Enhancing Biodiversity Target Assessments with Decision-Optimized Multispecies Predictions

Predicting biodiversity change and translating those insights into effective conservation actions remain central challenges for meeting global and national biodiversity targets. Species Distribution Models (SDMs) are widely used to anticipate shifts in species ranges, yet their outputs—typically probabilities of occurrence—often require binarization before they can be used in policy-relevant applications such as priority area selection, biodiversity indicators, or multispecies composition assessments. Conventional thresholding approaches are often heuristic, introducing substantial biases in community estimates, especially when datasets include numerous rare or imbalanced species distributions.

We present a new decision-driven binarization framework developed to enhance the policy relevance of SDM-based predictions by selecting multispecies assemblages that directly optimize targeted evaluation metrics. This formulation treats binarization as a decision problem, allowing predictions to be explicitly aligned with conservation objectives without any use of additional data for calibrating the binarization. In parallel, we introduce a computationally efficient alternative—the Set Size Expectation (SSE) method—which predicts assemblages based on expected biodiversity richness and scales effectively to national and global modelling efforts. Both approaches have demonstrated strong empirical performance against literature reference methods across diverse taxa, environmental contexts and wide gradients in evaluated species richness

By providing more accurate and objective multispecies presence–absence predictions, our framework strengthens the ability of predictive models to support forward-looking conservation strategies. By allowing decision-optimized multispecies predictions, it improves the translation of SDM outputs into actionable insights for biodiversity monitoring, scenario evaluation, and spatial planning—key components for achieving the goals set by global biodiversity agreements.

Building on these advances, our current research integrates the optimal binarization step into a sampling pipeline that generates realistic species assemblages capable of capturing fine-scale multimodal distributions but also measuring prediction uncertainties. This development offers a more nuanced and ecologically grounded representation of biodiversity patterns, further enhancing their utility for monitoring and decision-support in the face of accelerating environmental change.