DESCRIPTION

Individual-based dispersal model for connectivity analysis (time-based)

This module provides information about the isolation or connectivity of individual fragments derived of a landcover classification. Unlike r.pi.energy this module provides information about the time from emigration to immigration. The individual based dispersal model results are based on the step length and range, the perception distance and the attractivity to move towards patches.

NOTES

The suitability matrix impacts the step direction of individuals. If individuals are moving beyond the mapset borders the indivuals are set back to their original source patches.

EXAMPLE

An example for the North Carolina sample dataset:

The connectivity of patches of the landclass96 class 5 are computed using the time from emigration to immigration. The step length is set to 5 pixel, the output statistics are set to average time and variance of searchtime. For each patch 1000 individuals were released and the model stopped when at least 80% of all individuals sucessfully immigrated: 

r.pi.searchtime input=landclass96 output=searchtime1 keyval=5 step_length=5 stats=average,variance percent=80 n=1000
constrain the angle of forward movement to 10 degrees: 
r.pi.searchtime input=landclass96 output=searchtime1 keyval=5 step_length=5 stats=average,variance percent=80 n=1000 step_range=10
setting the perception range to 10 pixel: 
r.pi.searchtime input=landclass96 output=searchtime1 keyval=5 step_length=5 stats=average,variance percent=80 n=1000 perception=10
increasing the attraction to move towards patches to 10: 
r.pi.searchtime input=landclass96 output=searchtime1 keyval=5 step_length=5 stats=average,variance percent=80 n=1000 multiplicator=10
limiting the amount of steps to 10: 
r.pi.searchtime input=landclass96 output=searchtime1 keyval=5 step_length=5 stats=average,variance percent=80 n=1000 maxsteps=10
output of each movement location for a defined step frequency. Here every 10th step is provided as output raster: 
r.pi.searchtime input=landclass96 output=searchtime1 keyval=5 step_length=5 stats=average,variance percent=80 n=1000 out_freq=10
output of a raster which immigration counts: 
r.pi.searchtime input=landclass96 output=searchtime1 keyval=5 step_length=5 stats=average,variance percent=80 n=1000 out_immi=immi_counts
output of a binary immigration matrix. Each patch emigration and immigration for all patch combinations is recorded as 0 or 1: 
r.pi.searchtime input=landclass96 output=searchtime1 keyval=5 step_length=5 stats=average,variance percent=80 n=1000 binary_matrix=binary_matrix.txt
output of a matrix with immigration counts for each patch: 
r.pi.searchtime input=landclass96 output=searchtime1 keyval=5 step_length=5 stats=average,variance percent=80 n=1000 immi_matrix=immi_counts.txt
the previous examples assumed a homogeneous matrix, a heterogenous matrix can be included using a raster file which values are taken as costs for movement (0-100): 
# it is assumed that our species is a forest species and cannot move 
# through water, hence a cost of 100, does not like urban areas 
# (class: 6, cost: 10) but can disperse through shrubland (class 4, 
# cost=1) better than through grassland (class 3, cost: 2):

r.mapcalc "suit_raster = if(landclass96==5,1,if(landclass96 == 1, 10, if (landclass96==3,2, if(landclass96==4,1,if(landclass96==6,100)))))"
r.pi.searchtime input=landclass96 output=searchtime1 keyval=5 step_length=5 stats=average,variance percent=80 n=1000 suitability=suit_raster

SEE ALSO

r.pi.searchtime.pr, r.pi.searchtime.mw, r.pi

AUTHORS

Programming: Elshad Shirinov
Scientific concept: Dr. Martin Wegmann
Department of Remote Sensing
Remote Sensing and Biodiversity Unit
University of Wuerzburg, Germany

Port to GRASS GIS 7: Markus Metz

Last changed: $Date$