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
Spatial management of vulnerable benthic ecosystem components such as deep-sea corals and sponges requires adequate maps of their distribution. These maps are often based on statistical models of survey data. The objective of this project was to validate the predictions of existing presence or absence and abundance models of deep-sea corals and sponges in the Aleutian Islands that were based on bottom trawl survey data. Model validation was conducted by comparing bottom trawl survey model predictions to the observations of an in situ camera survey conducted at randomly selected locations. The measures of goodness of fit (area-under-the-receiver-operator-curve, AUC) for the bottom trawl survey model predictions of camera survey observations ranged from 0.59 to 0.77 (for sponges and coral, respectively) and indicated that the bottom trawl survey models predicted the probability of presence for corals accurately across the Aleutian Islands. The bottom trawl survey models explained as little as 3% of the variability in Stylasteridae density and up to 17% of the variability in coral density. These results indicate that models of deep-sea coral distributions based on presence and absence data from bottom trawl surveys can be accurate and can provide useful information for spatial management of these vulnerable taxa. However, for some other taxa, such as sponges, care should be taken interpreting the results of bottom trawl survey models. An interesting finding of this study was that the residuals from the bottom trawl survey model-camera density relationships were negative in areas that remained open to fishing after 2005, possibly indicating an effect of continued bottom trawling on the abundance of corals in these areas. This study highlights the importance of validating models of species distribution using independent surveys, so that the results can be used with confidence to support decision-making processes.
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