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Determining the magnitude and causes of intrinsic variability is a main issue in the analysis of bivalve growth. Inter-individual variability in bivalve growth has been attributed to differences in the physiological performance. This hypothesis has been commonly tested comparing the physiological rates of fast and slow growers after size differentiation has occurred. This experimental design may detect a link between growth and physiological performance, but we cannot interpret the posterior physiological performance as a driver for the prior growth variability. Considering these limitations, this work introduces a new methodological framework for the analysis of bivalve growth variability. We have conducted sequential measurements of size and physiological performance (feeding, digestion and metabolic rates) in even-sized mussels growing under homogeneous environmental conditions. This experimental design allows us to distinguish between changes over time within individuals, i.e. growth and trends in the physiological rates, from differences between individuals with respect to a baseline level. In addition, Functional Data Analysis provides powerful tools to summarize all the information obtained in the exhaustive sampling scheme and to test whether differences in the physiological performance enhance growth dispersion. Our results report an increasing dispersion in both size and physiological performance over time. Although mussels grew during the experiment, it is difficult to detect any increasing or decreasing temporal pattern in their feeding, digestion and metabolic rates due to the large inter-individual variability. Comparison between the growth and physiological patterns of mussels with final size above (fast growers) and below (slow growers) the median found that fast growers had larger feeding and digestion rates and lower metabolic expenditures during the experimental culture than mussels with slow growth, which agrees with the hypothesis of a physiological basis for bivalve growth variability.

plos one

Characterizing individual variability in mussel (Mytilus galloprovincialis) growth and testing its physiological drivers using Functional Data Analysis