Linking seafloor mapping and ecological models to improve classification of marine habitats: opportunities and lessons learnt in the Recherche Archipelago, Western Australia

Katrina Baxter

    Research output: ThesisDoctoral Thesis

    162 Downloads (Pure)

    Abstract

    [Truncated abstract] Spatially explicit marine habitat data is required for effective resource planning and management across large areas, although mapped boundaries typically lack rigour in explaining what factors influence habitat distributions. Accurate, quantitative methods are needed. In this thesis I aimed to assess the utility of ecological models to determine what factors limit the spatial extent of marine habitats. I assessed what types of modeling methods were able to produce the most accurate predictions and what influenced model results. To achieve this, initially a broad scale marine habitat survey was undertaken in the Recherche Archipelago, on the south coast of Western Australia using video and sidescan sonar. Broad and more detailed functional habitats types were mapped for 1054km2 of the Archipelago. Broad habitats included high and low profile reefs, sand, seagrass and extensive rhodolith beds, although considerable variation could be identified from video within these broad types. Different densities of seagrass were identified and reefs were dominated by macroalgae, filter feeder communities, or a combination of both. Geophysical characteristics (depth, substrate, relief) and dominant benthic biota were recorded and then modelled using decision trees and a combination of generalised additive models (GAMs) and generalised linear models (GLMs) to determine the factors influencing broad and functional habitat variation. Models were developed for the entire Archipelago (n=2769) and a subset of data in Esperance Bay (n=797), which included exposure to wave conditions (mean maximum wave height and mean maximum shear stress) calculated from oceanographic models. Additional distance variables from the mainland and islands were also derived and used as model inputs for both datasets. Model performance varied across habitats, with no one method better than the other in terms of overall model accuracy for each habitat type, although prevalent classes (>20%) such as high profile reefs with macroalgae and dense seagrass were the most reliable (Area Under the Curve >0.7). ... This highlighted not only issues of data prevalence, but also how ecological models can be used to test the reliability of classification schemes. Care should be taken when mapping predicted habitat occurrence with broad habitat models. It should not be assumed that all habitats within the type will be defined spatially, as this may result in the distribution of distinctive and unique habitats such as filterfeeders being underestimated or not identified at all. More data is needed to improve prediction of these habitats. Despite the limitations identified, the results provide direction for future field sampling to ensure appropriate variables are sampled and classification schemes are carefully designed to improve descriptions of habitat distributions. Reliable habitat models that make ecological sense will assist future assessments of biodiversity within habitats as well as provide improved data on the probability of habitat occurrence. This data and the methods developed will be a valuable resource for reserve selection models that prioritise sites for management and planning of marine protected areas.
    Original languageEnglish
    QualificationDoctor of Philosophy
    Publication statusUnpublished - 2007

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