A lodgepole pine density management diagram

A diagram is presented that can greatly facilitate density management of lodgepole pine (Pinus contorta) stands. Together with site index tab!es or curves, the diagram can be used to estimate average tree sizes and total yields produced under various density management regimes. Its use is illustrated with three alternative regimes. " West. J. Appl. For. 1:6-11,.Jan. 1986 The control of growing stock to achieve specific management objectives is critical to effective forest management. The actual manipulation of stand density is relatively easy, but arriving at levels of growing stock consistent with particular management objectives and constraints is much more difficult (Davis 1966). In this paper we present a density management diagram for lodgepole pine, then we compare the diagram with a computer growth and yield model, and finally we illustrate use of the diagram with three alternative regimes. Ultimately, the control of growing stock requires a biologically meaningful and easily applied index of stand density. The best measures of stand density are those that incorporate both the number and average size of individuals in a population (Curtis 1970, Curtis 1982, Daniel et al. 1979, West 1982). A familiar and easily applied example is Reineke's (1933) stand density index (SDI), which expresses the relation between the number of trees per acre (TPA) and their quadratic mean diameter (Dq): SD! = TPA (Dq/10) •.6 A major advantage of this and other size-density indexes is its independence of site quality and stand age (Curtis 1982, Daniel et al. 1979, Long 1985). Various graphical aides have been developed for use in density management using indexes based on sizedensity relationships. Wilson's (1979) 1 The help of Hank Cheatham, Bob Cottingham, and Reese Pope is gratefully acknowledged. This is Journal Paper No. 2959 of the Utah Agricultural Experiment Station "Stand Density Sheets" allow stocking to be controlled, using an index of stand density based on TPA and average height. Stocking guides of the type first developed by Gingrich (1967) are commonly based on Crown Competition Factor (CCF), another size-density index; the relationships represented by their various stocking lines are easily understood and reproduced using SDI (Daniel et al. 1979). The utility of these basic graphical aides can be greatly enhanced with inclusion of additional size parameters. Ando (1968) developed what he referred to as "stand density control diagrams" for most of the important commercial timber species in Japan. Similar diagrams have been produced for coastal Douglas-fir (Pseudotsuga menziesii [Mirb.] Franco) (Drew and Fiewelling 1979) and loblolly pine (Pinus taeda L.) (Fiewelling 1981). An obstacle in the construction of density management diagrams is the scarcity of appropriate data with which to build them. Previously, diagrams have been built using data from research growth plots, but for many species and locales in the Rocky Mountains such data are limited. We examined the feasibility of constructing a diagram with common timber inventory data for lodgepole pine. CONSTRUCTION OF THE LODGEPOLE PINE DENSITY MANAGEMENT DIAGRAM USDA Forest Service Stage II inventory data were compiled for 519 lodgepole pine stands from the Targhee, Salmon, and Medicine Bow National Forests, located in Idaho, Wyoming, and Colorado (Table 1). The only constraint in selecting stands for inclusion in the data set was that lodgepole pine represent at least 80% of the total basal area of eachostand. The Targhee and Medicine Bow data were used to fit the regression equations used to construct the diagram; the Salmon data were then used to validate these quations. Data from each of the stands •nduded estimates of total stand volume (ftS/ac to 4-in top), density (TPA), basal area (ft2/ac) as well as average he•oht (Ht•), and age of site trees (open grown or dominant). Using these variables we calculated Dq, $DI, and mean tree volume (MVOL). A nonlinear least-squares curve-fitting routine was used to develop regression models relating TPA, Dq, Ht•, and MVOL. The first equation, relating MVOœ and TPA to Dq, has a coefficient of determination (R 2) of 87% and a standard error of 0.7 in. Dq = (54.4 MVOL + 5.14) o.36• (1.0 0.00759 TPA) o.•46 The second equation, relating Ht• and Dq to MVOL, has a coefficient of determination (R 2) of 94% and a standard error of 0.8 ft 3. MVOL = ((0.00396 + 0.000779 Dq 227) (1.27 Ht•l.o9)) o.916 Neither regression model was biased with respect to the independent vanables, site index, or stand age. The lodgepole pine density management diagram (Figure 1) has Dq and TPA on the two major axes. These were chosen because, of the variables included, they are the most commonly used and easiest to estimate in the field. The diameter axis ranges from 1-30 in, while the densities range from 50-5000 TPA. Both axes are plotted on a logarithmic scale. The parallel diagonal lines represent SDL The uppermost line corresponds to an SDI of 700, approximating the maximum SD! represented in the data set Table 1. Description oflodgepole pine data including maximum and minimum values. Number of Volume Basal rea Site height Site tree age stands (ft3/ac) TPA (ft2/ac) (ft) (yr) Medicine Bow N.F. 294