Speaker
Description
Changes in species composition (beta diversity) provide key insights into the ecological processes structuring communities. Identifying the drivers of beta diversity is essential for predicting how communities may reorganize under increasing anthropogenic pressures.
Here, we present a workflow that integrates camera-trap observations with remote-sensing data to model and predict spatial and temporal patterns of mammal community turnover. We combine hierarchical N-mixture models to derive abundance proxies that account for imperfect detection across species, camera stations, years, and seasons, with generalized dissimilarity models to explicitly model beta diversity. This framework allows community dissimilarity and uniqueness to be quantified and mapped across heterogeneous landscapes.
We apply our workflow to a Mediterranean ecosystem, the Doñana National Park in southwestern Spain, to illustrate its methodological potential. First, we show that incorporating proxies of mammal species abundances, rather than presence-only data, substantially improves explanatory power and enhances our ability to explain variation in beta diversity. Second, we identify the main drivers of mammal community turnover in the study region, namely habitat heterogeneity, distance to permanent water sources, and the proportion of bare soil. Third, we demonstrate shifts in community composition associated with the presence of domestic species, particularly free-ranging horses.
Overall, this workflow allows the use of camera-trap data to infer spatial patterns in community composition, identify areas hosting distinct unique assemblages, assess the influence of domestic or invasive species on native communities, and forecast community-level responses to land-use modification and climate change.
| Status Group | Postdoctoral Researcher |
|---|---|
| FOR TALKS: Poster Presentation Option | No, I prefer to present only as a talk. |