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Climate extremes may be more important than climate means when predicting species range shifts

Climatic Change volume 163, pages 579–598 (2020)Cite this article

Abstract

It is well known that temperatures across the globe are rising, but climatic conditions are becoming more variable as well. Forecasts of species range shifts, however, often focus on average climatic changes while ignoring increasing climatic variability. In particular, many species distribution models use space-for-time substitution, which focuses exclusively on the effect of average climatic conditions on the target species across a geographic range, and is blind to the possibility of range-wide population collapse with increasing drought frequency, drought severity, or climate effects on other co-occurring species. Relegated to assessments of broad demographic patterns that ignore underlying biological responses to increasing climatic variability, this prevalent method of distribution forecasting may systematically underpredict climate change impacts. We compare six models of survival and abundance of a subcanopy tree species, Taxus brevifolia, over 40 years of past climate change to disentangle multiple sources of uncertainty: model formulation, scale of climate effect, and level of biological organization. We show that drought extremes increased Taxus individual- and population-scale mortality across a wide geographic climate gradient, precluding detection of a monotonic relationship with average climate. Individual-scale climatic extremes models derived from longitudinal data had the highest predictive accuracy (82%), whereas mean climate models had the lowest accuracy (< 65%). Our results highlight that conclusions drawn from forecasts of average warming alone likely underpredict climate change impacts by ignoring indicators of range-wide population declines for species sensitive to increasing climatic variability.

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Fig. 1

Data availability

Data are available from the Pacific Northwest Permanent Sample Plot Program (http://pnwpsp.forestry.oregonstate.edu) and the Smithsonian ForestGEO data portal (https://forestgeo.si.edu).

References

  1. Acker SA, McKee WA, Harmon ME, Franklin JF (1998) Long-term research on forest dynamics in the Pacific Northwest: a network of permanent forest plots. Man Biosphere Series 21:93–106

    Google Scholar 

  2. Adams HD, Macalady AK, Breshears DD, Allen CD et al (2010) Climate-induced tree mortality: earth system consequences. EOS Trans Am Geophys Union 91:153–154

    Google Scholar 

  3. Agrawal AA (2001) Phenotypic plasticity in the interactions and evolution of species. Science 294:321–326

    Google Scholar 

  4. Allen CD, Breshears DD, McDowell NG (2015) On underestimation of global vulnerability to tree mortality and forest die-off from hotter drought in the Anthropocene. Ecosphere 6:1–55

    Google Scholar 

  5. Allen CD, Macalady AK, Chenchouni H, Bachelet D et al (2010) A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests. For Ecol Manag 259:660–684

    Google Scholar 

  6. Anderegg LD, Anderegg WR, Berry JA (2013) Not all droughts are created equal: translating meteorological drought into woody plant mortality. Tree Physiol 33:701–712

    Google Scholar 

  7. Anderson-Teixeira KJ, Davies SJ, Bennett AC, Gonzalez-Akre EB et al (2015) CTFS-ForestGEO: a worldwide network monitoring forests in an era of global change. Glob Chang Biol 21:528–549

    Google Scholar 

  8. Archer E (2020) rfPermute: estimate permutation p-values for random Forest importance metrics. R package version 2.1.81. https://CRAN.R-project.org/package=rfPermute

  9. Bates D, Maechler M, Bolker B, Walker S (2015) Fitting linear mixed-effects models using lme4. J Stat Softw 67(1):1–48. https://doi.org/10.18637/jss.v067.i01

    Article  Google Scholar 

  10. Bentz BJ, Régnière J, Fettig CJ, Hansen EM et al (2010) Climate change and bark beetles of the western United States and Canada: direct and indirect effects. BioScience 60:602–613

    Google Scholar 

  11. Bertrand R, Lenoir J, Piedallu C, Riofrío-Dillon G et al (2011) Changes in plant community composition lag behind climate warming in lowland forests. Nature 479:517–520

    Google Scholar 

  12. Biging GS, Dobbertin M (1995) Evaluation of competition indices in individual tree growth models. For Sci 41:360–377

    Google Scholar 

  13. Birch JD, Lutz JA, Hogg EH, Simard SW et al (2019) Density-dependent processes fluctuate over 50 years in an ecotone forest. Oecologia 191(4):909–918

    Google Scholar 

  14. Blois JL, Williams JW, Fitzpatrick MC, Jackson ST, Ferrier S (2013) Space can substitute for time in predicting climate-change effects on biodiversity. Proc Natl Acad Sci 110:9374–9379

    Google Scholar 

  15. Boisvert-Marsh L, Périé C, de Blois S (2014) Shifting with climate? Evidence for recent changes in tree species distribution at high latitudes. Ecosphere 5:1–33

    Google Scholar 

  16. Bréda N, Huc R, Granier A, Dreyer E (2006) Temperate forest trees and stands under severe drought: a review of ecophysiological responses, adaptation processes and long-term consequences. Ann For Sci 63:625–644

    Google Scholar 

  17. Breshears DD, Cobb NS, Rich PM, Price KP et al (2005) Regional vegetation die-off in response to global-change-type drought. Proc Natl Acad Sci 102:15144–15148

    Google Scholar 

  18. Brun P, Kiørboe T, Licandro P, Payne MR (2016) The predictive skill of species distribution models for plankton in a changing climate. Glob Chang Biol 22:3170–3181

    Google Scholar 

  19. Buisson L, Thuiller W, Casajus N, Lek S, Grenouillet G (2010) Uncertainty in ensemble forecasting of species distribution. Glob Chang Biol 16:1145–1157

    Google Scholar 

  20. Busing RT, Halpern CB, Spies TA (1995) Ecology of Pacific yew (Taxus brevifolia) in western Oregon and Washington. Conserv Biol 9:1199–1207

    Google Scholar 

  21. Carey C, Alexander MA (2003) Climate change and amphibian declines: is there a link? Divers Distrib 9:111–121

    Google Scholar 

  22. Chen IC, Hill JK, Ohlemüller R, Roy DB, Thomas CD (2011) Rapid range shifts of species associated with high levels of climate warming. Science 333:1024–1026

    Google Scholar 

  23. Chevin L-M, Collins S, Lefèvre F (2013) Phenotypic plasticity and evolutionary demographic responses to climate change: taking theory out to the field. Funct Ecol 27(4):967–979

    Google Scholar 

  24. Clark JS, Bell DM, Hersh MH, Nichols L (2011) Climate change vulnerability of forest biodiversity: climate and competition tracking of demographic rates. Glob Chang Biol 17:1834–1849

    Google Scholar 

  25. Condit R, Aguilar S, Hernandez A, Perez R et al (2004) Tropical forest dynamics across a rainfall gradient and the impact of an El Niño dry season. J Trop Ecol 20:51–72

    Google Scholar 

  26. Coulson T, Catchpole EA, Albon SD, Morgan BJT et al (2001) Age, sex, density, winter weather, and population crashes in Soay sheep. Science 292:1528–1531

    Google Scholar 

  27. Cox PM, Betts RA, Jones CD, Spall SA, Totterdell IJ (2000) Acceleration of global warming due to carbon-cycle feedbacks in a coupled climate model. Nature 408:184–187

    Google Scholar 

  28. Cutler, D. R., T. C. Edwards, K. H. Beard, A. Cutler, et al. 2007. Random forests for classification in ecology. Ecology 88:2783–2792

  29. Dai A (2013) Increasing drought under global warming in observations and models. Nat Clim Chang 3:52–58

    Google Scholar 

  30. Daly C, Halbleib M, Smith JI, Gibson WP et al (2008) Physiographically sensitive mapping of climatological temperature and precipitation across the conterminous United States. Int J Climatol 28:2031–2064

    Google Scholar 

  31. Dalton MM, Mote PW, Snover AK (2013) Climate change in the Northwest: implications for our landscapes, waters, and communities. Island Press, Washington, D.C.

    Google Scholar 

  32. Daniels LD, Veblen TT (2003) Regional and local effects of disturbance and climate on altitudinal treelines in northern Patagonia. J Veg Sci 14:733–742

    Google Scholar 

  33. Das A, Battles J, van Mantgem PJ, Stephenson NL (2008) Spatial elements of mortality risk in old-growth forests. Ecology 89:1744–1756

    Google Scholar 

  34. Das A, Battles J, Stephenson NL, van Mantgem PJ (2011) The contribution of competition to tree mortality in old-growth coniferous forests. For Ecol Manag 261:1203–1213

    Google Scholar 

  35. Das AJ, Larson AJ, Lutz JA (2018) Individual species-area relationships in temperate coniferous forests. J Veg Sci 29(2):317–324

    Google Scholar 

  36. Das AJ, Stephenson NL, Davis KP (2016) Why do trees die? Characterizing the drivers of background tree mortality. Ecology 97:2616–2627

    Google Scholar 

  37. Das AJ, Stephenson NL, Flint A, Das T, Van Mantgem PJ (2013) Climatic correlates of tree mortality in water-and energy-limited forests. PLoS One 8:e69917

    Google Scholar 

  38. Davis KT, Dobrowski SZ, Higuera PE, Holden ZA et al (2019) Wildfires and climate change push low-elevation forests across a critical climate threshold for tree regeneration. Proc Natl Acad Sci 116:6193–6198

    Google Scholar 

  39. Davis MB, Shaw RG (2001) Range shifts and adaptive responses to Quaternary climate change. Science 292:673–679

    Google Scholar 

  40. Dubos N, Morel L, Crottini A, Freeman K et al (2020) High interannual variability of a climate-driven amphibian community in a seasonal rainforest. Biodivers Conserv 29:893–912

    Google Scholar 

  41. Easterling DR, Meehl GA, Parmesan C, Changnon SA et al (2000) Climate extremes: observations, modeling, and impacts. Science 289:2068–2074

    Google Scholar 

  42. Elith J, Leathwick JR (2009) Species distribution models: ecological explanation and prediction across space and time. Annu Rev Ecol Evol Syst 40:677–697

    Google Scholar 

  43. Ettinger A, HilleRisLambers J (2017) Competition and facilitation may lead to asymmetric range shift dynamics with climate change. Glob Chang Biol 23:3921–3933

    Google Scholar 

  44. Field CB, Barros V, Stocker TF, Dahe Q (2012) Managing the risks of extreme events and disasters to advance climate change adaptation: special report of the intergovernmental panel on climate change. Cambridge University Press, Page A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change

    Google Scholar 

  45. Fisher RA, Koven CD, Anderegg WRL, Christoffersen BO et al (2018) Vegetation demographics in earth system models: a review of progress and priorities. Glob Chang Biol 24:35–54

    Google Scholar 

  46. Fordham DA, Akçakaya HR, Araújo MB, Elith J et al (2012) Plant extinction risk under climate change: are forecast range shifts alone a good indicator of species vulnerability to global warming? Glob Chang Biol 18:1357–1371

    Google Scholar 

  47. Fordham DA, Mellin C, Russell BD, Akçakaya RH et al (2013) Population dynamics can be more important than physiological limits for determining range shifts under climate change. Glob Chang Biol 19:3224–3237

    Google Scholar 

  48. Franklin J (2010) Moving beyond static species distribution models in support of conservation biogeography. Divers Distrib 16:321–330

    Google Scholar 

  49. Franklin JF, DeBell DS (1988) Thirty-six years of tree population change in an old-growth Pseudotsuga–Tsuga forest. Can J For Res 18:633–639

    Google Scholar 

  50. Franklin JF, Shugart HH, Harmon ME (1987) Tree death as an ecological process. BioScience 37:550–556

    Google Scholar 

  51. Franklin JF, Spies TA, Van Pelt R, Carey AB et al (2002) Disturbances and structural development of natural forest ecosystems with silvicultural implications, using Douglas-fir forests as an example. For Ecol Manag 155:399–423

    Google Scholar 

  52. Franklin J, Serra-Diaz JM, Syphard AD, and Regan HM (2016) Global change and terrestrial plant community dynamics. Proceedings of the National Academy of Sciences:201519911

  53. Freund JA, Franklin JF, Larson AJ, Lutz JA (2014) Multi-decadal establishment for single-cohort Douglas-fir forests. Can J For Res 44(9):1068–1078

    Google Scholar 

  54. Furniss TJ, Larson AJ, Kane VR, Lutz JA (2020) Wildfire and drought moderate the spatial elements of tree mortality. Ecosphere 11(8):e03214

    Google Scholar 

  55. Gandrud C (2015) simPH: an R package for illustrating estimates from Cox proportional hazard models including for interactive and nonlinear effects. J Stat Softw 65(3):1–20 http://www.jstatsoft.org/v65/i03/

    Google Scholar 

  56. Garcia ES, Swann ALS, Villegas JC, Breshears DD et al (2016) Synergistic ecoclimate teleconnections from forest loss in different regions structure global ecological responses. PLoS One 11(11):e0165042

    Google Scholar 

  57. Gaylord ML, Kolb TE, Pockman WT, Plaut JA et al (2013) Drought predisposes piñon–juniper woodlands to insect attacks and mortality. New Phytol 198:567–578

    Google Scholar 

  58. Gedir JV, Cain JW, Harris G, Turnbull TT (2015) Effects of climate change on long-term population growth of pronghorn in an arid environment. Ecosphere 6:1–20

    Google Scholar 

  59. George TL, Fowler AC, Knight RL, McEwen LC (1992) Impacts of a severe drought on grassland birds in western North Dakota. Ecol Appl 2:275–284

    Google Scholar 

  60. Gilman SE, Urban MC, Tewksbury J, Gilchrist GW, Holt RD (2010) A framework for community interactions under climate change. Trends Ecol Evol 25:325–331

    Google Scholar 

  61. Grabherr G, Gottfried M, Gruber A, Pauli H (1995) Patterns and current changes in alpine plant diversity. In: Chapin FS, Körner C (eds) Arctic and alpine biodiversity: patterns. Causes and Ecosystem Consequences. Springer, Berlin Heidelberg, Berlin, Heidelberg, pp 167–181

    Google Scholar 

  62. Harrington CA, Reukema DL (1983) Initial shock and long-term stand development following thinning in a Douglas-fir plantation. For Sci 29:33–46

    Google Scholar 

  63. Harrell Jr FE (2020) rms: regression modeling strategies. R package version 6.0–1. https://CRAN.R-project.org/package=rms

  64. Harsch MA, Hulme PE, McGlone MS, Duncan RP (2009) Are treelines advancing? A global meta-analysis of treeline response to climate warming. Ecol Lett 12:1040–1049

    Google Scholar 

  65. Hegyi, F. 1974. A simulation model for managing jack-pine stands. RoyalColl. For, Res. Notes 30:74–90

  66. Hijmans RJ, Graham CH (2006) The ability of climate envelope models to predict the effect of climate change on species distributions. Glob Chang Biol 12:2272–2281

    Google Scholar 

  67. HilleRisLambers J, Anderegg LD, Breckheimer I, Burns KM et al (2015) Implications of climate change for turnover in forest composition. Northwest Science 89:201–218

    Google Scholar 

  68. Hostetler SW, Alder JR (2016) Implementation and evaluation of a monthly water balance model over the US on an 800 m grid. Water Resour Res 52:9600–9620

    Google Scholar 

  69. Hutyra LR, Munger JW, Nobre CA, Saleska SR et al (2005) Climatic variability and vegetation vulnerability in Amazônia. Geophys Res Lett 32:L24712

    Google Scholar 

  70. IPCC (2019) Climate change and land: an IPCC special report on climate change, desertification, land degradation, sustainable land management, food security, and greenhouse gas fluxes in terrestrial ecosystems [P.R. Shukla, J. Skea, E. Calvo Buendia, V. Masson-Delmotte, H.-O. Pörtner, and others]. ipcc.ch/srccl

  71. Iverson LR, McKenzie D (2013) Tree-species range shifts in a changing climate: detecting, modeling, assisting. Landsc Ecol 28:879–889

    Google Scholar 

  72. Keith H, Mackey BG, Lindenmayer DB (2009) Re-evaluation of forest biomass carbon stocks and lessons from the world’s most carbon-dense forests. Proc Natl Acad Sci 106:11635–11640

    Google Scholar 

  73. Knapp AK, Beier C, Briske DD, Classen AT et al (2008) Consequences of more extreme precipitation regimes for terrestrial ecosystems. AIBS Bull 58:811–821

    Google Scholar 

  74. Kolassa S, Schütz W (2007) Advantages of the MAD/mean ratio over the MAPE. Foresight, The International Journal of Applied Forecasting, pp 40–43

    Google Scholar 

  75. Larson AJ, Franklin JF (2010) The tree mortality regime in temperate old-growth coniferous forests: the role of physical damage. Can J For Res 40:2091–2103

    Google Scholar 

  76. Larson AJ, Lutz JA, Donato DC, Freund JA et al (2015) Spatial aspects of tree mortality strongly differ between young and old-growth forests. Ecology 96(11):2855–2861

    Google Scholar 

  77. Larson AJ, Lutz JA, Gersonde RF, Franklin JF, Hietpas FF (2008) Productivity influences the rate of forest structural development. Ecol Appl 18(4):899–910

    Google Scholar 

  78. Lassoie, J. P., T. M. Hinckley, and C. C. Grier. 1985. Coniferous forests of the Pacific Northwest. Pages 127–161 Physiological ecology of North American plant communities. Springer

  79. Lawrence DM, Fisher RA, Koven CD, Oleson KW et al (2019) The community land model version 5: description of new features, benchmarking, and impact of forcing uncertainty. J Adv Model Earth Syst 11:4245–4287

    Google Scholar 

  80. Lenoir J, Gégout J-C, Guisan A, Vittoz P et al (2010) Going against the flow: potential mechanisms for unexpected downslope range shifts in a warming climate. Ecography 33:295–303

    Google Scholar 

  81. Lenoir J, Svenning J-C (2015) Climate-related range shifts—a global multidimensional synthesis and new research directions. Ecography 38:15–28

    Google Scholar 

  82. Levis S, Bonan G, Vertenstein M, Oleson K (2004) The community land Model’s dynamic global vegetation model (CLM-DGVM): technical description and user’s guide. NCAR Tech Note 459:1–50

    Google Scholar 

  83. Lian, X., S. Piao, L. Z. X. Li, Y. Li, et al. 2020. Summer soil drying exacerbated by earlier spring greening of northern vegetation. Science advances 6:eaax0255

  84. Liaw A, Wiener M (2002) Classification and regression by randomForest. R News 2(3):18–22 https://CRAN.R-project.org/doc/Rnews/

    Google Scholar 

  85. Littell JS, Oneil EE, McKenzie D, Hicke JA et al (2010) Forest ecosystems, disturbance, and climatic change in Washington State, USA. Clim Chang 102:129–158

    Google Scholar 

  86. Lutz JA (2015) The evolution of long-term data for forestry: large temperate research plots in an era of global change. Northwest Science 89(3):255–269

    Google Scholar 

  87. Lutz JA, Furniss TJ, Johnson DJ, Davies SJ et al (2018) Global importance of large-diameter trees. Glob Ecol Biogeogr 27:849–864

    Google Scholar 

  88. Lutz JA, Halpern CB (2006) Tree mortality during early forest development: a long-term study of rates, causes, and consequences. Ecol Monogr 76(2):257–275

    Google Scholar 

  89. Lutz JA, Larson AJ, Freund JA, Swanson ME, Bible KJ (2013) The importance of large-diameter trees to forest structural heterogeneity. PLoS One 8:e82784

    Google Scholar 

  90. Lutz JA, Larson AJ, Furniss TJ, Donato DC et al (2014) Spatially nonrandom tree mortality and ingrowth maintain equilibrium pattern in an old-growth Pseudotsuga–Tsuga forest. Ecology 95:2047–2054

    Google Scholar 

  91. Lutz JA, van Wagtendonk JW, Franklin JF (2010) Climatic water deficit, tree species ranges, and climate change in Yosemite National Park. J Biogeogr 37:936–950

    Google Scholar 

  92. Matthews WJ, Marsh-Matthews E (2003) Effects of drought on fish across axes of space, time and ecological complexity. Freshw Biol 48:1232–1253

    Google Scholar 

  93. Mattson WJ, Haack RA (1987) The role of drought in outbreaks of plant-eating insects. Bioscience 37:110–118

    Google Scholar 

  94. McCabe GJ, and Markstrom SL (2007) A monthly water-balance model driven by a graphical user interface. Geological Survey (US). Open-File Report 2007–1088

  95. McDowell N, Pockman WT, Allen CD, Breshears DD et al (2008) Mechanisms of plant survival and mortality during drought: why do some plants survive while others succumb to drought? New Phytol 178:719–739

    Google Scholar 

  96. Moorcroft PR (2006) How close are we to a predictive science of the biosphere? Trends Ecol Evol 21:400–407

    Google Scholar 

  97. Neumann M, Mues V, Moreno A, Hasenauer H, Seidl R (2017) Climate variability drives recent tree mortality in Europe. Glob Chang Biol 23:4788–4797

    Google Scholar 

  98. Pan Y, Birdsey RA, Phillips OL, Jackson RB (2013) The structure, distribution, and biomass of the world’s forests. Annu Rev Ecol Evol Syst 44:593–622

    Google Scholar 

  99. Parmesan C, Root TL, Willig MR (2000) Impacts of extreme weather and climate on terrestrial biota. Bull Am Meteorol Soc 81:443–450

    Google Scholar 

  100. Parmesan C, Yohe G (2003) A globally coherent fingerprint of climate change impacts across natural systems. Nature 421:37–42

    Google Scholar 

  101. Purves D, Pacala S (2008) Predictive models of forest dynamics. Science 320:1452–1453

    Google Scholar 

  102. R Core Team (2020) R: a language and environment for statistical computing. In: R Foundation for statistical computing. Austria. URL, Vienna https://www.R-project.org/

    Google Scholar 

  103. Rapacciuolo G, Maher SP, Schneider AC, Hammond TT et al (2014) Beyond a warming fingerprint: individualistic biogeographic responses to heterogeneous climate change in California. Glob Chang Biol 20:2841–2855

    Google Scholar 

  104. Renwick KM, Curtis C, Kleinhesselink AR, Schlaepfer D et al (2018) Multi-model comparison highlights consistency in predicted effect of warming on a semi-arid shrub. Glob Chang Biol 24:424–438

    Google Scholar 

  105. Sillett SC, Van Pelt R, Freund JA, Campbell-Spickler J et al (2018) Development and dominance of Douglas-fir in North American rainforests. For Ecol Manag 429:93–114

    Google Scholar 

  106. Silvertown J, Franco M, Pisanty I, Mendoza A (1993) Comparative plant demography–relative importance of life-cycle components to the finite rate of increase in woody and herbaceous perennials. J Ecol 81:465–476

    Google Scholar 

  107. Sitch S, Huntingford C, Gedney N, Levy PE et al (2008) Evaluation of the terrestrial carbon cycle, future plant geography and climate-carbon cycle feedbacks using five dynamic global vegetation models (DGVMs). Glob Chang Biol 14:2015–2039

    Google Scholar 

  108. Smithwick EAH, Harmon ME, Remillard SM, Acker SA, Franklin JF (2002) Potential upper bounds of carbon stores in forests of the Pacific Northwest. Ecol Appl 12:1303–1317

    Google Scholar 

  109. Snyder PK, Delire C, Foley JA (2004) Evaluating the influence of different vegetation biomes on the global climate. Clim Dyn 23:279–302

    Google Scholar 

  110. Stark SC, Breshears DD, Garcia ES, Law DJ et al (2016) Toward accounting for ecoclimate teleconnections: intra-and inter-continental consequences of altered energy balance after vegetation change. Landsc Ecol 31:181–194

    Google Scholar 

  111. Stephenson N (1998) Actual evapotranspiration and deficit: biologically meaningful correlates of vegetation distribution across spatial scales. J Biogeogr 25:855–870

    Google Scholar 

  112. Suttle K, Thomsen MA, Power ME (2007) Species interactions reverse grassland responses to changing climate. Science 315:640–642

    Google Scholar 

  113. Svenning J-C, Normand S, Skov F (2008) Postglacial dispersal limitation of widespread forest plant species in nemoral Europe. Ecography 31:316–326

    Google Scholar 

  114. Swann AL, Laguë MM, Garcia ES, Field JP et al (2018) Continental-scale consequences of tree die-offs in North America: identifying where forest loss matters most. Environ Res Lett 13:055014

    Google Scholar 

  115. Therneau T, Crowson C, Atkinson E (2013) Using time dependent covariates and time dependent coefficients in the Cox model. CRAN vignettes:1–27

  116. Thomas CD, Cameron A, Green RE, Bakkenes M et al (2004) Extinction risk from climate change. Nature 427:145–148

    Google Scholar 

  117. Thomas P (2013) Taxus brevifolia. IUCN, The IUCN Red List of Threatened Species

    Google Scholar 

  118. Thuiller W (2003) BIOMOD–optimizing predictions of species distributions and projecting potential future shifts under global change. Glob Chang Biol 9:1353–1362

    Google Scholar 

  119. Thuiller W (2004) Patterns and uncertainties of species’ range shifts under climate change. Glob Chang Biol 10:2020–2027

    Google Scholar 

  120. Tredennick AT, Hooten MB, Adler PB (2017) Do we need demographic data to forecast plant population dynamics? Methods Ecol Evol 8:541–551

    Google Scholar 

  121. Urban MC (2015) Accelerating extinction risk from climate change. Science 348:571–573

    Google Scholar 

  122. Urban MC, Tewksbury JJ, Sheldon KS (2012) On a collision course: competition and dispersal differences create no-analogue communities and cause extinctions during climate change. Proc R Soc Lond B Biol Sci 279:2072–2080

    Google Scholar 

  123. VanDerWal J, Murphy HT, Kutt AS, Perkins GC et al (2013) Focus on poleward shifts in species’ distribution underestimates the fingerprint of climate change. Nat Clim Chang 3:239–243

    Google Scholar 

  124. Voelker SL, DeRose RJ, Bekker MF, Sriladda C et al (2018) Anisohydric water use behavior links growing season evaporative demand to ring-width increment in conifers from summer-dry environments. Trees 32:735–749

    Google Scholar 

  125. Walther GR (2003) Plants in a warmer world. Perspect Plant Ecol Evol Syst 6:169–185

    Google Scholar 

  126. Wason JW, Dovčiak M (2017) Tree demography suggests multiple directions and drivers for species range shifts in mountains of Northeastern United States. Glob Chang Biol 23:3335–3347

    Google Scholar 

  127. Williams JW, Jackson ST (2007) Novel climates, no-analog communities, and ecological surprises. Front Ecol Environ 5:475–482

    Google Scholar 

  128. Wisz MS, Pottier J, Kissling WD, Pellissier L et al (2013) The role of biotic interactions in shaping distributions and realised assemblages of species: implications for species distribution modelling. Biol Rev 88:15–30

    Google Scholar 

  129. Zhu K, Woodall CW, Clark JS (2012) Failure to migrate: lack of tree range expansion in response to climate change. Glob Chang Biol 18:1042–1052

    Google Scholar 

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Acknowledgments

We thank J.R. Alder (USGS) for the 800-m climate projection data. WFDP research was conducted under 5-year special use permits from the US Forest Service Gifford Pinchot National Forest and the US Forest Service Pacific Northwest Research Station. We thank the Pacific Northwest Permanent Sample Plot Program for data (provided through the H. J. Andrews Experimental Forest research program, National Science Foundation LTER DEB 1440409, US Forest Service Pacific Northwest Research Station, and Oregon State University). We are grateful for the foresight of J. F. Franklin in establishing these longitudinal plots.

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Upon request to the corresponding author.

Funding

National Science Foundation Graduate Research Fellowship Program, Utah State University Quinney College of Natural Resources Graduate Fellowship, and the Utah Agricultural Experiment Station (journal paper 9255).

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  1. Department of Wildland Resources, Utah State University, Logan, UT, 84321, USA

    Sara J. Germain & James A. Lutz

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  1. Sara J. Germain
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SJG and JAL conceived the study, SJG designed and performed analyses and wrote the initial manuscript, and SGJ and JAL revised and approved the final manuscript.

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Correspondence to Sara J. Germain.

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Germain, S.J., Lutz, J.A. Climate extremes may be more important than climate means when predicting species range shifts. Climatic Change 163, 579–598 (2020). https://doi.org/10.1007/s10584-020-02868-2

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Keywords

  • Longitudinal data
  • Permanent sample plots
  • Population decline
  • Smithsonian ForestGEO
  • Taxus brevifolia
  • Wind River Forest Dynamics Plot (WFDP)