Potential Impacts of Climate and Land Use Change on Ecosystem Processes in the Great Northern and Appalachian Landscape Conservation Cooperatives

  • Forrest Melton
  • Jun Xiong
  • Weile Wang
  • Cristina Milesi
  • Shuang Li
  • Ashley Quackenbush
  • David M. Theobald
  • Scott J. Goetz
  • Patrick Jantz
  • Ramakrishna Nemani


Ecosystem processes are the physical, chemical, and biological actions or events that link organisms and their environment. These processes include water and nutrient cycling, plant growth and decomposition, and regulation of community dynamics (Millennium Ecosystem Assessment 2003). The ecological characteristics of many parks and protected areas are dependent on the ecosystem functions that result from interactions between ecosystem processes, characteristics, and structures. Ecosystem functions, such as the regulation of water flows, soil retention and formation, and the provisioning of habitat and maintenance of biological diversity, in turn, provide the foundation for the ecosystem services supported by parks and protected areas (Hansen and DeFries 2007). As such, the preservation of ecosystem processes can be an important conservation target that complements conservation goals for species and habitats. Defining these targets is the first step in the Climate-Smart Conservation framework (Glick, Stein, and Edelson 2011; Stein et al. 2014).


Gross Primary Production Ecosystem Process Snow Water Equivalent Projected Increase Vegetation Productivity 
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.


  1. Bierwagen, B. G., D. M. Theobald, C. R. Pyke, A. Choate, P. Groth, J. V. Thomas, and P. Morefield. 2010. National housing and impervious surface scenarios for integrated climate impact assessments. Proceedings of the National Academy of Sciences 107 (49): 20887–92.CrossRefGoogle Scholar
  2. Breshears, D. D., O. B. Myers, C. W. Meyer, F. J. Barnes, C. B. Zou, C. D. Allen, N. G. McDowell, and W. T. Pockman. 2009. Tree die-off in response to global change-type drought: Mortality insights from a decade of plant water potential measurements. Frontiers in Ecology and the Environment 7:185–89. Scholar
  3. Carlson, T. N. 2004. Analysis and prediction of surface runoff in an urbanizing watershed using satellite imagery. Journal of the American Water Resources Association 40 (4): 1087.CrossRefGoogle Scholar
  4. Christensen, N. S., A. W. Wood, N. Voisin, D. P. Lettenmaier, and R. N. Palmer. 2004. The effects of climate change on the hydrology and water resources of the Colorado River basin. Climatic Change 62 (1–3): 337–63.CrossRefGoogle Scholar
  5. Dale, V. H., S. Brown, R. A. Haeuber, N. T. Hobbs, N. Huntly, R. J. Naiman, W. E. Riebsame, M. G. Turner, and T. J. Valone. 2000. Ecological principles and guidelines for managing the use of land. Ecological Applications 10 (3): 639–70.Google Scholar
  6. Daly, C., G. H. Taylor, W. P. Gibson, T. W. Parzybok, G. L. Johnson, and P. A. Pasteris. 2000. High-quality spatial climate data sets for the United States and beyond. Transactions of the ASAE—American Society of Agricultural Engineers 43 (6): 1957–62.CrossRefGoogle Scholar
  7. Farquhar, G. D., S. von Caemmerer, and J. A. Berry. 1980. A biochemical model of photosynthetic CO2 assimilation in leaves of C3 species. Planta 149 (1): 78–90.CrossRefGoogle Scholar
  8. Field, C. B., R. B. Jackson, and H. A. Mooney. 1995. Stomatal responses to increased CO2: Implications from the plant to the global scale. Plant, Cell & Environment 18 (10): 1214–25.CrossRefGoogle Scholar
  9. Fisichelli, N. A., G. W. Schuurman, W. B. Monahan, and P. S. Ziesler. 2015. Protected area tourism in a changing climate: Will visitation at US national parks warm up or overheat? PLOS ONE 10 (6): e0128226. doi:  10.1371/journal.pone.0128226.CrossRefGoogle Scholar
  10. Gesch, D., M. Oimoen, S. Greenlee, C. Nelson, M. Steuck, and D. Tyler. 2002. The National Elevation Dataset. Photogrammetric Engineering and Remote Sensing 68 (1): 5–32.Google Scholar
  11. Glick, P., B. A. Stein, and N. Edelson, eds. 2011. Scanning the Conservation Horizon: A Guide to Climate Change Vulnerability Assessment. Washington, DC: National Wildlife Federation.Google Scholar
  12. Goetz, S., F. Melton, W. Wang, C. Milesi, and D. Theobald. 2009. Modeling strategies for adaptation to coupled climate and land use change in the United States. In Proceedings of the World Bank 2009 Marseille Cities and Climate Change Urban Research Symposium. World Bank. Scholar
  13. Grimm, N. B., F. S. Chapin III, B. Bierwagen, P. Gonzalez, P. M. Groffman, Y. Luo, F. Melton, K. Nadelhoffer, A. Pairis, P. A. Raymond, J. Schimel, and C. E. Williamson. 2013. The impacts of climate change on ecosystem structure and function. Frontiers in Ecology and the Environment 11 (9): 474–82.CrossRefGoogle Scholar
  14. Groffman, P. M., P. Kareiva, S. Carter, N. B. Grimm, J. Lawler, M. Mack, V. Matzek, and H. Tallis. 2014. Ecosystems, biodiversity, and ecosystem services. Chap. 8 in Climate Change Impacts in the United States: The Third National Climate Assessment, edited by J. M. Melillo, T. C. Richmond, and G. W. Yohe, 195–219. US Global Change Research Program. doi: 10.7930/J0TD9V7H.Google Scholar
  15. Hansen, A. J., and R. DeFries. 2007. Ecological mechanisms linking protected areas to surrounding lands. Ecological Applications 17 (4): 974–88.CrossRefGoogle Scholar
  16. Hansen, A. J., J. J. Rotella, M. L. Kraska, and D. Brown. 2000. Spatial patterns of primary productivity in the Greater Yellowstone Ecosystem. Landscape Ecology 15:505–22.CrossRefGoogle Scholar
  17. Heinsch, F. A., Z. Maosheng, S. W. Running, J. S. Kimball, R. R. Nemani, K. J. Davis, P. V. Bolstad, et al. 2006. Evaluation of remote sensing based terrestrial productivity from MODIS using regional tower eddy flux network observations. IEEE Transactions on Geoscience and Remote Sensing 44 (7): 1908–25.CrossRefGoogle Scholar
  18. Homer, C., C. Huang, L. Yang, B. Wylie, and M. Coan. 2004. Development of a 2001 national land-cover database for the United States. Photogrammetric Engineering & Remote Sensing 70 (7): 829–40.CrossRefGoogle Scholar
  19. Ichii, K., W. Wang, H. Hashimoto, F. Yang, P. Votava, A. R. Michaelis, and R. R. Nemani. 2009. Refinement of rooting depths using satellite-based evapotranspiration seasonality for ecosystem modeling in California. Agricultural and Forest Meteorology 149 (11): 1907-18.CrossRefGoogle Scholar
  20. Ichii, K., M. A. White, P. Votava, A. Michaelis, and R. R. Nemani. 2008. Evaluation of snow models in terrestrial biosphere models using ground observation and satellite data: Impact on terrestrial ecosystem processes. Hydrological Processes 22 (3): 347–55.CrossRefGoogle Scholar
  21. Jennings, M. D. 2000. Gap analysis: Concepts, methods, and recent results. Landscape Ecology 15 (1): 5–20.CrossRefGoogle Scholar
  22. Law, B. 2007. AmeriFlux network aids global synthesis. Eos, Transactions American Geophysical Union, 88 (28): 286–86.CrossRefGoogle Scholar
  23. Melillo, J. M., T. C. Richmond, and G. W. Yohe, eds. 2014. The Third National Climate Assessment. US Global Change Research Program. doi: 10.7930/J0TD9V7H.Google Scholar
  24. Millennium Ecosystem Assessment. 2005. Ecosystems and Human Well-being: Synthesis. Washington, DC: Island Press.Google Scholar
  25. Monahan, W. B., T. Cook, F. Melton, J. Connor, and B. Bobowski. 2013. Forecasting distributional responses of limber pine to climate change at management-relevant scales in Rocky Mountain National Park. PLOS ONE 8 (12): e83163. doi:  10.1371/journal.pone.0083163.CrossRefGoogle Scholar
  26. Monahan, W. B., and N. A. Fisichelli. 2014. Climate exposure of US national parks in a new era of change. PLOS ONE 9 (7): e101302. doi:  10.1371/journal.pone.0101302.CrossRefGoogle Scholar
  27. Myneni, R. B., S. Hoffman, Y. Knyazikhin, J. L. Privette, J. Glassy, Y. Tian, Y. Wang, X. Song, Y. Zhang, G. R. Smith, et al. 2002. Global products of vegetation leaf area and fraction absorbed PAR from year one of MODIS data. Remote Sensing of Environment 83 (1): 214–31.CrossRefGoogle Scholar
  28. Nemani, R., H. Hashimoto, P. Votava, F. Melton, W. L. Wang, A. Michaelis, L. Mutch, C. Milesi, S. Hiatt, and M. White. 2009. Monitoring and forecasting protected area ecosystem dynamics using the Terrestrial Observation and Prediction System (TOPS). Remote Sensing of Environment 113 (7): 1497-1509.CrossRefGoogle Scholar
  29. Nemani, R. R., C. D. Keeling, H. Hashimoto, W. M. Jolly, S. C. Piper, C. J. Tucker, R. B. Myneni, and S. W. Running. 2003. Climate-driven increases in global terrestrial net primary production from 1982 to 1999. Science 300 (5625): 1560–63.CrossRefGoogle Scholar
  30. Nemani, R., P. Votava, A. Michaelis, M. White, F. Melton, J. Coughlan, K. Golden, H. Hashimoto, K. Ichii, L. Johnson, et al. 2007. Terrestrial Observation and Prediction System (TOPS): Developing ecological nowcasts and forecasts by integrating surface, satellite and climate data with simulation models. In Research and Economic Applications of Remote Sensing Data Products, edited by U. Aswathanarayana and R. Balaii. London: Taylor and Francis.Google Scholar
  31. NRCS (Natural Resources Conservation Service), US Department of Agriculture, Soil Survey Staff. 2015. Web soil survey.
  32. Rose, S., and N. E. Peters 2001. Effects of urbanization on streamflow in the Atlanta area (Georgia, USA): A comparative hydrological approach. Hydrological Processes 15 (8): 1441–57.CrossRefGoogle Scholar
  33. Stein, B. A., P. Glick, N. Edelson, and A. Staudt, eds. 2014. Climate-Smart Conservation: Putting Adaptation Principles into Practice. Washington, DC: National Wildlife Federation.Google Scholar
  34. Taylor, K. E., R. J. Stouffer, and G. A. Meehl. 2012. An overview of CMIP5 and the experiment design. Bulletin of the American Meteorological Society 93 (4): 485–98.CrossRefGoogle Scholar
  35. Theobald, D. M. 2005. Landscape patterns of exurban growth in the USA from 1980 to 2020. Ecology and Society 10:32. Scholar
  36. Theobald, D. M., and N. T. Hobbs. 1998. Forecasting rural land-use change: A comparison of regression- and spatial transition-based models. Geographical and Environmental Modelling 2:65–82.Google Scholar
  37. Thornton, P. E., B. E. Law, H. L. Gholz, K. L. Clark, E. Falge, D. S. Ellsworth, A. H. Goldstein, R. K. Monson, D. Hollinger, M. Falk, J. Chen, and J. P. Sparks. 2002. Modeling and measuring the effects of disturbance history and climate on carbon and water budgets in evergreen needleleaf forests. Agricultural and Forest Meteorology 113:185–222.CrossRefGoogle Scholar
  38. Thornton, P. E., and N. A. Rosenbloom. 2005. Ecosystem model spin-up: Estimating steady state conditions in a coupled terrestrial carbon and nitrogen cycle model. Ecological Modelling 189:25–48.CrossRefGoogle Scholar
  39. Thornton, P. E., and S. W. Running. 1999. An improved algorithm for estimating incident daily solar radiation from measurements of temperature, humidity, and precipitation. Agricultural and Forest Meteorology 93:211–28.CrossRefGoogle Scholar
  40. Thornton, P. E., S. W. Running, and M. A. White. 1997. Generating surfaces of daily meteorological variables over large regions of complex terrain. Journal of Hydrology 190:214–51.CrossRefGoogle Scholar
  41. Thrasher, B., J. Xiong, W. Wang, F. Melton, A. Michaelis, and R. Nemani. 2013. Downscaled climate projections suitable for resource management. Eos, Transactions American Geophysical Union 94 (37): 321–23.CrossRefGoogle Scholar
  42. Van Mantgem, P. J., and N. L. Stephenson. 2007. Apparent climatically induced increase of tree mortality rates in a temperate forest. Ecology Letters 10 (10): 909–16.CrossRefGoogle Scholar
  43. Wang, W., J. Dungan, H. Hashimoto, A. R. Michaelis, C. Milesi, K. Ichii, and R. R. Nemani. 2011. Diagnosing and assessing uncertainties of terrestrial ecosystem models in a multimodel ensemble experiment: 1. Primary production. Global Change Biology 17 (3): 1350–66.CrossRefGoogle Scholar
  44. Westerling, A. L., H. G. Hidalgo, D. R. Cayan, and T. W. Swetnam. 2006. Warming and earlier spring increase western US forest wildfire activity. Science 313 (5789): 940–43.CrossRefGoogle Scholar
  45. Westerling, A. L., M. G. Turner, E. A. Smithwick, W. H. Romme, and M. G. Ryan. 2011. Continued warming could transform Greater Yellowstone fire regimes by mid-21st century. Proceedings of the National Academy of Sciences of the United States of America 108 (32): 13165–70.CrossRefGoogle Scholar
  46. White, M. A., and R. R. Nemani. 2004. Soil water forecasting in the continental United States: Relative forcing by meteorology versus leaf area index and the effects of meteorological forecast errors. Canadian Journal of Remote Sensing 30:717–30.CrossRefGoogle Scholar
  47. White, M. A., and Nemani, R. R. 2006. Real-time monitoring and short-term forecasting of land surface phenology. Remote Sensing of Environment 104 (1): 43–49.CrossRefGoogle Scholar
  48. White, M. A., P. E. Thornton, S. W. Running, and R. R. Nemani. 2000. Parameterization and sensitivity analysis of the BIOME-BGC terrestrial ecosystem model: Net primary production controls. Earth Interactions 4:1–85.CrossRefGoogle Scholar
  49. Yang, F., K. Ichii, M. A. White, H. Hashimoto, A. R. Michaelis, P. Votava, A. Zhu, A. Huete, S. W. Running, and R. R. Nemani. 2007. Developing a continental-scale measure of gross primary production by combining MODIS and AmeriFlux data through Support Vector Machine approach. Remote Sensing of Environment 110 (1): 109–22.CrossRefGoogle Scholar
  50. Zhao, M., F. A. Heinsch, R. R. Nemani, and S. W. Running. 2005. Improvements of the MODIS terrestrial gross and net primary production global data set. Remote Sensing of Environment 95 (2): 164–76.CrossRefGoogle Scholar

Copyright information

© Island Press 2016

Authors and Affiliations

  • Forrest Melton
  • Jun Xiong
  • Weile Wang
  • Cristina Milesi
  • Shuang Li
  • Ashley Quackenbush
  • David M. Theobald
  • Scott J. Goetz
  • Patrick Jantz
  • Ramakrishna Nemani

There are no affiliations available

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