Since none of the submitted birds had leg bands, all rescued hummingbirds were assumed to be first-time rescues for the purpose of this study. Two mixed-effects logistic regression models were developed to predict the final disposition of hummingbirds (survival or death) during the rehabilitation process. The first model looked at all the individuals while the second model was developed included only a subset of individuals who received preliminary treatment. Survival was defined when birds were transferred to flight cage facilities for further rehabilitation and/or released, or when nestlings were transferred to nurseries and no death or euthanasia was reported by rehabilitation centers. Species and sex groups were included as random effects. Model candidates were fitted and were compared with each other to identify best-fitting models based on AIC and ANOVA test. For the first model (model 1), factors related to demography and whether treatment was provided were tested, and reasons for admissions were explored. A second model predicting survival was developed that included only a subset of individuals whose records indicated that they received preliminary treatment at rehabilitation centers (model 2). Binary variables for each treatment option (heat, nectar/oral fluids, steroid, NSAID, antibiotic) were generated and included in the model. We assumed that reason for admission also accounted for the physical condition of the bird at the time of admission, which may have significantly affected the treatment options. The models were developed in R using the “glmmTMB” package (Brooks et al., 2017). For both models, an interaction term between age and seasons was included and resulting models were tested against the baseline model using the ANOVA test. Ten thousand simulations, only of the best fitting model 2, were used to predict the probability of survival for all the birds and outcomes were plotted against risk factors categories. The data used for the study, python and R code used for pre-processing the data, creating models and generating figures are openly stored in a Zenodo repository https://zenodo.org/record/4311820.

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