| 1. |
- Lichtenberg, Elinor M., et al.
(author)
-
A global synthesis of the effects of diversified farming systems on arthropod diversity within fields and across agricultural landscapes
- 2017
-
In: Global Change Biology. - : Wiley. - 1354-1013 .- 1365-2486. ; 23:11, s. 4946-4957
-
Journal article (peer-reviewed)abstract
- Agricultural intensification is a leading cause of global biodiversity loss, which can reduce the provisioning of ecosystem services in managed ecosystems. Organic farming and plant diversification are farm management schemes that may mitigate potential ecological harm by increasing species richness and boosting related ecosystem services to agroecosystems. What remains unclear is the extent to which farm management schemes affect biodiversity components other than species richness, and whether impacts differ across spatial scales and landscape contexts. Using a global metadataset, we quantified the effects of organic farming and plant diversification on abundance, local diversity (communities within fields), and regional diversity (communities across fields) of arthropod pollinators, predators, herbivores, and detritivores. Both organic farming and higher in-field plant diversity enhanced arthropod abundance, particularly for rare taxa. This resulted in increased richness but decreased evenness. While these responses were stronger at local relative to regional scales, richness and abundance increased at both scales, and richness on farms embedded in complex relative to simple landscapes. Overall, both organic farming and in-field plant diversification exerted the strongest effects on pollinators and predators, suggesting these management schemes can facilitate ecosystem service providers without augmenting herbivore (pest) populations. Our results suggest that organic farming and plant diversification promote diverse arthropod metacommunities that may provide temporal and spatial stability of ecosystem service provisioning. Conserving diverse plant and arthropod communities in farming systems therefore requires sustainable practices that operate both within fields and across landscapes.
|
|
| 2. |
- Alejandre, Elizabeth M., et al.
(author)
-
Characterization Factors to Assess Land Use Impacts on Pollinator Abundance in Life Cycle Assessment
- 2023
-
In: Environmental Science and Technology. - : American Chemical Society (ACS). - 0013-936X .- 1520-5851. ; 57:8, s. 3445-3454
-
Journal article (peer-reviewed)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.
|
|
| 3. |
- Gardner, Emma, et al.
(author)
-
Reliably predicting pollinator abundance : Challenges of calibrating process-based ecological models
- 2020
-
In: Methods in Ecology and Evolution. - 2041-210X. ; 11:12, s. 1673-1689
-
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.
|
|
