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.

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 F. sylvatica, P. abies and A. alba,  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 (<5, 5-15, 16-38 or >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., Armillaria 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 (<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.

References

Aronesty, E. 2013. The Open Bioinformatics Journal, 7:1–8.

Caporaso, J.G., et al. 2011. Proceedings of the National Academy of Sciences of the United States of America, 108:4516–4522.

Edgar, R.C. 2013. Nature Methods, 10:996–998. 

Edgar, R.C., et al. 2011. Bioinformatics, 27:2194–2200.

Cole, J.R., et al. 2014. Nucleic Acids Research, 42:D633-642.

Ihrmark, K., et al. 2012. FEMS Microbiology Ecology, 82:666–677.

Kõljalg, U., et al. 2013. Molecular Ecology, 22:5271–5277.

Nilsson, R.H., et al. 2010. Fungal Ecology, 3:284–287.

Větrovský, T., et al. 2018. Bioinformatics, 34:2292–2294.