Evaluating Biodiversity Credit Metrics Using Metacommunity Modelling

Abstract

Global biodiversity enhancement is central to the UN Sustainable Development Goals and climate change mitigation. Achieving the Kunming-Montreal Global Biodiversity Frameworks 30 by 30 target requires an estimated additional US$700 billion annually. Biodiversity credit markets seek to address this funding gap by assigning financial value to biodiversity and ecosystem services. However, limited understanding of the metrics underpinning these credits pose significant barriers to their scalability and effectiveness. This pioneering study compares six credit metrics with six established biodiversity metrics to assess whether methodology choice influences metric responses to different ecosystem perturbations, identifying metrics best suited for specific interventions, and exploring comparability across metrics. A spatially explicit, multi-layered metacommunity simulation model, capable of reproducing a variety of empirically established macro-ecological patterns, was adapted to track ecosystem responses to six perturbation experiments and to record changes in the twelve tracked biodiversity metrics. Results reveal substantial divergence in how credit metrics assign value to nature, particularly between those estimating ecosystem services and those assessing species extinction risk. These findings underscore the need for careful alignment between metric selection and the ecological objectives of biodiversity projects and suggest that the development of a universal biodiversity credit is unlikely. Furthermore, in addition to metrics estimating ecosystem services, our results suggest that projects should incorporate metrics that are sensitive to declines in species-level abundances, thereby reflecting extinction risk.