Dataset Information

Experimental evidence that wildflower strips increase pollinator visits to crops.

ABSTRACT: Wild bees provide a free and potentially diverse ecosystem service to farmers growing pollination-dependent crops. While many crops benefit from insect pollination, soft fruit crops, including strawberries are highly dependent on this ecosystem service to produce viable fruit. However, as a result of intensive farming practices and declining pollinator populations, farmers are increasingly turning to commercially reared bees to ensure that crops are adequately pollinated throughout the season. Wildflower strips are a commonly used measure aimed at the conservation of wild pollinators. It has been suggested that commercial crops may also benefit from the presence of noncrop flowers; however, the efficacy and economic benefits of sowing flower strips for crops remain relatively unstudied. In a study system that utilizes both wild and commercial pollinators, we test whether wildflower strips increase the number of visits to adjacent commercial strawberry crops by pollinating insects. We quantified this by experimentally sowing wildflower strips approximately 20 meters away from the crop and recording the number of pollinator visits to crops with, and without, flower strips. Between June and August 2013, we walked 292 crop transects at six farms in Scotland, recording a total of 2826 pollinators. On average, the frequency of pollinator visits was 25% higher for crops with adjacent flower strips compared to those without, with a combination of wild and commercial bumblebees (Bombus spp.) accounting for 67% of all pollinators observed. This effect was independent of other confounding effects, such as the number of flowers on the crop, date, and temperature. Synthesis and applications. This study provides evidence that soft fruit farmers can increase the number of pollinators that visit their crops by sowing inexpensive flower seed mixes nearby. By investing in this management option, farmers have the potential to increase and sustain pollinator populations over time.

SUBMITTER: Feltham H

PROVIDER: S-EPMC4569045 | biostudies-literature | 2015 Aug

REPOSITORIES: biostudies-literature

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Experimental evidence that wildflower strips increase pollinator visits to crops.

Feltham Hannah H Park Kirsty K Minderman Jeroen J Goulson Dave D

Ecology and evolution 20150801 16

Wild bees provide a free and potentially diverse ecosystem service to farmers growing pollination-dependent crops. While many crops benefit from insect pollination, soft fruit crops, including strawberries are highly dependent on this ecosystem service to produce viable fruit. However, as a result of intensive farming practices and declining pollinator populations, farmers are increasingly turning to commercially reared bees to ensure that crops are adequately pollinated throughout the season. W ...[more]

PMID: 26380683

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Project description:Wildflower strips (WFS) are increasingly used to counteract the negative consequences of agricultural intensification. To date, it is poorly understood how WFS promote flower visitation and pollination services in nearby insect-pollinated crops. We therefore ask whether WFS enhance pollination service in adjacent strawberry crops, and how such an effect depends on the distance from WFS. Over 2 years, we examined the effects of experimentally sown WFS compared to grassy strips on pollination services in adjacent strawberry (Fragaria ananassa) crops across a total of 19 study sites. Moreover, we examined flower visitation, species richness and community composition of the most important insect pollinator taxa at different within-field locations varying in distance to WFS. We found increased pollination services at the edge of WFS compared to locally reduced pollination services at the center, which resulted in no significant difference in seed set between WFS and control fields. Total flower visits and species richness of pollinators were higher in WFS than in adjacent strawberry fields. Moreover, wild bee visitation was enhanced in adjacent strawberry crops near WFS compared to field centers, and intermediate at field edges near grassy strips. Our study demonstrates that diverse WFS can increase wild bee visitation and pollination services in the field edges of adjacent strawberry crops, but that overall visitation and pollination services do not increase. Moreover, our findings show that major pollinator taxa exhibit distinct responses, resulting in a shift of pollinator community composition as a function of distance to WFS with direct effects on crop pollination. Our results that WFS enhance rather than reduce crop pollination services near WFS should distract possible concerns by farmers that WFS may locally absorb rather than export crop pollinators. Considering the spatial restricted enhancement of wild bees and associated pollination services we suggest to establish WFS in the center of crop fields.

Project description:Monitoring animals in their natural habitat is essential for advancement of animal behavioural studies, especially in pollination studies. Non-invasive techniques are preferred for these purposes as they reduce opportunities for research apparatus to interfere with behaviour. One potentially valuable approach is image-based tracking. However, the complexity of tracking unmarked wild animals using video is challenging in uncontrolled outdoor environments. Out-of-the-box algorithms currently present several problems in this context that can compromise accuracy, especially in cases of occlusion in a 3D environment. To address the issue, we present a novel hybrid detection and tracking algorithm to monitor unmarked insects outdoors. Our software can detect an insect, identify when a tracked insect becomes occluded from view and when it re-emerges, determine when an insect exits the camera field of view, and our software assembles a series of insect locations into a coherent trajectory. The insect detecting component of the software uses background subtraction and deep learning-based detection together to accurately and efficiently locate the insect among a cluster of wildflowers. We applied our method to track honeybees foraging outdoors using a new dataset that includes complex background detail, wind-blown foliage, and insects moving into and out of occlusion beneath leaves and among three-dimensional plant structures. We evaluated our software against human observations and previous techniques. It tracked honeybees at a rate of 86.6% on our dataset, 43% higher than the computationally more expensive, standalone deep learning model YOLOv2. We illustrate the value of our approach to quantify fine-scale foraging of honeybees. The ability to track unmarked insect pollinators in this way will help researchers better understand pollination ecology. The increased efficiency of our hybrid approach paves the way for the application of deep learning-based techniques to animal tracking in real-time using low-powered devices suitable for continuous monitoring.

Dataset Information

Experimental evidence that wildflower strips increase pollinator visits to crops.

Publications

Experimental evidence that wildflower strips increase pollinator visits to crops.

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