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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.
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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.
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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.
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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.
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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.
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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).
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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.
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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.
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<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.
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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.
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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.
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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.
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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.
11
Passadore, Cecilia, Andrés Domingo, and Eduardo R. Secchi. "Depredation by killer whale (Orcinus orca) and false killer whale (Pseudorca crassidens) on the catch of the Uruguayan pelagic longline fishery in Southwestern Atlantic Ocean." ICES Journal of Marine Science 72, no. 5 (January 16, 2015): 1653–66. http://dx.doi.org/10.1093/icesjms/fsu251.
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Abstract This study analyses depredation by killer whales (Orcinus orca) and false killer whales (Pseudorca crassidens) on catches of the Uruguayan pelagic longline fishery in the Southwestern Atlantic Ocean between 1998 and 2007. Data were collected by scientific observers from the National Observer Program of the Tuna Fleet operating in the area between 19°–40.5°S and 20°–54°W. Depredation occurred in 67 of the 1029 sets and was restricted to the area from 25°–40.5°S to 27°–53°W, though larger proportions of depredation (DP: percentage of total fish caught damaged by cetaceans) were observed in the Brazil–Malvinas (Falkland) Confluence area (34°–37°S and 51°–53°W) where most of the fishing effort was concentrated. Depredation occurred year-round though intra-annual variability in its intensity was recorded. The overall DP was 0.37% and was slightly higher in autumn. The spatial analysis showed that DP within grids of 1 × 1° was homogeneous and generally <2.5%. Ten out of 57 fish species caught by the fishery were depredated by cetaceans. Swordfish (Xiphias gladius) showed the highest DP per species (1.17%). Using the frequency of resources selectivity index of Ivlev, it was determined that swordfish was selected as a preferred prey in 43.9% of the sets with depredation. Generalized linear models indicated that distance to coast, year, and vessel were significant variables in explaining the number of fish depredated per fishing event. The presence of killer whales in the fishing ground seems not to affect the catch per unit effort by the longline fishery. The losses caused by depredation of cetaceans on the catch are low with probably minor economic effects to the Uruguayan longline fishing industry.
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Rochman, Fathur, Irwan Jatmiko, and Arief Wujdi. "BIOLOGY AND CPUE SPATIAL DISTRIBUTION OF ESCOLAR Lepidocybium flavobrunneum (Smith, 1843) IN EASTERN INDIAN OCEAN (EVOLVING FISHERIES: TODAY’S BY-CATCH IS TOMORROW’S TARGET CATCH)." Indonesian Fisheries Research Journal 22, no. 1 (December 23, 2016): 27. http://dx.doi.org/10.15578/ifrj.22.1.2016.27-36.
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Discharge of by catch is a significant problem in world fishery. Every commercial fishery such as tuna longline has a suite of bycatch species, escolar fish (LEC). LEC as by catch product has received a little attention because of its lower economic value and given its importance as a secondary market. With time, however, market can become establish for this presently undesirable species. Acknowledging that today’s by catch might become tomorrow’s target fish. The aims of this study areto provide information on biological aspect and catch per unit of effort (CPUE) spatial distribution of escolar (Lepidocybium flavobrunneum) as by catch in Indonesian longline fishery operating in the Eastern Indian Ocean. Total escolar samples of 1,815 were taken from scientific observer data from 2011-2013. The study area of escolar was between 0.897-33.175°S and 85.366– 138.733°E of Eastern Indian Ocean. Results show that the escolar length (cmFL) is distributed from 27-178 cmFL (median=83 cmFL, mode=85 cmFL, mean=83.95 cmFL and n= 1.812) and dominated by the size of 85 cmFL. The length weight relationship was determined to be W=0.0002FL2.2926(W in kg, FL in cm). In terms of CPUEs distribution, the lower CPUEs(<1.0001) generally occurred near shore between Indonesia and Australia (10-20°S and 110 125°E).The highest CPUEs of escolar (>1.0001 to 7.382) generally occurred in Western Australian, precisely on grid between 10-35°S and 85-110°E. These grids would be a potential for fishing LEC with the best time to catch in June to August.
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Setyadji, Bram, Budi Nugraha, and Lilis Sadiyah. "THE EFFECT OF DEPTH OF HOOKS, SET AND SOAK TIME TO THE CATCH PER UNIT OF EFFORT OF TUNA IN THE EASTERN INDIAN OCEAN." Indonesian Fisheries Research Journal 22, no. 2 (January 24, 2017): 61. http://dx.doi.org/10.15578/ifrj.22.2.2016.61-68.
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Yellowfin (Thunnus albacares) and bigeye (T. obesus) tuna have been intensively exploited by longline fleets since 1980’s, however, a large proportion of zero catch per set of target species still accurred. Zero catch data contributed significantly to the low catch per unit of effort (CPUE) compared to other countries at the same fishing area. Therefore, understanding the factors contributed to the CPUE of tuna is essential, in order to improve longline fishing efficiency. A total of 2.115 set-by-set data were obtained from Indonesian Scientific Observer Program. The onboard observations were carried out at commercial tuna longline operated in Eastern Indian Ocean from August 2005 to December 2014. Several analytical approaches were conducted in this paper. First, General Linear Model (GLM) was applied in order to model the relationship between CPUE with all the variables involved. Second, boxplot diagram, polynomial and linear regression were applied to fit the relationship between CPUE with set time, soak time and depth (represented by hook position) respectively. The result showed that, there was no significant relationship between set time and CPUE of bigeye and yellowfin tuna. Soak time was positively related with CPUE of yellowfin and affect adversely on bigeye. Depth also have significant relationship with CPUE of tuna, where catch of yellowfin decreased linearly with hook depth, whereas catch of bigeye was performed the opposite. Improvement in tuna longline fishery in eastern Indian Ocean can be achieved through implementation of the specific soak time and hook depth for each target species, i.e. yellowfin and bigeye tuna.
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Murray, T. E., J. A. Bartle, S. R. Kalish, and P. R. Taylor. "Incidental Capture of seabirds by Japanese southern bluefin tuna longline vessels in New Zealand waters, 1988-1992." Bird Conservation International 3, no. 3 (September 1993): 181–210. http://dx.doi.org/10.1017/s0959270900000897.
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SummaryFishery observers recorded incidental capture of seabirds during 785 days on Japanese bluefin tuna longline vessels around New Zealand between April and August each year, 1988-1992. High numbers of albatrosses Diomedea spp. and petrels Procellaria spp. were caught on longline hooks during setting and drowned. Twelve seabird taxa were recorded, six of them breeding only in New Zealand. Most were breeding adults, except for Grey-headed and Black-browed Albatrosses. No bias in sex ratio was evident except for Grey Petrels, of which nearly all were female. Winter-breeding species were most often caught. Birds were not caught randomly, but in a highly aggregated fashion suggestive of complex behavioural interactions with the fishery. Most albatrosses were caught by day in the south whereas most petrels were caught by night north-east of New Zealand. Highest capture rates occurred at dawn and dusk off north-east New Zealand in June-August. Very large catches at specific sites contributed disproportionately to the overall catch rate. The estimated minimum number of total seabirds caught in New Zealand waters declined from 3,652 in 1988 to 360 in 1992, probably as a result of mitigation measures introduced progressively by the industry and by government regulation. Use of tori lines to prevent birds seizing baits had an effect, as did setting in total darkness in the south. Considerably more work needs to be done on the development of improved mitigation measures. Greater observer coverage is required to measure accurately the mortality of individual seabird species on tuna longlines throughout the Southern Ocean and to determine the effectiveness of mitigation measures.
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Jatmiko, Irwan, Humber Andrade, and Budi Nugraha. "DELTA MODEL APPROACH FOR CPUE STANDARDIZATION OF SWORDFISH (Xiphias gladius Linnaeus, 1978) CAUGHT BY INDONESIAN LONGLINE FLEET IN THE EASTERN INDIAN OCEAN." Indonesian Fisheries Research Journal 23, no. 1 (October 17, 2017): 7. http://dx.doi.org/10.15578/ifrj.23.1.2017.7-15.
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Relative abundance indices as calculated based on commercial catches are the input data to run stock assessment models to gather useful information for decision making in fishery management. A Generalized Linear Model (GLM) was used to calculate relative abundance indices and effect of longline fishing gear configuration. Data were collected by a scientific observer program from August 2005 to November 2013. Most of the boats monitored were based in the Benoa Port, Bali. Catches are often equal to zero because swordfish is a bycatch for Indonesian longline fleets. Therefore, a hurdle model and a binomial distribution was used to model the proportion of positive catch rates, while a gamma distribution were used to model the positive longline sets. Correlations between the proportion of positive sets and year () and quarter () were weak. However, linear correlation between the proportion of positive sets and the length of branch lines () and number of hooks between floats () were negative and significant. The probability of success is higher for surface longline with small number of hooks and short branch lines. Models with year in interactions as random effects did not converge. Models with year in interactions as fixed effects did converge, but the estimation of standard errors of year coefficients were high. Meaningful estimations were obtained only when using the simplest model, in which year is not in interactions. The low proportional decrease of deviance indicates that most of the variability of catch rates of swordfish caught by Indonesian longline boats are not related to year, quarter, number of hooks between floats and the length of branch lines. Other variables and information, like the daytime while the longlines deployed in the water (day or night), type of bait, size and type of hooks, and if the fishermen use light-sticks to attract the fish, are necessary to better understand the catch rate, and improve the estimations of the relative abundance indices.
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Setyadji, Bram, and Zulkarnaen Fahmi. "THE IMPACT OF ENVIRONMENTAL CHANGES ON BLACK MARLIN, Makaira indica (Cuvier, 1832) ABUNDANCES IN THE EASTERN INDIAN OCEAN." Indonesian Fisheries Research Journal 26, no. 1 (April 27, 2020): 41. http://dx.doi.org/10.15578/ifrj.26.1.2020.41-49.
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Black marlin (Makaira indica) is commonly caught as frozen by-catch from Indonesian tuna longline fleets. Its contribution estimated 18% (~2,500 tons) from total catch in Indian Ocean. Relative abundance indices as calculated based on commercial catches are the input data for several to run stock assessment analyses that provide models to gather information useful information for decision making and fishery management, however, little are known about the influence of environmental factors to its abundance. In this paper, the abundance was represented as standardized index in order to eliminate any bias on other factors which might influence it. Data were collected from August 2005 to December 2017 through scientific observer program (2005-2017) and national observer program (2016-2017). Most of the vessels monitored were based in Benoa Port, Bali. Overall, time trends of abundance was fluctuated, although, there was increasing trend since 2010 then dropped significantly into relatively similar figure in 2005. Even though, Sea Surface Temperature (SST) and Sea Surface Height (SSH) were statistically significant when incorporating into the models, but it allegedly wasn’t the main driver in determining the abundance of black marlin. Instead, it was more likely driven by spatio-temporal factors (year and area) effect rather than environmental changes.
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Ha, Vu Viet, Tran Van Thanh, Hoang Ngoc Son, and Nguyen Thi Dieu Thuy. "ĐÁNH GIÁ RỦI RO SINH THÁI CỦA NGHỀ KHAI THÁC CÁ NGỪ ĐẠI DƯƠNG Ở BIỂN VIỆT NAM ĐỐI VỚI CÁC LOÀI KHAI THÁC THỨ CẤP." Tạp chí Khoa học và Công nghệ biển 19, no. 1 (May 31, 2019): 103–14. http://dx.doi.org/10.15625/1859-3097/19/1/9449.
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Ecological risk assessments of the oceanic tuna fisheries on the secondary species in the Sea of Vietnam were conducted following the productivity and susceptibility analysis (PSA) method suggested by Marine Stewardship Council. The secondary species were identified through compilation and analysis of data collected in 67 observer trips conducted on board of commercial tuna fisheries by Research Institute for Marine Fisheries (RIMF) and WWF-Vietnam during the period 2000-2016. The consequence analysis pointed out that there were 12 secondary species of tuna fisheries which were taken into PSA analysis. The results indicated that most of species were at medium and low risk level. Species considered at medium risk are Pelagic thresher (Alopias pelagicus), Blue shark (Prionace glauca), Scalloped hammerhead (Sphyrna lewini), Wahoo (Acanthocybium solandri), Escolar (Lepidocybium flavobrunneum), Indo-Pacific sailfish (Istiophorus platypterus) and at low risk are Longtail tuna (Thunnus tonggol), Swordfish (Xiphias gladius), Snake mackerel (Gempylus serpens), Black marlin (Makaira indica), Indo-Pacific blue marlin (M. mazara) and Common dolphinfish (Coryphaena hippurus). Yellowfin Tuna (Thunnus albacares) and Bigeye Tuna (T. obesus) are target species and both at ecologically low risk level. The results also showed that tuna handline fishery has less impacts on group of secondary species compared to longline fishery.
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Miller, T. J., and J. R. Skalski. "Integrating design- and model-based inference to estimate length and age composition in North Pacific longline catches." Canadian Journal of Fisheries and Aquatic Sciences 63, no. 5 (May 1, 2006): 1092–114. http://dx.doi.org/10.1139/f06-022.
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Age and size structure are attributes of fishery stocks important for predicting future productivity. As such, estimating age and length composition of catches has long been an important fisheries management activity. Many observer programs sample catches to obtain length measurements and otoliths (or other structures for ageing) from targeted species. In North Pacific groundfish fisheries, observers collect these data through a stratified multiphase sampling design. Sampling variance and covariance estimates for catch- or proportions-at-length or -age that reflect the randomization inherent in the sampling design provide important measures of uncertainty that correspond to measurement error components in length- or age-structured stock assessment models. We compare sampling variances and covariances of Pacific cod (Gadus macrocephalus) proportions-at-length and sablefish (Anoplopoma fimbria) proportions-at-age with those provided by the overdispersed multinomial model sometimes used in these assessment models. For example, the sampling variance estimates for 2002 Pacific cod proportion-at-length estimates in the Bering Sea Aleutian Islands are at most 13% of the variances provided by multinomial and square-root sample size assumptions. Furthermore, some proportion estimates are positively correlated, whereas only negative correlation occurs with the multinomial distribution.
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Gardner, Beth, Patrick J. Sullivan, Stephen J. Morreale, and Sheryan P. Epperly. "Spatial and temporal statistical analysis of bycatch data: patterns of sea turtle bycatch in the North Atlantic." Canadian Journal of Fisheries and Aquatic Sciences 65, no. 11 (November 2008): 2461–70. http://dx.doi.org/10.1139/f08-152.
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Loggerhead ( Caretta caretta ) and leatherback ( Dermochelys coriacea ) sea turtle distributions and movements in offshore waters of the western North Atlantic are not well understood despite continued efforts to monitor, survey, and observe them. Loggerhead and leatherback sea turtles are listed as endangered by the World Conservation Union, and thus anthropogenic mortality of these species, including fishing, is of elevated interest. This study quantifies spatial and temporal patterns of sea turtle bycatch distributions to identify potential processes influencing their locations. A Ripley’s K function analysis was employed on the NOAA Fisheries Atlantic Pelagic Longline Observer Program data to determine spatial, temporal, and spatio-temporal patterns of sea turtle bycatch distributions within the pattern of the pelagic fishery distribution. Results indicate that loggerhead and leatherback sea turtle catch distributions change seasonally, with patterns of spatial clustering appearing from July through October. The results from the space–time analysis indicate that sea turtle catch distributions are related on a relatively fine scale (30–200 km and 1–5 days). The use of spatial and temporal point pattern analysis, particularly K function analysis, is a novel way to examine bycatch data and can be used to inform fishing practices such that fishing could still occur while minimizing sea turtle bycatch.
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Walsh, William A., Pierre Kleiber, and Marti McCracken. "Comparison of logbook reports of incidental blue shark catch rates by Hawaii-based longline vessels to fishery observer data by application of a generalized additive model." Fisheries Research 58, no. 1 (October 2002): 79–94. http://dx.doi.org/10.1016/s0165-7836(01)00361-7.
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Santander-Neto, J., R. Barreto, FM Santana, and RPT Lessa. "Age, growth and demography of the silky shark Carcharhinus falciformis from the southwestern Atlantic." Endangered Species Research 45 (July 29, 2021): 237–49. http://dx.doi.org/10.3354/esr01131.
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The silky shark Carcharhinus falciformis is considered one of the least productive pelagic shark species. The estimation of growth and demographic parameters presented here is fundamental to a sound knowledge of population status of the species in the Atlantic Ocean. Data was collected through an onboard observer program of the Brazilian chartered pelagic longline fishing fleet that operates in the Equatorial Southwestern Atlantic. Vertebral analysis produced the von Bertalanffy growth parameters for pooled sexes L∞ = 283.05 cm; k = 0.0987 yr-1 and t0 = -3.47 yr. Males reached sexual maturity at 8.6 yr and females at 9.9 yr. Longevity was estimated at 27.2 yr. Age structure analysis indicated that 80.5% of the catch was composed of juveniles, with recruitment to the fishery from the first year of life (age 1+). These biological parameters are responsible for the species’ low resistance to fishing pressure, and our demographic analysis (Leslie Matrix) shows an annual population decline of 12.7% yr-1 under the current fishing scenario for the period analyzed. Therefore, conservation measures must be enacted to reestablish the population of silky sharks to safe levels for the maintenance of this species in the South Atlantic.
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Rochman, Fathur, Irwan Jatmiko, and Bram Setyadji. "Spatial Distribution, Behaviour, and Biological Aspect of Albacore (Thunnus alalunga) Caught in Eastern Indian Ocean." ILMU KELAUTAN: Indonesian Journal of Marine Sciences 22, no. 3 (August 22, 2017): 111. http://dx.doi.org/10.14710/ik.ijms.22.3.111-120.
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This study highlighted the occurrence of the Indonesian tuna longline fishery targeting albacore (Thunnus alalunga) caught in the Eastern Indian ocean. The data used in this study based on the Research Institute for Tuna Fisheries (RITF) observer program in Benoa. This paper presents the current information on biological aspect (size distribution and length-weight relationship) and fish behavior (swimming layer and feeding periodicity) of albacore. Total albacore samples of 3,152 were taken from scientific observer data from 2010-2013. The study area of albacore was between 0-33°S and 75-131°E. Albacore length distributed from 70-196 cmFL (median=93 cmFL, mode=100 cmFL, mean=92.12 cmFL) and dominated at size of 95 cmFL. The highest percentage length of albacore was >90 cm (L50) occured in the area between (30-35°S and 80-95°E) and (10-15°S and 120-125°E). The length weight relationship was determined to be W=0.0045 FL1.8211 (W in kg, FL in cm). The expected season to catch the ALB was from April to July with the peak season in June and July. The swimming layer of albacore based on minilogger data were distributed from at 118 to 341 m depth and mostly catch at depth of 156 m with temperature degree 18°C. The feeding periodicity of albacore’s are start from 7:45am to 17:59 pm, mostly active at 10 am to 11 am. The majority of ALB caught by Indonesian longliner was mature condition and negative allometric growth. The ALB peak season was in June-July and the best time to catch was 10 am to 11 am at depth of 156 m. Keywords : albacore, feeding periodicity, swimming layer, CPUE
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Jordaan, Gareth L., Jorge Santos, and Johan C. Groeneveld. "Effects of inconsistent reporting, regulation changes and market demand on abundance indices of sharks caught by pelagic longliners off southern Africa." PeerJ 6 (October 24, 2018): e5726. http://dx.doi.org/10.7717/peerj.5726.
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The assumption of a proportional relationship between catch-per-unit-effort (CPUE) and the abundance of sharks caught by pelagic longliners is tenuous when based on fisher logbooks that report only retained specimens. Nevertheless, commercial logbooks and landings statistics are often the only data available for stock status assessments. Logbook data collected from local and foreign pelagic longline vessels operating in four areas off southern Africa between 2000 and 2015 were used to construct standardized CPUE indices for blue sharks Prionace glauca and shortfin makos Isurus oxyrinchus. Generalized linear mixed models were used to explore the effects of year, month, vessel, fleet and presence of an observer on blue shark and shortfin mako variability. Landing statistics and auxiliary information on the history of the fishery, regulation changes, and market factors were superimposed on the CPUE indices, to test hypotheses that they would influence CPUE trends. Indices in the West and Southwest (Atlantic) areas were elevated for both species, compared to the South and East (Indian Ocean). The scale of year-on-year CPUE increments, up to an order of magnitude for blue sharks, reflected occasional targeting and retention, interspersed with periods where blue sharks were not caught, or discarded and not reported. Increments were smaller for higher value shortfin makos, suggesting that indices were less affected by unreported discarding. CPUE indices and landings of both shark species have increased in recent years, suggesting increased importance as target species. Analysis of logbook data resulted in unreliable indicators of shark abundance, but when trends were interpreted in conjunction with landings data, disaggregated by area and month, and with hindsight of market demand and regulation changes, anomalies could be explained.
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Crear, Daniel P., Tobey H. Curtis, Stephen J. Durkee, and John K. Carlson. "Highly migratory species predictive spatial modeling (PRiSM): an analytical framework for assessing the performance of spatial fisheries management." Marine Biology 168, no. 10 (September 6, 2021). http://dx.doi.org/10.1007/s00227-021-03951-7.
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AbstractSpatial management for highly migratory species (HMS) is difficult due to many species’ mobile habits and the dynamic nature of oceanic habitats. Current static spatial management areas for fisheries in the United States have been in place for extended periods of time with limited data collection inside the areas, making any analysis of their efficacy challenging. Spatial modeling approaches can be specifically designed to integrate species data from outside of closed areas to project species distributions inside and outside closed areas relative to the fishery. We developed HMS-PRedictive Spatial Modeling (PRiSM), which uses fishery-dependent observer data of species’ presence–absence, oceanographic covariates, and gear covariates in a generalized additive model (GAM) framework to produce fishery interaction spatial models. Species fishery interaction distributions were generated monthly within the domain of two HMS longline fisheries and used to produce a series of performance metrics for HMS closed areas. PRiSM was tested on bycatch species, including shortfin mako shark (Isurus oxyrinchus), billfish (Istiophoridae), and leatherback sea turtle (Dermochelys coriacea) in a pelagic longline fishery, and sandbar shark (Carcharhinus plumbeus), dusky shark (C. obscurus), and scalloped hammerhead shark (Sphyrna lewini) in a bottom longline fishery. Model validation procedures suggest PRiSM performed well for these species. The closed area performance metrics provided an objective and flexible framework to compare distributions between closed and open areas under recent environmental conditions. Fisheries managers can use the metrics generated by PRiSM to supplement other streams of information and guide spatial management decisions to support sustainable fisheries.
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Uhrin, Amy V., William A. Walsh, and Jon Brodziak. "Relative abundance of derelict fishing gear in the Hawaii-based pelagic longline fishery grounds as estimated from fishery observer data." Scientific Reports 10, no. 1 (May 8, 2020). http://dx.doi.org/10.1038/s41598-020-64771-1.
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Fader, Joseph E., Brianna W. Elliott, and Andrew J. Read. "The Challenges of Managing Depredation and Bycatch of Toothed Whales in Pelagic Longline Fisheries: Two U.S. Case Studies." Frontiers in Marine Science 8 (February 26, 2021). http://dx.doi.org/10.3389/fmars.2021.618031.
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Direct interactions with fisheries are broadly recognized as the leading conservation threat to small cetaceans. In open-ocean environments, one of the primary gear types implicated in these interactions is the pelagic longline. Unlike accidental entanglement in driftnets or deliberate entrapment by purse-seines, interactions between cetaceans and longlines are often driven by attraction of the animals to feed on bait or fish secured on the gear, a behavior known as depredation. Many small and medium-sized delphinid species have learned to exploit such opportunities, leading to economic costs to fisheries and a risk of mortality to the animals from either retaliation by fishermen or hooking or entanglement in fishing gear. Two pelagic longline fisheries in the United States experience depredation and bycatch by odontocete depredators: the Hawai‘i deep-set longline fishery, which is depredated primarily by false killer whales (Pseudorca crassidens), and the Atlantic pelagic longline fishery depredated primarily by short-finned pilot whales (Globicephala macrorhynchus). These fisheries are among the most intensively documented and managed pelagic longline fisheries in the world, with high levels of observer coverage, and bycatch mitigation measures required to reduce the mortality of seabirds, sea turtles and cetaceans. Both fisheries have active, multi-stakeholder “Take Reduction Teams,” enacted under the U.S. Marine Mammal Protection Act (MMPA), that are tasked to develop measures to reduce the bycatch of cetaceans below statutory reference points. Consequently, these two Teams represent model processes within which to address depredation and bycatch, having access to detailed, high-quality data on the nature and frequency of interactions with cetaceans, meaningful stakeholder involvement, resources to test potential solutions, and the institutional will to improve outcomes. We review how mitigation strategies have been considered, developed, and implemented by both Teams and provide a critical analysis of their effectiveness in addressing these problems. Notably, in the absence of straightforward avoidance or deterrence strategies, both Teams have developed gear and handling strategies that depend critically on comprehensive observer coverage. Lessons offered from these Teams, which have implemented consensus-driven management measures under a statutory framework, provide important insights to managers and scientists addressing other depredation problems.