Projecting boreal bird responses to climate change: the signal exceeds the noise.

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.

The density of anthropogenic features explains seasonal and behaviour-based functional responses in selection of linear features by a social predator.

Anthropogenic linear features facilitate access and travel efficiency for predators, and can influence predator distribution and encounter rates with prey. We used GPS collar data from eight wolf packs and characteristics of seismic lines to investigate whether ease-of-travel or access to areas presumed to be preferred by prey best explained seasonal selection patterns of wolves near seismic lines, and whether the density of anthropogenic features led to functional responses in habitat selection. At a broad scale, wolves showed evidence of habitat-driven functional responses by exhibiting greater selection for areas near low-vegetation height seismic lines in areas with low densities of anthropogenic features. We highlight the importance of considering landscape heterogeneity and habitat characteristics, and the functional response in habitat selection when investigating seasonal behaviour-based selection patterns. Our results support behaviour in line with search for primary prey during summer and fall, and ease-of-travel during spring, while patterns of selection during winter aligned best with ease-of-travel for the less-industrialized foothills landscape, and with search for primary prey in the more-industrialized boreal landscape. These results highlight that time-sensitive restoration actions on anthropogenic features can affect the probability of overlap between predators and threatened prey within different landscapes.