Spatial distributions of variables in marine environmental and fisheries research. Part 1: Geostatistics and autocorrelated environmental and fisheries data
The geographical information very often is lost during calculation of indices for the abundance from surveys. Isoplet diagrams showing the distribution of both fish abundance and temperature/salinity are usually produced by hand by skilled personnel. In order to find an objective approach we explore...
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ftimr:oai:imr.brage.unit.no:11250/105552 2023-05-15T15:39:00+02:00 Spatial distributions of variables in marine environmental and fisheries research. Part 1: Geostatistics and autocorrelated environmental and fisheries data Stensholt, Boonchai K. Sunnanå, Knut 1996 application/pdf http://hdl.handle.net/11250/105552 eng eng ICES ICES CM Documents;1996/D:16 This report is not to be cited without prior reference to the authors http://hdl.handle.net/11250/105552 28 s. environmental data miljødata fisheries research fiskeriforskning distribution utbredelse stock assessment bestandsberegning VDP::Mathematics and natural science: 400::Geosciences: 450::Oceanography: 452 VDP::Agriculture and fishery disciplines: 900::Fisheries science: 920::Fish health: 923 Working paper 1996 ftimr 2021-09-23T20:15:04Z The geographical information very often is lost during calculation of indices for the abundance from surveys. Isoplet diagrams showing the distribution of both fish abundance and temperature/salinity are usually produced by hand by skilled personnel. In order to find an objective approach we explored a new stochastic method for abundance calculation and isoplet diagram drawing, using the geostatistics presented in this paper. The potential of geostatistical methods is to analyse the structural pattern of spatially dependent variables, model it and use it to estimate the unknown values at the unsampled locations. Moreover it gives the variance of the estimation error at a location and the variance of the global estimated value which other methods do not give. At each location we can store the estimated values of different spatial dependent variables for the purpose of studying the relationship among them and presenting graphic plots showing the geographic distribution pattem of each variable. Furthermore the structural pattem of each variable can be used to detect the interrelationship among them. This presentation demonstrates these points with the following data: Temperature, salinity, bottom depth, and 0-group cod density in the Barents sea. Report Barents Sea Institute for Marine Research: Brage IMR Barents Sea |
institution |
Open Polar |
collection |
Institute for Marine Research: Brage IMR |
op_collection_id |
ftimr |
language |
English |
topic |
environmental data miljødata fisheries research fiskeriforskning distribution utbredelse stock assessment bestandsberegning VDP::Mathematics and natural science: 400::Geosciences: 450::Oceanography: 452 VDP::Agriculture and fishery disciplines: 900::Fisheries science: 920::Fish health: 923 |
spellingShingle |
environmental data miljødata fisheries research fiskeriforskning distribution utbredelse stock assessment bestandsberegning VDP::Mathematics and natural science: 400::Geosciences: 450::Oceanography: 452 VDP::Agriculture and fishery disciplines: 900::Fisheries science: 920::Fish health: 923 Stensholt, Boonchai K. Sunnanå, Knut Spatial distributions of variables in marine environmental and fisheries research. Part 1: Geostatistics and autocorrelated environmental and fisheries data |
topic_facet |
environmental data miljødata fisheries research fiskeriforskning distribution utbredelse stock assessment bestandsberegning VDP::Mathematics and natural science: 400::Geosciences: 450::Oceanography: 452 VDP::Agriculture and fishery disciplines: 900::Fisheries science: 920::Fish health: 923 |
description |
The geographical information very often is lost during calculation of indices for the abundance from surveys. Isoplet diagrams showing the distribution of both fish abundance and temperature/salinity are usually produced by hand by skilled personnel. In order to find an objective approach we explored a new stochastic method for abundance calculation and isoplet diagram drawing, using the geostatistics presented in this paper. The potential of geostatistical methods is to analyse the structural pattern of spatially dependent variables, model it and use it to estimate the unknown values at the unsampled locations. Moreover it gives the variance of the estimation error at a location and the variance of the global estimated value which other methods do not give. At each location we can store the estimated values of different spatial dependent variables for the purpose of studying the relationship among them and presenting graphic plots showing the geographic distribution pattem of each variable. Furthermore the structural pattem of each variable can be used to detect the interrelationship among them. This presentation demonstrates these points with the following data: Temperature, salinity, bottom depth, and 0-group cod density in the Barents sea. |
format |
Report |
author |
Stensholt, Boonchai K. Sunnanå, Knut |
author_facet |
Stensholt, Boonchai K. Sunnanå, Knut |
author_sort |
Stensholt, Boonchai K. |
title |
Spatial distributions of variables in marine environmental and fisheries research. Part 1: Geostatistics and autocorrelated environmental and fisheries data |
title_short |
Spatial distributions of variables in marine environmental and fisheries research. Part 1: Geostatistics and autocorrelated environmental and fisheries data |
title_full |
Spatial distributions of variables in marine environmental and fisheries research. Part 1: Geostatistics and autocorrelated environmental and fisheries data |
title_fullStr |
Spatial distributions of variables in marine environmental and fisheries research. Part 1: Geostatistics and autocorrelated environmental and fisheries data |
title_full_unstemmed |
Spatial distributions of variables in marine environmental and fisheries research. Part 1: Geostatistics and autocorrelated environmental and fisheries data |
title_sort |
spatial distributions of variables in marine environmental and fisheries research. part 1: geostatistics and autocorrelated environmental and fisheries data |
publisher |
ICES |
publishDate |
1996 |
url |
http://hdl.handle.net/11250/105552 |
geographic |
Barents Sea |
geographic_facet |
Barents Sea |
genre |
Barents Sea |
genre_facet |
Barents Sea |
op_source |
28 s. |
op_relation |
ICES CM Documents;1996/D:16 This report is not to be cited without prior reference to the authors http://hdl.handle.net/11250/105552 |
_version_ |
1766370463176458240 |