Hierarchical variance decomposition of fish scale growth and age to investigate the relative contributions of readers and scales

Correct estimation of interindividual variability is of primary importance in models aiming to quantify population dynamics. In a fisheries context, individual information such as age and growth is often extracted using scales; however, the rationale for using a given scalimetric method (i.e. number...

Full description

Bibliographic Details
Published in:Marine and Freshwater Research
Main Authors: Aulus Giacosa, Lucie, Aymes, Jean-Christophe, Gaudin, Philippe, Vignon, Matthias
Other Authors: Ecologie Comportementale et Biologie des Populations de Poissons (ECOBIOP), Institut National de la Recherche Agronomique (INRA)-Université de Pau et des Pays de l'Adour (UPPA)
Format: Article in Journal/Newspaper
Language:English
Published: HAL CCSD 2019
Subjects:
Online Access:https://hal.science/hal-02414999
https://hal.science/hal-02414999/document
https://hal.science/hal-02414999/file/2019_Aulus_MarineFreshwaterResearch.pdf
https://doi.org/10.1071/MF19059
Description
Summary:Correct estimation of interindividual variability is of primary importance in models aiming to quantify population dynamics. In a fisheries context, individual information such as age and growth is often extracted using scales; however, the rationale for using a given scalimetric method (i.e. number of scales per individual and number of readers) is rarely discussed, but different sources of variance may affect the results. As a case study, we used scale growth and age of brown trout (Salmo trutta) caught in the Kerguelen Islands. Based on a nested design (readings of four scales per fish by two independent readers), we decomposed variance in growth and age according to fish (interindividual level), scales (intraindividual level) and readers by using repeatability analysis. The results highlight that most variation is attributable to fish. Readers and scales contribute little to interindividual variance, suggesting that inference was insensitive to intraorganism biological variation. Using additional scales or readers was an inefficient use of sampling resources. We argue that variance decomposition should be widely used for studies aimed at modelling natural variability in life history traits. This would improve our knowledge of the implications of measurement error, helping rationalise and define appropriate sampling strategies