When Imagery and Physical Sampling Work Together: Toward an Integrative Methodology of Deep-Sea Image-Based Megafauna Identification

Imagery has become a key tool for assessing deep-sea megafaunal biodiversity, historically based on physical sampling using fishing gears. Image datasets provide quantitative and repeatable estimates, small-scale spatial patterns and habitat descriptions. However, taxon identification from images is...

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Autores principales:	Mélissa Hanafi-Portier, Sarah Samadi, Laure Corbari, Tin-Yam Chan, Wei-Jen Chen, Jhen-Nien Chen, Mao-Ying Lee, Christopher Mah, Thomas Saucède, Catherine Borremans, Karine Olu
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	deep-sea megafauna image-based identification biodiversity assessment identification keys integrative methodology towed camera Science Q General. Including nature conservation, geographical distribution QH1-199.5
Acceso en línea:	https://doaj.org/article/e5bf75a143a947169bcb5e381cd1ec72
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spelling	oai:doaj.org-article:e5bf75a143a947169bcb5e381cd1ec722021-11-19T05:52:29ZWhen Imagery and Physical Sampling Work Together: Toward an Integrative Methodology of Deep-Sea Image-Based Megafauna Identification2296-774510.3389/fmars.2021.749078https://doaj.org/article/e5bf75a143a947169bcb5e381cd1ec722021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fmars.2021.749078/fullhttps://doaj.org/toc/2296-7745Imagery has become a key tool for assessing deep-sea megafaunal biodiversity, historically based on physical sampling using fishing gears. Image datasets provide quantitative and repeatable estimates, small-scale spatial patterns and habitat descriptions. However, taxon identification from images is challenging and often relies on morphotypes without considering a taxonomic framework. Taxon identification is particularly challenging in regions where the fauna is poorly known and/or highly diverse. Furthermore, the efficiency of imagery and physical sampling may vary among habitat types. Here, we compared biodiversity metrics (alpha and gamma diversity, composition) based on physical sampling (dredging and trawling) and towed-camera still images (1) along the upper continental slope of Papua New Guinea (sedimented slope with wood-falls, a canyon and cold seeps), and (2) on the outer slopes of the volcanic islands of Mayotte, dominated by hard bottoms. The comparison was done on selected taxa (Pisces, Crustacea, Echinoidea, and Asteroidea), which are good candidates for identification from images. Taxonomic identification ranks obtained for the images varied among these taxa (e.g., family/order for fishes, genus for echinoderms). At these ranks, imagery provided a higher taxonomic richness for hard-bottom and complex habitats, partially explained by the poor performance of trawling on these rough substrates. For the same reason, the gamma diversity of Pisces and Crustacea was also higher from images, but no difference was observed for echinoderms. On soft bottoms, physical sampling provided higher alpha and gamma diversity for fishes and crustaceans, but these differences tended to decrease for crustaceans identified to the species/morphospecies level from images. Physical sampling and imagery were selective against some taxa (e.g., according to size or behavior), therefore providing different facets of biodiversity. In addition, specimens collected at a larger scale facilitated megafauna identification from images. Based on this complementary approach, we propose a robust methodology for image-based faunal identification relying on a taxonomic framework, from collaborative work with taxonomists. An original outcome of this collaborative work is the creation of identification keys dedicated specifically to in situ images and which take into account the state of the taxonomic knowledge for the explored sites.Mélissa Hanafi-PortierMélissa Hanafi-PortierSarah SamadiLaure CorbariTin-Yam ChanWei-Jen ChenJhen-Nien ChenMao-Ying LeeChristopher MahThomas SaucèdeCatherine BorremansKarine OluFrontiers Media S.A.articledeep-sea megafaunaimage-based identificationbiodiversity assessmentidentification keysintegrative methodologytowed cameraScienceQGeneral. Including nature conservation, geographical distributionQH1-199.5ENFrontiers in Marine Science, Vol 8 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	deep-sea megafauna image-based identification biodiversity assessment identification keys integrative methodology towed camera Science Q General. Including nature conservation, geographical distribution QH1-199.5
spellingShingle	deep-sea megafauna image-based identification biodiversity assessment identification keys integrative methodology towed camera Science Q General. Including nature conservation, geographical distribution QH1-199.5 Mélissa Hanafi-Portier Mélissa Hanafi-Portier Sarah Samadi Laure Corbari Tin-Yam Chan Wei-Jen Chen Jhen-Nien Chen Mao-Ying Lee Christopher Mah Thomas Saucède Catherine Borremans Karine Olu When Imagery and Physical Sampling Work Together: Toward an Integrative Methodology of Deep-Sea Image-Based Megafauna Identification
description	Imagery has become a key tool for assessing deep-sea megafaunal biodiversity, historically based on physical sampling using fishing gears. Image datasets provide quantitative and repeatable estimates, small-scale spatial patterns and habitat descriptions. However, taxon identification from images is challenging and often relies on morphotypes without considering a taxonomic framework. Taxon identification is particularly challenging in regions where the fauna is poorly known and/or highly diverse. Furthermore, the efficiency of imagery and physical sampling may vary among habitat types. Here, we compared biodiversity metrics (alpha and gamma diversity, composition) based on physical sampling (dredging and trawling) and towed-camera still images (1) along the upper continental slope of Papua New Guinea (sedimented slope with wood-falls, a canyon and cold seeps), and (2) on the outer slopes of the volcanic islands of Mayotte, dominated by hard bottoms. The comparison was done on selected taxa (Pisces, Crustacea, Echinoidea, and Asteroidea), which are good candidates for identification from images. Taxonomic identification ranks obtained for the images varied among these taxa (e.g., family/order for fishes, genus for echinoderms). At these ranks, imagery provided a higher taxonomic richness for hard-bottom and complex habitats, partially explained by the poor performance of trawling on these rough substrates. For the same reason, the gamma diversity of Pisces and Crustacea was also higher from images, but no difference was observed for echinoderms. On soft bottoms, physical sampling provided higher alpha and gamma diversity for fishes and crustaceans, but these differences tended to decrease for crustaceans identified to the species/morphospecies level from images. Physical sampling and imagery were selective against some taxa (e.g., according to size or behavior), therefore providing different facets of biodiversity. In addition, specimens collected at a larger scale facilitated megafauna identification from images. Based on this complementary approach, we propose a robust methodology for image-based faunal identification relying on a taxonomic framework, from collaborative work with taxonomists. An original outcome of this collaborative work is the creation of identification keys dedicated specifically to in situ images and which take into account the state of the taxonomic knowledge for the explored sites.
format	article
author	Mélissa Hanafi-Portier Mélissa Hanafi-Portier Sarah Samadi Laure Corbari Tin-Yam Chan Wei-Jen Chen Jhen-Nien Chen Mao-Ying Lee Christopher Mah Thomas Saucède Catherine Borremans Karine Olu
author_facet	Mélissa Hanafi-Portier Mélissa Hanafi-Portier Sarah Samadi Laure Corbari Tin-Yam Chan Wei-Jen Chen Jhen-Nien Chen Mao-Ying Lee Christopher Mah Thomas Saucède Catherine Borremans Karine Olu
author_sort	Mélissa Hanafi-Portier
title	When Imagery and Physical Sampling Work Together: Toward an Integrative Methodology of Deep-Sea Image-Based Megafauna Identification
title_short	When Imagery and Physical Sampling Work Together: Toward an Integrative Methodology of Deep-Sea Image-Based Megafauna Identification
title_full	When Imagery and Physical Sampling Work Together: Toward an Integrative Methodology of Deep-Sea Image-Based Megafauna Identification
title_fullStr	When Imagery and Physical Sampling Work Together: Toward an Integrative Methodology of Deep-Sea Image-Based Megafauna Identification
title_full_unstemmed	When Imagery and Physical Sampling Work Together: Toward an Integrative Methodology of Deep-Sea Image-Based Megafauna Identification
title_sort	when imagery and physical sampling work together: toward an integrative methodology of deep-sea image-based megafauna identification
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/e5bf75a143a947169bcb5e381cd1ec72
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When Imagery and Physical Sampling Work Together: Toward an Integrative Methodology of Deep-Sea Image-Based Megafauna Identification

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