Detecting anchored fish aggregating devices (AFADs) and estimating use patterns from vessel tracking data in small-scale fisheries

Abstract Monitoring the use of anchored fish aggregating devices (AFADs) is essential for effective fisheries management. However, detecting the use of these devices is a significant challenge for fisheries management in Indonesia. These devices are continually deployed at large scales, due to large...

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Autores principales:	Ahmad Catur Widyatmoko, Britta Denise Hardesty, Chris Wilcox
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/5d53e6bb27384910994ca6cec03fdf42
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spelling	oai:doaj.org-article:5d53e6bb27384910994ca6cec03fdf422021-12-02T17:41:12ZDetecting anchored fish aggregating devices (AFADs) and estimating use patterns from vessel tracking data in small-scale fisheries10.1038/s41598-021-97227-12045-2322https://doaj.org/article/5d53e6bb27384910994ca6cec03fdf422021-09-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-97227-1https://doaj.org/toc/2045-2322Abstract Monitoring the use of anchored fish aggregating devices (AFADs) is essential for effective fisheries management. However, detecting the use of these devices is a significant challenge for fisheries management in Indonesia. These devices are continually deployed at large scales, due to large numbers of users and high failure rates, increasing the difficulty of monitoring AFADs. To address this challenge, tracking devices were attached to 34 handline fishing vessels in Indonesia over a month period each. Given there are an estimated 10,000–50,000 unlicensed AFADs in operation, Indonesian fishing grounds provided an ideal case study location to evaluate whether we could apply spatial modeling approaches to detect AFAD usage and fish catch success. We performed a spatial cluster analysis on tracking data to identify fishing grounds and determine whether AFADs were in use. Interviews with fishers were undertaken to validate these findings. We detected 139 possible AFADs, of which 72 were positively classified as AFADs. Our approach enabled us to estimate AFAD use and sharing by vessels, predict catches, and infer AFAD lifetimes. Key implications from our study include the potential to estimate AFAD densities and deployment rates, and thus compliance with Indonesia regulations, based on vessel tracking data.Ahmad Catur WidyatmokoBritta Denise HardestyChris WilcoxNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Ahmad Catur Widyatmoko Britta Denise Hardesty Chris Wilcox Detecting anchored fish aggregating devices (AFADs) and estimating use patterns from vessel tracking data in small-scale fisheries
description	Abstract Monitoring the use of anchored fish aggregating devices (AFADs) is essential for effective fisheries management. However, detecting the use of these devices is a significant challenge for fisheries management in Indonesia. These devices are continually deployed at large scales, due to large numbers of users and high failure rates, increasing the difficulty of monitoring AFADs. To address this challenge, tracking devices were attached to 34 handline fishing vessels in Indonesia over a month period each. Given there are an estimated 10,000–50,000 unlicensed AFADs in operation, Indonesian fishing grounds provided an ideal case study location to evaluate whether we could apply spatial modeling approaches to detect AFAD usage and fish catch success. We performed a spatial cluster analysis on tracking data to identify fishing grounds and determine whether AFADs were in use. Interviews with fishers were undertaken to validate these findings. We detected 139 possible AFADs, of which 72 were positively classified as AFADs. Our approach enabled us to estimate AFAD use and sharing by vessels, predict catches, and infer AFAD lifetimes. Key implications from our study include the potential to estimate AFAD densities and deployment rates, and thus compliance with Indonesia regulations, based on vessel tracking data.
format	article
author	Ahmad Catur Widyatmoko Britta Denise Hardesty Chris Wilcox
author_facet	Ahmad Catur Widyatmoko Britta Denise Hardesty Chris Wilcox
author_sort	Ahmad Catur Widyatmoko
title	Detecting anchored fish aggregating devices (AFADs) and estimating use patterns from vessel tracking data in small-scale fisheries
title_short	Detecting anchored fish aggregating devices (AFADs) and estimating use patterns from vessel tracking data in small-scale fisheries
title_full	Detecting anchored fish aggregating devices (AFADs) and estimating use patterns from vessel tracking data in small-scale fisheries
title_fullStr	Detecting anchored fish aggregating devices (AFADs) and estimating use patterns from vessel tracking data in small-scale fisheries
title_full_unstemmed	Detecting anchored fish aggregating devices (AFADs) and estimating use patterns from vessel tracking data in small-scale fisheries
title_sort	detecting anchored fish aggregating devices (afads) and estimating use patterns from vessel tracking data in small-scale fisheries
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/5d53e6bb27384910994ca6cec03fdf42
work_keys_str_mv	AT ahmadcaturwidyatmoko detectinganchoredfishaggregatingdevicesafadsandestimatingusepatternsfromvesseltrackingdatainsmallscalefisheries AT brittadenisehardesty detectinganchoredfishaggregatingdevicesafadsandestimatingusepatternsfromvesseltrackingdatainsmallscalefisheries AT chriswilcox detectinganchoredfishaggregatingdevicesafadsandestimatingusepatternsfromvesseltrackingdatainsmallscalefisheries
_version_	1718379707197554688

Detecting anchored fish aggregating devices (AFADs) and estimating use patterns from vessel tracking data in small-scale fisheries

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