RT journal article
T1 Pollination supply models from a local to global scale
A1 Giménez-García, Ángel
A1 Allen-Perkins, Alfonso
A1 Bartomeus, Ignasi
A1 Balbi, Stefano
A1 Knapp, Jessica L.
A1 Hevia, Violeta
A1 Woodcock, Ben Alex
A1 Smagghe, Guy
A1 Miñarro, Marcos
A1 Eeraerts, Maxime
A1 Colville, Jonathan F.
A1 Hipólito, Juliana
A1 Cavigliasso, Pablo
A1 Nates-Parra, Guiomar
A1 Herrera Vega, José Manuel
A1 Cusser, Sarah
A1 Simmons, Benno I.
A1 Wolters, Volkmar
A1 Jha, Shalene
A1 Freitas, Breno M.
A1 Horgan, Finnbar G.
A1 Artz, Derek R.
A1 Sidhu, C. Sheena
A1 Otieno, Mark
A1 Boreux, Virginie
A1 Biddinger, David J.
A1 Klein, Alexandra-Maria
A1 Joshi, Neelendra K.
A1 Stewart, Rebecca I. A.
A1 Albrecht, Matthias
A1 Nicholson, Charlie C.
A1 O'Reilly, Alison D.
A1 Crowder, David William
A1 Burns, Katherine L. W.
A1 Nabaes Jodar, Diego Nicolás
A1 Garibaldi, Lucas Alejandro
A1 Sutter, Louis
A1 Dupont, Yoko L.
A1 Dalsgaard, Bo
A1 Da Encarnação Coutinho, Jeferson Gabriel
A1 Lázaro, Amparo
A1 Andersson, Georg K. S.
A1 Raine, Nigel E.
A1 Krishnan, Smitha
A1 Dainese, Matteo
A1 Van Der Werf, Wopke
A1 Smith, Henrik G.
A1 Magrach, Ainhoa
A2 Biología
K1 Mediterranean Region
K1 bee
K1 biome
K1 machine learning
K1 pollination
K1 pollinator
AB 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.
PB Copernicus Publications
SN 1399-1183
YR 2023
FD 2023
LK http://hdl.handle.net/10498/31480
UL http://hdl.handle.net/10498/31480
LA eng
DS Repositorio Institucional de la Universidad de Cádiz
RD 12-nov-2026