Multi-scale marine biodiversity patterns inferred efficiently from habitat image processing

Authors

  • Camille Mellin,

    Corresponding author
    1. Australian Institute of Marine Science, PMB No. 3, Townsville MC, Townsville, Queensland 4810 Australia
    2. Environment Institute and School of Earth and Environmental Sciences, University of Adelaide, South Australia 5005 Australia
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  • Lael Parrott,

    1. Complex Systems Laboratory, Department of Geography, University of Montreal, C.P. 6128 Succursale Centre-Ville, Montreal, Quebec H3C 3J7 Canada
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  • Serge Andréfouët,

    1. Institut de Recherche pour le Développement, UR 227 COREUS 2, BP A5, 98848 Nouméa, New Caledonia
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  • Corey J. A. Bradshaw,

    1. Environment Institute and School of Earth and Environmental Sciences, University of Adelaide, South Australia 5005 Australia
    2. South Australian Research and Development Institute, P.O. Box 120, Henley Beach, South Australia 5022 Australia
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  • M. Aaron MacNeil,

    1. Australian Institute of Marine Science, PMB No. 3, Townsville MC, Townsville, Queensland 4810 Australia
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  • M. Julian Caley

    1. Australian Institute of Marine Science, PMB No. 3, Townsville MC, Townsville, Queensland 4810 Australia
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Abstract

Cost-effective proxies of biodiversity and species abundance, applicable across a range of spatial scales, are needed for setting conservation priorities and planning action. We outline a rapid, efficient, and low-cost measure of spectral signal from digital habitat images that, being an effective proxy for habitat complexity, correlates with species diversity and requires little image processing or interpretation. We validated this method for coral reefs of the Great Barrier Reef (GBR), Australia, across a range of spatial scales (1 m to 10 km), using digital photographs of benthic communities at the transect scale and high-resolution Landsat satellite images at the reef scale. We calculated an index of image-derived spatial heterogeneity, the mean information gain (MIG), for each scale and related it to univariate (species richness and total abundance summed across species) and multivariate (species abundance matrix) measures of fish community structure, using two techniques that account for the hierarchical structure of the data: hierarchical (mixed-effect) linear models and distance-based partial redundancy analysis. Over the length and breadth of the GBR, MIG alone explained up to 29% of deviance in fish species richness, 33% in total fish abundance, and 25% in fish community structure at multiple scales, thus demonstrating the possibility of easily and rapidly exploiting spatial information contained in digital images to complement existing methods for inferring diversity and abundance patterns among fish communities. Thus, the spectral signal of unprocessed remotely sensed images provides an efficient and low-cost way to optimize the design of surveys used in conservation planning. In data-sparse situations, this simple approach also offers a viable method for rapid assessment of potential local biodiversity, particularly where there is little local capacity in terms of skills or resources for mounting in-depth biodiversity surveys.

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