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Owing in large part to Robert MacArthur’s classic research, wood warblers in the family Parulidae are textbook exemplars of species competition and niche partitioning. Conventional wisdom suggests that subtle differences in foraging behaviour are the principal means by which these nearly morphologically indistinguishable species are able to co-occur and avoid extinction. Yet, MacArthur’s study was in fact quite limited in scale, and he said little about the relevance of evolution to the study system. Here, we reassess MacArthur’s conclusions across an expanded set of syntopic warbler species in a forest in northern New York. We combine morphometrics, quantitative foraging data and faecal metabarcoding—a direct measure of warbler diet—to study competition and niche partitioning in an evolutionary framework. We find close relationships between morphology and foraging behaviour, but little connection between warbler ecomorphology and the 2237 invertebrate taxa detected in their diets. Instead, diet remains phylogenetically conserved—closely related warblers eat similar suites of invertebrates, regardless of where they forage. Finally, we present evidence that these species not only partition niche space in the present day but also that competition has shaped their behaviours over evolutionary time. 

Synchronized seasonal excretion of multiple coronaviruses coincides with high rates of coinfection in immature bats. This repo contains instructions and source code for reproducing the statistical analyses in the manuscript. Repo Contents scripts: contains the source .R and .stan files to reproduce the anaysis. Each file is detailed below in the specific sections corresponding to the statistical analyses. data: contains the raw source data and model generated output. figures: contains the final output figures from the manuscript. These can be recreated with the CovOZ_Figures_Submission_Clean.R script. 1. System Requirements Hardware Requirements Our source code requires only a standard computer. Much of the Markov chain Monte Carlo code is run in parallel so a computer with ample memory and multiple cores can be advantageous. The runtimes below are generated using a macbook with the recommended specs (64 GB RAM, 8 cores at 2.7 GHz). The code will also work on linux or windows computer. Software Requirements Reproducing the statistical analyses requires a current version of R and stan. We use version 4.4.1 of R and version 2.32.2 of stan. Package dependencies and versions Users will need the following packages install the following packages to execute the code. Our versions are effective October 1, 2024 tidyverse 2.0.0 lubridate 1.9.3 stringr 1.5.1 rstan 2.32.6 cowplot 1.1.3 ggtext 0.1.2 jpeg 0.1-10 scales 1.3.0 tictoc 1.2.1 2. Installation Guide Running the analysis requires: installing R. Depending on wifi speeds, installing R usually takes a few minutes. installing stan. Depending on wifi speeds, installing stan usually takes a few minutes. installing the necessary R packages (listed above). Depending on wifi speeds, installing packages usually takes about 30 seconds per package. 3. Demo This source code is not an R package with a formal demo, but rather source code is included for the various analyses in section 4. 4. Instructions for Use 4.1 Coinfection Analysis Runs chi-squared tests on coinfections of beta 2d.iv and beta 2d.v. Generates summary statistics, test statistics, and p-values from manuscript. input files: individual_variant_covariates.csv script file: coinfection_final.R run time: approximately 1 second 4.2 Individual Level Dynamics of Infection: Dynamic Binary Regression Runs individual level dynamic binary regression models. Produces output file that can recreate figures. input files: individual_variant_covariates.csv script files: logistic_curves_final.R GP_regression.stan output files: logistic_curve_out.RData run time: approximately 66 minutes 4.3 Dynamics of Circulation at the Population Level Runs combined (individual and pooled data) dynamic models. Produces output file that can recreate figures. input files: combined_out_variant.csv script files: cluster_curves_final.R GP_withLL.stan output files: cluster_curves.csv run time: approximately 25 minutes 4.4 Manuscript Figures Combined script that uses output files created by previous scripts to recreate all figures in the manuscript. input files: model_output/cluster_curves.csv combined_out_variant.csv individual_variant_covariates.csv model_output/logistic_curve_out.RData script files: CovOZ_Figures_Submission_Clean.R output files: Figure2_final.png Figure3_final.png Figure4_A-D_final.png Figure6_AP.png Figure7.png SIFigure8.png SIFigure9.png run time: approximately 16 seconds 4.5 Model Comparison Integrated Compares LOOIC values for sets of model frameworks. input files: combined_out_variant.csv script files: Pred_Comparisons.R GP_withLL.stan output files: preds.RData run time: approximately 2 hours 4.6 Model Comparison Individual Compares LOOIC values for sets of model frameworks. input files: combined_out_variant.csv script files: logistic_curves_loo.R GP_regression.stan GP_regression_add.stan GP_regression_interact.stan output files: logistic_curve_loo_age.RData logistic_curve_loo_age_add_sex.RData logistic_curve_loo_age_interact_sex.RData run time: approximately 6:45 hours 

Abstract Understanding the drivers of seasonal disease outbreaks remains a fundamental challenge in disease ecology. Periodic outbreaks can be driven by several seasonally varying factors, including pulses of susceptible individuals through births, changes in host behaviour and social aggregation and variation in host immunity. However, when these potential drivers overlap temporally, isolating their relative contributions to outbreak patterns becomes challenging.We studied Hendra virus, a zoonotic pathogen with seasonal spillovers from bats to horses and humans. Multiple seasonal factors have been hypothesized to drive Hendra virus transmission, including food shortages, birth pulses and changes in host aggregation, but their temporal overlap has made identifying primary drivers difficult.We conducted a 4‐year longitudinal study ofPteropusbats to test whether seasonal birth pulses and the resulting influx of susceptible juveniles drive Hendra virus transmission. Using a Bayesian ageing model, we aged sexually immature bats and placed them into birth cohorts. We used our age predictions to model how viral shedding and antibody responses changed as bats aged. We trackedBartonellaspp. Infection—a bacterial pathogen requiring close contact for transmission—as an indicator of transmission opportunities within each cohort for comparison.We found no evidence that seasonal birth pulses of immunologically naïve juveniles drove Hendra virus transmission. Two out of three cohorts showed substantially reduced maternal antibody transfer compared to the 2018 cohort, with seroprevalence near zero at our earliest sampling timepoints and showed no clear evidence of synchronized seroconversion. Furthermore,Bartonellainfection rates were consistent across cohorts, indicating that opportunities for pathogen transmission remained consistent across cohorts despite varying viral shedding patterns.Our findings demonstrate that birth pulses alone cannot explain observed patterns of Hendra virus outbreaks. These results highlight the importance of using multiple lines of evidence to evaluate competing mechanisms underlying seasonal disease dynamics, particularly when potential drivers coincide temporally. 

The black flying fox ( Pteropus alecto ) is a natural reservoir for Hendra virus, a paramyxovirus that causes fatal infections in humans and horses in Australia. Increased excretion of Hendra virus by flying foxes has been hypothesized to be associated with physiological or energetic stress in the reservoir hosts. The objective of this study was to explore the leukocyte profiles of wild-caught P . alecto , with a focus on describing the morphology of each cell type to facilitate identification for clinical purposes and future virus spillover research. To this end, we have created an atlas of images displaying the commonly observed morphological variations across each cell type. We provide quantitative and morphological information regarding the leukocyte profiles in bats captured at two roost sites located in Redcliffe and Toowoomba, Queensland, Australia, over the course of two years. We examined the morphology of leukocytes, platelets, and erythrocytes of P . alecto using cytochemical staining and characterization of blood films through light microscopy. Leukocyte profiles were broadly consistent with previous studies of P . alecto and other Pteropus species. A small proportion of individual samples presented evidence of hemoparasitic infection or leukocyte morphological traits that are relevant for future research on bat health, including unique large granular lymphocytes. Considering hematology is done by visual inspection of blood smears, examples of the varied cell morphologies are included as a visual guide. To the best of our knowledge, this study provides the first qualitative assessment of P . alecto leukocytes, as well as the first set of published hematology reference images for this species.