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.
Highlights
The importance of ecosystems to human well-being was documented by the Millennium Ecosystem Assessment, which recognised that the majority of pollinators are in decline or threatened [1]
Model performance species distribution model (SDM) provided a significantly better fit than expected by chance alone for all the species (Fig. 1 shows the results for the AUC of the testing phase; a similar pattern was observed for the AUC of the training phase)
For crops benefiting from insect pollination, we assumed that likelihood of species occurrence can be used as a proxy for potential pollinator service provision, implying two main premises: the first one is that the two variables scale proportionally; the second one is that a unit difference in the likelihood of occurrence in one species means the same change in service provision as in a different species
Summary
The importance of ecosystems to human well-being was documented by the Millennium Ecosystem Assessment, which recognised that the majority of pollinators are in decline or threatened [1]. Crop pollination is a key ecosystem service vital to the maintenance of both wild plant communities and agricultural productivity. Over three quarters of the world’s major crops benefit from insect pollination, with an economic value estimated to be around J 153 billion globally in 2005 and approximately J 500 million in the United Kingdom [2,3,4]. The recent declines observed in pollinators, mainly bees [5,6], may impact on the production of and profits from pollinator-dependent crops. Long-term trends of global crop production suggest that to compensate for a 3–8% yield reduction expected in absence of animal pollination, the expansion of agricultural land would be much greater Long-term trends of global crop production suggest that to compensate for a 3–8% yield reduction expected in absence of animal pollination, the expansion of agricultural land would be much greater (ca. 25%, and proportionally much greater in the developing world), which in turn could accelerate habitat destruction and contribute to further pollination loss [7]
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