Parallel elevational replacement of hosts and parasites in a highly susceptible songbird genus

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Williamson, Silas E. Fischer, Selina M. Bauernfeind, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8060400/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 07 Apr, 2026 Read the published version in Oecologia → Version 1 posted 4 You are reading this latest preprint version Abstract Elevational replacement distribution patterns underpin montane diversity and reflect the interaction of both biotic and abiotic pressures, but the degree to which parasites exhibit elevational zonation remains unclear. Investigating infection patterns in related host species across elevational gradients can reveal whether parasites and hosts show concordant patterns of elevational turnover, potentially due to shared historical and ecological factors. Here, we assessed patterns of elevational replacement in haemosporidian parasite assemblages that infect three congeneric songbird species: Bell’s vireo ( Vireo bellii) , gray vireo (V. vicinior) , and plumbeous vireo ( V. plumbeus ), each of which breeds across distinct elevations and habitats in the southwestern United States. We screened a total of 248 individuals using cytochrome b PCR and microscopy. We identified 19 haemosporidian haplotypes, including eight novel lineages. We found that each of the three vireo species exhibited high haemosporidian prevalence (55.0–86.2%), with nearly all infections from the genus Haemoproteus (subgenus Parahaemoproteus ). Haemosporidian assemblages varied across elevations; each sampled range of elevations harbored abundant, yet host-specific lineages with different environmental associations. Bell’s and plumbeous vireos, but not gray vireos, hosted several phylogenetically distinct, putative generalist lineages, likely reflecting spillover from more diverse local breeding bird communities. Repeated infections in individuals across breeding seasons, together with moderate parasitemia (x̄ ≈ 1%) suggest that these focal vireo species harbor chronic infections during their respective breeding seasons. These results demonstrate that elevational replacement patterns in avian hosts may be mirrored by their haemosporidian parasites, particularly among host-specialized lineages. Avian malaria diversity elevational range haemosporidian turnover Figures Figure 1 Figure 2 Figure 3 Introduction Mountains harbor exceptional biodiversity, in part because elevational gradients tend to be finely partitioned by related species with ranges that are elevationally parapatric (Cadena and Loiselle, 2007 ; von Humboldt and Bonpland, 2009 ; Freeman et al., 2016 ). Yet, it remains unclear whether parasites of elevational replacement host species mirror this range stratification. Generally, host-parasite interactions can strongly influence biogeographic patterns, with reciprocal effects on fitness (Combes, 1997 , 2001 ; Poulin, 2011 ; Hasik and Siepielski, 2022 ), population dynamics (Albon et al., 2002 ), and community structure (Minchella and Scott, 1991 , McNew et al., 2021 ). Strong host-specificity, combined with shared environmental constraints, should result in parasite turnover that parallels the turnover of hosts along environmental gradients. Conversely, aspects of parasite life-history––such as insulation from climatic factors (e.g., endoparasites), dependency on secondary hosts and vectors (e.g., complex life cycle), and degree of host-specificity (e.g., host range and degree of host switching)––should be expected to cause discordant patterns from those observed in hosts (Gage et al. 2008 ; Krasnov and Poulin, 2010 ; Ellis et al., 2015 ). Regional surveys across elevational gradients reflect these complex relationships. In various mountain systems, parasite turnover has been found to exceed host turnover (Galen and Witt, 2014 ; Barrow et al., 2021 ), and host turnover has been found to predict parasite turnover (Ellis et al., 2015 ; Williamson et al., 2019 ). Gradients of precipitation and temperature, as well as landscape features, have also been found to shape parasite communities (Illera et al., 2017 ; McNew et al., 2021 ). Investigating the parasite assemblages of closely related host taxa that are elevational replacements provides a natural experiment for disentangling the relative roles of host associations, parasite traits, and environmental gradients in shaping parasite biogeography, while controlling for host phylogeny, life history, and parasite susceptibility (Medeiros et al., 2013 ; Barrow et al., 2019 ). Among blood parasites, haemosporidians (order Haemosporida; Danilewsky, 1885) are a cosmopolitan group of protozoans with complex life cycles dependent upon vertebrate taxa, including birds, mammals, and reptiles (Ricklefs and Fallon, 2002 ; Duval et al., 2007 ; Boysen et al., 2022 ), and invertebrate vectors in the order Diptera (Linnaeus, 1758). A long history of co-evolution with avian hosts (Ricklefs et al., 2004 ; Lauron et al., 2015 ; Galen et al., 2018 ) has given rise to a remarkable diversity of avian haemosporidian lineages (Valkiūnas, 2005 ; Hellgren et al., 2007 ; Perkins, 2014 ), with many still yet to be described (Borner et al., 2016 ). Given the high diversity and complexity of haemosporidian parasites, it has remained challenging to characterize the distribution patterns of haemosporidian lineages across large geographic scales (Clark, 2018 ). However, within regions and genera, spatial trends appear—reflecting the influence of climate directly, through physiological interactions during transmission and development (Ikemoto 2008 ; Mordecai et al., 2013 ), and indirectly, by influencing host and vector abundance and susceptibility (Filion et al., 2020 ). As a result, the fluctuating climate of temperate and montane regions produce seasonal, annual, and habitat-level waves of infection (Bensch et al., 2007 ; Pérez-Rodríguez et al., 2013 ; Lutz et al., 2015 ; Reinoso-Pérez et al., 2024 ), whereas areas with higher, less seasonal temperatures, such as tropical regions and lowlands, can have persistent haemosporidian pressure (Zamora-Vilchis et al., 2012 ). Infection intensity, commonly measured as parasitemia (defined as the ‘estimated percentage of haemosporidians circulating in host blood’) provides insight into host condition and stage of haemosporidian infection (i.e., acute or chronic; Valkiūnas, 2005 ). Like prevalence (here defined as ‘proneness to infection’), parasitemia is also influenced by environmental factors, with peaks in temperate regions during the breeding season, when warmer, wetter conditions favor vector activity (Valkiūnas, 2005 ; Reinoso-Pérez et al., 2024 ) and when chronically infected hosts experience tissue-to-blood recrudescence (Atkinson and van Riper, 1991 ). Therefore, montane regions provide a valuable system for studying avian haemosporidian infection dynamics because of the sharp local variation in both climate and habitat over relatively small geographic distance (e.g., Illera et al., 2017 ; Williamson et al., 2019 ; Rodríguez-Hernández et al., 2021 ). The southwestern U.S. presents a particularly compelling region for studying haemosporidian biogeography and dynamics due to its climatic heterogeneity, wide range of habitat types, and steep elevational gradients in sky island mountains. Although arid overall, the region exhibits strong variation in precipitation timing and intensity, shaped by both temperate seasonality and the North American monsoon system. These climatic patterns interact with topographic variation (reviewed in Coblentz and Riitters, 2004 ) to create a mosaic of ecologically distinct elevational zones ranging from lowland riparian corridors with high relative humidity and permanent water sources, to mid-elevation desert scrublands and savannas reliant on seasonal rainfall, to cooler montane forests with shorter growing seasons and higher annual precipitation (Sheppard et al., 2002 ). While community-level surveys suggest moderate overall prevalence of haemosporidian infection among avian communities in the region (mean: ~36%; Barrow et al., 2021 ), and while environmental conditions––particularly related to elevation, have been shown to strongly influence spatial patterns of infection (Williamson et al., 2019 )––previous studies have primarily described environmental and ecological contributions across mid-high elevation habitats and sky islands. Therefore, further work is needed to expand our understanding of whether observed environmental and elevational trends persist across broader gradients and habitat types, especially among particularly susceptible hosts. Among the many avian species previously surveyed in the desert Southwest, the plumbeous vireo ( Vireo plumbeus ) has consistently stood out for its high haemosporidian prevalence across sites and years (Marroquin-Flores et al., 2017 ; Barrow et al., 2021 ). Migratory populations of this insectivorous songbird breed in mid- to high-elevation coniferous woodlands (from ~ 1,200–3,000 m) across the western U.S. and Mexico (Barlow, 1977 ; Curson and Goguen, 1998 ) and spend the nonbreeding season along the Pacific Slope and in the lowlands of central Mexico (Sibley and Monroe, 1990 ). Two closely related migratory vireos, Bell’s vireo ( Vireo bellii ) and gray vireo ( Vireo vicinior ), have relatively similar breeding distributions, yet inhabit distinct habitats and elevations. gray vireos breed in arid mid-elevation juniper ( Juniperus spp.) savannas and chaparral, ~ 400–1,900 m (Hubbard, 1970 ; Barlow, 1977 ) and spend the nonbreeding season primarily in lowland desert and coastal areas of Sonora and Baja California (Barlow et al., 1999 ; Fischer et al., 2025 ). Bell’s vireos breed in lowland riparian habitats, generally below ~ 1,500 m, with comparatively high access to water and vegetative cover (Brown, 1993 ) and spend the nonbreeding season along the Pacific coast of Mexico, from Baja California and Sonora south to El Salvador. Given the similarities in ecology and life history among vireos, local differences in breeding elevational range make these three focal species an ideal comparative system through which to examine haemosporidian infection patterns across elevational gradients. In this study, we tested whether haemosporidian biogeography, community assemblage, and infection dynamics vary predictably among three species of vireos that occupy distinct elevations and habitats at sites in New Mexico and Utah, USA. Using a combination of molecular screening and microscopy, we (1) compared parasite prevalence and infection intensity among host species, including the first parasite survey for regionally vulnerable gray vireos; (2) characterized haemosporidian haplotypes and host specificity; and (3) tested the extent to which community composition was linked to elevational zones and/or their associated host species and habitats. We predicted finding extensive parasite sharing between elevational replacement vireo species, considering the proximity and interdigitated nature of their ranges. Based on previously published haemosporidian surveys in vireos, we anticipated finding high infection prevalence across all three host species; however, we also expected prevalence and parasitemia to vary among species and environments. Lastly, because the Southwest is arid, we predicted higher prevalence and parasite diversity in more mesic habitats suitable for dipterid vectors, such as in the low-elevation riparian corridors used by Bell’s vireos and high-elevation woodlands occupied by plumbeous vireos. Materials and methods Sample collection We sampled 248 wild-caught individuals of three vireo species (Bell’s vireo: n = 20, gray vireo: n = 170, and plumbeous vireo: n = 58; Fig. 1 A; Table S1 ). Sampled individuals included adults and juveniles (Table S1 ). All individuals were sampled during the summer months (May–August), with the majority sampled prior to monsoon season (~ early-July–September) from 1995–2019 across 28 unique sites in New Mexico and a single site in Utah (Document S1; Tables S2-3). Detailed sampling protocols for Bell’s vireos are published in Gyllenhaal, 2024 , gray vireos in Fischer, 2020 , Fischer et al., 2022 and Fischer et al., 2025 , and plumbeous vireos are described in Marroquin-Flores et al., 2017 and Barrow et al., 2021 . Parasite screening and genetic data collection We extracted DNA from pectoral muscle ( n = 82) and whole blood ( n = 166) using the QIAGEN DNeasy Blood and Tissue Kit following manufacturer’s recommendations. Birds were screened for Haemoproteus and Plasmodium parasites using three nested polymerase chain reaction (PCR) protocols to amplify a 478-base pair (bp) fragment of the haemosporidian mitochondrial cyt b gene (Hellgren et al., 2004 ; Waldenström et al., 2004 ). We amplified parasite DNA with the outer primer pairs HaemNFI/HaemNR3 and HaemNF/HaemNR2, followed by the nested primer pair HaemF/HaemR2. We prepared outer PCR reactions in 25 µL volumes, containing 1.25 U of AmpliTaq Gold DNA Polymerase (Applied Biosystems, Foster City, CA, USA), 1× PCR Buffer II, 2.5 mM MgCl2, 0.2 mM dNTPs, 0.5 µM of each primer, and 20 ng of template DNA. Thermal cycling conditions were modified from Galen and Witt ( 2014 ), with an initial denaturation at 95°C for 8 mins, followed by 20 cycles of 94°C for 30 seconds, 50°C for 30 secs, 72°C for 45 secs, and a final extension at 72°C for 10 mins. For nested PCR, we used 1 µL of the outer PCR product as the template, with reaction conditions identical to the outer PCR, except for an increase to 35 cycles. Each reaction set included negative and positive controls to monitor for contamination and confirm amplification success, respectively. We visualized PCR products on 2% agarose gels stained with SYBR Safe Gel Stain (Invitrogen, Carlsbad, CA, USA) to verify the presence of amplicons of the expected size. Successfully amplified products were purified using ExoSap-IT (Affymetrix, Inc., Santa Clara, CA, USA) and submitted for Sanger sequencing at Psomagen (Rockville, MD, USA). gray vireo blood samples from recaptured individuals (i.e., samples from the same individuals from 2017 and 2018) underwent PCR amplification, but only a single sample was sequenced (Table S2 ). Determining prevalence and haplotype diversity We determined positive infections upon successful amplification of haemosporidian cyt b sequences and calculated pathogen prevalence within each host species and calculated 95% binomial confidence intervals using the ‘exact’ method available in the binom package in R (version 4.3.2; R Core Team, 2023; Sundar, 2006 ). Haemosporidian cyt b forward and reverse reads were trimmed to remove primers, resulting in the target fragment size of 478 bp and were then assembled using the default alignment algorithm in Geneious (version 2025.03; Biomatters Ltd; Kearse et al., 2012 ; Table S4 ). To identify haplotypes, we compared cleaned sequences to published records stored in the public databases GenBank (National Center for Biotechnology Information, US National Library of Medicine) and MalAvi (Bensch et al., 2009 ) by using the Basic Local Alignment Search Tool (BLAST). Additionally, to ensure the accuracy of both established and novel haplotypes, we downloaded all haemosporidian cyt b haplotype sequence files from MalAvi and compared the number of differences, if any, to our sequences via a distance matrix calculated in Geneious. Haplotypes that differed by one or more base pairs (~ 0.2% sequence divergence) from published sequences on GenBank or MalAvi were considered novel and named following MalAvi conventions. Phylogenetic analysis We estimated phylogenetic relationships among haemosporidian haplotypes using a maximum-likelihood (ML) framework in RAxML, v8.2.10 (Stamatakis, 2014 ). Using the GTR + G model of nucleotide substitution, we conducted a rapid bootstrap analysis with 1000 replicates, after which we searched for the best-scoring ML tree. We rooted the tree with Leucocytozoon (COLBF21, GenBank Accession MK947795) based on the current phylogenetic hypothesis for avian haemosporidians (Borner et al., 2016 ; Galen et al., 2018 ). Nodes with < 50% bootstrap support were collapsed in TreeGraph2 (Stöver and Müller, 2010 ). We determined the relationships among the three vireo study species based on the posterior distribution of likely trees available on birdtree.org with the ‘Hackett All Species’ option (Hackett et al., 2008 ; Jetz et al., 2012 ). We downloaded 100 phylogeny subsets and selected the first tree because the relationships were consistent among the 100 trees. Estimating parasite species richness and sampling completeness We used the iNEXT R package (version 3.0.1; Chao et al., 2014 ; Hsieh et al., 2024) to estimate parasite lineage diversity and evaluate sampling completeness for host species with adequate data (i.e., gray and plumbeous vireos). In separate analyses for each host species, we used infection counts of each parasite haplotype to generate rarefaction and extrapolation curves for parasite species richness (q = 0), with the endpoint set at 400 individuals (i.e., infections). We applied the ‘iNEXT()’ function to compute species richness estimates, including observed richness, extrapolated richness, and associated confidence intervals using standard function parameters. Additionally, we used the output to estimate sampling completeness (Chao et al., 2014 ) and defined this value as the minimum number of individual birds required to detect 95% of the total estimated parasite haplotypes infecting each host species. To visualize the rarefaction curves, we used the ‘ggiNEXT()’ function and ggplot2 package for style modifications (Wickham, 2016 ). Microscopy and parasitemia calculations We obtained available blood smears from the MSB for plumbeous vireos ( n = 28) and made and air dried blood smears in the field for gray vireos ( n = 53). Each slide was fixed with absolute methanol and stained for 50 mins with Giemsa solution (pH 7.0; Sigma-Aldrich, St. Louis, MO, USA). We then examined each slide to confirm infection status using light microscopy on an Olympus BX 53 Microscope following the protocol described in Valkiūnas ( 2005 ), where 10,000 erythrocytes were scanned at 1000x magnification with an oil immersion lens to identify and count Parahaemoproteus and Plasmodium infections (Valkiūnas, 2005 ). We calculated parasitemia, or the estimated percentage of erythrocytes infected with haemosporidian parasites, as the number of infected erythrocytes out of 10,000 screened. To account for lack of normality in the data, we used a bootstrap method with 1,000 replications to calculate parasitemia 95% confidence intervals via the R package boot (Kushary, 2000 ). Characterizing environmental variation We characterized elevation (m) and climatic variation using 19 bioclimatic variables at 30 sec (~ 1 km²) resolution from the WorldClim 2.1 database (Fick and Hijmans, 2017 ). We used principal components analysis (PCA) for temperature (Bio1–11) and precipitation (Bio12–19) variables to create a composite measure of temperature and precipitation across the gradient. The first two axes for temperature (hereafter, tempPC1 and tempPC2) explained a combined 84.2% of the variation and the first two axes for precipitation (hereafter, precipPC1 and precipPC2) explained 95.1% of the variation (Fig. S1 ; Table S5 -6). Variable loadings indicated that tempPC1 primarily represented year-round temperatures, with higher values relating to lower average temperatures, while tempPC2 represented greater seasonality and less temperature stability, with higher values relating to more extreme temperature fluctuations throughout the year and day-night cycle. Similarly, precipPC1 described the overall precipitation amount, with higher values indicating wetter conditions, whereas precipPC2 represented precipitation seasonality, with higher values representing more stable and less seasonal precipitation (Table S5 -6). Assessing infection dynamics among host species and infection types We tested whether haemosporidian infection status differed between categorical variables such as host species and tissue type (i.e., pectoral muscle or whole blood samples), using Pearson’s chi-squared tests via the stats package. To examine the relative magnitude of effect for each comparison, we calculated Cramér's V effect sizes were using the ‘CramerV()’ function in the rcompanion package (Mangiafico, 2024 ). Next, to assess differences in parasitemia, we first normalized the data using a natural log ln(1 + x) transformation and removed two influential outliers with parasitemia > 4% (gray vireo IDs: 272132427 and 272132642). We then evaluated residual variance with the ‘var.test()’ function in the stats package and compared log-transformed parasitemia between gray and plumbeous vireos and infection types (i.e., single vs. coinfected) using two-tailed Student’s t -tests. Intraspecific infection prevalence and parasitemia across environments We employed a two-pronged approach to assess environmental associations with infection prevalence: First, we used univariate, non-parametric tests to characterize and compare intraspecific infection patterns across abiotic variables relevant to parasite and vector ecology, including elevation, overall temperature (tempPC1) and precipitation (precipPC1), and the seasonality of temperature (tempPC2) and precipitation (precipPC2). Then, we built intraspecific multivariate linear models and applied nested and exhaustive model selection to evaluate the strength and direction of environmental effects. We adopted this approach to account for the structure of our data (i.e., non-overlapping elevational and environmental bands distinctly correlated to species identity) and limitations of our dataset (i.e., limited sample sizes, degree of environmental variation, and infection class imbalances). We first assessed data normality both visually and then statistically, using the Shapiro-Wilk test using the ‘shapiro.test()’ function from the stats package. Given the non-normal distribution of environmental and geographic data in our dataset, we applied a rank-sum approach via Mann-Whitney U-tests to compare the distributions of environmental variables between infected and uninfected birds. Wilcoxon effect sizes of each comparison were then calculated via the ‘wilcox_effsize()’ function in the rstatix package (Kassambara, 2023 ). We then constructed species-specific generalized linear models using glm() from the stats package. Prevalence model sets used a binomial distribution (hereafter: “BEVI_prev” for Bell’s vireo, “GRVI_prev” for gray vireo, and “PLVI_prev” for plumbeous vireo sets), while parasitemia model sets used a Gaussian distribution (“GRVI_para”, “PLVI_para”). We evaluated linearity between the logit and predicted values in prevalence models and assessed residual diagnostics in parasitemia models and with ‘autoplot()’ within the ggfortify package (Tang et al., 2016 ). Multicollinearity between variables was assessed by calculating variance inflation factors (VIF) via the car package. As expected, there was high collinearity with aspects of geographic and topographic variation, i.e. latitude and elevation, with other environmental variables. We retained elevation in all model sets because it captured variation in habitat and vector dynamics important to infection that were not explained by temperature and precipitation alone (Ishtiaq and Barve, 2018 ); however, we removed latitude from all linear models to reduce overfitting. For model sets with adequate sampling (i.e., GRVI_prev, PLVI_prev, GRVI_para, PLVI_para), we evaluated all possible additive combinations of five predictor variables: elevation, tempPC1, tempPC2, precipPC1, precipPC2. We used the ‘dredge’ function in the MuMIn package (Bartoń, 2024 ) for exhaustive model selection. For host datasets with < 30 data points (BEVI_prev and PLVI_para) we restricted candidate models to simple, biologically relevant structures to avoid overfitting. Specifically, we included univariate models nested within simple additive models containing at most two predictor variables, as well as null and global models for comparison. We then compared model performance and evaluated the trade-off between model fit and complexity using Akaike’s Information Criterion corrected for small sample sizes (AICc; Table 1 ). Table 1 Top five infection probability fixed effects additive models Bell’s vireo ( n = 20) Rank Predictors K AICc ΔAICc Weight 1 tempPC2 + precipPC1 3 22.6 - 0.324 2 tempPC1 2 23.6 0.967 0.200 3 precipPC1 2 24.7 0.348 0.113 4 tempPC1 + precipPC1 3 25.9 0.199 0.064 5 Elevation + tempPC1 3 25.9 0.197 0.064 gray vireo ( n = 170) Rank Predictors K AICc ΔAICc Weight 1 Elevation + tempPC1 3 204 - 0.419 2 Elevation + precipPC1 + tempPC1 4 206 1.72 0.177 3 Elevation + precipPC2 + tempPC1 4 206 1.73 0.177 4 Elevation + tempPC1 + tempPC2 4 206 2.07 0.149 5 Elevation + tempPC1 + tempPC2, precipPC1 + precipPC2 5 207 3.35 0.078 plumbeous vireo ( n = 58) Rank Predictors K AICc ΔAICc Weight 1 Null model 1 52.1 - 0.301 2 precipPC2 2 52.4 0.409 0.246 3 precipPC1 2 53.3 1.17 0.168 4 tempPC2 2 53.4 1.29 0.158 5 PrecipPC1 + precipPC2 2 53.9 1.73 0.127 [2] Table 1. Top five models assessing environmental predictors of haemosporidian infection for each species. Sample sizes for datasets and directionality of best model outputs are listed at the top. Models were ranked by AICc, with ΔAICc values relative to the top model and corresponding Akaike weights (Weight) indicating model support. To examine the effect of each predictor in top-ranked models, we standardized and calculated model-averaged regression coefficients using the MuMIN package (Table 2 ). For top-ranked models where the null model was not favored, we assessed goodness of fit via Hosmer-Lemeshow tests as part of the ResourceSelection package (Lele et al., 2023 ) and checked for overdispersion by dividing residual deviance by the degrees of freedom. Predictive accuracy was also assessed using 10-fold cross-validation via the caret package (Kuhn, 2008 ). Table 2 Bell’s vireo gray vireo plumbeous vireo Param β SE LCL UCL β SE LCL UCL β SE LCL UCL Intercept 0.85 0.88 -0.99 2.7 1.1 0.36 0.43 1.8 1.7 0.40 0.98 2.5 Elevation 0.06 0.37 -0.70 0.82 -1.7 0.76 -3.2 -0.21 - - - - tempPC1 -0.94 1.5 -3.9 2.0 1.1 0.55 0.01 2.1 - - - - tempPC2 1.8 2.7 -3.5 7.1 -0.05 0.72 -1.5 1.4 0.05 0.18 -0.31 0.41 precipPC1 2.8 3.5 -4.2 9.8 -0.16 0.59 -1.3 1.0 0.10 0.24 -0.38 0.58 precipPC2 - - - - -0.28 0.90 -2.1 1.5 0.17 0.31 -0.44 0.78 [3] Table 2 . Standardized model-averaged regression coefficients used to estimate effects of predictors and precision of effects across species-specific candidate model sets that assess environmental predictors of haemosporidian infection. Regression coefficients (β), standard error (SE), and 95% confidence limits (lower as LCL, upper as UCL) are given. Dashes indicate that the parameter was not present in a final model set, and NA values indicate that the parameter was not tested due to lack of variation present in model set. Results Haemosporidian prevalence, abundance, and diversity We uncovered an unusually high prevalence of haemosporidian infections in all three vireo species, with 175 infected birds of 248 screened (70.6%; 95% CI [64.5–76.2%]). The highest documented prevalence was found in plumbeous vireos (84.5% infected; 95% CI [72.6–92.7%]), followed by gray vireos (67.7%; 95% CI [60.0–74.7%]), and lastly, Bell’s vireos (55%; 95% CI [31.5–76.9%]; Fig. 1 B). Prevalence was significantly lower in Bell’s than in plumbeous vireos (χ²(1) = 5.72, p = 0.017, φ = 0.31), indicating a moderate effect size. While most birds were infected by single haemosporidian haplotype infections (88.1%; 95% CI [82.3–92.5%]), we found 21 individuals that were coinfected with two or more haplotypes (12.0%; 95% CI [7.54–17.7%]), and one gray vireo that was infected by three haplotypes (VIRVIC01, VIRVIC02, VIRVIC03). Of the 14 gray vireos recaptured about a year after initial sampling, 12 were infected with haemosporidians in both years, while two that were uninfected in 2017 tested positive in 2018. Nearly all infections were caused by parasites in the genus Haemoproteus , specifically the subgenus Parahaemoproteus (99.5%), which comprised 18 of 19 haplotypes identified. Plasmodium was rare in our data, with a single infection (lineage VIRPLU13) recorded from a plumbeous vireo (MSB:Bird:60726; Table S4 ). Of the 19 total parasite haplotypes identified, 8 were novel (Fig. 1 D; Table S4 ). Estimates of parasite species richness and sampling completeness We identified a total of three haemosporidian haplotypes from Bell’s vireos, four from gray vireos, and 14 from plumbeous vireos (Fig. 1 D). These differences were partly, but not entirely, attributable to differences in sample size. gray vireo Parahaemoproteus infections were substantially less diverse than those of plumbeous vireos despite more intensive screening efforts. Extrapolation from rarefaction curves showed considerably lower estimated parasite richness for gray vireos (4.99 ± 0.53 SE; 95% CI [4.00–6.03]; Table S7) and estimated that much smaller sample sizes would be needed to reach 95% sampling completeness (14 infections; Fig. 2 ; Table S8). This result contrasted our results for plumbeous vireos, where parasite richness was estimated to be ~ 10x higher than gray vireos (53.8 ± 23.3 SE; 95% CI [14.0–99.6]) and require much greater sampling effort (~ 26x more than estimated for gray vireos) to reach an estimated 95% completeness (estimated 364 infections). Bell’s vireos also exhibited low lineage diversity (3.92 ± 0.784 SE; 95% CI [3.00-5.45] and an estimated 95% sample completeness at only 33 infections, consistent with the limited diversity recovered (Table S7-S8). However, we caution overinterpreting these results due to the small Bell’s vireo sample size ( n = 12 infected) in our study and recommend additional sampling to generate more precise estimates. Of the 19 total haplotypes identified, we found evidence of potential host specificity with at least one species-specific haplotype infecting each of the three vireo species (Table S4 ). Of note, gray vireos were infected by novel haemosporidian haplotypes only, (VIRVIC01-VIRVIC04). This finding contrasted with plumbeous vireos, which showed novel haplotypes (i.e. VIRPLU10-13) and haplotypes previously found only in plumbeous vireos (i.e. VIRPLU03, VIRPLU07-09), as well as lineages known to infect other non-vireo species (i.e. SETAUD14, TROAED12, VIGIL07, VIRPLU01, and VIRPLU04). Sampling was comparatively lower for Bell’s vireos; no novel lineages were detected. We did detect, however, a recently described lineage (VIRBEL01) which was previously documented in Bell’s vireos sampled from Arizona and Texas (Fecchio et al., 2023 ), as well as other more generalist lineages (i.e. TROAED12 and VIGIL07; Table S4 ). Intensity of infection (parasitemia) across hosts and haplotypes We screened 102 individuals via microscopy (gray n = 71, plumbeous n = 31). A total of 81 samples showed evidence of infection and had parasitemia calculated (Parahaemoproteus : gray n = 53 and plumbeous n = 27; Plasmodium : plumbeous n = 1). gray vireos typically had slightly higher infection loads (1.00%; 95% CI [0.782–1.27%]) compared to plumbeous vireos (0.786%; 95% CI [0.584–1.01%]), partly due to two outlier gray vireo samples with > 4% parasitemia (ID 272132427 and 27213264; Fig. 1 C). After removing these outliers, we found that mean parasitemia did not differ significantly between host species (t(77) = 0.656, p = 0.514). When we compared single-haplotypes and coinfections, we also found no significant difference in parasitemia between species (t(73) = -0.728, p = 0.469), though we note the sampling pool of coinfected birds was small compared to single infections ( n = 7 and n = 68, respectively). Our comparison of infections using PCR and microscopy was largely consistent, with few detection errors identified between the two methods. We detected four positively infected birds via microscopy that did not successfully amplify by PCR (Table S1 ). These samples had a range of parasitemia spanning 0.70% to 2.68%. Additionally, we failed to detect haemosporidian infections via microscopy for two birds that were later successfully amplified using PCR; one individual was the single coinfected gray vireo with three Parahaemoproteous haplotypes that were identified by sequencing (Table S1 ). Environmental patterns of infection status and parasitemia Parahaemoproteus prevalence showed idiosyncratic patterns among host species and their associated habitats (Fig. 3 ; Table S9). For example, we found that infected Bell’s vireos tended to be found at lower latitudes ( z = -2.09, p = 0.040), with warmer year-round temperatures (tempPC1; z = -2.25, p = 0.027 Fig. 3 ; Fig. S2 ; Table S9). Similarly, probability of infection increased as overall precipitation (precipPC1) and temperature seasonality (tempPC2) increased (Table 1 ; Table 2 ). In contrast, we found higher proportions of infected gray vireos at lower elevations ( z = -2.07, p = 0.038), Fig. 1 C; Table S9) and at sites with more stable precipitation (precipPC2; z = 1.72, p = 0.057), Fig. 3 ; Table S9). However, this result was dampened when we excluded the eleven infected gray vireos from the Abajo Mountain site in Utah, which were outliers in many of the environmental PCA dimensions. The top model, however, showed that infection was negatively associated with elevation and positively associated with temperature (tempPC1; Table 1 ; Table 2 ) even when excluding the Utah site. Lastly, plumbeous vireo infections tended to be higher in areas with more seasonally stable precipitation (precipPC2; z = 2.04, p = 0.042), Fig. 3 ). Despite the top-ranked infection probability model showing no significant linear trends, precipitation (precipPC1 and precipPC2) was present in the majority of top candidate models (Table 1 ). We did not find any association between parasitemia and environmental variables in plumbeous vireos, with the null model having the strongest support and no clear predictor variable significantly reported across top models (Table S10). gray vireos, however, showed a significant pattern of increased parasitemia as overall precipitation (precipPC1) increased (Table S10), but this model had low explanatory power (R² = 0.05). Discussion Elevational replacement and haemosporidian diversity patterns Avian haemosporidians exhibit variable patterns of turnover across elevations (Galen and Witt, 2014 ; Illera et al., 2015; McNew et al., 2021 ), which is often independent of geographic distance between hosts (Williamson et al., 2019 ; Barrow et al., 2021 ). We demonstrated a strong case of parallel elevational replacement in hosts and parasites, with high turnover among Parahaemoproteus assemblages infecting vireo species breeding at different elevations. This elevational replacement pattern could be the result of a few different scenarios. Such turnover may reflect shared ecological constraints on hosts, vectors, and parasites along environmental gradients (Ellis et al., 2020 ), or species-specific immune or tolerance strategies (Armour et al., 2025 ). Regardless of mechanism, our results indicate that spatial structuring of parasite diversity is closely aligned with host elevational zonation. Of the 30 cyt b Parahaemoproteus and 12 Plasmodium haplotypes reported to infect vireos (MalAvi; downloaded 1/1/2025), our study adds seven novel Parahaemoproteus and one novel Plasmodium haplotype. The rarity of Plasmodium infections, even at lower elevations, contrasts many temperate systems (e.g. in Alaska: Oakgrove et al., 2014 ; California: Walther et al., 2016 ; Oklahoma: Wyckoff et al., 2024 ) but aligns with prior cross-elevational surveys in New Mexico (Marroquin-Flores et al., 2017 ; Williamson et al., 2019 ; Barrow et al., 2021 ). The low prevalence of Plasmodium at lower elevations in this region may reflect limited suitable habitat for mosquito vectors prior to the onset of monsoon rains. Broader and temporally replicated surveys of potential vectors and vector abundance in this region are needed to fully explain this apparent absence. Among Parahaemoproteus lineages, plumbeous vireos harbored a broad diversity of parasite haplotypes relative to the other two sampled species, while gray vireos exhibited a depauperate and likely specialized haemosporidian assemblage (Fig. 1 D). This contrast may indicate broader ecological flexibility or permissive host–parasite associations in plumbeous vireos, and possible specialization or reduced parasite exposure in gray vireos. Alternatively, low parasite diversity in gray vireos may reflect the depauperate breeding bird communities of the arid and structurally simple juniper savanna habitats. Conserved high susceptibility to haemosporidians among vireo species Vireos stand out among southwestern songbirds for their consistently high haemosporidian infection rates, providing a valuable opportunity to examine parasite dynamics in hyper-susceptible hosts. In the three species screened here, we found that 70.6% of 248 individuals screened had at least one haemosporidian infection, more than twice the average prevalence reported in community-wide surveys conducted in the region. For example, Marroquin-Flores et al. ( 2017 ) reported 36.6% haemosporidian prevalence across 49 bird species breeding in New Mexico, yet 92% of the vireos tested positive (Marroquin-Flores et al., 2017 ). A subsequent large-scale study by Barrow et al. ( 2021 ) reported similar patterns, with 36.1% prevalence across 61 species, but vireos again showed the highest infection rates in the communities surveyed (Barrow et al., 2021 ). This susceptibility appears to be conserved, with high haemosporidian prevalence documented in breeding sites of other species in the genus Vireo . Granthon and Williams ( 2017 ) reported 100% of the 62 PCR-screened red-eyed vireos ( Vireo olivaceus ) from Pennsylvania were infected, and Rodriquez et al. (2021) documented 59% infection in 22 individual warbling vireos ( V. gilvus ) sampled from Colorado, indicating that elevated susceptibility is widespread across the genus (Granthon and Williams, 2017 ; Rodriguez et al., 2021 ). Shared susceptibility within closely related species has been extensively documented (e.g. González et al., 2014 ; Lutz et al., 2015 ; Barrow et al., 2019 ; Pigeault et al., 2022 ) but attempts to identify the specific factors that contribute to high prevalence in vireos are still lacking. Of the correlated host traits recorded in other avian clades, thus far, open cup nest type and insectivorous diets likely increase exposure risk in this group (Braga et al., 2011 ; Rodriguez et al., 2021 ), while additional behavioral and physiological factors, including prolonged incubation, nest-site microhabitat, and possible tolerance-based immune strategies, may further contribute (González et al., 2014 ; Lutz et al., 2015 ; Muriel, 2020 ; Pigeault et al., 2022 ). Recapture data further support sustained parasite pressure: of the 14 gray vireos sampled in two consecutive years, 12 remained infected and the remaining two acquired infections by the second year (Table S2 ). Although distinguishing chronic infections from reinfections requires additional data, these results indicate that infection is persistent during the breeding season. Continued longitudinal sampling and investigation of exposure and immune response in vireos will be key to determining the mechanisms driving their elevated susceptibility. Environmental determinants of haemosporidian infection vary by host species Previous studies have linked haemosporidian prevalence to topographic and climatic variables (Pérez-Rodríguez et al., 2013 ; Rodriguez et al., 2021 ), though the strength and direction vary regionally and by haemosporidian clade observed (Williamson et al., 2019 ). In the arid southwest, where water availability shapes vector distributions (Valkiūnas, 2004; Santiago‐Alarcon et al., 2012), we expected haemosporidian prevalence to correspond with water availability (i.e. riparian habitats) and areas with higher, more seasonally stable precipitation (i.e. montane woodlands). Instead, prevalence increased with elevation and precipitation: Bell’s vireos showed the lowest prevalence, while gray vireos exhibited unexpectedly high infection rates. It remains unclear whether these patterns reflect species-specific traits or environmental differences, and additional sampling of Bell’s vireos may help resolve this uncertainty. Within species, infection probability was associated with different environmental factors in each taxon (Fig. 3 ; Fig. S2 -6; Table S9-10), indicating that no single abiotic predictor applies across vireos. Broader sampling across elevation, habitat moisture gradients, and co-distributed species will be needed to better understand cross-elevational trends in infection risk in southwestern avian communities. Moderate and consistent infection loads across environmental gradients Gray and plumbeous vireos exhibited similar average infection loads (~ 1% in gray vireos, 0.786% in plumbeous, range: 0.03–4.5%) with no significant differences between species (Fig. 1 B). These values slightly exceed the regional cross-species average (< 1%; Marroquin-Flores et al., 2017 ) and fall within the range of moderate infection intensities reported in experimental studies of other passerines (e.g. Asghar et al., 2012 ). We did note, however, two gray vireos with elevated parasitemia (4.5 and 4.49%) were sampled at distinct sites in the Sevilleta National Wildlife Refuge, indicating acute or recrudescence of chronic infections. The absence of high parasitemia in Bell’s and plumbeous vireos could be an artifact of limited sampling, as birds with severe infections often exhibit reduced mobility and capture probability (Mukhin et al., 2016 ), suggesting that heavily infected individuals in these species may be underrepresented. Additionally, we found no relationship between parasitemia and any tested environmental variables for plumbeous vireos. In gray vireos we saw a significant linear trend in parasitemia with increased overall precipitation (precipPC1; Table S10), but this model had weak explanatory power (R² = 0.05). These results are consistent with previous findings which suggest that haemosporidian parasitemia patterns are shaped by complex interactions between host and parasite biology not easily captured in models with abiotic factors alone (Loiseau et al., 2013 ). Conclusions We found that parasite elevational distributions mirror the elevational replacement pattern of host species among vireos in southwestern North America. This pattern may have resulted from host specialization, climate variation independently acting on hosts and parasites, climate variation acting indirectly through habitat or broader host and vector communities, or a combination of those forces. We found that vireos were consistently infected at high rates and largely harbor chronic, low-to-moderate intensity infections dominated by Parahaemoproteus , suggesting the potential for exclusive, reciprocal selective pressures that facilitate host-parasite coadaptation. The contrast in parasite richness between plumbeous vireos and gray vireos was striking: gray vireos harbored a limited number of haemosporidian lineages, all of which were host specific, while plumbeous vireos were infected with a diverse set of lineages. These differences imply species-specific variability in resistance, tolerance, or exposure to generalist haemosporidian lineages. In sum, our work adds to a growing body of evidence about clade-specific avian host susceptibility and host-parasite turnover across elevational gradients, highlighting vireos as an ideal focal group for future work on host-parasite dynamics across space and time. Declarations Acknowledgements The authors sincerely thank Bethany Abrahamson, Michael Andersen, Matthew Baumann, Alison Boyer, Serina Brady, Sara Brant, Mariel Campbell, Andrea Chavez, Mina Carnicom, Lida Crooks, Rosario Marroquin-Flores, Chauncey Gadek, Spencer Galen, Ethan Gyllenhaal, Mike Hartshorne, Andy Johnson, Michael Lelevier, Joseph Manthey, Xena Mapel, Taylor Martinez, Jenna McCullough, Moses Michelsohn, Kathleen Ramsay, George Rosenberg, C. Jonathan Schmitt, Donna Schmitt, Brenda Villanueva, and Toby Weisenhaus. Funding This work was supported by the National Science Foundation (Graduate Research Fellowship NSF-DGE-2439853 to DLFW; Postdoctoral Research Fellowship in Biology NSF-DBI-1611710 to LNB; and NSF-DEB-1146491 to CCW), the Bureau of Land Management Rio Puerco Field Office (via the Colorado Plateau Cooperative Eco-systems Studies Unit agreement), New Mexico Department of Game and Fish Share with Wildlife Grant to HMS/SEF, and a Sevilleta LTER Graduate Summer Research Fellowship and New Mexico Ornithological Society Ryan Beaulieu Research Grant to SEF. Conflict of interest The authors have no conflicts of interest to declare. Ethics approval Research activities were conducted under Institutional Animal Care and Use Protocol 16-200406-MC and appropriate state and federal scientific collecting permits (New Mexico Department of Game and Fish Authorization Number 3217; US Fish and Wildlife Permit Numbers MB094297-0 and MB0942970-1). Availability of data and material Specimen data are available in Supplementary Tables S1–S10 and are searchable via Arctos (arctos.database.museum; Cicero et al., 2024). Parasite sequences are archived in GenBank (accession nos. PV295634-PV295830). Vouchered specimens and frozen tissue samples are archived at the Museum of Southwestern Biology (https://msb.unm.edu/). All R code and datasets are available on FigShare (https://doi.org/10.6084/m9.figshare.28585943). Student highlight statement Understanding how biodiversity is structured across environmental gradients is fundamental to ecological theory, yet how these patterns extend to parasitic taxa remains a compelling area of investigation. Here, we assess whether the biogeographic pattern of elevational replacement observed in highly susceptible songbirds is mirrored by their haemosporidian parasites, and whether the processes driving this partitioning are shared. Our study is the first to show that, although the forces behind elevational turnover differ, strong host associations produce parallel zonation, particularly in host-specialized lineages. By linking host and parasite biogeography, this work demonstrates that the host itself serves as the structuring gradient for parasite diversity in montane regions. Authors’ contributions DLFW, JLW, CCW, and LNB conceptualized and developed methodologies for this project. JLW, SEF, HMS, SMB, KG, CCW, and LNB performed fieldwork and subsequent data curation. DLFW, JLW, and SMB performed molecular analysis and generated sequence data. JLW, SEF, HMS, KG, CCW, and LNB secured funding and provided supervision. DLFW and JLW performed formal analyses and created visualizations. DLFW and JLW wrote the original draft of the manuscript, and DLFW wrote the final version with input from all co-authors. 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PLoS ONE 7(6):e39208. 10.1371/journal.pone.0039208 Supplementary Files OecoDocumentS12025Wileyetal.docx OecoSupplementalFigures162025Wileyetal.docx OecoSupplementalTable12025Wileyetal.xlsx OecoSupplementalTable22025Wileyetal.xlsx OecoSupplementalTable42025Wileyetal.xlsx OecoSupplementalTables1102025Wileyetal.docx Cite Share Download PDF Status: Published Journal Publication published 07 Apr, 2026 Read the published version in Oecologia → Version 1 posted Reviewers agreed at journal 20 Dec, 2025 Reviewers invited by journal 11 Dec, 2025 Editor assigned by journal 08 Nov, 2025 First submitted to journal 07 Nov, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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22:25:36","extension":"html","order_by":24,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":248997,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8060400/v1/3e19fe2167f696a47a81791c.html"},{"id":98623952,"identity":"225ee786-eb8c-4900-9940-caf6eeba202d","added_by":"auto","created_at":"2025-12-19 17:07:49","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":794477,"visible":true,"origin":"","legend":"\u003cp\u003eA) Sampling distribution for each host species (Bell’s vireo: \u003cem\u003eVireo bellii, \u003c/em\u003egray vireo:\u003cem\u003e V. vicinior,\u003c/em\u003e and plumbeous vireo: \u003cem\u003eV. plumbeus\u003c/em\u003e) in the southwestern USA; B) Left: summary of infection prevalence, with significant differences between groups indicated by letters. Error bars represent 95% confidence intervals for positive infection proportions. Right: percent parasitemia for gray and plumbeous vireos. C) Proportion of individuals infected versus uninfected across elevational gradient. Asterisk denotes significant p-values (\u0026lt; 0.05); D) Phylogeny of haemosporidian cyt \u003cem\u003eb\u003c/em\u003ehaplotypes and their respective abundances by host. Stars denote novel haplotypes and bootstrap values \u0026gt;50 are shown at nodes. Bird images courtesy Birds of the World (Billerman et al., 2025).\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8060400/v1/1a0cc96edc97555fd85233e5.png"},{"id":98465196,"identity":"8b315db7-5665-4105-947e-fa5abd34b886","added_by":"auto","created_at":"2025-12-17 22:25:36","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":238049,"visible":true,"origin":"","legend":"\u003cp\u003eEstimate of haemosporidian lineage diversity in Bell’s (\u003cem\u003eVireo belli\u003c/em\u003e), gray (\u003cem\u003eV. vicinior\u003c/em\u003e) and plumbeous vireos (\u003cem\u003eV. plumbeus\u003c/em\u003e) based on iNEXT rarefaction and extrapolation. Bell’s vireo curves (yellow) were created using 12 avian haemosporidian infections and three haplotypes; gray vireo curves (gray) used 123 avian haemosporidian infections and four haplotypes; plumbeous vireo curves (blue) used 62 haemosporidian infections and 14 haplotypes. Points indicate the reference sample, solid line the rarefaction estimate, and dotted line the extrapolation. Total haplotype richness, rounded to nearest whole number, is estimated to be four for Bell’s vireos (95% CI [3-6]), five for gray vireos (95% CI [4 – 6]) and 54 for plumbeous vireos (95% CI [14 – 101]).\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8060400/v1/58348c69a137a0f76f296913.png"},{"id":98465199,"identity":"7dd97e02-999d-42da-98e5-45298d31d27d","added_by":"auto","created_at":"2025-12-17 22:25:36","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":547248,"visible":true,"origin":"","legend":"\u003cp\u003eIntraspecific distribution of infected and uninfected vireos across associated environmental variables. Higher tempPC1 values relate to cooler overall temperatures and higher precipPC2 relates to sites with more seasonally stable precipitation. Significantly different distributions are denoted by an asterisk (p \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8060400/v1/221c6a3a6b7f31c4a1189da6.png"},{"id":106809915,"identity":"afa0ee66-9b4c-4b16-a7e0-950725152dbc","added_by":"auto","created_at":"2026-04-13 16:13:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2876628,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8060400/v1/a4170f47-36e3-4fcf-97e5-83947399aeb8.pdf"},{"id":98465204,"identity":"f1f139ac-57a9-4634-8be0-7ca45b5c3942","added_by":"auto","created_at":"2025-12-17 22:25:36","extension":"docx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":21812,"visible":true,"origin":"","legend":"","description":"","filename":"OecoDocumentS12025Wileyetal.docx","url":"https://assets-eu.researchsquare.com/files/rs-8060400/v1/b12a8fd3b03b9f29df4611dd.docx"},{"id":98624222,"identity":"1fa58ff4-725c-4220-b2d7-bf15f2408285","added_by":"auto","created_at":"2025-12-19 17:08:10","extension":"docx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":1441927,"visible":true,"origin":"","legend":"","description":"","filename":"OecoSupplementalFigures162025Wileyetal.docx","url":"https://assets-eu.researchsquare.com/files/rs-8060400/v1/19a6f5657c1d8bc672ed0fd6.docx"},{"id":98465207,"identity":"7fc4fd51-35b4-45ec-80a8-a642dbb5af94","added_by":"auto","created_at":"2025-12-17 22:25:36","extension":"xlsx","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":149755,"visible":true,"origin":"","legend":"","description":"","filename":"OecoSupplementalTable12025Wileyetal.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8060400/v1/9ffa985150f64e3278cf5d6c.xlsx"},{"id":98465205,"identity":"a9a93691-b98e-414e-a5bc-5064e45082fe","added_by":"auto","created_at":"2025-12-17 22:25:36","extension":"xlsx","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":14375,"visible":true,"origin":"","legend":"","description":"","filename":"OecoSupplementalTable22025Wileyetal.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8060400/v1/bfd65bc1d04734ef452815a7.xlsx"},{"id":98623326,"identity":"59a3dc9e-ea67-4bc3-ac17-200128c2be35","added_by":"auto","created_at":"2025-12-19 17:05:48","extension":"xlsx","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":16537,"visible":true,"origin":"","legend":"","description":"","filename":"OecoSupplementalTable42025Wileyetal.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8060400/v1/d27c876aaae7f25cc91d0461.xlsx"},{"id":98624213,"identity":"e8177fff-8c88-4dc8-94e5-ff3ba333ec8f","added_by":"auto","created_at":"2025-12-19 17:08:09","extension":"docx","order_by":12,"title":"","display":"","copyAsset":false,"role":"supplement","size":69242,"visible":true,"origin":"","legend":"","description":"","filename":"OecoSupplementalTables1102025Wileyetal.docx","url":"https://assets-eu.researchsquare.com/files/rs-8060400/v1/61030e33dc7760f9f7957a0e.docx"}],"financialInterests":"","formattedTitle":"Parallel elevational replacement of hosts and parasites in a highly susceptible songbird genus","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMountains harbor exceptional biodiversity, in part because elevational gradients tend to be finely partitioned by related species with ranges that are elevationally parapatric (Cadena and Loiselle, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; von Humboldt and Bonpland, \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Freeman et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Yet, it remains unclear whether parasites of elevational replacement host species mirror this range stratification. Generally, host-parasite interactions can strongly influence biogeographic patterns, with reciprocal effects on fitness (Combes, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1997\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Poulin, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Hasik and Siepielski, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), population dynamics (Albon et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2002\u003c/span\u003e), and community structure (Minchella and Scott, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e1991\u003c/span\u003e, McNew et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Strong host-specificity, combined with shared environmental constraints, should result in parasite turnover that parallels the turnover of hosts along environmental gradients. Conversely, aspects of parasite life-history\u0026ndash;\u0026ndash;such as insulation from climatic factors (e.g., endoparasites), dependency on secondary hosts and vectors (e.g., complex life cycle), and degree of host-specificity (e.g., host range and degree of host switching)\u0026ndash;\u0026ndash;should be expected to cause discordant patterns from those observed in hosts (Gage et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Krasnov and Poulin, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Ellis et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRegional surveys across elevational gradients reflect these complex relationships. In various mountain systems, parasite turnover has been found to exceed host turnover (Galen and Witt, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Barrow et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and host turnover has been found to predict parasite turnover (Ellis et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Williamson et al., \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Gradients of precipitation and temperature, as well as landscape features, have also been found to shape parasite communities (Illera et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; McNew et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Investigating the parasite assemblages of closely related host taxa that are elevational replacements provides a natural experiment for disentangling the relative roles of host associations, parasite traits, and environmental gradients in shaping parasite biogeography, while controlling for host phylogeny, life history, and parasite susceptibility (Medeiros et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Barrow et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAmong blood parasites, haemosporidians (order Haemosporida; Danilewsky, 1885) are a cosmopolitan group of protozoans with complex life cycles dependent upon vertebrate taxa, including birds, mammals, and reptiles (Ricklefs and Fallon, \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Duval et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Boysen et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and invertebrate vectors in the order Diptera (Linnaeus, 1758). A long history of co-evolution with avian hosts (Ricklefs et al., \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Lauron et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Galen et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) has given rise to a remarkable diversity of avian haemosporidian lineages (Valkiūnas, \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Hellgren et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Perkins, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), with many still yet to be described (Borner et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGiven the high diversity and complexity of haemosporidian parasites, it has remained challenging to characterize the distribution patterns of haemosporidian lineages across large geographic scales (Clark, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). However, within regions and genera, spatial trends appear\u0026mdash;reflecting the influence of climate directly, through physiological interactions during transmission and development (Ikemoto \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Mordecai et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), and indirectly, by influencing host and vector abundance and susceptibility (Filion et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). As a result, the fluctuating climate of temperate and montane regions produce seasonal, annual, and habitat-level waves of infection (Bensch et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; P\u0026eacute;rez-Rodr\u0026iacute;guez et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Lutz et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Reinoso-P\u0026eacute;rez et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), whereas areas with higher, less seasonal temperatures, such as tropical regions and lowlands, can have persistent haemosporidian pressure (Zamora-Vilchis et al., \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eInfection intensity, commonly measured as parasitemia (defined as the \u0026lsquo;estimated percentage of haemosporidians circulating in host blood\u0026rsquo;) provides insight into host condition and stage of haemosporidian infection (i.e., acute or chronic; Valkiūnas, \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Like prevalence (here defined as \u0026lsquo;proneness to infection\u0026rsquo;), parasitemia is also influenced by environmental factors, with peaks in temperate regions during the breeding season, when warmer, wetter conditions favor vector activity (Valkiūnas, \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Reinoso-P\u0026eacute;rez et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and when chronically infected hosts experience tissue-to-blood recrudescence (Atkinson and van Riper, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1991\u003c/span\u003e). Therefore, montane regions provide a valuable system for studying avian haemosporidian infection dynamics because of the sharp local variation in both climate and habitat over relatively small geographic distance (e.g., Illera et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Williamson et al., \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Rodr\u0026iacute;guez-Hern\u0026aacute;ndez et al., \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe southwestern U.S. presents a particularly compelling region for studying haemosporidian biogeography and dynamics due to its climatic heterogeneity, wide range of habitat types, and steep elevational gradients in sky island mountains. Although arid overall, the region exhibits strong variation in precipitation timing and intensity, shaped by both temperate seasonality and the North American monsoon system. These climatic patterns interact with topographic variation (reviewed in Coblentz and Riitters, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) to create a mosaic of ecologically distinct elevational zones ranging from lowland riparian corridors with high relative humidity and permanent water sources, to mid-elevation desert scrublands and savannas reliant on seasonal rainfall, to cooler montane forests with shorter growing seasons and higher annual precipitation (Sheppard et al., \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). While community-level surveys suggest moderate overall prevalence of haemosporidian infection among avian communities in the region (mean: ~36%; Barrow et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and while environmental conditions\u0026ndash;\u0026ndash;particularly related to elevation, have been shown to strongly influence spatial patterns of infection (Williamson et al., \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u0026ndash;\u0026ndash;previous studies have primarily described environmental and ecological contributions across mid-high elevation habitats and sky islands. Therefore, further work is needed to expand our understanding of whether observed environmental and elevational trends persist across broader gradients and habitat types, especially among particularly susceptible hosts.\u003c/p\u003e \u003cp\u003eAmong the many avian species previously surveyed in the desert Southwest, the plumbeous vireo (\u003cem\u003eVireo plumbeus\u003c/em\u003e) has consistently stood out for its high haemosporidian prevalence across sites and years (Marroquin-Flores et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Barrow et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Migratory populations of this insectivorous songbird breed in mid- to high-elevation coniferous woodlands (from ~\u0026thinsp;1,200\u0026ndash;3,000 m) across the western U.S. and Mexico (Barlow, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1977\u003c/span\u003e; Curson and Goguen, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1998\u003c/span\u003e) and spend the nonbreeding season along the Pacific Slope and in the lowlands of central Mexico (Sibley and Monroe, \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e1990\u003c/span\u003e). Two closely related migratory vireos, Bell\u0026rsquo;s vireo (\u003cem\u003eVireo bellii\u003c/em\u003e) and gray vireo (\u003cem\u003eVireo vicinior\u003c/em\u003e), have relatively similar breeding distributions, yet inhabit distinct habitats and elevations. gray vireos breed in arid mid-elevation juniper (\u003cem\u003eJuniperus\u003c/em\u003e spp.) savannas and chaparral, ~\u0026thinsp;400\u0026ndash;1,900 m (Hubbard, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e1970\u003c/span\u003e; Barlow, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1977\u003c/span\u003e) and spend the nonbreeding season primarily in lowland desert and coastal areas of Sonora and Baja California (Barlow et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Fischer et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Bell\u0026rsquo;s vireos breed in lowland riparian habitats, generally below ~\u0026thinsp;1,500 m, with comparatively high access to water and vegetative cover (Brown, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1993\u003c/span\u003e) and spend the nonbreeding season along the Pacific coast of Mexico, from Baja California and Sonora south to El Salvador. Given the similarities in ecology and life history among vireos, local differences in breeding elevational range make these three focal species an ideal comparative system through which to examine haemosporidian infection patterns across elevational gradients.\u003c/p\u003e \u003cp\u003eIn this study, we tested whether haemosporidian biogeography, community assemblage, and infection dynamics vary predictably among three species of vireos that occupy distinct elevations and habitats at sites in New Mexico and Utah, USA. Using a combination of molecular screening and microscopy, we (1) compared parasite prevalence and infection intensity among host species, including the first parasite survey for regionally vulnerable gray vireos; (2) characterized haemosporidian haplotypes and host specificity; and (3) tested the extent to which community composition was linked to elevational zones and/or their associated host species and habitats. We predicted finding extensive parasite sharing between elevational replacement vireo species, considering the proximity and interdigitated nature of their ranges. Based on previously published haemosporidian surveys in vireos, we anticipated finding high infection prevalence across all three host species; however, we also expected prevalence and parasitemia to vary among species and environments. Lastly, because the Southwest is arid, we predicted higher prevalence and parasite diversity in more mesic habitats suitable for dipterid vectors, such as in the low-elevation riparian corridors used by Bell\u0026rsquo;s vireos and high-elevation woodlands occupied by plumbeous vireos.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSample collection\u003c/h2\u003e \u003cp\u003eWe sampled 248 wild-caught individuals of three vireo species (Bell\u0026rsquo;s vireo: \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;20, gray vireo: \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;170, and plumbeous vireo: \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;58; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA; Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Sampled individuals included adults and juveniles (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). All individuals were sampled during the summer months (May\u0026ndash;August), with the majority sampled prior to monsoon season (~\u0026thinsp;early-July\u0026ndash;September) from 1995\u0026ndash;2019 across 28 unique sites in New Mexico and a single site in Utah (Document S1; Tables S2-3). Detailed sampling protocols for Bell\u0026rsquo;s vireos are published in Gyllenhaal, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2024\u003c/span\u003e, gray vireos in Fischer, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, Fischer et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022\u003c/span\u003e and Fischer et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2025\u003c/span\u003e, and plumbeous vireos are described in Marroquin-Flores et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2017\u003c/span\u003e and Barrow et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eParasite screening and genetic data collection\u003c/h3\u003e\n\u003cp\u003eWe extracted DNA from pectoral muscle (\u003cem\u003en\u0026thinsp;=\u003c/em\u003e\u0026thinsp;82) and whole blood (\u003cem\u003en\u0026thinsp;=\u003c/em\u003e\u0026thinsp;166) using the QIAGEN DNeasy Blood and Tissue Kit following manufacturer\u0026rsquo;s recommendations. Birds were screened for \u003cem\u003eHaemoproteus\u003c/em\u003e and \u003cem\u003ePlasmodium\u003c/em\u003e parasites using three nested polymerase chain reaction (PCR) protocols to amplify a 478-base pair (bp) fragment of the haemosporidian mitochondrial cyt \u003cem\u003eb\u003c/em\u003e gene (Hellgren et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Waldenstr\u0026ouml;m et al., \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). We amplified parasite DNA with the outer primer pairs HaemNFI/HaemNR3 and HaemNF/HaemNR2, followed by the nested primer pair HaemF/HaemR2. We prepared outer PCR reactions in 25 \u0026micro;L volumes, containing 1.25 U of AmpliTaq Gold DNA Polymerase (Applied Biosystems, Foster City, CA, USA), 1\u0026times; PCR Buffer II, 2.5 mM MgCl2, 0.2 mM dNTPs, 0.5 \u0026micro;M of each primer, and 20 ng of template DNA. Thermal cycling conditions were modified from Galen and Witt (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), with an initial denaturation at 95\u0026deg;C for 8 mins, followed by 20 cycles of 94\u0026deg;C for 30 seconds, 50\u0026deg;C for 30 secs, 72\u0026deg;C for 45 secs, and a final extension at 72\u0026deg;C for 10 mins.\u003c/p\u003e \u003cp\u003eFor nested PCR, we used 1 \u0026micro;L of the outer PCR product as the template, with reaction conditions identical to the outer PCR, except for an increase to 35 cycles. Each reaction set included negative and positive controls to monitor for contamination and confirm amplification success, respectively. We visualized PCR products on 2% agarose gels stained with SYBR Safe Gel Stain (Invitrogen, Carlsbad, CA, USA) to verify the presence of amplicons of the expected size. Successfully amplified products were purified using ExoSap-IT (Affymetrix, Inc., Santa Clara, CA, USA) and submitted for Sanger sequencing at Psomagen (Rockville, MD, USA). gray vireo blood samples from recaptured individuals (i.e., samples from the same individuals from 2017 and 2018) underwent PCR amplification, but only a single sample was sequenced (Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eDetermining prevalence and haplotype diversity\u003c/h3\u003e\n\u003cp\u003eWe determined positive infections upon successful amplification of haemosporidian cyt \u003cem\u003eb\u003c/em\u003e sequences and calculated pathogen prevalence within each host species and calculated 95% binomial confidence intervals using the \u0026lsquo;exact\u0026rsquo; method available in the \u003cem\u003ebinom\u003c/em\u003e package in R (version 4.3.2; R Core Team, 2023; Sundar, \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHaemosporidian cyt \u003cem\u003eb\u003c/em\u003e forward and reverse reads were trimmed to remove primers, resulting in the target fragment size of 478 bp and were then assembled using the default alignment algorithm in Geneious (version 2025.03; Biomatters Ltd; Kearse et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Table \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e). To identify haplotypes, we compared cleaned sequences to published records stored in the public databases GenBank (National Center for Biotechnology Information, US National Library of Medicine) and MalAvi (Bensch et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) by using the Basic Local Alignment Search Tool (BLAST). Additionally, to ensure the accuracy of both established and novel haplotypes, we downloaded all haemosporidian cyt \u003cem\u003eb\u003c/em\u003e haplotype sequence files from MalAvi and compared the number of differences, if any, to our sequences via a distance matrix calculated in Geneious. Haplotypes that differed by one or more base pairs (~\u0026thinsp;0.2% sequence divergence) from published sequences on GenBank or MalAvi were considered novel and named following MalAvi conventions.\u003c/p\u003e\n\u003ch3\u003ePhylogenetic analysis\u003c/h3\u003e\n\u003cp\u003eWe estimated phylogenetic relationships among haemosporidian haplotypes using a maximum-likelihood (ML) framework in RAxML, v8.2.10 (Stamatakis, \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Using the GTR\u0026thinsp;+\u0026thinsp;G model of nucleotide substitution, we conducted a rapid bootstrap analysis with 1000 replicates, after which we searched for the best-scoring ML tree. We rooted the tree with \u003cem\u003eLeucocytozoon\u003c/em\u003e (COLBF21, GenBank Accession MK947795) based on the current phylogenetic hypothesis for avian haemosporidians (Borner et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Galen et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Nodes with \u0026lt;\u0026thinsp;50% bootstrap support were collapsed in TreeGraph2 (St\u0026ouml;ver and M\u0026uuml;ller, \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). We determined the relationships among the three vireo study species based on the posterior distribution of likely trees available on birdtree.org with the \u0026lsquo;Hackett All Species\u0026rsquo; option (Hackett et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Jetz et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). We downloaded 100 phylogeny subsets and selected the first tree because the relationships were consistent among the 100 trees.\u003c/p\u003e\n\u003ch3\u003eEstimating parasite species richness and sampling completeness\u003c/h3\u003e\n\u003cp\u003eWe used the \u003cem\u003eiNEXT\u003c/em\u003e R package (version 3.0.1; Chao et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Hsieh et al., 2024) to estimate parasite lineage diversity and evaluate sampling completeness for host species with adequate data (i.e., gray and plumbeous vireos). In separate analyses for each host species, we used infection counts of each parasite haplotype to generate rarefaction and extrapolation curves for parasite species richness (q\u0026thinsp;=\u0026thinsp;0), with the endpoint set at 400 individuals (i.e., infections). We applied the \u0026lsquo;iNEXT()\u0026rsquo; function to compute species richness estimates, including observed richness, extrapolated richness, and associated confidence intervals using standard function parameters. Additionally, we used the output to estimate sampling completeness (Chao et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) and defined this value as the minimum number of individual birds required to detect 95% of the total estimated parasite haplotypes infecting each host species. To visualize the rarefaction curves, we used the \u0026lsquo;ggiNEXT()\u0026rsquo; function and \u003cem\u003eggplot2\u003c/em\u003e package for style modifications (Wickham, \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eMicroscopy and parasitemia calculations\u003c/h2\u003e \u003cp\u003eWe obtained available blood smears from the MSB for plumbeous vireos (\u003cem\u003en\u0026thinsp;=\u003c/em\u003e\u0026thinsp;28) and made and air dried blood smears in the field for gray vireos (\u003cem\u003en\u0026thinsp;=\u003c/em\u003e\u0026thinsp;53). Each slide was fixed with absolute methanol and stained for 50 mins with Giemsa solution (pH 7.0; Sigma-Aldrich, St. Louis, MO, USA). We then examined each slide to confirm infection status using light microscopy on an Olympus BX 53 Microscope following the protocol described in Valkiūnas (\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), where 10,000 erythrocytes were scanned at 1000x magnification with an oil immersion lens to identify and count \u003cem\u003eParahaemoproteus\u003c/em\u003e and \u003cem\u003ePlasmodium\u003c/em\u003e infections (Valkiūnas, \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). We calculated parasitemia, or the estimated percentage of erythrocytes infected with haemosporidian parasites, as the number of infected erythrocytes out of 10,000 screened. To account for lack of normality in the data, we used a bootstrap method with 1,000 replications to calculate parasitemia 95% confidence intervals via the R package \u003cem\u003eboot\u003c/em\u003e (Kushary, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2000\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCharacterizing environmental variation\u003c/h3\u003e\n\u003cp\u003eWe characterized elevation (m) and climatic variation using 19 bioclimatic variables at 30 sec (~\u0026thinsp;1 km\u0026sup2;) resolution from the WorldClim 2.1 database (Fick and Hijmans, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). We used principal components analysis (PCA) for temperature (Bio1\u0026ndash;11) and precipitation (Bio12\u0026ndash;19) variables to create a composite measure of temperature and precipitation across the gradient. The first two axes for temperature (hereafter, tempPC1 and tempPC2) explained a combined 84.2% of the variation and the first two axes for precipitation (hereafter, precipPC1 and precipPC2) explained 95.1% of the variation (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e; Table \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e-6). Variable loadings indicated that tempPC1 primarily represented year-round temperatures, with higher values relating to lower average temperatures, while tempPC2 represented greater seasonality and less temperature stability, with higher values relating to more extreme temperature fluctuations throughout the year and day-night cycle. Similarly, precipPC1 described the overall precipitation amount, with higher values indicating wetter conditions, whereas precipPC2 represented precipitation seasonality, with higher values representing more stable and less seasonal precipitation (Table \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e-6).\u003c/p\u003e\n\u003ch3\u003eAssessing infection dynamics among host species and infection types\u003c/h3\u003e\n\u003cp\u003eWe tested whether haemosporidian infection status differed between categorical variables such as host species and tissue type (i.e., pectoral muscle or whole blood samples), using Pearson\u0026rsquo;s chi-squared tests via the \u003cem\u003estats\u003c/em\u003e package. To examine the relative magnitude of effect for each comparison, we calculated Cram\u0026eacute;r's V effect sizes were using the \u0026lsquo;CramerV()\u0026rsquo; function in the \u003cem\u003ercompanion\u003c/em\u003e package (Mangiafico, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Next, to assess differences in parasitemia, we first normalized the data using a natural log ln(1\u0026thinsp;+\u0026thinsp;x) transformation and removed two influential outliers with parasitemia\u0026thinsp;\u0026gt;\u0026thinsp;4% (gray vireo IDs: 272132427 and 272132642). We then evaluated residual variance with the \u0026lsquo;var.test()\u0026rsquo; function in the \u003cem\u003estats\u003c/em\u003e package and compared log-transformed parasitemia between gray and plumbeous vireos and infection types (i.e., single vs. coinfected) using two-tailed Student\u0026rsquo;s \u003cem\u003et\u003c/em\u003e-tests.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eIntraspecific infection prevalence and parasitemia across environments\u003c/h2\u003e \u003cp\u003eWe employed a two-pronged approach to assess environmental associations with infection prevalence: First, we used univariate, non-parametric tests to characterize and compare intraspecific infection patterns across abiotic variables relevant to parasite and vector ecology, including elevation, overall temperature (tempPC1) and precipitation (precipPC1), and the seasonality of temperature (tempPC2) and precipitation (precipPC2). Then, we built intraspecific multivariate linear models and applied nested and exhaustive model selection to evaluate the strength and direction of environmental effects. We adopted this approach to account for the structure of our data (i.e., non-overlapping elevational and environmental bands distinctly correlated to species identity) and limitations of our dataset (i.e., limited sample sizes, degree of environmental variation, and infection class imbalances).\u003c/p\u003e \u003cp\u003eWe first assessed data normality both visually and then statistically, using the Shapiro-Wilk test using the \u0026lsquo;shapiro.test()\u0026rsquo; function from the \u003cem\u003estats\u003c/em\u003e package. Given the non-normal distribution of environmental and geographic data in our dataset, we applied a rank-sum approach via Mann-Whitney U-tests to compare the distributions of environmental variables between infected and uninfected birds. Wilcoxon effect sizes of each comparison were then calculated via the \u0026lsquo;wilcox_effsize()\u0026rsquo; function in the \u003cem\u003erstatix\u003c/em\u003e package (Kassambara, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe then constructed species-specific generalized linear models using glm() from the \u003cem\u003estats\u003c/em\u003e package. Prevalence model sets used a binomial distribution (hereafter: \u0026ldquo;BEVI_prev\u0026rdquo; for Bell\u0026rsquo;s vireo, \u0026ldquo;GRVI_prev\u0026rdquo; for gray vireo, and \u0026ldquo;PLVI_prev\u0026rdquo; for plumbeous vireo sets), while parasitemia model sets used a Gaussian distribution (\u0026ldquo;GRVI_para\u0026rdquo;, \u0026ldquo;PLVI_para\u0026rdquo;). We evaluated linearity between the logit and predicted values in prevalence models and assessed residual diagnostics in parasitemia models and with \u0026lsquo;autoplot()\u0026rsquo; within the \u003cem\u003eggfortify\u003c/em\u003e package (Tang et al., \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Multicollinearity between variables was assessed by calculating variance inflation factors (VIF) via the \u003cem\u003ecar\u003c/em\u003e package. As expected, there was high collinearity with aspects of geographic and topographic variation, i.e. latitude and elevation, with other environmental variables. We retained elevation in all model sets because it captured variation in habitat and vector dynamics important to infection that were not explained by temperature and precipitation alone (Ishtiaq and Barve, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2018\u003c/span\u003e); however, we removed latitude from all linear models to reduce overfitting.\u003c/p\u003e \u003cp\u003eFor model sets with adequate sampling (i.e., GRVI_prev, PLVI_prev, GRVI_para, PLVI_para), we evaluated all possible additive combinations of five predictor variables: elevation, tempPC1, tempPC2, precipPC1, precipPC2. We used the \u0026lsquo;dredge\u0026rsquo; function in the \u003cem\u003eMuMIn\u003c/em\u003e package (Bartoń, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) for exhaustive model selection. For host datasets with \u0026lt;\u0026thinsp;30 data points (BEVI_prev and PLVI_para) we restricted candidate models to simple, biologically relevant structures to avoid overfitting. Specifically, we included univariate models nested within simple additive models containing at most two predictor variables, as well as null and global models for comparison. We then compared model performance and evaluated the trade-off between model fit and complexity using Akaike\u0026rsquo;s Information Criterion corrected for small sample sizes (AICc; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e\u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTop five infection probability fixed effects additive models\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBell\u0026rsquo;s vireo (\u003c/b\u003e\u003cb\u003en\u003c/b\u003e\u0026thinsp;\u003cb\u003e=\u0026thinsp;20)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eRank\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003ePredictors\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eK\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eAICc\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eΔAICc\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eWeight\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003etempPC2\u0026thinsp;+\u0026thinsp;precipPC1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e-\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.324\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003etempPC1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.967\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.200\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eprecipPC1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.348\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.113\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e4\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003etempPC1\u0026thinsp;+\u0026thinsp;precipPC1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.199\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.064\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e5\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eElevation\u0026thinsp;+\u0026thinsp;tempPC1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.197\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.064\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003egray vireo (\u003c/b\u003e\u003cb\u003en\u003c/b\u003e\u0026thinsp;\u003cb\u003e=\u0026thinsp;170)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eRank\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003ePredictors\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eK\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eAICc\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eΔAICc\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eWeight\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eElevation\u0026thinsp;+\u0026thinsp;tempPC1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e-\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.419\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eElevation\u0026thinsp;+\u0026thinsp;precipPC1\u0026thinsp;+\u0026thinsp;tempPC1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e1.72\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.177\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eElevation\u0026thinsp;+\u0026thinsp;precipPC2\u0026thinsp;+\u0026thinsp;tempPC1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e1.73\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.177\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e4\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eElevation\u0026thinsp;+\u0026thinsp;tempPC1\u0026thinsp;+\u0026thinsp;tempPC2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e2.07\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.149\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e5\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eElevation\u0026thinsp;+\u0026thinsp;tempPC1\u0026thinsp;+\u0026thinsp;tempPC2, precipPC1\u0026thinsp;+\u0026thinsp;precipPC2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e3.35\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eplumbeous vireo (\u003c/b\u003e\u003cb\u003en\u003c/b\u003e\u0026thinsp;\u003cb\u003e=\u0026thinsp;58)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eRank\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003ePredictors\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eK\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eAICc\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eΔAICc\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eWeight\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNull model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e-\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.301\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eprecipPC2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.409\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.246\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eprecipPC1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e1.17\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.168\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e4\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003etempPC2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e1.29\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.158\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e5\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrecipPC1\u0026thinsp;+\u0026thinsp;precipPC2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e1.73\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.127\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\u003cp\u003e[2] \u003cstrong\u003eTable 1.\u003c/strong\u003e Top five models assessing environmental predictors of haemosporidian infection for each species. Sample sizes for datasets and directionality of best model outputs are listed at the top. Models were ranked by AICc, with \u0026Delta;AICc values relative to the top model and corresponding Akaike weights (Weight) indicating model support.\u003c/p\u003e\n\u003cp\u003eTo examine the effect of each predictor in top-ranked models, we standardized and calculated model-averaged regression coefficients using the \u003cem\u003eMuMIN\u003c/em\u003e package (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). For top-ranked models where the null model was not favored, we assessed goodness of fit via Hosmer-Lemeshow tests as part of the \u003cem\u003eResourceSelection\u003c/em\u003e package (Lele et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and checked for overdispersion by dividing residual deviance by the degrees of freedom. Predictive accuracy was also assessed using 10-fold cross-validation via the \u003cem\u003ecaret\u003c/em\u003e package (Kuhn, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e\u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eBell\u0026rsquo;s vireo\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003egray vireo\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c13\" namest=\"c10\"\u003e \u003cp\u003eplumbeous vireo\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLCL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUCL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eLCL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eUCL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eLCL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eUCL\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElevation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003etempPC1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-3.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003etempPC2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eprecipPC1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-4.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eprecipPC2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\u003cp\u003e[3]\u0026nbsp;\u003cstrong\u003eTable 2\u003c/strong\u003e. Standardized model-averaged regression coefficients used to estimate effects of predictors and precision of effects across species-specific candidate model sets that assess environmental predictors of haemosporidian infection. Regression coefficients (\u0026beta;), standard error (SE), and 95% confidence limits (lower as LCL, upper as UCL) are given. Dashes indicate that the parameter was not present in a final model set, and NA values indicate that the parameter was not tested due to lack of variation present in model set.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eHaemosporidian prevalence, abundance, and diversity\u003c/h2\u003e \u003cp\u003eWe uncovered an unusually high prevalence of haemosporidian infections in all three vireo species, with 175 infected birds of 248 screened (70.6%; 95% CI [64.5\u0026ndash;76.2%]). The highest documented prevalence was found in plumbeous vireos (84.5% infected; 95% CI [72.6\u0026ndash;92.7%]), followed by gray vireos (67.7%; 95% CI [60.0\u0026ndash;74.7%]), and lastly, Bell\u0026rsquo;s vireos (55%; 95% CI [31.5\u0026ndash;76.9%]; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). Prevalence was significantly lower in Bell\u0026rsquo;s than in plumbeous vireos (χ\u0026sup2;(1)\u0026thinsp;=\u0026thinsp;5.72, p\u0026thinsp;=\u0026thinsp;0.017, φ\u0026thinsp;=\u0026thinsp;0.31), indicating a moderate effect size. While most birds were infected by single haemosporidian haplotype infections (88.1%; 95% CI [82.3\u0026ndash;92.5%]), we found 21 individuals that were coinfected with two or more haplotypes (12.0%; 95% CI [7.54\u0026ndash;17.7%]), and one gray vireo that was infected by three haplotypes (VIRVIC01, VIRVIC02, VIRVIC03). Of the 14 gray vireos recaptured about a year after initial sampling, 12 were infected with haemosporidians in both years, while two that were uninfected in 2017 tested positive in 2018.\u003c/p\u003e \u003cp\u003eNearly all infections were caused by parasites in the genus \u003cem\u003eHaemoproteus\u003c/em\u003e, specifically the subgenus \u003cem\u003eParahaemoproteus\u003c/em\u003e (99.5%), which comprised 18 of 19 haplotypes identified. \u003cem\u003ePlasmodium\u003c/em\u003e was rare in our data, with a single infection (lineage VIRPLU13) recorded from a plumbeous vireo (MSB:Bird:60726; Table \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e). Of the 19 total parasite haplotypes identified, 8 were novel (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD; Table \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eEstimates of parasite species richness and sampling completeness\u003c/h2\u003e \u003cp\u003eWe identified a total of three haemosporidian haplotypes from Bell\u0026rsquo;s vireos, four from gray vireos, and 14 from plumbeous vireos (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). These differences were partly, but not entirely, attributable to differences in sample size. gray vireo \u003cem\u003eParahaemoproteus\u003c/em\u003e infections were substantially less diverse than those of plumbeous vireos despite more intensive screening efforts. Extrapolation from rarefaction curves showed considerably lower estimated parasite richness for gray vireos (4.99\u0026thinsp;\u0026plusmn;\u0026thinsp;0.53 SE; 95% CI [4.00\u0026ndash;6.03]; Table S7) and estimated that much smaller sample sizes would be needed to reach 95% sampling completeness (14 infections; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; Table S8). This result contrasted our results for plumbeous vireos, where parasite richness was estimated to be ~\u0026thinsp;10x higher than gray vireos (53.8\u0026thinsp;\u0026plusmn;\u0026thinsp;23.3 SE; 95% CI [14.0\u0026ndash;99.6]) and require much greater sampling effort (~\u0026thinsp;26x more than estimated for gray vireos) to reach an estimated 95% completeness (estimated 364 infections). Bell\u0026rsquo;s vireos also exhibited low lineage diversity (3.92\u0026thinsp;\u0026plusmn;\u0026thinsp;0.784 SE; 95% CI [3.00-5.45] and an estimated 95% sample completeness at only 33 infections, consistent with the limited diversity recovered (Table S7-S8). However, we caution overinterpreting these results due to the small Bell\u0026rsquo;s vireo sample size (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;12 infected) in our study and recommend additional sampling to generate more precise estimates.\u003c/p\u003e \u003cp\u003eOf the 19 total haplotypes identified, we found evidence of potential host specificity with at least one species-specific haplotype infecting each of the three vireo species (Table \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e). Of note, gray vireos were infected by novel haemosporidian haplotypes only, (VIRVIC01-VIRVIC04). This finding contrasted with plumbeous vireos, which showed novel haplotypes (i.e. VIRPLU10-13) and haplotypes previously found only in plumbeous vireos (i.e. VIRPLU03, VIRPLU07-09), as well as lineages known to infect other non-vireo species (i.e. SETAUD14, TROAED12, VIGIL07, VIRPLU01, and VIRPLU04). Sampling was comparatively lower for Bell\u0026rsquo;s vireos; no novel lineages were detected. We did detect, however, a recently described lineage (VIRBEL01) which was previously documented in Bell\u0026rsquo;s vireos sampled from Arizona and Texas (Fecchio et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), as well as other more generalist lineages (i.e. TROAED12 and VIGIL07; Table \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eIntensity of infection (parasitemia) across hosts and haplotypes\u003c/h2\u003e \u003cp\u003eWe screened 102 individuals via microscopy (gray \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;71, plumbeous \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;31). A total of 81 samples showed evidence of infection and had parasitemia \u003cem\u003ecalculated (Parahaemoproteus\u003c/em\u003e: gray \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;53 and plumbeous \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;27; \u003cem\u003ePlasmodium\u003c/em\u003e: plumbeous \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1). gray vireos typically had slightly higher infection loads (1.00%; 95% CI [0.782\u0026ndash;1.27%]) compared to plumbeous vireos (0.786%; 95% CI [0.584\u0026ndash;1.01%]), partly due to two outlier gray vireo samples with \u0026gt;\u0026thinsp;4% parasitemia (ID 272132427 and 27213264; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). After removing these outliers, we found that mean parasitemia did not differ significantly between host species (t(77)\u0026thinsp;=\u0026thinsp;0.656, p\u0026thinsp;=\u0026thinsp;0.514). When we compared single-haplotypes and coinfections, we also found no significant difference in parasitemia between species (t(73) = -0.728, p\u0026thinsp;=\u0026thinsp;0.469), though we note the sampling pool of coinfected birds was small compared to single infections (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7 and \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;68, respectively).\u003c/p\u003e \u003cp\u003eOur comparison of infections using PCR and microscopy was largely consistent, with few detection errors identified between the two methods. We detected four positively infected birds via microscopy that did not successfully amplify by PCR (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). These samples had a range of parasitemia spanning 0.70% to 2.68%. Additionally, we failed to detect haemosporidian infections via microscopy for two birds that were later successfully amplified using PCR; one individual was the single coinfected gray vireo with three \u003cem\u003eParahaemoproteous\u003c/em\u003e haplotypes that were identified by sequencing (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eEnvironmental patterns of infection status and parasitemia\u003c/h2\u003e \u003cp\u003e \u003cem\u003eParahaemoproteus\u003c/em\u003e prevalence showed idiosyncratic patterns among host species and their associated habitats (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e; Table S9). For example, we found that infected Bell\u0026rsquo;s vireos tended to be found at lower latitudes (\u003cem\u003ez\u003c/em\u003e = -2.09, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.040), with warmer year-round temperatures (tempPC1; \u003cem\u003ez\u003c/em\u003e = -2.25, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.027 Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e; Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e; Table S9). Similarly, probability of infection increased as overall precipitation (precipPC1) and temperature seasonality (tempPC2) increased (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In contrast, we found higher proportions of infected gray vireos at lower elevations (\u003cem\u003ez\u003c/em\u003e = -2.07, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.038), Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC; Table S9) and at sites with more stable precipitation (precipPC2; z\u0026thinsp;=\u0026thinsp;1.72, p\u0026thinsp;=\u0026thinsp;0.057), Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e; Table S9). However, this result was dampened when we excluded the eleven infected gray vireos from the Abajo Mountain site in Utah, which were outliers in many of the environmental PCA dimensions. The top model, however, showed that infection was negatively associated with elevation and positively associated with temperature (tempPC1; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) even when excluding the Utah site. Lastly, plumbeous vireo infections tended to be higher in areas with more seasonally stable precipitation (precipPC2; \u003cem\u003ez\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.04, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.042), Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Despite the top-ranked infection probability model showing no significant linear trends, precipitation (precipPC1 and precipPC2) was present in the majority of top candidate models (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe did not find any association between parasitemia and environmental variables in plumbeous vireos, with the null model having the strongest support and no clear predictor variable significantly reported across top models (Table S10). gray vireos, however, showed a significant pattern of increased parasitemia as overall precipitation (precipPC1) increased (Table S10), but this model had low explanatory power (R\u0026sup2; = 0.05).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eElevational replacement and haemosporidian diversity patterns\u003c/h2\u003e \u003cp\u003eAvian haemosporidians exhibit variable patterns of turnover across elevations (Galen and Witt, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Illera et al., 2015; McNew et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), which is often independent of geographic distance between hosts (Williamson et al., \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Barrow et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). We demonstrated a strong case of parallel elevational replacement in hosts and parasites, with high turnover among \u003cem\u003eParahaemoproteus\u003c/em\u003e assemblages infecting vireo species breeding at different elevations. This elevational replacement pattern could be the result of a few different scenarios. Such turnover may reflect shared ecological constraints on hosts, vectors, and parasites along environmental gradients (Ellis et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), or species-specific immune or tolerance strategies (Armour et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Regardless of mechanism, our results indicate that spatial structuring of parasite diversity is closely aligned with host elevational zonation.\u003c/p\u003e \u003cp\u003eOf the 30 cyt \u003cem\u003eb Parahaemoproteus\u003c/em\u003e and 12 \u003cem\u003ePlasmodium\u003c/em\u003e haplotypes reported to infect vireos (MalAvi; downloaded 1/1/2025), our study adds seven novel \u003cem\u003eParahaemoproteus\u003c/em\u003e and one novel \u003cem\u003ePlasmodium\u003c/em\u003e haplotype. The rarity of \u003cem\u003ePlasmodium\u003c/em\u003e infections, even at lower elevations, contrasts many temperate systems (e.g. in Alaska: Oakgrove et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; California: Walther et al., \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Oklahoma: Wyckoff et al., \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) but aligns with prior cross-elevational surveys in New Mexico (Marroquin-Flores et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Williamson et al., \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Barrow et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The low prevalence of \u003cem\u003ePlasmodium\u003c/em\u003e at lower elevations in this region may reflect limited suitable habitat for mosquito vectors prior to the onset of monsoon rains. Broader and temporally replicated surveys of potential vectors and vector abundance in this region are needed to fully explain this apparent absence.\u003c/p\u003e \u003cp\u003eAmong \u003cem\u003eParahaemoproteus\u003c/em\u003e lineages, plumbeous vireos harbored a broad diversity of parasite haplotypes relative to the other two sampled species, while gray vireos exhibited a depauperate and likely specialized haemosporidian assemblage (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). This contrast may indicate broader ecological flexibility or permissive host\u0026ndash;parasite associations in plumbeous vireos, and possible specialization or reduced parasite exposure in gray vireos. Alternatively, low parasite diversity in gray vireos may reflect the depauperate breeding bird communities of the arid and structurally simple juniper savanna habitats.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eConserved high susceptibility to haemosporidians among vireo species\u003c/h2\u003e \u003cp\u003eVireos stand out among southwestern songbirds for their consistently high haemosporidian infection rates, providing a valuable opportunity to examine parasite dynamics in hyper-susceptible hosts. In the three species screened here, we found that 70.6% of 248 individuals screened had at least one haemosporidian infection, more than twice the average prevalence reported in community-wide surveys conducted in the region. For example, Marroquin-Flores et al. (\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) reported 36.6% haemosporidian prevalence across 49 bird species breeding in New Mexico, yet 92% of the vireos tested positive (Marroquin-Flores et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). A subsequent large-scale study by Barrow et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) reported similar patterns, with 36.1% prevalence across 61 species, but vireos again showed the highest infection rates in the communities surveyed (Barrow et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis susceptibility appears to be conserved, with high haemosporidian prevalence documented in breeding sites of other species in the genus \u003cem\u003eVireo\u003c/em\u003e. Granthon and Williams (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) reported 100% of the 62 PCR-screened red-eyed vireos (\u003cem\u003eVireo olivaceus\u003c/em\u003e) from Pennsylvania were infected, and Rodriquez et al. (2021) documented 59% infection in 22 individual warbling vireos (\u003cem\u003eV. gilvus\u003c/em\u003e) sampled from Colorado, indicating that elevated susceptibility is widespread across the genus (Granthon and Williams, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Rodriguez et al., \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Shared susceptibility within closely related species has been extensively documented (e.g. Gonz\u0026aacute;lez et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Lutz et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Barrow et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Pigeault et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) but attempts to identify the specific factors that contribute to high prevalence in vireos are still lacking. Of the correlated host traits recorded in other avian clades, thus far, open cup nest type and insectivorous diets likely increase exposure risk in this group (Braga et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Rodriguez et al., \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), while additional behavioral and physiological factors, including prolonged incubation, nest-site microhabitat, and possible tolerance-based immune strategies, may further contribute (Gonz\u0026aacute;lez et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Lutz et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Muriel, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Pigeault et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRecapture data further support sustained parasite pressure: of the 14 gray vireos sampled in two consecutive years, 12 remained infected and the remaining two acquired infections by the second year (Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). Although distinguishing chronic infections from reinfections requires additional data, these results indicate that infection is persistent during the breeding season. Continued longitudinal sampling and investigation of exposure and immune response in vireos will be key to determining the mechanisms driving their elevated susceptibility.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eEnvironmental determinants of haemosporidian infection vary by host species\u003c/h2\u003e \u003cp\u003ePrevious studies have linked haemosporidian prevalence to topographic and climatic variables (P\u0026eacute;rez-Rodr\u0026iacute;guez et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Rodriguez et al., \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), though the strength and direction vary regionally and by haemosporidian clade observed (Williamson et al., \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In the arid southwest, where water availability shapes vector distributions (Valkiūnas, 2004; Santiago‐Alarcon et al., 2012), we expected haemosporidian prevalence to correspond with water availability (i.e. riparian habitats) and areas with higher, more seasonally stable precipitation (i.e. montane woodlands). Instead, prevalence increased with elevation and precipitation: Bell\u0026rsquo;s vireos showed the lowest prevalence, while gray vireos exhibited unexpectedly high infection rates. It remains unclear whether these patterns reflect species-specific traits or environmental differences, and additional sampling of Bell\u0026rsquo;s vireos may help resolve this uncertainty. Within species, infection probability was associated with different environmental factors in each taxon (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e; Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e-6; Table S9-10), indicating that no single abiotic predictor applies across vireos. Broader sampling across elevation, habitat moisture gradients, and co-distributed species will be needed to better understand cross-elevational trends in infection risk in southwestern avian communities.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eModerate and consistent infection loads across environmental gradients\u003c/h2\u003e \u003cp\u003eGray and plumbeous vireos exhibited similar average infection loads (~\u0026thinsp;1% in gray vireos, 0.786% in plumbeous, range: 0.03\u0026ndash;4.5%) with no significant differences between species (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). These values slightly exceed the regional cross-species average (\u0026lt;\u0026thinsp;1%; Marroquin-Flores et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and fall within the range of moderate infection intensities reported in experimental studies of other passerines (e.g. Asghar et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). We did note, however, two gray vireos with elevated parasitemia (4.5 and 4.49%) were sampled at distinct sites in the Sevilleta National Wildlife Refuge, indicating acute or recrudescence of chronic infections. The absence of high parasitemia in Bell\u0026rsquo;s and plumbeous vireos could be an artifact of limited sampling, as birds with severe infections often exhibit reduced mobility and capture probability (Mukhin et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), suggesting that heavily infected individuals in these species may be underrepresented.\u003c/p\u003e \u003cp\u003eAdditionally, we found no relationship between parasitemia and any tested environmental variables for plumbeous vireos. In gray vireos we saw a significant linear trend in parasitemia with increased overall precipitation (precipPC1; Table S10), but this model had weak explanatory power (R\u0026sup2; = 0.05). These results are consistent with previous findings which suggest that haemosporidian parasitemia patterns are shaped by complex interactions between host and parasite biology not easily captured in models with abiotic factors alone (Loiseau et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eWe found that parasite elevational distributions mirror the elevational replacement pattern of host species among vireos in southwestern North America. This pattern may have resulted from host specialization, climate variation independently acting on hosts and parasites, climate variation acting indirectly through habitat or broader host and vector communities, or a combination of those forces. We found that vireos were consistently infected at high rates and largely harbor chronic, low-to-moderate intensity infections dominated by \u003cem\u003eParahaemoproteus\u003c/em\u003e, suggesting the potential for exclusive, reciprocal selective pressures that facilitate host-parasite coadaptation. The contrast in parasite richness between plumbeous vireos and gray vireos was striking: gray vireos harbored a limited number of haemosporidian lineages, all of which were host specific, while plumbeous vireos were infected with a diverse set of lineages. These differences imply species-specific variability in resistance, tolerance, or exposure to generalist haemosporidian lineages. In sum, our work adds to a growing body of evidence about clade-specific avian host susceptibility and host-parasite turnover across elevational gradients, highlighting vireos as an ideal focal group for future work on host-parasite dynamics across space and time.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors sincerely thank Bethany Abrahamson, Michael Andersen, Matthew Baumann, Alison Boyer, Serina Brady, Sara Brant, Mariel Campbell, Andrea Chavez, Mina Carnicom, Lida Crooks, Rosario Marroquin-Flores, Chauncey Gadek, Spencer Galen, Ethan Gyllenhaal, Mike Hartshorne, Andy Johnson, Michael Lelevier, Joseph Manthey, Xena Mapel, Taylor Martinez, Jenna McCullough, Moses Michelsohn, Kathleen Ramsay, George Rosenberg, C. Jonathan Schmitt, Donna Schmitt, Brenda Villanueva, and Toby Weisenhaus.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Science Foundation (Graduate Research Fellowship NSF-DGE-2439853 to DLFW; Postdoctoral Research Fellowship in Biology NSF-DBI-1611710 to LNB; and NSF-DEB-1146491 to CCW), the Bureau of Land Management Rio Puerco Field Office (via the Colorado Plateau Cooperative Eco-systems Studies Unit agreement), New Mexico Department of Game and Fish Share with Wildlife Grant to HMS/SEF, and a Sevilleta LTER Graduate Summer Research Fellowship and New Mexico Ornithological Society Ryan Beaulieu Research Grant to SEF.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no conflicts of interest to declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eResearch activities were conducted under Institutional Animal Care and Use Protocol 16-200406-MC and appropriate state and federal scientific collecting permits (New Mexico Department of Game and Fish Authorization Number 3217; US Fish and Wildlife Permit Numbers MB094297-0 and MB0942970-1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSpecimen data are available in Supplementary Tables S1\u0026ndash;S10 and are searchable via Arctos (arctos.database.museum; Cicero et al., 2024). Parasite sequences are archived in GenBank (accession nos. PV295634-PV295830). Vouchered specimens and frozen tissue samples are archived at the Museum of Southwestern Biology (https://msb.unm.edu/). All R code and datasets are available on FigShare (https://doi.org/10.6084/m9.figshare.28585943).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudent highlight statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eUnderstanding how biodiversity is structured across environmental gradients is fundamental to ecological theory, yet how these patterns extend to parasitic taxa remains a compelling area of investigation. Here, we assess whether the biogeographic pattern of elevational replacement observed in highly susceptible songbirds is mirrored by their haemosporidian parasites, and whether the processes driving this partitioning are shared. Our study is the first to show that, although the forces behind elevational turnover differ, strong host associations produce parallel zonation, particularly in host-specialized lineages. By linking host and parasite biogeography, this work demonstrates that the host itself serves as the structuring gradient for parasite diversity in montane regions. \u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDLFW, JLW, CCW, and LNB conceptualized and developed methodologies for this project. JLW, SEF, HMS, SMB, KG, CCW, and LNB performed fieldwork and subsequent data curation. DLFW, JLW, and SMB performed molecular analysis and generated sequence data. JLW, SEF, HMS, KG, CCW, and LNB secured funding and provided supervision. DLFW and JLW performed formal analyses and created visualizations. 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PLoS ONE 7(6):e39208. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1371/journal.pone.0039208\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0039208\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"oecologia","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"oeco","sideBox":"Learn more about [Oecologia](https://www.springer.com/journal/442)","snPcode":"442","submissionUrl":"https://submission.nature.com/new-submission/442/3","title":"Oecologia","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Avian malaria, diversity, elevational range, haemosporidian, turnover","lastPublishedDoi":"10.21203/rs.3.rs-8060400/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8060400/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eElevational replacement distribution patterns underpin montane diversity and reflect the interaction of both biotic and abiotic pressures, but the degree to which parasites exhibit elevational zonation remains unclear. Investigating infection patterns in related host species across elevational gradients can reveal whether parasites and hosts show concordant patterns of elevational turnover, potentially due to shared historical and ecological factors. Here, we assessed patterns of elevational replacement in haemosporidian parasite assemblages that infect three congeneric songbird species: Bell\u0026rsquo;s vireo (\u003cem\u003eVireo bellii)\u003c/em\u003e, gray vireo \u003cem\u003e(V. vicinior)\u003c/em\u003e, and plumbeous vireo (\u003cem\u003eV. plumbeus\u003c/em\u003e), each of which breeds across distinct elevations and habitats in the southwestern United States. We screened a total of 248 individuals using cytochrome \u003cem\u003eb\u003c/em\u003e PCR and microscopy. We identified 19 haemosporidian haplotypes, including eight novel lineages. We found that each of the three vireo species exhibited high haemosporidian prevalence (55.0\u0026ndash;86.2%), with nearly all infections from the genus \u003cem\u003eHaemoproteus\u003c/em\u003e (subgenus \u003cem\u003eParahaemoproteus\u003c/em\u003e). Haemosporidian assemblages varied across elevations; each sampled range of elevations harbored abundant, yet host-specific lineages with different environmental associations. Bell\u0026rsquo;s and plumbeous vireos, but not gray vireos, hosted several phylogenetically distinct, putative generalist lineages, likely reflecting spillover from more diverse local breeding bird communities. Repeated infections in individuals across breeding seasons, together with moderate parasitemia (x̄ \u0026asymp; 1%) suggest that these focal vireo species harbor chronic infections during their respective breeding seasons. These results demonstrate that elevational replacement patterns in avian hosts may be mirrored by their haemosporidian parasites, particularly among host-specialized lineages.\u003c/p\u003e","manuscriptTitle":"Parallel elevational replacement of hosts and parasites in a highly susceptible songbird genus","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-17 22:25:25","doi":"10.21203/rs.3.rs-8060400/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2025-12-20T15:01:07+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-12T02:21:11+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-08T12:48:54+00:00","index":"","fulltext":""},{"type":"submitted","content":"Oecologia","date":"2025-11-07T18:00:31+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"oecologia","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"oeco","sideBox":"Learn more about [Oecologia](https://www.springer.com/journal/442)","snPcode":"442","submissionUrl":"https://submission.nature.com/new-submission/442/3","title":"Oecologia","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"9c688cbf-f144-4b2f-92f1-85eae38173d8","owner":[],"postedDate":"December 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-04-13T16:09:33+00:00","versionOfRecord":{"articleIdentity":"rs-8060400","link":"https://doi.org/10.1007/s00442-026-05892-8","journal":{"identity":"oecologia","isVorOnly":false,"title":"Oecologia"},"publishedOn":"2026-04-07 15:58:07","publishedOnDateReadable":"April 7th, 2026"},"versionCreatedAt":"2025-12-17 22:25:25","video":"","vorDoi":"10.1007/s00442-026-05892-8","vorDoiUrl":"https://doi.org/10.1007/s00442-026-05892-8","workflowStages":[]},"version":"v1","identity":"rs-8060400","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8060400","identity":"rs-8060400","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}