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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-tonoise 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. [ABSTRACT FROM AUTHOR]

Environmental factors controlling the distribution and abundance of boreal avifauna are not fully understood, limiting our ability to predict the consequences of a changing climate and industrial development activities underway. We used a compilation of avian point-count data, collected over 1990-2008 from nearly 36 000 locations, to model the abundance of individual forest songbird species within the Canadian boreal forest. We evaluated 30 vegetation and 101 climatic variables, representing most of the widely-used dimensions of climate space, along with less usual measures of inter-annual variability. Regression tree models allowed us to calculate the relative importance of climate and vegetation variable classes according to avian migration strategy without the need for a priori variable selection or dimension reduction. We tested for hierarchical habitat selection by formulating hypotheses on the locations of variables within the model tree structures. Climate variables explained the majority (77%) of deviance explained over 98 species modelled. As may be expected at high latitudes, we found energy availability (temperature, 65%) to be more important than moisture availability (precipitation, 12%). The contributions of inter- and intra-annual climate variability (28%) were about half that of mean conditions. The relatively large contribution of remotely-sensed vegetation metrics (23%) highlighted the importance of local vegetation heterogeneity controlled by non-climatic factors. The two most important vegetation variables were landcover type and April leaf area index. When selected, these generally occurred in a model's right subtree, consistent with predictions from hierarchical habitat selection theory. When occupying the root node, landcover effectively delineated the historical forest-prairie ecotone, reflecting the current disequilibrium between climate and vegetation due to human land use. Our findings suggest a large potential for avian distributional shifts in response to climate change, but also demonstrate the importance of finer scale vegetation heterogeneity in the spatial distribution of boreal birds. [ABSTRACT FROM AUTHOR]

Previous studies of avian nest predation have focused on how human-induced changes in the landscape influence the frequency of predation.However, natural variation in the abundance of predators due to their choice of habitat can also influence predation rate. To determine if predation on artificial nests was influenced by forest stand type, we placed ground and shrub nests containing quail and plasticine eggsin contiguous coniferous, mixedwood and deciduous stands in the southern boreal mixedwood forest of central Canada. Nest predators were identified using remotely triggered cameras and marks left in plasticine eggs, while the relative abundance of nest predators such as squirrels and corvids were estimated using acoustic-visual surveys. Using the fate of quail eggs to calculate predation rate, we found that predation was significantly higher in coniferous (67%) than in deciduous(17%) or mixedwood (25%) forest, with similar predation on ground (37%) and shrub (29%) nests. Using plasticine eggs to calculate predation rate, nests in coniferous forest still suffered higher rates of predation, although predation rates were 15--20% higher, and ground nests suffered significantly higher rates of predation than shrub nests.Quail eggs seemed to suffer lower rates of predation because small mammals were unable to penetrate the shell, but could leave marks on plasticine eggs. The higher predation rate in coniferous forest was likely caused by higher abundance of red squirrels Tamiasciurus hudsonicus, the presence of fishers Martes pennanti and a simplified understory which may have made it easier for predators to find nests relative to the deciduous and niixedwood forest. Plasticine eggs provide newinsights into nest predation by identifying predation events by smaller predators such as mice that are missed when using quail eggs. [ABSTRACT FROM AUTHOR]