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 or pathogen in order to simulate spread of the pest or pathogen into the future using simulation.
r.pops.spread example
Figure: Infected hosts in a landscape, a typical model result

About the model

The module is using the PoPS Core library which is implementing the PoPS model and it is a central part of the Pest or Pathogen Spread (PoPS) project.

PoPS logo
Figure: Logo of the Pest or Pathogen Spread framework the PoPS is part of

The PoPS model is a stochastic spread model of pests and pathogens in forest and agricultural landscapes. It is used for various pest, pathogens, and hosts including animals, not just plants, as hosts. It was originally developed for Phytophthora ramorum and the original version of the model was written in R, later with Rcpp (Tonini, 2017), and was based on Meentemeyer (2011) paper.

The current implementation of the GRASS GIS module is using PoPS Core header-only C++ library which implements the PoPS model. The primary development of PoPS Core and of this module happens in a separate repositories and GRASS GIS Addons repository contains the latest release of the model. An alternative steering version of this module exists which includes a set of features supporting geospatial simulation steering (Petrasova, 2020) which is useful for exploring adaptive management scenarios.

Model parameters

Two basic epidemiological model types (model_type) are available for a transition of hosts between susceptible and infected classes: 1) susceptible-infected (SI) for an immediate transition when a disperser establishes on the host and 2) susceptible-exposed-infected (SEI) for an intermediate state when the host first becomes exposed and only after a latency period (latency_period) is over. This page lists above the numerous inputs and parameters, although many of them have default values, some need careful consideration and calibration. The best way how to identify options relevant to a given use case is to go through one of the available tutorials.

Calibration

Typically, the model needs to be calibrated. You can obtain the calibration from a published work, colleague, calibrate the model manually (in GRASS GIS), or use the R interface to PoPS called rpops which has dedicated functions for calibration.

NOTES

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

To cite this module, please refer to How to cite section in the readme file.

SEE ALSO

r.spread, r.grow, r.lake, r.futures

Tutorials and other resources:

AUTHORS

(in alphabetical order)

Chris Jones*
Margaret Lawrimore*
Vaclav Petras*
Anna Petrasova*

Previous contributors:

Zexi Chen*
Devon Gaydos*
Francesco Tonini*

* Center for Geospatial Analytics, NC State University