A predictive framework to understand forest responses to global change.

Journal Article (Review;Journal Article)

Forests are one of Earth's critical biomes. They have been shown to respond strongly to many of the drivers that are predicted to change natural systems over this century, including climate, introduced species, and other anthropogenic influences. Predicting how different tree species might respond to this complex of forces remains a daunting challenge for forest ecologists. Yet shifts in species composition and abundance can radically influence hydrological and atmospheric systems, plant and animal ranges, and human populations, making this challenge an important one to address. Forest ecologists have gathered a great deal of data over the past decades and are now using novel quantitative and computational tools to translate those data into predictions about the fate of forests. Here, after a brief review of the threats to forests over the next century, one of the more promising approaches to making ecological predictions is described: using hierarchical Bayesian methods to model forest demography and simulating future forests from those models. This approach captures complex processes, such as seed dispersal and mortality, and incorporates uncertainty due to unknown mechanisms, data problems, and parameter uncertainty. After describing the approach, an example by simulating drought for a southeastern forest is offered. Finally, there is a discussion of how this approach and others need to be cast within a framework of prediction that strives to answer the important questions posed to environmental scientists, but does so with a respect for the challenges inherent in predicting the future of a complex biological system.

Full Text

Duke Authors

Cited Authors

  • McMahon, SM; Dietze, MC; Hersh, MH; Moran, EV; Clark, JS

Published Date

  • April 2009

Published In

Volume / Issue

  • 1162 /

Start / End Page

  • 221 - 236

PubMed ID

  • 19432650

Electronic International Standard Serial Number (EISSN)

  • 1749-6632

International Standard Serial Number (ISSN)

  • 0077-8923

Digital Object Identifier (DOI)

  • 10.1111/j.1749-6632.2009.04495.x


  • eng