Project 3. Prediction

Contact Roland Pitcher

The overall objective is to develop models that predict patterns of marine biodiversity, with specifiable precision from more broadly available physical surrogates, at regional and national scales around Australia . More specifically, the Prediction Project will:

• Examine the performance of a number of statistical/mathematical methods for modeling the relationships between biodiversity and the physical environment,
• Examine the extent to which selected taxa (such as fishes) are biological surrogates for other taxa (such as invertebrate groups) and other biodiversity measures (eg. species richness, diversity indices, evenness).
• As a first step, collate suitable biological data that provide that best available match to the broadest range of physical data output from the Surrogates Project.

Strong 3-way links are proposed between the Prediction project and the Surrogates project, and with the Biodiversity project, which will be maintained by cross-membership of key staff in each.

Project Tasks

Biological Data Audit and Acquisition
Scope available biological datasets, select suitable ones, acquire metadata, organise data in relational database, extract data in format suitable for surrogates and prediction analyses (0.01° grid).

Predict Patterns of Continental Shelf Seabed Biodiversity from Physical Surrogates
Develop and test a number of statistical modelling methods at a range of end-user application spatial scales and as appropriate for the scale of the available surrogates, to predict functional assemblage patterns on the Continental shelf. Using data on multiple species derive biodiversity attributes/indices and test for relationship to predicted functional assemblage patterns

Predict Patterns of Continental Slope Biodiversity from Physical Surrogates
See task 2. Task uses existing slope data.

Predict Patterns of Temperate Shallow Reefs from Physical Surrogates
See Task 2. Tasks uses existing wide-ranging shallow temperate reef visual censuses, supplemented with new physical data to develop predictive models.

Predict Patterns of Tropical Coral Reefs from Physical Surrogates
See Task 2. Task will use existing wide-ranging shallow tropical reef visual censuses (Lord Howe, Coral Sea, NWS to the Abrolhos), supplemented with new physical data from the surrogates project.

Identified PhD Projects

• Machine learning and pattern recognition methods for predicting and mapping marine biodiversity
  Contact Matthew Browne

• Relationships between fish population and the physical structure of rocky reefs, including interaction with fishing pressure inside and outside marine protected areas (this project has also been approved for the QMS PhD Program)
  Contact Neville Barrett