Projecting boreal bird responses to climate change: the signal exceeds the noise. Issue 1 (1st January 2015)

• Record Type: Journal Article
• Authors: Stralberg, D.; Matsuoka, S. M.; Hamann, A.; Bayne, E. M.; Sólymos, P.; Schmiegelow, F. K. A.; Wang, X.; Cumming, S. G.; Song, S. J.
• Is Part Of: Ecological applications. Volume 25:Issue 1(2015)
• Page Start: 52
• Page End: 69
• Publication Date: 2015-01-01
• Subjects: avian density -- boosted regression trees -- boreal birds -- boreal forest -- climate change -- global climate models -- signal-to-noise ratio -- species distribution models -- uncertainty -- variance partitioning; Ecology -- Periodicals; Environmental protection -- Periodicals; Biology, Economic -- Periodicals; 577.05
• DOI: 10.1890/13-2289.1
• Languages: English
• ISSNs: 1051-0761

Abstract

Abstract : 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.