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Population densities of Hylobates agilis in forests with different disturbance histories in Ulu Muda Forest Reserve, Malaysia

Abstract Small ape habitat throughout Malaysia is rapidly being lost, degraded, and fragmented, and the effects of these changes on the abundance on this taxon are currently unknown. This study assessed the group density of Hylobates agilis in virgin forest, previously logged forest (1960s–1990s), and recently logged forest (2015–2017) of the Ulu Muda Forest Reserve (UMFR), Kedah, Malaysia. We conducted fixed‐point active acoustic triangulation at nine survey areas to estimate group density. We used vegetation “speed plots” and satellite imagery to quantify habitat characteristics and used model selection to identify ecological predictors of group density variation. The estimated group density of H. agilis in UMFR was 4.03 ± 0.14 groups km−2, with an estimated total of 2927 ± 102 groups in areas below 450 m a.s.l. in UMFR. Group density did not differ significantly among habitat types. The best ecological predictors for group density were canopy cover and proportion of deforested area. Areas with recent deforestation were associated with relatively high group densities, suggesting compression of the populations persisting in these habitat types. The consistently high group densities detected in all forest types emphasizes the importance of degraded forest as habitat for H. agilis. Because of the threats to small apes in Malaysia, and the uncertain status of most populations, we recommend a nationwide population census and regular monitoring to inform conservation planning and implementation. Most urgently, we call for immediate and permanent protection of UMFR and other forests in the Greater Ulu Muda landscape to protect the globally significant populations of H. agilis, as well as other charismatic and threatened megafauna, birds, and flora in the area.


| INTRODUCTION
Small apes (Hylobatidae) comprise 20 extant species, all of which are threatened and most of which have declining populations (Fleagle, 2013; International Union for Conservation of Nature, 2020; Rawson et al., 2011). Despite being broadly distributed across South and Southeast Asia and southern China, the habitat requirements of this taxon are fairly specific (Fleagle, 2013;Lappan & Whittaker, 2009). Small apes are vulnerable to deforestation, habitat degradation, and fragmentation as they are exclusively arboreal and live in the middle and upper canopy layers of the rainforest (Brockelman & Ali, 1987;Gittins, 1983;Whittaker, 2009), and they have not been reported to persist in monoculture plantations (Choudhury, 2006;Zhang et al., 2010). As true brachiators, small apes rely on continuous and dense canopy cover for locomotion (Cannon & Leighton, 1994), and their movements are limited by even small gaps in the forest canopy (Cheyne et al., 2013). For example, where small roads bisect the forest, Hylobates lar and Hylobates pileatus reduce the use of areas near the roadside and are less likely to cross roads than predicted by a null movement model (Asensio et al., 2021). As a result, the home ranges of roadside groups are partly delineated by roads, even though the roads are narrow and relatively permeable to gibbon movement (Asensio et al., 2021).
Hylobates agilis has a spatially disjunct distribution, with populations on Sumatra and on mainland Asia. The distribution range of H.
agilis on mainland Asia is restricted to a narrow band stretching from southernmost Thailand (Yala Province) to the area between the Muda River and Perak River in three northern states (Kelantan, Kedah, and Perak) in Peninsular Malaysia (Gittins, 1978;Marshall, 1981;Mootnick, 2006). H. agilis lives in groups averaging ca. four individuals (Lee et al., 2015), usually with a pair-bonded adult male and female, and up to four offspring. They defend territories of ca. 30 ha (Gittins, 1980) and produce loud, high-pitched vocal duets, usually in the morning hours. H. agilis is listed as Endangered on the IUCN Red List, with a declining population, primarily due to habitat loss from anthropogenic disturbance, with poaching as a secondary threat in some areas (Geissmann et al., 2020). The situation for the population on mainland Asia is particularly dire, with much of its former range having been deforested, degraded, or fragmented in the last few decades, resulting in local extinctions of many populations (Lappan, in prep).
Given rapid anthropogenic loss, modification, and degradation of small ape habitat throughout Malaysia (Lappan & Ruppert, 2019), detailed knowledge about small ape abundance and occurrence, and their ability to persist in disturbed and undisturbed environments is critical for the development of an effective national conservation plan. In Malaysia, most studies on small apes took place in the 1970s and 1980s (e.g., Chivers, 1974Chivers, , 1977Chivers, , 1980Johns, 1985Johns, , 1986.
Though these provide useful baseline data on group density, group composition, and behavioral ecology, little information about the distribution and abundance of Malaysian taxa has been published in the past three decades.
The aim of this study was to assess the population status of H. agilis in the Ulu Muda Forest Reserve (UMFR) in Kedah, Malaysia, part of the largest remaining forested area within the H. agilis distribution range on mainland Asia. We conducted fixed-point active acoustic triangulation at nine areas in UMFR to estimate group densities and the total number of H. agilis groups in lowland forest (<450 m a.s.l.) in UMFR. Because of the reported sensitivity of small apes to habitat disturbance, we predicted that group density would be higher in virgin forest than in previously logged and recently selectively logged forest habitats in the area. We also aimed to determine what habitat characteristics best predict H. agilis group densities at UMFR. 1050 km 2 , consisting mainly of lowland dipterocarp forest with elevation ranging from 97 to 1256 m a.s.l. (Woo & Perumal, 2019), with an area of 725.6 km 2 below 450 m a.s.l., 358.8 km 2 between 450 and 850 m a.s.l., and 87.9 km 2 above 850 m a.s.l. (Figure 3). The recorded daily minimum and maximum temperatures at the study area range between 15.2-25.8°C and 23.5-38.6°C, respectively. UMFR receives 2160-3000 mm/year of rainfall (Tan et al., 2019).
Despite its recognition as a critical water catchment area for the states of Penang, Kedah, and Perlis in Malaysia and its legal status as "Permanent Reserved Forest" (PRF), most of the UMFR is classified as Production Forest within the jurisdiction of the Forestry Department, meaning that concessions can be issued to contractors to carry out selective logging, in which trees of designated species above certain sizes (e.g., 55 cm diameter at breast height [DBH] for nondipterocarps, 65 cm DBH for dipterocarps) are felled in a controlled manner and carried along a skid track constructed using a bulldozer (Saiful & Latiff, 2014). Land with this status has also been cleared to make way for timber tree plantations or subjected to excision, where the PRF status is removed and the area becomes open to conversion for other land use (Law, 2020).
With the help of trained field assistants (Supporting Information: Table S1), we conducted active acoustic surveys using fixed-point triangulation following Ali (1987) andSrikosamatara (1993) in nine survey areas (SA; Table 2) from April 2018 to February 2019, covering a total SA of 33.4 km 2 . Trained field assistants with experience as principal investigators on other small ape population surveys helped to train new field assistants for the first few field trips, before the latter became experienced enough to lead others. We placed three SAs in each of three forest types (virgin, previously logged, and recently logged). All SAs were placed in forests below 450 m a.s.l. due to limitations in terms of logistics and budget.
Forests were classified into habitat types using Landsat images (Hansen et al., 2013) and historical information from local forest guides. The habitat types were: (1) virgin forest, approximately 5 km northeast from the Earth Lodge, a small ecotourism lodge for tourists and researchers (6°06′54″°N, 100°57′50″ E), (2) previously logged forest, located around the upstream area of Lake Muda, which was selectively logged between the 1960s and 1990s (Suksuwan, 2008), and (3) recently logged forest, located around the Ulu Legong Hot Spring (5°49′00″ N, 100°56′08″ E), where the forest is adjacent to villages and plantations, and was selectively logged between 2015 and 2017 (Table 2 and Figure 2). We selected the SA based on accessibility (e.g., presence of trails, topography), with the criterion that SAs were placed a minimum distance of 2 km apart to prevent pseudo-replication (i.e., sampling the same H. agilis group(s) twice).

| GROUP DENSITY
We estimated the group density (D) of H. agilis in UMFR following Brockelman and Ali (1987) T A B L E 1 Forest reserves in the greater Ulu Muda landscape (Suksuwan, 2008 where n is the total number of groups heard at each SA. p(m) is the calling probability, which is the estimated proportion of groups expected to be detected during a survey period of m days, to account for the fact that all groups in a SA may not vocalize during the acoustic survey period . We estimated p m ( ) from the data recorded during the surveys using the spreadsheet package by Thinh and Rawson (2011). A is the area of the SA (in km 2 ).
We used fixed-point active acoustic triangulation following Brockelman and Ali (1987) and Brockelman and Srikosamatara (1993) to estimate the location of duetting H. agilis pairs and to estimate the number of groups detected in each SA. In each of the nine SAs, we established three listening posts (LP) spaced 300-400 m apart, resulting in a total of 27 LPs. All LPs were placed on prominent locations such as small hills or along ridges of 300-400 m a.s.l. to improve our ability to detect duets coming from all directions (Brockelman & Ali, 1987). We conducted surveys in each SA for three consecutive days from 7 a.m. to 11 a.m. (UTC + 08:00). These times were selected because Gittins and Raemakers (1980, p. 74) showed T A B L E 2 Survey areas with forest type, location, and elevation range. that ascend before descending, followed by a coda (i.e., notes sung by the male at the end of the female's great call), were recorded, because they indicate the presence of a pair defending a territory (Haimoff & Gittins, 1985).
To determine the number of groups per SA, the locations of the groups whose duets were detected from two or more LPs were estimated using triangulation. Home ranges for Hylobates spp.
average about 30 ha in size (Bartlett, 2011), so we followed Brockelman and Ali (1987) and Brockelman and Srikosamatara (1993) in assuming that duets that were produced at locations >500 m apart were considered as distinct groups, as 500 m is the approximate diameter of the taxon's home range. Other relatively recent studies of H. agilis have also used this assumption (Lee et al., 2015;O'Brien et al., 2004). Groups heard singing from locations that could not be triangulated (because they were not heard from two LPs simultaneously) were excluded if they were heard only once over the 3-day survey period. In case a group was heard duetting from two LPs at the same estimated location but at different times or was heard calling more than once at the same location, it was also included in our density estimates if the estimated distance to the singing group from the LP was <1000 m . This modification was applied to the standard protocol because of the hilly topography as songs detected at one LP were in some cases not detectable from other LPs because land features obstructed the sound.
We estimated the maximum detection distance as 1000 m, as previous studies have shown that calls of Hylobates spp. can travel that far under good listening conditions (Brockelman & Ali, 1987), and groups that were further away from the LPs were unlikely to be detected (Vu et al., 2018) because their songs would generally be obscured by nearer songs or background noises. From our surveys, almost all songs that we could triangulate were estimated to be within a 1000 m (radius) from any of the LPs. We excluded songs that were triangulated to locations that appeared to be outside of the maximum detection distance (i.e., >1000 m from all LPs).
To estimate the area sampled for each SA more accurately, we areas that were acoustically obstructed from all three LPs̶ the sound of duets can be heard from up or down a slope (i.e., from a valley adjacent to a LP or from a higher point on the same slope), but not from beyond an intervening hill or ridge; therefore, groups that sing in a valley directly behind a hill or ridge from the perspective of a LP are unlikely to be heard from that LP (Brockelman & Ali, 1987), (2) water bodies (lakes and rivers); (3) riparian areas (areas < 50 m from rivers and lakes), because in UMFR these areas often have low canopy cover due to intermittent flooding; and (4) human settlements or areas that have been converted for agriculture .
To estimate the probability of acoustic detection (or calling probability p m ( )) during the 3-day survey, we used the methods of Thinh and Rawson (2011), but with the modification that only groups that were detected from a location <600 m from the nearest LP were included in the calling probability estimation ( Figure 4). Songs of groups with home ranges near an LP were likely to be detected whenever they were produced during the sampling period, as all of their songs should be produced at distances of <1000 m from the nearest LP (i.e., locations within the SA). However, groups with home ranges that are only partially within the SA may produce songs that are not detected from the LPs because they are produced >1000 m The weather during the surveys was consistently dry, with sporadic drizzles, mostly in the afternoon. This sporadic rainfall was unlikely to meaningfully affect the calling probability as groups were able to sing before and after the rain.

| TOTAL NUMBER OF GROUPS
To estimate the total number of groups (G) in lowland forest areas (i.e., <450 m a.s.l.) in UMFR and to place 95% confidence intervals around that estimate, we used the software package designed by Thinh and Rawson (2011) et al., 2013), water bodies, riparian areas (areas < 50 m from the reservoir and rivers, which tended to have low forest cover due to episodic inundation), and islands within the reservoir, to avoid overestimating the area of potential habitat .
We also excluded areas >450 m a.s.l. as we did not survey SA with elevation >450 m a.s.l.
PANG ET AL.

| HABITAT CHARACTERISTICS
We compared group densities across the three forest types using one-way analysis of variance (ANOVA). To compare habitat quality among forest types and identify the best ecological predictor(s) of H. agilis group density, we quantified indicators of habitat quality in two ways: (1) vegetation "speed plotting" and (2) satellite imagery.
Following Hamard et al. (2010), we established ten 10 m × 10 m vegetation plots (VP; Supporting Information: Figure S1) around the three LPs in each SA, resulting in 90 VPs in total. We placed three VPs approximately 50 m from each LP, with one each to the north (0°), southeast (125°), and southwest (225°) of the LP (Supporting Information: Figure S1). We placed the final (tenth) plot for each SA in the approximate midpoint of the three LPs (Supporting Information: Figure S1). We recorded the following vegetation characteristics in each VP: (1) mean canopy cover percentage (scored visually using a GRS Densitometer™; estimated from three points within the VP; rounded to the nearest 5%), (2) total number of trees ≥10 cm DBH, To determine whether SA across the three forest types were similar in habitat characteristics, we used the Kruskal-Wallis test (Kruskal & Wallis, 1952) to compare vegetation variables (i.e., mean canopy cover, mean DBH, mean tree height, number of trees km −2 , and elevation) from VPs across SA in different forest types.
We also measured percentage of forest cover in the year 2000 and proportion of forest cover lost between 2000 and 2018 for each SA using satellite imagery from the Hansen et al. (2013). The year 2000 cloud-free composite LandSat image assigns a value of 0 to 100 to each pixel, which corresponds with the estimated percentage of forest cover for that pixel (Hansen et al., 2013). We calculated the mean percentage of forest cover in 2000 for each SA as: where α is the value for forest cover of each pixel, b is the number of pixels within the SA assigned each value of α.
To calculate the proportion of land deforested between the year 2000 and the study period (2018/19) for each SA, we measured the area (km 2 ) deforested between 2000 and 2018 from the LandSat images (Hansen et al., 2013) and then divided this area by the total forested area (km 2 ) in 2000 for the same SA. An area was considered deforested in the LandSat image when canopy cover was <25% at 5 m tree height (Hansen et al., 2013). As the variables mean forest cover percentage in the year 2000 and proportion of area deforested between 2000 and 2018 are proportional data, we transformed these variables to approximate a normal distribution using the arcsinesquare-root transformation before statistical analysis.
To better understand the relationship between habitat characteristics and group densities, we built Linear Models (LM) with H. agilis group density as the outcome variable and different combinations of habitat variables as predictor variables. We then conducted model selection using the corrected Akaike Information Criterion (AICc; Akaike, 1974), an information criterion that includes a correction for small sample size (Hurvich & Tsai, 1989), to determine which model variable(s) best explain variation in group density, while prioritizing model simplicity (Wagenmakers & Farrell, 2004  The group density estimates for individual SA ranged from 3.29 to 4.99 groups km −2 (Table 2). Group density did not significantly differ across habitat types (one-way ANOVA: F 2,8 = 1.640, p = 0.270).

| TOTAL NUMBER OF GROUPS IN LOWLAND FOREST IN UMFR
We estimated that 725.6 km 2 of forested habitat within UMFR was <450 m a.s.l. Since gibbon density did not differ between virgin, previously logged, and recently logged forest, we used the overall mean group density to estimate that lowland (<450 m a.s.l.) forest in UMFR may support 2927 ± 102 H. agilis groups.
Although we surveyed only areas with elevation <450 m a.s.l., this elevation range covered ca. 62% of the UMFR landscape ( Figure 3), while the remaining habitat is hill (approximately 31%) and lower montane forest (approximately 7%).

| VEGETATION CHARACTERISTICS ACROSS SA
The mean tree DBH, canopy cover, and tree density (number of trees/km 2 ) did not significantly differ across forest types (Table 3); however, mean tree height was higher in logged forests (recently logged and previously logged) than in virgin forest (Table 3), and previously logged forests had a lower mean elevation than recently logged forests (Table 3).

| ECOLOGICAL PREDICTORS OF GIBBON DENSITY
The best model for vegetation plot data included only the predictor variable mean percentage of canopy cover (Table 4a). This model predicted that for with every 1% increase in canopy cover, the group density increased by ca. 0.05 group km −2 , and the 95% confidence intervals surrounding the estimate excluded zero. The second-best model included only the predictor variable mean DBH and the thirdbest model included mean percentage of canopy cover and mean DBH.
These models had substantially less support than the best model, as indicated by the much lower Akaike weights, and the 95% confidence intervals for mean DBH in both models included zero, suggesting that these models are not reliable.
For the remotely sensed data, the best model included mean   (Haining, 2001).
The three SAs in each forest type were placed approximately 2 km apart, so they were generally in the same landscape. The higher mean tree height, canopy cover, bigger mean DBH, and greater tree density may also be attributed by competitor removal, in which some trees can thrive after other trees were removed by logging activity.
Competition among neighbors is an inhibitor for tree growth (Coomes & Allen, 2007;Zhang et al., 2017). Other factors that we did not measure, such as differences in soil quality or plant species distributions, may also have caused tree heights to differ across forest types even before the logging.
H. agilis group density was consistently high across all forest types and comparable to the highest densities reported for H. agilis in other high-quality habitats (see Table 5 (Palombit, 1997), and feeding on higher and larger trees than sympatric Hylobates species (Gittins & Raemakers, 1980). However, H. agilis and S. syndactylus are both highly frugivorous, with a preference for figs, and their substantial dietary overlap renders them ecological competitors (Chivers, 1974;Elder, 2013;Palombit, 1997 (Gittins & Raemakers, 1980 T A B L E 4b Three best models for group density among models including seven different combinations of predictor variables from remotely sensed data (Hansen et al., 2013;National Lee et al. (2015) used multiple methods to estimate density. The value reported here is based on fixed-radius point counts. The author also reports group density estimates with ±coefficient of variation (CV) instead of 95% CI, which is not included in this table.
PANG ET AL. and had smaller home ranges than S. symphalangus in adjacent areas of undamaged habitat for ≥12 years post-fire (Lappan et al., 2021). If habitat quality remains poor, degraded areas may become population sinks for small apes, resulting in long-term population declines or local extinctions (O'Brien et al., 2003).
Therefore, while the high population densities found in recently logged forests in UMFR may indicate that the selective logging did not substantially affect habitat quality, it may also reflect population compression in these areas. In our sample, forests We were not able to directly assess the effects of food availability or season in this study due to resource limitations, but both of these factors influence small ape's ranging behavior and activity budgets (Inoue et al., 2016) Marshall, 2009;O'Brien et al., 2004;Yanuar, 2009), so we did not extrapolate to estimate H. agilis populations in higherelevation areas of UMFR. It is important to note that we assumed that our sample is representative of lowland areas of the UMFR more broadly in developing our estimate of the total population. However, our SA covered only about 5% of the total lowland forest area in UMFR, and the SA were not placed randomly in the landscape, although they were distributed across habitat types. Therefore, these numbers should be interpreted with some caution.

| Ecological predictors
Mean percentage of canopy cover in the vegetation plots, and mean percentage of forest cover in 2000 and proportion of forest lost between 2000 and 2018 from the remotely sensed data were included in the best models for group density, which is consistent with the findings of many other studies indicating the importance of intact forest canopies for other small apes species (e.g., Brockelman & Ali, 1987;Fleagle, 2013;Gittins, 1983;Whittaker, 2009). The density of H. lar (but not S. syndactylus) also had a positive relationship with size (DBH) and height of trees (Hankinson et al., 2021) However, more detailed ground-level vegetation assessment is necessary for ground-truthing and to provide finer-grained information, especially for studies involving behavior or diet selection.
Models including elevation (m) as a predictor variable explained the variation in group density poorly (Table 4b). The linear regression model also failed to detect a statistically significant effect of elevation on gibbon density across the SA (F 7,7 = 0.391, p = 0.0552; Supporting Information: Table S2), which could be explained by the limited range of elevations (0-450 m) covered by this study, given that H. agilis is known to thrive in these elevations (Lee et al., 2015). as well as more than 300 species of birds, including all ten species of Malaysian hornbills (Bashir, 2014). The habitat also provides critical ecosystem services such as water filtration and supply, carbon storage, flood mitigation, oxygen production, and climate control that are crucial for economic and social sustainability across the region as well as biodiversity conservation (Suksuwan, 2008 (Law, 2020). We also suggest that future studies consider the abundance and distribution of important food trees to infer (seasonal) food availability in small ape habitats, variables which have been shown to influence group density and home range size.

| Management and research recommendations
We further recommend studies of the behavioral ecology and population dynamics of small apes, especially in degraded and fragmented habitats, to support design of effective methods for restoration of important habitats and improvement of connectivity between forest fragments. Better understanding of the factors affecting group density is essential for the development of sound management practices and prioritization of conservation actions, such as construction of canopy bridges to re-establish connectivity between forest fragments, translocation/reintroduction of rehabilitated small apes to viable but unoccupied habitat fragments, and habitat restoration projects.

ACKNOWLEDGMENTS
Financial support for this project was provided by USM Research