Stock Assessment Models Research Articles

Misinterpreting retrospective patterns in fishery stock assessment

Abstract Estimates of stock size for a specific year change when a new year of data is added to a stock assessment model, and some assessments exhibit a unidirectional pattern to these retrospective differences. Assuming that the most recent stock assessment is the most reliable, retrospective patterns are often misinterpreted as a measure of estimation bias that can be corrected. The logical fallacy of this interpretation is exposed when another new year of data is added, and the estimates that were assumed to be true are now considered to be biased. True values of estimated parameters are needed to infer bias. For example, simulation-estimation experiments can produce retrospective patterns from misspecified estimation models that assume time-varying processes are stationary. These simulations show that retrospective patterns are not a reliable measure of bias, and retrospective adjustments may be further from true values. Therefore, the terminology of retrospective “bias” and “correction” is misleading. Retrospective patterns can be an informative diagnostic to identify and confront model misspecification, and if retrospective patterns cannot be reduced with respecified models, they can be communicated as measure of uncertainty for consideration in the precautionary management.

Engineering a prototype for a next-generation stock assessment modeling platform

Environmental and anthropogenic processes have led to widespread changes in productivities and spatial distributions of marine fishery resources. As geographic distributions of fish stocks and subsequently fishing fleets shift, improved spatiotemporal data and spatial modeling will become necessary to estimate abundance and productivity. The goal of this paper is to propose a strategy for engineering a modeling platform for spatially explicit, next-generation stock assessment models. We recount our approach for developing a system prototype, the Metapopulation Assessment System (MAS), that is easy to use, modular, and extensible. The MAS prototype was designed to support complex metapopulation modeling, which includes handling multiple populations, areas, fleets, and surveys and sex differentiation. We describe the components of the software life cycle, engineering, and infrastructure design to support a spatially explicit, next-generation stock assessment system. Software infrastructure was designed and implemented with 3 components: GitHub for collaboration, version control, and code organization; the C++ language for coding fishery system dynamics and estimation modeling; and the R language for coding the input-output interface and systematic testing. Systematic testing was a key component of the MAS development life cycle and was applied to guarantee that system requirements, such as accurate estimation of quantities of interest, are successfully implemented. We conclude with a discussion of some lessons learned and future challenges for ongoing efforts to implement a next-generation stock assessment platform.

Engineering a prototype for a next-generation stock assessment modeling platform: Supplementary Materials

Engineering a prototype for a next-generation stock assessment modeling platform: Supplementary Materials

The Benefits of Hierarchical Ecosystem Models: Demonstration Using EcoState, a New State‐Space Mass‐Balance Model

ABSTRACTEcosystem models predict changes in productivity and status for multiple species, and are important for incorporating climate‐linked dynamics in ecosystem‐based fisheries management. However, fishery regulations are primarily informed by single‐species stock assessment models, which estimate unexplained variation in dynamics (e.g., recruitment, survival, fishery selectivity, etc) using random effects. We review the general benefits of estimating random effects in ecosystem models: (1) better representing biomass cycles and trends for focal species; (2) conditioning interactions upon observed biomass for predators and prey; (3) easier replication of model results using formal estimation rather than informal model “tuning;” and (4) attributing process errors via comparison amongst different models. We then demonstrate these by introducing a new state‐space model EcoState (and associated R‐package) that extends mass balance dynamics from Ecopath with Ecosim. This model estimates mass balance (Ecopath) and time‐dynamics (Ecosim) parameters directly via their fit to time‐series data (biomass indices and fisheries catches) while also estimating the magnitude of process errors using RTMB. A real‐world application involving Alaska pollock (Gadus chalcogrammus) in the eastern Bering Sea suggests that fluctuations in krill consumption are associated with cycles of increased and decreased pollock production. A self‐test simulation experiment confirms that estimating process errors can improve estimates of productivity (growth and mortality) rates. Overall, we show that state‐space mass‐balance models can be fitted to time‐series data (similar to surplus‐production stock assessment models), and can attribute time‐varying productivity to both bottom‐up and top‐down drivers including the contribution of individual predator and prey interactions.

European anchovy's abundance, more affected by climatic conditions than fishing activities in the northwest African waters.

The marine waters off the coast of northwest Africa are known for being highly productive upwelling regions in the Eastern Atlantic Ocean. The present study evaluated the combined effects of climate change and sustainable fishing levels on the long-term sustainability of European anchovy (Engraulis encrasicolus), a target pelagic species found along the West African coastal waters. The present study used survey biomass time series from survey vessels and species catch time series from commercial fisheries operating in the region. Hence, we investigated the relationship between fishing dynamics and environmental factors. The status of anchovy stock was determined by analyzing changes in different initial depletion rates using a state-space Bayesian surplus production model. We used Principal component analysis to identify the essential environmental data set, while Generalized Additive Models (GAM) were employed to determine the influence of the environment on fisheries. The stock assessment model revealed that fishing intensity did not affect the abundance of anchovy in the region even when tested under different biomass depletion scenarios. However, by employing GAMs, the research assessed the impact of environmental variables on fish biomass, indicating that sea surface temperature (SST) significantly influences anchovy abundance in the region. The latitudinal gradient also had a significant influence on this species' abundance. These findings underscore the vulnerability of the small-scale fisheries sector in the tropical upwelling zone to climate change. The study concludes that even in the face of potential negative impacts from environmental factors, optimizing fishing methods can contribute to the preservation of pelagic resources in the region. It emphasizes the importance of managing fishery resources in a sustainable, economically viable, and socially acceptable way.

Expected improvements in precision when integrating opportunistic close-kin mark-recapture data into fisheries stock assessments

Close-Kin Mark-Recapture (CKMR) sampling, by providing information on abundance and survival rates (and potentially other quantities), offers a promising new data source for fisheries stock assessments. Sample design in order to achieve a desired precision is somewhat straightforward in simple CKMR models; however when integrated within a full stock assessment model with many other data sources, the value of the data (in terms of a reduction in uncertainty of model estimates) is less clear. Herein I demonstrate, using self-test simulations, the expected improvements in precision and accuracy of derived quantities and estimated parameters within statistical catch-at-age models when opportunistic CKMR sampling is conducted and the data integrated within the assessment. By opportunistic CKMR sampling I mean to describe the genetic sampling of individuals that comprise the age composition data, such that increases in CKMR sampling would also increase the age composition samples (and vice versa). I examine the expected improvements across three life history types (cod-like, flatfish-like, and sardine-like) and different amounts of data available to the assessment, including the uncertainty and inclusion of an abundance index and the sample size and time series length of CKMR and age composition samples. Results suggest CKMR data can provide considerable improvements in accuracy and precision of spawning stock biomass at the end of the time series and parameters defining natural mortality and scale of the population, provided an adequate annual sample size is collected relative to the spawning abundance of the stock during the period of CKMR inference. The time-series length of CKMR data and uncertainty or inclusion of an abundance index played a much more moderate role in how much improvement CKMR data provided over models fit without CKMR. This result was likely a function of the model being privy to an effectively known catch time series and known steepness, allowing it to estimate stock scale and trend reasonably well without CKMR data given informative composition data. I recommend simulation analyses including stock assessments as estimation models be carried out for those considering routinely collecting and integrating CKMR data into fisheries stock assessments.

Population genomics informs the management of harvested snappers across north-western Australia

Failure to consider population structure when managing harvested fishes increases the risk of stock depletion, yet empirical estimates of population structure are often lacking for important fishery species. In this study, we characterise genetic variation in single nucleotide polymorphisms (SNPs) to assess population structure for three harvested species of tropical snappers across the broad (up to 300 km wide) and extensive (~ 4000 km) continental shelf of north-western Australia. Comparisons across ~ 300 individuals per species, showed remarkably similar patterns of genetic structure among Lutjanus sebae (red emperor), L. malabaricus (saddletail snapper) and Pristipomoides multidens (goldband snapper) despite subtle differences in biological and ecological traits. Low levels of genetic subdivision were reflected in an isolation by distance relationship where genetic connectivity increased with geographic proximity. This indicates extensive but not unlimited dispersal across the north-western Australian shelf. Our findings provide evidence of connectivity between current management areas, violating the assumption of multiple independent stocks. Spatial stock assessment models may be more suitable for the management of these species however demographic connectivity rates cannot be accurately estimated from the conventional population genetic approaches applied in this study. We recommend that managers aim to maintain adequate spawning biomass across current management areas, and assess stocks at finer scales, where practical.

‘‘Slim pickings?’’: Extreme large recruitment events may induce density-dependent reductions in growth for Alaska sablefish (Anoplopoma fimbria) with implications for stock assessment

Growth processes mediate survival and fecundity within fish populations, which are fundamental in regulating population dynamics. Therefore, accurate estimates of population scale and sustainable exploitation levels in contemporary fishery stock assessment models rely heavily on understanding and accurately characterizing growth processes. However, empirical studies that relate population-level changes in growth patterns to observable ecosystem and population conditions remain scarce. In particular, few studies directly consider the influence of intra-specific competition (i.e., cohort effects) on growth variability, and its associated implications for stock assessment estimates. Focusing on Alaska sablefish as a case study, we illustrate how multiple unprecedented large recruitment events since 2014 resulted in density-dependent declines in growth on the population-level scale, using a state–space growth model. Furthermore, we demonstrate how incorporating cohort-specific growth variability within the Alaska sablefish stock assessment model resulted in substantial differences in estimates of spawning biomass and recommended harvest levels. Overall, findings from this study underscore the significance of cohort effects on growth processes and their implications for stock assessment models and associated harvest recommendations.

Fisheries shocks provide an opportunity to reveal multiple recruitment sources of sardine in the Sea of Japan

The abrupt decline in sardine catches in the Sea of Japan and the East China Sea (SJ-ECS) in 2014 and 2019 and the recovery in the following years call into question the current assumption that sardines in the SJ-ECS form a self-recruiting subpopulation. To test this hypothesis, we analysed otolith stable oxygen and carbon isotope profiles (δ18O, δ13C) of age-0 and age-1 sardines from 2010 and 2013–2015 year-classes captured in the SJ-ECS, as geographic markers for nursery areas. Age-0 sardines generally showed a significant ontogenetic decrease in otolith δ18O from larval to juvenile stages. However, the majority of age-1 captured in spring 2011, 2015 and 2016 showed non-decreasing otolith δ18O profiles, suggesting that the age-0 off the Japanese coast were not the main source of recruitment. Different migration groups were thus indicated: the “locals” growing up off the Japanese coast and the migrating “nonlocals”. The isotope profiles of the “nonlocals” overlapped with those of age-0 captured in the subarctic North Pacific, suggesting that they may be migrants from the Pacific, or perhaps an unobserved northward migration group in the SJ-ECS. Our results highlight the considerable uncertainty in the population structure assumed in current stock assessment models for Japanese sardine.

Time varying M with starvation mortality in a state-space stock assessment model: Part 2: Atlantic cod (Gadus morhua) on the southern Grand Bank of Newfoundland

State-space models are now a common tool for modeling time-varying ecological phenomena. This extends to state-space stock assessment models (SSAMs), recognized as pivotal components within the evolving landscape of next-generation stock assessment methodologies. Though methods are rapidly evolving, the estimation of time-varying rates of natural mortality (M) remains a challenge, and the sensitivity of stock assessments and management advice to assumed M values underscores the pressing need for improved estimation methods. Using southern Grand Bank (SGB) Atlantic cod as a case study, we introduce a novel approach to estimate time-varying M. We first convert a length-based starvation M index into an age-based index, which we then include in an age-based SSAM to estimate two components of M: starvation M and a remainder component. This produces a new SGB cod SSAM with time-varying total stock M. This model produces a large decrease (68 %) in the size of the model process errors (i.e., their standard deviation) and better fit compared to a model that did not account for time-varying M, indicating that the starvation M index improves our model of stock productivity. By leveraging readily available information on fish body condition and the proportion of fish in really poor condition, the proposed methods offer a valuable solution to the challenges associated with estimating time-varying M. The proposed methods offer a tractable solution to the common struggles associated with quantifying changes in fish productivity, which is crucial for the management of dynamic systems.

Main life history traits of the largehead hairtail, Trichiurus lepturus (Linnaeus, 1758) from the Senegalese North coast

Abstract Trichiurus lepturus is a benthopelagic species of socio-economic importance in Senegal. Despite its intensive exploitation, there is a lack of studies on its life traits in West African waters, particularly in Senegal. This study aims to determine the biological parameters of this species in the northern coastal zone of Senegal. Samples were collected monthly from April 2015 to March 2016 in Cayar, one of the main landing sites in Senegal. A total of 354 specimens were caught with longlines. Condition factor varied between the years with a peak in May. Positive allometric growth (b = 3.24) was found for this species. The reproductive period mainly occurred from April to July, and the sizes at first sexual maturity were 831 mm and 766 mm for females and males, respectively. Absolute fecundity was highly variable (39,976.88 ± 19,793.02 oocytes). The growth parameters estimated were asymptotic length (L∞) = 134 cm and the growth coefficient (K) = 0.0016. The maximum observed age was 1 year and 7 months. These parameters could be integrated into stock assessment models, providing a better estimate of the exploitation level of this resource and aiding in the formulation of more scientifically informed management measures for sustainable exploitation.

Bias in fishery stock assessment models.

Bias in fishery stock assessment models.

Developing a fine-scale spatial operating model of eastern oyster population dynamics in Chesapeake Bay, Maryland, U.S.A.

Management decisions for the eastern oyster (Crassostrea virginica) fishery in Maryland are made at a finer spatial scale than the spatial data resolution of the current stock assessment. This mismatch creates concerns that the consequences of management actions and fishing activities are not being adequately represented when assessing fishery status. To produce a model that could support a participatory modeling process intended to differentiate results of fine-scale management actions we developed a method for conditioning a down-scaled oyster stock assessment model to produce a spatially-explicit operating model at the scale of individual oyster bars for eastern oysters in Chesapeake Bay, Maryland. To ensure that parameter values of the operating model were consistent with data at multiple scales we fitted the model to bar-specific harvest data during 2004–2020 and regional abundance estimates from the current Maryland Oyster Stock Assessment during 1999–2020. The operating model can then predict effects of different management actions, such as planting hatchery-reared oysters, addition of substrate, or modifying fishing regulations, at a bar-specific scale and compare outcomes among different scenarios. Model outputs included a suite of management performance metrics, including but not limited to oyster abundance and fishery harvest, that were important to stakeholders. These bar-specific scenarios would not have been possible using an operating model with the same spatial resolution as the current Maryland Oyster Stock Assessment. Accounting for fine scale spatial processes can be important to engaging participants and our model provides an expedient option to develop an operating model that downscales stock assessment models.

Taking stock of global fisheries.

Current stock assessment models overestimate productivity and recovery trajectory.

Confronting transitions in fishery fleet structure and selectivity: practical recommendations for integrated age-structured stock assessments based on simulation analysis

Dynamic shifts in fleet structure and gear usage lead to complex implications for representing fishery selectivity in stock assessment models. There is generally a lack of consensus on how assessment models should be configured to confront changes in fishery fleet structure or associated selectivity forms, while balancing complexity-parsimony tradeoffs. We conducted a simulation analysis to evaluate the performance of alternative assessment models when confronted with fleet transitions among gear types, which included differences in (1) rates of transition (i.e., a fast or slow transition among gears), and (2) selectivity forms for each modeled fleet (i.e., asymptotic or dome-shaped). In general, explicitly modeling fleet structure (i.e., multi-fleet models) performed well, but demonstrated bias in biomass estimates and management reference points when selectivity forms were mis-specified. Single-fleet models were only unbiased when time-varying selectivity (e.g., using time blocks or continuous formulations) was estimated to account for changes among gear types. Our results suggest that single-fleet models with time-varying fishery selectivity are adequate for operational management advice, but research oriented multi-fleet models should be used as validation tools to explore model consistency within single-fleet models.

Empirical validation of integrated stock assessment models to ensuring risk equivalence: A pathway to resilient fisheries management.

The Precautionary Approach to Fisheries Management requires an assessment of the impact of uncertainty on the risk of achieving management objectives. However, the main quantities, such as spawning stock biomass (SSB) and fish mortality (F), used in management metrics cannot be directly observed. This requires the use of models to provide guidance, for which there are three paradigms: the best assessment, model ensemble, and Management Strategy Evaluation (MSE). It is important to validate the models used to provide advice. In this study, we demonstrate how stock assessment models can be validated using a diagnostic toolbox, with a specific focus on prediction skill. Prediction skill measures the precision of a predicted value, which is unknown to the model, in relation to its observed value. By evaluating the accuracy of model predictions against observed data, prediction skill establishes an objective framework for accepting or rejecting model hypotheses, as well as for assigning weights to models within an ensemble. Our analysis uncovers the limitations of traditional stock assessment methods. Through the quantification of uncertainties and the integration of multiple models, our objective is to improve the reliability of management advice considering the complex interplay of factors that influence the dynamics of fish stocks.

Sensitivity of the stock assessment for the Antarctic krill fishery to time‐varying natural and fishing mortality

AbstractThe stock assessment model for the Antarctic krill fishery is a population model operating on daily timesteps, which permits modeling within‐year patterns of some population dynamics. We explored the effects of including within‐year patterns in natural and fishing mortality on catch limits of krill, by incorporating temporal presence of key predator species and contemporary temporal trends of the fishing fleet. We found that inclusion of within‐year variation in natural and fishing mortalities increased catch limits. Fishing mortality had a greater effect than natural mortality despite differences in top‐down predation on krill, and potentially increased catch limits by 24% compared to the baseline model. Additionally, the stock assessment model allowed a higher catch limit when fishing was during peak summer months than autumn. Number of days with active fishing was negatively related to precautionary catch limits. Future stock assessments should incorporate contemporary spatiotemporal fishing trends and consider implementing additional ecosystem components into the model.

Spatial and seasonal variations in the growth of the populations of tongue sole fish (Cynoglossus quadrilineatus Bleeker, 1851) along four different stations of Makran coast by using the von Bertalanffy model

BackgroundGrowth is a most important life-history trait that defines the maximum size and age, maturation stage, and mortality of any fish species and is also involved in stock assessment models commonly used in fisheries conservation or management. ObjectivesThe present study focused on assessing the growth patterns and related parameters to the population of tongue sole (Cynoglossus quadrilineatus) along different sites of the Makran coast in temperate regions. MethodologyA total of 987 tongue sole fishes were collected seasonally from four different sites along the Makran coast (Gwadar, Pasni, Ormara, and Sur Bandar). Growth parameters i.e., length-weight relationships, length-frequency distribution, von Bertalanffy (VBGF) growth model, and stock status were analyzed. ResultsThe overall results of the length-weight relationship (LWRs) of this species showed only a significantly positive allometric growth pattern (b < 3.0; p < 0.05) observed during three seasons along the four selected sites of Makran Coast. The estimated von Bertalanffy (VBGF) growth parameters of C. quadrilineatus at four stations were described as; maximum observed length (Lmax) = 31.65 cm, asymptotic length (L∞) = 33.32 cm, size at sexual maturity (Lm) = 17.98 cm, Lopt = 20.55 cm, growth coefficient per year (k) = 0.55 year -1, t0 = 0.04 year -1, f = 2.79, Tmax = 2.80 years, tm = 0.78 year, M = 1.64 per year, topt = 1.30 years. The present study also noted seasonal variations in the length frequency distribution (LFD) data between the four selected sites. ConclusionsThus, our study concluded that the von Bertalanffy growth model (VBGF) is the best-fitted model for analyzing the growth patterns of C. quadrilineatus at different sites and during various seasons along the Makran Coast. This model demonstrated superior predictive ability and effectively described changes in growth patterns or body size across sub-populations of C. quadrilineatus at the four study sites. Thus, our research provides valuable insights into the population dynamics and life history traits of this tongue sole population in the Makran coast. Moreover, as the first study on the growth of this species, our findings offer essential information for informing sustainable exploitation, conservation, and fishery management strategies.

Value of data in stock assessment models with misspecified initial abundance and fishery selectivity

AbstractThe age‐structured assessment model available in the MSEtool R package assesses stock status and exploitation for varying data availability, from limited to rich datasets. We investigated model accuracy in relation to data availability, population exploitation levels, initial population assumption and fishery selectivity misspecification. Estimates were accurate in all conditions when data were available for a stock in an unfished state. However, for estimates to be accurate without complete exploitation data, total catch and abundance index data needed to span more than two stock generations. When the data time series was shorter than two generations, fishery mean lengths spanning one generation improved relative estimates (e.g. depletion), but precise estimates of unfished recruitment required fishery age‐ or length‐structured data.

Building confidence: Developing image-based methods to incorporate fishery-collected data in the American lobster stock assessment

The American lobster, Homarus americanus, supports the most valuable single-species fishery in North America; however to-date, a reliable and robust method to determine age does not exist, and thus some of the more traditional catch-at-age stock assessment methods cannot be used to determine status. In lieu of this, the Atlantic States Marine Fisheries Commission’s American Lobster Stock Assessment Model uses a probabilistic growth transition matrix to determine how lobsters of different sizes will grow and recruit into the fishery. Developing and updating the growth transition matrix to reflect current growth dynamics requires estimates of molt increment and interval. Tagging studies are an inexpensive method to collect this type of information, but relying on fishing industry reported recapture data and buy-in can be challenging, resulting in varied return rates and uncertainty in measurement accuracy. Here, we report on a subset of recaptured lobsters released in the Gulf of Maine and Georges Bank regions between 2015 and 2020, where in addition to recapture locations, harvesters were encouraged to submit images of recaptured lobsters alongside a standard lobster gauge as a scale to estimate carapace length. Some fishermen were able to provide both direct measurements and images. Images were analyzed in ImageJ to estimate individual lobster carapace length (CL); and each image was assigned a quality score. For all groups, image-derived lengths were correlated with measured observations, regardless of the overall image quality, with only a slight underestimation at the maximum end of CL. This image-based method for length estimation provides high-quality length predictions regardless of image quality and can significantly increase the likelihood of harvesters contributing data with broad potential to engage commercial fisherman in collaborative science. Using this method we were able to expand the dataset of length records from this project by 251 or 33 %, providing additional data at a rate similar to tagging an additional 6000 lobsters.