DOI: 10.7291/d1s68v
title: Juvenile salmon growth across different habitat types in Central Valley, CA

description: The size of an organism is an important factor for a variety of
 physiological and ecological processes. For fishes, larger size can
 increase long-term survival and provide a population-level benefit.
 Therefore, threatened and endangered species management often focuses on
 supporting high-quality habitat that provides growth opportunities. There
 are numerous habitat characteristics that can affect growth including food
 availability, temperature, and habitat complexity. Understanding how
 growth responds to habitat characteristics of different quality is the
 first step in determining what could lead to increased growth and
 potentially increased individual survival. We use Bayesian techniques to
 determine which of the differing methods is the best for modeling the
 effects of habitat type and temperature on growth. To apply this method,
 we gather data from previous studies of the growth benefits of differing
 habitats and temperature regimes on Chinook salmon on the Sacramento
 River, CA, USA. We find a consistent growth benefit of floodplain rearing
 across multiple studies and show that a Ratkowsky model is the best for
 modeling this growth data. This information can specifically help managers
 model and protect the endangered and threatened Chinook populations in
 this system and more generally understand fish growth across differing
 habitat types.  
description: We conducted searches on Web-of-Science for
 articles that matched the following term set for either the title or
 topic: (delta OR bay OR river OR floodplain) AND (Chinook) AND (feeding OR
 growth OR consumption OR size) AND (Sacramento OR California OR
 "Central Valley"). We then filtered the 253 resulting articles
 based on title and abstract and finally selected those which contained
 data on fish growth rates, temperature, and habitat type. We also flagged
 articles that did not present these data, but which indicated that
 researchers had collected data of those types. We contacted the authors of
 these articles requesting their data, and authors of other studies to find
 reports or unpublished data. This effort resulted in 10 studies with
 useable data (Sommer et
 al. 2001, 2020; Jeffres et al. 2008, 2020; Katz et al. 2014, 2019; Jeffres
 2017; Takata et al. 2017; Cordoleani et al. 2020,
 2021). While most of the
 studies used caged fish, one included data from tagged free swimming fish
 (Takata et al.
 2017). These studies
 covered two habitat types; lower velocity, higher temperature, more
 eutrophic floodplain habitat and faster velocity, lower temperature, more
 oligotrophic riverine habitat. As there were no data for very high or low
 temperatures (field data range 10.2–16.4 °C), we used lab data for
 temperatures &gt; 23 °C (Brett et al. 1982; Yanke
 2006) and temperatures
 &lt; 6 °C (Stauffer
 1973) to anchor the fits
 close to the minimum and maximum growth temperatures. As feeding
 conditions in the floodplain are likely closer to the ad libitum
 conditions of the lab, we only used the high temperature lab data to
 assist in the fitting of the floodplain data.  
 From each study, we extracted the start and end
 date of the experiment, the start mass or length, the end mass or length,
 the growth rate, the habitat type (floodplain or riverine), and the mean
 temperature over the experimental window. We chose to use the mean
 temperature over the whole window because in some reports finer scale data
 were not available. This reduced the noisiness of the data and allows
 management-models with course time resolutions to use our results. For
 studies that only reported mass, we converted mass to length (length [cm]
 = (mass [g] / 0.0189)<sup>0.34</sup>. We chose to model length
 as it is often the metric used in management, measured in the field when
 processing a large number of captured fish, and used in management models.
 These parameters are based on averages from literature
 (Petrusso and Hayes 2001;
 MacFarlane and Norton 2002; Kimmerer et al. 2005; Chapman et al. 2013;
 Michel et al. 2013). 
description: R programing languague