EU-Aktion zur Verbesserung der wissenschaftlichen Basis für Bestandsabschätzungen und -vorhersagen

Im Rahmen der Bewirtschaftung der Fischressourcen des Nordatlantiks werden für alle Nutzfischarten jährlich Bestandsgrößen und Nachwuchsraten erarbeitet, die Grundlagen für die Festlegung künftiger Fangmengen dieser Arten sind. Da in diese Berechnungen Daten Unterschiedlicher Zuverlässigkeit eingehen und den Berechnungsmethoden verschiedene Voraussetzungen zugrunde liegen, bestehen Unsicherheiten in der Präzision der Vorhersagen. Deshalb werden die benutzten Modelle und ihre Ergebnisse laufend kritisch überprüft und weiterentwickelt. Aufgrund der Einschränkungen der bisher benutzten Methoden und den Wünschen der Fischwirtschaft, Vorhersagen für Bestandsentwicklungen für mindestens 5 Jahre zu entwickeln, startete einer Reihe europäischer Fischerei- bzw. Meeresforschungsinstitute eine gemeinsame Aktion mit dem Ziel, die wissenschaftliche Basis für Bestandsabschätzungen und -vorhersagen zu verbessern. Diese "ConcertedAction: Sustainable fisheries. How can the scientific basis for fish stock assessments and predictions be improved? (SAP)" wird vom Generaldirektor für Fischerei der Kommission der Europäischen Gemeinschaft als Projekt FAIR CT 97-3805 seit dem 01.01.1998 für 40 Monate gefördert.

Establishing species–habitat associations for 4 eteline snappers with the use of a baited stereo-video camera system

With the use of a baited stereo-video camera system, this study semiquantitatively defined the habitat associations of 4 species of Lutjanidae: Opakapaka (Pristipomoides filamentosus), Kalekale (P. sieboldii), Onaga (Etelis coruscans), and Ehu (E. carbunculus). Fish abundance and length data from 6 locations in the main Hawaiian Islands were evaluated for species-specific and size-specific differences between regions and habitat types. Multibeam bathymetry and backscatter were used to classify habitats into 4 types on the basis of substrate (hard or soft) and slope (high or low). Depth was a major influence on bottomfish distributions. Opakapaka occurred at depths shallower than the depths at which other species were observed, and this species showed an ontogenetic shift to deeper water with increasing size. Opakapaka and Ehu had an overall preference for hard substrate with low slope (hard-low), and Onaga was found over both hard-low and hard-high habitats. No significant habitat preferences were recorded for Kalekale. Opakapaka, Kalekale, and Onaga exhibited size-related shifts with habitat type. A move into hard-high environments with increasing size was evident for Opakapaka and Kalekale. Onaga was seen predominantly in hard-low habitats at smaller sizes and in either hard-low or hard-high at larger sizes. These ontogenetic habitat shifts could be driven by reproductive triggers because they roughly coincided with the length at sexual maturity of each species. However, further studies are required to determine causality. No ontogenetic shifts were seen for Ehu, but only a limited number of juveniles were observed. Regional variations in abundance and length were also found and could be related to fishing pressure or large-scale habitat features.

Multiscale analysis of factors that affect the distribution of sharks throughout the northern Gulf of Mexico

Identification of the spatial scale at which marine communities are organized is critical to proper management, yet this is particularly difficult to determine for highly migratory species like sharks. We used shark catch data collected during 2006–09 from fishery-independent bottom-longline surveys, as well as biotic and abiotic explanatory data to identify the factors that affect the distribution of coastal sharks at 2 spatial scales in the northern Gulf of Mexico. Centered principal component analyses (PCAs) were used to visualize the patterns that characterize shark distributions at small (Alabama and Mississippi coast) and large (northern Gulf of Mexico) spatial scales. Environmental data on temperature, salinity, dissolved oxygen (DO), depth, fish and crustacean biomass, and chlorophyll-a (chl-a) concentration were analyzed with normed PCAs at both spatial scales. The relationships between values of shark catch per unit of effort (CPUE) and environmental factors were then analyzed at each scale with co-inertia analysis (COIA). Results from COIA indicated that the degree of agreement between the structure of the environmental and shark data sets was relatively higher at the small spatial scale than at the large one. CPUE of Blacktip Shark (Carcharhinus limbatus) was related positively with crustacean biomass at both spatial scales. Similarly, CPUE of Atlantic Sharpnose Shark (Rhizoprionodon terraenovae) was related positively with chl-a concentration and negatively with DO at both spatial scales. Conversely, distribution of Blacknose Shark (C. acronotus) displayed a contrasting relationship with depth at the 2 scales considered. Our results indicate that the factors influencing the distribution of sharks in the northern Gulf of Mexico are species specific but generally transcend the spatial boundaries used in our analyses.

Shallow-water benthic habitats of St. John, U.S. Virgin Islands

Coral reef ecosystems of the Virgin Islands Coral Reef National Monument, Virgin Islands National Park and the surrounding waters of St. John, U.S. Virgin Islands are a precious natural resource worthy of special protection and conservation. The mosaic of habitats including coral reefs, seagrasses and mangroves, are home to a diversity of marine organisms. These benthic habitats and their associated inhabitants provide many important ecosystem services to the community of St. John, such as fishing, tourism and shoreline protection. However, coral reef ecosystems throughout the U.S. Caribbean are under increasing pressure from environmental and anthropogenic stressors that threaten to destroy the natural heritage of these marine habitats. Mapping of benthic habitats is an integral component of any effective ecosystem-based management approach. Through the implementation of a multi-year interagency agreement, NOAA’s Center for Coastal Monitoring and Assessment - Biogeography Branch and the U.S. National Park Service (NPS) have completed benthic habitat mapping, field validation and accuracy assessment of maps for the nearshore marine environment of St. John. This work is an expansion of ongoing mapping and monitoring efforts conducted by NOAA and NPS in the U.S. Caribbean and replaces previous NOAA maps generated by Kendall et al. (2001) for the waters around St. John. The use of standardized protocols enables the condition of the coral reef ecosystems around St. John to be evaluated in context to the rest of the Virgin Island Territories and other U.S. coral ecosystems. The products from this effort provide an accurate assessment of the abundance and distribution of marine habitats surrounding St. John to support more effective management and conservation of ocean resources within the National Park system. This report documents the entire process of benthic habitat mapping in St. John. Chapter 1 provides a description of the benthic habitat classification scheme used to categorize the different habitats existing in the nearshore environment. Chapter 2 describes the steps required to create a benthic habitat map from visual interpretation of remotely sensed imagery. Chapter 3 details the process of accuracy assessment and reports on the thematic accuracy of the final maps. Finally, Chapter 4 is a summary of the basic map content and compares the new maps to a previous NOAA effort. Benthic habitat maps of the nearshore marine environment of St. John, U.S. Virgin Islands were created by visual interpretation of remotely sensed imagery. Overhead imagery, including color orthophotography and IKONOS satellite imagery, proved to be an excellent source from which to visually interpret the location, extent and attributes of marine habitats. NOAA scientists were able to accurately and reliably delineate the boundaries of features on digital imagery using a Geographic Information System (GIS) and fi eld investigations. The St. John habitat classification scheme defined benthic communities on the basis of four primary coral reef ecosystem attributes: 1) broad geographic zone, 2) geomorphological structure type, 3) dominant biological cover, and 4) degree of live coral cover. Every feature in the benthic habitat map was assigned a designation at each level of the scheme. The ability to apply any component of this scheme was dependent on being able to identify and delineate a given feature in remotely sensed imagery.

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.