Biological, ecological, and physiological research have provided hypotheses for the influence of factors affecting mushroom productivity, but testing hypotheses with replicated experiments on a stand or at landscape scales is very expensive and inferences derived from the results are limited to the stand conditions and forest types where the experiments were conducted. For instance, the Young Stand Thinning and Diversity Study on the Willamette National Forest in Oregon is an example of a well-replicated landscape level silvicultural experiment, but chanterelle productivity is only one of many dependent variables. A much more cost-effective and broadly applicable approach to predicting mushroom productivity will be development of a quantitative ecosystem process model that funcitons over a broad range of forest types, stand conditions, and site factors. Fortuitously, we believe that a confluence of scientific advances has made development of such a model possible.
3PG is an acronym for Physiological Principles
Predicting Growth. It is a generalized forest carbon
allocation model, published by Landsberg
and Waring (1997), that works with any forest biome and can
be run as an Excel spreadsheet by practicing foresters given a
few days of training. The model uses relatively simple and readily
available inputs such as species growth tables, latitude, aspect,
weather records, edaphic variables, stand age, and stand density
to derive monthly estimates of gross primary productivity, carbon
allocation, and stand growth. The model has the capacity for specifying
thinning regimes, although the function needs further refinement.
In recent iterations, the 3PG model has been linked to satellite
imagery of canopy photosynthetic capacity to model forest growth
across landscapes (Coops et al. 1998).
Intended as a practical management tool, the model is under constant
revision to incorporate new research data, simplify application,
and broaden its usefulness. Belowground processes and allocation
are one of the least developed aspects of this model and we hope
to contribute to the model's development with our envisioned research