United States 

Department of 

Agriculture Individual-Tree Probability 

Forest Service of Survival Model for the 

Northeastern Forest 

Experiment Station Northeastern United States 

Research Paper NE-642 

Richard M. Teck 

Donald E. Hilt 
Abstract 

Describes a distance-independent individual-tree probability of survival model for 

the Northeastern United States. Survival is predicted using a six-parameter 

logistic function with species-specific coefficients. Coefficients are presented for 

28 species groups. The model accounts for variability in annual survival due to 

species, tree size, site quality, and the tree's competitive position within the stand. 

Model performance is evaluated using the chi-square goodness-of-fit test. Resuits 

are presented for the calibration data and an independent validation data set. The 

model has been incorporated into NE-TWIGS. 

The Authors 

RICHARD M. TECK and DONALD E. HILT are, respectively, forester and research 

forester with the USDA Forest Service, Northeastern Forest Experiment Station, 

Delaware, Ohio. 

Manuscript received for publication 20 February 1990 

Northeastern Forest Experiment Station 

100 Matsonford Road, Radnor, PA 19087 

August 1990 
Introduction 

More than 30 forest cover types occupy the Northeastern 

United States. Mixed-species stands dominate the region 

and a multitude of past cutting practices present resource 

managers with complex management decisions. Accurate 

and reliable growth and yield information improves our 

management capabilities. An individual-tree modeling 

approach is desirable when predicting growth under these 

diverse conditions because it provides the necessary 

information for tracking species, tree size, and tree quality, 

the three essential components for economic analyses in 

northeastern forest stands. 

Models that predict the probability of survival of individual 

trees are an essential component of forest-growth 

prediction. Linking survival models with individual-tree 

diameter growth, height growth, and ingrowth allows us to 

predict forest stand development over time. 

According to Waring (1987), "Trees die when they cannot 

acquire or mobilize sufficient resources to heal injuries or 

otherwise sustain life." The interaction of factors influencing 

individual-tree survival remains one of the least-understood 

elements of forest growth and yield estimation. Of the 

thousands of seedlings produced by a typical mature tree, 

only a few survive to full maturity. Most die as a direct or 

indirect consequence of failing to compete successfully for 

light, water, or soil nutrients (Peet and Christensen 1987). 

This type of mortality, commonly referred to as self-thinning 

(Lee 1971), can occur at any stage of stand development 

and is discussed in detail by Kramer and Kozlowski (1979). 

By contrast, catastrophic mortality is caused by major fires, 

windstorms, epidemic insect attacks, and other external 

agents. It is irregular in occurrence and more difficult to 

predict (Lee 1971). In this paper we address only the 

mortality caused by self-thinning, and endemic external 

agents such as insects and disease. 

Here, we discuss the development and performance of an 

individual-tree survival model for the Northeastern United 

States. The model has been incorporated into the ND 

TWIGS forest-growth projection system (Hilt and Teck 1989; 

Teck 1990), and is similar in form to those used to predict 

individual-tree survival in the Lake States (Buchman et al. 

1983) and the Central States (Miner et al. 1988). 

Coefficients are presented for 28 species groups. The 

model was developed with USDA Forest Service Forest 

Inventory and Analysis (FIA) data from the following 

Northeastern States: Connecticut, Delaware, Kentucky, 

Maine, Maryland, Massachusetts, New Hampshire, New 

Jersey, New York, Ohio, Pennsylvania, Rhode Island, 

Vermont, and West Virginia. 

Individual-tree measurements collected by the Northeastern 

Forest Experiment Station's FIA unit were used in 

developing the model. More than 4,400 115-acre permanent 

plots measured throughout the 14 Northeastern States were 

used in this study. Data were collected in the 1960ts, 19701s, 

and 1980's. Only one remeasurement period was available 

for each state except for Maine, which was remeasured 

twice. The remeasurement period averaged 12 years. 

The data covered a wide range of age, site, and stocking 

conditions. Basal area ranged from 30 to 255 ft2/acre (Table 

1). Site index (base age 50 years), recorded on each 

115-acre plot for the dominant species, ranged from 30 to 

90. Site-index conversion equations were used to assign the 

appropriate site index to each tree depending on its 

species. Quadratic mean stand diameter ranged from 5 to 

13 inches, indicating a wide range in the age of the stands 

that were sampled. 

Information recorded for each tree (more than 5 inches in 

d.b.h) included species, initial d.b.h (DBH), and a status 

code indicating yhether the tree was alive or dead. 

Plots that were cut heavily between remeasurements 

(residual basal area less than 30 percent of initial 

conditions) were excluded. Plots that showed levels of 

excessive mortality (more than 70 percent of initial basal 

area) also were eliminated to exclude episodes of 

catastrophic mortality from the data. 

Every fourth plot was removed from the data set and 

reserved for validating the model. The final calibration data 

set, containing 59,465 trees, was divided into 28 species 

groups for analysis (Table 1). The validation data set 

contained 19,058 observations (Table 2). 

Methods 

Each of the 28 species groups was analyzed seperatdly. 

Our goal was to develop a single model form, and compute 

species-specific coefficients for that model. A single model 

form for all species is desirable because it simplifies model 

recalibration and localization of parameters. 

Individual-tree survivallmortality models have been 

developed for various forest types and geographic regions. 

Many of these models predict survival as a function of tree 

size, and a combination of tree- and other stand-level 

variables such as tree vigor, basal area per acre, crown 

ratio, and site quality. It is desirable to model the annual 

survival rate with a function that can be defined between 0 

and 1 (Buchman and Shifley 1983) since survival probability 

lies within this range (Hamilton 1986). 

The sigmoid shape of the logistic function lends itself to 

modeling survival. Since Lee (1971) developed his linear 

survival model for lodgepole pine, most individual-tree survival1 

mortality models (Table 3) have been based on the logistic 

function, a nonlinear function bounded between 0 and 1, or 

some generalized form of the logistic function (Hamilton 1986). 

Although other mathematical functions have been used to 

model survival (Hett 1971; Moser 1972), Rennolls and Peace 

(1986) speculate that the flexibility of the logistic function is 

one of the primary reasons for its widespread use. 
The general form of the logistic function is: 

where 

p(S) = probability of survival, OsS< 1, 

bo = "intercept", 

b1 . . . bn =set of n regression coefficients, 

XI . . . Xn =set of n predictor variables, and 

exp = base of the natural logarithm. 

Many potential predictor variables were evaluated for 

inclusion in the model. Plot variables included site index 

(SI), basal area per acre, trees per acre, and quadratic 

mean stand diameter. Individual-tree variables included 

basal area and d.b.h. 

Since a tree's survival is influenced by its competitive 

position within the stand, we analyzed three distanceindependent competition indices: (1) the ratio of d.b.h to 

quadratic mean stand diameter, (2) ratio of tree basal area 

to plot basal area, and (3) basal area larger than or equal to 

the subject tree (BAL). The latter had the highest correlation 

with survival. 

We then compared the predictive capabilities of several 

models in Table 3 with a sample from our data. Not all of 

the models were evaluated since several contained 

predictor variables not available in our data. Results from 

this comparison showed that the Central States model 

(North Cent. For. Exp. Stn. 1983) was better at predicting 

mortality trends in the sample data set than the other 

models. 

The data for each species group were than seperated into 

DBH x BAL x SI cells. The upper and lower boundaries of 

each cell were selected so that there were approximately 

equal numbers of trees within each cell. The mean value for 

each of the three predictor variables and the annual survival 

rate within each cell were used in a preliminary analysis to 

select the model form. 

Since a cell can contain trees with different remeasurement 

intervals, the annual survival rate for each cell was 

computed as (Buchman et al. 1983): 

Table 1 .-Individual-tree and plot characteristics of the calibration data set 

Species No. of Site indexa plot TPA~ Plot basal area DBH BALc No. of trees 

group plots Min. Avg. Max. Min. Avg- Max. Min. Avg. Max. Min. Avg. Max. Min. Avg. Max. Total Dead 

1 American beech 

2 Balsam fir 

3 Black cherry 

4 'Blackoak 

5 Chestnut oak 

6 Eastern hemlock 

7 Hickory 

8 Loblolly pine 

9 Noncommercial 

10 N. red oak 

11 N. white-cedar 

12 Other hardwoods 

13 Other pines 

14 Paper birch 

15 Quaking aspen 

16 Red maple 

17 Red pine 

18 Red spruce 

19 Scarlet oak 

20 Sugar maple 

21 Tamarack 

22 Virginia pine 

23 White ash 

24 White oak 

25 White pine 

26 White spruce 

27 Yellow birch 

28 Yellow-poplar 

Total 

Ft '/acre Inches 

aTotal height (in feet) at age 50. 

bNumber of trees per acre. 

CBasal area of trees larger than or equal to subject tree. 
Table 2.-Individual-tree and plot characteristics of the validation data set 

Species No. of Site indexa plot TPA~ Plot basal area DBH BALC No. of trees 

!PUP plots Min. Avg. Max. Min. Avg. Max. Min. Avg. Max. Min. Avg. Max. Min. Avg. Max. Total Dead 

1 American beech 

2 Balsam fir 

3 Black cherry 

4 Black oak 

5 Chestnut oak 

6 Eastern hemlock 

7 Hickory 

8 Loblolly pine 

9 Noncommercial 

10 N. red oak 

11 N. white cedar 

12 Other hardwoods 

13 Other pines 

14 Paper birch 

15 Quaking aspen 

16 Red maple 

17 Red pine 

18 Red spruce 

19 Scarlet oak 

20 Sugar maple 

21 Tamarack 

22 Virginia pine 

23 White ash 

24 White oak 

25 White pine 

26 White spruce 

27 Yellow birch 

28 Yellow-poplar 

Total 

Inches 

5.0 28.8 9.5 

5.0 17.6 7.2 

5.0 23.7 8.7 

5.0 41.0 10.9 

5.0 26.5 8.9 

5.0 27.1 8.9 

5.0 28.1 8.1 

5.0 20.2 9.4 

5.0 13.2 6.3 

5.0 29.6 9.7 

5.0 23.2 8.8 

5.0 25.8 8.8 

5.0 17.6 8.7 

5.0 20.7 8.0 

5.0 18.7 7.8 

5.0 31.6 8.3 

5.1 17.8 9.1 

5.0 21.5 7.9 

5.0 31.3 10.0 

5.0 37.2 9.6 

5.2 13.1 8.1 

5.0 16.4 9.0 

5.0 26.7 8.7 

5.0 32.4 9.3 

5.0 39.1 10.0 

5.0 16.7 7.9 

5.0 29.7 9.6 

5.0 28.1 10.2 

aTotal height (in feet) at age 50. 

bNumber of trees per acre. 

CBasal area of trees larger than or equal to subject tree. 

Table 3.-Previously developed suwivallmortality models based on the logistic function 

Mathematical function Citation Region 

1. M =[1 + EXP-(bl + b2-PD + b30PDGR + b4*Cl)]-' Monserud (1 976) Wisconsin 

2. M = [I + EXP-(bl + b2.RBA + b3-DBH + b4-TPA)]-I Krumland et al. (1978) Northern California 

3. M =[I + EXP-(b1 + b2*DGRb3)]-' + b4 Buchman (1 979) Lake States 

4. M =[I + EXP-(b1 + b2-DBH + b3*DBH2)]-I Wykoff et al. (1 982) Northern Rocky Mtns. 

5. S = bl-[1 + EXP-(b2 + b3-DGRb4 + b5*(DBH-1)b6(b7-(DB~-1)))]-1 Buchman et al. (1983) Lake States 

6. M =[I+ EXP-(bl + b2-DBH + b3oDGR5)J-I Hilt (1 985) Central hardwoods 

7. M =[I + EXP-(b1 + b2-DBH0.5 + b3*BA0,5 + W-DGR + b5DBH-I Hamilton (1 986) Northern Idaho 

+ b6-XI + b70X2 + b8*X3 + b9-RDBH + b10-DDBH)]-I 

8. S = 1-[I + EXP(b1 + b2-(DBH + l)b3-(b4-D~~ + b5eBAL)I-I North Cent. For. Exp. Central States 

Stn. (1 983) 

9. M = [I + EXP-(b1 + b2*TAB1+ b3eTPA + b4-PLC + Bolton and Meldahl Georgia 

b5eDBH + b6-SI)]-I (unpublished) 

S = predicted survival rate; M = predicted mortality rate; PD =predicted diameter; PDGR =predicted diameter growth rate; 

CI =competition index; RBA = relative basal area; TPA = trees per acre; DGR =diameter growth rate; DBH =diameter at breast 

height; DGR5 = 5 year periodic annual diameter growth; BA = stand basal area; SI =site index; XI,. . . ,X3 = species-specific 

constants; QMD = mean stand diameter; RDBH = DBHIQMD; DDBH = DGRIDBH; BAL = basal area per acre larger than or 

equal to subject tree; TAB1 = number of trees larger than subject tree; PLC = predicted live crown ratio; bl , . . . ,blO =speciesspecific coefficients. 
where S is the annual survival rate, Ni is the total number of 

trees in the ith group at the first measurement; Xi is the number 

of remeasured live trees in the ith group; and Ti is the number 

of years between remeasurements in the ith group. 

The final model chosen was: 

Equation (3) was fit to the cell data for each species group 

using weighted nonlinear least squares regression (SAS 

NLlN procedure). Each cell was weighted by the number of 

observations within the cell. The survival model uses the 

same form of the logistic function as the model developed 

by Shifley for the Central States (North Cent. For. Exp. Stn. 

1983). However, our model also includes site index as a 

predictor variable. 

Results 

where 

S =the tree's annual probability of survival 

DBH =diameter at breast height (inches), 

BAL = basal area per acre, in square feet, greater than or 

equal to the basal area of the subject tree, 

SI = species-specific site index (base age = 50), 

exp = base of the natural logarithm, and 

bl-be =parameters to be estimated. 

Species-specific coefficients for the 28 species groups are 

listed in Table 4. The survival rates predicted by this model 

form show the following trends: (1) survival rates decrease 

with increasing competition (BAL) for a given DBH and SI 

(Fig. 1); (2) survival rates increase with increasing DBH for a 

given value of BAL, reach a maximum, and then begin to 

decrease as DBH continues to increase (Fig. 2); the 

diameter at which survival peaks is species-specific and is 

dependent upon the interaction of 82, 63, and 64; (3) 

survival rates decrease with increasing SI for a given DBH 

and BAL (Fig. 3); (4) survival rates for a given DBH and SI 

Table 4.-Individual-tree probability of survival model coefficients 

Species group B 1 82 83 

1 American beech 

2 Balsam fir 

3 Black cherry 

4 Black oak 

5 Chestnut oak 

6 Eastern hemlock 

7 Hickory 

8 Loblolly pine 

9 Non commercial 

10 N. red oak 

11 N. white-cedar 

12 Other hardwoods 

13 Other pines 

14 Paper birch 

15 Quaking aspen 

16 Red maple 

17 Red pine 

18 Red spruce 

19 Scarlet oak 

20 Sugar maple 

21 Tamarack 

22 Virginia pine 

23 White ash 

24 White oak 

25 White pine 

26 White spruce 

27 Yellow birch 

28 Yellow-poplar 
BAL (ft*/act-e) 

Figure 1 .-Estimated annual survival rates for red spruce (SI = 50, d.b.h. = 10 inches), 

demonstrating effect of BAL on survival rate. 

10 20 

DBH (inches) 

Figure 2.-Estimated annual survival rates for red spruce (SI = 50), demonstrating effect 

of DBH on survival rate. 
BAL (ftzlacre) 

Figure 3.-Estimated annual survival rates for red spruce (d.b.h. = 10 inches), 

demonstrating effect of SI on survival rate. 

approach a minimum as inter-tree competition (BAL) 

increases. 

Observed and estimated number of survivor trees by 

species group and their associated annual survival rates are 

given in Table 5 for the calibration data set. The predicted 

survival rate for 19 of the 28 species groups was within 1 

percent of the observed survival rate. The absolute 

difference between the predicted annual survival rate for the 

other five species groups was between 1 and 2.2 percent of 

the observed annual survival rate. For example, the 

observed annual survival rate for paper birch is 99.19 

percent, and the predicted annual survival rate is 99.01 

percent. Therefore, the absolute difference between the 

observed and predicted annual survival rate is 0.18 percent. 

The model underpredicted survival for 7 species groups and 

overpredicted survival for 21 groups. 

A chi-square goodness-of-fit statistic was used to test the 

model. The X2 statistic is: 

where 

X2 = chi-square statistic, 

Oi =observed number of survivor trees in the ith cell, 

Ei = expected number of survivor trees in the ith cell, and 

n =number of cells. 

With the exception of balsam fir, the X2 test showed no 

significant difference between the observed and predicted 

survival distributions. 

Validation 

Observed and estimated number of survivor trees by 

species group and their associated annual survival rates for 

the validation data set are shown in Table 6. The predicted 

survival rate was within 1 percent of the observed survival 

rate for 23 of the 28 species groups. The absolute 

difference between the predicted and observed survival 

rates for the other five species groups was less than 2.6 

percent. The model underpredicted annual survival for 9 

species groups and overpredicted annual survival for 18 

groups. 

Chi-square goodness-of-fit statistics by species group for 

the validation data are shown in Table 6. There were no 

significant differences between predicted and observed 

survival rates for 27 of the 28 species groups. The 

exception was balsam fir. 
Table 5.-Performance of probability of survival model for calibration data set, by species group 

Survival 

Observed Estimated Degrees Critical 

Species No. of Mean No. of Annual No. of Annual Absolute of Chi- value 

group trees interval trees rate trees rate differencea freedomb squareC (a = .05) Significanced 

American beech 

Balsam fir 

Black cherry 

Black oak 

Chestnut oak 

Eastern hemlock 

Hickory 

Loblolly pine 

Non commercial 

N. red oak 

N. white cedar 

Other hardwoods 

Other pines 

Paper birch 

Quaking aspen 

Red maple 

Red pine 

Red spruce 

Scarlet oak 

Sugar maple 

Tamarack 

Virginia pine 

White ash 

White oak 

White pine 

White spruce 

Yellow birch 

Yellow-poplar 

Years 

11.6 

11.4 

12.0 

12.0 

12.1 

11.9 

11.8 

10.4 

11.6 

12.1 

11.3 

11.7 

13.0 

11.1 

11.6 

11.8 

11.7 

11.4 

12.0 

11.8 

11 .o 

10.7 

11.9 

12.0 

12.0 

11.3 

11.6 

11.0 

aEstimated survival rate minus the observed survival rate. I 

bn-p-l where n = number of cells and p = number of model parameters (6). 

CSum of chi-square statistics for all cells within a species group based on number of observed and predicted survival trees. 

d* * = Significant at a = 0.5; NS = not significant at a = 0.5. 

Discussion 

The model described predicts the annual survival rates for 

28 major species groups indigenous to the Northeast. The 

model accounts for variability in annual survival due to 

species, tree size, site quality, and the tree's competitive 

position within the stand. 

Since the data contained only trees greater than or equal to 

5 inches d.b.h., predicted survival rates for smaller trees are 

extrapolations contingent upon model form and the 

parameters associated with larger trees. This does not 

preclude the use of this model for predicting survival rates 

of trees in the smaller d.b.h classes. However, until data are 

available for validating the survival rates for these smaller 

classes, we recommend that users be cautious when 

applying the model to very young stands. 

Efforts were taken to eliminate plots that experienced 

episodes of catastrophic mortality during the 

remeasurement period. If the survival model is applied to 

forest stands in which some catastrophic mortality has 

occurred, the model may have a tendency to overpredict 

survival. We recommend that users take this into 

consideration. 

Users also should recognize that a very small change in 

predicted survival rates can result in a substantial difference 

in the number of trees predicted to survive for a given 

length of time. For example, suppose we are interested in 

predicting how many American beech and black cherry 

trees will survive in a 30-year projection. Using the 

compound survival formula (equation 6) and assuming an 

initial starting condition of 1,000 trees per acre for each 

species, and an overall mean annual survival rate for each 

species of 98.70 and 99.43 percent, respectively (Table 5), 

we would predict that 675 American beech and 842 black 

cherry trees per acre will be.alive after 30 years: 
where NS, is the predicted number of survivors after n 

years; SR is the annual survival rate; Ni is the initial number 

of trees; and n is the number of years in the projection. 

In this example, 84.2 percent of the black cherry survived 

compared with 67.5 percent of the American beech, a 

difference of 16.7 percent. The small difference of 0.73 

percent in the predicted annual survival rate between the 

two species resulted in a 16.7 percent difference in the 

predicted number of survivors. This difference will increase 

as the length of the projection increases due to the 

compound nature of survival prediction. 

The survival model has several desirable properties. 

Survival is dependent on a tree's competitive position within 

a stand. As BAL increases, the ability of that tree to 

compete successfully for moisture, sunlight, and nutrients 

decreases, resulting in a decreased likelihood of survival. 

Suppressed trees (small d.b.h.) in crowded young stands 

and overmature trees have higher probabilities of mortality. 

Finally, survival rates decrease with increasing site quality 

since more productive sites usually have higher stand 

volumes but fewer trees per acre than less productive sites. 

A three-dimensional response surface of the survival model 

for red spruce and three major species of the northern 

hardwood forest type is shown in Figure 4. We are satisified 

that the predicted survival rates exhibit biologically 

reasonable trends when the models are extrapolated for use 

under conditions not encountered in the data. 

This survival model has been incorporated into NE-TWIGS, 

an individual-tree growth and yield projection system for 

mixed-species forests of the Northeastern United States 

(Hilt and Teck 1989; Teck 1990). 

Table 6.-Performance of probability of survival model for validation data set, by species group 

Survival 

Observed Estimated Degrees Critical 

Species No. of Mean No. of Annual No. of Annual Absolute of Chi- value 

group trees interval trees rate trees rate differencea freedomb squareC (a = .05) Significanced 

American beech 

Balsam fir 

Black cherry 

Black oak 

Chestnut oak 

Eastern hemlock 

Hickory 

Loblolly pine 

Non commercial 

N. red oak 

N. white cedar 

Other hardwoods 

Other pines 

Paper birch 

Quaking aspen 

Red maple 

Red pine 

Red spruce 

Scarlet oak 

Sugar maple 

Tamarack 

Virginia pine 

White ash 

White oak 

White pine 

White spruce 

Yellow birch 

Yellow-poplar 

Years 

11.7 

11.3 

11.7 

12.1 

11.4 

11.6 

11.8 

10.7 

11.5 

12.1 

11.4 

11.9 

12.8 

11.0 

11.9 

11.7 

11.7 

11.3 

11.5 

11.8 

11.1 

11.2 

12.2 

12.3 

12.0 

11.5 

11.7 

11.0 

aEstimated survival rate minus the observed survival rate. 

bn-p-l where n =number of cells and p = number of model parameters (6). 

CSum of chi-square statistics for all cells within a species group based on number of observed and predicted survival trees. 

d* * = Significant at a = 0.5; NS = not significant at a = 0.5. 
Yellow Birch (Site lndex = 56) 

Sugar Maple (Site lndex = 56) American Beech (Site lndex = 56) 

Figure 4.-Three-dimensional response surface of survival model for red spruce and 

three major species of northern hardwood forest type on average site. 

Red Spruce (Site lndex = 50) 
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47-58. 

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growth and yield projection system for the 

Northeastern United States (Version 3.0 overview). 

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Teck, Richard M.; Hilt, Donald E. 1990. Individual-tree probability of 

survival model for the Northeastern United States. Res. Rep. 

NE-642. Radnor, PA: U.S. Department of Agriculture, Forest Service, 

Northeastern Forest Experiment Station. 10 p. 

Describes a distance-independent individual-free probability of survival 

model for the Northeastern United States. Survival is predicted using a sixparameter logistic function with species-specific coefficients. Coefficients 

are presented for 28 species groups. The model accounts for variability in 

annual survival due to species, tree size, site quality, and the tree's 

competitive position within the stand. Model performance is evaluated using 

the chi-square goodness-of-fit test. Results are presented for the calibration 

data and an independent validation set. The model has been incorporated 

into NE-TWIGS. , 

ODC: 56!.2:242(74) 

Keywords: Logistic function; mortality; survival model; survival prediction 

*U. S. GOVERNMENT PRINTING OFFICE: 1990/748-070100036 
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 

Berea, Kentucky, in cooperation with Berea College 

Burlington, Vermont, in cooperation with the University of Vermont 

Delaware, Ohio 

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

Hamden, Connecticut, in cooperation with Yale University 

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 University 

Warren, Pennsylvania 

Persons of any race, color, national origin, sex, age, religion, or with any handicapping condition are welcome to use and enjoy all facilities, programs, and services of 

the USDA. Discrimination in any form is strictly against agency policy, and should be 

reported to the Secretary of Agriculture, Washington, DC 20250.