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- Eeraerts, Maxime, et al.
(author)
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Synthesis of highbush blueberry pollination research reveals region-specific differences in the contributions of honeybees and wild bees
- 2023
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In: Journal of Applied Ecology. - 0021-8901. ; 60:12, s. 2528-2539
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Research review (peer-reviewed)abstract
- Highbush blueberry production has expanded worldwide in recent decades. To safeguard future yields, it is essential to understand if insect pollination is limiting current blueberry production and which insects contribute to pollination in different production regions. We present a systematic review including a set of meta-analyses on insect-mediated pollination in highbush blueberry. We summarize the geographic distribution of research, the abundance of different pollinator taxa and their relative pollination contributions. Using raw data from 21 studies, totalling 496 site replicates, we determine the degree of pollination service and pollen limitation (i.e. combining open pollination levels with experimental bagged and/or hand pollination treatments), as well as the contribution of honeybees and wild bees to pollination (i.e. observational, open pollination). Most studies originate from North America, focusing on only a few cultivars. Honeybees are the dominant pollinator, and wild bees are occasionally abundant. Wild bees are more efficient pollinators on a single-visit basis compared to honeybees, which increases their relative pollination contribution compared to their relative abundance. Insect-mediated pollination services increased blueberry fruit set, berry weight and seed set (R2 values: 64.8%, 75.9% and 75.2% respectively). We often detected pollen limitation, indicated by an increase in fruit set, berry weight and seed set (R2: 10.1%, 18.2% and 21.5%, respectively), with additional hand pollination. Increasing visitation of honeybees and wild bees contributed to blueberry pollination by increasing fruit set (R2: 5.4% and 3.5%), berry weight (R2: 6.5% and 2.8%) and seed set (R2: 6.4% and 3.8%) respectively. Bee contributions to fruit set and berry weight were variable across regions. Synthesis and application: A diverse community of insects, primarily bees, contributes to highbush blueberry pollination and yield. However, pollination deficits are common. The finding that both honeybees and wild bees enhance pollination highlights the possibility of adopting different management strategies that utilize honeybees, wild bees or both depending on the specific context and region. This further emphasizes the general importance of conserving pollinator health and diversity. Our synthesis highlights data gaps and areas for future research to better understand the pollination contribution of different pollinators to crops that are expanding globally.
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| 2. |
- Giménez-García, Angel, et al.
(author)
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Pollination supply models from a local to global scale
- 2023
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In: Web Ecology. - 1399-1183. ; 23:2, s. 99-129
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Journal article (peer-reviewed)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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