Species Distribution Models

Ensuring the right tree species are planted in the right place is crucial for reforestation success and long-term ecosystem health.

Terya is developing a Species Distribution Model (SDM) powered by machine learning to guide species selection and placement. This model takes into account environmental variables (climate data, soil type, topography, current vegetation cover) and predicts the suitability of different tree species across the landscape. By analyzing thousands of data points, the AI can recommend a diverse mix of native species optimally matched to each micro-habitat, improving survival rates and resilience. Advanced ecological ML tools help identify which native species are most likely to thrive under the local conditions today and under future climate scenarios (New Tree Tech: AI, drones, satellites and sensors give reforestation a boost). For example, if a certain area is projected to get drier, the model might favor drought-tolerant species there. The SDM is trained using historical species occurrence records (from botanic surveys and forest inventories) combined with environmental layers; algorithms like MaxEnt or ensemble decision trees ensure robust predictions of habitat suitability. In practice, this means when planning new planting, the team can refer to AI-generated maps highlighting zones where species like Acacia or Moringa would do best. This data-driven approach increases biodiversity and the adaptive capacity of the restored forest. It also feeds into community knowledge – local planters can be informed by the model’s suggestions, blending AI insights with traditional ecological knowledge.