Analyzing Catch–Effort Data Allowing for Randomness in the Catching Process

1986 ◽  
Vol 43 (1) ◽  
pp. 174-186 ◽  
Author(s):  
William J. Reed
Keyword(s):  
Time Series ◽  
Dynamic Model ◽  
Stochastic Dynamics ◽  
Maximum Sustainable Yield ◽  
Reliable Data ◽  
Model Parameters ◽  
Sustainable Yield ◽  
Deterministic Dynamic ◽  
Method Of Maximum Likelihood ◽  
Bivariate Time Series

For many fisheries the only reliable data is a (bivariate) time series of catches and efforts. Most existing methods of analyzing such data implicitly assume that the main source of randomness is in the dynamics of the population, while ignoring randomness in the catching process. The assumption of a deterministic catch production function (usually of the Schaefer form C = qEX) must be contrary to the experience of almost everyone who has ever gone fishing. In this paper a stochastic catch model coupled with a deterministic dynamic model is used in the analysis of catch–effort data and shown to give very plausible results. Estimates (with confidence intervais) of catchability, maximum sustainable yield, and other dynamic model parameters are obtained numerically by the method of maximum likelihood. The incorporation of stochastic dynamics with the stochastic catch model is difficult.

Using demographic methods to construct Bayesian priors for the intrinsic rate of increase in the Schaefer model and implications for stock rebuilding

2001 ◽  
Vol 58 (9) ◽  
pp. 1871-1890 ◽  
Author(s):  
M K McAllister ◽  
E K Pikitch ◽  
E A Babcock
Keyword(s):  
Intrinsic Rate ◽  
Maximum Sustainable Yield ◽  
Production Model ◽  
Intrinsic Rate Of Increase ◽  
Point Estimate ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Sustainable Yield ◽  
Rate Of Increase ◽  
Demographic Methods

Even though Bayesian methods can provide statistically rigorous assessments of the biological status of fisheries resources, uninformative data (e.g., declining catch rate series with little variation in fishing effort) can produce highly imprecise parameter estimates. This can be counteracted with the use of informative Bayesian prior distributions (priors) for model parameters. We develop priors for the intrinsic rate of increase (r) in the Schaefer surplus production model using demographic methods and illustrate the utility of this with an application to large coastal sharks in the Atlantic. In 1996, a U.S. stock assessment obtained a point estimate for r of 0.26. For such long-lived and low-fecund organisms, this could potentially be too high. Yet it was used to predict that within about 10 years, a 50% reduction in the 1995 catch level should result in >50% chance of increasing the population to the abundance required to produce maximum sustainable yield. In contrast, a Bayesian assessment that used demographic analysis to construct a prior for r with a median of 0.07 and coefficient of variation (CV) of 0.7 indicated that within 30 years, this policy would have only a very small chance of increasing the population to maximum sustainable yield.

Estimation of maximum sustainable yield of Gelidium robustum seaweed fishery in Mexico

2005 ◽  
Vol 85 (4) ◽  
pp. 775-778 ◽  
Author(s):  
M. Casas-Valdez ◽  
D. Lluch-Belda ◽  
S. Ortega-García ◽  
S. Hernández-Vázquez ◽  
E. Serviere-Zaragoza ◽  
...  
Keyword(s):  
Dynamic Model ◽  
West Coast ◽  
Baja California ◽  
Maximum Sustainable Yield ◽  
Sustainable Yield ◽  
Red Seaweed ◽  
The West ◽  
Surplus Production ◽  
Production Models ◽  
Gelidium Robustum

Surplus production models were used to assess the fishery condition of red seaweed Gelidium robustum off the west coast of the Baja California Peninsula from 1985 to 1997. The maximum sustainable yield and optimum effort estimated by the Schaefer model were 705 tn and 457 teams, while the Fox model estimated 670 tn and 510 teams. The determination coefficients were r2=0·62 for the Fox and r2=0·58 for the Schaefer model. These results suggest that the resource is not overexploited. Fitting the data to Hilborn & Walters' dynamic model was not satisfactory.

scholarly journals Parametric Estimation of the Stochastic Dynamics of Sea Surface Winds

2014 ◽  
Vol 71 (9) ◽  
pp. 3465-3483 ◽  
Author(s):  
William F. Thompson ◽  
Adam H. Monahan ◽  
Daan Crommelin
Keyword(s):  
Time Series ◽  
Stochastic Dynamics ◽  
Surface Wind ◽  
Estimation Procedure ◽  
Parametric Estimation ◽  
Sea Surface ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Surface Winds ◽  
Sea Surface Winds

Abstract In this study, the parameters of a stochastic–dynamical model of sea surface winds are estimated from long time series of sea surface wind observational data. The model was introduced by A. H. Monahan, who developed an idealized model from a highly simplified representation of the momentum budget of a surface atmospheric layer of fixed depth. Such estimation of model parameters is challenging, in particular for a multivariate model with nonlinear terms as is considered here. The authors use a method developed recently by Crommelin and Vanden-Eijnden, which approaches the estimation problem variationally, finding the spectrally “best fit” stochastic differential equation to a time series of observations. While the estimation procedure assumes forcing that is white in time, observed time series are generally better approximated as forced by red noise. Using a red-noise-forced linear system, the authors first show that the estimation procedure can still be used to estimate model parameters. Because the assumption of white noise is violated, these estimates lead to model autocorrelation functions that differ from the observed time series. Application of the estimation procedure to the wind data is further complicated by the fact that the boundary layer model is inconsistent with certain observed features of the wind. When these mismatches between the model and observations are accounted for, the estimation procedure generally results in parameter estimates consistent with the climatological features of the associated meteorological fields. Important exceptions to this result are the layer thickness and layer-top eddy diffusivity, which are poorly estimated where the vector winds are close to Gaussian.

scholarly journals Trends in Pacific Canadian groundfish stock status

2021 ◽  
Author(s):  
Sean C Anderson ◽  
Brendan M Connors ◽  
Philina A English ◽  
Robyn E Forrest ◽  
Rowan Haigh ◽  
...  
Keyword(s):  
Time Series ◽  
Credible Interval ◽  
Maximum Sustainable Yield ◽  
Sustainable Yield ◽  
Pacific Cod ◽  
Strait Of Georgia ◽  
Individual Transferable Quotas ◽  
Stock Status ◽  
Stock Assessments ◽  
Trawl Surveys

We assembled estimated biomass (B) time series from stock assessments for 24 Pacific Canadian groundfish stocks and modelled average and stock status through 2020 based on biomass relative to each stock's (1) Limit Reference Point (B/LRP), (2) Upper Stock Reference (B/USR), and (3) biomass at maximum sustainable yield (B/BMSY). The overall mean B/LRP in 2020 was 3.2 (95% credible interval [CI]: 2.6-3.9). The overall mean B/USR and B/BMSY in 2020 was 1.5 (95% CI: 1.3-1.9) and 1.4 (95% CI: 1.1-1.7), respectively. Average stock status declined from 1950 to around 2000 and has remained relatively stable since then. The change around 2000 followed the implementation of ITQs (individual transferable quotas) for the trawl fleet and the commencement of the synoptic trawl surveys. As of their last assessment, four stocks (Strait of Georgia Lingcod [Area 4B], coastwide Bocaccio, and inside and outside Quillback Rockfish) had a greater than 5% probability of being below their LRP (i.e., in the "critical zone"); Pacific Cod in Area 3CD had a 4.6% probability. Roughly one-third of stocks had a greater than 1 in 4 chance of being below their USR (i.e., in the "cautious zone"). Conversely, two-thirds of assessed groundfish stocks had a high (>75%) probability of being above the USR (i.e., in the "healthy zone").

A COMPARATIVE STUDY BETWEEN UNIVARIATE AND BIVARIATE TIME SERIES MODELS FOR CRUDE PALM OIL INDUSTRY IN PENINSULAR MALAYSIA

2020 ◽  
Vol 5 (1) ◽  
pp. 374
Author(s):  
Pauline Jin Wee Mah ◽  
Nur Nadhirah Nanyan
Keyword(s):  
Time Series ◽  
Palm Oil ◽  
Moving Average ◽  
Forecast Accuracy ◽  
Peninsular Malaysia ◽  
Time Series Models ◽  
Crude Palm Oil ◽  
Univariate Time Series ◽  
Import And Export ◽  
Bivariate Time Series

The main purpose of this study is to compare the performances of univariate and bivariate models on four time series variables of the crude palm oil industry in Peninsular Malaysia. The monthly data for the four variables, which are the crude palm oil production, price, import and export, were obtained from Malaysian Palm Oil Board (MPOB) and Malaysian Palm Oil Council (MPOC). In the first part of this study, univariate time series models, namely, the autoregressive integrated moving average (ARIMA), fractionally integrated autoregressive moving average (ARFIMA) and autoregressive autoregressive (ARAR) algorithm were used for modelling and forecasting purposes. Subsequently, the dependence between any two of the four variables were checked using the residuals’ sample cross correlation functions before modelling the bivariate time series. In order to model the bivariate time series and make prediction, the transfer function models were used. The forecast accuracy criteria used to evaluate the performances of the models were the mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE). The results of the univariate time series showed that the best model for predicting the production was ARIMA  while the ARAR algorithm were the best forecast models for predicting both the import and export of crude palm oil. However, ARIMA  appeared to be the best forecast model for price based on the MAE and MAPE values while ARFIMA  emerged the best model based on the RMSE value.  When considering bivariate time series models, the production was dependent on import while the export was dependent on either price or import. The results showed that the bivariate models had better performance compared to the univariate models for production and export of crude palm oil based on the forecast accuracy criteria used.

MSY needs no epitaph—but it was abused

2020 ◽  
Author(s):  
Daniel Pauly ◽  
Rainer Froese
Keyword(s):  
Maximum Sustainable Yield ◽  
Sustainable Yield

Abstract The maximum sustainable yield (MSY) concept is widely considered to be outdated and misleading. In response, fisheries scientists have developed models that often diverge radically from the first operational version of the concept. We show that the original MSY concept was deeply rooted in ecology and that going back to that version would be beneficial for fisheries, not least because the various substitutes have not served us well.

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