DOI: 10.5061/dryad.sxksn030s
title: Data from: Fungal communities are important determinants of bacterial community composition in deadwood

description: Fungal-bacterial interactions play a key role in the functioning of many
 ecosystems. Thus, understanding their interactive dynamics is of central
 importance for gaining predictive knowledge on ecosystem functioning.
 However, it is challenging to disentangle the mechanisms behind species
 associations from observed co-occurrence patterns and little is known
 about the directionality of such interactions. Here we apply joint species
 distribution modelling to high-throughput sequencing data on co-occurring
 fungal and bacterial communities in deadwood to ask whether fungal and
 bacterial co-occurrences result from shared habitat use (i.e. dead wood’s
 properties), or whether there are fungal-bacterial interactive
 associations after habitat characteristics are taken into account.
 Moreover, we test the hypothesis that the interactions are mainly
 modulated through fungal communities influencing bacterial communities.
 For that, we quantified how much the predictive power of the joint species
 distribution models for bacterial and fungal community improved when
 accounting for the other community. Our results show that fungi and
 bacteria form tight association networks (i.e. some species pairs co-occur
 more frequently and other species pairs co-occur less frequently 
 than expected by chance) in deadwood that include common (or opposite)
 responses to the environment, as well as (potentially) biotic
 interactions. Additionally, we show that information about the fungal
 occurrences and abundances increased the power to predict the bacterial
 abundances substantially, whereas information about the bacterial
 occurrences and abundances increased the power to predict the fungal
 abundances much less. Our results suggest that fungal communities may
 mainly affect bacteria in deadwood. Importance Understanding the
 interactive dynamics between fungal and bacterial communities is important
 to gain predictive knowledge on ecosystem functioning. However little is
 known about the mechanisms behind fungal-bacterial associations and the
 directionality of species interactions. Applying joint species
 distribution modelling to high throughput sequencing data on co-occurring
 fungal-bacterial communities in deadwood, we found evidence that
 non-random fungal-bacterial associations derive from shared habitat use,
 as well as (potentially) biotic interactions. Importantly, the combination
 of cross-validations and conditional cross-validations helped us to answer
 the question about the directionality of the biotic interactions,
 providing evidence that suggests that fungal communities may mainly affect
 bacteria in deadwood. Our modelling approach may help gaining insight into
 the directionality of interactions between different components of the
 microbiome in other environments.
description: Study area and experimental design
 The study area where these datasets were collected was located in
 the 25-ha Zofin ForestGEO® Dynamics Plot in the Novohradské Hory
 mountains, Czech Republic (www.forestgeo.si.edu, 48°39´57´´N,
 14°42´24´´E). This area is part of the 42-ha core zone of the Žofínský
 prales National Nature Reserve (est. 1838) which has never been managed
 and it thus represents a virgin forest.  All trees
 belonging to <i>F. sylvatica</i>, <i>P. abies</i>
 and <i>A. alba</i>,  with DBH between 30 cm and 100 cm and,
 first recorded as dead and lying in 1975, 1997, 2008 or 2013, were
 identified. Trees decomposing as standing before they were downed were
 omitted to exclude logs with unclear decay lengths. Hence, a tree species
 (beech, spruce and fir), decay length (&lt;5, 5-15, 16-38 or
 &gt;38 years) and DBH was assigned to each log. Then, within each tree
 species and decay length class, logs were selected randomly, making a
 total of 118 logs. To obtain representative samples, four subsamples were
 obtained from each log in October 2013 using an electric drill with a bit
 diameter of 8 mm. Materials from all four subsamples of each log were
 pooled to give one composite sample per log. In each of the samples wood
 physical and chemical characteristics were measured and, fungal community
 data (barcoded gITS7 and ITS4 primers targeting fungal ITS2 (Ihrmark et
 al., 2012)) and bacterial community data (barcoded 515F and 806R primers
 targeting the V4 region of the bacterial 16S rRNA gene (Caporaso et al.,
 2011)) was collected using high throughput sequencing.
 Data processing The
 sequencing data were processed using SEED v 2.0.3 (Vetrovsky et al.,
 2018)  as described in (Baldrian et al., 2016) and (Tlaskal et al., 2017).
 For bacteria, pair-end reads were merged using fastq-join (Aronesty 2013).
 For fungi, only forward read sequences beginning with the primer gITS7
 were considered since for certain highly abundant wood-decomposing fungi
 (e.g., <i>Armillaria</i> spp.), ITS2 is longer than 550 bases
 and these sequences would be missed during pair-end joining. The whole or
 partial ITS2 was extracted from fungal amplicons using ITS Extractor 1.0.8
 (Nilsson et al., 2010). Sequences of inferior quality (mean Phred score
 below 30, all sequences with ambiguous bases) or length (&lt;40 bases)
 were removed. Chimeric sequences were detected and deleted using UCHIME
 implementation in USEARCH 7.0.1090 (Edgar et al., 2011). Sequences were
 clustered using UPARSE implemented in USEARCH (Edgar 2013) at a 97%
 similarity level. Consensus sequences were constructed for each cluster,
 and the closest hits at the species level were identified using BLASTn
 against UNITE (55) and GenBank for fungi and, Ribosomal Database Project
 (56) and GenBank for bacteria. The minimum and maximum read counts were
 1598 and 21375 for fungi, and, 1606 and 15113 for bacteria, respectively.
 This resulted in 4519 fungal and 21260 bacterial OTUs, out of which 263
 and 11601 were global singletons and were removed. Therefore, the final
 data consisted of 4256 fungal and 9659 bacterial OTUs.
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 2013. <i>The Open Bioinformatics Journal</i>, 7:1–8.
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