| 1. |
- Garratt, Michael P D, et al.
(author)
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Opportunities to reduce pollination deficits and address production shortfalls in an important insect pollinated crop
- 2021
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In: Ecological Applications. - : Wiley. - 1051-0761 .- 1939-5582.
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Journal article (peer-reviewed)abstract
- Pollinators face multiple pressures and there is evidence of populations in decline. As demand for insect-pollinated crops increases, crop production is threatened by shortfalls in pollination services. Understanding the extent of current yield deficits due to pollination and identifying opportunities to protect or improve crop yield and quality through pollination management is therefore of international importance. To explore the extent of 'pollination deficits', where maximum yield is not being achieved due to insufficient pollination, we use an extensive dataset on a globally important crop, apples. We quantified how these deficits vary between orchards and countries as well as compare 'pollinator dependence' across different apple varieties. We found evidence of pollination deficits and in some cases, risks of over-pollination were even apparent where fruit quality could be reduced by too much pollination. In almost all regions studied we found some orchards performing significantly better than others, in terms of avoiding a pollination deficit and crop yield shortfalls due to sub-optimal pollination. This represents an opportunity to improve production through better pollinator and crop management. Our findings also demonstrate that pollinator dependence varies considerably between apple varieties in terms of fruit number and fruit quality. We propose that assessments of pollination service and deficits in crops can be used to quantify supply and demand for pollinators and help target local management to address deficits although crop variety has a strong influence on the role of pollinators.
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| 2. |
- Hutchinson, Louise A., et al.
(author)
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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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In: Agriculture, Ecosystems and Environment. - : Elsevier BV. - 0167-8809 .- 1873-2305. ; 315
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Journal article (peer-reviewed)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. |
- Gardner, Emma, et al.
(author)
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Reliably predicting pollinator abundance : Challenges of calibrating process-based ecological models
- 2020
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In: Methods in Ecology and Evolution. - 2041-210X. ; 11:12, s. 1673-1689
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Journal article (peer-reviewed)abstract
- Pollination is a key ecosystem service for global agriculture but evidence of pollinator population declines is growing. Reliable spatial modelling of pollinator abundance is essential if we are to identify areas at risk of pollination service deficit and effectively target resources to support pollinator populations. Many models exist which predict pollinator abundance but few have been calibrated against observational data from multiple habitats to ensure their predictions are accurate. We selected the most advanced process-based pollinator abundance model available and calibrated it for bumblebees and solitary bees using survey data collected at 239 sites across Great Britain. We compared three versions of the model: one parameterised using estimates based on expert opinion, one where the parameters are calibrated using a purely data-driven approach and one where we allow the expert opinion estimates to inform the calibration process. All three model versions showed significant agreement with the survey data, demonstrating this model's potential to reliably map pollinator abundance. However, there were significant differences between the nesting/floral attractiveness scores obtained by the two calibration methods and from the original expert opinion scores. Our results highlight a key universal challenge of calibrating spatially explicit, process-based ecological models. Notably, the desire to reliably represent complex ecological processes in finely mapped landscapes necessarily generates a large number of parameters, which are challenging to calibrate with ecological and geographical data that are often noisy, biased, asynchronous and sometimes inaccurate. Purely data-driven calibration can therefore result in unrealistic parameter values, despite appearing to improve model-data agreement over initial expert opinion estimates. We therefore advocate a combined approach where data-driven calibration and expert opinion are integrated into an iterative Delphi-like process, which simultaneously combines model calibration and credibility assessment. This may provide the best opportunity to obtain realistic parameter estimates and reliable model predictions for ecological systems with expert knowledge gaps and patchy ecological data.
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