OOS 32-8 - Challenges and opportunities for uniting citizen science and planned surveys for predicting species distributions

Thursday, August 10, 2017: 10:30 AM
Portland Blrm 254, Oregon Convention Center
Robert J. Fletcher Jr., Wildlife Ecology and Conservation, University of Florida, Gainesville, FL, Trevor Heffley, Statistics, Kansas State University and Robert A. McCleery, Department of Wildlife Ecology and Conservation, University of Florida, Gainesville, FL

Understanding and accurately predicting species distributions lies at the heart of many problems in ecology, evolution, and conservation. Citizen science provides a wealth of opportunistic data that can used for the development of models to interpret species distributions, which typically span broad spatio-temporal scales. Yet data from citizen science programs have well-known biases. In contrast, planned survey data are often preferred because of fewer biases, yet these data typically occur at fine spatio-temporal scales. We illustrate how these two types of data can be united using integrated distribution models, thereby addressing limitations in each type of data. Using two examples, one uniting camera traps with citizen science data from an online mapping program with fox squirrels (Sciurus niger shermani) and a second on the use of point transect data in birds coupled with Ebird data in the southeastern United States, we show how integrated distribution models can be developed and used for understanding and predicting species distributions.


We show three ways in which integrated distribution models may be useful: they can reduce of bias in parameter estimates for environmental relationships, they provide a means to estimate species prevalence (which is required for estimating occurrence probabilities), and they can capture broader environmental conditions, which may improve transferability of model predictions in space and time. Yet we also illustrate some ongoing challenges of the use integrated distribution models, including weighting of different components of the likelihood, proper estimation of sample selection bias, and imperfect detection. Our examples illustrate how inferences on environmental relationships and model predictions can fundamentally change with integrated distribution models. We conclude by outlining a prospectus for rapid improvement in the development and application of integrated distribution models.