Contrasting stock status trends obtained from survey and fishery CPUE, taking Larimichthys polyactis in Yellow Sea Large Marine Ecosystem as an example

Catch per unit effort modelling for stock assessment: A summary of good practices

“…Where catchability increases are considered likely but estimates are unavailable, ignoring them will positively bias stock status estimates (e.g., Han et al, 2023;Ye and Dennis, 2009). Wilberg et al (2009) recommend a default assumption that catchability varies over time and multiple methods of including time-varying catchability should be applied.…”

Assessment of Seabream Fisheries Stock of Oman Using the Monte Carlo Catch Maximum Sustainable Yield and the Bayesian Schaefer Model Methods

“…Another issue is the rise in the capacity of fishing gears [51]. Improvements in vessel power and the use of modern instrumentation result in increased fishing efficiency, allowing each unit of effort to catch more fish than previously for the same stock abundance [52]. When a gear harvests more than the targeted species it becomes difficult to estimate stocks.…”

The Effect of Process Variability and Data Quality on Performance of a State-Space Stock Assessment Model

“…Thanks to advances in fisheries stock assessment modeling software like Template Model Builder (TMB) that can approximate the high-level integrals, state-space stock assessment models that maximize the marginal likelihood and estimate many of the variances have emerged as a popular addition to the modeling toolbox (Aanes et al, 2020;Kristensen et al, 2016). In fact, state-space and the marginal likelihood have become largely synonymous in quantitative fisheries discourse, though Bayesian methods still flourish, particular in state-space surplus production models (Han et al, 2023;Soto et al, 2022). Where once a stock assessment model could perhaps only account for stochasticity in one underlying process like recruitment or catchability, state-space modeling software like SAM or the Woods Hole Assessment Model (WHAM) now default to including variability in several underlying processes (Johnson et al, 2014;Linton and Bence, 2008;Nielsen and Berg, 2014;.…”