Title: Research agenda for integrated landscape modeling
Author: Cushman, Samuel A.; McKenzie, Donald; Peterson, David L.; Littell, Jeremy; McKelvey, Kevin S.;
Source: Gen. Tech. Rep. RMRS-GTR-194. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. 50 p.
Publication Series: General Technical Report (GTR)
Description: Reliable predictions of how changing climate and disturbance regimes will affect forest ecosystems are crucial for effective forest management. Current fire and climate research in forest ecosystem and community ecology offers data and methods that can inform such predictions. However, research in these fields occurs at different scales, with disparate goals, methods, and context. Often results are not readily comparable among studies and defy integration. We discuss the strengths and weaknesses of three modeling paradigms: empirical gradient models, mechanistic ecosystem models, and stochastic landscape disturbance models. We then propose a synthetic approach to multi-scale analysis of the effects of climatic change and disturbance on forest ecosystems. Empirical gradient models provide an anchor and spatial template for stand-level forest ecosystem models by quantifying key parameters for individual species and accounting for broad-scale geographic variation among them. Gradient imputation transfers predictions of fine-scale forest composition and structure across geographic space. Mechanistic ecosystem dynamic models predict the responses of biological variables to specific environmental drivers and facilitate understanding of temporal dynamics and disequilibrium. Stochastic landscape dynamics models predict frequency, extent, and severity of broad-scale disturbance. A robust linkage of these three modeling paradigms will facilitate prediction of the effects of altered fire and other disturbance regimes on forest ecosystems at multiple scales and in the context of climatic variability and change.
Keywords: climate change, climate regime, disturbance regime, modeling paradigms, empirical gradient model, mechanistic ecosystem model, stochastic landscape disturbance model
- We recommend that you also print this page and attach it to the printout of the article, to retain the full citation information.
- This article was written and prepared by U.S. Government employees on official time, and is therefore in the public domain.
- You may send email to firstname.lastname@example.org to request a hard copy of this publication. (Please specify exactly which publication you are requesting and your mailing address.)
XML: View XML
Cushman, Samuel A.; McKenzie, Donald; Peterson, David L.; Littell, Jeremy; McKelvey, Kevin S. 2007. Research agenda for integrated landscape modeling. Gen. Tech. Rep. RMRS-GTR-194. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. 50 p.
Get the latest version of the Adobe Acrobat reader or Acrobat Reader for Windows with Search and Accessibility