Habitat modelling – Methods and examples Gdansk Martin Isæus
Wave exposure SWM Simplified Wave Model (Isaeus 2004)
SWM 2007 Wave Exposure
Wave exposure SWM
Wave exposure SWM, recalculated to seafloor
EUNIS, 6 classes
EUNIS, 9 classes
Spatial modelleing Statistiskt samband Modell GRASP, Maxent Prediktion
Marine geology Blue – Till overlays sedimentary rock Light blue – till Orange – Sand and gravel
Wave exposure STWAVE Storgrunden STWAVE
Quality of bathymetry Multi-beam Persgrunden R 2 = 0.95 Sjökort Markallen R 2 = 0.59
Resolution of indata visible in output Fucus at Finngrunden, Bothnian Sea
Presence of fish Stensnultra cvROC=0,843 ROC=0,889 cvCOR=0,63 COR=0,682 VindVal Fisk GIS
Probability of Blue mussel Foto Vattenkikaren
Cover of Fucus vesiculosus (Foto H. Kautsky)
Zoarces viviparus CPU
Predator fish, biomass Forsmark area, Bothnian Sea (SKB)
Probability of Nephrops burrows (BALANCE) Spearman Corr 0.659
Why EUNIS? -HELCOM Ministerial Meeting 2007 – BSAP, Baltic marine habitat classification system by 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)
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
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
Analyses aims -Describe species associations in Baltic phytobenthic communities -Test which environmental factors are important to explain these associations
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)
Species associations
Species-environment correlation Depth ”Salinity” % hard substrate
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)
EUNIS Suggestion on how to include Baltic
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
Discussion -Data not representing the whole Baltic
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
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!
Examples on species distributions in relation to wave exposure
Sites for Biological exposure index (BEI)
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)
Utsjöbanks inventering 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