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Title: Vegetation Demographics in Earth System Models: a review of progress and priorities

Abstract

Numerous current efforts seek to improve the representation of ecosystem ecology and vegetation demographic processes within Earth System Models (ESMs). These developments are widely viewed as an important step in developing greater realism in predictions of future ecosystem states and fluxes. Increased realism, however, leads to increased model complexity, with new features raising a suite of ecological questions that require empirical constraints. Here, we review the developments that permit the representation of plant demographics in ESMs, and identify issues raised by these developments that highlight important gaps in ecological understanding. These issues inevitably translate into uncertainty in model projections but also allow models to be applied to new processes and questions concerning the dynamics of real-world ecosystems. We argue that stronger and more innovative connections to data, across the range of scales considered, are required to address these gaps in understanding. The development of first-generation land surface models as a unifying framework for ecophysiological understanding stimulated much research into plant physiological traits and gas exchange. Constraining predictions at ecologically relevant spatial and temporal scales will require a similar investment of effort and intensified inter-disciplinary communication.

Authors:
 [1];  [2];  [3];  [4];  [5];  [6];  [2];  [7];  [2];  [1];  [8];  [9];  [10];  [11];  [12];  [2];  [13];  [14];  [1];  [15] more »;  [16];  [17];  [18];  [19];  [4];  [20];  [8];  [21] « less
+ Show Author Affiliations
  1. National Center for Atmospheric Research, Boulder, CO (United States)
  2. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
  3. Univ. of Utah, Salt Lake City, UT (United States). Dept. of Biology
  4. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
  5. Boston Univ., MA (United States). Dept. of Earth and Environment
  6. Univ. of Texas, Austin, TX (United States). Dept. of Integrative Biology
  7. Univ. of Maryland, College Park, MD (United States). Dept. of Geographic Sciences
  8. Univ. of Florida, Gainesville, FL (United States). Dept. of Biology
  9. Embrapa Agricultural Informatics, Campinas SP (Brazil)
  10. Univ. of Texas, Austin, TX (United States). Dept. of Geological Sciences
  11. Univ. of Notre Dame, IN (United States). Dept. of Biological Sciences
  12. Smithsonian Tropical Research Inst., Panama (Republic of Panama)
  13. Brookhaven National Lab. (BNL), Upton, NY (United States). Environmental and Climate Sciences Dept.
  14. Japan Agency for Marine-Earth Science and Technology (JAMSTEC), Yokohama (Japan)
  15. Lund Univ. (Sweden). Dept. of Physical Geography and Ecosystem Science
  16. Princeton Univ., NJ (United States). Program in Atmospheric and Oceanic Sciences
  17. Smithsonian Tropical Research Institute, Apartado Postal, 0843-03092 Panamá, República de Panamá
  18. Ghent Univ., Gent (Belgium). Dept. of Applied Ecology and Environmental Biology
  19. Columbia Univ., New York, NY (United States). Center for Climate Systems Research
  20. Princeton Univ., NJ (United States). Dept. of Geosciences
  21. Harvard Univ., Cambridge, MA (United States). Dept. of Organismic and Evolutionary Biology
Publication Date:
Research Org.:
Brookhaven National Laboratory (BNL), Upton, NY (United States); Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Biological and Environmental Research (BER)
OSTI Identifier:
1392227
Alternate Identifier(s):
OSTI ID: 1402525; OSTI ID: 1439230
Report Number(s):
BNL-114231-2017-JA
Journal ID: ISSN 1354-1013; R&D Project: 21087; YN0100000
Grant/Contract Number:  
SC0012704; SC0014363; DE‐SC0012704; AC02-05CH11231
Resource Type:
Accepted Manuscript
Journal Name:
Global Change Biology
Additional Journal Information:
Journal Volume: 24; Journal Issue: 1; Journal ID: ISSN 1354-1013
Publisher:
Wiley
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; demographics; earth system model; vegetation; DGVM; ecosystem; carbon cycle

Citation Formats

Fisher, Rosie A., Koven, Charles D., Anderegg, William R. L., Christoffersen, Bradley O., Dietze, Michael C., Farrior, Caroline, Holm, Jennifer A., Hurtt, George, Knox, Ryan G., Lawrence, Peter J., Lichststein, Jeremy W., Longo, Marcos, Matheny, Ashley M., Medvigy, David, Muller-Landau, Helene C., Powell, Thomas L., Serbin, Shawn P., Sato, Hisashi, Shuman, Jacquelyn, Smith, Benjamin, Trugman, Anna T., Viskari, Toni, Verbeeck, Hans, Weng, Ensheng, Xu, Chonggang, Xu, Xiangtao, Zhang, Tao, and Moorcroft, Paul. Vegetation Demographics in Earth System Models: a review of progress and priorities. United States: N. p., 2017. Web. doi:10.1111/gcb.13910.
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Fisher, Rosie A., Koven, Charles D., Anderegg, William R. L., Christoffersen, Bradley O., Dietze, Michael C., Farrior, Caroline, Holm, Jennifer A., Hurtt, George, Knox, Ryan G., Lawrence, Peter J., Lichststein, Jeremy W., Longo, Marcos, Matheny, Ashley M., Medvigy, David, Muller-Landau, Helene C., Powell, Thomas L., Serbin, Shawn P., Sato, Hisashi, Shuman, Jacquelyn, Smith, Benjamin, Trugman, Anna T., Viskari, Toni, Verbeeck, Hans, Weng, Ensheng, Xu, Chonggang, Xu, Xiangtao, Zhang, Tao, & Moorcroft, Paul. Vegetation Demographics in Earth System Models: a review of progress and priorities. United States. https://doi.org/10.1111/gcb.13910
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Fisher, Rosie A., Koven, Charles D., Anderegg, William R. L., Christoffersen, Bradley O., Dietze, Michael C., Farrior, Caroline, Holm, Jennifer A., Hurtt, George, Knox, Ryan G., Lawrence, Peter J., Lichststein, Jeremy W., Longo, Marcos, Matheny, Ashley M., Medvigy, David, Muller-Landau, Helene C., Powell, Thomas L., Serbin, Shawn P., Sato, Hisashi, Shuman, Jacquelyn, Smith, Benjamin, Trugman, Anna T., Viskari, Toni, Verbeeck, Hans, Weng, Ensheng, Xu, Chonggang, Xu, Xiangtao, Zhang, Tao, and Moorcroft, Paul. Mon . "Vegetation Demographics in Earth System Models: a review of progress and priorities". United States. https://doi.org/10.1111/gcb.13910. https://www.osti.gov/servlets/purl/1392227.
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@article{osti_1392227,
title = {Vegetation Demographics in Earth System Models: a review of progress and priorities},
author = {Fisher, Rosie A. and Koven, Charles D. and Anderegg, William R. L. and Christoffersen, Bradley O. and Dietze, Michael C. and Farrior, Caroline and Holm, Jennifer A. and Hurtt, George and Knox, Ryan G. and Lawrence, Peter J. and Lichststein, Jeremy W. and Longo, Marcos and Matheny, Ashley M. and Medvigy, David and Muller-Landau, Helene C. and Powell, Thomas L. and Serbin, Shawn P. and Sato, Hisashi and Shuman, Jacquelyn and Smith, Benjamin and Trugman, Anna T. and Viskari, Toni and Verbeeck, Hans and Weng, Ensheng and Xu, Chonggang and Xu, Xiangtao and Zhang, Tao and Moorcroft, Paul},
abstractNote = {Numerous current efforts seek to improve the representation of ecosystem ecology and vegetation demographic processes within Earth System Models (ESMs). These developments are widely viewed as an important step in developing greater realism in predictions of future ecosystem states and fluxes. Increased realism, however, leads to increased model complexity, with new features raising a suite of ecological questions that require empirical constraints. Here, we review the developments that permit the representation of plant demographics in ESMs, and identify issues raised by these developments that highlight important gaps in ecological understanding. These issues inevitably translate into uncertainty in model projections but also allow models to be applied to new processes and questions concerning the dynamics of real-world ecosystems. We argue that stronger and more innovative connections to data, across the range of scales considered, are required to address these gaps in understanding. The development of first-generation land surface models as a unifying framework for ecophysiological understanding stimulated much research into plant physiological traits and gas exchange. Constraining predictions at ecologically relevant spatial and temporal scales will require a similar investment of effort and intensified inter-disciplinary communication.},
doi = {10.1111/gcb.13910},
journal = {Global Change Biology},
number = 1,
volume = 24,
place = {United States},
year = {Mon Sep 18 00:00:00 EDT 2017},
month = {Mon Sep 18 00:00:00 EDT 2017}
}
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    • Geoscientific Model Development, Vol. 12, Issue 11
    • DOI: 10.5194/gmd-12-4585-2019

    Climate sensitive size-dependent survival in tropical trees
    journal, August 2018

    • Johnson, Daniel J.; Needham, Jessica; Xu, Chonggang
    • Nature Ecology & Evolution, Vol. 2, Issue 9
    • DOI: 10.1038/s41559-018-0626-z

    Uncertainty Quantification of Extratropical Forest Biomass in CMIP5 Models over the Northern Hemisphere
    journal, July 2018

    Forest age improves understanding of the global carbon sink
    journal, February 2019

    • Zhu, Kai; Song, Yiluan; Qin, Clara
    • Proceedings of the National Academy of Sciences, Vol. 116, Issue 10
    • DOI: 10.1073/pnas.1900797116

    Machine learning and artificial intelligence to aid climate change research and preparedness
    journal, November 2019

    • Huntingford, Chris; Jeffers, Elizabeth S.; Bonsall, Michael B.
    • Environmental Research Letters, Vol. 14, Issue 12
    • DOI: 10.1088/1748-9326/ab4e55

    Modelling tropical forest responses to drought and El Niño with a stomatal optimization model based on xylem hydraulics
    journal, October 2018

    • Eller, Cleiton B.; Rowland, Lucy; Oliveira, Rafael S.
    • Philosophical Transactions of the Royal Society B: Biological Sciences, Vol. 373, Issue 1760
    • DOI: 10.1098/rstb.2017.0315

    popler: An r package for extraction and synthesis of population time series from the long‐term ecological research (LTER) network
    journal, November 2019

    • Compagnoni, Aldo; Bibian, Andrew J.; Ochocki, Brad M.
    • Methods in Ecology and Evolution, Vol. 11, Issue 2
    • DOI: 10.1111/2041-210x.13319

    Twenty‐five years of GCB : Putting the biology into global change
    journal, December 2019

    Covariations between plant functional traits emerge from constraining parameterization of a terrestrial biosphere model
    journal, May 2019

    • Peaucelle, Marc; Bacour, Cédric; Ciais, Philippe
    • Global Ecology and Biogeography, Vol. 28, Issue 9
    • DOI: 10.1111/geb.12937

    Plant traits inform predictions of tundra responses to global change
    journal, November 2018

    • Myers‐Smith, Isla H.; Thomas, Haydn J. D.; Bjorkman, Anne D.
    • New Phytologist, Vol. 221, Issue 4
    • DOI: 10.1111/nph.15592

    Not the same old(‐growth) forests
    journal, November 2018

    • Longo, Marcos; Keller, Michael
    • New Phytologist, Vol. 221, Issue 4
    • DOI: 10.1111/nph.15636

    The importance of physiological, structural and trait responses to drought stress in driving spatial and temporal variation in GPP across Amazon forests
    journal, January 2019

    • Flack-Prain, Sophie; Meir, Patrick; Malhi, Yadvinder
    • Biogeosciences, Vol. 16, Issue 22
    • DOI: 10.5194/bg-16-4463-2019

    Accounting for forest age in the tile-based dynamic global vegetation model JSBACH4 (4.20p7; git feature/forests) – a land surface model for the ICON-ESM
    journal, January 2020

    • Nabel, Julia E. M. S.; Naudts, Kim; Pongratz, Julia
    • Geoscientific Model Development, Vol. 13, Issue 1
    • DOI: 10.5194/gmd-13-185-2020

    Topography and neighborhood crowding can interact to shape species growth and distribution in a diverse Amazonian forest
    journal, August 2018

    • Fortunel, Claire; Lasky, Jesse R.; Uriarte, María
    • Ecology, Vol. 99, Issue 10
    • DOI: 10.1002/ecy.2441

    A heuristic classification of woody plants based on contrasting shade and drought strategies
    journal, January 2019

    Forecasting semi‐arid biome shifts in the Anthropocene
    journal, January 2020

    • Kulmatiski, Andrew; Yu, Kailiang; Mackay, D. Scott
    • New Phytologist, Vol. 226, Issue 2
    • DOI: 10.1111/nph.16381

    Widespread drought‐induced tree mortality at dry range edges indicates that climate stress exceeds species' compensating mechanisms
    journal, August 2019

    • Anderegg, William R. L.; Anderegg, Leander D. L.; Kerr, Kelly L.
    • Global Change Biology, Vol. 25, Issue 11
    • DOI: 10.1111/gcb.14771

    Thinning Can Reduce Losses in Carbon Use Efficiency and Carbon Stocks in Managed Forests Under Warmer Climate
    text, January 2018

    Greater stem growth, woody allocation, and aboveground biomass in Paleotropical forests than in Neotropical forests
    journal, February 2019

    • Taylor, Philip G.; Cleveland, Cory C.; Soper, Fiona
    • Ecology, Vol. 100, Issue 3
    • DOI: 10.1002/ecy.2589

    Functional traits along a transect
    journal, September 2017

    A stomatal control model based on optimization of carbon gain versus hydraulic risk predicts aspen sapling responses to drought
    journal, July 2018

    • Venturas, Martin D.; Sperry, John S.; Love, David M.
    • New Phytologist, Vol. 220, Issue 3
    • DOI: 10.1111/nph.15333

    Important role of forest disturbances in the global biomass turnover and carbon sinks
    journal, August 2019

    Uncertainty Quantification of Extratropical Forest Biomass in CMIP5 Models over the Northern Hemisphere
    journal, July 2018

    Approaching the potential of model-data comparisons of global land carbon storage
    journal, March 2019

    Advancing global change biology through experimental manipulations: Where have we been and where might we go?
    journal, November 2019

    • Hanson, Paul J.; Walker, Anthony P.
    • Global Change Biology, Vol. 26, Issue 1
    • DOI: 10.1111/gcb.14894

    Ecosystem heterogeneity and diversity mitigate Amazon forest resilience to frequent extreme droughts
    journal, May 2018

    • Longo, Marcos; Knox, Ryan G.; Levine, Naomi M.
    • New Phytologist, Vol. 219, Issue 3
    • DOI: 10.1111/nph.15185

    Water stress limits transpiration and growth of European larch up to the lower subalpine belt in an inner-alpine dry valley
    journal, July 2018

    • Obojes, Nikolaus; Meurer, Armin; Newesely, Christian
    • New Phytologist, Vol. 220, Issue 2
    • DOI: 10.1111/nph.15348

    The influence of canopy radiation parameter uncertainty on model projections of terrestrial carbon and energy cycling
    journal, July 2019

    Quantifying seasonal and diurnal variation of stomatal behavior in a hydraulic-based stomatal optimization model
    journal, December 2018

    The importance of physiological, structural and trait responses to drought stress in driving spatial and temporal variation in GPP across Amazon forests
    journal, January 2019

    • Flack-Prain, Sophie; Meir, Patrick; Malhi, Yadvinder
    • Biogeosciences, Vol. 16, Issue 22
    • DOI: 10.5194/bg-16-4463-2019
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