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Indices of relative abundance are one of the most important inputs into a stock assessment model. For many species, we must rely on several indices that routinely conflict with each other and which may result in biased and uncertain outputs. Here, we explored whether reconciled trends obtained from dynamic factor analysis (DFA) applied to conflicting indices can be used as a trend of relative abundance input into a stock assessment model. We simulated an age-structured population of two coastal shark species in the southeast United States to generate multiple disagreeing indices, reconciled the indices using DFA, and then inserted both the multiple conflicting survey indices and the simplified DFA-predicted trend into respective stock assessment models. We compared the results of each stock assessment model to simulated values to evaluate the relative performance of each approach. We found that the DFA-based assessment generally performed similarly to the conflicting index-based assessment and may be a useful assessment tool in situations where conflicting indices with different selectivities, catchabilities, variances, and missing data are present. DFA assessment results were more consistent across simulation scenarios and outperformed many conflicting index assessments when surveys underwent shifts in catchability and the underlying stock abundance exhibited contrast. [ABSTRACT FROM AUTHOR]

Stock-wide trends in fish relative abundance are challenging to obtain when a single, comprehensive survey is unavailable, and multiple, spatially, and/or temporally fragmented surveys must be relied upon instead. Indices of abundance from multiple surveys frequently have differing trends, resulting in obscured true abundance patterns of the resource. We use an age-structured simulation model of two coastal shark species in the southeast United States to explore the performance of dynamic factor analysis (DFA) for reconciling multiple indices of abundance that are in conflict. Survey-specific time-variation in catchability was induced to generate conflicting indices of abundance. Key simulation sensitivities included survey variability, abundance pattern in the resource, and missing years of survey data. We caution against using DFA when there is no contrast in the underlying stock abundance or when trends in catchability in all surveys result in no survey that is representative of stock abundance. When multiple representative surveys were available, DFA proved useful across species in estimating stock-wide trends from conflicting survey indices with different selectivities, catchabilities, variances, and, to a lesser extent, with missing data. Our results suggest that resolving contrasting patterns among multiple time-series of relative abundance can improve understanding of the temporal trend in stock abundance. [ABSTRACT FROM AUTHOR]

Relative abundance trends of highly migratory species (HMS) have played a central role in debates over the health of global fisheries. However, such trends have mostly been inferred from fishery catch rates, which can provide misleading signals of relative abundance. While many biases are accounted for through traditional catch rate standardization, pelagic habitat fished is rarely directly considered. Using a method that explicitly accounts for temperature regimes, we analysed data from the US pelagic longline fishery to estimate relative abundance trends for 34 HMS in the Atlantic Ocean from 1987 through 2013. This represents one of the largest studies of HMS abundance trends. Model selection emphasized the importance of accounting for pelagic habitat fished with water column temperature being included in nearly every species’ model, and in extreme cases, a temperature variable explained 50–60% of the total deviance. Our estimated trends represent observations from one fishery only, and a more integrated stock assessment should form the basis for conclusions about stock status overall. Nonetheless, our trends serve as indicators of stock abundance and they suggest that a majority of HMS (71% of analysed species) are either declining in relative abundance or declined initially with no evidence of rebuilding. Conversely, 29% of the species exhibited stable, increasing, or recovering trends; however, these trends were more prevalent among tunas than either billfishes or sharks. By estimating the effects of pelagic habitat on fishery catch rates, our results can be used in combination with ocean temperature trends and forecasts to support bycatch avoidance and other time-area management decisions. [ABSTRACT FROM AUTHOR]

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 by catch 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. [ABSTRACT FROM AUTHOR]

Brooks, E. N., Powers, J. E., and Cortés, E. 2010. Analytical reference points for age-structured models: application to data-poor fisheries. – ICES Journal of Marine Science, 67: 165–175.Analytical solutions for biological reference points are derived in terms of maximum lifetime reproductive rate. This rate can be calculated directly from biological parameters of maturity, fecundity, and natural mortality or a distribution for this rate can be derived from appropriate metadata. Minimal data needs and assumptions for determining stock status are discussed. The derivations lead to a re-parameterization of the common stock–recruit relationships, Beverton–Holt and Ricker, in terms of spawning potential ratio. Often, parameters in stock–recruit relationships are restricted by tight prior distributions or are fixed based on a hypothesized level of stock resilience. Fixing those parameters is equivalent to specifying the biological reference points. An ability to directly calculate reference points from biological data, or a meta-analysis, without need of a full assessment model or fisheries data, makes the method an attractive option for data-poor fisheries. The derivations reveal an explicit link between the biological characteristics of a species and appropriate management. Predicted stock status for a suite of shark species was compared with recent stock assessment results, and the method successfully identified whether each stock was overfished. [ABSTRACT FROM PUBLISHER]

Jiao, Y., Hayes, C., and Cortés, E. 2009. Hierarchical Bayesian approach for population dynamics modelling of fish complexes without species-specific data. – ICES Journal of Marine Science, 66: 367–377.Modelling the population dynamics of fish complexes is challenging, and many species have been assessed and managed as a complex that was treated as a single species. Two Bayesian state-space surplus production models with multilevel priors (hierarchical models) were developed to simulate variability in population growth rates of species in a complex, using the hammerhead shark complex (Sphyrna spp.) of the Atlantic and Gulf of Mexico coasts of the US as an example. The complex consists of three species: scalloped (Sphyrna lewini), great (Sphyrna mokarran), and smooth hammerhead (Sphyrna zygaena). Bayesian state-space surplus production models with multilevel priors fitted the hammerhead data better than a model based on single-level priors. The hierarchical Bayesian approach represents an intermediate strategy between traditional models that do not include variability among species, and highly parameterized models that assign an estimate of parameters to each species. By ignoring the variability among species, confidence intervals of the estimates of stock status indicators can be unrealistically narrow, possibly leading to high-risk management strategies being adopted. Use of multilevel priors in a hierarchical Bayesian approach is suggested for future hammerhead shark stock assessments and for modelling fish complexes lacking species-specific data. [ABSTRACT FROM PUBLISHER]