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Habitat modelling – Methods and examples Gdansk 2008-06-10 Martin Isæus www.aquabiota.se
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Wave exposure SWM Simplified Wave Model (Isaeus 2004)
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SWM 2007 Wave Exposure
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Wave exposure SWM
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Wave exposure SWM, recalculated to seafloor
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EUNIS, 6 classes
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EUNIS, 9 classes
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Spatial modelleing Statistiskt samband Modell GRASP, Maxent Prediktion
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Marine geology Blue – Till overlays sedimentary rock Light blue – till Orange – Sand and gravel
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Wave exposure STWAVE Storgrunden STWAVE
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Quality of bathymetry Multi-beam Persgrunden R 2 = 0.95 Sjökort Markallen R 2 = 0.59
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Resolution of indata visible in output Fucus at Finngrunden, Bothnian Sea
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Presence of fish Stensnultra cvROC=0,843 ROC=0,889 cvCOR=0,63 COR=0,682 VindVal Fisk GIS
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Probability of Blue mussel Foto Vattenkikaren
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Cover of Fucus vesiculosus (Foto H. Kautsky)
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Zoarces viviparus CPU
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Predator fish, biomass Forsmark area, Bothnian Sea (SKB)
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Probability of Nephrops burrows (BALANCE) Spearman Corr 0.659
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Why EUNIS? -HELCOM Ministerial Meeting 2007 – BSAP, Baltic marine habitat classification system by 2011 -EUNIS - EU Classification system, which also Russia is interested in -HELCOM Habitat Red List, BALANCE Marine Landscapes, Natura2000 habitats -National classifications (Eg. Baltic countries, Germany)
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This initiative – to get the process started -Swedish Environmental Protection Agency (SEPA) -Working group: AquaBiota Water Research (Sweden), Alleco (Finland), Stockholm University (Sweden) -David Connor, JNCC (UK) -Workshop in Stockholm Mars 2007 with participants from Lithuania, Estonia, UK, Germany, Netherlands, Finland, Sweden
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Top-down / Bottom-up -Biological relevance -Which parameters structure the biota? -Which biological assemblages occur? -Statistical analyses -System hierarchy -Comparable to other systems? -GIS layers available? -Manageable complexity? -Relevant for management? -BalMar – classification tool
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Analyses aims -Describe species associations in Baltic phytobenthic communities -Test which environmental factors are important to explain these associations
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Data ->300 diving transects from Swedish and Finnish coasts, >3200 data points -Cover of macroalgae, plants and sessile animals (common species) -Depth, substrate, wave exposure, salinity Analyses -Cluster and nMDS (species associations) -CCA (species-environment correlations)
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Species associations
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Species-environment correlation Depth ”Salinity” % hard substrate
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MVS for identification of categories Depth<0.6 Depth<1.5Depth>1.5 Depth>0.6Depth<7.3Depth>7.3 n=234 Cla glo n=274 Fuc ves n=1173 Myt edu Fur lum Cer ten n=517 Myt edu Sph arc Rho con Multivariate regression tree (MRT)
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EUNIS Suggestion on how to include Baltic
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BalMar -Classification software using EUNIS criteria -Suggests habitat classes biological field data -Using dominant species for classifications, this method should be evaluated -When the method is agreed upon, data sets are classified rapidly
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Discussion -Data not representing the whole Baltic
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Conclusions -All 4 factors relevant, more data for class limits -Only phytobenthic data so far, need for deeper and more sheltered habitats, sediment -Acceptable EUNIS hierarchy -Need for better GIS layers - sediment, wave exposure whole Baltic, bathymetry, salinity
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Next steps -Invite all Baltic nations, with data and participation in the process -A few workshops -Habitat descriptions, harmonisation between countries, conversion tables -Continuation of small group work -Funding for the continuation -Ready by 2011!
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Examples on species distributions in relation to wave exposure
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Sites for Biological exposure index (BEI)
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BioEx R 2 = 39.6 STWAVE R 2 = 36.2 SWM R 2 = 55.2 FWM R 2 = 48.9 Wave models vs. Biological exposure index (BEI)
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Utsjöbanks inventering 2 2008-09 Ca 20 bankar Kummelbank Grisbådarna Hanöbanken Klippbanken Märketskallen Grundskallegrunden Argos yttergrund Finngrunden västra banken Sylen Eystrasaltbanken Norra/Södra Långrogrundet Vernersgrund Sydostbrotten Falkens grund Svenska Björn Utklippan Ursulas grund Campsgrund Klintgrund
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