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NAME

r.maxent.predict - Use a Maxent model to create a suitability distribution layer

KEYWORDS

modeling, Maxent

SYNOPSIS

r.maxent.predict
r.maxent.predict --help
r.maxent.predict [-ecfp] lambdafile=name [rasters=name[,name,...]] [variables=string] [projectionlayers=name] [alias_file=name] output=name [maxent=name] [java=name] [memory=memory in MB] [--overwrite] [--help] [--verbose] [--quiet] [--ui]

Flags:

-e
Automatically adapt resolution
When the ns and ew resolution are not the same, nearest neighbor resampling will be used to ensure both are the same.
-c
Do not apply clamping
Do not apply clamping when projecting.
-f
Fade effect clamping
Reduce prediction at each point in projections by the difference between clamped and non-clamped output at that point.
-p
Print Maxent command
Print the Maxent command used to create the prediction layer. For debugging.
--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:

lambdafile=name [required]
Lambda model file
Lambda model file created by Maxent or the r.maxent.train addon.
rasters=name[,name,...]
Names of the input raster layers
Names of the raster layers representing the environmental variables used in the Maxent model.
variables=string
variable names
Names of the environmental parameter(s) as used in the model. These need to be given in the same order as the corresponding raster layers. If left out, the names are assumed to be the same as the name of the raster layers.
projectionlayers=name
Location of folder with set of environmental variables.
Directory with set of rasters representing the same environmental variables as used to create the Maxent model. The names of the raster layers, excluding the file extension, need to be the same as the variable names used to create the Maxent model.
alias_file=name
csv file with variable and layer names
A csv file with in the first column the names of the explanatory variables used in the model, and in the second column the names of corresponding raster layers. Make sure both are provided in the same order.
output=name [required]
Prediction raster layer
The name of the raster layer with the predicted suitability scores
maxent=name
Location Maxent jar file
Give the path to the Maxent executable file (maxent.jar)
java=name
Location java executable
If Java is installed, but cannot be found, the user can provide the path to the java executable file. Note, an alternative is to use the r.maxent.setup addon.
memory=memory in MB
Maximum memory to be used (in MB)
Maximum memory to be used by Maxent (in MB)
Default: 300

Table of contents

DESCRIPTION

The r.maxent.predict module is a front-end to the Maxent software, providing a convenient way to run the Maxent software, and create output layers in GRASS GIS.

workflow, from data preparation, training a model to model prediction
A workflow, from data preparation, training a model to model prediction using three GRASS GIS addons.

It is part of a set of three addons that can be used to prepare the input data for the Maxent model (v.maxent.sdm), to train a maxent presence only model (r.maxent.train), and to use the model to create prediction layers (this module). The r.maxent.predict modules uses the model (the .lambdas file) created with r.maxent.train (or the Maxent software directly) and applies this to a set of environmental raster layers. These should represent the same variables as used to create the model, but can represent a different area, of future conditions.

The r.maxent.train creates a file maxent_explanatory_variable_names.csv, which you can check for the names of the predictor variables. If these are different from the input raster layers, you can provide the variable names using the variables parameter. Alternatively, you can provide a CSV file with the names of the explanatory variables (first column) and the names of the corresponding raster layers (second column).

Maxent includes the option of “clamping” projections. This constrains the values for environmental values in the projected range to the limit of that variable that is found in the training range. By default, Maxent applied clamping. You can disable this by setting the -c flag. You also have the option to reduce the prediction at each point in projections by the difference between clamped and non-clamped output at that point. Use the -f to enable this option.

NOTES

This addon requires the Maxent software (version ≥ 3.4). You can download the software from the Maxent website. The r.maxent.setup module provides an helper function to enable GRASS GIS to use the Maxent software.

EXAMPLE

The examples below use a dataset that you can download from here. It includes a vector point layer with observation locations of the pale-throated sloth (Bradypus tridactylus) from GBIF, the IUCN RedList range map of the species, a boundary layer of the South American countries from NaturalEarth and a number of bioclim raster layers from WorldClim version 2.1, representing the climate conditions representing the period 1970-2000 and the climate conditions predicted for 2061–2080 based on the GCM BCC-CSM2-MR and SSP 585.

The zip file contains a folder sampledata. This is a location with five subfolders PERMANENT, sloth, current, future and model01. Copy this Location to a GRASS Database (use an existing one or create one first). If you are not familiar with the concept of Locations and Mapsets, please first read the explanation about the GRASS GIS database.

Unzip the file, start up GRASS GIS, open the GRASS GIS database to which you copied the folder sampledata, switch to the Location sampledata and open the mapset model01. This mapset should have access to the other mapsets.

1: Data preparation

You can use the v.maxent.swd to create the required input layers. The code below creates the SWD file with the locations where the species has been recorded (species_output) and a SWD file with randomly created background point locations (bgr_ouput). The SWD files contain, for each location, the values of the raster layers selected with the evp_maps parameter. With the parameter export_rasters you tell the addon to export the raster layers as well. 
v.maxent.swd -t \
 species=Bradypus_tridactylus \
 evp_maps=bio02,bio03,bio08,bio09,bio13,bio15,bio17 \
 evp_cat=sa_eco_l2@current \
 alias_cat=landuse \
 nbgp=10000 \
 bgr_output=bgrd_swd.csv \
 species_output=spec_swd.csv \
 export_rasters=envlayers

The output is a folder with the so-called SWD files with the XY coordinates for the species presence location (spec_swd.csv) and the background locations (bgrd_swd.csv. Both also include the values of the input raster layers for the given point locations. In addition, there is the subfolder envlayers with the environmental raster layers in ascii format.

2: Train the model

Use the output of v.maxent.swd as input for r.maxent.train. First create a subfolder output_model1, so we can write the output to that folder.

The projectionlayers parameter is optionally. If you set it, a raster prediction layer will be created that represent the potential suitability distribution under current conditions (the conditions used to train the model).

With the -y and -b flags the point layers with the sample predictions and the predictions at the background point locations are created. Their values correspond to the values of the raster prediction layer. 

r.maxent.train -y -b -g \
  samplesfile=spec_swd.csv \
  environmentallayersfile=bgrd_swd.csv \
  togglelayertype=landuse \
  projectionlayers=envlayers \
  samplepredictions=model_1_samplepred \
  backgroundpredictions=model_1_bgrdpred \
  predictionlayer=model_1_suitability_current \
  outputdirectory=output_model1

When r.maxent.train is finished, go to the output folder and open the Bradypus_tridactylus.html file for an explanation of the different model outputs and model evaluation statistics. For a more detailed explanation, see the tutorial on the Maxent website.

In your current mapset, you'll find the raster prediction layer, and the sample and background point layers with the predicted values.

Output layers in GRASS GIS
The example creates the prediction raster layer 'model_1_suitability_current', the sample point layer 'model_1_samplepred' and the background point layer 'model_bgrdpred' (for the latter, only part of the map is shown here).

3: Create a prediction layer

The third step is to use the model created in the previous step to predict the species suitability distribution under future climates. Note, we are going to make the (unrealistic) assumption that the ecosystems do not change. 
r.maxent.predict
  lambda=output_model1/Bradypus_tridactylus.lambdas \
  rasters=bio02_ssp585,bio03_ssp585,bio08_ssp585,bio09_ssp585,bio13_ssp585,bio15_ssp585,bio17_ssp585,sa_eco_l2 \
  variables=bio02,bio03,bio08,bio09,bio13,bio15,bio17,landuse \
  output=model_1_ssp585

The resulting layer is written to the current mapset as model_1_ssp585 (right map in the figure below). The results suggest the area with suitable conditions will increase under future climates compared the that under the current conditions (left map in the figure below). This result is unexpected, and warrants further investigation.

Predicted suitability map for Bradypus tridactylus for the period 2061-2080 based on the GCM BCC-CSM2-MR and SSP 585.
Predicted suitabilty for the period 2061-2080 based on the GCM BCC-CSM2-MR and SSP 585.

REFERENCES

SEE ALSO

AUTHOR

Paulo van Breugel, https://ecodiv.earth

HAS green academy University of Applied Sciences
Innovative Biomonitoring research group
Climate-robust Landscapes research group

SOURCE CODE

Available at: r.maxent.predict source code (history)

Latest change: Sunday Nov 17 17:00:54 2024 in commit: 98393ab8a50f6a43692b7597c1a11fa9eef2c529

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