Community-based long-term management to address reinvasion of restored grassland vernal wetlands
Data files
Nov 05, 2025 version files 5.01 MB
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2019_2023.Rmd
64.31 KB
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Del_Sol_and_Camino_Corto_Permanent_Quadrats_2019_2023.csv
3.64 MB
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Del_Sol_Camino_Corto_Thatch_Biomass.csv
17.76 KB
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ds_cc_vp_polygons.csv
2.31 KB
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edna_angiosperms_by_quadrat.csv
1.26 MB
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metadata.csv
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README.md
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Abstract
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.
Dataset DOI: 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.
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-m2 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 BiofungicideTM 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 aov and anova 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 emmeans 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 adonis2 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 pairwise_adonis function developed by Maurice Goodman (O’Leary et al. 2021). We also conducted a Similarity Percentages analysis on community matrices using the simper 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.” Journal of Statistical Software 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.” The R journal 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. Nature Methods, 13(7), 581–583.
Gentry J (2024). Annotate: Annotation for microarrays. 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." Ecological Engineering 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." Estuaries and Coasts 44, no. 8 (2021): 2250-2264.
Osborne, Jason. "Improving your data transformations: Applying the Box-Cox transformation." Practical Assessment, Research, and Evaluation 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/>.