渔业科学进展
漁業科學進展
어업과학진전
MARINE FISHERIES RESEARCH
2014年
4期
1-6
,共6页
冯波%颜云榕%张宇美%易木荣%卢伙胜
馮波%顏雲榕%張宇美%易木榮%盧夥勝
풍파%안운용%장우미%역목영%로화성
鸢乌贼%资源%灯光罩网%光诱模型%南海
鳶烏賊%資源%燈光罩網%光誘模型%南海
연오적%자원%등광조망%광유모형%남해
Sthenoteuthis oualaniensis%Biomass%Light falling net%Light attractive model%South China Sea
为了解南海鸢乌贼(Sthenototeuthis oualaniensis)资源量与分布状况，本研究利用建立在灯光罩网船上的北斗星通渔业信息采集网络，搜集南海鸢乌贼生产数据，并根据灯光罩网作业特点，创建了光诱资源量评估模型，使用克里金插值法，绘制出南海鸢乌贼的分布密度图，估计其总资源量和年可捕量。研究结果表明，鸢乌贼在南海有着广泛的分布，以110.5°-111.5°E、11°-12°N之间的海域和115.5°-116.5°E、9.5°-11.5°N之间的海域资源密度最高，在4 t/km2以上；以112°-112.5°E、14.5°-15°N 之间的海域和113°-115°E、15°-16.5°N 之间的海域单位努力量渔获量(CPUE)最高，达1 kg/(kW·d·km2)以上。根据克里金插值法估算，在南海108°-118°E、9°-20°N之间的359个渔区，鸢乌贼资源量为204.94104t，总可捕量为99.40104t。评估认为，南海鸢乌贼资源开发潜力大，是未来南沙渔业开发的主要种类。
為瞭解南海鳶烏賊(Sthenototeuthis oualaniensis)資源量與分佈狀況，本研究利用建立在燈光罩網船上的北鬥星通漁業信息採集網絡，搜集南海鳶烏賊生產數據，併根據燈光罩網作業特點，創建瞭光誘資源量評估模型，使用剋裏金插值法，繪製齣南海鳶烏賊的分佈密度圖，估計其總資源量和年可捕量。研究結果錶明，鳶烏賊在南海有著廣汎的分佈，以110.5°-111.5°E、11°-12°N之間的海域和115.5°-116.5°E、9.5°-11.5°N之間的海域資源密度最高，在4 t/km2以上；以112°-112.5°E、14.5°-15°N 之間的海域和113°-115°E、15°-16.5°N 之間的海域單位努力量漁穫量(CPUE)最高，達1 kg/(kW·d·km2)以上。根據剋裏金插值法估算，在南海108°-118°E、9°-20°N之間的359箇漁區，鳶烏賊資源量為204.94104t，總可捕量為99.40104t。評估認為，南海鳶烏賊資源開髮潛力大，是未來南沙漁業開髮的主要種類。
위료해남해연오적(Sthenototeuthis oualaniensis)자원량여분포상황，본연구이용건립재등광조망선상적북두성통어업신식채집망락，수집남해연오적생산수거，병근거등광조망작업특점，창건료광유자원량평고모형，사용극리금삽치법，회제출남해연오적적분포밀도도，고계기총자원량화년가포량。연구결과표명，연오적재남해유착엄범적분포，이110.5°-111.5°E、11°-12°N지간적해역화115.5°-116.5°E、9.5°-11.5°N지간적해역자원밀도최고，재4 t/km2이상；이112°-112.5°E、14.5°-15°N 지간적해역화113°-115°E、15°-16.5°N 지간적해역단위노역량어획량(CPUE)최고，체1 kg/(kW·d·km2)이상。근거극리금삽치법고산，재남해108°-118°E、9°-20°N지간적359개어구，연오적자원량위204.94104t，총가포량위99.40104t。평고인위，남해연오적자원개발잠력대，시미래남사어업개발적주요충류。
In this study we evaluated the biomass and distribution ofSthenoteuthis oualaniensisin South China Seabased on the data collected by Bdstar Navigation fishery information collection network that was mounted on the light falling net vessels. Considering the operation process of the light falling vessel, we built a light fishing stock assessment model and introduced a probability function to calculate the sweeping area and the biomass of S. oualaniensis. We then used kriging method to predict the density ofS. oualaniensis and the CPUE. We subsequently generated a map of the distribution ofS. oualaniensis and estimated the total biomass and the allowable catch. Our analysis showed thatS. oualaniensis were widely distributed in South China Sea with high density (4 t/km2) in the area of 110.5°-111.5°E, 11°-12°N and 115.5°-116.5°E, 9.5°-11.5°N; in the area of 112°-112.5°E, 14.5°-15°N and 113°-115°E, 15°-16.5°N, the value of CPUE was as high as 1 kg/(kW·d·km2). The results of Kriging interpolation suggested that in the area of 108°-118°E, 9°-20°N there was a biomass of 2.05 million tons and an allowable catch of 994,000 tons in 359 fishing areas. The annual allowable catch could be 392,000 tons in 105 fishing areas inferred from CPUE. We assessed that there were 630,700 tons ofS. oualaniensis in the area of Nansha Islands and it could be one of the future target species in the deep-sea fisheries. Here we only provided a crude estimate because all the parameters in our model were obtained by the sample vessels. To make an accurate estimate, further investigation will be needed on fishing vessels and fishing ground. It was found that the enhanced machine and light power did not necessarily increase the fishing efficiency. Although a higher light power could enlarge the illuminated area, davits could not support a larger falling net. Moreover, our model could also be used to assess the light arrangement, practice distance, and cost effectiveness in light fisheries.
