Integrating Remote Sensing, Citizen Science, and AI in Next-Generation Species Distribution Models: Emerging Opportunities and Challenges

Species Distribution Models (SDMs) have become central to ecological research, conservation planning, and biodiversity monitoring. However, traditional approaches relying primarily on climatic predictors and presence-only data face significant limitations. In this study, we explore the integration of three rapidly expanding data sources — remote sensing, citizen science, and artificial intelligence (AI) — to build a next-generation framework for SDMs. Using bibliometric analysis and case study synthesis, we evaluate recent trends and highlight methodological advances that leverage high-resolution satellite imagery, UAV data, large-scale citizen science platforms, and deep learning algorithms. Results demonstrate that hybrid SDMs incorporating heterogeneous data improve predictive accuracy, enhance spatiotemporal resolution, and support real-time ecological forecasting. We argue that this paradigm shift can overcome long-standing issues of sampling bias, model generalizability, and data scarcity, particularly in under-studied regions. Our findings suggest a roadmap for developing SDMs capable of supporting urgent conservation decisions under accelerating global change.