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

Gardner, Emma ; Breeze, Tom D. ; Clough, Yann LU ; Smith, Henrik G. LU ; Baldock, Katherine C.R. ; Campbell, Alistair ; Garratt, Michael P.D. ; Gillespie, Mark A.K. ; Kunin, William E. and McKerchar, Megan , et al. (2020) In Methods in Ecology and Evolution 11(12). p.1673-1689
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... (More)

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.

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organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
calibration, credibility assessment, Delphi panels, ecosystem services, pollinators, process-based models, validation
in
Methods in Ecology and Evolution
volume
11
issue
12
pages
17 pages
publisher
John Wiley and Sons
external identifiers
  • scopus:85091733658
ISSN
2041-210X
DOI
10.1111/2041-210X.13483
language
English
LU publication?
yes
id
a8361172-f040-43b8-a80e-dcc88c272d43
date added to LUP
2020-11-02 12:25:36
date last changed
2021-01-06 07:13:08
@article{a8361172-f040-43b8-a80e-dcc88c272d43,
  abstract     = {<p>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.</p>},
  author       = {Gardner, Emma and Breeze, Tom D. and Clough, Yann and Smith, Henrik G. and Baldock, Katherine C.R. and Campbell, Alistair and Garratt, Michael P.D. and Gillespie, Mark A.K. and Kunin, William E. and McKerchar, Megan and Memmott, Jane and Potts, Simon G. and Senapathi, Deepa and Stone, Graham N. and Wäckers, Felix and Westbury, Duncan B. and Wilby, Andrew and Oliver, Tom H.},
  issn         = {2041-210X},
  language     = {eng},
  number       = {12},
  pages        = {1673--1689},
  publisher    = {John Wiley and Sons},
  series       = {Methods in Ecology and Evolution},
  title        = {Reliably predicting pollinator abundance : Challenges of calibrating process-based ecological models},
  url          = {http://dx.doi.org/10.1111/2041-210X.13483},
  doi          = {10.1111/2041-210X.13483},
  volume       = {11},
  year         = {2020},
}