How chronobiology can affect local population dynamics and spatial distribution in Antarctic Krill

• Main Authors: Groeneveld, Jürgen, Johst, Karin, Kawaguchi, So, Meyer, Bettina, Teschke, Mathias, Grimm, Volker
• Format: Conference Object
• Language: unknown
• Published: 2014
• Subjects: Antarctic; Antarc*; Antarctic Krill; Euphausia superba; Sea ice
• Online Access: https://epic.awi.de/id/eprint/43432/; https://hdl.handle.net/10013/epic.ac8ce3eb-263d-464f-8b8e-5fb00523a096

We investigate how individual growth and population structure of Antarctic krill (Euphausia superba) would be affected by changes in the spatio-temporal dynamics of the sea ice cover. This is of high interest since krill has adapted to a particular environmental regime which is likely to change dramatically over the coming years. The response of krill will in particular depend on its chronobiology: when and why does krill, after a period of decreased metabolic activity during winter, switch back to an active metabolic state? If this switch is purely triggered by the Zeitgeber day length, the metabolically active period of krill and the availability of food resources would become out of phase with potentially drastic consequences for krill populations. Alternatively, the switch might also be triggered by food availability. To explore the consequences of different environmental scenarios and assumptions about krill chronobiology, we developed a spatially explicit individual-based simulation model. The model operates on a daily time step. Each time step ice cover extent and day length for each grid cell in the model are updated. In the model demographic and behavioural processes are simulated every time step. Particularly all modelled krill individuals grow depending on food availability, move, reproduce given their reproductive and metabolic state, and die with a certain probability. Growth and reproduction are modelled according to a simplified version of dynamic energy budget theory (DEBKiss). Simulations run for several years until quasi-stationary population characteristics have emerged. Population metrics such as length distribution and heterogeneity in reproductive state within the population are observed. We will present the model and demonstrate its potential by contrasting results for selected environmental and chronobiological scenarios. The model’s design and implementation are open so that suggestions regarding alternative assumptions and scenarios can easily be implemented and explored.