This paper summarizes the methods for evaluating the selectivity of fishing gear, including species- and size-selection resulting from various kinds of fishing gear and sorting devices, which have been developed by the authors over the last 30 years. Firstly, we briefly outline the master curve method to estimate the trawl selection curve of target mesh size, based on the Baranov’s geometrical similarity assumption that there is a geometrical similarity in the combination of body size and mesh opening at a given retention probability. Secondly, we review the development process of the SELECT method for estimating a selection curve with an explanation of the factors affecting split parameters in the SELECT method, such as fishing effort, catchability, and sampling fraction. Thirdly, we describe the conditions for data pooling in replicated comparative fishing experiments with fishing gears of different specifications. Finally, we outline a contact probability model for evaluating contact selection and available selection of towed net with a sorting device, such as grids, square mesh windows, and dredge tooth.

It is well known that there is bias using sampling gear, such as a framed midwater trawl (FMT), when measuring the density and length distribution of target species. This limit is characterized by the catch efficiency of the gear. To improve the accuracy of abundance estimates from catch, it is necessary to determine the catch efficiency of the sampling gear. Acoustic monitoring can be used to determine the catch efficiency of the gear, as its non-invasive, wide-range sweeps provide more reliable estimates of absolute abundance of the target species. In this study, we proposed a method to estimate the catch efficiency of FMT using acoustic data for juvenile walleye pollock (Gadus chalcogrammus). Experimental hauls using FMT with black netting were conducted 17 times in nighttime, as a result, juveniles of fork length ranged from 10 to 70 mm was caught. The estimated efficiency by each haul decreased with the fork length increases with sampling periods. Additionally, the efficiency was estimated by the fork length class of 10 mm intervals using multiple regression, then its variation with length showed same tendency with the result mentioned above.

This is an abstract of the paper that won Best Paper Award 2020 of the Society of Fisheries Engineers. This paper is part of the Shimonoseki Pufferfish Research Project conducted by the National Fisheries University from 2016 to 2019. This paper, as the title suggests, is a mathematical model of a puffer fish pattern. The reason for reproducing pufferfish patterns is to build a system to identify pufferfish species from their body patterns. The research was initiated based on the idea that if the patterns of puffer fish could be reproduced, it could be used effectively to identify puffer fish. Identifying pufferfish species for human consumption is conducted by experts who hold a license to cook pufferfishes. Nevertheless, it is difficult to identify the parental species and the poisoned parts of interspecific hybrid pufferfishes. Therefore, putative hybrids are completely excluded from the distribution process. Developing a system to identify hybrid pufferfishes will decrease the erroneous identification of pufferfish species. In this study, to apply to such identification system, pufferfish skin patterns were replicated using a cellular automata (CA) model. Here the CA model was based on Turing patterns through the exchange of binary values between neighboring cells. Despite the simplicity of the model, which uses five parameters (three parameters related to basic color pattern and two parameters for creating a large black spot) to produce skin patterns, it can produce characteristic skin patterns of all edible species of Takifugu.

Post-harvest freshness of trawl-caught deep-water mud shrimp Solenocera melantho was assessed by measuring the concentration of ATP-related compounds in the tail muscle. In the field study, trawls were towed touching the seabed to catch the shrimps for 15 min, and then the tows were continued off the seabed for 35 and 65 min to prevent more shrimps being caught and to simulate trawling with S. melantho in the codend at the towing speed of 2.0 knots. Underwater video camera was connected to record the behavior of the catches inside the codend during towing. S. melantho were stored on ice for 5 days after harvesting. S. melantho were observed to move toward to the codend, and continued to be stationary without moving after reaching the codend during the trawling. The concentrations of ATP-related compounds during the storage were not different significantly in the tow duration of 35 and 65 min. ATP were found when the catches were retrieved from the codend on the deck of the vessel. Decomposition speed of ATP is high, and AMP and IMP were accumulated during the storage. The onset of HxR was observed after 2-day storage, and Hx was not found during the storage of 5 days.

Studies of ocean waves have been developed by treating time-series data of ocean waves as linear stochastic processes (e.g., the spectral model proposed by Pierson and Moskowitz). However, they are based on the small amplitude wave theory, which presents limitations due to the wave height being sufficiently small compared with the wavelength, and the wavelength being sufficiently small compared with the water depth. In the above theory, the irregularity of ocean wave time-series data is due to the linear superposition of many component waves. Therefore, if the measured time-series data of ocean waves do not exhibit sufficient linearity, then an analysis based on the power spectrum density function cannot be reliable performed. A method to evaluate the linearity of measured ocean wave time-series data is discussed herein using phase-randomized surrogate data, and a linear wave is defined as a linear superposition of many component waves. Specifically, the geometric features of the reconstructed trajectories of the measured time-series data are quantified to evaluate the linearity.

In this study, work days in a set-net fishery were classified using onshore fish sorting work time and catch weight per day as classification indicators. Specifically, the daily onshore fish sorting operation of set-net fishermen in the Suzu area (Kuroshio Town, Kochi Prefecture, Japan) was analyzed using a latent class regression model, which was constructed using daily work time and catch weight as dependent and independent variables, respectively. Observations of work days and interview surveys were also conducted to verify the modeling results. The analysis period included 175 days from October 24, 2018, to July 16, 2019. Daily catch weight and work time were confirmed by referring to operation logs and sales reports from the fisherman of Suzu. The analysis revealed that these 175 work days could be divided into the following three classes: class 1, days when two sorting tables were used; class 2, days when an irregular situation occurred, i.e., when equipment was damaged or an unusual catch was acquired; and class 3, days when one sorting table was used. By combining latent class regression analysis with observations of work, the actual state of the fishery operation could be determined with greater accuracy.

More than fifty years have passed since many seawalls were constructed along reclaimed land in Tokyo Bay, thus they have become deteriorated. Currently, the extending the lifespan of the seawalls and creating suitable habitats for aquatic organisms, are recognized as vital issues. In addition, the reuse of massive industrial by-products for the port facilities maintenance remains an issue. We therefore designed a counterweight mound in front of an existing seawall, by employing civil engineering materials made of dredged soil and steelmaking slag in Tokyo Bay. The results of monitoring surveys of the mound demonstrate that the mound successively fosters an algal community, including various benthos and fish populations.

Fishermen are severely concerned that the underwater noise generated by offshore wind power will have an irreversible effect on the behavior of marine organisms and fishing activities. Accordingly, the surrounding environment of offshore wind farms has continually investigated as part of the environmental impact assessment. This paper has been summarized the effects of underwater noise generated from offshore wind power on the behavior of marine mammals, marine reptiles (sea turtles) and fishes. The main items are as follows. 1) Characteristics of underwater noise generated from offshore wind power. 2) Auditory characteristic of marine organisms. 3) The effects of underwater noise generated from offshore wind power on the behavior of marine organisms.