IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0151811.html
   My bibliography  Save this article

Exploring Climate Niches of Ponderosa Pine (Pinus ponderosa Douglas ex Lawson) Haplotypes in the Western United States: Implications for Evolutionary History and Conservation

Author

Listed:
  • Douglas J Shinneman
  • Robert E Means
  • Kevin M Potter
  • Valerie D Hipkins

Abstract

Ponderosa pine (Pinus ponderosa Douglas ex Lawson) occupies montane environments throughout western North America, where it is both an ecologically and economically important tree species. A recent study using mitochondrial DNA analysis demonstrated substantial genetic variation among ponderosa pine populations in the western U.S., identifying 10 haplotypes with unique evolutionary lineages that generally correspond spatially with distributions of the Pacific (P. p. var. ponderosa) and Rocky Mountain (P. p. var. scopulorum) varieties. To elucidate the role of climate in shaping the phylogeographic history of ponderosa pine, we used nonparametric multiplicative regression to develop predictive climate niche models for two varieties and 10 haplotypes and to hindcast potential distribution of the varieties during the last glacial maximum (LGM), ~22,000 yr BP. Our climate niche models performed well for the varieties, but haplotype models were constrained in some cases by small datasets and unmeasured microclimate influences. The models suggest strong relationships between genetic lineages and climate. Particularly evident was the role of seasonal precipitation balance in most models, with winter- and summer-dominated precipitation regimes strongly associated with P. p. vars. ponderosa and scopulorum, respectively. Indeed, where present-day climate niches overlap between the varieties, introgression of two haplotypes also occurs along a steep clinal divide in western Montana. Reconstructed climate niches for the LGM suggest potentially suitable climate existed for the Pacific variety in the California Floristic province, the Great Basin, and Arizona highlands, while suitable climate for the Rocky Mountain variety may have existed across the southwestern interior highlands. These findings underscore potentially unique phylogeographic origins of modern ponderosa pine evolutionary lineages, including potential adaptations to Pleistocene climates associated with discrete temporary glacial refugia. Our predictive climate niche models may inform strategies for further genetic research (e.g., sampling design) and conservation that promotes haplotype compatibility with projected changes in future climate.

Suggested Citation

  • Douglas J Shinneman & Robert E Means & Kevin M Potter & Valerie D Hipkins, 2016. "Exploring Climate Niches of Ponderosa Pine (Pinus ponderosa Douglas ex Lawson) Haplotypes in the Western United States: Implications for Evolutionary History and Conservation," PLOS ONE, Public Library of Science, vol. 11(3), pages 1-24, March.
  • Handle: RePEc:plo:pone00:0151811
    DOI: 10.1371/journal.pone.0151811
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0151811
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0151811&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0151811?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Laura Gray & Andreas Hamann, 2013. "Tracking suitable habitat for tree populations under climate change in western North America," Climatic Change, Springer, vol. 117(1), pages 289-303, March.
    2. Lintz, Heather E. & McCune, Bruce & Gray, Andrew N. & McCulloh, Katherine A., 2011. "Quantifying ecological thresholds from response surfaces," Ecological Modelling, Elsevier, vol. 222(3), pages 427-436.
    3. Austin, Mike, 2007. "Species distribution models and ecological theory: A critical assessment and some possible new approaches," Ecological Modelling, Elsevier, vol. 200(1), pages 1-19.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Mathys, A.S. & Coops, N.C. & Simard, S.W. & Waring, R.H. & Aitken, S.N., 2018. "Diverging distribution of seedlings and mature trees reflects recent climate change in British Columbia," Ecological Modelling, Elsevier, vol. 384(C), pages 145-153.
    2. Václavík, Tomáš & Meentemeyer, Ross K., 2009. "Invasive species distribution modeling (iSDM): Are absence data and dispersal constraints needed to predict actual distributions?," Ecological Modelling, Elsevier, vol. 220(23), pages 3248-3258.
    3. Muñoz-Mas, Rafael & Vezza, Paolo & Alcaraz-Hernández, Juan Diego & Martínez-Capel, Francisco, 2016. "Risk of invasion predicted with support vector machines: A case study on northern pike (Esox Lucius, L.) and bleak (Alburnus alburnus, L.)," Ecological Modelling, Elsevier, vol. 342(C), pages 123-134.
    4. Meineri, Eric & Dahlberg, C. Johan & Hylander, Kristoffer, 2015. "Using Gaussian Bayesian Networks to disentangle direct and indirect associations between landscape physiography, environmental variables and species distribution," Ecological Modelling, Elsevier, vol. 313(C), pages 127-136.
    5. Marmion, Mathieu & Luoto, Miska & Heikkinen, Risto K. & Thuiller, Wilfried, 2009. "The performance of state-of-the-art modelling techniques depends on geographical distribution of species," Ecological Modelling, Elsevier, vol. 220(24), pages 3512-3520.
    6. Kaiping Wang & Weiqi Wang & Niyi Zha & Yue Feng & Chenlan Qiu & Yunlu Zhang & Jia Ma & Rui Zhang, 2022. "Spatially Heterogeneity Response of Critical Ecosystem Service Capacity to Address Regional Development Risks to Rapid Urbanization: The Case of Beijing-Tianjin-Hebei Urban Agglomeration in China," Sustainability, MDPI, vol. 14(12), pages 1-21, June.
    7. Sellami, Mohamed Habib & Sifaoui, Mohamed Salah, 2008. "Modelling of heat and mass transfer inside a traditional oasis: Experimental validation," Ecological Modelling, Elsevier, vol. 210(1), pages 144-154.
    8. Di Traglia, Mario & Attorre, Fabio & Francesconi, Fabio & Valenti, Roberto & Vitale, Marcello, 2011. "Is cellular automata algorithm able to predict the future dynamical shifts of tree species in Italy under climate change scenarios? A methodological approach," Ecological Modelling, Elsevier, vol. 222(4), pages 925-934.
    9. Mouton, Ans M. & De Baets, Bernard & Goethals, Peter L.M., 2010. "Ecological relevance of performance criteria for species distribution models," Ecological Modelling, Elsevier, vol. 221(16), pages 1995-2002.
    10. Aertsen, Wim & Kint, Vincent & van Orshoven, Jos & Özkan, Kürşad & Muys, Bart, 2010. "Comparison and ranking of different modelling techniques for prediction of site index in Mediterranean mountain forests," Ecological Modelling, Elsevier, vol. 221(8), pages 1119-1130.
    11. Lyndsie S Wszola & Victoria L Simonsen & Erica F Stuber & Caitlyn R Gillespie & Lindsey N Messinger & Karie L Decker & Jeffrey J Lusk & Christopher F Jorgensen & Andrew A Bishop & Joseph J Fontaine, 2017. "Translating statistical species-habitat models to interactive decision support tools," PLOS ONE, Public Library of Science, vol. 12(12), pages 1-13, December.
    12. Basille, Mathieu & Calenge, Clément & Marboutin, Éric & Andersen, Reidar & Gaillard, Jean-Michel, 2008. "Assessing habitat selection using multivariate statistics: Some refinements of the ecological-niche factor analysis," Ecological Modelling, Elsevier, vol. 211(1), pages 233-240.
    13. Rufino, Marta M. & Albouy, Camille & Brind'Amour, Anik, 2021. "Which spatial interpolators I should use? A case study applying to marine species," Ecological Modelling, Elsevier, vol. 449(C).
    14. Mouton, Ans M. & De Baets, Bernard & Van Broekhoven, Ester & Goethals, Peter L.M., 2009. "Prevalence-adjusted optimisation of fuzzy models for species distribution," Ecological Modelling, Elsevier, vol. 220(15), pages 1776-1786.
    15. Stoklosa, Jakub & Huang, Yih-Huei & Furlan, Elise & Hwang, Wen-Han, 2016. "On quadratic logistic regression models when predictor variables are subject to measurement error," Computational Statistics & Data Analysis, Elsevier, vol. 95(C), pages 109-121.
    16. Suárez-Seoane, Susana & García de la Morena, Eladio L. & Morales Prieto, Manuel B. & Osborne, Patrick E. & de Juana, Eduardo, 2008. "Maximum entropy niche-based modelling of seasonal changes in little bustard (Tetrax tetrax) distribution," Ecological Modelling, Elsevier, vol. 219(1), pages 17-29.
    17. Hopkins, Robert L. & Burr, Brooks M., 2009. "Modeling freshwater fish distributions using multiscale landscape data: A case study of six narrow range endemics," Ecological Modelling, Elsevier, vol. 220(17), pages 2024-2034.
    18. Maximilian Axer & Robert Schlicht & Rico Kronenberg & Sven Wagner, 2021. "The Potential for Future Shifts in Tree Species Distribution Provided by Dispersal and Ecological Niches: A Comparison between Beech and Oak in Europe," Sustainability, MDPI, vol. 13(23), pages 1-20, November.
    19. Pie, Marcio R. & Meyer, Andreas L.S. & Firkowski, Carina R. & Ribeiro, Luiz F. & Bornschein, Marcos R., 2013. "Understanding the mechanisms underlying the distribution of microendemic montane frogs (Brachycephalus spp., Terrarana: Brachycephalidae) in the Brazilian Atlantic Rainforest," Ecological Modelling, Elsevier, vol. 250(C), pages 165-176.
    20. Moreno-Amat, Elena & Mateo, Rubén G. & Nieto-Lugilde, Diego & Morueta-Holme, Naia & Svenning, Jens-Christian & García-Amorena, Ignacio, 2015. "Impact of model complexity on cross-temporal transferability in Maxent species distribution models: An assessment using paleobotanical data," Ecological Modelling, Elsevier, vol. 312(C), pages 308-317.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0151811. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.