Depensation in fish stocks: a hierarchic Bayesian meta-analysis

1997 ◽  
Vol 54 (9) ◽  
pp. 1976-1984 ◽  
Author(s):  
M Liermann ◽  
R Hilborn
Keyword(s):  
Probability Distribution ◽  
Hierarchical Modeling ◽  
Meta Analysis ◽  
Data Set ◽  
Fish Stocks ◽  
Stock Recruitment ◽  
Taxonomic Groups ◽  
Bayesian Prior ◽  
Different Levels

The probability of different levels of depensation within four taxonomic groups was calculated using a Bayesian technique called hierarchical modeling. With this method we combined spawner-recruit data from many stocks within a taxon to estimate the distribution describing the variability of depensation within that taxon. The spawner-recruit model we use allows for both depensation (lower than expected recruits at low population levels) and hypercompensation (where recruits are higher than expected at low population levels). The end product of our analysis is a probability distribution that can be used as a Bayesian prior when analyzing a new data set. We examined four taxonomic groups (the salmonids, gadiforms, clupeiforms and pleuronectiforms) and found that, for all of the taxa, the most likely values fell close to or within the range of no depensation. However, because the distributions were very broad we suggest that analysis of stock recruitment data should incorporate spawner-recruit curves that include the possibility of depensation and hypercompensation.

scholarly journals Quirky patterns in time-series of estimates of recruitment could be artefacts

2014 ◽  
Vol 72 (1) ◽  
pp. 111-116 ◽  
Author(s):  
M. Dickey-Collas ◽  
N. T. Hintzen ◽  
R. D. M. Nash ◽  
P-J. Schön ◽  
M. R. Payne
Keyword(s):  
Time Series ◽  
Stock Assessment ◽  
Meta Analysis ◽  
Reference Points ◽  
Fish Stocks ◽  
Stock Recruitment ◽  
Modelling Assumptions ◽  
Meta Analyses ◽  
Recruitment Model ◽  
Recruitment Time

Abstract The accessibility of databases of global or regional stock assessment outputs is leading to an increase in meta-analysis of the dynamics of fish stocks. In most of these analyses, each of the time-series is generally assumed to be directly comparable. However, the approach to stock assessment employed, and the associated modelling assumptions, can have an important influence on the characteristics of each time-series. We explore this idea by investigating recruitment time-series with three different recruitment parameterizations: a stock–recruitment model, a random-walk time-series model, and non-parametric “free” estimation of recruitment. We show that the recruitment time-series is sensitive to model assumptions and this can impact reference points in management, the perception of variability in recruitment and thus undermine meta-analyses. The assumption of the direct comparability of recruitment time-series in databases is therefore not consistent across or within species and stocks. Caution is therefore required as perhaps the characteristics of the time-series of stock dynamics may be determined by the model used to generate them, rather than underlying ecological phenomena. This is especially true when information about cohort abundance is noisy or lacking.

Estimating steepness of the stock-recruitment relationship in Chilean fish stocks using meta-analysis

2018 ◽  
Vol 200 ◽  
pp. 61-67 ◽  
Author(s):  
Rodrigo Wiff ◽  
Andrés Flores ◽  
Sergio Neira ◽  
Bruno Caneco
Keyword(s):  
Meta Analysis ◽  
Fish Stocks ◽  
Stock Recruitment

Towards a new recruitment paradigm for fish stocks

1997 ◽  
Vol 54 (4) ◽  
pp. 969-977 ◽  
Author(s):  
D J Gilbert
Keyword(s):  
Time Series ◽  
Null Hypothesis ◽  
Meta Analysis ◽  
Fish Stock ◽  
Test Statistic ◽  
Mean Values ◽  
Fish Stocks ◽  
Independent Events ◽  
Alternative Hypotheses ◽  
Stock Recruitment

The stock recruitment paradigm involves the hypothesis that recruitment (R) to a fish stock is positively related to the spawning stock biomass (SSB) of the stock, at low SSB. I propose a ``recruitment states'' hypothesis wherein R is independent of SSB but has different mean values during successive periods. Meta-analysis was used to test the null hypothesis that recruitment is a series of random, independent events, against these two alternative hypotheses, for 153 marine spawning bony fish stocks and 31 salmonid stocks. A test statistic for the stock recruitment paradigm, based on estimating derivatives from the first differences of the time series, was not significant for the marine stocks. The null hypothesis was rejected for the salmonid stocks. Recruitment states models significantly fitted time series for the marine stocks. Ricker models also significantly fitted these data, conflicting with the derivatives test result. However, because SSB is dependent on R, lagged by the age at maturity, a period in a low recruitment state would tend to lead to a period of low SSB. Therefore, the significance of the fit to the Ricker model may have been spurious. The recruitment states model best explained the meta-dataset for the marine stocks.

The Negative Intelligence–Religiosity Relation: New and Confirming Evidence

2019 ◽  
Vol 46 (6) ◽  
pp. 856-868 ◽  
Author(s):  
Miron Zuckerman ◽  
Chen Li ◽  
Shengxin Lin ◽  
Judith A. Hall
Keyword(s):  
Cognitive Ability ◽  
Cognitive Style ◽  
Religious Beliefs ◽  
Meta Analysis ◽  
Theoretical Work ◽  
Data Set ◽  
Analytic Cognitive Style

Zuckerman et al. (2013) conducted a meta-analysis of 63 studies that showed a negative intelligence–religiosity relation (IRR). As more studies have become available and because some of Zuckerman et al.’s (2013) conclusions have been challenged, we conducted a new meta-analysis with an updated data set of 83 studies. Confirming previous conclusions, the new analysis showed that the correlation between intelligence and religious beliefs in college and noncollege samples ranged from −.20 to −.23. There was no support for mediation of the IRR by education but there was support for partial mediation by analytic cognitive style. Thus, one possible interpretation for the IRR is that intelligent people are more likely to use analytic style (i.e., approach problems more rationally). An alternative (and less interesting) reason for the mediation is that tests of both intelligence and analytic style assess cognitive ability. Additional empirical and theoretical work is needed to resolve this issue.

scholarly journals Indicators of AEI Applied to the Delaware Estuary

2002 ◽  
Vol 2 ◽  
pp. 169-189 ◽  
Author(s):  
Lawrence W. Barnthouse ◽  
Douglas G. Heimbuch ◽  
Vaughn C. Anthony ◽  
Ray W. Hilborn ◽  
Ransom A. Myers
Keyword(s):  
Meta Analysis ◽  
Juvenile Fish ◽  
Fish Populations ◽  
Weight Of Evidence ◽  
Data Sets ◽  
Delaware Estuary ◽  
Fish Stocks ◽  
Trends Analysis ◽  
Definition Of

We evaluated the impacts of entrainment and impingement at the Salem Generating Station on fish populations and communities in the Delaware Estuary. In the absence of an agreed-upon regulatory definition of “adverse environmental impact” (AEI), we developed three independent benchmarks of AEI based on observed or predicted changes that could threaten the sustainability of a population or the integrity of a community.Our benchmarks of AEI included: (1) disruption of the balanced indigenous community of fish in the vicinity of Salem (the “BIC” analysis); (2) a continued downward trend in the abundance of one or more susceptible fish species (the “Trends” analysis); and (3) occurrence of entrainment/impingement mortality sufficient, in combination with fishing mortality, to jeopardize the future sustainability of one or more populations (the “Stock Jeopardy” analysis).The BIC analysis utilized nearly 30 years of species presence/absence data collected in the immediate vicinity of Salem. The Trends analysis examined three independent data sets that document trends in the abundance of juvenile fish throughout the estuary over the past 20 years. The Stock Jeopardy analysis used two different assessment models to quantify potential long-term impacts of entrainment and impingement on susceptible fish populations. For one of these models, the compensatory capacities of the modeled species were quantified through meta-analysis of spawner-recruit data available for several hundred fish stocks.All three analyses indicated that the fish populations and communities of the Delaware Estuary are healthy and show no evidence of an adverse impact due to Salem. Although the specific models and analyses used at Salem are not applicable to every facility, we believe that a weight of evidence approach that evaluates multiple benchmarks of AEI using both retrospective and predictive methods is the best approach for assessing entrainment and impingement impacts at existing facilities.

scholarly journals ESTUDO DA PRODUÇÃO DE ENERGIA EÓLICA A PARTIR DE MEDIÇÕES ANEMOMÉTRICAS DA ESTAÇÃO DE CRAÍBAS/AL E SIMULAÇÕES COM O MODELO WASP

2016 ◽  
Vol 38 ◽  
pp. 477
Author(s):  
Thays Paes de Oliveira ◽  
Rosiberto Salustiano da Silva Junior ◽  
Roberto Fernando Fonseca Lyra ◽  
Sandro Correia Holanda
Keyword(s):  
Power Generation ◽  
Probability Distribution ◽  
Wind Turbine ◽  
Wind Direction ◽  
Wind Farm ◽  
Scale Parameters ◽  
Wind Potential ◽  
Generation Capacity ◽  
Intensity Density ◽  
Different Levels

Wind energy is seen as one of the promising generation of electricity, as a source of cheap and renewable, is benefit to reduce the environmental impacts of the dam. Along with the hydroelectric networks, the energy produced by the wind will help to increase power generation capacity in the country. That from speed data and direction municipality Wind Craíbas in the corresponding period 2014 - 2015, estimated the wind potential of the region. The tool used in the treatment of the collected data was the Wasp, making simulations of three different levels of measurement, producing a fictitious wind farm with powerful wind turbine. With the model, WASP helps estimate the probability distribution of Weibull and scale parameters A and K. he predominant wind direction is southeast and the best wind power and intensity density levels took place in 70m and 100m high , with about 201 W / m² and 243 W / m² respectively. But when evalua ted the inclusion of fictitious wind farm, the best use happened at 100m tall with production around 73.039 GWh , which can be attributed this improvement to increased efficiency of the wind turbine used in the simulation.

scholarly journals Do Attentional Lapses Account for the Worst Performance Rule?

2021 ◽  
Vol 10 (1) ◽  
pp. 2
Author(s):  
Christoph Löffler ◽  
Gidon T. Frischkorn ◽  
Jan Rummel ◽  
Dirk Hagemann ◽  
Anna-Lena Schubert
Keyword(s):  
Time Distribution ◽  
Hierarchical Modeling ◽  
Reaction Times ◽  
Empirical Support ◽  
Response Time Distribution ◽  
Self Report ◽  
Task Completion ◽  
Published Data ◽  
Data Set ◽  
Attentional Lapses

The worst performance rule (WPR) describes the phenomenon that individuals’ slowest responses in a task are often more predictive of their intelligence than their fastest or average responses. To explain this phenomenon, it was previously suggested that occasional lapses of attention during task completion might be associated with particularly slow reaction times. Because less intelligent individuals should experience lapses of attention more frequently, reaction time distribution should be more heavily skewed for them than for more intelligent people. Consequently, the correlation between intelligence and reaction times should increase from the lowest to the highest quantile of the response time distribution. This attentional lapses account has some intuitive appeal, but has not yet been tested empirically. Using a hierarchical modeling approach, we investigated whether the WPR pattern would disappear when including different behavioral, self-report, and neural measurements of attentional lapses as predictors. In a sample of N = 85, we found that attentional lapses accounted for the WPR, but effect sizes of single covariates were mostly small to very small. We replicated these results in a reanalysis of a much larger previously published data set. Our findings render empirical support to the attentional lapses account of the WPR.

A meta-analysis of the variables related to turnover intention among IT personnel

Kybernetes ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ahmet Hakan Özkan
Keyword(s):  
Job Satisfaction ◽  
Organizational Commitment ◽  
Effect Size ◽  
Turnover Intention ◽  
Meta Analysis ◽  
It Industry ◽  
Data Set ◽  
Content Type ◽  
It Personnel ◽  
The Relationship

PurposeThis study aims to investigate the relationships between job satisfaction, organizational commitment and turnover intention of information technology (IT) personnel.Design/methodology/approach3,844 studies which are published between 1998 and 2019 are screened on ScienceDirect, Scopus and ProQuest databases. 10,523 subjects formed the first data set regarding the relationship between job satisfaction and turnover intention, 7,903 subjects formed the second data set regarding the relationship between organizational commitment and turnover intention, 843 subjects formed the third data set regarding the relationship between empowerment and turnover intention, and 3,430 subjects formed the fourth data set regarding the relationship between job satisfaction and organizational commitment.FindingsResults showed that the effect size of the relationship between job satisfaction and organizational commitment is the strongest (r = 0.59). The effect size of the relationship between job satisfaction and turnover intention (r = −0.50), and the effect size of the relationship between organizational commitment and turnover intention r = −0.51) were also large. But the effect size of the relationship between empowerment and turnover intention was medium (r = −0.34).Originality/valueThis study is rare, and it can be used by the managers working in the IT industry.

A systematical approach to classification problems with feature space heterogeneity

Kybernetes ◽  
2019 ◽  
Vol 48 (9) ◽  
pp. 2006-2029
Author(s):  
Hongshan Xiao ◽  
Yu Wang
Keyword(s):  
Factor Analysis ◽  
Meta Analysis ◽  
Feature Space ◽  
Classification Performance ◽  
Classification Algorithm ◽  
Significant Feature ◽  
Data Sets ◽  
Data Set ◽  
Classification Techniques ◽  
Content Type

Purpose Feature space heterogeneity exists widely in various application fields of classification techniques, such as customs inspection decision, credit scoring and medical diagnosis. This paper aims to study the relationship between feature space heterogeneity and classification performance. Design/methodology/approach A measurement is first developed for measuring and identifying any significant heterogeneity that exists in the feature space of a data set. The main idea of this measurement is derived from a meta-analysis. For the data set with significant feature space heterogeneity, a classification algorithm based on factor analysis and clustering is proposed to learn the data patterns, which, in turn, are used for data classification. Findings The proposed approach has two main advantages over the previous methods. The first advantage lies in feature transform using orthogonal factor analysis, which results in new features without redundancy and irrelevance. The second advantage rests on samples partitioning to capture the feature space heterogeneity reflected by differences of factor scores. The validity and effectiveness of the proposed approach is verified on a number of benchmarking data sets. Research limitations/implications Measurement should be used to guide the heterogeneity elimination process, which is an interesting topic in future research. In addition, to develop a classification algorithm that enables scalable and incremental learning for large data sets with significant feature space heterogeneity is also an important issue. Practical implications Measuring and eliminating the feature space heterogeneity possibly existing in the data are important for accurate classification. This study provides a systematical approach to feature space heterogeneity measurement and elimination for better classification performance, which is favorable for applications of classification techniques in real-word problems. Originality/value A measurement based on meta-analysis for measuring and identifying any significant feature space heterogeneity in a classification problem is developed, and an ensemble classification framework is proposed to deal with the feature space heterogeneity and improve the classification accuracy.

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