Habitat Suitability Modeling

Habitat-suitability modeling studies the effect of the abiotic characteristics of the habitat on the presence, abundance, or diversity of a given taxonomic group of organisms. For example, one might study the influence of soil characteristics, such as soil temperature, water content, and proportion of mineral soil on the abundance and species richness of Collembola (springtails), the most abundant insects in soil. The study uses neural networks to build a number of predictive models for collembolan diversity. Another study of habitat-suitability modeling by neural networks is given by Ozesmi and Ozesmi.

Several habitat-suitability modeling applications of other DM methods are surveyed by Fielding. Fielding applies a number of methods, including discriminant analysis, logistic regression, neural networks, and genetic algorithms, to predict nesting sites for golden eagles. Bell uses decision trees to describe the winter habitat of prong-horn antelope. Jeffers uses a genetic algorithm to discover rules that describe habitat preferences for aquatic species in British rivers.

The author has been involved in a number of habitat-suitability studies using rule induction and decision trees. Rule induction was used to relate the presence or absence of a number of species in Slovenian rivers to physical and chemical properties of river water, such as temperature, dissolved oxygen, pollutant concentrations, chemical oxygen demand, etc. Regression trees were used to study the influence of soil characteristics, such as soil texture, moisture, and acidity on the abundance (total number of individuals) and diversity (number of species) of Collembola (springtails). Decision trees were also used to model habitat suitability for red deer in Slovenian forests using geographic information system (GIS) data, such as elevation, slope, and forest composition. Finally, decision trees that model habitat suitability for brown bears have been induced from GIS data as well as data on brown bear sightings. The model has then been used to identify the most suitable locations for the construction of wildlife bridges/underpasses that would enable the bears to safely cross the highway passing through the bear habitat.

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