Estimating the sustainability of towed fishing-gear impacts on seabed habitats: a simple quantitative risk assessment method applicable to data-limited fisheries

1. Impacts of bottom fishing, particularly trawling and dredging, on seabed (benthic) habitats are commonly perceived to pose serious environmental risks. Quantitative ecological risk assessment can be used to evaluate actual risks and to help guide the choice of management measures needed to meet sustainability objectives. 2. We develop and apply a quantitative method for assessing the risks to benthic habitats by towed bottom-fishing gears. The meth od is based on a simple eq uation for relative benthic status (RBS), derived by solving the logistic population growth equation for the equilibrium state. Estimating RBS requires only maps of fishing intensity and habitat type – and parameters for impact and recovery rates, which may be taken from meta-analyses of multiple experimental studies of towed-gear impacts. The aggregate status of habitats in an assessed region is indicated by the distribution of RBS values for the region. The application of RBS is illustrated for a tropical shrimp-trawl fishery. 3. The status of trawled habitats and their RBS value depend on impact rate (depletion per trawl), recovery rate and exposure to tra wling. In the shrimp-trawl fishery region, gravel habitat was most sensitive, and though less exposed than sand or mudd y-sand, was most affected overall (regional RBS = 91% relative to un-trawled RBS = 100%). Muddy-sand was less sensitive, and though relatively most exposed, was less affected overall (RBS = 95%). Sand was most heavily trawled but least sensitive and least affected overall (RBS = 98%). Region-wide , >94% of habitat area had >80% RBS because most tra wling and impacts were confined to small areas. RBS was also applied to the region’s benthic invertebrate communities with similar results. 4. Conclu sions. Unlike qualitative or categorical trait-based risk assessments, the RBS method provides a quantitative estimate of status relative to an unimpacted baseline, with minimal requireme nts for input data. It could be applied to bottom