scholarly journals strandCet: R package for estimating natural and non-natural mortality-at-age of cetaceans from age-structured strandings

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e5768 ◽  
Author(s):  
Camilo Saavedra
Keyword(s):  
Total Mortality ◽  
R Package ◽  
Analytical Framework ◽  
Population Projections ◽  
Sources Of Information ◽  
Mortality Models ◽  
Credible Intervals ◽  
Leslie Matrices ◽  
Age Structured ◽  
Derived Data

Mortality is one of the most important parameters for the study of population dynamics. One of the main sources of information to calculate the mortality of cetaceans arises from the observed age-structure of stranded animals. A method based on an adaptation of a Heligman-Pollard model is proposed. A freely accessible package of functions (strandCet) has been created to apply this method in the statistical software R. Total, natural, and anthropogenic mortality-at-age is estimated using only data of stranded cetaceans whose age is known. Bayesian melding estimation with Incremental Mixture Importance Sampling is used for fitting this model. This characteristic, which accounts for uncertainty, further eases the estimation of credible intervals. The package also includes functions to perform life tables, Siler mortality models to calculate total mortality-at-age and Leslie matrices to derive population projections. Estimated mortalities can be tested under different scenarios. Population parameters as population growth, net production or generation time can be derived from population projections. The strandCet R package provides a new analytical framework to assess mortality in cetacean populations and to explore the consequences of management decisions using only stranding-derived data.

scholarly journals KBoost: a new method to infer gene regulatory networks from gene expression data

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Luis F. Iglesias-Martinez ◽  
Barbara De Kegel ◽  
Walter Kolch
Keyword(s):  
Breast Cancer ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
R Package ◽  
Breast Cancer Patients ◽  
Sources Of Information ◽  
Cancer Subtypes ◽  
Gene Regulatory

AbstractReconstructing gene regulatory networks is crucial to understand biological processes and holds potential for developing personalized treatment. Yet, it is still an open problem as state-of-the-art algorithms are often not able to process large amounts of data within reasonable time. Furthermore, many of the existing methods predict numerous false positives and have limited capabilities to integrate other sources of information, such as previously known interactions. Here we introduce KBoost, an algorithm that uses kernel PCA regression, boosting and Bayesian model averaging for fast and accurate reconstruction of gene regulatory networks. We have benchmarked KBoost against other high performing algorithms using three different datasets. The results show that our method compares favorably to other methods across datasets. We have also applied KBoost to a large cohort of close to 2000 breast cancer patients and 24,000 genes in less than 2 h on standard hardware. Our results show that molecularly defined breast cancer subtypes also feature differences in their GRNs. An implementation of KBoost in the form of an R package is available at: https://github.com/Luisiglm/KBoost and as a Bioconductor software package.

scholarly journals Modeling the implications of stock mixing and life history uncertainty of Atlantic bluefin tuna

2017 ◽  
Vol 74 (11) ◽  
pp. 1990-2004 ◽  
Author(s):  
Lisa A. Kerr ◽  
Steven X. Cadrin ◽  
David H. Secor ◽  
Nathan G. Taylor
Keyword(s):  
Temporal Distribution ◽  
Bluefin Tuna ◽  
Analytical Framework ◽  
Magnitude Distribution ◽  
Atlantic Bluefin Tuna ◽  
Age Structured ◽  
Stock Mixing ◽  
Overlap Model ◽  
Mixed Stock ◽  
Central Atlantic

Atlantic bluefin tuna (Thunnus thynnus) is currently managed as two separate eastern and western stocks, despite information indicating considerable stock mixing. Using a simulation model, we explored how scenarios of population-specific migration and uncertainty in aspects of bluefin tuna biology affect the magnitude, distribution, and mixed stock nature of the resource and catch of its associated fisheries. The analytical framework was a stochastic, age-structured, stock-overlap model that was seasonally and spatially explicit with movement of eastern- and western-origin tuna informed by tagging and otolith chemistry data. Alternate estimates of movement and assumptions regarding maturity and recruitment regime for western-origin fish were considered. Simulation of the operating model indicated considerable stock mixing in the western and central Atlantic, which resulted in differences between the stock and population view of western bluefin tuna. The relative biomass of the western population and its spatial and temporal distribution in the Atlantic was sensitive to model assumptions and configurations. Simulation modeling can provide a means to ascertain the potential consequences of stock mixing on the assessment and management of fishery resources.

Life in the Slow Lane: Ecology and Conservation of Long-Lived Marine Animals

1999 ◽  
Keyword(s):  
Simulation Model ◽  
Maximum Size ◽  
Population Projections ◽  
Population Declines ◽  
Sandbar Shark ◽  
Carcharhinus Plumbeus ◽  
Marine Animals ◽  
Important Species ◽  
Age Structured ◽  
Stage Class

<em>Abstract.</em> —The sandbar shark <em>Carcharhinus plumbeus </em> is the most important species caught in the commercial shark fishery operating off the U.S. Atlantic and Gulf of Mexico coasts. Previous demographic studies of this and other species of sharks have utilized age-structured, deterministic life tables that provided point estimates of maximum rates of increase. To reduce some of the uncertainty in estimates of age at maturity and longevity—especially acute in the case of the sandbar shark—I constructed a stage-based model based on an Usher matrix that utilizes the more reliable estimates of size at maturity and maximum size for this species in the northwest Atlantic. Because demographic variability also can affect estimated rates of increase, I introduced stochasticity into the model by randomly selecting fecundity rates from an empirically determined distribution, and natural mortality rates from estimates obtained through four life history methods. The simulation model was applied to females only. Population projections 20 years forward in time without exploitation predicted slowly growing populations at approximately 1.3%/year. Application of a constant instantaneous mortality rate (<EM>F</EM> ) of 0.1 to each stage-class separately indicated that removal of large juveniles would produce the greatest population declines, whereas removal of age-0 individuals would be sustainable. The simulation model was then used to predict potential outcomes under three hypothetical harvesting scenarios using the current U.S. commercial quota indicating that all strategies produced pronounced population declines.

scholarly journals There and Back to the Present: An Evolutionary Tale on Biological Diversity

2021 ◽  
Author(s):  
Leandro Duarte ◽  
Gabriel Nakamura ◽  
Vanderlei Debastiani ◽  
Renan Maestri ◽  
Maria Joao Veloso da Costa Ramos Pereira ◽  
...  
Keyword(s):  
Biological Diversity ◽  
Environmental Gradients ◽  
Dispersal Limitation ◽  
R Package ◽  
Analytical Framework ◽  
Niche Evolution ◽  
Ecological Patterns ◽  
Local Species ◽  
Individual Based Simulation ◽  
Approximate Bayesian

Ecologists often agree on the importance of macroevolution for niche-mediated distribution of biological diversity along environmental gradients. Yet, macroevolutionary diversification and dispersal in time and space generate uneven geographic distribution of phylogenetic pools, which affects the imprint let by macroevolution on local species pools. In this article we introduce an individual-based simulation approach coupled to Approximate Bayesian Computation (ABC) that allows to parameterize the adaptation rate of species niche positions along the evolution of a monophyletic lineage, and the intensity of dispersal limitation, associated with the distribution of biological diversity between assemblages potentially connected by dispersal (metacommunity). The analytical tool was implemented in an R package called mcfly. We evaluated the statistical performance of the analytical framework using simulated datasets, which confirmed the suitability of the analysis to estimate adaptation rate and dispersal limitation parameters. Further, we evaluated the role played by niche evolution and dispersal limitation on species diversity distribution of Phyllostomidae bats across the Neotropics. The framework proposed here shed light on the links between niche evolution, dispersal limitation and the distribution of biological diversity, and thereby improved our understanding of evolutionary imprints on ecological patterns. Perhaps more importantly, it offers new possibilities for solving the eco-evolutionary puzzle.

scholarly journals Mathematical modeling of COVID-19 transmission and mitigation strategies in the population of Ontario, Canada

2020 ◽  
Author(s):  
Ashleigh R. Tuite ◽  
David N. Fisman ◽  
Amy L. Greer
Keyword(s):  
Median Number ◽  
Case Finding ◽  
Mitigation Strategies ◽  
System Capacity ◽  
Base Case ◽  
Social Distancing ◽  
Effective Dynamic ◽  
Credible Intervals ◽  
Age Structured ◽  
Limited Testing

AbstractBackgroundWe evaluated how non-pharmaceutical interventions could be used to control the COVID-19 pandemic and reduce the burden on the healthcare system.MethodsUsing an age-structured compartmental model of COVID-19 transmission in the population of Ontario, Canada, we compared a base case with limited testing, isolation, and quarantine to scenarios with: enhanced case finding; restrictive social distancing measures; or a combination of enhanced case finding and less restrictive social distancing. Interventions were either implemented for fixed durations or dynamically cycled on and off, based on projected ICU bed occupancy. We present median and credible intervals (CrI) from 100 replicates per scenario using a two-year time horizon.ResultsWe estimated that 56% (95% CrI: 42-63%) of the Ontario population would be infected over the course of the epidemic in the base case. At the epidemic peak, we projected 107,000 (95% CrI: 60,760-149,000) cases in hospital and 55,500 (95% CrI: 32,700-75,200) cases in ICU. For fixed duration scenarios, all interventions were projected to delay and reduce the height of the epidemic peak relative to the base case, with restrictive social distancing estimated to have the greatest effect. Longer duration interventions were more effective. Dynamic interventions were projected to reduce the proportion of the population infected at the end of the two-year period. Dynamic social distancing interventions could reduce the median number of cases in ICU below current estimates of Ontario’s ICU capacity.InterpretationWithout significant social distancing or a combination of moderate social distancing with enhanced case finding, we project that ICU resources would be overwhelmed. Dynamic social distancing could maintain health system capacity and also allow periodic psychological and economic respite for populations.

scholarly journals Endogenous fishing mortalities: a state-space bioeconomic model

2017 ◽  
Vol 74 (9) ◽  
pp. 2437-2447 ◽  
Author(s):  
José-María Da-Rocha ◽  
Javier García-Cutrín ◽  
María-José Gutiérrez ◽  
Ernesto Jardim
Keyword(s):  
Total Mortality ◽  
Fishing Effort ◽  
Bioeconomic Model ◽  
Fishing Mortality ◽  
Northern Spain ◽  
Management Regime ◽  
Target Management ◽  
Age Structured ◽  
Age Structured Model ◽  
Stock Abundance

Abstract A methodology that endogenously determines catchability functions that link fishing mortality with contemporaneous stock abundance is presented. We consider a stochastic age-structured model for a fishery composed by a number of fishing units (fleets, vessels or métiers) that optimally select the level of fishing effort to be applied considering total mortalities as given. The introduction of a balance constrain which guarantees that total mortality is equal to the sum of individual fishing mortalities optimally selected, enables total fishing mortality to be determined as a combination of contemporaneous abundance and stochastic processes affecting the fishery. In this way, future abundance can be projected as a dynamic system that depends on contemporaneous abundance. The model is generic and can be applied to several issues of fisheries management. In particular, we illustrate how to apply the methodology to assess the floating band target management regime for controlling fishing mortalities which is inspired in the new multi-annual plans. Our results support this management regime for the Mediterranean demersal fishery in Northern Spain.

scholarly journals Microfinance Institutions Fostering Sustainable Development by Region

2020 ◽  
Vol 12 (7) ◽  
pp. 2682 ◽  
Author(s):  
Icíar García-Pérez ◽  
María Ángeles Fernández-Izquierdo ◽  
María Jesús Muñoz-Torres
Keyword(s):  
Sustainable Development ◽  
Holistic Approach ◽  
Analytical Framework ◽  
Microfinance Institutions ◽  
Sources Of Information ◽  
Keyword Analysis ◽  
Academic Literature ◽  
Financial Exclusion ◽  
The Relationship ◽  
Different Sources

In the last few years, considerable attention has been paid to microfinance as a relevant participant in the formal financial system, whose target audience is people who are otherwise at risk of financial exclusion. In parallel, sustainability and the promotion of Sustainable Development (SD) are imposed as the theoretical frame when facing any study. This, connected with cultural and organizational dimensions theories, are the analytical framework for the analysis of the relationship between the context of performance in which Microfinance Institutions (MFIs) operate and their activity in promoting sustainability. A holistic approach is necessary to make operational these concepts; for that reason, financial, environmental, social and governance dimensions (FESG), and the balance among them, have to be considered. The main objective of the paper is to explore to what extent MFIs are fostering SD, and how this promotion is performed by region. For the analysis, two different sources of information have been studied: sectoral academic literature that focuses on the different sustainability dimensions, and MIX Market sustainability data obtained from the MFIs. A keyword analysis of the selected papers has been executed to be conscious of the most investigated aspects by region; on the data provided by the institutions, a Kruskal-Wallis H test has been performed to learn what the main Sustainability Indicators (SIs) are that are reported affirmatively. To obtain comprehensive research, a comparative study of the results offers the convergences, divergences and gaps of information in each of the regions. The findings show significant differences depending on the region, and confirm that operationalization should be adjusted at the regional context of the MFIs. The paper, with the inherent limitations due to data quality, also offers recommendations for the better promotion of sustainability in each of the regions.

scholarly journals The importance of length and age composition data in statistical age-structured models for marine species

2014 ◽  
Vol 72 (1) ◽  
pp. 31-43 ◽  
Author(s):  
Kotaro Ono ◽  
Roberto Licandeo ◽  
Melissa L. Muradian ◽  
Curry J. Cunningham ◽  
Sean C. Anderson ◽  
...  
Keyword(s):  
Life History ◽  
Life Histories ◽  
Stock Assessment ◽  
Marine Species ◽  
Reference Points ◽  
Assessment Model ◽  
Sources Of Information ◽  
Age Composition ◽  
Composition Data ◽  
Age Structured

Abstract Management of marine resources depends on the assessment of stock status in relation to established reference points. However, many factors contribute to uncertainty in stock assessment outcomes, including data type and availability, life history, and exploitation history. A simulation–estimation framework was used to examine the level of bias and accuracy in assessment model estimates related to the quality and quantity of length and age composition data across three life-history types (cod-, flatfish-, and sardine-like species) and three fishing scenarios. All models were implemented in Stock Synthesis, a statistical age-structured stock assessment framework. In general, the value of age composition data in informing estimates of virgin recruitment (R0), relative spawning-stock biomass (SSB100/SSB0), and terminal year fishing mortality rate (F100), decreased as the coefficient of variation of the relationship between length and age became greater. For this reason, length data were more informative than age data for the cod and sardine life histories in this study, whereas both sources of information were important for the flatfish life history. Historical composition data were more important for short-lived, fast-growing species such as sardine. Infrequent survey sampling covering a longer period was more informative than frequent surveys covering a shorter period.

scholarly journals Reproducible analysis of disease space via principal components using the novel R package syndRomics

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Abel Torres-Espín ◽  
Austin Chou ◽  
J Russell Huie ◽  
Nikos Kyritsis ◽  
Pavan S Upadhyayula ◽  
...  
Keyword(s):  
Complex Disease ◽  
Principal Component ◽  
R Package ◽  
Analytical Framework ◽  
Underlying Disease ◽  
Primary Outcome Measure ◽  
Biomedical Data ◽  
Disease States ◽  
Reproducible Analysis ◽  
Main Components

Biomedical data are usually analyzed at the univariate level, focused on a single primary outcome measure to provide insight into systems biology, complex disease states, and precision medicine opportunities. More broadly, these complex biological and disease states can be detected as common factors emerging from the relationships among measured variables using multivariate approaches. ‘Syndromics’ refers to an analytical framework for measuring disease states using principal component analysis and related multivariate statistics as primary tools for extracting underlying disease patterns. A key part of the syndromic workflow is the interpretation, the visualization, and the study of robustness of the main components that characterize the disease space. We present a new software package, syndRomics, an open-source R package with utility for component visualization, interpretation, and stability for syndromic analysis. We document the implementation of syndRomics and illustrate the use of the package in case studies of neurological trauma data.

Using Internet-Derived Data to Measure Religion: Understanding How Google Can Provide Insight into Cross-National Religious Differences

2021 ◽  
Author(s):  
Amy Adamczyk ◽  
Jacqueline Scott ◽  
Steven Hitlin
Keyword(s):  
Best Practices ◽  
Social Issues ◽  
Sources Of Information ◽  
Social Media Data ◽  
Religious Buildings ◽  
Derived Data ◽  
Media Data ◽  
Insight Into ◽  
Existing Data ◽  
Cross National

Abstract Internet and social media data provide new sources of information for examining social issues, but their potential for scholars interested in religion remains unclear. Focusing on cross-national religion data, we test the validity of measures drawn from Google and Twitter against well-known existing data. We find that Google Trend (GT) searches for the dominant religions’ major holidays, along with “Buddhism,” can be validated against traditional sources. We also find that GT and traditional measures account for similar amounts of variation, and the GT measures do not differ substantially from established ones for explaining several cross-national outcomes (e.g., fertility, circumcision, and alcohol use), as well as new ones (e.g., interest in religious buildings and sex). The Twitter measures do not perform as well. Our study provides insight into best practices for generating and using these measures, and offers evidence that internet-generated data can replicate existing measures that are less accessible and more expensive.

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