IDEAS home Printed from https://ideas.repec.org/a/gam/jlands/v8y2019i7p108-d246228.html
   My bibliography  Save this article

Habitat Climate Change Vulnerability Index Applied to Major Vegetation Types of the Western Interior United States

Author

Listed:
  • Patrick J. Comer

    (NatureServe, 1680 38th Street, Suite 120, Boulder, CO 80301, USA)

  • Jon C. Hak

    (NatureServe, 1680 38th Street, Suite 120, Boulder, CO 80301, USA)

  • Marion S. Reid

    (NatureServe, 1680 38th Street, Suite 120, Boulder, CO 80301, USA)

  • Stephanie L. Auer

    (NatureServe, 2511 Richmond Highway, Suite 930, Arlington, VA 22202, USA)

  • Keith A. Schulz

    (NatureServe, 1680 38th Street, Suite 120, Boulder, CO 80301, USA)

  • Healy H. Hamilton

    (NatureServe, 2511 Richmond Highway, Suite 930, Arlington, VA 22202, USA)

  • Regan L. Smyth

    (NatureServe, 2511 Richmond Highway, Suite 930, Arlington, VA 22202, USA)

  • Matthew M. Kling

    (Department of Integrative Biology, University of California, Berkeley, CA 94720, USA)

Abstract

We applied a framework to assess climate change vulnerability of 52 major vegetation types in the Western United States to provide a spatially explicit input to adaptive management decisions. The framework addressed climate exposure and ecosystem resilience; the latter derived from analyses of ecosystem sensitivity and adaptive capacity. Measures of climate change exposure used observed climate change (1981–2014) and then climate projections for the mid-21st century (2040–2069 RCP 4.5). Measures of resilience included (under ecosystem sensitivity) landscape intactness, invasive species, fire regime alteration, and forest insect and disease risk, and (under adaptive capacity), measures for topo-climate variability, diversity within functional species groups, and vulnerability of any keystone species. Outputs are generated per 100 km 2 hexagonal area for each type. As of 2014, moderate climate change vulnerability was indicated for >50% of the area of 50 of 52 types. By the mid-21st century, all but 19 types face high or very high vulnerability with >50% of the area scoring in these categories. Measures for resilience explain most components of vulnerability as of 2014, with most targeted vegetation scoring low in adaptive capacity measures and variably for specific sensitivity measures. Elevated climate exposure explains increases in vulnerability between the current and mid-century time periods.

Suggested Citation

  • Patrick J. Comer & Jon C. Hak & Marion S. Reid & Stephanie L. Auer & Keith A. Schulz & Healy H. Hamilton & Regan L. Smyth & Matthew M. Kling, 2019. "Habitat Climate Change Vulnerability Index Applied to Major Vegetation Types of the Western Interior United States," Land, MDPI, vol. 8(7), pages 1-27, July.
    • Handle: RePEc:gam:jlands:v:8:y:2019:i:7:p:108-:d:246228
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2073-445X/8/7/108/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2073-445X/8/7/108/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. James H. Thorne & Hyeyeong Choe & Peter A. Stine & Jeanne C. Chambers & Andrew Holguin & Amber C. Kerr & Mark W. Schwartz, 2018. "Climate change vulnerability assessment of forests in the Southwest USA," Climatic Change, Springer, vol. 148(3), pages 387-402, June.
    2. Gregorutti, Baptiste & Michel, Bertrand & Saint-Pierre, Philippe, 2015. "Grouped variable importance with random forests and application to multiple functional data analysis," Computational Statistics & Data Analysis, Elsevier, vol. 90(C), pages 15-35.
    3. W. A. Kurz & C. C. Dymond & G. Stinson & G. J. Rampley & E. T. Neilson & A. L. Carroll & T. Ebata & L. Safranyik, 2008. "Mountain pine beetle and forest carbon feedback to climate change," Nature, Nature, vol. 452(7190), pages 987-990, April.
    4. Scott R. Loarie & Philip B. Duffy & Healy Hamilton & Gregory P. Asner & Christopher B. Field & David D. Ackerly, 2009. "The velocity of climate change," Nature, Nature, vol. 462(7276), pages 1052-1055, December.
    5. Sheehan, T. & Bachelet, D. & Ferschweiler, K., 2015. "Projected major fire and vegetation changes in the Pacific Northwest of the conterminous United States under selected CMIP5 climate futures," Ecological Modelling, Elsevier, vol. 317(C), pages 16-29.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Patrick J. Comer & Jon C. Hak & Patrick McIntyre, 2022. "Addressing Climate Change Vulnerability in the IUCN Red List of Ecosystems—Results Demonstrated for a Cross-Section of Major Vegetation-Based Ecosystem Types in the United States," Land, MDPI, vol. 11(2), pages 1-16, February.
    2. Pinki Mondal & Sonali Shukla McDermid, 2021. "Editorial for Special Issue: “Global Vegetation and Land Surface Dynamics in a Changing Climate”," Land, MDPI, vol. 10(1), pages 1-4, January.
    3. Han Li & Wei Song, 2021. "Spatiotemporal Distribution and Influencing Factors of Ecosystem Vulnerability on Qinghai-Tibet Plateau," IJERPH, MDPI, vol. 18(12), pages 1-21, June.

    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. Erickson, Adam & Nitschke, Craig & Coops, Nicholas & Cumming, Steven & Stenhouse, Gordon, 2015. "Past-century decline in forest regeneration potential across a latitudinal and elevational gradient in Canada," Ecological Modelling, Elsevier, vol. 313(C), pages 94-102.
    2. Hyeyeong Choe & James H. Thorne, 2019. "Climate exposure of East Asian temperate forests suggests transboundary climate adaptation strategies are needed," Climatic Change, Springer, vol. 156(1), pages 51-67, September.
    3. Mouhamadou Bamba Sylla & Nellie Elguindi & Filippo Giorgi & Dominik Wisser, 2016. "Projected robust shift of climate zones over West Africa in response to anthropogenic climate change for the late 21st century," Climatic Change, Springer, vol. 134(1), pages 241-253, January.
    4. Metsaranta, J.M. & Kurz, W.A., 2012. "Inter-annual variability of ecosystem production in boreal jack pine forests (1975–2004) estimated from tree-ring data using CBM-CFS3," Ecological Modelling, Elsevier, vol. 224(1), pages 111-123.
    5. Mouhamadou Sylla & Nellie Elguindi & Filippo Giorgi & Dominik Wisser, 2016. "Projected robust shift of climate zones over West Africa in response to anthropogenic climate change for the late 21st century," Climatic Change, Springer, vol. 134(1), pages 241-253, January.
    6. 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.
    7. Bruno R Ribeiro & Lilian P Sales & Paulo De Marco Jr. & Rafael Loyola, 2016. "Assessing Mammal Exposure to Climate Change in the Brazilian Amazon," PLOS ONE, Public Library of Science, vol. 11(11), pages 1-13, November.
    8. Xie, Yalin & Lei, Xiangdong & Shi, Jingning, 2020. "Impacts of climate change on biological rotation of Larix olgensis plantations for timber production and carbon storage in northeast China using the 3-PGmix model," Ecological Modelling, Elsevier, vol. 435(C).
    9. Sohngen, Brent & Tian, Xiaohui, 2016. "Global climate change impacts on forests and markets," Forest Policy and Economics, Elsevier, vol. 72(C), pages 18-26.
    10. Eleftherios Giovanis & Oznur Ozdamar, 2025. "The transboundary effects of climate change and global adaptation: the case of the Euphrates–Tigris water basin in Turkey and Iraq," Empirical Economics, Springer, vol. 68(4), pages 1935-1972, April.
    11. Pedro Delicado & Daniel Peña, 2023. "Understanding complex predictive models with ghost variables," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 32(1), pages 107-145, March.
    12. Fabrizio Maturo & Rosanna Verde, 2023. "Supervised classification of curves via a combined use of functional data analysis and tree-based methods," Computational Statistics, Springer, vol. 38(1), pages 419-459, March.
    13. Disha Sachan & Pankaj Kumar & Md. Saquib Saharwardi, 2022. "Contemporary climate change velocity for near-surface temperatures over India," Climatic Change, Springer, vol. 173(3), pages 1-19, August.
    14. Avery P. Hill & Christopher B. Field, 2021. "Forest fires and climate-induced tree range shifts in the western US," Nature Communications, Nature, vol. 12(1), pages 1-10, December.
    15. Decheng Zhou & Lu Hao & John B. Kim & Peilong Liu & Cen Pan & Yongqiang Liu & Ge Sun, 2019. "Potential impacts of climate change on vegetation dynamics and ecosystem function in a mountain watershed on the Qinghai-Tibet Plateau," Climatic Change, Springer, vol. 156(1), pages 31-50, September.
    16. Antoine Adde & Diana Stralberg & Travis Logan & Christine Lepage & Steven Cumming & Marcel Darveau, 2020. "Projected effects of climate change on the distribution and abundance of breeding waterfowl in Eastern Canada," Climatic Change, Springer, vol. 162(4), pages 2339-2358, October.
    17. Zhiyuan Xiang & Meifang Zhao & U. S. Ogbodo, 2020. "Accumulation of Urban Insect Pests in China: 50 Years’ Observations on Camphor Tree ( Cinnamomum camphora )," Sustainability, MDPI, vol. 12(4), pages 1-15, February.
    18. Katherine Dagon & Daniel P. Schrag, 2019. "Quantifying the effects of solar geoengineering on vegetation," Climatic Change, Springer, vol. 153(1), pages 235-251, March.
    19. Luke Shoo & Ary Hoffmann & Stephen Garnett & Robert Pressey & Yvette Williams & Martin Taylor & Lorena Falconi & Colin Yates & John Scott & Diogo Alagador & Stephen Williams, 2013. "Making decisions to conserve species under climate change," Climatic Change, Springer, vol. 119(2), pages 239-246, July.
    20. Katherine A. Zeller & Rebecca Lewison & Robert J. Fletcher & Mirela G. Tulbure & Megan K. Jennings, 2020. "Understanding the Importance of Dynamic Landscape Connectivity," Land, MDPI, vol. 9(9), pages 1-15, August.

    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:gam:jlands:v:8:y:2019:i:7:p:108-:d:246228. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.