Academic literature on the topic 'Japanese longline fishery observer data'
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Journal articles on the topic "Japanese longline fishery observer data"
1
Stevens, JD. "Blue and Mako Shark by-catch in the Japanese Longline Fishery off South-eastern Australia." Marine and Freshwater Research 43, no. 1 (1992): 227. http://dx.doi.org/10.1071/mf9920227.
Full textAbstract:
During the last 10 years, up to 70 Japanese longline vessels have fished Tasmanian waters of the Australian Fishing Zone each season, targeting bluefin tuna (Thunnus maccoyii). The average seasonal fishing effort in Tasmanian waters is about 3.3 million hooks, and data from observers suggests that, this results in a by-catch of some 34 000 blue sharks (Prionace glauca) each year, representing a weight of about 275 tonnes. In the last few years, fishing effort has increased as the catch rates of southern bluefin tuna have declined. The sharks are discarded after removal of the fins. The actual number of blue sharks hooked is much higher than 34000 because many are released by either shaking or cutting them off the line, although they are often damaged in the process. The majority of blue sharks caught are immature or adolescent females. Smaller numbers of shortfin mako sharks (Isurus oxyrinchus) are also caught and retained both for their fins and their meat. Tasmanian waters represent only one area of the Australian Fishing Zone fished by Japanese longliners.
2
Semba, Yasuko, Kotaro Yokawa, Hiroaki Matsunaga, and Hiroshi Shono. "Distribution and trend in abundance of the porbeagle (Lamna nasus) in the southern hemisphere." Marine and Freshwater Research 64, no. 6 (2013): 518. http://dx.doi.org/10.1071/mf12272.
Full textAbstract:
Knowledge of a species’ distribution is an important element in its effective management and conservation. The porbeagle (Lamna nasus) is a common by-catch shark in the tuna longline fishery in the southern hemisphere, but its distribution and abundance are largely unknown. The investigation of observer data from the tuna longline fishery and other fishery survey data has revealed that (1) porbeagles are distributed in the pelagic waters across the oceans of the southern hemisphere, (2) juveniles and adults are distributed in cooler environments than are neonates, (3) pregnant females occur in the pelagic waters of the Indian Ocean and the Tasman Sea, most being frequently recorded around the Cape of Good Hope between June and July and (4) the standardised catch per unit effort (CPUE) based on tuna longline fishery (1994–2011) and driftnet survey (1982–1990) data indicate no continuous decreasing trend in the abundance of the southern porbeagle, contrary to the declining trend reported in a limited region in the South Atlantic. Considering its circumglobal distribution, stock status of this population should be assessed using information from the areas of its major distribution, including pelagic waters, and international coordination across oceans is necessary for the effective management of this population.
3
Rochman, Fathur, and Budi Nugraha. "PRODUCTIVITY AND ECONOMIC ANALYSIS OF THE INDIAN OCEAN LONGLINE FISHERY LANDED AT BENOA PORT BALI INDONESIA." Indonesian Fisheries Research Journal 20, no. 2 (December 1, 2014): 77. http://dx.doi.org/10.15578/ifrj.20.2.2014.77-86.
Full textAbstract:
This study highlighted the occurrence of productivity and economic analysis of Indian Ocean longline fishery which was landed in Benoa port Bali Indonesia. The aim of this study is to determine feasibility of tuna longline effort based on business analysis and current condition. The data used in this study based on the Research Institute for Tuna Fisheries (RITF) observer program in Benoa from 2010-2011. This paper presents the current information on Catch per Unit of Effort (CPUE) and feasibility analysis based on the recent economic parameters. The CPUEs of tuna longline vessel in 2010 and 2011 respectively were 288.35 kg/effort and 281.97 kg/effort. The feasibility analysis of Indian Ocean tuna longline effort showed that tuna longline efforts remains profitable<br />and feasible with payback periods (year-3, month - 2 and day- 18), internal rate of return (53%), average rate of return (61.24%) and net present value between Rp 1.709.897.950,- (first year) and Rp 85.331.099.211,- (at the end of 25 years).
4
White, William T., Leontine Baje, Sharon A. Appleyard, Andrew Chin, Jonathan J. Smart, and Colin A. Simpfendorfer. "Shark longline fishery of Papua New Guinea: size and species composition and spatial variation of the catches." Marine and Freshwater Research 71, no. 6 (2020): 627. http://dx.doi.org/10.1071/mf19191.
Full textAbstract:
This study provides the first detailed investigation of the catches of the shark longline fishery in Papua New Guinea. Fisheries observers collected data on shark catches from a total of 318 longline sets between May and June 2014, before its closure in July 2014. In all, 14694 sharks were recorded with a total estimated biomass of 439 tonnes (Mg). Eighteen species of sharks were recorded in the observer data, with the most dominant species being Carcharhinus falciformis, which constituted more than 90% of the total catches by both weight and number of individuals. The level of observer misidentification was low (<10%), which reflected the use of region-specific identification guides by well-trained fisheries observers. The most diverse catches were in the Solomon Sea area, whereas catches in most other areas, particularly the Bismarck Sea areas, were less diverse and more strongly dominated by C. falciformis. Size and sex ratios varied by species, highlighting the importance of obtaining species-level information from the fishery being investigated. Any consideration by fisheries managers to reopen this fishery needs to consider the effect this will have on the species targeted and the livelihoods of coastal fishers who also rely on the same resources.
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Jatmiko, Irwan, Budi Nugraha, and Fayakun Satria. "CAPAIAN PERKEMBANGAN PROGRAM PEMANTAU PADA PERIKANAN RAWAI TUNA DI INDONESIA (Achievement of the Development of Observer Program on Tuna Longline Fishery in Indonesia)." Marine Fisheries : Journal of Marine Fisheries Technology and Management 6, no. 1 (September 30, 2016): 23. http://dx.doi.org/10.29244/jmf.6.1.23-31.
Full textAbstract:
<p>ABSTRACT<br />Fisheries data is one of the important aspects to understand the basic biology, species distributions and population dynamics of fish stock. One of the efforts to collect data is conducting observer program on tuna longline to improve the understanding of all aspects on fishing operation at the sea. The objectives of this study are to describethe historical development of observers, composition and conservation status of tuna longline vessels catch in Indonesia. Data collection was conducted by observer from August 2005 to November 2013. The method used in this research is descriptive method in which this study aimed to describe the phenomenon that occurs in the tuna longline fishery and catch composition. Tuna longline catches can be categorized into fivegroups, namely, tunas, billfishes, sharks and rays, birds and turtles and other fish. The results showed that the composition of longline tuna catches was dominated by other fish groups with 48.10% followed by tunas 33.85%. Other fish group was dominated by bycatch that have economic value (by product). Data and information gained from observer are very important, so its activity should be perceived as necessity for better fisheries management, rather than as mandatory from Regional Fisheries Management Organization (RFMO) regulations.</p><p><br />Keywords: catch composition, fisheries management, observer, tuna longline</p><p>-------</p><p><br />ABSTRAK</p><p>Data perikanan merupakan salah satu aspek penting untuk memahami biologi dasar, distribusi spesies dan dinamika populasi stok ikan. Salah satu upaya untuk memperoleh data secara tepat adalah dengan melaksanakan program pemantau di atas kapal rawai tuna untuk meningkatkan pemahaman tentang semua aspek pada operasi penangkapan di laut. Penelitian ini bertujuan untuk mengetahui sejarah perkembangan pemantau, mengetahui komposisi dan status konservasi hasil tangkapan pada kapal rawai tuna di Indonesia. Pengumpulan data dilakukan mulai bulan Agustus 2005 hingga November 2013 di kapal rawai tuna yang sebagian besar berbasis di Pelabuhan Benoa, Bali. Metode yang digunakan dalam penelitian ini adalah metode deskriptif dimana penelitian ini ditujukan untuk menggambarkan fenomena yang terjadi pada perikanan rawai tuna dan komposisi hasil tangkapan. Hasil tangkapan rawai tuna dapat dikategorikan ke dalam lima kelompok yaitu: tuna, ikan berparuh, hiu dan pari, burung dan penyu serta ikan lainnya. Hasil penelitian menunjukkan bahwa komposisi hasil tangkapan kapal rawai tuna didominasi oleh kelompok ikan lainnya, yaitu sebesar 48,10%, diikuti oleh kelompok tuna 33,85%. Kelompok ikan lainnya ini kebanyakan hasil tangkapan sampingan yang mempunyai nilai ekonomis. Data dan informasi yang diperoleh dari program pemantau ini sangat penting sehingga pelaksanaannya harus dilihat sebagai kebutuhan untuk pengelolaan perikanan yang lebih baik, bukan hanya atas dasar kepatuhan terhadap peraturan dari Regional Fisheries Management Organization (RFMO).</p><p><br />Kata kunci: komposisi hasil tangkapan, pengelolaan perikanan, pemantau, rawai tuna</p>
6
Bi, Rujia, Yan Jiao, Haakon Bakka, and Joan A. Browder. "Long-term climate ocean oscillations inform seabird bycatch from pelagic longline fishery." ICES Journal of Marine Science 77, no. 2 (January 14, 2020): 668–79. http://dx.doi.org/10.1093/icesjms/fsz255.
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Abstract Seabirds are facing increasing threats in both marine and terrestrial habitats, and many populations have experienced dramatic declines over past decades. Fisheries bycatch is the most pervasive at-sea threat and is of increasing concern in fisheries management and marine conservation. We predicted spatial and temporal heterogeneities of seabird bycatch probability in the US Atlantic pelagic longline fishery (PLL) through an interactive Barrier model based on observer data from the National Marine Fisheries Service Pelagic Observer Program. The Barrier model prevents bias caused by physical barriers such as coastlines by defining the spatial correlation function as a collection of paths between points and eliminating any paths across physical barriers. The integrated nested Laplace approximations methodology and stochastic partial differential equations approach were applied to fit the model, greatly reducing execution time. Seabird bycatch had a hotspot of high bycatch probability in the mid-Atlantic bight in most years, and the hotspot varied in presence and location yearly. The inter-annual variations in bycatch hotspot are correlated with Gulf Stream meanders. Special area and time fishing restrictions predicted by relationships with Gulf Stream positions might enable the US Atlantic PLL to avoid peak areas and periods of seabird bycatch and thereby support seabird conservation.
7
Brodziak, Jon, and William A. Walsh. "Model selection and multimodel inference for standardizing catch rates of bycatch species: a case study of oceanic whitetip shark in the Hawaii-based longline fishery." Canadian Journal of Fisheries and Aquatic Sciences 70, no. 12 (December 2013): 1723–40. http://dx.doi.org/10.1139/cjfas-2013-0111.
Full textAbstract:
One key issue for standardizing catch per unit effort (CPUE) of bycatch species is how to model observations of zero catch per fishing operation. Typically, the fraction of zero catches is high, and catch counts may be overdispersed. In this study, we develop a model selection and multimodel inference approach to standardize CPUE in a case study of oceanic whitetip shark (Carcharhinus longimanus) bycatch in the Hawaii-based pelagic longline fishery. Alternative hypotheses for shark catch per longline set were characterized by the variance to mean ratio of the count distribution. Zero-inflated and non-inflated Poisson, negative binomial, and delta-gamma models were fit to fishery observer data using stepwise variable selection. Alternative hypotheses were compared using multimodel inference. Results from the best-fitting zero-inflated negative binomial model showed that standardized CPUE of oceanic whitetip sharks decreased by about 90% during 1995–2010 because of increased zero catch sets and decreased CPUE on sets with positive catch. Our model selection approach provides an objective way to address the question of how to treat zero catches when analyzing bycatch CPUE.
8
Chan, Hing Ling, and Minling Pan. "Fishing trip cost modeling using generalized linear model and machine learning methods – A case study with longline fisheries in the Pacific and an application in Regulatory Impact Analysis." PLOS ONE 16, no. 9 (September 7, 2021): e0257027. http://dx.doi.org/10.1371/journal.pone.0257027.
Full textAbstract:
Fishing trip cost is an important element in evaluating economic performance of fisheries, assessing economic effects from fisheries management alternatives, and serving as input for ecosystem and bioeconomic modeling. However, many fisheries have limited trip-level data due to low observer coverage. This article introduces a generalized linear model (GLM) utilizing machine learning (ML) techniques to develop a modeling approach to estimate the functional forms and predict the fishing trip costs of unsampled trips. GLM with Lasso regularization and ML cross-validation of model are done simultaneously for predictor selection and evaluation of the predictive power of a model. This modeling approach is applied to estimate the trip-level fishing costs using the empirical sampled trip costs and the associated trip-level fishing operational data and vessel characteristics in the Hawaii and American Samoa longline fisheries. Using this approach to build models is particularly important when there is no strong theoretical guideline on predictor selection. Also, the modeling approach addresses the issue of skewed trip cost data and provides predictive power measurement, compared with the previous modeling efforts in trip cost estimation for the Hawaii longline fishery. As a result, fishing trip costs for all trips in the fishery can be estimated. Lastly, this study applies the estimated trip cost model to conduct an empirical analysis to evaluate the impacts on trip costs due to spatial regulations in the Hawaii longline fishery. The results show that closing the Western and Central Pacific Ocean (WCPO) could induce an average 14% increase in fishing trip costs, while the trip cost impacts of the Eastern Pacific Ocean (EPO) closures could be lower.
9
Rochman, Fathur, Bram Setyadji, and Arief Wujdi. "STANDARDIZING CPUE OF ALBACORE TUNA (Thunnus alalunga Bonnaterre, 1788) ON TUNA LONGLINE FISHERY IN EASTERN INDIAN OCEAN." Indonesian Fisheries Research Journal 23, no. 1 (October 17, 2017): 29. http://dx.doi.org/10.15578/ifrj.23.1.2017.29-38.
Full textAbstract:
Albacore (Thunnus alalunga) is the third dominant catch of Indonesian tuna longline fishery operating in the eastern Indian Ocean. The percentage production of albacore catch was reaching up 6% of the total catch of tuna groups in Indonesia. Thi study aims to examine a relative abundance indices using standardized catch per unit of effort (CPUE) of longliner based on albacore tuna. This information will give a valuable input and information to support stock assessment particularly in the regional basis. In this study, we use Generalized Linear Model (GLM) with Tweedie distribution to standardize the CPUE and to estimate relative abundance indices based on the Indonesian longline dataset time series. Data were collected from January 2006 to October 2015 (106 trip observer and 8.989 fishing days) by conducting direct onboard observation on tuna longline vessels operating in the Indian Ocean. The result show that year, area,hooks between floats, year*season, year*area and year* hooks between floats significantly influenced the nominal CPUE of albacore. The highest value of Standardized CPUE appeared in 2014 and probably related to the large number of foreign fishing vessels with a high capacity (over 60 GT) targeting frozen tuna including albacore. In 2015, standardized CPUE value was sharply decreased due to the ban of foreign vessels in Indonesia.
10
Chang, Shui-Kai, and Tzu-Lun Yuan. "Deriving high-resolution spatiotemporal fishing effort of large-scale longline fishery from vessel monitoring system (VMS) data and validated by observer data." Canadian Journal of Fisheries and Aquatic Sciences 71, no. 9 (September 2014): 1363–70. http://dx.doi.org/10.1139/cjfas-2013-0552.
Full textAbstract:
Estimating geo-referenced fishing effort is vital to develop advice for effective fisheries management. Many studies in recent decades have attempted to obtain complete, high-resolution effort data from vessel monitoring systems (VMSs). The main challenge in this regard is to develop a classification method for differentiating fishing activities (e.g., fishing days) from nonfishing activities in VMS data. This study developed a simple, novel classification criterion for a large-scale tuna longline (LTLL) fishery that has not been studied before. LTLL operations were first explored using observer data. Three approaches were designed for developing fishing-day classification criteria, using maximizing sum of sensitivity and specificity (SS) as the major performance measure and minimizing difference of SS as a reference. At least one VMS report with speed in the range of 2–5 kn (1 kn = 1.852 km·h–1) detected during the time-of-day period of 14:00–23:00 h was recommended as the criterion for defining a fishing day. Possible explanations for the differences between the estimated fishing days from VMS data and those reported on logbooks are discussed; most causes were related to specific features of the fishery.
Dissertations / Theses on the topic "Japanese longline fishery observer data"
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Klaer, Neil E., and n/a. "Analysis of seabird by-catch in the Australian Fishing Zone using Japanese longline fishery observer data." University of Canberra. Applied Science, 1998. http://erl.canberra.edu.au./public/adt-AUC20060814.150108.
Full textAbstract:
Data collected by Australian observers, combined with logbook data supplied by
all Japanese vessels fishing in the Australian Fishing Zone were used to estimate
the total seabird catch and catch rates (and associated variances) within the
Australian Fishing Zone by season and area. The species composition of subsamples
of captured seabirds was used in conjunction with the estimates of total
by-catch to provide estimates of by-catch by species.
The total by-catch of all seabird species by Japanese longline within Australian
waters was estimated to be 2,981 (cv 17%) for the 1992 fishing year, 3,590 (cv
15%) in 1993 and 2,817 (cv 19%) in 1994. However, the observed seabird bycatch
may underestimate the total number of seabirds killed by 27%. The increase
in total seabird by-catch between 1992 and 1993 is partly due to the use of
monofilament nylon longline gear by two vessels in 1993, and the decrease from
1993 to 1994 was mainly due to a corresponding reduction in fishing effort.
For the three years examined, 78% of the total seabird by-catch was albatrosses,
with black-browed albatross (Diomedea melanophrys) and shy albatross (D.
cauta) caught in the greatest numbers. Catches of yellow-nosed albatross (D.
chlororhynchos), wandering albatross (D. exulans}, and grey-headed albatross (D.
chrysostoma) were also substantial. The proportions of individual species caught
showed considerable inter-annual variability.
Most seabirds caught and killed by longline fishing are captured during line
setting. Data collected by the observers were also used to determine the influence
of various environmental factors and mitigation measures on seabird catch rates.
Generalised linear models were used to determine the statistical significance of
the effect of each factor on the seabird catch rate. Results show that the
environmental factor that has the most influence is whether line setting was
carried out at night or during the day. For the data examined, the chance of
catching seabirds during day sets was five times greater than for night sets. For
night sets, the chance of catching seabirds during the full half-phase of the moon
was five times greater than during the new half-phase.
Other environmental factors with significant effects were the area and season
fished. Wind, cloud and sea conditions were not found to have a significant
influence. Considerable variation in the seabird by-catch rate among vessels was
found, which is probably due to differences in their implementation of mitigation
measures, as well as the clumped distribution of seabirds by area and time. Interannual
variation in the by-catch rate was found to be statistically significant but
the differences among years was small in comparison to other factors.
An examination of the influence of mitigation measures for sets made during the
day in summer in the Tasmanian area showed that the level of bait thawing and
unidentified factors related to individual vessels were most significant in
determining the seabird by-catch rate, followed by the use of a bait throwing
device. It was not possible to examine the influence of the use of bird scaring tori
poles and lines as these were used during all sets examined in detail. For this data
set, the amount of cloud cover had an influence, while moon phase, sea conditions
and wind strength did not.
2
CHEN, PO-WEN, and 陳柏妏. "Analysis of observer data and standardized catch rate of blue shark caught by the Taiwanese large scale longline fishery in the Atlantic Ocean." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/sn295u.
Full textAbstract:
碩士國立高雄海洋科技大學
漁業生產與管理研究所
106
This study was based on observers’ records of the Taiwanese tuna longline fishing fleets operating in the Atlantic Ocean in the period 2004-2015. Observers’ data analysis included those of operation area, fishing effort, species composition, shark by-catch species composition, shark discard/release rates, and catch per unit effort (CPUE) of blue shark from Atlantic Ocean. Five areas: A (north of 20°N), B (5°N-20°N), C (5°N-15°S), D (15°S-50°S, west to 20°W) and E (15°S-50°S, 20°W-20°E) were categorized based on the blue shark nominal CPUE distribution. To cope with the high zero shark catch percentage, the CPUE of blue shark, calculated as the number of fish caught per 1,000 hooks, was standardized using a two-step delta-lognormal approach that treats the proportion of positive sets and the CPUE of positive catches separately. Standardized indices with 95% bootstrapping confidence intervals were reported. Major shark by-catch species were blue shark (Prionace glauca) (75.74%), bigeye thresher shark (Alopias superciliosus) (8.91%), shortfin mako (Isurus oxyrinchus) (4.53%) in the North Atlantic Ocean and blue shark (67.70%), crocodile shark (Pseudocarcharias kamoharai) (14.38%), cookiecutter shark (Isistius brasiliensis) (5.68%) in the South Atlantic Ocean. The standardized CPUE showed a stable trend for blue sharks from 2007 to 2014. The results obtained in this study can be improved if longer time series observers' data are available.
Book chapters on the topic "Japanese longline fishery observer data"
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"Biology and Management of Dogfish Sharks." In Biology and Management of Dogfish Sharks, edited by Christina L. Conrath and Robert J. Foy. American Fisheries Society, 2009. http://dx.doi.org/10.47886/9781934874073.ch10.
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Abstract.—Data from research surveys conducted by the National Marine Fisheries Service’s (NMFS) Alaska Fisheries Science Center and the International Pacific Halibut Commission (IPHC) were examined along with catch data from NMFS’s North Pacific Groundfish Observer Program to determine the distribution, abundance, and status of spiny dogfish <em>Squalus acanthias </em>in Alaska. Dogfish are present in the Gulf of Alaska, Bering Sea, and Aleutian Islands but survey and observer data indicate they are most abundant in the central and eastern Gulf of Alaska in waters less than 200 m deep. All data sources indicate that dogfish have increased in abundance in the Gulf of Alaska in recent years. Log-transformed catch per unit effort (CPUE) of dogfish calculated from the NMFS trawl survey data ranged from 0.47 sharks/km2 in 1984 to 0.91 sharks/km2 in 2007, with a peak of 1.17 sharks/km2 in 2003. Log-transformed CPUE of spiny dogfish calculated from the NMFS longline survey data ranged from 0.041 sharks/100 hooks in 1979 to 0.16 sharks/100 hooks in 2007, with peaks in abundance of 0.23 sharks/100 hooks in 1983 and 1998. Log-transformed CPUE of dogfish calculated from the IPHC annual longline survey data showed a similar trend and ranged from 0.84 sharks/100 hooks in 1997 to 1.18 sharks/100 hooks in 2006, with a peak value of 1.38 sharks/100 hooks in 2003. Estimates of the incidental catch of dogfish by Alaska fisheries in the Gulf of Alaska calculated from NMFS observer data from 1990 to 2007 were variable and ranged from 140 to 865 metric tons (mt), with peak values of over 650 mt in years 1998, 1999, 2006, and 2007. Dogfish are currently managed as a member of the “other species” complex by the North Pacific Fishery Management Council in Alaska waters. The increasing abundance of this species in the Gulf of Alaska in recent years and the increasing probability of a fishery developing may necessitate a species-specific management strategy for dogfish in Alaska waters.