Model Features How PrognosisBC Works Software Components Data Inputs Model Outputs Growth Assumptions PrognosisBC Sub-models Other Available Components Example Applications

Model Features

Prognosis^BC Version 3.3 forecasts future stand conditions based on the expected growth and mortality of individual trees within a stand. The model can predict the development of both even- and uneven-aged, single or mixed-species stands composed of:

The model is best used for projecting existing stands from a ground-based inventory, though it can also initiate and project a stand from bare ground conditions, and a prototype regeneration model can now be invoked.

Three hardwood species: aspen, birch and cottonwood, are included in this new release, addressing a shortcoming of Version 2.0.

More information about new features can be found on this page.

How Prognosis^BC Works

Software Components

Prognosis^BC has three software components:

• DataProg is the data translator component of Prognosis^BC. It is used to translate an imported data file (representing an existing stand) into a tree list that SimProg can use to project stand growth.
• SimProg is the user interface that controls Prognosis^BC simulations. It projects the growth and development of a stand described in a tree list (imported from DataProg), based on user-specified site information, merchantable volume limits, and stand treatments.
• ViewProg is the graphical reporting component of Prognosis^BC. It allows users to compare the responses of different stand structures to various treatments over time.

These components are described in more detail below.

DataProg

While DataProg can be used to create a new tree list from scratch, it was not designed to be a data entry tool. If more than 50 records are to be entered, it would be more efficient to use a commercial spreadsheet program for initial data entry, save that file in comma-separated (CSV) format, then use DataProg to translate the CSV-format file into a tree list.

DataProg's template feature allows users to save data format specifications as templates that can be applied to similar data files.

SimProg

SimProg produces simple graphical or tabular stand summary results for each simulation run. It can also launch ViewProg to display more detailed results by species and/or DBH class.

ViewProg

ViewProg is a system for reporting the structure of stands projected with SimProg. Its graphic flexibility allows users to compare the responses of different stand structures to various treatments over time. Two types of reports are available for comparing runs:

• Stand & Stock Tables - These reports display stand structure by species and diameter class for each time step in the projection. For each combination of species and diameter class, stand composition is quantified in terms of either density (stems/hectare) or merchantable volume per hectare.
• Species Composition - These reports display changes in stand-level attributes (stem density, basal area, merchantable volume, total volume) over time, by species.

Both types of report can be displayed for one run, or one type of report can be displayed for two different runs. Both report types can be viewed as charts or tables. Because of the multitude of charts generated during comparisons, charts are not saved in ViewProg. Charts can be printed in black and white or colour, or can be copied into other applications (e.g., MS Word or PowerPoint) for future reference. Only tabular reports can be saved as files.

Prognosis^BC hardware requirements are described on this page.

Data Inputs

The data needed to run Prognosis^BC are typically derived from operational cruises or permanent sample plots. DataProg translates these plot data into an input tree list file format that can be subsequently used by SimProg. The minimum information needed in a tree list is plot ID, tree ID, tree count, and DBH for each inventoried tree. The tree count can be based on a fixed area or prism-based inventory.

Input files must be text files (i.e., non-binary) with one record of data per tree, and columns must be either fixed width or delimited by spaces or commas. It is also possible (but time-consuming) to enter data directly using DataProg to create a tree list.

Model Outputs

SimProg projects the growth and mortality of the stand from bare ground or existing stand conditions, based on user-specified parameters, and produces stand summary results for each simulation run. SimProg results can be viewed as tables or graphs of various statistics (e.g. stem density, basal area, total volume, merchantable volume, top height) over time.

ViewProg displays stand and stock tables, and changes in species composition through time, for the stands projected with SimProg.

Growth Assumptions

Prognosis^BC is a non-spatial individual tree model in which growth of large trees is driven by incremental diameter growth. When present in the inventory, observed diameter increment data can be incorporated into a projection. The increment model accumulates increments over successive time steps (commonly 10 years), eliminating the need to know the age of the stand.

The growth of seedlings and saplings with DBH <7.5cm is modelled with periodic height increments. The predicted growth of stems >7.5cm is modelled using periodic diameter increments. Other assumptions are listed in the following table.

Factor	Assumption	Impact
Spatial location	The model does not account for the effects of tree location in the stand (evenly spaced vs. clumped) on stand development. Under similar conditions, the model will project the same yields for stands with evenly spaced, clumped, and/or randomly spaced trees.	No impact
Crown ratio	Trees with long crowns grow faster than trees with short crowns.	High ratio = increased growth
Relative size (indicator of canopy position)	A tree's vigour will improve if its canopy position improves (i.e., when larger trees are removed).	Increase in relative size = increase in growth
Stand density	While trees in an open stand grow faster, the overall volume/hectare may be lower than that of a denser stand with less growth per individual tree.	Increase in stand density = decrease in tree growth
Thinning	Thinning response is a function of the reduction in stand density and whether the relative size of the residual tree changes as a result of thinning.	Increase in relative size of residual tree and/or reduction in stand density = increase in tree growth

Prognosis^BC Sub-models

Prognosis^BC is based on sub-models that predict diameter growth, height growth, crown development, mortality, and regeneration for individual trees. The sequence of calculation steps is shown below.

Other Available Components of Prognosis^BC

The core USFS Forest Vegetation Simulator growth and yield model has been extended by numerous modules that make it possible to simulate the effects of fire, pests, and disease. Of these extensions, only the Western Root Disease model has been formally implemented in the Prognosis^BC system. A prototype implementation of the Fire and Fuels Extension (FFE) has been adapted from the Northern Idaho variant and is available for research purposes. A prototype environmental indicators extension - PrognosisEI - was also developed in the late 1990s and completed in 1999. Users interested in finding out more about the additional capabilities of Prognosis^BC should refer to this page and contact the Support Centre for more information: Prognosis^BC Support or call toll-free at 1.866.515.3772.

Western Root Disease Extension: WRD

Initial root disease conditions may be based on: tree inventory damage codes; residual inoculum found in stumps; approximate densities of infected and uninfected trees inside root disease patches; and information on the number, size, and location of disease patches in a stand.

The growth and shrinkage of disease patches is modelled using a separate simulation of the movement of the infection front into other areas of disease-free trees. The details of pathogen behaviour can be tailored to local conditions. To represent the persistence of inoculum following a clearcut harvest, a separate simulation at a finer time scale is used to model the transfer of inoculum to young trees in the regeneration phase.

The extension also simulates the effects of windthrow events and of four types of bark beetles known to interact with root disease: those that depend on density of susceptible stems, windfalls, or on the density and location of root-disease-infected stems.

More information about the Western Root Disease model can be found on this page

Fire and Fuels Extension: FFE

Linked to Prognosis, the FFE model consists of 3 main components: live trees, fuels, and fire effects. Prognosis maintains the live trees and simulates their natural growth, mortality, and the impacts of silvicultural activities. The FFE tracks snags (standing dead trees) and woody debris (logs or tree parts on the ground). Snags are created through stand management or tree mortality. As the snags age, they lose their crown, break, and fall down to become woody debris. Woody debris is also created through crown loss from live trees and debris from management actions. This dead organic matter decays over time, with rates that are dependent on the size and type of material, and site-dependent factors. As well as all the standard management that is part of Prognosis, the FFE contains specific options for managing snags and woody debris, such as salvage logging or mechanical treatments.

The FFE simulates fire behaviour and effects such as fuel consumption, crowning, tree mortality and smoke production. Users may choose to simulate the effect of the fire in the stand. In this case, the model will simulate the consumption of woody debris and crowns, trees killed by fire, and production of smoke. As a result of these events the stand structure will be changed. Alternatively, the FFE can predict the potential effect of two different types of fire. In this case, the model will produce output on the potential mortality, fuel consumption, smoke production, and will produce indices of crowning potential.

More information about the Fire and Fuels Extension can be found on this page

PrognosisEI

PrognosisEI combines the forecast stand conditions from Prognosis^BC with other ecological data to project future environmental values. Timber and environmental projections are reported as plain text files that can be exported to customized ArcView tools for spatial analysis and automated display of maps and graphs.

The first case-study application of PrognosisEI was a 3,300 hectare watershed-level study in the West Arm Demonstration Forest near Nelson, BC. Model behaviour was tested in twelve alternative scenarios that included no management, large clearcuts, partial cuts, and patch cuts. Case study findings showed that the model indicators, representing a wide range of stand- and watershed-level timber and non-timber values, were responsive to scenario differences.

More information about PrognosisEI can be found on this page

Example Applications