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Connectivity among riparian corridors
in the Triangle area, NC
Using riparian buffers as a vector for understanding
herpetological dispersal among wilderness areas in a
developed landscape
Tony DiBiase
30 April 2013
Abstract
Dispersal of amphibian and reptilian species in highly fragmented landscape is often contingent on the
presence of protected riparian corridors, which provide suitable vectors between habitat patches in
otherwise developed land. Landscape connectivity via riparian corridors is often contingent on the size
of the buffer established. To determine an adequate buffer length and analyze connectivity within
riparian ecosystems in the Triangle region of North Carolina, I used a geographic information system
(GIS) integrating graph-theory approaches. I first developed a cost-resistance surfaced based on
biophysical proxies for dispersal potential and two buffer lengths (60 and 300 meters), then used the
GeoHAT tool to build a network of linked habitat nodes that provided a measure of total connected area
per patch. The analysis revealed an overall lower cost-threshold for maximal connectivity in the 300m.
buffer, though a higher connected-area per patch for the smaller buffer design. These results provide a
manager with a framework for understanding habitat connectivity in developed landscapes, as well as a
menu of critical important habitats in the Triangle for increased conservation protection of dispersal
activity.
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Introduction
The dispersal of amphibians and reptiles across a landscape confronts a significant challenge
when faced with densely developed urban regions, where animal movement is often hindered by
physical obstructions, habitat loss, and increased mortality risks from roads (Machtans et al 1996).
Connectivity between patches of protected habitat is critical for dynamic ecological functionality,
playing an essential role in species dispersal, genetic flow, animal migration, and a diverse range of
landscape-scale processes. Protected riparian corridors present a potentially significant vector for
dispersal activity in these urban ecosystems, providing transitional spaces between protected terrestrial
and aquatic reserves (Fischer et al 2000; Shirley 2006). Riparian buffers, which are often protected at a
certain distance by municipal regulation to protect water quality (Naiman 1993; McBride et al 2005),
represent unique habitat corridors, intersecting the urban mosaic in relatively continuous paths that
establish a network of linked habitat patches (Bennett 1999, Semlitsch et al 2003, McBride et al 2005).
Given the relatively fragmented nature of protected wilderness areas in the midst of urban
development, effective design of riparian buffer zones is necessary to maximize for landscape
connectivity. Often, stream buffers are set in regards to water resources alone, at a distance of around
30-60 meters rather than at a threshold necessary to preserve biodiversity in a riparian ecosystem,
which would require 150-300 meters varying on the species protected (Fischer et al 2000; Semlitsch et al
2003; Shirley 2006). The relative size of a buffer zone around riparian habitat additionally affects
landscape connectivity, providing different edge matrices depending on the possible pathways provided
by differing buffer lengths (Naiman et al 1993; Beier et al 1998). Several studies have used geospatial
technology to model connectivity in riparian habitats, testing amphibian dispersal rates by buffer size
(Semlitsch et al 2003), the movement of bird species in riparian buffers (Machtans et al 1996; Shirley
2006), the effect of surrounding development on watershed connectivity (McBride et al 2006), and the
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efficacy of corridor design for habitat connectivity (Beier et al 1998). The specific focus of this study is to
implement the Geospatial Habitat Analysis Toolkit (GeoHAT) (Fay 2012) using graph theory approaches
to compare relative levels of connectivity established by two potential riparian buffer sizes in the
Triangle region of North Carolina. I expect the point at which the edge network of habitat nodes
becomes maximally connected to occur significantly earlier for the 300 meter buffer than for the smaller
60 meter buffer, as well as the resulting graph networks to be larger. I used result of this connectivity
analysis to highlight the importance of effective buffer design to preserving species dispersal and
biodiversity across a landscape, and provide managers a specific list of high conservation-priority edges
and protected areas critical to functional landscape connectivity.
Methods
Study Area
The ‘Triangle’ region of North Carolina, bounded by the cities of Durham, Chapel Hill, and
Raleigh and spanning seven counties (Durham, Wake, Chatham, Lee, Orange, Johnston, and Harnett)
provides a useful study area to understand habitat connectivity using riparian corridors. The region is
characteristic of rapidly growing centers of urban development, projected to grow in population by 25%
by 2020 (NC OSBM 2000). The Triangle exhibits significant habitat fragmentation between the roughly
350 protected ‘State Natural Heritage Areas’ (SNHA) (McDonald et al 2004). The ecosystem dominant in
the region is similar to much of the North Carolina Piedmont ecoregion, formed from secondary
successional evergreen forest stands composed of Pinus taeda or mixed hardwood stands comprised of
Liriodenrdon tulipifera, Acer rubrum, Quercus alba, Quercus rubra, and Liquidambar styraciflua
(Christensen 1977).
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Spatial Analysis
Geospatial analysis was conducted using ESRI ArcMap version 10.1 (ESRI 2011), using a land
cover dataset from the National Land Cover Dataset (MRLC 2006), hydrographical and elevation datasets
from the National Hydrographic Dataset (NHD+), U.S. Census county shapefiles from the TIGER dataset
(TIGER 2010), and State Natural Heritage Area shapefiles from North Carolina’s Department of
Environment and Natural Resources (NC DENR 2012). A mask layer was created by clipping seven
county shapefiles within the study area from the TIGER dataset (Durham, Orange, Lee, Chatham,
Johnston, Harnett, and Wake), dissolving them together, and converting them to a raster grid. All of the
other datasets were projected to NAD 1983 UTM Zone 12N and clipped to the extent of the study area,
at a cell resolution of 30 meters. Data processing and analysis was conducted using the ArcMap model
builder (ESRI 2011, see the appendix for detailed processing steps). The NHD+ elevation dataset was
filled, then converted to a Relative Slope position grid to visualize the probably moisture contained for
each cell. SNHA features with less than 100 acres in size (165 out of the original 362 SNHA areas) were
deleted from the dataset to minimize computer processing time, as well as to select for larger,
ecologically viable patches with regions of core habitat (Beier et al 1998; McBride et al 2006). Land
cover and relative slope position datasets were reclassified from 0-100 based on their suitability for
species dispersal (Figure 1)1 and combined to form a cost resistance surface. Euclidean distance was
calculated from the clipped NHD+ flowline features, and reclassified to form two ad-hoc buffers,
weighting cells with a distance of either 60 or 300 meters2 from the stream at a cost of ‘0’, cells beyond
that threshold until 1000 meters at a cost of ’1000’, and distances beyond 1km. as ‘nodata’. Reclassified
1
Figure 1 lists the specific weights assigned to each LULC and RSP category. Suitability ranked based on positive
habitat for herpetological dispersal, and was set based on literature review from Beier et al (1998), Semlitsch et al
(2003), Olson et al (2007), and Ficetola et al (2009).
2
Based on buffer distance sizes for municipal water quality protection (Fischer et al 2000; McBride et al 2006) and
riparian biodiversity/species connectivity (Semlitsch et al 2003).
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raster distance was used in proxy of a discrete vector buffer to account for potential least-cost pathways
outside of the river buffer; rather than arbitrarily categorize the buffer as the only acceptable dispersal
zone, Euclidean distance was used to weight areas closer to a stream as less costly, modeling the
resistance surface from the perspective of an animal making sequential decisions (Semlitsch et al 2003;
Shirley 2006). The distance-buffers were added to the cost-resistance surfaces to create a uniform cost
surface for both buffer types.
Using the Geospatial Habitat Assessment Tool (GeoHAT) (Fay 2012), an edge list was created
between pairwise comparisons of SNHA patches. This list was used to plot graph diameter (the longest
edge length between the extreme points in a network) against different cost-distance thresholds in R (R
Core Team 2012), resulting in a summary plot that peaked at the point of maximal connectivity. A
threshold of 390,000 cost-distance units was used for the 60 meter buffer, and 210,000 cost-distance
units for the 300 meter buffer. These results were used to compare the graph network for the 60 and
300 meter buffer in terms of graph connected area as well as establish a list of critical edges and nodes
that necessitate increased conservation effort.
Results
Connectivity analysis using a graph-theory approach yielded a map of the 156 SNHA areas
greater than 100 acres within the Triangle area (Figure 1), a cost-surface demonstrating land-use and
slope position resistance along with buffer lengths (Figure 2), and a graph network of connected SNHA
regions weighted by the relative area linked to each reserve (Figure 3). The vast majority of SNHA
polygons appear to be closely nearby riparian areas, though when the final graph is maximally
connected several SNHA reserves remain disconnected to the network (Figure 3). The 300-meter buffer
appears to have a significant overall effect on the cost resistance surface raster, producing much
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stronger and larger buffer zones than the relatively noisy surface produced by the 60-meter buffer
(Figure 2); this difference highlights the ecological effect of extending buffer lengths to account for
biodiversity above water quality. The graph-network created by both buffers appear very similar,
though the 60-meter diagram appears to have more connections between SNHA, which is likely caused
by the significantly larger cost-threshold than used in the 300-meter buffer graph (Figure 3); patches
with the highest degree of centrality and connected area are highlighted in dark blue. The graph system
reached maximal connectivity at a threshold of 210,000 cost-distance units for the 300-meter buffer
versus 490,000 cost-distance units for the 60-meter buffer (Figure 4). Connected area was assessed for
each patch both in absolute terms and using an inverse-weighted distance function; interestingly no
single patch (other than 1351) occur more than once for a single category, demonstrating a distribution
of critical nodes for connectivity depending on the category (Table 1). The mean connected area for the
300-meter buffer is roughly 9km. less than that for the 60-meter buffer, with an overall maximum of
5km. less by patch (Table 2). The distributions of connected areas by plot appear to be similar for both
the 60-meter and 300-meter buffer zones (Figure 5).
Discussion
The results of this analysis suggest that additional study is necessary regarding connectivity
linked to riparian buffer regions. Due to limitations in computational power, least-cost paths between
SNHA patches were not determined, barring ‘patch efficiency’ metrics from being calculated;
quantitative analysis of the most critical edges in the network were not determined. Additional study of
the least-cost paths would allow for a listing of the most important routes between SNHA polygons,
allowing for the creation of a corridor design map rather than simply a listing of critical nodes. Further,
‘bridge’ patches which connect graph subnetworks cannot be quantitatively assessed without a measure
of patch efficiency, which requires the cost-backlink rasters from the least-cost path analysis. The cost-
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threshold used to assess the graph network differed greatly based on the size of the buffer (210,000
versus 490,000 cost-distance units), but the numbers reflect the point at which the graph was maximally
connected rather than measuring the innate biological ability of herpetological species to disperse on
the landscape; a better cost-threshold would be determined from the spatial dispersal rate from field
surveys rather than an optimized measure of graph diameter. The cost-surface used for analysis was
based in criteria suggested by Beier (1998) and Semlitsch et al (2003) to model riparian corridors, but
the cost-resistance surface would be improved by integrating additional variables based on specific
habitat suitability for the focal species.
The connectivity analysis results were significantly different than expected, with the smaller
riparian buffer leading to a larger overall connected area per SNHA patch than for the larger buffer
(Table 2). This result strongly contrasts to the original hypothesis that a larger riparian buffer size would
yield a stronger degree of landscape connectivity between SNHA patches. Interestingly, while the
absolute level of connected area is significantly greater per patch for the smaller buffer size, the inverseweighted distance area for all patches in both buffers appear to be similar in size, demonstrating the
variability of connected area based on spatial arrangement of patches beyond simply the presence of a
connection—given that the IDW area decays the relative influence of patches further away from the
node, the similar results for the IDW area for both buffers suggests a different spatial array of patches
within the network. The relatively abnormal results for the connectivity analysis can likely be explained
by the large difference in cost-distance thresholds used to limit each network. The 300-meter buffer
reaches maximal connectivity over 100,000 cost-distance units earlier than the 60-meter buffer, yielding
a graph similar in shape to the network for the 60-meter buffer, but with less overall connections—the
300-meter buffer reaches the same optimal connectivity threshold more efficiently than the 60-meter
buffer, explaining the overall greater area in the smaller buffer network despite achieving the same level
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of connectivity. Comparing the two buffer lengths depends on the measure of connectivity that is used,
allowing managers to determine connectivity based on dispersal ability or connected area: optimal
connectivity is reached (in terms of maximizing the diameter of the graph) significantly earlier for the
larger buffer, but the smaller buffer size lends a greater overall amount of area connected. Simulating
the 300-meter buffer at a larger threshold similar to the 60-meter buffer would generate additional
paths between nodes and lead to much greater area connected. Due to the extremely low connectivity
threshold value and the fairly similar IDW connected area results, the 300-meter buffer likely does
provide additional landscape connectivity between SNHA patches in the Triangle, despite having less
absolute connected area in total; the 300-meter ‘biodiversity’ buffer yields additional landscape
connectivity over the 60-meter ‘water-quality’ buffer. A conservation approach to protect
herpetological dispersal in the Triangle between SNHA patches must focus on establishing these larger
buffers, as well as providing additional protection to the critical habitat nodes outlined in this analysis
(Table 1) to ensure the viability of the dispersal network. Specifically, Crabtree Creek, White Pines
Natural Area, Sandy Branch Sand Ridge and Bay Complex, and Sam Knob are all SNHA regions that have
the highest number of connected area in terms of absolute and IDW area, and occur for both the 60 and
300 meter buffers, meriting significant conservation management and monitoring to preserve these
highly connected nodes.
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REFERENCES
Beier, Paul; Noss, Reed. 1998. Do Habitat Corridors Provide Connectivity? Conservation Biology
12:6,1241-1252.
Bennett, Andrew. 1999. Linkages in the Landscape: The Role of Corridors and Connectivity in
Wildlife Conservation. IUCN, Gland, Switzerland and Cambridge, UK.
Christensen, NL. 1977. Changes in structure, pattern and diversity associated with climax forest
maturation in Piedmont, North Carolina. American Midland Naturalist 97:1,176-188.
ESRI 2012. ArcGIS Desktop: Release 10.1. Redlands, CA: Environmental Systems Research
Institute.
Fay, John, and DL Urban. 2012. Geospatial Habitat Assessment Tool: GeoHAT. Version 1.01.
Nicholas School of the Environment, Duke University
http://code.env.duke.edu/projects/fay/wiki/GeoHat
Fischer, Richard; Fischenich, J.C. 2000. Design Recommendations for Riparian Corridors and
Vegetated Buffer Strips. United States Department of Defense. Army Technical Note
ADA378426.
Ficetola, Gentile; Padoa-Schioppa, Emilio; De Bernardi, Fiorenza. 2008. Influence of Landscape
Elements in Riparian Buffers on the Conservation of Semiaquatic Amphibians. Conservation
Biology 23:1,114-123.
Olson, DH; Anderson, PD; Frissell, CA; Hartwell, HW; Bradford, DF. 2007. Biodiversity
management approaches for stream-riparian areas: Perspectives for Pacific Northwest
headwater forests, microclimates, and amphibians. Forest Ecology and Management 246:1,81107.
Matchtans, Craig; Villard, Marc-Andre; Hannon, Susan. 2002. Use of Riparian Buffer Strips as
Movement Corridors by Forest Birds. Conservation Biology 10:5,1366-1379.
McBride, Maeve; Booth, Derek. 2007. Urban Impacts on physical stream condition: effects of
spatial scale, connectivity, and longitudinal trends. Journal of American Water Resources
Association 41:3,565-580.
McDonald, Rober; Urban, Dean. 2004. Forest edges and tree growth rates in the North Carolina
piedmont. Ecology 85:8, 2258-2266.
MRLC. 2006. National Land Cover Database. Multi-Resolution Land Characteristics Consortium.
http://www.mrlc.gov/index.php
Naiman, Robert; Decamps, Henri; Pollock, Michael. 1993. The Role of Riparian Corridors in
Maintaining Regional Biodiversity. Ecological Applications 3:2,209-212.
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NC DENR. 2013. Significant Natural Heritage Areas. Raleigh, NC. North Carolina Division of
Environment and Natural Resources, Division of Parks and Recreation, Natural Heritage
Program. Available on North Carolina OneMap portal:
http://data.nconemap.com/geoportal/catalog
North Carolina OSBM. 2000. County/State Population Projections. North Carolina Office of State
Budget and Management.
NHD+ (a). 2012. Elevation Dataset. National Hydrologic Dataset Plus. http://www.horizonsystems.com/NHDPlus/NHDPlusV2_home.php
NHD+ (b). 2012. NHD Flowlines. National Hydrologic Dataset Plus. http://www.horizonsystems.com/NHDPlus/NHDPlusV2_home.php
R Core Team 2012. R: a language and environment for statistical computing. R Foundation for
Statistical Computing, Vienna, Austria. URL: hhtp://www.R-project.org/.
Semlitsch, Raymond; Bodie, Russell. 2003. Biological Criteria for Buffer Zones around Wetlands
and Riparian Habitats for Ampibians and Reptiles. Conservation Biology 17,5,1219-1228.
Shirley, Susan. 2006. Movement of forest birds across river and clearcut edges of varying
riparian buffer strip widths. Forest Ecology and Management 223:3,190-199.
TIGER. 2010. County boundary shapefiles. United States Census TIGER Data. Available
http://www.census.gov/geo/maps-data/data/tiger.html
Acknowledgements
Much thanks to John Fay, my instructor from Duke University’s Nicholas School of the Environment, for
providing me with the SNHA data, elevation, flowline, and LULC data, as well as providing access and
support for the GeoHAT tool used extensively in this report; as well as to Dean Urban for extensive
conversations regarding the application of graph theory approaches to environmental management.
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Table 1: Most critical patches for connected and IDW Area for 60m and 300m buffers
Patch ID is listed above, referenced to the name of the patch below:
Patch
Connecte
ID
d Area
300
METER
S
1399
10320
1351
9725
1677
9194
Patch Name
Sam Knob
White Pines Natural Area
Black Pine Ridge
Maple Swamp Gordonia
9171 Forest
9060 Scranton Hardwood Forest
364
851
Patch
ID
IDW Area
1542
1904
1399
482
1351
5804
5174
4975
4348
4228
Patch Name
Sandy Branch Sand Ridge
and Bay Complex
Snake Mountain
Sam Knob
Crabtree Creek
White Pines Natural Area
Patch
Connected
ID2
Area3
60
METER
S
2878
9533
1791
9461
817
9072
Patch Name4
Youngs Mountain
Johnson Mill Bray
Jackson Blackjacks
3107
8996 Second Creek Natural Area
1351
8826 White Pines Natural Area
Patch
ID
IDW Area
Patch Name
Sandy Branch Sand Ridge
1542
5729 and Bay Complex
1351
5135 White Pines Natural Area
482
4987 Crabtree Creek
1963
4723 Bennett Mountain
1399
4428 Sam Knob
Table 2: Summary Statistics for Connected Areas
Statistic
Connected area (300m) IDW area (300m)
Connected Area (60m) IDW area (60m)
Min
0
0
0
0
1st Quartile
733
298
875
369
Median
1994
732
2641
993
Mean
2698
1246
3259
1455
3rd Quartile
4083
2025
5122
2359
Max
10320
5804
9533
5729
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Figure 4: Distance threshold v. graph diameter for both buffer lengths
Figure 5: Histogram of connected areas by buffer
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Figure 6: Histogram of IDW-connected area by buffer
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Model Builder Images: Data Management
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Model Builder Images: relative slope position
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Model Builder Images: Cost Surfaces
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Model Builder Images: Graph Analysis
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