European Settlement-Era 

Vegetation of the 

Monongahela National 

Forest, West Virginia

Melissa A. Thomas-Van Gundy 

and Michael P. Strager

United States 

Department of 

Agriculture

Forest Service

Northern 

Research Station

General Technical 

Report NRS-101

White oak

Scarlet oak

American chestnut

White oak

Maple 

American chestnut

Maple 

Sugar maple

Sugar maple

Black locust

American 

chestnut

White oak

White oak
Abstract 

Forest restoration would be greatly helped by understanding just what forests looked like a century or more 

ago. One source of information on early forests is found in old deeds or surveys, where boundary corners were 

described by noting nearby trees known as witness trees. This paper describes the creation and analysis of a 

database of witness trees from original metes and bounds surveys of what became the Monongahela National 

Forest in West Virginia. We include an estimate of positional error from the conversion of paper maps to digital 

format. The final database contains 15,589 corners and 22,328 trees of 49 species from deeds dating from 

1752 to 1899. White oak was the most frequent witness tree, followed by sugar maple, American beech, and 

American chestnut, and distribution patterns were recognizable across the study area.

In early forests of the study area, magnolia, sugar maple, and black cherry were found on high-elevation 

ridges. Red spruce, hemlock, birch, and American beech were found on high-elevation toe slopes. Basswood 

was found in high-elevation coves, and red oak was associated with bench landforms at high elevations. At 

moderate elevations American chestnut and chestnut oak were associated with ridges, white pine and yellow 

pine occurred on benches, and an unknown species called spruce-pine was found on valley landforms. 

Blackgum was associated with toe slopes on low elevations, and black walnut was found on low-elevation 

benches. Low-elevation valleys contained white oak, elm, and sycamore. An important finding from this 

analysis is that some associations between species and environmental variables differed based on the 

ecological setting. Indicator kriging, using presence-absence data, resulted in probability of occurrence maps 

for selected species. We estimate that white oak covered 26 percent of the study area, sugar maple 19 

percent, American chestnut 3 percent, and red spruce 2 percent. 

The Authors

Melissa A. Thomas-Van Gundy is a research forester with the U.S. Forest Service, Northern Research

Station, Parsons, WV.

Michael P. Strager is an associate professor with West Virginia University, Division of Forestry and

Natural Resources, Morgantown, WV.

Cover Photo: The witness trees of one parcel, the Andrew G. Mathews grant of 95 acres in 1843, are shown. The image is 

a portion of a digitally scanned map from the 1930s of land grants covering what later became the Monongahela National 

Forest near Clover Lick in Pocahontas County, West Virginia. (Photo by Melissa Thomas-Van Gundy, U.S. Forest Service.)

Manuscript received for publication February 2012

Published by: For additional copies:

U.S. FOREST SERVICE U.S. Forest Service

11 CAMPUS BLVD SUITE 200 Publications Distribution

NEWTOWN SQUARE PA 19073-3294 359 Main Road

Delaware, OH 43015-8640

August 2012 Fax: 740-368-0152

Visit our homepage at: http://www.nrs.fs.fed.us
1

Introduction

Information on historical forest conditions is sparse for 

much of the eastern United States due to early settlement 

and forest clearing by Europeans and intensive timber 

harvesting at the start of the 20th century. Nevertheless, 

descriptions and quantitative assessments of early forests 

and grasslands are useful in restoration ecology and can 

come from many sources such as land survey records, 

travelers’ accounts, and photographs (Whitney 1994). In 

the absence of large old-growth forests, these historical 

references are often the best source of information on 

forest characteristics at the time of European settlement. 

They can provide clues to Native American influences and 

other disturbances on the landscape, and can provide an 

ecological baseline to inform restoration actions. 

Unlike early surveys in the Midwest, systematic grid 

surveys such as those under the Government Land Office 

(GLO) were uncommon in the original colonies. There, 

land transfers followed survey methods called metes and 

bounds. Metes and bounds surveys consist of a series of 

bearings and distances with trees, posts, rock piles, or 

natural features recorded to describe corners where bearings 

changed. These trees used as the parcel corner or located 

close to the corner are called witness trees. Deeds or grants 

documenting transfer of ownership of a parcel of land 

also document tree species existing at the time of transfer 

through these witness trees (Abrams and Ruffner 1995; 

Black and Abrams 2001a, b; Rentch and Hicks 2005; 

Whitney and DeCant 2003). 

Witness tree data give a snapshot of forest composition 

at the time of early European settlement. Witness tree 

data have been used to document changes in species 

composition in current forests (Abrams and McCay 1996, 

Rentch and Hicks 2005) and other changes in forest 

conditions (Dyer 2001, Wang et al. 2009, Whitney and 

DeCant 2003) or show consistency in species distribution 

patterns (Strahler 1972). Relationships between vegetation 

and site conditions can also be determined through analysis 

of witness tree locations on the landscape (Abrams and 

McCay 1996, Black and Abrams 2001a, Wang 2007). 

Species abundance may also provide information on 

historical disturbance regimes. When combined with 

archeological data, witness tree information can also reveal 

Native American influences on forest composition (Black 

et al. 2006). This glimpse into the past may be useful for 

understanding historical conditions and informing current 

land management. 

Surveyor bias toward certain tree species has been addressed 

in witness tree studies based on GLO methods (Bourdo 

1956, Liu et al. 2011) and metes and bounds (Black and 

Abrams 2001a). In contrast with GLO surveys, in metes 

and bounds surveys the surveyor was not required to scribe 

information on witness or bearing trees. Therefore, a bias 

toward smooth-barked trees was not likely. Unusual tree 

species may have been more likely to be used as a witness 

tree, as was found for Public Land Survey records in 

Wisconsin (Liu et al. 2011), because these trees would have 

made the corner easier to re-locate. Longer-lived species 

would likely be chosen over others if available. 

Like most other eastern states, West Virginia retains only 

a few fragments of old-growth forests documenting early 

forest composition. Often these fragments are found 

in uncommon ecological settings; existing old-growth 

remnants may underrepresent more common ecological 

settings and forest types. In contrast, witness tree 

information from land grants and deeds contains a record 

of forest composition at the time of European settlement 

across a wide range of ecological settings. 
2

In 2005, paper maps from the 1930s depicting witness 

trees found on what is now the Monongahela National 

Forest (MNF) were converted to a digital format. The 

witness tree dataset built from the 1930s paper maps 

was explored to answer questions on the composition of 

European settlement-era forests of the MNF. A previous 

analysis of a subset of these data was made by Abrams 

and McCay (1996). The analysis presented here is based 

on a larger number of points and uses different methods; 

we hope it will give greater detail of the early forest that 

can inform ecosystem restoration actions. Specifically, 

the objectives of this analysis were to: (1) quantify the 

positional error of this witness tree database as a result 

of conversion to digital format, (2) characterize the 

spatial relationships of the witness trees, (3) characterize 

the species-site relationships in the early forests, and 

(4) interpolate among witness corners to provide 

continuous forest composition from the witness trees. 

STUDY AREA

The MNF is located in east-central West Virginia (Fig. 

1) and has complex topography as most of the area is 

located in the Allegheny Mountains and Ridge and 

Valley physiographic sections, with a small portion in the 

Northern Cumberland Mountains section (Cleland et al. 

2007). This complexity results in a variety of landforms 

and conditions supporting diverse vegetation. The study 

area (MNF proclamation boundary buffered by 5 km) is 

approximately 1,014,000 ha, and includes all or portions 

of the following counties: Barbour, Grant, Greenbrier, 

Nicholas, Pendleton, Pocahontas, Preston, Randolph, 

Tucker, and Webster. 

This unglaciated area includes the faulted and folded 

mountains of the Ridge and Valley physiographic section 

and the uplifted and eroded Allegheny Mountains. The 

Allegheny Front divides the two physiographic sections, 

creating a rain shadow effect to the east. Sedimentary rocks 

of Pennsylvanian, Mississippian, Devonian, Silurian, and 

Ordovician age underlie the study area. Lithology includes 

sandstones, shales, siltstones, coal, and limestone. Differing 

substrates and rates of erosion help create the varied soils 

and topography of the study area. 

The physiographic sections that cover the MNF can be 

further subdivided to describe the diversity of ecological 

conditions. Subsections in the study area are as follows: 

Eastern Allegheny Mountain and Valley (EAMV), 

Eastern Coal Fields (ECF), Northern High Allegheny 

Mountain (NHAM), Ridge and Valley (RV), Southern 

High Allegheny Mountains (SHAM), Western Allegheny 

Mountain and Valley (WAMV), and Western Allegheny 

Mountains (WAM) (Cleland et al. 2007; Table 1). In 

general, the RV subsection is warm and dry and the 

WAMV subsection is dry with moderate temperatures. The 

ECF subsection is warm and moderate in overall moisture. 

The EAMV subsection is moderate in both moisture and 

temperature regimes, and the WAM subsection is cool with 

moderate moisture. The SHAM and NHAM subsections 

are both wetter and cooler than the other subsections, but 

NHAM has the lowest average temperatures (Table 2). 

Table 1.—Ecological subsections of the Monongahela National Forest study area (Cleland et al. 2007), area within 

the proclamation boundary, and percentage of total for each subsection. 

Subsection Hectares Percent of study area

Eastern Allegheny Mountain and Valley 161,518 15.9

Eastern Coal Fields 35,078 3.5

Northern High Allegheny Mountain 215,591 21.2

Ridge and Valley 137,390 13.6

Southern High Allegheny Mountains 243,468 24.0

Western Allegheny Mountain and Valley 48,184 4.8

Western Allegheny Mountains 151,138 14.9

Other 21,426 2.1

 Total 1,013,793 100.0
3

Figure 1.—Study area location and physiographic subsections. Thicker boundary line is between Northern Ridge and 

Valley, and Allegheny Mountains sections. 

Subsection

Monongahela NF proclamation boundary

Ridge & Valley

0 5 10 20 30 40

Kilometers

West Virginia

Monongahela NF

W. Allegheny Mtns.

E. Coal Fields

W. Allegheny Mtn. & Valley

S. High Allegheny Mtns.

N. High Allegheny Mtns.

E. Allegheny Mtn. & Valley

Elkins

Petersburg

Franklin

Marlinton

Webster Springs

Parsons
4

Potential Natural Vegetation (percent)

 Appalachian Mixed Northeastern Northern Oak-hickory-

	 	 oak forest	 mesophytic forest	 spruce-fir forest	 hardwoods	 pine forest

EAMV 58.9 0.0 0.2 40.9 0.0

ECF 28.1 51.7 0.0 20.2 0.0

NHAM 0.0 0.0 14.3 80.4 5.4

RV 49.1 0.0 0.7 9.2 40.9

SHAM 4.5 23.3 21.1 51.2 0.0

WAMV 85.9 1.1 0.0 13.0 0.0

WAM 26.6 38.0 0.0 35.3 0.1

Appalachian oak forest is the primary potential natural 

vegetation for the RV, EAMV, and WAMV subsections. 

(Common and scientific names of species are found in 

Table 3.) In contrast, a mixture of northern hardwoods 

and red spruce is the primary potential natural vegetation 

for the NHAM and SHAM subsections. The mixed 

mesophytic type is the primary potential natural vegetation 

for the WAM and ECF subsections (Cleland et al. 2007; 

Table 2). 

The extractive logging boom (and associated fires and soil 

loss) that reshaped the original forest of West Virginia 

occurred between 1870 and 1920, reaching a peak in 

1909. As with European settlement elsewhere, however, 

there was small-scale extraction as evidenced by the first 

sawmill in Tucker County in about 1776 (Stephenson 

1993). In the upland counties of the MNF, commercial 

timber was first removed in areas close to navigable rivers 

and streams starting around 1865 (Stephenson 1993) with 

interstate rail lines reaching the region in the 1850s and 

1870s (Lewis 1998). Large-scale forest removal occurred 

after narrow-gauge railroads were built into the remote 

upland forest starting around 1884 (Stephenson 1993); 

rail lines reached the headwaters of the Greenbrier River 

in Pocahontas County in 1903 (Lewis 1998). Other 

technologies that made large-scale timber removal possible 

included the Shay locomotive to navigate the narrowgauge rail lines and the bandsaw. The largest expansion of 

sawmills utilizing bandsaws across West Virginia occurred 

between 1890 and 1910 (Lewis 1998). The MNF became 

a National Forest in 1920, although the first land was 

purchased in 1915 near Parsons, WV, under the authority 

of the Weeks Act of 1911. 

Methods

Witness Tree Locations

In the 1930s, personnel on the MNF obtained copies of 

the first land grant or deed for parcels that would later 

become the MNF from county courthouses. MNF staff 

then used the bearings and distances listed in the deeds and 

land grants to plot the parcels. As these deeds were under 

Table 2.—Selected subsection climate and potential natural vegetation attributes (Cleland et al. 2007). Elevation 

figures are summarized from a 1:4800 scale digital elevation model of the study area. Subsection abbreviations:

EAMV = Eastern Allegheny Mountain and Valley, ECF = Eastern Coal Fields, NHAM = Northern High Allegheny 

Mountain, RV = Ridge and Valley, SHAM = Southern High Allegheny Mountains, WAMV = Western Allegheny 

Mountain and Valley, and WAM = Western Allegheny Mountains.

Subsection Ave. ann. Ave. ann. Ave. Jan. Ave. ann. Ave. ann. Min. Max. Mean

 max. temp. min. temp. min. temp. snowfall precipitation elev. elev. elev.

 (oC) (oC) (oC) (cm) (cm) (m) (m) (m)

EAMV 16.5 2.6 -8.8 120.5 111.0 523.3 1,461.8 824.5

ECF 17.5 4.5 -6.8 94.9 113.7 526.1 956.8 761.0

NHAM 14.5 2.0 -9.4 263.5 128.2 511.5 1,454.5 1,020.2

RV 17.3 4.1 -7.1 82.6 102.3 284.7 1,482.5 712.8

SHAM 15.2 2.4 -9.0 224.5 138.1 446.8 1,478.6 1,043.7

WAMV 17.1 4.3 -7.0 85.6 99.7 529.1 1,101.5 731.7

WAM 15.1 3.0 -8.8 187.8 122.2 418.2 1,226.8 733.0
5

the metes and bounds system of land survey and not GLO 

surveys, only the species of corner trees are given in the 

original deed. The individual deeds were referenced by the 

owner’s name, date of deed, and a location number. The 

resulting parcel maps each covered an area approximately 

11,300 ha and overlap to varying degrees. There are 83 

of these maps covering the area roughly contained by the 

proclamation boundary of the MNF. In 2005, the 1930s 

paper maps were scanned and georeferenced to be used in 

a geographic information system (GIS) and to preserve the 

information. The digital versions of the 1930s paper maps 

were used for digitizing the corners of individual parcels. 

Point features were manually digitized in a GIS (ArcGIS 

9.2, 2002, ESRI, Redlands, CA) with corners placed using 

the 2005 digital maps as visual guides. The 2005 digital 

maps were of parcel boundaries, often with no indication 

as to corner number or witness tree species. To assign 

the witness trees to the correct corners, the deed survey 

descriptions were used to determine corner number and 

tree species, or other marker, defining that corner. Along 

with tree species, the locator number and date of deed 

or survey were recorded in the ArcGIS attribute file. A 

crosswalk of common names appearing in the deeds and 

current scientific names was used in attributing the corners 

(Table 3). If a species was not noted for a witness tree in 

the deed (e.g., oak, maple, birch), those witness trees were 

recorded at the genus level. There is duplication of corners 

as the deeds themselves reference adjoining parcels. For 

this database, duplicate points were retained if new species 

were included as witness trees or the survey was made 10 or 

more years after the first survey using the shared corner. 

Spatial and Attribute Uncertainty

Errors in corner location were introduced in the creation 

of the digital maps and dataset from the hand-drawn paper 

maps. The 1930s maps were scanned at a resolution of 

200 dots per inch (JPEG format) and georeferenced using 

the ESRI ArcGIS georeferencing extension. At least four 

corners or tie points were used from the scanned maps to 

reference actual coordinates. Latitude and longitude were 

noted on most of the 1930s maps, making georeferencing 

easier. Georeferencing of maps continued until root mean 

square error rates for all four corners were less than 10 m 

(Strager 2008). 

Corner points were placed through manual digitizing at a 

mapping scale of 1:5,000 using many features of the maps 

as guides and the deed for bearings and distances. The 

scanned base maps included hand-drawn parcels, most of 

which were highlighted by colored pencil lines. For some 

parcels only the colored pencil line existed on the maps to 

aid in placing corners. Some tracts were preprinted on the 

map with corners identified by circles. All of these map 

markings introduced positional error when used to place 

corners in space. In addition, because of the scale used to 

create the digital point (1:5,000), the actual digital point 

was not placed at the exact middle of the guide marker, 

introducing another error factor. 

To estimate total positional error across all points 

(Equation 1), random points were located in the study 

area in GIS and used to locate map elements to sample. 

Measurements were made using the ArcGIS measuring 

tool at a map scale of 1:1,000. At the 50 random locations, 

the widths of pencil lines, colored pencil lines, and printed 

lines were measured. Also at 50 random locations, the 

diameters of printed circles were measured. The distance 

from the electronic data point to the center of the base 

map corner location was measured at 50 random locations. 

These 50 measurements per map element were not 

necessarily taken at the same locations because not all map 

elements were found at the same location. Error terms 

1 through 4 were averaged and divided in half (Kelly et 

al. 2008) since the target placement of the digital corner 

would have been the center of these map elements. Total 

error was calculated by summing the squares of each term 

and taking the square root of the total (Equation 1; Kelly 

et al. 2008). Included in this total calculation is an average 

root mean square error of 9 m for the georeferencing 

process (Strager 2008). 

Equation 1

etotal = (e1

2

 + e2

2

 + e3

2

 + e4

2

 + e5

2

 + e6

2

)

1/2

where:

e1 = 1⁄2 average width of hand-drawn pencil parcel lines

e2 = 1⁄2 average width of hand-drawn colored pencil lines 

e3 = 1⁄2 average width of printed parcel lines

e4 = 1⁄2 average diameter of printed corner circles

e5 = distance to actual corner from digitized corner

e6 = 9 m, average root mean square error associated with 

georeferencing
6

Table 3.—Common and scientific names of witness trees cited in deeds dated 1752 to 1899 on the Monongahela

National Forest. A question mark after a scientific name represents uncertainty in the assignment of a scientific

name to that name used in deeds. 

Common name used in deeds	 Scientific name	 Stems tallied

Balsam fir, fir, balsam Abies balsamea 4

Striped maple Acer pensylvanicum 1

Sugar or hard maple, sugar tree, sugar Acer saccharum a 2,252

Maple Acer spp., A. rubrum? 1,265

Buckeye Aesculus spp. 48

Serviceberry, service, sarvice Amelanchier spp.a 62

Black or sweet birch Betula lenta 3

River birch Betula nigra 6

Birch Betula spp. 1,007

Hornbeam, ironwood, hophornbeam, or bluebeech Carpinus caroliniana; Ostrya virginiana 303

Hickory Carya spp. 1,010

Chestnut Castanea dentata 1,373

Dogwood Cornus spp. 318

Hazel, witch hazel Corylus spp.?, Hamamelis virginiana 6

Hawthorn, white thorn, thorn Crataegus spp. 34

American beech Fagus grandifolia 1,938

Ash Fraxinus americana 421

Holly Ilex opaca 1

Butternut, white walnut Juglans cinerea 136

Black walnut, walnut Juglans nigra 128

Red cedar, cedar Juniperus virginiana 3

Yellow-poplar, poplar, tulip tree, tulip Liriodendron tulipiferaa 455

Indian wood, Indian bitter Maclura pomifera? a, b 5

Magnolia, cucumber, elkwood Magnolia acuminata or M. fraseri c 260

Apple, crab apple, plum, and peach Malus spp. 10

Mulberry Morus spp. 5

Spruce-pine None, likely red spruced or hemlocka 399

Blackgum, gum, sour gum Nyssa sylvatica 284

Sourwood Oxydendrum arboretum 10

Red spruce, spruce, black spruce, yew pine Picea rubens a ,e 683

Yellow, pitch, Virginia, or black pine Pinus rigida, P. virginiana, or P. pungens 116

Pine Pinus spp. 1,030

White pine Pinus strobus 214

Sycamore Plantanus occidentalis 82

Aspen, cottonwood Populus spp. 1

Black or wild cherry Prunus serotina 265

White oak Quercus alba 3,779

Scarlet, span, Spanish, or pin oak Quercus coccineab 443

Chestnut or rock oak Quercus prinus a 1,111

Northern red oak Quercus rubra 736

Oak Quercus spp. 18

Black oak Quercus velutina 501

Locust Robinia pseudoacacia 220

(Table 3 continued on next page)
7

Willow Salix spp. 1

Sassafras Sassafras albidum 6

Yew Taxus canadensis, possibly spruce? 11

Basswood, yellow or white lynn, lin Tilia spp.a 911

Hemlock, hemlock-spruce Tsuga canadensis b 354

Elm Ulmus spp. 90

Unknown 2

aStrausbaugh and Core 1978; bU.S. Forest Service undated a; cWebster-dictionary.org; dStrahler 1972; eU.S. Forest Service undated b. 

Table 3.— Continued.

Landform Bias

Bias toward certain landforms was noted in similar metes 

and bounds witness tree datasets (Black and Abrams 

2001a). To assess the degree of bias toward landforms in 

the study area, witness tree locations were compared to 

a systematic sample (Black and Abrams 2001a). A 0.8-

km square grid was created over the study area and the 

resulting center points tallied by landform (landforms used 

in this analysis are defined below). A Chi-square test was 

used to compare the landform frequencies from the metes 

and bounds (irregular) survey to the systematic survey. The 

0.8-km grid size was chosen for systematic sampling to 

simulate a GLO grid survey. 

Spatial Analyses

Tree species are not located randomly in space as 

environmental and biological factors influence their 

presence and abundance at any given location (Cooper 

1859, Küchler 1964, Whittaker 1956). Ecological data 

often violate the assumptions of many statistical models 

by exhibiting spatial autocorrelation (Legendre 1993). 

Spatial autocorrelation is the property of pairs of random 

variables having values that are more similar (positive 

autocorrelation) or less similar (negative autocorrelation) 

than expected for random pairs of observations (Legendre 

1993). One benefit of spatial autocorrelation is that if 

positive spatial autocorrelation is found, then predictions 

can be made of unknown values using surrounding known 

values. Spatial heterogeneity is an inherent property of 

ecosystems, not the product of a random process, and for 

this reason is important to describe (Legendre 1993). 

To describe the spatial characteristics of the witness tree 

dataset, clusters of high and low values and spatial outliers 

were determined for selected species across the study area 

through the calculation of Anselin’s local Moran’s I (Anselin 

1995) in ArcMap 9.3.1 (2009, ESRI, Redlands, CA). To 

reduce errors associated with small sample sizes, only those 

species with 50 or more occurrences were assessed (McCune 

et al. 2002, Whitney 1990). At each witness tree location, a 

relative frequency of each species was calculated by dividing 

the number of trees of each species by the total number 

of trees at the corner (Wang et al. 2009). This relative 

frequency was used as the attribute value for the calculation 

of spatial autocorrelation by species. 

The study area as a whole was used, as opposed to ecological 

subsections to reduce edge effects due to the shapes of the 

subsections. The results of the local Moran’s I calculations 

are Z-scores with high positive scores indicating surrounding 

points have similar values, either similar high values or similar 

low values. Low negative Z-scores indicate a statistically 

significant (α = 0.05) spatial outlier. The Z-scores were 

used to classify the study points into statistically significant 

High-High (HH) or Low-Low (LL) points (α = 0.05), 

where HH points are those with a high abundance of a 

particular species surrounded by other high-abundance 

points of that same species and LL points are the opposite. 

Significant spatial outliers were classified as High-Low 

(HL) if the point has a high value and is surrounded 

by points with low values, or Low-High (LH) if the 

opposite occurs. Euclidian distance was used in all spatial 

calculations with weights calculated by inverse distance; 

data were standardized by row totals to account for 

potential sampling bias. 
8

Indicator Species Analysis

If patterns in species abundance are determined from the 

mapping of local spatial autocorrelation, then it may be 

possible to describe underlying environmental variables 

that are influencing species distribution. To characterize 

the associations between tree species and ecophysical 

characteristics, environmental variables associated with the 

corner points (buffered by the error term) were extracted 

from existing spatial datasets and also derived from a 

digital elevation model (DEM). Ecophysical characteristics 

assessed were: topographic roughness, moisture index, 

aspect, landform, elevation, and soil series. 

Topographic roughness is a measure of surface variability 

that may influence the distribution of species in an area 

or cause patterns in other physical variables influencing 

species distributions. For this analysis, a topographic 

roughness index (TRI) for each cell was calculated as the 

square root of the sum of squared differences in elevation 

between a cell and its eight neighboring cells (Riley et al. 

1999). The moisture index was calculated for each cell as 

a function of flow accumulation and slope (Equation 2; 

Anderson et al. 1998).

Equation 2

Moisture index = ln (flow accumulation +1) / (slope +1) 

Elevation, moisture index, and TRI were all calculated as 

averages around the corner location buffered by the positional 

error term calculated in Equation 1. These averages were 

then classified in ArcMap into high, medium, and low based 

on unbiased quantiles with each class containing an equal 

(or nearly equal) number of features. The use of quantiles, 

three equal-sized groups of the raw calculated factors, 

reduces the influence of outliers and extremes (Isaaks and 

Srivastava 1989). The use of quantiles reduced the likelihood 

that cutoffs would be biased toward any given species’ 

environmental requirements. 

The use of quantiles resulted in elevations of 298.4 to 

731.7 m classed as low, 731.8 to 892.5 m as moderate, and 

>892.5 m as high elevation. The moisture index ranged 

from -4.26 to 7.3 with breaks at -2.78 for low/moderate 

and -1.53 for moderate/high. The TRI ranged from 1 

to 224.7 m, with 1 to 39.9 m as low, 40.0 to 71.6 m as 

moderate, and 71.7 to 224.7 m classed as high TRI. 

Aspect, slope, elevation, topographic roughness, and flow 

accumulation (a component of the moisture index) were 

derived through ArcMap Spatial Analyst from an 18-meter 

DEM of the study area resampled from a 3-meter DEM 

to reduce computing time. Aspect was transformed so that 

0 - 22.5 degrees and 337.5 - 360 degrees both resulted 

in north aspect. Landform and soil series were extracted 

from existing spatial datasets of the MNF with the corners 

buffered by the positional error term and all landforms and 

soil series within that radius tallied. 

Landform data are from the MNF ecological classification 

system and were assigned during soil surveys (Natural 

Resources Conservation Service [NRCS] 2010a). 

Landforms are as follows: ridge, bench, toe slope, side 

slope, cove, and valley. In the original database, slope 

landforms were separated into generic and mountain with 

an elevation cutoff for mountain slopes at approximately 

300 m. The generic and mountain slope landforms were 

combined for this analysis because much of the MNF is 

above 300 m in elevation and elevation was assessed as a 

separate variable regardless of landform. Narrow ridges, 

broad ridges, saddles, shoulders, knobs, and peaks were 

combined for the ridge landform category. Side slopes 

and middle/back slope landforms were combined for the 

side slope category, and floodplains, newer terraces, older 

terraces, alluvial fans, valley floors, flats, plains, and valleys 

were combined for the valley category. 

Soil series were obtained from the MNF soils GIS data 

layer based on the county soil surveys originally mapped 

at a scale of 1:20,000. Soil series that described less than 1 

percent of the corners were dropped from the analysis, as 

were the associated survey corners. The original soil series 

used in the MNF GIS layer were combined by slope and 

stoniness categories. Complexes of soils were summarized 

by the first soil series listed in the complex; for example, 

we grouped Berks-Weikert soils with Berks soils for this 

analysis (Table 4). 

The frequency counts of species and ecophysical variables 

by subsection were analyzed by indicator species analysis in 

PC-ORD (McCune and Mefford 2006); significance was 

tested through Monte Carlo methods (4,999 permutations; 

α = 0.05). Categorical ecophysical variables (TRI, moisture 

index, aspect, landform, elevation, and soils) were used 

as grouping factors and each witness tree record served 
9

Table 4.—Soil series of the Monongahela National Forest used in indicator species analysis. Soils data are from

the Monongahela National Forest GIS soils data layer, originally from county soil surveys. For analysis, soil series 

were grouped by first soil listed in a series or complex, and slope and stoniness classes were grouped together.

Allegheny Allegheny Loam

Atkins Atkins Loam

Atkins Silt Loam

Atkins-Philo-Potomac Complex

Belmont Belmont Silt Loam

Belmont Stony Silt Loam-Rock Outcrop Complex

Belmont-Cateache Silt Loams

Berks Berks Channery Loam

Berks Channery Silt Loam

Berks Channery Silt Loam, Moist

Berks, Weikert, and Calvin Soils

Berks-Dekalb Complex

Berks-Weikert Channery Silt Loams

Berks-Weikert Shaly Silt Loams

Blackthorn Blackthorn Channery Loam

Blackthorn Channery Sandy Loam

Blackthorn Very Channery Loam

Blackthorn-Dekalb-Elliber

Buchanan Buchanan and Ernest Stony Soils

Buchanan Channery Fine Sandy Loam

Buchanan Channery Loam

Calvin Calvin Channery Silt Loam

 Calvin Silt Loam, High Base Substratum

 Calvin Stony Silt Loam, High Base Substratum

Calvin-Dekalb-Berks Complex

Calvin-Dekalb-Hazelton Complex

Cateache Cateache Channery Silt Loam

 Cateache Silt Loam

Dekalb Dekalb Channery Loam

 Dekalb Channery Sandy Loam

Dekalb Extremely Stony Loam

 Dekalb Rubbly Loam

 Dekalb, Hazleton, and Lehew Stony Soils

 Dekalb, Hazleton, and Lehew Very Stony Soils

 Dekalb-Elliber

 Dekalb-Elliber-Blackthorn

Dekalb-Hazleton Complex 

Dekalb-Rock Outcrop Complex

Ernest Ernest Rubbly Silt Loam

 Ernest Silt Loam

Gilpin Gilpin Channery Silt Loam

Gilpin Silt Loam

Gilpin Stony Silt Loam

Gilpin-Buchanan Complex

Gilpin-Dekalb Complex

Gilpin-Dekalb Stony Complex

Gilpin-Laidig Association

Laidig Laidig and Buchanan Soils

 Laidig Channery Loam

 Laidig Channery Silt Loam

 Laidig Stony Loam

 Laidig Very Stony Loam

Lily Lily Loam

 Lily Sandy Loam

Macove Macove Channery Silt Loam

Mandy Mandy Channery Silt Loam

Meckesville Meckesville Stony Silt Loam

Opequon Opequon Silt Loam 

Opequon-Caneyville Silty Clay Loams

Potomac Potomac Cobbly Loam

Potomac Fine Sandy Loam

Potomac Loam

Potomac Very Cobbly Fine Sandy Loam

Potomac Very Gravelly Fine Sandy Loam

Potomac Gravelly Loam

Shouns Shouns Channery Loam

Shouns Silt Loam

Weikert Weikert Channery Silt Loam

Soil Series 

Used in 

Analysis Original GIS Layer

Soil Series

Used in 

Analysis Original GIS Layer
10

as a plot. Tree species with more than 50 occurrences in 

the study area were included in indicator species analysis. 

After this initial filter, a species had to have at least 10 

occurrences with a study variable to be included in the 

indicator species analysis (Whitney 1982). Indicator species 

analysis combines the species abundance in a given group 

with the faithfulness of occurrence of a species to a group 

and has been used to describe site-species relationships 

(Godefroid et al. 2007, Phillips et al. 2003). The indicator 

value is the product of the proportional abundance of a 

species in a group relative to the abundance of that species 

in all groups and the mean proportion of sample units in 

each group that contain the species (Dufrene and Legendre 

1997, McCune et al. 2002). If a species is a perfect 

indicator of a given group, it should always be present in 

that group and exclusive to that group. 

Spatial Interpolation

To create continuous coverage for selected species, species 

abundances were converted to presence or absence and 

indicator kriging (IK) was used to map the probability 

of occurrence between locations. Indicator kriging has 

been used on presence/absence witness tree data to create 

spatially continuous representations of presettlementera vegetation (Wang 2007), and is the only kriging 

method appropriate for binary data. Semivariograms 

were constructed and fitted in ArcMap. The lag size was 

varied to reduce the nugget effect, which is the amount 

of variation at the origin (zero distance between pairs 

of observations) of the semivariogram and represents 

measurement error in the data or variation at a scale finer 

than measured in the model. Sparse data may also lead 

to a greater than expected nugget effect. Anisotropy, the 

property of spatial data where differences in values differ 

by distance and direction between pairs of observations, 

was found in the distributions of some species and 

was accounted for in the final models to adjust for the 

directional influence of the spatial autocorrelation. The 

directional trend was incorporated into the models by 

setting the directional search to the direction of the axis 

of the anisotropic ellipse. Spherical models were fit to the 

semivariograms because the sample variograms showed 

linear behavior near the origin (Isaaks and Srivastava 

1989); stable models were used for some species to reduce 

error. Interpolations were made for common species (white 

oak and sugar maple), and species of interest for restoration 

efforts (red spruce and American chestnut). Species with 

similar site variable associations (based on indicator species 

analysis) were assessed either alone or in combination 

(e.g., white oak and white pine). By using a threshold 

of 30-percent probability of occurrence (Manies and 

Mladenoff 2000, Wang 2007), the area covered by each 

species was calculated for the study area. 

Results

Species Abundances

The full database consists of 15,692 corners from 

approximately 1,450 individual parcel descriptions from 

deeds dated 1752 to 1914. For the subsequent analyses 

reported here, corners and associated trees dated after 1899 

were removed. Corners dated after 1899 totaled 103, with an 

associated 141 trees from five deeds, leaving 15,589 corners 

representing 22,328 witness trees (Fig. 2). About 54 percent 

of the corners recorded only one witness tree, two witness 

trees were recorded on about 38 percent of the corners, and 

about 7 percent of the corners recorded three witness trees. 

Corners listing four to six trees represented about 1 percent 

of the total. About 24 percent of the corners date to the late 

1700s. The greatest numbers of corners were established in 

the 1840s and 1850s at 17.8 percent and 29.3 percent of the 

total, respectively (Fig. 3). Some corners in the dataset were 

not used for analysis as they fell outside the state boundary 

or occurred on minor subsections. 

Forty-nine species (or genera) were used at least once as 

witness trees in the deeds, 28 of which were overstory 

species with sufficient numbers of occurrences for spatial 

analysis (Table 3). Spruce-pine (likely red spruce or 

hemlock) and Indianwood witness trees were retained as 

separate species although a current common or scientific 

name could not be confidently assigned to them. 

As expected, species composition varied by ecological 

subsection. White oak was the most common species 

across the study area, with a mean relative frequency of 

19.4 percent. White oak accounted for 17 to 38 percent 

of the witness trees in the EAMV, RV, WAMV, and WAM 

subsections based on mean relative abundance (Table 5). 

American chestnut was the most frequent species in the ECF 

subsection (18.4 percent), American beech in the NHAM 

(18.2 percent), and sugar maple in the SHAM subsection 

(18.2 percent). After white oak, the most abundant species 
11

Figure 2.—Locations of corners from all deeds in relation to the proclamation boundary of the Monongahela National 

Forest and this boundary buffered by 5 kilometers.

Corner

Monongahela NF proclamation boundary

5 km buffer

0 5 10 20 30 40

Kilometers
12

across the study area were sugar maple (10.1 percent), 

American beech (8.4 percent), American chestnut (6.3 

percent), and chestnut oak (5.2 percent). Surprisingly, red 

maple was not cited in any deeds, although it is likely that 

“maple” refers to red maple. If this is the case, then red 

maple made up 5.0 percent of the relative abundance across 

the study area, and ranked 6th overall. It was most common 

in the ECF subsection, where its relative abundance was 

11.4 percent, second only to American chestnut. 

When we summarized species as absolute counts by 

subsection, similar patterns of species abundances emerge 

(Table 5). White oak was still the most abundant witness 

tree across the study area and for four of the subsections, 

ranking 4th (ECF), 7th (NHAM), and 9th (SHAM) in the 

others. Of those species with greater than 50 occurrences 

across the study area, elm and serviceberry were the least 

common witness trees. 

Spatial Error and Landform Bias

During creation of the dataset, the conversion of paper 

maps to digital format and the manual placement of corner 

points using the maps as guides introduced positional 

error. The measurements taken for Equation 1 resulted 

in an estimate of positional error of 20.9 m around each 

witness tree corner. This error estimate was rounded to 

21 m and used as a radius to buffer the corner locations 

for the calculation of elevation, moisture index, TRI, and 

tallies of landform, aspect, and soil series associated with 

the corners for indicator species analysis. 

The Chi-square comparison of landform frequencies from 

witness tree locations to systematic sampling showed no 

difference (p = 0.575). Thus, there was no bias in witness 

tree locations by landform in this study area and dataset. 

0.1% 0.1% 0.5%

7.0%

16.1%

2.9% 

1.7% 

7.1%

9.7%

17.9%

29.3%

3.2%

1.7% 2.0% 

0.7%

0

500

1,000

1,500

2,000

2,500

3,000

3,500

4,000

4,500

5,000

1752

1760-17691770-17791780-17891790-17991800-18091810-18191820-18291830-18391840-18491850-18591860-18691870-18791880-18891890-1899

Figure 3.—Number of corners per decade, and percent of total number of corners, based on deed date. Twelve deeds with 

unknown dates were not included. Number of 

Corners
13

Local Clustering

The corner locations themselves, regardless of species, were 

significantly clustered when spatial autocorrelation was 

assessed across the study area. The local clustering analysis 

determined whether species were significantly clustered 

in one of four categories: high-high (HH), low-low (LL), 

high-low (HL), or low-high (LH). Overall, LL points were 

found only for sugar maple, American beech, and white 

oak (Figs. 4 and 5). Analysis of white oak witness trees, 

shows a distribution of LL points highlighting the near 

absence of white oak in the wetter, colder, and higherelevation subsections (SHAM and NHAM) and part of 

the WAM subsection (Fig. 5). Most HH points for white 

oak were in the EAMV subsection. There were HH points 

across the study area for American chestnut, but most were 

located in the EAMV and WAM subsections (Fig. 5). Most 

Table 5.—Mean relative frequency and occurrence (N) by species and ecological subsection, for species or genus

with 50 or more occurrences across the study area. Relative frequencies were calculated at each point by dividing

the number of trees of each species or genus by the total number of trees at the corner; occurrence is based on the 

presence of the species or genus at a corner. 

Species or genus EAMV ECF NHAM RV SHAM WAMV WAM Study area

White oak 32.5 (1,794) 10.7 (44) 5.7 (129) 28.3 (568) 3.9 (175) 38.2(351) 17.0 (600) 19.4(3,661)

Sugar maple 4.5 (298) 10.1 (44) 17.1 (407) 7.0 (177) 18.2 (902) 7.3 (80) 7.3 (295) 10.1(2,201)

American beech 1.2 (81) 8.3 (36) 18.2 (447) 0.8 (21) 17.2 (881) 2.9 (26) 9.1 (377) 8.4(1,869)

American chestnut 6.3 (393) 18.4 (76) 4.0 (115) 4.0 (89) 4.7 (226) 6.1 (67) 9.3 (376) 6.3(1,342)

Chestnut oak 6.0 (364) 3.0 (13) 2.3 (59) 12.3 (255) 1.0 (50) 2.2 (22) 7.6 (293) 5.2(1,056)

Maple 5.6 (387) 11.4 (58) 6.6 (192) 2.0 (59) 4.7 (271) 3.9 (51) 5.0 (223) 5.0(1,241)

Pine 9.1 (528) 0 (0) 1.8 (35) 8.5 (170) 4.0 (190) 4.2 (40) 0.6 (22) 5.0 (985)

Birch 1.2 (97) 1.8 (10) 6.1 (171) 1.1 (25) 9.1 (484) 1.8 (17) 4.7 (187) 4.2 (991)

Hickory 6.1 (431) 2.2 (13) 1.3 (39) 4.5 (119) 1.7 (101) 5.1 (57) 5.3 (237) 4.1 (997)

Basswood 2.6 (182) 4.7 (23) 5.9 (143) 3.1 (76) 6.9 (353) 1.5 (16) 2.4 (89) 3.9 (882)

Northern red oak 2.8 (190) 2.9 (15) 3.4 (93) 4.8 (123) 3.1 (167) 2.2 (27) 2.6 (112) 3.1 (727)

Spruce 1.4 (94) 0 (0) 3.0 (90) 0.8 (16) 5.9 (291) 0.4 (3) 3.5 (126) 2.9 (620)

Black oak 2.7 (198) 4.0 (17) 0.2 (7) 5.6 (121) 0.7 (31) 5.0 (58) 1.4 (56) 2.2 (488)

Yellow-poplar 1.1 (76) 10.4 (46) 1.3 (35) 1.2 (25) 1.1 (52) 1.4 (15) 4.7(203) 2.0 (452)

Scarlet oak 0.9 (68) 0.5 (3) 2.1 (54) 2.0 (44) 0.9 (44) 3.8 (46) 4.3(179) 2.0 (438)

Spruce-pine 2.9 (169) 3.0 (11) 2.4 (59) 1.5 (33) 1.4 (66) 0.2 (2) 0.8 (30) 1.8 (370)

Ash 0.7 (58) 0.5 (3) 2.1 (64) 1.7 (43) 2.4 (146) 0.7 (9) 2.3 (95) 1.7 (418)

Hemlock 0 (0) 0 (0) 6.5 (175) 0.3 (7) 3.4 (139) 0 (0) 0.2 (6) 1.6 (327)

Blackgum 1.1 (84) 2.5 (10) 0.6 (17) 1.3 (35) 0.6 (28) 1.2 (13) 2.1 (94) 1.2 (281)

Black walnut/butternut 0.7 (41) 0.9 (5) 1.1 (29) 2.3 (63) 1.4 (66) 1.4 (16) 0.9 (38) 1.2 (258)

Magnolia 0.1 (6) 0.5 (2) 2.2 (55) 0.5 (12) 1.2 (71) 0 (0) 2.4(110) 1.1 (256)

Dogwood 1.8 (147) 1.4 (8) 0.3 (11) 1.5 (46) 0.2 (11) 1.8 (24) 1.3 (68) 1.1 (315)

Hophornbeam/hornbeam 1.1 (86) 0.2 (1) 1.3 (45) 1.2 (34) 1.7 (102) 0.8 (8) 0.4 (23) 1.1 (299)

Black cherry 0.7 (45) 0.2 (1) 1.8 (55) 0.2 (5) 2.2 (125) 0.6 (7) 0.5 (21) 1.0 (259)

White pine 3.1 (191) 0 (0) 0.1 (2) 0.5 (10) 0 (0) 0 (0) 0.3 (10) 1.0 (213)

Black locust 0.7 (63) 0.3 (2) 0.7 (19) 0.9 (26) 1.0 (54) 1.2 (15) 0.9 (38) 0.8 (217)

Yellow pine 1.4 (75) 0 (0) 0 (0) 0.3 (6) 0 (4) 2.9 (21) 0.1 (4) 0.6 (110)

Sycamore 0.4 (19) 0.4 (1) 0.1 (2) 0.6 (13) 0.2 (10) 0.3 (3) 1.2 (38) 0.5 (86)

Elm 0.4 (23) 0 (0) 0.3 (10) 0.8 (21) 0.3 (17) 1.1 (11) 0.2 (7) 0.4 (89)

Serviceberry <0.1 (8) 0.5 (3) 0.5 (16) <0.1 (2) 0.1 (11) 0 (0) 0.4 (19) 0.2 (59)

 Total (N) 6,196 445 2,575 2,244 5,068 1,005 3,976 21,509
14

Figure 4.—Results of local cluster analysis for sugar maple and American beech witness trees. Analysis results in the 

identification of hot spots (HH), cold spots (LL), and spatial outliers (HL or LH). Points without a cluster designation are not 

displayed. 

American 

Beech

LL

 LH

 HH

 HL

Sugar Maple

LL

 LH

 HH

 HL

American 

Beech

LL

 LH

 HH

 HL

of the HH points for red spruce witness trees were located 

in the SHAM subsection (Fig. 6). For witness trees tallied 

as simply maple, there were more HH points in the EAMV 

subsection, and this species did not show a pattern similar 

to sugar maple, suggesting these were red maples (Fig. 7). 

The significant LL points for American beech all were found 

in the RV and EAMV subsections; HH points were found 

in SHAM, NHAM, and WAM subsections (Fig. 4). The 

distribution of local clustering was similar for sugar maple, 

although no LL clusters appear for sugar maple in the RV 

subsection (Fig. 4). Higher concentrations of yellow-poplar 

were found in the WAM subsection and very few HH points 

for this species were found in either SHAM or NHAM 

subsections (Fig. 8). Significant HH or HL points for 

basswood were scattered throughout the study area (Fig. 9). 

The oaks (other than white oak) and hickories all had similar 

distributions of HH points, with few found in the moist 

and higher-elevation SHAM and NHAM subsections (Figs. 

10, 11, and 12). Northern red oak is the exception to this 

pattern, with more HH points found in these subsections 

compared to the other oak species. Yellow pine and pine had 

similar distributions of high abundance points with nearby 

high abundance points (HH); most were found in the 

EAMV or RV subsections (Fig. 17). In contrast, there were 

no HH points for hemlock in the EAMV subsection; HH 

points for this species were generally located in the northern 

half of the study area and at higher elevations (Fig. 7). 

Most HH points for birch witness trees were found in the 

higher-elevation subsections (Fig. 6). Cluster analysis for 

sycamore shows no HH points in the NHAM subsection 

(Fig. 14). The cluster of HH points of black walnut 

(Fig. 15) in the RV subsection appears unusual and may 

represent more mesic conditions along coves and/or stream 

bottoms within this generally dry subsection. Groups of 

HH points were found for blackgum in the WAM and 

EAMV subsections (Fig. 16). For magnolia species, most 

HH points were located in the WAM subsection or the 

border of WAM and SHAM (Fig. 13). Analyses were made 

for other species: black cherry (Fig. 8), ash (Fig. 9), sprucepine (Fig. 13), elm (Fig. 14), butternut (Fig. 15), and black 

locust (Fig. 16); the graphical results are displayed, but 

their cluster distributions are not discussed further. 

Sugar Maple

LL

 LH

 HH

 HL
15

Figure 5.—Results of local cluster analysis for white oak and American chestnut witness trees. Analysis results in the 

identification of hot spots (HH), cold spots (LL), and spatial outliers (HL or LH). Points without a cluster designation are not 

displayed. 

White Oak

LL

 LH

 HH

 HL

American 

Chestnut

LH

 HH

 HL

Figure 6.—Results of local cluster analysis for red spruce and birch witness trees. Analysis results in the identification of 

hot spots (HH), cold spots (LL), and spatial outliers (HL or LH). Points without a cluster designation are not displayed. 

Red Spruce

LH

 HH

 HL

Birch

 LH

 HH

 HL
16

Figure 8.—Results of local cluster analysis for yellow-poplar and black cherry witness trees. Analysis results in the 

identification of hot spots (HH), cold spots (LL), and spatial outliers (HL or LH). Points without a cluster designation are not 

displayed.

Yellow-poplar

 LH

 HH

 HL

Black Cherry

 HH

 HL

Figure 7.—Results of local cluster analysis for maple and hemlock witness trees. Analysis results in the identification of hot 

spots (HH), cold spots (LL), and spatial outliers (HL or LH). Points without a cluster designation are not displayed. 

Maple

 LH

 HH

 HL

Hemlock

 LH

 HH

 HL
17

Figure 10.—Results of local cluster analysis for chestnut oak and black oak witness trees. Analysis results in the 

identification of hot spots (HH), cold spots (LL), and spatial outliers (HL or LH). Points without a cluster designation are not 

displayed.

Chestnut 

Oak

 LH

 HH

 HL

Black Oak

 LH

 HH

 HL

Figure 9.—Results of local cluster analysis for ash and basswood witness trees. Analysis results in the identification of hot 

spots (HH), cold spots (LL), and spatial outliers (HL or LH). Points without a cluster designation are not displayed.

Ash

 LH

 HH

 HL

Basswood

 LH

 HH

 HL
18

Figure 12.—Results of local cluster analysis for hickory and white pine witness trees. Analysis results in the identification 

of hot spots (HH), cold spots (LL), and spatial outliers (HL or LH). Points without a cluster designation are not displayed.

Hickory

 LH

 HH

 HL

White Pine

 LH

 HH

 HL

Figure 11.—Results of local cluster analysis for Northern red oak and scarlet oak witness trees. Analysis results in the 

identification of hot spots (HH), cold spots (LL), and spatial outliers (HL or LH). Points without a cluster designation are not 

displayed.

Northern 

Red Oak

LH

 HH

 HL

Scarlet Oak

LH

 HH

 HL
19

Figure 13.—Results of local cluster analysis for spruce-pine and magnolia witness trees. Analysis results in the 

identification of hot spots (HH), cold spots (LL), and spatial outliers (HL or LH). Points without a cluster designation are not 

displayed.

Spruce-pine

 LH

 HH

 HL

Magnolia

 LH

 HH

 HL

Figure 14.—Results of local cluster analysis for sycamore and elm witness trees. Analysis results in the identification of hot 

spots (HH), cold spots (LL), and spatial outliers (HL or LH). Points without a cluster designation are not displayed.

Sycamore

 HH

 HL

Elm

 HH

 HL
20

Figure 16.—Results of local cluster analysis for blackgum and black locust witness trees. Analysis results in the identification 

of hot spots (HH), cold spots (LL), and spatial outliers (HL or LH). Points without a cluster designation are not displayed.

Blackgum

 HH

 HL

Black Locust

 LH

 HH

 HL

Figure 15.—Results of local cluster analysis for black walnut and butternut witness trees. Analysis results in the identification 

of hot spots (HH), cold spots (LL), and spatial outliers (HL or LH). Points without a cluster designation are not displayed.

Black Walnut

 HH

 HL

Butternut

 LH

 HH

 HL
21

Figure 17.—Results of local cluster analysis for pine and yellow pine witness trees. Analysis results in the identification of 

hot spots (HH), cold spots (LL), and spatial outliers (HL or LH). Points without a cluster designation are not displayed. 

Species-Site Associations

Indicator species analysis showed that across the study area 

red spruce, hemlock, birch, American beech, magnolia, 

basswood, sugar maple, ash, northern red oak, and black 

cherry were all associated with higher elevations; red spruce, 

hemlock, birch, and American beech were found on toe 

slopes (Table 6). At high elevations, Mandy soils on toe 

slopes were associated with red spruce and birch, hemlock 

was found on toe slopes on Buchanan soils, and black cherry 

was associated with Mandy soils on ridges. American beech, 

magnolia, and basswood were found on Meckesville soils 

and sugar maple and ash were associated with Belmont 

soils. Northern red oak witness trees stand out in this highelevation group as being found on southeast aspects, on 

Cateache soils, and with sites low in moisture. 

Moderate elevations supported maple, pine, white pine, 

yellow pine, American chestnut, chestnut oak, scarlet oak, 

and spruce-pine, with American chestnut and chestnut 

oak more likely on ridges and locations with Lily soils, 

high TRI, and low moisture. Spruce-pine witness trees 

were more likely on valley landforms in the Potomac soil 

series with low TRI and high moisture. Low-elevation 

sites with high moisture were more likely to support black 

walnut, white oak, elm, and sycamore. White oak, elm, and 

sycamore were associated with the valley landform. Hickory 

and yellow-poplar witness trees were associated with sites 

of high TRI and low elevation. Low-elevation toe slopes on 

Lily soils were associated with blackgum witness trees. 

No elevation class was significantly associated with 

butternut or black locust witness trees. Butternut was 

associated with toe slopes on Cateache soils; black locust, 

with southeast slopes over Laidig soils. 

The species-site associations differed by subsection for 

some species and ecophysical variables (Tables 7-13). The 

most frequent witness tree species, white oak, was found on 

Weikert soils when assessed across the study area but also 

was associated with Laidig soils (NHAM subsection; Table 

9). Weikert and Laidig soils are geographically associated, 

forming in place from sandstone, siltstone, and shale 

(NRCS 2010b). White oak witness trees were associated 

with toe slopes in the RV, SHAM (Table 11), and WAMV 

Pine

 LH

 HH

 HL

Yellow Pine

 HH

 HL
22

(Table 12) subsections and valleys in the WAM subsection 

(Table 13). The association of white oak with TRI class 

varied by subsection as well, covering all three classes: high 

in the NHAM, moderate in the EAMV, and low in the 

WAM subsections. In the NHAM and SHAM subsections, 

white oaks were found on areas of low moisture. 

Sugar maple, the next most frequent witness tree, differed 

in associated soil series between the study area as a whole 

and the RV and WAM subsections, with Belmont, 

Calvin, and Meckesville soils determined to be significant. 

These three soil series are geographically associated with 

each other (NRCS 2010b). Indicator species analysis 

determined two other landform associations for sugar 

maple witness trees: toe slopes in the EAMV subsection 

and valleys in the ECF subsection (Table 8), along with 

the association with ridges in the study area overall. Some 

differences of interest between analyses by subsection 

versus study area as a whole can be found for sugar maple 

in the EAMV subsection, where sugar maple was found at 

low elevations, in areas of high moisture, and on northeastfacing toe slopes. Basswood, which was often found in 

similar areas as sugar maple, was also associated with toe 

slopes, Belmont soils, and low elevations in the EAMV 

subsection. 

While associated with an upland soil series when assessed 

across the study area, American beech witness trees 

were associated with Potomac and Atkins soils and cove 

landforms, in the SHAM and ECF subsections. In the 

EAMV subsection, American beech was associated with 

high-elevation, frigid Mandy soils. 

American chestnut witness trees were mainly associated 

with ridges across the study area and in the EAMV, ECF, 

WAMV, and WAM subsections, but were associated with 

toe slopes in the SHAM subsection. Chestnut oaks had 

associations very similar to American chestnut in the study 

area as a whole and in the EAMV and WAM subsections. 

Northern red and black oak witness trees were found on 

a variety of landforms based on subsection and followed 

similar patterns. Both northern red and black oaks were 

found on ridges in the EAMV subsections, valleys in the 

ECF subsections, and toe slopes in the RV subsection. 

Witness trees cited simply as maple differ in soil series; 

these trees are associated with the alluvial Atkins soil in 

the SHAM subsection and Laidig soils in the study area as 

a whole. The species (or group of species) represented by 

the maple witness trees exhibits a range of site associations 

by subsection from toe slope (EAMV) and ridge (ECF) to 

bench (SHAM) landforms, on southwest (SHAM) aspects, 

and from areas of high (EAMV) to low (NHAM) moisture. 

Pine (no species given) witness trees were associated with 

the alluvial Allegheny soils in the study area overall, but 

with the residual Mandy soil in the SHAM subsection. 

Also in the SHAM subsection, these witness trees were 

found to be associated with high elevations. 

Red spruce witness trees were associated with a variety 

of landforms depending on subsection. This species was 

found on toe slopes (study area and EAMV), ridges (RV), 

bench (SHAM), and valley (WAM) landforms. Overall, 

this species was associated with high elevations, but when 

assessed by subsection, red spruce was associated with low 

elevations in the WAM subsection. Northerly aspects were 

favored by red spruce with northeast aspects across the 

study area and north (EAMV) and northwest (SHAM) 

aspects also significant. 

Black cherry witness trees were found on the alluvial Atkins 

soil and toe slope landforms in the WAM subsection. Across 

the study area, this species was associated with Mandy soils, 

a high-elevation residual soil, and ridge landforms. Similarly, 

blackgum witness trees were associated with Atkins soils 

and valley landforms in the ECF subsection, ridges and Lily 

soils in the EAMV subsection, and Ernest soils in the RV 

subsection. Ash in the EAMV subsection were associated 

with Mandy soils of high moisture in contrast to the mainly 

limestone-derived Belmont soils associated with this species 

across the study area. 
23

 Species or genus Elevation Moisture TRI Landform Aspect Soil series

 Red spruce high moderate low toe slope NE Mandy

Hemlock high low toe slope Buchanan

 Birch high toe slope NE Mandy

 American beech high moderate toe slope NE Meckesville

 Magnolia high high ridge Meckesville

 Basswood high high cove E Meckesville

 Sugar maple high moderate high ridge E Belmont

 Ash high bench Belmont

 Red oak high low high bench SE Cateache

 Black cherry high low ridge Mandy

 Maple moderate low Laidig

 Pine moderate high low bench W Allegheny

 White pine moderate bench Weikert

Yellow pine moderate low bench Lily

American chestnut moderate low high ridge Lily

Chestnut oak moderate low high ridge NW Lily

 Scarlet oak moderate high Lily

 Spruce-pine moderate high low valley Potomac

 Black walnut low high bench Opequon

 White oak low high low valley SE Weikert

Elm low high valley W Atkins

 Sycamore low high low valley Atkins

Hickory low high W Macove

Yellow-poplar low high Gilpin

 Black oak low low SE Lily

 Blackgum low toe slope Lily

 Butternut toe slope Cateache

 Black locust SE Laidig

Table 6.—Significant associations (α = 0.05; n ≥10) between tree species or genus and environmental variables for

the study area. Blank cells indicate no significant association for that species-variable combination.
24

Table 7.—Significant associations (α = 0.05; n ≥10) between tree species or genus and environmental variables for

the EAMV subsection. Blank cells indicate no significant association for that species-variable combination.

 Species or genus Elevation Moisture TRI Landform Aspect Soil series

 Red spruce high toe slope N Shouns

 Birch high high toe slope E Mandy

 American beech high toe slope NE Mandy

 Red oak high high ridge

 Black oak high low ridge SE 

 American chestnut high low high ridge NW Lily

Chestnut oak high low high NW Lily

 Scarlet oak high high toe slope

 Black locust high ridge

 Pine moderate high low bench Allegheny

Yellow pine moderate low high

 White oak moderate moderate

 White pine low cove

 Sugar maple low high toe slope NE Belmont

 Basswood low high high toe slope E Cateache

 Black walnut low bench

Hickory low high

Elm low valley

 Sycamore low high valley Atkins

 Butternut low ridge

 Black cherry low Mandy

 Ash high low bench Mandy

Yellow-poplar high E 

 Blackgum moderate ridge Lily

 Maple high low toe slope

 Spruce-pine high toe slope

 Magnolia Cateache
25

Table 8.—Significant associations (α = 0.05; n ≥10) between tree species or genus and environmental variables for

the ECF subsection. Blank cells indicate no significant association for that species-variable combination.

 Species or genus Elevation Moisture TRI Landform Aspect Soil series

 Black oak moderate valley Atkins

 Basswood moderate

Yellow-poplar moderate

 Spruce-pine low high valley

Chestnut oak ridge

 Red oak valley

 Sugar maple valley

 Blackgum valley Atkins

 American beech cove Atkins

 Maple ridge

 American chestnut ridge

 Scarlet oak

 Ash

 Butternut

 Magnolia

 Black locust

Hickory

 Birch

 Red spruce

Hemlock

 Black cherry

 Pine

 White pine

Yellow pine 

 Black walnut

 White oak

Elm

 Sycamore
26

Table 9.—Significant associations (α = 0.05; n ≥10) between tree species or genus and environmental variables for

the NHAM subsection. Blank cells indicate no significant association for that species-variable combination.

 Species or genus Elevation Moisture TRI Landform Aspect Soil series

Hemlock high high low

 Black cherry high low

 Sugar maple high high

 American beech moderate toe slope

 White oak moderate low high Laidig

Hickory low SW Allegheny

Yellow-poplar low high

 Blackgum low

 Birch bench N

 Red oak high bench S

 Basswood high

Chestnut oak low bench S

 Scarlet oak

 Maple low

 Magnolia low

 Spruce-pine high low

 Butternut NE 

 Black oak

 Red spruce

Elm

 American chestnut

 Ash

 Pine

 White pine

Yellow pine 

 Sycamore

 Black walnut

 Black locust
27

 Species or genus Elevation Moisture TRI Landform Aspect Soil series

 Sugar maple high ridge Calvin

American chestnut high

 Basswood high

 Ash moderate

 Maple moderate

 Scarlet oak moderate cove Shouns

 Black oak low high low toe slope

Yellow-poplar low high valley Potomac

 Pine bench Macove

Yellow pine bench

 Sycamore valley SW

 Spruce-pine high

 Red oak toe slope

 White oak toe slope

 Red spruce ridge

Chestnut oak W Lily

Hickory Allegheny

 White pine Ernest

 Blackgum low Ernest

 Black locust Buchanan

 Birch

 American beech

Hemlock

 Magnolia

 Black cherry

 Black walnut

Elm

 Butternut

Table 10.—Significant associations (α = 0.05; n ≥10) between tree species or genus and environmental variables for

the RV subsection. Blank cells indicate no significant association for that species-variable combination.
28

Table 11.—Significant associations (α = 0.05; n ≥10) between tree species or genus and environmental variables for

the SHAM subsection. Blank cells indicate no significant association for that species-variable combination.

 Species or genus Elevation Moisture TRI Landform Aspect Soil series

 Red spruce high moderate low bench NW Berks

Hemlock high high low toe slope Ernest

 Pine high low ridge Mandy

 White oak moderate low toe slope S Potomac

 Sugar maple moderate high ridge Belmont

 Blackgum moderate moderate toe slope Atkins

Elm moderate

 Maple low low bench SW Atkins

Yellow-poplar low valley

 Black walnut low

 American beech cove Potomac

 Spruce-pine valley

 Magnolia N Meckesville

 Red oak low high bench SE Berks

Hickory high side slope SE 

 Birch toe slope

 American chestnut toe slope Laidig

Chestnut oak SE 

 Butternut high

 Sycamore bench SW

 Ash bench

 White pine

 Basswood high

 Black cherry ridge

 Black locust SE Laidig

 Black oak

 Scarlet oak

Yellow pine
29

Table 12.—Significant associations (α = 0.05; n ≥10) between tree species or genus and environmental variables for

the WAMV subsection. Blank cells indicate no significant association for that species-variable combination.

 Species or genus Elevation Moisture TRI Landform Aspect Soil series

 Birch high NE 

 Sugar maple high

 Red oak low

 Basswood moderate N

 White oak toe slope

 Black oak Calvin

 Black locust SE Calvin

American chestnut ridge

 Maple high

 Pine SE 

Elm W

 White pine

Yellow pine

Chestnut oak

 Ash

Yellow-poplar

 American beech

 Blackgum

 Black walnut

 Sycamore

 Spruce-pine

 Butternut

 Red spruce

Hemlock

 Magnolia

 Black cherry

 Scarlet oak

Hickory
30

 Species or genus Elevation Moisture TRI Landform Aspect Soil series

 Sugar maple high E Meckesville

 Basswood high high Cateache

 Scarlet oak high high

 Butternut high cove

 Magnolia high ridge SE 

 Black cherry high toe slope Atkins

Yellow-poplar moderate ridge

 American chestnut moderate low high ridge Allegheny

Chestnut oak moderate low high Allegheny

 Maple moderate moderate

 Red spruce low high low valley NE Allegheny

 White oak low low valley

 Sycamore low high low toe slope

 Blackgum low high toe slope

 Birch high cove NE 

 American beech high NE 

 Ash ridge

 Black walnut high low toe slope

 Black oak N

 Spruce-pine toe slope

 Black locust toe slope

 White pine valley

 Red oak high

Pine 

Yellow pine 

Hickory

Elm

Hemlock

Table 13.—Significant associations (α = 0.05; n ≥10) between tree species or genus and environmental variables for

WAM subsection. Blank cells indicate no significant association for that species-variable combination.
31

Figure 18.—Indicator kriging results for sugar maple and sugar maple or basswood witness trees. Interpolations were made 

on presence/absence of the selected species. Results are given in terms of percent probability of occurrence.

Sugar Maple or Basswood 

Probability of Occurrence

0-5%

5-10%

10-20%

20-30%

30-40%

40-50%

50-60%

60-70%

0-5%

5-10%

10-20%

20-30%

30-40%

40-50%

50-60%

60-70%

70-80%

Subsections 

Monongahela NF 

proclamation boundary

Sugar Maple Probability 

of Occurrence

Indicator Kriging

Spatial interpolation through IK was calculated for the 

presence/absence of sugar maple, red spruce, white oak, and 

American chestnut witness trees and the combinations of 

sugar maple or basswood, sugar maple or American beech, 

red spruce or birch, white oak or white pine, American 

chestnut or chestnut oak, and red, scarlet, black or chestnut 

oak occurrences. The calculated anisotropy, although 

varying by species, tracked well with the Allegheny Front, 

the geological formation of known importance in the study 

area that runs northeast-southwest. 

The graphical results of IK (Figs. 18 – 22) are consistent 

with the local clustering results. The lowest probabilities 

for the occurrence of sugar maple and sugar maple or 

American beech were found in the EAMV subsection 

(Figs. 18 and 19). Red spruce was more likely to be found 

in the cool and moist SHAM and NHAM subsections 

(Fig. 19). White oak and American chestnut had low 

probabilities of occurrence in the cool and moist NHAM 

and SHAM subsections (Figs. 20 and 21). The probability 

of occurrence for white oak or white pine was greatest 

in the EAMV subsection (Fig. 21). When the locations 

of northern red, scarlet, black, or chestnut oak were 

interpolated, the lowest probability of occurrence was 

found in the SHAM and NHAM subsections (Fig. 22). 

With 30-percent probability of occurrence as a threshold, 

white oak remained dominant across the study area 

among single species analyzed at about 26 percent of the 

study area (Table 14). Including corners where white 

pine was recorded did not increase the area covered by 

the two species. Sugar maple, the next most abundant 

witness tree based on counts, covered about 19 percent 

of the study area after interpolating between points, with 

coverage increasing to 27 percent when basswood points 

were included or 41 percent when American beech points 

were included (Table 14). When assessed alone, American 

chestnut covered 3 percent of the study area, increasing to 

12 percent when chestnut oak points were included. Red 

spruce alone was estimated to cover 2 percent of the study 

area, increasing to nearly 10 percent when birch points 

were included. Oak species other than white oak were 

estimated as present on 18 percent of the study area. 
32

Figure 20.—Indicator kriging results for red spruce or birch and white oak witness trees. Interpolations were made on 

presence/absence of the selected species. Results are given in terms of percent probability of occurrence.

Red Spruce or Birch 

Probability of Occurrence

0-5%

5-10%

10-20%

20-30%

30-40%

40-50%

50-60%

60-70%

70-80%

0-5%

5-10%

10-20%

20-30%

30-40%

40-50%

50-60%

60-70%

70-80%

White Oak Probability 

of Occurrence

Figure 19.—Indicator kriging results for sugar maple or American beech and red spruce witness trees. Interpolations were 

made on presence/absence of the selected species. Results are given in terms of percent probability of occurrence.

Sugar Maple or American 

Beech Probability of 

Occurrence

Red Spruce Probability 

of Occurrence

0-5%

5-10%

10-20%

20-30%

30-40%

40-50%

50-60%

60-70%

70-80%

0-5%

5-10%

10-20%

20-30%

30-40%

40-50%

Subsections 

Monongahela NF 

proclamation boundary

Subsections 

Monongahela NF 

proclamation boundary
33

Figure 22.—Indicator kriging results for American chestnut or chestnut oak and red, scarlet, black, or chestnut oak witness 

trees. Interpolations were made on presence/absence of the selected species. Results are given in terms of percent probability 

of occurrence.

American Chestnut or 

Chestnut Oak Probability

of Occurrence

Red, Scarlet, Black or 

Chestnut Oak Probability 

of Occurrence

0-5%

5-10%

10-20%

20-30%

30-40%

40-50%

50-60%

60-70%

70-80%

80-90%

0-5%

5-10%

10-20%

20-30%

30-40%

40-50%

50-60%

60-70%

70-80%

80-90%

Subsections 

Monongahela NF 

proclamation boundary

Figure 21.—Indicator kriging results for white oak or white pine and American chestnut witness trees. Interpolations were 

made on presence/absence of the selected species. Results are given in terms of percent probability of occurrence.

White Oak or White Pine 

Probability of Occurrence

American Chestnut 

Probability of Occurrence

0-5%

5-10%

10-20%

20-30%

30-40%

40-50%

50-60%

60-70%

70-80%

80-90%

0-5%

5-10%

10-20%

20-30%

30-40%

40-50%

50-60%

Subsections 

Monongahela NF 

proclamation boundary
34

Table 14.—Percent of the study area in selected species based on IK results using 30-percent probability of

occurrence as threshold.

Species Hectares Percent of study area

Sugar maple or American beech 418,815 41.3

Sugar maple or basswood 278,897 27.5

White oak or white pine 264,749 26.1

White oak 261,656 25.8

Sugar maple 188,297 18.6

Northern red, scarlet, black, or chestnut oak 185,791 18.3

American chestnut or chestnut oak 118,049 11.6

Red spruce or birch 97,621 9.6

American chestnut 29,162 2.9

Red spruce 21,457 2.1

Discussion

The witness trees listed as pine in the SHAM subsection 

were likely red spruce. The indicator species analysis 

showed these trees recorded as pine were significantly 

associated with ridge landforms of low TRI and high 

elevation on Mandy soils. Soils in the Mandy series are 

strongly to extremely acid and have a frigid temperature 

regime (NRCS 2010b). A frigid soil temperature regime is 

likely to favor red spruce over hardwoods in undisturbed 

forests and not likely to support any pine species. The 

witness trees recorded as spruce-pine were likely hemlock 

as they were associated with Potomac soils, valley 

landforms, and high moisture when assessed across the 

study area. Interestingly, no hemlock was recorded from 

deeds in the EAMV subsection although 169 spruce-pines 

were; they were found to be associated with high-moisture 

toe slopes. 

Indicator kriging of red spruce points alone resulted 

in only 2 percent of the study area with greater than 

30-percent probability of occurrence of red spruce. This 

value increased to nearly 10 percent when birch points 

were included in the analysis (Table 14; Fig. 22). The 

proportion of spruce-dominated forests in the MNF 

during the period prior to European settlement has been 

estimated at 10 to 25 percent (U.S. Forest Service 2006a). 

Assuming that the majority of the birch corners in the 

surveys were yellow birch, our IK estimate is at the low end 

of one made for the MNF based on potential vegetation 

developed as part of the MNF ecological land type 

classification (U.S. Forest Service 2006a). Restoration of 

red spruce-dominated forests is the goal of a management 

prescription applied to approximately 17 percent of the 

MNF under the current Forest Plan (U.S. Forest Service 

2006b). The witness tree database could be used to validate 

areas proposed for restoration of spruce-dominated forests. 

Across the study area, white pine witness trees were 

significantly associated with cove, bench, and valley 

landforms, similar to findings in the presettlement forest 

of central Pennsylvania (Nowacki and Abrams 1992). 

This result lends support to the idea that white pine 

was restricted to more mesic sites because of periodic 

understory fires (Abrams 2001). Larger white pines are 

considered fire resistant because of thick, insulating bark, 

although white pine seedlings and saplings are killed by 

understory fire (Carey 1993). In a mixed oak-white pine 

forest, dominance by either group is controlled by the 

frequency of understory fires, with longer fire-free periods 

resulting in white pine recruitment to the overstory 

(Abrams 2001).

Current difficulties regenerating northern red oak on 

mesic sites make northern red oak witness tree information 

important to land managers. Northern red oak witness 

trees rank 11th in terms of abundance across the study 

area, and their highest ranking (6th) was in the RV 

subsection. Based on indicator species analysis, northern 

red oaks were found on sites with low moisture and high 

TRI, at high elevations, and with southeast aspects. 
35

A broader range of site conditions for northern red oak 

may be found by exploring the HH point locations 

determined from the local clustering analysis. These points 

could be queried for site conditions other than those 

assessed in this analysis and field visits could determine 

other site factors that may be important for supporting 

northern red oak. 

Other assessments of witness trees have used contingency 

table analysis to determine species-site relationships 

(Abrams and McCay 1996; Black and Abrams 2001a, b; 

Dyer 2001; Strahler 1972; Wang 2007; Whitney 1990; 

Whitney and DeCant 2003). While this nonparametric 

method gives a measure of significant positive or negative 

association with environmental variables, the G statistic 

can give inaccurate results with small expected values 

(common for datasets including many site variables 

and species) (Dowdy et al. 2004), and the test assumes 

independence of the samples (Maddox and Wisnewski 

2008). Since positive spatial autocorrelation was found in 

the data, we chose to use indicator species analysis with 

significance tested through Monte Carlo methods. Logistic 

regression could also be used to determine significant 

ecophysical variables associated with each species. Indicator 

species analysis was used in this analysis as a tool to 

describe the significant site-species associations of the 

species at each corner and not to predict occurrence of any 

species at a given point. Nothing in this analysis precludes 

future regression analysis to predict species occurrence, but 

this dataset is not a random sample and exhibits positive 

spatial autocorrelation that would need to be addressed in 

any regression model. 

As was found for witness trees in the Central Hardwood 

Region of Ohio, Pennsylvania, and West Virginia (MNF 

was not included in that study area) (Rentch and Hicks 

2005), white oak dominated the MNF witness tree record. 

Unlike the MNF data, black oak was the next most 

abundant species (Rentch and Hicks 2005). For the MNF, 

black oak ranked 13th overall, reaching 5th in the WAMV 

and RV subsections (Table 5). American chestnut was more 

common on the MNF than in the area assessed by Rentch 

and Hicks (2005), ranking 4th across the study area, 1st 

in the ECF subsection, 3rd in two subsections (WAM 

and WAMV), and 4th in the EAMV subsection (Table 

5). Northern red oak was found to be a relatively minor 

component of the early forest in both studies. 

An estimate of early forest composition reported that 

American chestnut made up about 12 percent of the forest 

of West Virginia (Brooks 1910). In the current study, 

American chestnut made up about 6 percent of the witness 

trees across the study area, and covered about 3 percent of 

the study area based on IK. When American chestnut was 

combined with chestnut oak, which occurred on similar 

site conditions, approximately 12 percent of the study area 

is estimated to include either species (Table 14). 

The results of indicator species analysis suggest logical 

groupings of species into forest types. Sugar maple and 

basswood are similar in their site associations across the 

study area as a whole and in the EAMV subsection. Sugar 

maple and American beech across the study area were 

found to be associated with areas of moderate moisture, 

high elevation, and two geographically associated soils. 

Birch (likely yellow birch) and red spruce also had many 

similarities when assessed across the study area. Sycamore 

and elm witness trees across the study area were both 

associated with similar ecophysical variables. Chestnut oak 

and American chestnut often showed the same significant 

associations of soil, landform, and elevation. White oak 

stands out in comparison to the other oaks because of 

its significant association with valley landforms and 

areas of high moisture, while the other oaks, in general, 

were associated with areas of lower moisture. White 

oak and white pine were associated with the same soils 

and landforms across the study area. This same species 

combination and landform association was found in 

presettlement forests of the Ridge and Valley Province in 

Pennsylvania (Nowacki and Abrams 1992). 

Ultimately, surveyors chose from species present on the 

site, where environmental factors constrained which species 

could occur at any given survey corner. Any mapping of 

forest types from the analysis of witness tree data is likely 

to be valid even though the trees used to create groupings 

were not strictly chosen at random by the original surveyor 

(Manies and Mladenoff 2000). Although the surveyor 

chose the species to record as a witness tree, site factors 

affected which species were available to choose from so that 

even if species bias exists, the mapping of vegetation at a 

landscape scale should not be significantly affected (Manies 

and Mladenoff 2000). 
36

We believe that evidence for surveyor bias toward certain 

species in the MNF witness tree dataset was limited since 

surveyors used a variety of tree species as witness trees. 

Smaller-stature trees such as dogwood and serviceberry 

were documented in the deeds, although not in large 

numbers. The number of species used by the surveyors 

(Table 3) implies broad knowledge of common trees in 

the study area. Most telling is the very low occurrence of 

the generic oak (only 18 occurrences). An overwhelming 

number of deeds (and presumably surveyors) used specific 

oak names in parcel descriptions. 

Elevation does not appear to be a driver in the distribution 

of tree species in the WAMV and ECF subsections. Only 

two species in the WAMV subsection and only four 

species in the ECF subsection displayed an association 

with any elevation class. The ECF subsection is the 

westernmost subsection in the study area and the farthest 

from the Allegheny Front. This distance from the more 

mountainous areas may allow for other environmental 

drivers to have greater influence on species distributions 

than does elevation. The study area includes only a portion 

of the larger WAMV subsection and is dominated by the 

Greenbrier River Valley, which may be influencing the 

analysis of environmental variables. 

A previous study of witness tree data for the MNF 

determined that presettlement Ridge and Valley section 

forests were dominated by mixed oak (white oak, chestnut 

oak, black oak, and northern red oak), pines, American 

chestnut, and hickory on ridges (Abrams and McCay 

1996). Valley floors of the Ridge and Valley section were 

dominated by white oak, sugar maple, pines, basswood, 

and hemlock. In the Allegheny Mountains section, 

presettlement forests were mainly American beech, 

hemlock, sugar maple, red maple, birch, and pine, with 

American beech, hemlock, and pine on the mountaintops 

and hemlock, maple, and birch on valley floors. 

Unlike Abrams and McCay (1996), the current analysis used 

subsections instead of sections and more landforms were 

included. Species trends were generally similar in that oaks 

were found on drier landscapes and mesic landforms were 

dominated by northern hardwoods (American beech-birchmaple). With this current analysis, however, more detail 

is available for development of predictive models to assign 

forest types or species to certain landforms and subsections. 

In the 1996 analysis, red spruce witness trees were 

conspicuously absent, with only one tallied for the Ridge 

and Valley Province and six in the Allegheny Mountains 

(Abrams and McCay 1996), compared to the 683 used in 

the current study. In the current study, duplicate corners 

were retained in the dataset if new tree species were 

added in subsequent surveys. The 1996 study covered 

approximately 80,000 ha of the MNF and included deeds 

ranging from 1780 to 1856. The current study was based 

on more than 15,000 corners and 22,000 trees; the 1996 

study included only 1,015 trees. 

Indicator kriging of witness trees from GLO surveys 

was not found to be useful in estimating the actual area 

occupied by different vegetation types because the spatial 

resolution of witness points was too coarse to recreate finerscale and patchy patterns (Maines and Mlandenoff 2000). 

As a result, Manies and Mladenoff (2000) recommend that 

IK be used only to describe areas greater than 10,000 ha. 

In their study, positive spatial autocorrelation (which IK 

relies on) was not detected for all species. Of the species 

modeled through IK for the MNF dataset, only scarlet oak 

showed no spatial autocorrelation at the study area scale. 

Overall, probabilities of occurrence for most species and 

even combinations of species were low, and none reached 

100 percent. Environmental variables could be used 

with the witness tree locations through co-IK for better 

interpolation between points.

Conclusions

Significant clustering patterns in the distribution of species 

of witness trees indicate positive spatial autocorrelation. 

This result was not unexpected as vegetation is known 

to be associated with environmental variables. These 

spatial patterns need to be considered when creating 

predictive models (Miller 2005). Using only those species/

witness tree locations with clustered distributions should 

facilitate finding the strongest associations between species 

occurrences and modeled variables. 

Information presented here could also be used to model 

past extents of species and compare results to current 

forests. Even without these models, the witness tree data 

could be compared to current forests in more general 

terms such as species composition by subsection. While 

the witness trees were treated as one point in time for 
37

this analysis, the dataset does cover 148 years and future 

analysis of the dataset could include temporal analysis. 

Land managers can immediately use the results of this 

analysis to describe European settlement-era forests and aid 

in determining whether restoration goals are appropriate 

given this new information. Managers could also explore 

site conditions and patterns for individual species through 

the indicator species analysis and results of local clustering. 

This analysis has shown that while some species may be 

abundant in the witness tree record (white oak and sugar 

maple), their distributions were not homogenous across 

the study area. Analysis by ecological subsection captured 

the variability in the study area and its influence on the 

distribution of tree species. 

ACKNOWLEDGMENTS

We would like to recognize the work of Michael Metz 

and the other undergraduates at West Virginia University 

who constructed the digital database. The manuscript was 

greatly improved by reviews from Todd Hutchinson and 

John Brown. 

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Forest restoration would be greatly helped by understanding just what forests looked like a century or 

more ago. One source of information on early forests is found in old deeds or surveys, where boundary 

corners were described by noting nearby trees known as witness trees. This paper describes the creation 

and analysis of a database of witness trees from original metes and bounds surveys of what became 

the Monongahela National Forest in West Virginia. We include an estimate of positional error from the 

conversion of paper maps to digital format. The final database contains 15,589 corners and 22,328 

trees of 49 species from deeds dating from 1752 to 1899. White oak was the most frequent witness 

tree, followed by sugar maple, American beech, and American chestnut, and distribution patterns were 

recognizable across the study area.

In early forests of the study area, magnolia, sugar maple, and black cherry were found on high-elevation 

ridges. Red spruce, hemlock, birch, and American beech were found on high-elevation toe slopes. 

Basswood was found in high-elevation coves, and red oak was associated with bench landforms at 

high elevations. At moderate elevations American chestnut and chestnut oak were associated with 

ridges, white pine and yellow pine occurred on benches, and an unknown species called spruce-pine 

was found on valley landforms. Blackgum was associated with toe slopes on low elevations, and 

black walnut was found on low-elevation benches. Low-elevation valleys contained white oak, elm,

and sycamore. An important finding from this analysis is that some associations between species and 

environmental variables differed based on the ecological setting. Indicator kriging, using presenceabsence data, resulted in probability of occurrence maps for selected species. We estimate that white 

oak covered 26 percent of the study area, sugar maple 19 percent, American chestnut 3 percent, and 

red spruce 2 percent. 

KEY WORDS: historical biogeography, land surveys, indicator species analysis, indicator kriging, 

spatial analysis

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