scholarly journals Artificial upward trends in Greek marine landings: a case of presentist bias in European fisheries

2019 ◽  
Author(s):  
Athanassios C. Tsikliras ◽  
Donna Dimarchopoulou ◽  
Androniki Pardalou
Keyword(s):  
Time Series ◽  
Small Scale ◽  
Marine Fisheries ◽  
Upward Trend ◽  
Stock Assessments ◽  
Fisheries Policy ◽  
Catch Reconstruction ◽  
Combined Time Series ◽  
Original Time ◽  
Analytical Errors

AbstractAccording to the official landings as reported by the international databases for Greece, the declining trend of the Greek marine fisheries landings that had been continuous since the mid 1990s has been reversed during the last two years, with the total marine fisheries landings showing elevated catches after 2016. We claim that this upward trend is an artifact that is attributed to the combined reporting of the landings of additional fleets since 2016 that had been separately reported before and resulted in 20-30% inflation of the landings. In 2016, the Greek statistical authorities included the landings of 10 000 small-scale coastal vessels with engine horsepower lower than 20 HP together with the remaining coastal vessels, purse-seiners and trawlers whose landings formed the official reported Greek marine fisheries landings from 1970 to 2015. We acknowledge that this act of partial catch reconstruction improved the resolution of the landings and the officially reported values are now more realistic. However, the artificial, albeit inadvertent, inflation of the official Greek marine fisheries landings as they appear in international databases is a clear case of ‘presentist bias’ and may distort stock assessments and ecosystem modeling. As the currently misleading data stand, they are cause for substantial misinterpretation and analytical errors that can influence fisheries policy and have serious implications for fisheries management. We suggest that researchers should refrain from using the combined time-series and that a correction should be applied to the original time series (1970-2015) to account for the entire small-scale coastal fleet.

Small-scale marine fisheries policy in Vietnam

Marine Policy ◽  
2009 ◽  
Vol 33 (2) ◽  
pp. 419-428 ◽  
Author(s):  
Robert Pomeroy ◽  
Kim Anh Thi Nguyen ◽  
Ha Xuan Thong
Keyword(s):  
Small Scale ◽  
Marine Fisheries ◽  
Fisheries Policy

scholarly journals Reconstruction of Marine Fisheries Catches for the Republic of Malta (1950-2010)

2017 ◽  
Vol 18 (2) ◽  
pp. 241 ◽  
Author(s):  
M. KHALFALLAH ◽  
M. DIMECH ◽  
A. ULMAN ◽  
D. ZELLER ◽  
D. PAULY
Keyword(s):  
Small Scale ◽  
Statistical System ◽  
Recreational Fisheries ◽  
Marine Fisheries ◽  
Time Period ◽  
Catch Reconstruction ◽  
The 1950S ◽  
The Republic

 The marine fisheries catches of Malta were reconstructed for the period 1950-2014, including for reported and previously unreported commercial large- and small-scale catches, unmonitored fisheries catches, i.e., subsistence and recreational fisheries, as well as major discards. The present study updates and improves a previous catch reconstruction for Malta for the 1950-2010 time period. Reconstructed marine fisheries catches for Malta are nearly 1.3 times the official landings reported by the FAO and national authorities on behalf of Malta, increasing from around 1,200 t·year-1 in the 1950s to 3,700 t·year-1 in the 2010s. The discrepancy between reported and reconstructed total catches is mostly due to the subsistence catches estimated, which here consist exclusively of on-board consumption and take-home catch of commercial fishers. While the Maltese fisheries statistical system includes procedures to estimate ‘unmonitored’ commercial landings, this contribution documents that it would be beneficial to also account for non-commercial catches.

scholarly journals Superiority of Hybrid Soft Computing Models in Daily Suspended Sediment Estimation in Highly Dynamic Rivers

2021 ◽  
Vol 13 (2) ◽  
pp. 542
Author(s):  
Tarate Suryakant Bajirao ◽  
Pravendra Kumar ◽  
Manish Kumar ◽  
Ahmed Elbeltagi ◽  
Alban Kuriqi
Keyword(s):  
Time Series ◽  
Wavelet Transform ◽  
Suspended Sediment ◽  
River Basin ◽  
Time Series Data ◽  
Series Data ◽  
Discrete Wavelet ◽  
Original Time Series ◽  
Inference System ◽  
Original Time

Estimating sediment flow rate from a drainage area plays an essential role in better watershed planning and management. In this study, the validity of simple and wavelet-coupled Artificial Intelligence (AI) models was analyzed for daily Suspended Sediment (SSC) estimation of highly dynamic Koyna River basin of India. Simple AI models such as the Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) were developed by supplying the original time series data as an input without pre-processing through a Wavelet (W) transform. The hybrid wavelet-coupled W-ANN and W-ANFIS models were developed by supplying the decomposed time series sub-signals using Discrete Wavelet Transform (DWT). In total, three mother wavelets, namely Haar, Daubechies, and Coiflets were employed to decompose original time series data into different multi-frequency sub-signals at an appropriate decomposition level. Quantitative and qualitative performance evaluation criteria were used to select the best model for daily SSC estimation. The reliability of the developed models was also assessed using uncertainty analysis. Finally, it was revealed that the data pre-processing using wavelet transform improves the model’s predictive efficiency and reliability significantly. In this study, it was observed that the performance of the Coiflet wavelet-coupled ANFIS model is superior to other models and can be applied for daily SSC estimation of the highly dynamic rivers. As per sensitivity analysis, previous one-day SSC (St-1) is the most crucial input variable for daily SSC estimation of the Koyna River basin.

An essay on ‘combined’ time series processes

2000 ◽  
Vol 19 (1) ◽  
pp. 77-93 ◽  
Author(s):  
Christopher Wlezien
Keyword(s):  
Time Series ◽  
Combined Time Series

scholarly journals The Systematic Bias of Entropy Calculation in the Multi-Scale Entropy Algorithm

Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 659
Author(s):  
Jue Lu ◽  
Ze Wang
Keyword(s):  
Time Series ◽  
Standard Deviation ◽  
Coarse Graining ◽  
Sample Entropy ◽  
Systematic Bias ◽  
Original Time Series ◽  
Multi Scale ◽  
Calculation Results ◽  
Surrogate Measure ◽  
Original Time

Entropy indicates irregularity or randomness of a dynamic system. Over the decades, entropy calculated at different scales of the system through subsampling or coarse graining has been used as a surrogate measure of system complexity. One popular multi-scale entropy analysis is the multi-scale sample entropy (MSE), which calculates entropy through the sample entropy (SampEn) formula at each time scale. SampEn is defined by the “logarithmic likelihood” that a small section (within a window of a length m) of the data “matches” with other sections will still “match” the others if the section window length increases by one. “Match” is defined by a threshold of r times standard deviation of the entire time series. A problem of current MSE algorithm is that SampEn calculations at different scales are based on the same matching threshold defined by the original time series but data standard deviation actually changes with the subsampling scales. Using a fixed threshold will automatically introduce systematic bias to the calculation results. The purpose of this paper is to mathematically present this systematic bias and to provide methods for correcting it. Our work will help the large MSE user community avoiding introducing the bias to their multi-scale SampEn calculation results.

HIGHER ORDER COMPLEXITY OF TIME SERIES

2004 ◽  
Vol 14 (08) ◽  
pp. 2979-2990 ◽  
Author(s):  
FANJI GU ◽  
ENHUA SHEN ◽  
XIN MENG ◽  
YANG CAO ◽  
ZHIJIE CAI
Keyword(s):  
Time Series ◽  
Mental Arithmetic ◽  
Higher Order ◽  
Second Order ◽  
Stationary Time Series ◽  
Eeg Signals ◽  
Eyes Closed ◽  
Logistic Mapping ◽  
Original Time ◽  
Different Order

A concept of higher order complexity is proposed in this letter. If a randomness-finding complexity [Rapp & Schmah, 2000] is taken as the complexity measure, the first-order complexity is suggested to be a measure of randomness of the original time series, while the second-order complexity is a measure of its degree of nonstationarity. A different order is associated with each different aspect of complexity. Using logistic mapping repeatedly, some quasi-stationary time series were constructed, the nonstationarity degree of which could be expected theoretically. The estimation of the second-order complexity of these time series shows that the second-order complexities do reflect the degree of nonstationarity and thus can be considered as its indicator. It is also shown that the second-order complexities of the EEG signals from subjects doing mental arithmetic are significantly higher than those from subjects in deep sleep or resting with eyes closed.

Study of Thermal Activity in The Mixing Layer During First Generated Single Cloud by Using Combined Observation From Boundary Layer Radar, Doppler Lidar and Time Lapse Camera 

2021 ◽  
Author(s):  
Ginaldi Ari Nugroho ◽  
Kosei Yamaguchi ◽  
Eiichi Nakakita ◽  
Masayuki K. Yamamoto ◽  
Seiji Kawamura ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Time Series ◽  
Atmospheric Boundary Layer ◽  
Vertical Velocity ◽  
Time Lapse ◽  
Small Scale ◽  
Thermal Activity ◽  
Doppler Lidar ◽  
Cumulus Cloud ◽  
Scale Perturbation

<p>Detailed observation of small scale perturbation in the atmospheric boundary layer during the first generated cumulus cloud are conducted. Our target is to study this small scale perturbation, especially related to the thermal activity at the first generated cumulus cloud. The observation is performed during the daytime on August 17, 2018, and September 03, 2018. Location is focused in the urban area of Kobe, Japan. High-resolution instruments such as Boundary Layer Radar, Doppler Lidar, and Time Lapse camera are used in this observation. Boundary Layer Radar, and Doppler Lidar are used for clear air observation. Meanwhile Time Lapse Camera are used for cloud existence observation. The atmospheric boundary layer structure is analyzed based on vertical velocity profile, variance, skewness, and estimated atmospheric boundary layer height. Wavelet are used to observe more of the period of the thermal activity. Furthermore, time correlation between vertical velocity time series from height 0.3 to 2 km and image pixel of generated cloud time series are also discussed in this study.</p>

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