## Intro

In the previous articles you have learned how to prepare the data for the analysis and how to train a model using SDMtune. In this article you will learn how to use a trained model to make predictions. We will use the default model that we trained in the previous article to predict the distribution of the virtual species.

## Make prediction

First we load the SDMtune package:

library(SDMtune)
#>
#>    _____  ____   __  ___ __
#>   / ___/ / __ \ /  |/  // /_ __  __ ____   ___
#>   \__ \ / / / // /|_/ // __// / / // __ \ / _ \
#>  ___/ // /_/ // /  / // /_ / /_/ // / / //  __/
#> /____//_____//_/  /_/ \__/ \__,_//_/ /_/ \___/  version 1.1.5
#>
#> To cite this package in publications type: citation("SDMtune").

New locations are predicted with the function predict(). The function takes three main arguments:

• a trained model given as SDMmodel() object;
• a new dataset, used to make prediction (can be a data.frame, an SWD() object or a raster stack object);
• the output type, that for Maxent models can be: raw, logistic or cloglog.

Next we get the prediction for our training locations using the cloglog output type:

pred <- predict(default_model, data = data, type = "cloglog")

The output in this case is a vector containing all the predicted values for the training locations:

head(pred)
#> [1] 0.8246888 0.6886814 0.5675941 0.7317644 0.7472581 0.6938709

We can get the prediction only for the presence location with:

p <- data@data[data@pa == 1, ]
pred <- predict(default_model, data = p, type = "cloglog")
tail(pred)
#> [1] 0.9571988 0.9222179 0.6833005 0.2801192 0.6050091 0.7394861

For models trained with the Maxent method, the function performs the prediction in R without calling the MaxEnt Java software. This results in a faster computation for large datasets and might result in a slightly different output compared to the Java software.

## Create a distribution map

We can use the same function to create a distribution map starting from the predictors raster stack’ object that we created in the first article.

map <- predict(default_model, data = predictors, type = "cloglog")

In this case the output is a raster object:

map
#> class      : RasterLayer
#> dimensions : 192, 186, 35712  (nrow, ncol, ncell)
#> resolution : 0.5, 0.5  (x, y)
#> extent     : -125, -32, -56, 40  (xmin, xmax, ymin, ymax)
#> crs        : +proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0
#> source     : memory
#> names      : layer
#> values     : 2.401041e-07, 0.9665651  (min, max)

The map can be saved in a file directly when running the prediction, we just have to pass additional arguments to the predict() function. In the next example we save the map in a file called “my_file” in the GeoTIFF format:

map <- predict(default_model, data = predictors, type = "cloglog", file = "my_map", format = "GTiff")

The predict() function has other arguments useful when predicting large datasets:

• progress: can be set to "text" to visualize a progress bar;
• extent: can be passed to reduce the prediction to the given extent.

## Plot a distribution map

To plot the distribution map we can use the function plotPred():

plotPred(map)

The function plotPred() plots a map with a color ramp similar to the one used by the MaxEnt Java software. We can pass additional arguments to customize the map. In the next example we provide a custom color ramp and we add a title to the legend:

plotPred(map, lt = "Habitat\nsuitability", colorramp = c("#2c7bb6", "#abd9e9", "#ffffbf", "#fdae61", "#d7191c"))

### Try yourself

Restrict the prediction to Chile and plot the prediction. To see the solution highlight the following cell:

# First create the extent that surrounds Cile
e = raster::extent(c(-77, -60, -56, -15))
# Now use the extent to make the prediction
map_e <- predict(default_model, data = predictors, type = "cloglog", extent = e)
# And finally plot the prediction
plotPred(map_e)

## Plot a presence/absence map

To plot a presence/absence map we need a threshold value that splits the prediction in presence and absence values. The function thresholds() returns some commonly used threshold values starting from an SDMmodel() object. In the next example we print the threshold values for the default_model object using the type "cloglog":

ths <- thresholds(default_model, type = "cloglog")
thresholds(default_model, type = "cloglog")
Threshold Cloglog value Fractional predicted area Training omission rate
Minimum training presence 0.0149851 0.4992 0.0000
Equal training sensitivity and specificity 0.5690769 0.2076 0.2075
Maximum training sensitivity plus specificity 0.3900450 0.2806 0.0775

For example we want to create a presence/absence map using the threshold that maximize the training sensitivity plus specificity. We use the function plotPA() passing the threshold value as argument:

plotPA(map, th = ths[3, 2])

We can also save the map in a file with the following code:

plotPA(map, th = ths[3, 2], filename = "my_pa_map", format = "GTiff")

Both functions plotPred() and plotPA() have the argument hr`to plot the the map with high resolution, useful when the map is used in a scientific publication. For all the arguments of the functions that we have used in this article please refer to the documentation.

### Try yourself

Repeat the same analysis using a model trained with the Maxnet method.