Comparing the results of recall surveys and standardized searches in understanding bird-window collisions at houses

Every year a large number of birds die when they collide with windows. The actual number is difficult to ascertain. Previous attempts to estimate bird-window collision rates in Canada relied heavily on a prior citizen-science study that used memory-based surveys. Such an approach to data collection has many potential biases. We built upon this study and its recommendations for future research by creating a citizen-science program that actively searched for collision evidence at houses and apartments for an extended period with the objective to see how standardized approaches to data collection compared with memory recall. Absolute collision estimates as well as relative differences were compared between residence types in the two studies, and we found considerable differences in absolute values for collisions but similar rankings of collision rates between residence types. Collision recall rates in our study (56.5%) were very similar those in the prior 2012 study, where 50.5% of participants remembered a bird colliding with a window at some time in the past. Fatality estimates, however, were 1.4 times higher in the 2012 study than in our study based on standardized searches. Rural houses with a bird feeder consistently had the highest number of collisions. This suggests that memory recall surveys may be a useful tool for understanding the relative importance of different risk factors causing bird-window collisions.

Detectability of migrating raptors and its effect on bias and precision of trend estimates

Annual counts of migrating raptors at fixed observation points are a widespread practice, and changes in numbers counted over time, adjusted for survey effort, are commonly used as indices of trends in population size. Unmodeled year-to-year variation in detectability may introduce bias, reduce precision of trend estimates, and reduce power to detect trends. We conducted dependent double-observer surveys at the annual fall raptor migration count at Lucky Peak, Idaho, in 2009 and 2010 and applied Huggins closed-capture removal models and information-theoretic model selection to determine the relative importance of factors affecting detectability. The most parsimonious model included effects of observer team identity, distance, species, and day of the season. We then simulated 30 years of counts with heterogeneous individual detectability, a population decline (λ = 0.964), and unexplained random variation in the number of available birds. Imperfect detectability did not bias trend estimation, and increased the time required to achieve 80% power by less than 11%. Results suggested that availability is a greater source of variance in annual counts than detectability; thus, efforts to account for availability would improve the monitoring value of migration counts. According to our models, long-term trends in observer efficiency or migratory flight distance may introduce substantial bias to trend estimates. Estimating detectability with a novel count protocol like our double-observer method is just one potential means of controlling such effects. The traditional approach of modeling the effects of covariates and adjusting the index may also be effective if ancillary data is collected consistently.