Predictive modelling of the benthic habitats in five turbid and shallow estuaries along the south-west coast of Australia  

Authors / CoAuthors

Tran, M. | Anderson, T.J. | Booth, D. | Li, J.

Abstract

Identifying the spatial distribution and extent of biotic habitats in estuaries is important in managing our natural resources. Although a number of remote-sensing habitat distribution mapping methods have been successful in clear, shallow-water coastal environments and deeper marine environments, these methods fail in highly turbid and shallow estuarine environments. In this study, we used underwater video to record percent cover of biotic habitats of seagrasses, green and red macroalgae, polychaete mounds and mussel clumps in five estuaries on the south-west coast of Western Australia. Random Forest (RF) was used to model, predict and map their habitats across the estuaries based on the video data and spatially continuous environmental variables. RF is an ensemble, machine-learning method that is robust and able to handle non-linear relationships frequently encountered with ecological data. RF is often used within terrestrial ecosystems but less so in marine ecosystems (e.g. predicting seabed sediment and hardness for marine habitats). The results of this study show that RF models performed well for predictions of biotic habitats, with 79-90% of variation explained by depth, latitude, longitude and water quality variables. This study confirms that RF also has important applications in estuaries, with results refining existing baseline maps and highlighting the importance of biophysical processes driving plant and invertebrate species distribution. Underwater video and RF modelling offer a valuable approach to mapping the spatial distribution of highly turbid biotic habitats.

Product Type

nonGeographicDataset

eCat Id

75824

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Keywords

• External PublicationAbstract

( Theme )

• environmental

( Theme )

• marine

Australian and New Zealand Standard Research Classification (ANZSRC)

• Ecological Applications

• Published_Internal

Publication Date

2013-01-01T00:00:00

Creation Date

Security Constraints

Australian Government Security ClassificationSystem    

Classification - unclassified

Legal Constraints

Creative Commons Attribution 4.0 International Licence    

Access - restricted

Use - license

Status

Purpose

Maintenance Information

notPlanned

Topic Category

biota

Series Information

Lineage

Unknown

Parent Information

Extents

Reference System

Spatial Resolution

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Source Information

This abstract will be submitted to the International Congress of MODelling and SIMulation (MODSIM) conference 2013. This abstract will be submitted to the International Congress of MODelling and SIMulation (MODSIM) conference 2013.