Time-variant productivity in biomass dynamic models on seasonal and long-term scales

The productivity of fish populations varies naturally over time, dependent on integrated effects of abundance, ecological factors, and environmental conditions. These changes can be expressed as gradual or abrupt shifts in productivity as well as fluctuations on any time scale from seasonal oscillat...

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Bibliographic Details
Published in:ICES Journal of Marine Science
Main Authors: Mildenberger, Tobias, Berg, Casper Willestofte, Pedersen, Martin Wæver, Kokkalis, Alexandros, Nielsen, J. Rasmus
Format: Article in Journal/Newspaper
Language:English
Published: 2020
Subjects:
Eastern Baltic cod (Gadus morhua)
Fisheries management
Fish stock assessment
Maximum sustainable yield
Population dynamics
Seasonality
SPiCT
Surplus production model
Gadus morhua
Online Access:https://orbit.dtu.dk/en/publications/30a52f3e-b8c5-4745-adf1-442de22c4d15
https://doi.org/10.1093/icesjms/fsz154
https://backend.orbit.dtu.dk/ws/files/265048299/tvp_maintext.pdf
Description
Summary:The productivity of fish populations varies naturally over time, dependent on integrated effects of abundance, ecological factors, and environmental conditions. These changes can be expressed as gradual or abrupt shifts in productivity as well as fluctuations on any time scale from seasonal oscillations to long-term changes. This study considers three extensions to biomass dynamic models that accommodate time-variant productivity in fish populations. Simulation results reveal that neglecting seasonal changes in productivity can bias derived stock sustainability reference levels and, thus, fisheries management advice. Results highlight the importance of biannual biomass indices and their timing relative to the peaks of the seasonal processes (i.e. recruitment, growth, mortality) for the estimation of seasonally time-variant productivity. The application to real-world data of the eastern Baltic cod ( Gadus morhua ) stock shows that the model is able to disentangle differences in seasonal fishing mortality as well as seasonal and long-term changes in productivity. The combined model with long-term and seasonally varying productivity performs significantly better than models that neglect time-variant productivity. The model extensions proposed here allow to account for time-variant productivity of fish populations leading to increased reliability of derived reference levels.

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