Variability of larval Baltic sprat (Sprattus sprattus L.) otolith growth: a modeling approach combining spatially and temporally resolved biotic and abiotic environmental key variables |
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Authors: | HANS‐HARALD HINRICHSEN RUDI VOSS BASTIAN HUWER CATRIONA CLEMMESEN |
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Affiliation: | 1. Leibniz Institute of Marine Sciences at Kiel University, Düsternbrooker Weg 20, D‐24105 Kiel, Germany;2. Sustainable Fishery, Department of Economics, University of Kiel, Kiel, Germany;3. Technical University of Denmark, National Institute of Aquatic Resources, Charlottenlund, Denmark |
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Abstract: | Plankton sampling was conducted in the Baltic to obtain sprat larvae. Their individual drift patterns were back‐calculated using a hydrodynamic model. The modelled positions along the individual drift trajectories were subsequently used to provide insight into the environmental conditions experienced by the larvae. Autocorrelation analysis revealed that successive otolith increment widths of individual larvae were not independent. Otolith increment width was then modelled using two different generalized additive model (GAM) analyses (with and without autocorrelation), using environmental variables determined for each modelled individual larval position as explanatory variables. The results indicate that otolith growth was not only influenced by the density of potential prey but was controlled by a number of simultaneously acting environmental factors. The final model, not considering autocorrelation, explained more than 80% of the variance of otolith growth, with larval age as a factor variable showing the strongest significant impact on otolith growth. Otolith growth was further explained by statistically significant ambient environmental factors such as temperature, bottom depth, prey density and turbulence. The GAM analysis, taking autocorrelation into account, explained almost 98% of the variability, with the previous otolith increment showing the strongest significant effect. Larval age as well as ambient temperature and prey abundance also had a significant effect. An alternative approach applied individual‐based model (IBM) simulations on larval drift, feeding, growth and survival starting as exogenously feeding larvae at the back‐calculated positions. The IBM results revealed optimal growth conditions for more than 97% of the larvae, with a tendency for our IBM to slightly overestimate larval growth. |
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Keywords: | Bornholm Basin generalized additive model analysis growth rates individual‐based model otolith microstructure analysis recruitment |
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