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
To study family-specific variation in the survival of pink salmon Oncorhynchus gorbuscha, we partitioned family size into four life history divisions: (1) maternal fecundity, (2) deposition of fertilized eggs and egg loss from the redd, (3) freshwater survival (and male potency), and (4) marine survival. We directly measured the variability in fecundity and then measured the family-specific variability of freshwater survival in several Alaskan hatchery populations. Next, we measured freshwater survival in spatially clustered groups of wild pink salmon (not identified to a specific dam or sire) in Prince William Sound, Alaska. Drawing on estimates of the family-specific variation of marine survival in pink salmon from previous studies, we concluded that family-specific egg deposition processes and family-specific variability in the marine environment were the primary sources of the overall variability in pink salmon family size, at least in the populations studied. We hypothesize that the freshwater environment generally induces lower variability in family size than does the marine environment. If this is so, it appears that pink salmon populations are more finely adapted to the freshwater environment, presumably because this environment is more constant. Finally, we speculate that the marine environment is too unpredictable to permit the same level of adaptation of many traits closely linked to marine survival.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.