Lessons learned from comparing spatially explicit models and the Partners in Flight approach to estimate population sizes of boreal birds in Alberta, Canada

Abstract

Estimating the population abundance of landbirds is a challenging task complicated by the amount, type, and quality of available data. Avian conservationists have relied on population estimates from Partners in Flight (PIF), which primarily uses roadside data from the North American Breeding Bird Survey (BBS). However, the BBS was not designed to estimate population sizes. Therefore, we set out to compare the PIF approach with spatially explicit models incorporating roadside and off-road point-count surveys. We calculated population estimates for 81 landbird species in Bird Conservation Region 6 in Alberta, Canada, using land cover and climate as predictors. We also developed a framework to evaluate how the differences between the detection distance, time-of-day, roadside count, and habitat representation adjustments explain discrepancies between the 2 estimators. We showed that the key assumptions of the PIF population estimator were commonly violated in this region, and that the 2 approaches provided different population estimates for most species. The average differences between estimators were explained by differences in the detection-distance and time-of-day components, but these adjustments left much unexplained variation among species. Differences in the roadside count and habitat representation components explained most of the among-species variation. The variation caused by these factors was large enough to change the population ranking of the species. The roadside count bias needs serious attention when roadside surveys are used to extrapolate over off-road areas. Habitat representation bias is likely prevalent in regions sparsely and non-representatively sampled by roadside surveys, such as the boreal region of North America, and thus population estimates for these regions need to be treated with caution for certain species. Additional sampling and integrated modeling of available data sources can contribute towards more accurate population estimates for conservation in remote areas of North America.

Forecasting the Cumulative Effects of Multiple Stressors on Breeding Habitat for a Steeply Declining Aerial Insectivorous Songbird, the Olive-sided Flycatcher (Contopus cooperi)

“…Fourth, 72% of the point counts in our dataset were within 100 m of a road. Although we did not find a relationship between the occurrence of olive-sided flycatchers and the presence of roads in the southern Peace River region, roadside surveys can under-sample certain habitats in boreal forest, and underrepresent bird densities (Sólymos et al, 2020). Simulations of future habitat availability would be best suited to areas with higher densities of the target species (which can, however, be difficult for species that have experienced steep declines) and more comprehensive land cover and point count data, and subsequently, lower error in habitat suitability models.…”

Projected effects of climate change on boreal bird community accentuated by anthropogenic disturbances in western boreal forest, Canada

“…These predictive models were based on point count data, including surveys from the North American Breeding Bird Survey (BBS; http://pwrc.usgs.gov/bbs/), Boreal Avian Modelling Project (BAM; http://borealbirds.ualberta.ca) and the ABMI (http://abmi.ca). The models were built following the methodology outlined in Ball et al (2016) and Sólymos et al (2020). Land cover associations were based on the dominant land cover (native vegetation and human footprint) type within a 150‐m radius buffer around the points.…”

Quantifying Long-Term Bird Population Responses to Simulated Harvest Plans and Cumulative Effects of Disturbance

“…The SDMs that we used for boreal birds were produced by the Boreal Avian Modelling Project (BAM), Alberta Biodiversity Monitoring Institute (ABMI), and Environment and Climate Change Canada (ECCC). The modeling process has been described in detail elsewhere and has been applied in other simulation and modeling studies (Ball et al, 2016;Sólymos et al, 2020b). Briefly, point count data were collated and standardized (n = 141,557 survey visits from 33,002 unique stations) from multiple boreal bird studies in Alberta's boreal forests .…”