DOI: 10.5061/dryad.2rbnzs807
title: Data from: Beyond the trail: understanding non-native plant invasions in mountain ecosystems

description: In an era of rapid climate change and increased human pressure on
 mountains, the introduction and spread of non-native plant species have
 become a critical focus of research. Road construction and motorized
 vehicle traffic are often cited as major sources of disturbance and
 dispersal vectors facilitating the encroachment of non-native plants into
 these ecosystems. In contrast, the impacts of outdoor activities, such as
 hiking, remain less studied, despite hiking trails being a key component
 of tourist movements in mountains and providing access to higher
 elevations that are often free of plant invasions. Our aim was to conduct
 a multi-regional assessment of the abiotic, biotic, and anthropogenic
 drivers of non-native plant species distribution along hiking trails. To
 achieve this, we implemented a standardized sampling design across nine
 mountain regions on six continents. By establishing T-shaped sample sites
 parallel to trails and leading perpendicular into adjacent vegetation, we
 assessed the main drivers contributing to non-native species presence,
 richness, and cover. We found that at the global scale abiotic (climatic)
 variables explained most of the variation of non-native species presence
 and richness, while for non-native species cover, biotic factors were most
 important. Anthropogenic factors, including distance to the trail and
 livestock grazing, played a relatively minor role. While the total number
 of non-native species differed across regions, the patterns explaining
 plant invasions were consistent. Our results show that trails were
 important conduits for non-native plants into mountain areas, even if
 anthropogenic drivers had a lower impact on non-native species
 distribution along trails than previously observed along roads.
 Importantly, non-native species were not constrained to trails, suggesting
 that wandering off-trail by hikers and domestic animals plays an important
 role in the spread of non-native species away from trail edges. This
 highlights the importance of limiting off-trail visitor use in areas of
 high conservation value. 
description: The dataset has been collected using the MIREN T trail protocol,
 as described in Liedtke et al. (2020) [<strong>Liedtke, R., Barros,
 A., Essl, F., Lembrechts, J.J., Wedegärtner, R.E.M., Pauchard, A.
 & Dullinger, S. (2020) Hiking trails as conduits for the spread of
 non-native species in mountain areas. Biological Invasions, 22,
 1121–1134.]. </strong>The dataset   includes nine mountain regions
 on six continents, including countries in North America (USA), South
 America (Argentina and Chile), Europe (Sweden, Norway, Czech Republic),
 Africa (South Africa), Asia (China) and Oceania (Australia) (Table 1). It
 covers 55 mountain trails, 680 sample sites and more than 2000
 plots. Each regional contributor/s submitted the
 dataset to the lead authors (Barros, A., Fuentes Lillo, E., Lembrechts,
 J.) using the template format developed through the MIREN T trail
 protocol. The dataset includes information on: environmental information
 (site code, transect, plot, elevation, geographic coordinates, distance to
 trail head, distance to trail edge, trail use intensity, exotic
 herbivory), bioclimatic variables (Bio 1, Bio 5, Bio 6, Bio 12), and
 vegetation variables (native and non-native species richness, native and
 non-native cover, non-native occurrence, shrub cover, tree cover). In
 addition, the occurrence of each non-native species and its frequency was
 provided by each regional contributor. This information is included
 Supplementary Information in the manuscript.  The
 information was curated in Excel and R and all statistical analyses were
 performed using R software.  We include in this
 repository the Excel file and the scripts used to undertake the
 statistical analyses.
description: # Data from: Beyond the trail: understanding non-native plant invasions in
 mountain ecosystems
 [https://doi.org/10.5061/dryad.2rbnzs807](https://doi.org/10.5061/dryad.2rbnzs807) ## Description of the data and file structure #### File: Barros_et_al._2024.R **Description:**  The R script was used to conduct the analyses for the manuscript entitled: "**Beyond the trail: understanding non-native plant invasions in mountain ecosystems".** #### File: Trail_data_Final.xlsx **Description:** The Excel file contains information on the MIREN T trail survey for the 9 mountain regions and 55 trails. The dataset includes 680 sample sites and 2112 plots.  ##### Variables * Country = name of the country where the study was conducted (categorical) * Region = the MIREN region within the country where the study was conducted (categorical) * Trail= the name of the trail where the MIREN T trail survey was conducted (categorical) * Transect= the number of the transect of the MIREN T trail survey (integer) * Elevation= meters above sea level (integer) * Latitude= recorded in decimal degrees * Longitude= recorded in decimal degrees * Nn_rich= non-native richness (integer) * Nn_cover= non-native cover (percentage cover, continuous from 0 to 100) * Nn_occurrence= non-native occurrence (binomial, 0 or 1) * Sh_cover= shrub cover (percentage cover, continuous from 0 to 100) * N_rich= native richness (integer) * Canopy = canopy/tree cover (percentage cover, continuous) * Dist. trail= distance to trail head (in meters, continuous) * Plot = Plot 1, 2, 3. As referred in the MIREN T trail protocol (ordinal) * Exoticherb= presence/absence of exotic herbivores in the plot (categorical, yes/no) * Trail_intensity= the level of use of the trail based on visitor counters, logbooks or contributors knowledge (categorical: Low, Medium, High). * Bio1 = bioclimatic variable,  annual mean temperature, expressed in degree celsius. * Bio5  = bioclimatic variable, maximum temperature of the warmest month, expressed in degree celsius. * Bio6  = bioclimatic variable, minimum temperature of coldest month, expressed in degree celsius. * Bio12 = bioclimatic variable, annual precipitation, expressed in millimeters. **Note:** When data was not recorded on a plot the symbol '-' is given.  ## Code/software The R software (version 4.4.1) is needed to upload and run the scripts. The packages used to run the script are the following: * library(readxl) * library(vegan) * library(ape) * library(dplyr) * library(factoextra) * library(FactoMineR) * library(glmmTMB) * library(MuMIn) * library(tidyverse) * library(ggplot2) * library(lme4) * library(performance) * library(AER) * library(DHARMa) * library(TMB) * library(bbmle) * library(ggeffects) * library(gridExtra) The workflow followed is described in the R script.  ## Access information Other publicly accessible locations of the data: * Not applicable Data was derived from the following sources: * Not Applicable