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Biodiversity and Conservation

, Volume 28, Issue 12, pp 3199–3220 | Cite as

Rethinking restoration targets for American chestnut using species distribution modeling

  • Jessica Cavin BarnesEmail author
  • Jason A. Delborne
Original Paper

Abstract

Given the scale and speed of contemporary environmental changes, intensive conservation interventions are increasingly being proposed that would assist the evolution of adaptive traits in threatened species. The ambition of these projects is tempered by a number of concerns, including the potential maladaptation of manipulated organisms for contemporary and future climatic conditions in their historical ranges. Following the guidelines of the International Union for the Conservation of Nature, we use a species distribution model (SDM) to consider the potential impact of climate change on the distribution and quantity of suitable habitat for American chestnut (Castanea dentata), a functionally extinct forest species that has been the focus of various restoration efforts for over 100 years. Consistent with other SDMs for North American trees, our model shows contraction of climatically suitable habitat for American chestnut within the species’ historical range and the expansion of climatically suitable habitat in regions to the north of it by 2080. These broad changes have significant implications for restoration practice. In particular, they highlight the importance of germplasm conservation, local adaptation, and addressing knowledge gaps about the interspecific interactions of American chestnut. More generally, this model demonstrates that the goals of assisted evolution projects, which often aim to maintain species in their native ranges, need to account for the uncertainty and novelty of future environmental conditions.

Keywords

Assisted evolution Climate change Forest biotechnology Maxent Niche model Range shift 

Notes

Acknowledgements

We would like to thank Rob Dunn for his help in imagining and implementing this project and Matt Fitzpatrick for his insights into ecological modeling and help with data management. Additionally, we are grateful for suggestions made by Andy Newhouse, Fred Gould, Emmanuel Frimpong, and two anonymous reviewers, which improved the accuracy and clarity of the paper. This work was supported by a National Science Foundation (NSF) Integrative Graduate Education and Research Traineeship (IGERT) (Grant No. 1068676) and the NSF Science, Technology, and Society (STS) Program (Grant No. 1632670).

Funding

This work was supported by a National Science Foundation (NSF) Integrative Graduate Education and Research Traineeship (IGERT) (Grant No. 1068676) and the NSF Science, Technology, and Society (STS) Program (Grant No. 1632670).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

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© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.Department of Forestry and Environmental ResourcesNorth Carolina State UniversityRaleighUSA
  2. 2.Genetic Engineering and Society CenterNorth Carolina State UniversityRaleighUSA

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