FN Archimer Export Format
PT J
TI When Imagery and Physical Sampling Work Together: Toward an Integrative Methodology of Deep-Sea Image-Based Megafauna Identification
BT 
AF Hanafi-Portier, Melissa
   Samadi, Sarah
   Corbari, Laure
   Chan, Tin-Yam
   Chen, Wei-Jen
   Chen, Jhen-Nien
   Lee, Mao-Ying
   Mah, Christopher
   Saucède, Thomas
   Borremans, Catherine
   Olu, Karine
AS 1:1,2;2:2;3:2;4:3;5:4;6:4;7:5;8:6;9:7;10:1;11:1;
FF 1:;2:;3:;4:;5:;6:;7:;8:;9:;10:PDG-REM-EEP-LEP;11:PDG-REM-EEP-LEP;
C1 Laboratoire Environnement Profond, IFREMER, REM/EEP, Centre de Bretagne, Plouzané, France
   UMR 7205 ISYEB, Équipe “Explorations, Espèces et Spéciations”, Muséum National d’Histoire Naturelle, Paris, France
   Institute of Marine Biology and Center of Excellence for the Oceans, National Taiwan Ocean University, Keelung, Taiwan
   Institute of Oceanography, National Taiwan University, Taipei, Taiwan
   Marine Fisheries Division, Fisheries Research Institute, Council of Agriculture, Keelung, Taiwan
   Department of Invertebrate Zoology, National Museum of Natural History, Smithsonian Institution, Washington, DC, United States
   Biogéosciences, UMR 6282, CNRS, Université Bourgogne Franche-Comté, Dijon, France
C2 IFREMER, FRANCE
   MNHN, FRANCE
   UNIV NATL TAIWAN OCEAN NTOU, TAIWAN
   UNIV NATL TAIWAN NTU, TAIWAN
   FRI, TAIWAN
   NHM, USA
   UNIV BOURGOGNE FRANCHE COMTE, FRANCE
SI BREST
SE PDG-REM-EEP-LEP
IN WOS Ifremer UPR
   DOAJ
   copubli-france
   copubli-univ-france
   copubli-int-hors-europe
IF 5.247
TC 4
UR https://archimer.ifremer.fr/doc/00735/84656/89747.pdf
   https://archimer.ifremer.fr/doc/00735/84656/89748.zip
LA English
DT Article
CR ATIMO VATAE
   BENTHEDI I
   BIOMAGLO
   BIOPAPUA
   MADANG 2012
   MADEEP
   PAMELA-MOZ01
   PAMELA-MOZ04
BO Antea
   Le Suroît
   Alis
   L'Atalante
   Pourquoi pas ?
DE ;deep-sea megafauna;image-based identification;biodiversity assessment;identification keys;integrative methodology;towed camera;physical sampling
AB 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. 
PY 2021
PD NOV
SO Frontiers in Marine Science
SN 2296-7745
PU Frontiers Media SA
VL 8
UT 000883848800001
DI 10.3389/fmars.2021.749078
ID 84656
ER

EF