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Weighting of size-composition data (length or weight composition of the catches) can have a large influence on the results of contemporary integrated stock assessment models in the presence of model misspecification. Model misspecification leads to conflicting information among data sets, and the choice of data weighting will determine the results. Information content on absolute abundance and abundance trends contained in size-composition data is particularly susceptible to misspecification of the biological processes. Biological processes are often misspecified in assessment models for exploited fish stocks due to lack of information. The misspecification can be in a functional form (e.g., the growth curve) or in the values assumed for pre-specified parameters. Our application to bigeye tuna in the eastern Pacific Ocean shows how one needs to “get the biology right”, i.e. minimize model misspecification, to reduce the dependency of stock assessment results on the weighting of the various data components. The stock assessment results are sensitive to the conversion from processed weight to total weight, a common, but often overlooked, component of model specification, and to the asymptotic length of the growth curve. The results are also sensitive to the weighting of the composition data. Application of the Age-Structured Production Model diagnostic shows that recruitment variation must be taken into account to interpret the absolute abundance and trend information contained in a CPUE-based index of relative abundance. Unfortunately, recruitment cannot typically be estimated from the relative index of abundance alone, so composition data are needed. The abundance estimates from an age-structured production model with estimated recruitment deviates are too uncertain (i.e., have wide confidence intervals) to be of use for management advice. Therefore, there is a trade-off between using composition data to estimate recruitment and its influence on estimates of absolute abundance through a catch-curve type process. We conclude that (i) integrated analysis, the current approach for assessing fish stocks, is supported by our results; (ii) composition data are needed to estimate recruitment; and (iii) addressing key model misspecifications should be a major component of integrated analysis. [ABSTRACT FROM AUTHOR]

Fish population estimators (for abundance, mortality and movement) from conventional tagging (mark-recapture) programmes require the assumption that tagged individuals are equally vulnerable to recapture as the (relevant portion of the) untagged population. Most commercial tuna species are distributed over millions of square km, with poorly understood stock structure and movement dynamics, while tags tend to be released in much smaller areas that are selected for logistical reasons. If the tags do not mix sufficiently with the general population this could substantially bias tuna stock assessments. We outline two simple quantitative methods for evaluating tag mixing, and apply them to western Pacific Ocean skipjack tuna ( Katsuwonis pelamis ). The first approach compares, in a discrete time window, the spatial distributions of tag recoveries from pairs of release events that were separated in space and/or time. Significant differences between the recovery distributions indicate that tags from the two release events are not fully mixed with each other, such that one or both release events cannot be fully mixed with the general population either. The second approach tests whether tag density (number of recovered tags per unit of catch) is spatially homogeneous for an individual release event. Both tests focus on detecting incomplete mixing and neither can prove that full mixing has been achieved. The analyses provide strong evidence that tag mixing assumptions are not being met in recent western Pacific skipjack tuna stock assessments. Simple diffusion models were fit to the tag density observations and used to simulate the potential magnitude of mixing biases using simple estimators for abundance and natural and fishing mortality. Options for reformulating the skipjack assessment are discussed. [ABSTRACT FROM AUTHOR]

The growth modeling framework to integrate age-at-length and tag–recapture length-increment data sources is well established in fishery science. Surprisingly, the use of the so-called “Laslett–Eveson–Polacheck (LEP)” statistical approach has been infrequent since its development in the early 2000s. Likely, this is due to the complex treatment of process error by the methodology and its resulting high computational demands. Using bigeye tuna in the eastern Pacific Ocean as an example, this research presents a modified LEP method to strengthen the benefits of integrating the two sources of data into a single-step modeling approach. The bigeye tuna case study is typical in the tuna world in which age estimates from direct otolith readings commonly cover the young segment of the species life-cycle only, causing growth model predictions extrapolated beyond the data to be unreasonably high and contain uncertain values of the asymptotic length parameter L ∞ . In the case of bigeye, several improvements resulted from integration of an auxiliary tag–recapture dataset containing recoveries from large (older) fish: the estimated growth curve “bent” over toward a much lower and more plausible value of L ∞ in order to explain the tag–recapture observations from older adult bigeye and the uncertainty (precision) of most growth parameters was greatly reduced (increased). The estimate of variability of the length-at-age was lower and estimated with greater precision. It is argued that it is reasonable to relax the complex assumptions made in the original LEP approach of treating L ∞ as a random variable and assuming dependency between the lengths at tagging ( l 1 ) and recovery ( l 2 ) while treating the unknown age at release ( A ) for each tagged fish as a random variable. These modifications greatly reduce computational demand, potentially making the LEP approach more widely used, and simplifying the interpretation of variation of length-at-age for use in stock assessment models that fit to length composition data. In addition, opportunities are opened to integrate the methodology inside modern statistical stock assessment models. [ABSTRACT FROM AUTHOR]

Highlights: [•] We profile over a population scale to evaluate the effect of each data component. [•] We structure model using both additional process and reduction of data weights. [•] Low priority data components with large gradients are less emphasis on the model. [•] Differences in model structures result in differences in the estimated dynamics. [•] We recommend using additional model process rather than statistical down-weighting. [ABSTRACT FROM AUTHOR]

Abstract: The impacts of the steepness parameter of the Beverton–Holt spawner–recruitment curve on fisheries stock assessment and management performance were evaluated, using data for bigeye tuna (Thunnus obesus) in the eastern Pacific Ocean. The analysis was conducted using the Stock Synthesis model. The estimates of maximum sustainable yield (MSY) and related management reference points are sensitive to the value of steepness (ranging from 0.4 to 1.0) of the Beverton–Holt spawner–recruitment relationship assumed in the assessment model. Steepness also impacts the evaluation of stock status. A lesser value assumed for steepness leads to a more pessimistic evaluation of the stock status regarding whether the stock is overfished or overfishing is occurring. However, a lesser value does not reduce the estimates of MSY due to the interaction between steepness and the estimates of other model parameters. When managing the stock under effort limitation using MSY-based fishing mortality, a lower assumption of steepness yields less expected losses in catch than does a higher assumption. This is due to the flatness of the yield curve when the steepness is high. A lower assumption of steepness also maintains a higher level of spawning biomass and recruitment. [Copyright &y& Elsevier]

Abstract: The wahoo, Acanthocybium solandri (Cuvier), is a high trophic level predator in tropical and sub-tropical marine pelagic waters worldwide. Wahoo are primarily caught as a retained incidental catch in purse seine and pelagic longline fisheries that target tuna and swordfish. The global commercial catch of wahoo has increased in the Pacific Ocean from 130t in 1993 to a peak in recent years of 1339t in 2006. Wahoo are also a highly prized sportfish in recreational fisheries, and although catches are poorly understood, they may be higher than the commercial catch in some regions. Despite their commercial and recreational importance, little quantitative information exists on their biology and stock structure, particularly in the Pacific Ocean. This dearth of information may have significant consequences for stock assessments and management. Suggested research priorities for wahoo in the Pacific Ocean are to: (1) investigate their biology in the region, particularly age, growth and reproductive parameters, which are essential for stock assessment; (2) use holistic methods to determine stock structure on a timescale appropriate for assessment and management; (3) conduct quantitative diet analyses that may contribute to ecosystem models, and (4) quantify catch from recreational fisheries to include in estimates of total mortality. [Copyright &y& Elsevier]

Kopf, R. K., Davie, P. S., Bromhead, D., and Pepperell, J. G. 2011. Age and growth of striped marlin (Kajikia audax) in the Southwest Pacific Ocean. – ICES Journal of Marine Science, 68: 1884–1895.This study describes the first validated model of age and growth developed for striped marlin (Kajikia audax). Daily periodicity of otolith microincrements was corroborated by back-calculated hatch dates that matched the known spawning season in the Southwest Pacific Ocean (SWPO). Yearly annulus formation in fin-spine sections was corroborated by daily otolith microincrements and by a marginal increment analysis. Ages of females ranged from 140 d to 8.5 years in fish between 990 mm and 2872 mm lower-jaw fork length (LJFL), and ages of males from 130 d to 7.0 years in fish between 1120 mm and 2540 mm LJFL. Sex-specific differences in growth were significant, with females growing to a larger asymptotic size and greater age than males. An instantaneous growth rate of 3.1 mm d–1 at 6 months and an estimated length of 1422–1674 mm LJFL by age 1 year makes this species among the fastest growing bony fish. Implications of these findings are discussed in relation to commercial longline and recreational fisheries management of striped marlin in the SWPO and in relation to the biology of pelagic fish growth. [ABSTRACT FROM PUBLISHER]

Abstract: We used hold-out cross validation model selection to determine the most appropriate form of the selectivity curve, using a nonparametric approach to represent selectivity. The cross-validation method is based on setting aside a portion of the catch-at-age (or catch-at-length) data to use as a test data set. The remaining catch-at-age data, along with other data (e.g. relative indices of abundance) are used to estimate the parameters of the stock assessment model, including the selectivity parameters. These parameter estimates are then used to predict the catch at age for the test data set. The selectivity model that produces the closest predictions to the test data set is chosen as the selectivity model to use in the assessment. The selectivity model we use is nonparametric, based on estimating an individual selectivity parameter for each age and then applying smoothness penalties to constrain how much the selectivity can change from age to age. The smoothness penalties we consider are the first, second, and third differences, a length-based penalty, and a monotonic penalty. The penalties are applied on the logarithm of selectivity to avoid scale-related problems and improve stability. The method was applied to the assessment of bigeye tuna in the eastern Pacific Ocean. We found that the estimated management quantities were relatively robust within the set of smoothness penalties that gave low cross-validations scores. We also found that poor choices for the smoothness penalties could give very different results. Poor choices include both under-smoothing (e.g. no penalties) and over-smoothing (penalties that are too large). The most influential factor was the inclusion of a monotonic penalty. [Copyright &y& Elsevier]

The article examines 14 nuclear microsatellite loci (nDNA) in the yellow tang fish or Zebrasoma flavescens to assess interactions between the remote reefs of the Hawaiian Archipelago and the species-rich communities of the Central and West Pacific Ocean. Population differentiation among three West Pacific sites and Hawaii were shown by concordant frequency shifts in both nDNA and mitochondrial cytochrome b (mDNA). The implications of fine scale differentiation within the Hawaiian Archipelago for management of the yellow tang are explored. Estimates of yellow tang historical demography indicate that Hawaii exports rather than imports yellow tang larvae.

Abstract: The use of fishery impact plots and maximum sustainable yield estimations by fishery to evaluate fishery impacts are illustrated using the bigeye tuna stock assessment in the eastern Pacific Ocean (EPO). The stock status of the bigeye tuna stock in the EPO is evaluated using the general stock assessment model Stock Synthesis II. Fishery impact plots are developed to examine the effects of the different fishery groups on the bigeye tuna spawning biomass by simulating the population dynamics while removing each fishery. Maximum sustainable yields and associated quantities are calculated using the selectivity of each fishery group. Historically, the longline fleets have had the greatest impact on the stock of bigeye tuna, but since about 1993 the purse-seine fleet has increased its impact, and its catches and effect on the stock are currently greater than those of the longline fishery. However, the theoretical long-term yields obtainable from a purse-seine fishery are much less than those obtainable from a longline fishery. [Copyright &y& Elsevier]

Abstract: Assumptions about model structure and data are a critical part of the fisheries stock assessment procedure. Sensitivity to these assumptions can be greater than the estimation uncertainty conditioned on the assumptions. Therefore, it is important that model structure and data assumptions be evaluated in stock assessments. We use the stock assessment of bigeye tuna in the eastern Pacific Ocean to illustrate several issues related to typical model and data assumptions. For this purpose, we treat the Taiwanese longline fishery as a separate entity, rather than combining data for that fishery with those for other longline fisheries, as conducted in the latest assessment of bigeye. Using the bigeye case study, we illustrate that incorrect assumptions about the relationship between catch-per-unit-effort (CPUE) and abundance and the form of the selectivity curve can greatly impact the assessment results, even when the fishery associated with the data is only a minor component of the total catch. We also illustrate the importance that understanding the data and the system under investigation plays in avoiding making inappropriate assumptions. [Copyright &y& Elsevier]

Abstract: Today, the tuna fishery of the western and central Pacific is one of the world''s largest fisheries. Annual catches exceed 2 millionmt—approximately 50% of the global tuna catch—with an estimated landed value of USD 3 billion (in 2005). The fishery expanded rapidly from the mid 1980s, following the development of the purse-seine fishery. From the mid 1990s, it was recognised that a Regional Fisheries Management Organisation was required to facilitate cooperation in the management of the resource. Following almost a decade of negotiations and planning, the Western and Central Pacific Fishery Commission (WCPFC) was established in 2004. During the interim period, stock assessments for the key tuna species were developed and revealed that for two species (yellowfin and bigeye tuna) there was concern regarding the increased levels of fishing mortality. However, declarations and resolutions calling for restraint in the expansion of fishing effort were largely unheeded over the last decade and, to date, the WCPFC has been unable to introduce any measures to effectively reduce (or limit) the level of fishing mortality on yellowfin and bigeye tuna. This paper proposes a number of mechanisms for improving the performance of the WCPFC with respect to meeting the conservation and management objectives of the commission and argues that Pacific Island nations need to collectively take the lead to ensure the effective management of the resource. [Copyright &y& Elsevier]

Abstract: In the Northwest Pacific, the squid jigging fisheries targeted the west winter–spring cohort of neon flying squid (Ommastrephes bartramii) from August to November. Total annual catch by the Chinese mainland squid jigging fleet during 2000–2005 ranged from 64,100 to 104,200t. The unique life history of this squid species makes the use of traditional age- or length-structured models difficult in evaluating the effect of intensive commercial jigging on this stock. We fitted a modified depletion model to the Chinese jigging fisheries data to estimate the squid stock abundance during 2000–2005. Monthly biological data were randomly sampled from the five squid jigging vessels during the fishing seasons. Effects of using different natural mortality rates (M) and three different error assumptions were evaluated in fitting the depletion model. Based on sensitivity analyses, the log-normal error model was found to be preferred for the squid assessment. The assessment results indicated that the initial (pre-fishing season) annual population sizes ranged from 199 to 704 million squid with the M value of 0.03–0.10 during 2000–2005. The proportional escapement (M =0.03–0.10) for different fishing seasons over the time period of 2000–2005 ranged from 15.3% (in 2000) to 69.9% (in 2001), with an average of 37.18%, which was close to the management target of 40%. Thus, the current fishing mortality of the squid jigging fishery was considered to be sustainable. We inferred its annual maximum allowable catch ranging from 80,000 to 100,000t. This study suggests that the modified depletion model provides an alternative method for assessing short-lived species such as O. bartramii. [Copyright &y& Elsevier]

The Hawaiian green sea turtle genetic stock is endemic to the Hawaiian Archipelago. This stock was depleted over the past century mainly due to over-exploitation that ceased during the 1970s following protection under the US Endangered Species Act. Nesting trends suggest the stock has been recovering but no formal stock assessment has been undertaken. So, we used a Bayesian state-space surplus-production model to describe Hawaiian green turtle population dynamics given limited data and uncertainty about sea turtle demography. Data series comprised commercial landings of green turtles reported from the Archipelago (1944–1973) and nester abundance recorded at the primary rookery on East Island, French Frigate Shoals (1973–2004). The model incorporated process and observation error and was fitted using Markov chain Monte Carlo simulation with a mix of informative and non-informative priors. We estimated that the Hawaiian green turtle stock was ca. 20% of pre-exploitation biomass when monitoring and protection began in the 1970s. The stock is estimated to be now ca. 83% of pre-exploitation biomass with an intrinsic growth rate ca. 5.4% pa (95% Bayesian credible interval: 3.1–8.9%). Rebound or recovery potential (also exploitation rate at MSP) of this stock was estimated to be 3.4% (1.6–6.2%), which is consistent with estimates for other long-lived late-maturing marine species. So, this once-seriously-depleted green turtle stock is well on the way to recovery and a limited harvest might now be demographically feasible. These findings are relevant for supporting informed public policy debate on the restoration of indigenous hunting rights in the Archipelago. Parameter estimates and model structure from the Bayesian surplus-production model were incorporated in an interactive easy-to-use stochastic simulation model to help support policy analysts in stock recovery planning and to explore sustainable harvest potential. [Copyright &y& Elsevier]

The increased computational demands of modern statistical stock assessment models have made the standard methods to provide uncertainty estimates for forward projections impractical for timely results in many applications. However, forward projections and their associated estimates of uncertainty are an important and popular piece of management advice. We describe a less computationally intense method to estimate uncertainty in forward projections that includes both parameter uncertainty and future demographic stochastic uncertainty. This frequentist method uses penalized likelihood as an approximation to mixed effects and can be viewed as treating the future projection period as part of the estimation model rather than performing stochastic projections. This allows confidence intervals to be calculated using normal approximation based on the delta method. The method is tested using simulation analysis and compared with Bayesian analysis and with projections based on point estimates of the parameters. The method is applied to yellowfin tuna in the eastern Pacific Ocean. [Copyright &y& Elsevier]

Since 1979, fishery-independent data and fishery-dependent data have been used to estimate trends in the abundance of the northeastern stock of spotted dolphins in the eastern tropical Pacific. Data collected aboard tuna vessels have been used to estimate trends in relative abundance, while data collected from fishery-independent research vessels have been used to estimate trends in actual abundance. One of the largest discrepancies between the two data sources is that tuna vessels tend to report dolphin schools that are 400%-500% larger than schools observed by research vessels. After comparing research vessel and tuna vessel observations overlapping in space and time, it appears that either measurement error or selective reporting of large schools is the most likely explanation for the disparity. Comparing single-species schools against portions of mixed-species schools introduced bias. Revised fishery-dependent abundance estimates were generated using only single-species dolphin schools, resulting in estimates that appear more similar to recent fishery-independent abundance estimates. Biases associated with mixed-species schools did not appear to be correctable in fishery-dependent data, suggesting that data on mixed-species schools should not be considered if fishery-dependent data are to be combined with fishery- independent data for stock assessment purposes. [ABSTRACT FROM AUTHOR]