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

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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 ( and some occurrence datasets analysed during the current study ( are available online.


  1. 1.

    Smith, A. B., Godsoe, W., Rodríguez-Sánchez, F., Wang, H.-H. & Warren, D. Niche estimation above and below the species level. Trends Ecol. Evol. 34, 260–273 (2019).

  2. 2.

    Walter, J. A. et al. The geography of spatial synchrony. Ecol. Lett. 20, 801–814 (2017).

  3. 3.

    Stephens, R. B., Hocking, D. J., Yamasaki, M. & Rowe, R. J. Synchrony in small mammal community dynamics across a forested landscape. Ecography 40, 1198–1209 (2017).

  4. 4.

    Post, E. & Forchhammer, M. C. Synchronization of animal population dynamics by large-scale climate. Nature 420, 168–171 (2002).

  5. 5.

    Koenig, W. D. Spatial autocorrelation and local disappearances in wintering North American birds. Ecology 82, 2636–2644 (2001).

  6. 6.

    IPCC Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) (Cambridge Univ. Press, 2013).

  7. 7.

    Koenig, W. D. & Liebhold, A. M. Temporally increasing spatial synchrony of North American temperature and bird populations. Nat. Clim. Change 6, 614 (2016).

  8. 8.

    Hällfors, M. H. et al. Addressing potential local adaptation in species distribution models: implications for conservation under climate change. Ecol. Appl. 26, 1154–1169 (2016).

  9. 9.

    Ikeda, D. H. et al. Genetically informed ecological niche models improve climate change predictions. Glob. Change Biol. 23, 164–176 (2017).

  10. 10.

    Banta, J. A. et al. Climate envelope modelling reveals intraspecific relationships among flowering phenology, niche breadth and potential range size in Arabidopsis thaliana. Ecol. Lett. 15, 769–777 (2012).

  11. 11.

    Pearman, P. B., D’Amen, M., Graham, C. H., Thuiller, W. & Zimmermann, N. E. Within-taxon niche structure: niche conservatism, divergence and predicted effects of climate change. Ecography 33, 990–1003 (2010).

  12. 12.

    Maguire, K. C., Shinneman, D. J., Potter, K. M. & Hipkins, V. D. Intraspecific niche models for ponderosa pine (Pinus ponderosa) suggest potential variability in population-level response to climate change. Syst. Biol. 67, 965–978 (2018).

  13. 13.

    Prasad, A. M. & Potter, K. M. Macro-scale assessment of demographic and environmental variation within genetically derived evolutionary lineages of eastern hemlock (Tsuga canadensis), an imperiled conifer of the eastern United States. Biodivers. Conserv. 26, 2223–2249 (2017).

  14. 14.

    Hotaling, S. et al. Demographic modelling reveals a history of divergence with gene flow for a glacially tied stonefly in a changing post-Pleistocene landscape. J. Biogeogr. 45, 304–317 (2018).

  15. 15.

    Castillo Vardaro, J. A., Epps, C. W., Frable, B. W. & Ray, C. Identification of a contact zone and hybridization for two subspecies of the American pika (Ochotona princeps) within a single protected area. PLoS ONE 13, e0199032 (2018).

  16. 16.

    Sexton, J. P., McIntyre, P. J., Angert, A. L. & Rice, K. J. Evolution and ecology of species range limits. Annu. Rev. Ecol. Evol. Syst. 40, 415–436 (2009).

  17. 17.

    Guralnick, R. Differential effects of past climate warming on mountain and flatland species distributions: a multispecies North American mammal assessment. Glob. Ecol. Biogeogr. 16, 14–23 (2007).

  18. 18.

    Beever, E. A., Ray, C., Wilkening, J. L., Brussard, P. F. & Mote, P. W. Contemporary climate change alters the pace and drivers of extinction. Glob. Change Biol. 17, 2054–2070 (2011).

  19. 19.

    MacArthur, R. A. & Wang, L. C. H. Behavioral thermoregulation in the pika, Ochotona princeps: a field study using radiotelemetry. Can. J. Zool. 52, 353–358 (1974).

  20. 20.

    Millar, C. I. & Westfall, R. D. Distribution and climatic relationships of the American pika (Ochotona princeps) in the Sierra Nevada and western Great Basin, USA: periglacial landforms as refugia in warming climates. Arct. Antarct. Alp. Res. 42, 76–88 (2010).

  21. 21.

    Galbreath, K. E., Hafner, D. J. & Zamudio, K. R. When cold is better: climate-driven elevation shifts yield complex patterns of diversification and demography in an alpine specialist (American pika, Ochotona princeps). Evolution 63, 2848–2863 (2009).

  22. 22.

    Galbreath, K. E., Hafner, D. J., Zamudio, K. R. & Agnew, K. Isolation and introgression in the Intermountain West: contrasting gene genealogies reveal the complex biogeographic history of the American pika (Ochotona princeps). J. Biogeogr. 37, 344–362 (2010).

  23. 23.

    Ray, C., Beever, E. A. & Rodhouse, T. J. Distribution of a climate-sensitive species at an interior range margin. Ecosphere 7, e01379 (2016).

  24. 24.

    Merriam, C. H. The geographic distribution of life in North America, with special reference to Mammalia. Proc. Biol. Soc. Wash. 7, 1–64 (1892).

  25. 25.

    Varner, J. & Dearing, M. D. The importance of biologically relevant microclimates in habitat suitability assessments. PLoS ONE 9, e104648 (2014).

  26. 26.

    Schwalm, D. et al. Habitat availability and gene flow influence diverging local population trajectories under scenarios of climate change: a place-based approach. Glob. Change Biol. 22, 1572–1584 (2016).

  27. 27.

    Ryo, M., Yoshimura, C. & Iwasaki, Y. Importance of antecedent environmental conditions in modeling species distributions. Ecography 41, 825–836 (2018).

  28. 28.

    Barve, N. et al. The crucial role of the accessible area in ecological niche modeling and species distribution modeling. Ecol. Model. 222, 1810–1819 (2011).

  29. 29.

    Marcer, A., Mendez-Vigo, B., Alonso-Blanco, C. & Pico, F. X. Tackling intraspecific genetic structure in distribution models better reflects species geographical range. Ecol. Evol. 6, 2084–2097 (2016).

  30. 30.

    Raes, N. Partial versus full species distribution models. Nat. Conserv. 10, 127–138 (2012).

  31. 31.

    Omernik, J. M. & Griffith, G. E. Ecoregions of the conterminous United States: evolution of a hierarchical spatial framework. Environ. Manag. 54, 1249–1266 (2014).

  32. 32.

    Ecological Regions of North America: Toward a Common Perspective (Commission for Environmental Cooperation, 1997; map updated 2006).

  33. 33.

    Varner, J., Horns, J. J., Lambert, L. S., Westberg, E., Ruff, J. S., Wolfenberger, K., Beever, E. A. & Dearing, M. D. Plastic pikas: behavioural flexibility in low-elevation pikas (Ochotona princeps). Behav. Process. 125, 63–71 (2016).

  34. 34.

    McDonald, K. A. & Brown, J. H. Using montane mammals to model extinctions due to global change. Conserv. Biol. 6, 409–415 (1992).

  35. 35.

    Varner, J. & Dearing, M. D. Dietary plasticity in pikas as a strategy for atypical resource landscapes. J. Mammal. 95, 72–81 (2014).

  36. 36.

    Smith, J. A. & Erb, L. P. Patterns of selective caching behavior of a generalist herbivore, the American pika (Ochotona princeps). Arct. Antarct. Alp. Res. 45, 396–403 (2013).

  37. 37.

    Castillo, J. A. et al. Replicated landscape genetic and network analyses reveal wide variation in functional connectivity for American pikas. Ecol. Appl. 26, 1660–1676 (2016).

  38. 38.

    Rowe, K. C. et al. Spatially heterogeneous impact of climate change on small mammals of montane California. Proc. R. Soc. B 282, 20141857 (2015).

  39. 39.

    Moritz, C. et al. Impact of a century of climate change on small-mammal communities in Yosemite National Park, USA. Science 322, 261–264 (2008).

  40. 40.

    Tingley, M. W., Koo, M. S., Moritz, C., Rush, A. C. & Beissinger, S. R. The push and pull of climate change causes heterogeneous shifts in avian elevational ranges. Glob. Change Biol. 18, 3279–3290 (2012).

  41. 41.

    Santos, M. J., Smith, A. B., Thorne, J. H. & Moritz, C. The relative influence of change in habitat and climate on elevation range limits in small mammals in Yosemite National Park, California, U.S.A. Clim. Change Responses 4, 7 (2017).

  42. 42.

    Morelli, T. L. et al. Anthropogenic refugia ameliorate the severe climate-related decline of a montane mammal along its trailing edge. Proc. R. Soc. B 279, 4279–4286 (2012).

  43. 43.

    De Frenne, P. et al. Microclimate moderates plant responses to macroclimate warming. Proc. Natl Acad. Sci. USA 110, 18561–18565 (2013).

  44. 44.

    Austin, M. P. Spatial prediction of species distribution: an interface between ecological theory and statistical modelling. Ecol. Model. 157, 101–118 (2002).

  45. 45.

    Johnston, A. N. et al. Ecological consequences of anomalies in atmospheric moisture and snowpack. Ecology 100, e02638 (2019).

  46. 46.

    Silva, G. S. C. et al. Transcontinental dispersal, ecological opportunity and origins of an adaptive radiation in the Neotropical catfish genus Hypostomus (Siluriformes: Loricariidae). Mol. Ecol. 25, 1511–1529 (2016).

  47. 47.

    Nolan, C. et al. Past and future global transformation of terrestrial ecosystems under climate change. Science 361, 920–923 (2018).

  48. 48.

    Beale, C. M., Brewer, M. J. & Lennon, J. J. A new statistical framework for the quantification of covariate associations with species distributions. Methods Ecol. Evol. 5, 421–432 (2014).

  49. 49.

    VanDerWal, J., Shoo, L. P., Graham, C. & William, S. E. Selecting pseudo-absence data for presence-only distribution modeling: how far should you stray from what you know? Ecol. Model. 220, 589–594 (2009).

  50. 50.

    Pyron, R. A., Costa, G. C., Patten, M. A. & Burbrink, F. T. Phylogenetic niche conservatism and the evolutionary basis of ecological speciation. Biol. Rev. 90, 1248–1262 (2015).

  51. 51.

    Breiner, F. T., Guisan, A., Bergamini, A. & Nobis, M. P. Overcoming limitations of modelling rare species by using ensembles of small models. Methods Ecol. Evol. 6, 1210–1218 (2015).

  52. 52.

    Petitpierre, B., Kueffer, C., Broennimann, O., Randin, C., Daehler, C. & Guisan, A. Climatic niche shifts are rare among terrestrial plant invaders. Science 335, 1344–1348 (2012).

  53. 53.

    Daly, C., Gibson, W. P., Taylor, G. H., Johnson, G. L. & Pasteris, P. A knowledge-based approach to the statistical mapping of climate. Clim. Res. 22, 99–113 (2002).

  54. 54.

    Hafner, D. J. North American pika (Ochotona princeps) as a late Quaternary biogeographic indicator species. Quat. Res. 39, 373–380 (1993).

  55. 55.

    Hafner, D. J. & Smith, A. T. Revision of the subspecies of the American pika, Ochotona princeps (Lagomorpha: Ochotonidae). J. Mammal. 91, 401–417 (2010).

  56. 56.

    Omernik, J. M. Ecoregions of the conterminous United States. Map (scale 1:7,500,000). Ann. Assoc. Am. Geogr. 77, 118–125 (1987).

  57. 57.

    Omernik, J. M. in Biological Assessment and Criteria: Tools for Water Resource Planning and Decision Making (eds Davis, W. S. & Simon, T. P.) 49–62 (Lewis, 1995).

  58. 58.

    Sarr, D. A., Duff, A., Dinger, E. C., Shafer, S. L., Wing, M., Seavy, N. E. & Alexander, J. D. Comparing ecoregional classifications for natural areas management in the Klamath Region, USA. Nat. Areas J. 35, 360–377 (2015).

  59. 59.

    Fenneman, N. M. & Johnson, D. W. Physical Divisions of the United States (US Geological Survey, 1946).

  60. 60.

    Phillips, S. J. et al. Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data. Ecol. Appl. 19, 181–197 (2009).

  61. 61.

    Merow, C., Smith, M. J. & Silander, J. A. Jr. A practical guide to MaxEnt for modeling species’ distributions: what it does, and why inputs and settings matter. Ecography 36, 1058–1058 (2013).

  62. 62.

    Boria, R. A., Olson, L. E., Goodman, S. M. & Anderson, R. P. Spatial filtering to reduce sampling bias can improve the performance of ecological niche models. Ecol. Model. 275, 73–77 (2014).

  63. 63.

    Fourcade, Y., Engler, J. O., Rödder, D. & Secondi, J. Mapping species distributions with MAXENT using a geographically biased sample of presence data: a performance assessment of methods for correcting sampling bias. PLoS ONE 9, e97122 (2014).

  64. 64.

    Fourcade, Y., Besnard, A. G. & Secondi, J. Paintings predict the distribution of species, or the challenge of selecting environmental predictors and evaluation statistics. Glob. Ecol. Biogeogr. 27, 245–256 (2018).

  65. 65.

    Efron, B., Hastie, T., Johnstone, I. & Tibshirani, R. Least angle regression. Ann. Stat. 32, 407–499 (2004).

  66. 66.

    Zou, H. & Hastie, T. Regularization and variable selection via the elastic net. J. R. Stat. Soc. B 67, 301–320 (2005).

  67. 67.

    Araujo, M. B. & New, M. Ensemble forecasting of species distributions. Trends Ecol. Evol. 22, 42–47 (2007).

  68. 68.

    Boyce, M. S., Vernier, P. R., Nielsen, S. E. & Schmiegelow, F. K. A. Evaluating resource selection functions. Ecol. Model. 157, 281–300 (2002).

  69. 69.

    Hirzel, A. H., Le Lay, G., Helfer, V., Randin, C. & Guisan, A. Evaluating the ability of habitat suitability models to predict species presences. Ecol. Model. 199, 142–152 (2006).

  70. 70.

    Smith, A. B. On evaluating species distribution models with random background sites in place of absences when test presences disproportionately sample suitable habitat. Divers. Distrib. 19, 867–872 (2013).

  71. 71.

    Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).

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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.

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.

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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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