EnsemblES - Using ensemble techniques to capture the accuracy and sensitivity of ecosystem service models

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If the United Nations sustainable development goals (SDGs; https://sustainabledevelopment.un.org/) are to be achieved, it is vital to understand the interactions between people and nature. A significant aspect of these interactions can be classed as 'nature's contributions to people' (termed ecosystem services; ES). However, the empirical ES data needed to quantify these relationships are sparse in all parts of the World. Using recent advances in data availability from remote sensing, models are increasingly able to provide credible information where empirical data are lacking. Specifically, ES models produce maps of estimated ES (typically based on land cover and other driving variables) and so can provide the understanding of the spatial distribution and heterogeneity ES required to aid planning and optimisation of land use decisions. However, most ES modelling applications rely on a single model for each ES and few applications explicitly validate ES models against independent datasets. As a consequence, the uncertainties associated with each application of ES models (and the datasets that underpin) them remain largely unknown. This is a particular issue as the results of local-scale validation are likely not to be transferable to new locations or to the regional and national scales at which ES model outputs are most widely used.EnsemblES seeks to address these issues by: 1) investigating ES model input sensitivity, varying initial conditions at the start of model simulations; 2) combining the outputs of multiple ES models (from multiple initial conditions) into 'ensembles' of models using a variety of techniques including when data on individual model performance is vs is not available; and 3) validating these model ensembles against independent data, highlighting a) the accuracy of ES ensembles, and b) whether coefficients of variation of the ensemble is a good predictor of ensemble uncertainty.