DOI: 10.5061/dryad.mpg4f4r5w
title: Data from: Successful recovery of native plants post-invasive removal in forest understories is driven by native community features

description: Temperate forest understories hold the majority of the plant diversity
 present in these ecosystems and play an essential role in the recruitment
 and establishment of native trees. However, the long-term persistence of
 healthy forest understories is threatened by the impacts of invasive
 plants. As a result, a common practice is the removal of the agent of
 invasion. Despite this, we know little about the success of these
 practices and lack a comprehensive understanding of what intrinsic and
 extrinsic factors shape the recovery. In a multi-year field experiment, we
 investigated (Q1) whether native propagule availability drove native
 community recovery, (Q2) what the characteristics of successfully
 recovering communities were, and (Q3) under which environmental conditions
 recovery rates were faster. After initial removal of invasives, we seeded
 native species to manipulate assembly history and mimic restoration
 practices, we also implemented a repeated, vs. once, removal treatment,
 all in a full-factorial design. We collected data on plant species
 composition and abundance (i.e., species level percent cover) and on
 environmental conditions (i.e., light and soil water availability) in the
 three subsequent summers. Our results show that native community recovery
 rates were independent of seeding additions or frequency of invasive plant
 removal. The fastest rates of recovery were associated with high native
 species richness, native communities with higher values of specific leaf
 area (SLA), and low drought stress years. Our results suggest that
 restoration practices post-invasive plant removal should be tailored to
 enhance natural dispersal, or artificial addition if the resident
 community is species-poor, of native species with traits compatible with
 high resource availability, such as species with high SLA. In addition to
 the importance of the native community characteristics, our results
 underscore the need for assessing environmental conditions, favoring
 management practices during years of low drought stress to maximize native
 community recovery.
description: # Data from: **Successful recovery of native plants post-invasive removal
 in forest understories is driven by native community features** The files
 in DRYAD allow readers to run models to reproduce results and figures of
 the paper "Successful recovery of native plants post-invasive removal
 in forest understories is driven by native community features",
 currently submitted to Ecological Applications. The code to reproduce
 results and figures can be found at
 [https://github.com/laispetri/TemperateForestRestoration.git](https://github.com/laispetri/TemperateForestRestoration.git) ## Description of the data and file structure The code was build in RMarkdown. All packages needed are listed in the beginning of code, along with extra information on how to organize folders within the working directory. Here is the description of the CSV files: * "data.csv": ID = unique identifier per observation (integer); forest\_stand = site (character); plot = plot original number (integer); subplotOriginal = treatment number [one-time removal/multi-year removal = 1/7 - natural regeneration; 3/9 - forbs and grasses added; 4/10 - forbs added] (integer); Cover2019 = native percent cover in 2019 (double); Cover1 = native percent cover in 2020 (double); Cover2 = native percent cover in 2021 (double); Cover3 = native percent cover in 2022 (double); RichNJN1 = native richness in June of 2020 (integer); RichNJN2 = native richness in June of 2021 (integer); RichNJN3 = native richness in June of 2022 (integer); smJL1 = volumetric soil water content (%) in July of 2020 (double); smJL2 = volumetric soil water content (%) in July of 2021 (double); smJL3 = volumetric soil water content (%) in July of 2022 (double); forbAG1 = native forb percent cover in August of 2020 (double); grassAG1 = native graminoids percent cover in August of 2020 (double); woodyAG1 = native woody percent cover in August of 2020 (double); forbAG2 = native forb percent cover in August of 2021 (double); grassAG2 = native graminoids percent cover in August of 2021 (double); woodyAG2 = native woody percent cover in August of 2021 (double); SLA61 = native community weighted mean of specific leaf area in June of 2020 (double); SLA62 = native community weighted mean of specific leaf area in June of 2021 (double); SLA63 = native community weighted mean of specific leaf area in June of 2022 (double); light1 = available % light in peak grenness of 2020 (double); light2 = available percent light in peak grenness of 2021 (double); light3 = available % light in peak grenness of 2022 (double); subplot = ordered subplot number based on subplotOriginal (integer). * "richnessN.csv": forest\_stand = site (character); plot = plot original number (integer); subplot = treatment number [one-time removal/multi-year removal = 1/7 - natural regeneration; 3/9 - forbs and grasses added; 4/10 - forbs added] (integer); year = year in which the data was collected (integer); PctCover\_100\_N = native percent cover (double); Richness\_N = number of native species within a plot (integer). * "coverI2019.csv": forest\_stand = site (character); plot = plot original number (integer); subplot = treatment number [one-time removal/multi-year removal = 1/7 - natural regeneration; 3/9 - forbs and grasses added; 4/10 - forbs added] (integer); CoverI2019 = invasive percent cover within a subplot (double); year = year in which the data was collected (integer). * "dominantI.csv": plot = plot original number (integer); subplot = treatment number [one-time removal/multi-year removal = 1/7 - natural regeneration; 3/9 - forbs and grasses added; 4/10 - forbs added] (integer); year = year in which the data was collected (integer); sppName = invasive species with the largest percent cover within a subplot (character). * "traitsSeedMix.csv": Species\_name: native species added via seeding (character); Presence = present [1] or absent [0] across all sampled subplots x years(integer); frequency = number of times a species was recorded across all sampled subplots x years (integer); MixType = type of seed mix added (character); SLA\_mm2.mg = species specific leaf area value in mm2.mg (double); SLAmean = mean specfic leaf area of all native species recorded across all sampled subplots x years (double); SLAsd = standard deviation of specfic leaf area of all native species recorded across all sampled subplots x years(double). * "traitsDiversity.csv": acronym: species acronym (character); SLA\_mm2.mg = specific leaf area value in mm2.mg. * "VPD.csv": ID = unique identifier (integer); VPD\_cumsum = cumulative sum of vapor pressure deficit from May to August; year = year the data was collected. Cells with missing data are filled with NAs. ## Sharing/Access information All four CSV files needed to run this code can be found in DRYAD [https://doi.org/10.5061/dryad.mpg4f4r5w](https://doi.org/10.5061/dryad.mpg4f4r5w). Please, cite the original paper (once it is published) if using any data or code shared here.