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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)







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



Quality of bathymetry l.jpg
Quality of bathymetry

Sjökort Markallen

R2 = 0.59

Multi-beam Persgrunden

R2 = 0.95


Slide12 l.jpg

Resolution of indata visible in outputFucus at Finngrunden, Bothnian Sea


Slide13 l.jpg

VindValFiskGIS

Presence of fish Stensnultra

cvROC=0,843

ROC=0,889

cvCOR=0,63

COR=0,682


Slide14 l.jpg

Probability of Blue mussel

Foto Vattenkikaren


Cover of fucus vesiculosus l.jpg
Cover of Fucus vesiculosus

(Foto H. Kautsky)



Slide17 l.jpg

Forsmark area, Bothnian Sea (SKB)

Predator fish, biomass


Slide18 l.jpg

Probability of Nephrops burrows

(BALANCE)

Spearman Corr 0.659


Why eunis l.jpg
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)


This initiative to get the process started l.jpg
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 l.jpg
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 l.jpg
Analyses aims

  • Describe species associations in Baltic phytobenthic communities

  • Test which environmental factors are important to explain these associations


Slide24 l.jpg
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 environment correlation l.jpg

”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)



Balmar l.jpg
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


Slide30 l.jpg

Discussion

  • Data not representing the whole Baltic


Conclusions l.jpg
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 l.jpg
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!




Slide35 l.jpg

Wave models vs. Biological exposure index (BEI) exposure

BioEx

R2 = 39.6

SWM

R2 = 55.2

FWM

R2 = 48.9

STWAVE

R2 = 36.2


Utsj banks inventering 2 l.jpg
Utsjöbanks exposureinventering 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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