Applications of ecological niche and species distribution models in agricultural, livestock, and forestry systems: A comprehensive review

Declining global agricultural productivity driven by climate variability and pest proliferation creates unprecedented food security challenges that traditional management approaches cannot adequately address. Ecological Niche Models (ENM) and Species Distribution Models (SDM) have emerged as powerful frameworks for predicting spatial distributions under future climate scenarios. These approaches enable identification of optimal cultivation zones and development of targeted adaptation strategies that enhance resilience across agricultural systems while supporting proactive management for global food security. This review explores the development and use of ENM and SDM in agriculture, livestock, and forestry, emphasizing their role in identifying production areas, assessing risks from pests, diseases, and weeds, and informing management decisions. It also addresses key methodological aspects and their growing importance in sanitary planning, food security, and climate adaptation. We conducted a systematic literature review to examine ENM and SDM applications in productive systems. The analysis recorded specific uses, target organisms, study objectives, and key elements of model construction, parameterization, validation, transferability, and input data. The review defined the current scope of ENM and SDM in productive systems and identified critical knowledge gaps. It highlights the value of the BAM framework to guide modeling design and interpretation. The findings provide a conceptual base for broader applications and identify future research and implementation opportunities. ENM and SDM transform complex ecological and production data into actionable insights that support policy, social, economic, and management decisions across agriculture, forestry, and livestock sectors. Their flexibility across scales enables tailored solutions. Technological advances will enhance their impact, positioning these models as essential tools for sustainable food security. • BAM framework provides the conceptual basis to apply ENM and SDM in productive systems. • ENM and SDM identify suitable areas and risks for productive systems and associated organisms. • Framing ENM and SDM with policy and socioeconomic data could better guide decisions. • Interdisciplinary approaches using AI and remote sensing advances maximize model potential. • Resolving conceptual ambiguities will unlock ENM and SDM's full potential in agriculture.