Bärbel Müller-Karulis

Modeling Social—Ecological Scenarios in Marine Systems

Human activities have substantial impacts on marine ecosystems+ including rapid regime shifts with large consequences for human well-being. We highlight the use of model-based scenarios as a scientific tool for adaptive stewardship in the face of such consequences. The natural sciences have a long history of developing scenarios but rarely with an in-depth understanding of factors influencing human actions. Social scientists have traditionally investigated human behavior, but scholars often argue that behavior is too complex to be represented by broad generalizations useful for models and scenarios. We address this scientific divide with a framework for integrated marine social-ecological scenarios, combining quantitative process-based models from the biogeochemical and ecological disciplines with qualitative studies on governance and social change. The aim is to develop policy-relevant scenarios based on an in-depth empirical understanding from both the natural and the social sciences, thereby contributing to adaptive stewardship of marine social-ecological systems.

Seasonal variation of average phytoplankton concentration in the Kattegat—a periodical point model

Abstract Seasonal variations in primary production, phytoplankton biomass, chlorophyll-a, dissolved inorganic phosphorus and nitrogen concentrations in the upper 10 m of the Kattegat were analysed by means of monitoring data from 1993–1997. Spatial optimal analysis, based on a stochastic model, was used to reconstruct weekly constituent fields onto a spatial grid. The reconstructed fields were spatially integrated, resulting in a relatively smooth seasonal variability of the average variables. A simple dynamical model, set up as a periodical boundary problem, is suggested for the average phytoplankton concentration, dissolved inorganic nitrogen and entrainment depth as state variables. The model is forced by the solar radiation, nitrogen load from the land sources and atmosphere as well as by nitrogen supply from the lower nutrient-rich layer. The latter process is modelled proportional to the water entrainment into the upper euphotic layer and is driven by atmospheric forcing, river runoff and the Baltic water inflow. Four model coefficient values were fitted by minimising the root mean square difference between the integrated monitoring data and the model output. The suggested diagnostic model reflects the main features in seasonal variability of phytoplankton and nitrogen concentrations by average values, including the magnitude and timing of such dynamic events as the spring and late summer phytoplankton blooms. The importance of different forcing factors is quantified and estimates of unobserved components such as new primary production can be computed.

Hidden variables in a Dynamic Bayesian Network identify ecosystem level change

Abstract Ecosystems are known to change in terms of their structure and functioning over time. Modelling this change is a challenge, however, as data are scarce, and models often assume that the relationships between ecosystem components are invariable over time. Dynamic Bayesian Networks (DBN) with hidden variables have been proposed as a method to overcome this challenge, as the hidden variables can capture the unobserved processes. In this paper, we fit a series of DBNs with different hidden variable structures to a system known to have undergone a major structural change, i.e. the Baltic Sea food web. The exact setup of the hidden variables did not considerably affect the result, and the hidden variables picked up a pattern that agrees with previous research on the system dynamics.