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Run 'Ri.Asia' model trained on SLF invaded range in Japan and South Korea

Samuel M. Owens1

2026-07-30

Source: vignettes/080_run_regional_invaded_asian_MaxEnt_model.Rmd
080_run_regional_invaded_asian_MaxEnt_model.Rmd

Overview

This vignette trains the second regional-scale model, the Ri.Asia model, which is based on the invaded range in east Asia. This vignette follows the same structure as vignette 070. In the next vignette, we will train the final regional-scale model based on the native range for SLF in east Asia.

Setup

CURRENT MODEL VERSION: v4

# general tools
library(tidyverse)  #data manipulation
library(here) #making directory pathways easier on different instances
# here::here() starts at the root folder of this package.
library(devtools)
library(knitr)

# SDMtune and dependencies
library(SDMtune) # main package used to run SDMs
library(dismo) # package underneath SDMtune
library(rJava) # for running MaxEnt
library(plotROC) # plots ROCs

# spatial data handling
library(raster) 
library(terra) 

library(viridis)

library(scari)

Note: I will be setting the global options of this document so that only certain code chunks are rendered in the final .html file. I will set the eval = FALSE so that none of the code is re-run (preventing files from being overwritten during knitting) and will simply overwrite this in chunks with plots.

SDMtune will run MaxEnt through java via the rJava package. You will need to ensure that your machine has the proper version of java installed (x32 or x64) for your operating system.

checkMaxentInstallation(verbose = TRUE)

This chunk sets the java memory allocation (Xmx). I will increase the memory allocation from 512m (the default) to 8GB (8000mb) of memory. This should be edited according to your system, because you should still leave about 20% of your system memory available for other processes. If you have a 32-bit system, you cannot set the max above 4000mb.

# xmx sets java memory allocation
options(java.parameters = "-Xmx8000m")

# xss sets java stack size
# options(java.parameters = c("-Xss2560k", "-Xmx2048m"))

1. Format Data for Model

I will load in the datasets I will need for the MaxEnt models. These are labeled at the beginning of the object name by the parameter they will be used in the SDMtune::train() function (x, p or a). I will begin by loading in the covariate data and then by loading in the points datasets. The env_covariates dataset labeled invaded_asian contains a raster of each of the environmental covariates used to train the model- these were the covariates that were masked using the K-G climate zones in the last vignette.

1.1 Input Data- env covariates 

# path to directory
mypath <- file.path(here::here() %>% 
                       dirname(),
                     "maxent/historical_climate_rasters/chelsa2.1_30arcsec")


# the env covariate scale used to train the model
x_invaded_asian_env_covariates_list <- list.files(path = file.path(mypath, "v1_maxent_10km"), pattern = "\\_regional_invaded_asian_KG.asc$", full.names = TRUE)  %>%
  # dont include Access to cities
  grep(pattern = "atc_2015", value = TRUE, invert = TRUE)

# the scale used to make xy predictions
x_global_hist_env_covariates_list <- list.files(path = file.path(mypath, "v1_maxent_10km"), pattern = "\\.asc$", full.names = TRUE) %>%
    grep("bio2_1981-2010_global.asc|bio11_1981-2010_global.asc|bio12_1981-2010_global.asc|bio15_1981-2010_global.asc", ., value = TRUE)

The CMIP6 versions of these covariates will only be used for projection purposes.

# path to directory
  mypath <- file.path(here::here() %>% 
                       dirname(),
                     "maxent/future_climate_rasters/chelsa2.1_30arcsec")

# the env covariates for performing xy predictions for global slf and IVR points

# SSP126
x_global_126_env_covariates_list <- list.files(path = file.path(mypath, "2041-2070_ssp126_GFDL", "v1_maxent_10km"), pattern = "\\_global.asc$", full.names = TRUE) %>%
  # dont include Access to cities
  grep(pattern = "atc_2015", value = TRUE, invert = TRUE)

# SSP370
x_global_370_env_covariates_list <- list.files(path = file.path(mypath, "2041-2070_ssp370_GFDL", "v1_maxent_10km"), pattern = "\\_global.asc$", full.names = TRUE) %>%
  # dont include Access to cities
  grep(pattern = "atc_2015", value = TRUE, invert = TRUE)

# SSP585
x_global_585_env_covariates_list <- list.files(path = file.path(mypath, "2041-2070_ssp585_GFDL", "v1_maxent_10km"), pattern = "\\_global.asc$", full.names = TRUE) %>%
  # dont include Access to cities
  grep(pattern = "atc_2015", value = TRUE, invert = TRUE)

I will create rasters of the environmental covariates, stack and gather summary statistics. I will also shorten their names and exclude possible operators from layer names (for example, using the dash symbol was found to interfere with SDMtune making predictions for tables downstream).

# layer name object. Check order of layers first
env_layer_names <- c("bio11", "bio12", "bio15", "bio2")
# stack env covariates
x_invaded_asian_env_covariates <- terra::rast(x = x_invaded_asian_env_covariates_list)

# attributes
nlyr(x_invaded_asian_env_covariates)
names(x_invaded_asian_env_covariates)
minmax(x_invaded_asian_env_covariates)
# ext(x_invaded_asian_env_covariates)
# crs(x_invaded_asian_env_covariates)

# I will change the name of the variables because they are throwing errors in SDMtune
names(x_invaded_asian_env_covariates) <- env_layer_names



# global rasters
# stack env covariates
x_global_hist_env_covariates <- terra::rast(x = x_global_hist_env_covariates_list)

# attributes
nlyr(x_global_hist_env_covariates)
names(x_global_hist_env_covariates)
minmax(x_global_hist_env_covariates)
# ext(x_global_hist_env_covariates)
# crs(x_global_hist_env_covariates)

# I will change the name of the variables because they are throwing errors in SDMtune
names(x_global_hist_env_covariates) <- env_layer_names
# confirmed- SDMtune doesnt like dashes in column names (it is read as a mathematical operation)
# SSP126
x_global_126_env_covariates <- terra::rast(x = x_global_126_env_covariates_list)

# attributes
nlyr(x_global_126_env_covariates)
names(x_global_126_env_covariates)
minmax(x_global_126_env_covariates)
# ext(x_global_126_env_covariates)
# crs(x_global_126_env_covariates)

names(x_global_126_env_covariates) <- env_layer_names


# SSP370
x_global_370_env_covariates <- terra::rast(x = x_global_370_env_covariates_list)

# attributes
nlyr(x_global_370_env_covariates)
names(x_global_370_env_covariates)
minmax(x_global_370_env_covariates)
# ext(x_global_370_env_covariates)
# crs(x_global_370_env_covariates)

names(x_global_370_env_covariates) <- env_layer_names


# SSP585
x_global_585_env_covariates <- terra::rast(x = x_global_585_env_covariates_list)

# attributes
nlyr(x_global_585_env_covariates)
names(x_global_585_env_covariates)
minmax(x_global_585_env_covariates)
# ext(x_global_585_env_covariates)
# crs(x_global_585_env_covariates)

names(x_global_585_env_covariates) <- env_layer_names
rm(x_invaded_asian_env_covariates_list)
rm(x_global_hist_env_covariates_list)
rm(x_global_126_env_covariates_list)
rm(x_global_370_env_covariates_list)
rm(x_global_585_env_covariates_list)

1.2 Input data- presences / absences (training and testing)

I need to also load in the SLF presence and background points datasets created in vignette 060.

# slf presences
p_slf_points <- read.csv(file = file.path(here::here(), "vignette-outputs", "data-tables", "slf_all_coords_final_2026-07-30.csv")) %>%
  dplyr::select(-species)

# training presences
p_slf_points_invaded_asian_train <- read.csv(
  file = file.path(here::here(), "vignette-outputs", "data-tables", "regional_invaded_asian_train_slf_presences_v4.csv")
  )
# test presences
p_slf_points_invaded_asian_test <- read.csv(
  file = file.path(here::here(), "vignette-outputs", "data-tables", "regional_invaded_asian_test_slf_presences_v4.csv")
  )

# background points
a_regional_invaded_asian_background_points <- read.csv(
  file.path(here::here(), "vignette-outputs", "data-tables", "regional_invaded_asian_background_points_v4.csv")
  )

I showed the process for selecting the presences in the last vignette, but this time I will just load in the presences dataset created in vignette 060 (regional model setup).

Model training object

Now I will create the “samples with data” object that SDMtune requires.

regional_invaded_asian_train <- SDMtune::prepareSWD(
  species = "Lycorma delicatula",
  env = x_global_hist_env_covariates,
  p = p_slf_points_invaded_asian_train, 
  a = a_regional_invaded_asian_background_points,
  verbose = TRUE # print helpful messages
  )

regional_invaded_asian_train@coords # coordinates
regional_invaded_asian_train@pa # presence / absence (background counted as absence)
regional_invaded_asian_train@data # extracted data from

I usually look at how many records were dropped before I save it, because SDMtune gives an undefined warning about how many samples were discarded. In this case, we only lost 3 presence records, so this is acceptable. I will also save the output to be used later.

SDMtune::swd2csv(swd = regional_invaded_asian_train, file_name = c(
  file.path(here::here(), "vignette-outputs", "data-tables", "regional_invaded_asian_train_slf_presences_with_data_v4.csv"),
  file.path(here::here(), "vignette-outputs", "data-tables", "regional_invaded_asian_background_points_with_data_v4.csv")
  ))

Model testing object

The testing / validation SWD object will be created in the same fashion, except using the presences from the invaded range in North America.

regional_invaded_asian_test <- SDMtune::prepareSWD(
  species = "Lycorma delicatula",
  env = x_global_hist_env_covariates, 
  p = p_slf_points_invaded_asian_test, 
  a = a_regional_invaded_asian_background_points,
  verbose = TRUE # print helpful messages
  )

regional_invaded_asian_test@coords # coordinates
regional_invaded_asian_test@pa # presence / absence (background counted as absence)
regional_invaded_asian_test@data # extracted data from 
SDMtune::swd2csv(swd = regional_invaded_asian_test, file_name = c(
  file.path(here::here(), "vignette-outputs", "data-tables", "regional_invaded_asian_test_slf_presences_with_data_v4.csv"),
  file.path(here::here(), "vignette-outputs", "data-tables", "NULL.csv")
  ))

# remove second copy of background data points
file.remove(file.path(here::here(), "vignette-outputs", "data-tables", "NULL.csv"))

2. Train Invaded Regional Model

First, I will train a maxEnt model. This and the other regional scale model will NOT be cross-validated. Cross-validation randomly selects a percentage of the presence data for training and testing, without regard for spatial location. This process makes sense for the global model, which is fed ALL presence data. Due to the spatial scale of the regional models, it makes more sense to spatially separate the training and testing data if given the opportunity.

I will use the following list of hyperparameters to train the initial model, which I selected via tuning for the global model.

  • ALL feature classes (fc) used (l = linear, q = quadratic, p = product, h = hinge, t = threshold)
  • regularization multiplier (reg) set to 1.5 (more regularized than default of 1)
  • iterations = 5000. This is the max number of iterations for the optimization algorithm to perform before stopping training. Increasing this number from the default of 500 allows the algorithm to make more refined predictions.
regional_invaded_asian_model <- SDMtune::train(
  method = "Maxent",
  data = regional_invaded_asian_train,
  fc = "qpht", # feature classes set to ALL except linear
  reg = 1.5,
  iter = 5000, # number of iterations
  progress = TRUE
)

Summary Statistics

This function produces all summary statistics for this model. For the complete and annotated workflow used to create this function, see 051_compute_MaxEnt_summary_statistics_workflow.Rmd.

mypath <- file.path(here::here() %>% 
                       dirname(),
                     "maxent/models/slf_regional_invaded_asian_v4")
scari::compute_MaxEnt_summary_statistics(
  model.obj = regional_invaded_asian_model, 
  model.name = "regional_invaded_asian", 
  mypath = mypath, 
  create.dir = TRUE, # create subdirectory
  env.covar.obj = x_invaded_asian_env_covariates, # env covariates raster stacked
  train.obj = regional_invaded_asian_train, # training data used to create model
  test.obj = regional_invaded_asian_test, # data you wish to use to test the model
  plot.type = "cloglog", # types of univariate and marginal response curves to be created
  jk.test.type = c("train", "test") # types of jackknife curves to be created
  )

Looking at the summary statistics, the most explanatory variable (according to the jackknife test, training data in Korea and Japan) is the mimimum winter temperature (Bio 11). By itself, it can produce nearly the same AUC as the full model. When left out, Bio 11 is also the most explanatory (basically, a model excluding any variable except Bio 15 is substantially inhibited in its predictive power). Bio 11 is similarly the the most impactful variable in the testing data when producing a model on its own. The permutation importance plot validates that bio 11 is the most important.

Here are the jackknife plots:

The variables behaved differently in the response curves. Looking at the univariate response curves (the result of creating a model with only that variable), Bio 11 resembles a bell-shaped curve, with a peak around 0°C. Bio 12 is a narrow peak at around 1300mm precipitation which trails off in the positive direction, and Bio 2 generally has activity between 5 and 10°C, with a sharp peak at around 9°C. These three variables roughly agree with the N. American invaded model in their minima and maxima (our main concern), but differ some in shape.

Here are the response curves:

Bio 15 is a bit strange in that it is bimodal, with peak activity below 30% and then between 70-100%. This suggests that most SLF populations are present at either high or low levels of precipitation seasonality (the quality of being variable in precipitation over the course of a year). This could have to do with the monsoonal effects of the rainy/dry seasons in Korea. This partially contrasts the N American invaded model, which has populations exclusively below 30%. The models agree on activity in this area, but Korea has most populations in the 70-100% range (see the rug plots).

The model itself had a training AUC of 0.956 and a test AUC of 0.775.

Here is the ROC curve:

3. Create Outputs for Analysis

3.1 Create distribution map for area of interest

Lastly, I will use the SDMtune::predict() function to predict the suitability for the range of interest. I will threshold the map by the fixed_1, MTSS and 10_percentile thresholds. The workflow for this function is wrapped into the function create_MaxEnt_suitability_maps().

mypath <- file.path(here::here() %>% 
                       dirname(),
                     "maxent/models/slf_regional_invaded_asian_v4")
regional_invaded_asian_model <- read_rds(file = file.path(mypath, "regional_invaded_asian_model.rds"))
scari::create_MaxEnt_suitability_maps(
  model.obj = regional_invaded_asian_model,
  model.name = "regional_invaded_asian", 
  mypath = mypath, 
  create.dir = FALSE, 
  env.covar.obj = x_global_hist_env_covariates, 
  describe.proj = "globe_1981-2010", # name of area or time period describe.proj to
  clamp.pred = TRUE,
  map.thresh = TRUE, # whether thresholded versions of these maps should be created
  map.thresh.extra = "fixed_1",
  thresh = c("fixed_1", "10_percentile", "MTSS"),
  summary.file = file.path(mypath, "regional_invaded_asian_summary.csv")
)

I will also create suitability maps for the projected 2041-2070 climate data.

The suitability maps for this model are shown below. I will load in the maps that were thresholded by both the MTSS and MTP thresholds. Bear in mind that I chose the MTSS threshold for analyses downstream.

The most prominent suitable areas above the MTSS (the colored areas) were predicted in completely different areas than the previous two models. This model highlights significant suitability in tropical regions of South America, sub-Saharan Africa, India and southeast Asia. It seems to omit most temperate regions. This is an excellent find, because it this model may predict suitable areas that would not be detected if the data were run as a global average (the traditional method for SDM). There is no noticeable trend in the predicted future suitability maps, with the exception of expansion in suitable areas in the tropics as the scenario increases. Here are the maps:

The historical data (1981-2010):

The predicted future data (2041-2070) under the ssp126 climate change scenario:

The predicted future data (2041-2070) under the ssp370 climate change scenario:

The predicted future data (2041-2070) under the ssp585 climate change scenario:

3.2 Predict suitability for all SLF presences

I will get projected suitability values, calculated on the cloglog scale (between 0 and 1) for each of the SLF presence points. These suitability values will be added back to the original data frame and saved. First, I will use SDMtune::prepareSWD() to extract raster values from each layer used to build the model. I will save this output.

During data analysis, I will create a scatter plot of these suitability values in the global vs regional models.

mypath <- file.path(here::here() %>% 
                       dirname(),
                     "maxent/models/slf_regional_invaded_asian_v4")

# slf presence data
slf_presences <- read.csv(file = file.path(here::here(), "vignette-outputs", "data-tables", "slf_all_coords_final_2026-07-30.csv")) %>%
  dplyr::select(-species)
scari::predict_xy_suitability(
  xy.obj = slf_presences,
  xy.type = "Lycorma delicatula",
  env.covar.obj = x_global_hist_env_covariates,
  model.obj = regional_invaded_asian_model,
  mypath = mypath,
  predict.type = "cloglog",
  output.name = "regional_invaded_asian_slf_all_coords_1981-2010"
)

3.3 Predict suitability for IVR locations

I will perform the same action as above, but for the locations of important wineries around the world.

mypath <- file.path(here::here() %>% 
                       dirname(),
                     "maxent/models/slf_regional_invaded_asian_v4")

# load in all IVR points
IVR_locations <- readr::read_rds(file.path(here::here(), "data", "wineries_esri54017.rds"))

IVR_locations <- IVR_locations %>%
  dplyr::select(x, y)
scari::predict_xy_suitability(
  xy.obj = IVR_locations,
  xy.type = "IVR locations",
  env.covar.obj = x_global_hist_env_covariates,
  model.obj = regional_invaded_asian_model,
  mypath = mypath,
  predict.type = "cloglog",
  output.name = "regional_invaded_asian_wineries_1981-2010",
  buffer.pred = TRUE
)

We have the Ri.Asia model trained, so now we will move on to train the final regional-scale model based on the native range in east Asia.

References

  1. Elith, J., Phillips, S. J., Hastie, T., Dudík, M., Chee, Y. E., & Yates, C. J. (2011). A statistical explanation of MaxEnt for ecologists: Statistical explanation of MaxEnt. Diversity and Distributions, 17(1), 43–57. https://doi.org/10.1111/j.1472-4642.2010.00725.x

  2. Feng, X. (2022). Shandongfx/nimbios_enm [HTML]. https://github.com/shandongfx/nimbios_enm (Original work published 2018).

  3. Gallien, L., Douzet, R., Pratte, S., Zimmermann, N. E., & Thuiller, W. (2012). Invasive species distribution models – how violating the equilibrium assumption can create new insights. Global Ecology and Biogeography, 21(11), 1126–1136. https://doi.org/10.1111/j.1466-8238.2012.00768.x

  4. Maryam Bordkhani. (n.d.). Threshold rule [Online post].

  5. Radosavljevic, A., & Anderson, R. P. (2014). Making better Maxent models of species distributions: Complexity, overfitting and evaluation. Journal of Biogeography, 41(4), 629–643. https://doi.org/10.1111/jbi.12227

  6. Sobek-Swant, S., Kluza, D. A., Cuddington, K., & Lyons, D. B. (2012). Potential distribution of emerald ash borer: What can we learn from ecological niche models using Maxent and GARP? Forest Ecology and Management, 281, 23–31. https://doi.org/10.1016/j.foreco.2012.06.017

  7. Phillips, S. J., Anderson, R. P., & Schapire, R. E. (2006). Maximum entropy modeling of species geographic distributions. Ecological Modelling, 190(3), 231–259. https://doi.org/10.1016/j.ecolmodel.2005.03.026

  8. Steven Phillips. (2017). A Brief Tutorial on Maxent. http://biodiversityinformatics.amnh.org/open_source/maxent/.

  9. Steven J. Phillips, Miroslav Dudík, & Robert E. Schapire. (2023). Maxent software for modeling species niches and distributions (Version 3.4.3 (Java)) [Computer software]. http://biodiversityinformatics.amnh.org/open_source/maxent/.

  10. Srivastava, V., Roe, A. D., Keena, M. A., Hamelin, R. C., & Griess, V. C. (2021). Oh the places they’ll go: Improving species distribution modelling for invasive forest pests in an uncertain world. Biological Invasions, 23(1), 297–349. https://doi.org/10.1007/s10530-020-02372-9

  11. VanDerWal, J., Shoo, L. P., Graham, C., & Williams, S. E. (2009). Selecting pseudo-absence data for presence-only distribution modeling: How far should you stray from what you know? Ecological Modelling, 220(4), 589–594. https://doi.org/10.1016/j.ecolmodel.2008.11.010

  12. Vignali, S., Barras, A. G., Arlettaz, R., & Braunisch, V. (2020). SDMtune: An R package to tune and evaluate species distribution models. Ecology and Evolution, 10(20), 11488–11506. https://doi.org/10.1002/ece3.6786