In the Species Trait Experiment you can analyse the effect of environmental variables on 1 or more species traits, and also test how traits differ among multiple species.

How to run an STM in the BCCVL

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

Step 1: Description tab

  • Enter the name for your experiment in the first box. 
  • (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: Algorithms tab

You can run two different trait analyses in the BCCVL:

1. Test the effect of environmental variables on traits: this will run an additive model that will test how the selected species traits are affected by all selected environmental variables (e.g. trait 1 ~ env1 + env2 + env3).

2. Test the differences in traits among species: this will run a model that will test how the selected traits differ among species (e.g. trait 1 ~ species). Note: your input dataset that you select in the next tab will need to include multiple species to be able to run this test.

  • Select the algorithm/s you would like to use to calibrate your model. You can choose one or all to run your experiment.
  • (optional) Configuration options: 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. 

Step 3: Input Datasets tab

  • Select your pre-loaded species trait dataset by clicking the Select A Dataset button. Note: If you click this and you have no loaded species trait 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 STM. Click Select
  • Click Next.

Step 4: Configuration tab

  • The first 5 rows of data will be shown in this tab. You can select which traits and/or environmental variables should be used in the analyses by using the drop down menus under each column. You need to indicate whether your trait data are continuous, nominal (categorical data with no order, such as colour) or ordinal (categorical data with an order, such as cover: low-medium-high), and whether your environmental variables are continuous or categorical. Note: you have to nominate at least a column for latitude, longitude and one trait variable. Environmental variables are optional here, as BCCVL-provided environmental data can be selected in the next tab.

  • Note: Whether or not you select a 'species' column will define whether you run the experiment for individual trait measurements or for species-level trait measurements. Species-level analysis will use the entire dataset as a whole in the analysis. If you want to account for repeated measurements per species, select the species column as random factor and make sure you select a Generalized Linear Mixed Model on the Algorithm tab.
  • Click Next

Step 5: Climate & Environmental Data tab

Note: This tab is optional if your input dataset includes environmental data and you have selected these columns on the Configuration tab. You can choose to add environmental layers from datasets provided by the BCCVL. If you have not selected any environmental variables in the Configuration, this tab is required.

  • 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/or climate layers click Next.

Step 6: Constraints tab

This is an optional tab that allows you to train your model to a particular area (e.g. Queensland only). It means that only the data in the selected constrained area is used in the model. The default constraint is the convex hull (= minimum polygon) around all data points. This convex hull is shown as a blue outline 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. The different constraint options are:

  • Use Convex Hull
    • You can add a buffer around the convex hull by nominating a distance in km and click Add Offset. The buffer will be added on the map.
  • Select constraints by pre-defined region
    • Select one of the region types that are currently available in the BCCVL: Australian States and Territories, Local Government Areas, National Resource Management Regions, 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.
    • Click Add To Map.
    • 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 Draw On 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.

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.

A log file will now be sent to our virtual machines where your experiment will be run. You will receive an email when your results are ready to view. This can be done from the Experiments page. 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.