The Species Distribution Model Experiment (SDM) lets you investigate the potential distribution of a species under current climatic and environmental conditions. The BCCVL currently provides 17 different algorithms across 4 different categories to run your species distribution model. Read more about Species Distribution Modelling here.

Note: You will need to run a Species Distribution Model before you can run a Climate Change or Biodiverse Experiment.

How to run an SDM in the BCCVL

  • At the top of the page click on the Experiments tab. 
  • Under the primary experiments heading click on Species Distribution Modelling Experiment

Step 1: Description tab

  • Enter the name for your experiment in the first box (e.g. Current fox (Vulpes vulpes) distribution). 
  • (optional) You can also add a description of your experiment in the box below if you want to convey more information. Some researchers use this box to record their research question or hypotheses for later referral.
  • Click Next.

Step 2: Occurrences tab

  • Select your pre-loaded species occurrence dataset by clicking the Select A Dataset button. Note: If you click this and you have no loaded species occurrence datasets you will need to visit the dataset page and import or upload the required data.
  • In the pop-up box select the dataset you wish to use in your SDM. Click Select
  • (optional) You can visualise your occurrence data by clicking the green eye icon.
  • Click Next.

Step 3: Absences tab

You have two options for adding absence data

  • Uploaded true absence data: If you have your own absence data you need to import this into your experiment as below.
    • Select yes under the question whether you have true absence data.
    • Click the Select A Dataset button that appears.
    • In the pop-up box select the pre-loaded absence dataset you wish to use in your SDM. Click Select
    • (optional) You can visualise your absence data by clicking the green eye icon. 
    • Click Next.
  • Pseudo-absence data: The BCCVL can randomly generate pseudo-absence data points for your experiment.
    • Select no under the question whether you have true absence data.
    • You can customize the pseudo-absence data generation under the Algorithms tab, so you can move to the next tab.
    • Click Next.

Step 4: Climate & Environmental Data tab

  • Click the Select Available Datasets button.
  • In the pop-up box you can enter search terms to filter for required datasets or browse through by scrolling.
  • Once you have found the dataset/s you are looking for select them and click Select Layers
  • When back on the Climate & Environmental Data tab you can select/deselect data layers in the blue box.
  • (optional) You can visualise each of the data layers by clicking the green eye icon, and on the right hand side of the map you can toggle which data layer you want to visualise.
  • Once you have selected all your environmental and climate layers click Next.

Step 5: Constraints tab

This is an optional tab that allows you to train your model to a particular area or region (e.g. Queensland only). The default constraint is the convex hull (= smallest polygon around all occurrence records - blue on the map). The green box indicates the area of the selected environmental/climate variables. If this area is smaller than the convex hull, the model will only be trained in that area.If you do not want to change the geographic extent of your model simply click Next. If you do want to constrain your model, there are three options:

  • Constrain by a pre-defined region

    • Select one of the four region types that are currently available in the BCCVL: Australian States and Territories, Local Government Areas, National Resource Management Regions, IBRA 7 regions.
    • Find the region of your interest in the drop down menu.
    • Click Add To Map.
  • Constrain by drawing a bounding box on the map

    • Click Draw On Map to draw a rectangular bounding box on the map to which the model will be constrained.
  • Constrain by defining the coordinates of the bounding box 

    • Enter coordinates for the northern, eastern, southern and western limits respectively to define the bounding box.
    • Click Input Constraints to add the defined bounding box to the map.

Note that the model will be trained on the selected area, and the results will include a predicted distribution map for the constrained area, as well as a projection to the geographic extent of your environmental/climate layers.

Step 6: Algorithms tab

  • Select the algorithm/s you would like to use to calibrate your model. You can choose one or many (or even all!) to run your experiment. Don't know which one to select? Look up detailed information of each algorithm under Algorithm Information (SDMs).
  • (optional) Configuration:
    • If you don't have true absence data and you want to use a presence/absence model, the BCCVL will automatically generate pseudo-absence data. Under Pseudo Absence Configuration you can set the settings for this generation. By default the generation will be random throughout the geographic extent of the area selected in the constraints tab, with a 1:1 ratio of presence:absence data. More information about the different options is available on the Absence data page.
    • Other configuration options are available for some algorithms. These options can be changed by changing the value or make a different selection from the drop down menu. The configuration options are currently set to the standard default values of the R packages so you do not need to make changes. However, configuring the model to best fit your data might give a better result.
  • Click Next.

Note: The BCCVL currently provides 17 different algorithms. Choosing what algorithm best suits your experiment and data or selecting the optimal configuration options can be confusing. For further information on each algorithm and the configuration options click on the title of the algorithm you want to explore under Algorithm Information (SDMs).

Step 7: Run tab

  • Ensure you are happy with your experiment design.
  • If all tabs are green then your experiment is ready to go.
  • Click Start Experiment.
  • If any of your tabs are red, revisit it and ensure you have filled in each component correctly.