Pollination supply models from a local to global scale

IF 2.3 3区 环境科学与生态学 Q2 ECOLOGY Web Ecology Pub Date : 2023-10-04 DOI:10.5194/we-23-99-2023
Angel Giménez-García, Alfonso Allen-Perkins, Ignasi Bartomeus, Stefano Balbi, Jessica L. Knapp, Violeta Hevia, Ben Alex Woodcock, Guy Smagghe, Marcos Miñarro, Maxime Eeraerts, Jonathan F. Colville, Juliana Hipólito, Pablo Cavigliasso, Guiomar Nates-Parra, José M. Herrera, Sarah Cusser, Benno I. Simmons, Volkmar Wolters, Shalene Jha, Breno M. Freitas, Finbarr G. Horgan, Derek R. Artz, C. Sheena Sidhu, Mark Otieno, Virginie Boreux, David J. Biddinger, Alexandra-Maria Klein, Neelendra K. Joshi, Rebecca I. A. Stewart, Matthias Albrecht, Charlie C. Nicholson, Alison D. O'Reilly, David William Crowder, Katherine L. W. Burns, Diego Nicolás Nabaes Jodar, Lucas Alejandro Garibaldi, Louis Sutter, Yoko L. Dupont, Bo Dalsgaard, Jeferson Gabriel da Encarnação Coutinho, Amparo Lázaro, Georg K. S. Andersson, Nigel E. Raine, Smitha Krishnan, Matteo Dainese, Wopke van der Werf, Henrik G. Smith, Ainhoa Magrach
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引用次数: 0

Abstract

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 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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从地方到全球范围的授粉供应模式
摘要生态集约化已经受到学术界的极大关注,但农民仍然很少采用,因为监测不同生态功能的状态并不简单。建模工具是衡量生态功能的一种更容易获得的替代方法,可以帮助促进农民和其他决策者使用这些工具。在作物授粉的情况下,建模传统上遵循机械或数据驱动的方法。机械模型模拟传粉媒介的栖息地偏好和觅食行为,而数据驱动模型将地理参考变量与实际观测结果联系起来。在这里,我们测试了这两种方法来预测授粉供应,并使用新发布的全球传粉者访问不同作物的数据集来验证这些预测。对于机械方法,我们使用最广泛使用的模型之一,而对于数据驱动方法,我们从一组全面的最先进的机器学习模型中进行选择。此外,我们探索了一种混合方法,其中数据派生的输入,而不是专家评估,告知机制模型。我们发现,在全球范围内,机器学习模型效果最好,在传粉者访问率的预测和观测之间提供了0.56的秩相关系数。反过来,这种机制模型在全球范围内对大黄蜂以外的野生蜜蜂也适用。以温带或地中海森林为特征的生物群落在机制模型预测和观测之间表现出更好的一致性,这可能是由于更全面的生态学知识,因此这些生物群落的输入变量参数化得更好。鉴于不同生物群系中物种的组成不同,本研究强调了在多个生物群系之间传递输入变量的挑战。我们的研究结果为在不同空间尺度上哪种授粉供给模型表现最佳提供了明确的指导,这是在生态集约化下生态系统服务交付模型中弥合利益相关者与学术界差距的第一步。
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来源期刊
Web Ecology
Web Ecology Agricultural and Biological Sciences-Ecology, Evolution, Behavior and Systematics
CiteScore
4.60
自引率
0.00%
发文量
6
审稿时长
17 weeks
期刊介绍: Web Ecology (WE) is an open-access journal issued by the European Ecological Federation (EEF) representing the ecological societies within Europe and associated members. Its special value is to serve as a publication forum for national ecological societies that do not maintain their own society journal. Web Ecology publishes papers from all fields of ecology without any geographic restriction. It is a forum to communicate results of experimental, theoretical, and descriptive studies of general interest to an international audience. Original contributions, short communications, and reviews on ecological research on all kinds of organisms and ecosystems are welcome as well as papers that express emerging ideas and concepts with a sound scientific background.
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