A Simulation Study to Evaluate Biases in Population Characteristics Estimation Associated with Varying Bin Numbers in Size‐Based Age Subsampling. (22nd April 2020) - Hilling, Corbin D.; Jiao, Yan; Bunch, Aaron J.; Phelps, Quinton E.
Abstract: In temperate waters, growth and mortality of bony fishes are frequently estimated from age information derived from the examination of annular rings on hard structures (e.g., otoliths). However, determining ages from hard structures can be time consuming, often requires sacrificing fish, and has associated costs for supplies and personnel time in processing or reading structures. Subsampling based on a target number of fish per length bin is commonly used to reduce time and costs but may introduce biases into the estimation of population characteristics. We wanted to understand how interactive effects of bin width, gear selectivity, and length‐at‐age variability influence the estimation of growth parameters, total instantaneous mortality ( Z ), and age frequency. We developed a simulation model to generate populations under the assumption that growth followed the von Bertalanffy growth model; we then sampled from those populations for age analysis based on no gear selectivity, dome‐shaped selectivity, and logistic selectivity. Furthermore, we wanted to determine whether observed biases could be corrected by using a weighting procedure during growth model fitting. Fifteen subsampling schemes were evaluated, with five different length bin widths and three target subsample sizes for each bin (subsampling levels). Gear selectivity, variability in length at age, and estimation procedures had a greater and more predictable influence on growth parameters than bin widths for size‐based subsampling. Dome‐shaped gear selectivity was associated with biases in growth parameter and Z estimation. Weighted regression based on weighting factors calculated from the original sample's length frequency generally improved the consistency of growth parameter estimates among subsampling schemes but did not always improve accuracy. No bin widths or subsample sizes were clearly superior across modeled scenarios. Consequently, alteration of bin widths seems less useful in reducing biases than using alternative estimation methods for population characteristics of interest and considering other external factors.