1. Logistic regression predicts the probability of occurrence of a species as a function of environmental variables. This technique was applied to a large data set describing the distribution of two common gammarid species, Gammarus fossarum and G. pulex, in streams in the Netherlands, to evaluate its usefulness in defining habitat requirements. 2. A method is presented that derives optimum habitat ranges for environmental variables from logistic regression equations. The calculated optimum habitat ranges, which are related to the maximum likelihood of presence in the field, agreed with habitat requirements and ecological tolerances in the literature. 3. Single logistic regressions provide good descriptions of the optimum habitat requirements and multiple logistic regressions give insight into the relative importance of each environmental variable. It is the combination that makes logistic regression a valuable tool for constructing habitat suitability indices. 4. Current velocity, pH, Kjeldahl nitrogen, total phosphorus, ammonium nitrogen, conductivity, width and depth are, in this sequence, the most important environmental variables in predicting the probability of occurrence of G. fossarum, whereas current velocity, Kjeldahl nitrogen, pH and depth are the most important variables for the prediction of the probability of occurrence of G. pulex.
Freshwater Biology – Wiley
Published: Jun 1, 1998
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