FN Archimer Export Format
PT J
TI Modelling the distribution of rare and data-poor diadromous fish at sea for protected area management
BT 
AF ELLIOTT, Sophie A. M.
   ACOU, Anthony
   BEAULATON, Laurent
   GUITTON, Jerome
   REVEILLAC, Elodie
   RIVOT, Etienne
AS 1:1,4,6;2:1,2;3:1,5;4:4;5:3;6:1,4;
FF 1:;2:;3:;4:;5:;6:;
C1 Management of Diadromous Fish in their Environment OFB-INRAE-Institut Agro-UPPA, 35042 Rennes, France
   UMS OFB-CNRS-MNHN PatriNat, Station marine du Museum National d’Histoire Naturelle, 35800 Dinard, France
   Littoral, Environnement et Sociétés (LIENSs), UMR 7266, La Rochelle Université-CNRS, 17000 La Rochelle, France
   DECOD (Ecosystem Dynamics and Sustainability), L’Institut Agro, Ifremer, INRAE, Rennes, France
   Service Conservation et Gestion Durable des Espèces Exploitées, OFB, DRAS, 35042 Rennes, France
   Game & Wildlife Conservation Trust, Salmon & Trout Research Centre, East Stoke, Wareham BH20 6BB, UK
C2 INST AGRO RENNES-ANGERS, FRANCE
   MNHN, FRANCE
   UNIV LA ROCHELLE, FRANCE
   INST AGRO RENNES-ANGERS, FRANCE
   OFB, FRANCE
   GWCT, UK
UM DECOD
IN WOS Cotutelle UMR
   copubli-france
   copubli-europe
   copubli-univ-france
IF 4.1
TC 4
UR https://archimer.ifremer.fr/doc/00820/93182/99782.pdf
LA English
DT Article
DE ;Species Distribution Model;Diadromous fish;Rare species;Imperfect detection;Marine Protected Areas;Bycatch
AB Anthropogenic pressures have resulted in declines in diadromous fish. Many diadromous fish which were commercially important are now threatened and protected. Little is known about their marine life history phases, and no observation-based Species Distribution Model exists for this group of species at sea. Yet, fisheries dependent and independent data could provide new insights into the distribution of diadromous fish at sea.We collated a database of 168 904 hauls from fisheries observer bycatch data and scientific fisheries surveys, from eastern Atlantic and Mediterranean waters. The distribution of eleven rare and data-poor diadromous fish (shads, lampreys, salmonids, the European eel, the thinlip mullet, smelt and the European flounder) were modelled. A Bayesian site occupancy model, that incorporates imperfect detection to account for repeat de-tections and non-detections, the non-random nature of fishing gear type and spatial autocorrelation was used. From the model outputs, we explored bycatch risk and the role of MPAs, required under the Marine Strategy Framework Directive and Habitat Directive and assessed.Diadromous fish were observed within relatively shallow coastal areas. Species specific gear bycatch trends were observed. Core distribution areas corresponded to their known water basin presence, indicating connec-tivity with their freshwater habitats. Numerous Habitat Directive Marine Protected Areas were found to be of relevance.Given the coastal distribution of these species, they are exposed to higher anthropogenic pressures from both terrestrial and marine environments. Risk of bycatch at sea for most species appears to be low. Nonetheless, for threatened individuals, even a small amount of bycatch may impact their populations, especially since mis-reporting is likely to be high. Differences in catchability between gears highlight potential benefits of limiting access of certain gears within protected areas to reduce bycatch. 
PY 2023
PD JAN
SO Progress In Oceanography
SN 0079-6611
PU Pergamon-elsevier Science Ltd
VL 210
UT 000912896200001
DI 10.1016/j.pocean.2022.102924
ID 93182
ER

EF