Biological communities are changing drastically in response to global climate change (Walther et al., 2002), changes in use by human populations (Krishtalka et al., 2002), and introduction of exotic species (Carlton, 1996; Enserink, 1999). To study the impact of such changes in the marine environment, biologists require a detailed understanding of the diversity and distributions of marine organisms on macroscopic scales, such as across entire ocean basins, in order to improve understanding of the actual distributions of species, and gain an overall impression of the potential community structures that exist in particular habitats. A major obstacle to such an improved understanding is the fact that existing biodiversity records are both incomplete and idiosyncratic, consisting of museum collection and fisheries survey data covering only a small, and usually biased, fraction of the marine biosphere. The ability to predict distributions of species based on existing specimen records would allow investigators to predict the presence or absence of species in previously unsampled waters. If such predictions prove robust, then responses of species to global climate change could be predicted (Peterson et al., 2002b), potential impacts of introductions of exotic species on native faunas could be anticipated proactively (Peterson and Vieglais, 2001), and assessment of potential future conservation areas could be performed (Peterson et al., 2000). In this paper, we extend the application of one tool, the Genetic Algorithm for Ruleset Prediction (GARP) to the marine realm, and demonstrate its potential usefulness in predicting geographic distributions of littoral and benthic fishes across a major and heterogeneous ocean region.

Wiley, E.O., K.M. McNyset, A.T. Peterson, C.R. Robins, and A.M. Stewart. 2003. Niche modeling perspective on geographic range predictions in the marine environment using a machine-learning algorithm. Oceanography 16(3):120–127, https://doi.org/10.5670/oceanog.2003.42.

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