Biodiversity Models and Nature Futures Scenarios as Transformative Agents in Achieving the KM-GBF

With biodiversity increasingly under pressure from human activities and global environmental change, decisive actions to halt and reverse its decline is essential. Global policy frameworks such as the Kunming–Montreal Global Biodiversity Framework (KM-GBF) offer guidance to governments and corporations by outlining clear goals and targets for achieving this transition. Effective implementation and monitoring of progress toward these goals require robust modelling tools that incorporate past trends and project future trajectories under different scenarios. Moreover, within the KM-GBF is the need to recognise clear recognition of diverse values of nature – such as those reflected in the Nature Futures Framework - to ensure that multiple worldviews are considered in locally-relevant contexts. Models incorporating different value perspectives provide important insights into how physical, environmental, and socio-economic interventions could support the synergistic and coherent implementation of the KM-GBF across sectors and value systems.  However, there remains no representative accounting of models that can inform holistically on the KM-GBF goals and targets. Here we present a review of the current biodiversity modelling landscape around 60 models identified through a multi-evidence-based approach. We evaluate current capacities, strengths and limitations, and adaptability of these models in providing a toolbox for modelling and projecting biodiversity change for policy and decision-making processes. Through this review and analyses, we found that biodiversity and nature’s contribution to people (NCP) focussed goals (i.e., goal A and B) and biodiversity and NCP-centred targets (i.e., targets 1- 8) are well informed by indicators produced by the models. However, there remains critical gaps in informing the ecological restoration, genetic diversity and invasive alien species. The review also presented opportunities for model intercomparison and complementary modelling with common and comparable metrics/variables produced and shared across the models. Major shortcomings relate to the lack of indicators on drivers that can inform on how policy options were reflected in model parameterization and under-representation of cultural values of nature. Future developments can accelerate progress through integrative scenario analysis and improved detection and attribution capabilities for more robust predictions of how different future pathways affect may progress and synergize in responding to evolving societal and environmental challenges.