FN Archimer Export Format PT J TI Benthos distribution modelling and its relevance for marine ecosystem management BT AF REISS, Henning BIRCHENOUGH, Silvana BORJA, Angel BUHL-MORTENSEN, Lene CRAEYMEERSCH, Johan DANNHEIM, Jennifer DARR, Alexander GALPARSORO, Ibon GOGINA, Mayya NEUMANN, Hermann POPULUS, Jacques RENGSTORF, Anna M. VALLE, Mireia VAN HOEY, Gert ZETTLER, Michael L. DEGRAER, Steven AS 1:1;2:2;3:3;4:4;5:5;6:6;7:7;8:3;9:7;10:8;11:9;12:10;13:3;14:11;15:7;16:12; FF 1:;2:;3:;4:;5:;6:;7:;8:;9:;10:;11:PDG-ODE-DYNECO-AG;12:;13:;14:;15:;16:; C1 Univ Nordland, Fac Biosci & Aquaculture, N-8049 Bodo, Norway. Cefas, Lowestoft Lab, Lowestoft NR33 0HT, Suffolk, England. AZTI Tecnalia, Div Marine Res, Pasaia 20110, Spain. Inst Marine Res, N-5817 Bergen, Norway. IMARES Wageningen UR Inst Marine Resources & Ecos, NL-4400 AB Yerseke, Netherlands. Helmholtz Ctr Polar & Marine Res, Alfred Wegener Inst, D-27570 Bremerhaven, Germany. Leibniz Inst Balt Sea Res Warnemunde, D-18119 Rostock, Germany. Marine Res Dept, Senckenberg Meer, D-26382 Wilhelmshaven, Germany. Ifremer Ctr Brest Technopole Brest Iroise, F-29280 Plouzane, France. Natl Univ Ireland, Sch Nat Sci, Earth & Ocean Sci, Galway, Ireland. Inst Agr & Fisheries Res, Dept Aquat Environm & Qual, Bioenvironm Res Grp, B-8400 Oostende, Belgium. Royal Belgian Inst Nat Sci, Operat Directorate Nat Environm, Marine Ecol & Management, B-1200 Brussels, Belgium. C2 UNIV NORDLAND, NORWAY CEFAS, UK AZTI, SPAIN IMR, NORWAY IMARES, NETHERLANDS INST A WEGENER, GERMANY LEIBNIZ INST BALT SEA RES (IOW), GERMANY MARINE RES DEPT, GERMANY IFREMER, FRANCE UNIV NATL IRELAND, IRELAND ILVO, BELGIUM ROYAL BELGIAN INST NAT SCI, BELGIUM SI BREST SE PDG-ODE-DYNECO-AG IN WOS Ifremer jusqu'en 2018 copubli-europe IF 2.626 TC 104 UR https://archimer.ifremer.fr/doc/00203/31405/29915.pdf LA English DT Article DE ;ecosystem approach;environmental monitoring;habitat suitability modelling;macrofauna;mapping;marine spatial planning (MSP);predictive modelling;species distribution modelling AB Marine benthic ecosystems are difficult to monitor and assess, which is in contrast to modern ecosystem-based management requiring detailed information at all important ecological and anthropogenic impact levels. Ecosystem management needs to ensure a sustainable exploitation of marine resources as well as the protection of sensitive habitats, taking account of potential multiple-use conflicts and impacts over large spatial scales. The urgent need for large-scale spatial data on benthic species and communities resulted in an increasing application of distribution modelling (DM). The use of DM techniques enables to employ full spatial coverage data of environmental variables to predict benthic spatial distribution patterns. Especially, statistical DMs have opened new possibilities for ecosystem management applications, since they are straightforward and the outputs are easy to interpret and communicate. Mechanistic modelling techniques, targeting the fundamental niche of species, and Bayesian belief networks are the most promising to further improve DM performance in the marine realm. There are many actual and potential management applications of DMs in the marine benthic environment, these are (i) early warning systems for species invasion and pest control, (ii) to assess distribution probabilities of species to be protected, (iii) uses in monitoring design and spatial management frameworks (e.g. MPA designations), and (iv) establishing long-term ecosystem management measures (accounting for future climate-driven changes in the ecosystem). It is important to acknowledge also the limitations associated with DM applications in a marine management context as well as considering new areas for future DM developments. The knowledge of explanatory variables, for example, setting the basis for DM, will continue to be further developed: this includes both the abiotic (natural and anthropogenic) and the more pressing biotic (e.g. species interactions) aspects of the ecosystem. While the response variables on the other hand are often focused on species presence and some work undertaken on species abundances, it is equally important to consider, e.g. biological traits or benthic ecosystem functions in DM applications. Tools such as DMs are suitable to forecast the possible effects of climate change on benthic species distribution patterns and hence could help to steer present-day ecosystem management. PY 2015 PD JAN SO Ices Journal Of Marine Science SN 1054-3139 PU Oxford Univ Press VL 72 IS 2 UT 000350154300002 BP 297 EP 315 DI 10.1093/icesjms/fsu107 ID 31405 ER EF