Climatic Change

, Volume 126, Issue 1–2, pp 151–162 | Cite as

Northward migration under a changing climate: a case study of blackgum (Nyssa sylvatica)

  • Johanna DesprezEmail author
  • Basil V. IannoneIII
  • Peilin Yang
  • Christopher M. Oswalt
  • Songlin Fei


Species are predicted to shift their distribution ranges in response to climate change. Region-wide, empirically-based studies, however, are still limited to support these predictions. We used a model tree species, blackgum (Nyssa sylvatica), to study climate-induced range shift. Data collected from two separate sampling periods (1980s and 2007) by the USDA’s Forestry and Inventory Analysis (FIA) Program were used to investigate changes in abundance and dominance, and shifts in distribution, of blackgum in four ecoregions of the eastern United States. Our results indicated new recruitment of blackgum in the northern portion of its range, along with increases in both density and annual rates of change in importance value (IV). Conversely, declines in recruitment were found in the southern portion of blackgum’s range, along with decreases in density and IV. The center portion of blackgum’s range had mixed patterns of change (i.e., both increases and decreases) throughout. A northward range expansion was also detected by comparing blackgum’s historic range to where it was detected during our two more-recent sampling periods. Our findings suggest that blackgum is migrating north in response to climate change. Our study also suggests two broader implications about tree migration patterns in response to climate change: (1) species can respond to changing climate in relatively short time periods, at least for generalist species such as blackgum, and (2) climate-induced vegetation dynamic patterns can be detected at the regional level, but are inherently complex.


Kriging Stem Density Range Shift Valuable Ecosystem Service Importance Value 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



We would like to thank Gabriela Nuñez-Mir, Teresa Clark and Louis Desprez for editing prior versions of this manuscript, and three anonymous reviewers for providing constructive comments on an earlier version of the manuscript.

Author Attribution

Johanna Desprez contributed to the idea development, conducted most data analysis, most of the writing and manuscript preparation. Basil V. Iannone III provided intellectual input, statistical and organizational advice, and contributed to editing and writing. Peilin Yang conducted data analysis. Chris Oswalt contributed to data compilation and organization. Songlin Fei contributed the initial idea, participated in data analysis, and manuscript preparation.

Conflict of Interest

Not Applicable


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Copyright information

© Springer Science+Business Media Dordrecht (outside the USA) 2014

Authors and Affiliations

  • Johanna Desprez
    • 1
    Email author
  • Basil V. IannoneIII
    • 1
  • Peilin Yang
    • 2
  • Christopher M. Oswalt
    • 3
  • Songlin Fei
    • 1
  1. 1.Department of Forestry and Natural ResourcesPurdue UniversityWest LafayetteUSA
  2. 2.Department of ForestryUniversity of KentuckyLexingtonUSA
  3. 3.USDA Forest Service Southern Research StationKnoxvilleUSA

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