University of Tehran Journal of Natural Environment 2008-7764 77 Ecology and Biodiversity Management 2024 07 22 Comparison of the performance of the linear and non-linear models in habitat suitability estimation (case study: Razi scraper, Capoeta razii) Comparison of the performance of the linear and non-linear models in habitat suitability estimation (case study: Razi scraper, <i>Capoeta razii</i>) 49 60 95191 10.22059/jne.2023.367109.2610 FA Hadi Poorbagher Department of Fisheries, Faculty of Natural Resources, University College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran. Soheil Eagderi Department of Fisheries, Faculty of Natural Resources, University College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran. Fateh Moezzi Department of Fisheries, Faculty of Natural Resources, University College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran. Journal Article 2023 10 22 The statistical models with the best performance in habitat suitability studies of fish species are of high importance. The present study compared the performance of linear (linear regression model (LM) and generalized linear model (GLM)) and non-linear models (artificial neural networks (ANN) and support vector machine (SVM)) in estimating habitat suitability index (HSI) for <em>Capoeta razii</em> in a southern Caspian Sea basin. The environmental parameters were altitude, depth, width, velocity, temperature, pH, electrical conductivity (EC), total dissolved solids (TDS), bottom stone diameter, and total count of stones with diameter > 15 cm per m². The linear models had weak predictive performance (higher RMSE values) compared to ANN and SVM models. The SVM was the best model with the predictors of altitude, pH, temperature, and stone diameter, and ANN was the best model using the rest of the parameters. The arithmetic mean model (AMM) showed better performance in estimating HSI compared to the geometric mean model (GMM). The distribution of HSI values along the sampling stations in the Caspian Sea basin (the Taleghan River) showed high diversity in the habitat condition of the fish species.      The statistical models with the best performance in habitat suitability studies of fish species are of high importance. The present study compared the performance of linear (linear regression model (LM) and generalized linear model (GLM)) and non-linear models (artificial neural networks (ANN) and support vector machine (SVM)) in estimating habitat suitability index (HSI) for <em>Capoeta razii</em> in a southern Caspian Sea basin. The environmental parameters were altitude, depth, width, velocity, temperature, pH, electrical conductivity (EC), total dissolved solids (TDS), bottom stone diameter, and total count of stones with diameter > 15 cm per m². The linear models had weak predictive performance (higher RMSE values) compared to ANN and SVM models. The SVM was the best model with the predictors of altitude, pH, temperature, and stone diameter, and ANN was the best model using the rest of the parameters. The arithmetic mean model (AMM) showed better performance in estimating HSI compared to the geometric mean model (GMM). The distribution of HSI values along the sampling stations in the Caspian Sea basin (the Taleghan River) showed high diversity in the habitat condition of the fish species.      https://jne.ut.ac.ir/article_95191_fe4a91f8d04f4fa9843c6ad06dcbfb19.pdf