MaxEnt brings comparable results when the input data are being completed; Model parameterization of four species distribution models

• Published in: Ecology and evolution 2023-02, Vol.13 (2), p.e9827-n/a
• Main Authors: Ahmadi, Mohsen, Hemami, Mahmoud‐Reza, Kaboli, Mohammad, Shabani, Farzin
• Format: Article
• Language: English
• Subjects: Accuracy; Applied Ecology; Bias; Biodiversity Ecology; Biogeography; Entropy; Environmental management; Generalized linear models; Geographical distribution; habitat suitability; MaxEnt; Maximum entropy; model tuning; Mountains; Parameterization; Performance prediction; Rare species; Spatial Ecology; spatially imbalanced‐biased; Statistical models; Training; Variables; Weighting
• ISSN: 2045-7758; 2045-7758
• DOI: 10.1002/ece3.9827

Species distribution models (SDMs) are practical tools to assess the habitat suitability of species with numerous applications in environmental management and conservation planning. The manipulation of the input data to deal with their spatial bias is one of the advantageous methods to enhance the performance of SDMs. However, the development of a model parameterization approach covering different SDMs to achieve well‐performing models has rarely been implemented. We integrated input data manipulation and model tuning for four commonly‐used SDMs: generalized linear model (GLM), gradient boosted model (GBM), random forest (RF), and maximum entropy (MaxEnt), and compared their predictive performance to model geographically imbalanced‐biased data of a rare species complex of mountain vipers. Models were tuned up based on a range of model‐specific parameters considering two background selection methods: random and background weighting schemes. The performance of the fine‐tuned models was assessed based on recently identified localities of the species. The results indicated that although the fine‐tuned version of all models shows great performance in predicting training data (AUC > 0.9 and TSS > 0.5), they produce different results in classifying out‐of‐bag data. The GBM and RF with higher sensitivity of training data showed more different performances. The GLM, despite having high predictive performance for test data, showed lower specificity. It was only the MaxEnt model that showed high predictive performance and comparable results for identifying test data in both random and background weighting procedures. Our results highlight that while GBM and RF are prone to overfitting training data and GLM over‐predict nonsampled areas MaxEnt is capable of producing results that are both predictable (extrapolative) and complex (interpolative). We discuss the assumptions of each model and conclude that MaxEnt could be considered as a practical method to cope with imbalanced‐biased data in species distribution modeling approaches. The input data for species distribution modeling (SDM) are always completing, and data of the unknown and range‐restricted species are mostly spatially imbalanced‐biased. Model parameterization is necessary to improve the predictive performance of the species distribution models. The MaxEnt model provides reproducible results when the input data are being developed.