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Data collected by fruit body– and DNA-based survey methods yield consistent species-to-species association networks in wood-inhabiting fungal communities

Cite this dataset

Saine, Sonja; Ovaskainen, Otso; Somervuo, Panu; Abrego, Nerea (2020). Data collected by fruit body– and DNA-based survey methods yield consistent species-to-species association networks in wood-inhabiting fungal communities [Dataset]. Dryad.


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.


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 ( 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).

Usage notes

The zip file contains three data files and four R script files. Excel file covariates includes the environmental covariates used in the analyses. The columns are the covariates (DC = decay class, GC = ground contact, and uprooted and disjunct corresponding to fall type) and the rows are the samples (i.e. the 100 study logs). Excel file new_FB_names 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 protax_0.5 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 (Script_S1_fit_model), 2) computing mixing statistics (Script_S2_compute_mixing_statistics), 3) plotting the mixing statistics (Script_S3_show_mixing), and 4) estimating the model parameters and for plotting variance partitioning, the effects of the covariates and the association networks (Script_S4_parameter_estimates).


University of Helsinki

Academy of Finland, Award: 308651

Academy of Finland, Award: 309581

Jane and Erkko Aatos foundation

Jane and Erkko Aatos foundation