scholarly journals A state-space stock assessment model for American plaice on the Grand Bank of Newfoundland

2020 ◽  
Vol 51 ◽  
pp. 45-104
Author(s):  
A M J Perreault ◽  
L J Wheeland ◽  
L J Wheeland ◽  
M J Morgan ◽  
N G Cadigan
Keyword(s):  
State Space ◽  
Stock Assessment ◽  
Assessment Model

scholarly journals A state-space stock assessment model for northern cod, including under-reported catches and variable natural mortality rates

2016 ◽  
Vol 73 (2) ◽  
pp. 296-308 ◽  
Author(s):  
Noel G. Cadigan
Keyword(s):  
State Space ◽  
Gadus Morhua ◽  
Stock Assessment ◽  
Mortality Rates ◽  
Assessment Model ◽  
Data Sources ◽  
Natural Mortality ◽  
Similar Data ◽  
Subjective Data ◽  
Northern Cod

A state-space assessment model for the northern cod (Gadus morhua) stock off southern Labrador and eastern Newfoundland is developed here. The model utilizes information from offshore trawl surveys, inshore acoustic surveys, fishery catch age compositions, partial fishery landings, and tagging. This is done using an approach that avoids the use of subjective data-weighting. Estimates of fishing mortality rates (F) are usually conditional on assumptions about natural mortality rates (M) in stock assessment models. However, by integrating much of the information on northern cod, it is possible to estimate F and M separately. It is also possible to estimate a change in the offshore survey catchability by including inshore acoustic biomass estimates. The proposed model also accounts for biased total catch statistics, which is a common problem in stock assessments. The main goal of the model is to provide realistic projections of the impacts of various levels of future fishery catches on the recovery of this stock. The projections incorporate uncertainty about M and catch. This is vital information for successful future fisheries. The model has been developed for the specific data sources available for northern cod, but it could be adapted to other stocks with similar data sources.

A state-space spatial survey-based stock assessment (SSURBA) model to inform spatial variation in relative stock trends

2020 ◽  
Vol 77 (10) ◽  
pp. 1638-1658
Author(s):  
Rajeev Kumar ◽  
Noel G. Cadigan ◽  
Nan Zheng ◽  
Divya A. Varkey ◽  
M. Joanne Morgan
Keyword(s):  
State Space ◽  
Stock Assessment ◽  
State Space Model ◽  
Logistic Function ◽  
Fish Growth ◽  
Assessment Model ◽  
Fish Length ◽  
Stock Size ◽  
Age Cohort ◽  
Size At Age

An age-structured, spatial survey-based assessment model (SSURBA) is developed and applied to the Grand Banks stock (NAFO Divisions 3LNO) of American plaice (Hippoglossoides platessoides) in Newfoundland and Labrador. The state-space model is fit to annual spatial (i.e., three divisions) stock size-at-age research vessel (RV) survey indices that are assumed to be proportional to abundance. We model index catchability (q) as a logistic function of fish length, which varies with age, cohort, and the time of the survey; therefore, the model facilitates the estimation of q values that change spatially and temporally following changes in fish growth and survey gears. The SSURBA model produces division-level estimates of fishing mortality rates (F), stock productivity, and stock size relative to the logistic catchability assumption with q = 1 for fully selected ages. The spatial model allows us to include additional survey information compared with the space-aggregated assessment model (all of 3LNO) that is currently used to assess stock status. The model can provide estimates of relative catch, which we compare with reported catch trends to partially validate the model.

scholarly journals Accounting for correlated observations in an age-based state-space stock assessment model

2016 ◽  
Vol 73 (7) ◽  
pp. 1788-1797 ◽  
Author(s):  
Casper W. Berg ◽  
Anders Nielsen
Keyword(s):  
State Space ◽  
Stock Assessment ◽  
Age Groups ◽  
Assessment Model ◽  
Fish Stock ◽  
Confidence Bounds ◽  
Independence Assumption ◽  
Fish Stocks ◽  
Fish Stock Assessment ◽  
Improved Model

Abstract Fish stock assessment models often rely on size- or age-specific observations that are assumed to be statistically independent of each other. In reality, these observations are not raw observations, but rather they are estimates from a catch-standardization model or similar summary statistics based on observations from many fishing hauls and subsamples of the size and age composition of the data. Although aggregation mitigates the strong intra-haul correlation between sizes/ages that is usually found in haul-by-haul data, violations of the independence assumption can have a large impact on the results and specifically on reported confidence bounds. A state-space assessment model that allows for correlations between age groups within years in the observation model for catches and surveys is presented and applied to data on several North Sea fish stocks using various correlation structures. In all cases the independence assumption is rejected. Less fluctuating estimates of the fishing mortality is obtained due to a reduced process error. The improved model does not suffer from correlated residuals unlike the independent model, and the variance of forecasts is decreased.

scholarly journals Simulation testing methods for estimating misreported catch in a state-space stock assessment model

2020 ◽  
Vol 77 (3) ◽  
pp. 911-920 ◽  
Author(s):  
Charles T Perretti ◽  
Jonathan J Deroba ◽  
Christopher M Legault
Keyword(s):  
Random Walk ◽  
State Space ◽  
Stock Assessment ◽  
Assessment Model ◽  
Testing Methods ◽  
Multiple Sources ◽  
Estimation Model ◽  
Estimation Errors ◽  
Simulation Testing ◽  
Over Time

Abstract State-space stock assessment models have become increasingly common in recent years due to their ability to estimate unobserved variables and explicitly model multiple sources of random error. Therefore, they may be able to better estimate unobserved processes such as misreported fishery catch. We examined whether a state-space assessment model was able to estimate misreported catch in a simulated fishery. We tested three formulations of the estimation model, which exhibit increasing complexity: (i) assuming no misreporting, (ii) assuming misreporting is constant over time, and (iii) assuming misreporting follows a random walk. We tested these three estimation models against simulations using each of the three assumptions and an additional fourth assumption of uniform random misreporting over time. Overall, the worst estimation errors occurred when misreporting was ignored while it was in fact occurring, while there was a relatively small cost for estimating misreporting when it was not occurring. Estimates of population scale and fishing mortality rate were particularly sensitive to misreporting assumptions. Furthermore, in the uniform random scenario, the relatively simple model that assumed misreporting was fixed across ages and time was more accurate than the more complicated random walk model, despite the increased flexibility of the latter.

scholarly journals Choosing the observational likelihood in state-space stock assessment models

2017 ◽  
Vol 74 (5) ◽  
pp. 779-789 ◽  
Author(s):  
Christoffer Moesgaard Albertsen ◽  
Anders Nielsen ◽  
Uffe Høgsbro Thygesen
Keyword(s):  
State Space ◽  
Stock Assessment ◽  
A Priori ◽  
Reference Points ◽  
Assessment Model ◽  
Model Fit ◽  
Total Allowable Catch ◽  
Stock Biomass ◽  
Spawning Stock ◽  
Using Data

Data used in stock assessment models result from combinations of biological, ecological, fishery, and sampling processes. Since different types of errors propagate through these processes, it can be difficult to identify a particular family of distributions for modelling errors on observations a priori. By implementing several observational likelihoods, modelling both numbers- and proportions-at-age, in an age-based state-space stock assessment model, we compare the model fit for each choice of likelihood along with the implications for spawning stock biomass and mean fishing mortality. We propose using AIC intervals based on fitting the full observational model for comparing different observational likelihoods. Using data from four stocks, we show that the model fit is improved by modelling the correlation of observations within years. However, the best choice of observational likelihood differs for different stocks, and the choice is important for the short-term conclusions drawn from the assessment model; in particular, the choice can influence total allowable catch advise based on reference points.

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