United States 

Department of 

Agriculture 

Forest Service 

Northeastern Forest 

Experiment Station 

Research Paper NE-676 

Test of Four Stand Growth 

Simulators for the 

Northeastern United States 

Thomas M. Schuler 

David A. Marquis 

Richard L. Ernst 

Brian T. Simpson 
Abstract 

SILVAH, FIBER, NE-TWIGS, and OAKSIM simulators, commonly used in the 

northeastern United States, were evaluated by comparing predicted stand development 

with actual stand development records for periods ranging from 15 to 50 years. Results 

varied with stand parameter, forest type, projection length, and geographic area. 

Except in the spruce-fir forest type where FIBER stands out as the best simulator, no 

single simulator is clearly superior to the others for all locations within a forest type. In 

general, FIBER, NE-TWIGS, and SILVAH performed best in the northern hardwood 

(beech-birch-maple) forest type; NE-TWIGS and SILVAH performed best in the 

Allegheny hardwood (cherry-maple) forest type; SILVAH and OAKSIM performed best 

in the oak-hickory forest type; and SILVAH was most suitable in the transition hardwood 

(mixture of northern hardwoods and oaks) forest type. The results give growth and yield 

model users more information for selecting the simulator most suitable for their 

particular needs. The results also can be used as a diagnostic tool for growth and yield 

model development. 

The Authors 

THOMAS M. SCHULER, research forester, received his bachelor's degree in forestry 

from Purdue University and a master's degree in silviculture from Colorado State 

University. In 1992, he joined the forest management research project at the U.S. 

Department of Agriculture, Forest Service, Timber and Watershed Laboratory, 

Parsons, West Virginia. 

DAVID A. MARQUIS, (retired) coordinator for the Northeast Decision Model received 

his bachelor of science degree in forestry from Pennsylvania State University and his 

master's and doctoral degrees in forest ecology and silvicuiture from Yale University. 

RICHARD L. ERNST, currently a mensurationist for the Washington Office in Fort 

Collins, Colorado, received his bachelor's and master's degrees in forest management 

and operations research from Pennsylvania State University. 

BRIAN T. SIMPSON, currently a forester for the Northeastern Forest Experiment 

Station, Morgantown, West Virginia, received his bachelor's degrees in geology and 

forestry and his master's degree in forestry from West Virginia University. 

The computer program described in this publication is available on request with the 

understanding that the U.S. Department of Agriculture cannot assure its accuracy, 

completeness, reliability, or suitability for any other purpose than that reported. The 

recipient may not assert any proprietary rights thereto nor represent it to anyone as 

other than a Government-produced computer program. For cost information, write 

Northeastern Forest Experiment Station, 5 Radnor Corporate Center, Suite 200, P.O. 

Box 6775, Radnor, PA, 19087-8775. [FSM 6621.51. 

Cover Figure-Range of geographic locations and forest types evaluated by the four 

growth and yield simulators included in this study. 

Manuscript received for publication 4 May 1993 

USDA FOREST SERVICE 

5 RADNOR CORP CTR STE 200 

PO BOX 6775 

RADNOR PA 19087-8775 

September 1993 
Introduction 

Forest land managers need reliable growth and yield 

information to manage the vast areas of forest land in the 

eastern United States. To provide this information, 

computer programs that simulate stand growth and yield 

have been developed and are widely available for use on 

microcomputers. Several stand growth simulators have 

been developed for forests of the northeastern United 

States. Although projection estimates are carefully 

evaluated during the development phase of each simulator, 

there is little information available to aid the potential user 

in selecting the best simulator for their particular 

circumstances when several simulators are available for a 

given area. The relative performance of various models and 

their suitability for use in a particular forest type and 

geographic area, in comparison to other simulators, is 

largely unknown. 

A test of four simulators was undertaken to provide 

performance information needed in a multiple-resource, 

silvicultural decision model currently being developed by 

the Northeastern Forest Experiment Station, US. 

Department of Agriculture, Forest Service (Marquis 1991). 

The results of the test will be used by the decision model to 

aid i~n the selection of a simulator for a given projection 

where a specific simulator is not designated by the user. 

The results also can be used by individuals to evaluate 

various simulators as to their utility for particular situations. 

Methods 

There were four stand growth simulators included in the 

test: SILVAH V4.03 (Marquis 1986, Marquis and Ernst 

1992), OAKSIM (Hilt 1985a, 1985b), NE-TWIGS V2.0 (Hilt 

and Teck 1989, Teck 1990), and FIBER V2.0 (Solomon and 

others 1987). All four simulators are distance-independent, 

individual tree, or stand table-projection types of simulators. 

They all begin with a list of trees by species and diameter at 

breast height (d.b.h.) and can generate similar tree lists or 

stand tables upon completion of the projection. All are 

available for public use on IBM-compatible microcomputers. 

Brief Description of SILVAH Version 4.03 

SILVAH was developed for use in hardwood forests of the 

Allegheny region of Pennsylvania, New York, West Virginia, 

Maryland, and Ohio. The SILVAH system is primarily an 

expert system used in making silvicultural decisions, but it 

also ilncludes a stand growth simulator. Since this work was 

completed, SILVAH V4.5 has been released. 

Growlth data in SILVAH comes from two major sources. 

Data on the cherry-maple and beech-birch-maple types 

were generated from long-term research plots in 

northwestern Pennsylvania, some of which span 53 years of 

continuous record. Data on the oak-hickory type in SILVAH 

came from OAKSIM. The equations for oak were taken from 

that simulator and used for all oak species. Some growth 

equation adjustments included in OAKSIM are not included 

in SILVAH. 

Variables used in SILVAH growth and mortality equations 

include species, d.b.h., relative diameter (d.b.h./stand 

diameter), relative stand density for all species except oak 

(Marquis and others 1992), total basal area for oaks, and 

site index for oaks. SILVAH is a modified stand tableprojection simulator. Usual projection periods are 5 years, 

but periods as short as 1 year are permitted. Minimum tree 

size is 1.0 inch d.b.h.; maximum tree size is 40.0 inches 

d.b.h. SILVAH does not estimate ingrowth into the 1-inch 

d.b.h. class. 

Brief Description of OAKSIM 

OAKSIM was developed for use with oak-hickory forests of 

the upland central hardwood region. Growth and mortality 

data were obtained from 20-year records on 77 permanent 

plots in southern Ohio and southeastern Kentucky. A single 

growth equation is used for all species, but there are four 

mortality equations applied to the species groupings of black 

oak, white oak, hickory and other trees, and understory trees. 

Variables used in OAKSIM equations include: d.b.h., 

quadratic mean stand diameter, total stand basal area, and 

species (mortality only). OAKSIM is a distance-independent, 

individual-tree simulator. It works from a tree list and treats 

each tree in the list as a single entity, either killing it or 

growing it in its entirety. 

OAKSIM projects only in 5-year growth periods. The 

minimum d.b.h. is 2.6 inches and the maximum d.b.h. is 30.0 

inches. OAKSIM does not estimate ingrowth. 

Brief Description of NE-TWIGS Version 2.0 

NE-TWIGS was developed for use throughout the 

northeastern United States. It utilized the large data base 

pertaining to the entire northeastern United States of the 

Forest Inventory and Analysis Unit, Northeastern Forest 

Experiment Station, USDA Forest Service. Growth records 

on these plots range in time from 8 to 14 years and include all 

forest types growing in the Northeast. 

Variables used in NE-TWIGS to estimate growth and 

mortality include species, d.b.h., basal area in trees larger 

than the tree in question, and site index. NE-TWIGS is a 

distance-independent, individual-tree simulator that 

calculates potential growth, then reduces that growth by 

competitive position. 

NE-TWIGS projects growth annually in cycles of 1 to 5 years. 

Minimum d.b.h. is 1.0 inch and maximum d.b.h. is 30.0 

inches. NE-TWIGS allows the user to select ingrowth as an 

option, but requires the user to specify manually the ingrowth 

tree characteristics. The ingrowth function was not utilized for 

these tests. Since this work was completed, NE-TWIGS V3.0 

has been released and has automatic ingrowth functionality. 
Brief Description of FlBER Version 2.0 

FlBER was developed for the spruce-fir and northern 

hardwood forest types of New England. It is based on data 

from 359 spruce-fir research plots in Maine and 48 northern 

hardwood plots in New Hampshire. In addition, some 2,500 

growth plots primarily from forest industry were utilized. 

These plots are located throughout Maine, New Hampshire, 

Vermont, and northern New York. 

Variables used in the FlBER growth and mortality equations 

include d.b.h., initial stand basal area, residual stand basal 

area, percent hardwoods, and percent species. FlBER is a 

two-stage matrix model. One stage is a set of linear 

regression coefficients that predict transition probabilities of 

tree growth and mortality as a function of stand density, tree 

size, and proportion of hardwoods. The predicted 

probabilities are the elements of stand projection matrices 

used to project the distribution of diameters over a 5-year 

period. 

FlBER projects in 5-year increments only. Minimum d.b.h. is 

5.0 inches and maximum d.b.h. is 30.0 inches. FlBER was 

the only simulator of the four tested that contained an 

automated ingrowth function. FlBER estimates ingrowth into 

the 5.0-inch class. The ingrowth function cannot be disabled 

and was used for these tests. 

Test Data and Procedures 

Actual stand growth records were obtained from permanent 

research plots in Maine, New Hampshire, Vermont, 

Massachusetts, New York, Pennsylvania, West Virginia, 

Ohio, and Kentucky. Forest types on these plots included 

aspen-birch, cherry-maple or Allegheny hardwoods, beechbirch-maple or northern hardwoods, oak-hickory, spruce-fir, 

transition hardwoods (mixtures of northern hardwoods and 

oaks), and several miscellaneous types. 

After the data were assembled, we found that five forest 

types were well represented and could be included in the 

analyses. Forest type determinations were designated using 

the proporiion of total stand basal area represented by a 

particular species. Quantitative algorithms defined by 

Marquis and Ernst (1992) were used to determine forest 

type during initial stand analysis. 

All plots in the simulator test data set had a minimum of 15 

years of actual growth record. Some had growth records as 

long as 50 years. Each growth record was categorized as 

either a short or long period. A short period was defined as 

20 years and a long period as 40 years. Since 

remeasurements often did not coincide exactly with the 20- 

and 40-year periods, deviations of up to 5 years were 

permitted for the short period, and deviations up to 10 years 

were permitted for the long period. Thus, the 20-year period 

contained growth records of 15 to 25 years and the 40-year 

period contained growth records of 30 to 50 years. No 

stands were in the 26- to 29-year length. 

The actual records were categorized as either short or long 

so that model performance for each projection length could 

be evaluated. However, in all simulations, attempts were 

made to match the actual growth period as closely as 

possible. If the actual growth records covered 22 years, the 

simulation was done for 22 years if permitted by the model. 

FlBER and OAKSIM were structured to make growth 

projections in multiples of 5 years only. Thus, a 22-year 

period was projected as a 20-year period with FlBER and 

OAKSIM, but projected for the actual time period with 

SILVAH and NE-TWIGS. 

The test data included both cut and uncut stands. About 

one-third of the observations were stands that had some 

form of cutting during a projection period. Removals from 

simulated tree lists duplicated actual cutting procedures as 

closely as possible. Procedures that could not be duplicated 

with the simulator interface were done manually. In such 

cases, stands were projected to the time of the cut and 

saved. The projected tree list was edited to reflect cut 

characteristics and submitted again to the simulator to finish 

the projection. 

The stand summary procedures from SILVAH were used to 

tabulate and report stand parameters, such as basal area 

and number of trees per acre for all tests. The results of 

each simulator's projection and the actual, observed stand 

data, in the form of a tree list, were entered into a modified 

version of SILVAH that excluded all trees less than 5.0 

inches d.b.h. from stand summaries. This eliminated 

variation in test results due to differences in individual 

model minimum acceptable diameters and permitted direct 

comparisons among simulators. Summary statistics were 

transferred by this modified version of SILVAH to a 

spreadsheet where the stand summaries from actual and 

simulated stands were accumulated. 

In all, there were 185 separate stands included in the tests. 

These provided 186 short-period observations and 28 longperiod observations. The largest number of observations 

were from the northern hardwood forest type. Allegheny 

hardwoods, oak-hickory, and spruce-fir types also were well 

represented. States with 20 or more observations included 

Maine, Vermont, Massachusetts, New York, Pennsylvania, 

and West Virginia. Table 1 lists the number of observations 

by period, forest type, and state. 

Stand Parameters 

All simulators were used in all forest types and geographic 

areas. These methods resulted in using some simulators in 

forest types for which their use was never intended. In all 

analyses, six key per-acre stand parameters were used to 

represent stand conditions. The stand parameters consisted 

of number of trees, basal area in the pole-size class (5.0 to 

11.0 inches d.b.h.), basal area in the sawtimber-size class 

(12.0 inches d.b.h. and greater), total basal area (5.0 inches 

d.b.h. and greater), medial stand diameter calculated as 

2 d.b.h.aEd.b.h.2, and percentage of the basal area in the 
primary species group for the forest type being tested 

(Table 2). Medial stand diameter was chosen as an 

expression of stand diameter because it has been shown to 

approximate the average diameter of the dominant, 

codominant, and intermediate trees of the stand (Marquis 

1991). 

Table 1 .-Number of observations by forest type, state, 

and projection period. 

- - - 

Forest type, 

projection period, No. of 

state Observations 

Spruce-fir 

Short period, all states 32 

Short period, ME 28 

Northern hardwoods 

Short period, all states 77 

Short period, ME 20 

Short period, MA 14 

Short period, VT 30 

Short period, PA 10 

L,ong period, all states 15 

Long period, NH 10 

Long period, PA 5 

Allegheny hardwoods 

Short period, all states 32 

Short period, PA 24 

Short period, WV 6 

Long period, all states 8 

Long period, PA 6 

Transition hardwoods 

Short period, all states 12 

Short period, WV 8 

Oak-hickory 

Short period, all states 29 

Short period, KY 5 

Short period, OH 7 

- Short period, NY 14 

Originally, a seventh parameter was included to represent 

the proportion of stand basal area in a secondary species 

group. This worked well in some forest types where there 

were two distinct species groups represented, such as 

transition hardwoods in which oaks and maple-beech groups 

were both prevalent. But the use of two species groups in 

forest types in which only one major species group exists, 

such as northern hardwoods, introduced a large error that 

was not meaningful. As a result, the secondary species group 

was dropped as a test parameter. 

The six key stand parameters together represent major stand 

characteristics that a forest manager might want to simulate: 

stand density or stocking, stand structure, average tree size, 

and species composition. Stocking is represented by total 

basal area and number of trees. Structure is reprsented by 

the distribution of basal area between the pole- and sawtimbersize classes. The basal area in these two size classes also 

provides an indirect measure of pulpwood and sawtimber 

volumes as well as timber values. Average tree size of the 

dominant, codominant, and intermediate trees is represented 

by the medial diameter. And species composition is represented 

by the percentage of basal area in the primary species group. 

Simulator Performance Measures 

Methodology for simulator evaluation has been investigated 

(Reynolds and others 1981 ; Ek and Monserud 1979; 

Goulding 1979; Brand 1983; Buchman and Shifley 1983; 

Zuuring and others 1988) revealing problems in evaluating 

multiple simulators. After considerable analysis, we chose to 

rank simulator performance based on three separate 

measures. Each measure is described. 

In order to evaluate simulator performance, we developed a 

procedure that generated several statistics in order to 

evaluate the goodness of fit of each simulator to the actual 

data. The procedure calculated the mean difference between 

the actual ending stand parameters and the simulated stand 

parameters. This difference was an estimate of bias. The 

closer the bias to zero, the better on average the model 

predicted across all stands in the test. 

We also calculated a tolerance interval, which indicated the 

spread in the prediction orthevariation in thedifference among 

the individual stands. The tolerance interval parameters were 

set so that 75 percent of the stands would fall within the 

tolerance interval width 95 percent of the time. Thus, the 

smaller the tolerance interval width, the better the simulator. 

The tolerance interval and bias were computed using SYSTAT1. 

Both bias and tolerance interval are important. A simulator that 

overpredicts on half the stands and underpredicts on the other 

half may have a small average bias, but a large tolerance 

interval. A simulator that always underpredicts may have a 

small tolerance interval but a large average bias. The ideal 

simulator is one that has both a small average bias and a 

small tolerance interval. 

lThe use of trade, firm, or corporation names in this publication 

is for the information and convenience of the reader. Such 

use does not constitute an official endorsement or approval 

by the U.S. Department of Agriculture or the Forest Service of 

any product or service to the exclusion of others that may be 

suitable. 
Table 2.-The primary species group used for each forest type evaluated 

Forest type Primary species group 

Northern hardwoods Sugar maple (Acer saccharurn Marsh), beech (Fagus grandifolia Ehrh.), and 

birch (Betula sp.) 

Allegheny hardwoods Black cherry (Prunus serotina Ehrh.), white ash (Frdnus 

americana L.), yellow-poplar (Liriodendron tulipifera L.) 

Transition hardwoods All oaks (Quercus sp.) 

Oak-hickory All oaks (Quercus sp.) 

Spruce-fir All conifers (Abies sp., Picea sp., Tsuga sp.) 

Table 3.-Spruce-fir percent mean bias, percent tolerance interval, and percent of stands within 15 percent of 

the actual values by stand parameter, period length, and state 

Basal area (total) 

Projection period, 

states 

. . 

Short period, all states -25 33 22 -32 38 -17 28 25 FIBER 

Short period, ME I -26 34 18 I -33 38 1 -17 29 25 FIBER 

Basal area (saw) 

Short period, all states SILVAH 

Short period, ME 23 25 -42 27 

23 22 1 27 

SILVAH 

SILVAH 

Basal area (poles) 

Bias 

Short period, all states -37 45 -32 42 -25 40 22 FIBER 

Short period, ME 3 45 :: 1-34 43 ::1-27 41 21 FIBER 

OAKSIM 

Medial stand diameter 

Tol. 

Int. 15% Bias 

NE-TWIGS 

Number of trees 

Tol . 

Int. Bias 

Short period, all states 

Short period, ME 

Short period, all states -36 47 22 -38 48 -31 45 28 FIBER 

Sho~t period, ME 1 -4 48 14 / -41 49 1 -35 4 21 FIBER 

Best 

15% 

FIBER 

13 20 66 15 FIBER,OAKSIM 

15 21 61 1,: 16 : FIBER 

Percent basal area in primary Species Group 

To1 . Int. Bias 

Overall 

15% 

Tol. 

Int. 15% 
Finally, stand parameters after simulation were compared 

with actual stand values at the same age, and the 

proportion of stands estimated within 15 percent of the 

actual value was tabulated. These tabulations were made 

separately for each of the six stand parameters, then an 

average was calculated for all six parameters for each 

simulator. The results provide a measure of how well the 

simulators performed on the test data set. 

Model Selection 

The results from the bias, tolerance interval, and percent of 

stands within 15 percent of the actual values were 

tabulated. Both bias and tolerance interval width were 

converted to percentages of the average actual observations. 

An "overall" percent bias and percent tolerance interval for 

the six parameters was obtained simply by averaging the 

percentage estimates for the individual parameters. The 

simulator that performed the best in at least two of the three 

"overall" criteria--bias, tolerance interval width, and percent 

of stands estimated within 15 percent of the actual value-- 

was selected as the simulator for the automatic simulatorselection algorithm for the Northeast Decision Model. 

Results and Discussion 

In the spurce-fir forest type, FlBER was the clear choice for 

the best performing simulator. This forest type was limited 

to Maine and an "all states combined" category. The "all 

states combined" category was dominated by the Maine 

observations as is evident by the similarity of the results of 

the tests for each of the six stand parameters and the 

"overall" averages presented in Table 3. 

NE-TWIGS, FIBER, and SILVAH made reasonable 

estimates of the percent of basal area in the primary 

species group. FlBER predictions were closest to the actual 

observations for total basal area, basal area in pole-sized 

trees, and number of trees. SILVAH had the smallest bias 

and the narrowest tolerance interval for basal area of 

sawlog-sized trees. 

All af the simulators underpredicted total basal area, polesize basal area, and number of trees. This may be related to 

the large percentage of cut stands in the data set for this 

particular forest type. Although the cuttings were simulated 

within the stand projections, the reduced basal area in 

these stands may have resulted in changes in the rates of 

density-dependent mortality, growth rates of the residual 

trees, and ingrowth of new trees into measured size classes 

not accounted for by the simulators. 

Northern Hardwoods 

FIBER, NE-TWIGS, and SILVAH each provided the closest 

estimates for different categories (Table 4). FlBER 

projections were closest to the actual observations in the 

"overall" category for both the short and long projection 

periods of "all states combined," the short periods in Maine 

and Massachusetts, and the long period in New Hampshire. 

NE-TWIGS projections were closest in the "overall" 

category for both the short and long projection periods in 

Pennsylvania, while SILVAH projections were closest in the 

"overall" category for the short projection period in 

Vermont. The northern hardwood forest type was 

represented by more observations than any other forest 

type included in this evaluation. The northern hardwood test 

data included observations from five states and both short 

and long projection periods. SILVAH, NE-TWIGS, and 

FIBER were developed, in part, with data from the northern 

hardwood forest. The "overall" results reflect the calibration 

of these models for this forest type. 

Individual parameter estimates of pole-sized basal area, 

number of trees, and the percent of basal area in the 

primary species group were estimated the closest by FlBER 

in the majority of the location and period length categories. 

The projections of sawlog-sized basal area exhibited 

considerable variability. SILVAH was closest to the actual 

observations for short projections in Vermont, Maine, and 

"all states combined." NE-TWIGS was closest for long 

projections in Pennsylvania and "all states combined." The 

results for other locations and projection periods were less 

clear, such as in sawlogsized basal area estimates for the 

short period in Massachusetts and Pennsylvania and the 

long period in New Hampshire. Medial stand diameter 

estimates also showed considerable variability, with each of 

the models performing best in at least one category. 

Allegheny Hardwoods 

In most cases, growth and yield model users will be well 

served by using NE-TWIGS for this forest type. NE-TWIGS 

projections were closest to the actual observations in the 

"overall" category for the short and long projection periods 

in Pqnnsylvania and "all states combined" (Table 5). 

SILVAH projections were closest to the actual observations 

in the "overall" category for the short projection period in 

West Virginia. The results of the individual parameter 

estimates for total and sawlog-sized basal area projections 

clearly show that NE-TWIGS provided the best estimates. 

SILVAH generally provided the closest estimates for the 

percent of basal area in the primary species group, while 

OAKSIM generally had the best results for predicting 

number of trees and polesized basal area. All of the 

simulators provided reasonable estimates of medial stand 

diameter, and model differences were not clearly apparent 

for this parameter. 

Transition Hardwoods 

Based on the results for "all states combined," we felt that 

SILVAH would be the most appropriate choice in this forest 

type for most situations. Although our test data included 
Table 4.-Northern hardwood percent mean bias, percent tolerance interval, and percent of stands within 15 

percent of the actual values by stand parameter, period length, and state 

Basal area (total) 

Projection period, 

states 

Short period, all states 

Short period, ME 

shoa period, MA 

Short period, VT 

Short period, PA 

Long period, all states 

Long period, NH 

Long period, PA 

Short period, all states 

Short period, ME 

Shod period, MA 

Short period, VT 

Short period, PA 

Long period, all states 

Long period, NH 

Long period, PA 

Basal area (saw) 

SILVAH 

Short period, ail states -12 38 32 -16 38 27 

Short period, ME I -36 I0 15 / -42 53 0 

Bias 

Short period, MA 

Short period, VT 

Short period, PA 

OAKSXM 

Long period, PA 

Tol. 

Int. Bias 

FIBER 

FIBER 

FIBER 

SILVAH 

TWIGS, FIBER 

FIBER 

FIBER 

NONE 

15% 

NETWIGS 

SILVAH 

SILVAH 

None 

SILVAH 

OAKSlM,TWIGS 

TWIGS 

None 

TWIGS 

To1 . Int. Bias 

Basal area (~oles) 

Best 

15% 

FIBER 

w, 

-15 40 29 FIBER 

-45 55 0 FIBER 

-23 36 21 FIBER 

Bias 

Tol. 

Int. 15% 

Tol. 

Int. 15% 
Table 4.-continued 

-- 

Projection period, 

states 

Short period, all states 

Short period, ME 

Short period, MA 

Short period, VT 

Short period, PA 

Long period, all states 

Long period, NH 

Long period, PA 

Short period, all states 

Short period, ME 

Short period, MA 

Short period, VT 

Short period, PA 

Long period, all states 

Long period, NH 

Long period, PA 

Short period, all states 

Short period, ME 

Short period, MA 

Short period, VT 

Short period, PA 

Long period, all states 

Long period, NH 

Long period, PA 

Medial stand diameter 

Number of trees 

OAKSIM 

Percent basal area in primary species group 

Bias 

41 OOpq Short period, ME 1-13.10 25.50 45.83 1-13.33 28.83 35.001-14.50 27.67 . .$- ,,,: 

NE-TWIGS 

Short aeriod. MA 1 -9.17 18.67 53.67 1-17.17 

Tol. 

Int. 

Shoa period, all states 

-. . 

Short period, VT 

Short period, PA 

Long period, all states 

FIBER Best 

15% Bias 15% 

Long period, NH 1 7.33 25.00 31.671-20.67 30.67 35.00 

Bias 

Tol. 

kit. 

SILVAH 

FIBER 

FIBER 

OAKSIM 

TWIGS 

FIBER 

FIBER 

TWIGS 

FIBER 

FIBW 

FIBER 

TWIGS 

TWIGS 

FIBER 

FIBER 

TWIGS 

FIBER 

FIBER 

FIBER 

FIBER 

FIBER 

FIBER 

FIBER 

FIBER 

FIBER 

FIBER 

SILVAH 

TWIGS 

FIBER 

FIBER 

TWIGS 

Tol. 

Int. 15% 
Table 5.-Allegheny hardwood percent mean bias, percent tolerance interval, and percent of stands within 15 

percent of the actual values by test parameter, period length, and state 

Projection period, I SILVAH I OAKSIM I NE-TWIGS I FIBER I 

states 

Short period, WV / -30 26 :: / ....................... 

....................... 

::-: 

Long period, all states g$$1 35 ............ 

............ ma.:. ............. 

Shortperiod, all states E$@$$ 20 56 .:.:.:.:.: ............ ............ ........... I Short period, PA 4 17 63 

Long period, PA 1 12 27 50 1 -30 

-19 

-18 

Short period, all states 38 -32 

. Short period, PA 46 -28 

Bias 

Short period, WV -35 13 

Long period, all states 38 19 

Long period, PA 55 17 

Short period, PA 14= 

Tol. Best 

Int. 

Short period, WV ............. I ........ .............. 

.............. 

-17 26 ......... 

....................... Long period, all states EE:; 36 ................... 

Biis 

Longperiod, PA I 15 32 

15% 

Tol. 

Int. 

Basal area (total) 

15% 15% 

TWIGS 

TWIGS 

TWIGS 

None 

TWIGS 

Bias 

Basal area (saw) 

Bias 

+:. 13 SJLVAH ... .:.:. 

1 14 -1724 l4 14 50 I TWIGS 

Tol. 

Int. 

Tol. 

Int. 

TWIGS 

TWIGS 

TWIGS 

15% 

Basal area boles) 

50 26 22 50 

54 23 21 50 

Medial stand diameter 

SJLVAH 

TWIGS 

-31 39 17 -12 26 33 

'-17 32 38 -16 27 25 \\\ 

-2',,.<8 50 -15 27 33 

Short period, PA 12 5 

OAKSIM 

OAKSF 

OAKSIM 

Short period, WV 

Long period, all states 

Long period, PA 

Number of trees 

Short period, all states 

Short period, PA 

Short period, WV 

Long period, all states 

Long period, PA 

....................... 

....................... 

....................... $$:= -10 22 ..... " ...;. :.:.: ............ ................... 1 47 1 OAKSIM 

71 1-11 21 461 OAKSIM 

33 1 -22 27 17 1 OAKSIM 
Percent basal area in vrimatv species group 

Projection period, 

statel 

I 

- 

........... Short period, all states 

Shont period, PA ..... ........... .:.:.:.:.:. 

...... ::s::z 

........ Shofi period, WV .............A + ,.*. ........... ........... 

..... ...... 

Long all stetes I- ......... 2 

Shoa period, all states 

Short period, PA 

Shoa period, WV 

Long period, all states 

Long period, PA 

SILVAH 

Overall 

SILVAH 

SILVAH,OAKSIM 

SILVAH 

SILVAH 

OAKSIM 

15% 

OAKSIM 

Bias 

stands from West Virginia, Massachusetts, and Vermont, 

only West Virginia had adequate representation to allow 

independent performance evaluations. The similarity of the 

results for "all states combined" and West Virginia reflect the 

proportion of West Virginia stands in this category. 

Bias 

Tol. 

Int. 

FlBER projections were closest to the actual observations for 

total basal area and basal area of sawlog-sized trees (Table 

6). SlLVAH was the closest to the actual observations of polesized basal area, while both SILVAH and FlBER were nearly 

equal in estimates of medial stand diameter. FlBER was the 

best at projecting number of trees for West Virginia, although 

there were no clear distinctions for the "all states combined" 

category. SILVAH and NE-TWIGS were the best predictors of 

species composition. In this category, FIBER'S performance 

was adversely affected by model design that did not include a 

category for oak species comprising the primary species 

group category as designated by our tests. For the FlBER 

simulations, we converted all oak species to white ash, which 

was ane of the FlBER species categories. Thus, FlBER 

projections did not include the oak species component and, 

consequently, somewhat biased the results. SILVAH had the 

best results for the "overall" category for "all states 

combined." The "overall" results for West Virginia did not 

exhibit any clearly superior simulators, according to our 

ranking technique. 

NE-TWIGS 

In the oak-hickory forest type, SILVAH, OAKSIM, and NETWIGS had similar results. There were no long observation 

Bias 

To1 . Int. 

mGS 

TWlGS 

SILVAH 

TWIGS 

TWIGS 

Best 

FIBER 

15% 

periods in this forest type. OAKSIM was the best 

predictor of total basal area and also proved to be the 

best "overall" simulator for New York. SILVAH predictions 

resulted in the best "overall" performance for Ohio and 

the "all states combined" categories. The "overall" 

results for Kentucky did not exhibit any clearly superior 

simulators, according to our ranking techniques. OAKSIM 

clearly provided the best predictions of total basal area 

for all locations (Table 7). SILVAH, NE-TWIGS, and 

OAKSIM all provided reasonable estimates of basal area 

of sawlog-sized and pole-sized basal area, medial stand 

diameter, and number of trees. Most notable from these 

results was the absence of FIBER, which overpredicted 

all measures of basal area and medial stand diameter 

and underpredicted number of trees. SILVAH and NETWIGS provided the best estimates of species 

composition. The species composition estimates 

provided by OAKSIM also were adequate in most cases. 

However, as stated earlier, the design of OAKSIM 

requires that species be grouped into four categories. 

Once species of the same category are combined, it is 

not possible with OAKSIM to identify the trees within a 

group as to their individual species designations. This 

design feature reflects the intent of the developers to 

limit the use of OAKSIM to upland oak stands where 

combining similar species did not compromise growth 

and yield projections. Similarly, the inadequate 

estimates of species composition obtained from FIBER 

reflect the use of the simulator beyond its scope of 

applicable forest types. 

Kiss 

To1 . 

Int. 15% 

Tol. 

Int. 15% 
Table 6.-Transition hardwood percent mean bias, percent tolerance interval, and percent of stands within 15 

percent of the actual values by stand parameter, period length, and state 

Proiection oeriod. I SILVAH I OAKSIM I NE-TWIGS I FIBER I Best 

Basal area (total) 

. , 

states 

Short period, all states FIBW 

Short period, WV FIBER 

Basal area (saw) 

Short period, all states -35 13 17 -154 40 -28 33 FIBER 

Short period, WV I -39 11 0 / -53 10 1: / -32 38 FIBER 

Bias 

Basal area (poles) -. 

Short period, all states 2 20 58 -6 28 50 -1 26 SILVAH 

Short period, WV 20 . 50 1 -21 27 50 I I2 16 SILVAH 

Tol. 

Int. Bias 

Medial stand diameter 

15% 

Short period, all states -3 6 4 6 92 SILVAH 

Short period, WV -3 5 4 6 88 FIBER 

To1 . Int. 

Number of trees 

15% Bias 15% Bias 

Short period, all states None 

Short period, WV FIBER 

Tol . 

Int. 

Percent basal area in orimaw soecies rrrouo 

Tol. 

Int. 

Shod period, all states -19 24 42 

15% 

Overall 
Table 7.--Oak-hickory percent mean bias, percent tolerance interval, and percent of stands within 15 percent 

of the actual values by stand parameter, period length, and state 

Basal area (total) 

Projection period, 

ates 

Short period, all states 

Short period, KY 

Short period, OH 

Short period, NY 

Short period, all states 

Short period, KY 

Short period, OH 

Short period, NY 

Shod period, all state 

Short period, KY 

Short period, OH 

Short period, NY 

SILVAH 

Shoa period, all states 

Shod period, KY 

Short period, OH 

Short period, NY 

Short period, all states 

Short period, KY 

Short period, OH 

Short period, NY 

15% 

OAKSIM 

Bias 

Basal area (saw) 

To1 . Int. 

Basal area (poles) 

15% 

NETWIGS 

Bias 

Medial stand diameter 

Bias 

Tol. 

Int. 

Number of trees 

FIBER Best 

OAKSM 

OAKSIM 

OAKSIM 

OAKSIM 

TWIGS 

SILVAH 

TWIGS 

OAKSIM 

SILVAH 

Tol . 

Int. Bias 

20 SILVAH,OAKSIM 

29 None 

50 sILvAH,oAKSIM 

15% 

48 OAKSIM 

20 SILVAH,TWIGS 

71 TWIGS 

43 OAKSIM 

To1 . Int. 

52 OAKSIM 

0 TWIGS 

57 SILVAH 

57 OAKSIM 

15% 

Short period, all states 

Short period, KY 

Short period, OH -25 24 29 

Overall 

0 SILVAH,TWIGS 

0 TWIGS 

0 TWIGS 

0 I TWIGS 

Short period, KY 

Shoa period, OH 
Table 8.-Simulators that will be selected by the automatic selection algorithm used by the Northeast 

Decision Model 

Forest type Model Also suitable Not suitable 

Projection period, state selection 

Spruce-fir 

Short period, all states FIBER None SILVAH,NE-TWIGS ,OAKSIM 

Short period, ME FIBER None SILVAH,NE-TWIGS, OAKSIM 

Northern hardwoods 

Short period, all states FIBER NE-TWIGS, SILVAH OAKSIM 

Short period, ME FIBER NE-TWIGS,SILVAH OAKSIM 

Short period, MA FIBER NE-TWIGS,SILVAH OAKSIM 

Short period, VT SILVAH NE-TWIGS,FIBER OAKSIM 

Short period, PA NE-TWIGS FIBER,SILVAH OAKSIM 

Long period, all states FIBER NE-TWIGS ,SILVAH OAKSIM 

Long period, NH FIBER NE-TWIGS,SILVAH OAKSIM 

Long period, PA NE-TWIGS FIBER,SILVAH OAKSIM 

Allegheny hardwoods 

Short period, all states NE-TWIGS SILVAH OAKSIM,FIBER 

Short period, PA NE-TWIGS SILVAH OAKSIM,FIBER 

Short period, WV SILVAH NE-TWIGS OAKSIM,FIBER 

Long period, all states NE-TWIGS SILVAH OAKSIM ,FIBER 

Long period, PA NE-TWIGS SILVAH OAKSIM,FIBER 

Transition hardwoods 

Short period, all states SILVAH NE-TWIGS OAKSIM,FIBER 

Short period, WV SILVAH1 NE-TWIGS OAKSIM,FIBER 

Oak-hickory 

Short period, all states SILVAH OASKIM,NE-TWIGS FIBER 

Short period, KY SILVAH1 OAKSIM,NE-TWIGS FIBER 

Short period, OH SILVAH OAKSIM,NE-TWIGS FIBER 

Short period, NY OAKSIM SILVAH,NE-TWIGS FIBER 

'Because simulator performance measures yielded inconclusive results, the simulator selection was based on 

the "all states combined" category. 
Recommendations for the Northeast 

Decision Model 

The simulator that ranked the best on the basis of the three 

"overall" evaluation criteria used will be the recommended 

simulator for that forest type and state in the Northeast 

Decision Model. These designations are shown in Table 8. 

Table 8 also lists growth and yield simulators that should not 

be used with particular forest types. Suitability determinations 

were made by a combination of test results and the intended 

use of the simulator by the developers. In no case do we 

recommend that a particular growth and yield simulator be 

used where the developers recommend otherwise. 

FIBER proved to be the most reliable simulator for spruce-fir 

and was the only choice in that forest type. FIBER also 

proved to be the best choice for most locations in the 

northern hardwood forest type, although NE-TWIGS and 

SILVAH also were selected as the best simulators for 

Pennsylvania and Vermont northern hardwoods, respectively. 

NE-TWIGS had the best results for most locations in 

Allegheny hardwoods, although SILVAH was the chosen 

simulator for Allegheny hardwoods in West Virginia. In 

transition hardwoods, SILVAH was selected as the best 

simulator for the "all states combined" category. Based on 

the results from the "all states combined" category, SILVAH 

also was selected as the most appropriate simulator for West 

Virginia transition hardwoods in which conclusive results 

from our simulator performance measures were not obtained. 

In the oak-hickory forest type, OAKSIM was selected as the 

best simulator for New York, while SILVAH was selected for 

Ohio and "all states combined." Again, based on the results 

from "all states combined," SILVAH also was selected as the 

most appropriate simulator for Kentucky oak-hickory forests 

in wlhich conclusive results were not obtained. 

The Northeast Decision Model will include all four simulators 

tested here, and users will be free to select any of these four, 

and possibly others, to use in any forest type or geographic 

area. If users select a simulator that has given poor results in 

these tests for a particular type or region, a warning will be 

issued, but the user will be free to employ the simulator of 

their choice. 

In addition, the Northeast Decision Model will include an 

automatic simulator selection algorithm, based on forest type 

and ecological land type or geographic region. When the user 

chooses automatic simulator selection, the decision model 

will select the growth and yield simulator that performed best 

in these tests. 

Conclusions 

As we evaluated the results in a continuum of forest types 

from north to south, certain general trends were observed. 

Most notable was the performance of FIBER. FIBER 

projections clearly provided the best estimates for the 

spruce-fir stands of Maine and Vermont; provided the best 

estimates for most, but not all, applications in the northern 

hardwood stands found throughout the New England states 

to Pennsylvania; and was replaced by other simulators as 

the most suitable in the Allegheny hardwood stands of 

Pennsylvania and West Virginia and the oak-hickory stands 

of Ohio and Kentucky. 

All models generally performed better in the geographic 

region and forest type for which they were developed. This 

validates regional growth and yield modeling efforts that 

have been undertaken. However, several simulators 

performed reasonably well in types and geographic areas in 

which they were never intended to be used. While it seems 

appropriate to recommend the simulator designed for a 

particular forest type and geographic area over another that 

works as well there but was not built with data from that 

situation, it also seems reasonable to extend the geographic 

range of the better performing simulators when there is not 

a simulator available designed for that locale. 

Except in the spruce-fir forest type, no individual simulator 

stands out as clearly superior to the others for all locations. 

There are always at least two, and often more that logically 

could be used. 

All simulators that proved suitable for use in particular forest 

types and geographic regions seem to do a good job, on the 

average. That is, when run on numerous stands of a 

particular type, they provide reasonable estimates of stand 

parameters. But all the simulators can produce very large 

errors on individual stands. Errors of the magnitude of 30 to 

50 percent occurred even on 20-year projections. This 

primarily was a consequence of some large individual trees 

within a small sampling unit dying during the projection 

period. Thus, these simulators probably are best used for 

development of yield tables and for general management 

and planning purposes where they can be run on multiple 

stands and the averages are used to characterize that class 

of stand. Caution should be exercised when using these 

simulators to determine the future value or appropriate 

management for an individual stand. 

Acknowledgments 

We thank Northeastern ForesrExperiment Station 

employees, Donald Seegrist (retired) for the development of 

the bias and tolerance interval analyses, and Don Hilt 

(deceased), Paul Sendak, H. Clay Smith, Dale Solomon, 

Susan Stout, and Mark Twery, for providing long-term stand 

development data. 
Literature Cited 

Brand, Gary J.; Holdaway, Margaret R. 1983. Users need 

performance information to evaluate models. Journal 

of Forestry. 81 (4): 235-237. 

Buchman, Roland G.; Shifley, Stephen R. 1983. Guide to 

evaluating forest growth projection systems. Journal 

of Forestry. 81 (4): 232-234. 

Ek, Alan R.; Monserud, Robert A. 1979. Performance and 

comparison of stand growth models based on 

individual tree and diameter-class growth. Canadian 

Journal of Forest Research. 9: 231-244. 

Goulding, C.J. 1979. Validation of growth models used in 

forest management. New Zealand Journal of Forestry. 

24(1): 108-124. 

Hilt, Donald E. 1985a. OAKSIM: An individual-tree growth 

and yield simulator for managed, even-aged, upland 

oak stands. Res. Pap. NE-562. Radnor, PA: U.S. 

Department of Agriculture, Forest Service, Northeastern 

Forest Experiment Station. 21 p. 

Hilt, Donald E. 1985b. User's guide to OAKSIM: An 

individual-tree growth and yield simulator for 

managed, even-aged, upland oak stands. Gen. Tech. 

Rep. NE-104. Radnor, PA: US. Department of 

Agriculture, Forest Service, Northeastern Forest 

Experiment Station. 22 p. 

Hilt, Donald E.; Teck, Richard M. 1989. NE-TWIGS: An 

individual-tree growth and yield projection system for 

the northeastern United States. The Compiler 7(2): 

10-1 6. 

Marquis, David A. 1986. SILVAH: A stand analysis, 

prescription, and management simulator program for 

hardwood stands on the Alleghenies. In: Wiant, Harry 

V., Jr.; Yandle, David 0.; Kidd, William E., Jr., eds. 

Forestry Microcomputer Software Symposium: 1986 

June 30July 2; Morgantown, WV. Morgantown, WV: 

West Virginia University: 224-240. 

Marquis, David A. 1991. The Northeast Forest Experiment 

Station Expert Decision Model. Forest Perspectives. 

l(2): 16. 

Marquis, David A.; Ernst, Richard L. 1992. User's guide to 

SILVAH: stand analysis, prescription, and 

management simulator program for hardwood stands 

of the Alleghenies. Gen. Tech. Rep. NE-162. Radnor, 

PA: US. Department of Agriculture, Forest Service, 

Northeastern Forest Experiment Station. 124 p. 

Marquis, David A.; Ernst, Richard L.; Stout, Susan L. 1992. 

Prescribing silvicultural treatments for hardwood 

stands of the Alleghenies. -Gen. Tech. Rep. NE-96 

(Revised). Broomall, PA: US. Department of Agriculture, 

Forest Service, Northeastern Forest Experiment Station. 

102 p. 

Reynolds, Marion R.; Burkhard, Harold E.; Daniels, Richard 

F. 1981. Procedures for statistical validation of 

stochastic simulation models. Forest Science. 27: 

349-364. 

Solomon, Dale S.; Hosmer, Richard A.; Hayslett, Homer T., 

Jr. 1987. FIBER Handbook: A growth model for 

spruce-fir and northern hardwood forest types. Res. 

Pap. NE-602. Radnor, PA: U.S. Department of 

Agriculture, Forest Service, Northeastern Forest 

Experiment Station. 19 p. 

Teck, Richard M. 1990. NE TWIGS 3.0: An individual-tree 

growth and yield projection system for the 

Northeastern United States. The Compiler. 8(1): 25-27. 

Zuuring, Hans R.; Arney, James D.; Milner, Kelsey S. 1988. 

Generic graphical analyses for tree growth model 

invalidation. In: Ek, Alan R.; Shifley, Stephen R.; Burk, 

Thomas E., eds. Forest growth-modelling and prediction, 

Vol. 2; Proceedings of a symposium; 1987 August 23-27; 

Minneapolis, MN. Gen. Tech. Rep. NC-120. St. Paul, 

MN: U.S. Department of Agriculture, Forest Service, 

North Central Forest Experiment Station: 828-834. 
Schuler, Thomas M.; Marquis, David A.; Ernst, Richard L.; Simpson, Brian 

T. 1993. Test of four stand growth simulators for the northeastern 

United States. Res. Pap. NE-676. Radnor, PA: U.S. Department of 

Agriculture, Forest Service, Northeastern Forest Experiment Station. 14 p. 

Evaluates SILVAH, FIBER, NE-TWIGS, and OAKSIM, simulators commonly used in the 

northeastern United States, by comparing predicted stand development with actual stand 

development records for periods ranging from 15 to 50 years. Results varied with stand 

parameter, forest type, projection length, and geographic area. Except in the spruce-fir forest 

type where FIBER stands out as the best simulator, no single simulator is clearly superior to 

the others for all locations within a forest type. In general, FIBER, NE-TWIGS, and SILVAH 

performed best in the northern hardwood (beech-birch-maple) forest type; NE-TWIGS and 

SILVAH performed best in the Allegheny hardwood (cherry-maple) forest type; SILVAH and 

OAKSIM performed best in the oak-hickory forest type; and SILVAH was most suitable in the 

transition hardwood (mixture of northern hardwoods and oaks) forest type. Results give 

growth and yield model users more information for selecting the simulator most suitable for 

their particular needs and the results also can be used as a diagnostic tool for growth and 

yield model development. 

ODC: 564 

Keywords: Growth and yield, SILVAH, FIBER, NE-TWIGS, OAKSIM, 

predicted stand development 

aU. S. GOVERNMENT PRINTING OFFICE: 1993/750-123160025 
Headquarters of the Northeastern Forest Experiment Station is in Radnor, Pennsylvania. Field laboratories are maintained at: 

Amherst, Massachusetts, in cooperation with the University of Massachusetts 

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Delaware, Ohio 

Durham, New Hampshire, In cooperation with the University of New Hampshire 

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Morgantown, West Virginia, in cooperation with West Virginia University 

Orono, Maine, in cooperation with the University of Maine 

Parsons, West Virginia 

Princeton, West Virginia 

Syracuse, New York, in cooperation with the State University of New York, 

College of Environmental Sciences and Forestry at Syracuse University 

University Park, Pennsylvania, in cooperation with The Pennsylvania State Universlty 

Warren, Pennsylvania 

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