A Double-Observer Approach for Estimating Detection Probability and Abundance From Point Counts

Abstract

Although point counts are frequently used in ornithological studies, basic assumptions about detection probabilities often are untested. We apply a double-observer approach developed to estimate detection probabilities for aerial surveys (Cook and Jacobson 1979) to avian point counts. At each point count, a designated “primary” observer indicates to another (“secondary”) observer all birds detected. The secondary observer records all detections of the primary observer as well as any birds not detected by the primary observer. Observers alternate primary and secondary roles during the course of the survey. The approach permits estimation of observer-specific detection probabilities and bird abundance. We developed a set of models that incorporate different assumptions about sources of variation (e.g. observer, bird species) in detection probability. Seventeen field trials were conducted, and models were fit to the resulting data using program SURVIV. Single-observer point counts generally miss varying proportions of the birds actually present, and observer and bird species were found to be relevant sources of variation in detection probabilities. Overall detection probabilities (probability of being detected by at least one of the two observers) estimated using the double-observer approach were very high (>0.95), yielding precise estimates of avian abundance. We consider problems with the approach and recommend possible solutions, including restriction of the approach to fixed-radius counts to reduce the effect of variation in the effective radius of detection among various observers and to provide a basis for using spatial sampling to estimate bird abundance on large areas of interest. We believe that most questions meriting the effort required to carry out point counts also merit serious attempts to estimate detection probabilities associated with the counts. The double-observer approach is a method that can be used for this purpose.

Songbird Abundance And Parasitism Differ Between Urban And Rural Shrublands

“…To avoid problems resulting from differences in the probability of detection among species, we analyzed species separately, and do not directly compare abundances among species. We acknowledge the recent emphasis on collecting distance or multiple-observer data to estimate effects of observer, time, species, and treatment on probability of detection (Thompson et al 1998, Nichols et al 2000, but the necessary data were not collected in this study.…”

Estimating the effects of detection heterogeneity and overdispersion on trends estimated from avian point counts

“…A sufficient condition underpinning the robust estimation of trends from uncorrected point counts is that the number of birds counted is strongly correlated with the actual number of birds present, e.g., E(C ) ¼ pN, where C is the number of birds counted, p is the detection probability, and N is true abundance (Johnson 2008). The validity of this assumption is often asserted to depend, in turn, on constant detection probability (Barker and Sauer 1995, Nichols et al 2000, Rosenstock et al 2002, Williams et al 2002. However, Johnson (2008) shows that two weaker conditions are sufficient for valid trend inference: (1) that p be independent of N, and (2) that the variance of p be ''small'' in relation to the variance of N (Johnson 2008).…”

Site‐selection bias and apparent population declines in long‐term studies

“…Accurately detecting and measuring population declines is a major challenge for several reasons. Studying population dynamics over time is labor intensive and expensive, and because total population censuses are rarely possible, biologists must deal with substantial measurement uncertainty (e.g., Reynolds et al 2011;d'Eon-Eggertson et al 2015;Rueda-Cediel et al 2018), especially for species with low or variable detection probabilities (Nichols et al 2000;Bailey et al 2004). Furthermore, populations often fluctuate randomly, exhibit temporally autocorrelated changes, or both, and such phenomena reflect complex underlying dynamics (e.g., Mosnier et al 2015;McCain et al 2016;Öst et al 2016).…”