DOI: 10.5061/dryad.5qfttdzc1
title: Data for: Drainage impacts on the productivity of wetland species Spartina alterniflora and Salicornia pacifica

description: Coastal wetlands display ecohydrological zonation such that vertical
 differences of plant zones are driven by varying groundwater levels over
 tidal cycles. It is unclear how variable levels of tidal drainage directly
 impact biotic and abiotic factors in coastal wetland ecosystems. To
 determine the impacts of drainage levels, simulated tides in mesocosms
 with varying degrees of drainage were created with Spartina alterniflora,
 the salt marsh coastal ecosystem dominant species on the United
 States Atlantic Coast, and Salicornia pacifica, the Pacific Coast
 dominant. We measured biomass production and photosynthesis as indicators
 of plant health, and we also measured soil and porewater characteristics
 to help interpret patterns of productivity. These measures included above
 and belowground biomass, porewater pH, salinity,
 ammonium concentration, sulfide concentration, soil redox
 potential, net ecosystem exchange, photosynthesis rate,
 respiration rate, and methane flux. We found the greatest plant production
 in soils with intermediate drainage levels, with production
 values that were 13.7% higher for S. alterniflora and 57.7%
 higher for S. pacifica in the intermediate flooding levels than found
 in more inundated and more drained conditions. Understanding how
 drainage impacts plant species is important for predicting
 wetland resilience to sea level rise, as increasing water levels
 alter ecohydrological zonation.
description: This experiment was conducted using two ecosystem-dominant salt
 marsh species <em>Spartina alterniflora</em> and
 <em>Salicornia pacifica</em>.  Mesocosms were used to quantify
 the effects of the varying drainage treatments on the coastal wetland
 plant growth and soil conditions. The mesocosm soil was prepared by
 combining 25% marsh soil that was collected from the Tuckerton Peninsula,
 New Jersey (39.5388°, -74.3254°), 25% multipurpose sand (Lowes,
 Philadelphia, PA, USA), and 50% mushroom compost (Primex Garden Center,
 Glenside, PA, USA). Soils were screened at 2-mm to remove shell hash and
 coarse organic matter, homogenized with a cement mixer, and added to plant
 pots. The <em>S. alterniflora</em> plants were contained in
 pots that were made from cylindrical PVC pipes that were 20-cm long x
 10-cm in diameter and were screened at the bottom to allow for
 water exchange. The <em>S. pacifica</em> plants were contained
 in 20-cm long square (7.6 x 7.6-cm) treepots, also screened at the
 bottom.  We conducted a 2 x 4 factorial experiment
 where two types of plants were exposed to four different drainage
 treatments, with a total of 64 experimental units. Pots were placed in 150
 L tidal mesocosms constructed from stock tanks and painted white.
 Simulated tides were created using tubing to pump water between the
 experimental tank and a reservoir, using timers to create a once-daily
 three-hour high tide and a 21-hour low tide. Inside four separate tanks,
 there were four drainage treatments: where the low tide water level was
 never drained (0-cm), and where the low tide water levels were drained to
 5-cm, 10-cm, and 20-cm below the surface of the plant pot, The plants were
 propagated outdoors receiving natural precipitation. Plants were
 propagated in freshwater for one week, transitioned to 10 ppt salinity for
 one week, and finally changed over to 20 ppt salinity for the remainder of
 the experiment. Saline conditions were created utilizing Instant Ocean and
 adjusted weekly. Plants were propagated for 12 weeks from 13 May 2021 to 3
 August 2021. To measure the productivity of the wetland
 species <em>Spartina alterniflora</em> and
 <em>Salicornia pacifica</em>, the aboveground and belowground
 biomass of each treatment was measured. Aboveground and belowground
 biomass was harvested at the end of week 12. Stalks growing from the soil
 were cut, plants were washed over a 2-mm sieve, dried at 60°C, and
 weighed. Belowground biomass was separated from the soil by hand, rinsed
 over a sieve, dried at 60°C, and then weighed. Biomass values were scaled
 to a square meter through multiplication by a constant.
 Measurements of the effects of soil drainage on abiotic
 parameters included porewater salinity, pH, ammonium, and hydrogen
 sulfide. Porewater was collected with a push pointsampler on 7 August 2021
 (PPX36, MHE, East Tawas, MI, USA) at a depth of 20-cm. Samples for
 ammonium, pH, and salinity were frozen for later analysis. Subsamples (2
 mL) were fixed with 0.22% zinc acetate solution, and frozen, for later
 analysis of hydrogen sulfide. Samples were analyzed for salinity and pH
 using a calibrated portable pH/Conductivity meter (Star A325
 pH/Conductivity Portable Multiparameter Meter, Thermo Scientific Orion).
 UV-vis spectrophotometry was used for measurements of porewater ammonium
 using the indophenol blue method. Hydrogen sulfide was measured
 spectrophotometrically on the zinc acetate-fixed samples following the
 methylene blue method. All samples below the detection limit (~0.25 µM)
 were assigned a sulfide concentration of zero for the purposes of data
 analysis. Redox potential measurements were taken at depths of 5-cm and
 15-cm for the S<em>partina
 </em><em>alterniflora</em> soils, and depths of 3-cm and
 9-cm for <em>Spartina pacifica</em> soils to reflect the size
 of the pots using a bench-top redox meter (Bench-Top pH/mV Meter, Sper
 Scientific). Carbon dioxide and methane fluxes were
 measured to calculate the photosynthesis, respiration, and net ecosystem
 exchange (NEE) rates of the plants. The fluxes were measured in the field
 on 7 August 2021 from 10 a.m. to 4 p.m. (Eastern Standard Time), using an
 LGR ultra-portable greenhouse gas analyzer via cavity ring-down
 spectroscopy (ABB-Los Gatos Research, San Jose, CA, USA). Positive fluxes
 were defined as those in which gas concentrations increased over time
 within the chamber, and negative fluxes as those in which concentrations
 decreased (Nakanoa et al., 2004). Photosynthesis rates were obtained by
 measuring the carbon dioxide concentration as a function of time in the
 light and dark conditions, and then subtracting the dark measurement from
 the light measurement. Respiration rates were obtained by measuring the
 carbon dioxide concentration as a function of time in the dark
 conditions.  All statistical analyses were performed in
 R 4.2.3 (R Core Team, 2023). Bar graphs and scatter plots were made using
 the package ggplot2. The responses of the study plants
 <em>Spartina</em> and <em>Salicornia </em>to
 drainage were analyzed using permutational multivariate analysis of
 variance (perMANOVAs; Anderson, 2001), utilizing the R package
 <em>vegan</em>. The dependent variables included above and
 belowground biomass, porewater pH, salinity, ammonium concentration,
 sulfide concentration, soil redox potential, NEE, photosynthesis rate,
 respiration rate, and methane flux. We partitioned the variability and
 obtained F-statistics on matrices of Euclidean distances calculated from
 the original raw data. P-values were calculated using 10,000 random
 permutations of the appropriate exchangeable units. Where values of
 certain variables were missing, the means of a treatment group factor were
 added to the matrix to ensure each variable had the same number of values.
 Redox was rank-transformed prior to analysis to remove negative values.
 Because the interaction term was significant, we conducted contrast tests
 using the same function (adonis2), with a Bonferroni correction applied
 for the number of contrasts (0.05/16 = 0.0031). Because
 the results of the contrasts did not reveal strong groupings, we conducted
 follow-up two-way ANOVAs to identify the factors contributing to
 significant differences between the species and drainage levels. ANOVAs
 were performed on rank-transformed data to account for the non-normal
 error structure. ANOVAs were run in base R using the
 <em>aov</em> command. Follow-up least-square means comparisons
 tests were completed on main effects and interactions, where appropriate,
 using package <em>lsmeans</em> using the Tukey or Sidak
 methods for p-value adjustments as appropriate. A
 principal component analysis (PCA) was used for dimension reduction of the
 multivariate measurements to examine variability between treatments and
 plant species, as well as to determine species and treatment grouping
 patterns with respect to the dependent variables. The eigenvalues provide
 a measure of the amount of variance explained by each principal component.
 The data matrix was constructed using the 11 dependent variables which
 included above and belowground biomass, porewater pH, salinity, ammonium
 concentration, sulfide concentration, soil redox potential, NEE,
 photosynthesis rate, respiration rate, and methane flux.  The packages
 used for the PCA include <em>cor</em>,
 <em>ggcorrplot</em>, <em>factoextra</em>. To
 perform the PCA, the data was first normalized, and a correlation matrix
 was computed and visualized. Visualization of the PCA was constructed as a
 biplot of the first vs. second principal components. A Kaiser-Meyer-Olkin
 (KMO) test was used to determine how suited the data is for factor
 analysis, utilizing the <em>EFAtools</em> package. A
 Bartlett's test of sphericity was used to test for significant
 correlations among at least some of the variables, utilizing the psych
 package. As many of the measured variables varied in
 response to the drainage treatments in a multi-dimensional way, we
 constructed loess curves (span=0.40) to describe the relationship between
 treatment and the response variables of above and belowground biomass,
 porewater salinity, porewater pH, porewater ammonium and sulfide
 concentration, NEE, and redox potential. A loess curve is a nonparametric
 method for smoothing a series of data and was used because of the
 non-linear structure of the relationship between drainage and response
 variables.
description: # Data for: Drainage impacts on the productivity of wetland species
 Spartina alterniflora and Salicornia pacifica ## Description of the data
 and file structure: "all\_data\_7.csv"; Description: Record of
 all measurements recorded throughout the project, separated by plant
 species Drainage treatments: zero (drained 0-cm), five (drained 5-cm), ten
 (drained 10-cm), twenty (drained 20-cm); Plant species: *Spartina
 alterniflora* (SA), *Salicornia pacifica* (SP); Parameters: * Aboveground
 Biomass (g/m2) * Belowground Biomass (g/m2) * Porewater pH * Porewater
 Salinity (ppt) * Soil Redox Potential (mV) * Methane Flux (uM/m2/s) *
 Photosynthetic uptake (uM/m2/s of CO2, where negative values indicate
 uptake) * Respiration Rate (uM/m2/s of CO2) * Net Ecosystem Exchange (NEE)
 (uM/m2/s) * Porewater Ammonium (ug/L) * Porewater Sulfide (uM/L)
 "all\_data\_8.csv"; Description: Same as
 "all\_data\_7.csv" EXCEPT that the column heading names are
 altered and the treatments (e.g., 0-cm) are listed numerically rather than
 in text. Both versions of the file are uploaded to match inputs for R code
 (below). ## Sharing/Access information Links to other publicly accessible
 locations of the data: Data is described in the publication titled
 Drainage Impacts on the Productivity of Wetland Species *Spartina
 alterniflora* and *Salicornia pacifica;* ## Code/Software “Permanova.R”;
 Software: Run using coding language R version 4.2.3; Use: Code for to test
 for associations between the parameters; Sheets from the file
 “all\_data\_7.csv” are the data input for this code; Packages:
 “Datawizard” version 0.8.0 (Patil et al. 2022); "Vegan" version
 2.6-4 (Oksanen et al. 2022); “Permanova.R”; Software: Run using coding
 language R version 4.2.3; Use: Code for to test for associations between
 the parameters; Sheets from the file “all\_data\_7.csv” are the data input
 for this code; Packages: “Datawizard” version 0.8.0 (Patil et al. 2022);
 "Vegan" version 2.6-4 (Oksanen et al. 2022); “Anovas”; Software:
 Run using coding language R version 4.2.3; Use: Code to compare variances
 across the means of different groups; Sheets from the file
 “all\_data\_7.csv” are the data input for this code; Packages: “lsmeans”
 version 2.30-0 (Lenth et al. 2018); “Correlations and PCA Code.R”;
 Software: Run using coding language R version 4.2.3; Use: Code for to
 calculate correlations and to create correlation plot; Code for to perform
 PCA and to create PCA plot; Sheets from the file “all\_data\_8.csv” are
 the data input for this code; Packages: "cor" version 0.4.4
 (Kuhn et al. 2021), "ggcorrplot" version 0.1.4 (Kassambara
 2022), factoextra version 1.0.7 (Kassambara 2020); “Bar plots and scatter
 plots1.R”; Software: Run using coding language R version 4.2.3; Use: Code
 for to create bar plots and scatter plots plots; Sheets from the file
 “all\_data\_8.csv” are the data input for this code; Packages:
 "ggplot2" version 3.4.2 (Wickham 2005)