Recently, graph-theory has been characterised as an efficient and useful tool for conservation planning (e.g. Bunn et al. 2000, Calabrese & Fagan 2004, Minor & Urban 2008, Zetterberg et. al. 2010). As a part of the r.connectivity.* tool-chain, r.connectivity.corridors is intended to make graph-theory more easily available to conservation planning.
r.connectivity.corridors is th 3rd tool of the r.connectivity.* toolchain.
r.connectivity.corridors loops through the attribute-table of the edge- output vector map from r.connectivity.network and computes the corridor for each edge of a user-defined set of edges. r.connectivity.corridors can account for the importance of the corridors for the entire networkby reclassifying them with regards to network measures from r.connectivity.network.
Finally, all individual corridors are being put together using r.series. In this step the values of the cells in all corridor maps are summed up, which indicates the importance of an area (raster cell) for the network of the given patches (either the number of corridors a cell is part of, or other graph-theoretical measures for corridor importance).
!!!Corridors are only computed for an undirected graph.!!!
Output raster maps are named according to a user defined prefix and suffix.
The following example is based on the North Carolina dataset!
Please be aware that all input parameter of the following example are purely hypothetical (though they intend to imitate a real life situation) and chosen only for the matter of the exercise. Parameters have to be adjusted in other cases according to ecological knowledge in order to give meaningful results!
Let us assume we want to analyse the habitat connectivity for a hypothetical species of the Hardwood Swamps in North Carolina. This species lives only in the larger core area of the swamps (> 1ha) while the borders are no suitable habitats.
It is not the most mobile of species and can cover (under optimal conditions) maximal 1.5 km.
Prepare input data
Befor we can run the connectivity analysis with the r.connectivity.*-tools we need to prepare the the example input data. Because we want to use cost distance as a distance measure we have to provide a cost raster map in addition to the required vector map of (habitat) patches:
Create input patch vector map
Create input patch vector map
Create a cost raster (se also: r.cost)
g.region -p rast=landuse96_28m@PERMANENT align=landuse96_28m@PERMANENT
###Patch input vector map
##Extract habitat patches
#Condition 1: Category 11 = Bottomland Hardwoods/Hardwood Swamps
#Condition 2: no border-cells are suitable
r.to.vect input=patches output=patches feature=area
#Add a column for the population proxy (in this case area in hectares)
v.db.addcol map=patches layer=1 'columns=area_ha double precision'
#Upload area to attribute table (later used as population proxy)
v.to.db map=patches type=point,line,boundary,centroid layer=1 qlayer=1 option=area units=hectares columns=area_ha
#Extract core habitat areas with more than 1 ha
v.extract input=patches output=patches_1ha type=area layer=1 where="area_ha>1"
For the cost raster, we assume that areas which are developed with high intensity are absolute barriers for our species (not crossable at all).
Other developed and managed areas can be crossed, but only at high costs (they are avoided where possible). Other Hardwood landcover types offer best opportnity for the dispersal of our species (they are prefered), while the costs for crossing the other landcover types is somewhere inbetween.
Hint: One might also add infrastructure like e.g. roads
#Reclassify land use map
#Windows users may have to use the GUI version of r.reclass and paste the rules there...
echo '0 = 56 #not classified (2*resolution (28m))
1 = NULL #High Intensity Developed (absolut barrier)
2 = 140 #Low Intensity Developed (5*resolution (28m))
3 = 112 #Cultivated (4*resolution (28m))
4 = 70 #Managed Herbaceous Cover (2,5*resolution (28m))
6 = 28 #Riverine/Estuarine Herbaceous (1*resolution (28m))
7 = 42 #Evergreen Shrubland (1,5*resolution (28m))
8 = 42 #Deciduous Shrubland (1,5*resolution (28m))
9 = 42 #Mixed Shrubland (1,5*resolution (28m))
10 = 28 #Mixed Hardwoods (1*resolution (28m))
11 = 28 #Bottomland Hardwoods/Hardwood Swamps (1*resolution (28m))
15 = 56 #Southern Yellow Pine (2*resolution (28m))
18 = 28 #Mixed Hardwoods/Conifers (1*resolution (28m))
20 = 42 #Water Bodies (1,5*resolution (28m))
21 = 84 #Unconsolidated Sediment (3*resolution (28m))' | r.reclass input=landuse96_28m@PERMANENT output=costs --overwrite
Create the network (r.connectivity.distance)
In the first step the network dataset is created, and the cost distance between all possible pairs of vertices (patches) is calculated.
Our species is known to be uable to cover more than 1.5 km distance. In order to identify potential for improving the connectectivity of the landscape for our speies we set the cutoff distance (maximum search distance for connections) to three times dispersal potential of our species (4500). In lack of real population data we use the area (ha) as a proxy. The distance between two patches is measured as the maximum distance along the closest 500m of boundary of a patch (ca. 18 border cells with 28m resolution). All non map output is stored in a folder named hws_connectivity in the current working directory.
r.connectivity.distance --verbose patches=patches_1ha costs=costs pop_proxy=area_ha prefix=hws_connectivity folder=./hws_connectivity cutoff=4500 border_dist=18
Analyse the network (r.connectivity.network)
In the next step network analysis is performed. We define the connectivity cutoff (maximum cost distance where connectivity is assumed) to be 1500.0 cost distance units, which represents the maximum known dispersal ability of our species. The dispersal kernel is adjusted to characteristics of this species by setting the exponent to -3, which gives a steeper decrease in successful dispersal with increasing cost distance. Only very few dispersal units of our species are expected to cross more than ca. 900 cost distance units successfully (se: ./hws_connectivity/kernel.eps after running the command or run it with the x-flag (-x) for only displaying the kernel). Connectivity metrics reslults are stored in te attribute table of the vector maps on vertex (patches, map name: "prefix" _ vertex_measures) and edge level (connections, map name: "prefix" _ edge_measures). An overview over connectivity metrics on the graph level (the entire network) is stored in "folder" (./hws_connectivity). We requested also an R-plot of the dispersal kernel and an R-plot givig an overview over network characteristics to be stored in the same folder.
Users with a multi-processor computer (e.g. dual-core) may speed up processing by setting cores > 1 in r.connectivity.network.
r.connectivity.network -y -i folder=./hws_connectivity connectivity_cutoff=1500.0 lnbh_cutoff=2.0 cl_thres=10 exponent=-3 kernel_plot=./hws_connectivity/kernel.eps overview_plot=./hws_connectivity/overview.eps cores=1
Calculate corridors (r.connectivity.corridors)
In this example two alternative (or to some extent complementary) sets of corridors are calculated:
Corridor set 1:
For the first set, corridors are only computed for the minimum spanning tree (in literture sometimes called the backbone of an ecological network). This is specified in the "where"-parameter. Edge betweenness values are used as weights, which represent the centrality of a connection (edge) in a network (and is therewith a measure of importance). The output is a set of corridor raster maps (named: "prefix" _corridor_ "edge_id" _ "weight") and a summary over all computed corridors (named: "prefix" _corridor_ "edge_id" _ "weight" _sum_ "suffix").
Corridor set 2:
r.connectivity.corridors --verbose folder=./hws_connectivity suffix=mst weights=cf_eb_ud where="cf_mst_ud > 1" cores=1
For the second set, corridors are computed for all edges in the graph (network). This takes a little more time. Corridors are then weighted by the modeled, undirected (= in oth directions) flow of propagules ("cf_u").
Users with a multi-processor computer (e.g. dual-core) may speed up corridor processing by setting cores > 1 r.connectivity.corridors.
r.connectivity.corridors --verbose folder=./hws_connectivity suffix=alle weights=cf_u cores=1
An other means for speeding up processing would be to use integer weights (e.g. by converting edge betweenness to integer afte multiplying with 10 to the power of the relevant number of digits). Then r.reclass is used in the script instead of r.recode.
When all analysis is finnished some cleanup may be appropriate, in order to save disk space:
#Add the f-lag (-f) to the following two commands if you are sure you want to delete all intermediate maps from r.connectivity.*.
Stefan Blumentrath, Norwegian Institute for Nature Research (NINA)
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