Point-count surveys are often used to collect data on the abundance and distribution of birds, generally as an index of relative abundance. Valid comparison of these indices assumes that the detection process is comparable over space and time. These restrictive assumptions can be eliminated by estimating detection probabilities directly. We generalize a recently proposed removal model for estimating detection probabilities using a time-of-detection approach, which can account for more sources of variation in point-count data. This method is specifically designed to account for variation in detection probabilities associated with singing rates of birds. Our model accounts for both availability bias and detection bias by modeling the combined probability that a bird sings during the count, and the probability that it is detected given that it sings. The model requires dividing the count into several intervals and recording detections of individual birds in each interval. We develop maximum-likelihood estimators for this approach and provide a full suite of models based on capture-recapture models, including covariate models. We present two examples of this method: one for four species of songbirds surveyed in Great Smoky Mountains National Park using three unequal intervals, and one for the Pearly-eyed Thrasher (Margarops fuscatus) surveyed in Puerto Rico using four equal intervals. Models incorporating individual heterogeneity were selected for all data sets using information-theoretic model-selection techniques. Detection probabilities varied among count-time intervals, which suggests that birds may be responding to observers. We recommend applying this method to surveys with four or more equal intervals to reduce assumptions and to take full advantage of standard capture-recapture software. The time-of-detection approach provides a better understanding of the detection process, especially when singing rates of individual birds affect detection probabilities.
Estimación de la Abundancia en Puntos de Conteo Mediante el Método del Tiempo de Detección