Classifying benthic biotopes on sub-tropical continental shelf reefs: How useful are abiotic surrogates?

• Surveyed benthic taxa on rocky reef habitats from 30 to 70 m.
• Five distinct benthic biotopes were defined and described.
• Patterns of benthic assemblage structure were compared to abiotic surrogates.
• Best abiotic surrogates explained 42% of variation in assemblage structure.
• Results show the need to establish links when using abiotics for conservation planning.

Biodiversity of marine areas beyond the reach of conventional diving technology (>30 m) is poorly known, yet subjected to increasing stresses from expanding recreational and commercial fishing, minerals exploration and other anthropogenic influences. In part, resource managers address this by using abiotic surrogates for patterns of biodiversity in planning marine protected areas or other management measures. However, the efficacy of these surrogates varies from place to place, and is often not quantified at the scale used by MPA designers and managers. This study surveyed and classified benthic assemblages of continental shelf rocky reefs across three depth categories from 30 to 70 m, using a suspended HD camera array, which is both non-destructive and cost-effective compared to any other methods of sampling at these depths. Five distinct benthic biotopes were defined, characterised primarily by variations in abundances of sea whips, sponges, kelp, and urchins. Derived patterns of benthic assemblage structure were compared to abiotic surrogates available at the scale (local) used in MPA planning. The individual factors with most influence on the classification were recreational fishing pressure, water temperature at the bottom, and distance from nearest estuary. The best combination of abiotic surrogates had a relatively strong relationship with the benthic assemblage, explaining 42% of the variation in assemblage structure (BIOENV ρ = 0.65), however the performance of a classification based on commonly used physical surrogates was relatively poor, explaining only 22% of variation. The results underline the limitations of using abiotic variables for habitat mapping at the local scale, and the need for robust surveys to quantify patterns of biodiversity.