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Minte-Vera C.V.,Inter American Tropical Tuna Commission | Maunder M.N.,Inter American Tropical Tuna Commission | Maunder M.N.,Center for the Advancement of Population Assessment Methodology | Aires-da-Silva A.M.,Inter American Tropical Tuna Commission | And 2 more authors.
Fisheries Research | Year: 2017

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. © 2017 Elsevier B.V.


Deroba J.J.,National Oceanic and Atmospheric Administration | Butterworth D.S.,University of South Africa | Methot R.D.,National Oceanic and Atmospheric Administration | DeOliveira J.A.A.,CEFAS - Center for Environment, Fisheries and Aquaculture Science | And 32 more authors.
ICES Journal of Marine Science | Year: 2014

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. © 2014 Published by Oxford University Press 2014. This work is written by US Government employees and is in the public domain in the US.


Johnson K.F.,University of Washington | Monnahan C.C.,University of Washington | McGilliard C.R.,National Oceanic and Atmospheric Administration | McGilliard C.R.,University of Washington | And 13 more authors.
ICES Journal of Marine Science | Year: 2014

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. © 2014 © International Council for the Exploration of the Sea 2014. All rights reserved.


Ono K.,University of Washington | Licandeo R.,University of British Columbia | Muradian M.L.,University of Washington | Cunningham C.J.,University of Washington | And 13 more authors.
ICES Journal of Marine Science | Year: 2014

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. © 2014 © International Council for the Exploration of the Sea 2014. All rights reserved.


Hurtado-Ferro F.,University of Washington | Szuwalski C.S.,University of Washington | Szuwalski C.S.,University of California at Santa Barbara | Valero J.L.,Center for the Advancement of Population Assessment Methodology | And 11 more authors.
ICES Journal of Marine Science | Year: 2014

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. © 2014 © International Council for the Exploration of the Sea 2014. All rights reserved.


Francis R.I.C.C.,123 Overtoun Terrace | Aires-da-Silva A.M.,Inter American Tropical Tuna Commission | Maunder M.N.,Inter American Tropical Tuna Commission | Maunder M.N.,Center for the Advancement of Population Assessment Methodology | And 2 more authors.
Fisheries Research | Year: 2015

In age-structured stock assessments it would be useful to be able to include all available information on growth, including age-length observations and length increments from tagging experiments. However, it was suggested in 1988 that combing the growth information from these two sources was problematic because the age- and length-based growth information they contain are not directly comparable. We evaluate two approaches that have since been made to this problem and conclude that though both approaches achieve comparability the simpler method was better suited for use in stock assessments, in part because of lesser computational demands. We show how the simpler approach is improved by allowing for correlation between length deviates at tagging and recapture, which increases biological plausibility and corrects a negative bias in estimates of variability in length at age. © 2015 Elsevier B.V.


Wang S.-P.,National Taiwan Ocean University | Maunder M.N.,Inter American Tropical Tuna Commission | Maunder M.N.,Center for the Advancement of Population Assessment Methodology | Nishida T.,Japan National Research Institute of Fisheries And Environment of Inland Sea | Chen Y.-R.,National Taiwan Ocean University
Fisheries Research | Year: 2015

Results from stock assessment modelling are often highly sensitive to model assumptions. It is therefore important that models are correctly specified and sensitivity analyses are conducted to evaluate the impact of model uncertainty. Model misspecification, changes in parameters over time, and data weighting are common issues that arise when developing stock assessment models. We conducted a stock assessment for swordfish Xiphias gladius in the Indian Ocean, using an integrated age-structured model, and evaluated estimates of management quantities under alternative assumptions about (1) changes in catchability for CPUE-based indices of abundance, (2) changes in gear selectivity, (3) weighting of abundance indices, and (4) the impact of sex-specific growth. The results indicated that assuming time-blocks for both catchability and selectivity may be appropriate to reflect the changes in fishing operations of Japanese and Taiwanese longline fleets. This assumption also provided better model performance and more optimistic assessment results because it implied that the decline in indices of abundance resulted from changes in catchability rather than depletion of biomass. Inappropriate choices for selectivity curves can deleteriously affect model performance, and attempting to account for model misspecification through downweighting of data may not be appropriate. Care must be taken when modelling changes in selectivity because selectivity can be distorted to accommodate changes in catchability. More generally, substantial changes in catchability (e.g., due to changes in targeting) may not be fully addressed in CPUE standardization and may require modelling changes over time in catchability within the stock assessment model. Finally, we found that misspecification of growth is at least as influential, if not more so, than misspecification of catchability and selectivity, or data weighting. © 2014 Elsevier B.V.


Anderson S.C.,Simon Fraser University | Monnahan C.C.,University of Washington | Johnson K.F.,University of Washington | Ono K.,University of Washington | Valero J.L.,Center for the Advancement of Population Assessment Methodology
PLoS ONE | Year: 2014

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. © 2014 Anderson et al.


Crone P.R.,Southwest Fisheries Science Center | Crone P.R.,Center for the Advancement of Population Assessment Methodology | Valero J.L.,Center for the Advancement of Population Assessment Methodology
Fisheries Research | Year: 2014

Modeling selectivity, the relative capture probability expressed as a function of fish age or length, in statistical catch-at-age models remains one of the most influential and uncertain parameterizations in developing robust stock assessments to provide resource management advice. Selectivity parameterization affects point estimates of management quantities and associated uncertainty, as well as the estimation of other model parameters, such as fishing and natural mortality, growth, recruitment, and spawner-recruit relationships. The choice of biological data (length or age) and selectivity assumptions (length- or age-based) made by assessment analysts can directly impact final estimates of important management quantities. In this paper, Pacific mackerel and Pacific sardine stock assessments based on the integrated age-structured Stock Synthesis model are used in concert with simulation methods to evaluate the influence such decisions have on the quality (bias and precision) of estimates of maximum sustainable yield, current spawning stock biomass, and depletion. Findings from this evaluation indicate that: (1) when age data are used, the selectivity assumption (length- or age-based) was generally less influential and did not impact the quality of derived management quantities; (2) when length data are used, misspecification of selectivity generally produced more variable findings and lower quality estimates for quantities of maximum sustainable yield and current biomass; (3) estimates of depletion were generally more robust and precise, irrespective of the biological data or selectivity assumption used in the model; and (4) formal examination of selectivity as illustrated in this paper is useful for identifying other parameters potentially misspecified in the overall model. © 2014.

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