Observer bias and the detection of low‐density populations

Abstract

Monitoring programs increasingly are used to document the spread of invasive species in the hope of detecting and eradicating low‐density infestations before they become established. However, interobserver variation in the detection and correct identification of low‐density populations of invasive species remains largely unexplored. In this study, we compare the abilities of volunteer and experienced individuals to detect low‐density populations of an actively spreading invasive species, and we explore how interobserver variation can bias estimates of the proportion of sites infested derived from occupancy models that allow for both false negative and false positive (misclassification) errors. We found that experienced individuals detected small infestations at sites where volunteers failed to find infestations. However, occupancy models erroneously suggested that experienced observers had a higher probability of falsely detecting the species as present than did volunteers. This unexpected finding is an artifact of the modeling framework and results from a failure of volunteers to detect low‐density infestations rather than from false positive errors by experienced observers. Our findings reveal a potential issue with site occupancy models that can arise when volunteer and experienced observers are used together in surveys.

Detecting diversity: emerging methods to estimate species diversity

“…Observer experience and training instructions did not reduce the prevalence of errors [79]. Models have been proposed to account for both false negatives and false positives [78], but issues with parameter identifiability arise when they are applied to data with heterogeneous detection [89]. A more recent parameterization of the original model, which can be applied to data with two or more detection methods, might solve some of the identifiability issues [79] The hierarchical modeling approach of MSOMs, particularly within a Bayesian framework, offers its own challenges.…”

Annual monitoring reveals rapid upward movement of exotic plants in a montane ecosystem

“…Sampling effort and observer experience are known factors affecting data quality, especially in the detection of rare, inconspicuous, or species with low-density populations (Fitzpatrick et al 2009;Moore et al 2011). To ensure optimal observer accuracy, field work was spread across three consecutive days to avoid tiredness.…”

Accounting for false positive detection error induced by transient individuals

“…However, it is evident that, in reality, false positive observations are common and non-trivial (Royle and Link 2006;McClintock et al 2010a;Miller et al 2011). Until now, studies addressing the issue of false positives have done so exclusively in the context of species misidentification, a result of either animals being difficult to distinguish from closely related co-occurring species (McClintock et al 2010a(McClintock et al , 2010bMiller et al 2011), or high variability in observer identification skills (Royle and Link 2006;Fitzpatrick et al 2009). Arguably, however, an equally common source of false-positive observation error is the detection of evidence that an individual is only temporarily present at, or had at some earlier point temporarily visited, a site that is, in truth, unoccupied because the presence of that individual is transient rather than permanent.…”