Baseline (1996–2017) and projected (2011-2100) maps of predicted mean indicated breeding pairs (IBP) and standard deviation (SD) for the 12 waterfowl species in Eastern Canada. Projected means were summarized for the three 30-year periods under the “low” (RCP4.5) and “high” (RCP8.5) greenhouse gas concentration trajectories. From the projected abundances, we also computed maps of climate suitability indices per 12 species. PDF maps and spatial data objects (TIFF files) for the four time-lag schemes under “low” (RCP4.5) and “high” (RCP8.5) greenhouse gas concentration trajectories.

Maps representing near-national waterfowl density and abundance of 13 individual species and 4 species groups (goldeneyes, mergansers, scoters, and scaup). Predictive models were built using Boosted Regression Tree analysis, data from the Waterfowl Breeding Population and Habitat Survey, and several environmental datasets. Methods used to create 17 species-level models are described in Barker et al. 2014 (ACE 9(2): 7). Guild-Level Maps (cavity nesters, ground nesters, and overwater Nesters) were produced by summing species-level observations and then built models at the guild level. The prediction of total waterfowl abundance was produced by summing the predictions from guild-level models (cavity nesters, ground nesters, and overwater nesters). Species specific model uncertainty are also provided.

Species specific mean projections and uncertainty estimates (coefficient of variation) of80 boreal-breeding songbird species generated using boosted regression tree models for the current period (based on climate data from 1961-1990) and three future time periods (2011–2040, 2041­–2070, 2071–2100).

We calculated population estimates for 81 landbird species in Bird Conservation Region 6 in Alberta, Canada, using spatially explicit models on roadside and off-road point-count surveys that incorporate land cover and climate as predictors. We compared our results with population estimates from Partners in Flight (PIF) and 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.