scholarly journals DiaThor: R package for computing diatom metrics and biotic indices

2022 ◽  
Vol 465 ◽  
pp. 109859
Author(s):  
M.M. Nicolosi Gelis ◽  
M.B. Sathicq ◽  
J. Jupke ◽  
J. Cochero
Keyword(s):  
R Package ◽  
Biotic Indices

scholarly journals DiaThor: R package for computing diatom metrics and biotic indices

2021 ◽  
Author(s):  
Maria Mercedes Nicolosi Gelis ◽  
Maria Belen Sathicq ◽  
Jonathan Jupke ◽  
Joaquin Cochero
Keyword(s):  
Water Quality ◽  
Web Application ◽  
Water Quality Assessment ◽  
R Package ◽  
Morphological Data ◽  
Sensitivity Index ◽  
Biotic Indices ◽  
Ecological Data ◽  
Diatom Index ◽  
Open Platform

1- Diatoms are widely used to detect changes in water quality due to their specific sensibility to a variety of environmental conditions. Among the different diatom-based tools to measure water quality, the biotic indices, ecological and morphological traits are the most commonly used. 2- DiaThor, contains 19 functions that provide morphological data of the samples (number and shape of chloroplasts, total biovolume), ecological data (species richness, evenness, diversity, size classes, ecological guilds, ecological preferences) and biotic indices (Descy Index, EPID Index, Indice Diatomique Artois-Picardie, Swiss Diatom Index, Pampean Diatom Index, ILM index, Specific Pollution sensitivity Index, Lobo Index, Sladecek index, SPEAR(herbicides) index and the Trophic index). A web application (in Shiny) was also developed to provide access to the package for those users not familiar with the R environment. 3- The structure of the package, how it functions and the structure of the input and output data are explained. To demonstrate the most common use of DiaThor, an example of the package performance is also provided. 4- The DiaThor package aims to contribute to the water quality assessment based on diatom assemblages, while also providing researchers with an open platform to suggest new statistics and functionalities to be integrated into future builds.

scholarly journals BBI: an R package for the computation of Benthic Biotic Indices from composition data

2018 ◽  
Vol 2 ◽  
pp. e25649 ◽  
Author(s):  
Tristan Cordier ◽  
Jan Pawlowski
Keyword(s):  
Deep Sea ◽  
High Throughput Sequencing ◽  
Environmental Dna ◽  
R Package ◽  
Biotic Indices ◽  
Oil Drilling ◽  
Composition Data ◽  
Ecological Quality Status ◽  
Custom Script ◽  
Quality Status

The monitoring of impacts of anthropic activities in marine environments, such as aquaculture, oil-drilling platforms or deep-sea mining, relies on Benthic Biotic Indices (BBI). Several indices have been formalised to reduce the multivariate composition data into a single continuous value that is ascribed to a discrete ecological quality status. Such composition data is traditionally obtained from macrofaunal inventories, which is time-consuming and expertise-demanding. Important efforts are ongoing towards using High-Throughput Sequencing of environmental DNA (eDNA metabarcoding) to replace or complement morpho-taxonomic surveys for routine biomonitoring. The computation of BBI from such composition data is usually being undertaken by practitioners with excel spreadsheets or through custom script. Hence, the updating of reference morpho-taxonomic tables and cross studies comparison could be hampered. Here we introduce the R package BBI for the computation of BBI from composition data, either obtained from traditional morpho-taxonomic inventories or from metabarcoding data. Its aim is to provide an open-source, transparent and centralised method to compute BBI for routine biomonitoring.

Marine halogenated compound analysis: from an R package to the isolation of new griseophenone derivatives

Planta Medica ◽  
2016 ◽  
Vol 81 (S 01) ◽  
pp. S1-S381
Author(s):  
C Roullier ◽  
Y Guitton ◽  
S Prado ◽  
O Grovel ◽  
YF Pouchus
Keyword(s):  
R Package ◽  
Halogenated Compound

The Orchard Plot: Cultivating a Forest Plot for Use in Ecology, Evolution and Beyond

2019 ◽  
Author(s):  
Shinichi Nakagawa ◽  
Malgorzata Lagisz ◽  
Rose E O'Dea ◽  
Joanna Rutkowska ◽  
Yefeng Yang ◽  
...  
Keyword(s):  
Meta Analysis ◽  
R Package ◽  
Effect Sizes ◽  
Forest Plot ◽  
Point Estimates ◽  
Aggregate Effect ◽  
The Individual ◽  
Meta Analyses ◽  
Heterogeneous Effect ◽  
Intuitive Interpretation

‘Classic’ forest plots show the effect sizes from individual studies and the aggregate effect from a meta-analysis. However, in ecology and evolution meta-analyses routinely contain over 100 effect sizes, making the classic forest plot of limited use. We surveyed 102 meta-analyses in ecology and evolution, finding that only 11% use the classic forest plot. Instead, most used a ‘forest-like plot’, showing point estimates (with 95% confidence intervals; CIs) from a series of subgroups or categories in a meta-regression. We propose a modification of the forest-like plot, which we name the ‘orchard plot’. Orchard plots, in addition to showing overall mean effects and CIs from meta-analyses/regressions, also includes 95% prediction intervals (PIs), and the individual effect sizes scaled by their precision. The PI allows the user and reader to see the range in which an effect size from a future study may be expected to fall. The PI, therefore, provides an intuitive interpretation of any heterogeneity in the data. Supplementing the PI, the inclusion of underlying effect sizes also allows the user to see any influential or outlying effect sizes. We showcase the orchard plot with example datasets from ecology and evolution, using the R package, orchard, including several functions for visualizing meta-analytic data using forest-plot derivatives. We consider the orchard plot as a variant on the classic forest plot, cultivated to the needs of meta-analysts in ecology and evolution. Hopefully, the orchard plot will prove fruitful for visualizing large collections of heterogeneous effect sizes regardless of the field of study.

Systematics, Biogeography, and Morphological Character Evolution of the Hemiepiphytic Subfamily Monsteroideae (Araceae)

2019 ◽  
Vol 104 (1) ◽  
pp. 33-48 ◽  
Author(s):  
Alejandro Zuluaga ◽  
Martin Llano ◽  
Ken Cameron
Keyword(s):  
Dna Sequences ◽  
Pacific Islands ◽  
R Package ◽  
Morphological Characters ◽  
Ancestral State ◽  
Seed Shape ◽  
Long Distance ◽  
The Pacific ◽  
And Migration ◽  
The Tropics

The subfamily Monsteroideae (Araceae) is the third richest clade in the family, with ca. 369 described species and ca. 700 estimated. It comprises mostly hemiepiphytic or epiphytic plants restricted to the tropics, with three intercontinental disjunctions. Using a dataset representing all 12 genera in Monsteroideae (126 taxa), and five plastid and two nuclear markers, we studied the systematics and historical biogeography of the group. We found high support for the monophyly of the three major clades (Spathiphylleae sister to Heteropsis Kunth and Rhaphidophora Hassk. clades), and for six of the genera within Monsteroideae. However, we found low rates of variation in the DNA sequences used and a lack of molecular markers suitable for species-level phylogenies in the group. We also performed ancestral state reconstruction of some morphological characters traditionally used for genera delimitation. Only seed shape and size, number of seeds, number of locules, and presence of endosperm showed utility in the classification of genera in Monsteroideae. We estimated ancestral ranges using a dispersal-extinction-cladogenesis model as implemented in the R package BioGeoBEARS and found evidence for a Gondwanan origin of the clade. One tropical disjunction (Monstera Adans. sister to Amydrium Schott–Epipremnum Schott) was found to be the product of a previous Boreotropical distribution. Two other disjunctions are more recent and likely due to long-distance dispersal: Spathiphyllum Schott (with Holochlamys Engl. nested within) represents a dispersal from South America to the Pacific Islands in Southeast Asia, and Rhaphidophora represents a dispersal from Asia to Africa. Future studies based on stronger phylogenetic reconstructions and complete morphological datasets are needed to explore the details of speciation and migration within and among areas in Asia.

LRMoE: An R Package for Flexible Actuarial Loss Modelling Using Mixture of Experts Regression Model

2020 ◽  
Author(s):  
Spark C. Tseung ◽  
Andrei Badescu ◽  
Tsz Chai Fung ◽  
Xiaodong Sheldon Lin
Keyword(s):  
Regression Model ◽  
R Package ◽  
Mixture Of Experts ◽  
Loss Modelling

EpiPen: An R Package to Investigate Two-Locus Epistatic Models

2014 ◽  
Vol 17 (4) ◽  
Author(s):  
Raymond K. Walters ◽  
Charles Laurin ◽  
Gitta H. Lubke
Keyword(s):  
Power Analysis ◽  
R Package ◽  
Simulation Studies ◽  
Nucleotide Polymorphisms ◽  
Single Nucleotide ◽  
Epistatic Interactions ◽  
Model Interpretation ◽  
Genome Wide ◽  
Using Data ◽  
Power Analyses

Epistasis is a growing area of research in genome-wide studies, but the differences between alternative definitions of epistasis remain a source of confusion for many researchers. One problem is that models for epistasis are presented in a number of formats, some of which have difficult-to-interpret parameters. In addition, the relation between the different models is rarely explained. Existing software for testing epistatic interactions between single-nucleotide polymorphisms (SNPs) does not provide the flexibility to compare the available model parameterizations. For that reason we have developed an R package for investigating epistatic and penetrance models, EpiPen, to aid users who wish to easily compare, interpret, and utilize models for two-locus epistatic interactions. EpiPen facilitates research on SNP-SNP interactions by allowing the R user to easily convert between common parametric forms for two-locus interactions, generate data for simulation studies, and perform power analyses for the selected model with a continuous or dichotomous phenotype. The usefulness of the package for model interpretation and power analysis is illustrated using data on rheumatoid arthritis.

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