The Migratory Modelling Experiment (MM) lets you investigate how the potential distribution of your species might change throughout the year. This experiment runs a Species Distribution Modelling experiment for subsets of your species occurrence data. For each subset you can choose a different set of climate and environmental variables. All subsets will be run using the same algorithm.


Run an MM on EcoCommons

On the top of the page click on “Analysis Hub” and then on “Modelling Wizards”. Under “Primary experiments” choose “Migratory Modelling Experiment”.


Step 1: Description tab

  • Enter the title for your experiment in the first box (e.g. Summer vs winter distribution of Monarch butterfly). 

  • (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 questions or hypotheses for later referral.

  • Click “Next” on the bottom of the page.

Step 2: Occurrences tab

  • Select your pre-loaded multi-species occurrence dataset by clicking on "+Select an occurrence dataset".
    Note: If you click this and you have no loaded species occurrence datasets you will need to visit the dataset page and upload the required data.
  • In the pop-up box select the dataset you wish to use in your MM. 
  • (optional) You can visualise your occurrence data by clicking the green eye icon next to your loaded data.
  • Click "Next" on the bottom of the page.

Step 3: Absences tab

You have two options for adding absence data: 

  • Uploaded true absence data: If you have your own absence data with a column labeled 'month' with the same input values as the 'month' column in your occurrence data, you can select this for your experiment as follows:

  • Select “Yes” under the question whether you have true absence data.
  • Click on “+ Select an absence dataset”. 
  • In the pop-up box select the pre-loaded absence dataset you wish to use in your MM. 
  • (optional) You can visualise your absence data by clicking the green eye icon. 
  • Click “Next” on the bottom of the page.
  • Pseudo-absence data: The EcoCommons can randomly generate pseudo-absence or background (if using Maxent as algorithm) points for your experiment.

  • Select “No” under the question whether you have true absence data.
  • You can change the pseudo-absence generation settings (absence-presence ratio and strategy) or the background generation settings (number of background points). 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 absence:presence data. All algorithms in the experiment will use these settings, unless you change it for specific algorithms on the Algorithms tab.
  • Click “Next” on the bottom of the page.

Step 4: Subsets & Environmental Data tab

  • Click on  “+ Add subset”.

  • Click on " Select datasets" on the left site of the white box. 

  • In the pop-up box you can enter search terms to filter for required datasets or filter by collection, resolution and/or domain.

  • Once you have found the dataset/s you are looking for select them and click “Close”.

  • When back on the Climate & Environmental Data tab you can select/deselect data layers on the left site of the white box.

  • On the right sight, add a Title for the subset, and indicate which values in the 'month' column should be used for this subset. 
    Note: The values in the 'month' column need to be numbers but can represent other periods than months (e.g. 1 = winter, 2 = summer, or 1 = breeding, 2 = feeding).
  • You can add more subsets by clicking the Add Subset button.
  • For each subset you can choose a different set of climate/environmental variables.
  • Once you have selected all your environmental and climate layers for each subset click “Next” on the bottom of the page.

Note: If you choose data layers that do not have the same resolution you can choose whether they should be scaled to the finest or coarsest resolution.

Step 5: Constraints tab

In this section you can select the area in which to train your model. This means that only the occurrence records from the constrained area are used, and pseudo-absence or background points are only generated in this area. It is good practice to remove parts of the geographic or environmental space where you are certain your species will not be found.

The default constraint is the convex hull (= minimum polygon) around all occurrence records indicated by a blue outline on the map. The different constraint options are:

  • Use Convex Hull for each individual species

  • You can add a buffer around the convex hull by nominating a distance in km. The buffer will be added on the map once you click outside the white box. 
  • Select constraints by pre-defined region

  • Select one of the region types that are currently available in the BCCVL: Local Government Areas, National Resource Management Regions, Australian States and Territories, IBRA 7 regions, River Regions, Drainage Divisions Level 1 or 2, Marine Ecoregions of the World, Integrated Marine and Coastal Regionalisation of Australia (IMCRA4) Provincial or Meso-scale Bioregions.
  • Find the region of your interest in the drop-down menu. You can select multiple regions.
  • You can also add a buffer around the pre-defined region constraints.
  • Use Environmental Envelope

  • This is the geographic extent of where all selected climate/environmental datasets overlap.
  • Draw constraints on Map

  • Click on “Start drawing”, then click on the map to draw a shape on the map to which the model will be constrained.
  • Upload Shapefile 

  • Select a shapefile from your computer to use as the constraint.
  • Note: 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.

  • Once you selected the constrained area click “Next” on the bottom of the page

Step 6: Algorithms tab

  • Select the algorithm you would like to use to calibrate your model. For the MM you can choose only one algorithm to run your experiment. Don't know which one to select? You can read about each algorithm here.
    Note: Each subset will be modelled with the same algorithm configuration settings

  • (optional) Configuration:

  • If you want to change the pseudo-absence/background selection settings for a particular algorithm, you can do that here. If you change nothing, the settings from the Absence tab will be used for all selected algorithms. 
  • Other configuration options are available for most algorithms. These options can be changed by changing the value or making a different selection from the drop down menu. The configuration options are currently set to the standard default values of the R packages. More information about each configuration option can be found on the support page for that particular algorithm.
  • Click “Next” on the bottom of the page.

Step 7: Run tab

  • Ensure you are happy with your experiment design.

  • If none of the tabs have a triangle with an exclamation mark, your experiment is ready to go.

  • Click “Start Experiment”.

  • If any of your tabs have a triangle with an exclamation mark, revisit them and ensure you have filled in each component correctly.


A log file will now be sent to our virtual machines where your experiment will be run. 

You can view the progress of your job under “My job”. Once your job is finished you can view the results by either clicking “View all results” inside your job or click on the “My results” tab under Workspace. 

For now, sit back and relax, grab a coffee, or do some other work without being hampered by a slower computer that is running heavy models in the background.