Functional traits determine the different effects of prey, predators, and climatic extremes on desert reptiles

Terrestrial reptiles are particularly vulnerable to climate change. Their highest density and diversity can be found in hot drylands, ecosystems which demonstrate extreme climatic conditions. However, reptiles are not isolated systems but part of a large species assemblage with many trophic dependencies. While direct relations among climatic conditions, invertebrates, vegetation, or reptiles have already been explored, to our knowledge, species’ responses to direct and indirect pathways of multiple climatic and biotic factors and their interactions have rarely been examined comprehensively. We investigated direct and indirect effects of climatic and biotic parameters on the individual (body condition) and population level (occupancy) of eight abundant lizard species with different functional traits in an arid Australian lizard community using a 30‐yr multi‐trophic monitoring study. We used structural equation modeling to disentangle single and interactive effects. We then assessed whether species could be grouped into functional groups according to their functional traits and their responses to different parameters. We found that lizard species differed strongly in how they responded to climatic and biotic factors. However, the factors to which they responded seemed to be determined by their functional traits. While responses on body condition were determined by habitat, activity time, and prey, responses on occupancy were determined by habitat specialization, body size, and longevity. Our findings highlight the importance of indirect pathways through climatic and biotic interactions, which should be included into predictive models to increase accuracy when predicting species’ responses to climate change. Since one might never obtain all mechanistic pathways at the species level, we propose an approach of identifying relevant species traits that help grouping species into functional groups at different ecological levels, which could then be used for predictive modeling.