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Data from: Successful recovery of native plants post-invasive removal in forest understories is driven by native community features

Petri, Laís1; Ibáñez, Inés1

Published Sep 21, 2023 on Dryad. https://doi.org/10.5061/dryad.mpg4f4r5w

Cite this dataset

Petri, Laís; Ibáñez, Inés (2023). Data from: Successful recovery of native plants post-invasive removal in forest understories is driven by native community features [Dataset]. Dryad. https://doi.org/10.5061/dryad.mpg4f4r5w

Abstract

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.

README: 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

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; 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; 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; 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; 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.

Please, cite the original paper (once it is published) if using any data or code shared here.

Funding

National Science Foundation, Award: DEB-1252664

University of Michigan–Ann Arbor, Matthaei Botanical Gardens and Nichols Arboretum