Best publishing practices to improve user confidence in scientific software

<p>Preprint of a paper (currently in revision at Ideas in Ecology and Evolution) on practices to improve the reliability of scientific software.</p

Macroevolution of ecological networks

<p>Presented at the BlueFern symposium, Oct. 2014, University of Canterbury</p

The ecological and evolutionary dynamics of species interaction networks

<p>Slides presented at the Mathematics for Planet Earth workshop "Mathematics for an Evolving Biodiversity", Montréal, Sept. 2013</p

Relationships between raw and realized modularity

<p>Relationships between raw and realized modularity for 290 networks, including the results of null models</p> <p><strong>results.dat<br></strong></p> <p><strong>w</strong> - web number</p> <p><strong>q</strong> - raw (Louvain) modularity</p> <p><strong>nm</strong> - number of modules</p> <p><strong>qr</strong> - realized modularity</p> <p><strong>ed</strong> - number of edges</p> <p><strong>no</strong> - number of nodes</p> <p><strong>co</strong> - connectance</p> <p><strong>qe</strong> - random expectation of Louvain modularity</p> <p><strong>eqe</strong> - variance of the random modularity expectation</p> <p><strong>qre</strong> - random expectation of realised modularity</p> <p><strong>eqre</strong> - variance of the random realized modularity expectation</p> <p><strong>rq</strong> - rank (based on modularity)</p> <p><strong>rqr</strong> - ranked (based on realized modularity)</p> <p><strong>dq</strong> - empirical - random modularity</p> <p><strong>dqr </strong>- empirical - random realized modularity</p> <p> </p> <p><strong>altmeasures.dat</strong></p> <p><strong>w</strong> - network (unipartite) number</p> <p>Wa(R) - modularity and realized modularity with the walktrap method</p> <p><strong>Sp(R)</strong> - with the spinglass algorithm</p> <p><strong>Eb(R)</strong> - with the edge-betweenness method</p

Mapping ecological concepts using twitter

<p>Interactions between key concepts mentionned on Twitter in tweets containing words from the field of ecology. See the URL for more details on the methodology.</p> <p>These data come from a series of relatively short sampling sessions.</p

Data and code to reproduce analyses from "Compositional turnover in host and parasite communities does not change network structure"

This repository contains data and code necessary to reproduce the analyses from <div><br></div><div>> Dallas, T and T Poisot. 2017. "Compositional turnover in host and parasite communities does not change network structure" <i>Ecography</i></div><div><br></div><div><br></div><div>`D.R` contains functions to calculate network dissimilarity</div><div><br></div><div>`Dallas2017.Rmd` contains text and code to reproduce analyses and figures from manuscript</div><div><br></div><div>`NHMdata.RData` contains data obtained from the London Natural History Musuem's host-helminth database.</div

Morphometric measurements of Lamellodiscus haptoral parts

<p>List of measurements of 147 parasites individuals (size of haptor and reproductive organs, in tenth of millimeters). For an explanation of the measurements, refer to Fig. 1 in</p> <p>Timothée Poisot, Yves Desdevises (2010) Putative speciation events in <em>Lamellodiscus</em> (Monogenea: Diplectanidae) assessed by a morphometric approach. <em>Biological Journal of the Linnean Society</em> 99 (3) 559-569.</p

Species and their interactions respond to different environmental variables

<p>Slides for the ESA 2015 meeting, Baltimore.</p

Phage-bacteria networks isolated in soil

<p>Collection of 5 networks of bipartite interactions between bacteria (fluorescent Pseudomonads) and lytics phage isolated in soil in Montpellier, France. For details on the methods, see</p> <p>Poisot, Lounnas & Hochberg (2013) The structure of natural microbial enemy-victim networks. Ecological Processes</p> <p>Each network gives the interaction strength between 24 phages and 19 bacteria.</p

Network structure of host-parasite networks from Central Europe

<p>Network metric for host-parasites communities, based on adjacency matrices, divided into all parasites (ALL), facultative parasites only (FAC). and obligatory parasites only (OPC).</p> <p><strong>network :</strong> unique network identifier (community number, parasite type, host type)<br><strong>connectance :</strong> number of infections / community richness<br><strong>size :</strong> total richness of the community (hosts + parasites)<br><strong>parasites :</strong> number of parasites<br><strong>hosts :</strong> number of hosts<br><strong>nestedness :</strong> NODF measure of nestedness<br><strong>average_host_range :</strong> mean host range, measured using the RR metric - values closer to 0 indicate generality<br><strong>number_modules :</strong> number of community modules found<br><strong>modularity :</strong> Qbip modularity, optimized using the LP-BRIM method<br><strong>null_nestedness :</strong> average NODF of 1000 null replicates<br><strong>nestedness_pvalue</strong> : significancy of the deviation between null and empirical nestedness values<br><strong>null_modularity :</strong> average Qbip of 1000 null replicates<br><strong>modularity_pvalue :</strong> significancy of the deviation between null and empirical nestedness values<br><strong>null_model :</strong> type of null model, either I or II<br><strong>parasite_type :</strong> type of parasites considered (all, facultative, or obligatory)</p