Niche Modeling and Geographic Range Predictions in the Marine Environment Using a Machine - learning Algorithm

(Vieglais et al., 2000). Museum data are high quality because voucher specimens can be examined if identification is questionable. However, like all point data, museum specimens provide only a limited view of the actual species' range (Krohn, 1996), hence the need for predictive approaches. A limited number of ecological data sets are also available, worldwide, including physio-chemical parameters (NOAA, 1999) and bathymetry (Smith and Sandwell, 1997). Ecological niche modeling uses the primary point data and the ecological data to build a partial niche The niche model is defined in ecological space by ecological parameters. It can be projected into geographic space by dividing the area of interest into rows and columns to create a grid, and then identifying the grid cells where the ecological parameters match those of the niche model. The landscape for this study is the Central Western Atlantic, roughly bounded by the definitions of the Food and Agricultural Organizations definition of Fishing Area 31, approximately the area of the Atlantic Ocean, Caribbean Sea, and the Gulf of Mexico between 35°N and 5°N Latitude and west of 40°W Longitude. Many tools have been used to develop models of ecological niches. Among the simplest is BIOCLIM (Nix, 1986), which involves intersecting the ranges (slightly trimmed) inhabited by the species along each environmental axis (e.g. 0–50 m depth x annual surface temperature average of 20–22°C, etc.). Other approaches include general linear models, distance-based algorithms , and regression-tree analyses (Austin et al. These relatively straightforward algorithms, however, suffer from their focus on a search for a single decision rule, or a small set of decision rules. The reality of species' ranges is that many factors affect them, and indeed different decision rules may govern distributional limits in different sectors of a species' distribution 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 …