This script doesn't include pre-analysis steps such as data occurrence acquisition, area of calibration definition, variable selection, etc (I will try to include scripts for them ASAP). You must do these steps before proceeding.
I've tried to comment on the code as much as possible but you can write me if you have any doubts or suggestions @didelphisaurita on Twitter.
It is quite a simple script, nothing much sophisticated but it works :) I believe it can be particularly interesting for beginners and intermediate level R users as it doesn't have lots of complicated functions, if any, it's pretty straightforward. You may have to manually install some of the packages, I had some trouble installing some of them through devtools:install_github().
For my project I used the current climate and four future scenarios, so, for each scenario, there's a corresponding block of code. I didn't use loops because I had far less experience when I wrote the script and kept this way to keep the script simple.
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If you want to use fewer scenarios you just have to delete the corresponding blocks of code.
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if you want to run more scenarios you just have to copy, paste and change the object's names so it doesn't overwrite other scenario's objects.
The basic requirements to run the script is to set the variables and records path and it's good to go! You should look into the tuning parameters, though, I will mention them later.
To run the script you'll need a data folder in your working directory/root folder, the result folders are created automatically. The structure of the data folder is as follows (you can also see sdmfolder_example.zip file):
Structure:
├── sdm_example │ └── data │ ├── elev | | └── elevation.tif │ ├── raster │ │ ├── current | | | └── variables.tif │ │ ├── RCP26 | | | └── variables.tif │ │ ├── RCP45 | | | └── variables.tif │ │ ├── RCP60 | | | └── variables.tif │ │ └── RCP85 | | | └── variables.tif │ └── records └── records.csv
Elevation is actually optional. If you don't want to extract elevation, you can remove/comment on the part of the script that computes elevation or just run it anyway, you'll get lots of error messages but everything else should work just fine.
I believe that the parameters that you should pay the most attention to while fine-tuning are the following (but check the whole script):
Parameters:
# Number of background points, in my case 10K 113 randomBgSites <- dismo::randomPoints(current, 10000) #Minimum, maximum and intervals of master regularization (regMult) #Classes of features (classes) 185 tunedModel <- trainMaxNet(data=trainData, regMult= c(seq(1, 4, by = 0.5)), verbose=F, classes = "lqh", testClasses=TRUE, clamp=T, 191 out = c('model', 'tuning')) #Threshold of suitability, in my case 5% lowest values = 0.95 242 p95 <- round(length(occPredVals) * 0.95) #Distance from species MCP to cut off, in my case 200 km 376 buffered.mcp.records <- raster::buffer(mcp.records, width = 200*km
I chose a KFold=5 partitioning of the data so there are 5 models for each scenario/species.
1.Records and background points files with a KFold ID
2.Rdata on each model
3.Tuning parameters for each model
4.Continous Maxent models
5.Variance of the continuous models
6.Metrics of evaluation: CBI, TSS, and AUC
7.Threshold values
8.CBI weighted mean ensemble model
9.Thresholded model
10.Area and elevation of the thresholded models
11.Log file with species names, number of records, metrics, and threshold values
I use CBI as the main metric and to weight means, and TSS and AUC as better than random metrics.