Benthos distribution modelling and its relevance for marine ecosystem management

ICES Journal of Marine Science

Volumn 72, Issue 2, 2014, Pages 297-315

  Reiss, Henning ^a   Birchenough, Silvana ^b   Borja, Angel ^c   Buhl Mortensen, Lene ^d   Craeymeersch, Johan ^e   Dannheim, Jennifer ^f   Darr, Alexander ^g   Galparsoro, Ibon ^c   Gogina, Mayya ^g   Neumann, Hermann ^h   Populus, Jacques ⁱ   Rengstorf, Anna M ^j   Valle, Mireia ^c   Hoey, Gert Van ^k   Zettler, Michael L ^g   Degraer, Steven ^l  

^a NORD UNIVERSITY   (Norway)

^b CENTRE FOR ENVIRONMENT   (United Kingdom)

^c MARINE RESEARCH DIVISION   (Spain)

^d INSTITUTE OF MARINE RESEARCH   (Norway)

^e WAGENINGEN UNIVERSITY   (Netherlands)

^f ALFRED WEGENER INSTITUTE FOR POLAR AND MARINE RESEARCH   (Germany)

^g LEIBNIZ INSTITUTE FOR BALTIC SEA RESEARCH   (Germany)

^h SENCKENBERG DEUTSCHES ENTOMOLOGISCHES INSTITUT   (Germany)

ⁱ TECHNOPÔLE BREST IROISE   (France)

^j NATIONAL UNIVERSITY OF IRELAND   (Ireland)

^k INSTITUTE FOR AGRICULTURAL AND FISHERIES RESEARCH ILVO   (Belgium)

^l ROYAL BELGIAN INSTITUTE OF NATURAL SCIENCES   (Belgium)

more..

Author keywords

Ecosystem approach; environmental monitoring; habitat suitability modelling; macrofauna; mapping; marine spatial planning (MSP),predictive modelling; species distribution modelling.

Indexed keywords

ASSESSMENT METHOD; BENTHIC ENVIRONMENT; BENTHOS; BIOLOGICAL INVASION; ECOSYSTEM MANAGEMENT; ENVIRONMENTAL MONITORING; HABITAT CONSERVATION; MARINE ECOSYSTEM; NICHE; POPULATION DISTRIBUTION; POPULATION MODELING;

EID: 84964297861     PISSN: 10543139     EISSN: 10959289     Source Type: Journal    
DOI: 10.1093/icesjms/fsu107     Document Type: Review

References (187)

1. dlandsvik, B. 2008. Marine downscaling of a future climate scenario for the North Sea. Tellus Series A: Dynamic Meteorology and Oceanography, 60: 451-458.
2. Araujo, M. B., and New, M. 2007. Ensemble forecasting of species distributions. Trends in Ecology and Evolution, 22: 42-47.
3. Austin, M. P. 2002. Spatial prediction of species distribution: an interface between ecological theory and statistical modelling. Ecological Modelling, 157: 101-118.
4. Azzurro, E., Soto, S., Garofalo, G., and Maynou, F. 2013. Fistularia commersonii in the Mediterranean Sea: invasion history and distribution modeling based on presence-only records. Biological Invasions, 15: 977-990.
5. Ban, N. C., Alidina, H. M., and Ardron, J. A. 2010. Cumulative impact mapping: advances, relevance and limitations to marine management and conservation, using Canada's Pacific waters as a case study. Marine Policy, 34: 876-886.
6. Beale, C. M., and Lennon, J. J. 2012. Incorporating uncertainty in predictive species distribution modelling. Philosophical Transactions of the Royal Society B: Biological Sciences, 367: 247-258.
7. Beerling,D. J., Huntley, B., and Bailey, J. P. 1995. Climate and the distribution of Fallopia japonica-use of an introduced species to test the predictive capacity of response surfaces. Journal of Vegetation Science, 6: 269-282.
8. Bekkby, T., Rinde, E., Erikstad, L., Bakkestuen, V., Longva, O., Christensen,O., Isaeus, M., et al. 2008. Spatial probability modelling of eelgrass (Zostera marina) distribution on the west coast of Norway. ICES Journal of Marine Science, 65: 1093-1101.
9. Bergström, U., Sundblad, G., Downie, A. L., Snickars, M., Boström, C., and Lindegarth, M. 2013. Evaluating eutrophication management scenarios in the Baltic Sea using species distribution modelling. Journal of Applied Ecology, 50: 680-690.
10. Bijleveld, A. I., van Gils, J. A., van der Meer, J., Dekinga, A., Kraan, C., van derVeer,H.W., and Piersma, T. 2012. Designing a benthic monitoring programme with multiple conflicting objectives. Methods in Ecology and Evolution, 3: 526-536.
11. Birchenough, S. N. R., Boyd, S. E., Vanstaen, K., Coggan, R. A., and Limpenny, D. S. 2010. Mapping an aggregate extraction site off the Eastern English Channel: a methodology in support of monitoring and management. Estuarine Coastal and Shelf Science, 87: 420-430.
12. Birchenough, S. N. R., Degraer, S., Reiss, H., Borja, A., Braeckman, U., Craeymeersch, J., De Mesel, I., et al. 2011. Response of marine benthos to climate change. In ICES Status Report on Climate Change in the North Atlantic, pp. 262. Ed. by P. C. Reid, and L. Valdés. ICES Cooperative Research Report.
13. Borja, A., Elliott, M., Andersen, J. H., Cardoso, A. C., Carstensen, J., Ferreira, J. G., Heiskanen, A. S., et al. 2013. Good Environmental Status of marine ecosystems: what is it and how do we know when we have attained it Marine Pollution Bulletin, 76: 16-27.
14. Bos, A. R., Dankers, N., Groeneweg, A. H., Carin,D., Hermus, R., Jager, Z., de Jong, D. J., et al. 2005. Eelgrass (Zostera marina L.) in sthe western Wadden Sea: monitoring, habitat suitability model, transplantations and communication. In Proceedings "Dunes and Estuaries 2005"-International Conference on Nature Restoration Practices in European Coastal Habitats, pp. 95-109. Ed. by J-L. Herrier, J. Mees, A. Salman, J. Seys, H. van Nieuwenhuyse, and I. Dobbelaere. VLIZ Special Publication, Koksijde, Belgium.
15. Botkin, D. B., Saxe, H., Araú jo, M. B., Betts, R., Bradshaw, R. H. W., Cedhagen, T., Chesson, P., et al. 2007. Forecasting the effects of global warming on biodiversity. BioScience, 57: 227-236.
16. Boyce, M. S.,Vernier, P. R., Nielsen, S. E., and Schmiegelow, F. K. A. 2002. Evaluating resource selection functions. Ecological Modelling, 157: 281-300.
17. Braeckman, U., Rabaut, M., Vanaverbeke, J., Degraer, S., and Vincx, M. 2014. Protecting the commons: the use of subtidal ecosystem engineers in marine management. Aquatic Conservation: Marine and Freshwater Ecosystems, in press, doi:10.1002/aqc.2448.
18. Bremner, J. 2008. Species' traits and ecological functioning in marine conservation and management. Journal of Experimental Marine Biology and Ecology, 366: 37-47.
19. Broennimann, O., Treier, U. A., Muller-Scharer, H., Thuiller, W., Peterson, A. T., and Guisan, A. 2007. Evidence of climatic niche shift during biological invasion. Ecology Letters, 10: 701-709.
20. Brown, C. J., Smith, S. J., Lawton, P., and Anderson, J. T. 2011. Benthic habitat mapping: a review of progress towards improved understanding of the spatial ecology of the seafloor using acoustic techniques. Estuarine, Coastal and Shelf Science, 92: 502-520.
21. Bryan, T. L., and Metaxas, A. 2007. Predicting suitable habitat for deepwater gorgonian corals on the Atlantic and Pacific Continental Margins of North America. Marine Ecology Progress Series, 330: 113-126.
22. Bucas, M., Bergström, U., Downie, A. L., Sundblad, G., Gullström, M., von Numers, M., S iaulys, A., et al. 2013. Empirical modelling of benthic species distribution, abundance, and diversity in the Baltic Sea: evaluating the scope for predictive mapping using different modelling approaches. ICES Journal of Marine Science, 70: 1233-1243.
23. Buckley, L. B.,Urban, M. C., Angilletta, M. J., Crozier, L. G., Rissler, L. J., and Sears, M.W. 2010. Can mechanism inform species' distribution models Ecology Letters, 13: 1041-1054.
24. Buhl-Mortensen, L., Vanreusel, A., Gooday, A. J., Levin, L. A., Priede, I.G.,Buhl-Mortensen,P.,Gheerardyn,H., et al. 2010.Biological structures as a source of habitat heterogeneity and biodiversity on the deep ocean margins. Marine Ecology: an Evolutionary Perspective, 31: 21-50.
25. Buhl-Mortensen, P., Dolan, M., and Buhl-Mortensen, L. 2009. Prediction of benthic biotopes on a Norwegian offshore bank using a combination of multivariate analysis and GIS classification. ICES Journal of Marine Science, 66: 2026-2032.
26. Buongiorno Nardelli, B., Tronconi, C., Pisano, A., and Santoleri, R. 2013. High and Ultra-High resolution processing of satellite Sea Surface Temperature data over Southern European Seas in the framework of MyOcean project. Remote Sensing of Environment, 129: 1-16.
27. Cade, B. S., and Noon, B. R. 2003. A gentle introduction to quantile regression for ecologists. Frontiers in Ecology and the Environment, 1: 412-420.
28. Carlström, J., Carlén, I., and Isæus, M. 2009. Ecosystem approach to marine spatial planning-Polish marine areas and the Natura2000 network. AquaBiota Report 2009, 02: 1-31.
29. Chen, C. S., Gao, G. P., Qi, J. H., Proshutinsky, A., Beardsley, R. C., Kowalik, Z., Lin, H. C., et al. 2009. A new high-resolution unstructured grid finite volume Arctic Ocean model (AO-FVCOM): an application for tidal studies. Journal of Geophysical Research: Oceans, 114: C08017, doi:10.1029/2008JC004941.
30. Cheung,W.W. L., Lam, V.W. Y., and Pauly,D. 2008.Modelling present and climate-shifted distribution of marine fishes and invertebrates. Fisheries Centre Research Reports, 16/3. 72 pp.
31. Cheung,W.W. L., Lam, V.W. Y., Sarmiento, J. L., Kearney, K.,Watson, R., and Pauly,D. 2009. Projecting global marine biodiversity impacts under climate change scenarios. Fish and Fisheries, 10: 235-251.
32. Cheung, W. W. L., Meeuwig, J. J., Feng, M., Harvey, E., Lam, V. W. Y., Langlois, T., Slawinski, D., et al. 2012. Climate-change induced tropicalisation of marine communities in Western Australia. Marine and Freshwater Research, 63: 415-427.
33. Cho, Y., Lee,W. C., Hong, S., Kim, H. C., and Kim, J. B. 2012. GIS-based suitable site selection using habitat suitability index for oyster farms in Geoje-Hansan Bay, Korea. Ocean and Coastal Management, 56: 10-16.
34. Cohen, J. E. 1978. Food Webs and Niche Space, p. 189. Princeton University Press, Princeton, NJ.
35. Crall, A. W., Jarnevich, C. S., Panke, B., Young, N., Renz, M., and Morisette, J. 2013. Using habitat suitability models to target invasive plant species surveys. Ecological Applications, 23: 60-72.
36. Crowder, L., and Norse, E. 2008. Essential ecological insights for marine ecosystem-based management and marine spatial planning. Marine Policy, 32: 772-778.
37. Darr, A., Gogina, M., and Zettler, M. L. 2014a. Detecting hot-spots of bivalve biomass in the south-western Baltic Sea. Journal of Marine Systems, 134: 69-80.
38. sDarr, A., Gogina, M., and Zettler, M. L. 2014b. Functional changes in benthic communities along a salinity gradient-a western Baltic case study. Journal of Sea Research, 85: 315-324.
39. Davies, A. J., and Guinotte, J. M. 2011. Global habitat suitability for framework-forming cold-water corals. PLoS One, 6: e18483.
40. Davies, C. E., Moss, D., and Hill, M. O. 2004. EUNIS Habitat Classification. Report to the European Topic Centre on Nature Protection and Biodiversity, Paris for European Environment Agency. 307 pp.
41. De la Moriniere, E. C., Pollux, B. J. A., Nagelkerken, I., Hemminga, M. A., Huiskes, A. H. L., and Van der Velde, G. 2003. Ontogenetic dietary changes of coral reef fishes in the mangrove-seagrass-reef continuum: stable isotopes and gut-content analysis. Marine Ecology Progress Series, 246: 279-289.
42. Degraer, S., Verfaillie, E.,Willems,W., Adriaens, E., Vincx, M., and Van Lancker,V. 2008. Habitat suitability modelling as a mapping tool for macrobenthic communities: an example fromthe Belgian part of the North Sea. Continental Shelf Research, 28: 369-379.
43. Diederich, S. 2005. Differential recruitment of introduced Pacific oysters and native mussels at the North Sea coast: coexistence possible Journal of Sea Research, 53: 269-281.
44. Dolbeth, M., Ferreira, O., Teixeira, H., Marques, J. C., Dias, J. A., and Pardal, M. A. 2007. Beach morphodynamic impact on a macrobenthic community along a subtidal depth gradient. Marine Ecology Progress Series, 352: 113-124.
45. Dormann, C. F. 2007. Promising the future Global change projections of species distributions. Basic and Applied Ecology, 8: 387-397.
46. Douvere, F., and Ehler,C.N. 2009. Newperspectives on sea use management: initial findings from European experience with marine spatial planning. Journal of Environmental Management, 90: 77-88.
47. Elith, J., and Graham, C. H. 2009. Do they How do theyWHYdo they differ On finding reasons for differing performances of species distribution models. Ecography, 32: 66-77.
48. Elith, J., Graham, C. H., Anderson, R. P., Dudk, M., Ferrier, S., Guisan, A., Hijmans, R. J., et al. 2006. Novel methods improve prediction of species' distributions from occurrence data. Ecography, 29: 129-151.
49. Elith, J., Kearney, M., and Phillips, S. 2010. The art of modelling rangeshifting species. Methods in Ecology and Evolution, 1: 330-342.
50. Elith, J., and Leathwick, J. R. 2009. Species distribution models: ecological explanation and prediction across space and time. Annual Review of Ecology, Evolution, and Systematics, 40: 677-697.
51. Ellis, J., Ysebaert,T., Hume,T., Norkko, A., Bult,T., Herman, P., Thrush, S., et al. 2006. Predicting macrofaunal species distributions in estuarine gradients using logistic regression and classification systems. Marine Ecology Progress Series, 316: 69-83.
52. Elsäßer, B., Farinãs-Franco, J. M., Wilson, C. D., Kregting, L., and Roberts, D. 2013. Identifying optimal sites for natural recovery and restoration of impacted biogenic habitats in a special area of conservation using hydrodynamic and habitat suitability modelling. Journal of Sea Research, 77: 11-21.
53. Engler, R., Guisan, A., and Rechsteiner, L. 2004. An improved approach for predicting the distribution of rare and endangered species from occurrence and pseudo-absence data. Journal of Applied Ecology, 41: 263-274.
54. Etnoyer, P., and Morgan, L. E. 2007. Predictive habitat model for deep gorgonians needs better resolution: comment on Bryan & Metaxas (2007). Marine Ecology Progress Series, 339: 311-312
55. European Commission. 2008 Roadmap for Maritime Spatial Planning: AchievingCommon Principles in the EU Communication from the Commission: COM(2008) 791 final
56. European Commission. 2010. Commission Decision of 1 September 2010 on criteria and methodological standards on good environmental status of marine waters (notified under document C(2010) 5956)(2010/477/EU). Official Journal of the European Union, L232: 12-24.
57. Falk,W., and Mellert, K. H. 2011. Species distribution models as a tool for forest management planning under climate change: risk evaluation of Abies alba in Bavaria. Journal of Vegetation Science, 22: 621-634.
58. Foley,M. M., Halpern, B. S., Micheli, F., Armsby,M. H., Caldwell, M. R., Crain, C. M., Prahler, E., et al. 2010. Guiding ecological principles for marine spatial planning. Marine Policy, 34: 955-966.
59. Franklin, J. 2009. Mapping Species Distribution: Spatial Inference and Prediction. Cambridge University Press, Cambridge.
60. Franklin, J. 2010. Moving beyond static species distribution models in support of conservation biogeography. Diversity and Distributions, 16: 321-330
61. Freeman, E. A., and Moisen, G. G. 2008. A comparison of the performance of threshold criteria for binary classification in terms of predicted prevalence and kappa. Ecological Modelling, 217: 48-58.
62. Frid, C. L. J., Paramor, O. A. L., Brockington, S., and Bremner, J. 2008. Incorporating ecological functioning into the designation and management of marine protected areas. Hydrobiologia, 606: 69-79.
63. Galparsoro, I., Borja,Á., Bald, J., Liria, P., and Chust, G. 2009. Predicting suitablehabitat for theEuropeanlobster (Homarus gammarus),onthe Basque continental shelf (Bay of Biscay), using Ecological-Niche Factor Analysis. EcologicalModelling, 220: 556-567.
64. Galparsoro, I., Borja, A., Kostylev, V. E., Rodriguez, J. G., Pascual, M., and Muxika, I. 2013. A process-driven sedimentary habitat modelling approach, explaining seafloor integrity and biodiversity assessment within the European Marine Strategy Framework Directive. Estuarine Coastal and Shelf Science, 131: 194-205.
65. Galparsoro, I., Connor, D.W., Borja, A., Aish, A., Amorim, P., Bajjouk, T., Chambers, C., et al. 2012. Using EUNIS habitat classification for benthic mapping in European seas: present concerns and future needs. Marine Pollution Bulletin, 64: 2630-2638.
66. Gimpel, A., Stelzenmüller, V., Cormier, R., Floeter, J., and Temming, A. 2013. A spatially explicit risk approach to support marine spatial planning in the German EEZ. Marine Environmental Research, 86: 56-69.
67. Glémarec, M. 1973. The benthic communitites of the European North Atlantic continental shelf. Oceanography and Marine Biology: an Annual Review, 11: 263-289.
68. Gogina, M., Glockzin, M., and Zettler, M. L. 2010a. Distribution of benthic macrofaunal communities in the western Baltic Sea with regard to near-bottom environmental parameters. 1. Causal analysis. Journal of Marine Systems, 79: 112-123.
69. Gogina, M., Glockzin, M., and Zettler, M. L. 2010b. Distribution of benthic macrofaunal communities in the western Baltic Sea with regard to near-bottom environmental parameters. 2. Modelling and prediction. Journal of Marine Systems, 80: 57-70.
70. Gogina, M., and Zettler, M. L. 2010. Diversity and distribution of benthic macrofauna in the Baltic Sea: data inventory and its use for species distribution modelling and prediction. Journal of Sea Research, 64: 313-321.
71. Gormley, A. M., Forsyth, D. M., Griffioen, P., Lindeman, M., Ramsey, D. S. L., Scroggie,M.P., andWoodford, L. 2011.Using presence-only and presence-absence data to estimate the current and potential distributions of established invasive species. Journal of Applied Ecology, 48: 25-34.
72. Gormley,K. S. G.,Porter, J. S., Bell,M.C., Hull, A.D., and Sanderson,W. G. 2013. Predictive habitat hodelling as a tool to assess the change in distribution and extent of an OSPAR priority habitat under an increased ocean temperature scenario: consequences for marine protected area networks and management. PLoS One, 8: e68263.
73. Gray, J. S., and Elliott, M. 2009. Ecology of Marine Sediments. Oxford University Press, New York.
74. Guisan, A., Broennimann, O., Engler, R., Vust, M., Yoccoz, N. G., Lehmann, A., and Zimmermann, N. E. 2006a. Using niche-based models to improve the sampling of rare species. Conservation Biology, 20: 501-511.
75. Guisan, A., Lehmann, A., Ferrier, S., Austin, M., Overton, J. M. C., Aspinall, R., and Hastie, T. 2006b. Making better biogeographical predictions of species' distributions. Journal of Applied Ecology, 43: 386-392.
76. Guisan, A., and Thuiller,W. 2005. Predicting species distribution: offering more than simple habitat models. Ecology Letters, 8: 993-1009.
77. Guisan, A., Tingley, R., Baumgartner, J. B., Naujokaitis-Lewis, I., Sutcliffe, P. R., Tulloch, A. I. T., Regan, T. J., et al. 2013. Predicting species distributions for conservation decisions. Ecology Letters, 16: 1424-1435.
78. Guisan, A., and Zimmermann, N. E. 2000. Predictive habitat distribution models in ecology. Ecological Modelling, 135: 147-186.
79. Halpern, B. S., Walbridge, S., Selkoe, K. A., Kappel, C. V., Micheli, F., D'Agrosa, C., Bruno, J. F., et al. 2008. A global map of human impact on marine ecosystems. Science, 319: 948-952.
80. Hansen, A. J. 2011. Contribution of source-sink theory to protected area science. In Sources, Sinks and Sustainability, pp. 339-360. Ed. by J. Liu, V. Hull, A. Morzillo, and J.Wiens. Cambridge University Press, New York.
81. Hare, J. A., Manderson, J. P., Nye, J. A., Alexander, M. A., Auster, P. J., Borggaard, D. L., Capotondi, A. M., et al. 2012. Cusk (Brosme brosme) and climate change: assessing the threat to a candidate marine fish species under the US Endangered Species Act. ICES Journal of Marine Science, 69: 1753-1768.
82. Heikkinen, R. K., Luoto, M., Araujo, M. B., Virkkala, R., Thuiller, W., and Sykes, M. T. 2006. Methods and uncertainties in bioclimatic envelope modelling under climate change. Progress in Physical Geography, 30: 751-777.
83. Hengl, T., Sierdsema, H., Radovic, A., and Dilo, A. 2009. Spatial prediction of species' distributions fromoccurrence-only records: combining point pattern analysis, ENFA and regression-kriging. Ecological Modelling, 220: 3499-3511.
84. Hering,D. Borja A. Carstensen J. Carvalho L. Elliott M. Feld C. K. Heiskanen A. S. et al. 2010 The European Water Framework Directive at the age of 10: a critical review of the achievements with recommendations for the future. Science of the Total Environment 408 4007-4019.
85. Hintzen, N.T., Bastardie, F., Beare,D., Piet, G. J., Ulrich, C., Deporte, N., Egekvist, J., et al. 2012. VMStools: open-source software for the processing, analysisand visualisation of fisheries logbook andVMSdata. Fisheries Research, 115: 31-43.
86. Hirzel, A. H., Le Lay, G., Helfer, V., Randin, C., and Guisan, A. 2006. Evaluating the ability of habitat suitability models to predict species presences. Ecological Modelling, 199: 142-152.
87. Holt, J., Butenschön, M.,Wakelin, S. L., Artioli, Y., and Allen, J. I. 2012. Oceanic controls on the primary production of the northwest European continental shelf: model experiments under recent past conditions and a potential future scenario. Biogeosciences, 9: 97-117.
88. Holt, J.,Wakelin, S., Lowe, J., and Tinker, J. 2010. The potential impacts of climate change on the hydrography of the northwest European continental shelf. Progress in Oceanography, 86: 361-379.
89. Houziaux, J-S., Craeymeersch, J.,Merckx, B.,Kerckhof, F.,Van Lancker, V., Courtens, W., Stienen, E., et al. 2011. "EnSIS"-Ecosystem Sensitivity to Invasive Species. Belgian Science Policy 2011-Research Programme Science for a Sustainable Development: Final Report, 105 pp.
90. IPCC. 2013. Summary for policymakers. In Climate Change 2013: The Physical Science Basis. Contribution ofWorking Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, p. 2216. Ed. by T. F. Stocker, D. Qin, G-K. Plattner, M. Tignor, S. K. Allen, J. Boschung, A. Nauels, et al. Cambridge University Press, Cambridge.
91. Jackson, J. B. C., Kirby,M. X., Berger,W. H., Bjorndal, K. A., Botsford, L. W., Bourque, B. J., Bradbury, R. H., et al. 2001. Historical overfishing and the recent collapse of coastal ecosystems. Science, 293: 629-638.
92. sJones, C. G., Lawton, J. H., and Shachak, M. 1994. Organisms as ecosystem engineers. Oikos, 69: 373-386.
93. Jones, M. C., Dye, S. R., Pinnegar, J. K., Warren, R., and Cheung, W.W. L. 2012. Modelling commercial fish distributions: prediction and assessment using different approaches. Ecological Modelling, 225: 133-145.
94. Jones, M. C., Dye, S. R., Pinnegar, J. K., Warren, R., and Cheung, W. W. L. 2013. Applying distribution model projections for an uncertain future: the case of the Pacific oyster in UK waters. Aquatic Conservation: Marine and Freshwater Ecosystems, 23: 710-722.
95. Kadmon, R., Farber, O., and Danin, A. 2003. A systematic analysis of factors affecting the performance of climatic envelope models. Ecological Applications, 13: 853-867.
96. Kaiser, M. J., Collie, J. S., Hall, S. J., Jennings, S., and Poiner, I. R. 2002. Modification of marine habitats by trawling activities: prognosis and solutions. Fish and Fisheries, 3: 114-136.
97. Katsanevakis, S., Stelzenmüller, V., South, A., Sørensen, T. K., Jones, P. J. S., Kerr, S., Badalamenti, F., et al. 2011. Ecosystem-based marine spatial management: review of concepts, policies, tools, and critical issues. Ocean and Coastal Management, 54: 807-820.
98. Kissling, W. D., Dormann, C. F., Groeneveld, J., Hickler, T., Kühn, I., McInerny, G. J., Montoya, J. M., et al. 2012. Towards novel approaches to modelling biotic interactions in multispecies assemblages at large spatial extents. Journal of Biogeography, 39: 2163-2178.
99. Knudby, A., Jupiter, S., Roelfsema, C., Lyons, M., and Phinn, S. 2013. Mapping coral reef resilience indicators using field and remotely sensed data. Remote Sensing, 5: 1311-1334.
100. Koubbi, P., Loots, C., Cotonnec, G., Harlay, X., Grioche, A., Vaz, S., Martin, C., et al. 2006. Spatial patterns and GIS habitat modelling of Solea solea, Pleuronectes flesus and Limanda limanda fish larvae in the eastern English Channel during the spring. Scientia Marina, 70: 147-157.
101. Lambert, G. I., Jennings, S., Hiddink, J. G., Hintzen, N. T., Hinz, H., Kaiser, M. J., and Murray, L. G. 2012. Implications of using alternative methods of vessel monitoring system (VMS) data analysis to describe fishing activities and impacts. ICES Journal of Marine Science, 69: 682-693.
102. Leathwick, J., Moilanen, A., Francis, M., Elith, J., Taylor, P., Julian, K., Hastie, T., et al. 2008. Novel methods for the design and evaluation of marine protected areas in offshorewaters. Conservation Letters, 1: 91-102.
103. Lee, J., South, A. B., and Jennings, S. 2010. Developing reliable, repeatable, and accessible methods to provide high-resolution estimates of fishing-effort distributions from vessel monitoring system (VMS) data. ICES Journal of Marine Science, 67: 1260-1271.
104. Legendre, P. 1993. Spatial autocorrelation-trouble or new paradigm Ecology, 74: 1659-1673.
105. Legendre, P., and Legendre, L. 1998. Numerical Ecology. Elsevier, Amsterdam.
106. Lek, S., and Guegan, J. F. 1999. Artificial neural networks as a tool in ecological modelling, an introduction. Ecological Modelling, 120: 65-73.
107. Lenoir, S., Beaugrand, G., and Lecuyer,E. 2011. Modelled spatial distribution of marine fish and projected modifications in the North Atlantic Ocean. Global Change Biology, 17: 115-129.
108. Link, J. 2002. Does food web theory work for marine ecosystems Marine Ecology Progress Series, 230: 1-9.
109. Liu, C. R., Berry, P. M., Dawson, T. P., and Pearson, R. G. 2005. Selecting thresholds of occurrence in the prediction of species distributions. Ecography, 28: 385-393.
110. Lobo, J. M., Jimenez-Valverde, A., and Hortal, J. 2010. The uncertain nature of absences and their importance in species distributionmodelling. Ecography, 33: 103-114.
111. Lobo, J. M., Jiménez-Valverde, A., and Real, R. 2008. AUC: a misleading measure of the performance of predictive distribution models. Global Ecology and Biogeography, 17: 145-151.
112. MacArthur, R. 1955. Fluctuations of animal populations and a measure of community stability. Ecology, 36: 533-536.
113. Markert, A., Wehrmann, A., and Kroncke, I. 2010. Recently established Crassostrea-reefs versus nativeMytilus-beds: differences in ecosystem engineering affects the macrofaunal communities (Wadden Sea of Lower Saxony, southern German Bight). Biological Invasions, 12: 15-32.
114. Maxwell,D. L., Stelzenmuller,V., Eastwood, P.D., and Rogers, S. I. 2009. Modelling the spatial distribution of plaice (Pleuronectes platessa), sole (Solea solea) and thornback ray (Raja clavata) in UK waters for marine management and planning. Journal of Sea Research, 61: 258-267.
115. May, R. M. 1983. The structure of food webs. Nature, 301: 566-568.
116. McCann, R. K., Marcot, B. G., and Ellis, R. 2006. Bayesian belief networks: applications in ecology and natural resource management. Canadian Journal of Forest Research, 36: 3053-3062.
117. McPherson, J. M., and Jetz,W. 2007. Effects of species' ecology on the accuracy of distribution models. Ecography, 30: 135-151.
118. Meißner, K., Darr, A., and Rachor, E. 2008. Development of habitat models for Nephtys species (Polychaeta: Nephtyidae) in the German Bight (North Sea). Journal of Sea Research, 60: 276-291.
119. Meißner, K., Fiorentino, D., Schnurr, S., Martinez Arbizu, P., Huettmann, F., Holst, S., Brix, S., et al. 2014. Distribution of benthic marine invertebrates at northern latitudes-an evaluation applying multi-algorithm species distribution models. Journal of Sea Research, 85: 241-254.
120. Meinesz, A., Belsher, T., Thibaut, T., Antolic, B., Mustapha, K. B., Boudouresque, C-F., Chiaverini, D., et al. 2001. The introduced green alga Caulerpa taxifolia continues to spread in the Mediterranean. Biological Invasions, 3: 201-210.
121. Méléder, V., Populus, J., Guillaumont, B., and Mouquet, P. 2010. Predictive modelling of seabed habitats-case study of subtidal kelp forests on the coast of Brittany, France. Marine Biology, 157: 1525-1541.
122. Millar,C. S., and Blouin-Demers, G. 2012. Habitat suitability modelling for species at risk is sensitive to algorithm and scale: a case study of Blanding's turtle, Emydoidea blandingii, in Ontario, Canada. Journal for Nature Conservation, 20: 18-29.
123. Miller, J. A. 2010. Species distribution modeling. Geography Compass, 4: 490-509.
124. Monk, J., Ierodiaconou, D., Bellgrove, A., Harvey, E., and Laurenson, L. 2011. Remotely sensed hydroacoustics and observation data for predicting fish habitat suitability. Continental Shelf Research, 31: 17-27.
125. Moore, C. H., Harvey, E. S., and Van Niel, K. 2010. The application of predicted habitat models to investigate the spatial ecology of demersal fish assemblages. Marine Biology, 157: 2717-2729.
126. Neumann, H., Kröncke, I., De Boois, I., and Reiss, H. 2013. Climate change facilitated range expansion of the non-native Angular crab Goneplax rhomboides into the North Sea. Marine Ecology Progress Series, 484: 143-153.
127. NyströmSandman, A.,Wikström, S. A., Blomqvist, M., Kautsky, H., and Isaeus, M. 2013. Scale-dependent influence of environmental variables on species distribution: a case study on five coastal benthic species in the Baltic Sea. Ecography, 36: 354-363.
128. Olenin, S., Elliott, M., Bysveen, I., Culverhouse, P. F., Daunys, D., Dubelaar, G. B. J., Gollasch, S., et al. 2011. Recommendations on methods for the detection and control of biological pollution in marine coastal waters. Marine Pollution Bulletin, 62: 2598-2604.
129. Ordonez, V., Rius, M., McQuaid, C. D., Pineda, M. C., Pascual, M., and Turon, X. 2013. Early biotic interactions among introduced and native benthic species reveal cryptic predation and shifts in larval behaviour. Marine Ecology Progress Series, 488: 65-79.
130. Panigada, S., Zanardelli, M., MacKenzie, M., Donovan, C., Mélin, F., and Hammond, P. S. 2008. Modelling habitat preferences for fin whales and striped dolphins in the Pelagos Sanctuary (Western Mediterranean Sea) with physiographic and remote sensing variables. Remote Sensing of Environment, 112: 3400-3412.
131. Pesch, R., Pehlke, H., Jerosch, K., Schröder,W., and Schlüter, M. 2008. Using decision trees to predict benthic communities within and near the German Exclusive Economic Zone (EEZ) of the North Sea. Environmental Monitoring and Assessment, 136: 313-325.
132. Peterson, A. T. 2003. Predicting the geography of species' invasions via ecological niche modeling. Quarterly Review of Biology, 78: 419-433.
133. Phillips, S. J., Dudik, M., Elith, J., Graham, C. H., Lehmann, A., Leathwick, J., and Ferrier, S. 2009. Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data. Ecological Applications, 19: 181-197.
134. Pimm, S. L. 1982. FoodWebs, p. 219. Chapman and Hall, London.
135. Pompe, S., Hanspach, J., Badeck, F., Klotz, S., Thuiller,W., and Kühn, I. 2008. Climate and land use change impacts on plant distributions in Germany. Biology Letters, 4: 564-567.
136. Pulliam, H. R. 2000. On the relationship between niche and distribution. Ecology Letters, 3: 349-361.
137. Quattrini, A. M., Ross, S.W., Carlson, M. C. T., and Nizinski, M. S. 2012. Megafaunal-habitat associations at a deep-sea coral mound off North Carolina, USA. Marine Biology, 159: 1079-1094.
138. Quinoñes-Rivera, Z. J.,Wissel, B., Rabalais, N. N., and Justic, D. 2010. Effects of biological and physical factors on seasonal oxygen dynamics in a stratified, eutrophic coastal ecosystem. Limnology and Oceanography, 55: 289-304.
139. Rabaut, M., Van de Moortel, L., Vincx, M., and Degraer, S. 2010. Biogenic reefs as structuring factor in Pleuronectes platessa (Plaice) nursery. Journal of Sea Research, 64: 102-106.
140. Ready, J., Kaschner, K., South, A. B., Eastwood, P. D., Rees, T., Rius, J., Agbayani, E., et al. 2010. Predicting the distributions of marine organisms at the global scale. Ecological Modelling, 221: 467-478.
141. Rees, S. E., Rodwell, L. D., Attrill, M. J., Austen, M. C., and Mangi, S. C. 2010. The value of marine biodiversity to the leisure and recreation industry and its application to marine spatial planning. Marine Policy, 34: 868-875.
142. Reijonen, A., Nöjd, A., Rousi, H., and Kotilainen, A. 2008. Marine landscapes and benthic habitats in the Archipelago Sea (the Baltic Sea)-a case study. BALANCE Report, 31. 57 pp.
143. Reiss, H., Cunze, S., König, K., Neumann, H., and Kröncke, I. 2011. Species distribution modelling of marine benthos: a North Sea case study. Marine Ecology Progress Series, 442: 71-86.
144. Rengstorf, A. M., Grehan, A., Yesson, C., and Brown, C. 2012. Towards high-resolution habitat suitability modeling of vulnerable marine ecosystems in the deep-sea: resolving terrain attribute dependencies. Marine Geodesy, 35: 343-361.
145. Rice, J., Arvanitidis, C., Borja, A., Frid, C., Hiddink, J. G., Krause, J., Lorance, P., et al. 2012. Indicators for sea-floor integrity under the European Marine Strategy Framework Directive. Ecological Indicators, 12: 174-184.
146. Richardson, A. J., Brown, C. J., Brander, K., Bruno, J. F., Buckley, L., Burrows, M. T., Duarte, C. M., et al. 2012. Climate change and marine life. Biology Letters, 8: 907-909.
147. Richardson, D. M., Iponga, D. M., Roura-Pascual, N., Krug, R. M., Milton, S. J., Hughes, G. O., and Thuiller,W. 2010. Accommodating scenarios of climate change and management in modelling the distribution of the invasive tree Schinus molle in South Africa. Ecography, 33: 1049-1061.
148. Robinson, L. M., Elith, J., Hobday, A. J., Pearson, R. G., Kendall1, B. E., Possingham, H. P., and Richardson, A. J. 2011. Pushing the limits in marine species distribution modelling: lessons from the land present challenges and opportunities. Global Ecology and Biogeography, 20: 789-802.
149. Sagehashi, M. 2008. Ecological informatics: Bayesian networks. In Encyclopedia of Ecology, pp. 3397-3403. Ed. by S. E. Jørgensen, and B. Fath Elsevier Amsterdam.
150. Salomidi, M., Katsanevakis, S., Borja, A., Braeckman, U., Damalas, D., Galparsoro, I., Mifsud, R., et al. 2012. Assessment of goods and services, vulnerability, and conservation status of European seabed biotopes: a stepping stone towards ecosystem-based marine spatial management. Mediterranean Marine Science, 13: 49-88.
151. Sara, G., Palmeri, V., Montalto, V., Rinaldi, A., and Widdows, J. 2013. Parameterisation of bivalve functional traits for mechanistic ecophysiological dynamic energy budget (DEB) models. Marine Ecology Progress Series, 480: 99-117.
152. Saraiva, S., van der Meer, J.,Kooijman, S., and Sousa, T. 2011.Modelling feeding processes in bivalves: a mechanistic approach. Ecological Modelling, 222: 514-523.
153. Saulquin, B., Hamdi, A., Gohin, F., Populus, J., Mangin, A., and Fanton d'Andon, O. 2013. Estimation of the diffuse attenuation coefficient KdPAR using MERIS and application to seabed habitat mapping. Remote Sensing of Environment, 128: 224-233.
154. Sbrocco, E. J., and Barber, P. H. 2013. MARSPEC: ocean climate layers for marine spatial ecology. Ecology, 94: 979. http://dx.doi.org/10.1890/12-1358.1.
155. Simberloff,D., Parker, I. M., andWindle, P. N. 2005. Introduced species policy, management, and future research needs. Frontiers in Ecology and the Environment, 3: 12-20.
156. Sinclair, S. J., White, M. D., and Newell, G. R. 2010. How useful are species distribution models for managing biodiversity under future climates Ecology and Society, 15: 8. http://www.ecologyandsociety.org/vol15/iss1/art8/.
157. Skov, H., Humphreys, E., Garthe, S., Geitner, K., Grémillet, D., Hamer, K. C., Hennicke, J., et al. 2008. Application of habitat suitability modelling to tracking data of marine animals as a means of analyzing their feeding habitats. Ecological Modelling, 212: 504-512.
158. Soberó n, J. 2007 Grinnellian and Eltonian niches and geographic distributions of species. Ecology Letters 10 1115-1123.
159. Stelzenmüller, V., Breen, P., Stamford, T., Thomsen, F., Badalamenti, F., Borja, A., Buhl-Mortensen, L., et al. 2013. Monitoring and evaluation of spatially managed areas: a generic framework for implementation of ecosystem based marine management and its application. Marine Policy, 37: 149-164.
160. Stelzenmüller, V., Lee, J., Garnacho, E., and Rogers, S. I. 2010. Assessment of a Bayesian Belief Network-GIS framework as a practical tool to support marine planning. Marine Pollution Bulletin, 60: 1743-1754.
161. Stockwell, D., and Peters, D. 1999. The GARP modelling system: problems and solutions to automated spatial prediction. International Journal of Geographical Information Science, 13: 143-158.
162. Thrush, S. F., Hewitt, J. E., Norkko, A., Nicholls, P. E., Funnell, G. A., and Ellis, J. I. 2003. Habitat change in estuaries: predicting broad-scale responses of intertidal macrofauna to sediment mud content. Marine Ecology Progress Series, 263: 101-112.
163. Thuiller,W., Brotons, L., Araujo, M. B., and Lavorel, S. 2004. Effects of restricting environmental range of data to project current and future species distributions. Ecography, 27: 165-172.
164. Thuiller,W., Richardson,D. M., Pysek, P., Midgley, G. F., Hughes, G.O., and Rouget, M. 2005. Niche-based modelling as a tool for predicting the risk of alien plant invasions at a global scale. Global Change Biology, 11: 2234-2250.
165. Tillin, H. M., Hiddink, J. G., Jennings, J., and Kaiser,M. J. 2006. Chronic bottom trawling alters the functional composition of benthic invertebrate communities on a sea-basin scale. Marine Ecology Progress Series, 318: 31-45.
166. Tsoar, A., Allouche,O., Steinitz,O., Rotem,D., and Kadmon, R. 2007.A comparative evaluation of presence-only methods for modelling species distribution. Diversity and Distributions, 13: 397-405.
167. Tyberghein, L., Verbruggen, H., Pauly, K., Troupin, C., Mineur, F., and DeClerck,O. 2012.Bio-ORACLE: a global environmental dataset for marine species distribution modelling. Global Ecology and Biogeography, 21: 272-281.
168. Václavk, T., and Meentemeyer, R. K. 2012. Equilibrium or not Modelling potential distribution of invasive species in different stages of invasion. Diversity and Distributions, 18: 73-83
169. Valavanis, V. D., Pierce, G. J., Zuur, A. F., Palialexis, A., Saveliev, A., Katara, I., and Wang, J. J. 2008. Modelling of essential fish habitat based on remote sensing, spatial analysis and GIS. Hydrobiologia, 612: 5-20.
170. Valle, M., Borja, Á., Chust, G., Galparsoro, I., and Garmendia, J. M. 2011. Modelling suitable estuarine habitats for Zostera noltii, using Ecological Niche Factor Analysis and Bathymetric LiDAR. Estuarine, Coastal and Shelf Science, 94: 144-154.
171. Valle, M., Chust, G., del Campo, A., Wisz, M. S., Olsen, S. M., Garmendia, J. M., and Borja, A. 2014. Projecting future distribution of the seagrass Zostera noltii under global warming and sea level rise. Biological Conservation, 170: 74-85.
172. Valle, M., van Katwijk, M. M., de Jong, D. J., Bouma, T. J., Schipper, A. M., Chust, G., Benito, B. M., et al. 2013. Comparing the performance of species distribution models of Zostera marina: implications for conservation. Journal of Sea Research, 83: 56-64.
173. Van Hoey, G., Permuy, D. C., Vandendriessche, S., Vincx, M., and Hostens, K. 2013. An ecological quality status assessment procedure for soft-sediment benthic habitats: weighing alternative approaches. Ecological Indicators, 25: 266-278.
174. Vaz, S., Martin, C. S., Eastwood, P. D., Ernande, B., Carpentier, A., Meaden, G. J., and Coppin, F. 2008. Modelling species distributions using regression quantiles. Journal of Applied Ecology, 45: 204-217.
175. Venables, W. N., and Dichmont, C. M. 2004. GLMs, GAMs and GLMMs: an overview of theory for applications in fisheries research. Fisheries Research, 70: 319-337.
176. Verween, A., Hendrickx, F., Vincx, M., and Degraer, S. 2007. Larval presence prediction through logistic regression: an early warning system against Mytilopsis leucophaeata biofouling. Biofouling, 23: 25-35.
177. Vierod, A. D. T., Guinotte, J. M., and Davies, A. J. 2014. Predicting the distribution of vulnerable marine ecosystems in the deep sea using presence-background models. Deep Sea Research II: Topical Studies in Oceanography, 99: 6-18.
178. Vincenzi, S., Zucchetta, M., Franzoi, P., Pellizzato, M., Pranovi, F., De Leo, G. A., and Torricelli, P. 2011. Application of a Random Forest algorithm to predict spatial distribution of the potential yield of Ruditapes philippinarum in the Venice lagoon, Italy. Ecological Modelling, 222: 1471-1478.
179. Wei, C. L., Rowe, G. T., Escobar-Briones, E., Boetius, A., Soltwedel, T., Caley,M. J., Soliman, Y., et al. 2010. Global patterns and predictions of seafloor biomass using Random Forests. PLoS One, 5: e15323.
180. Wenger, S. J., Som, N. A., Dauwalter, D. C., Isaak, D. J., Neville, H. M., Luce, C. H., Dunham, J. B., et al. 2013. Probabilistic accounting of uncertainty in forecasts of species distributions under climate change. Global Change Biology, 19: 3343-3354.
181. Willems, W., Goethals, P., Van den Eynde, D., Van Hoey, G., Van Lancker, V., Verfaillie, E., Vincx, M., et al. 2008. Where is the worm Predictive modelling of the habitat preferences of the tubebuilding polychaete Lanice conchilega. Ecological Modelling, 212: 74-79.
182. Wilson, M. F. J., O'Connell, B., Brown, C., Guinan, J. C., and Grehan, A. J. 2007. Multiscale terrain analysis of multibeam bathymetry data for habitat mapping on the continental slope. Marine Geodesy, 30: 3-35.
183. Wisz,M.S., and Guisan, A. 2009.Dopseudo-absence selection strategies influence species distribution models and their predictions An information-theoretic approach based on simulated data. BMC Ecology, 9: 8.
184. Wisz,M. S.,Hijmans,R. J.,Li,J.,Peterson,A.T.,Graham,C.H.,Guisan,A., and Distribut,N. P. S. 2008. Effects of sample size on the performance of species distribution models. Diversity and Distributions, 14: 763-773.
185. Ysebaert, T., Meire, P., Herman, P. M. J., and Verbeek, H. 2002. Macrobenthic species response surfaces along estuarine gradients: prediction by logistic regression. Marine Ecology Progress Series, 225: 79-95.
186. Zettler, M. L., Karlsson, A., Kontula, T., Gruszka, P., Laine, A. O., Herkül, K., Schiele, K. S., et al. 2014. Biodiversity gradient in the Baltic Sea: a comprehensive inventory of macrozoobenthos data. Helgoland Marine Research, in press, doi:10.1007/s10152-10013-10368-x.
187. Zuur, A. F., Saveliev, A. A., and Ieno, E. N. 2012. Zero Inflated Models and Generalized Linear Mixed Models with R. Highland Statistics, Newburgh.