each carcass while it is still fresh and conducting a necropsy, it is impossible to distinguish predation form post-mortality scavenging. However, we usually had ancillary information, in addition to the carcass having evidence of having been eaten (e.g., > 1 carcass at the site or the location inconsistent with a site normally used by Snail Kites), when predation was assigned as the probable cause of death (Table 4-31). Emaciation was the second most frequent cause of death for juveniles, but was not an observed cause of death for adults during this study. Here we caution that our data were collected during non-drought years. During widespread droughts, when food may be scarce, emaciation may be a more frequent cause of death for both age classes. Emaciation may also have been underestimated in our sample. For example, we suggested that some juvenile deaths may have been included as "censored" birds as a result of birds dispersing to habitats atypical of adults and subsequently, less adequately searched. These areas may also be more likely to have less predictable food resources. Two of the emaciated birds were found in marine environments where apple snails are completely lacking. Other causes of death included vehicle collisions, disease, and one probable gunshot. Vehicle collisions were observed for both age classes and one additional death from this cause was observed for an unbanded or radio-tagged adult (i.e., it is not included in this sample). Deaths from vehicle collisions may be more likely when nesting or foraging concentrations occur adjacent to roadways. One adult female died of an infection of the coelomic cavity. One juvenile may also have had an intestinal disease, but severe autolysis precluded conformation. The skeletal remains of one juvenile had a probable gunshot (shotgun) hole through its sternum, but we were unable to confirm if this was the cause of death. Table 4-31. Known mortality of snail kites banded or radio-tagged during this study. Year Age Location of Mortality Probable Cause of Death 1992 JUV WCA-3A Predation' 1992 JUV St Johns Marsh Predation' 1992 JUV Holmes Beach, FL Emaciation2 1992 JUV Florida Bay Emaciationz 1992 JUV Everglades Agricultural Area Vehicle Collision' 1992 JUV WCA-2A Unknown4 1992 JUV Agriculture Area (Collier Co.) Unknown4 1993 JUV West Palm Beach W.C.A. Predation' 1993 JUV Everglades National Park Predation' 1993 AD Lake Okeechobee Predation' 1993 AD Lake Okeechobee Predation' 1993 AD WCA-3A Infection of Coelomic Cavitys 1993 JUV Coquina W.C.D. Unknown6 1993 AD West Palm Beach W.C.A. Unknowns'7 1994 JUV WCA-3A Predation' 1994 JUV WCA-3A Predation' 1994 JUV WCA-3A Predation' 1994 JUV WCA-3A Predation' 1994 JUV Everglades National Park Predation' 1994 JUV Lake Kissimmee Predation' cont. ADULTS JUVENILES (n=13) (n-30) 385% 36.7% 7.0.0%% 7.746.2% 3.3 46.7% E,* 0 v-a c.El 0 m coao, n a P Man UO 0, \o Figure 4-18. The percentage ofmortality of adult and juvenile Snail Kites in each of 6 mortality classes. Particularly for causes of death in which a necropsy was not performed (e.g., predation), we can never determine the cause of death with certainty. However, we assigned mortality to each class only when ancillary evidence supported our conclusion. In the absence of such evidence, we assigned mortality to the unknown class. Table 31. Continued 1994 JUV 1994 JUV 1994 AD 1994 AD 1994 JUV 1994 JUV 1994 JUV 1994 AD 1994 AD Loxahatchee N.W.R. Loxahatchee N.W.R. Lake Kissimmee Lake Okeechobee Everglades Agricultural Area WCA-3A Big Cypress National Preserve WCA-2A Lake Okeechobee 1994 JUV Lake Kissimmee 1994 JUV 1994 JUV 1994 JUV 1994 JUV Everglades National Park East Lake Tohopekaliga Lake Okeechobee C- ll Basin 1994 AD East Lake Tohopekaliga 1995 AD Lake Okeechobee 1995 AD Oesceola Co. (private land) 1995 JUV Lake Marion 1995 JUV East Lake Tohopekaliga 1995 JUV Lake Marian 1995 AD Glades Co. (State Highway 78) 1995 AD St. Johns Marsh 1995 JUV Lake Marian Predation' Predation' Predation' Predation' Unknown (not predation)8 Unknown (not predation)s Unknown (not predation)8 Unknown (not predation)8 Unknown (not predation)s Unknown4 Unknown4 Unknown4 Unknown4 Unknown4 Unknown4 Predation' Predation' Predation' Predation' Emaciation2'9 Vehical Collision2 Unknown (not predation)8'10 Unknown4 Carcass showing clear signs of having been eaten (e.g., feathers plucked, bones broken) with additional ancillary evidence supporting having been taken by a predator (e.g., other carcasses found at the site, feathers of predator [Great-horned Owl], and location or conditions under which the carcass was found [e.g. on limbs of trees]). However, we can never be certain that some of these were not a result of scavenging after death. 2 Based on necropsies performed by the National Wildlife Health Research Center. SBird found still alive along farm road with broken wing. SCarcass too decomposed for evaluation. SBased on necropsy performed by the University of Florida Laboratory of Widlife Disease Research. 6 Skeletal remains of bird banded by Jon Buntz (GFC)found at base offence post. Small hole in sternum was consistent with shotgun pellet. 7 Bird was in excellent nutritional health and had 3 snails in esophagus. No external signs of trauma. 3 Carcass was completely intact (i.e., no sign ofpredation or scavenging), but was too decomposed for further evaluation. 9 Bird was severely emaciated; however, some evidence of intestinal disease which may have contributed to death. "o Decomposition was too severe to determine cause of death, but bird was emaciated. Previous Estimates of Survival There have been several previous reports of estimated survival (e.g.,Snyder et al 1989a, Beissinger 1995). To our knowledge these estimates emerged primarily from 3 sources of information: (1) the annual count, (2) a study by Snyder et al. (1989a) with banded birds, and (3) a study by Snyder et al. (1989a, 1989b) using radio transmittered birds. Snyder et al. (1989a) reported the results of resighting birds banded from 1968-1978. They did not use standard capture-recapture methodology (i.e., CJS models) presumably because of a lack of sufficient data for most years. They point out that only a fraction of the birds were checked for bands in any given year. Consequently, they estimated a range for minimum annual survival, taking into account all possible band loss. Thus, actual survival estimates from these data would range from their lower minimum estimate to 1.0. All of the resightings they report were from 1979 when their resighting effort was most intensive. Consequently, their estimates of minimum annual survival also confound adult and juvenile survival since all birds, except those banded in 1978, were banded as nestlings and resighted as adults. The estimate for birds banded in 1978 would not confound adult and juvenile survival, since the estimates were only for minimum survival of their first year (i.e., the estimate was for juveniles only). For 4 of 10 years their lower estimate was 0 (i.e., actual survival ranged from 0.0 to 1.0) and the average of their lower estimates was 0.47 (i.e., survival ranged from 0.47 to 1.00). They suggested that adult survival probably exceeds 0.9 under good conditions, but did not present any estimation procedure to support this suggestion. Beissinger (1995) later reported adult survival during high-water years was 0.95 (0.03 SD). He reported that these estimates were projected from the studies by Snyder et al. (1989a, 1989b); however, he reported no estimation procedure for either survival or its standard deviation; nor could we find such procedures in the sources cited. Beissinger later informed us (S. Beissinger, pers. comm.) that these were estimates in the sense of approximations and were not derived using a statistical estimator (e.g., maximum likelihood), and that the standard deviations were intended as a way of dealing with the uncertainty of the survivorship information in his stochastic model. Estimates of survival during droughts, particularly the 1981 drought, have been frequently reported based on changes between years in the annual count (e.g., Beissinger 1986, 1988, 1995, Takekawa and Beissinger 1989). The count in 1980 (before the 1981 drought) was 652 birds (Sykes et al. 1995)(reported as 654 by Rodgers et al. [1988]). The count made in December of 1981 (after the drought) was 109 birds (an 83% decline in the count)(Rodgers et al 1988, Sykes et al 1995); although an alternative count for 1981 of = 250 (done independently by Beissinger [1982, 1984] in March of 1982) is often substituted for the state- conducted count, implying a population decline of 60% (e.g., Beissinger 1984, 1986, 1988, 1995, Takekawa and Beissinger 1989). We suggest that using the annual count for estimating survival is not scientifically valid because it is subject to multiple sources of error that are inconsistent among years (discussed in detail in chapter on Monitoring Snail Kite Populations in Florida). Estimating survival from the annual survey during a drought is particularly suspect because it is well known that Snail Kites disperse in large numbers to peripheral wetlands during droughts, where counts are not conducted (Beissinger and Takekawa 1983, Takekawa and Beissinger 1989) and this dispersal has not been taken into account in these survival estimates. Snyder et al. (1989a, 1989b) reported that at least 7 of 8 (one was of unknown fate) radio transmittered birds survived from May 1981 (just prior to the peak of the 1981 drought) until their study period of 1982. Although their sample size was small, this implies an estimate of survival during droughts as 0.875 (rather than the 0.17 0.40 inferred from the annual count). If these data are considered as following a binomial distribution (and assuming that the unknown bird was dead), then a 95% confidence interval would be 0.65 1.00 and does not even include the estimates inferred from the annual count. Beissinger (1995) later suggests that these data are more applicable as lag-year (the year following the drought) estimates because radios were attached toward the latter part of the actual drought after these birds had survived some of the dropping water levels and the risks associated with dispersal to Lake Okeechobee. We disagree for several reasons. First, our data show that the risks associated with dispersal are encountered on a regular basis (i.e., approximately 25% of the population disperses every month) whether there is a drought or not. We do agree that the risks of predation may be greater in peripheral habitats because of increased proximity to upland habitats and their associated predators (particularly Great-horned Owls); however, dispersal to these habitats is not likely to be extensive until alternative wetlands have dried (i.e., at the peak of the drought). We also suggest that much of the mortality associated with droughts will occur during the peak of the drought and during the first winter after a drought, when depressed food levels are compounded by cold temperatures, which decrease food availability even further (Cary 1983, 1985). Both of these periods of high risk were included in their sample of radio-transmittered birds and therefore should better represent drought-year, rather than lag-year, conditions. However, regardless of how these data are interpreted, there still remains a need for credible estimates of drought-related survival. ChapterS. REPRODUCTION Beo-- the eof th" "pt o I" 0, debl bchh--,; ... Igy) hllb c h I g-"le, oa b-o b,,t 0 e 1 10 -W I'Mo t,, -1o, t( I 0 d by Beo O 1988,O ykeCtOb]199 5 I ). -h B foO 1 15 10 0 Rp"od"l ooool SoooKoa\ t lot 0 'cl.dollah 0000iici 00iIlll I u nu ed~t b el We h,,,, b hpl,,d a ,,pi- ll 0 01, oh o h- -0-y Ie ooouia o l,10adal 0,1,00 1'v '10p c a xpl Ill ooooop'oolloinyrcl ho dlrlnmi '00,000 ,ooohoooooooilr;lid 00 0nrd dolle 1,1I 3 0000000 ........ leleuiede',pl.. ot female young) produced pe female iofeach age Cass 1 Ihe population Untorunael., lor many species, including Snall Kbe. we c-nnol estlimae this paran- eler directly Rlihcr. It is delved h1om the proportion ol buds attempting to breed (a.), ihe propormn of breeding ittemp, that are 1 ucceful (S,), and the num- ber ol bleeding atlemllpt pc yea (fi,) For succesful 0estig atcmpt,, we all need o, knolw the lni0 be of young produced (Y,) and the sex ratio of the young piodu0ed (R,)(Brwn 1974,1 Caughicy 1977(Fig 5- 1) l Alni chaplel wc review the imotmalon that has beeli pi v~otl y led on e"ach ol these pat -meel , a0 well a 1'pren 0 ll baed oo 0 lew00 dh0ata SEMANTICS Mlundl; tandlngs houtme ,asules ot reproduce mn cl frequently be attrlbulble to a lick of car deimtlans ol what is belng mnastulcd anid/or to wiat i owr, to avoid Ihe Ilmet lype. we will begin or assessment of reproduction by providing operational definitions for terms discussed below. Breeding Attempt- There has been considerable disagreement among researchers regarding what constitutes a breeding attempt. For the purposes of this report, we consider a breeding attempt to begin with the laying of the first egg Steenhof (1987). Snyder et al. (1989a) considered a breeding attempt to begin with nest building, prior to the laying of the first egg. They suggested that to ignore the period before egg laying in analyses of reproductive success would be ill-advised because of the high proportion (>0.33) of nests they observed that failed prior to egg laying. We agree entirely with Snyder et al. (1989a) that, for many questions, the failure of nests prior to egg laying may have important biological implications. These failures may provide insight as to environmental conditions at the time of egg laying and also may provide information regarding behavioral aspects of mate choice. However, we disagree that nests during the nest-building stage for this species should be considered as a nesting attempt for estimation of reproductive parameters. We have several reasons for this conclusion. First, inclusion of "pre-laying" failures may include nests in which a pair bond has not even been established between a male and female. Nest building is initiated by the male as part of courtship (Beissinger 1988, Bennetts et al. 1988) and more than one male may direct courtship toward a single female (Beissinger 1987, pers. obs.). Thus, if two males initiated nest building as part of the courtship toward a single female, this would be considered as two nesting attempts using the definition of Snyder et al. (1989a), even though only one of these nests may produce young. Similarly, our observations indicate that a single male may exhibit courtship behavior, including nest building, towards several females in succession. This behavior may last as little as a few hours or may last several days and may then be redirected to a new female if a pair bond is not established. We observed a single radio-tagged male direct courtship to as many as five different females before a pair bond was established that resulted in egg laying. Using the definition of Snyder et al. (1989a), each of these courtship attempts would have been interpreted as a failed breeding attempt. In contrast, we view this as part of the mate selection (courtship) process rather than as a demographic parameter. Second, the passage of cold fronts and corresponding temperature change often results in reduced food availability (Cary 1985). Consequently, courtship is often terminated with the passage of cold fronts and resumed (often at a new location) when temperatures return to pre-front conditions (Beissinger 1988, Bennetts etal. 1988). Thus, if two cold fronts passed before eggs were actually laid, the pair would have been considered to have made three separate breeding attempts (with two failures) even if the pair successfully raised a brood. For demographic purposes, we view these postponements as courtship interruptions, rather than multiple breeding attempts with each interruption being considered as a breeding failure. Third, because nest building begins with the placement of the first stick and many more courtship nests are probably initiated than are ever detected, it creates a substantial bias in the estimate of success if these early starts are not detected (Mayfield 1961, Miller and Johnson 1978, Johnson 1979, Hensler and Nichols 1981). Finally, it is well known that nesting raptors tend to be considerably more sensitive to disturbance early in the nesting cycle (Grier and Fyfe 1987, Steenhof 1987). Although previous investigators have reported a high proportion of nest abandonment by Snail Kites prior to egg laying (e.g., Beissinger 1986, Snyder et al. 1989a), we have seen no accounting for how much of this abandonment might be attributable to disturbance by the investigators themselves. In contrast, abandonment of eggs or young by Snail Kites is extremely rare (Bennetts et al. 1994, Sykes et al. 1995). Thus, measuring nesting success after the first egg has been laid can reduce this potential source of confounding and minimize disturbance to this endangered species. Based on these concerns, we define a breeding attempt to begin with the laying of the first egg. Thus, unless otherwise stated, references to nests in this report implies the presence of eggs or young. Successful Nest- For the purposes of this report, a successful nest is one in which at least one young reaches fledging age (Steenhof 1987). Because birds after fledging may or may not be present at the nest, we defined fledging age as 80% of the average age of first flight (Steenhof and Kochert 1982). Snail Kites are capable of first flight at approximately 30 days of age (Chandler and Anderson 1974, Beissinger 1988, Bennetts et al. 1988); thus, we considered a nest as having been successful if it produced young that survived to at least 24 d (Bennetts et al. 1988). At this age, animals are reasonably assured of still being at the nest and mortality for most raptors between this time and fledging is minimal (Milsap 1981, Steenhof 1987). In addition, we banded birds at the time they were determined to be of fledging age. Consequently, any mortality that occurred after this age would have been included in our estimates of juvenile survival from our mark-resighting program. The Breeding Season The initiation of nests (i.e., egg laying) has been documented in all months of the year (Sykesl987c); although, for any given year, Snyder et al. (1989a) observed a maximum breeding season (interval over which nests were initiated) of 31.7 weeks (7.9 months) during an 18-year study in Florida. Although Snail Kites in Florida can potentially lay eggs in all months of the year, there is a very distinct seasonal distribution of nest initiations (Table 5-1) (Fig. 5-2). Nest initiations begin as early as November, but in most years widespread initiations usually do not begin until January or February. Peak initiations usually occur in March, but are often several weeks later, peaking in April, in the northern habitats (Toland 1994). Table 5-1. The number of nest initiations reported in each month during studies from 1966 through 1995. Sykes Snyder et al. Snyder et al. Bennetts et al. Toland This Proportion of Month (1987c) (1989a)1 (1989a)2 (1988)' (1994)4 Study5 Total" Total OCT 5 0 0 0 0 5 0.00 NOV 9 9 8 -- 0 0 17 0.01 DEC 5 35 35 0 7 47 0.04 JAN 26 90 81 14 3 74 184 0.16 FEB 35 98 78 102 22 66 201 0.17 MAR 36 147 119 125 49 106 310 0.26 APR 21 114 103 102 53 40 217 0.18 MAY 10 64 56 32 19 17 102 0.09 JUN 5 44 38 0 16 0 59 0.05 JUL 1 27 25 0 5 0 31 0.03 AUG 2 1 1 -- 0 0 3 0.00 SEP 1 0 0 0 0 1 0.00 Total 156 629 544 375 167 310 1177 Includes all years reported by Snyder et al. (1989a). SIncludes only years reported by Snyder et al. (1989a) to have wide seasonal coverage (1970-1982). Unpublised data from nests reported by Bennetts et al. (1988). Seasonal coverage was limited to January through July. Unpublished data courtesy ofB.R. Tolandfrom nests reported by Toland (1994). Based on data from 1994 and 1995. SBased only on data with wide seasonal coverage (Sykes 1987c, Snyder et aL 1989a [1970-1982 only], Toland 1994, and this study. S0.25- 020 S015 0 0.05 OCT NOV DEC JAN FEB MAR APR MAY JUN JUL AUG SEP Month Figure 5-2. The proportion of nest initiations for each month of the year based on cumulative data reported by Sykes (1987c), Snyder et at (1989a)(1970-1982 only), Toland (1994), and this study. The Breeding Population AGE OF FIRST REPRODUCTION Sykes (1979) reported that Snail Kites are capable of breeding at 3 years of age. However, Sykes (1979) suggested that some birds possibly breed at a younger age. Beissinger (1986) later reported both male and female birds breeding at one year of age and Snyder et al. (1989a) reported one female breeding at nine months. Our data are consistent with Beissinger (1986) and Snyder et al. (1989a). During this study, we commonly observed yearling Snail Kites attempting to breed. PROPORTION OF BIRDS ATTEMPTING TO BREED Adults- Nichols et al. (1980) suggested that the proportion of birds that attempted to breed during favorable conditions was quite high. They suggested that there was no reason to suspect that it was not 1.0 and, consequently, assumed that value for their demographic model. They reported, however, that this was a crude estimate for lack of a better one. Beissinger (1995) similarly reported that the proportion of adult Snail Kites attempting to breed during high-water years was 1.0, but also provided no empirical evidence. Although our data for this parameter are very limited, they are consistent with these earlier estimates. During 1995, we closely monitored 25 radio-transmittered adult Snail Kites for breeding activity in order to assess the proportion attempting to breed and the number of breeding attempts per year. Of these 23 adults, 14 were females and 9 were males. During the 1995 nesting season, we located each bird on the ground approximately bi-weekly to determine its breeding status (e.g., a nest, courtship, not breeding). Birds in which no breeding activity was detected were generally observed for 22 hrs and subsequent visits, usually within 10 days, were required to confirm a non-breeding status and to confirm any nests for birds exhibiting courtship. During 1995 (a relatively high water year throughout the kite's range), all 23 (100%) adults attempted to breed at least once. Our estimate is based on a relatively small sample (N=23) and on only one year; however, it does provide an empirical basis that most, if not all, adults may attempt to breed in some years. Sykes (1979) reported that he observed no nesting attempts during 1971 (a widespread severe drought; see Management and Conservation). Based on this observation, Nichols et al. (1980) assumed that no birds nested during 1971 for their demographic modeling effort. Beissinger (1986) reported that during the 1981 drought 80-90% of the kites did not attempt to nest, and Beissinger (1995) later reported that only 15% of adult Snail Kites attempt to breed during drought years. However, no empirical evidence was presented in support these estimates. Based on anecdotal evidence, we believe that the proportion of birds attempting to breed during drought years may be highly variable depending on the spatial extent of the drought (see discussions on Water Management and Snail Kites in Management and Conservation). We agree that during a severe widespread drought, most birds probably do not attempt to breed. However, in cases of more localized droughts, where portions of the kite's range may not be experiencing dry conditions, the proportion of birds attempting to breed may remain very high. For example, during 1991, the Everglades region was at the end of a 2-3 year drought (whether it was a 2 or 3 year drought depends on how a drought is defined). During this year almost no nesting activity was observed in the Everglades region (J.A. Rodgers Jr., pers. comm.). This would appear consistent that a small proportion of birds had attempted to breed. However, during this year, record numbers of birds were nesting on Lake Tohopekaliga (J.A. Rodgers and J. Buntz, pers. comm.) and in the upper St. Johns Marshes (B. Toland, pers. comm.), areas not influenced by the drought conditions in the Everglades. Our data on movement strongly suggest that the Florida population is one population that moves frequently throughout its range, rather than a meta-population of quasi-isolated subpopulations (see Movements). Thus, in years where drought is not widespread, birds may merely shift the location of nesting activities. Consequently, we suggest that this parameter may be quite variable and needs to take into account the severity and spatial extent of a given drought. Sykes (1979) observed relatively few (n=6) nests during 1972, the lag year following the 1971 drought. The average number of nests per year that Sykes (1979) reported from 1968-1976, excluding 1971, was 23. Based on this observation of reduced nesting during this lag year, Nichols et al. (1980) assumed a proportion of 0.5 adults attempted to nest during 1972. Beissinger (1995) reported that a proportion of 0.8 adults attempt to breed during lag years, although we could find no empirical support for this estimate in any of the sources cited. We suspect that, similarly to drought years, this parameter may be highly variable depending on the specific drought. Thus, we view this parameter as also being unknown and subject to high variability. Subadults-- Snail Kites have been reported to breed as young as 9 months old (Snyder et al. 1989a); thus, by a calendar-year definition Snail Kites are capable of breeding as juveniles (i.e., < 1 year old). However, these cases are ones in which the birds attempted to breed during the nesting season following the nesting season of their hatch year. Thus, this parameter should be defined as the proportion of birds attempting to breed during their second breeding season (the first is the one in which they hatched). Based on data from Snyder et al. (1989a), Beissinger (1995) reported that 25% of subadults attempt to breed during high water conditions. Snyder et al. (1989a) observed 8 banded subadults breeding during 1979 out of a minimum of 74 that had survived from their hatching year of 1978. Because Snyder et al. (1989a) only checked 50.8% of the nests for bands, they estimated that there were probably 16 subadult breeders out of a minimum of 74 banded subadults (22%). Of course, this estimate assumes that only the 74 subadults observed alive in 1979 had survived and that there was an equal probability of detecting a banded subadult that was breeding in the sample of nests that were checked and those that were not checked. During 1992, we estimated a similar percentage of 17% of the subadult birds attempting to breed (Bennetts and Kitchens 1992). Our estimate was based on only 2 breeding birds of 12 banded yearlings that we observed during the 1992 breeding season. Consequently, our estimate requires similar assumptions that we suggested above for Snyder et al. (1989a). During 1995 (a high water year throughout the kite's range), we also closely monitored 9 radio- transmittered juvenile Snail Kites for breeding activity (as described above). Of these 9 birds 3 (33%) attempted to breed. All of the estimates derived from the data of Snyder et al. (1989a), as well as from our own data, are very limited (i.e., small samples each from one year); however, they do consistently suggest that a relatively small proportion of subadults do attempt to breed during some years. Beissinger (1995) also reported that the proportion of subadults attempting to breed during drought years and lag years was 0.15. We could find no empirical basis for this estimate in any of the sources cited; but we agree with Beissinger that the average percentage would probably be lower when conditions are poor in part or all of their range. Nest Success Nest success has been among the most widely estimated parameters of reproduction of Snail Kites. However, it has probably also been among the most confusing. There are several areas of disagreement among researchers regarding estimation of nest success. The disagreements center primarily on which nests should be included in the sample and what estimator should be used. Consequently, nest success has been difficult to compare because different researchers have used different estimators and have included or excluded different categories of nests within their respective data sets. We have attempted to summarize below the major issues of contention. We have also summarized the literature on nest success and explicitly pointed out which estimator was used and what categories of nests were included or excluded in the sample. Thus, readers can make comparisons among studies and decide for themselves which estimates are most appropriate for their particular needs. AREAS OF DISAGREEMENT ESTIMATION OF NEST SUCCESS REGARDING Inclusion or Exclusion of Nests Found at Different Stages- At what stage a given nest is found can greatly influence its probability of success. Nests found late in the nesting cycle have a higher probability of success because they have less observation time during which they are at risk. A Snail Kite nest requires at least 57 days to fledge young (27 days of incubation and 30 days for nestlings to reach fledging age). Thus, a nest found during egg laying will have potentially >50 days "at risk" (provided it does not fail earlier) to be considered successful. In contrast, a nest found close to the time of fledging may have only a few days "at risk" to be considered successful. Consequently, estimates of nest success that were derived using nests found late in the nesting cycle tend to be biased high (Mayfield 1961, 1975, Miller and Johnson 1978, Hensler and Nichols 1981, Hensler 1985). Nests at different stages also are vulnerable to different risks. For example, rat snakes (Elaphe obsolete) are believed to be one of the major predators of Snail Kite nests (Bennetts and Caton 1988). Rat snakes will readily take eggs or young that are less than one week old; however, the larger size of older nestlings largely precludes predation by rat snakes. Consequently, nests found when young are > 1 week have an inherently lower risk of predation by rat snakes. Some researchers (e.g., Beissinger 1986, Snyder et al. 1989a) also have included nests prior to eggs having been laid (i.e., during nest building) in deriving estimates of success. We disagree with this practice for the reasons previously discussed (see definition of Breeding Attempt in earlier section of this chapter on Semantics). Because of these biases, estimates of nest success can be substantially influenced by what nests (i.e., found at what stage) are included or excluded for deriving a given estimate. This makes comparison of previous estimates of nest success for Snail Kites difficult because researchers have not used the same criteria for inclusion or exclusion of nests found at different stages when deriving their estimates. Steenhof and Kochert (1982) suggested three ways to minimize this type of sampling error for estimating nest success. First, they suggest estimating success based on a pre-determined sample of territorial pairs. However, because Snail Kites do not maintain nesting territories from one year to the next, this solution is not feasible for this species. Secondly, they suggested using estimates derived only from nests that were found during incubation (by definition, they considered a breeding attempt to have begun after they laying of the first egg). This suggestion is feasible for kites; but of the previously reported estimates, only Snyder et al. (1989a) reported estimates using this criterion (but they also included manipulated nests for which some strong assumptions were made; see discussion of Manipulated Nests below). Their third suggestion was to use the Mayfield Estimator (see discussion of Mayfield vs Conventional Estimators below), which is intended to account for the bias imposed by not finding all nests during early stages. Of the previously reported estimates, only Bennetts et al. (1988) reported estimates using this estimator. Given the differences in what nests were included or excluded in previous studies, we urge caution in making comparisons among previous studies. We also agree with Steenhof and Kochert (1982) that estimates of success should be derived either using only nests that were found during incubation or using the Mayfield estimator. Of these two approaches we prefer the latter (discussed below in section on Mayfield vs Conventional Estimators), although there remains disagreement among researchers regarding this conclusion. Manipulated Nests- Nests that occur in cattails may have a tendency to collapse under conditions of high winds or waves (Sykes and Chandler 1974). This led to a previous practice of placing nests that were subject to this type of failure in artificial nest baskets (Chandler and Andeson 1974, Sykes and Chandler 1974). Because this may influence the outcome of a given nest, whether to include or exclude these nests has been the subject of some debate (e.g., Beissinger 1986, Snyder et al. 1989a). Similarly, when these nests have been included in samples from which estimates of nest success were derived, there have been differences among researchers (e.g., Sykes 1979, Snyder et al. 1989a) as to how these nests were treated in the derivation of nest success. Sykes (1979, 1987b) included 43 nests that were placed in artificial nest baskets in his sample for estimating success. These nests were not treated differently than other nests. Snyder et al. (1989a) later criticized this use of manipulated nests. They suggested that the success of manipulated nests was higher than if they had not been manipulated, and that this would have biased Sykes's estimate of success upward. Snyder et al. also presented estimates of nest success using 94 manipulated nests. They argued that because these nests were in imminent danger of collapse, they considered them all as failures. They suggested that to exclude them, as was done by Beissinger (1986) and Beissinger and Snyder (1987), would have also biased success upward because these manipulated nests were not a random sample with regard to their probability of success (i.e., that they would have failed). In contrast to their suggestion, we have observed collapsed nests containing older (> 10 d old) nestlings that have been successful. Although we agree with Snyder et al. (1989a) that exclusion of these nests probably would have biased success upward, we also believe that including them all as failures probably would have biased their estimate slightly downward. Our tendency is to agree with the solution of Snyder et al. (1989a), but to accept that there might be a slight bias toward underestimation of success. An additional concern that has not been addressed by previous authors is that the susceptibility of nests to collapse may be influenced by the investigators themselves. The vulnerability of nests to collapse can be greatly influenced by the paths of airboats while conducting nests visits, particularly in cattails (Bennetts 1996). Airboat trails are often wide enough to allow increased susceptibility to wind damage and/or to weaken the structural support provided by the cattails adjacent to the nest. This type of damage can be minimized, if not eliminated, by maintaining a substantial distance from the nest during an approach and either wading in to nests or using a mirror pole from a distance to check them (Bennetts 1996). Nest baskets have not been used in recent years and we do not anticipate (or advocate) a recurrence of their use. Although some nest collapse still occurs in some areas, particularly on lakes (J.A. Rodgers, pers. comm.), we do not believe that the benefits of nest baskets warrant the effort or disturbance for their use as a general management tool. They may, however, be warranted for isolated special circumstances. Previous use of nest baskets had been initiated when numbers of Snail Kite probably were much lower than are currently found. We do, however, advocate that researchers must exercise extreme care to avoid influencing the outcome of nests being monitored. Mayfield vs Conventional Estimator- Mayfield (1961, 1975) proposed an estimator for nest success that was based on daily exposure (risk) such that a daily probability of success was derived using only those days in which a given nest was under observation. Overall success is then derived by applying the daily success over the length of the interval being estimated. This approach provides an estimate of success that is unbiased with respect to when a given nest was found, but requires an assumption that the probability of success is constant over the period (e.g., incubation) being estimated. Hensler and Nichols (1981) later showed, using Monte Carlo simulations, that this estimator was superior to the conventional estimator under a wide variety of conditions. Bennetts et al. (1988) used the Mayfield estimator for nest success of Snail Kites and found it to perform favorably for this species. They found some violation of the assumption of constancy (e.g., success differed between incubation and nestling stages); however, this assumption can be overcome by using separate estimates for periods that differ (Hensler and Nichols 1981). Snyder et al. (1989a) later argued that the Mayfield estimator was inappropriate for Snail Kites because the interval length for nest building was too variable to apply this estimator. We agree with Snyder et al. (1989a) that the Mayfield estimator would be inappropriate for estimation of success during the nest building stage. However, we also believe that the nest- building period is inappropriate to include in estimates of nesting success for this species (see discussion of Breeding Attempt in earlier section of this chapter on Semantics). Consequently, we disagree with Snyder et al. (1989a) that the Mayfield estimator is inappropriate for estimating nesting success of Snail Kites. Rather, we agree with Hensler and Nichols (1981), Miller and Johnson (1978), Steenhof and Kochert (1982), and Steenhof (1987), that this estimator is preferable to conventional estimates of nesting success because of its ability to produce unbiased estimates of nesting success. ESTIMATES OF NEST SUCCESS AND ITS PROCESS VARIANCE Given the wide disagreement among researchers regarding nesting success, we suggest that future researchers be specific about what is being included or excluded, and that consideration be given to reporting success both by conventional and Mayfield estimators so readers have the ability to compare their results. We have also provided a summary of the previously reported estimates, showing what nests (i.e., found at what stage) were included in each estimate, whether or not manipulated nests were included, and which estimator was used (Table 5-2). We estimated the mean annual nest success (S,) as 0.32 based on reported nest success from each year using estimates that were based on nests (in which at least one egg has been laid) that were found during the egg stage (Table 5-3). However, some years had extremely low sample sizes, which may have precluded a reliable estimate for that year. If we had excluded estimates for those years with < 10 nests, we would have estimated mean annual nest success (S,) as 0.28. Estimate of Process Variance- It is important to recognize that there are several distinct variance components associated with demographic parameters (White et al. 1982, Burnham et al. 1987). A demographic parameter (e.g., survival) may vary over time (temporal variation) or among locations (spatial variation). There is also likely to be heterogeneity among individual (individual variation) in their probability of survival due to genetic or phenotypic variation (DeAngelis and Gross 1982). Each of these sources of variation are a type of population variation (Burnham et al. 1987). There is also variation attributable to sampling populations. Unlike these previous sources of variation, sampling variation is not a measure of population variability, but rather is a measure of sampling error. This latter source of variation is important because it provides a measure of the certainty for a given parameter estimate. However, for demographic modeling, what is important is the actual variability of parameter over time, space, and among individuals (collectively called process variance). For modeling populations, sampling variation is a source of noise and should be removed from the overall variance estimate. Burnham et al. (1987) provided the theoretical framework and formulae for estimating process variance. We used this framework to estimate process variance for nest success based on estimates reported from 1968-1995 using only nests found after the first egg was laid, but before hatching. Based on the data from table 5-3, we estimated &o=0.08 and b=0.28. INFLUENCES OF NEST SUCCESS There are a multitude of factors that could potentially influence the outcome of Snail Kite nests. Factors that have been reported to significantly affect nest success include location (i.e., area)(Snyder et al. 1989a), water levels (Sykes 1987b, Bennetts et al. 1988, Snyder et al. 1989a, Toland 1994), date of initiation (Bennetts et al. 1988), nest substrate (Snyder et al. 1989a, Toland 1994), nest height (Bennetts et al. 1988, Toland 1994), distance to land (Sykes 1987c), and interspecific coloniality (Snyder et al. 1989a). We used logistic regression to test for the influence of each of these effects, except interspecific coloniality, on a sample of 854 nests using data from Bennetts et al. (1988), Toland (1994, unpubl. data), and this study. Our preliminary univariate analysis, which had a liberal rejection criterion of a=0.25 (see methods) for each effect indicated that all of these effects warranted retention for further analysis (Table 5-4). However, our results indicated that the specific substrate, rather than herbaceous versus woody, was warranted for further consideration. Similarly, our results indicated that a categorical threshold distance to land of less than or greater than 200m (Sykes 1987c) was warranted for further consideration, rather than the actual distance. Although our preliminary univariate analysis supported the retention of these effects, a multivariate analysis with each of the retained effects (but lacking interaction terms) indicated that only year and date of Table 5-2. Mean annaul nest success from major studies conducted since 1968. A successful nest is considered a nest in which at least one young fledged. Also shown are the estimator used to estimate success, whether or not nests placed in nest baskets were included in the estimate, and whether or not nests found in each of 3 stages were included in the estimate. Stage Found Annual Includes Nest Success Range Years Location(s)' N Estimator Baskets Building Inc. Nestling Source 54% 17-85% 68-76 1,2,3,4,5,6 175' Conv" Yes' No Yes Yes Sykes (1979) 56% 17-85% 68-78 1,2,3,4,5,6 204 Convy Yes' No Yes Yes Sykes (1987b) 21% 0-41% 78-83 2,3,4,5,6,7 331 Convy No Yes Yes No Beissinger (1986) 30% 23-36% 86-87 4 367 Mayfield" No No Yes Yes Bennetts et al. (1988) 38% 30-46% 86-87 4 367 Conv' No No Yes Yes Bennetts et al. (1988) 31% 21-40% 86-87 4 358 ConWf No Yes Yes No Bennetts et al. (1988) 35% 26-44% 86-87 4 317 Convy No No Yes No Bennetts et al. (1988)' 9%' 0-30% 68-83 4,5,6,7 236 Convy Yese Yes No No Snyder et al. (1989) 29%' 0-100% 68-83 4,5,6,7 256 Convy Yese No Yes No Snyder etal. (1989) 23%' 0-100% 68-83 4,5,6,7 499 Conv' Yes' Yes Yes No Snyder et al. (1989) 29% 8-55% 90-93 8 167 Convy No No Yes No Toland (1994) 58% 53-63% 94-95 1,2,3,4,5,6,7 233 Cony' No No Yes No This study SLocations are: WCA-1 (1), WCA-2A (2), WCA-2B (3), WCA-3A (4), Lake Okeechobee (5), Lake Kissimmee (6), Lake Tohopekaliga (7), and Upper St. Johns Marsh (8). SDiffers from 183 reported by source author because success was unknown for 8 of the 183 nests. Conventional- Success estimated as (# Successful Nests/Total # Nests) SNests placed in nest baskets were treated as all other nests. SSuccess estimated using maximum likelihood estimator (MLE) described by (Mayfield 1961, 1975, Hensler and Mchols 1981). SWas not reported by Bnennes e al. (1988), but data from their study were avalaible to derive estimate. SNot reported by source authors, but estimated from annual success in Snyder et al. (1989a, Table 2). Annual success for 1978-1983 was reported seperaely for lakes and WCA-3A by source authors, but combined here for estimate of mean annual success. All years were included in estimate regardless ofsample size. 'AU nests placed in nest baskets were considered to have failed. initiation were warranted at more restrictive rejection criteria of a =0.05 (Table 5-5). Our final model indicated an area, but not a year effect, as was indicated by our preliminary analyses (Table 5-6). However, area and year effects were highly confounded in these data because the studies included in this analyses that were conducted during different years were also conducted at different areas. Thus, we do not believe that we can reliably distinguish between these effects. Differences in success among areas and years are not surprising given the many causes of nest failures (Sykes 1987c, Bennetts et al. 1988, Snyder et al. 1989a). Our data indicated an effect from the date of initiation in all phases of this analysis; although it was not completely clear as to whether this effect was quadratic or linear. Overall nest success (all years combined) was highest during January with a generally decreasing trend over time (Fig. 5-3). However, the overall trend is somewhat misleading because it was heavily influenced by one year (1987) of exceptionally high success in January (Fig. 5-4). Most years had the peak of success inFebruary (3 of 7) or March (2 of 7). In only one year was peak success in January (1987) and one year in April (1993). In only one year did we observe nesting during December (1985), and success was lower than during January, February or March of that year. These temporal effects of success were undoubtedly confounded with year effects because studies conducted from 1991-1993 by Toland (1994, unpubl. data), which were included in this analysis, were conducted in the northern part of the kite's range where the date of initiation was often several weeks later than in the southern portion of their range. In contrast, most of the data from other years were from the southern Table 5-3. Annual nest success reported during studies from 1968 through 1995. Nest success was based on nests found during the egg stage. No. No. Nest Sampling Year Nests Successful Success (S) Var (S)' Source 1968 1 1 1.00 0.0000 Snyder et al. (1989a) 1969 -- - 1970 1 1 1.00 0.0000 Snyder et al. (1989a) 1971 -- -- - 1972 3 1 0.33 0.0741 Snyder et al. (1989a) 1973 18 4 0.22 0.0096 Snyder et al. (1989a) 1974 13 0 0.00 0.0000 Snyder et al. (1989a) 1975 15 0 0.00 0.0000 Snyder et al. (1989a) 1976 18 0 0.00 0.0000 Snyder et al. (1989a) 1977 13 2 0.15 0.0100 Snyder et al. (1989a) 1978 59b 25 0.42 0.0041 Snyder et al. (1989a) 1979 78b 42 0.54 0.0032 Snyder et al. (1989a) 1980 2 0 0.00 0.0000 Snyder et al. (1989a) 1981 5b 0 0.00 0.0000 Snyder et al. (1989a) 1982 12b 1 0.08 0.0064 Snyder et al. (1989a) 1983 18b 5 0.28 0.0111 Snyder et al. (1989a) 1984 -- - 1985 - 1986 107 28 0.26 0.0018 Bennetts et al. (1988) 1987 210 92 0.44 0.0012 Bennetts et al. (1988) 1988 - 1989 - 1990 26 2 0.08 0.0027 Toland (1994) 1991 39 8 0.21 0.0042 Toland (1994) 1992 59 33 0.55 0.0042 Toland (1994) 1993 43 14 0.33 0.0051 Toland (1994) 1994 57 36 0.63 0.0041 This study 1995 176 94 0.53 0.0014 This study "Sampling variance was not reported by the source authors, but was estimated based on a binomial distribution. Annual success for 1978-1983 was reported seperately for lakes and WCA-3A by source authors, but combined herefor estimate of annual success. Table 5-4. Summary statistics from individual univariate logistic regression models for the factors effecting nest success. Source df X2' P> X21 Year 7 63.18 <0.001 Areab 3 45.90 <0.001 Nest Substrate (NSUB)' 6 29.50 <0.001 Herbaceous vs Woody Substrate 1 0.01 0.918 Date of Initiation (DOI)d 1 35.94 <0.001 DOI* DOI' 1 12.83 <0.001 Water Depth at DOI 1 19.98 <0.001 Nest Height (HOT) 1 5.18 0.023 Distance to Nearest Land (LAND) 1 0.31 0.578 Distance > 200 m (D200)' 1 2.42 0.120 " Chi Square was based on Wald Statistic (SAS Inc. 1988). b Areas were WCA-3A, WCA2B, Upper St, Johns Marsh. Because other areas had insufficient sample sizes to be effectively included, they were grouped into an 'other'category. SSubstrates were Willow, Pond Apple, Meleleuca, Wax Myrtle, Cypress, Cattail, and 'other' d Estimated Julian date offirst egg. 'A visual inspection indicated the possibility ofa quadratic, rather than linear, relationship ofDOI. SSykes (1987b) suggested that nests within 200m of land were more prone to failure. Table 5-5. Summary statistics from preliminary main-effects multivariate logistic regression model for the factors effecting nest success. Source df X2. P> I X21 Year 7 17.65 0.007 Area" 3 0.38 0.944 Nest Substrate (NSUB)' 6 8.66 0.194 Date of Initiation (DOI)d 1 7.66 0.006 DOI* DOI' 1 3.14 0.076 Water Depth at DOI 1 0.39 0.532 Nest Height (HGT) 1 1.31 0.253 Distance > 200 m (D200)' 1 2.49 0.114 a Chi Square was based on likelihood-ratio test of models between the fully saturated main-effects model with and without each main effect (Hosmer and Lemeshow 1989). b Areas were WCA-3A, WCA2B, Upper St, Johns Marsh. Because other areas had insufficient sample sizes to be effectively included, they were grouped into an 'other'category. SSubstrates were Willow, Pond Apple, Meleleuca, Wax Myrtle, Cypress, Cattail, and 'other'. d Estimated Julian date offirst egg. 'A visual inspection indicated the possibility of a quadratic, rather than linear, relationship ofDOI. f Sykes (1987b) suggested that nests within 200m of land were more prone to failure. 100 o80 ,0 0o0 0 N D J F M A M J J A S Month Figure 5-3. The percentage of nests that were successful during each month. Data used in this analysis were from Bennetts et a. (1988)(1986-1987), Toland (1994, unpubLdata)(1990-1993), and this study (1994-1995). portion of the kite's range. This probably also accounts for the interaction effect of year with date of initiation. Number of Young per Successful Nest In contrast to nest success, the number of young per successful nest probably is one of the least variable and has been the least controversial of the reproductive parameters. The relative lack of variability for this parameter is not surprising since it is not unusual for raptors to produce normal numbers of young per successful nest even when other aspects of reproduction (e.g., proportion of population attempting to breed or nest success) are depressed (Brown 1974, Steenhof 1987). For this reason, the number of young per successful nest is not particularly informative in the absence of these other reproductive parameters (Brown 1974). Several studies have reported estimates for the number of young per successful nest (Table 5-7) and the average from 20 years of reported data is 1.9. Annual estimates reported have ranged from a low of 1.4 (Sykes 1979, 1987b, Bennetts et al. 1988) to a high of 2.5 (Sykes 1979, 1987b)(Table 5-8). Number of Nesting Attempts Per Year Snail Kites are capable of raising more than one brood per year and attempts at multiple brooding may be fairly widespread (Snyder et al. 1989a). However, the extent of multiple nesting attempts has been poorly documented. Snyder et al. (1989a) estimated the number of attempts based on a verbal description of the following calculation: n No. Nesting Attempts = No. Attempts per pair n No. Pairs 2 where n, = the number of successful nesting attempts found on Lake Okeechobee and WCA-3A in 1978, s = the estimate of nest success (estimated as the number Table 5-6. Summary statistics from the final most parsimonious (based on AIC and LRTs) logistic regression modelfor the factors effecting nest success. Source df f2. p> I x21 Year 7 9.80 0.200 Area' 3 10.78 0.013 Date of Initiation (DOI) 1 5.34 0.021 DOI DOl1 1 2.66 0.103 YR DOI 1 16.63 0.020 a Chi square was based on a LRT between models with and without the source term. SAlthough the main effect ofyear was not sign ficant at a=0.05, it was retained in thefinal model because of its significant interaction with date of initiation (DOI). c Multivariate model indicated that this term did not need to be retained in subsequent models; however, the AIC was lower with this term included. Year and area effects also were confounded because some years included a restricted sample of areas (see Table 5-2). dA LRT ofmodels with and without this term indicated that this term was not warranted; although the AIC was slightly lower with the term included. a0 0 I)40 0 0 N D J W 80 dC 60 O 0 CD a40 is 20 Z wi so dl00-r Uao CD so 8 4601 dl z ID 1986 F M A M J J A S SN D J J A S 1991 I Is O N D J F M A M J J A S IILn~ 0 i -- T--- - O N D F A 1i00 1992 J A S 1993 0Ill7 SN A J J A S 1994 11[ O N D J F M A M J J A S 80 1995 o I i6i-iil v il S D J F M A M Month Figure 5-4. The percentage of nests that were successful during each month of each year. Data used in this analysis were from Bennets et aL (1988)(1986-1987), Toland (1994, unpubl data)(1991-1993), and this study (1994-1995). J J A S o 0 - In- Z 0o (a so 0 - successful nests/ number nests observed) for nests found at the nest building stage at Lake Okeechobee and WCA- 3A that were successful, and n, = the number of Snail Kites counted during the 1977 annual count on Lake Okeechobee and WCA-3A. Using the values reported by Snyder et al. (1989a) produces the following estimate: 60 0.29 207 .9 2.7 attempts per pair 152 76 2 We have several concerns about this estimate derived by Snyder et al. (1989a). First, it is important to recognize that this estimate is only applicable to the number of nest building attempts (i.e., courtship attempts by our definition; see discussion of Semantics above). It is not an estimate for the number of nesting attempts, if a nesting attempt is defined as having produced at least one egg. If we apply their procedure using nests in which at least one egg was laid and using an estimate of success derived from nests found during incubation (25 successful nests of 59 found during incubation =0.42)(this is less biased than using nests found after hatching; see above) then an analogous estimate for the number of nesting attempts (in which at least one egg was laid) would have been: 60 0.42 142 2-- 1= 1.9 attempts per pair 152 76 2 Regardless of how a nesting attempt is defined, we believe that the approach used by Snyder et al. (1989a) is inherently biased and produces an unreliable estimate for this parameter. First, there are several assumptions inherent in their calculations for which their estimate is not robust to violation. They correctly pointed out that they assumed (1) the count from 1977 was an accurate census, (2) there was a 1:1 sex ratio, (3) all birds counted during 1977 were potential breeders in 1978, and (4) no birds died between the 1977 count and the end of the 1978 breeding season. As we discuss in considerable detail in a later chapter (see discussion of the Annual Count in Chapter on Monitoring the Florida Snail Kite Population), the annual count is not a reliable census of the population, as was assumed, and evidence suggests that it is not even a reliable index to the population. Given that it is highly improbable that all birds are counted during the annual count (Rodgers et al. 1988), it is likely that the number of pairs would have been underestimated, which would have resulted in the number of attempts per pair to have been overestimated. We also have no means of evaluating whether assuming a 1:1 sex ratio was reasonable, but in the absence of such information we agree with Snyder et al. (1989a) that this was a reasonable assumption. We disagree that all birds counted during the 1977 annual count can be reasonably assumed to be breeders in 1978. Our data, as well as the observations by Snyder et al. (1989a) indicate that juveniles have a lower probability of attempting to breed than do adults. Thus, all birds included in the 1977 count that were juveniles were not potential breeders. This would tend to inflate the denominator (i.e., to reduce the number of pairs in their calculation) and, consequently produce a negative bias; however, there is no reliable way to estimate the extent of the bias, since the proportion of juveniles in the 1977 count was unknown. We agree with Snyder et al. (1989a) that they had to assume that no birds died between the 1977 annual count and the end of the 1978 breeding season; however, the true assumption was substantially more extensive than they reported. Because their calculation assumed the same population at both time periods (i.e., during the count and during the breeding season) they really assumed that Lake Okeechobee and WCA-3A (the only areas used in their calculation) were a closed population. That is, that there were not only no deaths, but also that there were no births, no immigration, and no emigration. If the time period between the 1977 annual count and the end of the 1978 breeding season were very short, this would be a reasonable assumption. However, the count was conducted in December of 1977 (see the Annual Count in Chapter on Monitoring the Florida Snail Kite Population) and initiation of the last reported nest of the 1978 breeding season was in August of 1978 (Snyder et al. 1989a). Thus, the interval over which this assumption applied was 8-9 months. We are not especially concerned about the assumption of no births because this assumption can be met by excluding young- of-the-year juveniles from their analysis. Although it is unlikely that no deaths occurred over this period of time, we would also not expected a large bias from this effect due to high adult survival. In contrast to births and deaths, we find the assumption of no immigration or emigration of substantial concern. Our data suggests that the probability of an adult bird moving in any given month is approximately 0.25 (see Movement). Thus, over an 8-9 month period it is extremely unlikely that there was no immigration or emigration to or from these areas. Furthermore, our data suggest that at the time the annual count is conducted, there is a greater probability that birds will be in habitats not typically used for nesting. This could result in a substantial overestimate of the number of attempts per pair. Snyder et al. (1989a) suggested that a bi-modal seasonal distribution of nests that are spaced about 34 Table 5-7. The annual mean and overall (i.e., all years and locations combined) number young per successful nest from major studies conducted since 1968. A successful nest was considered a nest in which at least one young fledged. Annual x Overall No. Per No. Per Total No. Successful Successful Annual Years Successful Nest' Nest2 Range Included Location(s)' Nests Source 2.0 1.9 1.4 2.5 1968-1976 1, 2, 3, 4, 5, 6 84 Sykes (1979) 2.0 2.0 1.4-2.5 1968-1978 1, 2, 3, 4, 5, 6 103 Sykes (1987b) 2.0 -* 1978-1983 4,5,6,7 106 Beissinger (1986) 1.5 1.6 1.4 1.7 1986-1987 4 149 Bennetts et al. (1988) 2.0 --' 1968-1983 4,5,6,7 --4 Snyder et al. (1989) 1.9 2.0 1.5-2.1 1990-1993 8 57 Toland (1994) 1.8 1.8 1.6- 1.9 1994-1995 1,2,3.4,5,6,7,8 144 This study The annual average number ofyoung per successful nest SThe total (all years) number ofyoung per total number of successful nests 'Locations are: WCA-1 (1), WCA-2A (2), WCA-2B (3), WCA-3A (4), Lake Okeechobee (5), Lake Kissimmee (6), Lake Tohopekaliga (7), Upper St. Johns Marsh (8), SNot reported and/or insufficient information provided to estimate Table 5-8. The number of successful nests, youngfledged, and number of young per successful nest reported for each year from 1968 through 1995. No. Successful No. No. Young per Nests Young Fledged Successful Nest Source 1968 11 24 2.2 Sykes (1979, 1987b) 1969 8 13 1.6 Sykes (1979, 1987b) 1970 8 12 1.5 Sykes (1979, 1987b) 1971 0 0 --' Sykes (1979, 1987b) 1972 3 7 2.3 Sykes (1979, 1987b) 1973 12 29 2.4 Sykes (1979, 1987b) 1974 6 11 1.8 Sykes (1979, 1987b) 1975 14 35 2.5 Sykes (1979, 1987b) 1976 22 30 1.4 Sykes (1979, 1987b) 1977 8 20 2.5 Sykes (1987b) 1978 11 20 1.8 Sykes (1987b) 1979 54 1082 2.0 Beissinger (1986) 1980 _- -3 3 1981 0 0 -' Beissinger (1986) 1982 2 42 2.0 Beissinger (1986) 1983 10 202 2.0 Beissinger (1986) 1984 _3 -3 .3 -3 1985 -3 3 1986 45 65 1.4 Bennetts et al. (1988) 1987 104 172 1.7 Bennetts et al. (1988) 1988 _3 _- 1989 3 _3 -3 1990 2 3 1.5 Toland (1994) 1991 8 17 2.1 Toland (1994) 1992 33 68 2.1 Toland (1994) 1993 14 26 1.9 Toland (1994) 1994 50 80 1.6 This study 1995 94 181 1.9 This study ' No successful nests from which to estimate 2 Not reported, but inferred from number of successful nests and number young per successful nest ' No reported information months apart during some years indicated widespread multiple brooding. We agree that a bi-modal distribution of nest initiations may be an indication of multiple brooding during those years. However, a bi-modal distribution may also be attributable in some years to the starting and stopping of nest initiations due to early season temperature changes; although these two explanations are not mutually exclusive. We often observed nest initiations during December or January that coincided with periods of unseasonably warm weather, particularly in the southern habitats. As colder temperatures resumed nest initiations that had not reached the egg laying stage were likely to terminate. Initiations would resume when warm temperatures returned, often a month or two later. This would create a peak of initiations early during the season (usually in January) followed by a second larger peak one or two months later (usually in February or March) after warm temperatures became prevalent. However, birds that successfully initiated early were also likely to re-nest because completion or failure of their first nest occurred well within the primary breeding season. Snyder et al. (1989a) correctly limited their inference to 1978. Beissinger (1995) later extended this inference to a general estimate for the number of attempts per year; although he revised the estimate to from 2.7 to 2.2 suggesting that this would be conservative. In order to generalize this estimate to all years, Beissinger (1995) had to assume that 1978 was a "representative" year. Based on the data presented by Snyder et al. (1989a), 1978 was far from representative. Based on both number of nests found and on number of nestlings banded, 1978 was an extremely high year for reproduction (Fig. 5-5). Consequently, application of this estimate to other years would very likely have resulted in an inflated estimate. 388888E.&,~. aso11111 8ee 7 88 68 70 71 72 73 74 75 76 77 78 79 80 81 82 83 Year 66 67 88 68 70 71 7 723 74 75 76 77 78 79 80 81 62 83 Year Figure 5-5. he number ofnests (top) and number of nestlings banded (bottom) during each year reported by Snyder et al. (1989a). Snyder et a. (1989a) estimated the number of breeding attempts per pair during 1978 and correctly limited their inference of this estimate to 1978. Based on these data, we suggest that estimates from 1978 can not be reliably extended to other years. Z z I Z E z -o 1 - ,0 - a Ii In contrast to the approach used by Snyder et al. (1989a), we estimated the extent of multiple broods using radio-transmittered birds. During 1995, we closely monitored 23 radio-transmittered adult Snail Kites for breeding activity in order to assess the number of breeding attempts per year. Of these 23 adults, 14 were females and 9 were males. During the 1995 nesting season, we located each bird on the ground approximately bi-weekly (x = 14.1 days 8.1 sd) and determined its breeding status (e.g., a nest, courtship, not breeding). Birds in which no breeding activity was detected were generally observed for 22 hrs and subsequent visits, usually within 10 days, were required to confirm a non-breeding status and to confirm any nests for birds exhibiting courtship. We found an average of 1.4 (0.6 sd) nesting attempts per bird (Table 5-9). The interval of our breeding status checks could have resulted in a failure to detect an occasional bird that initiated a nest that failed early during laying or incubation. Consequently, this estimate may be slightly low for 1995. However, most nest failure occurs during the first week after hatching (Bennetts et al. 1988), which would have required nesting activity for at least 4- 5 weeks. Consequently, the potential bias from having missed nests over a 14-day period probably was negligible. In addition, 1995 had favorable water conditions throughout the Snail Kite's range in Florida. Consequently, we might also expect our 1995 estimate to be higher than an annual average. In summary, we agree with Snyder et al. (1989a) that multiple brooding by Snail Kites in Florida is common during some years. However, our data suggest that the estimate of 2.7 attempts per year by Snyder et al (1989a) and even the "more conservative" estimate of 2.2 attempts per year used by Beissinger (1995) were substantial overestimates. A combination of differences in our estimation procedures, difference in our respective definitions of a breeding attempt, and annual variability of this parameter probably account for these discrepancies. Conditional Probability of Attempting to Breed- An alternative way to look at the number of attempts per year is using conditional probability for the proportion of birds attempting to breed (c,). That is, a" would be the probability that a bird attempted to breed, given that it had not attempted previously during that nesting season. Of 23 birds we monitored for breeding activity during 1995, all 23 attempted to breed at least once. Thus, our estimate of aC would be 1.0. Similarly, a2 would be the probability that a bird attempted to breed, given that it had previously made 1 attempt during that breeding season. Based on our data from 1995, we would RE P- -- -P-- IF. W VH W Table 5-9. Number of nesting attempts and number ofattempts that were successful for each of 23 adult Snail Kites during the 1995 breeding season. y S Number of No. Attempts Frequency Sex Attempts Successful 152.698 F 1 1 152.584 F 1 0 153.496 F 2 0 153.860 F 2 1 152.739 F 3 1 153.931 F 1 1 152.039 F 2 1 153.969 F 2 2 152.494 F 1 1 152.169 F 2 1 153.979 F 2 1 152.777 F 1 1 152.499 F 1 0 152.369 F 1 1 152.869 M 1 1 153.900 M 1 1 152.128 M 1 1 153.390 M 1 1 153.290 M 1 1 152.848 M 1 0 152.858 M 1 1 152.539 M 1 0 152.379 M 2 1 estimate a2 to be 0.34 (8 of 23). The probability that a bird attempted to breed, given that it had previously made two attempts during that breeding season (a3) was 0.13 (1 of 8). The variance for these estimates could be derived based on a binomial distribution, although the formula traditionally used for this estimate: &,(1 a,) Var(&,) = -- ni is intended for large samples (White and Garrott 1990). Hollander and Wolfe (1973) provide alternative procedures that could be used for smaller samples. Number of Successful Broods per Year- Snyder et al. (1989a) suggested that in some years it was possible for Snail Kites to successfully raise four broods. This was based on the length of the breeding season for certain years (e.g., 1978 and 1979) and the assumption that it would take 10 weeks (70 d) to raise a brood if mate desertion occurred and 16 weeks (112 d) if no mate desertion occurred. Although this is certainly theoretically possible, we believe that the probability of a Snail Kite successfully raising even three broods in a given year is very close to zero. We base our conclusion on several points. First, empirical data do not support Snyder et al.'s (1989a) conclusion. There have been no documented cases of Snail Kites successfully raising > 2 broods in a given year, and the occurrence of successfully raising 2 broods appears quite rare. Out of an 18-year study including 666 nesting attempts, Snyder et al. (1989a) documented only 3 cases of Snail Kites successfully raising 2 broods. Similarly, only 1 of 23 (4%) radio-transmittered birds that we closely monitored for breeding activity during 1995 (a good year), successfully raised two broods. We believe that Snyder et al. (1989a) overlooked some critical aspects of the breeding biology of Snail Kites when making this suggestion. First, although the inclusive dates from the first nest initiated in a given year to the last may span a period of 6-7 months, the initiation of nests is not evenly distributed throughout that period (see The Breeding Season above). The majority of nests (82% of the nests reported by Snyder et al. [1989a]) were initiated during a five month period from January through May. This is sufficient time for only two successful broods even when mate desertion occurs. The longest nesting season (time span over which nest initiations were observed) reported by Snyder et al. (1989a) over an 18-year period was only 31.7 weeks. Given that they suggest that it takes 10-16 weeks per successful brood, the longest nesting season they observed did not even have sufficient time to successfully raise four broods (even with mate desertion for all broods), and barely had sufficient time to successfully raise two broods without mate desertion. There also has been no consideration given to energetic costs of raising successive broods. Thus, we believe that a small percentage of birds (e.g., < 10%) may successfully raise two broods during some years; however, there is currently no empirical evidence to support the conclusion that Snail Kites successfully raise > 2 broods per year. Chapter 6. MOVEMENTS Natal Dispersal of Juveniles Natal dispersal s usually defined as the pr- manent movement ol an animal (usually a juvenile) away froman animals natal site to a new ste actual or potential breeding (.g Howard 1960, Greenwood dlspelsal ofa uvene from ts natal wetland wI h tie undertandg that a given indivdual mght, and prob- ably wdl, return to it natl wetland many times dur- ing its 1lfetme. The overall cumulative probabilityof uvenle Snail Kites dispeing from their natal wetld dunng their firt ye- was 0 81 based on an estmae derived using a KaplanMeeresimator (Fig 6-) Ony 8 of 65 (12%) radlo-transmitterd birds ovel a the-ye period that survived their enlire hist year and whoe locations were known remained in their nata welland for their entire irst year 64 TEMPORAL PATTERNS OF NATAL DISPERSAL Within-year patterns- Of the birds that dispersed during their first year (n=57), most (60%) did so within the first 60 days after fledging and all did so within the first 240 days (Fig. 6-2). -o i 40- 90 3 0 120 150 180 210 24 270 0 3 0 Days Since Fledging Figure 6-2. The percentage ofjuvenile Snail Kites that initially dispersed from their natal wetland in each 30-day time period since fledging. Differences among Years-Dispersal of juveniles from their natal wetland was lowest during 1992 and relatively higher in both 1993 and 1994 (Fig. 6-3). Differences were significant (at a =0.05) between 1992 and each of the other years, but not between 1993 and 1994 (Table 6-1)(Appendix 6-1). CD ------------ 3.0"3 IE- o 0.4 -J' O APR MAY JUN JUL AUG SEP OCT NOV DEC JAN FEB MAR APR Date Figure 6-3. Kaplan Meier estimatesfor the cumulative probability of dispersal in each of the three years. Confidence intervalsfor estimates are not shown to minimize cluttering, but are provided in detail in Appendix 6-1. Table 6-1. Results of log-rank tests between dispersal functions of juvenile Snail Kites dispersing from their natal wetlands during each study year (SY)(April 15 April 14). Comparison X2 df Prob 1992 vs. 1993 5.246 1 0.022 1992 vs. 1994 4.049 1 0.044 1993 vs. 1994 0.129 1 0.720 DIFFERENCES IN NATAL DISPERSAL BETWEEN NORTHERN AND SOUTHERN REGIONS We did not have a sufficient sample to compare differences in natal dispersal of juveniles among specific wetlands and/or among specific regions. However, because low waters levels occurred throughout the southern portion of our study area just prior to our study, we were interested in a spatial comparison of natal dispersal. Consequently, we pooled our samples from the southern regions (i.e., Everglades, Loxahatchee Slough, and Lake Okeechobee), which were substantially influenced by the previous drought, and the northern regions (i.e., the Kissimmee Chain-of-Lakes, and Upper St. Johns River Basin), which were relatively unaffected by the previous drought. This analysis indicated that natal dispersal from wetlands in the southern regions (i.e., most affected by the previous drought) was substantially lower than from wetlands in the northern regions during 1992, but not during 1993 or 1994 (Table 6-2)(Fig. 6-4)(Appendix 6-2). Table 6-2. Results of log-rank tests comparing dispersal functions ofjuvenile Snail Kites dispersing from the northern regions (i.e., Kissimme Chain of Lakes and Upper St. Johns River Basin) and southern regions (i.e., Everglades, Lake Okeechobee, and Loxahatchee Slough) during each study year (SY)(April 15 April 14). Comparison X2 df Prob 1992 7.530 1 0.006 1993 0.635 1 0.426 1994 <0.001 1 0.994 DIFFERENCES IN NATAL DISPERSAL BETWEEN LAKE AND MARSH HABITATS Our sample was also insufficient to compare the differences in natal dispersal among each individual habitat; but was sufficiently large to compare between lakes and marshes. We did not observe any differences in dispersal out of lakes compared to marshes for any juvenile cohort at a = 0.05; however differences were significant during 1993 at a = 0.10 (Table 6-3). Additionally, the number of animals in each sample group (i.e., birds that were alive and whose locations were known for each habitat type during each year) at any given time was relatively low (! 15)(Appendix 6-3). Thus, the power to detect even substantial differences in dispersal also was relatively low (see Fig. 3-1 in Methods section). There was, however, a consistent pattern for the estimates of the cumulative probability of dispersal out of marshes to be higher than from lakes for each cohort (Fig. 6-5). DISCUSSION OF NATAL DISPERSAL High rates of natal dispersal are often found for species that exhibit nomadic tendencies or that inhabit fluctuating or unpredictable environments (Baker 1978, Greenwood and Harvey 1982). Thus, the overall high rates of dispersal we found are not surprising. Two of the more commonly suggested reasons for high rates of natal dispersal are to gain access to resources (e.g., food or mates) in a saturated environment and inbreeding avoidance. The latter will be addressed in a Table 6-3. Results of log-rank tests comparing dispersal functions ofjuvenile Snail Kites dispersing from lake and marsh habitats during each study year (April 15 April 14). Comparison X2 df Prob 1992 0.599 1 0.439 1993 3.071 1 0.080' 1994 0.717 1 0.397 SUsing the alternative tests described by Cox and Oakes (1984)(Appendic 3-3) that are slightly less conservative (i e., have greater power, but higher risk of Type I error) we estimated '=3.801, P=0.051 and j=3.653, P=0.056for alternative tests I and 2, respectively. later section (see Natal Philopatry and Site Fidelity). Most species studied in which gaining access to resources has been suggested as a reason for dispersal have been territorial species (reviewed by Greenwood and Harvey 1982). In contrast, Snail Kites defend only a few meters around their nest site and only rarely defend feeding areas (Sykes et al. 1995). Thus, access to resources is unlikely to be limited by social behavior (except mate choice), and is more likely due to the overall availability of food or mates. Given the lack of social exclusion from resources, it might be predicted that if high dispersal were attributable to gaining increased access to resources, then dispersal should be high when local resources are depressed. Our results were not consistent with higher rates of dispersal during periods of low resources availability. Unfortunately, we did not initiate the collection of foraging data until 1993; however, based on hundreds of hours of field observations of birds, its was very apparent that food resources were substantially lower in 1992 (the first year of our study following the previous drought) compared to later years. Our foraging observations (during 1993 and 1994) as well as numerous previous reports (reviewed by Sykes et al. 1995) indicated that the capture of snails often requires < 3 minutes. In contrast, we often observed birds during 1992 that would forage in excess of 30 minutes without capturing a snail. In a few instances during our trapping period, we observed several foraging birds for hours without observing a single snail having been captured. Thus we would have predicted that dispersal would have been highest during 1992. Contrary to our prediction, dispersal was lowest during 1992. Furthermore, during 1992 dispersal was substantially lower in the southern regions where food was more depressed. Movement Probabilities In addition to the initial dispersal of juveniles from their natal wetland we also examined general movements of adult and juvenile Snail Kites. Here we treat movement using a conditional logistic regression model based on one month time intervals (see methods). Thus, our model expresses the conditional probability that, given a bird was alive and its location known at time t, that it would be in the same location (or conversely at a different location) at time t + 1. We then explored several potential effects on this probability. We based our analysis of general movements on 5,299 locations of radio transmittered birds (3,618 locations of adults and 1,681 of juveniles). THE EFFECT OF AGE AND SEX ON MOVEMENT PROBABILITIES A conditional logistic model of movement probability (i.e., the probability of a bird moving between time t and time t +1, given that it was alive and its location known at t+ 1) indicated effects of both age (X2=5.38, df=1, P=0.020) and sex (X2=6.16, df=2, P=0.046) when analyzed separately. However, our model of sex was confounded with age because sex was categorized as male, female, or unknown; where only juveniles were unknown. If these data were constrained to only adults, we found no evidence that sex had an influence on movement probability (X2=0.76, df=1, P=0.384). Because the unconstrained model with age (i.e., both age classes included) is a subset of the more general model of sex (with 1 fewer parameter), an alternative approach to test for the effects of sex is a LRT. The LRT between the unconstrained univariate models also indicated that sex does not add significantly to the fit of these data over a model with only age (LRT =0.76, 1 df, P=0.617). A comparison of AIC between these two models adds additional support for this conclusion (the model with age only had a lower AICXTable 6-4). Thus, we concluded that age, but not g 1. ..... o 0.8... - --- --. - = 0.6 J S .. -.o -o a 0.4 S_.... .- i n 0.2 jB-------------- mar&i E 0 3 A' M J J A S O N D J F M A M J J A S O N D J F M A M JJ A S O NO D JJ F M A 1992 1993 1994 1995 Date Figure 6-5. Kaplan Meier estimates for the cumulative probability of dispersal from lake and marsh habitats in each ofthe three study years. Confidence intervals for estimates are not shown to minimize cluttering, but are provided in detail in Appendix 6-3. Table 6-4. Summary statistics for conditional logistic regression model for the factors affecting the probability of movement between times t and t + 1 (at monthly time steps), given that an animal was alive at time t and its location known. Shown are the model description, number of estimable parameters (np), relative deviance (-2bln[l), and Akaike 's Information Criterion (AIC). The model shown in bold would be the one selected from this group based on AIC. Model np -21n(S) AIC Age 2 2502.95 2506.95 Sex 3 2502.19 2508.19 sex, had an influence on movement probabilities. Our estimates indicated that adults had a higher overall probability of movement (excluding any additional factors) than juveniles (Fig. 6-6). Age Class Figure 6-6. Conditional probabilities that adult andjuvenile Snail Kites that were alive and their location known at time t, were in the same location (or conversely at a different location) at time t + 1. Also shown are the standard errors (rectangles) and 95% confidence intervals (vertical lines). TEMPORAL EFFECTS ON MOVEMENT PROBABILITIES We began testing for an overall time effect using a univariate model based on separate parameter estimates for each month of each year of our study (i.e., 12 months for each of 3 years =36 parameters). This test showed a strong effect of time (X2=90.79, df=36, P<0.001)(Fig. 6-7). We then 16 E - 0 9. 04 16 0.4 a2- 0 91 50" 05 .0 .4 6) .0 AMJJASONDJFMAMJJASONDJFMAMJJASONDJFMA 192 1993 199M 1995 Month/Year A AMJJASONDJFMAMJJASONDJFMAMJJASONDJFMA 1992 1903 1994 1995 Month/Year Figure 6-7. Conditional probabilities that adult and juvenile Snail Kites that were alive and their location known at time t, were in the same location (or conversely at a different location) at time t + Ifor each month during this study (solid lines). Also shown are the 95% confidence intervals (dotted nnes). tested whether this effect could be accounted for with a more parsimonious model using separate months, but not for each year (i.e., 1 parameter for each month [12] rather than 36 for the previous model). This model also showed a significant monthly effect (x2=32.36, df= 11, P<0.001), but a LRT indicated that the more general model (with 36 parameters) was warranted (LRT=73.81, 24 df, P< 0.001). We next explored a series of models in which time was expressed as a seasonal, rather than monthly effect. These models reflected various combinations of 3 and 4 seasons in a sliding window approach (i.e., each iteration shifted the months included by one month) to determine how the months should be divided into seasons. This analysis indicated that one of the 3-season models (of 4 months/per season) was the most parsimonious based on AIC (Table 6-5); however, 2 additional models (the model with a separate parameter for each month and one of the 4-season models) had similarly low AIC values and could not be rejected based solely on AIC criteria. Next, we compared a suite of models including + I I i~ \q combinations of the above effects including their interaction terms. This analysis indicated that a model with season (the 3-season model selected from the above analysis), year, and the interaction between season and year was the most parsimonious based on AIC (Table 6- 6). As above, two alternative models (individual month model with 36 parameters and the model with season and Table 6-6. Summary statistics for conditional logistic regression models for potential temporal effects on the probability of movement between times t and t + I (at monthly time steps), given that an animal was alive at time t and its location known. Shown are the model description, number of estimable parameters (np), relative deviance (- 21ln[f), and Akaike's Information Criteria (AIC). The model shown in bold would be the one selected from these potential models based on AIC criteria. Model np -21n(-) AIC Time 36 2400.6 2472.6 Month 12 2474.4 2498.4 Season 3 2490.6 2496.6 Year 3 2477.4 2483.6 Month Year 14 2446.6 2474.6 Month Year Month*Year 36 2402.7 2474.7 Season Year 5 2460.9 2470.9 Season Year Season*Year 9 2451.3 2469.3 year without an interaction term) had similarly low AIC values and could not be rejected based solely on AIC. Likelihood ratio tests also indicated that the model with the lowest AIC was preferred (X2 =50.72, df=27, P=0.003 and X2 =9.55, df=4, P=0.049 for each of the alternative models, respectively). Thus, our data support that movement probabilities are influenced by season (Fig. 6-8), year (Fig. 6-9), and an interaction between season and year (Fig. 6-10). SPATIAL EFFECTS ON THE PROBABILITY OF MOVEMENT Here we explored whether the location of a given bird influenced whether or not the bird moved between times t and t + 1. For this analysis, we were not concerned with the destination of the bird (that will be explored latter in the section on Spatial Patterns of Movement); only its location at the time that a movement did or did not occur. We began this analysis with the general null hypothesis that the specific wetland where a bird was located at time t did not influence the probability of whether or not it moved to a different location at time t + 1. We rejected this null hypothesis based on a conditional logistic regression model (x2 =107.38, 16 df, P <0.001)(Fig. 6-11). We then tested the same hypothesis using the region, rather than the specific wetland, to determine if this might provide a more parsimonious model. This test also rejected the null hypothesis (x2 =53.82, 5 df, P <0.001); however, a comparison of these two models indicated that our data Table 6-5. Summary statistics for conditional logistic regression models for potential seasonal groupings affecting the probability of movement between times t and t + 1 (at monthly time steps), given that an animal was alive at time t and its location known. Shown are the model description, number of estimable parameters (np), relative deviance (-21n[-]), and Akaike's Information Criteria (AIC). The model shown in bold would be the one selected from these potential models based on AIC. Season Model np -2ln(S) AIC (JAN FEB MAR APR MAYJUN JUL AUG SEP OCTNOVDEC) 12 2474.44 2498.44 (JAN FEB MAR APR) (MAY JUN JUL AUG) (SEP OCT NOV DEC) 3 2490.66 2496.66 (FEB MAR APR MAY) (JUNJULAUGSEP) (OCT NOVDECJAN) 3 2498.75 2504.75 (MAR APR MAYJUN) (JULAUG SEP OCT) (NOVDEC JAN FEB) 3 2506.24 2512.24 (APR MAYJUNJUL) (AUG SEP OCTNOV) (DECJANFEB MAR) 3 2498.14 2504.14 (JAN FEB MAR) (APR MAY JUN) (JUL AUG SEP) (OCT NOVDEC) 4 2491.16 2499.16 (FEB MAR APR) (MAYJUN JUL) (AUG SEP OCT) (NOV DEC JAN) 4 2497.98 2505.98 (MAR APR MAY) (JUN JUL AUG) (SEP OCT NOV) (DEC JAN FEB) 4 2497.36 2505.36 SPRING SUMMER FALLWINTER (JAn-lp) (May-Au) (Sp-Dc) Season 035 0.30 - 025 0.10 0.05 SPRING SUMMER FALLWINTER (Jn-Ap) (My-Aug) (Sp-D-c) Season Figure 6-8. Conditionalprobabilities that adult andjuvenile Snail Kites that were alive and their location known at time t, were in the same location (or conversely at a different location) at time t + I during each season. Also shown are the standard errors (rectangles) and 95% confidence intervals (vertical lines). supported use of the more general model (i.e., specific wetlands) based on both a LRT (X2 =52.04, 11 df, P <0.001), and on AIC (Table 6-7). Table 6-7. Summary statistics for conditional logistic regression models of the probability of movement between times t and t + 1. The model with the lowest AIC (bold) would be selected if based solely on this criterion. Source -21n(g) np AIC Specific Wetland 2394.12 17 2428.12 Region 2446.16 6 2458.16 Ad 1 0.35 Adib 0.30 2 0.25 0.20 g | 4-i- 2 0.15 0.10 Study Year 0.35 0 - 5 0.20 0 215 - 0.05 - 0.00 19S2 1993 1994 Study Year Figure 6-9. Conditional probabilities that adult andjuvenile Snail Kites that were alive and their location known at time t, were in the same location (or conversely at a different location) at time t + 1 during each study year. Also shown are the standard errors (rectangles) and 95% confidence intervals (vertical lines). Pooling of Locations- We next explored whether we could improve our model by some limited selective pooling based on a combination of biological and statistical criteria. Our goal for this exploration was to determine if we could obtain a more parsimonious model by pooling areas in which the overall relative use and seasonal patterns of use were similar enough so as not to warrant separate parameter estimates. We did not attempt to pool some wetlands whose use patterns we felt were biologically different (e.g., wetlands that were used primarily during non-breeding with wetlands used primarily for breeding) even though we could have done so strictly based on statistical criteria. Thus, although a more parsimonious model for the effects of location on movement probability was possible, we preferred to maintain separate parameter estimates for some areas to better ensure the biological integrity of these models. We began our exploration of potential pooling with areas in the Southern Everglades. The first pooling we considered was Everglades National Park (ENP) and Northeast Shark River Slough (NESRS). Each of these SU FA SP SU FA SP 8U FA SP S192 1993 1994 Season/Study Year SU FA SP SU FA SP SU FA SP 1992 1983 1994 Season/Study Year Figure 6-10. Conditionalprobabilities that adult and juvenile Snail Kites that were alive and their location known at time 1, were in the same location (or conversely at a different location) at time t + 1 during each season of each study year. Also shown are the standard errors (rectangles) and 95% confidence intervals (vertical lines). 10 I a -10 w04A1 9CAS 303B BICY 43 4 EE TO4 O 3 C PEW Location Figure 6-11. Adjusted residuals from a crosstabulation of movement and location at time t. Residuals >0 indicate that birds in this area moved more frequently than expected and residuals <0 indicates that birds in that area moved less frequently than expected. I 2 030- 3 o.mo 0.20 0.10 - 0.00 o a.o these areas are administered by the National Park Service, are part of the Shark River Slough (ENP has areas not within the Shark River Slough, but these areas were not used by radio-transmittered kites during our study), receive low to moderate kite use, and are not impounded at their outflow (each have levees at their inflow). A statistical comparison indicated that separate parameter estimates for each of these areas was not warranted based on LRTs and AIC (Table 6-8). Next, we considered including WCA-3B with ENP and NESRS. WCA-3B is also within the Shark River Slough, but is impounded at its outflow and is administered by state agencies. However, its overall relative use and its seasonal patterns of use (each of these areas tended to be used most during early summer) were quite similar to ENP and NESRS. A statistical comparison indicated that separate parameter estimates for each of these areas also was not warranted based on LRTs and AIC (Table 6-8). We considered pooling Big Cypress National Preserve (BICY) with ENP, NESRS, and WCA3B; however, BICY was used more extensively during fall and early winter than these other areas and much of areas used in BICY consisted of cypress prairie habitat which was not generally available in these other areas. Consequently, we did not include BICY with these other areas, even though we probably could have justified doing so on a statistical basis. Next, we considered pooling the A.R.M. Loxahatchee National Wildlife Refuge (WCA-1), Water Conservation Area 2A (WCA2A), and Holey Land Wildlife Management Area (HOLEY). Each of these areas represents northern Everglades habitats, although their water management histories have differed. Our statistical comparison indicated that separate parameter estimates for each of these areas was not warranted based on LRTs and AIC (Table 6-8). We next considered areas within the Kissimmee Chain-of-Lakes. First we considered pooling Lakes Tohopekaliga (TOHO) and East Lake Tohopekaliga (ETOHO). Our statistical comparison indicated that separate parameter estimates for each of these areas was not warranted based on LRTs and AIC (Table 6-8). Next we considered including Lake Kissimmee (KISS) with TOHO and ETOHO. Lake Kissimmee received moderately heavy use compared to the other lakes, but all are in close proximity, the seasonal patterns of use were similar, and there was considerable interchange among these lakes. Our statistical comparison indicated that separate parameter estimates for each of these areas again was not warranted based on LRTs and AIC (Table 6-8). In contrast, other lakes within the Kissimmee- Chain-of-Lakes (e.g., Lakes Marion, Tiger, Walk-in-the Water, and Marian) received substantially less use than KISS, TOHO, and ETOHO and the seasonal pattern of use was quite different (i.e., they were used most frequently during non-breeding periods). The seasonal pattern of use of these smaller lakes more closely resembled that of the peripheral habitats. Consequently, we next considered pooling the smaller lakes of the Kissimmee-Chain-of-Lakes with the peripheral habitats. Our statistical comparison indicated that separate parameter estimates for each of these areas were not warranted based on LRTs and AIC (Table 6-8). Finally, we compared various combinations of pooling to the general unconstrained model and among each other. This analysis indicted that the model containing all of our proposed pooling was a substantial improvement over the unconstrained model (i.e., with no pooling). Additionally, the model with all of our proposed pooling had the lowest AIC; although overall differences among all of the models we compared were relatively small. Consequently, we used the most parsimonious model (Model 16) of this set of models in further analyses of the influence of location on movement probability. This model had 10 parameters compared to the unconstrained model with 17 parameters; but was still a substantial improvement (x2 =47.61, 7 df, P <0.001) over the 6-parameter model using regions, rather than location. HYDROLOGIC EFFECTS ON THE PROBABILITY OF MOVEMENT We tested the influence of water levels on the probability of movement for a subset of the data (1787 of 2994 months). We were unable to use the complete data because applicable hydrologic data were not readily available for all areas. Our subset, however, included most of the major wetlands used by kites. Probably the most notable exception was the Upper St. Johns marsh. Data that were applicable to the areas kites used most during this study (i.e., the Blue Cypress Water Management Area and Blue Cypress Marsh Water Conservation Area) were available only from 1991 to present. Because our analysis used long-term averages to assess relative water levels (see below), we did not believe that these data were sufficient for this Table 6-8. Summary statistics for conditional logistic regression models of the probability of movement between times t and t + I to evaluate the pooling of some parameters. A failure to reject a LRT indicates that the additional parameters of the more general (unconstrained) model may not be supported by these data. The model with the lowest AIC (bold) would be selected if based solely on AIC. Model No. Constraints (Pooling) LRT' (2) df P > X2 -21n(i) np AIC 1 Unconstrained -- 2394.12 17 2428.12 2 NESRS = ENP 2.78 1 0.10 2396.90 16 2428.90 3 NESRS = ENP=WCA-3B 2.80 2 0.25 2396.93 15 2426.93 4 HOLEY = WCA1 0.07 1 0.79 2394.20 16 2426.20 5 HOLEY = WCA1=WCA2A 0.14 2 0.93 2394.26 15 2424.26 6 TOHO= ETOHO 1.46 1 0.23 2395.59 16 2427.59 7 TOHO= ETOHO=KISS 1.48 2 0.48 2395.61 15 2425.61 8 PERIPHERAL = KISSCH 0.00 1 0.99 2394.12 16 2426.12 9 Reduced Model (2,4,6,8) 4.31 4 0.36 2398.44 13 2424.44 10 Reduced Model (2,4,7,8) 5.39 6 0.49 2399.52 11 2421.52 11 Reduced Model (2,5,6,8) 4.38 5 0.50 2398.51 12 2422.51 12 Reduced Model (2,5,7,8) 4.40 6 0.62 2398.52 11 2420.52 13 Reduced Model (3,4,6,8) 4.34 5 0.50 2398.46 12 2422.46 14 Reduced Model (3,4,7,8) 4.36 6 0.63 2398.48 11 2420.48 15 Reduced Model (3,5,6,8) 4.41 6 0.62 2398.53 11 2420.53 16 Reduced Model (3,5,7,8)2 4.43 7 0.73 2398.55 10 2418.55 ' Based on comparison with unconstrained model 2 Includes allproposed constraints (pooling) assessment. Long-term data were available for the adjacent Blue Cypress Lake (which were used for drought assessments; see Conservation and Management), but these data did not sufficiently represent the areas most used by kites during the years of our study 0==0.48 for monthly averages from gauge S-251E [the most applicable gauge for recent kite use] with Blue Cypress Lake), undoubtedly due to changes in water management policies during these most recent years. However, based on our results, exclusion of this area would have been highly unlikely to have influenced our conclusions. Assessing the effects of water levels on a biological response of Snail Kites (or any other species that can move large distances in short periods of time) can be extremely difficult because of the spatial and temporal variation in both the water levels and the animals. We were unable to use stage (i.e., elevation of the water surface) directly as our measure of water level because the ground elevation differences among areas result in stages among areas being incomparable. At first glance, it seems that local water depth is a suitable alternative to stage; however, we were very concerned about two problems that result from using depth. First, reliable ground elevation data (which are required to determine depth from stage) are sorely lacking for most areas. Although many of the gauges have known ground elevation, there was often considerable distance between the gauges and the areas used by birds such that these known elevations were not reliable indicators of elevations in the areas of interest. Secondly, the lake habitats in particular, often had steep elevation gradients such that a substantial range of water depths may have been used even by a single bird in any given day. The range of depths used by a given bird in a single foraging bout on some lakes (e.g., Lake Kissimmee) often exceeded the range of depths over hundreds of square kilometers in marsh habitats. Consequently, our assignment of elevations and subsequent depths would have been quite arbitrary for many areas. To overcome these problems we wanted a measure that was robust to spatial variation (i.e., "standardized" such that it was comparable among areas) but did not rely on our having to assign an arbitrary elevation to a local site as would have been required by using depth. We also wanted a measure that would adequately reflect the temporal variation of hydrologic conditions. For example, most areas in Florida exhibit seasonal variation in water levels. Consequently, we wanted a measure that enabled us to assess the influence of water levels independently of seasonal variation. Based on these considerations we used the departure from monthly average stages for each area as a measure of relative water levels. Specific gauges used for this analysis are provided in Appendix 6- 4. A monthly average over the 26-year period from 1969-1994 (i.e., an overall average of the annual monthly averages) was calculated for each area. The period of record was used for areas that did not have reliable records for all 26 years. The difference (i.e., the residual) between a specific month of a given year and the long term average for that month was used as our measure of relative water levels. The 26-year period of time was sufficiently long to encompass long-term variability and also coincided with the period of time that Snail Kites have been monitored on an annual basis. Because averages were calculated by month, rather than annually, seasonal variation was taken into account. Because this measure is using stage, rather than depth, it also did not require assignment of a local ground elevation. Consequently, we could apply the same measure to birds within, as well as among, wetlands. However, one general caveat that must be included is that this is a measure of relative, rather than absolute, water levels. If birds respond to absolute water conditions (e.g., depth) then this measure may be misleading. There has been considerable effort in recent years to collect reliable data on elevation that can be used to estimate depth. A re-analysis of these data may be warranted in the future as these improvements become more available; however some problems (e.g., steep elevation gradients) will persist even with improved information on depth. We began our analysis with a univariate approach to logistic regression (Hosmer and Limeshow 1989) using the departure from average stage (described above) as a continuous independent variable. This analysis initially indicated an effect of relative water level (X2=8.27, 1 df, P=0.004). However, because location (i.e., wetland) and water level are confounded and our previous analyses indicated an effect of location, we next tested a model that included the effects of both location and water level. This test was consistent with our earlier analysis indicating a location effect (X2=55.88, 9 df, P< 0.001); however when included in a model with location, the effect of water level on the probability of movement was no longer apparent (X2=0.01, 1 df, P=0.934). Although these results indicated that relative water level was not a major influence on the probability of movement, we must emphasize that the hydrologic conditions under which our study was conducted were generally high water conditions throughout the study area. Consequently, low water conditions that might have triggered movements generally did not occur during this study and inferences regarding the effects of low- water conditions on movement probabilities could not be made. However, previous studies (e.g., Beissinger and Takekawa 1983, Takekawa and Beissinger 1989) have indicated substantial dispersal of Snail Kites during low- water conditions. Given that apple snails may die or become unavailable to kites during dry conditions, these previous reports are certainly reasonable, and may indicate that the lack of an effect of water levels we observed applies only to generally high water conditions. THE EFFECT OF FOOD RESOURCES ON MOVEMENT One of the most commonly cited reasons for animals to move is the availability of food (e.g., Krebs et al. 1974, Greenwood and Swingland 1984, Pyke 1984). Animals move if food resources are low or if there is potential for better resources elsewhere (Pyke 1984). Nomadic species are particularly believed to move in response to sporadic food conditions (Andersson 1980). The most commonly suggested reasons suggested for Snail Kites to move are water levels and food (e.g., Beissinger 1988, Bennetts et al. 1994, Sykes et al. 1995) and low water levels are generally implied to represent low food availability. During 1993 and 1994 we conducted 343 hours of foraging observations (including 814 prey captures) to assess the influence of food resources on movement. To minimize confounding variation, all observations were conducted on adult birds between 2 hours after sunrise and 2 hours before sunset. In addition, we restricted our observations to days that were not unseasonably cold (i.e., during the passage of cold fronts), were not raining, and winds did not exceed 10 mph. For comparisons of food acquisition, we also used only complete observations of foraging bouts. That is, observations in which we observed an individual for the entire length of time it took to capture a snail. There was also potential confounding attributable to foraging behavior. Course hunting (hunting by low flight over the marsh) is the most commonly used method of prey capture in Florida (Beissinger 1983a, Sykes 1987a) and accounted for 671 of 814 (82%) of the captures we observed. Still (perch) hunting accounted for the remaining 143 (18%) captures and the proportion of use among these 2 behaviors was highly dependent on habitat type (X2=249.78, 3 df, P<0.001). Perch hunting was primarily observed in cypress prairie habitats (Fig. 6-12). In contrast, we had a more reliable sample of course hunting observations in each habitat type, season, and year. Thus, our comparisons among seasonal and annual food acquisition by kites was limited to course hunting to reduce confounding between these different behaviors. 100- S a i g20 0 -ARM Qr Mi0 tNoh Ljh- Ok-ho0b CypO Prfi Habitat Type P.ch Hunng 400 0Hun0in 4 Figure 6-12. The percentage of captures by perch hunting and aerial (course) hunting in each of 4 habitat types. We compared food acquisition among seasons and years using the foraging time per capture as the dependent variable in an ANOVA model. Our results showed a difference among years, seasons, and an interaction between year and season (Table 6-9). Capture times were lowest during summer, relatively higher during spring, and still higher during autumn of each year (Fig. 6-13). Capture times also tended to be lower during 1994 compared to 1993 for each season. These results suggest that higher movement probabilities corresponded with seasons and years of higher food availability (see Temporal Effects on Movement Probabilities above). This is the opposite result of what would be expected if movements were attributable to low food availability. Table 6-9. Analysis of variance table from model of foraging time per capture as the dependent variable. Mean square (MS) and F values are based on type III partial sums ofsquares (i.e., they are adjustedfor all other terms in the model and are not dependent on the order of entry)(SAS Inc. 1988). Source df MS F P Year 1 386.79 29.65 <0.001 Season 2 374.80 28.73 <0.001 Year x Season 2 166.65 12.78 <0.001 Error 175 13.04 In addition to comparisons among seasons and years, we also observed 8 movements of 7 radio- transmittered birds (one bird moved twice) for which we had foraging observations prior to a subsequent movement. This enabled obtaining foraging observations immediately after the movement for paired comparisons 1993 1994 E 25 1- SPR SUH FALJ sPR SUM FALL eason 20ear) 5 10 Figure 6-13. The mean ( SE)foraging time to capture snails. Sample sizes (number of complete bouts observed) are also shown. of food acquisition before and after moving. To reduce confounding, we restricted this analysis to those observations where the movement was within 30 days of obtaining the first foraging observation. This reduced the potential for seasonal differences in food acquisition to be confounded with differences between locations. To further reduce confounding, we matched the time of observations before and after moving. Thus, if the foraging observations before moving were conducted between 1100 and 1300 h, then observations after moving were also conducted between 1100 and 1300 h. We then tested the null hypothesis (Ho) that the difference in mean foraging time per capture before and after moving was zero. These data indicated no difference in food acquisition before and after moving (t=0.60, P=0.57). Furthermore, in 4 of the 8 movements we observed, birds increased the time required to capture snails (Fig. 6-14). In the remaining 4 cases, birds decreased the time required to capture 1 2 12 12 12 1 2 IS5OI 155171 11A140 70555 Location (Fl7p--hs 1 2 1 2 (53.83 153.6* Figure 6-14. The mean ( SE) foraging time to capture snails by radio-transminered birds before (location 1) and after (location 2) moving. Sample sizes (number of complete bouts observed) are also shown. snails. Although these data are limited, they do not support the hypothesis that birds are moving to enhance their foraging opportunities. However, the only case (bird 153.564) where we observed a second movement immediately following a previous movement, was when the time required to capture snails was higher than at all other locations. This suggests that there might be a threshold of food acquisition, below which birds will move if there are more favorable sites elsewhere. MODEL SELECTION AND DISCUSSION OF EFFECTS ON MOVEMENT PROBABILITIES We began our model selection using the procedure described by Hosmer and Limeshow (1989)(see Methods). First we conducted a univariate analysis of each potential term (i.e., age, season, study year, and location). Based on the preliminary analyses above, we used location, rather than region. We also used the 10-parameter model for location rather the full 17-parameter model. Because all terms were significant at the a level (0.25) suggested by Hosmer and Limeshow (1989), we next used each term in a multivariate model of only main terms (i.e., no interactions were included at this point). In both the univariate and multivariate analyses all terms were significant (Table 6-10) and consequently retained for further model selection procedures. Next we began exploring which, if any, interaction terms should be included in the model. A LRT between a fully saturated model (i.e., with all interaction terms) and the multivariate model without any interaction terms indicated that at least some interactions were warranted (Table 6-11, test 23). A comparison of the fully saturated model with a model excluding the 4- way interaction term indicated that the 4-way interaction was not warranted (Table 6-11, test 24). However, comparisons among models with various combinations of 2 and 3-way interactions indicated that some 2-way interaction terms (Table 6-11, tests 2,3,7,8,10,12, and 13) and some 3-way interaction terms (Table 6-11, tests 17,18,19,20,22) were warranted. Our tests did not support retaining an age location, age season, or age * year interaction terms (Table 6-11, tests 1, 4, and 5); but did support retaining 2-way interaction terms for season x location and year location at a= 0.05 (Table 6-11, tests 2 and 3) and a season year interaction at a= 0.10. All further tests using season x location and year location interaction terms indicated that these terms were warranted (Table 6-11, tests 7,8,10,12 and 13); however, retention of the season year interaction was not supported when included with these other interactions (Table 6-11, tests 9, 11, and 14). Thus, we retained season*location and year*location interaction | + + 6 4 3 2- 1 Table 6-10. Maximum likelihood analysis of variance table for univariate models (i.e., each source term represents a separate model) and multivariate models (i.e., each source term is contained within 1 model) of potential sources of variation of the probability of movement between times t and t + 1. Univariate Multivariate Source 2 df P < 2 1 df P < X2 Age 5.38 1 0.020 9.87 1 0.002 Season 17.85 2 <0.001 22.35 2 <0.001 Year 28.28 2 <0.001 25.48 2 <0.001 Location 103.36 9 <0.001 110.98 9 <0.001 SBased on Wald / statistic (SAS inc. 1988). Table 6-11. Likelihood ratio tests (LRTs) comparing conditional logistic regression models of the probability of movement between times t and t + 1. The null hypothesis (H) from a LRT is that the reduced model (i.e., the model with fewer parameters) fits the data equally well as the more general model (i.e., with more parameters). Thus, a rejection of H favors the more general model and failure to reject Hefavors the more reduced model. LRT No. General Model' Reduced Model' LRT df P > 1 A S Y L A*L A S Y L 13.61 9 0.14 2 ASYL S*L ASYL 45.22 18 <0.01 3 ASYL Y*L ASYL 30.82 18 0.03 4 ASYL A*S ASYL 1.01 2 0.60 5 ASYL A*Y ASYL 2.99 2 0.22 6 ASYL S*Y ASYL 8.12 4 0.09 7 A S Y L S*L Y*L ASYL S*L 34.44 18 0.01 8 A S Y L S*L Y*L ASYL Y*L 48.84 18 <0.01 9 A S Y L S*L S*Y A S Y L S*L 4.64 4 0.33 10 A S Y L S*L S*Y ASYL S*Y 41.74 4 <0.01 11 ASYL S*L Y*L S*Y A S Y L S*L Y*L 2.51 4 0.64 12 ASYL S*L Y*L S*Y A S Y L Y*L S*Y 45.12 18 <0.01 13 ASYL S*L Y*L S*Y A S Y L S*L S*Y 32.31 18 0.02 14 ASY L + all2-way Interactions A S Y L S*L Y*L 22.23 17 0.18 15 ASYL S*L Y*L A*S*Y ASY L S*L Y*L 5.16 4 0.27 16 ASYL S*L Y*L A*S*L A S Y L S*L Y*L 21.06 18 0.27 17 ASYL S*L Y*L A*Y*L A S Y L S*L Y*L 32.76 18 0.02 18 ASYL S*L Y*L S*Y*L A S Y L S*L Y*L 59.63 36 <0.01 19 AS YL S*LY*L S*Y*L A*Y*L A S Y L S*L Y*L S*Y*L 33.59 18 0.01 20 ASYL S*L Y*L S*Y*L A*Y*L A S Y L S*L Y*L A*Y*L 60.77 36 <0.01 21 A S Y L +all 2 & 3-way Interactions ASY L S*L Y*L S*Y*L A*Y*L 39.57 39 0.44 22 ASY L +all 2 & 3-way Interactions AS Y L + all 2-way Interactions 110.87 76 <0.01 23 Fully Saturated (all interactions) ASYL 247.78 16 <0.01 24 Fully Saturated (all interactions) A S Y L +all 2 & 3-way Interactions 35.02 36 0.52 'Model terms areA =Age, S=Season, Y=Study Year, L=Location (with poolingfrom Table 6-8) terms, but not a season* year term. Using a base model with these 2-way interaction terms we tested whether the data supported the addition of any 3-way interaction terms. Our results indicted that two of the 3-way interaction terms (Age*Year*Location and Season*Year*Location) were supported by the data (Table 6-11, tests 17,18, 19, and 20) and the remaining two were not (Table 6-11, tests 15 and 16). Thus, based on LRTs, our data support a model with all main effects, two 2-way interaction terms (Season*Location and Year*Location) and two 3-way interaction terms (Age*Year*Location and Season*Year*Location). The results from using AIC were not in complete agreement with LRTs. The model with the lowest AIC (Table 6-12, Model 7) was a model with all main effects and only one 2-way interaction term (Season*Location); however, the AIC of the model with both of the 2-way interaction terms supported by LRTs (Table 6-12, Model 12) was very close. AIC did not support inclusion of the 3-way interaction terms. Differences between models indicated by LRTs and AIC are not uncommon and represent conceptual differences between an approach of hypothesis testing (LRTs) and optimization (AIC). Both approaches were consistent in that they indicated that the effects of age, season, year, and location were all supported by the data and that there is an interaction between time and location. The methods only differ in suggesting to what degree of complexity the interactions are supported by the data. Our results indicated that considerable Table 6-12. Summary statistics used for model selection of conditional logistic regression models of the probability of movement between times t and t + 1. The model with the lowest AIC (bold) would be selected if based solely on AIC criterion. Model Model Description -21n(g) np AIC No. 1 Age (A) 2502.95 2 2506.95 2 Season (S) 2490.66 3 2496.66 3 Year (Y) 2477.48 3 2483.48 4 Location (L) 2398.55 10 2418.55 5 A S Y L 2339.23 15 2369.23 6 A S Y L A*L 2325.62 24 2373.62 7 A S Y L S*L 2294.01 33 2360.01 8 A S Y L Y*L 2308.41 33 2374.41 9 A S Y L A*S 2338.22 17 2372.22 10 A S Y L A*Y 2336.24 17 2370.24 11 A S Y L S*Y 2331.11 19 2369.11 12 A S Y L S*L Y*L 2259.57 51 2361.57 13 A S Y L S*L S*Y 2289.37 37 2363.37 14 A S Y L S*Y Y*L 2302.20 37 2376.20 15 ASY L S*L Y*L S*Y 2257.06 55 2367.06 16 A S Y L with all 2-way Interactions 2237.34 68 2373.34 17 A S Y L S*L Y*L A*S*Y 2254.41 55 2364.41 18 A S Y L S*L Y*L A*S*L 2238.06 69 2376.06 19 A S Y L S*L Y*L A*Y*L 2226.81 69 2364.81 20 A S Y L S*L Y*L S*Y*L 2199.63 87 2373.63 21 A S Y L S*L Y*L S*Y*L 2166.04 105 2376.04 22 A S Y L with all 2 & 3-way 2126.47 144 2414.47 23 Fully Saturated 2091.45 180 2451.45 movement occurs that is not directly related to water levels. We cautioned, however, that this analysis was conducted entirely during a period of relatively high water. Based on anecdotal evidence we believe that Snail Kites do move in response to low water levels. Thus, we believe that there is a threshold response to water levels. If water levels become low enough to negatively affect food resources, then kites will likely move from that area. However, during most years there is considerable movement that appears to be independent of current water levels. We suggest a hypothesis for this movement pattern below. Dispersal is also generally thought to be favored when local resources (e.g., food) are low or better conditions exist elsewhere (Horn 1984). In contrast, our results from both within-year and between year comparisons suggest that higher probabilities of movement occur when food resources are high. We also found that natal dispersal of juveniles was lower in areas where food resources were likely to have been depressed. At first it may seem counter-intuitive to leave an area if food resources are high; however, we suggest a hypothesis that this may be a reasonable strategy given the dynamic and unpredictable nature of a kite's environment. A virtual certainty about any specific wetland inhabited by Snail Kites is that it will go dry. What is not certain is which wetlands will go dry in which years. Thus, there is an advantage for kites to have experience regarding the availability of wetlands throughout their range so that when a local drying event does occur past experience reduces the need for "blind" searching for suitable alternative habitats. Thus, in years that food is not limiting, which for kites may be most years, high food resources may enable kites to "explore" their potential habitats with little risk of starvation. The resulting experience from many locations may then help kites to locate food resources faster during times when food is limiting. Spatial Patterns of Movement Here we ask the question that given a bird at location r at time t has moved, what is the probability that it will be at location s at time t + 1. Thus, our conditional setting is that a bird is alive, its location at times t and t + 1 are known, and it has moved from its location at time t. We approached this problem by first establishing whether the locations at time t and t + 1 are independent (i.e., our null hypothesis [Ho] is that the probability of moving to a given location does not depend on the bird's previous location). For our initial exploration we used a fully unconstrained data set with no pooling of locations). This hypothesis (Ho) is strongly rejected (2= 1014.41, df=256, P < 0.001). Thus, our data suggests that the probability of movement to a given location does depend on the bird's previous location. The residuals from this analysis provide some insight into how these movement patterns departed from what would have been expected if Ho were true (Fig. 6-15). EFFECT OF DISTANCE What becomes apparent from the above residuals is a tendency to move to areas in relative proximity to a bird's current location. For example, birds at WCA-3A had the greatest positive departure from expected when they moved to Everglades National Park and Big Cypress National Preserve, both of which are immediately adjacent to WCA-3A. The greatest negative departures from expected were movements to the lakes within the Kissimmee chain-of-lakes (at the opposite end of the kites' range in Florida. This pattern of higher than expected values for locations in proximity and lower than expected values for distal areas can be seen throughout these data. We further explored the relationship between distance and the location of movements by ranking each location (wetland) with respect to its relative distance from each other location. Thus, the closest wetland to any given wetland was given a rank of 1 and the furthest was given a rank 15. We excluded the peripheral habitats and the Kissimmee Chain-of-Lakes from this analysis because they were not contiguous and it would have been virtually impossible to perform an analysis for every wetland. When more than one wetland was immediately adjacent to another, we used the areas that were most frequently used by kites within each wetland to determine their relative ranking. We then tested for the effect of distance using the null hypothesis (HJ that the distance rank is equal for all movements (i.e., the expected values for a goodness-of-fit test among distance classes were equal). We strongly rejected H. (X2=320.83,13 df, P <0.001) and concluded that distance does have an influence on the locations to which a bird moves. Not surprisingly, the residuals from this analysis indicated that birds exhibited a greater than expected frequency of moving to a new location in proximity to their previous location, and a lower than expected frequency of moving to areas that were most distant from their previous location (Fig. 6-16). This does not imply that birds will not move long distances (our data show that they do); but rather, that long distance movements have a tendency to be made in short increments. -a to EVWdONMWWIPk(ENP) I P 1 E10 E Kim HOLEY. S P... t0 -Cyp -N- "P.W...BCY) cc I F (DI 10 BEf A W LOX "CAB JM TOHO K-CHA 5I NoWth EaM Shwk Slo ug P NES.C |-o| 10 WI -Dc :5 --_ . 0 Of KHE- WOOSB CA OE 0 XE i IS ETOHO PET u A M LLorhzw N4W.R. LOX) Hooy ULa W.MA (HOLE) z '0 M IM 0 -B 0 o B aw 6p d aW W0 a8 AW00400f ^ .--O-"-- jgg 0E NEwRS WCAB WCAO HOLEY OEE 0 O ErH0O PER V WOAOA WOS LOX IW It S TON K' to W NI OCB W"A2A HOLEY4 a* 000 ETOHO pow KY WCA WC- LOX WLo -t TOMO K1CH CI cc < oV E 91 000WIW0A0LEY OIWE KSa ETwHO PERt S0---- 3 LOxM t Skeo gho (WPBE) 0 SLJotnrMwhtSJM) * I cc :3 WI I 5 a W a- HL Er -10 OVCY W W LmOX W HL S S K-000 40 EEOOa WO SW.AA C LOXX 5 I TOHO" K-00 Location at Time t+1 Location at Time t+1 Figure 6-15. Adjusted residuals from the crosstabulation of the frequency of movements to each location (at time t + I)from each location at time t. A residual value of 1.96 is an approximate indicator ofthat residual being significant at an a = 0.05 level of significance. _81 I -1 I---. WEN Y 10 WCAOO WOO HOXLY WE US 01 T tOY WOCM WOOB tLOX 01B LU TO0 000 -a 10 ijiTohcdioi 05 LO o S Tgg^gl 1<.5 BCt WCA3 WOS LOX W6P 00 T0O K-0HA E L W' ElLT&opwlg>(ETOHO) o t-5 E0 0 0 'a 10------ OChneMl esW (KCHN 1 .5 a g a I PW-ItW H0bb (PER1F) II I C-0 ^ a~ o~ g g g- ____________< ll ---- ------ r 0e1 I0 WC0 S WOS1. H00 OEE MU E100 000 Location at Time t+1 Figure 6-15. Cont. 5 - _10- 2 3 4 5 6 7 8 9 1011 1 13 1 15 Distance Rank Figure 6-16. Standardized residuals from a contingency table of the frequency of movements to wetlands in each relative distance class. Expected values were derived under a null hypothesis (H.) of equal probability for each distance class. THE EFFECT OF AGE, SEX, AND TIME ON MOVEMENTS BETWEEN SPECIFIC LOCATIONS We tested for the effects of age, sex, and time (i.e., season and year) on the location-specific movement probabilities using a LRT between a log-linear model of specific location effects (using the reduced model from Table 6-10) and models which include the effects of each of these independent variables. We found no effect of age (X2= 17.7, df=23, P=0.773), sex (X2 =27.9, df=47, P=0.988), season (X2=39.04, df=44, P=0.684), or year (X2=11.72, df=48, P>0.999). This does not imply that there were no effects from these independent variables, but rather that if there were such effects, our data were insufficient to detect them. Given the large number of parameters (> 100) in these models this latter result would not be surprising. SEASONAL SHIFTS IN LATITUDE It has been previously suggested (e.g., Sykes 1983a, Sykes et al. 1995) that Snail Kites tend to move south during colder winters. Although no data have previously been presented in support of these suggestions, our data are consistent with this conclusion. There was a general tendency for Snail Kite occurrence to shift north during the summer (May-August) and south during Fall/Winter (September-December)(x2=27.68, df=4, P<0.001)(Fig. 6-17). We used a LRT to test whether this relationship was influenced by age, sex, or year by comparing a fully 15 10 MW 10 IWO 1issi0t 0098) 0 '.10 ^ 1 i-i- -i-i- r anIEU 000 00*00s- w WOOs Hoim 000 1000 0.00 am- I E P S WOYOL WCA o EY Oi 0 OM ETOHO tW Y 0C00 WOcS LOX WPB UM TOLHO K-C0 Location at Time t+l W I 1 1= li * IN do M I Easc~ w J--AWp May-Aug S. -D.C Season Figure 6-17. Adjusted residuals from a contingency table of the frequency of movements to wetlands in northern, central, and southern regions during each season. saturated model to one without each of these effects. These tests indicated that these seasonal shifts in latitude were influenced by age (LRT=15.68, df=4, P =0.004), sex (LRT=22.47, df=8, P=0.004), and year (LRT=51.60, df=8, P<0.001). Although we found evidence to suggest that there were effects of age, sex, and year on these seasonal shifts, the AIC was substantially lower for a reduced model without these effects (9,837 compared to 12,651 for the closest model). Although this result appears contradictory, it is not uncommon, and reflects the difference between hypothesis testing where a specific effect is being tested and model selection, which views model selection as more of an optimization problem (Spendelow et al. 1995). In other words, there may be real influences of age, sex, and year, but our data are insufficient to effectively incorporate separate parameter estimates for each of these influences. SHIFTS IN REGIONAL USE Factors such as resource abundance and disturbance that influence patterns of distribution at one scale may be expressed quite differently at a different scale (Wiens 1989). Consequently, we looked at broad- scale movement patterns over the duration of the study in addition to patterns occurring within monthly time steps. We accomplished this by examining how the proportion of birds in different regions shifted throughout the study. Because our capture protocol used the proportion of birds in different areas as a biasis for our sampling, we believe that our initial conditions were a reasonable representation of the distribution of birds at the start of this study. We then looked at the proportion of locations each month in each region to examine how these proportions changed over time. It was quite apparent from these data that shifts in regional use had occurred over the duration of the study. We tested for a linear trend over the 37 month period from April 1992 through April 1995 using linear regression on the proportion (after an arc-sin transformation) of locations in each region. We tested the null hypothesis (Ho) that the observed slope over this period was zero using a t test (SAS Inc. 1988). The proportion of birds using the Everglades Region decreased over this interval (t=6.27, P<0.001)(Fig. 6-18). The proption of birds using the Okeechobee and Upper St. Johns Regions decreased over the interval (t= -5.77, P<0.001 for the Okeechobee Region and t=-3.94, P <0.001 for the Upper St. Johns Region). The proportion of birds did not exhibit a linear trend for the Kissimmee Region (t=-0.028, P=0.916), the Loxahatchee Slough Region (t=-1.415, P=0.166), or the Peripheral Region (t=-1.088, P=0.284); although the seasonal shifts in some regions (e.g., the Peripheral Region) may have been better represented by a higher order polynomial regression. Effect of Hydrology- Our analysis of regional shifts is strictly correlative and can not reliably be used to infer cause and effect. However, it is quite possible that, at least some of the shifts in regional use, were attributable to hydrologic changes. For example, the Everglades Region experienced a relatively severe drought just prior to our study. It is quite likely that the low proportion of birds in this region at the beginning of our study was attributable to this drought. Our anecdotal observations of foraging birds in this region during 1992 clearly indicated that food resources were diminished. We often observed birds foraging for long periods (sometimes > 1-2 hours) without capturing a snail; whereas, under good conditions, foraging birds capture snails in just a few minutes (see Effect of Food Resources on Movement). The relatively high proportion of birds in the Okeechobee and Upper St. Johns Regions at the beginning of this study may well have been birds displaced from the Everglades Region. The Okeechobee Region also had experienced the drought preceding our study; however, virtually all of the birds we observed in this region at the beginning of our study were confined to the outer marsh of Lake Okeechobee which had not dried during the drought. Over the duration of our study the proportion of birds decreased at Lake Okeechobee and those birds present in this region shifted from the outer marsh to more interior marshes, that had been dry Everglades Region S- Okeechobee Region 80 so -40 Kxahatee ChainSlough Region so0 60 I40 20 40 - 20 - Upper St Johns River Region 40 S20 S- 0- Lonuhalchee Slough Region 015. so Peripheral Region 880so 60 o40 20 0 AMJJASONDJ FMAMJJASONDJ FMAMJJASO N O J FMA 1992 1993 1994 1995 Month (Year) Figure 6-18. The percentage of Snail Kite locations in each region during each month from April 1992 through April 1995. during the preceding drought. The Upper St. Johns Region had not experienced a drought just prior to our study. The Northern Lakes Region also had not experienced the recent drought and a higher proportion of birds in this region might also have been expected. However, the Northern Lakes Region had extremely high numbers of birds in 1991 (J. Rodgers Jr, J. Buntz, GFC, Pers. Comm.). Thus a high proportion of birds probably had been using this region, but had already dispersed by the start of our study. SEASONAL SHIFTS IN HABITAT USE During this study it became apparent that there were substantial seasonal shifts in the use of different habitats by Snail Kites. Consequently, we conducted a "post hoc" evaluation of these habitat shifts. Because this study was not designed to evaluate habitat relationships and because there is high variability in micro-habitat, our analysis was limited to five broad habitat categories (described in Study Area). We assigned each location to one of these five habitat classes. We then examined how the proportion of use of each habitat for each month shifted over the duration of the study. Our results indicated that Snail Kites exhibit strong seasonal patterns in their relative use of some habitats (Fig. 6-19). Relative use of Lake Okeechobee fluctuated greatly, but was not as predictable seasonally as other habitats. Use of Lake Okeechobee was relatively high for the first year of our study then dropped to very low use during the winter of 1993-1994, and increased again to moderate use (discussed above in Shifts in Regional Use). The use of other habitats exhibited more seasonal fluctuation. Peak use of graminoid marshes and northern lakes coincided with the periods of major breeding activity; whereas, cypress prairies and peripheral habitats were used more extensively outside of the primary breeding season. One potential explanation for the use of graminoid marshes and lake habitats during breeding is the relative predictability of hydrologic conditions in these habitats compared to cypress prairies or peripheral habitats. Cypress prairies tend to have shorter hydroperiods than are typically used by breeding birds. Given that a breeding attempt requires 10-16 weeks per clutch (Snyder et al. 1989a), the probability of an area drying out in cypress prairie during a breeding attempt is greater in this habitat compared to those typically used by nesting kites. Thus, the probability of a nesting attempt failing probably is also greater. Similarly, peripheral habitats were usually more ephimeral wetlands or were used for agricultural purposes and were subject to extreme fluctuations for Okeechobee a so . Nortarn LBa Gramlnold Marsh 1992 1993 1994 1995 Month (Year) Figure 6-19. The percentage of Snail Kite locations in each habitat during each month from April 1992 through April 1995. agricultural use. Thus, the probability of this habitat remaining in suitable hydrologic condition for the extent of a breeding attempt also was likely diminished. Although hydrologic predictability may provide an explanation for why graminoid marshes and lake habitats were more likely used during breeding seasons, it does not explain why cypress prairies and peripheral habitats were used during non-breeding periods. We can offer some potential hypotheses, all of which remain to be tested. First, birds in cypress habitats tend to hunt from perches, rather than by flight (see The Effect of Food Resources on Movement). This could offer an energetic advantage since perching requires less energetic expenditure than flight. Secondly, using alternative habitats to the primary breeding habitats could allow snail populations to be repelinished prior to the next breeding season. The summer months when birds shift to cypress and peripheral habitats also coincides with peak breeding activity of apple snails. Lastly, exploring alternative habitats when food resources are not limiting may better enable kites to locate food during periods of localized drought. This latter hypothesis was previously discussed in more detail in the section on Model Selection and Discussion of Effects on Movement Probabilities. The detectability of Snail Kites is probably very low in both cypress prairies and peripheral habitats compared to graminoid marshes, the northern lakes, or the Lake Okeechobee habitat types. Many of the peripheral habitats were either on private land (e.g., agricultural areas) or on public land in which access was limited (e.g., water control districts). Much of the cypress habitat also had very limited access (e.g., some management units of Big Cypress National Preserve do not allow airboat access) and even large numbers of birds in this habitat type were very easy to overlook because of the dense vegetation. On several occasions we had reports of a few birds in cypress habitats only to discover with more intensive searching that the "few" birds turned out to be a large number of birds (50-400). This seasonal use of habitats with low detectability can have important implications regarding population monitoring of Snail Kites in Florida. For example, the annual count conducted each year (see Monitoring of Snail Kite Populations in Florida) in Florida coincides with the period of relatively high use (July-January) of the peripheral and cypress prairie habitats. Consequently, the number of birds detected during these surveys may be greatly influenced the number of birds in these habitats. This can cause a substantial undercounting of birds and may influence the variability among counts depending on what proportion of the population is in these habitats in any given year. These problems are discussed in detail in the chapter on Monitoring ofSnail Kite Populations in Florida. Beissinger et al. (1983) reported that Snail Kites in Florida have long been known to "disappear" from their usual haunts in summer and subsequently "reappear" during mid-October. This observation led to the speculation that Snail Kites in Florida may move to Cuba (Beissinger et al. 1983). No birds that had been banded in Florida were found during an expedition to search for such birds in Cuba (Beissinger et al. 1983). Although movement to Cuba is certainly possible, our data suggest that a more simple explanation for the disappearance of kites during summer may be the shifts in habitat use. The time of disappearance of birds reported by Beissinger et al. (1983) coincides with the time that we observed increased use of peripheral and cypress habitats (where they are less likely to be observed). The reappearance in mid-October also coincides with the time that birds begin shifting back to the graminoid and lake habitats. Natal Philopatry and Site Fidelity Philopatry is usually defined as the tendency for an animal to remain at (or very near) its natal site (e.g., Mayr 1963, Shields 1984). However, Snail Kites by their nature do not typically remain anywhere, but may have an affinity to return to their natal site. Consequently, for our analyses, we have relaxed the definition of philopatry to the tendency for a bird to occur in its natal wetland even though birds may, and probably do, regularly also occur elsewhere. We also emphasize from the outset that our analyses of natal philopatry and site fidelity were intended only as a "post hoc" cursory exploration. Our study was not designed to answer questions regarding these topics, and we urge caution in interpretation of our results beyond preliminary exploration. However, because almost no information exists on natal philopatry or fidelity for this species (Sykes et al. 1995), we believed that this preliminary exploration was warranted. NATAL PHILOPATRY Our primary data for assessing natal philopatry were the resightings of 414 banded Snail Kites, whose natal wetlands were known. Of these sightings 157 (40%) were of birds that were sighted in their natal wetland. The proportion of birds observed in their natal wetland was not dependent on the breeding status (i.e., actively nesting or not nesting) (X2=0.45, 1 df, P=0.50) based on a subsample of 255 birds whose breeding status was known (Fig. 6-20). The proportion of birds observed in their natal wetland was, however, influenced by the bird's sex (X2=6.02, 1 df, P=0.014)(Fig. 6-21), natal wetland (x2=39.75, 7 df, P<0.001)(Fig. 6-22), and the year (x2= 10.12, 3 df, P=0.018)(Fig. 6-23). enag Not BRre 01=106) f-149) Breeding Status Figure 6-20. The percentage of banded birds that were observed to be actively breeding (including courtship) or not breeding that were or were not resighted in their natal wetland. Only birds whose breeding status was known were included in our sample. 4- a ,, to 0 -T a (-125) Sex 0=170) Figure 6-21. The percentage ofmale and female banded birds that were or were not resighted in their natal wetland. Differences in natal philopatry between males and females are not uncommon among bird species and the avoidance of inbreeding is a frequently suggested reason for these sexual differences (e.g., Bengtsson 1978, Greenwood et al. 1978); even though there are few examples of the harmful effects of inbreeding in birds (Greenwood and Harvey 1982). Differences among sexes in natal philopatry usually are reported for philopatric species and not generally known to occur in nomadic species (Greenwood and Harvey 1982). Snail Kites in Florida, however, probably have a more 120 |inNdWfand S100- 0 Not In Nd Wellald 06 S40 WCA3A WCA2B OKEE 08 SJM TOHO ETOHO OTHER 0_a V_ 62 6.114 0-60 t-M0 0_ -1M I.-i) Natal Wetland 2 4. WCA3A WCA2B OKEE KISS SJM TOHO ETOHO OTHER Natal Wetland Figure 6-22. The percentage of banded birds from each (natal) wetland that were or were not resighted in their natal wetland. Only birds whose whose natal origins were known (n=414) were included. Also shown are the adjusted residuals from a crosstabulation of natal wetland and whether or not they were resighted in their natal wetland. For visual clarity, only the residuals from birds that were resighted in their natal wetland are shown; however the residuals from birds that were not resighted in their natal wetland are an approximate mirror image of those shown. restricted range than most nomadic species and consequently, similarly to philopatric species, may have a greater probability of inbreeding. Thus, our results are not necessarily inconsistent with the inbreeding avoidance hypothesis; nor do we have any data to support this potential explanation for the differences in philopatry between male and female Snail Kites. The differences among years and specific wetlands are even less clear. Our data were insufficient to explore the details of interactions among these sources of variation; however, interaction effects would be likely. For example, if one part of the Snail Kites' range had an ongoing drought then it would be unlikely for birds that had fledged from that part of their range to be in their natal wetland until conditions improved. Consequently, our results should be viewed in light of current conditions and probably should not be extrapolated beyond the environmental conditions we encountered. The extent of fidelity to a given birds natal wetland is undoubtedly influenced by the current condition of that natal wetland and other wetlands throughout the state. SITE FIDELITY In the previous section we addressed the tendency for Snail Kites to return to their natal wetland. Using radio telemetry, we also examined whether individual birds have a tendency to return to the same wetlands from year to year, irrespective of their natal area and breeding status. To test fidelity, White and Garrott (1990) suggest using a chi-square test of independence for situations in which locations of animals can be assigned to discrete areas (e.g., our designations of wetlands). For our initial exploration, we used this procedure, without any pooling, for all individuals for which we had sufficient data. Because we have already shown that there are strong seasonal effects in locations used by Snail Kites, we conducted analyses independently for each season (using the season designation previously supported by our data). We did not include any individuals in this analysis for which we had fewer than five radio locations in each season of each year of comparison. Although five locations was an arbitrary criterion, we believed that fewer locations would likely have produced spurious results because we would have likely had representation from only a small portion of the season. Based on our sampling interval (= 14 d), five locations were likely to have represented locations from over half of the season of interest (the expected number of locations during any one season, if the radio were operational for the entire season, was = 8). Additionally, chi-square tests have been known to perform poorly when the expected values of cells are less than five (Cochran 1954). Our criterion was intended to reduce these problems. A departure from fidelity was common for Snail Kites in each season. During the late-winter/spring (January through April) season 5 of 7 (71%) of the individuals, for which we had sufficient data to test fidelity, showed a departure from fidelity at a=0.05 level of significance (Table 6-13). Similarly, 16 of 24 (67%) individuals showed a departure from fidelity during the summer season (May through August)(Table 6-14), and 4 of 8 (50%) showed a departure from fidelity during the autumn/early-winter season (September through December)(Table 6-15). Because each test was an individual analysis from an individual bird, we did not attempt any detailed model selection for additional effects (e.g., sex or year). We also did not believe that the data warranted any extensive meta-analysis for these effects. On a purely descriptive note, however, we found no obvious patterns with respect to sex or year. There was a slight tendency for a greater departure from fidelity during the non- breeding seasons (summer-early winter) early during the study, but given the small sample sizes, we are not convinced that this is anything more than random variation. We emphasized at the beginning of this section, that this was a "post hoc" exploratory analysis. In particular, there are two problems which need to be identified. First, except for the summer season, in which we had 24 individuals meeting our criteria, these tests were conducted using a very small number of individuals. These small sample sizes were likely a result of the battery life of our radios. The expected life of our radios was 12 months and this may have been an optimistic expectation from some of the manufacturers. Thus, our analyses were only possible for individuals whose radios exceeded their expected life (or were replaced) and whose locations were generally known. Because most radios were attached during spring, the summer season was the first full season following attachment and the most likely to have had individuals for which we had > 1 year of data. This is probably why our sample was so much higher for summer. The specifications of the mortality switches of our radios precluded some radio designs that would have Table 6-13. Test results from j tests of independence for the number of locations of individual radio- transmittered Snail Kites during the late-winter/spring season (January April) at each location between years. Freq Sex Year 1 Year 2 N 1' N 22 X2 df P 152.104 M 93 94 5 5 6.67 1 0.010 152.630 M 93 94 7 6 4.55 2 0.103 152.362 F 93 95 17 7 24.00 4 <0.001 152.030 F 94 95 5 7 2.86 1 0.091 152.480 F 94 95 8 7 4.77 1 0.029 152.499 F 94 95 6 8 14.00 3 0.003 152.584 F 94 95 7 6 13.00 2 0.002 STotal number of locations for individual during this season for year 1. Total number of locations for individual during this seasonforyear 2. SBecause df = rows-1 columns-1 and all individuals had locations in only years (rows) for this season, the total number of locations we observed for the individual equals df+1. Contact Us | Permissions | Preferences | Technical Aspects | Statistics | Internal | Privacy Policy © 2004 - 2010 University of Florida George A. Smathers Libraries.All rights reserved. Acceptable Use, Copyright, and Disclaimer Statement Last updated October 10, 2010 - - mvs