Pollination supply models from a local to global scale

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.This research has been supported by the 2017–2018 Belmont Forum and BiodivERsA joint call for research proposals, under the BiodivScen ERA-NET Cofund programme; the funding organizations AEI, NWO, ECCyT and NSF; the Spanish State Research Agency through María de Maeztu Unit of Excellence accreditation (MDM-2017-0714); the Basque Government BERC programme; the Comunidad de Madrid through the call Research Grants for Young Investigators from Universidad Politécnica de Madrid; the European Union FEDER Interreg Sudoe programme (SOE1/P5/E0129); the Research Foundation – Flanders (FWO) and Special Research Fund of Ghent University (BOF); INIA-RTA2013-00139-C03-01 (MinECo and FEDER) and PCIN2014-145-C02-02 (MinECo; EcoFruit project BiodivERsA-FACCE2014-74); the Global Environmental Facility–United Nations Environment Programme–Food and Agricultural Organization Global Pollination Project; the INTA Structural Project “Development of the organized, sustainable and competitive beekeeping sector (2019-PE-E1-I017-001)”; the Portuguese national research funding agency (FCT, contract IF/00001/2015); the Maria Zambrano International Talent Recruitment Programme funded by the Spanish Ministry of Universities; a Royal Commission for the Exhibition of 1851 Research Fellowship; the Brazilian National Council for Scientific and Technological Development (Conselho Nacional de Desenvolvimento Científico e Tecnológico, CNPq, no. 308358/2019-8); the Philippines Department of Agriculture, Bureau of Agricultural Research; the USDA NIFA Specialty Crop Research Initiative, from Project 2012-51181-20105: Developing Sustainable Pollination Strategies for U.S. Specialty Crops; the Felix Trust; the United States Department of Agriculture, National Institute for Food and Agriculture, through the Specialty Crop Research Initiative projects 2012-01534 (Developing Sustainable Pollination Strategies for U.S. Specialty Crops) and PEN04398 (Determining the Role of and Limiting Factors Facing Native Pollinators in Assuring Quality Apple Production in Pennsylvania; a Model for the Mid-Atlantic Tree Fruit Industry); the State Horticultural Association of Pennsylvania; the Alexander von Humboldt Foundation; the German Research Foundation; the German Academic Exchange Service; USDA NIFA SCRI (PEN04398) and USDA NIFA (ARK02527 ARK02710); Science Foundation Ireland; the Irish Research Council, Environmental Protection Agency and Eva Crane Trust; CNPq; the Global Environmental Facility (GEF); the Food and Agriculture Organization of the United Nations (FAO); the United Nations Environment Programme (UNEP); the Brazilian Biodiversity Fund (Fundo Brasileiro para a Biodiversidade, FUNBIO); the Ontario Ministry of Agriculture, Food and Rural Affairs (OMAFRA grant UofG2015-2466); the initiative Food from Thought: Agricultural Systems for a Healthy Planet, funded by the Canada First Research Excellence Fund (grant 000054); the Rebanks Family Chair in Pollinator Conservation by the Weston Family Foundation; the North-South Centre; ETH Zürich; the Mercator Foundation Switzerland through the ETH Zürich World Food System Center; and the Swedish Research Council Formas