habitat modelling methods and examples gdansk 2008 06 10
Download
Skip this Video
Download Presentation
Habitat modelling – Methods and examples Gdansk 2008-06-10

Loading in 2 Seconds...

play fullscreen
1 / 36

Habitat modelling Methods and examples Gdansk 2008-06-10 - PowerPoint PPT Presentation


  • 86 Views
  • Uploaded on

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.

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'Habitat modelling Methods and examples Gdansk 2008-06-10' - nevan


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
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

ad