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  Home > Research > LTER Program > LTER6 Focus Areas > LTER6 Integrated Research

  LTER6 Integrated Research

Potential Effects Of Future Change

Project objectives and relationship to LTER6 goals

A critical objective of LTER6 is to integrate our knowledge from previous LTER work and current studies to evaluate how our system – considering all three drivers and all three responders of the Central Question – might react to scenarios of future climate change (Goal II, objective 4). This task can only be achieved by using simulation models. However, it is not our intention to try to use models to predict the future. Instead, we aim to conduct "desk top" experiments with models to better understand the potential behavior of complex systems and to test hypotheses that cannot be approached in field experiments. Most of the models we plan to employ for this part of the study have been used in the past at our site, and some are being developed at our site specifically.


During LTER6 we are particularly interested in examining the interactions among our drivers (climate, land use and disturbance), as well as the influence of multiple drivers on responders. We hypothesize that the impacts of regional climate change on our ecosystem will be strongly influenced by local topography and canopy cover and that indirect impacts of climate change, because of disturbances, will be more important than direct effects of climate (Goal II, objective 2). To examine the influence of multiple drivers on responders, we will, for example, examine how the interactions of climate change, disturbance, and land-use (defined by the scenarios that were presented in the proposal but subject to some modification) will force changes in carbon and nutrient dynamics. We will start by comparing the sensitivity of responders to single drivers and then progress to combinations of drivers. We will also examine scenarios in which the disturbance driver is dependent on climate, expecting this will lead to the largest response. Comparisons between responders will be “controlled” by using common datasets to drive models, with all future scenarios such as climate and disturbance history as well as other driving variables. For each responder examined we will contrast the mean response and the spatial and short-term temporal variability of the response under future change scenarios (i.e., treatment) relative to that of the current situation (i.e., control). When models predict the same ecosystem responders, their predictions will be compared to gain insights on uncertainty. We will examine a range of potential future responses to changes in our three system drivers using multiple scenarios. The AOGCM simulations described in the IPCC Fourth Assessment Report provide a basis for projecting general climatic changes in our region (although we will use more recent assessments if they become available). Over the next 100 years these projections indicate an overall mean increase in temperature, with temperatures increasing in both summer and winter. While mean annual precipitation may not change or increase slightly, precipitation variability will likely increase. Given projected temperature and precipitation seasonal patterns, it is also likely that the Andrews Forest will experience a longer dry season. We will use a combination of synthetic climate data and downscaled AOGCM simulations of future climate data (derived from the Climate project of the LTER6 program) produced under one or more of the IPCC emissions scenario. We will contrast these climate scenarios with two extreme cases: 1) a continuation of the current climate mean and variability and 2) rapid change, a halving in the time for the IPCC Fourth Assessment Report projected changes to occur. Given that interactions between topography and large-scale weather patterns influence how climate is expressed locally, we will translate these regional scale changes to a local level using PRISM-related models.

Midterm Progress Details



Presentations or References

Berger, U., C. Piou, K. Schiffers, and V. Grimm. 2008. Competition among plants: Concepts, individual-based modeling approaches, and a proposal for a future research strategy. Perspectives in Plant Ecology, Evolution and Systematics 9:121-135.

Canham, C.D. 1988. Growth and canopy architecture of shade-tolerant trees: response to canopy gaps. Ecology 69:786-795.

Grimm, V., E. Revilla, U. Berger, F. Jeltsch, W.M. Mooij, S.F. Railsback, H.H. Thulke, J. Weiner, T. Wiegand, and D.L. DeAngelis. 2005. Pattern-Oriented Modeling of Agent-Based Complex Systems: Lessons from Ecology. Science 310:987-991.

Güneralp, B., and G. Gertner. 2007. Feedback loop dominance analysis of two tree mortality models: relationship between structure and behavior. Tree Physiology 27:269-280.

Kätterer, T., Andrén, O. The ICBM family of analytically solved models of soil carbon, nitrogen and microbial biomass dynamics - descriptions and application examples. Ecol. Model. 136, 191-207.

Krankina, O. N., M. E. Harmon, F. Schnekenberger, and C. A. Sierra. In preparation. Response of forest carbon stores to the Northwest Forest Plan of 1992.

Landsberg, J.J., and R.H. Waring. 1997. A generalised model of forest productivity using simplified concepts of radiation-use efficiency, carbon balance and partitioning. Forest Ecology and Management 95:209-228.

Lischke, H., Zimmermann, N., Bolliger, J., Rickebusch, S., Löffler, T.J., 2006. TreeMig: A forest-landscape model for simulating spatio-temporal patterns from stand to landscape scale. Ecol. Model. 199, 409-420.

Magnani, F., Mencuccini, M., Borghetti, M., Berbigier, P., Berninger, F., Delzon, S., Grelle, A., Hari, P., Jarvis, P.G., Kolari, P., Kowalski, A.S., Lankreijer, H., Law, B.E., Lindroth, A., Loustau, D.,

Manca, G., Moncrieff, J.B., Rayment, M., Tedeschi, V., Valentini, R., Grace, J., 2007. The human footprint in the carbon cycle of temperate and boreal forests. Nature 447, 848-850.

Medlyn, B., D. Barrett, J.J. Landsberg, P. Sands, and R. Clement. 2003. Conversion of canopy intercepted radiation to photosynthate: review of modeling approaches for regional scales. Functional Plant Biology 30:153-169.

Mitchell, S. R., M. E. Harmon, K. B. O'Connell, and F. Schnekenberger. In review. The optimal role of forests in climate change mitigation: Bioenergy production or carbon sequestration? Nature Climate Change.

Nitschke, C., Innes, J.L., 2008. A tree and climate assessment tool for modeling ecosystem response to climate change. Ecol. Model. 210, 263-277.

Ohmann, J. L., Gregory, M.J. 2002. Predictive mapping of forest composition and structure with direct gradient analysis and nearest neighbor imputation in coastal Oregon, U.S.A. Canadian Journal of Forest Research 32, 725-741.

Seidl, R., Rammer, W., 2011. iLand: the individual-based forest landscape and disturbance model. Model documentation. http://iland.boku.ac.at/ (accessed: 2011-06-27)

Seidl, R., Rammer, W., Scheller, R.M., Spies, T.A., 2011a. Simulating ecological complexity: a scalable, individual-based process model of forest ecosystem dynamics. Ecological Applications, in review.

Seidl, R., P.M. Fernandes, T.F. Fonseca, F. Gillet, A.M. Jönsson, K. Merganicova, S. Netherer, A. Arpaci, J.D. Bontemps, H. Bugmann, J.R. Gonzalez-Olabarria, P. Lasch, C. Meredieu, F. Moreira, M.J. Schelhaas, and F. Mohren. 2011b. Modelling natural disturbances in forest ecosystems: a review. Ecological Modelling 222, 903-924.

Smithwick, E. H., Harmon, M.E., Remillard, S.M., Acker, S.A., Franklin, J.F. 2002. Potential upper bounds of carbon stores in forests of the Pacific Northwest. Ecol. Appl. 12, 1303-1317.

Wu, J., and J.L. David. 2002. A spatially explicit hierarchical approach to modeling complex ecological systems: theory and applications. Ecological Modelling 153:7-26.