A protocol for classifying ecologically relevant marine zones, a statistical approach

Mapping ecologically relevant zones in the marine environment has become increasingly important. Biological data are however often scarce and alternatives are being sought in optimal classifications of abiotic variables. The concept of 'marine landscapes' is based on a hierarchical classification of geological, hydrographic and other physical data. This approach is however subject to many assumptions and subjective decisions. An objective protocol for zonation is being proposed here where abiotic variables are subjected to a statistical approach, using principal components analysis (PCA) and a cluster analysis. The optimal number of clusters (or zones) is being defined using the Calinski-Harabasz criterion. The methodology has been applied on datasets of the Belgian part of the North Sea (BPNS), a shallow sandy shelf environment with a sandbank-swale topography. The BPNS was classified into 8 zones that represent well the natural variability of the seafloor. The internal cluster consistency was validated with a split-run procedure, with more than 99% correspondence between the validation and the original dataset. The ecological relevance of 6 out of the 8 zones was demonstrated, using indicator species analysis. The proposed protocol, as exemplified for the BPNS, can easily be applied to other areas and provides a strong knowledge basis for environmental protection and management of the marine environment. A SWOT-analysis, showing the strengths, weaknesses, opportunities and threats of the protocol was performed. (C) 2009 Elsevier Ltd. All rights reserved.

[1]  I. Akoumianaki,et al.  Spatial variability and dynamics of macrobenthos in a Mediterranean delta front area: The role of physical processes , 2007 .

[2]  D. Chessel,et al.  ECOLOGICAL-NICHE FACTOR ANALYSIS: HOW TO COMPUTE HABITAT-SUITABILITY MAPS WITHOUT ABSENCE DATA? , 2002 .

[3]  Steven Degraer,et al.  Macrobenthic community structure of soft-bottom sediments at the Belgian Continental Shelf , 2004 .

[4]  Edward J. Gregr,et al.  Adaptive classification of marine ecosystems: Identifying biologically meaningful regions in the marine environment , 2007 .

[5]  B. Todd,et al.  Classification of Marine Sublittoral Habitats, with Application to the Northeastern North America Region , 2005 .

[6]  R. Courtney,et al.  Stock evaluation of giant scallop (Placopecten magellanicus) using high-resolution acoustics for seabed mapping , 2003 .

[7]  D. Fairbanks,et al.  Physio-climatic classification of South Africa's woodland biome , 2000, Plant Ecology.

[8]  Miklas Scholz,et al.  Conceptual classification model for Sustainable Flood Retention Basins. , 2009, Journal of environmental management.

[9]  L. Hubert,et al.  A general statistical framework for assessing categorical clustering in free recall. , 1976 .

[10]  P. Legendre.  Reply to the comment by Preston and Kirlin on "Acoustic seabed classification: improved statistical method" 1 , 2003 .

[11]  D. Mouillot,et al.  Indicator species analysis as a test of non-random distribution of species in the context of marine protected areas , 2002, Environmental Conservation.

[12]  Jan Lepš,et al.  Multivariate Analysis of Ecological Data , 2006 .

[13]  J. Heino,et al.  Assessing physical surrogates for biodiversity : Do tributary and stream type classifications reflect macroinvertebrate assemblage diversity in running waters? , 2006 .

[14]  Peter Barnes,et al.  Benthic Habitats and the Effects of Fishing , 2005 .

[15]  P. Legendre,et al.  SPECIES ASSEMBLAGES AND INDICATOR SPECIES:THE NEED FOR A FLEXIBLE ASYMMETRICAL APPROACH , 1997 .

[16]  N. Loneragan,et al.  Mapping and characterising subtropical estuarine landscapes using aerial photography and GIS for potential application in wildlife conservation and management , 2005 .

[17]  Philip M. Perrin,et al.  A CLUSTER ANALYSIS APPROACH TO CLASSIFYING IRISH NATIVE WOODLANDS , 2022, Biology and Environment: Proceedings of the Royal Irish Academy.

[18]  P Goovaerts,et al.  Benthic biotope index for classifying habitats in the Sado Estuary: Portugal. , 2005, Marine environmental research.

[19]  D. Wishart.  Clustan : user manual , 1978 .

[20]  J. Guinan,et al.  Multiscale Terrain Analysis of Multibeam Bathymetry Data for Habitat Mapping on the Continental Slope , 2007 .

[21]  Craig J. Brown,et al.  Small-scale Mapping of Sea-bed Assemblages in the Eastern English Channel Using Sidescan Sonar and Remote Sampling Techniques , 2002 .

[22]  Alan R. Orpin,et al.  Towards a statistically valid method of textural sea floor characterization of benthic habitats , 2006 .

[23]  D. Wright,et al.  A Benthic Terrain Classification Scheme for American Samoa , 2006 .

[24]  G. Fader,et al.  Benthic habitat mapping on the Scotian Shelf based on multibeam bathymetry, surficial geology and sea floor photographs , 2001 .

[25]  P. Shin,et al.  Sediment characteristics and colonization of soft-bottom benthos: a field manipulation experiment , 1997 .

[26]  M. Hill,et al.  Data analysis in community and landscape ecology , 1987 .

[27]  Marc Van Meirvenne,et al.  Multivariate geostatistics for the predictive modelling of the surficial sand distribution in shelf seas , 2006 .

[28]  R. Coccioni,et al.  Benthic foraminifera for heavy metal pollution monitoring: A case study from the central Adriatic Sea coast of Italy , 2008 .

[29]  Mark E. Taylor,et al.  Geophysical approaches to the classification, delineation and monitoring of marine habitats and their communities , 2003 .

[30]  Girish N. Punj,et al.  Cluster Analysis in Marketing Research: Review and Suggestions for Application , 1983 .

[31]  Antoine Guisan,et al.  Predictive habitat distribution models in ecology , 2000 .

[32]  George Tsirtsis,et al.  ASSESSMENT OF CLUSTERING ALGORITHMS IN DISCRIMINATING EUTROPHIC LEVELS IN COASTAL WATERS , 2008 .

[33]  V. Verneaux,et al.  Relationships of macrobenthos with dissolved oxygen and organic matter at the sediment-water interface in ten French lakes , 2004 .

[34]  Martin J. Baptist,et al.  The distribution of macrozoobenthos in the southern North Sea in relation to meso-scale bedforms , 2006 .

[35]  I. Evans.  Statistical Characterization of Altitude Matrices by Computer. Report 6. An Integrated System of Terrain Analysis and Slope Mapping. , 1979 .

[36]  J. M. Preston,et al.  Comment on "Acoustic seabed classification: improved statistical method" , 2003 .

[37]  T. Caliński,et al.  A dendrite method for cluster analysis , 1974 .

[38]  W. Thuiller,et al.  Predicting species distribution: offering more than simple habitat models. , 2005, Ecology letters.

[39]  J. H. Ward.  Hierarchical Grouping to Optimize an Objective Function , 1963 .

[40]  Steven Degraer,et al.  Management, research and budgetting of aggregates in shelf seas related to end-users (Marebasse) , 2007 .

[41]  André Hardy,et al.  An examination of procedures for determining the number of clusters in a data set , 1994 .

[42]  S. Deleu,et al.  Management, Research and Budgeting of Aggregates in Shelf Seas related to End-users (Marebasse): Scientific Report Year 1 (01/04/2002 - 31/03/2003) , 2003 .

[43]  P. Legendre,et al.  MAPPING OF MARINE SOFT-SEDIMENT COMMUNITIES: INTEGRATED SAMPLING FOR ECOLOGICAL INTERPRETATION , 2004 .

[44]  P. Bermejo-Barrera,et al.  Characterization of surface marine sediments from Ría de Arousa estuary according to extractable humic matter content. , 2006, Chemosphere.

[45]  G. W. Milligan,et al.  An examination of the effect of six types of error perturbation on fifteen clustering algorithms , 1980 .

[46]  László Orlóci,et al.  An Agglomerative Method for Classification of Plant Communities , 1967 .

[47]  Pierre Legendre,et al.  Acoustic seabed classification: improved statistical method , 2002 .

[48]  D. DeMaster,et al.  A synthesis of bentho-pelagic coupling on the Antarctic shelf: Food banks, ecosystem inertia and global climate change , 2006 .

[49]  C. Darwin.  The Origin of Species by Means of Natural Selection, Or, The Preservation of Favoured Races in the Struggle for Life , 1859 .

[50]  Steven Degraer,et al.  Habitat suitability modelling as a mapping tool for macrobenthic communities: An example from the Belgian part of the North Sea , 2008 .

[51]  W. P. Williams,et al.  A comparison of clustering methods for river benthic community analysis , 2004, Hydrobiologia.

[52]  Mark E. Taylor,et al.  National frameworks for marine conservation — a hierarchical geophysical approach , 2000 .

[53]  M. Zacharias,et al.  A Hierarchical Ecological Approach to Conserving Marine Biodiversity , 2000 .

[54]  Steven Degraer,et al.  Where is the worm? Predictive modelling of the habitat preferences of the tube-building polychaete Lanice conchilega , 2008 .

[55]  G. W. Milligan,et al.  An examination of procedures for determining the number of clusters in a data set , 1985 .

[56]  David W. Macdonald,et al.  Predicting mammal species richness and distributions: testing the effectiveness of satellite-derived land cover data , 1999, Landscape Ecology.