Predicting microhabitat selection in juvenile Atlantic salmon Salmo salar by the use of logistic regression and classification trees

• Published in: Freshwater Biology
• Main Authors: TURGEON, KATRINE, RODRÍGUEZ, MARCO A.
• Format: Article in Journal/Newspaper
• Language: English
• Published: Wiley 2005
• Subjects: Atlantic salmon; Salmo salar
• Online Access: http://dx.doi.org/10.1111/j.1365-2427.2005.01340.x; https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1111%2Fj.1365-2427.2005.01340.x; https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1365-2427.2005.01340.x

Summary 1. We compared the capacity of logistic regression (LR) and classification tree (CT) models to predict microhabitat use and the summer distribution of juvenile Atlantic salmon, Salmo salar , in two reaches of a small stream in eastern Quebec. 2. The models predicted the presence or absence of salmon at a location on the basis of habitat features (depth, current velocity, presence of instream and overhead cover, substratum particle size, and distance to stream bank) measured at that location. Models were validated by means of crossover field tests evaluating the performance of models developed for one reach (calibration trials) when applied to the other reach (validation trials). Model performance was evaluated with regard to accuracy, generality and ease of use and interpretation. Prediction maps based on habitat features were also built to compare the observed position of fish with those predicted by LR and CT models. 3. The spatial distribution of active fish differed markedly from that of resting fish, apparently as a result of the selection for water greater than about 30 cm depth by active fish and for the presence of rocky cover by resting fish. 4. All models made accurate predictions, validated by crossover trials. For both LR and CT models, the prediction maps reflected well the actual fish distributions. However, CT models were easier to build and interpret than LR models. CT models also had less variable performance and a smaller decline in predictive capability in crossover trials (for fish at rest), suggesting that they may be more transferable than LR models.