Integrating MaxEnt and Random Forest Models to Assess Habitat Suitability of Black-Necked Cranes, A Case Study in Nyingchi City

Abstract

Understanding the spatial distribution of rare species is fundamental to biodiversity conservation. The black-necked crane (Grus nigricollis), a flagship species of alpine wetlands and a first-class nationally protected species in China, serves as an important indicator for ecosystem health. Based on the had data and ecological environment data, this study used the Maximum Entropy model (MaxEnt) and Random Forest model (RF) to predict the suitable distribution area of the black-necked crane. The Random Forest model exhibited high predictive accuracy, with an AUC of 0.945, closely aligning with known crane distribution patterns. Key environmental determinants of habitat suitability were identified as distance to buildings (d_b), distance to roads (d_r), and isothermality (Bio3), with average contribution rates of 15.1%, 15.05%, and 5.85%, respectively. High-probability suitable areas were primarily concentrated in riparian wetlands of Nyingchi City, with an optimal habitat core at the T-shaped valley confluence of the Yarlung Tsangpo and Nyang rivers. Through comparative analysis of MaxEnt and RF, this study significantly reduced spatial uncertainties in habitat suitability predictions. These findings provide critical spatial baselines for targeted conservation strategies of this sacred plateau species, particularly in maintaining ecological connectivity under climate change scenarios.