Extinction debt and colonization credit delay range shifts of eastern North American trees

Abstract

Global climate change is already having an impact on species ranges. For species with slow demography and limited dispersal, such as trees, lags between climate change and distribution shifts are likely to increase in the future. Such lags can be of critical importance to management and biodiversity of forests, because they can result in ‘extinction debts’, where populations temporarily persist under unsuitable conditions, and ‘colonization credits’, where suitable locations are not occupied owing to slow demography and limited dispersal. Here we use a range dynamics model based on metapopulation theory to show that the distributions of 21 dominant trees in eastern North America are out of equilibrium with climate and demonstrate both extinction debt and colonization credit. Moreover, lags are more severe at northern range limits, suggesting that range contraction in response to warming temperatures will outpace expansion.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Figure 1: Schematic demonstrating how metapopulation dynamics can lead to range limits.
Figure 2: Maps of projected species ranges with extinction debt and colonization credit for selected species.
Figure 3: The responsiveness of species ranges to changes in temperature.
Figure 4: Predicted recruitment and mortality rates in equilibrium ranges and in areas facing extinction debt.

References

  1. 1

    Parmesan, C. Ecological and evolutionary responses to recent climate change. Annu. Rev. Ecol. Evol. Syst. 37, 637–669 (2006).

    Article  Google Scholar 

  2. 2

    Austin, M. Species distribution models and ecological theory: a critical assessment and some possible new approaches. Ecol. Modell. 200, 1–19 (2007).

    Article  Google Scholar 

  3. 3

    Guisan, A. & Thuiller, W. Predicting species distribution: offering more than simple habitat models. Ecol. Lett. 8, 993–1009 (2005).

    Article  Google Scholar 

  4. 4

    Thuiller, W. et al. Consequences of climate change on the tree of life in Europe. Nature 470, 531–534 (2011).

    CAS  Article  Google Scholar 

  5. 5

    Thuiller, W. et al. The European functional tree of bird life in the face of global change. Nat. Commun. 5, 3118 (2014).

    Article  Google Scholar 

  6. 6

    Blois, J. L ., Zarnetske, P. L ., Fitzpatrick, M. C . & Finnegan, S . Climate change and the past, present, and future of biotic interactions. Science 341, 499–504 (2013).

    CAS  Article  Google Scholar 

  7. 7

    Guisan, A. et al. Predicting species distributions for conservation decisions. Ecol. Lett. 16, 1424–1435 (2013).

    Article  Google Scholar 

  8. 8

    García-Valdés, R., Zavala, M. A., Araújo, M. B. & Purves, D. W. Chasing a moving target: projecting climate change-induced shifts in non-equilibrial tree species distributions. J. Ecol. 101, 441–453 (2013).

    Article  Google Scholar 

  9. 9

    Tilman, D., May, R. M., Lehman, C. L. & Nowak, M. A. Habitat destruction and the extinction debt. Nature 371, 65–66 (1994).

    Article  Google Scholar 

  10. 10

    Kuussaari, M. et al. Extinction debt: a challenge for biodiversity conservation. Trends Ecol. Evol. 24, 564–571 (2009).

    Article  Google Scholar 

  11. 11

    Dullinger, S. et al. Extinction debt of high-mountain plants under twenty-first-century climate change. Nat. Clim. Change 2, 619–622 (2012).

    Article  Google Scholar 

  12. 12

    Svenning, J.-C. & Sandel, B. Disequilibrium vegetation dynamics under future climate change. Am. J. Bot. 100, 1266–1286 (2013).

    Article  Google Scholar 

  13. 13

    Purves, D. W., Lichstein, J. W., Strigul, N. & Pacala, S. W. Predicting and understanding forest dynamics using a simple tractable model. Proc. Natl Acad. Sci. USA 105, 17018–17022 (2008).

    CAS  Article  Google Scholar 

  14. 14

    Botkin, D. B. JABOWA-II: a Computer Model of Forest Growth (Oxford Univ. Press, 1993).

    Google Scholar 

  15. 15

    Scheller, R. M. et al. Design, development, and application of LANDIS-II, a spatial landscape simulation model with flexible temporal and spatial resolution. Ecol. Modell. 201, 409–419 (2007).

    Article  Google Scholar 

  16. 16

    Boulangeat, I., Georges, D. & Thuiller, W. FATE-HD: a spatially and temporally explicit integrated model for predicting vegetation structure and diversity at regional scale. Glob. Change Biol. 20, 2368–2378 (2014).

    Article  Google Scholar 

  17. 17

    Snell, R. S. et al. Using dynamic vegetation models to simulate plant range shifts. Ecography 37, 1184–1197 (2014).

    Article  Google Scholar 

  18. 18

    Hartig, F. et al. Connecting dynamic vegetation models to data - an inverse perspective. J. Biogeogr. 39, 2240–2252 (2012).

    Article  Google Scholar 

  19. 19

    Levins, R. Some demographic and genetic consequences of environmental heterogeneity for biological control. Bull. Entomol. Soc. Am. 15, 237–240 (1969).

    Google Scholar 

  20. 20

    Holt, R. D. & Keitt, T. H. Alternative causes for range limits: a metapopulation perspective. Ecol. Lett. 3, 41–47 (2000).

    Article  Google Scholar 

  21. 21

    Holt, R. D., Keitt, T. H., Lewis, M. A., Maurer, B. A. & Taper, M. L. Theoretical models of species’ borders: single species approaches. Oikos 108, 18–27 (2005).

    Article  Google Scholar 

  22. 22

    Kellman, M. Sugar maple (Acer saccharum Marsh.) establishment in boreal forest: results of a transplantation experiment. J. Biogeogr. 31, 1515–1522 (2004).

    Article  Google Scholar 

  23. 23

    Tilman, D. Competition and biodiversity in spatially structured habitats. Ecology 75, 2–16 (1994).

    Article  Google Scholar 

  24. 24

    Lande, R. Extinction thresholds in demographic models of territorial populations. Am. Nat. 130, 624–635 (1987).

    Article  Google Scholar 

  25. 25

    Ebenhard, T. Colonization in metapopulations: a review of theory and observations. Biol. J. Linn. Soc. 42, 105–121 (1991).

    Article  Google Scholar 

  26. 26

    Yackulic, C. B., Nichols, J. D., Reid, J. & Der, R. To predict the niche, model colonization and extinction. Ecology 96, 16–23 (2015).

    Article  Google Scholar 

  27. 27

    IPCC. Climate Change 2013: The Physical Science Basis (Cambridge Univ. Press, 2013).

  28. 28

    Boulangeat, I. et al. Anticipating the spatio-temporal response of plant diversity and vegetation structure to climate and land use change in a protected area. Ecography 37, 1230–1239 (2014).

    Article  Google Scholar 

  29. 29

    Bertrand, R. et al. Changes in plant community composition lag behind climate warming in lowland forests. Nature 479, 517–520 (2011).

    CAS  Article  Google Scholar 

  30. 30

    Vellend, M. et al. Extinction debt of forest plants persists for more than a century following habitat fragmentation. Ecology 87, 542–548 (2006).

    Article  Google Scholar 

  31. 31

    Peñuelas, J., Ogaya, R., Boada, M. & Jump, A. S. Migration, invasion and decline: changes in recruitment and forest structure in a warming-linked shift of European beech forest in Catalonia (NE Spain). Ecography 30, 829–837 (2007).

    Article  Google Scholar 

  32. 32

    Scheffer, M., Hirota, M., Holmgren, M., van Nes, E. H. & Chapin, F. S. Thresholds for boreal biome transitions. Proc. Natl Acad. Sci. USA 109, 21384–21389 (2012).

    CAS  Article  Google Scholar 

  33. 33

    KelIy, C. K. & Bowler, M. G. Coexistence and relative abundance in forest trees. Nature 417, 437–440 (2002).

    CAS  Article  Google Scholar 

  34. 34

    Thuiller, W. et al. A road map for integrating eco-evolutionary processes into biodiversity models. Ecol. Lett. 16, 94–105 (2013).

    Article  Google Scholar 

  35. 35

    Pagel, J. & Schurr, F. M. Forecasting species ranges by statistical estimation of ecological niches and spatial population dynamics. Glob. Ecol. Biogeogr. 21, 293–304 (2012).

    Article  Google Scholar 

  36. 36

    Talluto, M. V. et al. Cross-scale integration of knowledge for predicting species ranges: a metamodelling framework. Glob. Ecol. Biogeogr. 25, 238–249 (2016).

    Article  Google Scholar 

  37. 37

    Woudenberg, S.W. et al. The Forest Inventory and Analysis Database: Database Description and Users Manual Version 4.0 for Phase 2 (RMRS-GTR-245) (US Department of Agriculture, Forest Service 2010).

  38. 38

    Ministère des Ressources naturelles. Normes d'inventaire forestier (Direction des inventaires forestier, Ministère des Ressources naturelles, 2013).

  39. 39

    Ontario Ministry of Natural Resources. Permanent Sample Plot and Permanent Growth Plot Reference Manual (Ministry of Natural Resources Growth and Yield Program, 2014).

  40. 40

    Porter, K. B., Maclean, D. A., Beaton, K. P. & Upshall, J. Base de données sur les placettes d‘échantillonnage permanentes du Nouveau-Brunswick (PSPDB v1.0): Guide de l’utilisateur et analyse (Ressources naturelles Canada, Service Canadien des forêts, 2001).

  41. 41

    McKenney, D. W. et al. Customized spatial climate models for North America. Bull. Amer. Meteor. Soc. 92, 1611–1622 (2011).

    Article  Google Scholar 

  42. 42

    Gelman, A., Jakulin, A., Pittau, M. G. & Su, Y.-S. A weakly informative default prior distribution for logistic and other regression models. Ann. Appl. Stat. 2, 1360–1383 (2008).

    Article  Google Scholar 

  43. 43

    Gelman, A. & Rubin, D. B. Inference from iterative simulation using multiple sequences. Stat. Sci. 7, 457–472 (1992).

    Article  Google Scholar 

  44. 44

    Bender, E. A., Case, T. J. & Gilpin, M. E. Perturbation experiments in community ecology: theory and practice. Ecology 65, 1–13 (1984).

    Article  Google Scholar 

Download references

Acknowledgements

We acknowledge funding from NSERC strategic grant 430393-12, the European Research Council’s Seven Framework Programme FP7/2007–2013 grant agreement no. 281422 (TEEMBIO), and the Quebec Centre for Biodiversity Science. We are grateful for feedback from L. J. Pollock.

Author information

Affiliations

Authors

Contributions

M.V.T., D.G., S.V. and I.B. conceived the study, S.V. built the database and M.V.T. wrote the code for the model, MCMC samplers, and ran the analyses. All authors contributed to writing the manuscript.

Corresponding author

Correspondence to Matthew V. Talluto.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Information

Supplementary Methods, Supplementary References, Supplementary Tables 1–3, Supplementary Figures 1–12. (PDF 15185 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Talluto, M., Boulangeat, I., Vissault, S. et al. Extinction debt and colonization credit delay range shifts of eastern North American trees. Nat Ecol Evol 1, 0182 (2017). https://doi.org/10.1038/s41559-017-0182

Download citation

Further reading

Search

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing