A Bayesian hierarchical formulation of the De Lury stock assessment model for abundance estimation of Falkland Islands' squid (Loligo gahi)

2004 ◽  
Vol 61 (6) ◽  
pp. 1048-1059 ◽  
Author(s):  
Murdoch K McAllister ◽  
Simeon L Hill ◽  
David J Agnew ◽  
Geoffrey P Kirkwood ◽  
John R Beddington
Keyword(s):  
Sampling Error ◽  
Stock Assessment ◽  
Assessment Model ◽  
Abundance Estimation ◽  
Falkland Islands ◽  
Parameter Estimates ◽  
Bayesian Hierarchical ◽  
Stock Assessments ◽  
Catch Rates ◽  
Using Data

In stock assessments of short-lived species, De Lury depletion models are commonly applied in which commercial catches and changing catch rates are used to estimate resource abundance. These methods are applied within fishing seasons to decide when to close the fishery and can be reliable if the data show a distinct decline in response to the catch removals. However, this is not always the case, particularly when sampling error variation masks trends in abundance. This paper presents a Bayesian hierarchical formulation of the De Lury model in which data from previous years are combined hierarchically in the same stock assessment model to improve parameter estimation for future stock assessments. The improved precision in parameter estimates is demonstrated using data for the Falkland Islands' Loligo gahi squid fishery.

A new way to visualize and report structural and data uncertainty in stock assessments

2020 ◽  
Vol 77 (8) ◽  
pp. 1275-1280
Author(s):  
Jason Cope ◽  
Vladlena Gertseva
Keyword(s):  
Sensitivity Analysis ◽  
Alternative Model ◽  
Reference Model ◽  
Stock Assessment ◽  
Assessment Model ◽  
Data Uncertainty ◽  
Model Comparisons ◽  
Modelling Framework ◽  
Stock Assessments ◽  
General Method

We present a visual and tabular representation of fisheries stock assessment model outputs to rapidly examine and effectively communicate sensitivity analysis results from numerous alternative model comparisons. This approach uses multiple output metrics to identify which alternative stock assessment model configurations relative to the reference model deserve further attention when quantifying intermodel uncertainty. An accompanying table of likelihood components, parameters, and model-derived quantities highlights where major changes exist compared with the reference model. The general method is applicable to any stock assessment and should aid in model behavior diagnosis and communicating uncertainty to managers. Specific examples and code are provided for the Stock Synthesis modelling framework.

scholarly journals Demonstration of a spatially explicit, tag-integrated stock assessment model with application to three interconnected stocks of yellowtail flounder off of New England

2014 ◽  
Vol 72 (1) ◽  
pp. 164-177 ◽  
Author(s):  
Daniel R. Goethel ◽  
Christopher M. Legault ◽  
Steven X. Cadrin
Keyword(s):  
New England ◽  
Integrated Assessment ◽  
Stock Assessment ◽  
Assessment Model ◽  
Spatially Explicit ◽  
Closed Population ◽  
Yellowtail Flounder ◽  
Limanda Ferruginea ◽  
Stock Assessments ◽  
Source Sink

Abstract Ignoring population structure and connectivity in stock assessment models can introduce bias into important management metrics. Tag-integrated assessment models can account for spatially explicit population dynamics by modelling multiple population components, each with unique demographics, and estimating movement among them. A tagging submodel is included to calculate predicted tag recaptures, and observed tagging data are incorporated in the objective function to inform estimates of movement and mortality. We describe the tag-integrated assessment framework and demonstrate its use through an application to three stocks of yellowtail flounder (Limanda ferruginea) off New England. Movement among the three yellowtail flounder stocks has been proposed as a potential source of uncertainty in the closed population assessments of each. A tagging study was conducted during 2003–2006 with over 45 000 tagged fish released in the region, and the tagging data were included in the tag-integrated model. Results indicated that movement among stocks was low, estimates of stock size and fishing mortality were similar to those from conventional stock assessments, and incorporating stock connectivity did not resolve residual patterns. Despite low movement estimates, new interpretations of regional stock dynamics may have important implications for regional fisheries management given the source-sink nature of movement estimates.

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.

A framework to incorporate environmental effects into stock assessments informed by fishery-independent surveys: a case study with American lobster (Homarus americanus)

2020 ◽  
Vol 77 (10) ◽  
pp. 1700-1710
Author(s):  
Cameron T. Hodgdon ◽  
Kisei R. Tanaka ◽  
Jocelyn Runnebaum ◽  
Jie Cao ◽  
Yong Chen
Keyword(s):  
Environmental Variability ◽  
Stock Assessment ◽  
Homarus Americanus ◽  
Assessment Model ◽  
American Lobster ◽  
Multiple Model ◽  
Environmental Covariates ◽  
Model Based ◽  
Stock Assessments ◽  
Abundance Indices

Stock assessments for a majority of the world’s fisheries often do not explicitly consider the effects of environmental conditions on target species, which can raise model uncertainty and potentially reduce forecasting quality. Model-based abundance indices were developed using a delta generalized linear mixed model that incorporates environmental variability for use in stock assessment to understand how the incorporation of environmental variability impacts our understanding of population dynamics. For this study, multiple model-based abundance indices were developed to test the incorporation of environmental covariates in a length-structured assessment of the American lobster (Homarus americanus) stock in the Gulf of Maine – Georges Bank on the possible improvement of stock assessment quality. Comparisons reveal that modelled indices with environmental covariates appear to be more precise than traditional indices, but model performance metrics and hindcasted fishery statuses revealed that these improvements to indices may not necessarily mean an improved assessment. Model-based abundance indices are not intrinsically better than design-based indices and should be tested for each species individually.

scholarly journals Utility of 18-kHz acoustic data for abundance estimation of Atlantic herring (Clupea harengus)

2012 ◽  
Vol 69 (6) ◽  
pp. 1086-1098 ◽  
Author(s):  
Ryan A. Saunders ◽  
Ciaran O'Donnell ◽  
Rolf J. Korneliussen ◽  
Sascha M. M. Fässler ◽  
Maurice W. Clarke ◽  
...  
Keyword(s):  
Stock Assessment ◽  
Clupea Harengus ◽  
Assessment Model ◽  
Target Strength ◽  
Abundance Estimation ◽  
Atlantic Herring ◽  
Acoustic Data ◽  
Substantial Problem ◽  
The Impact ◽  
Age Estimates

Abstract Saunders, R. A., O'Donnell, C., Korneliussen, R. J., Fässler, S. M. M., Clarke, M. W., Egan, A, and Reid, D. 2012. Utility of 18-kHz acoustic data for abundance estimation of Atlantic herring (Clupea harengus) – ICES Journal of Marine Science, 69: 1086–1098. Current acoustic survey protocols for Atlantic herring (Clupea harengus) abundance estimation are principally dependent upon 38-kHz backscatter data. This can constitute a substantial problem for robust stock assessment when 38-kHz data are compromised. Research vessels now typically collect multifrequency data during acoustic surveys, which could be used to remediate such situations. Here, we investigate the utility of using 18- and 120-kHz data for herring abundance estimation when the standard 38-kHz approach is not possible. Estimates of herring abundance/biomass in the Celtic Sea (2007–2010) were calculated at 18, 38, and 120 kHz using the standard 38-kHz target-strength (TS) model and geometrically equivalent TS models at 18 and 120 kHz. These estimates were compared to assess the level of coherence between the three frequencies, and 18-kHz-derived estimates were subsequently input into standard 38-kHz-based population models to evaluate the impact on the assessment. Results showed that estimates of herring abundance/biomass from 18 and 38 kHz acoustic integration varied by only 0.3–5.4%, and acoustically derived numbers-at-age estimates were not significantly (p > 0.05) different from 1:1. Estimates at 120 kHz were also robust. Furthermore, 18-kHz-derived estimates did not significantly change the assessment model output, indicating that 18-kHz data can be used for herring stock assessment purposes.

Relationship between settlement of southern rock lobster pueruli, Jasus edwardsii, and recruitment to the fishery in Tasmania, Australia

2001 ◽  
Vol 52 (8) ◽  
pp. 1271 ◽  
Author(s):  
C. Gardner ◽  
S. D. Frusher ◽  
R. B. Kennedy ◽  
A. Cawthorn
Keyword(s):  
Interannual Variation ◽  
Stock Assessment ◽  
Catch Rate ◽  
Assessment Model ◽  
Rock Lobster ◽  
Commercial Catch ◽  
Jasus Edwardsii ◽  
Catch Rates ◽  
Additional Support ◽  
Northeast Coast

Puerulus catches on artificial collectors were measured monthly at four sites around Tasmania from 1991 to April 2000, with the aim of predicting future changes in recruitment to the fishery. Support for the potential of catch-rate prediction in Tasmania was provided at the two sites that have overlap of several years between indices of puerulus settlement and indices of the abundance of recruits to the fishery. At Bicheno, on the northeast coast, correlations between annual puerulus index and commercial catch rates were highly significant, with a lag of 5 years (P< 0.01). Similar interannual trends in puerulus index and estimates from a stock-assessment model of the biomass of recruits to the fishery provided additional support for a link with puerulus index. A 5-fold interannual variation in puerulus index detected at Bicheno, with a peak in 1995, was preceded by 3 years of relatively low puerulus catch. The peak in puerulus index appears to lead to an increase in the abundance of sublegal males in research sampling 3 years later. Correlation between annual measures of puerulus index and catch rate also appeared significant at King Island (P= 0.06) although data at this site had less contrast.

Size-structured population modelling and risk assessment of the Victorian southern rock lobster, Jasus edwardsii, fishery

2001 ◽  
Vol 52 (8) ◽  
pp. 1495 ◽  
Author(s):  
David Hobday ◽  
André E. Punt
Keyword(s):  
Egg Production ◽  
Stock Assessment ◽  
Reference Points ◽  
Assessment Model ◽  
Data Series ◽  
Population Modelling ◽  
Rock Lobster ◽  
Southeastern Australia ◽  
Jasus Edwardsii ◽  
Catch Rates

Current annual landings of southern rock lobster, Jasus edwardsii, from southeastern Australia are around 5000 tonnes valued at A$140 million. The Victorian component of this catch during the 1998-99 fishing season was 550 t, valued at A$18 million. During the past 20 years catch rates have declined from 0.8 kg per pot lift to 0.6 and 0.3 kg per pot lift in the western and eastern management zones respectively. The fishery has been managed with input controls during this period, but at the time of writing, the direction of future management is not clear. A size-structured model was developed to assess risk associated with both effort (input) and catch (output) controlled harvest strategies in each zone. The stock-assessment model was fitted to historical catch data (in weight and by number) from 1951, catch rates, and the length-frequency by sex. The uncertainty associated with the estimates of exploitable biomass and egg production was assessed according to Bayesian methods. The output of the assessment formed the basis for projections intended to determine the risk associated with different future levels of effort and catch. Reference points based on estimated biomass and egg production relative to the start of the data series in 1951 were considered.

Detecting and correcting underreported catches in fish stock assessment: trial of a new method

2010 ◽  
Vol 67 (8) ◽  
pp. 1247-1261 ◽  
Author(s):  
Nicolas Bousquet ◽  
Noel Cadigan ◽  
Thierry Duchesne ◽  
Louis-Paul Rivest
Keyword(s):  
Gadus Morhua ◽  
Stock Assessment ◽  
Population Analysis ◽  
Assessment Model ◽  
Fish Stock ◽  
Stock Biomass ◽  
Stock Assessments ◽  
Spawning Stock ◽  
Age Structured ◽  
Fish Stock Assessment

Landings from fisheries are often underreported, that is, the true landings are greater than those reported. Despite this bias, reported landings are widely used in fish stock assessments, and this might lead to overoptimistic exploitation strategies. We construct a statistical stock assessment model that accounts for underreported landings using the theory of censoring with sequential population analysis (SPA). The new model is developed and implemented specifically for the cod stock ( Gadus morhua ) from the southern Gulf of St. Lawrence (Canada). This stock is known to have unreported overfishing during 1985–1992. We show in simulations that for this stock, the new censored model can correctly detect the problematic landings. These corrections are nearly insensitive to subjective boundaries placed on real catches and robust to modifications imposed in the simulation of landings. However, when surveys are too noisy, the new SPA for censored catches can result in increased uncertainty in parameters used for management such as spawning stock biomass and age-structured stock size.

scholarly journals Data needs and spatial structure considerations in stock assessments with regional differences in recruitment and exploitation

2017 ◽  
Vol 74 (11) ◽  
pp. 1918-1929 ◽  
Author(s):  
LaTreese S. Denson ◽  
David B. Sampson ◽  
Andi Stephens
Keyword(s):  
Spatial Structure ◽  
Survey Data ◽  
Spatial Configuration ◽  
Stock Assessment ◽  
Assessment Model ◽  
Environmental Index ◽  
Environmental Indices ◽  
Aggregated Data ◽  
Stock Assessments ◽  
Disaggregated Data

This study uses a simulation experiment to demonstrate that bias in estimates of spawning biomass is influenced by the spatial configuration of a stock assessment model, whether survey data are used or not and whether an environmental index is available to inform the spatial distribution of recruitment. Stocks with limited movement of postsettlement fish may be spatially structured due to environmental forces that affect larval dispersal and recruitment distribution or from nonuniform spatial exploitation. Data are frequently aggregated across space in stock assessments, thus disregarding this complex spatial structure and possibly introducing bias into estimates of stock status. An operating model (OM) is created that simulates data that are used in a set of estimation models to assess bias. The following experimental factors are considered: (i) using survey data and environmental indices in the assessment; (ii) using disaggregated data (two regions, as generated by the OM) or aggregated data (one region); and (iii) incorporating different patterns in the OM’s regional exploitation and environmentally driven recruitment distribution.

A general framework for integrating the standardization of catch per unit of effort into stock assessment models

2001 ◽  
Vol 58 (4) ◽  
pp. 795-803 ◽  
Author(s):  
Mark N Maunder
Keyword(s):  
Confidence Intervals ◽  
General Framework ◽  
Simulation Analysis ◽  
Stock Assessment ◽  
Integrated Approach ◽  
Assessment Model ◽  
The Other ◽  
Parameter Estimates ◽  
Explanatory Variables ◽  
Point Estimates

A general framework is presented for integrating the standardization of catch per unit of effort (CPUE) into stock assessment models. Catchability is modeled using both continuous and categorical explanatory variables. The likelihood for the CPUE data is combined with the other likelihoods from the stock assessment model; the parameters used to model catchability are estimated simultaneously with the other parameters of the stock assessment model. The method is applied to a New Zealand rock lobster (Jasus edwardsii) stock, and the results are compared with those obtained using a generalized linear model. The point estimates are similar for both methods, but the confidence intervals from the integrated framework are much narrower. Simulation analysis supports the findings that the integrated approach gives narrower confidence intervals that more accurately represent the uncertainty in the parameter estimates, provided the model is correct.

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