A temporally and spatially variable Rockfish Conservation Area (RCA) was established as a marine protected area along the US west coast in 2002 to protect stocks of rockfishes Sebastes spp. Since the RCA falls within the region sampled annually by the West Coast Groundfish Bottom Trawl Survey, we utilized data collected from 2003 to 2011 to evaluate whether establishment of the RCA influenced catch per unit effort (CPUE), species richness, and size distribution of demersal fishes. We compared CPUE and species richness among 3 management areas (continuously closed, periodically closed, and open to commercial bottom trawling) using analysis of covariance models that account for variability due to area, year, and depth. The most appropriate models for CPUE (35 species treated individually and aggregated into 6 subgroups) and species richness were selected using Akaike's information criterion. All of the best-fit models were highly significant (p < 0.0001), explaining 3 to 76% of the variation in catch. For 27 species and 5 subgroups, mean CPUE was significantly greater within the area continuously closed to commercial bottom trawling relative to areas periodically closed or open. The most appropriate model for richness included area and year, and mean richness was greatest in the area continuously closed to trawling. Species-specific length composition distributions were calculated from subsampled individual lengths for 31 species. Significant differences in length frequency distributions were observed, with a higher proportion (~65%) of larger fish most often present in areas continuously closed to commercial bottom trawling (20 of 31 species) relative to other areas. Our data suggest that the RCA is an effective management tool for conserving not only rockfishes, but also other demersal fish species.

The Northwest Fisheries Science Center (NWFSC) and the Pacific Islands Fisheries Science Center (PIFSC) are collaborating with researchers at Woods Hole Oceanographic Institution (WHOI) to develop the SeaBED autonomous underwater vehicles (AUV) to overcome the challenges in surveying fish in inaccessible habitats.Traditional survey techniques such as bottom trawls are of limited applicability in such areas due to the rocky, rugose terrain. Fish in marine protected areas must also be surveyed using non-lethal methods. Furthermore, monitoring deeper coral reefs are difficult since many important habitats are below depths that can be surveyed by divers. Hover-capable bottom-tracking AUVs offer a unique tool that is appropriate for work in such areas. We present preliminary results from two surveys: one of deep water corals on a mesophotic coral reef near Guam and another of demersal fishes on rocky reefs off southern California. We discuss some developments needed to utilize this tool for future routine surveys and assessments.

Resource managers in the United States and worldwide are tasked with identifying and mitigating trade-offs between human activities in the deep sea (e.g., fishing, energy development, and mining) and their impacts on habitat-forming invertebrates, including deep-sea corals, and sponges (DSCS). Related management decisions require information about where DSCS occur and in what densities. Species distribution modeling (SDM) provides a cost-effective means of identifying potential DSCS habitat over large areas to inform these management decisions and data collection. Here we describe good practices for DSCS SDM, especially in the context of data collection and management applications. Managers typically need information regarding DSCS encounter probabilities, densities, and sizes, defined at sub-regional to basin-wide scales and validated using subsequent, targeted data collections. To realistically achieve these goals, analysts should integrate available data sources in SDMs including fine-scale visual sampling and broad-scale resource surveys (e.g., fisheries trawl surveys), include environmental predictor variables representing multiple spatial scales, model residual spatial autocorrelation, and quantify prediction uncertainty.Winship et al. Deep-Sea Coral Modeling Good PracticesWhen possible, models fitted to presence-absence and density data are preferred over models fitted only to presence data, which are difficult to validate and can confound estimated probability of occurrence or density with sampling effort. Ensembles of models can provide robust predictions, while multi-species models leverage information across taxa, and facilitate community inference. To facilitate the use of models by managers, predictions should be expressed in units that are widely understood and validated at an appropriate spatial scale using a sampling design that provides strong statistical inference. We present three case studies for the Pacific Ocean that illustrate good practices with respect to data collection, modeling, and validation; these case studies demonstrate it is possible to implement our good practices in real-world settings.