r.pops.spread - GRASS GIS manual

NAME

r.pops.spread - A dynamic species distribution model for pest or pathogen spread in forest or agricultural ecosystems

KEYWORDS

raster, spread, model, disease, pest

SYNOPSIS

r.pops.spread

r.pops.spread --help

r.pops.spread [-ms] host=name total_plants=name infected=name [average=name] [average_series=basename] [single_series=basename] [stddev=name] [stddev_series=basename] [probability=name] [probability_series=basename] [outside_spores=name] [spread_rate_output=name] [treatments=name[,name,...]] [treatment_date=string[,string,...]] [treatment_length=integer[,integer,...]] [treatment_application=string] [moisture_coefficient_file=name] [temperature_coefficient_file=name] [weather_coefficient_file=name] [lethal_temperature=float] [lethal_month=integer] [temperature_file=name] start_time=integer end_time=integer seasonality=from,to step=string [reproductive_rate=float] [natural_dispersal_kernel=string] [natural_distance=float] natural_direction=string natural_direction_strength=float [anthropogenic_dispersal_kernel=string] [anthropogenic_distance=float] anthropogenic_direction=string [anthropogenic_direction_strength=float] [percent_natural_dispersal=float] [mortality_rate=float] [mortality_time_lag=integer] [mortality_series=basename] [random_seed=integer] [runs=integer] [nprocs=integer] [--overwrite] [--help] [--verbose] [--quiet] [--ui]

Flags:

-m: Apply mortality; After certain number of years, start removing dead hosts from the infected pool with a given rate
-s: Generate random seed (result is non-deterministic); Automatically generates random seed for random number generator (use when you don't want to provide the seed option)
--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

Parameters:

host=name [required]: Input host raster map; Number of hosts per cell.
total_plants=name [required]: Input raster map of total plants; Number of all plants per cell
infected=name [required]: Input raster map of initial infection; Number of infected hosts per cell
average=name: Average infected across multiple runs
average_series=basename: Basename for output series of average infected across multiple runs
single_series=basename: Basename for output series of infected as single stochastic run
stddev=name: Standard deviations
stddev_series=basename: Basename for output series of standard deviations
probability=name: Infection probability (in percent)
probability_series=basename: Basename for output series of probabilities
outside_spores=name: Output vector map of spores or pest units outside of modeled area
spread_rate_output=name: Output CSV file containg yearly spread rate in N, S, E, W directions
treatments=name[,name,...]: Raster map(s) of treatments (treated 1, otherwise 0)
treatment_date=string[,string,...]: Dates when treatments are applied (e.g. 2020-01-15)
treatment_length=integer[,integer,...]: Treatment length in days; Treatment length 0 results in simple removal of host, length > 0 makes host resistant for certain number of days
treatment_application=string: Type of treatmet application; Options: ratio_to_all, all_infected_in_cell; Default: ratio_to_all
moisture_coefficient_file=name: Input file with one moisture coefficient map name per line; Moisture coefficient
temperature_coefficient_file=name: Input file with one temperature coefficient map name per line; Temperature coefficient
weather_coefficient_file=name: Input file with one weather coefficient map name per line; Weather coefficient
lethal_temperature=float: Temperature at which the pest or pathogen dies; The temerature unit must be the same as for thetemerature raster map (typically degrees of Celsius)
lethal_month=integer: Month when the pest or patogen dies due to low temperature; The temperature unit must be the same as for thetemperature raster map (typically degrees of Celsius)
temperature_file=name: Input file with one temperature raster map name per line; The temperature should be in actual temperature units (typically degrees of Celsius)
start_time=integer [required]: Start year of the simulation; The first day of the year will be used
end_time=integer [required]: End year of the simulation; The last day of the year will be used
seasonality=from,to [required]: Seasonal spread (from,to); Spread limited to certain months (season), for example 5,9 for spread starting at the beginning of May and ending at the end of September; Default: 1,12
step=string [required]: Simulation step; How often the simulation computes new step; Options: week, month; week: Compute next simulation step each week; month: Compute next simulation step each month
reproductive_rate=float: Number of spores or pest units produced by a single host; Number of spores or pest units produced by a single host under optimal weather conditions; Default: 4.4
natural_dispersal_kernel=string: Natural dispersal kernel type; Options: cauchy, exponential; Default: cauchy
natural_distance=float: Distance parameter for natural dispersal kernel
natural_direction=string [required]: Direction of natural dispersal kernel; Typically prevailing wind direction; none means that there is no directionality or no wind; Options: N, NE, E, SE, S, SW, W, NW, NONE, none; Default: none
natural_direction_strength=float [required]: Strength of direction of natural dispersal kernel; The kappa parameter of von Mises distribution (concentration); typically the strength of the wind direction
anthropogenic_dispersal_kernel=string: Anthropogenic dispersal kernel type; Options: cauchy, exponential
anthropogenic_distance=float: Distance parameter for anthropogenic dispersal kernel
anthropogenic_direction=string [required]: Direction of anthropogenic dispersal kernel; Value none means that there is no directionality; Options: N, NE, E, SE, S, SW, W, NW, NONE, none; Default: none
anthropogenic_direction_strength=float: Strength of direction of anthropogenic dispersal kernel; The kappa parameter of von Mises distribution (concentration); typically the strength of the wind direction
percent_natural_dispersal=float: Percentage of natural dispersal; How often is the natural dispersal kernel used versus the anthropogenic dispersal kernel; Options: 0-1
mortality_rate=float: Mortality rate of infected hosts; Percentage of infected hosts that die in a given year (hosts are removed from the infected pool); Options: 0-1
mortality_time_lag=integer: Time lag from infection until mortality can occur in years; How many years it takes for an infected host to die (value 1 for hosts dying at the end of the first year)
mortality_series=basename: Basename for series of number of dead hosts; Basename for output series of number of dead hosts (requires mortality to be activated)
random_seed=integer: Seed for random number generator; The same seed can be used to obtain same results or random seed can be generated by other means.
runs=integer: Number of simulation runs; The individual runs will obtain different seeds and will be averaged for the output
nprocs=integer: Number of threads for parallel computing; Options: 1-

DESCRIPTION
NOTES
EXAMPLES
REFERENCES
SEE ALSO
AUTHORS

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.pops.spread 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

SOURCE CODE

Available at: r.pops.spread source code (history)