Ensemble model projection of climate-driven habitat redistribution for the endangered Brachymystax tsinlingensis

Climate change poses an escalating threat to global biodiversity, with freshwater salmonids exhibiting heightened vulnerability to temperature-driven distribution shifts. As one of the southernmost-distributed endangered salmonid species in the Northern Hemisphere, the Qinling lenok (Brachymystax tsinlingensis) serves as a critical indicator of climate impacts on stream-dwelling fishes. Despite its ecological significance, research on its spatial distribution remains limited. This study employed ensemble species distribution models (ESDMs) integrating four algorithms: artificial neural network (ANN), generalized additive model (GAM), gradient boosting machine (GBM), and maximum entropy model (MaxEnt), to predict habitat suitability under current and future climate scenarios. Using occurrence records and seven bioclimatic variables, the ESDMs showed robust predictive performance (AUC = 0.928; TSS = 0.988). Current suitable habitats (3799 km²) are concentrated in the tributaries of Weihe River Basin and the Hanjiang River Basin of the Qinling Mountains. Under CMIP6 projections for 2041-2060, all global climate models indicated a consistent contraction of suitable habitat: 41-60 % in MPI-ESM1-2-HR; over 60 % in EC-Earth3-Veg and 47-54 % in MRI-ESM2-0, accompanied by range shifts toward upper tributaries and minor centroid displacement near the junction of Baoji and Tianshui City. Variable importance analyses revealed that BIO5 (21 %), BIO4 (17 %), BIO7 (15 %), BIO18 (11 %) and BIO3 (10 %) were the key influential factors. Response curves indicated that Qinling lenok is sensitive to rapid temperature fluctuations. These findings underscore the Qinling Mountains' continued importance for this endangered species, necessitating conservation prioritization of high-altitude stream habitats and adaptive restocking strategies. This study provides critical insights for climate-resilient biodiversity management in mountainous regions.