Integrating auxiliary data in optimal spatial design for species distribution modelling

1. Traditional surveys used to create species distribution maps and estimate ecological relationships are expensive and time consuming. Citizen science offers a way to collect a massive amount of data at negligible cost and has been shown to be a useful supplement to traditional analyses. However, there remains a need to conduct formal surveys to firmly establish ecological relationships and trends. 2. In this paper, we investigate the use of auxiliary (e.g. citizen science) data as a guide to designing more efficient ecological surveys. Our aim is to explore the use of opportunistic data to inform spatial survey design through a novel objective function that minimizes misclassificaton rate (i.e. false positives and false negatives) of the estimated occupancy maps. We use an initial occupancy estimate from auxiliary data as the prior in a Bayesian spatial occupancy model, and an efficient posterior approximation that accounts for spatial dependence, covariate effects, and imperfect detection in an exchange algorithm to search for the optimal set of sampling locations to minimize misclassification rate. 3. We examine the optimal design as a function of the detection rate and quality of the citizen-science data, and compare this optimal design with several common ad hoc designs via an extensive simulation study. We then apply our method to eBird data for the brown-headed nuthatch in the Southeast US. 4. We argue that planning a survey with the use of auxiliary data improves estimation accuracy and may significantly reduce the costs of sampling.