Ecosystem management requires information to determine and mitigate adverse impacts of fishing on all ecosystem components. Deep-sea coral and sponge ecosystems often co-occur with fishing activities, and there is considerable research documenting the vulnerability and slow recovery of deep-sea coral and sponge communities to damage. The objective of the present analysis was to construct models that could predict the distribution, abundance and diversity of deep sea corals and sponges in the Aleutian Islands. Generalized additive models were constructed based on bottom trawl survey data collected from 1991 to 2011 and tested on data from 2012. The results showed that deep-sea coral and sponge distributions were strongly influenced by the maximum tidal currents at bottom trawl locations, possibly indicative of reduced sedimentation or increased food-delivery processes near the seafloor in areas of moderate to high current. Depth and location were also important factors affecting the distribution of deep-sea sponges and corals. The analysis resulted in acceptable models of presence or absence for all taxonomic groups and similar fits when models were applied to test data. The best-fitting models of abundance explained between 20 and 25% of the deviance in the abundance data. Current management protects ~50% of the coral and sponge habitat in the Aleutian Islands at depths to 500 m. The models constructed here will allow managers to evaluate ecological versus economic benefits between protecting coral and sponge habitat and allowing commercial fishing by examining the effect of spatial closures on the amount of coral and sponge habitat that is protected.

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Describing essential habitat is an important step toward understanding and conserving harvested species in ecosystem-based fishery management. Using data from fishery-independent ichthyoplankton, groundfish surveys, and commercial fisheries observer data, we utilized species distribution modeling techniques to predict habitat-based spatial distributions of federally managed species in Alaska. The distribution and abundance maps were used to refine existing essential fish habitat descriptions for the region. In particular, we used maximum entropy and generalized additive modeling to delineate distribution and abundance of early (egg, larval, and pelagic juvenile) and later (settled juvenile and adult) life history stages of groundfishes and crabs across multiple seasons in three large marine ecosystems (Gulf of Alaska, eastern Bering Sea, and Aleutian Islands) and the northern Bering Sea. We present a case study, featuring Kamchatka flounder (Atheresthes evermanni), from the eastern and northern Bering Sea to represent the >400 habitat-based distribution maps generated for more than 80 unique species–region–season–life-stage combinations. The results of these studies will be used to redescribe essential habitat of federally managed fishes and crabs in Alaska.

Projecting the future distributions of commercially and ecologically important species has become a critical approach for ecosystem managers to strategically anticipate change, but large uncertainties in projections limit climate adaptation planning.Although distribution projections are primarily used to understand the scope of potential change-rather than accurately predict specific outcomes-it is nonetheless essential to understand where and why projections can give implausible results and to identify which processes contribute to uncertainty. Here, we use a series of simulated species distributions, an ensemble of 252 species distribution models, and an ensemble of three regional ocean climate projections, to isolate the influences of uncertainty | 6587 BRODIE et al.

Recruitment for many marine fishes is believed to be determined at an early life history stage. Pacific herring (Clupea pallasi) spawn in the intertidal and shallow subtidal zones and have a demersal egg stage that is susceptible to egg removals during incubation. Data were collected by the Alaska Department of Fish and Game in four years in Prince William Sound, Alaska, to identify important factors contributing to egg removals. We constructed analysis of variance models based on physical and biological variables to determine which environmental factors control egg loss rates. The habitat variables examined at each study transect were depth, wave exposure, north-south location, substrate, vegetation, mean bird abundance, abundance of loose eggs, and fish predation. Depth of spawn was the primary factor determining egg loss. Cumulative time of air exposure over incubation was substituted into the model for depth. Using the model, the total estimated egg loss from spawning to hatching ranged from 67 to 100% with an average of 75% (SE = 3.3%) in 1995. Eggs were originally deposited from 4 to -6 m depth relative to mean low water. The majority of eggs that remained in the spawning beds to hatching were deposited from 1 to -4 m depth. Egg removals due to avian predation were probably responsible for extreme egg loss rates at shallow depths.

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For those marine fish species with specific habitat preferences, a habitat-based assessment may provide an alternative to traditional surveys. We conducted a habitat-based acoustic and stereo image stock assessment survey for rockfishes (Sebastes spp.) on a rocky ridge habitat in the eastern Bering Sea. Video analysis suggested that juvenile and adult rockfishes were more abundant on the seafloor in the rocky ridge area than on the surrounding sandy flats. Over the ridges, the distribution of rockfishes was uniformly low in the water column during nighttime surveys and higher during daytime surveys. The opposite pattern was observed in the video on the seafloor between night (high density) and day (lower density), indicating that fish in the water column during the day moved to the seafloor at night. Mean biomass of adult rockfishes for the rocky ridges was 1.54 Â 10 4 tonnes based on acoustic data. The biomass of juvenile fish was estimated to be 9.2 Â 10 2 tonnes. Utilization of similar survey methodologies on a larger scale might improve assessment of rockfishes not only in Alaska, but also throughout their range where fishery-independent biomass estimates have been difficult to obtain.Résumé : Chez les espèces de poissons marins qui possèdent des préférences spécifiques d'habitat, une évaluation basée sur l'habitat pourrait être une méthode de rechange aux inventaires traditionnels. Nous avons fait un inventaire acoustique et vidéo d'évaluation des stocks des sébastes (Sebastes spp.) basé sur l'habitat dans un milieu de crête rocheuse dans l'est de la mer de Béring. L'analyse vidéo indique que les sébastes jeunes et adultes sont plus abondants sur le fond marin dans la région de la crête rocheuse que sur les plats sablonneux adjacents. Sur les crêtes, la répartition des sébastes est uniformément basse dans la colonne d'eau durant les inventaires de nuit et plus élevée durant les inventaires de jour. Un patron contraire s'observe par vidéo sur le fond marin entre les inventaires de nuit (densité élevée) et de jour (densité basse), ce qui indique que les poissons qui sont dans la colonne d'eau durant le jour se déplacent vers le fond marin la nuit. La biomasse moyenne des sébastes adultes sur les crêtes rocheuses est de 1,54 Â 10 4 tonnes d'après les données acoustiques. La biomasse des jeunes poissons est estimée à 9,2 Â 10 2 tonnes. L'utilisation de méthodes semblables d'inventaire sur une plus grande échelle pourrait améliorer l'évaluation des sébastes, non seulement en Alaska, mais aussi sur toute leur aire de répartition dans laquelle des estimations des biomasses indépendantes de la pêche sont difficiles à obtenir.[Traduit par la Rédaction]

Supplement 1. Stereo drop camera descriptionThe two stereo drop camera systems were comprised of two machine-vision cameras spaced approximately 30 cm apart in underwater housings that were connected via ethernet cables to a computer also in an underwater housing. On the first drop-camera system, one of the paired cameras recorded monochromatic still images sized at 1.45 megapixels (JAI, CM-140GE), while the other camera collected 1.73 megapixel color still images (JAI, AB-201GE). On the second drop-camera system, one of the paired cameras recorded monochromatic still images sized at 1.45 megapixels (JAI, CM-140GE), while the other camera collected 2.82 megapixel color still images (Prosilica GX 1920C). Lighting was provided by four strobe lights constructed of four Bridgelux® BXRA LED arrays capable of producing 1,300 lumens at 10.4 W. The computer, cameras, and lights were powered by a 28 V NiMH battery pack. Synchronous images were collected and recorded from each of the cameras at a frequency of one image per second. Each of the systems was enclosed in an aluminum cage to protect the components from damage. Additionally, a 1/4 inch diameter coaxial cable provided a connection from the drop-camera system to the winch at the surface, allowing images from the monochrome camera were viewed in real time at a rate of four images per second. This allowed the height of the camera to be actively controlled to keep it just above the seafloor using a quick response electric winch. Supplement 2. Distribution modeling of structure forming invertebrates and cross-validation using bottom trawl survey and camera survey data Bottom trawl survey modelsThe initial distribution modeling was carried out using bottom trawl survey data collected on the NOAA Fisheries, Alaska Fisheries Science Center, eastern Bering Sea outer shelf and slope surveys from 2002 to 2012 and was reported in Sigler et al. (2015). Briefly, the invertebrate distributions were predicted using generalized additive models (GAM) to determine the relationships between environmental variables (latitude*longitude, depth, slope, long-term average bottom temperature, ocean color, mean current speed, maximum tidal current speed, sediment grain size and sediment sorting which is the standard deviation of grain size) and observations of presence in bottom trawl survey catches for each structure-forming invertebrate group. All modeling was carried out in R software using the mgcv package (Wood 2006) and diagnostics were performed using the PresenceAbsence package. A binomial distribution was used to model presence or absence data and backwards term selection was employed so that the full model including all variables was fit first and