Two basic methodological choices in wildland vegetation inventories: their consequences and implications.
In designing inventories of wildland vegetation, 2 of the many basic methodological choices are whether data are collected, reduced and stored in discrete classes or as continuous variables, and whether data are gathered as general purpose variables to bear upon many questions, or as specific purpose variables optimized for only 1 type of prediction. The effects of these choices on the accuracy with which vegetation inventories predict plant community production were examined by comparing regression models built on differing sets of independent variables 'inventoried' from a common data base. Contrary to expectations, discrete variables of classified community types were better predictors of plant community production than the same vegetation data reduced as continuous variables by 3 ordination techniques. Substitution of specific purpose soil and vegetation variables thought to be especially relevant to production did not improve correlations from those of analogous general purpose variables. These results do not show the anticipated loss of accuracy of general purpose inventory variables, but such findings cannot yet be generalized to other situations. Implications for the design of practical extensive survey methods for wildland vegetation are discussed.