1 1.10 Application of estuarine and coastal classifications in marine ...

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While these are useful guides for the development of classifications, the detailed architecture of the classification and resultant class distributions are also crucial to management when important decisions on management priorities and levels of action are weighted heavily on information provided in a classified map. The utility of a hierarchical classification increases further since many coastal classifications have been developed without a specific application in mind and without knowledge of future applications; many of which would be difficult to perceive given the wide range of current uses for coastal areas and types of coastal management. The recent progress in MSP has drawn heavily on existing classifications, the majority of which were not specifically designed with fully integrated MSP in mind. Significant efforts are underway to evaluate their utility and to better align and integrate local, regional, national and international classification schemes to provide interoperability across schemas and facilitate objective broader scale comparisons. 1.10.1.3 Examples of Hierarchical Classifications The hierarchical marine habitat classification developed for EUNIS (European Nature Information System) (http://eunis.eea.europa.eu/habitats.jsp) integrates and modifies several existing schemas, including the Marine Habitat Classification for Britain and Ireland (Connor et al. 2004; http://www.jncc.gov.uk/MarineHabitatClassification) and classifications developed for the OSPAR (north-east Atlantic), Helsinki (Baltic Sea) and Barcelona (Mediterranean Sea) Conventions to create a pan-European reference set of habitat units with common descriptions. The Marine Habitat Classification for Britain and Ireland is based on multivariate analyses of more than 30,000 biological samples and is well established as a standard tool for marine 10
conservation practitioners, industry regulators and those involved in environmental assessment, survey and management. A novel feature of the classification is the development of a key (analogous to a taxonomic key for individual species) for systematic identification of habitat types. The EUNIS classification scheme was designed to enable comparative referencing and reporting in nature conservation specifically linked to the legal obligations under EC Habitats Directive Annex I and the Bern Convention Resolution No. 4. EUNIS has been constructed to be truly hierarchical in design, with habitat units at each level aiming to be of equivalent ecological importance and with no duplication of lower level units within the higher types. The hierarchy allows mapping at different spatial scales, as demonstrated by the MESH project (Mapping European Seabed Habitats) in a web-based GIS application (http://www.searchmesh.net/) which has collated existing maps of differing levels of detail and standardized them according to the EUNIS scheme. The classification’s use of physical parameters (e.g. substratum, salinity, wave and current energy), linked clearly to changes in community types, provides the basis for predicting the distribution of marine biotopes from existing data (Feral, 1999). Predictive models of EUNIS habitat types have been developed for north-west Europe (Coltman et al. 2008). The comprehensive and hierarchical structure of the EUNIS classification supports diverse management applications including: 1) provision of broad categories for the assessment of the state and trends of nature in the European Environment Agency’s reporting process; 2) supporting the development of the EU NATURA 2000 conservation network and revision of Annex I of the Habitats Directive; 3) obtaining an overview of habitat distribution across Europe; to enable nations to place and assess their habitats in a European context; 4) conducting biodiversity assessments; 5) providing a practical system for the description and monitoring of 11
Page 1 and 2: 1.10 Application of estuarine and c
Page 3 and 4: SYNOPSIS Coastal and marine classif
Page 5 and 6: government and non-governmental man
Page 7 and 8: paucity of species and habitat data
Page 9: and many sub-catastrophic disturban
Page 13 and 14: levels of the hierarchy are defined
Page 15 and 16: distributions, both within and surr
Page 17 and 18: In a hierarchical framework for the
Page 19 and 20: 1.10.2.3 Australian Coastal Classif
Page 21 and 22: Ecological theory predicts that the
Page 23 and 24: outbreaks in the late 1970s (Green
Page 25 and 26: intertidal zones, and shallow coast
Page 27 and 28: 1.10.3.1 Identifying Priority Conse
Page 29 and 30: y the Massachusetts Oceans Act (200
Page 31 and 32: eight color coded zones to provide
Page 33 and 34: appropriate place to investigate fe
Page 35 and 36: 1.10.4.3 Massachusetts Ocean Plan T
Page 37 and 38: One of the foundational concepts un
Page 39 and 40: ecosystems has shown that considera
Page 41 and 42: In May 2004, Germany was the first
Page 43 and 44: improve the design and interpretati
Page 45 and 46: NOAA’s CoastWatch Change Analysis
Page 47 and 48: ground resolution of 30 m. Each cla
Page 49 and 50: also be used to assess the role of
Page 51 and 52: States (Gundlach and Hayes 1978). T
Page 53 and 54: information at the species level. I
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Page 57 and 58: map of the entire Australian shorel
Page 59 and 60: within 11 regions, leading to an ov
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enhancement of certain functions in
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achieved by identifying barriers to
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ecosystem service values extracted
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1.10.12 FUTURE DIRECTIONS AND PRIOR
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1.10.12.1 Linking Patterns and Proc
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approach. New classifications will
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Arundel, H. and Mount, R. 2007. Nat
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Connell, J. H. 1978. Diversity in t
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Duke. N.C., Meynecke, J.O., Dittman
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Green, A.L., C.E. Birkeland, R.H. R
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Hiddink, J. G., Jennings, S., Kaise
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Kostylev, V., Todd, B.J., Fader, G.
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Maxwell, D.L., Stelzenmüller, V.,
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Spatial and temporal patterns in fi
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Sharples, C. 2006. Indicative Mappi
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Wells, S., Ravilous, C., Corcoran,
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FIGURE LEGENDS Figure 1. A) Classes
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Figure 10. Ecological evaluation in
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Figure 21. Combining 'intolerance'
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selected planning units in gray ins
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Box 2. Classifications as thematic
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Figure 1. 1
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Figure 3. 3
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Figure 5. 5
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Figure 7. 7
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Figure 9. 9
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Figure 11. 11
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Figure 13. 13
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Figure 15. Fishing effort (hrs/day)
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Figure 17. 17
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Figure 19. 100% Conversion. 80% 60%
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Figure 21. 21
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Figure 23. 23
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Figure 25. 25
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Figure 27. 27
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Figure 29. 29
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Figure 31. 31
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Figure 33. 33
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