| 1. |
- Alejandre, Elizabeth M., et al.
(författare)
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Characterization Factors to Assess Land Use Impacts on Pollinator Abundance in Life Cycle Assessment
- 2023
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Ingår i: Environmental Science and Technology. - : American Chemical Society (ACS). - 0013-936X .- 1520-5851. ; 57:8, s. 3445-3454
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Tidskriftsartikel (refereegranskat)abstract
- While wild pollinators play a key role in global food production, their assessment is currently missing from the most commonly used environmental impact assessment method, Life Cycle Assessment (LCA). This is mainly due to constraints in data availability and compatibility with LCA inventories. To target this gap, relative pollinator abundance estimates were obtained with the use of a Delphi assessment, during which 25 experts, covering 16 nationalities and 45 countries of expertise, provided scores for low, typical, and high expected abundance associated with 24 land use categories. Based on these estimates, this study presents a set of globally generic characterization factors (CFs) that allows translating land use into relative impacts to wild pollinator abundance. The associated uncertainty of the CFs is presented along with an illustrative case to demonstrate the applicability in LCA studies. The CFs based on estimates that reached consensus during the Delphi assessment are recommended as readily applicable and allow key differences among land use types to be distinguished. The resulting CFs are proposed as the first step for incorporating pollinator impacts in LCA studies, exemplifying the use of expert elicitation methods as a useful tool to fill data gaps that constrain the characterization of key environmental impacts.
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| 2. |
- Hutchinson, Louise A., et al.
(författare)
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Using ecological and field survey data to establish a national list of the wild bee pollinators of crops
- 2021
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Ingår i: Agriculture, Ecosystems and Environment. - : Elsevier BV. - 0167-8809 .- 1873-2305. ; 315
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Tidskriftsartikel (refereegranskat)abstract
- The importance of wild bees for crop pollination is well established, but less is known about which species contribute to service delivery to inform agricultural management, monitoring and conservation. Using sites in Great Britain as a case study, we use a novel qualitative approach combining ecological information and field survey data to establish a national list of crop pollinating bees for four economically important crops (apple, field bean, oilseed rape and strawberry). A traits data base was used to establish potential pollinators, and combined with field data to identify both dominant crop flower visiting bee species and other species that could be important crop pollinators, but which are not presently sampled in large numbers on crops flowers. Whilst we found evidence that a small number of common, generalist species make a disproportionate contribution to flower visits, many more species were identified as potential pollinators, including rare and specialist species. Furthermore, we found evidence of substantial variation in the bee communities of different crops. Establishing a national list of crop pollinators is important for practitioners and policy makers, allowing targeted management approaches for improved ecosystem services, conservation and species monitoring. Data can be used to make recommendations about how pollinator diversity could be promoted in agricultural landscapes. Our results suggest agri-environment schemes need to support a higher diversity of species than at present, notably of solitary bees. Management would also benefit from targeting specific species to enhance crop pollination services to particular crops. Whilst our study is focused upon Great Britain, our methodology can easily be applied to other countries, crops and groups of pollinating insects.
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| 3. |
- Giménez-García, Angel, et al.
(författare)
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Pollination supply models from a local to global scale
- 2023
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Ingår i: Web Ecology. - 1399-1183. ; 23:2, s. 99-129
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Tidskriftsartikel (refereegranskat)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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