Modeling brook trout presence and absence from landscape variables using four different analytical methods

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Abstract

As a part of the Great Lakes Regional Aquatic Gap Analysis Project, we evaluated methodologies for modeling associations between fish species and habitat characteristics at a landscape scale. To do this, we created brook trout Salvelinus fontinalis presence and absence models based on four different techniques: multiple linear regression, logistic regression, neural networks, and classification trees. The models were tested in two ways: by application to an independent validation database and cross-validation using the training data, and by visual comparison of statewide distribution maps with historically recorded occurrences from the Michigan Fish Atlas. Although differences in the accuracy of our models were slight, the logistic regression model predicted with the least error, followed by multiple regression, then classification trees, then the neural networks. These models will provide natural resource managers a way to identify habitats requiring protection for the conservation of fish species.

Additional publication details

Publication type Book chapter
Publication Subtype Book Chapter
Title Modeling brook trout presence and absence from landscape variables using four different analytical methods
Series number 48
Subseries American Fisheries Society Symposia
Year Published 2006
Language English
Publisher American Fisheries Society
Contributing office(s) Great Lakes Science Center
Description 19 p.
Larger Work Type Book
Larger Work Subtype Conference publication
Larger Work Title Landscape influences on stream habitats and biological assemblages
First page 513
Last page 531
Online Only (Y/N) N
Additional Online Files (Y/N) N