COS 140-1
Crowd-sourcing ecology: Predicting plant attractiveness to pollinators from internet image searches

Friday, August 15, 2014: 8:00 AM
314, Sacramento Convention Center
Christie A. Bahlai, Ecology, Evolutionary Biology and Behavior Program, Michigan State University, East Lansing, MI
Douglas A. Landis, Center for Integrated Plant Systems Lab, Michigan State University, East Lansing, MI

Humans enjoy taking photographs of 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. In order to develop a broader list of forb species to be used in pollinator habitat restoration efforts, we used Google Image searches to determine the relative incidence of pollinator visitation to a wide variety of flowers.   Here we test the hypothesis that Plant+bee image abundance is positively associated with the observed attractiveness of the same species in controlled common garden field trials. To test this, we used a list of plant species that had been included in a recent common garden pollinator observation trial. We conducted a Google image search for each of the species by Latin name, plus “bee.” From the first 30 photos which successfully identified the plant, up to 200 images, we recorded the number of Apis (honey) bees, non-Apis(exclusively wild) bees,  and the number of bee-mimicking syrphid flies. We used these observations from search hits as predictor variables in weighted negative-binomial models for field-observed abundances of each of these groups. Bloom seasonality was used as a covariate in these models.


We found that non-Apis bees and, to a lesser extent, syrphids observed in google image searches were positively correlated with observations of these taxa under controlled field conditions (Pseudo R2 of 0.668 and 0.003 respectively).  Apis bee observations were not associated with internet images, but were slightly associated with bloom period. Our results suggest that this methodology is a useful screening tool to identify candidate plants for pollinator habitat restoration efforts directed at wild bee conservation. Habitat restoration and manipulation to provide resources for pollinators usually emphasizes seeding or direct planting of specific flowering forbs. Many of these efforts rely on a limited set of plants that are well-known to provide floral resources- for instance; apiculturists have developed lists of “Honey plants” which are widely used. Crowd-sourcing a list of attractive plants using the methodology described has the potential to enhance biodiversity by drawing attention to useful nectar plants which are often overlooked in restoration efforts. Increasing our understanding of the attractiveness of a greater diversity of plants increases the potential for habitat-customizability in pollinator –friendly forb mixes.