Modelling individual variability in growth of bull trout in the Walla Walla River Basin using a hierarchical von Bertalanffy growth model

Abstract

We examined growth in length of fluvial bull trout (Salvelinus confluentus) in the Walla Walla River Basin, Washington and Oregon. Our objectives were to quantify individual variability in growth; examine growth within and among years, life history forms, life stages and sexes; and estimate von Bertalanffy growth parameters. Individual variability was evaluated by modelling asymptotic length (L∞) and the growth coefficient (k) as random variables. All models were fit with Bayesian methods and were evaluated for fit by the deviance information criterion. By incorporating individual variability, population‐level estimates of L∞ and k appeared appropriate and estimated growth trajectories for specific bull trout fit individual observed patterns in growth. Growth trajectories and positive correlation between individual estimates of L∞ and k suggest that some individuals grow at a faster rate and reach a larger maximum size than other individuals and those differences are maintained throughout life. Selected models suggest that fluvial migrants have higher estimates of L∞ and k than residents, but there were only slight differences in parameter estimates among migrants from two adjacent spawning populations in the Walla Walla River Basin, as well as between males and females. Growth rates increased for fluvial migrants after subadult emigration. Individual variability in growth is consistent with the life history diversity assumed essential for bull trout population persistence. Quantifying this variability is important for modelling population dynamics and viability to conserve this threatened species.

New Insights into the Ecology of Adfluvial Bull Trout and the Population Response to the Endangered Species Act in the North Fork Lewis River, Washington

“…) but their growth was similar to reported growth rates from other migratory, fluvial individuals (Harris et al. ). Finally, the differences in growth rates likely explain interpopulation differences in length at age.…”

Drivers of growth for Atlantic cod (Gadus morhua L.) in Icelandic waters – A Bayesian approach to determine spatiotemporal variation and its causes

“…While there have been studies that have fit growth data using Bayesian techniques, most have described the growth of individual fishes using one of two methods. Fish are either measured for length throughout time, using mark–recapture studies (Bal, Rivot, Prévost, Piou & Baglinière, ; Tang et al ., ), along with a variant of the VBGF known as the Fabens version (Fabens et al ., ), or they have been harvested, measured for length and the otoliths (or other aging parts) used to back calculate age and length from measurements of annulii in the growth rings (Alós et al ., ; Linde et al ., 2011; de Zárate & Babcock, ; Contreras‐Reyes, Quintero & Wiff, ; Harris, Newlon, Howell, Koch & Haeseker, ). Surprisingly, not that many studies have fit a basic growth model to direct conditional length‐at‐age data using a Bayesian framework ( i.e ., individuals measured directly for both length and age at a single time point, but see Thorson & Minte‐Vera, ; Lopez Quintero et al ., 2017), but in many cases, a wealth of data exists from standardised surveys and there is useful information to garner from them.…”

“…Alternatively, increased fishing pressure over the past half-century could lead to a decline in the number of fishes at older ages (Hsieh, Yamauchi, Nakazawa & Wang, 2010) or fisheries-induced evolution of lifehistory traits such as growth (Heino et al, 2015). In this study we fit (Alós et al, 2009;Linde et al, 2011;de Zárate & Babcock, 2016;Contreras-Reyes, Quintero & Wiff, 2018;Harris, Newlon, Howell, Koch & Haeseker, 2018). Surprisingly, not that many studies have fit a basic growth model to direct conditional length-at-age data using a Bayesian framework (i.e., individuals measured directly for both length and age at a single time point, but see Thorson & Minte-Vera, 2016;Lopez Quintero et al, 2017), but in many cases, a wealth of data exists from standardised surveys and there is useful information to garner from them.…”

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

“…Indeed, preliminary analysis to quantify errors should be a prerequisite to any study because it could provide valuable insights for accurate modelling of individual variability. Such understanding of interindividual variability should serve to better estimate population dynamics and could have several applications in stock assessment and conservation (Harris et al 2018).…”

“…Growth parameters are highly dependent upon an accurate description of the individual age-length relationship (Hatch and Jiao 2016). Moreover, estimates of individual growth in population models can be significantly different while accounting for or failing to account for interindividual variability, leading to evolutionary misinterpretations or to inappropriate conservation decisions (Shelton and Mangel 2012;Vincenzi et al 2014;Harris et al 2018).…”