Status of coral reef ecosystems in a marine managed area in St. Croix, USVI [poster]

This poster presents information on the status and trends of coral reef ecosystems in St. Croix, US Virgin Islands (USVI). Data were collected by NOAA’s Center for Coastal Monitoring and Assessment Biogeography Branch (CCMA-BB) from 2001-2006 at 1,275 random locations in and around Buck Island Reef National Monument (BIRNM). The main objective was to quantify changes in fish species and assemblage diversity, abundance, biomass and size structure; to provide spatially explicit information on the distribution of key species or groups of species; and to compare community structure inside versus outside of BIRNM.

Quantifying annual variation in catchability for commercial and research fishing

Biomass indices, from commercial catch per unit of effort (CPUE) or random trawl surveys, are commonly used in fisheries stock assessments. Uncertainty in such indices, often ex-pressed as a coefficient of variation (CV), has two components: observation error, and annual variation in catchability. Only the former can be estimated directly. As a result, the CVs used for these indices either ignore the annual-variation component or assume a value for it (often implicitly). Two types of data for New Zealand stocks were examined: 48 sets of residuals and catchability estimates from stock assessments using either CPUE or trawl survey indices; and biomass estimates from 17 time series of trawl surveys with between 4 and 25 species per time series. These data show clear evidence of significant annual variation in catchability. With the trawl survey data, catchability was detectably extreme for many species in about one year in six. The assessment data suggest that this annual variability typically has a CV of about 0.2. For commercial CPUE the variability is slightly less, and a typical total CV (including both components) of 0.15 to 0.2. This is much less than the values of 0.3 to 0.35 that have commonly been assumed in New Zealand. Some estimates of catchability are shown to be implausible.

Applications in adaptive cluster sampling of Gulf of Alaska rockfish

Adaptive cluster sampling (ACS) has been the subject of many publications about sampling aggregated populations. Choosing the criterion value that invokes ACS remains problematic. We address this problem using data from a June 1999 ACS survey for rockfish, specifically for Pacific ocean perch (Sebastes alutus), and for shortraker (S. borealis) and rougheye (S. aleutianus) rockfish combined. Our hypotheses were that ACS would outperform simple random sampling (SRS) for S. alutus and would be more applicable for S. alutus than for S. borealis and S. aleutianus combined because populations of S. alutus are thought to be more aggregated. Three alternatives for choosing a criterion value were investigated. We chose the strategy that yielded the lowest criterion value and simulated the higher criterion values with the data after the survey. Systematic random sampling was conducted across the whole area to determine the lowest criterion value, and then a new systematic random sample was taken with adaptive sampling around each tow that exceeded the fixed criterion value. ACS yielded gains in precision (SE) over SRS. Bootstrapping showed that the distribution of an ACS estimator is approximately normal, whereas the SRS sampling distribution is skewed and bimodal. Simulation showed that a higher criterion value results in substantially less adaptive sampling with little tradeoff in precision. When time-efficiency was examined, ACS quickly added more samples, but sampling edge units caused this efficiency to be lessened, and the gain in efficiency did not measurably affect our conclusions. ACS for S. alutus should be incorporated with a fixed criterion value equal to the top quartile of previously collected survey data. The second hypothesis was confirmed because ACS did not prove to be more effective for S. borealis-S. aleutianus. Overall, our ACS results were not as optimistic as those previously published in the literature, and indicate the need for further study of this sampling method.

Report of the pilot EAFM course, 23-28 June, 2013, Kota Kinabalu, Malaysia

Participants were exposed to concepts and information about EAFM using a structured, participatory method of delivery. The learning strategy involved pre- and post-course assessment as well as 4-6 month follow up at outcome level to assess improves EAFM practices. The report outlines methodology details of content and the daily feedback process.

Training of Trainers (ToT) in essential EAFM, 30 June - 3 July, 2013, Kota Kinabalu, Malaysia

This built upon the previous Essential EAFM course. Participants were exposed to concepts about adult learning, course preparation,, participation engagement and how to deliver and manage interactive training sessions.