Emmanuel Paradis

Bio: Emmanuel Paradis is an academic researcher from Centre national de la recherche scientifique. The author has contributed to research in topics: Population & Biological dispersal. The author has an hindex of 33, co-authored 89 publications receiving 18033 citations. Previous affiliations of Emmanuel Paradis include British Trust for Ornithology & Institut de recherche pour le développement.

APE: Analyses of Phylogenetics and Evolution in R language

Abstract: Summary: Analysis of Phylogenetics and Evolution (APE) is a package written in the R language for use in molecular evolution and phylogenetics. APE provides both utility functions for reading and writing data and manipulating phylogenetic trees, as well as several advanced methods for phylogenetic and evolutionary analysis (e.g. comparative and population genetic methods). APE takes advantage of the many R functions for statistics and graphics, and also provides a flexible framework for developing and implementing further statistical methods for the analysis of evolutionary processes. Availability: The program is free and available from the official R package archive at http://cran.r-project.org/src/contrib/PACKAGES.html#ape. APE is licensed under the GNU General Public License.

ape 5.0: an environment for modern phylogenetics and evolutionary analyses in R.

Abstract: Summary After more than fifteen years of existence, the R package ape has continuously grown its contents, and has been used by a growing community of users The release of version 50 has marked a leap towards a modern software for evolutionary analyses Efforts have been put to improve efficiency, flexibility, support for 'big data' (R's long vectors), ease of use and quality check before a new release These changes will hopefully make ape a useful software for the study of biodiversity and evolution in a context of increasing data quantity Availability and implementation ape is distributed through the Comprehensive R Archive Network: http://cranr-projectorg/package=ape Further information may be found at http://ape-packageirdfr/

pegas: an R package for population genetics with an integrated-modular approach.

Abstract: Summary: pegas is a new package for the analysis of population genetic data. It is written in R and is integrated with two other existing R packages (ape and adegenet). pegas provides functions for standard population genetic methods, as well as low-level functions for developing new methods. The flexible and efficient graphical capabilities of R are used for plotting haplotype networks as well as for other functionalities. pegas emphasises the need to further develop an integrated‐modular approach for software dedicated to the analysis of population genetic data. Availability: pegas is distributed through the Comprehensive R Archive Network (CRAN): http://cran.r-project.org/web/packages/pegas/index.html Further information may be found at: http://ape.mpl.ird.fr/pegas/

Patterns of natal and breeding dispersal in birds

Abstract: 1. Dispersal is of critical ecological and evolutionary importance for several issues of population biology, particularly population synchrony, colonization and range expansion, metapopulation and source–sink dynamics, and population genetic structure, but it has not previously been possible to compare dispersal patterns across a wide range of species or to study movement outside the confines of local study areas. 2. Using resampling methods, we verified that statistically unbiased estimates of average dispersal distance and of intraspecific variance in dispersal distance could be extracted from the bird ringing data of the British Trust for Ornithology. 3. Using data on 75 terrestrial bird species, we tested whether natal and breeding dispersal were influenced by a species’ habitat requirements, diet, geographical range, abundance, morphology, social system, life history or migratory status. We used allometric techniques to ascertain whether these relationships were independent of body size, and used the method of phylogenetically independent contrasts to ascertain whether they were independent of phylogeny. 4. Both natal and breeding dispersal distances were lower among abundant species and among species with large geographical ranges. Dispersal distances and life-history variables were correlated independent of phylogeny, but these relationships did not persist after controlling for body size. All morphometrical variables (wing length, tarsus length and bill length) were not significantly correlated with dispersal distances after correcting for body size or phylogenetic relatedness. 5. Migrant species disperse further than resident ones, this relation was independent of body size but not of phylogeny. A significant positive relation was observed between breeding dispersal distance and long-term population decline among migrants, but not among residents. 6. The species living in wet habitats disperse further than those living in dry habitats, which could be explained by the greater patchiness of wet habitats in space and/or time. This relationship was observed only for breeding dispersal, suggesting that this habitat variable does not impose the same constraint on natal dispersal.

Analysis of Phylogenetics and Evolution with R

Abstract: Introduction.- First Steps in R for Phylogeneticists.- Phylogenetic Data in R.- Plotting Phylogenies.- Phylogeny Estimation.- Analysis of Macroevolution with Phylogenies.- Developing and Implementing Phylogenetic Methods in R.

The Theory of Island Biogeography

Abstract: Preface to the Princeton Landmarks in Biology Edition vii Preface xi Symbols Used xiii 1. The Importance of Islands 3 2. Area and Number of Speicies 8 3. Further Explanations of the Area-Diversity Pattern 19 4. The Strategy of Colonization 68 5. Invasibility and the Variable Niche 94 6. Stepping Stones and Biotic Exchange 123 7. Evolutionary Changes Following Colonization 145 8. Prospect 181 Glossary 185 References 193 Index 201

GenAlEx 6.5

Abstract: Summary: GenAlEx: Genetic Analysis in Excel is a cross-platform package for population genetic analyses that runs within Microsoft Excel. GenAlEx offers analysis of diploid codominant, haploid and binary genetic loci and DNA sequences. Both frequency-based (F-statistics, heterozygosity, HWE, population assignment, relatedness) and distance-based (AMOVA, PCoA, Mantel tests, multivariate spatial autocorrelation) analyses are provided. New features include calculation of new estimators of population structure: G′ST, G′′ST, Jost’s Dest and F′ST through AMOVA, Shannon Information analysis, linkage disequilibrium analysis for biallelic data and novel heterogeneity tests for spatial autocorrelation analysis. Export to more than 30 other data formats is provided. Teaching tutorials and expanded step-by-step output options are included. The comprehensive guide has been fully revised. Availability and implementation: GenAlEx is written in VBA and provided as a Microsoft Excel Add-in (compatible with Excel 2003, 2007, 2010 on PC; Excel 2004, 2011 on Macintosh). GenAlEx, and supporting documentation and tutorials are freely available at: http://biology.anu.edu.au/GenAlEx. Contact: rod.peakall@anu.edu.au

Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences

Abstract: Profiling phylogenetic marker genes, such as the 16S rRNA gene, is a key tool for studies of microbial communities but does not provide direct evidence of a community's functional capabilities. Here we describe PICRUSt (phylogenetic investigation of communities by reconstruction of unobserved states), a computational approach to predict the functional composition of a metagenome using marker gene data and a database of reference genomes. PICRUSt uses an extended ancestral-state reconstruction algorithm to predict which gene families are present and then combines gene families to estimate the composite metagenome. Using 16S information, PICRUSt recaptures key findings from the Human Microbiome Project and accurately predicts the abundance of gene families in host-associated and environmental communities, with quantifiable uncertainty. Our results demonstrate that phylogeny and function are sufficiently linked that this 'predictive metagenomic' approach should provide useful insights into the thousands of uncultivated microbial communities for which only marker gene surveys are currently available.

phytools: an R package for phylogenetic comparative biology (and other things)

Abstract: Summary 1. Here, I present a new, multifunctional phylogenetics package, phytools, for the R statistical computing environment. 2. The focus of the package is on methods for phylogenetic comparative biology; however, it also includes tools for tree inference, phylogeny input/output, plotting, manipulation and several other tasks. 3. I describe and tabulate the major methods implemented in phytools, and in addition provide some demonstration of its use in the form of two illustrative examples. 4. Finally, I conclude by briefly describing an active web-log that I use to document present and future developments for phytools. I also note other web resources for phylogenetics in the R computational environment.