GRASS GIS manual: r.pi.searchtime

NAME

r.pi.searchtime - Individual-based dispersal model for connectivity analysis (time-based)

KEYWORDS

raster

SYNOPSIS

r.pi.searchtime
r.pi.searchtime help
r.pi.searchtime [-acdi] input=name [suitability=string] output=name [out_immi=string] [immi_matrix=string] [binary_matrix=string] [threshold=float] keyval=integer step_length=integer [step_range=float] [perception=integer] [multiplicator=float] n=integer percent=float stats=string[,string,...] [maxsteps=integer] [out_freq=integer] [title="phrase"] [--overwrite] [--verbose] [--quiet]

Flags:

-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
--verbose: Verbose module output
--quiet: Quiet module output

Parameters:

input=name: Name of input raster map
suitability=string: Name of the costmap with values from 0-100
output=name: 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: Category value of the patches
step_length=integer: 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: Number of individuals
percent=float: Percentage of individuals which must have arrived successfully to stop the model-run
stats=string[,string,...]: 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

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

DESCRIPTION

This modules 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

AUTHORS

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

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