Abstract: Background Species distribution models (SDMs), grounded fundamentally in Hutchinson's ecological niche concept and biogeographical principles, constitute indispensable analytical frameworks for investigating, simulating, and projecting the potential geographical distributions of species under current and future climate change scenarios. The SDMs offer important support for scientific ecosystem management by forecasting emergent spatial ecological patterns and elucidating species' potential responses to diverse environmental disturbances, including shifting climatic regimes, habitat fragmentation, and anthropogenic land-use alterations. This comprehensive review synthesizes the concepts and types of SDMs, outlining prevalent methodologies and their diverse applications in ecosystem management. based on the ecological niche principle are important for studying, simulating, and predicting the potential distribution of species in the context of climate change, as well as for improving ecosystem management. Methods This paper summarizes the concepts and types of species distribution models and outlines three modeling approaches: environmental envelope techniques (e.g., BIOCLIM), which define species' fundamental niches by identifying environmental extremes from occurrence data;, regression analyses (e.g., GLM, MARS), employing statistical relationships between presence/absence records and predictor variables;, and machine learning (e.g., MaxEnt, RF, ANN) leveraging complex, non-linear pattern recognition capabilities. The fundamental predictive workflow was structured into three critical steps: (1)It also describes a three-step simulation and prediction method consisting of data preparation-compiling species occurrence records (presence-only, presence/absence) and spatially explicit environmental variables (climate, terrain, soil);, (2) model selection-choosing appropriate algorithms based on data quality and objectives;, and (3) model evaluation-applying metrics (AUC, TSS) to validate prediction accuracy and robustness. These methods enable the construction of reliable predictive models that incorporate species occurrence records and relevant environmental variables to infer potential geographic distributions. The efficacy of SDM outputs was assessed across five distinct ecosystem management contexts. Results Key applications demonstrated include:It reviews the applications of species distribution models in typical ecosystem management in the context of climate change. 1) Forest ecosystems: Quantify the dynamic impacts of climate-driven and disturbance-driven changes in species ranges; 2) Wetland ecosystems: Project invasive species spread and native biodiversity threats; 3) Agricultural ecosystems: Delineate crop suitability shifts and pest/disease expansion risks; 4) Urban ecosystems: Rank habitat suitability and assessing the potential for biodiversity conservation; 5) Marine ecosystems: Reveal surface-dwelling species' spatial dynamics under ocean warming. The study further highlights future directions for SDM development. These applications provide critical spatial predictions for developing proactive conservation and management strategies.1) Species distribution models in forest ecosystems can predict suitable areas for forest species in the context of climate change. 2) Species distribution models in wetland ecosystems can reduce wetland area and decrease biodiversity. 3) In agro-ecosystems, species distribution models simulate crop growth trends and prevent the impact of invasive species on crops. 4) In urban ecosystems, these models integrate ocean climate, biophysical, and distance data to protect the habitats of rare organisms. 5) In urban ecosystems, species distribution models can optimize urban ecological patterns and protect biodiversity when combined with urban ecological networks. In marine ecosystems, species distribution models can integrate marine climatic, biophysical, and distance data to protect the habitats of rare organisms. Conclusion The review underscores the vital role of SDMs as powerful tools for translating species-environment relationships into actionable insights for ecosystem management under global change. Future development directions include: 1) EnhancedProspects are presented in terms of deep integration of dynamic ecological mechanisms (e.g., dispersal, biotic interactions, adaptive evolution) to improve temporal projection realism; 2) Deeper integration of multi-source data (remote sensing, genomics, citizen science) with advanced AI/machine learning to refine model accuracy and resolution;, intelligent technology, and 3) Closer coupling with ecosystem service evaluation modeling in future species distribution models to optimize conservation resource allocation. SDMs constitute indispensable tools for predicting biotic redistribution patterns in changing environments, enabling evidence-based ecosystem management strategies. Advancing SDMs along these avenues is crucial for strengthening their scientific foundation and practical utility in guiding adaptive ecosystem management.This is intended to provide references for developing and applying species distribution models.