DOI: 10.5061/dryad.ht76hdrsk
title: Data from: Road disturbance shifts root fungal symbiont types and reduces the connectivity of plant-fungal co-occurrence networks in mountains

description: Roads are currently one of the most disruptive anthropogenic disturbances
 to mountain ecosystems worldwide. These disturbances can have a profound
 effect on roadside soil properties and vegetation, typically favouring
 fast-growing and ruderal species. However, their effect on
 plant-associated fungal communities and plant-fungal interactions remains
 largely unknown. In this study, we examined the changes in root-associated
 fungal communities as well as plant-fungal and fungal-fungal co-occurrence
 networks along mountain roads from four biogeographical regions. We found
 that roadsides consistently altered plant and fungal community
 composition, generally favouring arbuscular mycorrhizal fungi and putative
 plant pathogens at the expense of ectomycorrhizal fungi. Moreover,
 roadsides consistently reduced the complexity of plant-fungal and
 fungal-fungal co-occurrence networks (with 66% to 95% and 40% to 94%
 reduction in total edge density, respectively), even though the richness
 of fungal communities was not reduced and many of the naturally occurring
 highly-connected taxa were still present. Our findings suggest that
 altered and transient conditions in the roadsides may favour more
 generalist symbionts like AMF and pathogens with low fidelity for
 particular hosts as opposed to surrounding natural vegetation which is
 dominated by symbionts with higher specificity for the host (like
 ectomycorrhizal fungi). We conclude that road disturbance may have a
 consistent negative imprint on connectivity between plants and fungi; a
 consequence that deserves attention as it could render mountain roadside
 systems unstable and vulnerable to further pressures such as climate
 change and invasive species.
description: All sampling sites were located along mountain roads in four
 distinct and distant regions: Norway, Spain (Tenerife), Chile, and
 Argentina. The Norwegian sites were situated in a subarctic climate along
 three roads spanning elevations from 13 to 683 m a.s.l. (6-7 elevation
 points per road; 20 sites in total). Vegetation transitioned from birch
 forests with ericaceous understory at lower elevations to dwarf-shrub
 tundra at higher elevations. In Tenerife, sites were located along three
 paved roads from 24 to 2377 m a.s.l. (7-8 elevation points per road; 22
 sites in total) on the southern slopes of the Teide volcano, where
 vegetation ranged from thermophilous scrubs at lower elevations to Canary
 pine forests and high mountain scrublands at higher elevations. Chilean
 sites were located along two paved roads from 378 to 1645 m a.s.l. (8 and
 11 elevation points per road; 19 sites in total), with vegetation shifting
 from Mediterranean and temperate forests to resinous forests and
 scrublands at higher elevations. The Argentinean sites were distributed
 along three gravel roads spanning elevations from 1755 to 3782 m a.s.l.
 (6-7 elevation points per road; 20 sites in total), transitioning from
 shrublands and herbaceous steppes at lower elevations to sparse cushion
 shrub and herbaceous vegetation at higher elevations.
 The experimental setup expanded on a global road survey
 initiative to monitor plant species composition along mountain roads. Each
 sampling site (81 in total) consisted of two large plots in a T-shape: one
 parallel to the road (2 m × 50 m) and the other perpendicular, extending
 from 2 to 102 m into the background vegetation. The parallel plots were
 designated as roadside (R) plots, and the perpendicular plots as
 background (B) plots. Within each plot, five subplots of 20 cm × 20 cm
 were selected, spaced approximately 10 m apart in the roadside plots and
 at increasing distances (5 m, 10 m, 20 m, 40 m, and 80 m) in the
 background plots. In Norway, due to logistical constraints, up to four
 subplots (10 m, 20 m, 40 m, and 80 m) were sampled instead of five. A
 total of 810 samples were expected (81 sites × 2 plots × 5 subplots), but
 due to occasional sampling challenges and failed sequencing, the final
 dataset included 673 samples with matching plant and fungal
 data. Vegetation surveys were conducted during the peak
 growing season in 2017 for Norway, in 2019 for Tenerife, and in 2018-2019
 for Argentina and Chile. Presence/absence and percentage cover of all
 plant species were recorded for each subplot. During surveys, root samples
 were collected from a random subset of plants in each subplot. Roots from
 all plants in a subplot were pooled, washed with ionized water, and stored
 in Ziplock bags at 4 °C before being shipped for analysis of
 root-associated fungi using Illumina amplicon sequencing (ITS1 region).
 Subplots were designed to ensure root sampling of most observed plant
 species, maximizing the likelihood of capturing interactions between
 plants and their associated fungi. The dataset includes
 an OTU table of root-associated fungi per subplot together with taxonomy
 and lifestyles, plant community per subplot and metadata per subplot
 indicating region, latitude, longitude, mean annual temperature,
 elevation, distance from the road, etc.  
description: # Road disturbance shifts root fungal symbiont types and reduces the
 connectivity of plant-fungal co-occurrence networks in mountains
 [https://doi.org/10.5061/dryad.ht76hdrsk](https://doi.org/10.5061/dryad.ht76hdrsk) ## Description of the data and file structure The R_code_Miren.txt is a text file containing examples of the code used in the paper.  Data_Miren.xlsx is an Excel file with three sheets containing the data from the study. The data were collected along mountain roads (at different elevations) in four distinct regions: Norway, Spain (Tenerife), Chile and Argentina. In each region, there were 2-3 mountain roads, each containing several sites. Within sites, there were 2 large plots, one located next to the road (R plot) and one away from the road (B plot). For each big plot, there were 2-5 subplots, which in the B plot had different distances from the road. The samples were collected during the peak of the growing season in 2017 for Norway, in 2019 for Tenerife, and in 2018-2019 for Argentina and Chile. ### Files and variables #### File: R\_code\_Miren.txt **Description:** This R script is related to the analyses described in the manuscript entitled: "Road disturbance shifts root fungal symbiont types and reduces the connectivity of plant-fungal co-occurrence networks in the mountains". The script exemplifies how the main analyses in the manuscript were conducted and how the figures were created. ## File: Data\_Miren.xlsx **Description:** This file contains three sheets: 1: metadata for all samples (metadata), 2. fungal community data (fun_data) and 3. plant community data (plant_data) 1. metadata contains the following variables:  -Sample_ID: Sample (taken from subplots) label used in the lab that can be connected with the labels of fun_data and plant_data and with the raw fungal sequencing data submitted to NCBI database (accession number PRJNA1182238); -Latitude [DD]: subplot latitude expressed in decimal fractions of a degree; -Longitude [DD]: subplot longitude expressed in decimal fractions of a degree; -Elevation [m]: subplot elevation in meters; -Distance_road [m]: distance of the subplot from the road in meters; -MAT: mean annual temperature at the site in °C; -Region: the region where the subplot is found (ARG - Argentina, CHL - Chile, NOR - Norway, TEN - Tenerife); -Road: The two-letter codes internally used to distinguish the roads within the region where the site, plot and subplots are located;  -Site_nr: site number within the road; -Plot: plot type (R- roadside, B - background) within the site; -Subplot: subplot number within the plot; 2. OTU table contains Sample_ID in rows and the relative abundances of operational taxonomic units (OTUs) in columns: -Taxonomy and lifestyles for each OTU (phylum, class, order, family, genus, species, lifestyle, guild) are presented at the bottom of the file (highlighted in yellow); -The explanation about which databases were used to assign guilds and lifestyles is highlighted in orange. 3. Plant community data contains Sample_ID in rows and percentage cover of plant species in columns.