Predicting catch per unit effort from a multispecies commercial fishery in Port Phillip Bay, Australia

2020 ◽  
Vol 71 (4) ◽  
pp. 542
Author(s):  
Karina L. Ryan ◽  
Denny Meyer
Keyword(s):  
Time Series ◽  
Environmental Conditions ◽  
Environmental Variability ◽  
Moving Average ◽  
Commercial Fishing ◽  
Arima Models ◽  
Catch Per Unit Effort ◽  
Explanatory Variables ◽  
Unit Effort ◽  
Stock Abundance

Quantitative models that predict stock abundance can inform stock assessments and adaptive management that allows for less stringent controls when abundance is high and environmental conditions are suitable, or tightening controls when abundance is low and environmental conditions are least suitable. Absolute estimates of stock abundance are difficult and expensive to obtain, but data from routine reporting in commercial fisheries logbooks can provide an indicator of stock status. Autoregressive integrated moving average (ARIMA) models were constructed using catch per unit effort (CPUE) from commercial fishing in Port Phillip Bay from 1978–79 to 2009–10. Univariate and multivariate models were compared for short-lived species (Sepioteuthis australis), and species represented by 1–2 year-classes (Sillaginodes punctatus) and 5–6 year-classes (Chrysophrys auratus). Simple transfer models incorporating environmental variables produced the best predictive models for all species. Multivariate ARIMA models are dependent on the availability of an appropriate time series of explanatory variables. This study demonstrates an application of time series methods to predict monthly CPUE that is relevant to fisheries for species that are short lived or vulnerable to fishing during short phases in their life history or where high intra-annual variation in stock abundance occurs through environmental variability.

scholarly journals Modeling of Lake Malombe Annual Fish Landings and Catch per Unit Effort (CPUE)

Forecasting ◽  
2021 ◽  
Vol 3 (1) ◽  
pp. 39-55
Author(s):  
Rodgers Makwinja ◽  
Seyoum Mengistou ◽  
Emmanuel Kaunda ◽  
Tena Alemiew ◽  
Titus Bandulo Phiri ◽  
...  
Keyword(s):  
Time Series ◽  
Information Criterion ◽  
Coefficient Of Determination ◽  
Series Data ◽  
Efficiency Coefficient ◽  
Arima Models ◽  
Catch Per Unit Effort ◽  
Annual Fish ◽  
Unit Effort ◽  
The Mean

Forecasting, using time series data, has become the most relevant and effective tool for fisheries stock assessment. Autoregressive integrated moving average (ARIMA) modeling has been commonly used to predict the general trend for fish landings with increased reliability and precision. In this paper, ARIMA models were applied to predict Lake Malombe annual fish landings and catch per unit effort (CPUE). The annual fish landings and CPUE trends were first observed and both were non-stationary. The first-order differencing was applied to transform the non-stationary data into stationary. Autocorrelation functions (AC), partial autocorrelation function (PAC), Akaike information criterion (AIC), Bayesian information criterion (BIC), square root of the mean square error (RMSE), the mean absolute error (MAE), percentage standard error of prediction (SEP), average relative variance (ARV), Gaussian maximum likelihood estimation (GMLE) algorithm, efficiency coefficient (E2), coefficient of determination (R2), and persistent index (PI) were estimated, which led to the identification and construction of ARIMA models, suitable in explaining the time series and forecasting. According to the measures of forecasting accuracy, the best forecasting models for fish landings and CPUE were ARIMA (0,1,1) and ARIMA (0,1,0). These models had the lowest values AIC, BIC, RMSE, MAE, SEP, ARV. The models further displayed the highest values of GMLE, PI, R2, and E2. The “auto. arima ()” command in R version 3.6.3 further displayed ARIMA (0,1,1) and ARIMA (0,1,0) as the best. The selected models satisfactorily forecasted the fish landings of 2725.243 metric tons and CPUE of 0.097 kg/h by 2024.

Wind-Aided Recruitment of Canadian Arctic Cisco (Coregonus autumnalis) into Alaskan Waters

1988 ◽  
Vol 45 (5) ◽  
pp. 906-910 ◽  
Author(s):  
Robert G. Fechhelm ◽  
David B. Fissel
Keyword(s):  
Time Series ◽  
Canadian Arctic ◽  
Wind Data ◽  
Commercial Fishery ◽  
Catch Per Unit Effort ◽  
Longshore Currents ◽  
Unit Effort ◽  
Colville River ◽  
Young Of The Year ◽  
Mackenzie River

Summer wind data collected at Barter Island, Alaska, were compared with commercial fishery catches of arctic cisco (Coregonus autumnalis) at the Colville River, Alaska, for the period 1967–85. There was a significant (p = 0.036) association between yearly catch-per-unit-effort and the percent of easterly winds after adjusting for a 5-yr differential in the two time series. Results suggest that young-of-the-year fish which spawn in Canada's Mackenzie River are aided in their westward dispersal into Alaskan waters via wind-driven longshore currents. The greater the prevalence of easterly winds (westerly currents), the greater the recruitment. Increased recruitment manifests itself as an increase in Alaskan commercial fishery catch some 5-yr later when fish have grown to a size that renders them susceptible to commercial nets.

scholarly journals Influence of seasonality, salinity and temperature on catch trend of Penaeus indicus H. Milne-Edwards, 1837 in a coastal lagoon, India

2019 ◽  
Vol 66 (1) ◽  
Author(s):  
R.K. Raman ◽  
V.R. Suresh ◽  
S.K. Mohanty ◽  
K.S. Bhatta ◽  
S.K. Karna ◽  
...  
Keyword(s):  
Time Series ◽  
Coastal Lagoons ◽  
Moving Average ◽  
Positive Influence ◽  
Chilika Lagoon ◽  
Explanatory Variables ◽  
Structural Time Series ◽  
Water Temperature Data ◽  
Penaeus Indicus ◽  
Auto Regressive

The catch pattern of P. indicus in coastal lagoons is influenced by seasonal changes in physicochemical parameters of the lagoon ecosystem. In this study the effects of seasonality, salinity and water emperature of lagoon on P. indicus catch were analysed using Structural Time Series Model (STSM) and ARIMAX (Auto Regressive Integrated Moving Average with explanatory variables) modeling approach using monthly time series catch, salinity and water temperature data of the Chilika Lagoon (a Ramsar site) in India for the period from 2001 to 2015. Results showed a significant (p<0.05) increasing stochastic upward trend and two seasonal cycles for P. indicus catch in the lagoon. Salinity was found to have significant positive influence (p<0.05) and temperature to have insignificant positive influence on P. indicus catch in the lagoon.

scholarly journals ARIMA Time Series Models for Full Truckload Transportation Prices

Forecasting ◽  
2018 ◽  
Vol 1 (1) ◽  
pp. 121-134 ◽  
Author(s):  
Jason W. Miller
Keyword(s):  
Time Series ◽  
Moving Average ◽  
The United States ◽  
Arima Models ◽  
Autoregressive Integrated Moving Average ◽  
Estimated Parameters ◽  
Coefficients Of Variation ◽  
Labor Statistics ◽  
Bureau Of Labor Statistics ◽  
Practical Implications

The trucking sector in the United States is a $700 billion plus a year industry and represents a large percentage of many firms’ logistics spend. Consequently, there is interest in accurately forecasting prices for truck transportation. This manuscript utilizes the autoregressive integrated moving average (ARIMA) methodology to develop forecasts for three time series of monthly archival trucking prices obtained from two public sources—the Bureau of Labor Statistics (BLS) and Truckstop.com. BLS data cover January 2005 through August 2018; Truckstop.com data cover January 2015 through August 2018. Different ARIMA models closely approximate the observed data, with coefficients of variation of the root mean-square deviations being 0.007, 0.040, and 0.048. Furthermore, the estimated parameters map well onto dynamics known to operate in the industry, especially for data collected by the BLS. Theoretical and practical implications of these findings are discussed.

scholarly journals Modelling New Zealand Road Deaths

2021 ◽  
Vol 32 (2) ◽  
pp. 4-15
Author(s):  
Colin Morrison ◽  
Ernest Albuquerque
Keyword(s):  
Time Series ◽  
New Zealand ◽  
Moving Average ◽  
Arima Model ◽  
Time Series Forecasting ◽  
Time Data ◽  
Baseline Model ◽  
Explanatory Variables ◽  
Distributed Lag ◽  
Autoregressive Distributed Lag

New Zealand is developing an integrated road safety intervention logic model. This paper describes a core component of this wider strategic research carried out in 2018: a baseline model that extrapolates New Zealand road deaths to 2025. The baseline will provide context to what Waka Kotahi NZ Transport Agency is trying to achieve. It offers a way of understanding what impact interventions have in acting with and against external influences affecting road deaths and serious trauma. The baseline model considers autonomous change at a macro level given social and economic factors that influence road deaths. Identifying and testing relationships and modelling these explanatory variables clarifies the effect of interventions. Time-series forecasting begins by carefully collecting and rigorously analysing sequences of discrete-time data, then developing an appropriate model to describe the inherent structure of the series. Successful time-series forecasting depends on fitting an appropriate model to the underlying time-series. Several time-series models were investigated in understanding road deaths in the New Zealand context. In the final modelling an autoregressive integrated moving average (ARIMA) model and two differing autoregressive distributed lag (ARDL) models were developed. A preferred model was identified. This ARDL model was used to project road deaths to 2025.

scholarly journals Multi-scale CO2

2021 ◽  
Author(s):  
Xiaomeng Gu ◽  
Andrew Viggo Metcalfe ◽  
Gary Glonek
Keyword(s):  
Time Series ◽  
Seasonal Variation ◽  
Industrial Revolution ◽  
Moving Average ◽  
Arima Models ◽  
Arma Models ◽  
Deterministic Models ◽  
Increasing Trend ◽  
Exponential Trend ◽  
Sampling Intervals

Abstract Five time series of estimated atmospheric CO 2 with sampling intervals ranging from 0.5 million years to the relatively high frequency of one week are analysed. The yearly series shows a clear increasing trend since the beginning of the first Industrial Revolution around 1760. The weekly series shows a clear increasing trend and also seasonal variation. In both cases, the trend is fitted by a conceptual model that consists of a baseline value with an exponential trend superimposed. For the weekly series, the seasonal variation is modelled as an exponential of a sum of sine and cosine terms. The deviations from these deterministic models are treated as detrended and deseasonalised time series.Then,threesub-categoriesof autoregressive integrated moving average (ARIMA) models are fitted to the five time series: ARMA models which are stationary; FARIMA models which are stationary but have long memory and are fractal processes, and ARIMA models which are variations on a random walk and so non-stationary in the variance.The FARIMA and ARIMA models provide better fits to the data than the corresponding ARMA models. All the fitted models are close to the boundary of stability, and are consistent with claims that climate change due to an increase in atmospheric CO 2 may not quickly be reversed even if CO 2 emissions are stopped.

scholarly journals Influent Forecasting for Wastewater Treatment Plants in North America

2019 ◽  
Vol 11 (6) ◽  
pp. 1764 ◽  
Author(s):  
Gavin Boyd ◽  
Dain Na ◽  
Zhong Li ◽  
Spencer Snowling ◽  
Qianqian Zhang ◽  
...  
Keyword(s):  
Time Series ◽  
Wastewater Treatment ◽  
North America ◽  
Wastewater Treatment Plants ◽  
Moving Average ◽  
Wastewater Management ◽  
Sufficient Information ◽  
Arima Models ◽  
Analysis Model ◽  
Influent Flow

Autoregressive Integrated Moving Average (ARIMA) is a time series analysis model that can be dated back to 1955. It has been used in many different fields of study to analyze time series and forecast future data points; however, it has not been widely used to forecast daily wastewater influent flow. The objective of this study is to explore the possibility for wastewater treatment plants (WWTPs) to utilize ARIMA for daily influent flow forecasting. To pursue the objective confidently, five stations across North America are used to validate ARIMA’s performance. These stations include Woodward, Niagara, North Davis, and two confidential plants. The results demonstrate that ARIMA models can produce satisfactory daily influent flow forecasts. Considering the results of this study, ARIMA models could provide the operating engineers at both municipal and rural WWTPs with sufficient information to run the stations efficiently and thus, support wastewater management and planning at various levels within a watershed.

Effects of environmental variability on abundance of commercial marine species in the northern Gulf of California

2019 ◽  
Vol 83 (3) ◽  
pp. 195
Author(s):  
T. Mónica Ruiz-Barreiro ◽  
Francisco Arreguín-Sánchez ◽  
Arturo González-Baheza ◽  
Juan C. Hernández-Padilla
Keyword(s):  
Environmental Variables ◽  
Gulf Of California ◽  
Environmental Changes ◽  
Environmental Variability ◽  
Species Abundance ◽  
Marine Species ◽  
Additive Models ◽  
Catch Per Unit Effort ◽  
Cross Correlation Analysis ◽  
Stock Abundance

Studies have shown that environmental variables significantly affect variation in stock abundance of marine populations. The northern Gulf of California (NGC) is a highly productive region of interest due to its fish resources and diversity. Conservation of the marine species inhabiting the region is of public interest. Our study analysed the influence of physical environmental factors on several commercial marine species, using catch per unit effort (CPUE) as a proxy for abundance. Generalized additive models were used to test the significance of selected environmental variables on stock abundance. Deseasonalized cross-correlation analysis was used to examine time-lagged correlations between CPUE and abiotic variables to identify response timings. The results suggest that for most commercial species the sea surface temperature and the long-term climate Pacific Decadal Oscillation index are the predominant predictors for species abundance, followed by the Colorado River discharge. The Multivariate ENSO Index and the Pacific-North American pattern indices also showed specific effects on certain species. The NGC is a highly dynamic region, where species respond to environmental changes according to the characteristics of their life histories.

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