Title: Further advances in predicting species distributions
Author: Moisen, Gretchen G.; Edwards, Thomas C. Jr.; Osborne, Patrick E.
Source: Ecological Modelling. 199: 129-131.
Description: In 2001, a workshop focused on the use of generalized linear models (GLM: McCullagh and Nelder, 1989) and generalized additive models (GAM: Hastie and Tibshirani, 1986, 1990) for predicting species distributions was held in Riederalp, Switzerland. This topic led to the publication of special issues in Ecological Modelling (Guisan et al., 2002) and Biodiversity and Conservation (Lehmann et al., 2002). A second workshop, held in 2004, also in Riederalp, brought together a larger group of modeling specialists from the fields of ecology and statistics to discuss advances in predictive species distribution models since the first gathering. Three collections of papers resulted from this more recent workshop, one in the Journal of Applied Ecology (Guissan et al., in press), one in the Journal of Biogeography, (Araujo and Guisan, in press), and this special issue. The papers contained here offer a diverse look into current modelling issues for spatial prediction of both plant and animal species distributions at a variety of scales. Topics of these papers span all stages of the species distribution modelling process, including: choosing between purposive and probabilistic sampling schemes; handling problematic characteristics of ecological data such as disproportionate numbers of zero values, small sample sizes, and presence only data; comparing predictive modeling tools; and making best use of ecological theory in modelling practices. All of these topics are central to the proper modeling of species distributions, and this series of papers provides new insights to each topic, presenting a broad-based context in which to evaluate modeling tools.
Keywords: species distributions, generalized linear models (GLM), generalized additive models (GAM), modelling process
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Moisen, Gretchen G.; Edwards, Thomas C., Jr.; Osborne, Patrick E. 2006. Further advances in predicting species distributions. Ecological Modelling. 199: 129-131.
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