Pollination supply models from a local to global scale

dc.creatorGiménez-García, Angel
dc.creatorAllen-Perkins, Alfonso
dc.creatorBartomeus, Ignasi
dc.creatorBalbi, Stefano
dc.creatorKnapp, Jessica L.
dc.creatorHevia, Violeta
dc.creatorWoodcock, Ben Alex
dc.creatorSmagghe, Guy
dc.creatorMiñarro, Marcos
dc.creatorEeraerts, Maxime
dc.creatorColville, Jonathan F.
dc.creatorHipólito, Juliana
dc.creatorCavigliasso, Pablo
dc.creatorNates-Parra, Guiomar
dc.creatorHerrera, José M.
dc.creatorCusser, Sarah
dc.creatorSimmons, Benno I.
dc.creatorWolters, Volkmar
dc.creatorJha, Shalene
dc.creatorFreitas, Breno M.
dc.creatorHorgan, Finbarr G.
dc.creatorArtz, Derek R.
dc.creatorSidhu, C. Sheena
dc.creatorOtieno, Mark
dc.creatorBoreux, Virginie
dc.creatorBiddinger, David J.
dc.creatorKlein, Alexandra-Maria
dc.creatorJoshi, Neelendra K.
dc.creatorStewart, Rebecca I. A.
dc.creatorAlbrecht, Matthias
dc.creatorNicholson, Charlie C.
dc.creatorO'Reilly, Alison D.
dc.creatorCrowder, David William
dc.creatorBurns, Katherine L. W.
dc.creatorNabaes Jodar, Diego Nicolás
dc.creatorGaribaldi, Lucas Alejandro
dc.creatorSutter, Louis
dc.creatorDupont, Yoko L.
dc.creatorDalsgaard, Bo
dc.creatorda Encarnação Coutinho, Jeferson Gabriel
dc.creatorLázaro, Amparo
dc.creatorAndersson, Georg K. S.
dc.creatorRaine, Nigel E.
dc.creatorKrishnan, Smitha
dc.creatorDainese, Matteo
dc.creatorvan der Werf, Wopke
dc.creatorSmith, Henrik G.
dc.creatorMagrach, Ainhoa
dc.date2024-01-11T14:45:26Z
dc.date2024-01-11T14:45:26Z
dc.date2023
dc.date.accessioned2024-05-02T20:32:02Z
dc.date.available2024-05-02T20:32:02Z
dc.identifierhttp://repositorio.ucm.cl/handle/ucm/5171
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/9275356
dc.descriptionMechanistic 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.
dc.languageen
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 Chile
dc.rightshttp://creativecommons.org/licenses/by-nc-nd/3.0/cl/
dc.sourceWeb Ecology, 23(2), 99-129
dc.titlePollination supply models from a local to global scale
dc.typeArticle


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