COS 28-6
Using ecological niche modeling to predict the socio-economic backgrounds most likely to participate in citizen science

Tuesday, August 11, 2015: 9:50 AM
326, Baltimore Convention Center
Ashley R. Knoch, Department of Integrative Biology, Oklahoma State University, Stillwater, OK
Kristen A. Baum, Integrative Biology, Oklahoma State University, Stillwater, OK

Citizen science incorporates the public into scientific data collection and provides researchers with a unique tool to study phenomena at large spatial scales. The number of citizen science projects has increased in recent years, especially in the United States. Since citizen science can provide a valuable resource for researchers, it is important that scientists who utilize citizen scientist data understand the backgrounds of their participants. However, little is known about those who are actually participating. For example, if certain socio-economic backgrounds are more common than others, sampling biases could occur (e.g., some geographic areas could be under- or over-represented). We used the modeling program MaxEnt to evaluate if citizen scientist participation differs between rural or urban areas. Socio-economic factors used in the model included population density, as well as urban and crop land cover. Crop land cover was used as an indicator of rural populations. Occurrence points were latitude and longitude locations where citizen scientists made observations for the Monarch Larva Monitoring Project (MLMP).


Urban land cover was the greatest contributing environmental factor, followed by population density and finally crop cover. Areas that contained the best-predicted conditions overlapped highly with metropolitan locations. It is possible that many citizen science participants live in or near urbanized areas that are lower in population density than the city (i.e., suburbs). Suburban populations may have more contact with outreach activities than those in rural areas, and may be closer to natural areas that allow for citizen science participation than those in urban areas. However, outreach and recruitment may have contributed to higher than expected citizen science participation in some areas, suggesting these activities can be effective at increasing participation in underrepresented areas. Future analyses will incorporate additional socio-economic factors such as family size, income, and race. Having a greater understanding of who is participating in citizen science can allow researchers to target populations in geographic areas with low participation rates. With greater citizen science participation, researchers can better address questions at large spatial and temporal scales.