|Predicting Spatial Similarity of Freshwater Fish Biodiversity|
ISSNAF member and Postdoctoral Research Associate, Civil and Environmental Engineering at Princeton University
A major issue in modern ecology is to understand how ecological complexity at broad scales is regulated by mechanisms operating at the organismic level. What specific underlying processes are essential for a macroecological pattern to emerge? Here, we analyze the analytical predictions of a general model suitable for describing the spatial biodiversity similarity in river ecosystems, and benchmark them against the empirical occurrence data of freshwater fish species collected in the Mississippi–Missouri river system. Encapsulating immigration, emigration, and stochastic noise, and without resorting to species abundance data, the model is able to reproduce the observed probability distribution of the Jaccard similarity index at any given distance. In addition to providing an excellent agreement with the empirical data, this approach accounts for heterogeneities of different subbasins, suggesting a strong dependence of biodiversity similarity on their respective climates. Strikingly, the model can also predict the actual probability distribution of the Jaccard similarity index for any distance when considering just a relatively small sample. The proposed framework supports the notion that simplified macroecological models are capable of predicting fundamental patterns—a theme at the heart of modern community ecology.
Full text: Predicting spatial similarity of freshwater fish biodiversity, PNAS >>>
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