Stock Assessment Methods Research Articles

Stock assessment methods for sustainable fisheries

Abstract This special volume of the ICES Journal compiles contributions from the World Conference on Stock Assessment Methods for Sustainable Fisheries (July 2013, Boston, USA). The conference was the product of a strategic initiative on stock assessment methods that engaged many national and regional fishery management organizations to assure that scientists can apply the most appropriate methods when developing management advice. An inclusive workshop was designed to evaluate the performance of a variety of model categories by applying multiple models to selected case study data as well as simulated pseudo-data that had realistic measurement error. All model applications had difficulties in recovering the simulated stock and fishing mortality trends, particularly at the end of the assessment time series, when they are most important for informing fishery management. This general result suggests that the next steps in evaluating the performance of stock assessment methods should include stock status relative to sustainable reference points, catch advice, multi-model consideration, and alternative management procedures. Recognition of the limitations of conventional stock assessment methods should promote further development of data-limited approaches, methods with time-varying parameters, or spatial complexity, and a more revolutionary shift towards the application of multispecies and ecosystem models. The contributions in this volume address methodological themes that are expected to improve the scientific basis of fishery management. Furthermore, the limitations of stock assessment methods and associated uncertainty should be more extensively considered in fishery management strategies and tactical decisions. Recommendations developed during the conference called for the establishment of a global initiative to synthesize regional advances, form guidance on best practices, promote strategic investments, and highlight research needs for fish stock assessments.

Risk assessment of cartilaginous fish populations

Abstract We review three broad categories of risk assessment methodology used for cartilaginous fish: productivity-susceptibility analysis (PSA), demographic methods, and quantitative stock assessments. PSA is generally a semi-quantitative approach useful as an exploratory or triage tool that can be used to prioritize research, group species with similar vulnerability or risk, and provide qualitative management advice. Demographic methods are typically used in the conservation arena and provide quantitative population metrics that are used to quantify extinction risk and identify vulnerable life stages. Stock assessments provide quantitative estimates of population status and the associated risk of exceeding biological reference points, such as maximum sustainable yield. We then describe six types of uncertainty (process, observation, model, estimation, implementation, and institutional) that affect the risk assessment process, identify which of the three risk assessment methods can accommodate each type of uncertainty, and provide examples mostly for sharks drawn from our experience in the United States. We also review the spectrum of stock assessment methods used mainly for sharks in the United States, and present a case study where multiple methods were applied to the same species (dusky shark, Carcharinus obscurus) to illustrate differing degrees of model complexity and type of uncertainty considered. Finally, we address the common and problematic case of data-poor bycatch species. Our main recommendation for future work is to use Management Strategy Evaluation or similar simulation approaches to explore the effect of different sources of uncertainty, identify the most critical data to satisfy predetermined management objectives, and develop harvest control rules for cartilaginous fish. We also propose to assess the performance of data-poor and -rich methods through stepwise model construction.

Time-varying natural mortality in fisheries stock assessment models: identifying a default approach

Abstract A typical assumption used in most fishery stock assessments is that natural mortality (M) is constant across time and age. However, M is rarely constant in reality as a result of the combined impacts of exploitation history, predation, environmental factors, and physiological trade-offs. Misspecification or poor estimation of M can lead to bias in quantities estimated using stock assessment methods, potentially resulting in biased estimates of fishery reference points and catch limits, with the magnitude of bias being influenced by life history and trends in fishing mortality. Monte Carlo simulations were used to evaluate the ability of statistical age-structured population models to estimate spawning-stock biomass, fishing mortality, and total allowable catch when the true M was age-invariant, but time-varying. Configurations of the stock assessment method, implemented in Stock Synthesis, included a single age- and time-invariant M parameter, specified at one of the three levels (high, medium, and low) or an estimated M. The min–max (i.e. most robust) approach to specifying M when it is thought to vary across time was to estimate M. The least robust approach for most scenarios examined was to fix M at a high value, suggesting that the consequences of misspecifying M are asymmetric.

Ss3sim: An R Package for Fisheries Stock Assessment Simulation with Stock Synthesis

Simulation testing is an important approach to evaluating fishery stock assessment methods. In the last decade, the fisheries stock assessment modeling framework Stock Synthesis (SS3) has become widely used around the world. However, there lacks a generalized and scriptable framework for SS3 simulation testing. Here, we introduce ss3sim, an R package that facilitates reproducible, flexible, and rapid end-to-end simulation testing with SS3. ss3sim requires an existing SS3 model configuration along with plain-text control files describing alternative population dynamics, fishery properties, sampling scenarios, and assessment approaches. ss3sim then generates an underlying ‘truth’ from a specified operating model, samples from that truth, modifies and runs an estimation model, and synthesizes the results. The simulations can be run in parallel, reducing runtime, and the source code is free to be modified under an open-source MIT license. ss3sim is designed to explore structural differences between the underlying truth and assumptions of an estimation model, or between multiple estimation model configurations. For example, ss3sim can be used to answer questions about model misspecification, retrospective patterns, and the relative importance of different types of fisheries data. We demonstrate the software with an example, discuss how ss3sim complements other simulation software, and outline specific research questions that ss3sim could address.

Changes in the Salmonine Community of Lake Michigan and Their Implications for Predator–Prey Balance

AbstractWe combined statistical stock assessment methods with bioenergetic calculations to assess historical changes in abundance and consumptive demand of the hatchery‐supported salmonine community in Lake Michigan, with the goal of providing information needed to examine the lake's predator–prey balance. Especially for Chinook Salmon Oncorhynchus tshawytscha, the most dominant salmonine predator in the lake, our analysis revealed density‐dependent changes in growth, survival, production levels, consumptive demand, and fishery characteristics, suggesting that increased salmonine abundance possibly had substantial impacts on prey abundance that led to predators being food limited. Indeed, the estimated changes in the salmonine community were consistent with historical changes in prey abundances that were previously documented for Lake Michigan. Specifically, higher salmonine abundance and consumption were estimated for the early 1980s, during which time Alewife Alosa pseudoharengus abundance experienced a marked decline, leading to a Chinook Salmon mass mortality event in 1987. Similarly, increased salmonine abundance and consumption were estimated for the years since the early 2000s, and the Alewife population in Lake Michigan has been driven to historically low levels during these years. Increased salmonine abundance estimates in recent years were attributable to improved survival rates and natural reproduction of Chinook Salmon. Although past revisions to stocking rates may have been reasonable measures taken to stabilize the predator–prey system, our analysis suggests that recent reductions in stocking have not been sufficient to reduce predatory pressure on the Alewife population; however, they may have ameliorated potential effects of increased natural reproduction of Chinook Salmon. Along with a complementary assessment of the production dynamics of key prey species, our retrospective assessment of the dynamics of the Lake Michigan predator community and their consumptive demands can provide the basis for making future fishery management decisions from an ecosystem perspective.

Complex assessments or simple management procedures for efficient fisheries management: a comparative study

Abstract Complex stock assessment methods are data- and expertise-hungry, with the annual updates of catch-at-age data and models typically seen as an essential requirement for sound management. But are the heavy commitments of resources required for this level of annual intervention really necessary to achieve efficient long-term fishery management? This question is addressed through a retrospective analysis of management performance over the last 20 years for four North Atlantic fish stocks. The assessments for two of these stocks have exhibited fairly strong retrospective patterns. The actual assessment advice for these stocks was provided based on complex assessment methods making use of age data. The outcomes are compared with what could have been achieved with much simpler catch control rules based upon age-aggregated survey indices alone. Even for the stocks whose assessments exhibit retrospective patterns, these simple rules can achieve virtually equivalent catch and risk performance, with much less interannual TAC variability, compared with what actually occurred over the past 20 years.

Evaluating methods for setting catch limits in data-limited fisheries

The majority of global fish stocks lack adequate data to evaluate stock status using conventional stock assessment methods. This poses a challenge for the sustainable management of these stocks. Recent requirements to set scientifically based catch limits in several countries, and growing consumer demand for sustainably managed fish have spurred an emerging field of methods for estimating overfishing thresholds and setting catch limits for stocks with limited data. Using a management strategy evaluation framework we quantified the performance of a number of data-limited methods. For most life-histories, we found that methods that made use of only historical catches often performed worse than maintaining current fishing levels. Only those methods that dynamically accounted for changes in abundance and/or depletion performed well at low stock sizes. Stock assessments that make use of historical catch and effort data did not necessarily out-perform simpler data-limited methods that made use of fewer data. There is a high value of additional information regarding stock depletion, historical fishing effort and current abundance when only catch data are available. We discuss the implications of our results for other data-limited methods and identify future research priorities.

Simulation testing the robustness of stock assessment models to error: some results from the ICES strategic initiative on stock assessment methods

Abstract The World Conference on Stock Assessment Methods (July 2013) included a workshop on testing assessment methods through simulations. The exercise was made up of two steps applied to datasets from 14 representative fish stocks from around the world. Step 1 involved applying stock assessments to datasets with varying degrees of effort dedicated to optimizing fit. Step 2 was applied to a subset of the stocks and involved characteristics of given model fits being used to generate pseudo-data with error. These pseudo-data were then provided to assessment modellers and fits to the pseudo-data provided consistency checks within (self-tests) and among (cross-tests) assessment models. Although trends in biomass were often similar across models, the scaling of absolute biomass was not consistent across models. Similar types of models tended to perform similarly (e.g. age based or production models). Self-testing and cross-testing of models are a useful diagnostic approach, and suggested that estimates in the most recent years of time-series were the least robust. Results from the simulation exercise provide a basis for guidance on future large-scale simulation experiments and demonstrate the need for strategic investments in the evaluation and development of stock assessment methods.

Some explorations of the life history ratios to describe length composition, spawning-per-recruit, and the spawning potential ratio

AbstractEvaluating the status of data-poor fish stocks is often limited by incomplete knowledge of the basic life history parameters: the natural mortality rate (M), the von Bertalanffy growth parameters (L∞ and k), and the length at maturity (Lm). A common approach to estimate these individual parameters has been to use the Beverton–Holt life history invariants, the ratios M/k and Lm/L∞, especially for estimating M. In this study, we assumed no knowledge of the individual parameters, and explored how the information on life history strategy contained in these ratios can be applied to assessing data-poor stocks. We developed analytical models to develop a relationship between M/k and the von Bertalanffy growth curve, and demonstrate the link between the life history ratios and yield- and spawning-per-recruit. We further developed the previously recognized relationship between M/k and yield- and spawning-per-recruit by using information on Lm/L∞, knife-edge selectivity (Lc/L∞), and the ratio of fishing to natural mortality (F/M), to demonstrate the link between an exploited stock's expected length composition, and its spawning potential ratio (SPR), an internationally recognized measurement of stock status. Variation in length-at-age and logistic selectivity patterns were incorporated in the model to demonstrate how SPR can be calculated from the observed size composition of the catch; an advance which has potential as a cost-effective method for assessing data-poor stocks. A companion paper investigates the effects of deviations in the main assumptions of the model on the application of the analytical models developed in this study as a cost-effective method for stock assessment [Hordyk, A. R., Ono, K., Valencia, S., Loneragan, N. R., and Prince, J. D. 2015. A novel length based empirical estimation method of spawning potential ratio (SPR), and tests of its performance, for small-scale, data-poor fisheries. ICES Journal of Marine Science, 72: 217–231].

A multispecies statistical age-structured model to assess predator–prey balance: application to an intensively managed Lake Michigan pelagic fish community

Using a Bayesian modeling approach, we developed a multispecies statistical age-structured model to assess trade-offs between predatory demands and prey productivities, with the aim to inform management of top predators. Focusing on the Lake Michigan fish community, we assessed these trade-offs in terms of predation mortalities and productivities of alewife (Alosa pseudoharengus) and rainbow smelt (Osmerus mordax) and functional responses of salmonines. Our predation mortality estimates suggested that salmonine consumption has been a major driver of prey dynamics, with sharp declines in alewife abundance in the 1960s–1980s and the 2000s coinciding with increased predation rates. Our functional response analysis indicated that feedback mechanisms are unlikely to help maintain a predator–prey balance, with Chinook salmon (Oncorhynchus tshawytscha) and lake trout (Salvelinus namaycush) consumption declining only at the lowest prey densities, while the other salmonines consumed prey at a maximum rate across all observed prey densities. This study demonstrates that a multispecies modeling approach combining stock assessment methods with explicit consideration of predator–prey interactions can provide a basis for tactical decision-making from a broader ecosystem perspective.

Informing Recovery Goals Based on Historical Population Size and Extant Habitat: A Case Study of the Gulf Sturgeon

AbstractConservation efforts under regulatory programs such as the U.S. Endangered Species Act and Magnuson–Stevens Fisheries Conservation and Management Act seek to restore species in decline to a level where the populations are no longer in jeopardy. Key to designing effective conservation plans for the more than 2,500 species currently managed by these two U.S. programs is the establishment of realistic restoration targets given changes in habitats and ecosystems. Accomplishing this is difficult, particularly for populations that have experienced significant habitat alterations. We developed and assessed an approach for establishing quantifiable goals for the recovery of a threatened species by (1) estimating historical population biomass prior to directed fishing, (2) identifying large‐scale habitat modifications that limit available critical habitat, (3) assessing the relationship between historical baselines, habitat characteristics, and extant available habitat, and (4) using these relationships to inform recovery efforts in rivers where traditional stock assessment methods are not possible. In our case study of Gulf Sturgeon Acipenser oxyrinchus desotoi, we found that the current population levels in four of the seven river systems in the recovery plan are likely at or exceeding the mean carrying capacity, given the current levels of available habitat. In the remaining three rivers, extant Gulf Sturgeon populations are likely below their estimated carrying capacity levels. Our approach is of management importance because it establishes realistic recovery criteria through the assessment of changes in habitat from historic to present levels and is widely applicable across a species' range. An important application of this approach is to assess the potential benefits to fish species from management actions, such as dam removal or spawning habitat restoration, that are designed to restore habitat and promote species recovery.Received March 25, 2014; accepted September 29, 2014

A new role for effort dynamics in the theory of harvested populations and data-poor stock assessment

Research shows that population status can be predicted using catch data, but there is little justification for why these predictions work or how they account for changes in fisheries management. We demonstrate that biomass can be reconstructed from catch data whenever fishing mortality follows predictable dynamics over time (called “effort dynamics”), and we develop a state-space catch only model (SSCOM) for this purpose. We use theoretical arguments and simulation modeling to demonstrate that SSCOM can, in some cases, estimate population status from catch data. Next, we use meta-analysis to estimate effort dynamics for US West Coast groundfishes before and after fisheries management changes in the mid-1990s. We apply the SSCOM using meta-analytic results to data for eight assessed species and compare results with stock assessment and data-poor methods. Results indicate general agreement among all three methods. We conclude that effort dynamics provides a theoretical basis for using catch data to reconstruct biomass and has potential for conducting data-poor assessments. However, we still recommend that index and compositional data be collected to allow application of data-rich methods.

Evaluating alternative methods for monitoring and estimating responses of salmon productivity in the North Pacific to future climatic change and other processes: A simulation study

We used empirically based simulation modelling of 48 sockeye salmon (O. nerka) populations to examine how reliably alternative monitoring designs and fish stock assessment methods can distinguish between changes in density-dependent versus density-independent components of productivity and identify the relative contribution of a climate-driven covariate. We explored a wide range of scenarios for ocean and freshwater conditions and the response of salmon productivity (adult recruits per spawner) to those conditions. Our results show that stock assessments based on historical relationships between salmon productivity and climate-driven oceanographic conditions will likely perform poorly when those relationships change, even when such changes are anticipated and incorporated into stock assessment models in a timely manner. Estimating the relative importance of climate-driven oceanographic influences as a driver of sockeye productivity will be difficult, especially if climatic changes occur rapidly and concurrently with other disturbances. Thus, better understanding of the mechanisms by which climatic changes and other drivers influence salmon productivity may be essential to avoid undesirable management outcomes. As well, an expansion of monitoring of juvenile salmon abundances on more salmon stocks is needed to help distinguish the effects of different drivers.

Fish catch data: Less than what meets the eye

A recent opinion piece published in Nature summarises the differing views held by Pauly on the one hand, and by Hilborn and Branch on the other, regarding the challenge faced by fishery scientists in accurately determining the status of the world's fisheries. Both commentaries discuss whether the fisheries catch data published by FAO can by themselves be used to infer fishery status. The purpose of this short communication is to examine both views and to propose additional solutions to contribute to the understanding of fishery status globally. These may include expanding data-poor stock assessment methods as well as community-based data collection and monitoring programs, particularly in developing countries.

Review of integrated size-structured models for stock assessment of hard-to-age crustacean and mollusc species

Abstract Punt, A. E., Huang, T., and Maunder, M. N. 2013. Review of integrated size-structured models for stock assessment of hard-to-age crustacean and mollusc species. – ICES Journal of Marine Science, 70:16–33. Crustaceans and molluscs such as crabs, rock lobsters, prawns, abalone, and oysters constitute large and valuable fisheries. However, assessments of these species are hampered because they cannot be production aged, in contrast to many teleosts. The major data sources for these species, in addition to catch and abundance index data, are the size compositions of the catches and of any fishery-independent indices. Assessments of such species have been conducted using age-based methods of stock assessment, as well as surplus production models. However, size-structured methods are now preferred because they can make full use of size-composition data, are able to integrate multiple sources of data, and produce the types of outputs which are needed for management purposes. An advantage of size-based models over age-based models is that all processes can be size-based, and these processes can modify the (unmodelled) size-at-age distribution. We review these methods, highlighting the choices that need to be made when developing integrated size-structured stock assessments, the data sources which are typically available and how they are used for parameter estimation, and contrast a number of such assessments worldwide.

Identifying research priorities for management under uncertainty: The estimation ability of the stock assessment method used for eastern Bering Sea snow crab (Chionoecetes opilio)

The fishery for snow crab in the eastern Bering Sea was declared overfished in 1999 and had by 2010 failed to rebuild in the time specified. A key component of the rebuilding plan for snow crab was the stock assessment on which management advice was based. In common with all stock assessments, there are numerous potential sources of uncertainty, but it is not clear which uncertainty is most influential, and hence how research should be focused to best reduce uncertainty. A simulation framework is used to explore the estimation ability of the assessment method when its assumptions are violated. The focus of this evaluation is on how well management quantities such as mature male biomass and the overfishing level can be estimated. Three categories of uncertainty are considered: mis-specification of pre-specified parameters, data quantity and/or quality, and assumptions in the assessment related to fishing mortality. Management quantities are estimated reasonably well when all assumptions of the stock assessment method are correct. Additional data on growth and natural mortality would be most beneficial. However, penalties on fishing mortality introduce bias into estimates, and survey selectivity patterns are poorly determined by the estimation method.

Accounting for cohort-specific variable growth in fisheries stock assessments: A case study from south-eastern Australia

Modern statistical fishery stock assessment models rarely account for temporal variability in mean length-at-age, and almost never describe cohort-specific effects. This study employs techniques for discerning temporal change to mean length-at-age from fisheries data and introduces recently developed stock assessment methods to account for this variability. Using Stock Synthesis, a statistical catch-at-age modelling framework, a stock assessment to account for cohort-specific variability in mean length-at-age was developed for blue grenadier, Macruronus novaezelandiae, an important commercial species in Australia's Southern and Eastern Scalefish and Shark Fishery. Key outputs of a standard stock assessment model that assumes static growth were compared to those of an alternative model that accounts for observed variability in length-at-age. Comparisons show that accounting for variable growth provides a better fit to time-series data and results in significant differences to key population estimates. These differences have implications for the estimation of quantities important to management and thus to scientific advice regarding recommended catch levels.

Global marine yield halved as fishing intensity redoubles

AbstractThere is widespread concern and debate about the state of global marine resources and the ecosystems supporting them, notably global fisheries, as catches now generally stagnate or decline. Many fisheries are not assessed by standard stock assessment methods including many in the world's most biodiverse areas. Though simpler methods using widely available catch data are available, these are often discounted largely because data on fishing effort that contributed to the changes in catches are mostly not considered. We analyse spatial and temporal patterns of global fishing effort and its relationship with catch to assess the status of the world's fisheries. The study reveals that fleets now fish all of the world's oceans and have increased in power by an average of 10‐fold (25‐fold for Asia) since the 1950s. Significantly, for the equivalent fishing power expended, landings from global fisheries are now half what they were a half‐century ago, indicating profound changes to supporting marine environments. This study provides another dimension to understand the global status of fisheries.

Evaluating methods that classify fisheries stock status using only fisheries catch data

Methods that use only fisheries catch records to determine the status of exploited fish populations have been used to draw important conclusions regarding the world's fisheries. The reliability of two such approaches is evaluated by simulating a range of fisheries development and overfishing scenarios. The success rate and bias of stock status classification by two catch-based methods is compared with those of two stock assessment methods that explicitly model population dynamics and use additional fishing effort data. On average the catch-based methods correctly classified the status of stocks in 31% and 34% of the cases considered. Two simple stock assessments successfully classified stock status in 57% and 59% of the cases. The catch-based methods and the surplus production stock assessment were negatively biased and on average provided overly pessimistic conclusions regarding stock status. Catch-based methods were more negatively biased on average than the stock assessment approaches.

Evaluation of a spatially sex-specific assessment method incorporating a habitat preference model for blue marlin (Makaira nigricans) in the Pacific Ocean

Blue marlin, widely distributed throughout the Pacific Ocean, are sexually dimorphic, have certain preferred habitats, and migrate seasonally. These characteristics have been ignored in previous stock assessment models. A population dynamics model that includes spatial structure, and sex and age structure was therefore constructed and fitted to fisheries data for blue marlin, along with information on the relative density of the population over space derived from a habitat preference model that uses the oceanographic and biological variables sea-surface temperature, mixed layer depth, sea-surface height anomaly, and chlorophyll-a concentration. Monte Carlo simulation was then used to examine the estimation performance of the stock assessment method. Estimates of management-related quantities including current spawning stock biomass are substantially biased when the assessment method ignores seasonal movement and sexual dimorphism. We also found that (i) uncertainty about the relationship between catch rate and abundance influences estimation performance to a larger extent than uncertainty in catches, (ii) the outcomes of the assessment are sensitive to the values assumed for natural mortality and stock-recruitment steepness, and (iii) the ratio of current spawning stock biomass to that at pre-exploitation equilibrium appears to be the most robust among the quantities considered. We conclude that assessment methods for blue marlin in the Pacific Ocean need to take account of seasonal migration and sex structure to improve stock assessments.