Climatic Change

, Volume 51, Issue 3, pp 509-540

First online:

Tree Mortality in Gap Models: Application to Climate Change

  • Robert E. KeaneAffiliated withUSDA Forest Service, Rocky Mountain Research Station, Fire Sciences Laboratory
  • , Mike AustinAffiliated withDivision of Wildlife and Ecology, CSIRO
  • , Christopher FieldAffiliated withDepartment of Plant Biology, Carnegie Institution of Washington
  • , Andreas HuthAffiliated withCenter for Environmental Systems Research, University of Kassel
  • , Manfred J. LexerAffiliated withInstitute of Silviculture, University of Agricultural Sciences
  • , Debra PetersAffiliated withUSDA-ARS, Jornada Experimental Range MSC 3JER, NMSU
  • , Allen SolomonAffiliated withWestern Ecology Division, U.S. Environmental Protection Agency
  • , Peter WyckoffAffiliated withBotany Department, Duke University

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Gap models are perhaps the most widely used class of individual-based tree models used in ecology and climate change research. However, most gap model emphasize, in terms of process detail, computer code, and validation effort, tree growth with little attention to the simulation of plant death or mortality. Mortality algorithms have been mostly limited to general relationships because of sparse data on the causal mechanisms of mortality. If gap models are to be used to explore community dynamics under changing climates, the limitations and shortcomings of these mortality algorithms must be identified and the simulation of mortality must be improved. In this paper, we review the treatment of mortality in gap models, evaluate the relationships used to represent mortality in the current generation of gap models, and then assess the prospects for making improvements, especially for applications involving global climate change. Three needs are identified to improve mortality simulations in gap models: (1) process-based empirical analyses are needed to create more climate-sensitive stochastic mortality functions, (2) fundamental research is required to quantify the biophysical relationships between mortality and plant dynamics, and (3) extensive field data are needed to quantify, parameterize, and validate existing and future gap model mortality functions.