Sources of Measurement Error, Misclassification Error, and Bias in Auditory Avian Point Count Data

Abstract

Avian point counts vary over space and time due to actual differences in abundance, differences in detection probabilities among counts, and differences associated with measurement and misclassification errors. However, despite the substantial time, effort, and money expended counting birds in ecological research and monitoring, the validity of common survey methods remains largely untested, and there is still considerable disagreement over the importance of estimating detection probabilities associated with individual counts. Most practitioners assume that current methods for estimating detection probability are accurate, and that observer training obviates the need to account for measurement and misclassification errors in point count data. Our approach combines empirical data from field studies with field experiments using a system for simulating avian census conditions when most birds are identified by sound. Our objectives are to: identify the factors that influence detection probability on auditory point counts, quantify the bias and precision of current sampling methods, and find new applications of sampling theory and methodologies that produce practical improvements in the quality of bird census data.We have found that factors affecting detection probabilities on auditory counts, such as ambient noise, can cause substantial biases in count data. Distance sampling data are subject to substantial measurement error due to the difficulty of estimating the distance to a sound source when visual cues are lacking. Misclassification errors are also inherent in time of detection methods due to the difficulty of accurately identifying and localizing sounds during a count. Factors affecting detection probability, measurement errors, and misclassification errors are important but often ignored components of the uncertainty associated with point-count-based abundance estimates.

Inference about density and temporary emigration in unmarked populations

“…These models have been designed to explicitly account for sources of bias inherent to survey data; however, the assumptions of these models have been criticized (Bart et al 2004, Efford and Dawson 2009, Simons et al 2009). Geographic closure is among the most commonly cited assumption thought to be violated.…”

Comparison of Methods for Estimating Bird Abundance and Trends From Historical Count Data

“…For example, recording the exact distances to each detected bird maximizes flexibility for analyzing the data using distance sampling (Buckland et al 2001), but it may also increase violations of the assumption that birds are assigned to distance intervals without error (Alldredge et al 2007b(Alldredge et al , 2008. Similarly, lengthening the count period beyond 0-3 or 3-5 min by adding the interval 5-10 min has benefits in modeling individual heterogeneity and increasing the overall detection probability at a point (Barker et al 1993, Alldredge et al 2007a), but it increases probabilities that birds are recorded in the wrong time interval by observers (Simons et al 2009) or move during the counts, a violation of the closure assumption tied to most abundance estimators , Nichols et al 2009). There are parallel tradeoffs between collecting the ancillary data and violating the assumptions of the other abundance estimators as well (Johnson 2008, Efford and Dawson 2009, Rota et al 2009, Simons et al 2009, Chandler et al 2011.…”

“…Similarly, lengthening the count period beyond 0-3 or 3-5 min by adding the interval 5-10 min has benefits in modeling individual heterogeneity and increasing the overall detection probability at a point (Barker et al 1993, Alldredge et al 2007a), but it increases probabilities that birds are recorded in the wrong time interval by observers (Simons et al 2009) or move during the counts, a violation of the closure assumption tied to most abundance estimators , Nichols et al 2009). There are parallel tradeoffs between collecting the ancillary data and violating the assumptions of the other abundance estimators as well (Johnson 2008, Efford and Dawson 2009, Rota et al 2009, Simons et al 2009, Chandler et al 2011. Unfortunately, the literature on the new abundance estimators has rarely provided guidance on how or whether data collections should be kept consistent with the common standards (but see Farnsworth et al 2005).…”

“…This flexibility is important because (1) observers' abilities to collect complicated ancillary data will vary considerably among projects using mostly volunteers versus primarily highly trained research staff (O'Connor et al 2000); and (2) views on point-count protocols will continue to change, particularly as there remains considerable disagreement on which methods are best for conducting and analyzing point counts (Ellingson and Lukacs 2003, Hutto and Young 2003, Johnson 2008, Efford and Dawson 2009, Simons et al 2009). Reviving the common standards avoids the debates about methodology and moves the discussion back to how, collectively, we can best use our survey data to understand and address the large-scale issues facing bird populations.…”

How well do regional or national Breeding Bird Survey data predict songbird population trends at an intact boreal site?

“…This random effect of station was nested within a random effect for sampling site (the clusters of three point counts). An additional random effect of observer was included to account for variation in observer ability (Simons et al 2009). These random effects were included in all models.…”