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We examined the effects of habitat change on Cerulean Warbler (Dendroica cerulea) populations at stops along Breeding Bird Survey (BBS) routes in the central Appalachians. We used aerial photographs to compare early (1967/1971), middle (1982/1985), and late (2000/2003) periods and compared 1992 and 2001 National Land Cover Data (NLCD). Mean Cerulean Warbler detections per stop decreased at 68 BBS stops between the early (0.05) and middle (0.01) time periods and their distribution became more restricted (15 vs. 3% of stops), but the amount of deciduous/mixed forest increased. Mean detections at 240 stops decreased from the middle (0.09) to the late (0.06) time periods, but the deciduous/mixed forest land cover and fragmentation metrics did not change. The amounts of deciduous/mixed forest, core forest area, and edge density in the NLCD analysis decreased from 1992 to 2001, whereas the amount of non-forest land cover increased. The number of Cerulean Warbler detections did not change (1992 = 0.08, 2001 = 0.10; P = 0.11). The lack of concordance between Cerulean Warbler detections and broad habitat features suggests that smaller, microhabitat features may be most important in affecting Cerulean Warbler breeding habitat suitability. Received 10 October 2009. Accepted 31 March 2011., The Cerulean Warbler (Dendroica cerulea) is considered a species of high conservation concern by Partners in Flight and has been reviewed for listing as a threatened species (USDI 2006). Analyses [...]

The density of a closed population of animals occupying stable home ranges may be estimated from detections of individuals on an array of detectors, using newly developed methods for spatially explicit capture--recapture. Likelihood-based methods provide estimates for data from multi-catch traps or from devices that record presence without restricting animal movement ('proximity' detectors such as camera traps and hair snags). As originally proposed, these methods require multiple sampling intervals. We show that equally precise and unbiased estimates may be obtained from a single sampling interval, using only the spatial pattern of detections. This considerably extends the range of possible applications, and we illustrate the potential by estimating density from simulated detections of bird vocalizations on a microphone array. Acoustic detection can be defined as occurring when received signal strength exceeds a threshold. We suggest detection models for binary acoustic data, and for continuous data comprising measurements of all signals above the threshold. While binary data are often sufficient for density estimation, modeling signal strength improves precision when the microphone array is small. Key words: acoustic census methods; area search; camera trap; fecal DNA; maximum likelihood; microphone array; passive detector array; population density; proximity detector; signal strength; spatially explicit capture recapture.

Point counts are used widely to index bird populations. Variation in the proportion of birds counted is a known source of error, and for robust inference it has been advocated that counts be converted to estimates of absolute population size. We used simulation to assess nine methods for the conduct and analysis of point counts when the data included distance-related heterogeneity of individual detection probability. Distance from the observer is a ubiquitous source of heterogeneity, because nearby birds are more easily detected than distant ones. Several recent methods (dependent double-observer, time of first detection, time of detection, independent multiple-observer, and repeated counts) do not account for distance-related heterogeneity, at least in their simpler forms. We assessed bias in estimates of population size by simulating counts with fixed radius w over four time intervals (occasions). Detection probability per occasion was modeled as a half-normal function of distance with scale parameter [sigma] and intercept g(0) = 1.0. Bias varied with [sigma]/w; values of [sigma] inferred from published studies were often 50% for a 100-m fixed-radius count. More critically, the bias of adjusted counts sometimes varied more than that of unadjusted counts, and inference from adjusted counts would be less robust. The problem was not solved by using mixture models or including distance as a covariate. Conventional distance sampling performed well in simulations, but its assumptions are difficult to meet in the field. We conclude that no existing method allows effective estimation of population size from point counts. Key words: bias, capture-recapture, distance-related heterogeneity, mixture models, point counts, repeated counts.

Distance-sampling methods are commonly used in studies of animal populations to estimate population density. A common objective of such studies is to evaluate the relationship between abundance or density and covariates that describe animal habitat or other environmental influences. However, little attention has been focused on methods of modeling abundance covariate effects in conventional distance-sampling models. In this paper we propose a distance-sampling model that accommodates covariate effects on abundance. The model is based on specification of the distance-sampling likelihood at the level of the sample unit in terms of local abundance (for each sampling unit). This model is augmented with a Poisson regression model for local abundance that is parameterized in terms of available covariates, Maximum-likelihood estimation of detection and density parameters is based on the integrated likelihood, wherein local abundance is removed from the likelihood by integration. We provide an example using avian point-transect data of Ovenbirds (Seiurus aurocapiUus) collected using a distance-sampling protocol and two measures of habitat structure (understory cover and basal area of overstory trees). The model yields a sensible description (positive effect of understory cover, negative effect on basal area) of the relationship between habitat and Ovenbird density that can be used to evaluate the effects of habitat management on Ovenbird populations. Key words: abundance estimation; avian point counts; distance-sampling methodology; mixture models; Ovenbird; random effects.