The effectiveness of marine reserve systems constructed using different surrogates of biodiversity.

Biological sampling in marine systems is often limited, and the cost of acquiring new data is high. We sought to assess whether systematic reserves designed using abiotic domains adequately conserve a comprehensive range of species in a tropical marine inter-reef system. We based our assessment on data from the Great Barrier Reef, Australia. We designed reserve systems aiming to conserve 30% of each species based on 4 abiotic surrogate types (abiotic domains; weighted abiotic domains; pre-defined bioregions; and random selection of areas). We evaluated each surrogate in scenarios with and without cost (cost to fishery) and clumping (size of conservation area) constraints. To measure the efficacy of each reserve system for conservation purposes, we evaluated how well 842 species collected at 1155 sites across the Great Barrier Reef seabed were represented in each reserve system. When reserve design included both cost and clumping constraints, the mean proportion of species reaching the conservation target was 20-27% higher for reserve systems that were biologically informed than reserves designed using unweighted environmental data. All domains performed substantially better than random, except when there were no spatial or economic constraints placed on the system design. Under the scenario with no constraints, the mean proportion of species reaching the conservation target ranged from 98.5% to 99.99% across all surrogate domains, whereas the range was 90-96% across all domains when both cost and clumping were considered. This proportion did not change considerably between scenarios where one constraint was imposed and scenarios where both cost and clumping constraints were considered. We conclude that representative reserve systems can be designed using abiotic domains; however, there are substantial benefits if some biological information is incorporated.

Biological surrogacy in tropical seabed assemblages fails.

Surrogate taxa are used widely to represent attributes of other taxa for which data are sparse or absent. Because surveying and monitoring marine biodiversity is resource intensive, our understanding and management of marine systems will need to rely on the availability of effective surrogates. The ability of any marine taxon to adequately represent another, however, is largely unknown because there are rarely sufficient data for multiple taxa in the same region(s). Here, we defined a taxonomic group to be a surrogate for another taxonomic group if they possessed similar assemblage patterns. We investigated effects on surrogate performance of (1) grouping species by taxon at various levels of resolution, (2) selective removal of rare species from analysis, and (3) the number of clusters used to define assemblages, using samples for 11 phyla distributed across 1189 sites sampled from the seabed of Australia's Great Barrier Reef. This spatially and taxonomically comprehensive data set provided an opportunity for extensive testing of surrogate performance in a tropical marine system using these three approaches for the first time, as resource and data constraints were previously limiting. We measured surrogate performance as to how similarly sampling sites were divided into assemblages between taxa. For each taxonomic group independently, we grouped sites into assemblages using Hellinger distances and medoid clustering. We then used a similarity index to quantify the concordance of assemblages between all pairs of taxonomic groups. Surrogates performed better when taxa were grouped at a phylum level, compared to taxa grouped at a finer taxonomic resolution, and were unaffected by the exclusion of spatially rare species. Mean surrogate performance increased as the number of clusters decreased. Moreover, no taxonomic group was a particularly good surrogate for any other, suggesting that the use of any one (or few) group(s) for mapping seabed biodiversity patterns is imprudent; sampling several taxonomic groups appears to be essential for understanding tropical/subtropical seabed communities. Consequently, where resource constraints do not allow complete surveying of biodiversity, it may be preferable to exclude rare species to allow investment in a broader range of taxonomic groups.