Predicting plant attractiveness to pollinators with passive crowdsourcing
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 (managedmore »
- Authors:
-
- Michigan State Univ., East Lansing, MI (United States). Dept. of Entomology
- Publication Date:
- Research Org.:
- Univ. of Wisconsin, Madison, WI (United States); Michigan State Univ., East Lansing, MI (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Biological and Environmental Research (BER); National Science Foundation (NSF); Michigan State University
- OSTI Identifier:
- 1326681
- Grant/Contract Number:
- FC02-07ER64494; NSF DEB 1027253
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Royal Society Open Science
- Additional Journal Information:
- Journal Volume: 3; Journal Issue: 6; Journal ID: ISSN 2054-5703
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 59 BASIC BIOLOGICAL SCIENCES; 99 GENERAL AND MISCELLANEOUS; bee; data mining; nectar; pollination; search engine
Citation Formats
Bahlai, Christie A., and Landis, Douglas A. Predicting plant attractiveness to pollinators with passive crowdsourcing. United States: N. p., 2016.
Web. doi:10.1098/rsos.150677.
Bahlai, Christie A., & Landis, Douglas A. Predicting plant attractiveness to pollinators with passive crowdsourcing. United States. https://doi.org/10.1098/rsos.150677
Bahlai, Christie A., and Landis, Douglas A. Wed .
"Predicting plant attractiveness to pollinators with passive crowdsourcing". United States. https://doi.org/10.1098/rsos.150677. https://www.osti.gov/servlets/purl/1326681.
@article{osti_1326681,
title = {Predicting plant attractiveness to pollinators with passive crowdsourcing},
author = {Bahlai, Christie A. and Landis, Douglas A.},
abstractNote = {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.},
doi = {10.1098/rsos.150677},
journal = {Royal Society Open Science},
number = 6,
volume = 3,
place = {United States},
year = {Wed Jun 01 00:00:00 EDT 2016},
month = {Wed Jun 01 00:00:00 EDT 2016}
}
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