While an increasing number of studies indicate that the range, diversity and abundance of many wild pollinators has declined, the global area of pollinator-dependent crops has significantly increased over the last few decades. Crop pollination studies to date have mainly focused on either identifying different guilds pollinating various crops, or on factors driving spatial changes and turnover observed in these communities. The mechanisms driving temporal stability for ecosystem functioning and services, however, remain poorly understood. Our study quantifies temporal variability observed in crop pollinators in 21 different crops across multiple years at a global scale. Using data from 43 studies from six continents, we show that (i) higher pollinator diversity confers greater inter-annual stability in pollinator communities, (ii) temporal variation observed in pollinator abundance is primarily driven by the three-most dominant species, and (iii) crops in tropical regions demonstrate higher inter-annual variability in pollinator species richness than crops in temperate regions. We highlight the importance of recognizing wild pollinator diversity in agricultural landscapes to stabilize pollinator persistence across years to protect both biodiversity and crop pollination services. Short-term agricultural management practices aimed at dominant species for stabilizing pollination services need to be considered alongside longer term conservation goals focussed on maintaining and facilitating biodiversity to confer ecological stability.
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http://dx.doi.org/10.1098/rspb.2021.0212 | DOI Listing |
Front Plant Sci
November 2024
College of Water Sciences, Beijing Normal University, Beijing, China.
Sci Total Environ
December 2024
Cranfield Environment Centre, Cranfield University, Cranfield, Bedfordshire MK43 0AL, UK.
Inter-annual variations in crop production have significant implications for global food security, economic stability, and environmental sustainability. Existing crop yield prediction models primarily using meteorological variables may not adequately encapsulate the full breadth of weather influences on crop development processes, such as compound or extreme events. Incorporating weather patterns into crop models could provide a more comprehensive understanding of the environmental conditions affecting growth, enabling more accurate and earlier yield predictions.
View Article and Find Full Text PDFPlants (Basel)
September 2024
Department of Nutrition Management, Crop Research Institute, Drnovská 507, Ruzyně, 161 01 Prague, Czech Republic.
The stability and yield of barley grain are affected by several factors, such as climatic conditions, fertilisation, and the different barley varieties. In a long-term experiment in Prague, Czech Republic, established in 1955, we analysed the weather trends and how weather, fertilisation (10 treatments in total), and different barley varieties affected grain yield and stability. A total of 44 seasons were evaluated.
View Article and Find Full Text PDFDiscov Geosci
September 2024
Net Zero and Resilient Farming, Rothamsted Research, North Wyke, Okehampton, UK.
Climate change is likely to exacerbate land to water phosphorus (P) transfers, causing a degradation of water quality in freshwater bodies in Northwestern Europe. Planning for mitigation measures requires an understanding of P loss processes under such conditions. This study assesses how climate induced changes to hydrology will likely influence the P transfer continuum in six contrasting river catchments using Irish national observatories as exemplars.
View Article and Find Full Text PDFBMC Biol
September 2024
Department of Comparative Biomedicine and Food Science, University of Padova, Viale Dell'Università 16, Legnaro, 35020, Italy.
Background: Seafood is increasingly traded worldwide, but its supply chain is particularly prone to frauds. To increase consumer confidence, prevent illegal trade, and provide independent validation for eco-labelling, accurate tools for seafood traceability are needed. Here we show that the use of microbiome profiling (MP) coupled with machine learning (ML) allows precise tracing the origin of Manila clams harvested in areas separated by small geographic distances.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!