Back

Impacts of human-introduced species on the geography of life on Earth

Capinha, C.; Pabst, R.; Leroy, B.

2025-07-08 ecology
10.1101/2025.07.05.663167 bioRxiv
Show abstract

Human activities are increasingly transporting species beyond their native ranges, where they often establish and become permanent additions to recipient biotas. These introductions, documented for tens of thousands of taxa, are a departure from the natural constraints of dispersal that have long shaped species distributions. However, current knowledge on their impacts on global biogeography remains poorly understood and fragmented. Here, we review empirical evidence across 15 classical biogeographical rules to assess how non-native species are altering the spatial structure of global biodiversity. Our synthesis reveals that while some patterns, such as species-area relationships, are often reinforced under invasion, others, including the delineation of global biogeographic regions, relationships between island isolation-diversity and between body sizes and latitudinal gradient (i.e., Bergmans rule), already show profound reshaping in contemporary assemblages. Additionally, other patterns (e.g., latitudinal gradient of species diversity; body size and insularity) show context-dependent changes, shaped by factors such as spatial scale, taxonomic group, and introduction history. These transformations are often more pronounced at broad spatial scales and in highly invaded systems such as islands and temperate regions. Our findings demonstrate that biological invasions are selectively but profoundly reshaping the geography of life on Earth, with major implications for conservation, macroecology, and the future of biodiversity patterns in the Anthropocene.

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.