DOI: 10.5061/dryad.tmpg4f4wm
title: Data collected by fruit body– and DNA-based survey methods yield consistent species-to-species association networks in wood-inhabiting fungal communities

description: Inferring interspecific interactions indirectly from community data is of
 central interest in community ecology. Data on species communities can be
 surveyed using different methods, each of which may differ in the amount
 and type of species detected, and thus produce varying information on
 interaction networks. Since fruit bodies reflect only a fraction of the
 woodinhabiting fungal diversity, there is an ongoing debate in fungal
 ecology on whether fruit body– based surveys are a valid method for
 studying fungal community dynamics compared to surveys based on DNA
 metabarcoding. In this paper, we focus on species-to-species associations
 and ask whether the associations inferred from data collected by
 fruit-body surveys reflect the ones found from data collected by DNA-based
 surveys. We estimate and compare the association networks resulting from
 different survey methods using a joint species distribution model. We
 recorded both raw and residual associations that respectively do not and
 do correct for the influence of the abiotic predictors when estimating the
 species-to-species associations. The analyses of the DNA data yielded a
 larger number of species-to-species associations than the analyses of the
 fruit body–based data as expected. Yet, we estimated unique associations
 also from the fruit-body data. Our results show that the directions of
 estimated residual associations were consistent between the data types,
 whereas the raw associations were much less consistent, highlighting the
 need to account for the influence of relevant environmental covariates
 when estimating association networks. We conclude that even though
 DNA-based survey methods are more informative about the total number of
 interacting species, fruit-body surveys are also an adequate method for
 inferring association networks in wood-inhabiting fungi. Since the DNA and
 fruit-body data carry on complementary information on fungal communities,
 the most comprehensive insights are obtained by combining the two survey
 methods.
description: The data consist of wood-inhabiting fungal community data
 collected by fruit-body data published in Ovaskainen et al. 2013 (ISME J)
 and ITS2 sequence data published in Mäkipää et al. 2017 (ISME J). For the
 study Saine et al. 2020 (Oikos), we generated new molecular data by
 applying PROTAX-fungi (Abarenkov et al. 2018) to the original sequence
 data, as well as we updated the species nomenclature originally published
 in Ovaskainen et al. (2013) by following the Index Fungorum (http://www.indexfungorum.org/) database. Here, we provide the molecular species data obtained with PROTAX-fungi, the updated fruit-body nomenclature data and data on environmental characteristics along with the R scripts for reproducing the results from Saine et al. 2020 (Oikos).
description: The zip file contains three data files and four R script files.
 Excel file <em>covariates</em> includes the environmental
 covariates used in the analyses. The columns are the covariates
 (<em>DC</em> = decay class, <em>GC</em> = ground
 contact, and <em>uprooted </em>and
 <em>disjunct </em>corresponding to fall type) and the rows are
 the samples (<em>i.e. </em>the 100 study logs). Excel file
 <i>new_FB_names </i>includes the names of the species in the
 fruit-body data that were updated to match the species nomenclature in the
 DNA-base data. Text file <em>protax_0.5 </em>contains the
 renewed species-level identifications of the DNA-based data with 0.5
 reliability threshold. The four R script file correspond to a script used
 for 1) constructing and fitting the model
 (<em>Script_S1_fit_model</em>), 2) computing mixing statistics
 (<em>Script_S2_compute_mixing_statistics</em>), 3) plotting
 the mixing statistics (<em>Script_S3_show_mixing</em>), and 4)
 estimating the model parameters and for plotting variance partitioning,
 the effects of the covariates and the association
 networks (<em>Script_S4_parameter_estimates</em>).