Species Distribution Models for Crop Pollination: A Modelling Framework Applied to Great Britain

Chiara Polce; Mette Termansen; Jesus Aguirre-Gutiérrez; Nigel D. Boatman; Giles E. Budge; Andrew Crowe; Michael P. Garratt; Stéphane Pietravalle; Simon G. Potts; Jorge A. Ramirez; Kate E. Somerwill; Jacobus C. Biesmeijer

doi:10.1371/journal.pone.0076308

Species Distribution Models for Crop Pollination: A Modelling Framework Applied to Great Britain

Chiara Polce^*, Mette Termansen, Jesus Aguirre-Gutiérrez, Nigel D. Boatman, Giles E. Budge, Andrew Crowe, Michael P. Garratt, Stéphane Pietravalle, Simon G. Potts, Jorge A. Ramirez, Kate E. Somerwill, Jacobus C. Biesmeijer

^*Corresponding author for this work

36 Citations (Scopus)

Abstract

Insect pollination benefits over three quarters of the world's major crops. There is growing concern that observed declines in pollinators may impact on production and revenues from animal pollinated crops. Knowing the distribution of pollinators is therefore crucial for estimating their availability to pollinate crops; however, in general, we have an incomplete knowledge of where these pollinators occur. We propose a method to predict geographical patterns of pollination service to crops, novel in two elements: the use of pollinator records rather than expert knowledge to predict pollinator occurrence, and the inclusion of the managed pollinator supply. We integrated a maximum entropy species distribution model (SDM) with an existing pollination service model (PSM) to derive the availability of pollinators for crop pollination. We used nation-wide records of wild and managed pollinators (honey bees) as well as agricultural data from Great Britain. We first calibrated the SDM on a representative sample of bee and hoverfly crop pollinator species, evaluating the effects of different settings on model performance and on its capacity to identify the most important predictors. The importance of the different predictors was better resolved by SDM derived from simpler functions, with consistent results for bees and hoverflies. We then used the species distributions from the calibrated model to predict pollination service of wild and managed pollinators, using field beans as a test case. The PSM allowed us to spatially characterize the contribution of wild and managed pollinators and also identify areas potentially vulnerable to low pollination service provision, which can help direct local scale interventions. This approach can be extended to investigate geographical mismatches between crop pollination demand and the availability of pollinators, resulting from environmental change or policy scenarios.

Original language	English
Article number	e76308
Journal	PLoS ONE
Volume	8
Issue number	10
ISSN	1932-6203
DOIs	https://doi.org/10.1371/journal.pone.0076308
Publication status	Published - 14 Oct 2013
Externally published	Yes

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@article{84796554ff1049bda27ae1d40ca6e641,

title = "Species Distribution Models for Crop Pollination: A Modelling Framework Applied to Great Britain",

abstract = "Insect pollination benefits over three quarters of the world's major crops. There is growing concern that observed declines in pollinators may impact on production and revenues from animal pollinated crops. Knowing the distribution of pollinators is therefore crucial for estimating their availability to pollinate crops; however, in general, we have an incomplete knowledge of where these pollinators occur. We propose a method to predict geographical patterns of pollination service to crops, novel in two elements: the use of pollinator records rather than expert knowledge to predict pollinator occurrence, and the inclusion of the managed pollinator supply. We integrated a maximum entropy species distribution model (SDM) with an existing pollination service model (PSM) to derive the availability of pollinators for crop pollination. We used nation-wide records of wild and managed pollinators (honey bees) as well as agricultural data from Great Britain. We first calibrated the SDM on a representative sample of bee and hoverfly crop pollinator species, evaluating the effects of different settings on model performance and on its capacity to identify the most important predictors. The importance of the different predictors was better resolved by SDM derived from simpler functions, with consistent results for bees and hoverflies. We then used the species distributions from the calibrated model to predict pollination service of wild and managed pollinators, using field beans as a test case. The PSM allowed us to spatially characterize the contribution of wild and managed pollinators and also identify areas potentially vulnerable to low pollination service provision, which can help direct local scale interventions. This approach can be extended to investigate geographical mismatches between crop pollination demand and the availability of pollinators, resulting from environmental change or policy scenarios.",

author = "Chiara Polce and Mette Termansen and Jesus Aguirre-Guti{\'e}rrez and Boatman, {Nigel D.} and Budge, {Giles E.} and Andrew Crowe and Garratt, {Michael P.} and St{\'e}phane Pietravalle and Potts, {Simon G.} and Ramirez, {Jorge A.} and Somerwill, {Kate E.} and Biesmeijer, {Jacobus C.}",

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doi = "10.1371/journal.pone.0076308",

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T2 - A Modelling Framework Applied to Great Britain

AU - Polce, Chiara

AU - Termansen, Mette

AU - Aguirre-Gutiérrez, Jesus

AU - Boatman, Nigel D.

AU - Budge, Giles E.

AU - Crowe, Andrew

AU - Garratt, Michael P.

AU - Pietravalle, Stéphane

AU - Potts, Simon G.

AU - Ramirez, Jorge A.

AU - Somerwill, Kate E.

AU - Biesmeijer, Jacobus C.

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N2 - Insect pollination benefits over three quarters of the world's major crops. There is growing concern that observed declines in pollinators may impact on production and revenues from animal pollinated crops. Knowing the distribution of pollinators is therefore crucial for estimating their availability to pollinate crops; however, in general, we have an incomplete knowledge of where these pollinators occur. We propose a method to predict geographical patterns of pollination service to crops, novel in two elements: the use of pollinator records rather than expert knowledge to predict pollinator occurrence, and the inclusion of the managed pollinator supply. We integrated a maximum entropy species distribution model (SDM) with an existing pollination service model (PSM) to derive the availability of pollinators for crop pollination. We used nation-wide records of wild and managed pollinators (honey bees) as well as agricultural data from Great Britain. We first calibrated the SDM on a representative sample of bee and hoverfly crop pollinator species, evaluating the effects of different settings on model performance and on its capacity to identify the most important predictors. The importance of the different predictors was better resolved by SDM derived from simpler functions, with consistent results for bees and hoverflies. We then used the species distributions from the calibrated model to predict pollination service of wild and managed pollinators, using field beans as a test case. The PSM allowed us to spatially characterize the contribution of wild and managed pollinators and also identify areas potentially vulnerable to low pollination service provision, which can help direct local scale interventions. This approach can be extended to investigate geographical mismatches between crop pollination demand and the availability of pollinators, resulting from environmental change or policy scenarios.

AB - Insect pollination benefits over three quarters of the world's major crops. There is growing concern that observed declines in pollinators may impact on production and revenues from animal pollinated crops. Knowing the distribution of pollinators is therefore crucial for estimating their availability to pollinate crops; however, in general, we have an incomplete knowledge of where these pollinators occur. We propose a method to predict geographical patterns of pollination service to crops, novel in two elements: the use of pollinator records rather than expert knowledge to predict pollinator occurrence, and the inclusion of the managed pollinator supply. We integrated a maximum entropy species distribution model (SDM) with an existing pollination service model (PSM) to derive the availability of pollinators for crop pollination. We used nation-wide records of wild and managed pollinators (honey bees) as well as agricultural data from Great Britain. We first calibrated the SDM on a representative sample of bee and hoverfly crop pollinator species, evaluating the effects of different settings on model performance and on its capacity to identify the most important predictors. The importance of the different predictors was better resolved by SDM derived from simpler functions, with consistent results for bees and hoverflies. We then used the species distributions from the calibrated model to predict pollination service of wild and managed pollinators, using field beans as a test case. The PSM allowed us to spatially characterize the contribution of wild and managed pollinators and also identify areas potentially vulnerable to low pollination service provision, which can help direct local scale interventions. This approach can be extended to investigate geographical mismatches between crop pollination demand and the availability of pollinators, resulting from environmental change or policy scenarios.

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DO - 10.1371/journal.pone.0076308

M3 - Journal article

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SN - 1932-6203

VL - 8

JO - PLoS Computational Biology

JF - PLoS Computational Biology

IS - 10

M1 - e76308

ER -

Species Distribution Models for Crop Pollination: A Modelling Framework Applied to Great Britain

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