Description Usage Arguments Details Value Author(s) References See Also Examples
This function calculates the pairwise distances (Euclidean, cost path
distances and genetic distances) of populations using a friction matrix and
a spatial genind object. The genind object needs to have coordinates in the
same projected coordinate system as the friction matrix. The friction matrix
can be either a single raster of a stack of several layers. If a stack is
provided the specified cost distance is calculated for each layer in the
stack. The output of this function can be used with the functions
wassermann
or lgrMMRR
to test for the
significance of a layer on the genetic structure.
1 2  genleastcost(cats, fric.raster, gen.distance, NN = NULL,
pathtype = "leastcost", plotpath = TRUE, theta = 1)

cats 
a spatial genind object. see ?popgenreport how to provide coordinates in genind objects 
fric.raster 
a friction matrix 
gen.distance 
specification which genetic distance method should be used to calculate pairwise genetic distances between populations ( "D", "Gst.Nei", "Gst.Hedrick") or individuals ("Smouse", "Kosman", "propShared") 
NN 
Number of neighbours used when calculating the cost distance (possible values 4,8 or 16). As the default is NULL a value has to be provided if pathtype='leastcost'. NN=8 is most commonly used. Be aware that linear structures may cause artefacts in the leastcost paths, therefore inspect the actual leastcost paths in the provided output. 
pathtype 
Type of cost distance to be calculated (based on function in
the 
plotpath 
switch if least cost paths should be plotted (works only if pathtype='leastcost'. Be aware this slows down the computation, but it is recommended to do this to check least cost paths visually. 
theta 
value needed for rSPDistance function. see

to be written
returns a list that consists of four pairwise distance matrixes (Euclidean, Cost, length of path and genetic) and the actual paths as spatial line objects.
Bernd Gruber
Cushman, S., Wasserman, T., Landguth, E. and Shirk, A. (2013). ReEvaluating Causal Modeling with Mantel Tests in Landscape Genetics. Diversity, 5(1), 5172.
Landguth, E. L., Cushman, S. A., Schwartz, M. K., McKelvey, K. S., Murphy, M. and Luikart, G. (2010). Quantifying the lag time to detect barriers in landscape genetics. Molecular ecology, 41794191.
Wasserman, T. N., Cushman, S. A., Schwartz, M. K. and Wallin, D. O. (2010). Spatial scaling and multimodel inference in landscape genetics: Martes americana in northern Idaho. Landscape Ecology, 25(10), 16011612.
landgenreport
, popgenreport
,
wassermann
, lgrMMRR
1 2 3 4 5 6  ## Not run: %
glc < genleastcost(cats=landgen, fric.raster, "D", NN=8)
wassermann(eucl.mat = glc$eucl.mat, cost.mat = glc$cost.mats, gen.mat = glc$gen.mat)
lgrMMRR(gen.mat = glc$gen.mat, cost.mats = glc$cost.mats, eucl.mat = glc$eucl.mat)
## End(Not run)

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