Predicting origins of passerines migrating through Canadian migration monitoring stations using stable-hydrogen isotope analyses of feathers: a new tool for bird conservation

The Canadian Migration Monitoring Network (CMMN) consists of standardized observation and migration count stations located largely along Canada’s southern border. A major purpose of CMMN is to detect population trends of migratory passerines that breed primarily in the boreal forest and are otherwise poorly monitored by the North American Breeding Bird Survey (BBS). A primary limitation of this approach to monitoring is that it is currently not clear which geographic regions of the boreal forest are represented by the trends generated for each bird species at each station or group of stations. Such information on “catchment areas” for CMMN will greatly enhance their value in contributing to understanding causes of population trends, as well as facilitating joint trend analysis for stations with similar catchments. It is now well established that naturally occurring concentrations of deuterium in feathers grown in North America can provide information on their approximate geographic origins, especially latitude. We used stable hydrogen isotope analyses of feathers (δ²Hf) from 15 species intercepted at 22 CMMN stations to assign approximate origins to populations moving through stations or groups of stations. We further constrained the potential catchment areas using prior information on potential longitudinal origins based upon bird migration trajectories predicted from band recovery data and known breeding distributions. We detected several cases of differences in catchment area of species passing through sites, and between seasons within species. We discuss the importance of our findings, and future directions for using this approach to assist conservation of migratory birds at continental scales.

Temporal aggregation of migration counts can improve accuracy and precision of trends

Temporal replicate counts are often aggregated to improve model fit by reducing zero-inflation and count variability, and in the case of migration counts collected hourly throughout a migration, allows one to ignore nonindependence. However, aggregation can represent a loss of potentially useful information on the hourly or seasonal distribution of counts, which might impact our ability to estimate reliable trends. We simulated 20-year hourly raptor migration count datasets with known rate of change to test the effect of aggregating hourly counts to daily or annual totals on our ability to recover known trend. We simulated data for three types of species, to test whether results varied with species abundance or migration strategy: a commonly detected species, e.g., Northern Harrier, Circus cyaneus; a rarely detected species, e.g., Peregrine Falcon, Falco peregrinus; and a species typically counted in large aggregations with overdispersed counts, e.g., Broad-winged Hawk, Buteo platypterus. We compared accuracy and precision of estimated trends across species and count types (hourly/daily/annual) using hierarchical models that assumed a Poisson, negative binomial (NB) or zero-inflated negative binomial (ZINB) count distribution. We found little benefit of modeling zero-inflation or of modeling the hourly distribution of migration counts. For the rare species, trends analyzed using daily totals and an NB or ZINB data distribution resulted in a higher probability of detecting an accurate and precise trend. In contrast, trends of the common and overdispersed species benefited from aggregation to annual totals, and for the overdispersed species in particular, trends estimating using annual totals were more precise, and resulted in lower probabilities of estimating a trend (1) in the wrong direction, or (2) with credible intervals that excluded the true trend, as compared with hourly and daily counts.