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Abstract
The Southern Variant of the Forest Vegetation Simulator (FVS-SN) is made up of individual submodels that predict tree growth, recruitment and mortality. Forest managers on Ft. Bragg, North Carolina, discovered biologically unrealistic longleaf pine (
Pinus palustris) size-density predictions at large diameters when using FVS-SN to project red-cockaded woodpecker (
Picoides borealis) habitat. Inventory data from Ft. Bragg indicated the mortality submodel was responsible for the over-predictions. Three approaches to remedy longleaf pine mortality predictions in FVS-SN were explored: (1) using stand density modifier keywords, (2) using a tree size cap to influence mortality rates but not growth, and (3) iteratively invoking a mortality rate based on empirical data. Results showed the third approach was the only viable alternative. Details of this approach are described so that an FVS-SN user can effectively constrain predicted longleaf pine size-density combinations at realistic levels. Although the approach was successful, it required advanced knowledge of size-density relationships for longleaf pine. It also demands an advanced understanding of FVS-SN from the user. We suggest over-prediction of size-density relations at large diameters will be evident in any growth and yield model using similar mortality logic. Therefore our results provide a general framework for improving the accuracy of mortality predictions in FVS.
Parent Publication
Keywords
forest management,
forest planning,
growth and yield,
vegetation dynamics,
habitat modeling,
carbon inventory,
prognosis model,
landscape dynamics,
fire,
fuels,
climate change,
economics,
forest health
Citation
DeRose, R. Justin; Shaw, John D.; Vacchiano, Giorgio; Long, James N. 2008. Improving longleaf pine mortality predictions in the Southern Variant of the Forest Vegetation Simulator. In: Havis, Robert N.; Crookston, Nicholas L., comps. 2008. Third Forest Vegetation Simulator Conference; 2007 February 13–15; Fort Collins, CO. Proceedings RMRS-P-54. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. p. 160-166
