Estimating rates of fish movement from tag recoveries: conditioning by recapture

2002 ◽  
Vol 59 (6) ◽  
pp. 1054-1064 ◽  
Author(s):  
Richard McGarvey ◽  
John E Feenstra
Keyword(s):  
South Australia ◽  
Total Mortality ◽  
Transition Matrices ◽  
Release Rates ◽  
Short Term ◽  
Time Step ◽  
Fish Stocks ◽  
Movement Rates ◽  
Tag Loss ◽  
Spatial Cell

Tag-recovery data are commonly used to estimate movement rates of fish stocks. Fishers report tagged fish found in their catch; however, not all recoveries are reported to fishery researchers and the rate of nonreporting is usually not known or is imprecisely estimated. To obviate the problem of nonreporting, an estimator of movement rates is proposed that does not use the number originally tagged but is fitted to the relative proportions recaptured in each cell in each time step subsequent to release. Rates of processes that occur in the tag-release spatial cell, such as short-term tagging mortality and survival, cancel from the predicted likelihood probabilities. Similarly, rates in the recapture cell for processes of ongoing tag loss, natural mortality, and tag nonreporting, if they can be reasonably approximated as uniform across cells, also cancel. Estimators are presented assuming one of two levels of auxiliary fishery inputs: (i) total mortality by cell or time step, or (ii) if mortality can be approximated as spatially uniform, effort totals in each cell, by time step. Yearly movement transition matrices were estimated for King George whiting (Sillaginodes punctata) in South Australia among 11 spatial cells from tag recoveries gathered over a period of three decades.

Oil pollution and fisheries

1982 ◽  
Vol 297 (1087) ◽  
pp. 401-411 ◽  
Keyword(s):  
Water Quality ◽  
Adverse Effects ◽  
Oil Pollution ◽  
Coastal Areas ◽  
Short Term ◽  
Fish Stocks ◽  
Local Impacts ◽  
Reduced Water ◽  
Long Term Adverse Effects

The term ‘pollution’ is taken in its broadest sense and effects are recognized to be due to interference, tainting and toxicity. Each of these types of impact is discussed and assessed. It is concluded that no long-term adverse effects on fish stocks can be attributed to oil but that local impacts can be extremely damaging in the short term and that produce from specific localities can be tainted and unmarketable for long periods. In some coastal areas oil can be one among several contributors to reduced water quality, and the implications of this are discussed.

scholarly journals Near Real-Time Global Solar Radiation Forecasting at Multiple Time-Step Horizons Using the Long Short-Term Memory Network

Energies ◽  
2020 ◽  
Vol 13 (14) ◽  
pp. 3517 ◽  
Author(s):  
Anh Ngoc-Lan Huynh ◽  
Ravinesh C. Deo ◽  
Duc-Anh An-Vo ◽  
Mumtaz Ali ◽  
Nawin Raj ◽  
...  
Keyword(s):  
Deep Learning ◽  
Solar Radiation ◽  
Short Term Memory ◽  
Global Solar Radiation ◽  
Multiple Time ◽  
Short Term ◽  
Time Step ◽  
Term Memory ◽  
Multiple Time Step ◽  
Long Short Term Memory

This paper aims to develop the long short-term memory (LSTM) network modelling strategy based on deep learning principles, tailored for the very short-term, near-real-time global solar radiation (GSR) forecasting. To build the prescribed LSTM model, the partial autocorrelation function is applied to the high resolution, 1 min scaled solar radiation dataset that generates statistically significant lagged predictor variables describing the antecedent behaviour of GSR. The LSTM algorithm is adopted to capture the short- and the long-term dependencies within the GSR data series patterns to accurately predict the future GSR at 1, 5, 10, 15, and 30 min forecasting horizons. This objective model is benchmarked at a solar energy resource rich study site (Bac-Ninh, Vietnam) against the competing counterpart methods employing other deep learning, a statistical model, a single hidden layer and a machine learning-based model. The LSTM model generates satisfactory predictions at multiple-time step horizons, achieving a correlation coefficient exceeding 0.90, outperforming all of the counterparts. In accordance with robust statistical metrics and visual analysis of all tested data, the study ascertains the practicality of the proposed LSTM approach to generate reliable GSR forecasts. The Diebold–Mariano statistic test also shows LSTM outperforms the counterparts in most cases. The study confirms the practical utility of LSTM in renewable energy studies, and broadly in energy-monitoring devices tailored for other energy variables (e.g., hydro and wind energy).

Global Governance of Deep-Sea Fisheries

2004 ◽  
Vol 19 (3) ◽  
pp. 289-298 ◽  
Author(s):  
Moritaka Hayashi
Keyword(s):  
Global Governance ◽  
Deep Sea ◽  
Law Of The Sea ◽  
Governance System ◽  
Legal Regime ◽  
Short Term ◽  
Fish Stocks ◽  
The Law ◽  
Global Agreement ◽  
High Seas

AbstractThis article considers the gaps in the existing legal regime on deep-sea fisheries and explores a more effective global governance system. It is proposed that a new global agreement, modeled on the 1995 UN Fish Stocks Agreement, be negotiated covering deep-sea stocks as well as other high seas resources, so that all fisheries on the high seas may be covered. The proposed agreement would complete the gaps in high seas fisheries regime and serve as an effective link between the UN Convention on the Law of the Sea and regional fisheries bodies. As a short-term measure, FAO should prepare a set of guidelines covering all types of deep-sea fisheries, including shared and transboundary stocks as well as discrete high seas stocks. In addition, FAO's Committee on Fisheries should be strengthened in its global governance role, including co-ordination of all regional fisheries bodies

Abstract P290: Short-term Weight Change and Long-term Mortality in the Trials of Hypertension Prevention

Circulation ◽  
2016 ◽  
Vol 133 (suppl_1) ◽  
Author(s):  
Nancy R Cook ◽  
Lawrence J Appel ◽  
Paul K Whelton
Keyword(s):  
Weight Loss ◽  
Weight Change ◽  
Phase 1 ◽  
Total Mortality ◽  
Weight Loss Intervention ◽  
Phase 2 ◽  
Short Term ◽  
Hypertension Prevention ◽  
Long Term Mortality

Introduction: Although weight loss has favorable effects on intermediate outcomes, such as blood pressure and insulin resistance, few studies have examined its effects on long-term outcomes including total mortality. Methods: In the Trials of Hypertension Prevention (TOHP) individuals aged 30-54 years with high normal BP were randomized to a weight loss intervention, to one of several other lifestyle or dietary supplement interventions, or to usual care. All participants from Phase 1 (1987-90) and Phase 2 (1990-5) were followed for mortality through 2013. The association of weight change during any of the interventions with long-term mortality up to 18-24 years after the trial periods was examined among 3828 participants who fell into a high baseline weight stratum, defined as body mass index at least 26 kg/m2 in men and 24 kg/m2 in women. Results and Conclusions: There were 1477 high-weight participants in Phase 1 and 2351 in Phase 2, of whom 21% and 50%, respectively, were assigned to a weight loss intervention. Overall, mean weight change during the trial period was -1.8 lbs (-0.8% of baseline body weight) over 1.5 years in Phase 1 and 1.6 lbs (0.8%) over 3-4 years in Phase 2. A total of 556 (15%) lost > 5%, 1,101 (29%) lost <=5%, 1,567 (41%) gained less than 5%, and 604 (16%) gained > 5% in body weight. Corresponding hazard ratios (HRs) for total mortality were 0.82 (95% confidence interval (CI)=0.57-1.18), 0.94 (95% CI=0.72-1.23), 1.00 (reference), and 1.29 (95% CI=0.92-1.80) (p-trend = 0.046). There was a direct linear relationship with percent change in weight during the trial period and later mortality (HR=1.14 per 5% change, 95% CI=1.02-1.28, p=0.019). This association persisted throughout the course of mortality follow-up (Figure). In these healthy individuals taking part in lifestyle and nutrition supplement trials , short-term weight change was directly associated with mortality about two decades later. These results are consistent with a long-term beneficial effect of presumed intentional weight loss on total mortality.

Accumulation of Iron, Manganese, Zinc and Cadmium by the Australian Freshwater Mussel Velesunio ambiguus (Phillipi) and Its Potential as a Biological Monitor

1979 ◽  
Vol 30 (6) ◽  
pp. 741 ◽  
Author(s):  
WG Jones ◽  
KF Walker
Keyword(s):  
Body Weight ◽  
South Australia ◽  
Freshwater Mussel ◽  
Short Term ◽  
Manganese Zinc ◽  
Systematic Relationships ◽  
Iron Manganese ◽  
Australian Freshwater ◽  
Iron Concentrations

The accumulation of iron, manganese, zinc and cadmium by freshwater mussels in the River Murray, South Australia, and their response to changes in environmental iron concentrations are considered. Metal loads varied markedly between individuals from the same population. The variability is accounted for partly by systematic relationships between metal loads and body weight and age, but not sex. The distribution of metals between the major organs is discussed, but the analysis of separate organs showed no advantage for biological monitoring. Comparisons between iron concentrations in river water and in mussels showed no clear correspondence. The study suggests that V. ambiguus may not be a good short-term monitor of iron, but still may have potential as a long-term and site-comparison monitor of metals. once inherent variability is taken into account.

Short-Term Traffic Volume Forecast Method Based On CNN-LSTM-At

2021 ◽  
Author(s):  
Xing Xu ◽  
Chengxing Liu ◽  
Yun Zhao ◽  
Xuyang Yu ◽  
Xiang Wu
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Forecast Model ◽  
Traffic Volume ◽  
Model Verification ◽  
Short Term ◽  
Time Step ◽  
Sequence Prediction ◽  
Traffic Flow Prediction ◽  
Flow Prediction

Abstract In order to tackle existing traffic flow prediction problem, a Traffic Volume Forecast Model based on deep learning is designed. The model implements Convolutional Neural Network (CNN) to extract spatial matrix information, uses long and short-term neural network (LSTM) for sequence prediction, appends attention mechanism to time step on LSTM, and assigns weights to different time steps. By implementing model verification on the Chengdu taxi dataset, dividing data into various categories, cross validating different categories of data, and comparing the model with other models, it is concluded that the CNN-LSTM-At network model proposed in this article has higher accuracy compared with traditional network model.

scholarly journals CF-LSTM: Cascaded Feature-Based Long Short-Term Networks for Predicting Pedestrian Trajectory

2020 ◽  
Vol 34 (07) ◽  
pp. 12541-12548
Author(s):  
Yi Xu ◽  
Jing Yang ◽  
Shaoyi Du
Keyword(s):  
Trajectory Prediction ◽  
Short Term ◽  
Time Step ◽  
Social Rules ◽  
Human Interactions ◽  
Dynamic Velocity ◽  
Benchmark Datasets ◽  
Feature Based ◽  
Velocity Information ◽  
Previous Location

Pedestrian trajectory prediction is an important but difficult task in self-driving or autonomous mobile robot field because there are complex unpredictable human-human interactions in crowded scenarios. There have been a large number of studies that attempt to understand humans' social behavior. However, most of these studies extract location features from previous one time step while neglecting the vital velocity features. In order to address this issue, we propose a novel feature-cascaded framework for long short-term network (CF-LSTM) without extra artificial settings or social rules. In this framework, feature information from previous two time steps are firstly extracted and then integrated as a cascaded feature to LSTM, which is able to capture the previous location information and dynamic velocity information, simultaneously. In addition, this scene-agnostic cascaded feature is the external manifestation of complex human-human interactions, which can also effectively capture dynamic interaction information in different scenes without any other pedestrians' information. Experiments on public benchmark datasets indicate that our model achieves better performance than the state-of-the-art methods and this feature-cascaded framework has the ability to implicitly learn human-human interactions.

scholarly journals Estimating the mortality caused by great cormorant predation on fish stocks: pikeperch in the Archipelago Sea, northern Baltic Sea, as an example

2016 ◽  
Vol 73 (1) ◽  
pp. 84-93 ◽  
Author(s):  
Outi Heikinheimo ◽  
Pekka Rusanen ◽  
Katja Korhonen
Keyword(s):  
Age Groups ◽  
Total Mortality ◽  
Sensitivity Analyses ◽  
Fish Stock ◽  
Great Cormorant ◽  
Phalacrocorax Carbo ◽  
Fish Stocks ◽  
Archipelago Sea ◽  
Phalacrocorax Carbo Sinensis ◽  
The Impact

Estimates of the mortality rates caused by cormorants are needed to assess the impact on fish stock dynamics and fisheries. In this study, we calculated the annual instantaneous mortality caused by great cormorants (Phalacrocorax carbo sinensis) on young pikeperch (Sander lucioperca), using data from Archipelago Sea, southwestern coast of Finland. The pikeperch are vulnerable to cormorant predation mainly at the ages 2–4. The annual instantaneous mortality caused by cormorants was between 0.04 and 0.13, and the estimated effect on the pikeperch stock size at recruitment to the fishery ranged from 4% to 23%, respectively. The average annual cormorant-induced mortality accounted for 5%–34% of the total mortality in these age groups. The sensitivity analyses proved that the rates of mortality from other sources largely affect the estimated mortality from cormorant predation. In cases with strong fluctuations in the abundance of the prey fish stocks, ignoring the size and density dependence of the natural mortality may lead to overestimation of the importance of cormorants as competitors of fisheries.

scholarly journals Nearly Extensive Sequential Memory Lifetime Achieved by Coupled Nonlinear Neurons

2012 ◽  
Vol 24 (10) ◽  
pp. 2678-2699 ◽  
Author(s):  
Taro Toyoizumi
Keyword(s):  
Cognitive Processes ◽  
Neural Activity ◽  
Short Term Memory ◽  
Memory Performance ◽  
Linear Part ◽  
Network Size ◽  
Short Term ◽  
Time Step ◽  
Sequential Memory ◽  
The Brain

Many cognitive processes rely on the ability of the brain to hold sequences of events in short-term memory. Recent studies have revealed that such memory can be read out from the transient dynamics of a network of neurons. However, the memory performance of such a network in buffering past information has been rigorously estimated only in networks of linear neurons. When signal gain is kept low, so that neurons operate primarily in the linear part of their response nonlinearity, the memory lifetime is bounded by the square root of the network size. In this work, I demonstrate that it is possible to achieve a memory lifetime almost proportional to the network size, “an extensive memory lifetime,” when the nonlinearity of neurons is appropriately used. The analysis of neural activity revealed that nonlinear dynamics prevented the accumulation of noise by partially removing noise in each time step. With this error-correcting mechanism, I demonstrate that a memory lifetime of order [Formula: see text] can be achieved.

Short-term Forecasting in Marine Fish Stocks

1981 ◽  
Vol 38 (10) ◽  
pp. 1247-1254 ◽  
Author(s):  
Max Stocker ◽  
Ray Hilborn
Keyword(s):  
Time Series ◽  
Marine Fish ◽  
Management Practice ◽  
Model Fitting ◽  
Production Model ◽  
Catch Per Unit Effort ◽  
Short Term ◽  
Fish Stocks ◽  
Production Models ◽  
The Difference

The predictive power of stock production models and simple time series methods was considered for five marine fish stocks. The distinction between model fitting and forecasting future short-term catch is discussed, as is the difference between techniques to forecast short-term yield, and techniques to calculate long-term management practice. Fox's procedure for fitting Pella and Tomlinson's stock production model, Schnute's method for fitting Schaefer's model, and Gulland's method are all considered. We found that all methods except that of Gulland work well for some stocks, and the relative performance of the methods depends upon the exploitation history of the stock. In several instances one of the best forecasts of next year's catch per unit effort (CPUE) was the previous year's CPUE, emphasizing the fact that a good forecasting technique may have no utility in determining management policies.Key words: production models, catch and effort, fisheries, management, catch forecasting, time series

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