CAVE DENSITY OF THE GREENBRIER LIMESTONE GROUP, WEST VIRGINIA

Lee Stocks Jr. (lstocks@mansfield.edu)

Andrew Shears (ashears@mansfield.edu)

Department of Geography and Geology

Mansfield University

Mansfield, Pennsylvania 16933

1. INTRODUCTION

The Greenbrier Limestone Group (Figure 1), known in West Virginia as the “Big

Lime”, is an extensive, calcium-pure limestone unit of Mississippian Age (350-340 million

years). Deposited in a shallow ocean basin during the Carboniferous, the Big Lime is over

1000 feet thick in the Greenbrier Valley of West Virginia. The wet climate of central

Appalachia provides the hydraulics and corrosive carbonic acid action necessary to form

frequent and sizeable karst dissolution features, such as caves, sinkholes, and springs. Some of

the world’s largest caves form here as contact caves, where the Big Lime meets the underlying

McCrady Shale Formation, including Scott Hollow Cave (47.5 km), Organ Cave (61.9 km),

The Hole (37.0 km), and Maxwelton Sink Cave (18.3 km; Figure 2). Likewise, thousands of

sinkholes and pits have formed via dissolution of bedrock and collapse into subsurface cave

passages. These features create geohazards to infrastructure and provide pathways for aquifer

contamination through sediment and pollutant transport, thereby requiring a geographic

understanding of karst feature density.

FIGURE 1 FIGURE 2

EXPOSED BIG LIME IN STUDY AREA MAXWELTON SINK ENTRANCE

This research utilizes the geographic and geologic analysis capabilities of ArcGIS10

to produce a preliminary spatial analysis that can examine the relationships between this

extensive stratigraphic unit and the development of karst features, in order to explore any

structural or geographic controls on their genesis. Data is derived from the West Virginia

Speleological Survey database of over 4500 caves and pits, including length, depth, elevation,

and stratigraphic unit attributes. Hexagonal bins with a variety of diameters were used for used

for statistical analyses. Each of these tests showed statistically significant spatial relationships

for cave sites at all levels of analysis.

2. RATIONALE

The Greenbrier Limestone, the most abundant rock formation in the study area, is

composed mainly of marine limestones and shales of Mississipian Age. The Greenbrier

Limestone Group, which is 122 meters thick in areas (Figure 3), represents the predominant

exposed rock and produces thousands of caves in the Greenbrier County. The management and

use of these lands is crucial, as 10 percent of the Earth’s surface is karst (landform typified by

sinkholes and caves), and more than 25 percent of the nation’s drinking water comes from karst

aquifers and groundwater (Ford and Williams, 1992). Karst watersheds are more sensitive to

human disturbances than other watersheds because of fast moving aquifers, slow recharge rates,

and thin soils for filtration of pollutants (Doerfliger et al., 1997). Human-induced impacts in

karst areas are often associated with urban growth at the watershed level. This produces

hydrologic changes that increase sediment and pollutant delivery and impede infiltration as

impervious surfaces increase, such as roof tops, streets, buildings, and pavement (Doerfliger et

al., 1997). This increases impacts on surface and cave biota and water quality and quantity.

FIGURE 3 FIGURE 4

THICK GREENBRIER OUTCROP KARST FEATURES IN STUDY AREA

Human use of karst lands has altered the structure and functioning of these sensitive

environments (Verberg and Chen, 2000). Agriculture, forestry, and other land management

practices have drastically changed their morphology. Kastning and Kastning (1997) explain

the typical problems in karst associated with development of the built environment include

instability of soils, subsidence, and collapse of ground surface (Figure 4), erosion and

sedimentation of sinkholes, sinkhole flooding, and groundwater contamination. These issues

have become more frequent and extensive in West Virginia as surface development has

increased in subsurface recharge zones. Therefore, a geographic and spatial analysis of the

distribution of caves can elicit useful information that can aid local planning efforts in these

sensitive areas, and provide a better understanding of cave formation and location.

White et. al. (1986) define a cave as a natural opening in the Earth, large enough to

admit humans. Caves form as a result of a complex organization of geologic and hydrologic

processes. The interaction of these factors will determine where, when, and how an individual

cave entrance or passage may form. The vast majority of caves in the study area are formed in

limestone through hydraulic and corrosive action of carbonic acid. White and Culver (2012)

point out that caves are no longer viewed as geological anomalies standing on their own, rather

they are repositories of larger climatic and geologic systems with many subsystems. Passages

are fragments of conduit systems that were once part of a groundwater system for the region.

Active caves can indicate current hydrologic systems, whereas dry caves can explain how

drainage systems evolved. For example, Maxwelton Cave System in West Virginia (Figure 5)

is an actively forming cave being fed by a surface stream that disappears underground.

MCCRADY SHALE

E

E

GREENBRIER LIMESTONE

FIGURE 5

MAXWELTON SINK CAVE MAP SUPERIMPOSED ON ORTHOIMAGERY

A number of different spatial methods have been used to model sinkholes and hazard

potential in karst landscapes but very little has been done to analyze cave entrance relationships

or density in a particular geologic unit. Among the former, the most popular are those based on

proximity of neighboring sinkholes (Drake and Ford, 1972) or sinkhole density (Orndorff et

al.,2000). These methods are used to make inferences about the relationships between karst

features and geologic or hydrologic factors.

3. STUDY AREA

The study area for this research (Figure 6) is extensive, including all known, mapped

cave entrances in West Virginia. This database (Table 1) is maintained and updated by the

West Virginia Speleological Survey (WVASS), a collection of amateur cavers, professional

geologists, and caving groups that compile and organize county and area cave surveys.

WVASS publishes monthly bulletins, monographs, and special issues related to caves of

interest in West Virginia. The database is currently in Access format and contains 4,508 known

cave locations, with records organized by county. It contains various information, including

cave name, UTM or latitude/longitude coordinates, elevation, geologic unit, date of report,

length, depth, stream ingress/egress, biology, etc., as well as short descriptions of the cave

entrance, such as whether it is in a sinkhole, blind valley, sinking stream or other. This is an

invaluable database for multiple purposes.

The extracted caves formed in the Greenbrier Limestone Group subset included 4,375

records, of which 4,050 had plottable geographic coordinates. Although the database is

maintained by cavers and for cavers, the data is proprietary in nature, because of liability issues

that arise from publishing the location of caves found on privately held land. Use of the data

for this project was granted by its trustees under an agreement that identifiable cave locations

on privately-owned land are withheld from public release. Caves in the database with

georeferenced coordinates (4,050 caves) were plotted using ArcGIS, for purposes of mapping

(Figure 8), as well as point-based geospatial analyses.

FIGURE 6: STUDY AREA DATASET, EXTENT OF GREENBRIER LIMESTONE GROUP

BEDROCK AND SELECTED CAVE LOCATIONS IN GREENBRIER COUNTY

The hydrologic and environmental relationships between surface land use and cave

health are well established in the literature (Bhaduri et al., 1997). Likewise, the presence of

caves can provide geologic hazards via sinkhole collapse. Sudden sinkhole development in

karst areas has become an increasing threat as watersheds are urbanized. Human impacts can

change the hydrology and infiltration morphology resulting in localized flows, underground

soil piping and soil cover collapse (Newton, 1984). Sinkholes and cave entrances also funnel

contaminants into underlying aquifers, leading to regional impacts (Galloway et al., 1999).

TABLE 1

DATASET SAMPLE CNTY CAVE NAME ELEV GEOL. UNIT DPT CAVE DESCRIPTION LAT LONG

BAR Backward

Cave 2160

Greenbrier

Limestone 23

Entrance in small sinkhole.

Stream resurges to west * 795112

BAR Bugger Hole 2160 Greenbrier

Limestone 38 Ent in sinkhole75'X20' 390508 *

BAR Cow Cave 2640 Greenbrier

Limestone 31

Entrance in a shallow sink and is

steeply sloping passage * 795009

BAR Poling

Spring Cave 2150

Greenbrier Limestone

44 Entrance is 3' x 1.5'. Large

stream flows out entrance. 390554 *

GBR Burns

Cave #2 1870

Hillsdale

Limestone 60

Sump dove in 1991; 10,000' of

passage discovered. * 775541

GBR Burns

Cave #1 2120

Hillsdale

Limestone 52

Twin domes, ~50' each. Trash

pulled from sink uncovered

opening lead drop of 35'. 392538 *

GBR Falling Spr. Blowhole

2212 Patton

Limestone 200 Ent.70' above river in outcrop * 775248

4. METHODS

West Virginia caves formed in the Greenbrier Limestone Group were extracted from

the WVASS proprietary database and plotted, mapped, and binned in preparation for various

geospatial analyses. Spatial autocorrelation and multi-distance spatial cluster analysis were

performed on the point location data, which represents mapped cave entrances, to express

general characteristics of clustering in the statewide dataset. The points were also binned to a

hexagonal grid; the count attribute for each cell was used to perform cluster/outlier and hotspot

analysis, specifically to identify promising locations for future study and exploration.

A Global Moran’s I spatial autocorrelation (after: Moran, 1950) was used on the

point shapefile to determine the Moran’s I Index (Figure 7), a statistical expression of point

clustering from which a positive value identifies clustered points, while a negative value

identifies uniform or disperse patterns. Spatial autocorrelation is the correlation of a variable

with itself through space. If there is any systematic pattern in the spatial distribution of the

variable, it is spatially autocorrelated. When neighboring areas are more alike it has positive

spatial autocorrelation, but when they are different it exhibits negative autocorrelation. Random

patterns have no autocorrelation. Therefore, this statistic can test the assumption of

independence or randomness, providing insight into the genesis of cave entrances.

FIGURE 7: MORAN’S I SPATIAL AUTOCORRELATION ILLUSTRATION (ArcGIS10)

To further express localization of point clusters, a multi-distance spatial cluster

analysis based on Ripley’s K-function (Figure 8) was performed, using analysis distances in

100m increments from 100m to 1000m, then 1000m increments up to 10km for each point.

The multi-distance cluster analysis can determine whether caves exhibit statistically significant

clustering or dispersion over a range of distances.

FIGURE 8: RIPLEY’S K FUNCTION (ArcGIS10)

The statewide scale of analysis and extremely close proximity of many caves to their

neighbors suggested that binning would allow a more accurate analysis by creating a spatially

normalized surface for both visual and statistical comparisons of density. Because results of

the multi-distance spatial cluster analysis provided limited insight to appropriate bin sizes for

further clustering analysis, a 2500m diameter was chosen for the bins as a polygon size that

was both adequately large for mapping at the state scale, while also small enough to provide

areas manageable for further field study. The cave points were binned to a shapefile of a

hexagonal polygon grid at this diameter, with count attributes of caves in each bin used for

mapping and analysis. A map displaying counts for each 2500m bin (Figure 9) was created to

better visualize the distribution of caves in the state.

FIGURE 9: COUNT OF CAVES, BINNED TO 2500M DIAMETER HEXAGONAL GRID

Though several groupings are apparent in Figure 9, especially the one centered in

Greenbrier County, visual analysis alone cannot express the significance of possible clusters

statistically. The identification of such clusters is crucial to identifying areas for further study

and exploration. The creation of a binned polygon shapefile enabled the use of two additional

statistical cluster analysis techniques in ArcGIS. An Anselin Local Moran’s I cluster analysis,

with values of contiguous polygons used for neighbors, was utilized to identify high count,

statistically significant clusters of bin cells. Polygons valued as “High-High” by this test are

those which are both highly clustered internally, and surrounded by highly clustered cells.

Additionally, a Getis-Ord Gi analysis was performed to identify statistically significant “hot

spot” cells, based on the count attribute for cells and their contiguous neighbors. The Getis-

Ord Gi analysis was used to derive z-scores for each cell that describe how clustered caves are

based on count of that cell and its neighbors. A Z-score of +2.58 standard deviations for a cell

denoted that the caves there were clustered in a statistically significant fashion. For both

cluster analyses performed on these bin polygons, use of contiguous neighbors was chosen

because the hexagonal binning had already largely controlled for distance and non-uniform

polygon shapes.

5. RESULTS

A visual examination of Figures 6 and 9 showed that caves in the Greenbrier

Limestone Group appear to be spatially clustered, largely in areas known to be geologically

underlain by the Big Lime. Based on Figure 9, several groupings of caves were visually

apparent:

1. A linear grouping extending in two lines southwestward from central Randolph

County, joining together in Pocahontas County and continuing southwestward

through Greenbrier and into Monroe County.

2. Approximately four linear groupings, arranged in parallel lines from northeast to

southwest through Randolph and Pendleton Counties.

3. A grouping in the state’s eastern panhandle, Berkeley and Jefferson counties.

4. A small grouping spread across the border between Monongalia and Preston

Counties.

5. A grouping in southern Mercer County, which could be a continuation of the

Grouping 1 if features continued south into Virginia, outside the database’s spatial

extent.

However, visual analysis is limited in its value for expressing the spatial relationships

between these cave locations. Spatial autocorrelation and multi-distance cluster analyses of the

point location shapefile each revealed that caves in the Greenbrier Limestone Group were

spatially clustered in a statistically significant fashion. Spatial autocorrelation derived a Global

Moran’s I value of 0.444056, indicating that locations of caves are more clustered than random

or dispersed. The test’s z-score of +134.5 and p-value of 0.0 suggested that clustering detected

during I value calculations had a high degree of statistical significance. Hence, spatial

autocorrelation reaffirmed the visual analysis of Figure 9, in that caves of the Greenbrier

Limestone Group in West Virginia are spatially clustered.

Multi-distance spatial cluster analysis provided further insight into the nature of

clustering in the study area. Ripley’s K-function was determined twice: once using a range of

distances from 100m to 1000m in 100m increments (Figure 10), and a second time using a

range of distances from 1000m to 10km in 1000m increments (Figure 11).

FIGURE 10: K-FUNCTION UP TO 1000M FIGURE 11: K-FUNCTION UP TO 10KM

In both iterations of this analysis, the points are shown to be strongly and

significantly clustered because the Observed K value is much higher than the Expected K from

-----------Expected-K -----------Observed-K

-----------Expected-K -----------Observed-K -----------Confidence

a statistically random dataset, and well outside the confidence envelope. These results

suggested that clustering is not only statistically significant, but clustering is statistically

significant regardless of distance around a given point for clustering determination; in fact, the

likelihood of a point’s membership in a cluster actually increased in relation to expected values

when considering neighbors at longer distances. Cave locations are extremely likely to be a

part of a cluster of caves in West Virginia, and clustering is statistically significant when

neighbors up to 10km around each point are considered.

The binned hexagons with cave count attributes were used to perform further

geospatial analyses. Anselin Local Moran’s I cluster analysis identified 383 hexagonal cells in

the 2500m diameter binning grid denoted as “High-High” (Figure 12), meaning that caves were

not only highly clustered in each of those cells, but neighboring cells also hosted highly

clustered caves.

FIGURE 12: ANSELIN LOCAL MORAN’S I CLUSTER ANALYSIS, 2500M HEX BINS

This analysis further delineated boundaries between groupings visually identified

using Figure 9; the groups of clustered cells in Figure 12 are more clearly separated. The

largest grouping remained the series of cells stretching from southern Randolph and northern

Pocahontas counties southwestward through Greenbrier County and into Monroe County;

however, the clusters in southern Mercer County appear more distinct from this main grouping.

The paralleling linear groupings in eastern Randolph and Pendleton counties were broken into

six smaller groups with less linear extent. The panhandle grouping in Berkeley and Jefferson

counties, and the small cluster straddling the Monongalia and Preston county border are each

still visible, though each are de-emphasized through this mapping analysis.

Finally, further hot spot analysis was conducted through the calculation Getis-Ord Gi

statistics for each cell; the most important measure from Getis-Ord Gi is the z-score derived,

signifying statistical significance of clustering (Figure 13).

FIGURE 13: Z-SCORES FROM GETIS-ORD GI HOT SPOT ANALYSIS, 2500M HEX BIN

Using this statistical measure, some 610 cells were identified as being statistically

significant clusters, with z-scores greater than +2.58 standard deviations from mean of a normal

distribution. In the case of Getis-Ord Gi, the hotspots correspond pretty well with groupings

identified visually and using Anselin Local Moran’s I cluster analysis, but the hot spots on the

Getis-Ord Gi map are generally larger and more pronounced. The same major groupings –

from Pocahontas and Randolph counties southwestward into Monroe County, another including

the several groups across Randolph and Pendleton counties – were visible here in a slightly

larger form. The smaller groupings – the panhandle grouping in Berkeley and Jefferson

counties, the grouping straddling the Monongalia-Preston County border, and the group in

southern Mercer County – were far more pronounced with this analysis, whereas some other

smaller clusters emerged for the first time.

6. CONCLUSION

Preliminary efforts into exploring this dataset with geospatial and statistical methods

elicited promising results. Getis-GI, Moran’s I, and Ripley’s K-Function statistics invariably

show significant clustering and autocorrelation of caves in the Greenbrier Group. This is

expected as geology and topography are strong controls on cave formation and entrance

genesis. Progressive and more extensive exploratory statistics, coupled with fieldwork, will

provide more useful data and analysis that can be integrated into local policy decisions at the

local and regional levels, as well as contribute to a better understanding of the mechanisms of

cave development in the Greenbrier Group limes of West Virginia.

7. FUTURE WORK

Geographically Weighted Regression (GWR) is a statistical method to examine

relationships between different variables geographically. It provides an advantage over other

methods (i.e. Ordinary Least Squares regression) in that it makes a regression model for

individual points, as opposed to a global multivariate regression model that assumes relations

among variables are constant across the region of interest. In the geographic model, the

independent variables are inversely weighted by distance from the dependent variable.

Variables closer to the region modeled are weighted heavier than those farther away, with the

weight inversely proportional to the distance. The results can be used to determine the degree

variables contribute to the model predictions at specific points in space. If variables are found

to be significant contributors to the model prediction, their coefficients can be mapped to

provide a visual means of inference. Modern computing allows for the efficient spatial analysis

and visualization of large geographic datasets within a Geographic Information System, or GIS.

Performing a GWR on this dataset would provide further inferences to the distributional

relationship of caves in West Virginia.

Further understanding of cave distribution in relation to environmental variables on a

much smaller scale can be conducted using the results of this. Results from the Anselin

Moran’s I spatial cluster and Getis-Ord Gi hot spot analyses provided a number of hexagon

cells indicating highly clustered cave entrance locations, which were determined to be

statistically significant for both tests. Some of these cells, near Lewisburg in Greenbrier

County, will serve as sites for future study comparing caves to various environmental variables,

such as soil composition, slope, and land cover, on a local scale.

8. REFERENCES

Bhaduri, B., Grove, M., Lowry, C., and J. Harbor. 1997. Assessing Long-Term Hydrologic

Effects of Land Use Change. Journal of the American Water Works Association 89: 94-106

Drake, J. and D. Ford, 1971. The Analysis of Growth Patterns of Two-Generation Populations.

The Examples of Karst Sinkholes. Canadian Geography. 16:381-384

Ford, D. and P. Williams. 1992. Karst Geomorphology & Hydrology. Chapman & Hall, NY.

Galloway, D., D. Jones and S. Ingebritsen. 1999. USGS Circular 1182. Land Subsidence in the

United States. <www.usgs.gov>

Kastning, E.H. and K.M. Kastning. 1997. Buffer Zones in Karst Terranes. Karst-Water

Environment Symposium Proceedings, eds. T. Younos, T. Burbey, E. Kastning, J. Poff, 80-87.

Moran, P. 1950. Notes on Continuous Stochastic Phenomena. Biometrika. 37(1): 17-23

Newton, J. 1984. Review of Induced Sinkhole Development. Sinkholes: Their Geology,

Engineering and Environmental Impact. Proceedings of the First Multidisciplinary Conference

on Sinkholes and the Engineering and Environmental Impacts of Karst. Rotterdam pp. 1-13

Orndorff R., D. Weary, and K. Lagueux. 2000. GIS Analysis of Geologic Controls on the

Distribution of Dolines in the Ozarks of South-Central Missouri, USA. Acta Carsologica.

29(2):161–175

White, E., G. Aron, and W. White. 1986. The Influence of Urbanization on Sinkhole

Development in Central Pennsylvania. Environmental Geology 8(1-2):91-97.

White, W. and D. Culver. 2012. Encyclopedia of Caves. Academic Press. g