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Summary1. The 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. 2. 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. 3. We estimated singing rates and EDR for 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 and EDR for 73% and 59% of species, respectively. 4. 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 8Á5% using detection probabilities estimated from a combination of removal and distance sampling models. 5. 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.

Background: The global spread of the highly pathogenic avian influenza H5N1 virus has stimulated interest in a better understanding of the mechanisms of H5N1 dispersal, including the potential role of migratory birds as carriers. Although wild birds have been found dead during H5N1 outbreaks, evidence suggests that others have survived natural infections, and recent studies have shown several species of ducks capable of surviving experimental inoculations of H5N1 and shedding virus. To investigate the possibility of migratory birds as a means of H5N1 dispersal into North America, we monitored for the virus in a surveillance program based on the risk that wild birds may carry the virus from Asia.

For climate change projections to be useful, the magnitude of change must be understood relative to the magnitude of uncertainty in model predictions. We quantified the signal-to-noise ratio in projected distributional responses of boreal birds to climate change, and compared sources of uncertainty. Boosted regression tree models of abundance were generated for 80 boreal-breeding bird species using a comprehensive data set of standardized avian point counts (349,629 surveys at 122,202 unique locations) and 4-km climate, land use, and topographic data. For projected changes in abundance, we calculated signal-to-noise ratios and examined variance components related to choice of global climate model (GCM) and two sources of species distribution model (SDM) uncertainty: sampling error and variable selection. We also evaluated spatial, temporal, and interspecific variation in these sources of uncertainty. The mean signal-to-noise ratio across species increased over time to 2.87 by the end of the 21st century, with the signal greater than the noise for 88% of species. Across species, climate change represented the largest component (0.44) of variance in projected abundance change. Among sources of uncertainty evaluated, choice of GCM (mean variance component = 0.17) was most important for 66% of species, sampling error (mean= 0.12) for 29% of species, and variable selection (mean =0.05) for 5% of species. Increasing the number of GCMs from four to 19 had minor effects on these results. The range of projected changes and uncertainty characteristics across species differed markedly, reinforcing the individuality of species' responses to climate change and the challenges of one-size-fits-all approaches to climate change adaptation. We discuss the usefulness of different conservation approaches depending on the strength of the climate change signal relative to the noise, as well as the dominant source of prediction uncertainty.

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