Alternatives to genetic affinity as a context for within-species response to climate

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Abstract

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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Fig. 1: Pika minimum elevation (PME) and geographic subdivisions used to explore spatial heterogeneity in responses of the American pika to climate.
Fig. 2: Climate coherency.
Fig. 3: Coherency in responses to individual mechanistic climate variables.
Fig. 4: Coherency in responses to multivariate aspects of climate across subdivisions and background extents.
Fig. 5: Importance of subdivision unit in explaining responses to multivariate aspects of climate.

Data availability

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.

Code availability

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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Acknowledgements

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.

Author information

A.B.S. refined the shared data, revised the list of climatic predictors, performed all of the ecological niche modelling, devised all of the novel improvements to typical modelling norms, produced all tables and figures and cowrote and revised the manuscript. E.A.B. conceived of the idea, coordinated all the authors, contributed the largest number of the retained records, devised an initial list of climatic predictors, cowrote and revised the manuscript and convened the smaller group of analysts. A.E.K. developed the PME model, identified and refined the specific subdivision schemes and their constituent subunits and helped quality-check the pika dataset. A.N.J. helped select specific data sources for predictors, and helped identify mechanisms by which climatic variables may act on O. princeps. E.A.B., C.W.E., A.N.J., R.C.K., H.C.L., C.R. and T.J.R. iteratively advised on analytical approaches and research objectives, and edited drafts of the manuscript. C.D. provided (spatially and temporally) high-resolution (PRISM) data on our climatic predictor variables. J.V. and L.E.H. provided comprehensive editing of later drafts. All authors except A.B.S., A.E.K., H.C.L. and C.D. provided data on locations of O. princeps detections, provided input to analysis design and reviewed the manuscript.

Correspondence to Erik A. Beever.

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Competing interests

The authors declare no competing interests.

Additional information

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.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary methods, references, Figs. 1–5 and Tables 1, 6 and 7.

Reporting Summary

Supplementary Table 2

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.

Supplementary Table 3

Correlations between mechanistically derived climate predictors calculated using a 10-yr window immediately before each occurrence record and windows of shorter duration.

Supplementary Table 4

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.

Supplementary Table 5

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.

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