Standardizing CPUE of yellowfin tuna (Thunnus albacares) longline fishery in the tropical waters of the northwestern Indian Ocean using a deterministic habitat-based model

Song, Liming; Wu, Yumei

doi:10.1007/s10872-011-0055-y

Cited by 9 publications

(16 citation statements)

References 20 publications

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“…The different factor such as time of fishing (year), season, fishing area and hook between float (HBF) can cause an extreme value in nominal CPUE time series. It is also alligned with other researches (Song & Wu, 2011;Sadiyah et al, 2012). It seems that all variables used in this GLM are analysis are sufficiently representative for all confounding factor Standardizing CPUE of Albacore Tuna.....................................................in Eastern Indian Ocean (Rochman, F., et al) and the abundance and also described as real variables.…”

Section: Discussionmentioning

confidence: 87%

STANDARDIZING CPUE OF ALBACORE TUNA (Thunnus alalunga Bonnaterre, 1788) ON TUNA LONGLINE FISHERY IN EASTERN INDIAN OCEAN

Rochman

Setyadji

Wujdi

2017

Indonesian Fisheries Research Journal

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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.

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Section: Discussionmentioning

confidence: 87%

STANDARDIZING CPUE OF ALBACORE TUNA (Thunnus alalunga Bonnaterre, 1788) ON TUNA LONGLINE FISHERY IN EASTERN INDIAN OCEAN

Rochman

Setyadji

Wujdi

2017

Indonesian Fisheries Research Journal

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“…In some cases, CPUEs may underestimate population declines as fishers can compensate for reduced catch rates by changing fishing strategy and improving gear (Harley et al, 2001;Polacheck, 2006). While we accounted for some of these variations in our standardization processes, we were limited by available logbook data which did not contain information on potentially important factors, such as gear or method modifications (Chang et al, 2011;Song and Wu, 2011;Matsumoto et al, 2016), and possible underreporting and misreporting (Walsh et al, 2005). Therefore, we cannot rule out the impact of these factors on the reported trends.…”

Section: Discussionmentioning

confidence: 99%

Interactions Between a Large Marine Protected Area, Pelagic Tuna and Associated Fisheries

et al. 2020

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“…As one of the basic properties of seawater, salinity also has an important effect on fish behavior. Different kinds of fish have different salinity requirements and suffocation points (Song and Wu, 2011). For several decades, satellite sensors have been providing sea‐surface observations at various spatial and temporal scales.…”

Section: Introductionmentioning

confidence: 99%

An argo‐based experiment providing near‐real‐time subsurface oceanic environmental information for fishery data

ChunLing

Wang

Liu

2020

Fisheries Oceanography

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A better understanding of the relationships between oceanic environments and fishing conditions could make the utilization of fish more efficient, profitable, and sustainable. The current lack of high-precision subsurface seawater information has long been a constraint on fishery research. Using near-real-time Argo observations, this paper presents a new approach called gradient-dependent optimal interpolation. This approach provides daily subsurface oceanic environmental information according to fishery dates and locations. An experiment was conducted in the western and central Pacific Ocean using yellowfin tuna (YFT) catch data in August 2017. The results of seawater temperature and salinity represented differences of less than ±0.5°C and ±0.05, respectively, according to verification of error analysis and truth-finding comparisons. After applying the constructed temperature and salinity profiles, we described the relationship between subsurface information and yellowfin tuna catch distribution. Statistical analysis revealed that yellowfin tuna were more adapted to warmer and saltier seawater. At the near-surface (<5 m), the most suitable temperature was 28-29°C, although yellowfin tuna can endure a temperature range from 11 to 12°C at a depth of 300 m. The corresponding upper boundary of the thermocline was approximately 75 m, with a mean strength of 0.074°C/m, and the most suitable salinity for yellowfin tuna was 34.5-36.0 at depths shallower than 300 m. These results indicated that the constructed subsurface information was very close to the true values and they had high spatial and temporal accuracy.

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Standardizing CPUE of yellowfin tuna (Thunnus albacares) longline fishery in the tropical waters of the northwestern Indian Ocean using a deterministic habitat-based model

Cited by 9 publications

References 20 publications

STANDARDIZING CPUE OF ALBACORE TUNA (Thunnus alalunga Bonnaterre, 1788) ON TUNA LONGLINE FISHERY IN EASTERN INDIAN OCEAN

STANDARDIZING CPUE OF ALBACORE TUNA (Thunnus alalunga Bonnaterre, 1788) ON TUNA LONGLINE FISHERY IN EASTERN INDIAN OCEAN

Interactions Between a Large Marine Protected Area, Pelagic Tuna and Associated Fisheries

An argo‐based experiment providing near‐real‐time subsurface oceanic environmental information for fishery data

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