Integrating auxiliary data in optimal spatial design for species distribution modelling

Abstract

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. 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. 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. We argue that planning a survey with the use of auxiliary data improves estimation accuracy and may significantly reduce the costs of sampling.

The recent past and promising future for data integration methods to estimate species’ distributions

“…classification accuracy) and the constraints (e.g. sample size) on the design (Laber et al., ; Reich, Pacifici, & Stallings, ; Taha, ). The most important component is having clear objectives (Boulinier, Yoccoz, Nichols, & Boulinier, ) as this can have a major impact on the optimal design.…”

“…Reich et al. () apply these principles of spatial optimal design to ecological data while exploring the advantages of integrating auxiliary data to inform the design of species distribution modelling. Reich et al.…”

“…space-filling designs) or to improve covariance estimation by clustering sample locations. Reich et al (2018) apply these principles of spatial optimal design to ecological data while exploring the advantages of integrating auxiliary data to inform the design of species distribution modelling. Reich et al (2018) use a novel objective function that minimizes misclassification rate of occurrence probabilities and show that using auxiliary data refines the appropriate sampling locations and ultimately reduces uncertainty across a species' distribution.…”

The importance of simulation assumptions when evaluating detectability in population models

“…, Reich et al. , Williams et al. ), which could inform monitoring design while considering the resources and effort required for adequate inference.…”

Protecting juveniles, spawners or both: A practical statistical modelling approach for the design of marine protected areas

“…Though our statistical modelling approach relies on multiple fisheries‐independent and fisheries‐dependent datasets, we offer the caveat that sufficient monitoring might not be available to create predictions for the juveniles or spawners of all study species, as was the case in this study (Table ). Yet, the list of those life stages for which monitoring data are lacking for developing spatial SDMs can be communicated to the institutions responsible for data collection or considered in simulation experiments aiming at optimizing monitoring program designs (Reich, Pacifici, & Stallings, ; Thorson, ), so as to enable data gaps to be filled in the future. In the case of the U.S. GOM, the use of monitoring data collected by state agencies at fixed sampling stations (along with some modifications in our statistical models) would also enable one to generate predictions for coastal species (e.g.…”