Pairing field methods to improve inference in wildlife surveys while accommodating detection covariance

• Published in: Ecological Applications
• Main Authors: Clare, John, McKinney, Shawn T., DePue, John E., Loftin, Cynthia S.
• Other Authors: University of Maine, U.S. Geological Survey
• Format: Article in Journal/Newspaper
• Language: English
• Published: Wiley 2017
• Subjects: American marten; Martes americana
• Online Access: http://dx.doi.org/10.1002/eap.1587; https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1002%2Feap.1587; https://onlinelibrary.wiley.com/doi/pdf/10.1002/eap.1587; https://onlinelibrary.wiley.com/doi/full-xml/10.1002/eap.1587; https://esajournals.onlinelibrary.wiley.com/doi/am-pdf/10.1002/eap.1587; https://esajournals.onlinelibrary.wiley.com/doi/pdf/10.1002/eap.1587

Abstract It is common to use multiple field sampling methods when implementing wildlife surveys to compare method efficacy or cost efficiency, integrate distinct pieces of information provided by separate methods, or evaluate method‐specific biases and misclassification error. Existing models that combine information from multiple field methods or sampling devices permit rigorous comparison of method‐specific detection parameters, enable estimation of additional parameters such as false‐positive detection probability, and improve occurrence or abundance estimates, but with the assumption that the separate sampling methods produce detections independently of one another. This assumption is tenuous if methods are paired or deployed in close proximity simultaneously, a common practice that reduces the additional effort required to implement multiple methods and reduces the risk that differences between method‐specific detection parameters are confounded by other environmental factors. We develop occupancy and spatial capture–recapture models that permit covariance between the detections produced by different methods, use simulation to compare estimator performance of the new models to models assuming independence, and provide an empirical application based on American marten ( Martes americana ) surveys using paired remote cameras, hair catches, and snow tracking. Simulation results indicate existing models that assume that methods independently detect organisms produce biased parameter estimates and substantially understate estimate uncertainty when this assumption is violated, while our reformulated models are robust to either methodological independence or covariance. Empirical results suggested that remote cameras and snow tracking had comparable probability of detecting present martens, but that snow tracking also produced false‐positive marten detections that could potentially substantially bias distribution estimates if not corrected for. Remote cameras detected marten individuals more readily than passive ...