Performance of occupancy estimators when basic assumptions are not met: a test with field data where truth is known.

Abstract/Summary

Populations are rarely censused. Instead observations are affected by incomplete detection, misclassification, and detection heterogeneity that results from human and environmental constraints. Though numerous methods have been developed to deal with observational uncertainty, validation under field conditions are rare because truth is rarely known. We present the most comprehensive test of occupancy methods to date, using more than 33,000 auditory call observations collected under standard field conditions but where the true occupancy status of sites was known. Basic occupancy estimation approaches were biased when two key assumptions were not met: that no false positives occur and that no unexplained heterogeneity in detection parameters occurs. The greatest bias occurred for dynamic parameters (i.e., local colonization and extinction) and in many cases the degree of inaccuracy would render results largely useless. We examined three approaches to increase adherence or relax these assumptions: modifying the sampling design, employing estimators that account for false positive detections, and using covariates to account for site-level heterogeneity in both false negative and false positive detection probabilities. We demonstrate that bias can be substantially reduced by modifications to sampling methods and by using estimators that simultaneously account for false positive detections and site-level covariates to explain heterogeneity. Our results demonstrate that even small probabilities of misidentification and among-site detection heterogeneity can have severe effects on estimator reliability if ignored. We challenge researchers to place greater attention to both heterogeneity and false positives when designing and analyzing occupancy studies and provide 9 specific guidelines for design, implementation, and analysis of occupancy studies to meet this challenge.

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