DOI: 10.5061/dryad.7m0cfxq79
title: Community-based long-term management to address reinvasion of restored grassland vernal wetlands

description: Exotic annual grasses can dominate ecosystems by producing a thick layer
 of dead plant litter, hereafter “thatch”, that promotes the regeneration
 of exotic grasses and inhibits native plants. Vernal pool
 wetlands within a grassland matrix are threatened by these exotic annual
 grasses, meriting the need for long-term management.  We utilized
 the investment of local community members to test the efficacy of
 long-term thatch management on urban vernal pool plant
 assemblages. We recruited over 40 undergraduate students to
 perform manual annual summer thatch removal around the edges of 15 urban
 vernal pools for four years.  We coupled thatch removal with
 annual native seed addition because our analysis of environmental DNA
 (“eDNA”) in the soil seed bank revealed a lack of native plant species and
 an abundance of exotic plant species. We measured vegetation
 composition in a set of 180 permanent monitoring quadrats (12 per
 experimental pool) over five years to quantify the effect of annual thatch
 removal and native seed addition on thatch, bare ground, native plant
 species cover and richness, and exotic plant species cover and
 richness. Our annual thatch removal treatment successfully
 reduced thatch accumulation and increased bare ground, but it did not
 result in a consistent decrease in exotic plant cover or increase in
 native plant cover. Instead, the effects of thatch manipulation
 on plant composition were modulated by annual precipitation, with exotic
 species increasing during dry years and native species increasing during
 wet years. The addition of native plant seed caused an increase
 in native plant species richness, but only after three years of annual
 thatch removal and native seed addition. Our results indicate
 that the restoration of native vernal pool plants can be limited by
 invasive species, native seed availability, and annual
 precipitation. Our findings show how engaging the local community
 in the long-term restoration of urban ecosystems can address the
 persistent threat of invasion and build up capacity for native plant
 populations to increase over time.
description: Species Composition Surveys Plant species
 cover and richness were monitored annually starting in August 2019 before
 the first treatment year to determine baseline vegetation composition (all
 species were identifiable even after senescence), and then in June 2020 to
 2023.  Percent cover of each native and exotic species was measured in 12
 permanent 1-m<sup>2</sup> quadrats in each pool (including the
 six quadrats that were seeded in the experiment) using a quadrat with 1%
 subdivisions.  Total percent cover of each quadrat often exceeded 100% due
 to multiple canopy layers of graminoids and forbs.
 Additionally, we measured the richness of native and exotic plant
 species in the seed bank prior to experimental manipulations.  We
 collected and mixed three 8 cm-deep soil cores from each of the permanent
 monitoring quadrats using a 4 cm-diameter auger in July 2019.  We spread
 50 g of each homogenized sample over PRO-MIX© BX
 Biofungicide<sup>TM</sup> potting soil in germination trays. 
 Trays were set up outside in November 2019 and hand-watered weekly. 
 Mauchamp and colleagues (2002) concluded that only identifying species
 that germinated from field soil cores in a controlled environment in a
 traditional “grow-out” analysis was inadequate in capturing the total seed
 bank diversity in wetlands; therefore, we assessed seed bank diversity via
 soil DNA sequencing according to protocols developed by Stephanie Ma
 Lucero and colleagues (pers. comm.).  We sieved each homogenized soil
 sample through a 4 mm sieve, then mixed 5 g of the homogenized soil with
 liquid nitrogen and extracted DNA using the Qiagen DNeasy® PowerSoil® Pro
 Kit.  Extracted DNA was amplified using standard polymerase chain reaction
 (PCR) protocols, using the rbcL forward and reverse primers.  Samples
 underwent two PCR amplification cycles to attain sufficient DNA for
 sequencing, and amplicons were then cleaned using AMPure beads.  Amplicons
 were sequenced using an Illumina 600-cycle MiSeq Reagent Kit V3. 
 Sequences were cleaned using the “dada2” coding package and then matched
 with family, genus, and species using the National Center for
 Biotechnology Information’s Basic Local Alignment Search Tool (BLAST),
 BLAST+, and the “annotate” coding package (Gentry 2024; Callahan et al.
 2016).  Sequences were matched to taxa based on the BLAST Maximum Score
 metric. Data Analysis All analyses
 and visualization were performed in Rstudio version 1.4.1106 (R Core Team
 2023).  All graphs were generated using the functions in the package
 “ggplot2”. We compared the cumulative effects of
 treatments on diversity metrics using an analysis of variance via the
 <strong>aov</strong> and <strong>anova</strong>
 functions from the “stats” package.  We summed the percent cover of every
 exotic or native plant species to obtain the total exotic or native plant
 cover for each quadrat at each sampling time.  We also used the “vegan”
 package to calculate the Simpson’s Index including all native and exotic
 species for each quadrat at each time to assess how treatments affected
 species evenness and richness (Oksanen et al. 2022).
 Most of the datasets were not normally distributed based on
 diagnostic residual tests, which thus required the use of models that
 accounted for the particular distributions that best fit the datasets.  We
 constructed repeated measures generalized linear mixed-effects models
 (GLMMs) with sampling year, thatch manipulation treatment, and seeding
 treatment, and their interaction effects, included as fixed effects, and
 quadrat included as a random effect to account for repeated measures.  The
 thatch percent cover dataset followed a beta distribution based on
 diagnostic residual tests.  The bare ground percent cover and total native
 plant percent cover datasets were zero-inflated, so we constructed hurdle
 models with gamma distributions.  Moreover, the percent bare ground
 dataset exhibited unequal variances, so percent bare ground was
 log-transformed using the appropriate Box-Cox transformation (Osborne
 2019).  Total exotic plant percent cover among quadrats also followed a
 gamma distribution and exhibited unequal variances, so total exotic plant
 percent cover was square-root-transformed using the appropriate Box-Cox
 transformation.  Native and exotic plant species richness datasets
 followed a Poisson distribution.  GLMMs were generated using the “glmmTMB”
 and “lme4” packages (Brooks et al. 2017; Bates et al. 2014).  Post-hoc
 Tukey’s Honestly Significant Difference tests were performed on GLMM
 outputs using the <strong>emmeans</strong> function from the
 “emmeans” package to determine significant interactions among year, thatch
 treatment, and seeding addition on dependent variables (Lenth
 2023). Additionally, we determined differences in plant
 community composition among thatch and seeding treatments by using a
 permutational multivariate analysis of variance (PERMANOVA) via the
 <strong>adonis2</strong> function of the  “vegan” package
 (Oksanen et al. 2022).  Post-hoc pairwise comparisons between two-way
 factorial thatch manipulation and seeding treatments were evaluated using
 the <strong>pairwise_adonis</strong> function developed by
 Maurice Goodman (O’Leary et al. 2021).  We also conducted a Similarity
 Percentages analysis on community matrices using the
 <strong>simper</strong> function of the “vegan” package
 (Oksanen et al. 2022). Literature Cited
 Bates, Douglas, Martin Mächler, Ben Bolker, and Steve Walker.
 “Fitting linear mixed-effects models using lme4.” <em>Journal of
 Statistical Software</em> 67, no. 1 (2014).
 Brooks, Mollie E., Kasper Kristensen, Koen J. Van Benthem, Arni
 Magnusson, Casper W. Berg, Anders Nielsen, Hans J. Skaug, Martin Machler,
 and Benjamin M. Bolker. “glmmTMB balances speed and flexibility among
 packages for zero-inflated generalized linear mixed
 modeling.” <em>The R journal</em> 9, no. 2 (2017):
 378-400. Callahan, B. J., Paul J. McMurdie, Michael J.
 Rosen, Andrew W. Han, Amy Jo A. Johnson, and Susan P. Holmes. (2016).
 DADA2: High-resolution sample inference from Illumina amplicon
 data. <em>Nature Methods</em>, <em>13</em>(7),
 581–583. Gentry J (2024). <em>Annotate:
 Annotation for microarrays</em>. R package version 1.82.0.
 Lenth R (2023). emmeans: Estimated Marginal Means, aka
 Least-Squares Means. R package version 1.8.9, https://CRAN.R-project.org/package=emmeans. Mauchamp, André, Philippe Chauvelon, and Patrick Grillas. "Restoration of floodplain wetlands: opening polders along a coastal river in Mediterranean France, Vistre marshes." <em>Ecological Engineering</em> 18, no. 5 (2002): 619-632. Oksanen J, Simpson G, Blanchet F, Kindt R, Legendre P, Minchin P, O'Hara R, Solymos P, Stevens M, Szoecs E, Wagner H, Barbour M, Bedward M, Bolker B, Borcard D, Carvalho G, Chirico M, De Caceres M, Durand S, Evangelista H, FitzJohn R, Friendly M, Furneaux B, Hannigan G, Hill M, Lahti L, McGlinn D, Ouellette M, Ribeiro Cunha E, Smith T, Stier A, Ter Braak C, Weedon J (2022). vegan: Community Ecology Package. R package version 2.6-4. https://CRAN.R-project.org/package=vegan. O’Leary, Jennifer K., Maurice C. Goodman, Ryan K. Walter, Karissa Willits, Daniel J. Pondella, and John Stephens. "Effects of estuary-wide seagrass loss on fish populations." <em>Estuaries and Coasts</em> 44, no. 8 (2021): 2250-2264. Osborne, Jason. "Improving your data transformations: Applying the Box-Cox transformation." <em>Practical Assessment, Research, and Evaluation</em> 15, no. 1 (2019): 12. R Core Team (2023). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/&gt;.
description: # Community-based long-term management to address reinvasion of restored
 grassland vernal wetlands Dataset DOI:
 [10.5061/dryad.7m0cfxq79](10.5061/dryad.7m0cfxq79) ## Description of the
 data and file structure From 2019 to 2023, we monitored 15 vernal pools
 within Del Sol Vernal Pool Preserve (Del Sol) and Camino Corto Open Space
 (Camino Corto).  These grassland vernal pools were created or restored
 (e.g., topsoil grading, basin excavation, enhancement of berms around pool
 basins, native seed addition) in the 1980s and 1990s.  However, at the
 time of the experiment, the pools were surrounded by unrestored grassland
 that was dominated by exotic annual grasses.  These invasive grasses can
 produce a thick layer of dead plant matter, or “thatch”, that promotes the
 regeneration of exotic grasses while inhibiting native plants.  We set up
 an experiment to test the impacts of annual thatch manipulation, coupled
 with native seed addition in a fully-factorial design, on native and
 exotic plant cover and diversity.  We assigned each pool one of three
 treatments: 1) raking and removal of thatch from the pool (Thatch 
 Removal), 2) disturbance of thatch by raking without direct removal (i.e.,
 to stimulate faster decomposition; Thatch Disturbance); 3) no raking or
 removal (Control).  We then set up 12 1 m x 1 m quadrats in each pool (180
 quadrats total).  In six of the 12 quadrats, we seeded seven native
 species: *Juncus bufonius*, *Juncus occidentalis*, *Stipa pulchra*,
 *Grindelia camporum*, *Phalaris lemonnii*, and *Hordeum brachyantherum*. 
 Plant species cover and richness, as well as bare ground and thatch cover
 and depth, were monitored annually starting in 2019 before the first
 treatments to determine baseline vegetation composition, and then in
 2020-2023 after each year of annual  thatch manipulation treatments and
 seed addition.  Total percent cover of each quadrat often exceeded 100%
 due to multiple canopy layers of graminoids and forbs.  Additionally, we
 measured thatch biomass every 2-3 months during the summer/fall to measure
 thatch decomposition rates.  We did this by collecting, drying, and
 weighing thatch from 10 cm x 10 cm quadrats throughout each pool; we also
 collected, dried, and weighed the thatch that was raked from the 1 m x 1 m
 Thatch Removal quadrats. We also measured the below-ground diversity,
 i.e., the soil seed bank, of each experimental quadrat in 2019 to better
 understand the species composition baseline before treatments.  We did
 this in two ways: 1) collecting soil from each quadrat, cultivating it in
 a greenhouse, and recording the species that germinated; and 2) collecting
 soil from each quadrat and performing environmental DNA (“eDNA”) analysis
 to determine what plant DNA was present in the soil. We analyzed our
 above- and below-ground plant composition data using generalized linear
 mixed effects models (GLMMs) and non-metric multidimensional scaling
 (NMDS), and we used box plots to visualize our data.  We found that our
 annual thatch removal treatment successfully reduced thatch accumulation
 and increased bare ground, but it did not result in consistent decrease in
 exotic plant cover or increase in native plant cover.  Instead, the
 effects of thatch manipulation on plant composition were modulated by
 annual precipitation, with exotic species increasing during dry years and
 native species increasing during wet years.  The addition of native plant
 seed caused an increase in native plant species richness, but only after
 three years of annual thatch removal and native seed addition.  Our
 results indicate that the restoration of native vernal pool plants can be
 limited by invasive species, native seed ability, and annual
 precipitation. ### Files and variables #### File:
 Del_Sol_and_Camino_Corto_Permanent_Quadrats_2019_2023.csv
 **Description:** This dataset contains the above-ground species
 composition surveys of the 180 experimental quadrats, monitored
 2019-2023.  Data stored in tidy format as a .csv.  Each row corresponds to
 the percent cover recorded of one species in the specified quadrat at the
 specified date. ##### Variables * ObjectID: automatically-generated unique
 identifier for the row in the .csv file * GlobalID:
 automatically-generated unique identifier for the row in the Survey123
 cloud server * ParentGlobalID: automatically-generated unique identifier
 for each quadrat per date in the Survey123 cloud server * Monitoring
 Date: manually-entered date of observation * Monitor Names: name of the
 person performing the monitoring survey * Location: the site, either
 “camino_corto” or “del_sol”, in which the observed quadrat is located *
 Pool ID: unique name of the pool in which the observed quadrat is located
 * Zone: the portion of the pool, either the “edge” of the pool within 3 m
 of the upland grassland, or the “transition” from the edge of the pool to
 the native-dominated center of the pool, in which the observed quadrat is
 located * Quadrat ID: unique name of the observed quadrat * Quadrat
 Notes: optional column including any additional observations or edits to
 the observation * Percent Bare Ground: the estimated percentage of the
 observed quadrat not covered by vegetation or thatch * Percent Thatch: the
 estimated percentage of the observed quadratic covered by plant litter
 from the previous year * Thatch Depth (cm): the average depth of the
 thatch in the observed quadrat * Native Species
 Richness: automatically-calculated sum of all of the native species found
 in the observed quadrat * Sum of Native Cover: automatically-calculated
 sum of the percent cover of all the native species found in the observed
 quadrat * Nonnative Species Richness: automatically-calculated sum of all
 of the nonnative species found in the observed quadrat * Sum of Nonnative
 Cover: automatically-calculated sum of the percent cover of all the
 nonnative species found in the observed quadrat * Unknown Species
 Richness: automatically-calculated sum of all of the
 unknown/unidentifiable species found in the observed quadrat * Sum of
 Unknown Cover: automatically-calculated sum of the percent cover of all
 the unknown/unidentifiable species found in the observed quadrat * Sum of
 Other Cover: automatically-calculated sum of the estimated percentages of
 the quadrat covered by anything besides vegetation, thatch, and bare
 ground (e.g., trash) * Sum of All Cover: automatically-calculated sum of
 percent thatch, percent bare ground, percent other cover, and percent of
 all the species found in the quadrat * CreationDate: automatically-filled
 date that the survey entry was created for each quadrat (which usually
 matches the manually-entered Monitoring Date) * Creator: the username of
 the Survey123 account that was used to submit the survey of each quadrat *
 EditDate: automatically-filled date that a quadrat’s survey entry was
 edited, if any edits were needed after data were collected in the field
 (if not, it is simply the same as the CreationDate) * Editor: the username
 of the Survey123 account that was used to edit the survey of each quadrat,
 if any edits were needed after data were collected in the field (if not,
 it is simply the same as the Creator) * x: automatically-recorded
 longitude at which the observation was made, in decimal-degrees (using the
 GPS of the mobile device on which the survey was taken) *
 y: automatically-recorded latitude at which the observation was made, in
 decimal-degrees (using the GPS of the mobile device on which the survey
 was taken) * Year: the year in which the survey was taken *
 QuadratYear: the Quadrat ID and the Year concatenated, to create a unique
 identifier for each quadrat for each year * Treatment: blank column that
 can be filled in with metadata describing which thatch manipulation the
 quadrat received * Seeded: blank column that can be filled in with
 metadata describing which seeding treatment the quadrat received * Native
 Status: esignates each observed species as “native” or “exotic” to
 California * Species: the scientific name of the observed species *
 Percent Cover: the estimated percentage of the quadrat that is covered by
 the observed species * The last four “CreationDate”, “Creator”,
 “EditDate”, and “Editor” columns are duplicates of the previous columns of
 the same name that were recorded for each species in the observed quadrat.
 #### File: metadata.csv **Description:** This dataset contains metadata
 for each of the 180 experimental quadrats, designating its location and
 assigned treatments.  Data stored in tidy format as a .csv.  Each row
 corresponds to one quadrat.  ##### Variables * Location: the site, either
 “camino_corto” or “del_sol”, in which the quadrat is located * Pool
 ID: unique name of the pool in which the quadrat is located * Zone: the
 portion of the pool, either the “edge” of the pool within 3 m of the
 upland grassland, or the “transition” from the edge of the pool to the
 native-dominated center of the pool, in which the quadrat is located *
 Quadrat ID: unique name of the quadrat * Treatment: the thatch
 manipulation treatment assigned to each quadrat, either 1 (Thatch
 Removal), 2 (Thatch Disturbance), or 3 (Control) * Seeded: the native seed
 addition treatment assigned to each quadrat, either 0 (no seed added) or 1
 (native seed added) #### File: ds_cc_vp_polygons.csv **Description:** This
 dataset contains metadata on the 15 pools in the study, namely the size of
 each pool as recorded in the winter of 2019 by a Trimble GPS unit.  Data
 stored in tidy format as a .csv.  Each row corresponds to one pool.  #####
 Variables * Location: the site, either “camino_corto” or “del_sol”, in
 which the pool is located * Pool_ID: unique name of the pool *
 Notes: optional column for denoting any special circumstances regarding
 the pool * GPS_Date: automatically-filled date that the pool area was
 measured * GPS_Time: automatically-filled PST time that the pool area was
 measured * Acres: acreage of each pool * GlobalID: unique identifier for
 each pool that is automatically generated by the Trimble GPS unit *
 Shape__Area: area of the pool in square feet *
 Shape__Length: circumference of the pool in feet * Area_m2: area of each
 pool in square meters, calculated from “Shape__Area” #### File:
 Del_Sol_Camino_Corto_Thatch_Biomass.csv **Description:** This dataset
 contains the thatch biomass data for the 10 cm x 10 cm and 1 m x 1 m
 quadrats of collected, dried, and weighed thatch from 2019-2023.  Data
 stored in tidy format as a .csv.  Each row corresponds to the biomass of
 one thatch sample from the specified pool at the specified time. #####
 Variables * Month: numeric month in which the biomass was collected *
 Year: year in which the biomass was collected * Pool: the unique name of
 the pool from which the biomass was collected * Treatment: blank column
 that can be filled in with metadata describing which thatch manipulation
 or seeding treatment the quadrat received * Quadrat ID: the unique name of
 the quadrat from which the biomass was collected * Biomass (g): the dry
 weight of the biomass, in grams #### File: edna_angiosperms_by_quadrat.csv
 **Description:** This dataset contains the DNA sequences extracted from
 the soil samples collected from the 180 experimental quadrats in 2019. 
 Data stored in tidy format as a .csv.  Each row corresponds to a plant DNA
 fragment sequence extracted from the specified quadrat. ##### Variables *
 The first column is an automatically-generated unique identifier for the
 row in the .csv file. * quadrat_id: the unique name of the experimental
 quadrat * sequence: the nucleotide sequence * abundance: the number of
 times the sequence was detected in the quadrat *
 ...1: automatically-generated unique identifier of the sequence sample *
 x: duplicate column of "...1" * blast: blank column that can be
 filled with information from the BLAST result of each sequence *
 pool_id: the unique identifier of the pool from which the sample was
 collected * clade: the taxonomic clade that the sequence is classified
 under based on its identified genus * genus: scientific genus and
 identified for the sequence through BLAST * species: scientific species
 identified for the sequence through BLAST * notes: optional column
 including any specific notes about how BLAST results were interpreted for
 the sequence * Native (1) vs nonnative (0): designates each identified
 species as native (1) or nonnative (0) to California *
 genus_species: concatenated genus and species to provide the full
 scientific name identified for the sequence #### File: 2019_2023.Rmd
 **Description:** This is the RStudio Markdown file that contains the
 annotated code used for the cleaning, analysis, and visualization
 performed on the data to produce the results and figures in the
 manuscript. ## Code/software Included in this database is an RStudio
 Markdown file, 2019_2023.Rmd, which contains the annotated code used for
 the cleaning, analysis, and visualization performed on the data to produce
 the results and figures in the manuscript.   All analyses and
 visualization were performed in Rstudio version 1.4.1106 (R Core Team
 2023).  All graphs were generated using the functions in the package
 “ggplot2”.   The packages "tidyverse" and "janitor"
 were used to clean the raw data.  The packages "plotrix",
 "sjPlot", and "knitr" were used to visualize the
 data.  The packages "FSA", "lme4",
 "lmerTest", "emmeans", "glmmTMB",
 "car", "MASS", "vegan", "labdsv",
 and "indicspecies" were used to analyze the data. ## Access
 information Data was derived from the following sources: * Data was
 derived from Survey123 records. Survey123 cloud files are available upon
 request.