Long-term forest demography


Project PI

  • Jim Clark, Nicholas School of the Environment, Duke University

USFS collaborators

  • Chelcy Ford Miniat
  • Jim Vose

Postdoctoral associates, past and present

  • Sean McMahon, SERC
  • Jessica Metcalf, Princeton University
  • Soledad Benetiz Ponce, Duke University
  • Wei Wu, Assistant Professor, Univ Southern Mississippi

PhD students, past and present

  • Brian Beckage, Univ Vermont
  • Dave Bell,Univ Wyoming
  • Aaron Berdanier, Duke
  • Mike Dietze, Boston University
  •  Michelle Hersh, Eastern Michigan State
  • Janneke HilleRisLambers, Univ Washington
  • Ines Ibanez,Univ Michigan
  • Matthew Kwit, Duke
  • Shannon LaDeau, Cary Inst
  • Jason McLachlan, Notre Dame
  • Jacqueline Mohan,Univ Georgia
  • Emily Moran, UC Merced
  • Brad Tomasek, Duke
  • Mike Wolosin, Pew Center for Climate Change
  • Pete Wyckoff, Univ Minnesota, Morris
  • Kai Zhu, Nicholas School of the Environment, Duke University

Funding: National Science Foundation

Synopsis

The Long-term Forest Demographic (LTFD) Analysis was established to understand how climate and competition interact to control change in eastern forests.  The network, originally established as five stands in the southern Appalachians in 1991, has expanded to include the Piedmont and foothills and now eastern North and Central America.  The project has been supported by a number of NSF grants, including the Coweeta LTER.  It continues with support that includes  NSF’s Macrosystems Biology Program.

From the inception observations on natural variation in space and time were combined with experimental manipulation of the competitive environment.  The data set now extends over 20 yrs from 40,000 trees and > 350,000 tree-years.  We have quantified the interactions between temperature, drought, and competition for light and moisture vary widely between species and size classes.

Early studies quantified basic demographic rates, introducing the emerging hierarchical Bayes paradigm, organized as state-space models.  New modeling innovations have continued to play a large role in this research.

Recent publications

  • Bugalho, M.N., I. Ibánez, and J.S Clark. 2013. The effects of deer herbivory and forest type on tree recruitment vary with plant growth stage. Forest Ecology and Management, in press.
  • Clark, J.S., D. M Bell, M. Kwit, A. Powell, And K. Zhu. 2013. Dynamic inverse prediction and sensitivity analysis with high-dimensional responses: application to climate-change vulnerability of biodiversity.  Journal of Biological, Environmental, and Agricultural Statistics, in press.
  • Ghosh, S., D. M. Bell, J.S. Clark, A.E. Gelfand, and P. Flikkema.  2013. Process modeling for soil moisture using sensor network data. Statistical Science, in press.
  • Wu, W., Clark, J.S., and J. Vose. 2013. Response of hydrology to climate change in the southern Appalachian Mountains using Bayesian inference. Hydrologic Processes, in press.
  • Gelfand, A.E., S. Ghosh and J. S. Clark. 2013. Scaling integral projection models for analyzing size demography. Statistical Science, in press.
  • Ward, E.J., D.M. Bell, J.S. Clark and R. Oren. 2012. Hydraulic time constants for transpiration of loblolly pine at Duke FACE. Tree Physiology, 33, 123-134.
  • Ward, E.J., R. Oren, D.M. Bell, J.S. Clark, H.R. McCarthy, H. Seok-Kim and J.-C. Domec. 2012. The effects of long-term elevated CO2 and nitrogen fertilization on stomatal conductance estimated from scaled sap flux measurements at Duke FACE.  Tree Physiology, 33, 135-151.
  • Rapp, J.M., M. R. Silman, J. S. Clark, C.A. J. Girardin, D. Galiano, and R. Tito. 2012. Intra- and inter-specific tree growth across a long altitudinal gradient in the Peruvian Andes. Ecology, 93:2061-2072.
  • Clark, J.S., B.D. Soltoff, A.S. Powell, and Q.D. Read. 2012. Evidence from individual inference for high-dimensional coexistence: long term experiments on recruitment response. PLoS One, 7 e30050. doi:10.1371/journal.pone.0030050.
  • Moran, E.V. and J.S. Clark. 2012. Causes and consequences of unequal seed production in forest trees: a case study in red oaks. Ecology, 93:1082-1094.
  • Ghosh, S., A.E. Gelfand, K. Zhu, and J.S. Clark. 2012. The k-ZIG: flexible modeling for zero-inflated counts. Biometrics, 68:878-85.
  • Moran, E.V., J. Willis, and J.S. Clark. 2012. Genetic evidence for hybridization in red oaks. American Journal of Botany, 99, 92-100.
  • Clark, J.S., D. M. Bell, M. Kwit, A. Powell, R. Roper, A. Stine, B. Vierra, and K. Zhu. 2012. Individual‐scale inference to anticipate climate‐change vulnerability of biodiversity. Philosophical Transactions of the Royal Society B, 367, 236-246.
  • Uriarte M., J. S. Clark, J. K. Zimmerman, L. S. Comita, J. Forero-Montaña, and J. Thompson. 2012. Multi-dimensional tradeoffs in species responses to disturbance: Implications for diversity in a subtropical forest. Ecology, 93:191–205.
  • Ghosh, S., A. E. Gelfand, and J. S. Clark. 2012.  Inference for size demography from point pattern data using integral projection models. Journal of Agricultural, Biological and Environmental Statistics, 17, 641-677.
  • Zhu, K., C.W. Woodall, and J.S. Clark. 2012. Failure to migrate: lack of tree range expansion in response to climate change. Global Change Biology, DOI: 10.1111/j.1365-2486.2011.02571.
  • Wu, W., Clark, J.S., and J.M. Vose. 2012. Application of a full hierarchical Bayesian model in assessing streamflow response to a climate change scenario at the Coweeta Basin, NC, USA.  Journal of Resources and Ecology, 3, 118-128.
  • PLoS ONE 6(11): e27462. doi:10.1371/journal.pone.0027462
  • Colchero, F. and J.S. Clark. 2011. Bayesian inference on age-specific survival for censored and truncated data. Journal of Animal Ecology 80 DOI: 10.1111/j.1365-2656.2011.01898.x
  • Clark, J.S., D.M. Bell, M.H. Hersh, M. Kwit, E. Moran, C. Salk, A. Stine, D. Valle, and K. Zhu. 2011. Individual-scale variation, species-scale differences: inference needed to understand diversity.  Ecology Letters 14, 1273–1287.
  • Luo,Y. K. Ogle, C. Tucker, S. Fei, C, Gao, S. Ladeau, J. S. Clark, and D. S. Schimel. 2011. Ecological forecasting and data assimilation in a data-rich era. Ecological Applications 21, 1429–1442.
  • Agarwal, P., T. Mohave, H. Yu, and J. S. Clark. 2011. Exploiting temporal coherence in forest dynamics simulation. SCG ’11 Proceedings of the 27th Annual Symposium on Computational Geometry, Paris, France.
  • Clark, J.S., P. Agarwal, D.M. Bell , P. Flikkema , A. E. Gelfand, X. Nguyen , E. Ward, and J. Yang. 2011. Inferential ecosystem models, from network data to prediction. Ecological Applications, 21,1523–1536.
  • Luo Y.Q., J. Melillo, S.L. Niu, C. Beier, J.S. Clark, A.T. Classen, E. Davidson, J.S. Dukes, R.D. Evans, C.B. Field, C.I. Czimczik, M. Keller, B.A. Kimball, L. Kueppers, R.J. Norby, S.L. Pelini, E. Pendall, E. Rastetter, J. Six, M. Smith, M. Tjoelker, M. Torn. 2011. Coordinated approaches to quantify long-term ecosystem dynamics in response to global change. Global Change Biology, 17, 843-854, DOI: 10.1111/j.1365-2486.2010.02265.x.
  • Clark, J.S., D.M. Bell, M.H. Hersh, and L. Nichols. 2011. Climate change vulnerability of forest biodiversity: climate and resource tracking of demographic rates. Global Change Biology, 17, 1834–1849.
  • Wu, W., J.S. Clark, and J. Vose. 2010. Assimilating multi-source uncertainties of a parsimonious conceptual hydrological model using hierarchical Bayesian modeling, Journal of Hydrology, 394, 436-446.
  • Moran, E.V. and J.S. Clark. 2010. Estimating seed and pollen movement in a monoecious plant: a hierarchical Bayesian approach integrating genetic and ecological data. Molecular Ecology, 20, 1248–1262.
  • Clark, J.S., D. Bell, C. Chu, B. Courbaud, M. Dietze, M. Hersh, J. HilleRisLambers, I. Ibanez, S. L. LaDeau, S. M. McMahon, C.J.E. Metcalf, J. Mohan, E. Moran, L. Pangle, S. Pearson, C. Salk, Z. Shen, D. Valle, and P. Wyckoff. 2010. High dimensional coexistence based on individual variation: a synthesis of evidence. Ecological Monographs, 80, 569–608.
  • Clark, J.S. 2010. Individuals and the variation needed for high species diversity. Science 327:1129-1132.
  • Vieilledent, G., B. Courbaud, G. Kunstler, J.-F. Dhote, and J.S. Clark. 2010. Individual variability in tree allometry determines light resource allocation in forest ecosystems: a hierarchical Bayesian approach. Oecolgia, 163: 759-773.
  • Clark, J.S., D. Bell, M. Dietze, M. Hersh, I. Ibanez, S. LaDeau, S. M. McMahon, C.J.E. Metcalf, E. Moran, L. Pangle, and M. Wolosin. 2010. Models for demography of plant populations.  Pages 431 – 481 in T. O’Hagan and M. West (eds) Handbook of Bayesian Analysis, Oxford University Press.