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Abstract Changes in land use and land cover (LULC) due to agricultural expansion, commercial land management and other human‐driven modifications significantly influence the ecology of pathogens and vectors. This underscores the urgent need to understand how these respond to rapid and dynamic land use changes in these ecosystems and, critically, to identify strategies for mitigating their impacts.In tropical Central and South America, palm trees serve as primary habitats forRhodniuskissing bugs, vectors ofTrypanosoma cruzi, the etiologic agent of Chagas disease. This study investigates how LULC, weather and traits of the palmAttalea butyraceapredict the occurrence and infection ofRhodnius pallescens, integrating field data collection, molecular detection and spatial and hierarchical analyses across a rural landscape in Panama.Rhodnius pallescenswere collected from 46 palms in 11 communities with different landscape compositions including native forests, grasslands, successional forests and artificial structures. Robust occupancy modelling using land cover data at 10 m²resolution revealed that successional forest cover at 300 m spatial scale predicted greater occurrence ofR. pallescens, whereas native forest predicted lower occurrence. Quadratic models outperformed linear models, indicating occupancy peaks at intermediate land covers and palm tree traits.Real‐time PCR assays detectedTrypanosomainfections in 70% ofR. pallescensacross communities. Spatial autocorrelation analyses showed significant spatial clustering forT. cruzibut not forTrypanosoma rangeli. We used generalized additive mixed models to assess the influence of palm‐level and landscape‐scale attributes on parasite infection and identified significant nonlinear positive associations betweenT.cruziinfection and native forest and grassland, with high predictive accuracy (AUC = 0.90).Synthesis and applications. Findings here show that successional forest predicts greater kissing bug infestation risk in palm trees, whereas native forest predicts lower kissing bug occurrence but greater infection withT. cruzi. These insights can guide land use planning towards vegetation management practices that help minimizeT. cruzitransmission risks for rural communities. Importantly, vector surveillance should target forest‐grassland ecotones and consider forest successional stages near settlements, with intensified monitoring after disturbances; this approach is applicable to other vector‐borne pathogen systems shaped by land use change. 

Deforestation alters wildlife communities and modifies human–wildlife interactions, often increasing zoonotic spillover potential. When deforested land reverts to forest, species composition differences between primary and regenerating (secondary) forest could alter spillover risk trajectory. We develop a mathematical model of land-use change, where habitats differ in their relative spillover risk, to understand how land reversion influences spillover risk. We apply this framework to scenarios where spillover risk is higher in deforested land than mature forest, reflecting higher relative abundance of highly competent species and/or increased human–wildlife encounters, and where regenerating forest has either very low or high spillover risk. We find the forest regeneration rate, the spillover risk of regenerating forest relative to deforested land, and how rapidly regenerating forest regains attributes of mature forest determine landscape-level spillover risk. When regenerating forest has a much lower spillover risk than deforested land, reversion lowers cumulative spillover risk, but instaneous spillover risk peaks earlier. However, when spillover risk is high in regenerating and cleared habitats, landscape-level spillover risk remains high, especially when cleared land is rapidly abandoned then slowly regenerates to mature forest. These results suggest that proactive wildlife management and awareness of human exposure risk in regenerating forests could be important tools for spillover mitigation.