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Modelling and forecasting long-term dynamics of Western Baltic macrobenthic fauna in relation to climate signals and environmental change
Gröger, J.; Rumohr, H. (2006). Modelling and forecasting long-term dynamics of Western Baltic macrobenthic fauna in relation to climate signals and environmental change. J. Sea Res. 55(4): 266-277. https://dx.doi.org/10.1016/j.seares.2005.11.005
In: Journal of Sea Research. Elsevier/Netherlands Institute for Sea Research: Amsterdam; Den Burg. ISSN 1385-1101; e-ISSN 1873-1414, more
Peer reviewed article  
Available in  Authors 
Keywords
    Aquatic communities > Benthos
    Climatic changes
    Environmental effects
    Models
    Population dynamics
    Temporal variations > Long-term changes
    ANE, Germany, Schleswig-Holstein, Kiel Bight [Marine Regions]
    Marine/Coastal
Authors  Top 
  • Gröger, J.
  • Rumohr, H., more
Abstract
    Long-term macrobenthos data from Kiel Bight in the Western Baltic collected between 1968 and 2000 have been correlated with the winter NAO index (North Atlantic Oscillation Index) and other environmental data such as temperature, salinity and oxygen content in the bottom water in order to detect systematic patterns related to so far unexplained abiotic signals in the dynamics of zoobenthic species assemblages. The benthos data come from a cluster of five stations (Süderfahrt/ Millionenviertel) in Kiel Bay. Our investigations concentrated on the macrobenthic dynamics with a focus on the number of species m-2 (species richness). Using logarithms and the time series analysis approach of Box/Jenkins (ARIMA modelling, transfer function modelling) it was shown that species richness was strongly influenced by the winter NAO (adjusted for a linear time trend within the 1968-2000 period) and salinity (with a shift/lag of four years). Bootstrapping experiments (i.e. sampling from the error process) and analysis of prediction power (by means of the one- or more-years leaving-out method) showed that the parameter estimates behaved in a stable way, leading to a relatively robust model.
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