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Mapping Fishing Effort through AIS Data.

Natale F, Gibin M, Alessandrini A, Vespe M, Paulrud A - PLoS ONE (2015)

Bottom Line: After analysing a large AIS dataset for the period January-August 2014 and covering most of the EU waters, we show that AIS was adopted by around 75% of EU fishing vessels above 15 meters of length.Using the Swedish fleet as a case study, we developed a method to identify fishing activity based on the analysis of individual vessels' speed profiles and produce a high resolution map of fishing effort based on AIS data.The method was validated using detailed logbook data and proved to be sufficiently accurate and computationally efficient to identify fishing grounds and effort in the case of trawlers, which represent the largest portion of the EU fishing fleet above 15 meters of length.

View Article: PubMed Central - PubMed

Affiliation: European Commission Joint Research Centre Institute for the Protection and Security of the Citizen, Ispra, Italy.

ABSTRACT
Several research initiatives have been undertaken to map fishing effort at high spatial resolution using the Vessel Monitoring System (VMS). An alternative to the VMS is represented by the Automatic Identification System (AIS), which in the EU became compulsory in May 2014 for all fishing vessels of length above 15 meters. The aim of this paper is to assess the uptake of the AIS in the EU fishing fleet and the feasibility of producing a map of fishing effort with high spatial and temporal resolution at European scale. After analysing a large AIS dataset for the period January-August 2014 and covering most of the EU waters, we show that AIS was adopted by around 75% of EU fishing vessels above 15 meters of length. Using the Swedish fleet as a case study, we developed a method to identify fishing activity based on the analysis of individual vessels' speed profiles and produce a high resolution map of fishing effort based on AIS data. The method was validated using detailed logbook data and proved to be sufficiently accurate and computationally efficient to identify fishing grounds and effort in the case of trawlers, which represent the largest portion of the EU fishing fleet above 15 meters of length. Issues still to be addressed before extending the exercise to the entire EU fleet are the assessment of coverage levels of the AIS data for all EU waters and the identification of fishing activity in the case of vessels other than trawlers.

No MeSH data available.


Example of the validation of the method to detect fishing from non-fishing.Blue points correspond to notifications of fishing activity from the logbook data. Points in red represent the position of AIS messages classified as non-fishing, and point in black are messages classified as fishing. The underlying density raster represents a kernel density estimate of the fishing grounds on the basis of the logbook data. Data refers to the fishing activity of a single vessel between January and August 2014.
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pone.0130746.g002: Example of the validation of the method to detect fishing from non-fishing.Blue points correspond to notifications of fishing activity from the logbook data. Points in red represent the position of AIS messages classified as non-fishing, and point in black are messages classified as fishing. The underlying density raster represents a kernel density estimate of the fishing grounds on the basis of the logbook data. Data refers to the fishing activity of a single vessel between January and August 2014.

Mentions: The coordinates of the fishing operation from the logbooks were used to estimate an utilisation distribution (UD) according to a model in which the use of space can be described by a bivariate probability density function. The UD was estimated with the kernel method developed in [29] and using the R package adehabitarHR [30]. A raster was created from this UD and the values of this raster were associated to each message of the same vessel on the basis of a spatial overlay. Fig 2 gives an example of the validation approach. Each blue point corresponds to a position of a fishing operation from the logbook data. Points in red represent the position of AIS messages classified as non-fishing, and points in black, to messages classified as fishing. The underlying density layer represents the UD estimated from the logbook data. All data refer to the fishing activity of one vessel between January and August 2014.


Mapping Fishing Effort through AIS Data.

Natale F, Gibin M, Alessandrini A, Vespe M, Paulrud A - PLoS ONE (2015)

Example of the validation of the method to detect fishing from non-fishing.Blue points correspond to notifications of fishing activity from the logbook data. Points in red represent the position of AIS messages classified as non-fishing, and point in black are messages classified as fishing. The underlying density raster represents a kernel density estimate of the fishing grounds on the basis of the logbook data. Data refers to the fishing activity of a single vessel between January and August 2014.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4476776&req=5

pone.0130746.g002: Example of the validation of the method to detect fishing from non-fishing.Blue points correspond to notifications of fishing activity from the logbook data. Points in red represent the position of AIS messages classified as non-fishing, and point in black are messages classified as fishing. The underlying density raster represents a kernel density estimate of the fishing grounds on the basis of the logbook data. Data refers to the fishing activity of a single vessel between January and August 2014.
Mentions: The coordinates of the fishing operation from the logbooks were used to estimate an utilisation distribution (UD) according to a model in which the use of space can be described by a bivariate probability density function. The UD was estimated with the kernel method developed in [29] and using the R package adehabitarHR [30]. A raster was created from this UD and the values of this raster were associated to each message of the same vessel on the basis of a spatial overlay. Fig 2 gives an example of the validation approach. Each blue point corresponds to a position of a fishing operation from the logbook data. Points in red represent the position of AIS messages classified as non-fishing, and points in black, to messages classified as fishing. The underlying density layer represents the UD estimated from the logbook data. All data refer to the fishing activity of one vessel between January and August 2014.

Bottom Line: After analysing a large AIS dataset for the period January-August 2014 and covering most of the EU waters, we show that AIS was adopted by around 75% of EU fishing vessels above 15 meters of length.Using the Swedish fleet as a case study, we developed a method to identify fishing activity based on the analysis of individual vessels' speed profiles and produce a high resolution map of fishing effort based on AIS data.The method was validated using detailed logbook data and proved to be sufficiently accurate and computationally efficient to identify fishing grounds and effort in the case of trawlers, which represent the largest portion of the EU fishing fleet above 15 meters of length.

View Article: PubMed Central - PubMed

Affiliation: European Commission Joint Research Centre Institute for the Protection and Security of the Citizen, Ispra, Italy.

ABSTRACT
Several research initiatives have been undertaken to map fishing effort at high spatial resolution using the Vessel Monitoring System (VMS). An alternative to the VMS is represented by the Automatic Identification System (AIS), which in the EU became compulsory in May 2014 for all fishing vessels of length above 15 meters. The aim of this paper is to assess the uptake of the AIS in the EU fishing fleet and the feasibility of producing a map of fishing effort with high spatial and temporal resolution at European scale. After analysing a large AIS dataset for the period January-August 2014 and covering most of the EU waters, we show that AIS was adopted by around 75% of EU fishing vessels above 15 meters of length. Using the Swedish fleet as a case study, we developed a method to identify fishing activity based on the analysis of individual vessels' speed profiles and produce a high resolution map of fishing effort based on AIS data. The method was validated using detailed logbook data and proved to be sufficiently accurate and computationally efficient to identify fishing grounds and effort in the case of trawlers, which represent the largest portion of the EU fishing fleet above 15 meters of length. Issues still to be addressed before extending the exercise to the entire EU fleet are the assessment of coverage levels of the AIS data for all EU waters and the identification of fishing activity in the case of vessels other than trawlers.

No MeSH data available.