Regional ecological forecasting across scales: A manifesto for a biodiversity hotspot

Abstract

Abstract Iterative near‐term ecological forecasting has great promise to provide vital information to decision‐makers while improving our ecological understanding, yet several logistical and fundamental challenges remain. The ecoinformatics requirements are onerous to develop and maintain, posing a barrier to entry for regions where funding and expertise are limited, and there are fundamental challenges to developing forecasts that fulfil information needs spanning spatial, temporal and biological scales. Using the hyperdiverse Cape Floristic Region of South Africa as a case study, we propose that developing regionally focussed sets of ecological forecasts will help resolve logistical challenges faced by under‐resourced regions of the world, while comparison or coupling of models across scales will facilitate new fundamental insights. We review information needs and existing models for the region and explore how they could be developed into a set of linked iterative near‐term forecasts. Comparing or coupling ecological forecasts from different scales within the same domain has much potential to provide new insights for decision‐makers and ecologists alike. They allow us to quantitatively link processes in space and time, potentially revealing feedbacks, interconnections and emergent properties, while providing powerful tools for testing decision scenarios and identifying trade‐offs or unanticipated outcomes. While the development of multiple or combined ecological forecasts that span scales is not trivial, there are logistical gains to be made from developing shared ecoinformatics pipelines that feed multiple models. Even where useful forecasts do not yet exist, the pipelines can be of great value in their own right, delivering frequent and up‐to‐date information to decision‐makers while providing the basis for forecast development and other scientific research. Viewed together, regionally focussed approaches to ecological forecasting present a compelling opportunity to overcome logistical constraints and to integrate across multiple scales of organisation, ultimately improving our understanding and management of ecosystems.