Standardized geo-referenced catch, fishing effort and length-frequency data for the Indian Ocean Bigeye Tuna, Thunnus obesus (1952-2014)


Autoria(s): Wibawa, Teja Arief; Lehodey, Patrick; Senina, Inna
Cobertura

MEDIAN LATITUDE: -23.077288 * MEDIAN LONGITUDE: 87.220044 * SOUTH-BOUND LATITUDE: -47.500000 * WEST-BOUND LONGITUDE: 22.500000 * NORTH-BOUND LATITUDE: 25.500000 * EAST-BOUND LONGITUDE: 147.500000 * DATE/TIME START: 1952-11-15T00:00:00 * DATE/TIME END: 2014-12-15T00:00:00

Data(s)

29/08/2016

Resumo

Geo-referenced catch and fishing effort data of the bigeye tuna fisheries in the Indian Ocean over 1952-2014 were analysed and standardized to facilitate population dynamics modelling studies. During this sixty-two years historical period of exploitation, many changes occurred both in the fishing techniques and the monitoring of activity. This study includes a series of processing steps used for standardization of spatial resolution, conversion and standardization of catch and effort units, raising of geo-referenced catch into nominal catch level, screening and correction of outliers, and detection of major catchability changes over long time series of fishing data, i.e., the Japanese longline fleet operating in the tropical Indian Ocean. A total of thirty fisheries were finally determined from longline, purse seine and other-gears data sets, from which 10 longline and four purse seine fisheries represented 96% of the whole historical catch. The geo-referenced records consists of catch, fishing effort and associated length frequency samples of all fisheries.

Formato

application/zip, 2 datasets

Identificador

https://doi.pangaea.de/10.1594/PANGAEA.864154

doi:10.1594/PANGAEA.864154

Idioma(s)

en

Publicador

PANGAEA

Direitos

CC-BY: Creative Commons Attribution 3.0 Unported

Access constraints: unrestricted

Fonte

Supplement to: Wibawa, Teja Arief; Lehodey, Patrick; Senina, Inna (2016): Standardization of a geo-referenced fishing dataset for the Indian Ocean Bigeye Tuna, Thunnus obesus (1952-2014). Earth System Science Data Discussions, in review, doi:10.5194/essd-2016-40

Palavras-Chave #Code; Coverage; Date/Time; DATE/TIME; fishery, see article for details; Fishing effort; Gear; in square degree; L = longline, P = purse seine, O = other; L = longline, S = purse seine, O = other; Latitude; LATITUDE; Latitude, northbound; Latitude, southbound; Lat north; Lat south; LF(100-102cm); LF(10-12cm); LF(102-104cm); LF(104-106cm); LF(106-108cm); LF(108-110cm); LF(110-112cm); LF(112-114cm); LF(114-116cm); LF(116-118cm); LF(118-120cm); LF(120-122cm); LF(12-14cm); LF(122-124cm); LF(124-126cm); LF(126-128cm); LF(128-130cm); LF(130-132cm); LF(132-134cm); LF(134-136cm); LF(136-138cm); LF(138-140cm); LF(140-142cm); LF(14-16cm); LF(142-144cm); LF(144-146cm); LF(146-148cm); LF(148-150cm); LF(150-152cm); LF(152-154cm); LF(154-156cm); LF(156-158cm); LF(158-160cm); LF(160-162cm); LF(16-18cm); LF(162-164cm); LF(164-166cm); LF(166-168cm); LF(168-170cm); LF(170-172cm); LF(172-174cm); LF(174-176cm); LF(176-178cm); LF(178-180cm); LF(180-182cm); LF(18-20cm); LF(182-184cm); LF(184-186cm); LF(186-188cm); LF(188-190cm); LF(190-192cm); LF(192-194cm); LF(194-196cm); LF(196-198cm); LF(198-200cm); LF(20-22cm); LF(22-24cm); LF(24-26cm); LF(26-28cm); LF(28-30cm); LF(30-32cm); LF(32-34cm); LF(34-36cm); LF(36-38cm); LF(38-40cm); LF(40-42cm); LF(42-44cm); LF(44-46cm); LF(46-48cm); LF(48-50cm); LF(50-52cm); LF(52-54cm); LF(54-56cm); LF(56-58cm); LF(58-60cm); LF(60-62cm); LF(62-64cm); LF(64-66cm); LF(66-68cm); LF(68-70cm); LF(70-72cm); LF(72-74cm); LF(74-76cm); LF(76-78cm); LF(78-80cm); LF(80-82cm); LF(82-84cm); LF(84-86cm); LF(86-88cm); LF(88-90cm); LF(90-92cm); LF(92-94cm); LF(94-96cm); LF(96-98cm); LF(98-100cm); Lon east; Longitude; LONGITUDE; Longitude, eastbound; Longitude, westbound; Lon west; quarter of month; region; Resolution; T. obesus; T. obesus L F; Thunnus obesus; Thunnus obesus, length frequency; Time coverage; Units of catch are different for each fishery. Please see the article for details.; Units of effort are different for each fishery. Please see the article for details.
Tipo

Dataset