Background: Global environmental change—encompassing climate warming, land-use transformation, invasive species, and pollution—poses existential threats to insect and vertebrate populations worldwide. Biodiversity loss is accelerating at rates unprecedented in human history, necessitating robust predictive frameworks to guide proactive conservation.
Objective: This study aims to develop, validate, and compare predictive biodiversity models integrating multi-source environmental data for forecasting species distribution shifts, habitat suitability changes, and persistence probabilities under projected climate and land-use scenarios.
Methods: We employed a multi-model ensemble approach combining Species Distribution Models (SDMs), Machine Learning classifiers (Random Forest, XGBoost), and Population Viability Analysis (PVA). Data from GBIF, WorldClim 2.1, and remote-sensed land cover layers (2000–2024) were used across 342 focal species in tropical and temperate zones.
Results: Ensemble models achieved mean accuracy of 91.3% (AUC = 0.94). Under RCP 8.5 scenarios, 58% of modelled insect species faced habitat suitability declines exceeding 30% by 2050. Integrated landscape conservation strategies showed the highest species persistence probability (79.3%) compared to single-action interventions.
Conclusion: Predictive biodiversity models are indispensable tools for conservation planning. Ensemble approaches combining ecological and machine learning methods offer superior forecasting accuracy and practical utility for evidence-based decision-making under environmental uncertainty.