Читать книгу Climate Impacts on Sustainable Natural Resource Management - Группа авторов - Страница 40

In addition to biodiversity management, environmental researchers are also interested in Species Distribution Models (SDMs), which are widely used for predicting suitable habitat in space‐time for the choice of species (Bolliger et al. 2000; Raxworthy et al. 2003; Phillips et al. 2006; Baldwin 2009; Robinson et al. 2011). SDMs are beneficial in generating maps and results that identify suitable habitat areas and determine key environmental factors for driving species occurrence. These tools also report the threshold for suitable habitat demarcation and accuracy assessment. They are a valuable asset for many ecological studies and for species with narrow ranges it may also direct direction for future field surveys (Phillips et al. 2006). Integration of machine learning and geospatial techniques can easily handle such complex modeling problems.

Maximum Entropy (Maxent) is a presence‐only species distribution modeling technique used in the identification of a species' niche in environmental space. The prediction is based on the relation between observed occurrences to a set of climatic and non‐climatic environmental variables (Phillips et al. 2006; Pearson et al. 2007). Maxent is a free to download machine learning tool that provides a platform to integrate the species occurrence data with the bioclimatic variables using remote sensing and Geographical Information System and provides species habitat suitability and predicting future occurrence scenarios (Phillips et al. 2004; Phillips et al. 2006). Maxent has several advantages:

1 It can characterize probability distributions from incomplete information.

2 Presence data alone is sufficient and no absence data is required.

3 It uses both continuous and categorical environmental variables.

4 It produces an output with continuous prediction ranging from zero to one, where a higher value pixel indicates greater suitability for a given species at that pixel.