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Responses of fish to camera survey gear can influence abundance estimates, and fish behaviors such as attraction or avoidance of an approaching camera platform can bias fish counts. In addition, artificial lighting is generally required for camera-based surveys in boreal systems at > 30 m depth. This study recorded the responses of fish and their apparent orientation as seen in camera images taken in two different areas in Alaska. Stereo-image methods were used to estimate fish movement, range from camera, and the movement of the camera platform itself by estimating the spatial relationship between fish and the seafloor. Change in the apparent position of feature points along the seafloor and the fish was used to derive fish and camera speed. A total of 670 individual fish across 19 near-bottom species were analyzed from a dataset of 3463 image frames from 112 separate camera deployments. Individual fish species were combined into groups and analyzed for reactions, orientation toward the camera, nearest approach to the camera, and swimming speed. Most fish did not react strongly to the camera. A number of distinct patterns in reactions were observed, with roundfish exhibiting the highest level of attraction to the camera and flatfish showing the highest number of strong avoidance reactions. Significant differences in approach distance and fish swimming speed across analysis groups, reactions and regions were found, with highest fish speeds observed with roundfish and closest approach of the camera observed with demersal rockfish and flatfish. The findings demonstrate distinct differences in reactions among groups and suggest that the potential attraction response of roundfish might lead to positive biases for density. Flatfish were reactive mostly at close ranges, and rockfishes exhibited neutral and moderate reactions, which may signal reasonable estimates of abundance for these groups using camera-based surveys. [ABSTRACT FROM AUTHOR]

This paper describes the design and deployment of a new type of underwater stereo camera capable of triggering when animals are present in the field of view. Unobtrusive evaluation of the camera view field for potential targets is achieved using far-red illumination invisible to most fishes. The triggered camera (TrigCam) system is designed to be low cost by incorporating off-the-shelf commercial camera and computer components. It also incorporates several novel software and hardware developments such as the Cannon Hackers Development Kit which provides a high degree of control over the cameras, and a Raspberry Pi computer-on-board module for low-power, cost-efficient computing. The innovative triggering algorithm for fine control of the size and intensity of targets necessary to trigger an image is described in detail. Stereo image analysis provides estimates of fish size, position, and orientation to provide quantitative data from images. Test field deployments demonstrate operational capacity and illustrate potential applications for sampling marine organisms through example descriptions of analytical methods, including the trigger process and stereo image analysis. The TrigCam is intended to be an open source project to encourage continued development within the marine research community, with design information available through internet posts. [ABSTRACT FROM AUTHOR]

Abstract: Trawls are standard tools for surveying fisheries resources, yet they are selective in what they retain, and thus provide potentially misleading information about fish populations. In order to evaluate the potential for selective retention in a midwater survey trawl used in conjunction with acoustic surveys of walleye pollock, fish behavior was examined using an integrated approach of optical, acoustic and recapture net methods. A stereo-camera system was used to provide length, position and orientation information, and a dual-frequency identification sonar was used to track fish targets in the trawl. Fish escaping the trawl were sampled using recapture, or pocket, nets mounted to the outside of the trawl. Most fish were found to be oriented along the main trawl axis, facing the forward trawl opening. Nearest distance to the trawl panel did not appear to be length-dependent, however, at night when ambient light levels were lower, fish maintained less distance to the trawl panel compared to daytime observations. Consequently, significantly more fish escapes occurred at lower light levels. Trajectories of fish escaping the trawl were highly variable compared with fish that herded into the net, or those whose retention state was unknown. Greatest escapement into pocket nets was observed from the bottom panel of the trawl at night. These findings suggest that survey trawl samples will be less biased due to selectivity when trawls are conducted during the day. [Copyright &y& Elsevier]

Handegard, N. O., and Williams, K. 2008. Automated tracking of fish in trawls using the DIDSON (Dual frequency IDentification SONar). – ICES Journal of Marine Science. 65: 636–644.An application for the automated tracking of dual-frequency, identification sonar (DIDSON) data was developed and tested on fish observations taken in midwater trawls. The process incorporates target detection, multiple target tracking, and the extraction of behaviour information such as target speed and direction from the track data. The automatic tracker was evaluated using three test datasets with different target sizes, observation ranges, and densities. The targets in the datasets were tracked manually and with the automated tracker, using the manual-tracking results as the standard for estimating the performance of the automated tracking process. In the first and third dataset, where the targets were smaller and less dense, the automated tracking performed well, correctly identifying 74% and 57% of targets, respectively, and associating targets into tracks with <10% error compared with the manually tracked data. In the second dataset, where targets were dense and appeared large owing to the shorter observation range, 45% of targets were correctly identified, and the track error rate was 21%. Target speed and direction, derived from the tracking data, agreed well between the manual and automatic methods for all three test cases. Automated tracking represents a useful technique for processing DIDSON data, and a valuable alternative to time-consuming, manual data-processing, when used in appropriate conditions. [ABSTRACT FROM PUBLISHER]