Abstract
Abstract. Coupled physical–biological models usually resolve only parts of the trophic food chain; hence, they run the risk of neglecting relevant ecosystem processes. Additionally, this imposes a closure term problem at the respective “ends” of the trophic levels considered. In this study, we aim to understand how the implementation of higher trophic levels in a nutrient–phytoplankton–zooplankton–detritus (NPZD) model affects the simulated response of the ecosystem using a consistent NPZD–fish modelling approach (ECOSMO E2E) in the combined North Sea–Baltic Sea system. Utilising this approach, we addressed the above-mentioned closure term problem in lower trophic ecosystem modelling at a very low computational cost; thus, we provide an efficient method that requires very little data to obtain spatially and temporally dynamic zooplankton mortality. On the basis of the ECOSMO II coupled ecosystem model we implemented one functional group that represented fish and one group that represented macrobenthos in the 3-D model formulation. Both groups were linked to the lower trophic levels and to each other via predator–prey relationships, which allowed for the investigation of both bottom-up processes and top-down mechanisms in the trophic chain of the North Sea–Baltic Sea ecosystem. Model results for a 10-year-long simulation period (1980–1989) were analysed and discussed with respect to the observed patterns. To understand the impact of the newly implemented functional groups for the simulated ecosystem response, we compared the performance of the ECOSMO E2E to that of a respective truncated NPZD model (ECOSMO II) applied to the same time period. Additionally, we performed scenario tests to analyse the new role of the zooplankton mortality closure term in the truncated NPZD and the fish mortality term in the end-to-end model, which summarises the pressure imposed on the system by fisheries and mortality imposed by apex predators. We found that the model-simulated macrobenthos and fish spatial and seasonal patterns agree well with current system understanding. Considering a dynamic fish component in the ecosystem model resulted in slightly improved model performance with respect to the representation of spatial and temporal variations in nutrients, changes in modelled plankton seasonality, and nutrient profiles. Model sensitivity scenarios showed that changes in the zooplankton mortality parameter are transferred up and down the trophic chain with little attenuation of the signal, whereas major changes in fish mortality and fish biomass cascade down the food chain.
Highlights
The majority of spatially resolved marine ecosystem models are dedicated to a specific part of the marine food web
We aim to understand how the implementation of higher trophic levels in a nutrient–phytoplankton–zooplankton– detritus (NPZD) model affects the simulated response of the ecosystem using a consistent NPZD–fish modelling approach (ECOSMO E2E) in the combined North Sea–Baltic Sea system
We (i) present and discuss the spatial dynamics of the newly introduced functional groups, (ii) discuss the seasonality of the ecosystem components and introduce the MB and fish diet composition emerging from the model, (iii) present the comparison of the simulated fish biomass distribution to observed data and repeat the nutrient validation analysis as previously presented for ECOSMO II in Daewel and Schrum (2013), and (iv) discuss the model sensitivity with respect to ecosystem model closure
Summary
The majority of spatially resolved marine ecosystem models are dedicated to a specific part of the marine food web. Daewel et al, 2008; Megrey et al, 2007; Politikos et al, 2018; Vikebø et al, 2007) and multi-species models Some of these models are complex and already include many food web components such as OSMOSE (Shin and Cury, 2004, 2001) and ERSEM (Butenschön et al, 2016), the separation of trophic levels often constrains such models’ ability to simulate and distinguish between major control mechanisms on marine ecosystems (Cury and Shannon, 2004). The difficulty of resolving trophic feedback mechanisms increases the uncertainties when modelling the impacts of external controls on the trophic food chain (e.g. Daewel et al, 2014; Peck et al, 2015)
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