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Pollination supply models from a local to global scale

Giménez-García, Angel ; Allen-Perkins, Alfonso ; Bartomeus, Ignasi ; Balbi, Stefano ; Knapp, Jessica L. LU ; Hevia, Violeta ; Woodcock, Ben Alex ; Smagghe, Guy ; Miñarro, Marcos and Eeraerts, Maxime , et al. (2023) In Web Ecology 23(2). p.99-129
Abstract

Ecological intensification has been embraced with great interest by the academic sector but is still rarely taken up by farmers because monitoring the state of different ecological functions is not straightforward. Modelling tools can represent a more accessible alternative of measuring ecological functions, which could help promote their use amongst farmers and other decision-makers. In the case of crop pollination, modelling has traditionally followed either a mechanistic or a data-driven approach. Mechanistic models simulate the habitat preferences and foraging behaviour of pollinators, while data-driven models associate georeferenced variables with real observations. Here, we test these two approaches to predict pollination supply... (More)

Ecological intensification has been embraced with great interest by the academic sector but is still rarely taken up by farmers because monitoring the state of different ecological functions is not straightforward. Modelling tools can represent a more accessible alternative of measuring ecological functions, which could help promote their use amongst farmers and other decision-makers. In the case of crop pollination, modelling has traditionally followed either a mechanistic or a data-driven approach. Mechanistic models simulate the habitat preferences and foraging behaviour of pollinators, while data-driven models associate georeferenced variables with real observations. Here, we test these two approaches to predict pollination supply and validate these predictions using data from a newly released global dataset on pollinator visitation rates to different crops. We use one of the most extensively used models for the mechanistic approach, while for the data-driven approach, we select from among a comprehensive set of state-of-The-Art machine-learning models. Moreover, we explore a mixed approach, where data-derived inputs, rather than expert assessment, inform the mechanistic model. We find that, at a global scale, machine-learning models work best, offering a rank correlation coefficient between predictions and observations of pollinator visitation rates of 0.56. In turn, the mechanistic model works moderately well at a global scale for wild bees other than bumblebees. Biomes characterized by temperate or Mediterranean forests show a better agreement between mechanistic model predictions and observations, probably due to more comprehensive ecological knowledge and therefore better parameterization of input variables for these biomes. This study highlights the challenges of transferring input variables across multiple biomes, as expected given the different composition of species in different biomes. Our results provide clear guidance on which pollination supply models perform best at different spatial scales-the first step towards bridging the stakeholder-Academia gap in modelling ecosystem service delivery under ecological intensification.

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organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Web Ecology
volume
23
issue
2
pages
31 pages
publisher
European Ecological Federation in cooperation with Oikos
external identifiers
  • scopus:85178190049
ISSN
1399-1183
DOI
10.5194/we-23-99-2023
language
English
LU publication?
yes
id
765c3449-65c0-42f3-bbf3-390c7778a720
date added to LUP
2024-01-04 15:06:49
date last changed
2024-01-08 15:53:09
@article{765c3449-65c0-42f3-bbf3-390c7778a720,
  abstract     = {{<p>Ecological intensification has been embraced with great interest by the academic sector but is still rarely taken up by farmers because monitoring the state of different ecological functions is not straightforward. Modelling tools can represent a more accessible alternative of measuring ecological functions, which could help promote their use amongst farmers and other decision-makers. In the case of crop pollination, modelling has traditionally followed either a mechanistic or a data-driven approach. Mechanistic models simulate the habitat preferences and foraging behaviour of pollinators, while data-driven models associate georeferenced variables with real observations. Here, we test these two approaches to predict pollination supply and validate these predictions using data from a newly released global dataset on pollinator visitation rates to different crops. We use one of the most extensively used models for the mechanistic approach, while for the data-driven approach, we select from among a comprehensive set of state-of-The-Art machine-learning models. Moreover, we explore a mixed approach, where data-derived inputs, rather than expert assessment, inform the mechanistic model. We find that, at a global scale, machine-learning models work best, offering a rank correlation coefficient between predictions and observations of pollinator visitation rates of 0.56. In turn, the mechanistic model works moderately well at a global scale for wild bees other than bumblebees. Biomes characterized by temperate or Mediterranean forests show a better agreement between mechanistic model predictions and observations, probably due to more comprehensive ecological knowledge and therefore better parameterization of input variables for these biomes. This study highlights the challenges of transferring input variables across multiple biomes, as expected given the different composition of species in different biomes. Our results provide clear guidance on which pollination supply models perform best at different spatial scales-the first step towards bridging the stakeholder-Academia gap in modelling ecosystem service delivery under ecological intensification.</p>}},
  author       = {{Giménez-García, Angel and Allen-Perkins, Alfonso and Bartomeus, Ignasi and Balbi, Stefano and Knapp, Jessica L. and Hevia, Violeta and Woodcock, Ben Alex and Smagghe, Guy and Miñarro, Marcos and Eeraerts, Maxime and Colville, Jonathan F. and Hipólito, Juliana and Cavigliasso, Pablo and Nates-Parra, Guiomar and Herrera, José M. and Cusser, Sarah and Simmons, Benno I. and Wolters, Volkmar and Jha, Shalene and Freitas, Breno M. and Horgan, Finbarr G. and Artz, Derek R. and Sidhu, C. Sheena and Otieno, Mark and Boreux, Virginie and Biddinger, David J. and Klein, Alexandra Maria and Joshi, Neelendra K. and Stewart, Rebecca I.A. and Albrecht, Matthias and Nicholson, Charlie C. and O'Reilly, Alison D. and Crowder, David William and Burns, Katherine L.W. and Nabaes Jodar, Diego Nicolás and Garibaldi, Lucas Alejandro and Sutter, Louis and Dupont, Yoko L. and Dalsgaard, Bo and Da Encarnação Coutinho, Jeferson Gabriel and Lázaro, Amparo and Andersson, Georg K.S. and Raine, Nigel E. and Krishnan, Smitha and Dainese, Matteo and Van Der Werf, Wopke and Smith, Henrik G. and Magrach, Ainhoa}},
  issn         = {{1399-1183}},
  language     = {{eng}},
  month        = {{10}},
  number       = {{2}},
  pages        = {{99--129}},
  publisher    = {{European Ecological Federation in cooperation with Oikos}},
  series       = {{Web Ecology}},
  title        = {{Pollination supply models from a local to global scale}},
  url          = {{http://dx.doi.org/10.5194/we-23-99-2023}},
  doi          = {{10.5194/we-23-99-2023}},
  volume       = {{23}},
  year         = {{2023}},
}