Estimation of the area of potential thermal refuges using generalized additive models and multivariate adaptive regression splines: A case study from the Ste‐Marguerite River.

• Published in: River Research and Applications
• Main Authors: Saadi, Al Mahdi, Msilini, Amina, Charron, Christian, St-Hilaire, André, Ouarda, Taha B. M. J.
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: 2022
• Subjects: daily water temperature; generalized additive model (GAM); multivariate adaptive regression splines (MARS); thermal refuges; Canada; Marguerite; Nash; Gam; Sutcliffe; Marguerite River; Atlantic salmon; Salmo salar
• Online Access: https://espace.inrs.ca/id/eprint/12708/; https://doi.org/10.1002/rra.3886

Thermal refuges in rivers are becoming a critical habitat for ectotherm fish, including Atlantic salmon (Salmo salar). In this study, two statistical modelling approaches were used to estimate the areas of potential thermal refuges: generalized additive models (GAM) and multivariate adaptive regression splines (MARS). This allowed for the first development of a reliable statistical model that uses a few relevant predictors (air temperature, river discharge, main river, and tributary temperatures) to estimate tributary plume thermal refuge surface areas. GAM and MARS models were fitted independently for four sites on the Ste-Marguerite River, (Quebec, Canada). Model performances were evaluated using the leave-one-out cross validation (LOOCV) approach and the following criteria: the Akaike information criterion (AIC), root-mean-square error (RMSE), relative root-mean-square error (rRMSE), Nash-Sutcliffe efficiency coefficient (NASH), and finally the bias (BIAS). Using an array of thermographs deployed at the confluence of a cold tributary and the warmer main river stem, refuges were delineated at a daily time step. Model results indicate that the estimated areas are similar to the refuge surfaces interpolated using temperature measurements, with both models and for all sites. Results suggest that MARS performs better than GAM in terms of forecasting and estimating the variability of the area of thermal refuges at all study-stations. This relatively simple approach will be of use to water resources managers faced with the challenge of protecting thermal refuges for fish.