Abstract
Global concern regarding pollinator decline has intensified interest in enhancing pollinator resources in managed landscapes. These efforts frequently emphasize restoration or planting of flowering plants to provide pollen and nectar resources that are highly attractive to the desired pollinators. However, determining exactly which plant species should be used to enhance a landscape is difficult. Empirical screening of plants for such purposes is logistically daunting, but could be streamlined by crowdsourcing data to create lists of plants most probable to attract the desired pollinator taxa. People frequently photograph plants in bloom and the Internet has become a vast repository of such images. A proportion of these images also capture floral visitation by arthropods. Here, we test the hypothesis that the abundance of floral images containing identifiable pollinator and other beneficial insects is positively associated with the observed attractiveness of the same species in controlled field trials from previously published studies. We used Google Image searches to determine the correlation of pollinator visitation captured by photographs on the Internet relative to the attractiveness of the same species in common-garden field trials for 43 plant species. From the first 30 photographs, which successfully identified the plant, we recorded the number of Apis (managed honeybees), non-Apis (exclusively wild bees) and the number of bee-mimicking syrphid flies. We used these observations from search hits as well as bloom period (BP) as predictor variables in Generalized Linear Models (GLMs) for field-observed abundances of each of these groups. We found that non-Apis bees observed in controlled field trials were positively associated with observations of these taxa in Google Image searches (pseudo-R2 of 0.668). Syrphid fly observations in the field were also associated with the frequency they were observed in images, but this relationship was weak. Apis bee observations were not associated with Internet images, but were slightly associated with BP. Our results suggest that passively crowdsourced image data can potentially be a useful screening tool to identify candidate plants for pollinator habitat restoration efforts directed at wild bee conservation. Increasing our understanding of the attractiveness of a greater diversity of plants increases the potential for more rapid and efficient research in creating pollinator-supportive landscapes.
Highlights
Observations of pollinator decline have sparked global interest in managing for pollinator-supportive landscapes [1,2,3,4,5,6]
Habitat manipulations can provide a variety of ecosystem service benefits in addition to pollination services, non-crop habitat plantings have been an area of considerable research interest in recent years [3,8,9,10,11,12,13,14,15,16]
An average of 28.8 images for each plant species met our criterion for image quality and an average of 8.2 images (range field observations: Apis bees (b) field observations: non-Apis bees no. insects observed early middle late early middle late (c) 10 search results: Apis bees (d) search results: non-Apis bees no. images with insects
Summary
Observations of pollinator decline have sparked global interest in managing for pollinator-supportive landscapes [1,2,3,4,5,6]. Habitat loss, pathogens and parasites, exposure to agrochemicals, loss of genetic diversity, malnutrition and in the case of managed bees, apicultural management have all been identified as potential (and interacting) drivers of this pollinator decline [2,7]. Owing to these factors, there is an increasing concern that managed honeybees will not be able to meet demands for agricultural pollination [1]. Developing landscape management and conservation tools that support a wide variety of pollinators is desirable to foster stability and productivity in agricultural systems [2]. It is desirable to develop a screening tool to identify candidate plants for a variety of habitats
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