Learning from the Exploratories to make prediction beyond them: AI-based mapping and explanation of grassland biodiversity and ecosystem functions for entire landscape units (BEyond)

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Prediction, understanding and monitoring of spatio-temporal patterns is a major challenge in ecological research. The aim of this project is to learn from the unprecedented dataset of the Biodiversity Exploratories to predict patterns of biodiversity and ecosystem functioning beyond the Exploratories - for their entire landscape units. We choose an indirect modelling approach by including terrain, soil, climate, landuse and landscape structure as potential drivers in addition to remotely sensed data. Machine learning offers great opportunities for predictive mapping, due to the ability to learn non-linear and complex relationships between drivers and biodiversity variables. However, recent research indicates considerable limitations when trained models are applied to make predictions beyond intensively studied areas. Both, spatial overfitting and the learning of scientifically wrong relationships may significantly lead to a limited model transferability. A lack of model interpretability further prevents advancements for ecological research. To overcome these limitations we will develop and apply new methods for spatio-temporal predictive mapping that focus on increasing the model transferability coupled with methods of explainable artificial intelligence. We expect to be able to develop scientifically sound models that provide spatio-temporal continuous maps of selected biodiversity variables beyond the Exploratories. We further go beyond predictions and expect that insights into the former “black-box” models provide novel findings on patterns, drivers and interactions.