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

Research output: Contribution to journalArticle

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.

Details

Authors
  • Emma Gardner
  • Tom D. Breeze
  • Yann Clough
  • Henrik G. Smith
  • Katherine C.R. Baldock
  • Alistair Campbell
  • Michael P.D. Garratt
  • Mark A.K. Gillespie
  • William E. Kunin
  • Megan McKerchar
  • Jane Memmott
  • Simon G. Potts
  • Deepa Senapathi
  • Graham N. Stone
  • Felix Wäckers
  • Duncan B. Westbury
  • Andrew Wilby
  • Tom H. Oliver
Organisations
External organisations
  • University of Reading
  • University of Bristol
  • Northumbria University
  • Brazilian Agricultural Research Corporation
  • University of Leeds
  • Western Norway University of Applied Sciences
  • University of Worcester
  • University of Edinburgh
  • Lancaster University
Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Ecology
  • Environmental Sciences related to Agriculture and Land-use

Keywords

  • calibration, credibility assessment, Delphi panels, ecosystem services, pollinators, process-based models, validation
Original languageEnglish
Pages (from-to)1673-1689
Number of pages17
JournalMethods in Ecology and Evolution
Volume11
Issue number12
Early online date2020 Sep 7
Publication statusPublished - 2020 Dec
Publication categoryResearch
Peer-reviewedYes