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Reliably predicting...
Reliably predicting pollinator abundance : Challenges of calibrating process-based ecological models
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- Gardner, Emma (författare)
- University of Reading
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- Breeze, Tom D. (författare)
- University of Reading
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- Clough, Yann (författare)
- Lund University,Lunds universitet,BECC: Biodiversity and Ecosystem services in a Changing Climate,Centrum för miljö- och klimatvetenskap (CEC),Naturvetenskapliga fakulteten,Centre for Environmental and Climate Science (CEC),Faculty of Science
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- Smith, Henrik G. (författare)
- Lund University,Lunds universitet,BECC: Biodiversity and Ecosystem services in a Changing Climate,Centrum för miljö- och klimatvetenskap (CEC),Naturvetenskapliga fakulteten,Centre for Environmental and Climate Science (CEC),Faculty of Science
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- Baldock, Katherine C.R. (författare)
- University of Bristol,Northumbria University
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- Campbell, Alistair (författare)
- Brazilian Agricultural Research Corporation
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- Garratt, Michael P.D. (författare)
- University of Reading
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- Gillespie, Mark A.K. (författare)
- Western Norway University of Applied Sciences,University of Leeds
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- Kunin, William E. (författare)
- University of Leeds
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- McKerchar, Megan (författare)
- University of Worcester
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- Memmott, Jane (författare)
- University of Bristol
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- Potts, Simon G. (författare)
- University of Reading
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- Senapathi, Deepa (författare)
- University of Reading
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- Stone, Graham N. (författare)
- University of Edinburgh
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- Wäckers, Felix (författare)
- Lancaster University
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- Westbury, Duncan B. (författare)
- University of Worcester
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- Wilby, Andrew (författare)
- Lancaster University
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- Oliver, Tom H. (författare)
- University of Reading
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(creator_code:org_t)
- 2020
- 2020
- Engelska 17 s.
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Ingår i: Methods in Ecology and Evolution. - 2041-210X. ; 11:12, s. 1673-1689
- Relaterad länk:
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http://dx.doi.org/10... (free)
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https://lup.lub.lu.s...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- 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.
Ämnesord
- NATURVETENSKAP -- Biologi -- Ekologi (hsv//swe)
- NATURAL SCIENCES -- Biological Sciences -- Ecology (hsv//eng)
- LANTBRUKSVETENSKAPER -- Annan lantbruksvetenskap -- Miljö- och naturvårdsvetenskap (hsv//swe)
- AGRICULTURAL SCIENCES -- Other Agricultural Sciences -- Environmental Sciences related to Agriculture and Land-use (hsv//eng)
Nyckelord
- calibration
- credibility assessment
- Delphi panels
- ecosystem services
- pollinators
- process-based models
- validation
Publikations- och innehållstyp
- art (ämneskategori)
- ref (ämneskategori)
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Till lärosätets databas
- Av författaren/redakt...
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Gardner, Emma
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Breeze, Tom D.
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Clough, Yann
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Smith, Henrik G.
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Baldock, Katheri ...
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Campbell, Alista ...
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visa fler...
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Garratt, Michael ...
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Gillespie, Mark ...
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Kunin, William E ...
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McKerchar, Megan
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Memmott, Jane
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Potts, Simon G.
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Senapathi, Deepa
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Stone, Graham N.
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Wäckers, Felix
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Westbury, Duncan ...
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Wilby, Andrew
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Oliver, Tom H.
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visa färre...
- Om ämnet
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- NATURVETENSKAP
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NATURVETENSKAP
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och Biologi
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och Ekologi
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- LANTBRUKSVETENSKAPER
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LANTBRUKSVETENSK ...
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och Annan lantbruksv ...
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och Miljö och naturv ...
- Artiklar i publikationen
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Methods in Ecolo ...
- Av lärosätet
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Lunds universitet