Knowledge of the distribution and ecological associations of a species is a crucial ingredient for successful conservation management, biodiversity and sustainability research. However, ecological systems are inherently complex, our ability to directly observe them has been limited, and the processes that affect the distributions of animals and plants operate at multiple spatial and temporal scales.

Very recently, large citizen science efforts such as eBird, a very successful crowdsourcing project by the Cornell Lab of Ornithology that engages citizen scientists and avid birders, is enabling for the first time world-wide observations of bird distributions. eBird has collected more then 100 millions of bird observations to date from as many as 100 thousand human volunteers and submissions (checklists) continue to grow with an exponential rate. With this wealth of evidence comes a plethora of challenges as the data collection and sampling designs are unstructured, follow human activities and concentrations, and are subject to observer and environmental biases. For example, sparsely populated states in the US, such as Iowa and Nevada, have very low frequency of observations whereas East and West Coast states have the highest continental counts. Furthermore, temporal variability and biases are also evident as annual submission rates peak during spring and fall migration which is the most exciting times for birders to observe multiple species.

In our AAAI paper, we propose adaptive spatiotemporal species distribution models that can exploit the uneven distribution of observations from such crowdsourcing projects and can accurately capture multiscale processes. The proposed exploratory models control for variability in the observation process and can learn ecological, environmental and climate associations that drive species distributions and migration patterns. We offer for the first time hemisphere-wide species distribution estimates of long-distance migrants (Barn Swallow, Blackpoll Warbler, and Black-throated Blue Warbler in Figure above), utilizing more then 2.25 million eBird checklists.

Until recently, most biodiversity monitoring programs that collect data have been national in scope, hindering ecological study and conservation planning for broadly distributed species. The ability to produce comprehensive year-round distribution estimates that span national borders will make it possible to better understand the ecological processes affecting the distributions of these species, assess their vulnerability to environmental perturbations such as those expected under climate change, and coordinate conservation activities.

Citation:

D. Fink, T. Damoulas, J. Dave, (2013). Adaptive Spatio-Temporal Exploratory Models: Hemisphere-wide species distributions from massively crowdsourced eBird data. AAAI 2013, Washington, USA.

This is one of our series of posts on the latest research in Computational Sustainability being presented at conferences this summer. This time Theo Damoulas, a Research Associate in Computer Science, and a member of the Institute for Computational Sustainability, at Cornell University tells us about their new paper at AAAI in Washington this month.