Katrina Baxter University of Melbourne Supervision by Dr Mark Shortis

Classification and Prediction of Marine Habitats: the use of rule based learning to optimise field research Katrina Baxter University of Melbourne Supervision by Dr Mark Shortis

Optimising habitat mapping The marine environment is large and difficult to sample. There are many different survey methodsThere are a variety of habitats and many factors defining those habitats.Where do we start?

Study Site “Characterising the Fish Habitatsof the Recherché Archipelago”

Recherché Map

Opportunities to Predict Optimal methods of accurately classifying and also predicting where we find different marine habitats are required.Opportunities exist to integrate a number of techniques to achieve this, including GIS, remote sensing and rule based learning .

Outline • Videohabitat survey. • Results from initial rule based classification and prediction – a decision tree approach • Future work using remote sensing, GIS and spatial rule based classification approaches • Relevance of these approaches in guiding marine research, reserve planning and management.

Video Survey Broad scale “drop camera” habitat survey • Depth • Relief • Substrate • Exposure • Dominant Habitat • Presence/Absence data

Drop camera video survey

Rule based learning • Advantages of using decision trees to classify habitats • ability to learn and predict • ability to generalise • can use either continuous or categorical variables • results easily interpretable in form of if-then rules or tree format

Decision tree rules

Prediction results Legend.■ – accurately classified; ■ - misclassified; ■ - mixed sponge/ macroalgal habitat classified as macroalgae.

Spatial habitat classification & prediction Genetic algorithms also derive classification rules but allow us to undertake broader predictions of habitat type in a spatial context Side scan and Landsat can broadly define habitat types for use in a spatial model. Other data layers in the model may include: • Bathymetry • Exposure • Substrate • Wave energy

Landsat

Targeting data collection • Initial decision tree rules are being used to guide current sidescan data collection • - in deeper waters >30m. • in combination with interpretation of Landsat in shallow waters <30m

Management Implications Benefits of a Spatial Predictive Approach • Allow us to classify representative habitat types • Allows us to target field sampling - saves $$$ • Is managed within a GIS framework to assist management and effective long term monitoring • Provides increased understanding of the factors defining habitat types • We can assign measures of accuracy or reliability to the habitats we define • Allows us to plan reserves effectively

http:// www.marine.uwa.edu.au/recherche

Katrina Baxter University of Melbourne Supervision by Dr Mark Shortis