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Habitat modelling – Methods and examples Gdansk 2008-06-10. Martin Isæus www.aquabiota.se. 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.

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Habitat modelling methods and examples gdansk 2008 06 10 l.jpg

Habitat modelling – Methods and examples Gdansk2008-06-10

Martin Isæus

www.aquabiota.se


Wave exposure swm simplified wave model isaeus 2004 l.jpg

Wave exposure SWM Simplified Wave Model (Isaeus 2004)


Slide3 l.jpg

SWM 2007 Wave Exposure


Wave exposure swm l.jpg

Wave exposure SWM


Wave exposure swm recalculated to seafloor l.jpg

Wave exposure SWM, recalculated to seafloor


Eunis 6 classes l.jpg

EUNIS, 6 classes


Eunis 9 classes l.jpg

EUNIS, 9 classes


Spatial modelleing l.jpg

Statistiskt samband

GRASP, Maxent

Modell

Prediktion

Spatial modelleing


Marine geology l.jpg

Marine geology

Blue – Till overlays sedimentary rockLight blue – tillOrange – Sand and gravel


Wave exposure stwave storgrunden l.jpg

Wave exposure STWAVE Storgrunden

STWAVE


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Quality of bathymetry

Sjökort Markallen

R2 = 0.59

Multi-beam Persgrunden

R2 = 0.95


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Resolution of indata visible in outputFucus at Finngrunden, Bothnian Sea


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VindValFiskGIS

Presence of fish Stensnultra

cvROC=0,843

ROC=0,889

cvCOR=0,63

COR=0,682


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Probability of Blue mussel

Foto Vattenkikaren


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Cover of Fucus vesiculosus

(Foto H. Kautsky)


Zoarces viviparus cpu l.jpg

Zoarces viviparus CPU


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Forsmark area, Bothnian Sea (SKB)

Predator fish, biomass


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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)


Species associations l.jpg

Species associations


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”Salinity”

% hard substrate

Depth

Species-environment correlation


Mvs for identification of categories l.jpg

Depth<1.5

Depth>1.5

Depth>0.6

Depth<7.3

Depth>7.3

Depth<0.6

n=234

Cla glo

n=274

Fuc ves

n=1173

Myt eduFur lumCer ten

n=517

Myt eduSph arcRho con

MVS for identification of categories

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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Wave models vs. Biological exposure index (BEI)

BioEx

R2 = 39.6

SWM

R2 = 55.2

FWM

R2 = 48.9

STWAVE

R2 = 36.2


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Utsjöbanksinventering 2

Falkens grund

2008-09

Ca 20 bankar

Sydostbrotten

Norra/Södra Långrogrundet

Vernersgrund

Eystrasaltbanken

Sylen

Finngrunden västra banken

Campsgrund

Argos yttergrund

Grundskallegrunden

Märketskallen

Grisbådarna

Svenska Björn

Ursulas grund

Klintgrund

Kummelbank

Utklippan

Hanöbanken

Klippbanken


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