Lingering legacies: Past growth and parental experience influence somatic growth in a fish population

Body size and growth rate can influence individual and population success by mediating fitness. Understanding the factors that influence growth can be difficult to disentangle, however, because growth can be shaped by environmental conditions recently experienced, as well as legacy effects from conditions experienced earlier in life and by parents (via parental effects). To improve understanding of growth among annual cohorts (1982-2015) of Lake Erie Walleye (Sander vitreus), a species with life history and growth characteristics similar to many other long-lived, iteroparous fishes, we determined the role of the following hypothesized factors: H1) recent environmental conditions; H2) traits and experiences of the cohort, including growth, in the previous year; H3) early-life cohort density; H4) early-life body size; and H5) parental composition and environment. We evaluated the relative importance of these hypothesized factors using piecewise structural equation modeling in an informati..., Our evaluation of young adult growth was based on data collected annually during fall (i.e., September-November) surveys by the Ohio Department of Natural Resources-Division of Wildlife (ODNR-DOW). We tested different combinations of hypothesized ways in which past and recent environments can affect cohort-based growth in Walleye using model selection of these data within piecewise structured equation models. See the manuscript and appendix for further details., , # Lingering legacies: Past growth and parental experience influence somatic growth in a fish population

[https://doi.org/10.5061/dryad.34tmpg4t6](https://doi.org/10.5061/dryad.34tmpg4t6)

The dataset contains an R script and data file used to produce figures and all of the piecewise structured equation models (SEM).

## Description of the data and file structure

The R script file contains the code used to run the piecewise SEM analyses as well as to create related figures. The script contains additional information about analyses in comments throughout.

All of the data needed to conduct the analyses (predictor and response variables for piecewise SEMs) are compiled into the CSV file. Details about variable names are listed below. Within the dataset, each row represents a male or female annual cohort at ages 3, 4, or 5. Each cohort is represented by up to 6 rows in JAE_Almeida_et_al_compiled_data.csv - each sex (female and male) within the ages 3, 4, and 5.

Variables included in Alme...