Habitat modelling –

Methods and examples

Gdansk2008-06-10

Martin Isæus

www.aquabiota.se

Wave exposure SWM Simplified Wave Model (Isaeus 2004)

16

)*(16

1 i

ii

SWM

WFE

SWM 2007 Wave Exposure

Wave exposure SWM

Wave exposure SWM, recalculated to seafloor

EUNIS, 6 classes

EUNIS, 9 classes

Spatial modelleing

Abundans

Mil

jövari

abel

Statistiskt samband

Modell

GRASP, Maxent

Prediktion

Marine geology

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

Wave exposure STWAVE Storgrunden

STWAVE

-40

-30

-20

-10

0

-40 -30 -20 -10 0

Djup sjökort

Dju

p in

vent

erin

g

R2=0,59

Quality of bathymetry

y = 0.8959x + 3.8023

R2 = 0.9492

0

5

10

15

20

25

30

35

0 5 10 15 20 25 30

Multi-beam PersgrundenR2 = 0.95

Sjökort MarkallenR2 = 0.59

Resolution of indata visible in output

Fucus at Finngrunden, Bothnian Sea

0 6,400 12,8003,200 Kilometers

4

Vågexponering0 - 590,000

590,001 - 708,224

Djup-129.49 - -22

-21.99 - 0

Stensnultra sannolikhet0 - 0.05

0.06 - 0.25

0.26 - 0.5

0.51 - 0.75

0.76 - 1

Presence of fish StensnultracvROC=0,843ROC=0,889cvCOR=0,63COR=0,682

VindValFiskGIS

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 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

- 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

Bal_imp

Cer_ten

CharaCho_fil

Cla_glo

Cla_rup

Cordylo

Ele_cru

Fontinalis

Fuc_ser

Fuc_ves

Fur_lum

Myt_edu

Phy_pse

Pil_Ect

Pol_fuc

Pot_pec

Pot_per

Rho_con

Ruppia

Sph_arc

Zos_mar

axis 1

-1 0 1

axis

2

-1

0

1

bal_imp

cer_ten

chara

cho_filcla_glo

cla_rup

cordylo

ele_cru

fontinalis

fuc_ser

fuc_vesfur_lummyt_edu

phy_pse

pil_ect

pol_fuc

pot_pec

pot_per

rho_con

ruppia

sph_arc

zos_mar

axis 1

-1 0 1

axis

3-1

0

1

Species-environment correlation

bal_impcer_ten

chara

cho_fil

cla_glo cla_rup

cordylo

ele_cru

fontinalis

fuc_ser

fuc_ves fur_lum

myt_eduphy_pse

pil_ect

pol_fuc

pot_pec

pot_per

rho_con

ruppia

sph_arc

zos_mar

axis 1

-1 0 1

axis

2

-1

0

1

Depth

”Salinity”

% hard substrate

MVS for identification of categories

Depth<0.6

Depth<1.5 Depth>1.5

Depth>0.6 Depth<7.3 Depth>7.3

n=234

Cla glo

n=274

Fuc vesn=1173

Myt eduFur lumCer ten

n=517

Myt eduSph arcRho con

Multivariate regression tree(MRT)

EUNIS Suggestion on how to include Baltic

LittoralInfralittor

alCircalitto

ral

Offshore circalitto

ral

Bathyal (?sub-zones)

High energy

High energy

High energy

Moderate energy

Moderate energy

Moderate energy

Low energy

Low energy

Low energy

Variable(estuaries

, Kattegat)

30-0.5 (30-18 in

Kattegat)VarLR - -

BiogenicMarine, variable

34-18 LBR IBR CBR OBR BBR

18-7.57.5-44-0.5

Biogenic 18-0.5 RedLBR RedIBR RedCBR - -Marine >30 MarLGr MarIGr MarCGr MarOGr MarBGr

Variable 30-18 VarLGr - -

Reduced 18-0.5 RedLGr

RedIGr - 3 salinity

categories

RedCGv - 3 salinity categorie

s

? -

Marine >30 MarLSa MarISa MarCSa MarOSa MarBSaVariable 30-18 VarLSa - -

Reduced 18-0.5 RedLSa

RedISa - 3

categories

RedCSa - 3

categories

? -

Marine >30 MarLMu MarIMu MarCMu MarOMu MarBMuVariable 30-18 VarLMu - -

Reduced 18-0.5 RedLMu

RedIMu - 3 salinity categorie

s

RedCMu - 3 salinity categorie

s

? -

Marine >30 MarLMx MarIMx MarCMx MarOMx MarBMxVariable 30-18 VarLMx - -

Reduced 18-0.5 RedLMx

RedIMx - 3 salinity categorie

s

RedCMx - 3 salinity categorie

s

? -

Marine >30 MarLMp MarIMp - - -Variable 30-18 VarLMp VarIMp - - -

Reduced 18-0.5 RedLMp

RedIMp - 4

categories

- - -

Vegetated

sediments

Hard(epibiota)

Rock(bedrock, boulder,

stable cobble/p

ebble, other hard

substrata)

Marine(Atlantic, Mediterra

nean)

RockReduced(Baltic, Black,

lagoons)

?

>30 OCR BR

VarSR

Mixed(not

glacial till)

VarSMx

3 energy categorie

s

3 energy categorie

s

3 energy categorie

s-

Soft(infauna + epibiota)

Gravel & coarse sand

VarSGr

Sand

VarSSa

Mud

VarSMu

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!

Histogram (A1, Himanthalia elongata)

2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

LogSWM

11%

21%

32%

43%

54%

Pe

rce

nt

of

ob

s

Histogram (A1, Ascophyllum nodosum)

5 6 7 8 9 10 11 12 13 14

LogSWM

0102030405060708090

No

of

ob

s

Histogram (A3, Ruppia maritima)

5 6 7 8 9 10 11 12 13 14 15

LogSWM

02468

1012

No

of o

bs

Examples on species distributions in relation to wave exposure

Sites for Biological exposure index (BEI)

BioEx

R2 = 39.6

STWAVE

R2 = 36.2

SWM

R2 = 55.2

FWM

R2 = 48.9

Wave models vs. Biological exposure index (BEI)

Utsjöbanksinventering 2

2008-09Ca 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