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.distance is intended to make graph-theory more easily available to conservation planning.
r.connectivity.distance is the first tool of the r.connectivity.*-toolchain (followed by r.connectivity.network and r.connectivity.corridors).
r.connectivity.distance loops through all polygons in the input vector map and calculates the cost-distance to all the other polygons within a user-defined defined euclidean distance threshold.
It produces two vector maps that holde the network:
Attributes of the edge-map are:
|from_patch||category of the start patch||integer|
|to_patch||category of the destination patch||integer|
|dist||cost-distance from from_patch to to_patch||double precision|
Attributes of the vertex-map are:
|cat||category of the input patches||integer|
|pop_proxy||the user defined population proxy to be used in further analysis, representing a proxy for the amount of organisms potentially dispersing from a patch (e.g. habitat area)||double precision|
On user request (p-flag) the shortest paths between the possible combination of patches can be extracted (using r.drain), along with start and stop points.
In addition, r.connectivity.distance outputs a cost distance raster map for every input area which later on are used in r.connectivity.corridors (together with output from r.connectivity.network) for corridor identification.
Distance between patches is measured as border to border distance. With
the border_dist option, the user can define the number of cells
(n) along the border to be used for distance measuring.
The distance from a (start) patch to another (end) is measured as the n-th closest cell on the border of the other (end) patch. An increased number of border cells used for distance measuring increases also the width of possible corridors computed with r.connectivity.corridors later on.
If an output directory is given for the conefor_dir option is specified, also output suitable for further processing in CONEFOR will be produced, namely:
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.
### Set region g.region -p rast=landuse96_28m align=landuse96_28m ### Patch input vector map ## Extract habitat patches # Condition 1: Category 11 = Bottomland Hardwoods/Hardwood Swamps # Condition 2: no border-cells are suitable r.mapcalc expression="patches=if( \ landuse96_28m[0,1]==11&& \ landuse96_28m[0,-1]==11&& \ landuse96_28m[1,1]==11&& \ landuse96_28m[1,0]==11&& \ landuse96_28m[1,-1]==11&& \ landuse96_28m[-1,1]==11&& \ landuse96_28m[-1,0]==11&& \ landuse96_28m[-1,-1]==11&& \ landuse96_28m==11,1,null())" # Vectorize patches r.to.vect input=patches output=patches feature=area # Add a column for the population proxy (in this case area in hectares) v.db.addcolumn 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 output=costs rules=- --overwrite
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). We store the resulting network data also in a format that is suitable as input to CONEFOR software.
r.connectivity.distance -t -p input=patches_1ha pop_proxy=area_ha \ costs=costs prefix=hws_connectivity cutoff=4500 border_dist=18 \ conefor_dir=./conefor
To be continued with r.connectivity.network!
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