513 Modeling Brook Trout Presence and Absence from Landscape Variables Using Four Different Analytical Methods Paul J. Steen* School of Natural Resources and Environment, University of Michigan 170 Dana Building, Ann Arbor, Michigan 48109, USA Dora R. Passino-Reader U.S. Geological Survey, Great Lakes Science Center 1451 Green Road, Ann Arbor, Michigan 48105, USA Michael J. Wiley School of Natural Resources and Environment, University of Michigan 170 Dana Building, Ann Arbor, Michigan 48109, USA 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, neu- ral 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 logis- tic regression model predicted with the least error, followed by multiple regression, then clas- sification 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. American Fisheries Society Symposium 48:513–531, 2006 © 2006 by the American Fisheries Society *Corresponding author: psteen@umich.edu INTRODUCTION Knowledge of the habitats required to maintain the growth, survival, and reproduction of fresh- water fish species and populations is necessary for conservation planning and decision making. In practical application, however, habitat require- ments are often incompletely known. Therefore, biologists commonly use data on a fish’s habitat selection, based on field observations of species occurrence or densities (Rosenfeld 2003). Given data on habitat characteristics and observed fish distributions, correlative habitat associations can be used to predict the occurrence or densities of fish in locations where samples have not been col- lected. These predictions are useful for identify- ing habitat units important to target species but vulnerable to alteration and degradation by hu- mans, and lacking protective status. Such habi- tats represent “gaps” in conservation strategy. The goal of the U.S. Geological Survey, Gap Analysis Program (GAP) is to keep common spe- cies common by identifying those species not ad- equately represented in existing conservation areas (Scott et al. 1993). In the past decade, gap analy- ses have been performed in terrestrial systems across the United States and in the mid-1990s an aquatic gap pilot began in Missouri. In 2001, GAP funded the first regional aquatic gap analy- sis in the eight Great Lakes states: Minnesota,