Dependent double-observer method reduces false-positive errors in auditory avian survey data.

Bias introduced by detection errors is a well-documented issue for abundance and occupancy estimates of wildlife. Detection errors bias estimates of detection and abundance or occupancy in positive and negative directions, which can produce misleading results. There have been considerable design- and model-based methods to address false-negative errors, or missed detections. However, false-positive errors, or detections of individuals that are absent but counted as present because of misidentifications or double counts, are often assumed to not occur in ecological studies. The dependent double-observer survey method is a design-based approach speculated to reduce false positives because observations have the ability to be confirmed by two observers. However, whether this method reduces false positives compared to single-observer methods has not been empirically tested. We used prairie songbirds as a model system to test if a dependent double-observer method reduced false positives compared to a single-observer method. We used vocalizations of ten species to create auditory simulations and used naive and expert observers to survey these simulations using single-observer and dependent double-observer methods. False-positive rates were significantly lower using the dependent double-observer survey method in both observer groups. Expert observers reported a 3.2% false-positive rate in dependent double-observer surveys and a 9.5% false-positive rate in single-observer surveys, while naive observers reported a 39.1% false-positive rate in dependent double-observer surveys and a 49.1% false-positive rate in single-observer surveys. Misidentification errors arose in all survey scenarios and almost all species combinations. However, expert observers using the dependent double-observer method performed significantly better than other survey scenarios. Given the use of double-observer methods and the accumulating evidence that false positives occur in many survey methods across different taxa, this study is an important step forward in acknowledging and addressing false positives.

Validation of a novel time-to-event nest density estimator on passerines: An example using Brewer's sparrows (Spizella breweri).

Human impacts on natural resources increasingly necessitate understanding of the demographic rates driving wildlife population trends. Breeding productivity in many avian species is the demographic parameter that primarily influences population fluctuations. Nest density is a vital component of breeding productivity despite the fact that it is most often inferred exclusively from nest success. Unfortunately, locating every nest in a given area to determine nest density is often not feasible and can be biased by measurement error. The availability of a nest to be detected and the probability it will be detected during nest searching are two prominent sources of measurement error. A time-to-event nest density estimator has been developed that, unlike standard distance sampling methods, accounts for availability and can use nest data from outside structured surveys routinely collected to assess nest success. Its application is currently limited to Anseriformes, so we evaluated the general applicability of the time-to-event estimator in the order Passeriformes. To do this, we compared estimates of nest detection rate and nest density from the time-to-event estimator to distance sampling methods for 42 Brewer's sparrow (Spizella breweri) nests monitored in 2015. The time-to-event estimator produced similar but more precise nest detection and density estimates than distance sampling methods.