Detecting and forecasting complex nonlinear dynamics in spatially structured catch-per-unit-effort time series for North Pacific albacore (Thunnus alalunga)

2011 ◽  
Vol 68 (3) ◽  
pp. 400-412 ◽  
Author(s):  
Sarah M. Glaser ◽  
Hao Ye ◽  
Mark Maunder ◽  
Alec MacCall ◽  
Michael Fogarty ◽  
...  
Keyword(s):  
Nonlinear Dynamics ◽  
Time Series ◽  
North Pacific ◽  
Stock Assessment ◽  
North Pacific Ocean ◽  
Catch Per Unit Effort ◽  
Data Set ◽  
Unit Effort ◽  
Thunnus Alalunga ◽  
Low Dimensional

The presence of complex, nonlinear dynamics in fish populations, and uncertainty in the structure (functional form) of those dynamics, pose challenges to the accuracy of forecasts produced by traditional stock assessment models. We describe two nonlinear forecasting models that test for the hallmarks of complex behavior, avoid problems of structural uncertainty, and produce good forecasts of catch-per-unit-effort (CPUE) time series in both standardized and nominal (unprocessed) form. We analyze a spatially extensive, 40-year-long data set of annual CPUE time series of North Pacific albacore ( Thunnus alalunga ) from 1° × 1° cells from the eastern North Pacific Ocean. The use of spatially structured data in compositing techniques improves out-of-sample forecasts of CPUE and overcomes difficulties commonly encountered when using short, incomplete time series. These CPUE series display low-dimensional, nonlinear structure and significant predictability. Such characteristics have important implications for industry efficiency in terms of future planning and can inform formal stock assessments used for the management of fisheries.

scholarly journals Target-based catch-per-unit-effort standardization in multispecies fisheries

2018 ◽  
Vol 75 (3) ◽  
pp. 452-463 ◽  
Author(s):  
Hiroshi Okamura ◽  
Shoko H. Morita ◽  
Tetsuichiro Funamoto ◽  
Momoko Ichinokawa ◽  
Shinto Eguchi
Keyword(s):  
Stock Assessment ◽  
Catch Per Unit Effort ◽  
Target Variable ◽  
Traditional Methods ◽  
Data Set ◽  
Mixture Regression ◽  
Multispecies Fisheries ◽  
Unit Effort ◽  
Simple Simulation ◽  
Cpue Standardization

Standardized catch per unit effort (CPUE) is a fundamental component of fishery stock assessment. In multispecies fisheries, catchability can differ depending on which species is being targeted, and so the yearly trend extracted from the standardized CPUE is likely to be biased. We have, therefore, developed a method for predicting the unobserved variable related to targeted species from among multispecies composition data using a mixture regression model for the transformed residuals. In contrast with traditional methods, the proposed method predicts the target variable in CPUE standardization without removing a subset of the data. Keeping the entire data set avoids information loss, and so CPUE standardization with the predicted target variable should yield an unbiased estimate of the yearly trend. Simple simulation tests demonstrate that our method outperforms traditional methods. We illustrate the use of our method by applying it to CPUE data on arabesque greenling (Pleurogrammus azonus) caught in multispecies trawl fisheries in Hokkaido, Japan.

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.

Age and growth of albacore Thunnus alalunga in the North Pacific Ocean

2012 ◽  
Vol 80 (6) ◽  
pp. 2328-2344 ◽  
Author(s):  
K.-S. Chen ◽  
T. Shimose ◽  
T. Tanabe ◽  
C.-Y. Chen ◽  
C.-C. Hsu
Keyword(s):  
Pacific Ocean ◽  
North Pacific ◽  
North Pacific Ocean ◽  
Age And Growth ◽  
The North ◽  
Thunnus Alalunga ◽  
The North Pacific

Eels at the Edge: Science, Status, and Conservation Concerns

2009 ◽  
Keyword(s):  
New Zealand ◽  
Management System ◽  
Stock Assessment ◽  
Catch Per Unit Effort ◽  
Commercial Catch ◽  
The North ◽  
Anguilla Australis ◽  
Unit Effort ◽  
Access Rights ◽  
Quota Management

<em>Abstract.</em>—The New Zealand eel fishery comprises two species, the shortfin eel <em>Anguilla australis </em>and the New Zealand longfin eel <em>A. dieffenbachii</em>. A third species, the speckled longfin eel <em>A. reinhardtii</em>, is present in small numbers in some areas. Major fisheries in New Zealand are managed under the Quota Management System. Individual transferable quotas are set as a proportion of an annual total allowable commercial catch. The Quota Management System was introduced into the South Island eel fishery on 1 October 2000 and the North Island fishery on 1 October 2004. Freshwater eels have particular significance for customary Maori. Management policies allow for customary take and the granting of commercial access rights on introduction into the Quota Management System. Eel catches have remained relatively constant since the early 1970s. The average annual catch from 1989–1990 to 2001–2002 (fishing year) was 1,313 mt. Catch per unit effort remained constant from 1983 to 1989 and reduced from 1990 to 1999. Statistically significant declines in catch per unit effort for New Zealand longfin eel were found in some areas over the latter period. For management, an annual stock-assessment process provides an update on stock status.

scholarly journals Analysis of juvenile North Atlantic albacore (Thunnus alalunga) catch per unit effort by surface gears in relation to environmental variables

2005 ◽  
Vol 62 (7) ◽  
pp. 1475-1482 ◽  
Author(s):  
Nicolas Goñi ◽  
Haritz Arrizabalaga
Keyword(s):  
North Atlantic ◽  
Environmental Variables ◽  
Negative Relationship ◽  
Generalized Least Squares ◽  
Catch Per Unit Effort ◽  
Seasonal Thermocline ◽  
Unit Effort ◽  
Negative Effect ◽  
Thunnus Alalunga ◽  
Age Structured

Abstract The relationship between the catch per unit effort (cpue) of trolling and baitboat fisheries targeting juvenile North Atlantic albacore (Thunnus alalunga, Bonnaterre, 1788) and several environmental variables was studied. A multiple linear regression and a generalized least squares model (GLS) showed a significant negative relationship between age 2 albacore trolling and baitboat cpue, and the average agitation of the sea and the duration of insolation. No clear relationship was found between the juvenile albacore cpue and sea surface temperature, precipitation, and NAO or Gulf Stream Index. Underlying processes that could explain the negative effect of agitation and insolation are discussed, especially relating to the habitat of age 2 albacore above the seasonal thermocline. Results highlight the necessity of considering environmental variables in the standardization of albacore cpue series used for calibrating age-structured stock assessments.

A New Look at Snowpack Trends in the Cascade Mountains

2010 ◽  
Vol 23 (10) ◽  
pp. 2473-2491 ◽  
Author(s):  
Mark T. Stoelinga ◽  
Mark D. Albright ◽  
Clifford F. Mass
Keyword(s):  
Time Series ◽  
Global Warming ◽  
Pacific Northwest ◽  
North Pacific ◽  
Climate Model ◽  
North Pacific Ocean ◽  
Tropospheric Temperature ◽  
Anthropogenic Global Warming ◽  
The North ◽  
The North Pacific

Abstract This study examines the changes in Cascade Mountain spring snowpack since 1930. Three new time series facilitate this analysis: a water-balance estimate of Cascade snowpack from 1930 to 2007 that extends the observational record 20 years earlier than standard snowpack measurements; a radiosonde-based time series of lower-tropospheric temperature during onshore flow, to which Cascade snowpack is well correlated; and a new index of the North Pacific sea level pressure pattern that encapsulates modes of variability to which Cascade spring snowpack is particularly sensitive. Cascade spring snowpack declined 23% during 1930–2007. This loss is nearly statistically significant at the 5% level. The snowpack increased 19% during the recent period of most rapid global warming (1976–2007), though this change is not statistically significant because of large annual variability. From 1950 to 1997, a large and statistically significant decline of 48% occurred. However, 80% of this decline is connected to changes in the circulation patterns over the North Pacific Ocean that vary naturally on annual to interdecadal time scales. The residual time series of Cascade snowpack after Pacific variability is removed displays a relatively steady loss rate of 2.0% decade−1, yielding a loss of 16% from 1930 to 2007. This loss is very nearly statistically significant and includes the possible impacts of anthropogenic global warming. The dates of maximum snowpack and 90% melt out have shifted 5 days earlier since 1930. Both shifts are statistically insignificant. A new estimate of the sensitivity of Cascade spring snowpack to temperature of −11% per °C, when combined with climate model projections of 850-hPa temperatures offshore of the Pacific Northwest, yields a projected 9% loss of Cascade spring snowpack due to anthropogenic global warming between 1985 and 2025.

Age and growth of North Pacific albacore (Thunnus alalunga): Implications for stock assessment

2013 ◽  
Vol 147 ◽  
pp. 55-62 ◽  
Author(s):  
R.J. David Wells ◽  
Suzanne Kohin ◽  
Steven L.H. Teo ◽  
Owyn E. Snodgrass ◽  
Koji Uosaki
Keyword(s):  
North Pacific ◽  
Stock Assessment ◽  
Age And Growth ◽  
Thunnus Alalunga

scholarly journals PRODUKSI DAN PRODUKTIVITAS TUNA OLEH KAPAL TUNA LONGLINE YANG BERBASIS DI PPN PALABUHANRATU

2020 ◽  
Vol 10 (2) ◽  
pp. 173-181
Author(s):  
Mohammad Imron ◽  
Roza Yusfiandayani ◽  
Mulyono S. Baskoro
Keyword(s):  
Stock Assessment ◽  
Unit Effort ◽  
Thunnus Obesus ◽  
Thunnus Alalunga ◽  
Tuna Longline

Produktivitas penangkapan tuna dapat dilihat dari produksi penangkapan yang didaratkan di pelabuhan (landing) per upaya penangkapan (effort). Pelabuhan Perikanan Nusantara (PPN) Palabuhanratu menjadi salah satu pelabuhan perikanan yang aktivitas perikanannya tergolong aktif di wilayah pesisir selatan Pulau Jawa dan menjadi salah satu pusat kegiatan perikanan tangkap di wilayah Propinsi Jawa Barat. Produksi ikan tuna di PPN Palabuhanratu mengalami peningkatan yang cukup signifikan dari tahun 2010 sampai tahun 2019. Pada tahun 2014-2018 produksi ikan tuna di PPN Palabuhanratu mengalami penurunan yang cukup drastis. Pada tahun 2019, produksi kembali meningkat menjadi 1,091,612 ton. Landing Per Unit Effort (LPUE) digunakan dalam penelitian perikanan untuk mengindikasikan kelimpahan sumberdaya yang digunakan untuk melakukan stock assessment ketika mengestimasi kelimpahan relatif dari suatu spesies yang dieksploitasi. Komposisi hasil tangkapan tuna oleh kapal tuna longline terdiri atas ikan tuna sirip kuning (Thunnus albacores), tuna mata besar (Thunnus obesus), ikan tuna albakor (Thunnus alalunga). Produksi tuna yang didaratkan di PPN Palabuhanratu dari tahun 2010-2019 mengalami fluktuasi. Pada tahun 2010 produksi ikan tuna sirip kuning sebesar 444,952 ton, ikan tuna mata besar sebesar 979,189 ton, ikan tuna albakor sebesar 122,671 ton. Pada tahun 2019 produksi ikan tuna sirip kuning sebesar 617,992 ton, ikan tuna mata besar sebesar 240,487 ton, ikan tuna albakor sebesar 233,133 ton. Produktivitas tertinggi terjadi pada ikan tuna sirip kuning tahun 2014 dengan nilai LPUE sebesar 6,09 dengan produksi sebesar  2,448,171 ton dengan jumlah effort 402. Produktivitas mengalami fluktuasi setiap tahunnya.

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