DOI: 10.5061/dryad.3bk3j9kpc
title: Dataset for: Patterns of understory invasion in exotic, invasive timber stands

description: Current climate and land cover change threaten global mountaintops with
 increased spread of invasive species. Long-established plantations of
 exotic and invasive trees on these mountaintops can alter their
 surroundings, further increasing invader-facilitated invasion. Identifying
 the ecological conditions that promote such specific associations can help
 develop better management interventions.  The Western
 Ghats's Shola Sky Islands (&gt;1400m MSL) host vast stretches of
 exotic and invasive tree plantations that sustain colonisation of other
 invasive woody, herbaceous and fern species in their understories. Here we
 analysed vegetation and landscape variables from 232 systematically-placed
 plots in randomly selected grids using NMDS and Phi Coefficient
 approaches, to examine patterns of association (positive interactions)
 between understory invasive species with specific exotic and invasive
 overstory species. We also conducted GLMM with zero inflation to determine
 the influence of environmental variables where such associations
 occur.  We find that understory invasion of multiple species
 under the canopy of other exotic invasives is widespread across the Shola
 Sky Islands. Stands of Eucalyptus host the colonisation of 70% of
 non-native invasive species surveyed across the Shola Sky Islands. In
 particular, Lantana camara invasion is strongly associated with Eucalyptus
 stands.  We also found that climatic variables affect the
 colonisation of understorey woody invasive species, while invasion by
 exotic herbaceous species is associated with the density of road networks.
 Canopy cover impacts all invasives negatively, while incidence of fire was
 negatively associated with invasion by Lantana spp and the Pteridium spp.
  While the restoration of natural habitats largely targets the
 highly invasive Acacia, less invasive Eucalyptus and Pine are often not
 included. Our study suggests that retaining such exotic species in natural
 habitats, particularly protected areas, can hinder ongoing restoration
 efforts by facilitating further invasions by multiple woody and herbaceous
 species.  
description: The present study was conducted across the
 high-elevation montane forests of the Nilgiris and Anamalai-Palani hills
 landscapes (10.12⁰N 77.60⁰E to  11.50⁰N 76.70⁰E). We classified wooded
 habitats above 1400m MSL into shola forests and plantations following
 Arasumani et al. 2019. A point lattice of spacing 100 m between the points
 was overlaid using ArcGIS, and we randomly selected 0.5% of points.
 Squares of 200mx200m were placed over the lattice, with their centres
 coinciding with the selected points. We chose 143 random squares and
 surveyed up to eight circular plots of radius 7m within the grids placed
 systematically. Since our objective was to assess native regeneration
 under exotic overstorey, we did not include plots with native trees in
 their overstory in our analyses. The final numbers of plot samples were
 232. <br><br>Each grid cell consists of up to eight 7m-radius circular plots arranged systematically, and each plot has five circular sub-plots of radius 1m. The circumference at breast height (GBH) for all live trees and snags over 30 cm was measured within the circular plots. The trees were classified as native species, <em>Acacia</em> spp., <em>Eucalyptus</em> spp., pine spp., and others. The intensity of regeneration may depend on the amount of light reaching the floor, determined by the canopy cover, measured at five points inside the plot (four cardinal directions and the centre) using HabitApp, an android application (Bianchi et al., 2017). Within the subplots inside the plots, we measured the vegetation profile using a 5 m pole, calibrated at every 0.5 m. Calibrations at which vegetation touched the pole were noted. At each point, we also noted the presence of fire and counted the instances of invasive undergrowth. We identified the colonising invasive species at least up to the family level. From the locations collected for each plot, we determined the following variables through satellite images: elevation, slope, aspect, topographic position index and topographic convergence index. TPI was collected at a local scale, and TCI was collected at a scale of 30m after resampling the DEM.
description: We used the software R to conduct our analyses. The file is in
 CSV format and can be opened in Microsoft Excel.