Those who fail to learn from history are condemned to repeat it: A perspective on current stock assessment good practices and the consequences of not following them

Spatial awareness: Good practices and pragmatic recommendations for developing spatially structured stock assessments

“…Our good practices guide is dynamic and subject to rapid adaptation as the field of spatial stock assessment evolves over the coming years, especially as the amount and types of spatially resolved data continues to proliferate. Punt (2023) notes that analyst decisions are often influential to model outcomes. Therefore, understanding how and why decisions are made throughout the model development process can help guide good practices.…”

“…Understanding how demographic structure (i.e., growth, maturity, fecundity, and natural mortality rates) varies across the spatial domain must be considered within the context of the population structure and carefully implemented in conjunction with the assumed movement dynamics. Punt (2023) notes that adequately describing spatial variation in demographics, particularly growth, is important because it influences interpretation of depletion levels from observed length composition data. The primary decision to consider is whether demographic variation is driven by genetics (i.e., natal population), environment (i.e., current sub-population or area), or influenced by a combination of factors (Punt et al, 2020).…”

“…Thus, the integrated assessment framework (Maunder and Punt, 2013), which can incorporate almost any data source and model associated observation or sampling error, has improved the amount and information content of data and led to the inclusion of additional data types (e.g., citizen science, LEK or TEK, and genetics information; Sun et al, 2019;Goethel et al, 2022a). As part of the stock identification process (Cadrin et al, 2023), a thorough data inventory should be undertaken to determine the types of data available, the spatiotemporal scale of collection, and their representativeness of populationlevel processes (Punt, 2023).…”

“…The current basis for quantitative fisheries management is typically an integrated stock assessment, which models the population dynamics of the species to estimate stock status and sustainable catch limits by minimizing differences between predicted and observed values of fishery-dependent catch, age or length compositions, and auxiliary data sources (e.g., fishery-independent estimates of abundance, tagging data, or omics information; Maunder and Punt, 2013). As with any model attempting to emulate the complexities of coupled humannatural systems, stock assessments make simplifying assumptions to ensure tractability given data availability and knowledge gaps (Punt, 2023). The primary simplification in assessment models is the unit stock assumption, wherein the modeled stock is a single, well-mixed, panmictic population that is closed to immigration or emigration, is reproductively isolated, consists of a dynamic pool of individuals with identical vital rates, and that fishing effort is homogeneously distributed (Beverton and Holt, 1957;Cadrin, 2020).…”

Decadal changes in biomass and distribution of key fisheries species on Newfoundland’s Grand Banks

“…Stock assessments aim to evaluate the status of a population by evaluating biomass and fishing mortality relative to reference points to define catch limits [ 1 ]. Time series of biomass indices are often used to calibrate stock assessment models, primarily derived from fisheries-independent data collected from scientific surveys.…”