{'fraction_non_alphanumeric': 0.03776782471855707, 'fraction_numerical': 0.04725013113828027, 'mean_word_length': 4.990814599951656, 'pattern_counts': {'":': 0, '<': 3, '<?xml version=': 0, '>': 0, 'https://': 1, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 2, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Main Manuscript for Oxygen availability drives blood traits and the cell number-size tradeoff across Andean hummingbirds

Jessie L. Williamson1*, Ethan B. Linck1, Emil Bautista2, Ashley Smiley1,3, Jimmy A. McGuire3, Robert Dudley3, and Christopher C. Witt1* 1Museum of Southwestern Biology and Department of Biology, University of New Mexico, 302 Yale Blvd NE, Albuquerque, NM 87131, USA 2Centro de Ornitología y Biodiversidad (CORBIDI), Lima, Perú 3Museum of Vertebrate Zoology and Department of Integrative Biology, University of California, Berkeley, Berkeley, CA 94720, USA

Organisms that sustain high levels of aerobic activity under hypoxia, such as birds flying at high 34 altitude, must supply oxygen (O2) to respiring tissues despite reduced O2 availability in 'thin ' air. 35 To achieve this, the O2 transport functions of blood must be finely tuned to match O2 supply and 36 demand under a variety of ambient conditions (1-6). Most O2 in the blood is carried by 37 hemoglobin (Hb; (7), the concentration of which ([Hb]) is a key determinant of blood-O2 38 carrying capacity. Under hypobaric hypoxia, animals experience reduced arterial O2 saturation; 39 to compensate, they undergo erythropoiesis, which can increase [Hb] and therefore blood-O2 40 carrying capacity, enhancing O2 delivery to the tissues and compensating for reduced arterial O2 41 saturation (8). In the absence of compensatory increases in plasma volume (9), increased Hb 42 mass leads to elevated hematocrit (Hct) and, beyond a certain threshold, corresponding increases 43 in blood viscosity will begin to compromise O2 transport (10). A suite of interrelated physical 44 characteristics of blood-total red blood cell count (TRBC), mean cell volume (MCV), mean 45 cell hemoglobin (MCH), and mean cell hemoglobin concentration (MCHC ; Table S1)-can be 46 modulated to optimize blood-O2 transport, or to compensate for other cardiac, vascular, or 47

Within-species blood comparison 159
We used the individual-level dataset to model within species variation and accounted for 160 between-species differences by incorporating a species grouping variable. Using multilevel 161 Bayesian models for each of the six blood parameters, we estimated the effects of elevation, 162 body mass, precipitation, air temperature, species-typical Hb β A 13-β A 83 genotype, as well as 163 within-species variation in elevational position (an individual's sampled elevation relative to its 164 species elevational range), body mass, air temperature, and precipitation. Separately, we 165 excluded sex and wing-loading as potentially important predictors (see Methods). In total, we 166 compared six models per set using LOOIC and WAIC (Tables S3-S4; Methods). In addition to 167 fixed effects, models included species as a random effect, and a phylogenetically correlated 168 random intercept (i.e., covariance matrix of phylogenetic distances among taxa and 3A-F). Elevational position was also a strong predictor of Hct, indicating that individuals 176 located higher in their species' elevational range tended to have higher Hct ( Figure 3B). [Hb], 177 TRBC, and MCH also increased with increasing elevational position, although these 178 relationships were subtle ( Figure 3A, C, E). 179 The finding that [Hb], Hct, and TRBC increase with elevation was consistent with 180 theoretical expectations for O2-carrying capacity of blood and with prior empirical findings in 181 birds (3, 4, 25, 34, 38), humans (49, 50), mammals (23, 51), and other taxa (52, 53). In prior 182 work, substantial variation in slopes of these parameters across elevation may have been 183 attributable to variation in temporal, taxonomic, and spatial scales of comparison, as well as 184 constraints on the time-course of evolutionary adaptation (19, 32, 50). We expect increases in 185 Hct at higher elevations because an increase in the total mass of Hb enhances oxygenation under 186 lower ambient PO2 (13); unless there is a proportional increase in plasma volume or MCHC, an 187 increase in Hb mass is expected to be accompanied by an increase in the proportion of blood 188 comprised of red blood cells. TRBC also increased with elevation and was, along with MCV, 189 one of two key drivers of [Hb] adjustment ( Figure 2, Figure 4; 45). MCV did not vary 190 predictably in our linear models ( Figure 2D). Although increasing within-cell [Hb], or MCHC, 191 would seem to be an alternative mechanism of increasing O2-carrying capacity, MCHC was less 192 11 variable than other blood parameters (Table S2), likely due to physical constraints (54, 55); 193 accordingly, MCH and MCV were strongly correlated (Table S5; Figure 4C). Contrary to recent 194 empirical findings (4, 25), adjustments to MCHC appeared to be neither substantial nor 195 predictable with respect to elevation within or among species ( Figure 2F). 196 We found that MCV and MCH decreased in individuals occurring in warmer 197 environments ( Figures 3D, E), and that MCHC decreased in individuals occurring at warmer 198 localities within a species' range ( Figure 3F).  (Tables S3-S4; Methods). Moderate effects of the species grouping variable suggest that 207 variation in blood traits is partly explained by aspects of species biology that evolve too fast to 208 have resulted in phylogenetic autocorrelation. Species-typical Hb β A 13-β A 83 genotype did not 209 predict [Hb] or any other blood characteristics ( Figure 3A-F), providing no evidence that 210 evolutionary adjustments to O2-binding affinity affect the physical composition of blood or its 211 variation with elevation. 212 213

Among-species blood comparison 214
Using the species-level dataset, we evaluated the effects of elevation, body mass, species-typical 215 Hb β A 13-β A 83 genotype, and ambient temperature and precipitation on each of our 6 blood 216 response variables. We compared sets of models with LOOIC and WAIC (Table S3) (Table S6). The fact that this was true both within and 247 among species suggests that developmental and evolutionary fine-tuning of blood-O2 carrying 248 capacity occur similarly. 249 Using data for 32 well-sampled species, we estimated species-specific standardized 250 TRBC and MCV coefficients (β) in models with [Hb] as the response variable ( Figure S1), and 251 we used these to calculate a 0-1 index that we refer to as the Cell Number-Size Index (hereafter 252 CeNS). CeNS represents the proportional contribution of TRBC versus MCV to increasing [Hb], 253 calculated as: 254 Extending our analysis, we designed a set of models to test potential predictors of CeNS: 260 elevation, a quadratic elevation effect (elevation 2 ; to account for notable curvature in the data), 261 body mass, wing loading, species-typical Hb β A 13-β A 83 genotype, and phylogeny (see 262 Methods). Elevation, elevation 2 , and body mass strongly predicted CeNS ( Figure 4D-F; Table  263 S7). We found a trend of decreasing CeNS with higher wing loading, but the 95% credible 264 intervals overlapped zero ( Figure S2A-B). Species-typical Hb β A 13-β A 83 genotype did not 265 predict CeNS ( Figure S3), nor did we find phylogenetic signal in CeNS ( Figure S4). 266 Examining the tradeoff between cell number and volume provides a potential explanation 267 for the striking pattern that middle elevation species tend to adjust [Hb] by different mechanisms 268 than low and high elevation species ( Figure 4E). CeNS was uncorrelated with TRBC coefficients 269 (R 2 = 0.07, r = -0.27) but strongly correlated with MCV coefficients (R 2 = 0.51, r = -0.71), 270 indicating that variation in CeNS was primarily driven by the degree to which species modulate 271 cell size ( Figure S2, Figure S5. We suggest that because low and high elevation species tend to 272 possess relatively small cells, they may be more able to rely on shifts in cell size, rather than cell 273 number, to increase [Hb] ( Figure 2J; Figure 4). However, as cell size increased, species were 274 more likely to approach maximum size for red blood cells, beyond which they could incur a 275 functional detriment. As a result, species with larger red blood cells were more reliant on cell 276 number to adjust [Hb]. For middle elevation hummingbird species that have evolved under 277 moderate (~15-30%) reductions in O2 availability, TRBC appears to be the primary axis for 278 adjustment of blood-O2 carrying capacity per unit volume of blood. Species that have evolved at 279 the highest elevations are expected to be the best adapted to hypobaric hypoxia, and it is 280 therefore remarkable that their mode of [Hb] adjustment (primarily by MCV) resembles that of 281 hummingbird species in the lowlands, the putative ancestral elevation for hummingbirds (45). 282 15 283

Hummingbirds at extreme elevations 284
Do hummingbirds at extreme high elevations follow the same rules as hummingbirds 285 distributed at low and moderate elevations with respect to elevational blood variation? Our 286 models encompassed the entire elevational gradient and therefore could have missed important 287 phenomena such as threshold effects. To address this, we compared individual hummingbirds 288 sampled above and below 4,200 m elevation, a threshold that marks the upper ~10% of the 289 gradient. Hummingbirds sampled from these extreme high elevations had significantly smaller 290 cells and higher erythrocyte counts than others ( Figure S6). This finding suggests that high-291 altitude specialist hummingbirds tend to have smaller cells, consistent with findings in other high 292 altitude vertebrates (23, 29, 30, 52, 59); however, this pattern was only detectable under the 293 extreme conditions at the uppermost limits of the Andean elevational gradient. We proceeded to 294 formally test for elevational breakpoints using segmented regression (see Methods). Evidence for 295 objective blood trait thresholds along the elevational gradient was mixed, with breakpoint 296 estimates for individual traits ranging from ~1,400 to ~4,000 m ( Figure S7). Lack of consilience 297 in these estimates suggests that trait-specific thresholds are challenging to identify and may not 298 be generalizable across species and clades. 299 300 Phylogenetic signal in blood characteristics 301 Phylogenetic signal was low (λ = 0.0; K range = 0.14-0.21) across all blood traits for both 302 within-and among-species models. Blomberg's K and Pagel's λ results were consistent with 303 visual assessment of continuous trait maps, even after removing species with <3 individuals 304 16 sampled (Table S8; Figure S8). We additionally found no phylogenetic signal in CeNS (Figure  305 S4 influenced patterns of phylogenetic conservatism (34). We tested for and found no interaction of 316 clade and elevation on the six studied blood traits, with the exception of the interaction of 317 elevation and the Hermit clade in the [Hb] model set ( Figure S9). Interestingly, hermits are a 318 large, early diverging hummingbird clade that contains diminishing species diversity with 319 altitude (and no species that exceeds 3,000 m), and its apparent increased sensitivity of [Hb] to 320 elevation hints at a functional explanation for this biogeographic tendency. The existence of 321 clade-specific trait optima (34) and strong species effects, demonstrated in this study, may at first 322 seem difficult to reconcile with our findings that there was no within-family phylogenetic signal 323 in hummingbirds; however, we view these findings as being consistent with steady evolution of 324 trait optima by a mean-reverting, Ornstein-Uhlenbeck process, as described by Minias et al. (34), 325 and marked by occasional major jumps that coincide with changes in body plan or respiratory 326 demands. 327 A strength of our study was that hummingbirds vary only subtly in aspects of their 328 biology that could plausibly confound our analyses, such as metabolic rate, energy budget, torpor 329 use, mode of locomotion, foraging strategy, or clutch size. Phylogenetic conservatism in these 330 traits facilitates explanation of blood trait variation across the entire clade. A strong effect of 331 species identity in spite of these intrinsic controls hints that species optimize O2-carrying 332 functions of blood for subtle changes in lifestyle and environment, and on rapid timescales. This idiosyncrasies--not phylogenetic affinities--were the primary drivers of variation in blood traits 340 affecting O2-carrying functions. In response to elevational changes in O2 partial pressure, 341 hummingbirds make predictable adjustments to the physical composition of blood. Remarkably, 342 after controlling for many potentially confounding variables, decreasing PO2 drove increases in 343 [Hb] and Hct that were nearly identical in magnitude within and among hummingbird species, 344 respectively. This implies that developmental and evolutionary optimization of blood traits to 345 ambient PO2 occurs in fundamentally similar ways, responding to the same set of physical rules 346 that govern gas exchange at the blood-gas barrier. In addition to O2 availability, species identity 347 itself explained 13-21% of blood trait variation, suggesting that fast-evolving, unmodeled 348 aspects of species biology underpin substantial hematological variation. Our data revealed a 349 striking constraint space that defines the cell number-size tradeoff for hummingbird erythrocytes; values by subtracting 1.0 g/dL. When possible, at the time of blood collection, 10 µl whole blood 384 was preserved in a 1:200 NaCl dilution to obtain TRBC estimates. Within 12-hours of blood 385 collection, we pipetted a 10 µl sub-sample of blood-NaCl dilution into a hemocytometer and 386 allowed cells to settle for 1 min. After focusing the microscope, one or more photos were 387 obtained of cells in the hemocytometer grid. Later, cells were manually counted in the ImageJ 388 software (64) and TRBC was estimated following standard protocols (20, 21). 389 After drawing blood, birds were humanely killed by rapid cardiac compression. We 390 recorded collection elevation, latitude, longitude, sex, body mass, and date. Body mass was 391 typically recorded prior to blood sampling; however, body mass was corrected for estimated 392 volume of blood drawn for any individuals massed after blood sampling. Each collected bird was 393  (Table S1)  398 and following standard protocols (20). We examined distributions of the data using histograms 399 and qqPlots, and we eliminated outliers that fell outside of the expected distribution and that 400 differed from known 'typical' values (20, 25, 65). To reduce age-related outliers, we examined 401 values at the tails of each blood trait distribution that were associated with presence of a bursa 402 measuring ~2 x 2 mm. All juvenile values fell within the expected distribution of blood values. 403 We did not age individuals by plumage, as juvenile plumage lasts much longer than juvenile 404 blood values, which stabilize one to several months after fledging.  We used principal components analysis (PCA) for temperature (Bio1-11) and precipitation 424 (Bio12-19) variables to create a composite measure of temperature and precipitation across the 425 gradient. PC1 of the temperature variables (hereafter "temperature") explained 73.4% of the 426 variation in composite temperature, and loadings suggested that this axis corresponded to 427 increasing temperature across sites (see GitHub). PC1 of the precipitation variables (hereafter 428 "precipitation") explained 79.8% of the variation in composite precipitation, and loadings 429 suggested that this axis corresponded to increasing precipitation across sites (see GitHub). 430 To account for multiple measurement effects (i.e., traits measured from multiple 431 individuals within species), we calculated between-and within-species differences following 432 Villemereuil et al. (70). This approach uses within-group centering to separate each predictor 433 into two components: We first calculated species mean values (i.e., between-species variability) 434 and then subtracted the mean value of individual observations (i.e., within-species differences). 435 These measures of within-species variation for temperature, precipitation, and body mass were 436 included in models. Because hummingbird body masses may differ by sex, within-species 437 differences were calculated using mean body mass of males for males, mean body mass of 438 females for females, and species-level means for individuals of unknown sex (n=6). 439 We used wing area data from the 59 MSB species studied in Skandalis et al. (71) to 440 calculate wing loading in g/cm 2 , calculated as species mean body mass (g) divided by total wing 441 22 area (cm 2 ). We analyzed wing loading due its relevance to aerodynamic theory and hummingbird 442 flight at high altitudes (72-75). We initially constructed phylogenetic models (results not shown), and calculated phylogenetic 457 signal using the full and reduced models for each blood parameter, following the vignette and 458 recommendations of P. Bürkner (https://cran.r-project.org/web/packages/brms/ 459 vignettes/brms_phylogenetics.html), using the 'hypothesis' method. Estimates of phylogenetic 460 signal for all model sets (full and reduced in both within-and among species analyses) were low 461 (λ < 0.004); these estimates are supported by visualizations of continuous trait maps of mean 462 blood parameters (SI Appendix; Figure S8). Accordingly, we excluded phylogeny as an effect in 463 models and evaluated phylogenetic signal using Blomberg's K and Pagel's λ. Results from the 464 two tests were concordant and consistent across model subsets (Table S8) predictors whose 95% credible intervals (CIs) did not overlap zero in full models. Distribution 482 family differed by model set: Gaussian for [Hb], Hct, and TRBC models; skew-normal for MCV 483 and MCH models; and student's t-distribution for MCHC models. We ran four Markov chains 484 for 10,000 iterations with a burn-in period of 5,000, thinned every 10 steps. 485

24
We tested for interactions between clade and elevation by comparing three models: 1) the 486 top within-species model for each blood parameter; 2) the top model + (1|Clade); and 3) the top 487 model with an elev:Clade interaction. We found no meaningful interaction of elevation and clade 488 except for elev:CladeHermit in the [Hb] model, whose 95% credible intervals did not overlap 489 zero ( Figure S9). In accordance with lack of phylogenetic signal in our data, the effect of clade Species mean values were calculated for all species with n=2 or more individuals sampled. We 497 split Patagona gigas into "north" and "south" haplotypes in light of evidence that the complex 498 may be comprised of two functionally and genetically distinct populations separated by strong 499 migratory divides (82), and we analyzed data only from individuals sampled during the breeding 500 months to ensure correct population assignment. We designed model sets for each of our six 501 blood traits. For each response, we analyzed distributions (qqPlots, Cook's Distance, leverage) 502 and removed outliers that strongly influenced fit. For each model, predictor variables included 503 species-typical Hb β A 13-β A 83 genotype, mean elevation, mean body mass (log-transformed; one 504 mean for all individuals due to balanced sampling between sexes), mean temperature, and mean 505 precipitation. All variables were standardized to a mean of zero and standard deviation of one. 506 For each response, we compared: 1) an intercept-only null model, 2) a model with all 507 predictors, and 3) a reduced-predictor model with only those predictors whose 95% CIs did not 508 25 overlap zero in full models (reduced model 2). Given the notable quadratic shape of species 509 mean data for TRBC, MCV, MCH, and MCHC ( Figure  We then analyzed the factors that contribute to variation in CeNS. Our model response 538 variable was CeNS (0-1 index). Predictors, all species mean values, included elevation, a 539 quadratic elevation term "elevation 2 " to account for notable curvature in the data, body mass 540 (log-transformed), wing loading, and species-typical Hb β A 13-β A 83 genotype. We compared six 541 models: 1) an intercept-only null model, 2) a model with core predictors (elevation, body mass, 542 and wing loading), 3) a model with core predictors plus elevation 2 , 4) a model with core 543 predictors plus elevation 2 and species-typical Hb β A 13-β A 83 genotype, 5) a reduced version of 544 model 3, and 6) a reduced version of model 4. Reduced models included only those predictors 545 whose 95% CIs did not overlap zero in full models. In addition to model fit diagnostics, we 546 assessed correlations between variables by analyzing correlation matrices and by regressing 547 CeNS on each R 2 values from standardized species-specific CeNS models, unstandardized TRBC 548 and MCV beta estimates, and raw TRBC and MCV values ( Figure S5). Distribution family 549 differed by model set: Gaussian for individual-level, species mean, and CeNS prediction models, 550 and student's t-distribution for species-specific models. For all CeNS models we ran four Markov 551 chains for 20,000 iterations with a burn-in period of 10,000, thinned every 10 steps. We assessed 552 phylogenetic signal with Blomberg's K, Pagel's λ, and visually with contmaps ( Figure S4). 553 27 554

Model comparison and diagnostics 555
All models and sets were run using the brm() function with default priors (weakly informative 556 priors for the intercept and sigma; improper flat priors for predictors). To assess convergence we 557 visually examined trace plots, checked diagnostics, and confirmed that Rhat was <1.01 (81). We 558 used a posterior predictive check to assess whether model assumptions yielded good 559 approximations of the data generating process. There were no problems in any of our models 560 resulting from Bayesian fraction of missing information or divergent transitions. We compared 561 models and verified fits using the widely applicable information criterion (WAIC; (83), Bayesian 562 expected log pointwise predictive densities (ELPD; (84), and approximate leave-one-out cross-563 validation information criterion based on the posterior likelihoods (LOOIC; Tables S3, S6). We 564 estimated fixed effects (means and 95% CIs) from the posterior distributions for each predictor. 565 For relevant models, we estimated the proportion of the total variance attributed to the species 566 grouping variable, which accounts for unique aspects of blood parameters that are not captured 567 by the individual, life history, or environmental predictors. 568 569

Breakpoint analysis 570
We tested for objective thresholds in each blood trait using piecewise linear regression of 571 elevation (standardized) regressed on each blood trait. In this procedure, we first fit a simple 572 linear regression model, iteratively searched for objective "breakpoints" along our sampled 573 elevational gradient, selected the breakpoint with the lowest residual error, and refit the linear 574 regression model with separate estimates for slopes and intercepts below and above the 575 breakpoint, respectively. We constrained the model to estimate breakpoints that were each 400 m 576 28 from the lowest and highest points in our data (i.e., from 400 m to 4,178 m in elevation), to avoid 577 biasing breakpoints towards small sample sizes and each low and high elevations, and anomalous 578 species at both ends of the distribution. We evaluated goodness of fit using fit metrics and by 579 visually assessing QQplots, Cook's D, leverage, and residual distributions. We compared models 580 using AIC values. All breakpoint models fit significantly better than simple linear regression 581 models except for the TRBC breakpoint model; this model did fit better in all respects than the 582 simple linear regression model, but the fit was not significant at the p=0.05 level. We used a one-583 way ANOVA to compare whether individuals below and above model-estimated breakpoints 584 differed in their blood characteristics ( Figure S7 value from a single species. In all panels, solid trend lines correspond to relationships where 95% 832 posterior credible intervals in models did not overlap zero, while dashed trend lines correspond 833 to relationships where 89% credible intervals in models did not overlap zero (see Figure 3). 834 Credible intervals for both elevation (at the 95% level) and elevation 2 (at the 89% level) did not 835 overlap zero in the TRBC model (Panel I). Colors correspond to clades in Figure 1. R 2 values are 836 reported from Bayesian models reported in Figure 3. 837