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Maunder M.N.,Quantitative Resource Assessment LLC | Deriso R.B.,Inter American Tropical Tuna Commission
Canadian Journal of Fisheries and Aquatic Sciences | Year: 2011

Multiple factors acting on different life stages influence population dynamics and complicate the assessment and management of populations. To provide appropriate management advice, the data should be used to determine which factors are important and what life stages they impact. It is also important to consider density dependence because it can modify the impact of some factors. We develop a state-space multistage life cycle model that allows for density dependence and environmental factors to impact different life stages. Models are ranked using a two-covariates-at-a-time stepwise procedure based on AICc model averaging to reduce the possibility of excluding factors that are detectable in combination, but not alone. Impact analysis is used to evaluate the impact of factors on the population. The framework is illustrated by application to delta smelt (Hyposmesus transpacificus), a threatened species that is potentially impacted by multiple anthropogenic factors. Our results indicate that density dependence and a few key factors impact the delta smelt population. Temperature, prey, and predators dominated the factors supported by the data and operated on different life stages. The included factors explain the recent declines in delta smelt abundance and may provide insight into the cause of the pelagic species decline in the San Francisco Estuary. Source

Maunder M.N.,Inter American Tropical Tuna Commission | Maunder M.N.,University of California at San Diego | Piner K.R.,Southwest Fisheries Science Center
ICES Journal of Marine Science | Year: 2014

Interpretation of data used in fisheries assessment and management requires knowledge of population (e.g. growth, natural mortality, and recruitment), fisheries (e.g. selectivity), and sampling processes. Without this knowledge, assumptions need to be made, either implicitly or explicitly based on the methods used. Incorrect assumptions can have a substantial impact on stock assessment results and management advice. Unfortunately, there is a lack of understanding of these processes for most, if not all, stocks and even for processes that have traditionally been assumed to be well understood (e.g. growth and selectivity). We use information content of typical fisheries data that is informative about absolute abundance to illustrate some of the main issues in fisheries stock assessment. We concentrate on information about absolute abundance from indices of relative abundance combined with catch, and age and length-composition data and how the information depends on knowledge of population, fishing, and sampling processes. We also illustrate two recently developed diagnostic methods that can be used to evaluate the absolute abundance information content of the data. Finally, we discuss some of the reasons for the slowness of progress in fisheries stock assessment. © 2014 © International Council for the Exploration of the Sea 2014. All rights reserved. Source

Maunder M.N.,Inter American Tropical Tuna Commission | Harley S.J.,British Petroleum
Fisheries Research | Year: 2011

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. © 2011 Elsevier B.V. Source

Maunder M.N.,Inter American Tropical Tuna Commission | Punt A.E.,University of Washington
Fisheries Research | Year: 2013

Limited data, and the requirement to provide science-based advice for exploited populations, have led to the development of statistical methods that combine several sources of information into a single analysis. This approach, " integrated analysis" was first formulated by Fournier and Archibald in 1982. Contemporary use of integrated analysis involves using all available data, in as raw a form as appropriate, in a single analysis. Analyses that were traditionally carried out independently are now conducted simultaneously through likelihood functions that include multiple data sources. For example, the traditional analysis of converting catch-at-length data into catch-at-age data for use in an age-structured population dynamics models can be avoided by including the basic data used in this conversion, length-frequency and conditional age-at-length data, in the likelihood function. This allows for consistency in assumptions and permits the uncertainty associated with both data sources to be propagated to final model outputs, such as catch limits under harvest control rules. The development of the AD Model Builder software has greatly facilitated the use of integrated analyses, and there are now several general stock assessment models (e.g., Stock Synthesis) that allow many data types and model assumptions to be analyzed simultaneously. In this paper, we define integrated analysis, describe its history and development, give several examples, and describe the advantages of and problems with integrated analysis. © 2012 Elsevier B.V. Source

Ten separate experiments monitoring the simultaneous behaviors of 26 skipjack (Katsuwonus pelamis), 26 bigeye (Thunnus obesus), and 33 yellowfin (T. albacares) tunas within large multi-species aggregations associated with drifting fish aggregating devices (FADs) were investigated using ultrasonic telemetry in the equatorial eastern Pacific Ocean. Experiments were conducted during a research cruise aboard a chartered purse seine vessel. Purse seine sets were made on the tuna aggregations associated with FADs at the termination of six of the ten experiments. Seventeen of the 44 tagged tunas were not recaptured indicating the transient nature of the associative behavior of tunas with FADs. Although there was considerable overlap in the depths of the three species, by day and night, there were some species-specific differences and diel differences within species. While we documented spatial and temporal differences in the schooling behavior of the three tuna species, the differences do not appear sufficient such that modifications in purse seine fishing practices could effectively avoid the capture of small bigeye and yellowfin tunas, while optimizing the capture of skipjack tuna in purse seine sets on FADs. © 2013 Springer-Verlag Berlin Heidelberg. Source

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