A Short Species Distribution Modeling Tutorial

This repository contains a short tutorial for creating a species distribution model using QGIS, R, and MaxEnt. I prepared this documentation for the skills training sessions during the lab retreat of the Applied Plant Ecology Lab, Department of Biological Sciences, National University of Singapore held on 25-28 September 2017 in Malacca, Malaysia.

Table of Contents

• Download and Installation
• Study Area
• Prepare Datasets
• Model Species Distributions
• Further Reading
• References
• License
• Want to Contribute?

Download and Installation

Software

For this tutorial, download and install QGIS, R, and MaxEnt, all of which are free and open-source software. For QGIS and R, download the versions compatible with your machine's operating system. MaxEnt is a Java-based application and runs using various operating systems. The procedures shown in this tutorial uses a Mac OSX platform but it should be applicable to other operating systems.

Data

MaxEnt will require two types of input datasets:

1. Species occurrence data. The species occurrence records are the geographic point locations or coordinates of species observations. For this exercise, we will use the georeferenced database of selected threatened forest tree species in the Philippines compiled by Ramos et al. (2011). Download the database from the World Agroforestry Centre/ICRAF Dataverse here (150 KB, CSV file).

2. Environmental predictors. The environmental covariates consist of raster data that contain either continuous or categorical values such as precipitation, temperature, elevation, etc. We will be using the WorldClim raster datasets. WorldClim is a set of gridded global climate data layers, which can be used for mapping and ecological modeling. For this exercise, we will use WorldClim v.1.4 Current conditions (or interpolations of observed data from 1960-1990). We will need the highest resolution data available provided at 30 arc-seconds (~1 km); hence click the download by tile link and choose Tile 210. After clicking the tile, download the GeoTIFF file formats of the Altitude (~2 MB, ZIP file) and Bioclim (~29 MB, ZIP file) layers. You can read Hijmans et al. (2005) for more information about the climate data layers.

To prepare the datasets, we will also need administrative boundary data. We can use the administrative boundary vector data from the Global Administrative Database. On GADM's Download page, select "Philippines" and "Shapefile" from the Country and Format drop-down menus, respectively, and click the download link provided (~22 MB, ZIP file).

Study Area

Polillo Islands, Quezon Province, Philippines. The Polillo group of islands (approx. 14.861 N, 122.038 E) is situated in the northeast part of the Philippine archipelago. The Polillos comprise 27 small islands and islets, 25km off the east coast of Luzon. They form part of the Luzon Endemic Bird Area (EBA), ranked sixth in the world listing of critical EBAs (Stattersfield et al. 2000), whilst also constituting a highly distinct sub-centre of endemicity. Amongst these Polillo-specific endemics are a frog, several reptiles and seven birds, including a goshawk, hornbill, and parrots. The islands also support important populations of globally threatened species (e.g. Philippine cockatoo, Gray’s monitor lizard, Philippine jade vine), and are accorded high priority in all independent reviews of Philippine conservation priority areas. Open the image below to see more information about the conservation priorities in the Polillo Islands.

Prepare Datasets

1. First, we will create subsets from the environmental rasters to focus our modeling over our study area. To do this, we will create a polygon shapefile containing the extent of the study area and use this shapefile to clip all the raster map layers. Follow these steps using QGIS:

   • Load the PHL_adm2.shp shapefile by adding a vector layer from the Layer > Add Layer > Add Vector Layer... menu. This displays the municipal-level administrative boundaries.

   

   • To select our areas of interest, we will select the municipalities from the attribute table. Open the attribute table of PHL_adm2.shp by right-clicking the shapefile within the Layers Panel and then selecting Open Attribute Table from the menu. Inside the attribute table window, click the Select features using an expression icon. Once the Select by expression dialog box opens, enter the following expression:

    "NAME_2"  = 'Polillo' OR  "NAME_2"  = 'Burdeos' OR  "NAME_2"  = 'Panukulan' OR  "NAME_2"  = 'Patnanungan' OR  "NAME_2"  = 'Jomalig'
   

   

   This will select the municipalities belonging to our study area. Check the attribute table if you have selected five records, which includes the following municipalities: Polillo, Burdeos, Panukulan, Patnanungan, and Jomalig towns.

   • In the main QGIS window, right-click on PHL_adm2.shp and select Save As... from the menu. Once the Save vector layer as... dialog box opens, tick the Save only selected features to ensure that we save a new shapefile containing only the selected municipalities. Then, enter the file name of the output shapefile as polillos.shp to your working directory, and click OK. The new shapefile should appear in the QGIS Layers Panel.

   • Next, we will create a polygon from the extent of the municipalities shapefile that we have just saved. Go to Vector > Research Tools > Polygon from Layer Extent menu.

     • Under the Input Layer drop-down menu, select the newly created polillos.shp shapefile.
     • Under the Extent input line, select Save to File from the menu to save the file in your working directory. Then, click Run to create another shapefile called box.shp, which consists of a polygon covering the extent of the study area.

   

   • Next, go to Processing > Toolbox menu, which opens the Processing Toolbox panel. Search for the Clip raster with polygon function under the SAGA geoalgorithms and select this function. This will open the Clip Raster with Polygon dialog box.

     • Under the Input drop-down menu, click ... and navigate through your working directory and select one of the raster layers, say biol1_210.tif.
     • Under the Polygons input line, select the box.shp shapefile from the drop-down menu. The rasters will be clipped using the extent of this polygon.
     • Under the Clipped input line, click ... and select Save to File from the menu to save the file in your working directory using the same file name, but this time, change the output file type to ASC as this is the file type requirement used by MaxEnt. Then, click Run to generate the clipped raster file, biol1_210.asc.

   

   Repeat this for all other raster layers by following the same process. You may also opt to run this through batch processing by clicking on the Run As a Batch Process... button.

2. Next, we will extract the occurrence points of the species we are interested in modeling.

   • Inspect the threatened tree species database using tools like R or Excel. For this exercise, let us model the distributions of Cinnamomum mercadoi that were observed in the Polillo Islands.

   • To select the species from the CSV file, we will use a few lines of code in R as follows:

   # This line reads the CSV file and stores it in a variable
   # Note: change file path to your working directory
   data <- read.csv(file="Geoferenced_threatenedforesttreespecies.csv", header=TRUE, sep=",")
   
   # This line selects the species in our study area and stores it in a variable
   polillo_cm <- subset(data, Species=="Cinnamomum mercadoi" & Source=="Clements, 2001", select=c(2,10:11))
   
   # This line saves the selected species in a CSV file
   write.csv(polillo_cm, file="polillo_cm.csv", row.names=FALSE)

   Here, note that the search terms used include the species name and the source of the data based on the database. Also, only columns 2, 10, and 11 were selected and saved in the final CSV file, which corresponds to the columns 'Species', 'Lat', and 'Long'.

   Alternatively, you can download the R script and run this in R or RStudio.

3. We are almost ready to create our first species distribution model. But before we do that, load all of the clipped environmental rasters and the species occurrence file in QGIS:

   • Load the clipped environmental raster layers by adding them from the Layer > Add Layer > Add Raster Layer... menu. Remember that these are the .ASC files.

   • Load species occurrence data CSV file by adding it from the Layer > Add Layer > Add Delimited Text Layer... menu.

   • In the Create a Layer from a Delimited Text File dialog box, select the CSV file by navigating to the file in your working directory. Once it is opened, the species records and their coordinates will be shown in the lower part of the dialog box. In the X field and Y field drop-down menus, select 'Long' and 'Lat' columns, respectively.

Model Species Distributions

We are now ready to create our first species distribution model using MaxEnt.

1. Open MaxEnt and load the Samples and Environmental Layers by navigating to the respective directories of those files. Ensure that the tick boxes of all files are checked, and that the Environmental Layers files are all 'Continuous' types.

2. Also, in the main MaxEnt window, check tick boxes or select the following options:

   • Linear/Quadratic/Product/Threshold/Hinge features
   • Create response curves
   • Make pictures of predictions
   • Do jackknife to measure variable importance
   • Output format: Logistic
   • Output file type: asc

Leave the other advanced settings in their default for now. Then, click Run and wait for the processing to finish.

3. Once the MaxEnt software completes its data processing:

   • Load the resulting ASC file in QGIS from the Layer > Add Layer > Add Raster Layer... menu. Then, change the styling of the raster layer by going to Layer > Properties... menu, or double-clicking on the layer under the Layers Panel. Change the styling of the raster layer as shown on the image below. Alternatively, you can also load the layer styling using this QML file (only applicable to the logistic model output).

   

   The styling of the raster layer has been changed similar to the image below, which shows the logistic output of the MaxEnt's species distribution model.

   

Congratulations! You have now made your first species distribution model using QGIS, R, and MaxEnt.

Further Reading

To learn more about MaxEnt such as analysing and interpreting MaxEnt's outputs, adjusting model settings, etc. the following materials are suggested for further reading:

Elith, J., Graham, C.H., Anderson, R.P., Dudik, M., Ferrier, S., Guisan, A., et al. (2006) Novel methods improve prediction of species’ distributions from occurrence data. Ecography, 29, 129–151. (DOI)

Merow, C., Smith, M.J. & Silander, J.A. (2013) A practical guide to MaxEnt for modeling species’ distributions: what it does, and why inputs and settings matter. Ecography, 36, 1058–1069. (DOI)

Morales, N.S., Fernandez, I.C. & Baca-Gonzalez, V. (2017) MaxEnt’s parameter configuration and small samples: are we paying attention to recommendations? A systematic review. PeerJ, 5, e3093. (DOI)

Phillips, S.J., Anderson, R.P., Dudik, M., Schapire, R.E. & Blair, M.E. (2017) Opening the black box: an open-source release of Maxent. Ecography, 40, 887–893.(DOI)

Phillips, S.J., Anderson, R.P. & Schapire, R.E. (2006) Maximum entropy modeling of species geographic distributions. Ecological Modelling, 190, 231–259. (DOI)

Yackulic, C.B., Chandler, R., Zipkin, E.F., Royle, J.A., Nichols, J.D., Campbell Grant, E.H. & Veran, S. (2013) Presence-only modelling using MAXENT: when can we trust the inferences? Methods in Ecology and Evolution, 4, 236–243. (DOI)

References

Hijmans, R.J., Cameron, S.E., Parra, J.L., Jones, P.G. & Jarvis, A. (2005) Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology, 25, 1965–1978. (DOI)

Ramos, L.T., Torres, A.M., Pulhin, F.B. & Lasco, R.D. (2011) Developing a georeferenced database of selected threatened forest tree species in the Philippines. Philippine Journal of Science, 141, 165–177. (PDF)

Stattersfield, A.J., Crosby, M., Long, A.J. & Wege, D.C. (1998) Endemic Bird Areas of the World: Priorities for Biodiversity Conservation. The Burlington Press, Ltd., Cambridge, United Kingdom.

License

Creative Commons Attribution 4.0 International CC BY 4.0. Please note the Disclaimer of Warranties and Limitation of Liability under Section 5 of this license as follows:

a. Unless otherwise separately undertaken by the Licensor, to the extent possible, the Licensor offers the Licensed Material as-is and as-available, and makes no representations or warranties of any kind concerning the Licensed Material, whether express, implied, statutory, or other. This includes, without limitation, warranties of title, merchantability, fitness for a particular purpose, non-infringement, absence of latent or other defects, accuracy, or the presence or absence of errors, whether or not known or discoverable. Where disclaimers of warranties are not allowed in full or in part, this disclaimer may not apply to You.

b. To the extent possible, in no event will the Licensor be liable to You on any legal theory (including, without limitation, negligence) or otherwise for any direct, special, indirect, incidental, consequential, punitive, exemplary, or other losses, costs, expenses, or damages arising out of this Public License or use of the Licensed Material, even if the Licensor has been advised of the possibility of such losses, costs, expenses, or damages. Where a limitation of liability is not allowed in full or in part, this limitation may not apply to You.

c. The disclaimer of warranties and limitation of liability provided above shall be interpreted in a manner that, to the extent possible, most closely approximates an absolute disclaimer and waiver of all liability.

Want to Contribute?

In case you wish to contribute or suggest changes, please feel free to fork this repository. Commit your changes and submit a pull request. Thanks.