**-a**- Set for 8 cell-neighbors. 4 cell-neighbors are default
**-c**- Include cost of the path in the calculation of steps
**-d**- Output diversity map
**-i**- Output Shannon- and Simpson-index
**--overwrite**- Allow output files to overwrite existing files
**--help**- Print usage summary
**--verbose**- Verbose module output
**--quiet**- Quiet module output
**--ui**- Force launching GUI dialog

**input**=*name***[required]**- Name of input raster map
**suitability**=*string*- Name of the costmap with values from 0-100
**output**=*name***[required]**- Name for output raster map
**out_immi**=*string*- Name of the optional raster file for patch immigrants count
**immi_matrix**=*string*- Name for immigrants matrix ASCII-file
**binary_matrix**=*string*- Name for binary immigrants matrix ASCII-file
**threshold**=*float*- Percentage of individuals which must have immigrated successfully to be considered for the binary immigrants matrix
**keyval**=*integer***[required]**- Category value of the patches
**step_length**=*integer***[required]**- Length of a single step measured in pixels
**step_range**=*float*- Range to choose the next step direction from, in degrees [default = 180°]
**perception**=*integer*- Perception range
**multiplicator**=*float*- Attractivity of patches [1-inf]
**n**=*integer***[required]**- Number of individuals
**percent**=*float***[required]**- Percentage of individuals which must have arrived successfully to stop the model-run
**stats**=*string[,**string*,...]**[required]**- Statistical method to perform on the values
- Options:
*average, variance, standard deviation, median, min, max* **maxsteps**=*integer*- Maximum steps for each individual
**out_freq**=*integer*- Output an intermediate state of simulation each [out_freq] steps
**title**=*"phrase"*- Title for resultant raster map

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.

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

r.pi.searchtime input=landclass96 output=searchtime1 keyval=5 step_length=5 stats=average,variance percent=80 n=1000 step_range=10

r.pi.searchtime input=landclass96 output=searchtime1 keyval=5 step_length=5 stats=average,variance percent=80 n=1000 perception=10

r.pi.searchtime input=landclass96 output=searchtime1 keyval=5 step_length=5 stats=average,variance percent=80 n=1000 multiplicator=10

r.pi.searchtime input=landclass96 output=searchtime1 keyval=5 step_length=5 stats=average,variance percent=80 n=1000 maxsteps=10

r.pi.searchtime input=landclass96 output=searchtime1 keyval=5 step_length=5 stats=average,variance percent=80 n=1000 out_freq=10

r.pi.searchtime input=landclass96 output=searchtime1 keyval=5 step_length=5 stats=average,variance percent=80 n=1000 out_immi=immi_counts

r.pi.searchtime input=landclass96 output=searchtime1 keyval=5 step_length=5 stats=average,variance percent=80 n=1000 binary_matrix=binary_matrix.txt

r.pi.searchtime input=landclass96 output=searchtime1 keyval=5 step_length=5 stats=average,variance percent=80 n=1000 immi_matrix=immi_counts.txt

# 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

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: 2017-05-10 23:56:34 +0200 (Wed, 10 May 2017) $*

Available at: r.pi.searchtime source code (history)

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