A Species‐Specific Approach to Modeling Biological Communities and Its Potential for Conservation

Abstract: Community‐level approaches to biological conservation are now recognized as a major advance in most current single‐species conservation and management practices. Existing approaches for modeling bio‐ logical communities, have limited utility, however, because they commonly examine community metrics ( e.g., species richness, assemblage “types” ) and consequently do not consider the identity of the species that compose the community. This is a critical shortcoming because the functional differences among species ultimately translate into the differential vulnerability of biological communities to natural and human‐related environmental change. To address this concern I present a novel, species‐specific approach to modeling communities using a multiresponse, artificial neural network. This provides an analytical approach that facilitates the development of a single, integrative model that predicts the entire species membership of a community while still respecting differences in the functional relationship between each species and its environment. I used temperate‐lake fish communities to illustrate the utility of this modeling approach and found that predictions of community composition by the neural network were highly concordant with observed compositions of the 286 study lakes. Average similarity between observed and predicted community composition was 80% ( 22 out of the 27 species correctly classified ), and the model predicted a significant portion of the community composition in 91% of the lakes. I discuss the importance of the lake habitat variables for predicting community composition and explore the spatial distribution of model predictions in light of recent species invasions. The proposed modeling approach provides a powerful, quantitative tool for developing community predictive models that explicitly consider species membership, and thus each species' functional role in the community. Such models will contribute significantly to the study and conservation of biological communities.

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