Dynamic simulation of tuna longline gear using numerical methods

ABSTRACT:   The underwater shape and hook depth of tuna longline gear are important factors determining fishing performance. In this study, how the shape of tuna longline gear changes in response to sea conditions and gear rigging is explained. Physical models of underwater gear shape were made to simulate fishing gear and analyzed according to the direction and velocity of currents. Then experiments with small-scale models were conducted in a flume tank to confirm the accuracy of the simulation analysis. Finally, the simulation was examined relative to actual longline fishing gear. This approach provided an improvement over previous analytical methods that did not consider fishing gear shape in response to different sea conditions. A useful result is an improved understanding of the relationship between ocean currents and the configuration of longline gear (the shortening ratio, and number of hooks per basket). These factors affect hook depth which, in turn, affects selectivity. Application of these results could lead to more effective and efficient fishing under different sea conditions.     

金枪鱼延绳钓船机电混合推进系统的应用前景分析

通过分析金枪鱼延绳钓船的作业特点、现有延绳钓船配置及其潜在问题,介绍了电力推进系统的组成、特点和作用,探讨了机电混合推进系统开发的流程和需要注意的问题。归纳出机电混合推进系统具备的优点,包括：可以提高渔船放钓、收钓作业工况下的操纵灵活性,优化主机运行工况、降低故障率,并具有良好的节能减排效果。为渔船升级改造和渔业节能的研究提供参考依据。     

太平洋大眼金枪鱼延绳钓渔获分布及渔场环境浅析

本文主要根据收集到的渔获量数据、海水表层温度数据和有关文献资料 ,应用GIS技术对太平洋大眼金枪鱼延绳钓渔业进行了定量或定性分析。结果表明 :太平洋大眼金枪鱼延绳钓渔场主要分布在 2 0°N～2 0°S之间的热带海域 ,具纬向分布特征。对渔获产量同海表温度的分月统计显示 :太平洋大眼金枪鱼渔场最适月平均表层水温约 2 8～ 2 9℃ ,渔场出现频次为偏态分布型。最后 ,结合有关文献综合讨论分析了海表温度、溶解氧含量、海流等环境因子与金枪鱼渔场分布和形成机制的关系     

Separation of the Taiwanese regular and deep tuna longliners in the Indian Ocean using bigeye tuna catch ratios

ABSTRACT:   Taiwanese longline (LL) fisheries operating in the Indian Ocean usually target albacore tuna (ALB), swordfish (SWO) and yellowfin tuna (YFT) using regular LL. Bigeye tuna (BET), however, is targeted using deep LL. Thus, these two types of LL are considered to be different gears as they target different tuna species. Regular or deep LL fishing is defined by number of hooks per basket (NHB): regular LL if 6 ≤ NHB ≤ 10 and deep LL if 11 ≤ NHB ≤ 20. However, NHB information was available in only some of the recent LL data (1995–1999). This situation had caused problems of biased results in stock analysis in the past. Thus, the objective of our study was to explore an effective method to separate the two types of LL fishing by considering species composition. Some intervals of BET catch ratios were found to be effective in separating the regular and deep LL catches, i.e. 0.0 ≤ BET/(BET + ALB + SWO) ≤ 0.4 and 0.8 ≤ BET/(BET + ALB) ≤ 1.0, respectively. Using these two separators, the LL known data set (1995–1999) (learning data set) was classified. Correct classification occurred in 67.7% of the data, while 23.1% of the data were unclassified (11.9% due to zero catches and 11.2% due to classification into both LL types), and 9.2% were misclassifications. Then, using the methods developed, the LL unknown data set in the historical data (1979–1999) was classified and nominal CPUE values were calculated for four species. The CPUE trends based on this study were likely to be more reliable than those of previous studies.     

Characterizing seabird bycatch in the eastern Australian tuna and billfish pelagic longline fishery in relation to temporal,spatial and biological influences

• 1. Seabirds killed incidentally in Australia's eastern tuna and billfish (ETBF) longline fishery between September 2001 and June 2006 were examined to evaluate species composition and to relate, where possible, capture events to operational and environmental factors.
• 2. During this period 2.129 million hooks on 2202 shots were observed, and 369 birds were reported killed. The majority (78%) of these were flesh‐footed shearwaters (Puffinus carniepes), 53% of which were male and 44% female. Smaller numbers of medium to large sized albatrosses (Diomedeidae, predominantly female) and other shearwaters (Puffinus spp.) and petrels (Pterodroma spp.) dominated the remainder of the bycatch.
• 3. Of the 369 birds reported taken as bycatch, 280 were available for necropsy, and species identifications performed in situ by observers were assessed. While observer identifications were generally correct for common species, performance was poor for less common ones.
• 4. The geographical location (latitude) of shots, season, time of day at which shots were set, and bait type and life status (dead or alive) influenced the seabird bycatch rate. The majority of captures (87% overall) occurred between 30 and 35°S, with bycatch being lowest in winter, and remaining at similar levels across the other seasons.
• 5. The use of live fish bait was generally associated with increased captures of both seabirds overall, and flesh‐footed shearwaters in particular. Copyright © 2010 John Wiley & Sons, Ltd.

     

Development of automatic system for monitoring fishing effort in conger-eel tube fishery using radio frequency identification and global positioning system

ABSTRACT:   In this study, we developed a new automatic system, comprised of a radio frequency identification (RFID) system and global positioning system (GPS), for monitoring fishing effort, and effectiveness of effort, in fisheries using many pieces of fishing gear, such as hooks of longlines. The outline of this system is as follows. A single RFID tag with an identification (ID) number is attached to each piece of fishing gear. The RFID tag on the fishing gear passes the antenna of the RFID reader before being shot into the sea and after being hauled up from the sea. Data on the time and geographic location are measured by the GPS and recorded along with the ID number in a personal computer (PC). When a fish is caught, it is brought close to the fish-sorting table. Then, the second antenna of the RFID reader set at the side checks for a catch in the gear. The advantage of this system is that the fishing operation data can be collected without interfering with the fishermen's usual work. The prototype of the system was tested at a conger-eel tube fishery in Tokyo Bay and was able to record data on the time, the location and the catch of individual conger-eel tubes successfully.