Benthos distribution modelling and its relevance for marine ecosystem management

Type Article
Date 2015-01
Language English
Author(s) Reiss Henning1, Birchenough Silvana2, Borja Angel3, Buhl-Mortensen Lene4, Craeymeersch Johan5, Dannheim Jennifer6, Darr Alexander7, Galparsoro Ibon3, Gogina Mayya7, Neumann Hermann8, Populus Jacques9, Rengstorf Anna M.10, Valle Mireia3, Van Hoey Gert11, Zettler Michael L.7, Degraer Steven12
Affiliation(s) 1 : Univ Nordland, Fac Biosci & Aquaculture, N-8049 Bodo, Norway.
2 : Cefas, Lowestoft Lab, Lowestoft NR33 0HT, Suffolk, England.
3 : AZTI Tecnalia, Div Marine Res, Pasaia 20110, Spain.
4 : Inst Marine Res, N-5817 Bergen, Norway.
5 : IMARES Wageningen UR Inst Marine Resources & Ecos, NL-4400 AB Yerseke, Netherlands.
6 : Helmholtz Ctr Polar & Marine Res, Alfred Wegener Inst, D-27570 Bremerhaven, Germany.
7 : Leibniz Inst Balt Sea Res Warnemunde, D-18119 Rostock, Germany.
8 : Marine Res Dept, Senckenberg Meer, D-26382 Wilhelmshaven, Germany.
9 : Ifremer Ctr Brest Technopole Brest Iroise, F-29280 Plouzane, France.
10 : Natl Univ Ireland, Sch Nat Sci, Earth & Ocean Sci, Galway, Ireland.
11 : Inst Agr & Fisheries Res, Dept Aquat Environm & Qual, Bioenvironm Res Grp, B-8400 Oostende, Belgium.
12 : Royal Belgian Inst Nat Sci, Operat Directorate Nat Environm, Marine Ecol & Management, B-1200 Brussels, Belgium.
Source Ices Journal Of Marine Science (1054-3139) (Oxford Univ Press), 2015-01 , Vol. 72 , N. 2 , P. 297-315
DOI 10.1093/icesjms/fsu107
WOS© Times Cited 77
Keyword(s) ecosystem approach, environmental monitoring, habitat suitability modelling, macrofauna, mapping, marine spatial planning (MSP), predictive modelling, species distribution modelling
Abstract 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.
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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 (2015). Benthos distribution modelling and its relevance for marine ecosystem management. Ices Journal Of Marine Science, 72(2), 297-315. Publisher's official version : https://doi.org/10.1093/icesjms/fsu107 , Open Access version : https://archimer.ifremer.fr/doc/00203/31405/