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Connectivity Modelling of the Karuah-Myall
Catchments, NSW, Australia
Dr Alex M. Lechner and Darrel Tiang Chin Fung
22 December 2018
Connectivity Modelling of the Karuah-Myall Catchments, NSW, Australia
2
Connectivity Modelling of Karuah-Myall Catchments, NSW, Australia
Prepared for Midcoast Council
Report by: Dr Alex M. Lechner, and Darrel Tiang Chin Fung
School of Environmental and Geographical Sciences
University of Nottingham Malaysia Campus
Jalan Broga
43500 Semenyih
Selangor Darul Ehsan
Malaysia
E: Alex.Lechner@nottingham.edu.my
P: +60 (0)3 8725 3613
M: +60 (0)10 906 9167
Acknowledgements
We wish to acknowledge the help and feedback from Andrew Morris and Matthew Bell from the
Midcoast Council Catchment Management team for their invaluable feedback and guidance.
Connectivity Modelling of the Karuah-Myall Catchments, NSW, Australia
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Contents
Connectivity Modelling of the Karuah-Myall Catchments, NSW Australia .................. 1
Acknowledgements ......................................................................................................... 2
1.0 Introduction .............................................................................................................. 5
Connectivity Modelling Background ..................................................................... 5
Ecological characteristics of dispersal .................................................................. 5
Objectives ............................................................................................................ 6
2.0 Methods .................................................................................................................... 7
Study Area ........................................................................................................... 7
Modelling fine-scale connectivity .......................................................................... 8
Habitat and dispersal resistance surface from spatial data .................................. 9
Modelling Process ............................................................................................. 12
Analysis of protection status of important patches and linkages for connecting the
catchment ................................................................................................................. 13
Analysis of the Great Eastern Ranges ............................................................... 13
3.0 Results .................................................................................................................... 14
Least Cost Paths and Components ................................................................... 14
Patch-scale graph metrics - delta Integral Index of Connectivity and Clustering
Coefficient ................................................................................................................ 16
Sensitivity Analysis ............................................................................................ 18
Protected areas in the Karuah-Myall catchments and connectivity .................... 19
Karuah catchment and contribution to Great Eastern Ranges connectivity ........ 20
4.0 Conclusions ............................................................................................................ 22
5.0 Spatial data ............................................................................................................. 22
References ..................................................................................................................... 23
Connectivity Modelling of the Karuah-Myall Catchments, NSW, Australia
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Figures
Figure 1. Conceptual model of dispersal behaviour. Where movement between patches is
influenced by the interpatch-crossing and the gap-crossing distance. Dispersal cost from
landcover features also play a role in the likelihood of dispersal. ............................................... 6
Figure 2. True color remote sensed imagery with study area boundary. .................................... 7
Figure 3. Flow diagram describing the steps used in parameterising the connectivity model. .... 8
Figure 4. Habitat patches of the Karuah Catchment study area, including Least-Cost (LC) paths
and component boundaries. Including two insets showing Least cost paths in further detail. ....15
Figure 5. Habitat patches with component boundaries and delta Integral Index of Connectivity
(dIIC) for patches and linkages. Important linkages and patches are denoted by thick lines and
circles respectively. The circles located at the centroid of each patch describe patch-scale graph
metric values. ............................................................................................................................16
Figure 6. Habitat patches with component boundaries, LC paths and Clustering coefficient (CC)
for patches. This local scale graph metric computes the level of redundancy within a network.
Large circles represent crucial patches for landscape connectivity. The circles located at the
centroid of each patch describe patch-scale graph metric values. .............................................17
Figure 7. Number of Components (a) and Integral Index of Connectivity (b) versus interpatch
dispersal distance threshold. Both scenarios with resistance and non-resistance. ....................18
Figure 8. Habitat patches and protected areas with component boundaries and delta Integral
Index of Connectivity for patches and linkages. Important linkages and patches are denoted by
thick lines and circles respectively. The circles located at the centroid of each patch describe
patch-scale graph metric values. ...............................................................................................19
Figure 9. Habitat patches and the Great Eastern Ranges (GER) with component boundaries and
delta Integral Index of Connectivity for patches and linkages. Important linkages and patches are
denoted by thick lines and circles respectively. The circles located at the centroid of each patch
describe patch-scale graph metric values. ................................................................................20
Figure 10. (a) Full extent of the Great Eastern Ranges and the location of Karuah Catchment. (b)
Hypothetical pathways denoted by arrows that show how Karuah Catchment supports movement
between coastal vegetation and the GER. (c) Hypothetical pathways denoted by arrows showing
how Karuah Catchment supports movement between coastal vegetation within the catchment as
well as areas beyond the west of the catchment, and GER. ......................................................21
Tables
Table 1. Description of original land use zones, conversion to broad resistance classes and
resistance values. Note the original land use layer was updated manually and with Open Street
Map data as described in the text. ............................................................................................11
Connectivity Modelling of the Karuah-Myall Catchments, NSW, Australia
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1.0 Introduction
Connectivity Modelling Background
Conversion of natural ecosystems for human land uses leads to fragmentation which includes
loss of habitats and restriction of species movement. The consequential decrease in connectivity
has adverse effects on population viability resulting in greater extinction risk than from the loss of
habitat alone (Brook et al., 2008; Caughley, 1994; Fischer and Lindenmayer, 2006). The call for
better management of human modified landscapes is central to addressing the impact of
fragmentation on species movement and connectivity and ultimately viability of populations and
ecosystems.
Contemporary methods for modelling landscape connectivity include least-cost path analysis,
graph theory and circuit theory; providing a diverse toolbox for studying the different aspects of
connectivity (Adriaensen et al., 2003; Foltête et al., 2012; McRae et al., 2008; Urban and Keitt,
2001). Connectivity modelling methods including least-cost path analysis characterise non-
habitat/matrix based on dispersal costs, which represents the metabolic price and mortality risk
of moving across such areas (Adriaensen et al., 2003; Sawyer et al., 2011). Dispersal cost is
typified by a combination of landcover attributes, such as urbanisation, and species-specific
dispersal probability over various distances. Cost-weighted analysis is used to produce the least-
cost pathways connecting suitable habitat patches. Subsequently, using graph theoretic
approaches network measures are calculated to assess the significance of patches within a
connectivity network quantitatively (Minor and Urban, 2008; Rayfield et al., 2011). Circuit theory
provides an alternative approach to modelling dispersal, where the landscape is conceived to be
akin to an electrical circuit and resistance within the landscape is characterised by considering
current flow as analogous to individual movement probabilities across every raster grid cell
(McRae et al., 2008).
Ecological characteristics of dispersal
For this study connectivity was characterised based on the General Approach to Planning
Connectivity from Local Scales to Regional (GAP CLoSR) approach which models fine-scale
movement patterns and habitat based on three key parameters (Alex M. Lechner et al., 2015;
Lechner et al., 2017, n.d.; Lechner and Lefroy, 2014) (Figure 1):
1. A minimum habitat patch size required to support viable populations.
2. A gap-crossing distance, between connectivity elements such as scattered trees,
which limit the distances of open ground (gaps) which individuals will move across.
3. An interpatch-crossing distance threshold which is the maximum distance an animal
is able to move between patches of habitat.
Connectivity Modelling of the Karuah-Myall Catchments, NSW, Australia
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Figure 1. Conceptual model of dispersal behaviour. Where movement between patches is
influenced by the interpatch-crossing and the gap-crossing distance. Dispersal cost from
landcover features also play a role in the likelihood of dispersal.
For species to move long distances from one patch to another, structural connectivity elements
must exist such as scattered-trees or road side vegetation. Corridors or stepping stones of
vegetation are recognised as important facilitators of movement (Fischer and Lindenmayer, 2002;
Ree et al., 2004). The GAP CLoSR framework takes into account fine-scale dispersal behaviour
which is often absent from other connectivity modelling approaches.
Objectives
The objective of this study is to characterise connectivity within the Karuah-Myall Catchments,
New South Wales, Australia, using the GAP CLoSR framework (Lechner and Lefroy 2014) in
order to provide a strategic overview of connectivity to support the Midcoast council’s regional
planning efforts. We modelled connectivity for a “general representative species” dependent on
woody vegetation. This method characterises connectivity for the majority of native fauna which
utilises woody vegetation, and the plant species that rely on these fauna for dispersal.
Connectivity was modelled using a graph theoretic connectivity model, Graphab (Foltête et al.,
2012) where movement was characterised by least-cost paths and the importance of patches was
quantified using graph metrics. The results of this study were discussed in terms of the patches
and linkages which are critical for connecting the landscape, the contribution of protected areas
to conserving connectivity and the role of the two catchments in connectivity beyond its
boundaries with specific reference to the Great Eastern Ranges national wildlife corridor scheme.
Connectivity Modelling of the Karuah-Myall Catchments, NSW, Australia
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2.0 Methods
Study Area
2.1.1. Karuah River Catchment
The Karuah River catchment is located in the lower north coast of New South Wales, Australia. It
borders the Hunter River catchment in the south, and the Manning River catchment in the north.
The catchment is approximately 2410 km2. Land use within the Karuah River catchment ranges
from State Forest, agricultural land, National Parks, coal mining to urbanised areas. The
catchment is typified by narrow valleys to the north that widen to the mid and lower regions. The
catchment is valued for its rich biodiversity, diverse landforms and scenic values (Great Lakes
Coucil, 2014).
2.1.2. Myall Lakes Catchment
The Myall Lakes Catchment has an area of more than 400 km2 and is located in the mid north
coast of New South Wales, Australia. Myall River is the major tributary of this catchment, with its
headwaters extending to Craven Nature Reserve and the Kyle Range. Land use within the Myall
Lakes catchment is largely agricultural, with forestry and protected areas present in steeper areas.
Small urban areas are located in townships of Bulahdelah as well as the popular tourist
destinations in Tea Gardens-Hawks Nest (Great Lakes Council, 2015). Both Karuah and Myall
river discharges into Port Stephens to the south.
Figure 2. True color remote sensed imagery with study area boundary.
Connectivity Modelling of the Karuah-Myall Catchments, NSW, Australia
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Modelling fine-scale connectivity
This study adopted the GAP CLoSR framework (Lechner and Lefroy, 2014) which describes how
local and regional scale connectivity models can provide support for land use planning through
scenario analysis. The GAP CLoSR characterises connectivity based on fine-scale dispersal
behaviours and includes (Figure 3):
• identification of key ecological connectivity parameters;
• pre-processing spatial data based on these parameters to produce a 1) habitat layer
based on a minimum patch size, and 2) gap-crossing layer and 3) land cover layer to
produce a resistance surface based on dispersal costs;
• running these spatial data within existing connectivity modelling software.
Figure 3. Flow diagram describing the steps used in parameterising the connectivity model.
This study modelled connectivity for a “general representative species” which utilised native
woody vegetation. Such patches are likely habitats for the majority of native fauna in the region,
as well as floral species that rely on them for dispersal. Limitations of using such data are
described in Lechner and Lefroy (Lechner and Lefroy, 2014)
Connectivity Modelling of the Karuah-Myall Catchments, NSW, Australia
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Graphab was parameterised using dispersal values from Doerr et al.’s (2010) review of how
structural connectivity facilitates dispersal. This extensive review investigated 80 studies from 98
sources and as a result, synthesised average values for gap-crossing distance and interpatch-
crossing distance threshold. These values were relevant to this study as the majority of the
reviewed studies feature similar ecosystems and are also impacted by fragmentation mainly from
agriculture. The parameters were:
• 1000 m interpatch dispersal distance
• 100 m gap-crossing distance
• 10 ha minimum habitat patch size
Habitat and dispersal resistance surface from spatial data
2.3.1. Data processing
Key spatial datasets were supplied by the Midcoast council which including multiple land use and
land cover maps. Two vegetation land cover maps and one general land use map (5 m pixel size)
were chosen as the layers for which the habitat and resistance layers were derived from. The
habitat layer consisted of only native vegetation, whereas the resistance layer consisted of native
vegetation and other land cover classes plus the gap crossing surface processed from the native
vegetation layer. Manual edits and processing were required to prepare all spatial data for the
modelling process as the original data was considered inadequate for modelling fine-scale
connectivity.
The following geoprocessing was conducted in ArcGIS 10.6:
• Clip the vegetation cover and general land use layers to the extent of the study area.
• Produce a harmonised vegetation layer through combining most accurate components
of three land cover layers to produce the habitat and gap crossing layer.
• Manual corrections of land use and vegetation layers using ancillary data including Open
Street Maps.
• Manually digitise any missing vegetation considered as habitat using reference data.
• Manually digitise missing individual trees and grouped trees in open areas such as
pastures. This is a separate layer that will serve in the creation of the Gap Crossing layer
and was not included in the final vegetation layer.
We used ArcGIS and Google Earth Engine high resolution World View 2 base maps from
29/6/2016 and 8/2/2018 respectively as reference datasets. We relied mainly on the ArcGIS
base map due to the ease of access. Also, we found very little difference between the two
years.
2.3.2. Harmonisation of vegetation layers
The three primary land cover layers provided by Midcoast council had three different
representations of the distribution of land cover in the study area. These datasets were:
• CUT_GLC_HIGHRES_VEGASSESSMENT"
• NATIVE_VEGETATION_CANOPY
• Cut_MNC
Connectivity Modelling of the Karuah-Myall Catchments, NSW, Australia
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The layers were overlaid on each other and manually corrected using the reference data. This
produced a composite vegetation layer that was spatially complete and correct to 2016, the time
of the ArcGIS base map.
2.3.3. Open Street map update
Roads and waterways were updated on both the Habitat layer and the land cover layer to provide
a better representation of vegetation and land use patterns which impact on fine-scale movement.
For example, by adding roads we were able to identify discontinuities in vegetation patches which
were originally mapped as a single patch. We used Open Street Map (OSM) data to source roads,
streams and rivers that were not digitised in the existing datasets. The following edits were made
to the data from OSM:
• Filtering out small roads and tracks if they did not show up on Google Earth/Google
Maps. These were dirt roads or abandoned roads that has little to no traffic and thus
don’t affect movement.
• Buffering the roads and waterways by 3.75m on each side for a total width of 7.5m. This
step assures the roads are wide enough to show up when shown on the map without
having gaps or breaks in them.
• Combine with existing maps either through removing vegetation in the habitat layer or
altering classes in the land use layer
2.3.4. Habitat Layer update
The Habitat layer represents native vegetation or “habitat patches”. Using the ArcGIS base map
as a reference we manually digitise and/or altered vegetation considered to be habitat.
2.3.5. Land use resistance layer creation
Resistance to dispersal can be described by how movement costs associated with different kinds
of land cover reduce the maximum distance individuals can travel. For instance, a land cover
which doubles dispersal cost would reduce the interpatch-crossing distance threshold of 1 km to
500 m. For this region, the dispersal costs assigned to each pixel is was based on the paper by
Lechner and Lefroy (2014).
The classes in the land use layer were reclassified into four main classes based on the general
ways in which land cover affect movement: habitat, non-habitat (mainly agricultural land), roads
and water (Table 1). Dispersal costs varied from no cost (habitat and non-habitat) to water which
reduced distance by 300%.
The original land use layer was “Cut_GLC_highres_vegassessment”.
Connectivity Modelling of the Karuah-Myall Catchments, NSW, Australia
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Table 1. Description of original land use zones, conversion to broad resistance classes and
resistance values. Note the original land use layer was updated manually and with Open
Street Map data as described in the text.
Original Land use
General class
Resistance
Class
Resistance
Percentage
Resistance
Pixel Value*
SCLEROPHYLL SHRUBLAND,
SCLEROPHYLL FOREST, COASTAL DRY
FOREST, COASTAL HEADLAND,
WOODLAND, DRY HEATHLAND, WET
HEATHLAND, MANGROVE WOODLAND,
DRY RAINFOREST, RIPARIAN FOREST,
TALL SHRUBLAND.
Habitat
Other
100%
5
SAND COMPLEX, GRASSLAND,
SEDGELAND, RUSHLAND, FRESHWATER
MEADOW MIX.
Non-habitat
CLEARED, GOLF COURSES, PARKLAND,
PARKLAND/GRASSLAND, RESIDUAL PINE
FOREST, CLEARED PASTURE, MANAGED
PINE PLANTATION, ROCK, SAND,
CLEARED GRASSLAND.
Other
URBAN OR RESIDENTIAL DEVELOPMENT,
QUARRY, MINING STRIP, INDUSTRIAL
LAND, LANDFILLS, SCHOOLS, MINES-
COAL/CSG, FENCES.
Infrastructure
Infrastructure
200%
10
BRIDLEWAY, CONSTRUCTION ROAD, GLC
ROAD, MOTORWAY, MOTORWAY LINK,
RAIL, RESIDENTIAL ROAD, REST AREAS,
SECONDARY ROADS, SERVICE ROADS,
TERTIARY ROADS, TERTIARY LINK,
TRACKS.
Roads
Roads
200%
10
WATER, RIVER, STREAM
Water
Hydro
300%
15
*Resistance pixel values here are based on the pixel size of 5m. A resistance value of 5 will have no resistance and
values of 10 will have twice the resistance and 15 with have three times the resistance.
2.3.6. Gap-crossing Layer
The gap-crossing layer identified distances between structural connectivity elements and patches
beyond the gap-crossing distance threshold. Areas beyond this threshold act as barriers to
dispersal. Vegetation smaller than the minimum patch size is considered as structural connectivity
elements. In addition, we also manually digitised 14,125 trees (points) and 6,703 groups of trees
(polygons) which were not included in the original land cover mapping. The following steps were
taken to create the Gap Crossing layer:
• Individual trees were digitized as points from the reference layer then buffering by 2.5 m.
• Groups of scattered trees were digitized as polygons where distances between them
were less than 100 m.
• Both layers were then combined with the habitat layer.
Connectivity Modelling of the Karuah-Myall Catchments, NSW, Australia
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The gap crossing layer was created by buffering the combined layer by half the gap-crossing
distance threshold (50 m). Areas outside the buffer distance are considered as barriers to
movement. If connectivity elements are present within the gap-crossing distance, the buffers will
meet or overlap, allowing for movement. Dispersal will not happen outside the buffered areas.
2.3.7. Dispersal Resistance Layer
The dispersal resistance surface describes how landcover between patches restricts movement.
It is produced by combining the landcover resistance layer and the gap crossing layer. The gap-
crossing layer takes priority over other landcover classes. The resulting dispersal cost layer is
one that acknowledges fine-scale threshold dynamics as it ensures dispersal is impossible where
gaps are greater than the gap-crossing distance. The layer also allows for modelling of cumulative
costs where dispersal is possible but may be impeded by land use.
Modelling Process
A graph theoretic approach along with least-cost paths was used to assess connectivity within
the study area. Using graph theory, the landscape was characterised as a network of patches
connected by links described by least-cost paths (Dale and Fortin, 2010; Minor and Urban, 2008).
All modelling was conducted using Graphab 2.2 software (Foltête et al., 2012). The outputs from
the connectivity model were interpreted through visualising fragmentation and least-cost paths
and quantifying the importance of patches and linkages using graph metrics.
In the first step groups of patches that were connected to each other but isolated from other
patches were identified; known as components. Spatial patterns of these components are useful
for characterising fragmentation and barriers to connectivity at the regional scale (Alex M Lechner
et al., 2015). Large components describe multiple patches that are connected; characterising
regions that are connected. Numerous small components represented by a single or a number of
small patches describe regions where dispersal is highly constrained.
We calculated two patch scale graph metrics to characterise the importance of patches and
linkages for contributing to dispersal. The two patch scale metrics quantified were: delta Integral
index of connectivity (dIIC) (Pascual-Hortal and Saura, 2006; Saura and Pascual-Hortal, 2007)
and Clustering coefficient (CC) (Minor and Urban, 2008; Ricotta et al., 2000). IIC describes the
impact of the loss of habitat availability caused by the removal of the focal patch relative to the
connectivity network. The higher the value, the higher the connectivity. While the clustering
coefficient measures path redundancy between the patch and its neighbouring patches. A higher
value means alternative pathways exist for linking neighbouring patches.
In the final analysis we assessed the landscape-graph metrics, number of components (NC) and
the integral index of connectivity (IIC) versus a range of interpatch dispersal distance thresholds.
This analysis allowed for the identification of key dispersal distances for connecting the
catchment. It also functions as a sensitivity analysis, characterising how the interpatch dispersal
distance effects the results of the analysis.
Connectivity Modelling of the Karuah-Myall Catchments, NSW, Australia
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Analysis of protection status of important patches and linkages
To assess how important existing national parks, forest reserves and other protected areas are
for connecting patches in the study area we overlaid the connectivity modelling outputs with
protected area spatial data. A single protected area spatial dataset was produced, which
consisted of the following classes: National Parks, Forest Reserves, Coastal Wetland,
Environmental Conservation, Environmental Management, Flora Reserve, Forestry, Protected
Area, State Conservation Area and State Forest. We then identified whether patches and links
which had no protection status were important for connecting the study area.
Analysis of the Great Eastern Ranges
We assessed visually how the Karuah-Myall catchments contribute to the Great Eastern Ranges
(GER), national scale regional planning and connectivity initiative centred on the Great Dividing
Range and the Great Escarpment. It spans from Grampians, Western Victoria to Far North
Queensland (https://www.ger.org.au). The GER crosses the Karuah River catchment to the west.
Connectivity Modelling of the Karuah-Myall Catchments, NSW, Australia
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3.0 Results
Least Cost Paths and Components
A visual assessment of the outputs showed that the patches within the Karuah-Myall are nearly
all linked to each other, except for two isolated patches in the south east of the study area (Figure
4). These components are visualised in Figure 4 as two patches surrounded by blue lines
representing the component boundaries. The existence of least-cost paths between patches (red
lines in Figure 4) indicate that the cumulative cost-weighted distance between patches was less
than 1000 m and also that the gap-crossing distance between structural connectivity elements
was less than 100 m. Examples of the least-cost paths are shown in the two insets in Figure 4.
Least-cost paths avoid high resistance land covers such as settlements.
Connectivity Modelling of the Karuah-Myall Catchments, NSW, Australia
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Figure 4. Habitat patches of the Karuah-Myall Catchments, including Least-Cost (LC) paths
and component boundaries. The two insets provide a detailed view of the least-cost paths.
Connectivity Modelling of the Karuah-Myall Catchments, NSW, Australia
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Patch-scale graph metrics - delta Integral Index of Connectivity and Clustering
Coefficient
The patch-scale graph metric assessment characterised the importance of patches and linkages
for connecting habitat across the catchments (Figure 5 and Figure 6). Figure 5 describes the
distribution of dIIC values for patches and linkages. The distribution of dIIC values showed that a
central spine of high dIIC patches and linkages dominate the catchments. These are mostly large
patches and linkages connecting these large patches. This central spine runs from the largest,
northern-most node within Karuah river catchment, towards the east and down to the centre of
the study area before splitting into two linkages connecting other high dIIC nodes in the Karuah
river catchment to the west and Myall Lakes catchment to the east. These high dIIC linkages
between patches are critical for connecting the catchments.
Figure 5. Habitat patches with component boundaries and delta Integral Index of Connectivity
(dIIC) for patches and linkages. Important linkages and patches are denoted by thick lines and
circles respectively. The circles located at the centroid of each patch describe patch-scale
graph metric values.
Connectivity Modelling of the Karuah-Myall Catchments, NSW, Australia
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Figure 6 describes the Clustering Coefficient (CC) values for the study area. The CC
characterises the level of path redundancy within neighbouring patches. A high value indicates
that there are alternative pathways to reach neighbouring patches. As shown in Figure 6, there
are many nodes with low CC values. Many of these reside along the strip of cleared land in the
west of the study area. The same trend can be found throughout the south and in the east. These
patches with low CC values indicate that they are crucial to connectivity as they provide a unique
link between themselves and other patches.
Figure 6. Habitat patches with component boundaries, LC paths and Clustering coefficient
(CC) for patches. This local scale graph metric computes the level of redundancy within a
network. Large circles represent crucial patches for landscape connectivity. The circles
located at the centroid of each patch describe patch-scale graph metric values.
Connectivity Modelling of the Karuah-Myall Catchments, NSW, Australia
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Sensitivity Analysis
A sensitivity analysis was conducted to assess the significance of interpatch-crossing distance
thresholds for landscape-scale connectivity. This was done by modelling connectivity with and
without resistance. Figure 7 showed that there was very little difference in connectivity between
with and without resistance for both the number of components (NC) and IIC values. This
indicates that the greatest driver of connectivity within the two catchments is interpatch dispersal
distance, not resistance due to land cover. For both IIC and NC at a distance of around 50 m to
100 m there was a large decrease in NC and increase in IIC suggesting that species with these
movement distances or less are most likely to be affected by fragmentation in the catchments.
However, these species, which tend to be small sized, will have lower requirements for total patch
area so are less likely to be impacted by fragmentation.
Figure 7. Number of Components (a) and Integral Index of Connectivity (b) versus interpatch
dispersal distance threshold. Both scenarios with resistance and non-resistance.
0
50
100
150
200
250
300
350
0 100 200 300 400 500 600 700 800 900 1000
Number of Components
Interpatch dispersal distance threshold (m)
a.
Without Resistance With Resistance
0
0.005
0.01
0.015
0.02
0.025
0.03
0 100 200 300 400 500 600 700 800 900 1000
IIC
Interpatch dispersal distance threshold (m)
b.
IIC without resistance IIC with resistance
Connectivity Modelling of the Karuah-Myall Catchments, NSW, Australia
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Protected areas in the Karuah-Myall catchments and connectivity
Figure 8 describes delta IIC values for protected areas within the study area. The figure shows
that the majority of high dIIC nodes and linkages are within protected areas. The exception is a
region to the west from the north to south which has no protection status (Figure 8a). This area
includes high value patches and linkages in the north which connects west and east of the study
area. Another key region without protection is in the south (Figure 8b).
Figure 8. Habitat patches and protected areas with component boundaries and delta Integral
Index of Connectivity for patches and linkages. Important linkages and patches are denoted
by thick lines and circles respectively. The circles located at the centroid of each patch
describe patch-scale graph metric values.
Connectivity Modelling of the Karuah-Myall Catchments, NSW, Australia
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Karuah-Myall catchments and contribution to Great Eastern Ranges connectivity
Figure 9 describes dIIC values and the overlap with the Great Eastern Ranges. As seen in the
figure, there are few nodes and linkages with high dIIC values within the GER. There is one
significant node to the north and one more just below the middle. These are areas important for
connectivity.
Figure 9. Habitat patches and the Great Eastern Ranges (GER) with component boundaries
and delta Integral Index of Connectivity for patches and linkages. Important linkages and
patches are denoted by thick lines and circles respectively. The circles located at the centroid
of each patch describe patch-scale graph metric values.
Connectivity Modelling of the Karuah-Myall Catchments, NSW, Australia
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Figure 10 shows the Karuah catchment in the context of the GER. Figure 10 (a) shows the location of Karuah Catchment in relation to the GER
at the national scale. Figure 10 (b) shows visually that there is a cleared region between north and south forested areas in the GER to the west of
the Karuah catchment. The yellow arrow in Figure 10 (b) represents a hypothetical potential linkage enabling movement from patches in the north
to patches in the south of GER. The Karuah catchment is part of a region close to the east coast which potentially also provides another north to
south linkage. In Figure 10 (c), the arrows are used to visualise hypothetically how the Karuah catchment connects to the GER in the North-west,
and the South-west, shown by the light blue and dark blue arrows respectively. Although Myall Lakes catchment does not overlap with the GER,
it also plays a crucial role in connected coastal patches to the east of the study area and outside of the study area (Figure 10c)
Figure 10. (a) Full extent of the Great Eastern Ranges and the location of the study area. (b) Hypothetical North-South connection within the GER. (c)
Hypothetical pathways denoted by arrows showing how Karuah Catchment supports movement between coastal patches within the study area and
the GER.
Connectivity Modelling of the Karuah-Myall Catchments, NSW, Australia
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4.0 Conclusions
Our study showed that the Karuah-Myall catchments are well connected with only two isolated
patches. The study area is fragmented by two agricultural regions along the valley floors. Even
though these areas have been cleared, they still have a significant coverage of scattered trees
below the gap-crossing threshold distance. The sensitivity analysis shows that species with an
interpatch distance threshold of 50 m to 100 m or less are likely to be mostly affected by
fragmentation in the study area.
This study provided a coarse level general assessment of connectivity for the Karuah-Myall
catchments. While we only used a “general representative species” for the paramaterisation of
model the sensitivity analysis suggests that it is likely that the catchments are well connected for
the majority of species which depend on woody vegetation. Further assessments for species of
conservation concern which have more specific habitat requirements (i.e. utilise a subset of
woody vegetation as habitat or grasslands) and or have specific movement requirements not
captured by our resistance model (i.e. roads are barriers to movement) is potentially required.
The analysis identified that there are certain regions in the catchments which have no protection
status yet are important for connecting the landscape. In addition, the assessment of the
contribution of the Karuah catchment to the GER suggests that it provides important connections
from north to south. In addition, the Karuah catchment appears to also connect the coastal
forested areas in the Myall Lakes catchment to the vegetation in the GER. Critically, this
connection is dependent on number of key patches and linkages in the north (Figure 8b).
While the Karuah-Myall catchments appear to be well connected for a cleared pasture dominated
agricutural landscape, east-west linkages across the cleared valley floors should be prioritised to
preserve connectivity to ensure future connectivity. In addition, it appears as though both
catchments value for connectivity is not only for biodiversity within the catchments but beyond the
catchments as part of the GER.
This report and its associated mapping provides spatial data to assist MidCoast Council and other
relevant agencies design and implement regional connectivity conservation projects or conduct
further, more fine-scale analysis. The analysis does suggest that immediate priority focus areas
for enhanced connectivity status or function exist at a number of areas including the following:
• The Glen Nature Reserve west to Avon River State Forest (no protected status)
• Karuah National Park north-east to Myall River State Forest (no protected status)
• Karuah National Park to Monkerai Nature Reserve (contribution to Great Eastern Ranges
Initiative)
5.0 Spatial data
The spatial data for this project can be downloaded by contacting the authors.
The data is provided in three formats:
1. .mpks map package for ESRI ArcMap
2. .mdxs map package for ESRI ArcGIS Pro
3. Raw .shp and raster data.
Connectivity Modelling of the Karuah-Myall Catchments, NSW, Australia
23
References
Adriaensen, F., Chardon, J.P., Blust, G. De, Swinnen, E., Villalba, S., Gulinck, H.,
Matthysen, E., 2003. The application of ‘ least-cost ’ modelling as a functional
landscape model. Landsc. Urban Plan. 64, 233–247. https://doi.org/10.1016/S0169-
2046(02)00242-6
Brook, B.W., Sodhi, N.S., Bradshaw, C.J.A., 2008. Synergies among extinction drivers
under global change. Rev. Trends Ecol. Evol. 23, 453–460.
https://doi.org/10.1016/j.tree.2008.03.011
Caughley, G., 1994. Directions in conservation biology. J. Anim. Ecol. 63, 215–244.
Dale, M.R.T., Fortin, M.-J., 2010. From Graphs to Spatial Graphs. Annu. Rev. Ecol. Evol.
Syst. 41, 21–38. https://doi.org/doi:10.1146/annurev-ecolsys-102209-144718
Doerr, V.A.J., Doerr, E.D., Davies, M.J., 2010. Does structural connectivity facilitate
dispersal of native species in Australia’s fragmented terrestrial landscapes?, CEE
review 08-007 (SR44).
Fischer, J., Lindenmayer, D.B., 2006. Habitat Fragmentation and Landscape Change: An
Ecological and Conservation Synthesis, Global Ecology and Biogeography. Island
Press, Washington.
Fischer, J., Lindenmayer, D.B., 2002. The conservation value of paddock trees for birds in
a variegated landscape in southern New South Wales. 1. Species composition and
site occupancy patterns. Biodivers. Conserv. 11, 807–832.
Foltête, J.C., Clauzel, C., Vuidel, G., 2012. A software tool dedicated to the modelling of
landscape networks. Environ. Model. Softw. 38, 316–327.
Great Lakes Coucil, 2014. Karuah Catchment Management Plan - Community and
Stakeholder Forum Forum Outcomes Report.
Great Lakes Council, 2015. Great Lakes Council 2015 Waterway and Catchment Report.
Foster.
Lechner, A.M., Doerr, V., Harris, R.M.B., Doerr, E., E.C., L., n.d. A framework for multi-scale
connectivity modelling incorporating fine-scale dispersal behaviour. Landscape and
Urban planning. Landsc. Urban Plan.
Lechner, A.M., Harris, R.M.B., Doerr, V., Doerr, E., Drielsma, M., Lefroy, E.C., 2015. From
static connectivity modelling to scenario-based planning at local and regional scales.
J. Nat. Conserv. 28, 78–88. https://doi.org/10.1016/j.jnc.2015.09.003
Lechner, A.M., Lefroy, E.C., 2014. General Approach to Planning Connectivity from LOcal
Scales to Regional (GAP CLoSR): combining multi-criteria analysis and connectivity
science to enhance conservation outcomes at regional scale. Centre for Environment,
University of Tasmania. www.nerplandscapes.edu.au/publication/GAP_CLoSR.
Lechner, A.M., Sprod, D., Carter, O., Lefroy, E.C., 2017. Characterising landscape
connectivity for conservation planning using a dispersal guild approach. Landsc. Ecol.
32, 99–113. https://doi.org/10.1007/s10980-016-0431-5
Lechner, A.M., Sprod, D., Carter, O., Lefroy, E.C., 2015. Using dispersal guilds to assess
connectivity at the landscape scale: a case study in the Tasmanian Midlands.
Connectivity Modelling of the Karuah-Myall Catchments, NSW, Australia
24
https://doi.org/10.13140/RG.2.1.4408.2081
McRae, B.H., Dickson, B.G., Keitt, T.H., Shah, V.B., 2008. Using circuit theory to model
connectivity in ecology, evolution, and conservation. Ecology 89, 2712–2724.
Minor, E.S., Urban, D.L., 2008. A graph-theory framework for evaluating landscape
connectivity and conservation planning. Conserv. Biol. 22, 297–307.
Pascual-Hortal, L., Saura, S., 2006. Comparison and development of new graph-based
landscape connectivity indices: Towards the priorization of habitat patches and
corridors for conservation. Landsc. Ecol. 21, 959–967. https://doi.org/10.1007/s10980-
006-0013-z
Rayfield, B., Fortin, M.J., Fall, A., 2011. Connectivity for conservation: A framework to
classify network measures. Ecology 92, 847–858. https://doi.org/10.1890/09-2190.1
Ree, R. Van Der, Bennett, A.F., Gilmore, D.C., 2004. Gap-crossing by gliding marsupials :
thresholds for use of isolated woodland patches in an agricultural landscape. Biol.
Conserv. 115, 241–249. https://doi.org/10.1016/S0006-3207(03)00142-3
Ricotta, C., Stanisci, A., Avena, G., Blasi, C., 2000. Quantifying the network connectivity of
landscape mosaics: a graph-theoretical approach. Community Ecol. 1, 89–94.
Saura, S., Pascual-Hortal, L., 2007. A new habitat availability index to integrate connectivity
in landscape conservation planning: Comparison with existing indices and application
to a case study. Landsc. Urban Plan. 83, 91–103.
https://doi.org/10.1016/j.landurbplan.2007.03.005
Sawyer, S.C., Epps, C.W., Brashares, J.S., 2011. Placing linkages among fragmented
habitats: Do least-cost models reflect how animals use landscapes? J. Appl. Ecol. 48,
668–678.
Urban, D., Keitt, T., 2001. Landscape connectivity: A graph-theoretic perspective. Ecology
82, 1205–1218. https://doi.org/10.1890/0012-9658(2001)082[1205:LCAGTP]2.0.CO;2
