An analysis of landscape connectivity of the Grassland Biome in Mpumalanga using graph theory
MSc Project
Louise Fourie
Supervisor: Prof. M. Rouget
Introduction
South African Grassland Biome
• Second largest biome after savanna
• Most threatened and least protected biome in South Africa
• Only 1.6% formally protected
Introduction
•Habitat loss and fragmentation: Two primary threats to biodiversity
• Connectivity helps maintain viable populations in fragmented landscapes
• Important in biodiversity conservation
Aims
• To analyse the connectivity of grassland habitat patches in Mpumalanga using graph theory
• To investigate the importance of abandoned croplands for maintaining overall connectivity
Connectivity
The degree to which the landscape facilitates or prevents movement of organisms among available habitat patches
Connectivity
• Can be either structural or functional
Structural connectivity:
Arrangement of habitat and landscape without reference to specific species
Functional connectivity:
The behavioural responses of an organism to the arrangement of habitat in the landscape
Structural and functional connectivity
Structural connectivity between patch 1 and 2 relatively good
Patch 1
Functional connectivity
Movement of a specific species between patch 1 and 2 can be either easy or difficult depending on properties of the species
Patch 2
Measuring Connectivity
Different Measures available
•Nearest Neighbour distance
(use patch occupancy data and inter‐patch distance)
• Spatial pattern indices
(number, size, extent, shape or aspect of habitat patches)
• Observed immigration, emigration or dispersal rates
(actual observed movements of species)
Measuring Connectivity
Calabrese & Fagan (2004)
Graph theory
• Represent landscape as a set of nodes and edges
Edge (connection between nodes)
Node (habitat patch)
Graph theory
Advantages :
• Provide a detailed picture of connectivity
• Modest data requirements
• Very suitable for large scale landscape analysis needed for conservation planning
Graph theory
• Can use both structural and dispersal data
• Unify multiple aspects of habitat connectivity
• Can be applied at patch or landscape levels
• Many graph‐theory based indices available to evaluate structural or functional connectivity
Data
Data used
• Recent land cover for Mpumalanga (2008)
• Old fields identified from old 1:50,000 topographic maps
• Major roads
Methods
Natural grassland habitat patches in Mpumalanga
• Patches smaller than 5 ha removed
• 3 681 grassland habitat patches
• Total area of habitat patches
30 077 km2
• Of which 3 065 km2 is old fields
• 40% of the Grassland Biome in Mpumalanga transformed
Methods
Natural grassland habitat patches in Mpumalanga
500 m
F4
Natural
Old fields
Methods
Input
Output
Importance of patches for overall connectivity
Size of habitat patches
Conefor Sensinode
programme
Integral Index of Connectivity
Distances between patches
Number of components
Methods
• Number of Components (Clusters):
Component = set of nodes with a path between every pair of nodes
No connection between nodes of different components
Component 2
Component 1
Results
Number of Components
3500
3000
2500
Number of Components
2371
2000
1878
Without old fields
With old fields
1500
1000
500
318
53
47
243
0
0
200
400
600
Distance threshold (m)
800
1000
1200
• Above a distance threshold of 1000m there are less than 50 components left
Method
• Integral Index of Connectivity (IIC)
• Seen as the best binary Index to measure connectivity
• Include habitat area in measurement
• Ranges from 0 to 1
• Increase with improved connectivity
Results
• 25% improvement when old fields are added
Integral index of connectivity (IIC)
0.07
0.065
0.06
Without old fields
0.055
With old fields
0.05
0.045
0.04
0
200
400
600
800
Threshold distance (km)
1000
1200
Importance of habitat patches for overall connectivity
Distance threshold: 500 m
0 - 0.493
0.493 - 1.833
1.833 - 3.806
3.806 - 6.223
6.223 - 9.827
Weighted average of importance of patches in different vegetation types
(∑(dIIC x Area))/Total area of veg type
•
KwaZulu‐Natal Highland Thornveld**
Northern Escarpment Quartzite Sourveld**
Northern Escarpment Dolomite Grassland**
Barberton Montane Grassland**
Frankfort Highveld Grassland
Andesite Mountain Bushveld
Low Escarpment Moist Grassland
Soweto Highveld Grassland
Tsakane Clay Grassland
Rand Highveld Grassland
Eastern Highveld Grassland
KaNgwane Montane Grassland**
Northern Free State Shrubland
Long Tom Pass Montane Grassland**
Paulpietersburg Moist Grassland
Sekhukhune Montane Grassland**
Amersfoort Highveld Clay Grassland
Lydenburg Thornveld**
Steenkampsberg Montane Grassland**
Wakkerstroom Montane Grassland **
0
1
2
3
4
5
6
7
Average connectivity per vegetation type
•
Conservation implications
• The grassland habitat patches of Mpumalanga are well connected at a distance threshold of 500m
• The most connected vegetation types are:
Wakkerstroom montane grassland
Eastern Temparate freshwater wetlands
Steenkampsberg montane grassland
• The abandoned croplands present in this landscape increase the connectivity by 25%
• Landscape connectivity influence conservation value
Thank You