Abstract
Experiments that directly test larval fish individual-based model (IBM) growth predictions are uncommon since it is difficult to simultaneously measure all relevant metabolic and behavioural attributes. We compared observed and modelled somatic growth of larval herring ( Clupea harengus ) in short-term (50 degree-day) laboratory trials conducted at 7 and 13°C in which larvae were either unfed or fed ad libitum on different prey sizes (~100 to 550 µm copepods, Acartia tonsa ). The larval specific growth rate (SGR, % DW d -1 ) was generally overestimated by the model, especially for larvae foraging on large prey items. Model parameterisations were adjusted to explore the effect of 1) temporal variability in foraging of individuals, and 2) reduced assimilation efficiency due to rapid gut evacuation at high feeding rates. With these adjustments, the model described larval growth well across temperatures, prey sizes, and larval sizes. Although the experiments performed verified the growth model, variability in growth and foraging behaviour among larvae shows that it is necessary to measure both the physiology and feeding behaviour of the same individual. This is a challenge for experimentalists but will ultimately yield the most valuable data to adequately model environmental impacts on the survival and growth of marine fish early life stages.
Highlights
Biophysical individual-based models (IBMs) have been recognised as important tools for understanding how environmental conditions influence larval fish growth and survival
Compared to previous studies in which herring larvae were reared in the laboratory (Werner and Blaxter, 1980; McGurk, 1984; Folkvord et al, 2000), growth rates at optimum prey sizes in this study were quite high
We made a simple assessment of larval herring IBM physiological parameterisations by comparing observed and modelled growth rates of different sizes of larvae at different water temperatures that were provided different prey sizes
Summary
Biophysical individual-based models (IBMs) have been recognised as important tools for understanding how environmental conditions influence larval fish growth and survival (e.g. see reviews by Werner et al, 2001; Miller, 2007). Larval fish IBMs differ in the complexity of their biological components depending on the research questions asked. Some IBMs have been created to explore bottom-up regulation of survival and include highly detailed, mechanistic descriptions of growth physiology and foraging (Fiksen and MacKenzie, 2002; Lough et al, 2005; Ruzicka and Gallager, 2006). The reliability of physiologically-based growth estimates generated by these more complex IBMs depends on their ability to correctly depict how key abiotic and biotic factors influence the processes of foraging and growth.
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