期刊
ECOLOGICAL APPLICATIONS
卷 28, 期 5, 页码 1215-1222出版社
WILEY
DOI: 10.1002/eap.1720
关键词
bumble bees; crop pollination; fruit quality; honey bees; raspberry; simulation model
资金
- National Research Council of Argentina CONICET
- Argentine National Fund of Research [PICT 2012-3015, PICT 2015-2333]
- Natural Sciences and Engineering Research Council of Canada [RGPIN/107375-2012]
Globally, agriculture increasingly depends on pollinators to produce many seed and fruit crops. However, what constitutes optimal pollination service for pollinator-dependent crops remains unanswered. We developed a simulation model to identify the optimal pollination service that maximizes fruit quality in crops. The model depicts the pollination (i.e., autonomous self-fertilization, pollen deposition) and post-pollination (i.e., pollen germination, and time from germination to ovule fertilization) processes leading to fruit and seed set and allows for negative flower-pollinator interactions, specifically pistil damage. We parameterized and validated the model based on empirical observations of commercial raspberry in western Argentina. To assess the effects of pollination intensity for fruit production, we conducted simulations over a range of visit number per flower by the two primary managed pollinators worldwide, Apis mellifera and Bombus terrestris. Simulations identified that similar to 15-35 visits per flower by A. mellifera or similar to 10-20 visits by B. terrestris provide adequate pollination and maximize raspberry fruit quality (i.e., estimated as the proportion of ovules that develop into drupelets). Visits in excess of these optima reduce simulated fruit quality, and flowers receiving >670 honey bee visits or >470 bumble bee visits would produce fruits of poorer quality than those receiving no bee visits. The simulations generated consistent, unbiased predictions of fruit quality for 12 raspberry fields. This model could be adapted easily to other animal-pollinated crops and used to guide efficient pollinator management in any agro-ecosystem.
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