Challenging the geographic bias in recognising large-scale patterns of diversity change

Aim: Geographic structure is a fundamental organising principle in ecological and Earth sciences, and our planet is conceptually divided into distinct geographic clusters (e.g. ecoregions and biomes) demarcating unique diversity patterns. Given recent advances in technology and data availability, however, we ask whether geographically clustering diversity time-series should be the default framework to identify meaningful patterns of diversity change. Location: North America. Taxon: Aves. Methods: We first propose a framework that recognises patterns of diversity change based on similarities in the behaviour of diversity time-series, independent of their specific or relative spatial locations. Specifically, we applied an artificial neural network approach, the self-organising map (SOM), to group time-series of over 0.9 million observations from the North American Breeding Birds Survey (BBS) data from 1973 to 2016. We then test whether time-series identified as having similar behaviour are geographically structured. Results: We find little evidence of strong geographic structure in patterns of diversity change for North American breeding birds. The majority of the recognised diversity time-series patterns tend to be indistinguishable from being independently distributed in space. Main Conclusions: Our results suggest that geographic proximity may not correspond to shared temporal trends in diversity; assuming that geographic clustering is the basis for analysis may bias diversity trend estimation. We suggest that approaches that consider variability independently of geographic structure can serve as a useful addition to existing organising rules of biodiversity time-series.