United States

Department of

Agriculture

Forest Service

Northeastern

Research Station

General Technical

Report NE-323

Description of the Fork Mountain

Long-Term Soil Productivity Study:

Site Characterization

Mary Beth Adams

James Burger

Lucian Zelazny

John Baumgras
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Abstract

The effects of air pollution and timber harvesting on soil resources continue to be an

important issue in eastern hardwood forests. This publication describes the Fork Mountain

Long-term Soil Productivity Study (LTSP), located on the Fernow Experimental Forest, WV,

and the pretreatment stand, soil and climatic conditions. Extensive vegetation surveys,

biomass determinations, site characterization, and analyses of soil physical and chemical

characteristics are described herein. The Fork Mountain LTSP site is, based on most metrics,

a highly productive site with vegetative diversity typical of most second growth Appalachian

hardwood forests. Other than relatively low soil nutrient levels, site characteristics suggest

few problems with regeneration. Based on soil characteristics, the site may be susceptible to

leaching of base cations as a result of high levels of acidic deposition. Productivity and

nutrient characteristics, particularly calcium, varied across the site spatially, but are

accommodated in the experimental design. We continue to monitor the response of this

ecosystem to these treatments.

The Authors

MARY BETH ADAMS, Project Leader and soil scientist with the USDA Forest Service,

Northeastern Research Station, Parsons, WV.

JAMES BURGER, Professor of forestry and soil science, Virginia Tech University, School

of Forest Resources, Blacksburg, VA.

LUCIAN ZELAZNY, Thomas B. Hutcheson Professor of soil science, Virginia Tech

University, Department of Crop and Environmental Science, Blacksburg, VA.

JOHN BAUMGRAS, forest products technologist (retired), Northeastern Research Station,

Morgantown, WV.

Manuscript received for publication 12 April 2004

The 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.
1

Introduction

The forests of the central Appalachian region provide

recreational opportunities, valuable timber commodities,

wildlife habitat, and important ecosystem services. These

forests also are threatened by invasive plants, insects and

diseases, urban encroachment and development, strip

mining, wildfire, air pollution, and unregulated

recreational use. Forest decline has not yet been

documented in the central Appalachian region, but the

sustainable productivity, biodiversity, and health of

central Appalachian forest ecosystems are of increasing

concern. One hypothesized agent of decline that has

received significant attention of late is base cation

depletion of poorly buffered soils via forest harvesting

and atmospheric inputs of nitrogen (N) and sulfur (S)

(Adams 1999). Declines in soil base levels, particularly

calcium (Ca) and magnesium (Mg), with intensive

harvesting of forest products, have been documented in a

few instances in the United States (Fuller et al. 1987).

Because timber harvesting is expected to increase

substantially in the eastern United States (National

Research Council 1998), and because the shift in forest

utilization is toward more intensive fiber production and

removal of more organic matter, nutrient removal in

biomass could contribute significantly to base cation loss

from some forested sites. Possible base cation depletion

resulting from timber harvesting on public lands

managed by the USDA Forest Service has already been

questioned in timber sale appeals and identified as an

important issue in National Forest Management Plans.

The central Appalachian region receives some of the

largest inputs of acidic deposition in the United States

(Adams et al. 1994). Although S deposition has decreased

in some areas of the eastern United States, nitrogen

emissions are predicted to increase (Galloway et al.

1995). Nitrogen in excess of what the biota can

assimilate leads to N saturation. Symptoms of N

saturation have been documented for many ecosystems

(Fenn et al. 1998), and research from the Fernow

Experimental Forest (Adams et al. 1997, Peterjohn et al.

1996, Gilliam et al. 1996) suggests that some central

Appalachian forested sites have reached or are nearing N

saturation. One symptom of N saturation is accelerated

leaching of base cations from the soil; this raises concerns

about long-term soil nutrient levels. Sensitive soils are

those with a low cation exchange capacity, intermediate

base saturation, low pH, low amounts of bases released

via weathering, low sulfate absorption capacities, and

shallow depth (Adams et al. 2000). A large percentage of

soils in the Appalachian region exhibit these

characteristics. Because increased timber harvesting is

expected along with continued high levels of N

deposition in the central Appalachians, base cation

depletion and complications from N saturation are major

concerns for long-term productivity, biodiversity, and

sustainability of forest ecosystems.

To address these concerns, a study was initiated in 1996

on the Fernow Experimental Forest (Fig. 1). This study is

affiliated with the National Long-term Soil Productivity

(LTSP) Study (Tiarks et al. 1997), to evaluate timber

management impacts on long-term soil productivity and

evaluate the sustainability of managed stands. Our

objective is to evaluate the interaction of forest

management and air quality on long-term soil

productivity. Specifically, we are studying the influence of

nutrient removals on long-term forest productivity

Figure 1.—Location of Fernow Experimental Forest,

site of Fork Mountain Long-Term Soil Productivity

Study.



	




2

through examination of biomass production and nutrient

cycling over time. We will focus on key processes,

particularly those related to base cation retention and

supply, nitrogen saturation, and soil buffering. We also

will evaluate how soil changes affect forest communities

in central Appalachian hardwood forests, including

vegetative species composition, structure, diversity, and

productivity.

Our goals for this study include:

1. Characterize the productivity, diversity and

biogeochemistry of a forest system hypothesized to

be sensitive to base cation removal through harvest

removals and continuing inputs of nitrogen and

sulfur.

2. Determine the response of this forest community to

base cation removal.

3. Create new and modify existing vegetation/

nutrient/hydrologic models to describe and

simulate forest change in response to base removals,

nitrogen and sulfur inputs, and mitigating base

additions.

This publication addresses goal 1, provides a description

of the Fork Mountain LTSP site prior to implementation

of the experiment, and briefly discusses site variability.

Methods

Location of Experiment

This experiment is located on the Fernow Experimental

Forest in Tucker County, West Virginia (latitude 39o

 04'

N, longitude 79o

 41' W) on Fork Mountain, and was

established in 1996. The Fernow is located in the

Allegheny Mountain subsection of the Appalachian

Physiographic Province. The site has a southeast aspect,

with slopes ranging from 15 to 31 percent. Elevation

ranges from 798 m to 847 m. At the initiation of this

study, trees on this mostly undisturbed forested site were

about 85 years old and were typical of a relatively high

productivity (red oak site index50 = 80) central

Appalachian mixed hardwood forest. This site was last

harvested around 1910.

Description of Experiment

This experiment is designed to address the long-term

(one rotation or ~80 years) effects of base cation removals

on forest productivity, measured as aboveground

biomass, and to allow repeated measures of many

important parameters and processes. It is designed with

four replications of four treatments, and is blocked by

slope position (i.e. each row of four plots is one block;

see Fig. 2). The treatments are an uncut, untreated

control (CTRL); whole-tree harvesting (removal of all

aboveground biomass; WT); whole-tree harvesting +

ammonium sulfate fertilizer additions (WT+NS); wholetree harvesting + ammonium sulfate fertilizer + addition

of dolomitic lime (LIME). The ammonium sulfate

treatment is designed to accelerate base cation leaching

from the soil, and is based on other research (Adams et

al. 1997) which demonstrated a significant increase in

leaching of Ca and Mg in response to fertilization at rates

equal to twice ambient N and S deposition rates.

Ambient rates of deposition in throughfall are ~15 kg N/

ha/yr and 17 kg S/ha/yr (Helvey and Kunkle 1986).

Fertilizer treatments are applied three times per year

(March, July, November) to mimic natural deposition

patterns. Treatment of one-fourth of the treatment plots

with dolomitic lime will test whether relatively simple

amelioration techniques can be used to mitigate base

cation losses. Dolomitic lime is applied at a rate twice

that of the export rate of Ca from a nearby reference

watershed. Stream water export is approximately 11.25

kg Ca/ha/yr and 5.83 kg Mg/ha/yr (Adams et al. 1997).

Control plots will provide an untreated reference. (See

Fig. 2 for treatment assignments to plots.) On all

harvested plots, the vegetation will be allowed to

regenerate naturally.

Each growth plot is 0.2 ha in size (45.7 m on a side,

corrected for slope), and is bordered by a buffer strip (7.6

m wide) on each side, for a total treatment plot area of

0.4047 ha (Fig. 3).

Sampling and Measurements — Productivity

Productivity indicators include tree growth rates over

time and aboveground production, both in terms of

biomass and economic (timber) value. Therefore, the

vegetation was intensively sampled to determine initial
3

Overstory vegetation: Height and diameter at breast

height (dbh) of all trees greater than 2.54 cm diameter

were measured on each growth plot in the spring of

1996. Standing dead trees also were tallied. On the

untreated (control) growth plots, all trees greater than

2.54 cm diameter were tagged with permanent metal tags

so growth could be followed over time. In addition to

Forest Road 827

New skid road

Skid road

Control WT LIME WT+NS

WT WT+NS LIME Control

WT Control WT+NS LIME

WT+NS LIME WT Control

Block 

Block

Block IV

Block 

A

B C D

4 3 2 1

56 78

12 11 10 9

13 14 15 16

Top of slope

Bottom of slope

Figure 2.—Plot layout and treatment

assignments, Fork Mountain Long-Term Soil

Productivity Study. Letters within circles

indicate location of characterization soil pits.

See Appendix Table 19 for descriptions.

r=1.13m

0.0004 ha

regeneration plots

growth plot

treatment 

plot =

growth plot

+

buffers

x

x

x

x

x

x

x x x x

x

x

x

x

x x x

x

x

Figure 3.—Diagram of a treatment plot,

growth plot and regeneration plot on the

Fork Mountain Long-Term Soil

Productivity Study.

conditions of volume, basal area and density of tree

species, abundance of shrubs and herbs, and total

aboveground biomass. Also, an important component of

future productivity is reproductive capability, so

regeneration was evaluated in terms of existing tree

seedlings and sprouts and the regeneration potential of

stored seed in the forest floor (seed bed potential).
4

dbh and total height, height to 10 cm diameter top

(merchantable height), number of logs, crown class, and

condition also were recorded for all trees greater than

2.54 cm dbh. Basal area and density (stems/ha) were

calculated for each plot by species.

Pretreatment aboveground biomass also was determined

for each growth plot. Unbiased estimates of the green

whole-tree biomass weights of all trees greater than 12.7

cm dbh on each growth plot were obtained using the first

stage of the probability proportional to size (PPS)

method described by Valentine et al. (1987). The PPS

method provides a randomized approach to selecting

sample trees to be weighed, where the probability of

selecting a tree to be weighed is determined by the

estimated weight of that tree as a proportion of the sum

of the estimated weights of all trees on the sample plot.

Estimates of the green weight of each tree with a dbh

greater than 12.7 cm were obtained using the regression

equations developed for Appalachian hardwood tree

species by Brenneman and Daniels (1982). These

equations estimate whole-tree green weight, minus

foliage, as a function of tree dbh and height to a 10 cm

top diameter outside bark. The unbiased estimates of

plot weights are obtained by adjusting the original total

of estimated tree weights using the relationships between

the estimated and actual weights of the sampled trees.

The number of trees weighed on each growth plot, as a

percentage of the total number of trees with a dbh greater

than 12.7 cm, averaged 15.1 percent and ranged from 12

to 20 percent. Because the larger, heavier trees were more

likely to be sampled using PPS, the proportion of total

plot weight represented by sample trees exceeded the

proportion of tree numbers sampled. Accordingly, the

total weight of the trees sampled averaged 35 percent of

the estimated total plot weight, ranging from 23 to 46

percent.

The biomass estimates for trees greater than 12.7 cm dbh

do not include foliage weight. Tree weights were sampled

from March through November 1996. As a result, all

trees were not weighed without foliage. For trees weighed

with leaves on, green weight minus foliage was estimated

by deducting 4 percent of the total weight. Keays (1975)

and Young et al. (1979) reported that foliage accounts for

approximately 4 percent of the total green weight of

hardwood trees.

The dry-weight estimates were calculated by multiplying

the treatment plot green-weight estimates by a greenweight-to-dry-weight conversion factor (GDCF). The

GDCF was derived for each plot by dividing the sum of

the estimated dry weights for all trees on the plot greater

than 12 cm dbh by the sum of the estimated green

weights of these same trees. These tree weights were

estimated using both the green and dry biomass

equations developed by Brenneman et al. (1978) for

Appalachian hardwood tree species, such that the GDCF

obtained for each treatment plot reflects both the species

composition and diameter distribution unique to that

plot. For the 12 treatment plots, GDCF values were

relatively consistent, ranging from 0.559 to 0.609. A

similar process was used for the small trees, but equations

were derived from a sample of 127 small trees harvested

on Fork Mountain and elsewhere on the Fernow.

Because trees were not cut on the four control plots, it

was not possible to estimate biomass using the same PPS

methods applied to the treated plots. However, tree

weight data collected from the adjacent treatment plots

was incorporated into the estimates of control plot

biomass. The estimated and actual weights of trees

sampled from the treatment plots were used to develop

conversion factors for each tree species or species group.

These conversion factors then were applied to the

regression equation estimates of tree weights on the four

control plots to estimate total plot biomass. The

approach to converting green weight to dry weight

applied to the treatment plots also was applied to the

four control plots.

Trees between 2.54 and 12.7 cm dbh were cut at the

ground and mass determined on site, using a scale

mounted on a logging crane. All woody vegetation

smaller than 2.54 cm dbh and greater than 0.3 m also

was removed and weighed for each growth plot.

Regeneration, shrubs, and herb layer: Twenty 0.0004-ha

circular regeneration plots were established on each
5

growth plot ( Fig. 3), and the center marked with a metal

stake (radius =1.13 m). Each of these regeneration plots

was visited in late June/early July 1996. At each

regeneration plot, woody regeneration was counted by

species, height class, and origin (seedling vs. sprout). The

percentage surface cover was estimated for woody

regeneration and competing vegetation and the presence

of deer browse, slash, stoniness, and other characteristics

also were noted. All herbs and shrubs were then tallied on

one-fourth of each regeneration plot. An additional 20

regeneration plots were established in the buffer area

around each measurement plot and woody regeneration,

herbs and shrubs tallied on these plots. On five of these

buffer regeneration plots, one-fourth of each plot served

as a clip plot, where all aboveground vegetation (herbs

and shrubs, < 2.54 cm diameter, and < 1 m height) was

clipped at the ground line, placed in paper bags, ovendried, and weighed.

Seed bed potential: Because regeneration occurs

naturally from seeds and/or sprouts, we characterized the

seedbed to account for possible differences in species

composition of regeneration after treatments began.

Methods similar to those of Wendel (1987) were used. In

March 1996, five 30 cm X 30 cm X 10 cm deep samples

of the forest floor were collected from each of the

treatment plots. Four samples were collected from the

buffer areas along with one sample from the interior of

the growth plots. The samples were placed on newspaper

(to prevent soil loss during transport) in wooden trays

with hardware cloth bottoms. Each tray was labeled as to

sampling location and transported to the greenhouse.

Trays were watered regularly, and allowed to germinate

over the growing season. Regeneration was tallied in each

tray twice during the growing season: in July and

September. The counts were averaged per plot and

densities calculated. Only the September data are

presented here.

Leaf area index: Leaf area index was determined using a

LICOR LAI 2000 (Licor, Inc., Lincoln, NE).

Measurements were made in August 1996 at each

treatment plot corner for each of the 16 plots, using an

open sky as calibration; a mean of the four measurements

determined the leaf area index.

Litterfall biomass: Freshly fallen leaf litter samples were

collected in autumn 1996 from the control plots, using

0.9 m X 0.9 m collectors. One collector was placed in the

center of each control plot, the litter collected on a

weekly basis during the time of leaf fall, and samples were

dried and weighed.

Coarse woody debris: All down woody debris larger

than 10 cm diameter and longer than 30 cm, and all

stumps less than 1.4 m tall (snags were captured in tree

tally) were measured on each growth plot. Two diameters

(large end and small end; on very long pieces, several

diameters, to capture taper) and length of each piece of

down wood were recorded along with species and decay

class (See Adams and Owens 2001 for a description of

the decay classes; generally, Class I is the least decayed,

and Class III the most decayed). Samples also were

collected for nutrient analyses by species. Volume of

down wood was calculated using Smalian’s equation

(Husch et al. 1972) for log volume:

V = ((B+S)/2)L

where B = cross sectional area at butt end, S = cross

sectional area at small end, and L=length. Biomass was

calculated using published specific gravity values for each

species and decay class (Adams and Owens 2001).

Biomass of standing dead trees was estimated from

diameters and heights, and density values for Class II

dead wood from Adams and Owens (2001) for the

appropriate species.

Forest productivity will be measured using biomass

estimates from destructive sampling during the first 5

years, and thereafter estimated by equations. When the

stand reaches approximately 15 years of age, trees greater

than 2.54 cm dbh will be permanently tagged and

growth measured every 5 years. Vegetation indices

(species composition, density, frequencies, etc.) also will

be surveyed periodically.

Sampling and Measurements — Diversity

Species richness and diversity were estimated from

measurements described above. Richness is defined as the

total number of tree or plant species recorded on the

growth plots. Tree inventory data were used to calculate
6

relative density (RD), relative basal area (RBA), and an

importance value (IV) for each overstory species on each

growth plot, and overall, where importance value =

(RD+RBA)/2 (Jenkins and Parker 1997). Relative

importance values also were calculated for herbs and

shrubs (based on number of plants/species relative to

total number). The Shannon Diversity Index (H’) and

evenness also were calculated (Magurran 1988).

Sampling and Measurements —

Biogeochemistry

Measurements were made of vegetation and soil nutrient

pool sizes and solution chemistry. All tissue nutrient

analyses were conducted at the University of Maine Soil

and Plant Testing Laboratory, using protocols described

by Adams et al. (1995). Soil samples were analyzed at

Virginia Polytechnic Institute and State University (Lusk

1998), and solution samples were analyzed at the USDA

Forest Service Timber and Watershed Laboratory in

Parsons, WV (Edwards and Wood 1993).

Foliar chemistry: Foliage was collected from dominant/

codominant trees of four species (yellow-poplar, black

cherry, red maple, and sweet birch) per treatment plot

during August 1996. Samples were collected from the

upper crown of two trees/species/treatment plot, where

present. Samples were dried and ground (1mm mesh)

and analyzed for N, Ca, K, Mg, P Al, B, Cu, Fe, Mn,

and Zn.

During the assessment of buffer regeneration plots, all

herb species, except violets (Viola spp), on one-fourth of

each regeneration plot were removed at the ground line

and placed in paper bags for drying and weighing.

Violets were placed in a separate bag for drying and

weighing, to be used as a bioindicator of site nutrient

status. Viola was selected because it is ubiquitous, and

was found to be a sensitive indicator of nitrogen

differences in the soils (Gilliam and Turrill 1993).

Soil physical and chemical properties: In March and

April of 1996, soils were sampled from three randomly

located quantitative pits per treatment plot. A 30.5 cm X

30.5 cm square template was placed on the soil surface,

and the surface litter layers were collected from within

the template, by cutting around the edge with a knife (Oi

and Oe+Oa layers, separated), and placed in paper bags.

Then the top mineral soil layer (0-15 cm) was removed

within the same 30.5 X 30.5 cm area, gravels and cobblesized materials were removed, and all components

weighed separately. Then the hole was lined with a plastic

bag and backfilled with sand of a known density, level

with the soil surface, and the weight (and thus volume)

of the sand determined (Grossman and Reisch 2002).

This allowed bulk density to be calculated for the surface

horizon. A bulk density core was collected from the 15-

30 cm horizon using an AMC (Art, Manufacturing and

Supply, Inc., American Falls, ID) soil core bulk density

sampler. The soil was removed from the 15-30 cm

horizon, subsampled, and the process repeated for the

30-45 cm layer. These depths approximately correspond

with soil horizons (A, AB or BA, Bw or BC) determined

from the soil reconnaissance (See Appendix Table 19 for

soil profile descriptions). Soils were subsampled by

horizon for nutrient determination and the remaining

soil returned to the soil pits in the appropriate layered

order. Soil samples were air-dried, passed through a 2

mm (#10 ) sieve and stored in plastic bags at room

temperature. Coarse fragments (all materials > 2 mm)

and roots not passing through the sieve were washed and

their presence recorded as a percentage of total bulk

mass. Each soil sample was then analyzed in duplicate for

physical and chemical characteristics as described in Lusk

(1998): particle size; organic C; pH in water, 1N KCl,

and 0.1 M CaCl2, exchangeable base cations;

exchangeable acidity; total acidity; and cation exchange

capacity (CEC).

N cycling in the soil: Nitrogen mineralization and

nitrification were assessed using techniques described by

Gilliam et al. (1996). On each of four subplots within a

treatment plot, the surface litter layer was removed from

a small area, and soil samples collected to a depth of 10

cm and placed in plastic bags. One bag was returned to

the soil to incubate in situ for a month, and the other was

returned to the laboratory. All samples were stored on ice

for transport and until processing. Rocks and twigs were

removed and samples gently sieved, and ~15 g

subsamples weighed into plastic jars. To each 15 g

sample, 150 mL 1N KCl was added, the sample shaken

for 30 seconds, then filtered after 24 hours. Net
7

mineralization and net nitrification were determined

from the change in nitrate and ammonium pools over the

one-month incubation period and averaged across

subplots. An additional 10 g sample was used to

determine moisture content and organic matter content

(loss on ignition).

Soil solution chemistry: Suction lysimeters were installed

on the growth plots on May 6 and 8, 1996. Three

lysimeters per growth plot were installed to a depth of 1

m at approximately a 45-degree angle. Lysimeters were

placed to capture the variability of each growth plot.

Samples were collected approximately monthly when

sufficient solution was available. Soil solution was

analyzed for ammonia (NH3), nitrate (NO3), Ca, Mg,

Na, K, Cl, SO4, pH, and alkalinity, using methods and

QA/QC protocols detailed in Edwards and Wood (1993).

Other Measurements

Soil reconnaissance: Soils were described by Don Flegel,

USDA Natural Resources Conservation Service soil

scientist, from four pits distributed across the study area

(See Fig. 2). Descriptions are provided in Appendix

(Table 19). Based on these descriptions, soils are

classified as Calvin, Berks, or Hazleton series (loamyskeletal, mixed mesic Typic Dystrochrepts).

Meteorological measurements: The hydrometeorological

network of the Fernow has been described by Adams et

al. (1994). Data on rainfall volume and chemistry, air

temperature, and relative humidity are collected routinely

and provide data for most of Fork Mountain, including

the LTSP study site. In addition, Wind Mark Wind

sensors (Climatronics Corporation, Bohemia, NY) were

used to measure wind speed (miles/hour), wind direction

(degrees), standard deviation of wind direction (degrees),

solar radiation (watt/m2

), and quantum radiation (μ/s/m2).

The sensors are connected to a Campbell Scientific

CR10x data logger (Campbell Scientific Inc., Logan,

UT), recording readings every second, averaged every 15

minutes, and reported as hourly averages. The sensors are

located on a 9 m tower, approximately 1.6 km east of the

study site on the Fork Mountain ridge at approximately

the same elevation as the study plots.

Here we present data for November 1995 through

October 1997, beginning the year prior to treatment,

and incorporating the initial operation for the FM-LTSP

weather station, with supplemental information from

other Fernow monitoring stations and programs.

HOBO data loggers® (Onset Computer Corporation,

Bourne, MA) were used to monitor soil temperature in

four treatment plots: two control plots and two harvested

plots (WT). In each of the four treatment plots, four

HOBOs were placed at the 10 cm depth and left for 3-6

months, recording soil temperature every 4 hours, before

being returned to the lab for data retrieval. HOBOs were

placed in the soil in June 1996 prior to treatment,

removed during logging (beginning in August 1996),

then returned to the soil after logging treatments were

completed (May 1997).

Site characteristics: Various surface properties were

assessed at each of the regeneration plots: evidence of

deer browsing, presence of slash taller than 4 feet,

presence of soil disturbance, soil compaction, surface

stoniness (defined as > 35% surface stone), and surface

wetness. These parameters were classified according to

whether they might interfere with tree regeneration. For

each growth plot, the presence or absence of these

parameters was recorded at each regeneration plot, and

values expressed as percentage of regeneration plots with

these characteristics.

Statistical Analyses

For descriptive and characterization purposes, means and

standard deviations were calculated by plot and by block,

and ranges are provided for some variables. To analyze for

spatial variability, the data were analyzed using SAS

GLM program (SAS Institute, Cary, NC), with the

following model: VARIABLE= block treatment

block*treatment. Results were evaluated at the 95 percent

probability level to determine statistical significance.

Results and Discussion

Goal 1 objectives were to thoroughly characterize this site

prior to treatment and describe in detail the conditions

we observed. To this end, we describe the overall site

characteristics, but also will evaluate variability among
8

the four blocks, as appropriate. See Figure 2 or Table 1

for block and treatment assignments.

Growth plot characteristics: Plot surface characteristics

are shown in Table 1. Eighteen percent of the

regeneration plots showed evidence of browsing by deer.

This measurement indicated the presence or absence of

browsing on the regeneration plots; we have little

information on the extent of browsing. Only four growth

plots had evidence of deer browsing on more than 20

percent of the regeneration plots. No growth plots had

more than 50 percent of the regeneration plots browsed.

This suggests densities of approximately 6 deer/km2

;

which are slightly lower than other areas in West Virginia

and western Pennsylvania1

. Eight deer/ km2 is the density

at which deer interfere with establishment of

regeneration (McWilliams et al. 2003). Ninety-four

percent of the regeneration plots had no slash cover, and

5 percent had slash cover which was less than 4 feet tall;

and what slash was present generally covered less than 5

percent of the surface area. Ninety-four percent of the

regeneration plots showed no disturbance of the forest

floor, whereas about 3 percent showed disturbance of less

than 50 percent of the plot’s surface area. Surface

compaction was observed on only three growth plots.

These three measurements suggest that this secondgrowth stand had not been disturbed recently. Sixteen

percent of the regeneration plots were characterized as

stony (defined as greater than 35% surface cover in

stone). On four growth plots (1, 9, 14, 16), 25 percent or

more of the regeneration plots were recorded as stony:

On growth plot 9, 50 percent of the regeneration plots

were described as stony, and on growth plots 1 and 16,

35 percent of the regeneration plots were described as

stony. On growth plot 14, 25 percent of the regeneration

plots were described as stony. Surface wetness was

observed on only two growth plots. We conclude that

other than deer browsing, which was relatively low, these

plots experienced little recent disturbance. Also, relatively

Table 1.—Surface characteristics of growth plots on Fork Mountain LTSP site, with block and treatment

assignments.

Percentage of regeneration plots

Growth Block Treatment Browsed Slash Surface Compacted Stony Surface

plot disturbance soil surface wetness

1 I WT+NS 20 5 0 0 35 0

2 I LIME 10 0 10 10 20 10

3 I WT 5 5 0 0 15 0

4 I CONTROL 15 0 0 0 0 0

5 II WT 20 0 15 0 0 0

6 II WT+NS 5 0 5 0 10 0

7 II LIME 0 10 5 5 5 0

8 II CONTROL 0 5 5 0 15 0

9 III LIME 10 10 0 0 50 0

10 III WT+NS 50 0 5 0 15 0

11 III CONTROL 5 10 5 0 0 0

12 III WT 15 5 15 0 10 0

13 IV WT+NS 35 20 0 0 0 0

14 IV LIME 45 10 20 20 25 0

15 IV WT 20 10 0 0 20 0

16 IV CONTROL 40 0 15 0 35 5

 Average 18 6 6 2 16 1

1

W.M.Ford, 2004. Personal communication. Timber and

Watershed Laboratory, Parsons WV 26241
9

high concentrations of large surface stones represented

the only physical impediment to successful regeneration,

but these were not evenly distributed across the site.

Productivity: Nineteen commercial tree species, four

noncommercial tree species and three shrub species were

recorded in the overstory (Table 2; a complete list of all

plant species recorded, with scientific names can be

found in the Appendix, Table 20). Basal area for the site

was 23.54 m2

/ ha, with a density of 584 stems/ha. The

highest stem densities were recorded for the maples, with

sugar maple being the most abundant, followed by

striped maple and red maple. Northern red oak, black

cherry, and yellow-poplar also were relatively abundant.

However, the greatest basal area was found in sugar

maple, red oak, black cherry, and yellow-poplar (Table 2).

With the exception of sugar maple, these species were not

among the most abundant, indicating relatively few, but

fairly large red oak, black cherry and yellow-poplar trees.

Based on importance values, four species comprise

approximately two-thirds of the stand: sugar maple,

northern red oak, striped maple, black cherry.

Twenty-two species of standing dead trees were identified

(Table 3). Sugar maple was the most abundant standing

dead tree on the site, averaging 22 dead trees/ha,

followed by black locust. Black locust represented the

largest basal area of standing dead, followed by black

cherry, red oak, and white ash, suggesting the dead trees

of these species were fairly large, while there were many

small diameter dead sugar maple trees. Total basal area of

standing dead trees on the 16 growth plots was 2.5 m2

/

ha, which represents about 10 percent of total stand live

basal area, with a density of 88 standing dead trees/ha.

Table 2.—Tree (> 2.54 cm dbh) density, basal area and importance value by species for growth plots.

Species Density Basal area Importance

(stems/ha) (m2

/ha) value

Sugar maplea 248 4.79 31.4

Northern red oak 24 4.94 12.6

Striped maple 133 0.15 11.7

Black cherry 26 3.90 10.5

Red maple 47 2.15 8.6

Yellow-poplar 18 3.42 8.8

Fraser magnolia 14 0.50 2.3

White ash 10 0.96 2.9

Cucumber magnolia 9 0.55 2.0

Chestnut oak 5 0.58 1.6

Sweet birch 8 0.32 1.4

American beech 11 0.04 1.1

Bitternut hickory 3 0.32 0.9

Basswood 5 0.21 0.8

Black locust 3 0.25 0.8

White oak 1 0.28 0.7

Others:

Common serviceberry, Spice bush,

Sourwood, American Chestnut, < 5 each <0.10 each <0.1 each

Scarlet oak, American hornbeam,

Blackgum, Alternate-leafed dogwood,

Witch hazel

 Total 584 23.54

a

See Appendix Table 20 for scientific names
10

Analysis of existing woody regeneration identified 19

commercial tree species (Table 4), three noncommercial

tree species, and 12 shrub species (Table 5). The

regeneration consisted of 86 percent seedlings, with the

remainder root sprouts and stump sprouts. The most

abundant regeneration was from three species: black cherry

(26,933 stems/ha), striped maple (11,058 stems/ha) and

yellow-poplar (4,609 stems /ha). Each of these species

was found on all 16 growth plots, as were sugar maple

(4,594 stems/ha), red maple (3,428 stems/ha) and

northern red oak (2,401 stems/ha). White ash also was

relatively abundant and was found on 15 plots. Among

the shrubs and competing vegetation, greenbrier was most

abundant and found on all growth plots (5,212 stems/ha).

In the herb layer, 75 plant species were identified (Table

6; see Appendix Table 20 for a complete listing),

including 20 woody tree species. Viola spp. was the most

abundant herb and was found on all growth plots, at an

average density of 221,491 plants/ha, with stinging nettle

being next most common (93,280 plants/ha). Although

there were many plant species recorded, eight species

accounted for 75 percent of those recorded in the herb

layer survey: Viola spp., stinging nettle, black cherry

seedlings, Christmas fern, grapevine, Indian cucumber

root, New York fern, and yellow-poplar seedlings. The

Shannon Index of diversity based on the herb layer/

regeneration plots survey was 3.95 and evenness was

calculated as 0.908. This H′ value is slightly higher than

those reported by Eliott et al. (2002) in the southern

Appalachians for undisturbed forest and forest disturbed

by Hurricane Opal and salvage logging. Species richness

(r ) for the Fernow herb layer also was greater than that

reported for the southern Appalachian site.

In the survey of seed bed potential, 10 tree species were

identified: sweet birch, yellow-poplar, sassafras, black

cherry, black locust, sugar maple, red maple and chestnut

oak, fire cherry, and striped maple. We estimated

~183,000 potential tree seedlings/ha in these plots. Sweet

Table 3.—Standing dead trees by species, on growth plots.

Species Density total basal area Importance

(no./ha) (m2

/ha) value

Black locust 16 0.55 20.2

Sugar maple 22 0.13 14.6

Black cherry 9 0.26 10.3

Northern red oak 6 0.26 8.3

White ash 3 0.21 6.0

Sourwood 3 0.19 5.8

Striped maple 9 0.03 5.6

Red maple 6 0.09 5.0

Yellow-poplar 1 0.16 3.9

Sweet birch 3 0.11 3.6

Chestnut oak 2 0.11 3.5

Fraser magnolia 3 0.07 3.1

Bitternut hickory 1 0.10 2.6

Sassafras 1 0.08 2.1

Cucumber magnolia 1 0.05 1.5

Shagbark hickory, Eastern

hophornbeam, Yellow birch, <1 each <0.05 each 3.74 combined

Flowering dogwood, Witch hazel,

White oak, American chestnut 

 Total 88 2.46
11

Table 4.—Woody regeneration of commercial tree species, from regeneration plots.

Species No./ha Importance value

Black cherry 26,933 47.2

Striped maple 11,057 19.4

Yellow-poplar 4,609 8.1

Sugar maple 4,594 8.1

Red maple 3,428 6.0

Northern red oak 2,401 4.2

White ash 2,726 4.8

Sweet birch 416 0.7

Fraser magnolia 309 0.5

Eastern hophornbeam 216 0.4

Cucumber magnolia 154 0.3

Black locust 85 0.1

Chestnut oak 54 0.1

Bitternut hickory, shagbark

hickory, American beech, < 35 each 0.1

sassafras, blackgum, yellow

birch

 Total 57,017

Table 5.—Woody regeneration of noncommercial tree species and shrub species,

from regeneration plots.

Species No./ha Importance value

Greenbrier 5,212 41.0

Grapevine 3,219 25.3

Rubus spp. 3,004 23.6

Mapleleaf viburnum 548 4.3

American hornbeam 31 0.2

Flowering dogwood 23 0.2

Deciduous holly 209 1.6

Dutchman’s pipe 163 1.3

Alternate leaf dogwood 93 0.7

Downy serviceberry 85 0.7

Spicebush 31 0.2

Azalea, mountain laurel, fire cherry 7 each < 0.1

 Total 12,710
12

Table 6.—Herb species density and importance value, determined during regeneration survey.

Species No./ha Importance value

Viola 221,490 38.94

Stinging nettle 93,280 16.40

Black cherry 45,340 7.97

Christmas fern 17,450 3.07

Grapevine 15,470 2.72

Indian cucumber root 14,520 2.55

New York fern 14,080 2.48

Yellow-poplar 12,760 2.24

Twisted stalk 12,720 2.23

Indian turnip 12,480 2.19

Striped maple 12,170 2.14

unknown 11,800 2.07

Rubus 9,790 1.72

Greenbrier 9,670 1.70

Sedum 8,960 1.58

Deertongue grass 7,540 1.32

Sugar maple 6,020 1.06

White ash, black cohosh, grass, red maple, 2,000 - 5,000 each 4.3 total

bedstraw, upright smilax, wild licorice

Touch-me-not, blue cohosh, trillium, slender 500 - 2,000 each 3.3 total

toothwort, white snakeroot, Lycopodium,

false Solomon’s seal, mapleleaf viburnum

All others <500 each 2.1

Total 569,460

birch and yellow-poplar were the most numerous tree

seedlings followed by fire cherry and sassafras (Fig. 4).

The most numerous of the 24 semiwoody and shrub

competitors were Rubus spp., and grapevine. Wendel

(1987) evaluated abundance and distribution of

vegetation under four hardwood stands ranging in site

index from 64 to 80, using similar methods. On

Wendel’s sites, abundance ranged from 75,000 to

250,000 seedlings/ha. Wendel also found species

composition similar to what we reported: sweet birch was

consistently the most abundant of the regenerating tree

species that were common to all areas, and yellow-poplar

usually was ranked second in abundance. It is

noteworthy that on the Fork Mountain LTSP site, oak

seedlings, which were nearly absent in the regeneration

layer of this second-growth stand on Fork Mountain,

despite their presence in the overstory, also were nearly

absent in the seedbed survey. Because many oaks have

infrequent heavy mast years, this relatively low

abundance of oak seedlings could be due to low acorn

production or to high predation of acorns. Overall, the

lighter seeded species were most abundant in the seedbed

on the Fork Mountain site.

Biomass: Estimates of total aboveground plant biomass

(dry mass) ranged from 243 metric tons (T)/ ha to 385

T/ha, with the average of 312 T/ha (Table 7). Most of the

biomass was comprised of overstory trees larger than 12.7

cm dbh, with values ranging from 238 to 381 T/ha. This

is considerably greater than the 94 T/ha estimated by

Patric and Smith (1975) for 70-year-old trees on the

Fernow, and is also greater than values reported for most

hardwood forests in the eastern United States (see Adams

et al. [2000] for comparisons). The stand on Fork
13

Figure 4.—Number of seedlings of most common tree species found in seedbed potential survey.

Table 7.—Aboveground vegetative biomass. Estimated tree mass does not include foliage.

Plot Trees Trees Trees Herbs Total

> 12.7 cm 2.54 -12.7 cm < 2.54 cm

dbh dbh dbh

Metric ton/ha

1 296 3.95 0.67 0.32 301

2 347 4.55 0.74 0.09 352

3 340 3.58 0.99 0.24 345

4 381 3.06 0.81 0.28 385

5 308 1.96 1.70 0.18 312

6 362 2.60 0.99 0.17 366

7 320 4.59 1.43 0.09 326

8 238 3.51 1.37 0.14 243

9 262 7.92 1.06 0.03 271

10 299 5.32 1.70 0.07 306

11 302 3.45 1.55 0.14 307

12 312 4.50 1.91 0.08 319

13 305 6.28 1.95 0.11 314

14 285 5.17 1.32 0.05 291

15 274 4.66 1.17 0.07 280

16 261 5.75 1.48 0.13 269

Mean 306 (38.3) 4.43 (1.48) 1.30 (0.40) 0.14 (0.08) 312 (37.5)

(std.dev)

0

10

20

30

40

50

60

70

80

Sw eet birch Yellow -poplar Fire cherry Sassafras Black cherry Chest

Tree species

Seedlings (number X 1000/ha)
14

Mountain was approximately 90 years old at the time of

measurement and on a high productivity site. Although

basal area on this site was average for sites on the Fernow,

the high biomass may be attributed partly to the tall

trees: average height of the trees sampled was 29.3 m.

Litterfall production for 1996 was 3.80 T/ha, and leaf

area index averaged 4.5 across the site, both of which are

indicators of the high productivity of the Fork Mountain

site.

Average down woody debris (DWD) volume (exclusive

of stumps, and across decay classes) was 44.9 m3

/ha

(Table 8). This is comparable to other published values

for mixed hardwood forests, but less than reported for

Watershed 4, a reference watershed on the Fernow,

located about 1 km from the Fork Mountain LTSP site

(Adams et al. 2003). DWD volume on Watershed 4 was

estimated as 69.7 m3

/ha, and American chestnut was the

dominant species of down dead wood. American

chestnut DWD was nearly nonexistent on the Fork

Mountain LTSP site. Across all the plots on the Fork

Mountain LTSP research site, black cherry was the most

commonly encountered and represented the greatest

volume of DWD, followed by red maple, black locust

and northern red oak (Fig. 5). Note that density and

volume of DWD are well correlated on this site. Average

down wood biomass per plot was 17.3 T/ha (Table 8).

Eighty-four percent of the DWD was in wood decay

Class III, the most decayed. Presence as DWD may

reflect presence in the living stand or durability (decay

resistance) of the wood. Black locust is very decay

resistant, but is not very abundant in the overstory (Table

2). Black cherry is both abundant and relatively decay

resistant. There were approximately 52 stumps/ha, with

black cherry the most common, followed by northern red

oak, and most of the stumps were in wood decay Class II

or III. (Table 9). Stumps added an additional deadwood

volume of 0.384 m3

/ha, and stump mass was estimated at

168 kg/ha. An average of 90 standing dead trees were

recorded per hectare, with an average basal area of 2.46

m2

/ha (Table 10). Standing dead mass was estimated as

7.4 T/ha, for a total deadwood mass (DWD+stumps

+standing dead) of 24.9 T/ha. This represents less than

10 percent of the total aboveground biomass.

Figure 5.—Down dead wood on Fork Mountain Long-Term Soil Productivity Study, pretreatment

volume and density, by species. See Appendix, Table 20 for scientific names of tree species.

0

20

40

60

80

100

120

140

B.Hickory

S.hickory

Sweet birch

Chestnut 

White oak

Chestnut oak

N. red oak

Cucumbertree

Fraser magnolia

Yellow-poplar

Sassafras

Black cherry

Bl k l t

Volume (m 3/ha)
15

Table 8.—Down woody debris mass and volume.

Plot Dominant species, by volume volume mass

(m3

/ha) (T/ha)

1 sweet birch, yellow-poplar 42.8 14.9

2 white ash 23.1 11.5

3 black cherry, black locust 25.4 10.5

4 northern red oak, black cherry 65.4 25.5

5 white ash 49.4 20.0

6 white ash, black locust 56.9 22.5

7 black locust, unknown 79.0 24.5

8 Fraser magnolia 39.3 13.4

9 yellow-poplar 38.1 14.4

10 yellow-poplar, red maple 47.9 19.0

11 sassafras 31.6 15.9

12 black locust 51.4 23.8

13 chestnut 53.2 18.9

14 sugar maple, black cherry 41.0 14.2

15 cucumber magnolia 46.4 21.3

16 red maple, yellow-poplar 28.5 6.9

Mean (std. dev.) 44.9 (16.7) 17.3 (5.44)

Table 9.—Number of stumps by species and decay class. See Adams and Owens (2001) for details

on decay classification.

Wood decay class

Species I II III Total No./ha

Black cherry 6 25 43 74 23

Northern red oak 5 16 16 37 11

Red maple 1 10 3 14 4

Black locust 9 1 1 11 3

Sugar maple 3 0 3 6 2

Sweet birch 1 1 2 4 1

Yellow-poplar 1 2 1 4 1

Shagbark hickory 0 2 1 3 1

Sour wood 1 1 1 3 1

American chestnut 0 1 1 2 1

White ash 0 0 2 2 1

Fraser magnolia 1 1 0 2 1

Unknown 0 4 3 7 2

Total 28 64 77 169 52

% of total 17 38 45
16

Soil characteristics: Soil physical characteristics are

displayed in Table 11. Coarse fragment content, both

at the surface and to depth, ranged from as low as 8

percent to nearly 70 percent, with the greatest average

coarse fragment content in the 0-15 cm depth.

Although classified as loams, these soils contain

appreciable amounts of sand and porosity can be

quite high. The coarse fragment content also is high

in all three depths and these soils are considered well

drained.

Soil chemical data are presented in Tables 12 and 13.

These soils are acidic, with mean pH in water of 4.24,

4.45 and 4.42 for the 0-15, 15-30, 30-45 cm depths,

respectively, and low base saturation values in all

horizons. Base cation concentrations are relatively low

and reflect the acidic sandstone and/or shale of the

parent material bedrock. Carbon concentration is

greatest in the upper horizon, as is true for most

chemical values, averaging 7 percent. The forest floor

(litter) is relatively rich in most nutrients, but with

some differences between layers. The mass of Oe+Oa is

greater than the Oi layer, with greater concentrations of

N and P. Aluminum concentration in the Oe+Oa layer is

approximately 6.5 times that of the Oi layer. Note also

there are large differences in mean Fe concentrations

between the forest floor layers.

Biogeochemistry: Foliar nutrient concentrations in the

four tree species sampled are shown in Table 14. These

means fall within the range reported by Adams et al.

(1995) for sites nearby on the Fernow. As expected, foliar

nutrient concentrations varied among the species, with

yellow-poplar containing the highest levels of N, P and

Ca. Aluminum concentration also was highest in yellowpoplar foliage. Given the reported sensitivity of yellowpoplar to Al, this suggests further examination of the

nutrient relationships of this species are needed. Zinc

concentrations were elevated in sweet birch by almost a

factor of 10 relative to the other three species, while Mn

concentrations in yellow-poplar and black cherry were

considerably greater than for sweet birch or red maple.

These levels are not believed to be indicative of nutrient

deficiency or toxicity, due to the absences of visible

symptoms, although published critical values are not

available for most hardwood tree species. Mean values for

N, Ca, and Mg concentrations in birch, black cherry and

red maple foliage fell within the range of values observed

across the northeastern United States for these species

(Northeastern Ecosystem Research Cooperative foliar

chemistry database 2004). Foliar P concentrations in

black cherry, however, were at the lower end of the range

reported for the northeastern United States in the foliar

chemistry database for black cherry. The P

concentrations reported here are similar to those found

elsewhere on the Fernow2

, and may suggest the potential

for P limitation on this site. Additional data from this

and other nearby sites3

 lend support to this hypothesis,

and open additional research topics.

Mass and nutrient content of all vegetative and soil

components are shown in Table 15. The majority of K,

Ca, and Mg are found aboveground, while most of the N

2

M.B. Adams. Unpublished data. Timber and Watershed

Laboratory, Parsons WV 26241

3

W.T. Peterjohn. Unpublished data. West Virginia University,

Morgantown WV. 26506

Table 10.—Basal area and density of standing dead trees.

Plot Basal area Density

(m2

/ha) (stems/ha)

1 1.58 59

2 2.08 119

3 1.29 44

4 2.08 30

5 3.51 54

6 2.52 54

7 2.28 64

8 2.03 158

9 3.81 153

10 1.73 104

11 2.67 128

12 2.67 79

13 3.56 104

14 3.91 188

15 2.52 69

16 1.09 35

Mean (std. dev) 2.46 (0.87) 90 (48)
17

Table 11.—Mean soil physical properties plus range in values

0-15 cm (n=48) 15-30 cm (n=48) 30-45 cm (n=48)

Mean Minimum Maximum Mean Minimum Maximum Mean Minimum Maximum

% Sand 57.9 46 69 46.6 20 63 43.1 29 54

% Clay 13.6 8 18 19 9 28 29.4 23 39% Silt 28.6 18 36 34.6 21 59 27.4 22 32% Coarse 54.7 26 69.7 31.1 15.4 61.8 26.7 8.3 55.8

fragmenta

Bulk density 0.7 0.5 1.3 1.0 0.5 1.3 1.3 1.0 1.6

(fine earth fraction)b

Bulk density 1.2 0.9 1.7 1.2 0.9 1.4 1.5 1.3 1.6

(whole soil)

% porosity 55.1 47 64 42.6 39 49% capillary 36.9 32 42 33 29 40

porosity

Noncapillary 18.4 11 27 9.4 1 21

porosity

acoarse fragment % (mass basis) in 15-30 cm and 30-45 cm depth do not include large gravels or cobblesbfine earth fraction: fine earth= soil with coarse fragments (>2 mm) removed; bulk density estimates in 15-30 cm and 30-45 cm depths do not include

large gravels or cobbles.
18

Table 13.—Mean soil chemical characteristics, plus range in values.

0-15 cm (n=48) 15-30 cm (n=48) 30-45 cm (n=48)

Variable Mean Minimum Maximum Mean Minimum Maximum Mean Minimum Maximum% C 6.58 3.34 16.29 2.71 1.25 8.39 1.12 0.45 3.80pHwater 4.24 3.81 4.97 4.45 4.15 5.02 4.42 3.83 4.96

Total N (%) 0.42 0.22 0.85 0.22 0.12 0.42 0.14 0.09 0.26Ca (cmol+/kg) 0.54 0.19 2.80 0.17 0.05 0.76 0.13 0.04 0.13

Mg (cmol+/kg) 0.18 0.04 0.48 0.07 0.02 0.19 0.04 0.02 0.14

K (cmol+/kg) 0.33 0 0.69 0.34 0.06 9.09 0.12 0.03 0.24

Al (cmol+/kg) 3.52 0.93 6.63 3.43 0.74 6.02 3.92 0.93 8.16

Exch. acidity 5.30 2.42 10.23 4.12 2.31 6.82 4.68 1.98 10.78(cmol+/kg)

Total acidity 27.65 12.36 48.20 18.51 6.49 49.41 13.25 5.66 40.78(cmol+/kg)CEC(cmol+/kg) 28.70 13.27 49.76 19.09 6.86 50.12 13.55 5.92 41.39

% BS 3.67 1.09 9.11 2.99 1.04 38.31 1.61 0.70 3.73

Table 12.—Mass and nutrient concentrations of forest floor layers.

Oi layer (n=48) Oe+Oa layer (n=48)

Mean Minimum Maximum Mean Minimum Maximum

Mass (kg/ha) 6289 2014 13838 8801 247 70855

N (%) 1.26 0.87 1.70 1.63 1.12 2.16C (%) 48.12 39.88 50.05 41.94 0 93.73Ca (mg/kg) 8860 5130 13600 7869 2130 15400

K (mg/kg) 819 467 1290 985 699 1590

Mg (mg/kg) 734 455 1040 882 483 1420

P (mg/kg) 515 299 744 822 611 1110

Al (mg/kg) 548 172 2720 3501 741 8840

B (mg/kg) 20.3 11.9 27.9 19.9 14.1 29.9Cu (mg/kg) 23.45 4.44 731.00 10.06 6.27 16.60

Fe (mg/kg) 408 134 3470 3155 518 10500

Mn (mg/kg) 2134 860 3730 2712 698 4490

Zn (mg/kg) 34.8 21.2 93.7 52.6 34.6 153.0
19

is in the soil. However, these soil values represent

exchangeable base cation pools only. We have no

estimates of total cations for these soils, but

contributions from other pools are not expected to be

very large. Total Ca in other West Virginia soils derived

from variably acid sandstone and shale ranged from 400

to 1,096 kg/ha, for example (Jenkins 2002). Nearly

equivalent amounts of Ca are found in the forest floor

and in the vegetation, while less than 175 kg/ha is

available in the soil as exchangeable Ca. Approximately

half of the aboveground Ca can be found in the boles of

trees, with a nearly equivalent amount in tops and

branches (Adams et al. 1995). Managing base cations,

particularly Ca, in these soils requires balancing nutrient

removals in forest biomass (Adams 1999).

Table 15.—Mass and nutrient content. There are no data available for soil phosphorus.

Mass N P K Ca Mg

Kg/ha

Trees>12.7 cm dbh 305,800 625 40 282 1,446 61

(w/o foliage)

Small trees 198 9 <1 5 18 1

(w/o foliage)

Foliage 3,300 83 4 41 33 7

Herb layer 134 <1 <1 <1 <1 <1

CWD 24,868 48 2 10 42 4

 Total aboveground 334,300 766 48 339 1,540 74

 biomass

Forest floor 15,091 219 10 14 112 11

Soil

 0-15 cm 1,818,700 3,324 100 87 18

 15-30 cm 1,771,900 2,656 78 38 12

 30-45 cm 2,296,900 2,367 77 41 9

 Total 9,332 608 1,818 124

Table 14.—Mean (std. dev.) nutrient concentrations in foliage.

Sweet birch Yellow-poplar Black cherry Red maple

(n=8) (n=20) (n=19) (n=13)

N (%) 2.53 (0.17) 2.92 (0.22) 2.61 (0.26) 1.96 (0.15)

P (mg/kg) 1245 (128) 1569 (79) 1332 (143) 1165 (130)

K (mg/kg) 12800 (1538) 14157 (2310) 14446 (2444) 7996 (1971)

Ca (mg/kg) 10292 (1865) 12120 (3429) 9955 (1928) 6870 (1146)

Mg (mg/kg) 1891 (376) 2589 (435) 2817 (222) 962 (172)

Al (mg/kg) 62.48 (5.74) 265.5 (46.92) 128.74 (315.78) 33.76 (2.94)

Cu (mg/kg) 9.42 (0.72) 11.01 (0.77) 8.01 (0.94) 7.79 (1.09)

B (mg/kg) 41.24 (5.47) 31.39 (4.62) 27.53 (4.66) 28.68 (3.45)

Fe (mg/kg) 81.15 (9.62) 64.06 (6.75) 66.59 (4.36) 57.07 (5.43)

Mn (mg/kg) 4841 (1061) 17977 (1002) 18617 (772) 1955 (530)

Zn (mg/kg) 229 (63.2) 24.3 (3.0) 20.05 (3.6) 35.1 (6.6)
20

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

Nov-95

Dec-95

Jan-96

Feb-96

Mar-96

Apr-96

May-96

Jun-96

Jul-96

Aug-96

Sep-96

Date

Deposition (kg/ha)

c

s

0

0.5

1

1.5

2

2.5

3

3.5

4

Nov-95

Dec-95

Jan-96

Feb-96

Mar-96

Apr-96

May-96

Jun-96

Jul-96

Aug-96

S 96

Date

Deposition (kg/ha)

A

B

Figure 6.—(A) Wet deposition

of nitrogen and sulfur and

precipitation amount; (B)

Deposition of cations.

Deposition chemistry for the pretreatment period is

shown in Figures 6A and 6B. Deposition of sulfate and

nitrate are typically higher during the growing season

(Adams et al. 1994). The same is usually true for other

atmospheric constituents, and trends in deposition of all

constituents over time generally follow trends in

precipitation amounts. Most (> 60%) of the nitrogen in

deposition comes from nitrate. N deposition ranged

from 0.5 to 2 kg/ha/mo. Total N deposition for 1995

and 1996 was 8.6 and 12.1 kg/ha, respectively.

Net N mineralization rates of the surface soil ranged

from less than 0.5 g/m2

/mo to approximately 6 g/m2/mo,

with the greatest rates in July and August (Fig. 7). Net

nitrification exhibited a similar pattern and rates. Mean

monthly (growing season) rates of net mineralization and

net nitrification ranged from 4-6 g N/m2

 and 3-4 g N/

m2

, respectively (Gilliam and Adams 1999). These are

relatively high rates of processing compared with other

sites in the northeastern United States (Peterjohn et al.

1996), but are comparable to other published Fernow
21

Figure 7.—Net nitrogen mineralization and net nitrification in soils (0-10 cm), summer 1996.

Vertical bars represent ± 1 standard deviation.

values (Gilliam et al. 1996). High rates of nitrogen

cycling in the soil are symptomatic of nitrogen saturation, a

condition that has been well described elsewhere on the

Fernow (Peterjohn et al. 1996). It is not unreasonable to

assume that as the Fork Mountain site has some of the

same symptoms, it may also be nitrogen saturated.

During the pretreatment period, soil solution captured

by the lysimeters varied over space and time (Fig. 8), with

lysimeters in some plots capturing very little or no soil

solution. For purposes of describing the site,

concentrations were averaged by month and plot, and

also across plots. Monthly mean concentrations of most

Figure 8.—Capture of soil solution by tension lysimeters, summer 1996. A, B, and C represent different

lysimeters within each growth plot.

0

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

6/1/036/7/03

6/13/036/19/036/25/03

7/1/037/7/03

7/13/037/19/037/25/037/31/03

 Date

Plot number

0

2

4

6

8

10

12

06/01/9607/01/96

Date

Nitrogen (g/m2/mo)
22

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

06/12/9606/19/9606/26/9607/02/9607/09/9607/16/9607/23/9608/01/9608/13/9608/21/96

Date

Concentration (mg/L)

-4

-2

0

2

4

6

8

10

12

14

16

06/12/9606/19/9606/26/9607/02/9607/09/9607/16/9607/23/9608/01/9608/13/9608/21/96

Date

Concentration (mg/L)

analytes did not vary greatly over the summer (Fig. 9AB). The notable exception was NO3-N (Fig. 9a), which

exhibited an increase in concentration during the first

month of data collection, about 3 months after

installation. Thereafter, concentrations leveled off around

6-7 mg/L. Soil solution concentrations did not appear

related to precipitation amount. Mean solution pH from

all lysimeters decreased during the summer and fall of

1996 from about 5.8 to about 5.3.

Meteorology and Air Quality: Precipitation varied

somewhat from the long-term averages described by

Adams et al. (1994) (Fig. 10A), with 1996 being wetter

than the long-term mean and 1997 being drier. Monthly

mean temperatures did not vary greatly from the longterm mean (Fig. 10B). Note the frigid minimum

temperatures recorded in February 1996 and January

1997. Other meteorological data (wind speed and

direction, solar radiation, and relative humidity) are

A

B

Figure 9.—(A) Soil solution concentrations of nitrate, ammonium, and sulfate;

(B) Soil solution concentrations of cations. Vertical bars represent ± 1 standard deviation.
23

shown in Table 16. Low values for mean monthly rainfall

pH (Fig. 11A), and high ozone concentrations (Fig. 11B)

attest to potential air-quality related problems.

During the summer months soil temperature ranged

from 12 to 18 o

C at 10 cm depth. There were no

significant differences among the four HOBOs in each

plot or among the plots; daily means, minimums,

maximums are shown in Figure 12. Average soil

temperature during the summer of 1996 was around 16

o

C, but can change quickly in response to changes in air

temperature and particularly, precipitation.

A

B

Figure 10.—(A) Mean monthly precipitation amounts 1995 -1997, compared with 30-year

average monthly precipitation; (B) Monthly mean, long-term mean, and minimum and

maximum air temperature.

0

5

10

15

20

25

30

35

Nov-95

Dec-95

Jan-96

Feb-96

Mar-96

Apr-96

May-96

Jun-96

Jul-96

Aug-96

Sep-96

Oct-96

Nov-96

Dec-96

Jan-97

Feb-97

Mar-97

Apr-97

Date

Precipitation (cm)

-30

-20

-10

0

10

20

30

40

Nov-95

Dec-95

Jan-96

Feb-96

Mar-96

Apr-96

May-96

Jun-96

Jul-96

Aug-96

Sep-96

Oct-96

Nov-96

Dec-96

Jan-97

Feb-97

Mar-97

Apr-97

May-97

J 97

Date

Temperature (oC)

Mean Max Min Long-term m
24

Spatial Variability

A top-to-bottom of slope gradient was expected for

variables related to soil moisture and nutrients, including

productivity, because of the vast body of literature

describing increases in productivity in “cove” and other

depositional sites. This was the rationale for the block

design, blocking across the slope. On Fork Mountain,

total aboveground biomass was highest in Block I (Fig.

13A), reflecting the greatest mass of trees greater than

12.7 cm dbh, and decreased down the slope. However,

these differences were not statistically significant for total

aboveground biomass or for biomass of trees larger than

12.7 cm dbh (Table 17). Block effects were statistically

significant for the following biomass variables: small trees

(2.54-12.7 cm dbh), those trees with dbh less than 2.54

cm, and for herb biomass and standing dead wood mass,

although the patterns differed from that of the large trees.

The trend of decreasing mass downhill was consistent for

the mass of herbs (Fig. 13B), but not for the other variables.

Other variables that might be related to soil moisture

include physical properties, such as coarse fragment

content, and nutrient concentrations in soil and some

plant tissues. No significant differences in coarse

fragment content were detected for the upper horizon

(Table 17), but there was a significant effect for the 15-

30 and 30-45 cm depth, with significantly greater coarse

fragment content in Blocks I and II relative to Blocks III

and IV (Fig. 14). Block is a statistically significant effect

for some variables related to nutrients: foliar Ca

Table 16.—Meteorological data, monthly average values, Fork Mountain weather station,

1996-1997.

Date Wind speed Wind direction Solar radiation Relative humidity

miles/hr degrees watts/m2 %

1995 Nov 2.28 252 60.8 77.0

Dec 2.82 274 58.9 73.4

1996 Jan 2.99 230 59.3 73.8

Feb 2.99 249 87.6 71.0

Mar 2.69 246 127.1 81.6

Apr 2.87 255 172.1 76.9

May 2.20 220 177.1 81.7

Jun 1.56 210 240.7 71.2

Jul 1.61 211 204.8 75.7

Aug 1.33 193 220.0 77.3

Sep 1.87 203 140.1 78.3

Oct 1.86 233 107.7 74.8

Nov 3.85 193 48.7 72.6

Dec 4.91 202 50.3 76.1

1997 Jan 6.04 205 72.4 66.8

Feb 5.25 198 86.6 71.1

Mar 5.42 195 129.8 69.0

Apr 4.37 198 198.8 63.8

May 5.09 195 212.6 66.0

Jun 3.40 174 245.2 72.1

Jul 2.64 199 260.9 74.5

Aug 1.03 213 200.4 79.5

Sep 2.39 200 179.6 79.9

Oct 3.14 189 140.04 78.2
25

0

10

20

30

40

50

60

70

Nov-95

Dec-95

Jan-96

Feb-96

Mar-96

Apr-96

May-96

Jun-96

Jul-96

Aug-96

Sep-96

Oct-96

Nov-96

Dec-96

Jan 97

Date

ozone , 7 hr mean concentration (ppb)

NDDN ozone

3.4

3.6

3.8

4

4.2

4.4

4.6

4.8

5

Nov-95

Dec-95

Jan-96

Feb-96

Mar-96

Apr-96

May-96

Jun-96

Jul-96

Aug-96

Sep-96

Oct-96

Nov-96

Dec-96

Date

pH 

A

B

Figure 11.—(A) Mean monthly precipitation pH; (B) Atmospheric ozone concentrations at two ozonemonitoring sites near Fork Mountain Long-Term Soil Productivity Study. See text for details of locations.

concentrations in yellow-poplar, foliar N concentrations

in red maple; forest floor Oi layer C content; forest floor

Oi layer Ca, P, Mn concentrations; Oe + Oa layer Ca and

Mg concentrations; and soil base cation concentrations.

For most of these, nutrient concentrations are greatest in

Block I and decrease down slope, though not always

linearly. Generally, the upper row of plots (Block I) was

the richest in terms of soil nutrient concentrations. This

was particularly true for Ca, which may be explained by

particularly high Ca values for plots 1 and 4, perhaps due

to the presence of the gravel road approximately 10 m

above the top boundary of those plots. Soil Ca

concentrations in the 0-15 cm depth decreased down

slope (Fig. 15), as did total base cation concentrations,

foliar Ca in yellow-poplar, and forest floor Ca

concentrations. Therefore, while these nutrient data do

not appear to support the idea of nutrient enrichment

down the slope, they do provide support for the blocking
26

0

50

100

150

200

250

300

350

I II III

Block

Herb layer biomass (kg/ha)

0

50000

100000

150000

200000

250000

300000

350000

400000

I II III

Block

Aboveground biomass (kg/ha)

Trees>12.5 cm dbh small trees trees< 2.5 cm dbh D

Figure 12.—Daily mean, minimum and maximum soil temperature at 10 cm depth.

12

13

14

15

16

17

18

19

20

6/3/1996

6/10/19966/17/19966/24/1996

7/1/19967/8/1996

7/15/19967/22/19967/29/1996

8/5/1996

8/12/1996

Date

Temperature ( o C)

Figure 13.—(A). Total aboveground biomass, by component and block;

(B) Herb layer biomass, by block. Vertical bars represent ± 1 standard deviation.

A

B
27

Table 17.—Pretreatment analysis of variance results for Fork Mountain LTSP.

Variable Block Treatment

Biomass

Trees >12.7 cm dbh NS NS

Trees 2.5 -12.7 cm dbh 0.019** .086*

Trees <2.54 cm dbh 0.028** NS

Herbs 0.0017** .0140**

Forest Floor -Oi NS NS

Forest Floor-Oe+Oa NS NS

Forest Floor -total mass NS NS

Down woody debris NS NS

Stumps NS NS

Deadwood standing 0.085* 0.095*

Deadwood total NS NS

Total biomass NS NS

Nutrients - Foliage

Sweet birch -N conc. NS NS

Sweet birch - P conc. NS 0.0481**

Sweet birch K conc. NS NS

Swwet birch Ca conc. NS 0.0349**

Sweet birch Mg conc. NS NS

Yellow-poplar -N conc. NS NS

Yellow-poplar -P conc. NS NS

Yellow-poplar -K conc. NS NS

Yellow-poplar -Ca conc. 0.0331** NS

Yellow-poplar -Mg conc. NS NS

Black cherry N-conc. NS NS

Black cherry P-conc. NS NS

Black cherry K-conc. NS NS

Black cherry Ca-conc. NS NS

Black cherry Mgconc. NS NS

Red maple -N conc. 0.0136** 0.0544**

Red maple -P conc. NS NS

Red maple -K conc. NS NS

Red maple -Ca conc. NS NS

Red maple -Mgconc. NS NS

Coarse fragments in soil

0-15 cm NS NS

15-30 cm 0.023** NS

30-45 cm 0.07* NS

Nutrients - Forest Floor

Oi Nitrogen content NS NS

Oi Carbon content 0.0930* NS

Oi Ca conc. .0589* NS

Oi P Conc. 0.0670* NS

Oi Mn conc. 0.0046** NS

Oe+Oa Nitrogen content NS NS

Continued
28

Figure 14.—Percentage coarse fragment content in soil by horizon and block. Vertical bars

represent ± 1 standard deviation.

rationale: that variability down the length of the slope is

significant enough to capture by blocking.

Another way to evaluate spatial variability is by

evaluating plot means in relation to their location within

blocks (Fig. 16). When aboveground biomass is displayed

this way, in addition a slight down-slope trend, a trend

from the west to east side of the plots is evident. Note

that the upper northwest corner of the study area (plots

3, 4, 5, 6, 12) are similar in aboveground productivity, as

Table 17.—continued

Variable Block Treatment

Oe+Oa Carbon content NS NS

Oe+Oa Ca conc. 0.0030** NS

Oe+oa Mg conc 0.0416** NS

Nutrients in Soil

Ca conc. - 0-15 cm 0.0161** NS

Ca conc - 15-30 cm 0.0073** NS

Ca conc - 30-45 cm 0.0380** NS

Mg conc - 0-15 cm 0.0920* NS

Mg conc - 15-30 cm 0.0010** 0.0390**

Mg conc - 30-45 cm NS NS

K conc - 0-15 cm NS NS

K conc - 15-30 cm 0.0975* NS

K conc - 30-45 cm 0.0445** NS

Total bases- 0-15 cm 0.0113** NS

Total bases - 15-30 cm 0.0014** NS

Total bases - 30-45 cm 0.0204** NS

* Significant at p<.10

** Significant at p<.05

0

10

20

30

40

50

60

70

80

0-15 cm 15-30 cm

Soil horizon (depth)

% coarse fragments
29

measured by aboveground biomass, and those in the

lower, southeast corner (8,9,16) show some of the lowest

productivity.

Tree species and herb layer richness for each plot are

displayed similarly in Figures 17 and 18. The plots below

the midslope logging road (plots 9 through 16) exhibited

greater tree species richness than those above the road, as

well as a higher stem density. However, herb species

richness was greater on the upper two blocks of plots.

Tree species richness, diversity and evenness increased

down slope, while herb layer r and H’ exhibited a nearly

Figure 15.—Mean soil Ca concentration, in three horizons by block.

Vertical bars represent ± 1 standard deviation.

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

I II

Block 

Calcium (cmol+/kg)

Figure 16.—Spatial arrangement of

total aboveground biomass by plot

and block. Numbers on the bars are

growth plot numbers.

Block I

Block II

Block III

Block IV

0

50000

100000

150000

200000

250000

300000

350000

400000

450000

Aboveground biomass (kg/ha)

1

2

3

4

7

8

9

10

11

12

13

14

15

16

6

5

N
30

Figure 17.—Spatial arrangement of tree species richness

by plot and block. Note this graph is rotated from Figure 16

for ease of viewing. Numbers on the bars are growth plot

numbers. Figure 18.—Spatial arrangement of herb species richness by

plot and block. Numbers on the bars are growth plot numbers.

Figure 19.—Linear regression of growth plot aboveground biomass vs. plot species richness

for trees (triangles) and herb layer vegetation (circles).

Block I

Block II

Block III

Block IV

0

5

10

15

20

Tree species richness

1

2 3 4

8

7

6

5

9

10

11

12

13 14

15

16

N Block I

Block

II Block

III Block

IV

0

5

10

15

20

25

30

35

40

Tree species richness

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

N

y = 3E-05x + 19.818

R2

 = 0.0412

y = -8E-05x + 41.285

R2

 = 0.6603

0

5

10

15

20

25

30

35

40

200000 250000 300000 350000

Biomass (kg/ha)

Richness ( r)
31

Table 18.—Diversity measures, by block, Fork Mountain LTSP study site

Trees Herbs

Block Species Shannon Index Evenness Species Shannon Index Evenness

richness (r) of Diversity (H′) richness (r) of Diversity (H′)

I 16 2.63 0.948 55 3.85 0.962

II 17 2.69 0.949 49 3.75 0.964

III 20 2.89 0.965 41 3.55 0.957

IV 23 3.01 0.960 49 3.73 0.958

All 26 3.95 76 3.95 0.908

opposite pattern (Table 18). The spatial pattern of tree

species richness by plot is nearly opposite that for

productivity, including the west to east gradient. Those

plots (3, 4, 5, 6, 12) with the greatest aboveground

biomass had the lowest tree species richness and

generally, with some exceptions, the greatest herb layer

diversity. These relationships are shown in Figure 19. The

inverse relationship between tree species richness and

aboveground biomass is reasonably robust. The complex

trends across the site are also reflected in the 15-30 cm

horizon coarse fragments, where the down-slope trend is

apparent, but the across-slope gradient is less

pronounced. (Fig. 20).

Summary

We have learned a great deal about this particular site.

Based on most metrics, the Fork Mountain LTSP site is a

highly productivity site, with vegetative diversity typical

of most second-growth Appalachian hardwood forests.

Except for relatively low soil nutrient levels, the site

characteristics suggest few problems with regeneration.

Based on soil properties, the site may be susceptible to

leaching of base cations as a result of high levels of acidic

deposition. The trends in productivity and nutrients,

particularly Ca, were somewhat unexpected but are

accommodated within the experimental design.

Monitoring the response of this ecosystem to these

treatments continues.

Figure 20.—Spatial arrangement of percentage

soil coarse fragment content in 15 - 30 cm

horizon by block and plot. Numbers on the

bars are the growth plot numbers.

B horizon Coarse fragments in soil Block IV Block III Block II Block I

N

70

60

50

40

30

20

10

0

%
32

Acknowledgments

The authors thank the Fernow logging crew and

technicians who spent many months taking

measurements and installing treatments: Doug Owens,

Roy Poling, Scott Hockman, Mark Owens, Layne

Godwin, Pete Lipscomb, John Campbell. The assistance

of Clifford Phillips, Bob Rosier, Rick Hovatter, Robin

Bowley, and Darlene Madarish with plot establishment

and early sampling also is appreciated. Don Flegel

described the soil pits for us, and Christine Siegel-Issem

and Dave Mitchem from Virginia Tech provided valuable

help with the soil sampling. Frederica Wood has

provided timely assistance with data management. Many

thanks to Bob Powers for shepherding the LTSP idea

from its inception.

Literature Cited

Adams, M.B. 1999. Acidic deposition and sustainable

forest management in the central Appalachians.

Forest Ecology Management. 122: 17-28.

Adams, M.B.; Angradi, T.R.; Kochenderfer, J.N. 1997.

Stream water and soil responses to five years of

nitrogen and sulfur additions at the Fernow

Experimental Forest, West Virginia. Forest Ecology

and Management. 95: 79-91.

Adams, M.B.; Burger, J.A.; Jenkins, A.B.; Zelazny, L.

2000. Impact of harvesting and atmospheric

pollution on nutrient depletion of eastern US

hardwood forests. Forest Ecology and Management.

138: 301-319.

Adams, M.B.; Kochenderfer, J.N.; Angradi, T.R.;

Edwards, P.J. 1995. Nutrient budgets of two

watersheds on the Fernow Experimental Forest. In:

Gottschalk, K.W.; Fosbroke, S.L.C., eds. Proceedings

10th Central Hardwood Forest Conference; 1995

March 5-8; Morgantown, WV. Gen. Tech. Rep. NE107. Radnor, PA: U.S. Department of Agriculture,

Forest Service. Northeastern Forest Experiment

Station: 119-130.

Adams, M.B.; Kochenderfer, J.N.; Wood, F.; Angradi,

T.R.; Edwards, P. 1994. Forty years of

hydrometeorological data from the Fernow

Experimental Forest, West Virginia. Gen. Tech. Rep.

NE-184. Radnor, PA: U.S. Department of

Agriculture, Forest Service, Northeastern Forest

Experiment Station. 24 p.

Adams, M.B.; Owens, D.R. 2001. Specific gravity of

coarse woody debris for some Central Appalachian

hardwood forest species. Res. Pap. NE-716.

Newtown Square, PA: U.S. Department of

Agriculture, Forest Service, Northeastern Research

Station. 4 p.

Adams, M.B.; Schuler, T.M.; Ford, W.M.; Kochenderfer,

J.N. 2003. Large woody debris in a second-growth

central Appalachian hardwood stand: volume,

composition and dynamics. In: Van Sambeek, J.W.;

Dawson, J.O.; Ponder, F., Jr.; Lowenstein, E.F.;

Fralish, J.S., eds. Proceedings, 13th Central Hardwood

Forest Conference; 2002 April 1-3; Urbana IL. Gen.

Tech. Rep. NC-234. St Paul, MN: U.S. Department

of Agriculture, Forest Service, North Central Research

Station: 237-245.

Brenneman, B.B.; Daniels, R.F. 1982. A case study, with

alternatives, for calculating biomass yield. In:

Baldwin, V.C.; Lohrey, R.E., eds. Proceedings, 1982

Southern Forest Biomass Working Group Workshop;

1982 June 16-18; Alexandria, VA. New Orleans, LA:

U.S. Department of Agriculture, Forest Service,

Southern Forest Experiment Station: 105-110.

Brenneman, B.B.; Frederick, D.J.; Gardner, W.I.;

Schoenhofen, L.H.; Marsh, P.L. 1978. Biomass of

species and stands of West Virginia hardwoods. In:

Proceedings, Central Hardwood Conference II, 1978

November 14-16; West Lafayette, IN. West Lafayette,

IN: Purdue University: 160-178.

Edwards, P.J.; Wood, F. 1993. Field and laboratory

quality assurance/quality control protocols and

accomplishments for the Fernow experimental

Forest watershed acidification study. Gen. Tech.

Rep. NE-177. Radnor, PA: U.S. Department of
33

Agriculture, Forest Service, Northeastern Forest

Experiment Station. 15 p.

Elliott, K.J.; Hitchcock, S.L.; Krueger, L. 2002.

Vegetation response to large-scale disturbance in a

southern Appalachian forest: Hurricane Opal and

salvage logging. Journal of the Torrey Botanical

Society. 129(1): 48-59.

Fenn, M.R.; Poth, M.A.; Aber, J.D.; Baron, J.S.;

Bormann, B.T.; et al. 1998. Nitrogen excess in North

American ecosystems: predisposing factors,

ecosystem responses and management strategies.

Ecological Applications. 8(3): 706-733.

Fuller, R.D.; Driscoll, C.T.; Lawrence, G.B.; Nodvin,

S.C. 1987. Processes regulating sulphate flux after

whole-tree harvesting. Nature. 325: 707-710.

Galloway, J.N.; Schlesinger, W.H.; Levy, J.; Michaels, A.;

Schnoor, J.L. 1995. Nitrogen fixation:

anthropogenic enhancement, environmental

response. Global Biogeochemical Cycles. 9: 235-252.

Gilliam, F.S.; Adams, M.B. 1999. Effects of harvesting

on soil nitrogen (N) dynamics in a N-saturated

hardwood forest. In: Stringer, Jeffrey W.; Loftis,

David, L., eds. Proceedings 12th Central Hardwood

Forest Conference; 1999 February 28-March 2;

Lexington, KY. Gen. Tech. Rep. SRS-24. Asheville,

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

Southern Research Station: 29-36.

Gilliam, F.S.; Turrill, N.L. 1993. Herbaceous layer

cover and biomass in a young versus a mature

stand of a central Appalachian hardwood forest.

Bulletin of the Torrey Botanical Society. 120(4): 445-

450.

Gilliam, F.S.; Adams, M.B.; Yurish, B.M. 1996.

Ecosystem nutrient responses to chronic nitrogen

inputs at Fernow Experimental Forest, West

Virginia. Canadian Journal of Forest Research. 16:

196-205.

Grossman, R.B.; Reinsch, T.G. 2002. Bulk density and

linear extensibility. In: Dane, J.H.; Topp, G.C., eds.

Methods of soils analysis, part 4, physical methods.

Madison, WI: Soil Science Society of America: 201-

228.

Helvey, J.D.; Kunkle, S.H. 1986. Input-out budgets of

selected nutrients on an experimental watershed

near Parsons, West Virginia. Res. Pap. NE-584.

Broomall, PA: U.S. Department of Agriculture, Forest

Service, Northeastern Forest Experiment Station. 7 p.

Husch, B.; Miller, C.I.; Beers, T.W. 1972. Forest

mensuration. New York: Roland. 410 p.

Jenkins, A.B. 2002. Organic carbon and fertility of

forest soils on the Allegheny Plateau of West

Virginia. Morgantown, WV: West Virginia

University. 282 p. Master’s thesis.

Jenkins, M.A.; Parker, G.R. 1997. Changes in down

dead wood volume across a chronosequence of

silvicultural openings in southern Indiana forests.

In: Pallard, S.G.; Cecich, R.A.; Garrett, H.G.;

Johnson, P.S., eds. Proceedings, 11th Central

Hardwood Forest Conference; 1997 March 23-26;

Columbia, MO. Gen. Tech. Rep. NC-188. St. Paul,

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

North Central Forest Experiment Station: 162-169.

Keays, J.L. 1975. Complete tree utilization: an analysis

of the literature. Information Rep. VP-X-77.

Vancouver, BC: Canada Forest Products Laboratory.

79 p.

Lusk, M.G. 1998. Sulfate dynamics and base cation

release in a high elevation Appalachian Forest soils.

Blacksburg, VA: Virginia Polytechnic Institute and

State University. 168 p. Master’s thesis.

Magurran, A.E. 1998. Ecological diversity and its

measurement. London: Croom Helm. 179 p.

McWilliams, W.H.; Bowersox, T.W.; Brose, P.H.; Devlin,

D.A.; Finley, J.C.; et al. 2003. Indicators of
34

regenerative capacity for native broadleaf forests: A

case study of ungulate overpopulation in the

eastern US. In: World Forestry Congress XII

proceedings: forests, source of life, area A: forests for

people; 2003 September 21-28; Quebec City, PQ.

[place of publication unknown]: International Union

of Forestry Research Organization: 342

National Research Council. 1998. Forested landscapes

in perspective: prospects and opportunities for

sustainable management of America’s nonfederal

forest. Washington, DC: National Academy Press.

249 p.

Northeastern Ecosystem Research Cooperative. 2004.

Foliar chemistry database. Durham, NH: University

of New Hampshire, Complex Systems Research

Center. Available at: http://www.folchem.sr.unh.edu.

[January 5, 2004].

Patric, James H.; Smith, David W. 1975. Forest

management and nutrient cycling in eastern

hardwoods. Res. Pap. NE-324. Upper Darby, PA:

U.S. Department of Agriculture, Forest Service,

Northeastern Forest Experiment Station. 12 p.

Peterjohn, W.T.; Adams, M.B.; Gilliam, F.S. 1996.

Symptoms of nitrogen saturation in two central

Appalachian hardwood forest ecosystems.

Biogeochemistry. 35: 507-522.

Strausbaugh, P.D.; Core, E.L. 1952. Flora of West

Virginia. Morgantown, WV: Seneca Books. 1,079 p.

Tiarks, A.E; Buford, M.A.; Powers, R.F., Ragus, J.R.;

Page-Dumroese, D.S.; et al. 1997. North American

long-term soil productivity research program. In:

Communicating the role of silviculture in managing

the National Forests: proceedings of the National

silviculture workshop; 1997 May 19-22; Warren, PA.

Gen. Tech. Rep. NE-238. Radnor, PA: U.S.

Department of Agriculture, Forest Service,

Northeastern Forest Experiment Station: 140-147.

Valentine, H.T.; Gregoire, T.G.; Furnival, G.M. 1987.

Unbiased estimation of total tree weight by threestage sampling with probability proportion to size.

In: Wharton, E.H.; Cunia, T., comps. Estimating tree

biomass regressions and their error: proceedings of the

workshop on tree biomass regression functions and

their contribution to the error of forest inventory

estimates; 1986 May 26-30; Syracuse, NY. Gen. Tech.

Rep. NE- 117. Broomall, PA: U.S. Department of

Agriculture, Forest Service, Northeastern Forest

Experiment Station: 129-132.

Wendel, G.W. 1987. Abundance and distribution of

vegetation under four hardwood stands in northcentral West Virginia. Res. Pap. NE-607. Broomall,

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

Northeastern Forest Experiment Station. 6 p.

Young, H.E.; Ribe, J.H.; Hoppe, D.C. 1979. A biomass

study of the thinning potential and productivity of

immature forest stands in Maine. Bulletin 758.

Orono, ME: University of Maine, Life Sciences and

Agriculture Experiment Station. 38 p.
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36

APPENDIX

Table 19.—Characterization soil profiles, described by Natural Resources Conservation

Service, 1996. See Figure 2 for location of pits.

Identification: A

Location: 35' at SE 45o

 from boundary stake GP2/GP3

Parent material: sandstone colluvium over McCrady shale residue

Slope: 16%

Aspect: SE facing

Layers Description

Oa 0-1"

A 1-8", 20% coarse fragments, 6.4 YR 3/1, loam, very friable, many very fine to coarse

roots, clear, wavy boundary

BA 8-13", 25% coarse fragments, 7.5 YR 3/3, loam, friable, many very fine to coarse

roots, wavy, boundary

Bw1 13-24", 30% coarse fragments, 5 YR 5/4, silt loam, firm consistence, common very

fine to medium roots, gradual, wavy boundary

Bw2 24-36", 35% coarse fragments, 5 YR 4/3, silt loam, firm consistence, few very fine

and fine roots, clear wavy boundary

2C 36-53", 45% coarse fragments, 2.4 YR 4/3, silty clay, massive structure, firm

consistence, rare very fine to fine roots, clear wavy boundary

2Cr 53-55", 2.5 YR 3/3, and 2.5 YR 5/6, firm consistence

R > 55"

Identification: B

Location: Buffer strip between 6 & 5, 25' from stake corner 4, GP5

Parent Material: Sandstone residuum

Slope: 8%

Aspect: SE facing

Layers Description

Oa 0-0.5"

A1 0.5-2.5", 45% coarse fragments, 7.5 YR 2.5/1, loam, very friable, many very fine to

coarse roots, wavy boundary

A2 2.5-6.5", 35% coarse fragments, 7.5 YR 3/3, loam, very friable, many very fine to

coarse roots, clear wavy boundary

BA 6.5-9", 30% coarse fragments, 10YR 4/4, silt loam, friable, many very fine to coarse

roots, clear wavy boundary

Bw1 9-20", 35% coarse fragments, 10 YR 4/6, silt loam, friable, common very fine to

coarse roots, gradual, wavy boundary

Bw2 20-30", 40% coarse fragments, 10 YR 5/6, silt loam, friable, common very fine to

coarse roots, clear, wavy boundary

Bw3 30-43%, coarse fragments, 10 YR 5/6, loam, friable, few very fine and fine roots,

gradual, wavy boundary

C 43-55", 70% coarse fragments, 10 YR 5/8, loam, firm consistence, few fine roots

Continued
37

Table 19.—continued

Identification: C

Location: at end of plot 5 (buffer stake #5)

Parent material: Sandstone residuum

Slope: 8%

Aspect: SE

Layers Description

Oa 0-0.5"

A 0.5-3", 20% coarse fragments, 2.5 YR 3/2, loam, very friable, many very fine to

coarse roots, clear, wavy boundary

BA 3-7", 20% coarse fragments, 10 YR 3⁄4, loam, very friable, many very fine to coarse

roots, clear wavy boundary

Bw1 7-17", 25% coarse fragments, 10 YR 6/6, silt loam, friable, common very fine to

coarse roots, gradual wavy boundary

Bw2 17-20", 30% coarse fragments, 10 YR 6/6, loam, friable, few very fine and medium

rots, clear, wavy boundary

BC 28-34", 40% coarse fragments, 10 YR 6/6, loam, firm consistence, very few fine and

fine roots, gradual wavy boundary

2C 34-45", 55% coarse fragments, 10 YR 6/8 (with inclusions of 10 YR 5/3, 10 YR 5/2),

loam, firm consistence, clear wavy boundary

R > 45"

Identification: D

Location: Buffer stake # 7-8, 15' east on road

Parent material: weathered shale

Slope:

Aspect: SE

Layers Description

Oa 0-1", 

A 1-5", 20% coarse fragments, 10 YR 3/1, loam friable, many very fine to coarse

roots, clear wavy boundary

BA 5-9", 25% coarse fragments, 2.5 yr 4/3, loam, friable, many very fine to coarse

roots, clear wavy boundary

Bw1 9-29", 35% coarse fragments, 10 YR 5/6, silt loam, friable, common very fine to

coarse roots, gradual wavy boundary

Bw2 29-44", 65% coarse fragments, 10 YR 5/4 (with 10 YR 7/2 colors and 6.5 YR 5/6),

loam, very firm consistence, few very fine and fine roots, clear wavy boundary 

C 44-63", coarse fragments, 7.5 YR 5/6, loam with silt loam pockets, massive

structure, firm consistence, no roots, clear wavy boundary

CR 63-74", 2.5 YR 6/6 with red coatings (7.5 YR 5/6)
38

Table 20.—Plant species found on Fork Mountain LTSP plotsa

Trees

Red maple Acer rubrum L.

Sugar maple Acer saccharum Marsh.

Common serviceberry Amelanchier arborea (Michx. f.) Fernald

Sweet birch Betula lenta L.

Yellow birch Betula lutea Michx.

American hornbeam Carpinus caroliniana Walt.

Bitternut hickory Carya cordiformis (Wang) K. Koch

Shagbark hickory Carya ovata K. Koch

American chestnut Castanea dentata (Marsh.) Borkh.

Alternate-leaved dogwood Cornus alternifolia L.

Flowering dogwood Cornus florida L.

American beech Fagus grandifolia Ehrh.

White ash Fraxinus americana L.

Yellow-poplar Liriodendron tulipifera L.

Cucumber magnolia Magnolia acuminata L.

Fraser magnolia Magnolia fraseri Walt.

Blackgum Nyssa sylvatica Marsh.

Eastern hophornbeam Ostrya virginiana (Mill.) K. Koch

Sourwood Oxydendrum arboreum (L.) D.C.

Fire cherry Prunus pennsylvanica L.

Black cherry Prunus serotina Ehrh.

Chestnut oak Quercus montana Willd.

Northern red oak Quercus rubra L.

Black locust Robinia pseudo-acacia L.

Sassafras Sassafras albidum (Nutt.) Nees.

American basswood Tilia americana L.

Shrubs

Striped maple Acer pennsylvanicum L.

Deciduous holly Ilex sp.*

Mountain laurel Kalmia latifolia L.

Spicebush Lindera benzoin (L.) Blume

Azalea sp. Rhododendron sp.

Common elderberry Sambucus canadensis L.

Mapleleaf viburnum Viburnum acerifolium L.

Witch hazel Hamamelis virginiana L.

Woody Vines

Dutchman’s pipe Aristolochia durior Hill.

Virginia creeper Parthenocissus quinquefolia (L.) Planch.

Blackberry sp. Rubus sp.

Grape sp. Vitis sp.

Continued
39

Ferns and Allies

Clubmoss Lycopodium sp.

Hayscented fern Dennstaedtia punctiloba (Michx.) Moore

New York fern Dryopteris noveboracensis (L.) Gray

Beech fern Dryopteris sp.

Christmas fern Polystichum acrostichoides (Michx.) Schott.

Flowering Herbs

White baneberry Actaea alba L.

Mountain anemone Anemone lancifolia Pursh.

Indian turnip Arisaema triphyllum (L.) Schott.

Aster sp. Aster sp.

False nettle Boehmeria cylindrica (L.) Sw.

Sedge sp. Carex sp.

Blue cohosh Caulophyllum thalictroides (L.) Michx.

Black cohosh Cimicfuga racemosa (L.) Nutt.

Slender toothwort Dentaria heterophylla Nutt.

White snakeroot Eupatorium rugosum Houtt.

Wild licorice Gallium circaezans Michx.

Bedstraw sp. Gallium sp.

Wild geranium Geranium maculatum L.

St. Johnswort Hypericum sp.

Touch-me-not Impatiens sp.

Red henbit Lamium purpureum L.

Indian cucumber-root Medeola virginiana L.

Partridgeberry Mitchella repens L.

Basil balm Monarda clinopoda L.

Indian pipe Monotropa uniflora L.

Sweet cicely Osmorhiza claytoni (Michx.) Clarke

Ginseng Panax quinquefolius L.

Deertongue grass Panicum sp.

Pokeweed Phytolacca americana L.

Mayapple Podophyllum peltatum L.

Common cinquefoil Potentilla simplex Michx.

Lion’s foot Prenanthes trifoliata (Cass.) Fernald

Mountain mint Pycnantheum sp.

Buttercup Ranunculus sp.

Black snakeroot Sanicula canadensis L.

Sedum Sedum ternatum Michx.

False Solomon’s seal Smilacina racemosa (L.) Desf.

Upright smilax Smilax ecirrhata (Engelm.) S. Wats.

Greenbrier Smilax sp.

Twisted stalk Streptopus roseus Michx.

Table 20.—Continued

Continued
40

Cliff meadow rue Thalictrum sp. *

Virginia knotweed Tovara virginiana (L.) Raf.

Trillium Trillium sp.

Stinging nettle Urtica dioica L.

Violet Viola sp.

Orchid sp.

Grass sp.

Wild parsley*

a

All plant names according to Strausbaugh and Core (1952)

*not listed in Strausbaugh and Core

Table 20.—Continued
Printed on Recycled Paper

Adams, Mary Beth; Burger, James; Zelazny, Lucian; Baumgras, John. 2004.

Description of the Fork Mountain long-term soil productivity study: site

characterization. Gen. Tech. Rep. NE-323. Newtown Square, PA: U.S. Department

of Agriculture, Forest Service, Northeastern Research Station. 40 p.

The effects of air pollution and timber harvesting on soil resources continue to be an

important issue in eastern hardwood forests. This publication describes the Fork

Mountain Long-term Soil Productivity Study (LTSP), located on the Fernow

Experimental Forest, WV, and the pretreatment stand, soil and climatic conditions.

Extensive vegetation surveys, biomass determinations, site characterization, and

analyses of soil physical and chemical characteristics are described herein. The Fork

Mountain LTSP site is, based on most metrics, a highly productive site with vegetative

diversity typical of most second growth Appalachian hardwood forests. Other than

relatively low soil nutrient levels, site characteristics suggest few problems with

regeneration. Based on soil characteristics, the site may be susceptible to leaching of

base cations as a result of high levels of acidic deposition. Productivity and nutrient

characteristics, particularly calcium, varied across the site spatially, but are

accommodated in the experimental design. We continue to monitor the response of this

ecosystem to these treatments.

Keywords: diversity, acidic deposition, eastern hardwoods, Appalachian forests
Headquarters of the Northeastern Research Station is in Newtown Square,

Pennsylvania. Field laboratories are maintained at:

Amherst, Massachusetts, in cooperation with the University of Massachusetts

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

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

Warren, Pennsylvania

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(202)720-5964 (voice and TDD). USDA is an equal opportunity provider and employer.

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