Uniqueness of tree stand composition and soil microbial communities are related across urban spruce-dominated forests

The dataset contains data obtained from urban spruce-dominated forests in southern Finland where we have measured tree stand composition, forest management history, soil chemical properties, and soil microbial communities. Data files include (1) microbial OTU tables describing microbial community composition (sequence read counts of Operational Taxonomic Units) across the study plots, (2) taxonomic assignments and other metadata related to OTUs, and (3) measured and calculated variables describing the characteristics of sites and their microbial assemblages (site metadata).

Methods
Method description below are summarized from the original publication.

Study area and sample plots

We collected data from 73 forest plots distributed in three urban centers in southern Finland: Helsinki region (45 sites in cities of Helsinki, Espoo and Vantaa), Lahti (8 sites), and Tampere (20 sites). Sample plots consisted of four interconnected 20 m × 20 m squares that were placed inside forest stands.

Measurement of forest stand characteristics

All living trees ≥ 5 cm at breast height (1.3 m) and all cut tree stumps with diameter ≥ 10 cm at cut surface were measured within the study plots. We assessed the degree of urbanization around sample plots by calculating the proportion of built land surface within 200 m radius around sample plot center based on Corine Land Cover 2018 GIS-dataset.

Soil sampling

Soil sampling was done between May 19th and July 19th 2022. Samples were collected from 16 regularly spaced grid points across each 0.16 ha study plot. Minimum distance between two sampling points was 10 m. At each point, we collected ca. 0.5 dl of material from the humus layer between the undecomposed litter layer and mineral soil. After removing the undecomposed surface litter and roots, material was extracted with a DNA-sterilized hand shovel down to the boundary between humus layer and mineral soil to a maximum depth of 15 cm. Samples were sieved through 2 mm mesh and stored in -20°C until further processing.

Soil chemical analyses

We measured the carbon (C) and nitrogen (N) content of the soil samples from combustion products with an elemental analyzer. We measured ergosterol as biomarker of fungal biomass. Ergosterol was extracted from 0.25 g soil samples and quantified with liquid chromatography.

DNA extraction and sequencing

DNA was extracted from two ca. 200 mg portions of material from each sample. For bacterial metabarcoding, V4 region of the 16S ribosomal RNA gene was amplified with primers 515F (GTGCCAGCMGCCGCGGTAA) and 806R (GGACTACHVGGGTWTCTAAT). For fungal metabarcoding, the Internal Transcribed Spacer 2 (ITS2) of the nuclear ribosomal RNA coding region was amplified with primers ITS3-2024F (GCATCGATGAAGAACGCAGC) and ITS4-2409R (TCCTCCGCTTATTGATATGC). Indexed amplicons were sequenced with Illumina NovaSeq 6000 (paired-end 250 bp).

Bioinformatics

Sequence reads were demultiplexed, and index and primer sequences were removed from paired-end reads (data available under BioProject ID PRJNA1012880 in NCBI sequence read archive). Individual R1 and R2 reads were first quality filtered, and then assembled. Assembled reads were chimera filtered de novo. Remaining reads were clustered into OTUs with 98% similarity threshold. OTUs were taxonomically assigned with 80% confidence cutoff using Naïve Bayesian Classifier trained with bacterial SILVA (v138) and fungal UNITE (v9, dynamic, all eucaryotes) databases in mothur v.1.36.1. Fungal OTUs were further assigned to functional groups according to FungalTraits database based on genus-level taxonomical assignments. OTU tables were finally filtered by discarding bacterial OTUs that had less than 1‰ relative abundance and fungal OTUs that had less 0.1‰ relative abundance in all samples and OTUs that had maximum read counts in negative controls.

Data Preparation

We calculated diversity indices for all bacteria, ectomycorrhizal (ECM) fungi and saprotrophic (SAP) fungi. We considered SAP fungi to include soil, litter, wood and unspecified saprotrophs. We estimated Shannon diversity with sample read depth of 30k for bacteria, 60k for ECM fungi, and 55k for SAP fungi using the estimateD function in the R package iNEXT (v3.0.9). To quantify the relative contributions of sites to taxonomic turnover of bacteria, ECM fungi and SAP fungi, we calculated local contributions to species replacement (LCBDrepl) based on distance matrices representing the replacement component of beta diversity using the beta.div.comp and LCBD.comp functions in the R package adespatial (v0.3-21). We calculated LCBDrepl for bacteria based on the quantitative indices of replacement (Sørensen, Podani family) and LCBDrepl for fungi based on presence-absence (Jaccard, Podani family). We counted OTUs with relative abundance >0.1‰ as present. To avoid conflating fungal taxa with (potentially artefactual) intraspecific sequence variants and other sequence reads with uncertain taxonomic affinity, we restricted the analysis to fungal OTUs that were identified to species-level and pooled together OTUs that were assigned to the same species before determining presence-absence. To quantify management intensity in forest sites, we reduced the logging history data (basal areas of cut stumps in five different decay classes) into two main gradients by running a principal component analysis on square root transformed basal areas of cut stumps and extracting site scores for the two first principal components. To quantify forest tree diversity at each site, we calculated the number of ECM tree species (ECM tree richness), Shannon diversity of tree species (TreeDiv) based on relative basal areas, and an index of relative uniqueness of tree stand composition (LCBD-Trees). LCBD Trees was calculated from a distance matrix (Sørensen, Podani family) derived from tree composition data (square root transformed basal areas of tree species).