Reliably predicting pollinator abundance: challenges of calibrating process-based ecological models.

Gardner, E., Breeze, T.D., Clough, Y., Baldock, K.C.R., Campbell, A., Garratt, M., Gillespie, M.A.K., Kunin, W.E., McKerchar, Megan, Memmott, J., Potts, S.G., Senapathi, D., Stone, G.N., Wäckers, F., Westbury, Duncan B, Wilby, A. and Oliver, T.H. (2020) Reliably predicting pollinator abundance: challenges of calibrating process-based ecological models. Methods in Ecology and Evolution, 11 (12). pp. 1673-1689.

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Abstract

1. Pollination is a key ecosystem service for global agriculture but evidence of pollinator populationdeclines is growing. Reliable spatial modelling of pollinator abundance is essential if we are toidentify areas at risk of pollination service deficit and effectively target resources to support pollinatorpopulations. Many models exist which predict pollinator abundance but few have been calibratedagainst observational data from multiple habitats to ensure their predictions are accurate. 2. 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 GreatBritain. 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 onewhere we allow the expert opinion estimates to inform the calibration process. 3. All three model versions showed significant agreement with the survey data, demonstrating thismodel?s potential to reliably map pollinator abundance. However, there were significant differencesbetween the nesting/floral attractiveness scores obtained by the two calibration methods and fromthe original expert opinion scores. 4. Our results highlight a key universal challenge of calibrating spatially-explicit, process-basedecological models. Notably, the desire to reliably represent complex ecological processes in finelymapped landscapes necessarily generates a large number of parameters, which are challenging to calibrate with ecological and geographical data that is often noisy, biased, asynchronous and sometimesinaccurate. Purely data-driven calibration can therefore result in unrealistic parameter values, despite appearing to improve model-data agreement over initial expert opinion estimates. We thereforeadvocate a combined approach where data-driven calibration and expert opinion are integrated intoan iterative Delphi-like process, which simultaneously combines model calibration and credibility assessment. This may provide the best opportunity to obtain realistic parameter estimates and reliablemodel predictions for ecological systems with expert knowledge gaps and patchy ecological data.

Item Type:	Article
Additional Information:	The full-text of the online published article can be accessed via the official URL. © 2020 The Authors. Methods in Ecology and Evolution published by John Wiley & Sons Ltd on behalf of British Ecological Society This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Keywords:	calibration, credibility assessment, Delphi panels, ecosystem services, pollinators, process-based models, validation
Divisions:	Land and Property Management
Depositing User:	Professor Duncan Westbury
Date Deposited:	19 Apr 2024 12:48
Last Modified:	19 Apr 2024 12:48
URI:	https://rau.repository.guildhe.ac.uk/id/eprint/16605

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