DOI: 10.5061/dryad.xd2547dcp
title: Genomic and phenomic analysis of island ant community assembly

description: Island biodiversity has long fascinated biologists as it typically
 presents tractable systems for unpicking the eco-evolutionary processes
 driving community assembly. In general, two recurring themes are of
 central theoretical interest. First, immigration, diversification, and
 extinction typically depend on island geographical properties (e.g. area,
 isolation, and age). Second, predictable ecological and evolutionary
 trajectories readily occur after colonization, such as the evolution of
 adaptive trait syndromes, trends toward specialization, adaptive
 radiation, and eventual ecological decline. Hypotheses such as the taxon
 cycle draw on several of these themes to posit particular constraints on
 colonization and subsequent eco-evolutionary dynamics. However, it has
 been challenging to examine these integrated dynamics with traditional
 methods. Here we combine phylogenomics, population genomics and phenomics,
 to unravel community assembly dynamics among Pheidole (Hymenoptera,
 Formicidae) ants in the isolated Fijian archipelago. We uphold basic
 island biogeographic predictions that isolated islands accumulate
 diversity primarily through in situ evolution rather than dispersal, and
 population genomic support for taxon cycle predictions that endemic
 species have decreased dispersal ability and demography relative to
 regionally widespread taxa. However, rather than trending toward island
 syndromes, ecomorphological diversification in Fiji was intense, filling
 much of the genus-level global morphospace. Furthermore, while most
 endemic species exhibit demographic decline and reduced dispersal, we show
 that the archipelago is not an evolutionary dead-end. Rather, several
 endemic species show signatures of population and range expansion,
 including a successful colonization to the Cook islands. These results
 shed light on the processes shaping island biotas and refine our
 understanding of island biogeographic theory.
description: We provide filtered VCF files for restriction site associated DNA
 (RAD) markers for 942 Pheidole individuals mostly from the Fijian
 archipelago in order to facilitate island biogeographical investigation of
 community assembly dynamics. Also included are phylogenomic trees from RAD
 data. Additionally, we provide GPS coordinates and Python scripts in order
 to calculate the relations ships between population structure and
 geographic connectivity between islands according to current bathymetric
 data and past sea level change data.
description: There are three folders
 containing different groups of data files and/or R/Python scripts. These
 are called: 1.
 bathymetry 2.
 phylogenomics 3.
 VCFs 1.
 <i>bathymetry </i>folder This folder contains a work-flow
 for taking pairwise F<sub>ST</sub> estimates to conduct mantel
 tests against historic sea level connectivity estimates. The primary
 work-flow is outlined in the <i>bathymetry.R</i> script. This
 R script contains commented notes indicating when certain Python scripts
 (also included in the folder) are to be employed (apologies, some of the
 Python scripts are very old and look it, but they work). The folder
 contains all the files required to conduct Circuitscape
 analysis on the F<sub>ST</sub> estimates derived for
 <i>P. roosevelti</i> (EGPA number: EGPA0048). It also contains
 all files that will be generated by the scripts and indeed by Circuitscape. <i>ascSeaLevels.py</i> takes the <i>seaLevels.csv</i> file (from Haq et al 1987) to create a formatted bathymetry raster file as described in the Methods of the paper. The file <i>pops.csv</i> (used in <i>popMaker.py</i>) contains both the population code and a locality code that matches up with the <i>global_locality_full.csv</i> file to give each individual a GPS coordinate. <i>popMaker.py</i> uses the <i>EGPA0048.csv</i> SNP file to obtain taxa (but this step can be organized by the user) <i>pwDists2Matx.py</i> generates a formatted file of F<sub>ST</sub> estimates from the <i>EGPA0048_WC.csv</i> file (generated from popgen analysis) The R scripts use <i>global_locality_full.csv</i> and <i>pop.csv</i> to generate a main file (here <i>roos.txt</i>) of the relevant data. <i>ascMaskFudger.py</i> generates several files for geographic distance analysis (particularly make a polygon delineating the geographic range of each population). If there are not enough points to generate the polygon it will randomly make a few more within the range of the most extreme sample points. R scripts now write raster files using the <i>ascMaskFudger.py</i> outputs. <i>ascFijiMasker.py </i>creates a dummy mask file R scripts to format raster files and load data R scripts to load and format outputs then conduct mantel tests 2. <i>phylogenomics </i>folder In this folder are placed four phylogenetic trees derived from our RADseq data. These are: (i) the main Exabayes <i>Pheidole</i> phylogeny from Figure 2 (<i>pheiFJ_exa.tre</i>); (ii) the RAxML phylogeny from the same sequence files as the main <i>Pheidole</i> phylogeny (<i>pheiFJ_raxml.tre</i>); (iii) the Exabayes tree showing four distinct <i>P. knowlesi</i> lineages (with three obvious species clades plus one singleton clade) (<i>knowlesi_3spp.exabayes.tre</i>); and (iv) the Exabayes tree showing two distinct <i>P. vatu</i> lineages (<i>vatu_2spp.exabayes.tre</i>). 3. <i>VCFs</i> folder This folder contains VCF files from all species, generated from raw fastq files using parameter settings in Stacks and vcftools described in the paper’s Methods section. From these, population genomic analyses can be conducted. The <i>sequenceMetaData.csv</i> file contains both population designations and GPS coordinates for all sampled individuals plus the global database CASENT number if available. The <i>egpa</i> number represents a code that was given to each group of samples (i.e. species) for conducting analyses.