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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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Presentation Transcript
spatial modelleing

Statistiskt samband

GRASP, Maxent

Modell

Prediktion

Spatial modelleing
marine geology
Marine geology

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

quality of bathymetry
Quality of bathymetry

Sjökort Markallen

R2 = 0.59

Multi-beam Persgrunden

R2 = 0.95

slide13

VindValFiskGIS

Presence of fish Stensnultra

cvROC=0,843

ROC=0,889

cvCOR=0,63

COR=0,682

slide14

Probability of Blue mussel

Foto Vattenkikaren

slide17

Forsmark area, Bothnian Sea (SKB)

Predator fish, biomass

slide18

Probability of Nephrops burrows

(BALANCE)

Spearman Corr 0.659

why eunis
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
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
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
Analyses aims
  • Describe species associations in Baltic phytobenthic communities
  • Test which environmental factors are important to explain these associations
slide24
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)
mvs for identification of categories

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
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

Discussion

  • Data not representing the whole Baltic
conclusions
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
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

Wave models vs. Biological exposure index (BEI)

BioEx

R2 = 39.6

SWM

R2 = 55.2

FWM

R2 = 48.9

STWAVE

R2 = 36.2

utsj banks inventering 2
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