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

Abstract

Point counts are a common method for sampling avian distribution and abundance. Although methods for estimating detection probabilities are available, many analyses use raw counts and do not correct for detectability. We use a removal model of detection within an N-mixture approach to estimate abundance trends corrected for imperfect detection. We compare the corrected trend estimates to those estimated from raw counts for 16 species using 15 years of monitoring data on three national forests in the western Great Lakes, USA. We also tested the effects of overdispersion by modeling both counts and removal mixtures under three statistical distributions: Poisson, zero-inflated Poisson, and negative binomial. For most species, the removal model produced estimates of detection probability that conformed to expectations. For many species, but not all, estimates of trends were similar regardless of statistical distribution or method of analysis. Within a given combination of likelihood (counts vs. mixtures) and statistical distribution, trends usually differed by both stand type and national forest, with species showing declines in some stand types and increases in others. For three species, Brown Creeper, Yellow-rumped Warbler, and Black-throated Green Warbler, temporal patterns in detectability resulted in substantial differences in estimated trends under the removal mixtures compared to the analysis of raw counts. Overall, we found that the zero-inflated Poisson was the best distribution for our data, although the Poisson or negative binomial performed better for a few species. The similarity in estimated trends that we observed among counts and removal mixtures was probably a result of both experimental design and sampling effort. First, the study was originally designed to avoid confounding observer effects with habitats or time. Second, our time series is relatively long and our sample sizes within years are large.

Standardized Measures of Coastal Wetland Condition: Implementation at a Laurentian Great Lakes Basin-Wide Scale

“…This deviated to some extent from previous wetland count protocols, but analysis of a 360 o area better documents bird use of the entire wetland and associated riparian areas. Unlimited distance counts that include distance estimation are preferred for monitoring (Etterson et al 2009;Matsuoka et al 2012Matsuoka et al , 2014. Data recorded at each survey point before or immediately after the survey included: 1) point ID with recording of the GPS waypoint, 2) date, 3) start time, 4) name of observer(s), 5) weather conditions including air temperature, wind conditions, precipitation, cloud cover, ambient noise level, and if possible water temperature, and 6) verification of whether call-broadcast volume was checked.…”

Landscape matrix and species traits mediate responses of Neotropical resident birds to forest fragmentation in Jamaica

“…Based on this finding, we had no a priori reason to suspect systematic biases in species detectability in our study; thus, we used count data as indices of bird abundance in our analyses. Detected trends based on observed point counts are expected to reflect those based on true abundances, because we implemented standardized survey protocols to control for confounding detection biases due to landscape matrix effects and those due to observer, time, or sampling effort (Johnson 2008, Etterson et al 2009). …”

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

“…We therefore offer a simple reminder to investigators to follow the common standards whenever possible when conducting point-count surveys (Ralph et al 1993(Ralph et al , 1995a. We continue to advocate the common standards because they are relatively inexpensive and easy to follow, they have proved useful in a wide range of applications, and the resulting data can be analyzed with simple forms of abundance estimators to adjust for survey-specific detection probability , 2005, Thompson and La Sorte 2008, Etterson et al 2009, Reidy et al 2011, Matsuoka et al 2012, Sólymos et al 2013. The common standards include a wealth of useful recommendations on the design and conduct of surveys (Ralph et al 1993(Ralph et al , 1995a, nearly all of which are as pertinent today as they were 20 yr ago.…”