DESCRIPTION

Module r.pops.spread is a dynamic species distribution model for pest or pathogen spread in forest or agricultural ecosystems. The model is process based meaning that it uses understanding of the effect of weather on reproduction and survival of the pest/pathogen in order to simulate spread of the pest/pathogen into the future.

Module r.pops.spread is using Pest or Pathogen Spread library.

Figure: Logo of Pest or Pathogen Spread simulation

NOTES

The directions of wind consider north (N) to be grid north, if your true north is different direction, you need to make an adjustment.
The module currently does not handle NULL (no data) as input, so you need to change the NULLs to (most likely) zeros, for example: r.null map=infection null=0.

EXAMPLES

Obtaining list of rasters

Use R script to create weather coefficients based on a defined polynomial.

Example of creating file with list of input maps (unix-like command line):

g.list type=raster pattern="moisture_*" mapset=climate -m > moistures.txt
g.list type=raster pattern="temperature_*" mapset=climate -m > temperatures.txt

Note that the above assumes that the names will be ordered by time. This will happen automatically if they are, e.g. numbered as 001, 002, etc. (e.g. temperature_001 and not temperature_1). If they are numbered without the zero-padding, i.e. 1, 2, ..., 10, 11, ..., then in a unix-like command line, you can do pipe the result through sort with -n (| sort -n). For example, for map names like temperature_1, the following unix-like command will do the sorting:

g.list type=raster pattern="temperature_*" mapset=climate | sort -k2 -t_ -n > temperatures.txt

Note the underscore which tells sort where to split the name for sorting and the number 2 which indicates which part of the name to use for sorting after splitting. If you have the weather-related timeseries in a separate mapset, you can add this mapset to the search path of your current mapset so that you can have the rasters in the list without specifying the mapset. To add to the search path, use for example:

g.mapsets mapset=climate

Generating a constant coefficient

In case the moisture coefficient is not used, we can generate a constant raster map to be used as the coefficient:

r.mapcalc "const_1 = 1"

Then using unix-like command line, we can create a list of these rasters in a file based on the number of lines in a temperature list files we created earlier:

NUM_LINES=`cat temperatures.txt | wc -l`
echo const_1 > moistures.txt
for LINE in `seq 2 $NUM_LINES`; do echo const_1 >> moistures.txt; done;

Creating treatments

To account for (vector) treatments partially covering host cells:

# set resolution for treatments and convert to raster
g.region res=10 -ap
v.to.rast input=treatment output=treatment use=val

# resample to lower resolution (match host map resolution)
g.region align=host_map -p
r.resamp.stats -w input=treatment output=treatment_resampled method=count
# get maximum value, which is dependent on resolution
# e.g. when resampling from 10m to 100m, max will be 100 (100 small cells in 1 big cell)
r.info -r treatment_resampled
# result will be 0 to 1
r.mapcalc "treatment_float = test_treatment_resampled / 100"
# adjust host layer
r.mapcalc "treated_host = host - host * treatment_float"

Running the model

Example of the run of the model (unix-like command line):

r.spread.pest host=host total_plants=all infected=infected_2005 \
    moisture_coefficient_file=moistures.txt temperature_coefficient_file=temperatures.txt \
    output=spread step=week start_time=2005 end_time=2010 \
    reproductive_rate=4 dispersal_kernel=cauchy wind=NE random_seed=4

REFERENCES

Ross K. Meentemeyer, Nik J. Cunniffe, Alex R. Cook, Joao A. N. Filipe, Richard D. Hunter, David M. Rizzo, and Christopher A. Gilligan 2011. Epidemiological modeling of invasion in heterogeneous landscapes: spread of sudden oak death in California (1990-2030). Ecosphere 2:art17. DOI: 10.1890/ES10-00192.1
Tonini, Francesco, Douglas Shoemaker, Anna Petrasova, Brendan Harmon, Vaclav Petras, Richard C. Cobb, Helena Mitasova, and Ross K. Meentemeyer. Tangible geospatial modeling for collaborative solutions to invasive species management. Environmental Modelling & Software 92 (2017): 176-188. DOI: 10.1016/j.envsoft.2017.02.020

AUTHORS

Francesco Tonini* (original R version)
Zexi Chen* (C++ version)
Vaclav Petras* (parallelization, GRASS interface)
Anna Petrasova* (single species simulation)

* Center for Geospatial Analytics, NCSU