Random effect estimation of time-varying factors in Stock Synthesis

AbstractBiological processes such as fishery selectivity, natural mortality, and somatic growth can vary over time, but it is challenging to estimate the magnitude of time-variation of demographic parameters in population dynamics models, particularly when using penalized-likelihood estimation approaches. Random-effect approaches can estimate the variance, but are computationally infeasible or not implemented for many models and software packages. We show that existing models and software based on penalized-likelihood can be used to calculate the Laplace approximation to the marginal likelihood of parameters representing variability over time, and specifically demonstrate this approach via application to Stock Synthesis. Using North Sea cod and Pacific hake models as case studies, we show that this method has little bias in estimating variances for simulated data. It also provides a similar estimate of variability in hake recruitment (log-SD = 1.43) to that obtained from Markov chain Monte Carlo (MCMC) methods (log-SD = 1.68), and the method estimates a non-trivial magnitude (log-SD = 0.07) of variation in growth for North Sea cod. We conclude by discussing the generality of the proposed method and by recommending future research regarding its performance relative to MCMC, particularly when estimating multiple variances simultaneously.

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Markov-Modulated Continuous-Time Markov Chains to Identify Site- and Branch-Specific Evolutionary Variation in BEAST

Systematic Biology ◽

10.1093/sysbio/syaa037 ◽

2020 ◽

Vol 70 (1) ◽

pp. 181-189

Author(s):

Guy Baele ◽

Mandev S Gill ◽

Paul Bastide ◽

Philippe Lemey ◽

Marc A Suchard

Keyword(s):

High Performance ◽

Markov Models ◽

Marginal Likelihood ◽

Likelihood Estimation ◽

Phylogenetic Inference ◽

Time Variability ◽

Substitution Process ◽

Wide Range ◽

Markov Modulated ◽

Over Time

Abstract Markov models of character substitution on phylogenies form the foundation of phylogenetic inference frameworks. Early models made the simplifying assumption that the substitution process is homogeneous over time and across sites in the molecular sequence alignment. While standard practice adopts extensions that accommodate heterogeneity of substitution rates across sites, heterogeneity in the process over time in a site-specific manner remains frequently overlooked. This is problematic, as evolutionary processes that act at the molecular level are highly variable, subjecting different sites to different selective constraints over time, impacting their substitution behavior. We propose incorporating time variability through Markov-modulated models (MMMs), which extend covarion-like models and allow the substitution process (including relative character exchange rates as well as the overall substitution rate) at individual sites to vary across lineages. We implement a general MMM framework in BEAST, a popular Bayesian phylogenetic inference software package, allowing researchers to compose a wide range of MMMs through flexible XML specification. Using examples from bacterial, viral, and plastid genome evolution, we show that MMMs impact phylogenetic tree estimation and can substantially improve model fit compared to standard substitution models. Through simulations, we show that marginal likelihood estimation accurately identifies the generative model and does not systematically prefer the more parameter-rich MMMs. To mitigate the increased computational demands associated with MMMs, our implementation exploits recent developments in BEAGLE, a high-performance computational library for phylogenetic inference. [Bayesian inference; BEAGLE; BEAST; covarion, heterotachy; Markov-modulated models; phylogenetics.]

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Accounting for year effects and sampling error in temporal analyses of population and biodiversity change - Response to Seibold et al. 2019 “Arthropod decline in grasslands and forests is associated with landscape-level drivers”

10.32942/osf.io/cg3zs ◽

2020 ◽

Author(s):

Gergana N. Daskalova ◽

Isla Heather Myers-Smith ◽

Albert B Phillimore

Keyword(s):

Time Series ◽

Species Richness ◽

Sampling Error ◽

Random Effect ◽

Error Variance ◽

Future Research ◽

Public Confidence ◽

Biodiversity Change ◽

Change Response ◽

Over Time

An accumulating number of studies are reporting severe biomass, abundance and/or species richness declines of insects (Hallmann et al., 2017; Lister & Garcia, 2018; Seibold et al., 2019; Sánchez-Bayo & Wyckhuys, 2019). Collectively these studies aim to quantify the net change in invertebrate populations and/or community composition over time and to establish whether such changes can be attributed to anthropogenic drivers (Macgregor, Williams, Bell, & Thomas, 2019; Saunders, Janes, & O’Hanlon, 2019; Thomas, Jones, & Hartley, 2019; Montgomery et al., 2020; van Klink et al., 2020). Seibold et al. 2019 analysed a dataset of arthropod biomass, abundance and species richness from forest and grassland plots in a region of Germany and report significant declines of up to 78% over the time period of 2008 to 2018 (Seibold et al., 2019). However, their analysis did not account for the confounding effects of temporal pseudoreplication of observations from the same years. We show that simply by including a year random effect in the statistical models and thereby accounting for the common conditions experienced by observations from proximal sites in the same years, four of the five reported declines become non-significant out of six tests overall. To place their estimated effect sizes and those of other recent studies of insect declines in a broader geographic context, we analysed invertebrate biomass, abundance and species richness over time from 640 time series from 1167 sites around the world. We found that the average trend across the terrestrial and freshwater realms was not significantly distinguishable from no net change. Shorter time series that are likely to be most affected by sampling error variance – such as those reported in Seibold et al. 2019 – yielded the most extreme estimates of decline or increase. We suggest that the uncritical media uptake of extreme negative trends from short time series may be serving to exaggerate the speed of "insect Armageddon" and could eventually undermine public confidence in biodiversity research. We advocate that future research include all available data and use model structures that account for uncertainties to build a more robust understanding of biodiversity change during the Anthropocene and its variation among regions and taxa (Kunin, 2019; Saunders et al., 2019; Thomas et al., 2019; Didham et al., 2020; Dornelas & Daskalova, 2020).

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Research Creativity and Productivity in Political Science: A Research Agenda for Understanding Alternative Career Paths and Attitudes Toward Professional Work in the Profession

Political Science and Politics ◽

10.1017/s1049096519001215 ◽

2019 ◽

Vol 53 (1) ◽

pp. 79-83

Author(s):

Kim Quaile Hill

Keyword(s):

Research Productivity ◽

Career Paths ◽

Research Agenda ◽

Scientific Literature ◽

Future Research ◽

Scientific Career ◽

Scientific Careers ◽

Publication Success ◽

General Scientific ◽

Over Time

ABSTRACTA growing body of research investigates the factors that enhance the research productivity and creativity of political scientists. This work provides a foundation for future research, but it has not addressed some of the most promising causal hypotheses in the general scientific literature on this topic. This article explicates the latter hypotheses, a typology of scientific career paths that distinguishes how scientific careers vary over time with respect to creative ambitions and achievements, and a research agenda based on the preceding components for investigation of the publication success of political scientists.

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A Downward Spiral? A Panel Study of Misinformation and Media Trust in Chile

The International Journal of Press/Politics ◽

10.1177/19401612211025238 ◽

2021 ◽

pp. 194016122110252

Author(s):

Sebastián Valenzuela ◽

Daniel Halpern ◽

Felipe Araneda

Keyword(s):

News Media ◽

Panel Study ◽

Future Research ◽

Panel Survey ◽

Downward Spiral ◽

The Media ◽

Media Trust ◽

People’S Attitudes ◽

Over Time

Despite widespread concern, research on the consequences of misinformation on people's attitudes is surprisingly scant. To fill in this gap, the current study examines the long-term relationship between misinformation and trust in the news media. Based on the reinforcing spirals model, we analyzed data from a three-wave panel survey collected in Chile between 2017 and 2019. We found a weak, over-time relationship between misinformation and media skepticism. Specifically, initial beliefs on factually dubious information were negatively correlated with subsequent levels of trust in the news media. Lower trust in the media, in turn, was related over time to higher levels of misinformation. However, we found no evidence of a reverse, parallel process where media trust shielded users against misinformation, further reinforcing trust in the news media. The lack of evidence of a downward spiral suggests that the corrosive effects of misinformation on attitudes toward the news media are less serious than originally suggested. We close with a discussion of directions for future research.

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Multifinality, equifinality, and fanning: Developmental concepts and statistical implications

International Journal of Behavioral Development ◽

10.1177/01650254211020402 ◽

2021 ◽

pp. 016502542110204

Author(s):

Ben Hinnant ◽

John Schulenberg ◽

Justin Jager

Keyword(s):

Interindividual Variability ◽

Growth Models ◽

Practical Importance ◽

Simulated Data ◽

Developmental Changes ◽

Effect Sizes ◽

Over Time ◽

Developmental Window

Multifinality, equifinality, and fanning are important developmental concepts that emphasize understanding interindividual variability in trajectories over time. However, each concept implies that there are points in a developmental window where interindividual variability is more limited. We illustrate the multifinality concept under manipulations of variance in starting points, using both normal and zero-inflated simulated data. Results indicate that standardized estimates and effect sizes are inflated when predicting components of growth models with limited interindividual variance, which could lead to overinterpretation of the practical importance of findings. Conceptual implications are considered and recommendations are provided for evaluating developmental changes in common situations that researchers may encounter.

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A Bibliometric Network Analysis of Coronavirus during the First Eight Months of COVID-19 in 2020

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18030952 ◽

2021 ◽

Vol 18 (3) ◽

pp. 952 ◽

Cited By ~ 1

Author(s):

Leonardo B. Furstenau ◽

Bruna Rabaioli ◽

Michele Kremer Sott ◽

Danielli Cossul ◽

Mariluza Sott Bender ◽

...

Keyword(s):

Decision Making ◽

Network Analysis ◽

Web Of Science ◽

Future Research ◽

Future Trends ◽

Damage Prevention ◽

Over Time ◽

The Web

The COVID-19 pandemic has affected all aspects of society. Researchers worldwide have been working to provide new solutions to and better understanding of this coronavirus. In this research, our goal was to perform a Bibliometric Network Analysis (BNA) to investigate the strategic themes, thematic evolution structure and trends of coronavirus during the first eight months of COVID-19 in the Web of Science (WoS) database in 2020. To do this, 14,802 articles were analyzed, with the support of the SciMAT software. This analysis highlights 24 themes, of which 11 of the more important ones were discussed in-depth. The thematic evolution structure shows how the themes are evolving over time, and the most developed and future trends of coronavirus with focus on COVID-19 were visually depicted. The results of the strategic diagram highlight ‘CHLOROQUINE’, ‘ANXIETY’, ‘PREGNANCY’ and ‘ACUTE-RESPIRATORY-SYNDROME’, among others, as the clusters with the highest number of associated citations. The thematic evolution. structure presented two thematic areas: “Damage prevention and containment of COVID-19” and “Comorbidities and diseases caused by COVID-19”, which provides new perspectives and futures trends of the field. These results will form the basis for future research and guide decision-making in coronavirus focused on COVID-19 research and treatments.

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The incidence and prevalence of cardiovascular diseases in gout: a systematic review and meta-analysis

Rheumatology International ◽

10.1007/s00296-021-04876-6 ◽

2021 ◽

Author(s):

Peter Cox ◽

Sonal Gupta ◽

Sizheng Steven Zhao ◽

David M. Hughes

Keyword(s):

Systematic Review ◽

Cardiovascular Disease ◽

Cardiovascular Risk ◽

Cardiovascular Diseases ◽

Small Sample Size ◽

Meta Analysis ◽

Random Effect ◽

Small Sample ◽

Future Research ◽

Increased Risk

AbstractThe aims of this systematic review and meta-analysis were to describe prevalence of cardiovascular disease in gout, compare these results with non-gout controls and consider whether there were differences according to geography. PubMed, Scopus and Web of Science were systematically searched for studies reporting prevalence of any cardiovascular disease in a gout population. Studies with non-representative sampling, where a cohort had been used in another study, small sample size (< 100) and where gout could not be distinguished from other rheumatic conditions were excluded, as were reviews, editorials and comments. Where possible meta-analysis was performed using random-effect models. Twenty-six studies comprising 949,773 gout patients were included in the review. Pooled prevalence estimates were calculated for five cardiovascular diseases: myocardial infarction (2.8%; 95% confidence interval (CI)s 1.6, 5.0), heart failure (8.7%; 95% CI 2.9, 23.8), venous thromboembolism (2.1%; 95% CI 1.2, 3.4), cerebrovascular accident (4.3%; 95% CI 1.8, 9.7) and hypertension (63.9%; 95% CI 24.5, 90.6). Sixteen studies reported comparisons with non-gout controls, illustrating an increased risk in the gout group across all cardiovascular diseases. There were no identifiable reliable patterns when analysing the results by country. Cardiovascular diseases are more prevalent in patients with gout and should prompt vigilance from clinicians to the need to assess and stratify cardiovascular risk. Future research is needed to investigate the link between gout, hyperuricaemia and increased cardiovascular risk and also to establish a more thorough picture of prevalence for less common cardiovascular diseases.

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First encounter effects in testing of highly automated vehicles during two experimental occasions – The need for recurrent testing

it - Information Technology ◽

10.1515/itit-2020-0023 ◽

2021 ◽

Vol 0 (0) ◽

Author(s):

Jonas Andersson ◽

Azra Habibovic ◽

Daban Rizgary

Keyword(s):

Driver Behavior ◽

Added Value ◽

Future Research ◽

Gaze Behavior ◽

Repeated Testing ◽

Automated Vehicles ◽

Automated Driving ◽

Perceived Safety ◽

Repeated Interaction ◽

Over Time

Abstract To explore driver behavior in highly automated vehicles (HAVs), independent researchers are mainly conducting short experiments. This limits the ability to explore drivers’ behavioral changes over time, which is crucial when research has the intention to reveal human behavior beyond the first-time use. The current paper shows the methodological importance of repeated testing in experience and behavior related studies of HAVs. The study combined quantitative and qualitative data to capture effects of repeated interaction between drivers and HAVs. Each driver ( n = 8 n=8 ) participated in the experiment on two different occasions (∼90 minutes) with one-week interval. On both occasions, the drivers traveled approximately 40 km on a rural road at AstaZero proving grounds in Sweden and encountered various traffic situations. The participants could use automated driving (SAE level 4) or choose to drive manually. Examples of data collected include gaze behavior, perceived safety, as well as interviews and questionnaires capturing general impressions, trust and acceptance. The analysis shows that habituation effects were attenuated over time. The drivers went from being exhilarated on the first occasion, to a more neutral behavior on the second occasion. Furthermore, there were smaller variations in drivers’ self-assessed perceived safety on the second occasion, and drivers were faster to engage in non-driving related activities and become relaxed (e. g., they spent more time glancing off road and could focus more on non-driving related activities such as reading). These findings suggest that exposing drivers to HAVs on two (or more) successive occasions may provide more informative and realistic insights into driver behavior and experience as compared to only one occasion. Repeating an experiment on several occasions is of course a balance between the cost and added value, and future research should investigate in more detail which studies need to be repeated on several occasions and to what extent.

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How Collective-Action Failure Shapes Group Heterogeneity and Engagement in Conventional and Radical Action Over Time

Psychological Science ◽

10.1177/0956797620970562 ◽

2021 ◽

pp. 095679762097056

Author(s):

Morgana Lizzio-Wilson ◽

Emma F. Thomas ◽

Winnifred R. Louis ◽

Brittany Wilcockson ◽

Catherine E. Amiot ◽

...

Keyword(s):

Social Movements ◽

Collective Action ◽

Latent Profile Analysis ◽

Profile Analysis ◽

Social Identification ◽

Future Research ◽

Group Efficacy ◽

Before And After ◽

Using Data ◽

Over Time

Extensive research has identified factors influencing collective-action participation. However, less is known about how collective-action outcomes (i.e., success and failure) shape engagement in social movements over time. Using data collected before and after the 2017 marriage-equality debate in Australia, we conducted a latent profile analysis that indicated that success unified supporters of change ( n = 420), whereas failure created subgroups among opponents ( n = 419), reflecting four divergent responses: disengagement (resigned acceptors), moderate disengagement and continued investment (moderates), and renewed commitment to the cause using similar strategies (stay-the-course opponents) or new strategies (innovators). Resigned acceptors were least inclined to act following failure, whereas innovators were generally more likely to engage in conventional action and justify using radical action relative to the other profiles. These divergent reactions were predicted by differing baseline levels of social identification, group efficacy, and anger. Collective-action outcomes dynamically shape participation in social movements; this is an important direction for future research.

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Boosting heritability: estimating the genetic component of phenotypic variation with multiple sample splitting

BMC Bioinformatics ◽

10.1186/s12859-021-04079-7 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

The Tien Mai ◽

Paul Turner ◽

Jukka Corander

Keyword(s):

Random Effect ◽

Simulated Data ◽

Fixed Effect Model ◽

Accurate Estimate ◽

Limited Attention ◽

Effect Model ◽

Generic Strategy ◽

Multiple Sample ◽

Inference Strategy ◽

Resistance Data

Abstract Background Heritability is a central measure in genetics quantifying how much of the variability observed in a trait is attributable to genetic differences. Existing methods for estimating heritability are most often based on random-effect models, typically for computational reasons. The alternative of using a fixed-effect model has received much more limited attention in the literature. Results In this paper, we propose a generic strategy for heritability inference, termed as “boosting heritability”, by combining the advantageous features of different recent methods to produce an estimate of the heritability with a high-dimensional linear model. Boosting heritability uses in particular a multiple sample splitting strategy which leads in general to a stable and accurate estimate. We use both simulated data and real antibiotic resistance data from a major human pathogen, Sptreptococcus pneumoniae, to demonstrate the attractive features of our inference strategy. Conclusions Boosting is shown to offer a reliable and practically useful tool for inference about heritability.

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