On the use of abiotic surrogates to describe marine benthic biodiversity

A growing need to manage marine biodiversity sustainably at local, regional and global scales cannot be met by applying existing biological data. Abiotic surrogates of biodiversity are thus increasingly valuable in filling the gaps in our knowledge of biodiversity patterns, especially identification of hotspots, habitats needed by endangered or commercially valuable species and systems or processes important to the sustained provision of ecosystem services. This review examines the use of abiotic variables as surrogates for patterns in benthic biodiversity with particular regard to how variables are tied to processes affecting species richness and how easily those variables can be measured at scales relevant to resource management decisions. gradient variables such as salinity, oxygen concentration and temperature can be strong predictive variables for larger systems, although local stability of water quality may prevent usefulness of these factors at fine spatial scales. ical productivity has complex relationships with benthic biodiversity and although the development of local and regional models cannot accurately predict outside the range of their biological sampling, remote sensing may provide useful information. Indeed, interpolated values are available for much of the world’s seas, and these are continually being refined by the collection of remote sensing and field data. nt variables often exhibit complex relationships with benthic biodiversity. The strength of the relationship between any one sediment variable and biodiversity may depend on the state of another sediment variable in that system. Percentage mud, percentage gravel, rugosity and compaction hold the strongest independent predictive power. Rugosity and the difference between gravel and finer sediments can be established using acoustic methods, but to quantify grain size and measure compaction, a sample is necessary. patial variables such as latitude, longitude and depth are not direct drivers of biodiversity patterns but often correspond with driving gradients and can be of some use in prediction. In such cases it would be better to identify what the spatial variable is acting as a proxy for so boundaries for that variable are not overlooked. The utility of these potential surrogates vary across spatial scales, quality of data, and management needs. A continued focus on surrogate research will address the need of marine scientists and resource managers worldwide for accurate and robust predictions, extending from simple measures of diversity to species distributions and patterns of assemblage.