DOI: 10.5061/dryad.j6q573ndc
title: Data from: Phylogenetic relatedness drives protists assembly in marine and terrestrial environments

description: Aim: Assembly of protists communities is known to be driven mainly by
 environmental filtering, but the imprint of phylogenetic relatedness is
 unknown. In this study, we aim to test the degree at which co-occurrences
 and co-exclusions of protists in different phylogenetic relatedness
 classes are deviating from random expectation in two ecosystems in order
 to link them to ecological processes. Location: Global open-oceans and
 Neotropical rainforest soils Major taxa: Protists Time period: 2009-2013
 Methods: Protist metabarcoding data originated from two large scale
 studies. Co-occurrence and co-exclusion networks were constructed using a
 recent method combining a null distribution model with Spearman’s rank
 correlation coefficients among pairs of OTU. Phylogenetic relatedness was
 estimated using either global pairwise sequence distance or phylogenetic
 distance inferred from best maximum-likelihood trees derived from multiple
 alignments of OTU representative sequences. Significance of observed
 patterns relating networks and phylogenies were evaluated by distance
 classes against two null models in which either the tips of the
 phylogenetic trees or the network edges were randomized. Results:
 Closely-related protists co-occurred more often than expected by chance in
 all datasets, but also co-excluded less often than expected by chance in
 the marine dataset only. Concurrent excess of co-occurrences and
 co-exclusions were observed at intermediate phylogenetic distances in the
 marine dataset. Main conclusions: This suggest that environmental
 filtering and dispersal limitation are the dominant forces driving
 protists co-occurrences in both environments, while signal of competitive
 exclusion was only detected in the marine environment. Co-exclusion
 differences are potentially linked to the individual environments: marine
 waters are more homogeneous, while the rainforest soils contain a myriad
 of nutrient rich micro-environment reducing the strength of mutual
 exclusion.
description: Protistan OTUs from the world’s open oceans and seas came from de
 Vargas et al. (2015). This marine dataset is composed of 355 samples
 collected at the surface and deep chlorophyll maximum (DCM) in six oceans
 and two seas, which produced 366,800,845 protist reads of the V9
 hyper-variable region of the SSU-rRNA locus that clustered into 302,663
 OTUs. To allow for comparison, the version of this marine dataset used
 here was re-analyzed by Mahé et al. (2017). All filter-size classes
 libraries of either the surface or DCM at a single station were pooled
 together, thus the number of samples used here reduced to 47 for surface
 and 32 for DCM waters. This correspond to the "Dataset S1" of
 this archive which an OTU table provided into the standard JSON BIOM
 format. Protistan OTUs from three lowland Neotropical
 rainforests came from Mahé et al. (2017). This terrestrial dataset is
 composed of 144 samples collected at the soil surface, which produced
 46,652,206 protist reads of the V4 hyper-variable region of the SSU-rRNA
 locus that clustered into 26,860 OTUs. Sequence processing, OTU clustering
 with Swarm v2 (Mahé et al., 2015), and taxonomic assignments using the PR²
 database (Guillou et al., 2013). This correspond to the "Dataset
 S2" of this archive which an OTU table provided into the standard
 JSON BIOM format. The complete bash and R scripts to
 reproduce the analyses described in the linked publication (Lentendu
 & Dunthorn, 2021) are provided in the "File S1" in HTML
 format.
description: The two dataset files follow the BIOM format
 (http://biom-format.org/).<br>Ways to open these datasets are
 provided in section 2.1 and 2.2 of the "File S1".<br>The
 "File S1" is a standard HTML file which can be open with any web
 browser.<br>The full list of dependencies to run the analyses
 described in "File S1" is provide in the section 8 of this
 file.<br>All files were produced and analysed using Linux operating
 systems (Ubuntu 18.04.4 on a personal computer and Debian 4.19.12-1 on an
 HPC).