scholarly journals SPAM (Sex-Structured Pandalus Assessment Model): a stock assessment model for Pandalus stocks

2012 ◽  
Vol 69 (4) ◽  
pp. 770-783 ◽  
Author(s):  
Hilaire Drouineau ◽  
Louise Savard ◽  
Mathieu Desgagnés ◽  
Daniel Duplisea
Keyword(s):  
Stock Assessment ◽  
Sex Change ◽  
Assessment Model ◽  
Natural Mortality ◽  
The Novel ◽  
Fishing Mortality ◽  
Novel Structure ◽  
Shrimp Fisheries ◽  
Analytical Tools ◽  
Mortality Estimates

Despite the economic importance of Pandalus shrimp fisheries, few analytical tools have been developed to assess their stocks, and traditional stock assessment models are not appropriate because of biological specificities of Pandalus species. In this context, we propose SPAM (Sex-Structured Pandalus Assessment Model), a model dedicated to protandric hermaphrodite pandalids stock assessment. Pandalids are difficult to assess because the cues affecting sex change, size at recruitment, and mortality variability are not well understood or characterized. The novel structure of the model makes it possible to adequately describe variability in natural mortality by stage and in time, as well as variability in size at sex change and recruitment. The model provides traditional stock assessment outputs, such as fishing mortality estimates and numbers of individuals, and provides in addition yearly natural mortality estimates. The model is applied to the exploited shrimp stock of Pandalus borealis in Sept-Îles (Québec, Canada) as an illustrative example of the utility of the approach.

scholarly journals Stock assessment and projections incorporating discard estimates in some years: an application to the hake stock in ICES Divisions VIIIc and IXa

2010 ◽  
Vol 67 (6) ◽  
pp. 1185-1197 ◽  
Author(s):  
C. Fernández ◽  
S. Cerviño ◽  
N. Pérez ◽  
E. Jardim
Keyword(s):  
Time Series ◽  
Stock Assessment ◽  
Reference Points ◽  
Assessment Model ◽  
Marine Science ◽  
Fishing Mortality ◽  
Change Over Time ◽  
Age Structured ◽  
Available Information ◽  
Over Time

Abstract Fernández, C., Cerviño, S., Pérez, N., and Jardim, E. 2010. Stock assessment and projections incorporating discard estimates in some years: an application to the hake stock in ICES Divisions VIIIc and IXa. – ICES Journal of Marine Science, 67: 1185–1197. A Bayesian age-structured stock assessment model is developed to take into account available information on discards and to handle gaps in the time-series of discard estimates. The model incorporates mortality attributable to discarding, and appropriate assumptions about how this mortality may change over time are made. The result is a stock assessment that accounts for information on discards while, at the same time, producing a complete time-series of discard estimates. The method is applied to the hake stock in ICES Divisions VIIIc and IXa, for which the available data indicate that some 60% of the individuals caught are discarded. The stock is fished by Spain and Portugal, and for each country, there are discard estimates for recent years only. Moreover, the years for which Portuguese estimates are available are only a subset of those with Spanish estimates. Two runs of the model are performed; one assuming zero discards and another incorporating discards. When discards are incorporated, estimated recruitment and fishing mortality for young (discarded) ages increase, resulting in lower values of the biological reference points Fmax and F0.1 and, generally, more optimistic future stock trajectories under F-reduction scenarios.

Influence of errors in natural mortality estimates in cohort analysis

1997 ◽  
Vol 54 (7) ◽  
pp. 1608-1612 ◽  
Author(s):  
G Mertz ◽  
R A Myers
Keyword(s):  
Mortality Rate ◽  
Similar Pattern ◽  
Cohort Analysis ◽  
Transient Behavior ◽  
Natural Mortality ◽  
Exact Formulation ◽  
Fishing Mortality ◽  
Bias Factor ◽  
Special Case ◽  
Mortality Estimates

The accuracy of the estimation of cohort strength from catch data may be greatly degraded if a poor estimate of the natural mortality rate is entered into the calculation. A straightforward, exact formulation for the error in cohort reconstruction due to a misspecified natural mortality rate is presented. The special case of constant fishing mortality is particularly transparent, allowing the error to be segmented into easily interpreted terms. A change in the fishing mortality may result in a distinct hump in the transient behavior of the bias factor, rather than a simple monotonic adjustment. This implies a similar pattern in estimated cohort strength.

scholarly journals An integrated tagging model to estimate mortality rates of Albemarle Sound – Roanoke River striped bass

2017 ◽  
Vol 74 (7) ◽  
pp. 1061-1076 ◽  
Author(s):  
Julianne E. Harris ◽  
Joseph E. Hightower
Keyword(s):  
Striped Bass ◽  
Stock Assessment ◽  
Mortality Rates ◽  
Natural Mortality ◽  
Fishing Mortality ◽  
Albemarle Sound ◽  
High Reward ◽  
Accuracy And Precision ◽  
Summer Mortality ◽  
Roanoke River

We developed an integrated tagging model to estimate mortality rates and run sizes of Albemarle Sound – Roanoke River striped bass (Morone saxatilis), including (i) a multistate component for telemetered fish with a high reward external tag; (ii) tag return components for fish with a low reward external or PIT tag; and (iii) catch-at-age data. Total annual instantaneous mortality was 1.08 for resident (458–899 mm total length, TL) and 0.45 for anadromous (≥900 mm TL) individuals. Annual instantaneous natural mortality was higher for resident (0.70) than for anadromous (0.21) fish due to high summer mortality in Albemarle Sound. Natural mortality for residents was substantially higher than currently assumed for stock assessment. Monthly fishing mortality from multiple sectors (including catch-and-release) corresponded to seasonal periods of legal harvest. Run size estimates were 499 000–715 000. Results and simulation suggest increasing sample size for the multistate component increases accuracy and precision of annual estimates and low reward tags are valuable for estimating monthly fishing mortality rates among sectors. Our results suggest that integrated tagging models can produce seasonal and annual mortality estimates needed for stock assessment and management.

Detecting mortality variation to enhance forage fish population assessments

2018 ◽  
Vol 76 (1) ◽  
pp. 124-135 ◽  
Author(s):  
Nis S Jacobsen ◽  
James T Thorson ◽  
Timothy E Essington
Keyword(s):  
Test Performance ◽  
Size Structure ◽  
Stock Assessment ◽  
Reference Points ◽  
Forage Fish ◽  
Natural Mortality ◽  
Time Varying ◽  
Fishing Mortality ◽  
Catch Data ◽  
Over Time

Abstract Contemporary stock assessment models used by fisheries management often assume that natural mortality rates are constant over time for exploited fish stocks. This assumption results in biased estimates of fishing mortality and reference points when mortality changes over time. However, it is difficult to distinguish changes in natural mortality from changes in fishing mortality, selectivity, and recruitment. Because changes in size structure can be indicate changes in mortality, one potential solution is to use population size-structure and fisheries catch data to simultaneously estimate time-varying natural and fishing mortality. Here we test that hypothesis, using a simulation experiment to test performance for four alternative estimation models that estimate natural and fishing mortality from size structure and catch data. We show that it is possible to estimate time-varying natural mortality in a size-based model, even when fishing mortality, recruitment, and selectivity are changing over time. Finally, we apply the model to North Sea sprat, and show that estimates of recruitment and natural mortality are similar to estimates from an alternative multispecies population model fitted to additional data sources. We recommend exploring potential trends in natural mortality in forage fish assessments using tools such as the one presented here.

scholarly journals Time-varying natural mortality in fisheries stock assessment models: identifying a default approach

2014 ◽  
Vol 72 (1) ◽  
pp. 137-150 ◽  
Author(s):  
Kelli F. Johnson ◽  
Cole C. Monnahan ◽  
Carey R. McGilliard ◽  
Katyana A. Vert-pre ◽  
Sean C. Anderson ◽  
...  
Keyword(s):  
Stock Assessment ◽  
Assessment Method ◽  
Reference Points ◽  
Natural Mortality ◽  
Time Varying ◽  
Fishing Mortality ◽  
Total Allowable Catch ◽  
Robust Approach ◽  
Structured Population Models ◽  
Trade Offs

Abstract 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.

Integrating catch-at-age and multiyear tagging data: a combined Brownie and Petersen estimation approach in a fishery context

2006 ◽  
Vol 63 (3) ◽  
pp. 534-548 ◽  
Author(s):  
Tom Polacheck ◽  
J Paige Eveson ◽  
Geoff M Laslett ◽  
Kenneth H Pollock ◽  
William S Hearn
Keyword(s):  
Mortality Rate ◽  
Population Size ◽  
Stock Assessment ◽  
Natural Mortality ◽  
Fishing Mortality ◽  
Catch Data ◽  
Coefficients Of Variation ◽  
Southern Bluefin Tuna ◽  
Catch Rates ◽  
Comprehensive Framework

A comprehensive framework for modelling data from multiyear tagging experiments in a fishery context is presented that incorporates catch data into the traditional Brownie tag–recapture model. Incorporation of catch data not only allows for improved estimation of natural and fishing mortality rates, but also for direct estimation of population size at the time of tagging. These are the primary quantities required to be estimated in stock assessments — having an approach for directly estimating them that does not require catch rates provides a potentially powerful alternative for augmenting traditional stock assessment methods. Simulations are used to demonstrate the value of directly incorporating catch data in the model. Results from the range of scenarios considered suggest that in addition to providing a precise estimate of population size (coefficients of variation ranging from ~15% to 30%), including catch data can decrease biases in the mortality rate estimates (natural mortality especially) and improve precision of fishing mortality rate estimates (by as much as 60% at age 1). The model is applied to southern bluefin tuna (Thunnus maccoyii) tag–recapture and catch data collected in the 1990s to provide estimates of natural mortality, fishing mortality, and abundance for five cohorts of fish.

An age- and sex-structured assessment model for American eels (Anguilla rostrata) in the Potomac River, Maryland

2011 ◽  
Vol 68 (6) ◽  
pp. 1024-1037 ◽  
Author(s):  
Kari H. Fenske ◽  
Michael J. Wilberg ◽  
David H. Secor ◽  
Mary C. Fabrizio
Keyword(s):  
Large Scale ◽  
Assessment Model ◽  
Anguilla Rostrata ◽  
European Eel ◽  
Natural Mortality ◽  
Potomac River ◽  
Fishing Mortality ◽  
Catch Per Unit Effort ◽  
American Eel ◽  
Age And Sex

American eel ( Anguilla rostrata ) and European eel ( Anguilla anguilla ) populations have declined since the 1980s prompting concern about their status and the causes of decline, but stock assessment approaches to estimate effects of fishing on these populations are lacking. Since 1964, 16% of United States commercial American eel harvest came from the Potomac River, yet American eel abundance, production, and fishing mortality is poorly understood in this system. We developed an age- and sex-structured assessment model for 1980–2008 and compared results with the F50% biological reference point (BRP). The model included natural mortality, fishing mortality, and sex- and age-specific maturation mortality and selectivity. Between 1980 and 2008 estimated recruitment, biomass, and abundance decreased 82%–89%. In all years since 1993, the exploitation rate exceeded the F50% BRP. The model was moderately sensitive to changes in natural mortality, standard deviation for fishery and recruitment catch-per-unit-effort indices, and initial fishing mortality. The multidecadal decline in recruitment in Chesapeake eels matches those reported elsewhere for American and European eels, suggesting large-scale processes have affected anguillid eel recruitment in the North Atlantic.

scholarly journals A method for calculating a meta-analytical prior for the natural mortality rate using multiple life history correlates

2014 ◽  
Vol 72 (1) ◽  
pp. 62-69 ◽  
Author(s):  
Owen S. Hamel
Keyword(s):  
Life History ◽  
Mortality Rate ◽  
Stock Assessment ◽  
Meta Analysis ◽  
Assessment Model ◽  
Natural Mortality ◽  
Uncertainty Interval ◽  
Free Data ◽  
Stock Assessments ◽  
Measure Of Uncertainty

Abstract The natural mortality rate M is an important parameter for understanding population dynamics, and is extraordinarily difficult to estimate for many fish species. The uncertainty associated with M translates into increased uncertainty in fishery stock assessments. Estimation of M within a stock assessment model is complicated by its confounding with other life history and fishery parameters which are also uncertain, some of which are typically estimated within the model. Ageing error and variation in growth, which may not be fully modelled, can also affect estimation of M, as can various assumptions, including the form of the stock–recruitment function (e.g. Beverton–Holt, Ricker) and the level of compensation (or steepness), which may be fixed (or limited by a prior) in the model. To avoid these difficulties, stock assessors often assume point estimates for M derived from meta-analytical relationships between M and more easily measured life history characteristics, such as growth rate or longevity. However, these relationships depend on estimates of M for a great number of species, and those estimates are also subject to errors and biases (as are, to a lesser extent, the other life history parameters). Therefore, at the very least, some measure of uncertainty in M should be calculated and used for evaluating uncertainty in stock assessments and management strategy evaluations. Given error-free data on M and the covariate(s) for a meta-analysis, prediction intervals would provide the appropriate measure of uncertainty in M. In contrast, if the relationship between the covariate(s) and M is exact and the only error is in the estimates of M used for the meta-analysis, confidence intervals would appropriate. Using multiple published meta-analyses of M’s relationship with various life history correlates, and beginning with the uncertainty interval calculations, I develop a method for creating combined priors for M for use in stock assessment.

scholarly journals “The Elephant in the Room”: Exploring Natural Mortality Uncertainty in Statistical Catch at Age Models

2020 ◽  
Vol 7 ◽  
Author(s):  
Alessandro Mannini ◽  
Cecilia Pinto ◽  
Christoph Konrad ◽  
Paraskevas Vasilakopoulos ◽  
Henning Winker
Keyword(s):  
Stock Assessment ◽  
Assessment Process ◽  
Sensitivity Analyses ◽  
Fish Stock ◽  
Natural Mortality ◽  
Fishing Mortality ◽  
Food And Agriculture ◽  
Stock Assessments ◽  
The Mediterranean ◽  
The Impact

The natural mortality rate (M) of a fish stock is typically highly influential on the outcome of age-structured stock assessment models, but at the same time extremely difficult to estimate. In data-limited stock assessments, M usually relies on a range of empirically or theoretically derived M estimates, which can vary vastly. This article aims at evaluating the impact of this variability in M using seven Mediterranean stocks as case studies of statistical catch-at-age assessments for information-limited fisheries. The two main bodies carrying out stock assessments in the Mediterranean and Black Seas are European Union’s Scientific Technical Economic Committee for Fisheries (STECF) and Food and Agriculture Organization’s General Fisheries Commission for the Mediterranean (GFCM). Current advice in terms of fishing mortality levels is based on a single “best” M assumption which is agreed by stock assessment expert working groups, but uncertainty about M is not taken into consideration. Our results demonstrate that not accounting for the uncertainty surrounding M during the assessment process can lead to strong underestimation or overestimation of fishing mortality, potentially biasing the management process. We recommend carrying out relevant sensitivity analyses to improve stock assessment and fisheries management in data-limited areas such as the Mediterranean basin.

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