Bahlai and Landis test online image sharing as a predictive tool
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EmailEntomology post-doc Christie Bahlai and professor Doug Landis examined photographing and sharing images of flowers for its ability to capture ecological phenomena, the visitation rates of pollinators to flowers of different species.
Entomology post-doc Christie Bahlai and professor Doug Landis have published “Predicting Plant Attractiveness to Pollinators” with Royal Society Open Science. Noting that people passively collect data about the world around them, they examined a common leisure activity – photographing and sharing images of flowers – for its ability to capture ecological phenomena, the visitation rates of pollinators to flowers of different species.
In a methodology they termed “passive crowdsourcing,” they searched Google Images for pictures of blooms of 43 common flowering plants, and identified insects that were visible visiting the flowers in the photos. They then compared these observations to visitation rates observed in controlled experimental trials using these same plants. They found they could predict how often a flower was visited by wild bees by the number of visits observed in the internet images, although relationships were less clear for honey bees and bee-mimicking flies. This method could be used by scientists to make predictions about other ecological phenomena that may be documented by human use of the web.
