Revealing Sea Turtle Behavior in Relation to Fishing Gear Using Color-Coded Spatiotemporal Motion Patterns With Deep Neural Networks

Incidental capture, or bycatch, of marine species is a global conservation concern. Interactions with fishing gear can cause mortality in air-breathing marine megafauna, including sea turtles. Despite this, interactions between sea turtles and fishing gear—from a behavior standpoint—are not sufficie...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Janie L. Reavis, H. Seckin Demir, Blair E. Witherington, Michael J. Bresette, Jennifer Blain Christen, Jesse F. Senko, Sule Ozev
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	green turtle Chelonia mydas behavior recognition color-coding spatiotemporal features neural network Science Q General. Including nature conservation, geographical distribution QH1-199.5
Acceso en línea:	https://doaj.org/article/5e2f5ed0fb3345a4a1b3a93e64fb6920
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:5e2f5ed0fb3345a4a1b3a93e64fb6920
record_format	dspace
spelling	oai:doaj.org-article:5e2f5ed0fb3345a4a1b3a93e64fb69202021-12-01T02:22:16ZRevealing Sea Turtle Behavior in Relation to Fishing Gear Using Color-Coded Spatiotemporal Motion Patterns With Deep Neural Networks2296-774510.3389/fmars.2021.785357https://doaj.org/article/5e2f5ed0fb3345a4a1b3a93e64fb69202021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fmars.2021.785357/fullhttps://doaj.org/toc/2296-7745Incidental capture, or bycatch, of marine species is a global conservation concern. Interactions with fishing gear can cause mortality in air-breathing marine megafauna, including sea turtles. Despite this, interactions between sea turtles and fishing gear—from a behavior standpoint—are not sufficiently documented or described in the literature. Understanding sea turtle behavior in relation to fishing gear is key to discovering how they become entangled or entrapped in gear. This information can also be used to reduce fisheries interactions. However, recording and analyzing these behaviors is difficult and time intensive. In this study, we present a machine learning-based sea turtle behavior recognition scheme. The proposed method utilizes visual object tracking and orientation estimation tasks to extract important features that are used for recognizing behaviors of interest with green turtles (Chelonia mydas) as the study subject. Then, these features are combined in a color-coded feature image that represents the turtle behaviors occurring in a limited time frame. These spatiotemporal feature images are used along a deep convolutional neural network model to recognize the desired behaviors, specifically evasive behaviors which we have labeled “reversal” and “U-turn.” Experimental results show that the proposed method achieves an average F1 score of 85% in recognizing the target behavior patterns. This method is intended to be a tool for discovering why sea turtles become entangled in gillnet fishing gear.Janie L. ReavisH. Seckin DemirBlair E. WitheringtonMichael J. BresetteJennifer Blain ChristenJesse F. SenkoSule OzevFrontiers Media S.A.articlegreen turtleChelonia mydasbehavior recognitioncolor-codingspatiotemporal featuresneural networkScienceQGeneral. Including nature conservation, geographical distributionQH1-199.5ENFrontiers in Marine Science, Vol 8 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	green turtle Chelonia mydas behavior recognition color-coding spatiotemporal features neural network Science Q General. Including nature conservation, geographical distribution QH1-199.5
spellingShingle	green turtle Chelonia mydas behavior recognition color-coding spatiotemporal features neural network Science Q General. Including nature conservation, geographical distribution QH1-199.5 Janie L. Reavis H. Seckin Demir Blair E. Witherington Michael J. Bresette Jennifer Blain Christen Jesse F. Senko Sule Ozev Revealing Sea Turtle Behavior in Relation to Fishing Gear Using Color-Coded Spatiotemporal Motion Patterns With Deep Neural Networks
description	Incidental capture, or bycatch, of marine species is a global conservation concern. Interactions with fishing gear can cause mortality in air-breathing marine megafauna, including sea turtles. Despite this, interactions between sea turtles and fishing gear—from a behavior standpoint—are not sufficiently documented or described in the literature. Understanding sea turtle behavior in relation to fishing gear is key to discovering how they become entangled or entrapped in gear. This information can also be used to reduce fisheries interactions. However, recording and analyzing these behaviors is difficult and time intensive. In this study, we present a machine learning-based sea turtle behavior recognition scheme. The proposed method utilizes visual object tracking and orientation estimation tasks to extract important features that are used for recognizing behaviors of interest with green turtles (Chelonia mydas) as the study subject. Then, these features are combined in a color-coded feature image that represents the turtle behaviors occurring in a limited time frame. These spatiotemporal feature images are used along a deep convolutional neural network model to recognize the desired behaviors, specifically evasive behaviors which we have labeled “reversal” and “U-turn.” Experimental results show that the proposed method achieves an average F1 score of 85% in recognizing the target behavior patterns. This method is intended to be a tool for discovering why sea turtles become entangled in gillnet fishing gear.
format	article
author	Janie L. Reavis H. Seckin Demir Blair E. Witherington Michael J. Bresette Jennifer Blain Christen Jesse F. Senko Sule Ozev
author_facet	Janie L. Reavis H. Seckin Demir Blair E. Witherington Michael J. Bresette Jennifer Blain Christen Jesse F. Senko Sule Ozev
author_sort	Janie L. Reavis
title	Revealing Sea Turtle Behavior in Relation to Fishing Gear Using Color-Coded Spatiotemporal Motion Patterns With Deep Neural Networks
title_short	Revealing Sea Turtle Behavior in Relation to Fishing Gear Using Color-Coded Spatiotemporal Motion Patterns With Deep Neural Networks
title_full	Revealing Sea Turtle Behavior in Relation to Fishing Gear Using Color-Coded Spatiotemporal Motion Patterns With Deep Neural Networks
title_fullStr	Revealing Sea Turtle Behavior in Relation to Fishing Gear Using Color-Coded Spatiotemporal Motion Patterns With Deep Neural Networks
title_full_unstemmed	Revealing Sea Turtle Behavior in Relation to Fishing Gear Using Color-Coded Spatiotemporal Motion Patterns With Deep Neural Networks
title_sort	revealing sea turtle behavior in relation to fishing gear using color-coded spatiotemporal motion patterns with deep neural networks
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/5e2f5ed0fb3345a4a1b3a93e64fb6920
work_keys_str_mv	AT janielreavis revealingseaturtlebehaviorinrelationtofishinggearusingcolorcodedspatiotemporalmotionpatternswithdeepneuralnetworks AT hseckindemir revealingseaturtlebehaviorinrelationtofishinggearusingcolorcodedspatiotemporalmotionpatternswithdeepneuralnetworks AT blairewitherington revealingseaturtlebehaviorinrelationtofishinggearusingcolorcodedspatiotemporalmotionpatternswithdeepneuralnetworks AT michaeljbresette revealingseaturtlebehaviorinrelationtofishinggearusingcolorcodedspatiotemporalmotionpatternswithdeepneuralnetworks AT jenniferblainchristen revealingseaturtlebehaviorinrelationtofishinggearusingcolorcodedspatiotemporalmotionpatternswithdeepneuralnetworks AT jessefsenko revealingseaturtlebehaviorinrelationtofishinggearusingcolorcodedspatiotemporalmotionpatternswithdeepneuralnetworks AT suleozev revealingseaturtlebehaviorinrelationtofishinggearusingcolorcodedspatiotemporalmotionpatternswithdeepneuralnetworks
_version_	1718405903723528192

Revealing Sea Turtle Behavior in Relation to Fishing Gear Using Color-Coded Spatiotemporal Motion Patterns With Deep Neural Networks

Ejemplares similares