Aggregating fine-scale ecological knowledge to model coarser-scale attributes of ecosystems
Rastetter EB, King AW, Cosby BJ, Hornberger GM, O'Neill RV, Hobbie JE. 1992. Aggregating fine-scale ecological knowledge to model coarser-scale attributes of ecosystems. Ecological Applications. 2(1):55-70
As regional and global scales become more important to ecologists, methods must be developed for the application of existing fineùscale knowledge to predict coarserùscale ecosystem properties. This generally involves some form of model in which fineùscale components are aggregated. This aggregation is necessary to avoid the cumulative error associated with the estimation of a large number of parameters. However, aggregation can itself produce errors that arise because of the variation among the aggregated components. The statistical expectation operator can be used as a rigorous method for translating fineùscale relationships to coarser scales without aggregation errors. Unfortunately this method is too cumbersome to be applied in most cases, and alternative methods must be used. These alternative methods are typically partial corrections for the variation in only a few of the fineùscale attributes. Therefore, for these methods to be effective, the attributes that are the most severe sources of error must be identified a priori. We present a procedure for making these identifications based on a Monte Carlo sampling of the fineùscale attributes. We then present four methods of translating fineùscale knowledge so it can be applied at coarser scales: (1) partial transformations using the expectation operator, (2) moment expansions, (3) partitioning, and (4) calibration. These methods should make it possible to apply the vast store of fineùscale ecological knowledge to model coarserùscale attributes of ecosystems.
- Type
- Journal Article
- Authors
- Rastetter, E; King, Anthony; Cosby, Bernard; Hornberger, George; O'Neill, Robert; Hobbie, John
- Units
- ARCN , BELA , CAKR , GAAR , KOVA , NOAT
- Keywords
- aggregation, Arctic, Arctic LTER Site, Brooks Range Foothills, coarse-scale modeling, ecosystem structure, lumped models, Toolik Lake, Tundra