section for coastal ecology technical university of denmark

Section for Coastal Ecology Technical University of DenmarkNational Institute of Aquatic Resources

Habitat modeling: linking biology to abiotic predictors

Claus R. Sparrevohn&

Mats Lindegarth

Talk outline

First half: Conceptual part Second part: Methodology part Together with Mats

Fisheries science

Berverton and Holt 1957

-Exploitation pattern and level- Recruitment- Top down controlled

Can we map all marine habitats?

1: Large pelagic speciesCalifornia anchovy

2: Spawning volumeBaltic Cod

3: Nursery size hypothesisKattegat plaice

California anchovy

•Surface frontSpatial stable but seasonal unstable

California anchovy

•Taylor columnSpatial stable but temporal unstable

Baltic cod

ICES CTD stations 1994 to 2005

From Neuenfeldt and Geitner

Baltic cod?

ICES CTD stations Oxygen<2ml/l

From Neuenfeldt and Geitner

Baltic cod?

ICES CTD stations salinity<11 ppt

From Neuenfeldt and Geitner

Baltic cod?

ICES CTD stations Oxygen>2 ml/l, salinity>11 ppt

From Neuenfeldt and Geitner

suitable for cod eggs=reproductive volume

Flatfish nursery grounds

3D time series - Cod spawning habitat volume

Year1960 1965 1970 1975 1980 1985 1990 1995

Hab

itat v

olum

e [k

m3 ]

0

100

200

300

400

500

Baltic cod?

Historical spawning areas for cod in the Baltic Sea. From Bagge, O., Thurow, F., Steffensen, E., Bay, J. 1994. The Baltic Cod. Dana Vol. 10:1-28, modified by Aro, E. 2000. The spatial and temporal distribution patterns of cod (Gadus morhua callarias) in the Baltic Sea and their dependence on environmental variability – implications for fishery management. Academic dissertation. University of Helsinki and Finnish Game and Fisheries Research Institute, Helsinki 2000, ISBN-951-776-271-2, 75 pp.

3: Nursery size hypothesis

Nursery size hypothesis-Argues that there is a relationship between the size of the nursery and the stock

1) Sufficient supply of offshore spawned larvae

3: Nursery size hypothesis

1995 1997

3: Nursery size hypothesis

Year

Pla

ice

per 1

0 m

in

1910 1930 1950 1970 1990 2010

050

100

150

200

Background

•Involved in the InterReg project BALANCE: Mapping juvenile fish abundance based on predictor/fish count data relationships

Predictors:Wave-exposureDist. Shore to 5 mDist. Sample to shoreSlopeNo. Sand banksYearDepthSediment

3: Nursery size hypothesis

Conclusion

• Are all species limited by availability of suitable habitat•Habitat instability in time and place,• Year to year variations in population biomass.

Methods

Do we have the right statistical models and are we using them the right way?:• Different models: Linear vs. non linear models (GLM, GAM), Zero inflated and overdispersed data, use of hurdle models

• Regression threes (Mats)

Methods

Start with a simple GLM• Correlation between predictors• Trends in the residuals

What to do when we have trend in the residuals:• Extend the model with an interaction term• Extend the model with a non-linear predictor (e.g. predictort+predictor^2)• Transform your predictor• Use a GAM model

Methods

Zero inflated data:• Transform to presence/absence•Use other models

050

010

0015

00

Counts of plaice

Freq

0 6 13 21 29 37 45 53 61 69 77 85 93 102

Methods

Delta and hurdle models

Mixture model (ZIP, ZINB)

Thank you