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

• Published in: Marine and Freshwater Research
• Main Authors: Aulus-Giacosa, Lucie, Aymes, Jean-Christophe, Gaudin, Philippe, Vignon, Matthias
• Format: Article in Journal/Newspaper
• Language: English
• Published: 2019
• Subjects: Biodiversité et Ecologie; Biodiversity and Ecology; introduced specie;measurement error;sampling strategy;scalimetry; salmo trutta; espèce introduite; dynamique des populations; scalimétrie; erreur de mesure; stratégie d'echantillonnage; Kerguelen; Kerguelen Islands
• Online Access: http://prodinra.inra.fr/ft/BE5658DA-BD3C-4C7B-9F84-ECC0645EBF19; http://prodinra.inra.fr/record/490035; https://doi.org/10.1071/MF19059

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