Abstract
AbstractIn natural populations, vital rates such as survival and reproduction are influenced by a complex interplay of abiotic conditions (e.g., environment), density dependence, and individual factors (e.g., phenotypic traits). Studies at the extremes of species distributions, particularly high elevations, offer unique insights due to the intensified effects of abiotic stressors, which can amplify both direct and indirect effects on vital rates. In this study, we focus on a high‐elevation population of the common toad (Bufo bufo) located near the upper limit of its elevational range in the Swiss Alps. This setting provides a critical context for examining how extreme abiotic conditions interact with density dependence and individual factors to influence life history traits. Utilizing 28 years of capture–mark–recapture data and individual body size measurements from nearly 2500 toads, we applied in a Bayesian statistical framework a Cormack–Jolly–Seber model for estimating male survival probabilities, and a multistate model for assessing female survival and breeding probabilities, alongside sex‐specific growth curves. Our analysis indicates that survival probabilities are significantly impacted by interactions between abiotic conditions such as the active season length and temperature at emergence from hibernation, density dependence, and individual phenotypic traits such as body size. The breeding patterns of females showed a biennial cycle, with temperature at hibernation emergence influencing the likelihood of skipping breeding events and density affecting the resumption of breeding. These results highlight the role of abiotic conditions and density in shaping physiological and reproductive strategies in a high‐stress ecological niche. Moreover, we uncovered indications of indirect effects, where both abiotic conditions and density potentially affect asymptotic growth and thus survival, mediated through changes in body size. Our findings illustrate the complex dynamics at play in high‐elevation populations and the importance of long‐term, individual‐based data in studying these processes. This study underscores the value of integrating multiple sources of variation to understand population dynamics comprehensively, particularly in understudied, extreme environments where traditional ecological models may not fully capture the nuanced interdependencies of natural systems.
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