摘要
气候变化和人类活动严重影响物种的分布范围和生境适宜性。近年来,利用模型进行物种潜在适宜性生境预测已成为该领域研究的焦点之一。最大熵模型(MaxEnt)是一种基于物种分布数据和环境变量数据的机器学习模型,在物种生境预测中得到了广泛应用。本文首先介绍了MaxEnt模型的机理、建立过程、优化方法以及评估体系,然后综述了该模型在濒危物种和外来入侵物种潜在生境预测以及未来气候变化下物种的潜在分布模拟领域的应用,最后对MaxEnt模型目前面临的挑战以及未来的发展前景进行了分析与展望,以期MaxEnt模型在预测物种在自然环境中的分布和变化方面发挥更加重要的作用,为生物多样性保护和管理提供技术参考。
Climate change and anthropogenic activities are profoundly affecting species distribution range and habitat suitability. In recent years, using models to predict potential suitable habitats for different species has become one of the research focuses in this field. The maximum entropy model(MaxEnt), a machine learning model based on the data of species distribution and environmental variables, has been widely used in predicting species habitats. First, we introduced the mechanism, establishment process, optimization method and assessment system of the MaxEnt model. Then, we reviewed the application of the model in potential habitat prediction of endangered species and invasive species, and the simulation of the potential distribution of species under future climate change. Fina-lly, we proposed current challenges and future development prospects of the MaxEnt model, aiming to strengthen its role in predicting the natural distribution of species, and provide technical references for biodiversity conservation and management.
作者
杨佳悦
丁国玉
田秀君
YANG Jiayue;DING Guoyu;TIAN Xiujun(College of Environment,Beijing Jiaotong University,Beijing 100044,China)
出处
《应用生态学报》
北大核心
2025年第2期614-624,共11页
Chinese Journal of Applied Ecology
基金
国家重点研发计划项目(2022YFB2303101)资助。
