Search

An ecosystem approach to forage fish management is required because forage fish support large fisheries, are prey for many valued species in marine food webs, and provide important social and cultural benefits to humans. Complex ecosystem models are often used to evaluate potential ecosystem consequences of forage fish fisheries, but there is seldom sufficient data to parameterize them, and full consideration of uncertainty is impossible. Models of Intermediate Complexity for Ecosystem assessment (MICE) provide a link between full ecosystem models and tactical (usually single-species) models typically used in fisheries management. MICE are ideal tools to form the basis for management strategy evaluations that compare the ability of candidate strategies to achieve goals related to target fisheries and broader ecosystem protection objectives. A MICE model is developed for the California Current Ecosystem (CCE) that focuses on the fishery for the northern subpopulation of Pacific sardine ( Sardinops sajax ) and the indirect impacts of the fishery on place-based predators, in particular brown pelicans ( Pelecanus occidentalis ) and California sea lions ( Zalophus californianus ), in the Southern California Bight. The model includes three forage species (sardine, northern anchovy Engraulis mordax , and ‘other forage’), an ‘other prey’ category, and two predator species (brown pelican and California sea lion) and evaluates the impacts of variable forage availability on adult predator reproductive success and survival. Parameterization of the model is based on available monitoring data and assessment outputs. The model is used to assess the ecosystem and fishery consequences of the current sardine management systems for Mexico, the USA, and Canada, with a focus on identifying which among a long list of sources of uncertainty in the system are most consequential for predictions of fishery impacts on predators. Key sources of uncertainty to consider in ecosystem assessments for the CCE are how prey abundance and availability impact predator demography and the extent to which the dynamics of prey populations are driven by environmental factors. Data are available for some of these sources of uncertainty for CCE sardine management, but much uncertainty remains, necessitating exploration of sensitivity to alternative model formulations and parameter values when providing advice on management strategies to decision makers.

Somatic growth in fishes often varies through time. Despite this, most stock assessments either fix or estimate a time-invariant growth relationship because estimating time-varying growth parameters can be data intensive and subject to multiple sources of bias. Additionally, estimates of growth are often confounded with estimates of selectivity, particularly if selectivity is also time varying. Incorporating empirical weight-at-age (EWAA) information into assessments is a little-studied alternative that accounts for time-varying growth, but foregoes fixing or estimating growth and length–weight relationships. However, this method requires annual measures of fish weights at each age, and missing values must therefore be interpolated. We used Stock Synthesis in a simulation-testing framework to compare the effect of estimating a single time-invariant growth curve, time-varying growth curves, and incorporating EWAA information on management quantities and parameter estimates. We ran simulations across two fish life histories (hake-like and rockfish-like) and three data cases (data-rich, data-rich with a late-starting survey, and data-moderate). We found that when growth was time invariant, the EWAA approach was unbiased but had twice the median average relative error compared to a model that estimated growth from age and length data. However, for data-rich cases with time-varying growth, the EWAA method resulted in more accurate estimates of spawning stock biomass compared to the approach that estimated time-invariant and time-varying growth parameters, as evidenced by at least a five-fold reduction in range of median relative errors. The magnitude of this effect was greatest for the long-lived, slow-growing life history. For the relatively fast-growing species, estimates from the EWAA method were particularly sensitive to interpolating missing values.

Analysts conducting stock assessments using integrated, age-structured models must discretize length data into a limited number of bins (data bins). Furthermore, some modeling frameworks also allow users to specify a distinct structure for how lengths of fish are represented in the model (model bins). The effect of choices regarding the number and width of these bins on model output is unclear, and these choices are made inconsistently in assessments across regions and species. Here, we used the stock synthesis modeling framework, and the ss3sim stock assessment simulation package, to explore the effects of choices about length discretization on stock assessment performance for three fish life-history types and four data cases. We found that, with all other aspects of a model fixed, increasing the model bin width tended to increase estimates of spawning biomass, but this effect depended on the shape of length-based processes (e.g., growth, maturity, and selectivity). Thus, we suggest analysts using model bins wider than 1 cm explore the effect of this decision on derived management quantities. In the context of estimation, there generally was a predictable tradeoff between estimation accuracy and model run time, with finer model and data bins always improving estimation accuracy and model convergence, but increasing run time. In some cases, wider data bins reduced run time (by up to 50%) with little sacrifice in model estimation performance, particularly those using conditional age-at-length data. This study identifies key aspects to consider when binning length, and provides pertinent information for stock assessment best practice guidelines.

Retrospective patterns are systematic changes in estimates of population size, or other assessment model-derived quantities, that occur as additional years of data are added to, or removed from, a stock assessment. These patterns are an insidious problem, and can lead to severe errors when providing management advice. Here, we use a simulation framework to show that temporal changes in selectivity, natural mortality, and growth can induce retrospective patterns in integrated, age-structured models. We explore the potential effects on retrospective patterns of catch history patterns, as well as model misspecification due to not accounting for time-varying biological parameters and selectivity. We show that non-zero values for Mohn’s ρ (a common measure for retrospective patterns) can be generated even where there is no model misspecification, but the magnitude of Mohn’s ρ tends to be lower when the model is not misspecified. The magnitude and sign of Mohn’s ρ differed among life histories, with different life histories reacting differently from each type of temporal change. The value of Mohn’s ρ is not related to either the sign or magnitude of bias in the estimate of terminal year biomass. We propose a rule of thumb for values of Mohn’s ρ which can be used to determine whether a stock assessment shows a retrospective pattern.

The choice of how to model selectivity differs among approaches to fisheries stock assessment. VPA tends to make only weak assumptions regarding (age-specific) selectivity (asymptotic selectivity and temporal stability of selectivity for the most recent years). In contrast, selectivity is more parametric in “integrated” methods, and can be age-, length-, and age- and length-based. The use of parametric selectivity functions tends to reduce estimation variation because fewer parameters have to be estimated, but incorrect choices for the functional form for selectivity can lead to bias. This paper illustrates effects of poor choices for selectivity on the outcomes of stock assessments, outlines methods for evaluating whether a particular choice for selectivity is appropriate using residual diagnostics, and summarizes current ways to select among alternative functional forms for selectivity. This paper also provides a synthesis of the results of past simulation studies which have explored the ability to correctly parameterize selectivity.

There is widespread recognition that spatial structure is important for fisheries stock assessment, and several efforts have been made to incorporate spatial structure into assessment models. However, most studies exploring the impact of ignoring spatial structure in stock assessments have developed population models with multiple subpopulations, rather than exploring the impact spatial dynamics may have on performance of non-spatially structured assessment methods. Furthermore, the data available for stock assessments usually do not include tagging or other data necessary to estimate movement rates. One solution to this problem is to include several fleets, each with a different selectivity pattern to represent availability, within a spatially-aggregated assessment method. In this study, the impacts of ignoring spatial structure, and the effectiveness of using multiple selectivity patterns as a proxy for spatial structure, are evaluated for the northern subpopulation of Pacific sardine (or California sardine; Sardinops sagax ). A spatially-explicit operating model is used to explore three spatial factors: the existence of size-dependent seasonal migrations across large geographical areas, the influx of another stock into the area of the assessed stock, and the occurrence of recruitment outside the area where it is assumed to occur. Two other factors related to data were evaluated: data availability and data collection design. The assessment model (AM) is based on the 2010 stock assessment for Pacific sardine, implemented in Stock Synthesis, and includes two seasons per year and six fleets, each with a different selectivity pattern. Ignoring spatial structure is found to negatively impact estimation performance, with seasonal movement having the largest impact. The AM compensates for ignoring movement and spatial structure by adjusting the selectivity patterns, but selectivity alone is not able to account for all biases caused by spatial structure.

Center for the Advancement of Population Assessment Methodology, 8901 La Jolla ShoresDrive,La Jolla, CA 92037, USA*Corresponding author: tel: +1 206 543 4270; fax: +1 206 685 7471; e-mail: kfjohns@uw.eduJohnson,K.F.,Monnahan,C.C.,McGilliard,C.R.,Vert-pre,K.A.,Anderson,S.C.,Cunningham,C.J.,Hurtado-Ferro,F.,Licandeo,R.R.,Muradian,M.L.,Ono,K.,Szuwalski,C.S.,Valero,J.L.,Whitten,A.R.,andPunt,A.E.Time-varyingnaturalmortalityinfisheriesstockassessmentmodels:identifyingadefaultapproach. – ICESJournalofMarineScience,doi:10.1093/icesjms/fsu055.Received4 September 2013; revised 1 March 2014; accepted 3 March 2014.A typical assumption used in most fishery stock assessments is that natural mortality (M) is constant across time and age. However, M is rarelyconstant 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 inbiasedestimatesoffisheryreferencepointsandcatchlimits,withthemagnitudeofbiasbeinginfluencedbylifehistoryandtrendsinfishingmor-tality. Monte Carlo simulations were used to evaluate the ability of statistical age-structured population models to estimate spawning-stockbiomass,fishingmortality,andtotalallowablecatchwhenthetrueMwasage-invariant,buttime-varying.Configurationsofthestockassessmentmethod,implementedinStockSynthesis,includedasingleage-andtime-invariantMparameter,specifiedatoneofthethreelevels(high,medium,andlow)oranestimatedM.Themin–max(i.e.mostrobust)approachtospecifyingMwhenitisthoughttovaryacrosstimewastoestimateM.The least robust approach for most scenarios examined was to fix M at a high value, suggesting that the consequences of misspecifying M areasymmetric.Keywords: model misspecification, natural mortality,population models, reference points, simulation, Stock Synthesis, time-varying.

Management of marine resources depends on the assessment of stock status in relation to established reference points. However, many factors contribute to uncertainty in stock assessment outcomes, including data type and availability, life history, and exploitation history. A simulation–estimation framework was used to examine the level of bias and accuracy in assessment model estimates related to the quality and quantity of length and age composition data across three life-history types (cod-, flatfish-, and sardine-like species) and three fishing scenarios. All models were implemented in Stock Synthesis, a statistical age-structured stock assessment framework. In general, the value of age composition data in informing estimates of virgin recruitment (R0), relative spawning-stock biomass (SSB100/SSB0), and terminal year fishing mortality rate (F100), decreased as the coefficient of variation of the relationship between length and age became greater. For this reason, length data were more informative than age data for the cod and sardine life histories in this study, whereas both sources of information were important for the flatfish life history. Historical composition data were more important for short-lived, fast-growing species such as sardine. Infrequent survey sampling covering a longer period was more informative than frequent surveys covering a shorter period.

Hurtado-Ferro, F., Hiramatsu, K., and Shirakihara, K. 2010. Allowing for environmental effects in a management strategy evaluation for Japanese sardine. – ICES Journal of Marine Science, 67: 2012–2017. The Japanese sardine (Sardinops melanostictus) is a valuable, but highly variable, resource. After record catches during the late 1980s, a 4-year recruitment failure, coupled with overfishing, resulted in severe stock depletion. TAC-based management was introduced in 1997, but the trend has not been reversed and the biomass is now 2 orders of magnitude below the value in 1987. Although a strong correlation exists between winter sea surface temperature (SST) in the nursery area and recruitment, uncertainty about future environmental effects is not explicitly considered in the management. We evaluate the robustness of three catch rules to environmental uncertainty, as captured by three stock–recruitment models (one without an environmental factor and two based on correlations between recruitment and SST for two datasets), in terms of the risk of further depletion, average biomass, and average catch: a constant fishing mortality (CF), a strategy that follows the Japanese guidelines (JG), and a more conservative strategy (ENV) that uses a temperature threshold as a proxy for regime shifts to switch between alternative catch rules. ENV and JG performed better than CF, with ENV displaying better performance in the long term, but differences were negligible in the short term.