Calibrating indices of avian density from non‐standardized survey data: making the most of a messy situation

Abstract

SummaryThe analysis of large heterogeneous data sets of avian point‐count surveys compiled across studies is hindered by a lack of analytical approaches that can deal with detectability and variation in survey protocols.We reformulated removal models of avian singing rates and distance sampling models of the effective detection radius (EDR) to control for the effects of survey protocol and temporal and environmental covariates on detection probabilities.We estimated singing rates andEDRfor 75 boreal forest songbird species and found that survey protocol, especially point‐count radius, explained most of the variation in detectability. However, environmental and temporal covariates (date, time, vegetation) affected singing rates andEDRfor 73% and 59% of species, respectively.Unadjusted survey counts increased by an average of 201% from a 5‐min, 50‐m radius survey to a 10‐min, 100‐m radius survey (n= 75 species). This variability was decreased to 8·5% using detection probabilities estimated from a combination of removal and distance sampling models.Our modelling approach reduced computation when fitting complex models to large data sets and can be used with a wide range of statistical techniques for inference and prediction of avian densities.