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Habitat modeling: linking biology to abiotic predictors. Claus R. Sparrevohn & Mats Lindegarth. Section for Coastal Ecology Technical University of Denmark National Institute of Aquatic Resources. Talk outline. Second part: Methodology part Together with Mats.
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Habitat modeling: linking biology to abiotic predictors Claus R. Sparrevohn & Mats Lindegarth Section for Coastal Ecology Technical University of Denmark National Institute of Aquatic Resources
Talk outline Second part: Methodology part Together with Mats First half: Conceptual part
Fisheries science • Berverton and Holt 1957 • Exploitation pattern and level • Recruitment • Top down controlled
Can we map all marine habitats? 1: Large pelagic species California anchovy 2: Spawning volume Baltic Cod 3: Nursery size hypothesis Kattegat plaice
California anchovy • Surface front • Spatial stable but seasonal unstable
California anchovy • Taylor column • Spatial 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 suitable for cod eggs = reproductive volume From Neuenfeldt and Geitner
Flatfish nursery grounds 3D time series - Cod spawning habitat volume
Baltic cod? • Historicalspawningareas for cod in the BalticSea. From Bagge, O., Thurow, F., Steffensen, E., Bay, J. 1994. The BalticCod. Dana Vol. 10:1-28, modified by Aro, E. 2000. The spatial and temporal distribution patterns of cod (Gadusmorhuacallarias) in the BalticSea and theirdependenceonenvironmentalvariability – 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
Background • Involved in the InterReg project BALANCE: Mapping juvenile fish abundance based on predictor/fish count data relationships Predictors: Wave-exposure Dist. Shore to 5 m Dist. Sample to shore Slope No. Sand banks Year Depth Sediment
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
Methods Delta and hurdle models Mixture model (ZIP, ZINB)
