📝 SummarXiv

Abstract

Accelerating global biodiversity loss has highlighted the role of complex relationships and shared patterns among species in determining their responses to environmental changes. The structure of an ecological community, represented by patterns of dependence among constituent species, signals its robustness more than individual species distributions. We focus on obtaining community-level insights based on underlying patterns in abundances of bird species in Finland. We propose \texttt{barcode}, a modeling framework to infer latent binary and continuous features of samples and species, expanding the class of concurrent ordinations. This approach introduces covariates and spatial autocorrelation hierarchically to facilitate ecological interpretations of the learned features. By analyzing 132 bird species counts, we infer the dominant environmental drivers of the community, species clusters and regions of common profile. Three of the learned drivers correspond to distinct climactic regions with different dominant forest types. Three further drivers are spatially heterogeneous and signal urban, agricultural, and wetland areas, respectively.