Abstract
Abstract Pollination is a key ecosystem service for global agriculture but evidence of pollinator population declines is growing. Reliable spatial modelling of pollinator abundance is essential if we are to identify areas at risk of pollination service deficit and effectively target resources to support pollinator populations. Many models exist which predict pollinator abundance but few have been calibrated against observational data from multiple habitats to ensure their predictions are accurate. We selected the most advanced process‐based pollinator abundance model available and calibrated it for bumblebees and solitary bees using survey data collected at 239 sites across Great Britain. We compared three versions of the model: one parameterised using estimates based on expert opinion, one where the parameters are calibrated using a purely data‐driven approach and one where we allow the expert opinion estimates to inform the calibration process. All three model versions showed significant agreement with the survey data, demonstrating this model's potential to reliably map pollinator abundance. However, there were significant differences between the nesting/floral attractiveness scores obtained by the two calibration methods and from the original expert opinion scores. Our results highlight a key universal challenge of calibrating spatially explicit, process‐based ecological models. Notably, the desire to reliably represent complex ecological processes in finely mapped landscapes necessarily generates a large number of parameters, which are challenging to calibrate with ecological and geographical data that are often noisy, biased, asynchronous and sometimes inaccurate. Purely data‐driven calibration can therefore result in unrealistic parameter values, despite appearing to improve model‐data agreement over initial expert opinion estimates. We therefore advocate a combined approach where data‐driven calibration and expert opinion are integrated into an iterative Delphi‐like process, which simultaneously combines model calibration and credibility assessment. This may provide the best opportunity to obtain realistic parameter estimates and reliable model predictions for ecological systems with expert knowledge gaps and patchy ecological data.
Highlights
Pollination is a key ecosystem service underpinning the reproduction of many flowering plants, including many crops
Our aim is to identify an optimum set of parameters for the model that produces the best agreement with the observed survey data and enables the model to be used with confidence to predict the consequences of land-use change on UK pollinator populations and pollination service
|8 M ethods in Ecology and Evoluঞon occur within many survey areas, (b) landclasses that occur close to survey areas and/or cover a large area within the surrounding landscape, (c) landclasses that have floral attractiveness scores similar to adjacent landclasses, and (d) landclasses that have high floral/nesting expert opinion attractiveness scores, since ±50% of a high score results in a bigger absolute change in input attractiveness score than ±50% of a low score
Summary
Pollination is a key ecosystem service underpinning the reproduction of many flowering plants, including many crops. Pollinator populations are under increasing pressure from landscape simplification (Kennedy et al, 2013), agrochemical use (Rundlöf et al, 2015; Woodcock et al, 2017) and climate change (Kerr et al, 2015), and there is growing evidence of instability in pollinator-dependent crop yields (Garibaldi et al, 2011; Garratt et al, 2014) Unless addressed, these pressures are expected to cause significant declines in global pollinator diversity in the coming decades (Balfour et al, 2018; Rasmont et al, 2015), threatening global food security. Additional approaches are needed to help target resources to support pollinator populations
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