Investigating the Efficacy of Data Mining Techniques for Crowdsourced Biological Datasets

Ilona Regan, Dany Alkurdi, Sarun Paisarnsrisomsuk, Carolina Ruiz, Elizabeth Ryder, Rob Gegear

Abstract: The rapid worldwide decline of wild pollinators over recent years poses a significant environmental threat due to the critical keystone role that pollinators play in terrestrial ecosystems. In order to gain insight into the major anthropogenic factors causing these declines, researchers have collected large amounts of ecological data. Yet, they often lack the computational tools needed to analyze the information contained in such datasets. We investigate various data analysis techniques for the Beecology Project, which is a citizen science based effort to rapidly collect data on foraging habits of bumblebee species native to Massachusetts. Different data mining approaches were explored, including association analysis, trend analysis, classification, regression, and clustering. It was found that different techniques were more suitable depending on the biological research question. Future work will focus on making tools utilizing these approaches available online through the Beecology website, where they can be used by the public to determine how best to protect our native pollinators and the diverse ecosystems they support.