Multi-scale habitat selection modeling using combinatorial optimization of environmental covariates: A case study on nature reserve of red-crowned cranes

Abstract

Multi-scale habitat selection modeling (HSM) has garnered attention due to its ability to incorporate scale dependence of species. The key of multi-scale HSM is to select the appropriate combination of scales for different resources or environmental conditions, and then construct a set of multi-scale environmental covariates as the features of HSM. However, the existing scale selection methods do not determine the combination of scales under a unified model. In this study, a combinatorial optimization approach is proposed. We regard the combination of different scales as a search space, and use a heuristic optimization algorithm to search for the best-fitting model to determine the optimal scales for each resource or environmental condition. In a case study conducted in Yancheng National Nature Reserve, the proposed approach is applied to model the habitat selection of the endangered red-crowned crane. We compare the proposed method with single-scale, random-scale and other multi-scale approaches. The results show that the combination of scales selected based on the proposed method obtained the best accuracy in the spatial prediction of habitat suitability with a test AUC of 0.865 for the daytime scenario and 0.932 for the nighttime scenario. Moreover, the selected scales are utilized to generate response curves, providing suggestions for habitat restoration and management of the red-crowned crane population in the nature reserve.