A CEES project
How can we best manage the world's forest resources for wood yield, biodiversity, and / or carbon storage? Building on our work in forest modelling, this project uses computational approaches to objectively and automatically search for forest management 'programs' (e.g. tree harvesting schedules) that best achieve pre-stated goals.
The first to achieving this is a model of local (stand-scale) forest dynamics that can make accurate predictions of the outcome of alternative management programs. We believe we have the basic framework for such a model in the shape of the PPA, which was developed in Princeton by Steve Pacala, Drew Purves and others, and which has been shown to make accurate predictions in some cases (see this publication). However, to make the kind of accurate predictions needed for projecting (for example) wood yield over decadal timescales, it is necessary to use more sophisticated sub-models of growth and mortality than have been used within the PPA to date. Drew Purves and John Caspersen are working on this right now.
When this new version of the PPA is finalized, we will be nesting it within various optimization routines, in order to objectively find 'optimal' management programs. Finding these programs is far from trivial, because of the complexity of forest dynamics -- which is why we need this modelling approach at all. Will these programs look like management methods currently in use, or something new? And how much will the choice of program depend on tree species, climate, or what we are trying to achieve? Will the results be sufficiently compelling to change the way we manage forests in some regions?
- Nikolay Strigul, Denis Pristinski, Drew W Purves, Jonathan Dushoff, and Stephen W Pacala, Scaling from trees to forests: tractable macroscopic equations for forest dynamics, in Ecological Monographs, vol. 78, no. 4, pp. 523-545, November 2008
- Drew W Purves, Jeremy W Lichstein, Nikolay Strigul, and Stephen W Pacala, Predicting and understanding forest dynamics using a simple tractable model, in Proceedings of the National Academy of Sciences USA, 29 October 2008
