Accounting for within-species variability in the relationship between occurrence and climate is essential to forecasting species’ responses to climate change. Few climate-vulnerability assessments explicitly consider intraspecific variation, and those that do typically assume that variability is best explained by genetic affinity. Here, we evaluate how well heterogeneity in responses to climate by a cold-adapted mammal, the American pika (Ochotona princeps), aligns with subdivisions of the geographic range by phylogenetic lineage, physiography, elevation or ecoregion. We find that variability in climate responses is most consistently explained by an ecoregional subdivision paired with background sites selected from a broad spatial extent indicative of long-term (millennial-scale) responses to climate. Our work challenges the common assumption that intraspecific variation in climate responses aligns with genetic affinity. Accounting for the appropriate context and scale of heterogeneity in species’ responses to climate will be critical for informing climate-adaptation management strategies at the local (spatial) extents at which such actions are typically implemented.
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The raw PRISM weather variables that support the findings of this study are available from the PRISM Climate Group, but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. These data are available from the authors on reasonable request and with permission of author C. Daly.
The computer code (https://github.com/adamlilith/pika_climateCoherency) and some occurrence datasets analysed during the current study (https://doi.org/10.5066/P9LV1XCF) are available online.
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We thank C. Corkran, C. I. Millar, C. Shank, E. Willy, D. Wright, state and provincial Natural Heritage programmes and the Bow Valley Naturalists for contributing data on unequivocal detections of O. princeps. We thank M. Forister, J. Walter and C. Jarnevich for critical reviews of drafts of the manuscript. A full list of funding acknowledgements is provided in Supplementary Table 7. Any use of trade, firm or product names is for descriptive purposes only and does not imply endorsement by the US Government. The contents, findings and conclusions of this report are solely the responsibility of the authors and do not necessarily represent the views of the US Geological Survey, US Fish and Wildlife Service or US National Park Service.
The authors declare no competing interests.
Peer review information Nature Climate Change thanks Klaus Hackländer, Masahiro Ryo and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
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Supplementary methods, references, Figs. 1–5 and
Tables 1, 6 and 7.
Climate variables that have been associated with pika dynamics or hypothesized to drive population dynamics. The PRISM data source is from Daly et al. (2002) and Daymet from Thornton et al. (1997, 2000). Included are loadings for the first six axes of a principal component analysis, which was used to generate climate predictors (principal component axes) used in the multivariate climate coherency analyses.
Correlations between mechanistically derived climate predictors calculated using a 10-yr window immediately before each occurrence record and windows of shorter duration.
Spatial similarity between pairs of divisions/background extents. Similarity has the range [0, 1], with higher values connoting more spatial redundancy between units in divisions A and B.
Rank importance of each climate variable for each combination of subdivision and PME. Only predictors with a coherency significantly >0 are shown (solid bars in Fig. 3). See Supplementary Table 2 for definitions of each variable. AW Balance, atmospheric water balance. Dur, duration. RH, relative humidity. VPD, vapour pressure deficit. Var, variability. GS, growing season.