Nitrogen and carbon concentrations and stable isotope ratios: Data from a ^15^N tracer study in short-form _Spartina alterniflora_ and _Distichlis spicata_'
In NitrogenUptake2016: Data and Source Code From: Nitrogen Uptake and Allocation Estimates for Spartina Alterniflora and Distichlis Spicata

if(!require(knitr)){
  install.packages("knitr", repos='http://cran.us.r-project.org')
}
if(!require(rmarkdown)){
  install.packages("rmarkdown", repos='http://cran.us.r-project.org')
}
if(!require(plyr)){
  install.packages("plyr", repos='http://cran.us.r-project.org')
}
if(!require(reshape2)){
  install.packages("reshape2", repos='http://cran.us.r-project.org')
}
if(!require(ggplot2)){
  install.packages("ggplot2", repos='http://cran.us.r-project.org')
}
if(!require(scales)){
  install.packages("scales", repos='http://cran.us.r-project.org')
}
if(!require(MuMIn)){
  install.packages("MuMIn", repos='http://cran.us.r-project.org')
}
if(!require(rsq)){
  install.packages("rsq", repos='http://cran.us.r-project.org')
}
# then load the package:
library(knitr)
library(rmarkdown)
library(plyr)
library(reshape2)
library(ggplot2)
library(scales)
library(MuMIn)
library(rsq)
library(NitrogenUptake2016)


knitr::opts_chunk$set(echo = TRUE, comment=NA)

^a^United States Environmental Protection Agency, Office of Research and Development, 27 Tarzwell Drive, Narragansett, RI 02882, United States

^1^Corresponding author, Troy_Hill@nps.gov; Current address: National Park Service, South Florida Natural Resources Center, 950 North Krome Avenue, Homestead, FL 33030, United States

^b^Yale University, School of Forestry and Environmental Studies, 205 Prospect Street, New Haven, CT 06511, United States

^c^University of New Hampshire, Department of Natural Resources and the Environment, 46 College Road, Durham, NH 03824, United States

^d^Humboldt State University, College of Natural Resources and Sciences, 1 Harpst Street, Arcata, CA, 95521, United States

*Corresponding author: hill.troy@gmail.com

# if the NitrogenUptake2016 package isn't installed, use devtools to do so:
# devtools::install_github("troyhill/NitrogenUptake2016", build_vignettes = TRUE)

# set some constants
todaysDate <- substr(as.character(Sys.time()), 1, 10)
core.area <- pot.m2 <- 0.00801185 # mesocosm surface area (m2)
top.vol   <- core.area * 0.05 * 1e6 # cm3 in core: top 5 cm only
pointSize <- 2 # for ggplot graphics
pd <- pd2 <- position_dodge(1.2)
pd3 <- position_dodge(0.8)
grayColor <- 0.55
fig2Col   <- "gray55"

# Allometry ----------------------------------------------------------------

##### Establish relationships following Lu et al. 2016
CSP <- plyr::dlply(allometry, c("spp"), bCM)
CSP.coef <- plyr::ldply(CSP, stats::coef)
CSP.coef$lam <- plyr::ddply(allometry, c("spp"), function(df)  bCM(df, lam.only = TRUE))[, "V1"]
CSP.coef$BIC <- plyr::ldply(CSP, stats::BIC)[, "V1"]
CSP.coef$rsq <- plyr::ldply(CSP, MuMIn::r.squaredGLMM)[, "R2m"]
#CSP.coef

### estimate plant masses from allometry
for (i in 1:nrow(stemHeights)) {
  if (stemHeights$species[i] %in% "DS") {
    stemHeights$mass[i] <- (stemHeights$height_cm[i] * CSP.coef[1, 3] + CSP.coef[1, "(Intercept)"])^(1/CSP.coef$lam[1]) # DISP
  } else if (stemHeights$species[i] %in% "SA") {
    stemHeights$mass[i] <- (stemHeights$height_cm[i] * CSP.coef[2, 3] + CSP.coef[2, "(Intercept)"])^(1/CSP.coef$lam[2]) # SPAL
  }
}

###
### compare predicted (allometry) and observed masses at harvest
###


# get live + dead biomass for each pot at each date
dat.ld <- plyr::ddply(stemHeights, plyr::.(core_num, species, date), plyr::summarise,
                live = sum(mass[dead_live %in% "L"], na.rm = TRUE),
                dead = sum(mass[dead_live %in% "D"], na.rm = TRUE)
)

# add cohort number
dat.ld$day <- as.POSIXct(as.character(dat.ld$date), format = "%y%m%d", origin = "1960-01-01")
dat.ld$cohort <- NA

for (i in 1:length(unique(dat.ld$day))) {
  uniqueIDS <- unique(dat.ld$core_num[dat.ld$day == sort(unique(dat.ld$day))[i]])
  if (length(uniqueIDS) == 3) {
    dat.ld$cohort[dat.ld$core_num %in% uniqueIDS] <- ifelse(sum(is.finite(dat.ld$cohort)) == 0, 1, max(dat.ld$cohort, na.rm = TRUE) + 1)
  }
}
dat.ld$cohort[is.na(dat.ld$cohort)] <- 0
dat.ld$cohort[dat.ld$cohort == 5] <- 4


dat.ld$year <- 2016
dat.ld$dead <- 0
dat.ld$pot2  <- paste0(dat.ld$core_num, "-", dat.ld$species)



### allometry estimates
for (i in 1:length(unique(dat.ld$pot2))) {
  targPot <- unique(dat.ld$pot2)[i]
  maxDate <- max(dat.ld$day[dat.ld$pot2 %in% targPot])
  tempDat <- dat.ld[(dat.ld$pot2 %in% targPot) & (dat.ld$day == maxDate), ]
  if (i == 1) {
    dat.ld.sub  <- tempDat
  }
  if (i > 1) {
    dat.ld.sub <- rbind(dat.ld.sub, tempDat)
  }
}
dat.ld.sub$id <- paste0(dat.ld.sub$species, "-", dat.ld.sub$core_num)

### observed values
obs <- plyr::ddply(CN_mass_data[CN_mass_data$sample.type %in% c("leaf 1", "leaf 2", "leaf 3", "leaf 4", # "dead leaf",
                                            "leaf 5", "leaf 6", "leaf 7", "leaf 8", "leaf 9", "leaf 10", "leaf 11",
                                            "stems"), ], plyr::.(time, species, new.core.id), plyr::summarise,
             g        = sum(g_core, na.rm = T)
)
obs$core_num <- as.character(as.integer(substr(obs$new.core.id, 3, 4)))
obs$core_num[as.integer(obs$core_num) > 12] <- paste0(obs$species[as.integer(obs$core_num) > 12], as.integer(obs$core_num[as.integer(obs$core_num) > 12]) - 12, "_T0")
obs$id <- paste0(obs$species, "-", obs$core_num)

obs <- plyr::join_all(list(obs, dat.ld.sub[, c(4,9:10)]), by = "id")
names(obs)[which(names(obs) %in% "live")] <- "allom.est"
obs$diff <- (obs$allom.est - obs$g)             # magnitude accuracy of allometry prediction (g)
obs$diff.pct <- (obs$allom.est - obs$g) / obs$g # percent accuracy of allometry prediction
obs$species2 <- ifelse(obs$species %in% "SA", "italic(S.~alterniflora)", "italic(D.~spicata)")
summary(obs$diff)

# Relative growth rates ---------------------------------------------------
stemHeights$RGR <- stemHeights$lateStartDate <- NA

for(i in 1:length(unique(stemHeights$id[stemHeights$dead_live %in% "L"]))) { # 830 plants
  targPlant <- unique(stemHeights$id[stemHeights$dead_live %in% "L"])[i]
  subDat <- stemHeights[stemHeights$id %in% targPlant, ]
  if (nrow(subDat) > 1) {
    subDat$mass2 <- c(NA, subDat$mass[1:(nrow(subDat)-1)])
    subDat$day2 <- c(subDat$day[1], subDat$day[1:(nrow(subDat)-1)])
    subDat$RGR <- (log(subDat$mass) - log(subDat$mass2)) / as.numeric(difftime(subDat$day, subDat$day2, units = "days"))
    ### add RGRs to dat.live object
    stemHeights$RGR[(which(rownames(stemHeights) %in% rownames(subDat)))] <- subDat$RGR
    if (!is.na(subDat$height[1]) & (subDat$height[1] < 6)) {
      stemHeights$lateStartDate[which(rownames(stemHeights) %in% rownames(subDat))] <- 1 # indicates if high-growth period is captured
    }
  }
}

stemHeights$RGR[!is.finite(stemHeights$RGR)] <- NA

# mean growth rate for each plant, then each mesocosm
mean.rgr     <- plyr::ddply(stemHeights[stemHeights$dead_live %in% "L", ], plyr::.(id, new.core.id, species), plyr::summarise, meanRGR = mean(RGR, na.rm = TRUE))
mean.rgr.pot <- plyr::ddply(mean.rgr, plyr::.(new.core.id, species), plyr::summarise, RGR = mean(meanRGR, na.rm = TRUE))
mean.rgr.pot <- mean.rgr.pot[1:24, ]

# Stem density and biomass ------------------------------------------------

### summarize by pot and session
dat.live <- stemHeights[stemHeights$dead_live %in% "L", ]
ddHgt2 <- plyr::ddply(dat.live, plyr::.(date, day, core_num, species), plyr::numcolwise(sum, na.rm = TRUE))
ddHgt2$day <- as.POSIXct(ddHgt2$day, origin = "1960-01-01")
ddHgt2$cohort <- NA

for (i in 1:length(unique(ddHgt2$date))) {
  uniqueIDS <- unique(ddHgt2$core_num[ddHgt2$date == sort(unique(ddHgt2$date))[i]])
  if (length(uniqueIDS) == 3) {
    ddHgt2$cohort[ddHgt2$core_num %in% uniqueIDS] <- i
  }
}
ddHgt2$cohort <- (ddHgt2$cohort - 1) / 2
ddHgt2$cohort[is.na(ddHgt2$cohort)] <- 0


ddHgt4 <- plyr::ddply(ddHgt2, plyr::.(day, cohort, species), plyr::numcolwise(mean, na.rm = TRUE))
ddHgt4.se <- plyr::ddply(ddHgt2, plyr::.(day, cohort, species), plyr::numcolwise(se))
names(ddHgt4.se) <- paste0(names(ddHgt4.se), ".se")
ddHgt4 <- cbind(ddHgt4, ddHgt4.se[, c(5, 7, 9)])
ddHgt4$session <- as.POSIXct(ddHgt4$day, origin = "1960-01-01")
ddHgt4$species2 <- ifelse(ddHgt4$species %in% "SA", "italic(S.~alterniflora)", "italic(D.~spicata)")



####
#### Calculate Milner-Hughes NAPP
####
test <- nappCalc2(dataset = dat.ld[dat.ld$cohort > 0, ], liveCol = "live", siteCol = "pot2", timeCol = "day", summarize = "TRUE", annualReset = "FALSE")
napp <- test$summary
napp$pot2 <- as.character(napp$site)
napp$core_num <- as.numeric(sapply(strsplit(napp$pot2, "-"),  "[[", 1))
napp$species  <- sapply(strsplit(napp$pot2, "-"),  "[[", 2)

napp$cohort <- dat.ld$cohort[match(napp$pot2, dat.ld$pot2)]
napp$new.core.id <- ifelse(napp$core_num > 9, paste0(napp$species, napp$core_num), paste0(napp$species, "0", napp$core_num))


### build object to merge with master: average RGR, stem characteristics
# mean growth rate for each plant, then each mesocosm
mean.rgr     <- plyr::ddply(stemHeights[stemHeights$dead_live %in% "L", ], plyr::.(id, new.core.id, species), plyr::summarise, meanRGR = mean(RGR, na.rm = TRUE))
mean.rgr.pot <- plyr::ddply(mean.rgr, plyr::.(new.core.id, species), plyr::summarise, rgr = mean(meanRGR, na.rm = TRUE))
mean.rgr.pot <- mean.rgr.pot[1:24, ]

mean.dens     <- plyr::ddply(stemHeights[stemHeights$dead_live %in% "L", ], plyr::.(new.core.id, date), plyr::summarise, stem.density = sum(!is.na(height_cm)))
mean.dens.pot <- plyr::ddply(mean.dens, plyr::.(new.core.id), plyr::summarise, dens = mean(stem.density / pot.m2, na.rm = TRUE)) # mean of all stem density msrmts for a pot
mean.dens.pot <- mean.dens.pot[1:24, ]

rgr.stems <- plyr::join_all(list(mean.rgr.pot, mean.dens.pot), by = "new.core.id")

# Denitrification enzyme activity -----------------------------------------

dea$species  <- substr(dea$pot, 1, 2)
dea$species2 <- ifelse(dea$species %in% "SA", "italic(S.~alterniflora)", "italic(D.~spicata)")
dea$DEA.m2   <- dea$DEA * dea$bd_gcm3 * (top.vol / core.area) # * 5 * 1e4 # [flux per gram] * [g/cm3] * [5 cm depth] * [cm2/m2]
dea$IV.m2    <- dea$IV * dea$bd_gcm3  * (top.vol / core.area) # * 5 * 1e4
dea$gap.g    <- dea$DEA - dea$IV
dea$gap.m2   <- dea$DEA.m2 - dea$IV.m2

dd.dea <- plyr::ddply(dea, plyr::.(species, species2), plyr::summarise,
                flux.g = mean(DEA),
                flux.m2 = mean(DEA.m2),
                flux.g.se = se(DEA),
                flux.m2.se = se(DEA.m2)
)
dd.dea$type <- "DEA"

dd.iv <- plyr::ddply(dea, plyr::.(species, species2), plyr::summarise,
               flux.g = mean(IV),
               flux.m2 = mean(IV.m2),
               flux.g.se = se(IV),
               flux.m2.se = se(IV.m2)
)
dd.iv$type <- "GHG"

dd.dea <- rbind(dd.dea, dd.iv)


### stats on DEA/IV N2O flux
dea.m     <- reshape2::melt(dea, id.vars = c("species", "species2", "pot"), measure.vars = c(2, 3, 8:11))
dea.m$ID  <- paste0(dea.m$species, "-", dea.m$variable)

# dea.g.aov <- stats::aov(value ~ factor(variable) * factor(species), data = dea.m[(dea.m$variable %in% c("IV.m2", "DEA.m2")), ])
# car::Anova(dea.g.aov, type = 2, singular = TRUE) # difference between methods, but not species

# how much 15N was added to each pot?
KNO3    <- 2.24047           # grams of KNO3 added
soln    <- 2027.5            # final volume of spike solution (mls)
KNO3_mw <- 102.1032       # g/mol
N_mw    <- 15                # gN/mol KNO3
mls     <- 12                # mls added per mesocosm
spike   <- KNO3 * (N_mw / KNO3_mw) / soln * mls # total grams of 15N added to each mesocosm


# calculate atom pct from per mil values
CN_mass_data$n_ap <- ap(CN_mass_data$d15n)

# get background 15N AP
bkd <- plyr::ddply(CN_mass_data[as.character(CN_mass_data$time) %in% "t0", ], plyr::.(species, pool_label), plyr::summarise,
             n15 = mean(n_ap, na.rm = T),
             se.n15 = se(n_ap)
)

# subtract background to get 15N excess (if result is negative, 15Nxs = 0)
CN_mass_data$n15xs <- as.numeric(NA)
for(i in 1:nrow(CN_mass_data)) {
  if(!is.na(CN_mass_data$species[i])) {
    spp  <- CN_mass_data$species[i]
    pool <- CN_mass_data$pool_label[i]

    a <- bkd[(bkd$species %in% spp) & (bkd$pool_label %in% pool), "n15"]

    if((is.numeric(a) & (length(a) > 0))) { # excludes all pools we don't have bkd for
      difference <- CN_mass_data$n_ap[i] - a # NOT decimal fraction
      ifelse (!is.na(difference) & (difference > 0), CN_mass_data$n15xs[i] <- difference, CN_mass_data$n15xs[i] <- 0)
    } else {
      difference <- CN_mass_data$n_ap[i] - 0.370317701 # assume bkgd of 11 d15N
      ifelse ((difference > 0) & !is.na(difference), CN_mass_data$n15xs[i] <- difference, CN_mass_data$n15xs[i] <- 0)
    }
  }
}


### Additional correction to belowground live biomass, subtracting 15Nxs of dead MOM to account for sorption
CN_mass_data$n15xs_MOM <- CN_mass_data$n15xs # n15xs still useful for total recoveries
for(i in 1:nrow(CN_mass_data)) {
  if (CN_mass_data$time[i] %in% paste0("t", 1:4)) {
    if(!is.na(CN_mass_data$depth_top[i])) {
      spp    <- CN_mass_data$species[i]
      coreID <- CN_mass_data$new.core.id[i]
      pool   <- CN_mass_data$pool_label[i]
      depth_bottom <- CN_mass_data$depth_bottom[i]

      a <- CN_mass_data[(CN_mass_data$new.core.id %in% coreID) & (CN_mass_data$pool_label %in% paste0("dead biomass ", depth_bottom, "cm")) &
                   (CN_mass_data$depth_bottom == depth_bottom), "n15xs"]

      if((is.numeric(a) & (length(a) > 0))) { # excludes all pools we don't have bkd for
        difference <- CN_mass_data$n15xs[i] - a # NOT decimal fraction
        ifelse (!is.na(difference) & (difference > 0), CN_mass_data$n15xs_MOM[i] <- difference, CN_mass_data$n15xs_MOM[i] <- 0)
      } else {
        difference <- CN_mass_data$n_ap[i] - 0.370317701 # assume bkgd of 11 d15N
        ifelse ((difference > 0) & !is.na(difference), CN_mass_data$n15xs_MOM[i] <- difference, CN_mass_data$n15xs_MOM[i] <- 0)
      }
    }
  }
}



CN_mass_data$n15_g_pg_recov  <- CN_mass_data$n15xs  / 100 * CN_mass_data$n_pct  # for total recovery
CN_mass_data$n15_g_recov     <- CN_mass_data$n15_g_pg_recov  * CN_mass_data$g_core  # for total recovery

CN_mass_data$n15_g_pg <- CN_mass_data$n15xs_MOM / 100 * CN_mass_data$n_pct
CN_mass_data$n15_g    <- CN_mass_data$n15_g_pg  * CN_mass_data$g_core
CN_mass_data$n_core   <- CN_mass_data$n_pct * CN_mass_data$g_core

# get 15Nxs mass in each pool in each core
# total recovery using belowground pools (rather than bulk sediment)
mat2 <- plyr::ddply(CN_mass_data[!CN_mass_data$sample.type %in% c("bulk sediment"), ], plyr::.(time, species, new.core.id), plyr::summarise,
              n15_2 = sum(n15_g_recov, na.rm  = T)
)
mat2$recovery2 <- mat2$n15_2 / (spike) # "recovery" uses bulk belowground data, "recovery2" uses root pools
mat2$days <- as.numeric(substr(mat2$time, 2, 2)) * 7

### relps between napp and 15n recoveries (excludes dead biomass)
bgd <- plyr::ddply(CN_mass_data[CN_mass_data$sample.type %in% c("coarse roots", "fine roots", "rhizomes"), ],
             plyr::.(time, species, new.core.id), plyr::summarise,
             g        = sum(g_core, na.rm = T),
             n15    = sum(n15_g, na.rm  = T),
             n_core   = sum(n_core, na.rm = T)
)
bgd$g_pg <- bgd$n15 / bgd$g


# species-level belowground inventory over time
abv <- plyr::ddply(CN_mass_data[CN_mass_data$sample.type %in% c("decomp layer", "leaf 1", "leaf 2", "leaf 3", "leaf 4",
                                            "leaf 5", "leaf 6", "leaf 7", "leaf 8", "leaf 9", "leaf 10", "leaf 11",
                                            # "dead leaf", "standing dead", "microbe mat",
                                            "standing dead", "stems"), ], plyr::.(time, species, new.core.id), plyr::summarise,
             g        = sum(g_core, na.rm = T),
             n15      = sum(n15_g, na.rm  = T),
             n_core   = sum(n_core, na.rm = T)
)
abv$g_pg <- abv$n15 / abv$g

names(abv)[4:7] <- paste0(names(abv)[4:7], ".ag")
names(bgd)[4:7] <- paste0(names(bgd)[4:7], ".bg")

comb <- plyr::join_all(list(napp, abv, bgd, mat2), by = "new.core.id")

###
### aboveground data
###

### mass over time in aboveground compartments. mean +- se by species (sum across depth intervals)

ag2 <- plyr::ddply(CN_mass_data[CN_mass_data$sample.type2 %in% c("stems", "leaf"), ],
             plyr::.(time, species, new.core.id, sample.type2), plyr::summarise,
             g   = sum(g_core / pot.m2, na.rm  = T),
             n_pct = mean(n_pct, na.rm = TRUE),
             n   = sum(n_core / pot.m2, na.rm  = T),
             n15 = sum(n15_g, na.rm  = T),
             n15_pg = n15 / (g * pot.m2),
             c13 = mean(d13c, na.rm = TRUE),
             cn  = sum(c_pct * g_core, na.rm = TRUE) / n
)

ag2.sp <- plyr::ddply(ag2, plyr::.(time, species, sample.type2), plyr::numcolwise(mean))
ag2.se <- plyr::ddply(ag2, plyr::.(time, species, sample.type2), plyr::numcolwise(se))
names(ag2.se)[4:10] <- paste0(names(ag2.sp)[4:10], ".se")
ag2.sp <- cbind(ag2.sp, ag2.se[, 4:10])
ag2.sp$t <- as.integer(substr(ag2.sp$time, 2, 2))
ag2.sp$session <- unique(ddHgt4$session)[c(1, 3, 5, 7, 9)][as.numeric(as.factor(ag2.sp$time))]
ag2.sp$species2 <- ifelse(ag2.sp$species %in% "SA", "italic(S.~alterniflora)", "italic(D.~spicata)")


###
### belowground data
###

### mass over time in belowground compartments. mean +- se by species (sum across depth intervals)
bg <- plyr::ddply(CN_mass_data[CN_mass_data$sample.type %in% c("coarse roots", "fine roots", "rhizomes"), ],
            plyr::.(time, species, new.core.id, sample.type), plyr::summarise,
            g   = sum(g_core / pot.m2, na.rm  = T),
            n_pct = mean(n_pct, na.rm = TRUE),
            n   = sum(n_core / pot.m2, na.rm  = T),
            n15 = sum(n15_g, na.rm  = T),
            n15_pg = n15 / (g * pot.m2)
)

bg.sp <- plyr::ddply(bg, plyr::.(time, species, sample.type), plyr::numcolwise(mean))
bg.se <- plyr::ddply(bg, plyr::.(time, species, sample.type), plyr::numcolwise(se))
names(bg.se)[4:8] <- paste0(names(bg.se)[4:8], ".se")
bg.sp <- cbind(bg.sp, bg.se[, 4:8])
bg.sp$t <- as.integer(substr(bg.sp$time, 2, 2))
bg.sp$session <- unique(ddHgt4$session)[c(1, 3, 5, 7, 9)][as.numeric(as.factor(bg.sp$time))]
bg.sp$species2 <- ifelse(bg.sp$species %in% "SA", "italic(S.~alterniflora)", "italic(D.~spicata)")


# Total 15N inventories ---------------------------------------------------------

# aboveground
ag <- plyr::ddply(CN_mass_data[CN_mass_data$sample.type %in% c("belowground stems", "stems", "leaf", paste0("leaf ", 1:10)), ],
            plyr::.(time, species, new.core.id), plyr::summarise,
            g   = sum(g_core / pot.m2, na.rm  = T),
            n_pct = mean(n_pct, na.rm = TRUE),
            n   = sum(n_core / pot.m2, na.rm  = T),
            n15 = sum(n15_g, na.rm  = T) # g per pot
)

agd1 <- plyr::ddply(ag, plyr::.(time, species), plyr::summarise,
              n15.mean    = mean(n15, na.rm  = T),
              n15.se      = se(n15),
              recovery    = mean(n15 / spike, na.rm = TRUE),
              recovery.se = se(n15 / spike)
)
# species-level belowground inventory over time
bgd <- plyr::ddply(CN_mass_data[CN_mass_data$sample.type %in% c("coarse roots", "fine roots", "rhizomes"), ], #, "dead biomass"
             plyr::.(time, species, new.core.id), plyr::summarise,
             n15    = sum(n15_g, na.rm  = T)
)
bgd1 <- plyr::ddply(bgd, plyr::.(time, species), plyr::summarise,
              n15.mean    = mean(n15, na.rm  = T),
              n15.se      = se(n15),
              recovery    = mean(n15 / spike, na.rm = TRUE),
              recovery.se = se(n15 / spike)
)

bgd1$type <- "Belowground"
agd1$type <- "Aboveground"
mgd <- rbind(agd1, bgd1)

tot <- plyr::ddply(mat2, plyr::.(time, species), plyr::summarise,
             n15.mean    = mean(n15_2, na.rm  = T),
             n15.se      = se(n15_2),
             recovery    = mean(n15_2 / spike, na.rm = TRUE),
             recovery.se = se(n15_2 / spike)
)
tot$type <- "Total"

# total recovery combined above + belowground (includes dead aboveground but only live belowground)
tot.live <- plyr::ddply(CN_mass_data[CN_mass_data$sample.type %in% c("coarse roots", "fine roots", "rhizomes",
                                                 "leaf 1", "leaf 2", "leaf 3", "leaf 4",
                                                 "leaf 5", "leaf 6", "leaf 7", "leaf 8", "leaf 9", "leaf 10", "leaf 11",
                                                 "stems"), ], #, "dead biomass"
                  plyr::.(time, species, new.core.id), plyr::summarise,
                  n15    = sum(n15_g, na.rm  = T)
)
tot.live1 <- plyr::ddply(tot.live, plyr::.(time, species), plyr::summarise,
                   n15.mean    = mean(n15, na.rm  = T),
                   n15.se      = se(n15),
                   recovery    = mean(n15 / spike, na.rm = TRUE),
                   recovery.se = se(n15 / spike)
)


mgd <- rbind(mgd, tot)
# replace unique times with actual sampling dates using indexing
mgd$session <- unique(ddHgt4$session)[c(1, 3, 5, 7, 9)][as.numeric(as.factor(mgd$time))]

# N uptake rates ----------------------------------------------------------
# Aboveground: estimate from biomass and weighted average biomass N concentration
# get weighted average of aboveground N concentrations for each core
n_mean <- plyr::ddply(CN_mass_data[CN_mass_data$sample.type %in% c("belowground stems", "stems", "leaf", paste0("leaf ", 1:10)), ],
                plyr::.(species, time, new.core.id), plyr::summarise,
                tot_mass = sum(g_core / pot.m2, na.rm = TRUE),
                tot_n    = sum(n_core / pot.m2, na.rm = TRUE),
                tot_c    = sum(c_pct * g_core / pot.m2, na.rm = TRUE),
                n_wa = tot_n / tot_mass,
                c_wa = tot_c / tot_mass
)
n_bg_mean <- plyr::ddply(CN_mass_data[CN_mass_data$sample.type %in% c("fine roots", "coarse roots", "rhizomes"), ],
                   plyr::.(species, time, new.core.id), plyr::summarise,
                   tot_mass_bg = sum(g_core / pot.m2, na.rm = TRUE),
                   tot_n_bg    = sum(n_core / pot.m2, na.rm = TRUE),
                   tot_c_bg    = sum(c_pct * g_core  / pot.m2, na.rm = TRUE),
                   n_wa_bg = tot_n_bg / tot_mass_bg,
                   c_wa_bg = tot_c_bg / tot_mass_bg
)
bg_del <- plyr::ddply(bg, plyr::.(new.core.id), plyr::summarise, # I think all these values are per m2
                g_bg     = sum(g, na.rm = TRUE),
                g_froots = sum(g[sample.type %in% "fine roots"], na.rm = TRUE),
                g_roots  = sum(g[sample.type %in% c("fine roots", "coarse roots")], na.rm = TRUE),
                n_bg     = sum(n, na.rm = TRUE),
                n_pct_bg = mean(n_pct * g, na.rm = TRUE) / g_bg,
                n_pct_bg2 = n_bg / g_bg,
                n15_bg   = sum(n15, na.rm = TRUE)
)

# all relevant values are per m2
master <- plyr::join_all(list(napp[, c("napp.MH", "new.core.id")], # primary production
                        rgr.stems,
                        n_mean[, -c(1)], n_bg_mean[, -c(1:2)], # N inventories above and belowground
                        ag[, 3:7], bg_del # 15N recoveries and unweighted average pct N
), by = "new.core.id")
master$napp.MH  <- master$napp.MH / pot.m2 # g/m2
master$n15_core <- master$n15 + master$n15_bg # total live biomass inventory
master$recovery <- master$n15_core / spike
master$session  <- unique(ddHgt4$session)[c(3, 5, 7, 9)][as.numeric(as.factor(master$time))]

# use master$timeDiff for 15N rate calculations
# use master$timeDiff + 2 for NAPP rate calculations
master$timeDiff <- as.numeric(difftime(master$session, "2016-06-24", units = "days"))

master$n_uptake_15n     <- master$n15 * 1e3 / master$timeDiff / pot.m2 # mg N/day/m2 accumulating in aboveground tissue, using 15N
master$n_uptake_15n_bg  <- master$n15_bg * 1e3 / master$timeDiff /pot.m2 # mg N/day/m2
master$prodn_rate       <- master$napp.MH / (master$timeDiff + 2) # g/m2/day

master$n_uptake_biomass <- master$prodn_rate * 1e3 * master$n_wa # mg N/m2/day accumulating in aboveground tissue, using primary production rate and weighted average pct N
master$cn_ag    <- master$c_wa / master$n_wa # C:N ratio in aboveground tissue
master$cn_bg    <- master$c_wa_bg / master$n_wa_bg
master$cluster  <- ifelse(master$time %in% c("t1", "t2"), "1-2 weeks", "3-4 weeks")
master$n15_pgBG <- (master$n_uptake_15n + master$n_uptake_15n_bg) / master$g_bg # 15N uptake per gram belowground biomass: mg 15N/m2/day/g TOTAL BG biomass
master$n15_pgFR <- (master$n_uptake_15n + master$n_uptake_15n_bg) / master$g_froots # 15N uptake per gram coarse roots: mg 15N/m2/day/g fine roots
master$n15_pgCR <- (master$n_uptake_15n + master$n_uptake_15n_bg) / (master$g_roots - master$g_froots) # 15N uptake per gram coarse roots: mg 15N/m2/day/g coarse roots
master$n15_pgR  <- (master$n_uptake_15n + master$n_uptake_15n_bg) / master$g_roots # 15N uptake per gram fine+coarse root biomass: mg 15N/m2/day/g root biomass
master$n15_pgFR2 <- master$n15_core * 1e3 / master$g_froots
master$tot_15n_uptake <- master$n_uptake_15n + master$n_uptake_15n_bg # mg N/day/m2
master$days <- as.numeric(substr(master$time, 2, 2)) * 7


### apply relationship to estimate belowground production
summary(lm3_4 <- stats::lm(n_uptake_biomass ~ I(n_uptake_15n ) , data = master[master$time %in% c("t3", "t4"), ]))
lm3_4
master$bg_n_est <- as.numeric(master$n_uptake_15n_bg) * coefficients(lm3_4)[2] + coefficients(lm3_4)[1] # total N uptake belowground: mg N/m2/day
master$bg_n_est[master$time %in% c("t1", "t2")] <- NA
master$bgp_est <- master$bg_n_est / master$n_pct_bg2 / 1e3 # [estimated total N uptake (mg N/day)] / [mg N / mg biomass] / [1000 mg/g] = [g belowground biomass / day (/m2)]
master$bgp_biomass_est <- master$bgp_est * master$timeDiff # [g belowground biomass / day] * [timeDiff (days)] = total belowground production during period [g/m2]

### differences in total nitrogen interception between species
master$tot_N_int <- master$bg_n_est + master$n_uptake_biomass + ifelse(master$species %in% "SA", nmolHr_mgDay(dd.dea$flux.m2[2]), nmolHr_mgDay(dd.dea$flux.m2[1]))
master$tot_N_int[master$time %in% c("t1", "t2")] <- NA


dd.master <- ddply(master, .(time, species), numcolwise(mean))
dd.master.se <- ddply(master, .(time, species), numcolwise(se))
dd.master$n_uptake_biomass.se <- dd.master.se$n_uptake_biomass
dd.master$prodn_rate.se <- dd.master.se$prodn_rate
dd.master$session <- unique(ddHgt4$session)[c(3, 5, 7, 9)][as.numeric(as.factor(dd.master$time))]

# Results: a. Stem allometry, aboveground biomass and production ----------
table(allometry$spp[!is.na(allometry$sample)])
plyr::ldply(CSP, MuMIn::r.squaredGLMM)

# change over time in production?
summary(lm.sa.prod <- lm(napp.MH ~ session, data = master[master$species %in% "SA",]))
summary(lm.ds.prod <- lm(napp.MH ~ session, data = master[master$species %in% "DS",]))

plyr::ddply(master, plyr::.(species), plyr::summarise,
      production    = mean(prodn_rate, na.rm = TRUE),
      production.se = se(prodn_rate)
)
t.test(prodn_rate ~ species, data = master)

for (i in 1:length(unique(master$session))) {
  print(unique(master$session)[i])
  print(t.test(prodn_rate ~ species, data = master[(master$session %in% c(unique(master$session)[i])), ]))
}

# Results: b.   Leaf and stem biomass, N pools, and uptake ------------------
# leaf, stem biomass differences
leaf <- plyr::ddply(CN_mass_data[CN_mass_data$sample.type %in% c("leaf", paste0("leaf ", 1:10)), ],
              plyr::.(species, time, new.core.id), plyr::summarise,
              tot_mass = sum(g_core / pot.m2, na.rm = TRUE),
              tot_n    = sum(n_core / pot.m2, na.rm = TRUE),
              tot_c    = sum(c_pct * g_core / pot.m2, na.rm = TRUE),
              n_wa = tot_n / tot_mass,
              c_wa = tot_c / tot_mass)
leaf$t <- as.numeric(substr(leaf$time, 2, 2)) * 7

stem <- plyr::ddply(CN_mass_data[CN_mass_data$sample.type %in% c("belowground stems", "stems"), ],
              plyr::.(species, time, new.core.id), plyr::summarise,
              tot_mass = sum(g_core / pot.m2, na.rm = TRUE),
              tot_n    = sum(n_core / pot.m2, na.rm = TRUE),
              tot_c    = sum(c_pct * g_core / pot.m2, na.rm = TRUE),
              n_wa = tot_n / tot_mass,
              c_wa = tot_c / tot_mass
)
stem$t <- as.numeric(substr(stem$time, 2, 2)) * 7

leaf2 <- leaf
names(leaf2)[4:8] <- paste0(names(leaf)[4:8], ".leaf")
leaf2 <- cbind(leaf2, stem[4:8])
leaf2$n_wa_ag <- (leaf2$tot_n.leaf + leaf2$tot_n) / (leaf2$tot_mass.leaf + leaf2$tot_mass)

plyr::ddply(leaf[, -c(2, 3)], plyr::.(species), plyr::numcolwise(mean))
plyr::ddply(leaf[, -c(2, 3)], plyr::.(species), plyr::numcolwise(se))

# leaf biomass
summary(lm.sa.leaf <- lm(tot_mass ~ t, data = leaf[leaf$species %in% "SA",]))
summary(lm.ds.leaf <- lm(tot_mass ~ t, data = leaf[leaf$species %in% "DS",]))
t.test(tot_mass ~ species, data = leaf)

# leaf N content
summary(lm.sa.leafN <- lm(n_wa ~ t, data = leaf[leaf$species %in% "SA",]))
summary(lm.ds.leafN <- lm(n_wa ~ t, data = leaf[leaf$species %in% "DS",]))
t.test(n_wa ~ species, data = leaf)


# stems 
plyr::ddply(stem[, -c(2, 3)], plyr::.(species), plyr::numcolwise(mean))
plyr::ddply(stem[, -c(2, 3)], plyr::.(species), plyr::numcolwise(se))

# stem biomass
summary(lm.sa.stem <- lm(tot_mass ~ t, data = stem[stem$species %in% "SA",]))
summary(lm.ds.stem <- lm(tot_mass ~ t, data = stem[stem$species %in% "DS",]))
t.test(tot_mass ~ species, data = stem)

# stem N content
summary(lm.sa.leafN <- lm(n_wa ~ t, data = leaf[leaf$species %in% "SA",]))
summary(lm.ds.leafN <- lm(n_wa ~ t, data = leaf[leaf$species %in% "DS",]))
t.test(n_wa ~ species, data = leaf)

# mean N content for all
plyr::ddply(stem[, -c(2, 3)], plyr::.(), plyr::numcolwise(mean))
plyr::ddply(stem[, -c(2, 3)], plyr::.(), plyr::numcolwise(se))

# stem N stock
t.test(tot_n ~ species, data = stem)


# aboveground weighted average N content
summary(lm.sa.N.ag <- lm(n_wa_ag ~ t, data = leaf2[leaf2$species %in% "SA",]))
summary(lm.ds.N.ag <- lm(n_wa_ag ~ t, data = leaf2[leaf2$species %in% "DS",]))
t.test(n_wa_ag ~ species, data = leaf2)

plyr::ddply(leaf2, plyr::.(species, time), plyr::summarise,
      n_pct = mean(n_wa_ag),
      n_pct.se = se(n_wa_ag))
plyr::ddply(leaf2, plyr::.(species), plyr::summarise,
      n_pct = mean(n_wa_ag),
      n_pct.se = se(n_wa_ag))
plyr::ddply(leaf2, plyr::.(), plyr::summarise,
      n_pct = mean(n_wa_ag),
      n_pct.se = se(n_wa_ag))

### total N uptake (NAPP * %N)
summary(lm.sa.Nupt <- lm(n_uptake_biomass ~ session, data = master[master$species %in% "SA",]))
summary(lm.ds.Nupt <- lm(n_uptake_biomass ~ session, data = master[master$species %in% "DS",]))
t.test(n_uptake_biomass ~ species, data = master) # no difference bt species as a whole

for (i in 1:length(unique(master$session))) {
  print(unique(master$session)[i])
  print(t.test(n_uptake_biomass ~ species, data = master[(master$session %in% c(unique(master$session)[i])), ]))
}
plyr::ddply(master, plyr::.(species), plyr::summarise,
      n_uptake = mean(n_uptake_biomass),
      n_uptake.se = se(n_uptake_biomass))

# Results: c.   Belowground biomass and N pools -----------------------------
# belowground biomass and tissue N
rts <- ddply(CN_mass_data[CN_mass_data$sample.type %in% c("fine roots", "coarse roots", "rhizomes"), ],
             .(species, time, new.core.id, sample.type), summarise,
             tot_mass = sum(g_core / pot.m2, na.rm = TRUE),
             tot_n    = sum(n_core / pot.m2, na.rm = TRUE),
             tot_c    = sum(c_pct * g_core / pot.m2, na.rm = TRUE),
             n_wa = tot_n / tot_mass,
             c_wa = tot_c / tot_mass
)
rts$t <- as.numeric(substr(rts$time, 2, 2)) * 7

ddply(rts[, -c(2, 3)], .(species, sample.type), numcolwise(mean))
ddply(rts[, -c(2, 3)], .(species, sample.type), numcolwise(se))

# fine roots
organ <- "fine roots"
summary(lm(tot_mass ~ t, data = rts[(rts$sample.type %in% organ) & (rts$species %in% "SA"), ]))
summary(lm(tot_mass ~ t, data = rts[(rts$sample.type %in% organ) & (rts$species %in% "DS"), ]))
t.test(tot_mass ~ species, data = rts[(rts$sample.type %in% organ), ])
t.test(n_wa ~ species, data = rts[(rts$sample.type %in% organ), ])
t.test(tot_n ~ species, data = rts[(rts$sample.type %in% organ), ])

# coarse roots
organ <- "coarse roots"
t.test(tot_mass ~ species, data = rts[(rts$sample.type %in% organ), ])
summary(lm(tot_mass ~ t, data = rts[(rts$sample.type %in% organ) & (rts$species %in% "SA"), ]))
summary(lm(tot_mass ~ t, data = rts[(rts$sample.type %in% organ) & (rts$species %in% "DS"), ]))

for (i in 1:length(unique(rts$t))) {
  print(unique(rts$t)[i])
  print(t.test(n_wa ~ species, data = rts[(rts$t %in% c(unique(rts$t)[i])) & (rts$sample.type %in% organ), ]))
}

t.test(n_wa ~ species, data = rts[(rts$sample.type %in% organ), ])
t.test(tot_n ~ species, data = rts[(rts$sample.type %in% organ), ])



# rhizomes
organ <- "rhizomes"
summary(lm(tot_mass ~ t, data = rts[(rts$sample.type %in% organ) & (rts$species %in% "SA"), ]))
summary(lm(tot_mass ~ t, data = rts[(rts$sample.type %in% organ) & (rts$species %in% "DS"), ]))
t.test(tot_mass ~ species, data = rts[(rts$sample.type %in% organ), ])
t.test(n_wa ~ species, data = rts[(rts$sample.type %in% organ), ])
t.test(tot_n ~ species, data = rts[(rts$sample.type %in% organ), ])

Abstract

We present several datasets that provide information on primary production, nitrogen (N) uptake and allocation in two salt marsh grasses, short-form Spartina alterniflora and Distichlis spicata. These four datasets were generated during a month-long stable isotope (^15^N) tracer study described in the companion manuscript [1]. They include an allometry dataset containing mass and height data for individual plants harvested from Colt State Park, Bristol, Rhode Island and used to nondestructively estimate plant masses. A second dataset contains weekly stem height measurements collected over the course of the ^15^N tracer study. Also included are high resolution data from 49 vegetated compartments (leaves, stems, fine/coarse roots, rhizomes) and bulk sediment depth intervals, reporting the mass, carbon and N concentrations, and stable isotope ratios measured following the harvest of cores over time. Additionally, we provide a complementary dataset with estimates of microbial removal from potential and ambient denitrification enzyme assays. These data, along with source code used in their analysis, are compiled in the NitrogenUptake2016 R package available from the Comprehensive R Archive Network.

Specifications Table

Subject area Chemistry, Biology, Ecology

More specific subject area Biogeochemistry, plant ecology

Type of data R package, figures

How data were acquired Nutrient concentrations and stable isotope ratios: Elementar Vario Micro elemental analyzer connected to a continuous flow Isoprime 100 isotope ratio mass spectrometer Denitrification enzyme assays: N~2~O concentrations were measured on a Shimadzu GC2014 gas chromatographer with an electron capture detector Stem heights: ruler (±0.1 cm) Biomass: electronic scale (±0.001 g)

Data format Raw and analyzed

Experimental factors
Fifteen salt marsh sediment cores were collected from each of two vegetation types (short form Spartina alterniflora and Distichlis spicata). Cores were transferred to and grown in a laboratory greenhouse and deconstructed over four weeks following a single ^15^N tracer addition.

Experimental features
Three cores from each vegetation type were physically deconstructed at weekly intervals to measure ^15^N accumulation in a range of vegetative and sedimentary compartments. Additional data are provided describing the mass-height allometry of aboveground biomass, the dataset of weekly stem height measurements used to estimate growth rates, and results of sediment denitrification enzyme assays.

Data source location
Cores and allometry samples were collected from Colt State Park, Bristol, Rhode Island, USA (Latitude/Longitude: 41.6857, -71.2885). Greenhouse incubations conducted at USEPA Atlantic Ecology Division, Narragansett, Rhode Island, USA.

Data accessibility
Data are publicly available as an R package archived by the Comprehensive R Archive Network (https://cran.r-project.org/package=NitrogenUptake2016).

Value of the data

Stable isotope, nutrient concentration, and biomass data provide weekly snapshots of ^15^N tracer uptake and allocation to a suite of above- and belowground tissues.
Data allow investigation of nitrogen budgets and cycling in salt marsh ecosystems.
Growing season mass-height allometry data for Rhode Island, USA can be used in salt marsh studies from the northeast, compared to other geographic areas, and used in meta-analyses.
Denitrification enzyme data can be used in syntheses or meta-analyses.

1. Data

1.1. Experimental context

Salt marshes are exceptionally productive ecosystems [2]. High rates of primary production lead to substantial nutrient uptake by salt marsh plants [3], and biogeochemically active marsh sediments intercept nitrogen (N) dissolved in coastal waters [4-6]. To examine plant N uptake and possible artifacts associated with time scales of stable isotope tracer studies, we collected salt marsh cores (“mesocosms” hereafter), amended them with ^15^N and examined uptake and allocation over four weeks. In addition, high temporal and spatial resolution data were collected for two dominant salt marsh species, short-form Spartina alterniflora and Distichlis spicata. This work generated several datasets described in this paper and contained in the associated R package (NitrogenUptake2016). The R package can be installed and loaded by running the following commands in R:

install.packages("NitrogenUptake2016")
library(NitrogenUptake2016)

1.2. Mass-height allometry

Live plants were collected from Colt State Park (Bristol, Rhode Island, USA) in May, June, and July 2016. On each sampling date, three 25 cm x 25 cm quadrats were collected from monoculture areas of short-form Spartina alterniflora and Distichlis spicata. Stems were cut at the sediment surface, total plant height was measured to the nearest 0.1 cm, and a representative subsample of plants were individually dried to constant weight at 50^o^C. The "allometry" dataset includes 95 mass/height observations (units: grams and centimeters) from Distichlis spicata and 75 from short-form Spartina alterniflora.

?allometry

1.3. Stem heights

All live plants in each of the 24 mesocosms were tagged and the height from the base to the tallest feature was measured to the nearest 0.1 cm on 22 June, 29 June, 6 July, 13 July, and 20 July 2016. Additional height measurements were recorded as mesocosms were harvested incrementally over a four-week period. These data are included in the "stemHeights" dataset. New plants were tagged throughout the experiment, resulting in 3,315 height measurements from 839 unique plants.

?stemHeights

1.4. Mass, nutrient, and stable isotope concentrations

Mesocosm harvests occurred on 1 July, 8 July, 15 July, and 22 July 2016. During harvest, material in the mesocosms was separated into as many as 49 distinct vegetated compartments (leaves divided by node, stems, fine/coarse roots, rhizomes) and bulk sediment depth intervals. Aboveground and belowground compartments are described in the Experimental Methods section, below. In addition to the dry mass (50^o^C) of each compartment in each mesocosm, this dataset contains nitrogen and carbon concentrations and stable isotope ratios. The "CN_mass_data" dataset contains 1192 observations reporting the mass (g) of each compartment recovered from each mesocosm, the compartment’s volume (cm^3^; for belowground samples), carbon and nitrogen content (g $\cdot$ g^-1^), and stable carbon and nitrogen isotope ratios (per mil deviations from Vienna Pee Dee Belemnite (VPDB) and Air, respectively).

?CN_mass_data

1.5. Denitrification enzyme assays

The data are derived from two sets of acetylene inhibition assays conducted using bulk sediment from the 0-5 cm depth intervals of each of the six mesocosms harvested on 9 July 2016. The two assays measured denitrification enzyme activity (DEA) potential and "in vitro" or ambient denitrification. Both rates are provided on a per-gram basis (units: nmol N~2~O $\cdot$ g dry mass^-1^ $\cdot$ hr^-1^) in the "dea" dataset.

?dea

2. Experimental Design, Materials and Methods

2.1. Experimental context

Thirty salt marsh sediment mesocosms (10 cm x 35 cm PVC cores; 15 from monocultures of each species) were collected from the field, and three from each vegetation type were immediately harvested to represent time-zero conditions. The remaining 24 mesocosms were grown in a tidal tank in an outdoor greenhouse, and a single dose of 1.95 mg ^15^N was added to each mesocosm on 24 June 2016. Mesocosms were then harvested at weekly intervals over the subsequent four weeks.

2.2. Mass-height allometry

Live plants collected from Colt State Park, Bristol, RI, USA were used to establish allometric relationships between plant height and mass. Masses were modeled as a function of height, with species-specific allometry models parameterized using Box-Cox power transformations of biomass ($\lambda$). Masses of plants growing in the mesocosms were estimated from weekly plant height measurements using species-specific allometry equations of the form mass = ( height $\cdot$ a + b)^1/$\lambda$^. The R code used to construct these models from raw mass and height data is included in the vignettes in the "JEMBE" vignette in the R package "NitrogenUptake2016," accessible by running the following commands in R:

vignette(topic = "JEMBE", package = "NitrogenUptake2016")

2.3. Stem heights

Using source code provided in the package vignette, the species-specific allometry models shown in Table 1 from Hill et al. [1] were applied to stem heights collected over the course of the experiment to estimate biomass. Stem-level masses were summed by mesocosm for each measurement interval, and net primary production was calculated as the sum of positive live biomass increments at the mesocosm-scale [7, 8].

Mesocosm harvests provided an opportunity to examine the applicability of these models to plants grown in our greenhouse experiment. Aboveground biomass measured during mesocosm harvest was compared with predicted biomass; the sum of stem-level masses estimated from allometry models. These data are shown in Figure 1 (R^2^ = 0.80, y = 0.87x, P < 0.001).

# Figure 1 from Hill et al. (Data In Brief) - predicted vs obs biomass ---------------------------------------
ggplot(obs, aes(x = allom.est / pot.m2, y = g / pot.m2, colour = species2, shape = species2)) +
  geom_point(size = pointSize) + theme_classic() %+replace% theme(legend.title = element_blank()) +
  labs(x = expression("Predicted biomass (allometry; g"%.%m^-2~")"), y = expression("Measured biomass (g "%.%m^-2~")")) +
  ylim(0, 650) + xlim(0, 650) +
  scale_colour_grey(start = 0.5, end = 0.1, name = "", breaks = c(unique(obs$species2)[1], unique(obs$species2)[2]), labels = c(expression(italic(Distichlis)), expression(italic(Spartina)))) +
  scale_shape_manual(values = c(16, 17), name = "", breaks = c(unique(obs$species2)[1], unique(obs$species2)[2]), labels = c(expression(italic(Distichlis)), expression(italic(Spartina)))) +
  theme(legend.position = c(0.3, 0.9), legend.text.align = 0,
        legend.background = element_rect(fill = NA, colour = NA)) +
  geom_abline(slope = 1, intercept = 0, linetype = 2)

Figure 1. Relationship between allometry-based biomass estimates and biomass measured at harvest for each mesocosm (R^2^ = 0.80, y = 0.87x, P < 0.001), with dashed 1:1 line shown.

2.4. Mass, nutrient, and stable isotope concentrations

At collection (21 June 2016) and at weekly intervals following tracer addition, cohorts of three randomly selected mesocosms from each species was harvested for analysis of ^15^N accumulation in above- and belowground compartments. Aboveground biomass was clipped at the sediment surface, rinsed with deionized water and separated into live and standing dead plants. Live plants were further separated into stems, dead leaves, and live leaves. Live leaves were separated and numbered by position relative to the top of the plant. These node-level data are included in the provided dataset, although they were combined into a single leaf pool during data analysis [1].

After aboveground biomass was sampled, peat was extruded from the coring tube and sectioned into six depth intervals: 0-2 cm, 2-5 cm, 5-10 cm, 10-15 cm, 15-20 cm, and 20-30 cm. Surface litter and algal mat layers were separated when present. Each sediment depth interval was subsampled for belowground biomass and bulk density. One quarter of each depth interval volume was dried to constant weight at 50^o^C, and bulk density calculated as the dry mass divided by the sample volume. One quarter of each depth interval was archived in a freezer, and the remaining half of each depth interval was used for separation of belowground biomass. Within one week of harvest, belowground biomass was rinsed clean of sediment using deionized water and separated into live rhizomes, live coarse roots (>1 mm), live fine roots (less than or equal to 1 mm), and dead biomass. Live biomass was distinguished from dead by its white color and turgid structure. All samples were dried to constant weight at 50^o^C, ground using a Wiley mill and a size 40 screen, and stored in acid-washed scintillation vials. Samples containing ^15^N tracer samples were ground on a separate dedicated mill to prevent contamination.

Nitrogen and carbon concentrations and stable isotope ratios were measured using an Elementar Vario Micro elemental analyzer connected to a continuous flow Isoprime 100 isotope ratio mass spectrometer. Check standards, blanks, and replicated samples were run every ten samples. Replicate analyses of isotopic standard reference materials USGS 40 ($\delta$ ^13^C = -26.39 per mil; $\delta$ ^15^N = -4.52 per mil) and USGS 41 ($\delta$ ^13^C = 37.63 per mil; $\delta$ ^15^N = 47.57 per mil) were used to normalize isotopic values of working standards to the Air ($\delta$^15^N) and VPDB ($\delta$ ^13^C) scales [9]. Average recoveries for standard reference materials were ±1.1% for ^15^N, ±0.4% for total N, ±0.03% for ^13^C, and ±0.05% for total C. Coefficients of variation on sample replicates averaged 8.9% for $\delta$ ^15^N, 5.9% for total N, -0.5% for$\delta$ ^13^C, and 3.6% for total C.

These compartment-level data provide a time series of biomass and tissue N in aboveground (Fig. 2) and belowground (Fig. 3) tissues.

``` {r Figure 2 (Data In Brief), fig.width = 6, fig.height = 4, message = FALSE, echo = FALSE}

Figure 2 in Data In Brief manuscript - aboveground pools ---------------------------------------

ggplot(ag2.sp, aes(y = g, x = as.Date(session), colour = species2, shape = species2)) + geom_point(size = pointSize, position = pd2) + theme_classic() %+replace% theme(legend.title = element_blank()) + facet_grid(sample.type2 ~ .) + geom_errorbar(aes(ymin = g - g.se, ymax = g + g.se), width = 0, position = pd2) + scale_colour_grey(start = grayColor, end = 0.1, name = "", breaks = c(unique(ag2.sp$species2)[1], unique(ag2.sp$species)[2]), labels = c(expression(italic(Distichlis)), expression(italic(Spartina)))) + scale_shape_manual(values = c(16, 17), name = "", breaks = c(unique(ag2.sp$species2)[1], unique(ag2.sp$species)[2]), labels = c(expression(italic(Distichlis)), expression(italic(Spartina)))) + ylab(expression("Biomass (g "%.%m^-2~")")) + xlab("") + ylim(0, 325) + scale_x_date(breaks = as.Date(unique(ag2.sp$session)), labels = scales::date_format("%b-%d")) + theme(legend.position = c(2.2, 0.9), legend.text.align = 0, legend.background = element_rect(fill = NA, colour = NA))

ggplot(ag2.sp, aes(y = n_pct, x = as.Date(session), colour = species2, shape = species2)) + geom_point(size = pointSize, position = pd2) + theme_classic() %+replace% theme(legend.title = element_blank()) + facet_grid(sample.type2 ~ .) + geom_errorbar(aes(ymin = n_pct - n_pct.se, ymax = n_pct + n_pct.se), width = 0, position = pd2) + scale_colour_grey(start = grayColor, end = 0.1, name = "", breaks = c(unique(ag2.sp$species2)[1], unique(ag2.sp$species)[2]), labels = c(expression(italic(Distichlis)), expression(italic(Spartina)))) + scale_shape_manual(values = c(16, 17), name = "", breaks = c(unique(ag2.sp$species2)[1], unique(ag2.sp$species)[2]), labels = c(expression(italic(Distichlis)), expression(italic(Spartina)))) + ylab("Tissue N") + xlab("") + scale_y_continuous(labels = scales::percent) + scale_x_date(breaks = as.Date(unique(ag2.sp$session)), labels = scales::date_format("%b-%d")) + theme(legend.position = c(2.2, 0.9), legend.text.align = 0, legend.background = element_rect(fill = NA, colour = NA))

**Figure 2.** Mean (± SE; n = 3) leaf and stem biomass (left side) and N content (right side) at each harvest. _Distichlis_ shown as gray points, _Spartina_ as black triangles.




``` {r Figure 3 (Data In Brief), fig.width = 6, fig.height = 4, echo = FALSE}
# Figure 3 (Data In Brief) - belowground biomass pools --------------------------------------
ggplot(bg.sp, aes(y = g, x = as.Date(session), colour = species2, shape = species2)) + geom_point(size = pointSize, position = pd2) + theme_classic() +
  facet_grid(sample.type ~ .) + geom_errorbar(aes(ymin = g - g.se, ymax = g + g.se), width = 0, position = pd2) +
  scale_colour_grey(start = grayColor, end = 0.1, name = "", breaks = c(unique(bg.sp$species2)[1], unique(bg.sp$species)[2]), labels = c(expression(italic(Distichlis)), expression(italic(Spartina)))) +
  scale_shape_manual(values = c(16, 17), name = "", breaks = c(unique(bg.sp$species2)[1], unique(bg.sp$species)[2]), labels = c(expression(italic(Distichlis)), expression(italic(Spartina)))) +
  ylab(expression("Biomass (g "%.%m^-2~")")) +  xlab("") +
  scale_x_date(breaks = as.Date(unique(bg.sp$session)), labels = scales::date_format("%b-%d")) +
  theme(legend.text.align = 0, legend.position = c(2.2, 1),
        legend.background = element_rect(fill = NA, colour = NA), axis.text.x=element_text(angle=45,hjust=1))



ggplot(bg.sp, aes(y = n_pct, x = as.Date(session), colour = species2, shape = species2)) + geom_point(size = pointSize, position = pd2) + theme_classic() +
  facet_grid(sample.type ~ .) + geom_errorbar(aes(ymin = n_pct - n_pct.se, ymax = n_pct + n_pct.se), width = 0, position = pd2) +
  scale_colour_grey(start = 0.5, end = 0.1, name = "", breaks = c(unique(bg.sp$species2)[1], unique(bg.sp$species)[2]), labels = c(expression(italic(D.~spicata)), expression(italic(S.~alterniflora)))) +
  scale_x_date(breaks = as.Date(unique(bg.sp$session)), labels = scales::date_format("%b-%d")) +
  ylab("Tissue N content") +  xlab("") + scale_y_continuous(labels = scales::percent) +
  theme(legend.text.align = 0, legend.position = c(2.2, 1),
        legend.background = element_rect(fill = NA, colour = NA), axis.text.x=element_text(angle=45,hjust=1))

Figure 3. Mean (± SE) belowground biomass (left side) and N content (right side) at each harvest. Distichlis shown as gray points, Spartina as black triangles.

2.5. Denitrification enzyme assays

DEA potential was measured by incubating 6.5 g of sediment in 70 mL jars sealed with rubber septa. Sample containers were amended with 12 mL of nutrient-amended filtered seawater (7 mmol N $\cdot$ L^-1^ as KNO~3~; 17 mmol C $\cdot$ L^-1^ as D-glucose; and 6 mmol P $\cdot$ L^-1^ as KH~2~PO~4~) with 0.125 g $\cdot$ L-1 chloramphenicol added as a microbial inhibitor. Containers were alternately evacuated and flushed with N~2~ three times, and acetylene (10% of headspace volume) was added to block reduction of N~2~O to N~2~. In vitro denitrification was measured in nearly identical acetylene inhibition assays. The only difference was the use of 12 mL of un-amended filtered seawater rather than a nutrient solution [10].

In both assays, N~2~O accumulation in the jars was measured at 30 minute intervals for two hours. During sampling, 5 mL of sample headspace and 10 mL of N~2~ were transferred to evacuated 12 mL exetainers (Labco, UK). Sampled headspace was replaced with a 10% mixture of acetylene in N~2~. Nitrous oxide concentrations were analyzed by gas chromatography (GC; Shimadzu GC2014) using an electron capture detector (ECD). The GC column oven temperature was 75^o^C, the ECD temperature was 325^o^C, the carrier gas was helium and makeup gas was a 5% solution of methane in argon. The potential and in vitro DEA data provided here reflect the slope of the line of best fit for dilution-corrected N~2~O concentrations in the jar headspace. At least three points were used for each rate estimate, and all relationships had R^2^ of at least 0.92 (mean R^2^ = 0.97). A laboratory blank using DEA solution and no added sediment yielded zero flux (slope = 0.0; R^2^ = 0.0), so no blank correction was applied.

3. Acknowledgements

Financial support provided by the US EPA’s Sustainable and Healthy Communities Research Program, section 4.61.6. Caroline Kanaskie and Nathalie Sommer were supported by US EPA Greater Research Opportunities Fellowship Assistance Agreement nos. 91777301-0 and 91777501-0. The authors thank Rick McKinney for analytical assistance. The manuscript was improved by comments from Roxanne Johnson, Rick McKinney, and Glen Thursby. This report is contribution number ORD-026434 of the US EPA’s Office of Research and Development, National Health and Environmental Effects Research Laboratory, Atlantic Ecology Division. Although the information in this document has been funded by the US EPA, it does not necessarily reflect the views of the agency and no official endorsement should be inferred. Mention of trade names or commercial products does not constitute endorsement or recommendation for use.

4. References

Hill, T.D., et al., Nitrogen uptake and allocation estimates for Spartina alterniflora and Distichlis spicata. Journal of Experimental Marine Biology and Ecology, 2018. 507: p. 53-60.
Chmura, G.L., et al., Global carbon sequestration in tidal, saline wetland soils. Global Biogeochemical Cycles, 2003. 17(4): p. 1-12.
Craft, C., et al., Forecasting the effects of accelerated sea-level rise on tidal marsh ecosystem services. Frontiers in Ecology and the Environment, 2009. 7(2): p. 73-78.
Yang, W.H., B.H. Traut, and W.L. Silver, Microbially mediated nitrogen retention and loss in a salt marsh soil. Ecosphere, 2015. 6(1): p. 1-7.
Wigand, C., et al., Denitrification enzyme activity of fringe salt marshes in New England (USA). Journal of Environmental Quality, 2004. 33(3): p. 1144-1151.
Oczkowski, A., et al., Nitrogen retention in salt marsh systems across nutrient-enrichment, elevation, and precipitation regimes: a multiple-stressor experiment. Estuaries and Coasts, 2015. 39(1): p. 68-81.
Milner, C. and R.E. Hughes, Methods for the measurement of the primary production of grasslands. IBP Handbook no. 6. 1968, Oxford: Blackwell Scientific Publications.
Hill, T.D. and B.J. Roberts, Effects of seasonality and environmental gradients on Spartina alterniflora allometry and primary production. Ecology and Evolution, 2017. 7(22): p. 9676-9688.
Paul, D., G. Skrzypek, and I. Forizs, Normalization of measured stable isotopic compositions to isotope reference scales – a review. Rapid Communications in Mass Spectrometry, 2007. 21(18): p. 3006-3014.
Warneke, S., et al., A comparison of different approaches for measuring denitrification rates in a nitrate removing bioreactor. Water Research, 2011. 45(14): p. 4141-4151.

Any scripts or data that you put into this service are public.

NitrogenUptake2016 documentation built on May 1, 2019, 11:31 p.m.

rdrr.io home R language documentation Run R code online

CRAN packages Bioconductor packages R-Forge packages GitHub packages

Note that we can't provide technical support on individual packages. You should contact the package authors for that.

NitrogenUptake2016
Data and Source Code From: Nitrogen Uptake and Allocation Estimates for Spartina Alterniflora and Distichlis Spicata

Nitrogen and carbon concentrations and stable isotope ratios: Data from a ^15^N tracer study in short-form _Spartina alterniflora_ and _Distichlis spicata_'
In NitrogenUptake2016: Data and Source Code From: Nitrogen Uptake and Allocation Estimates for Spartina Alterniflora and Distichlis Spicata

Abstract

Specifications Table

Value of the data

1. Data

2. Experimental Design, Materials and Methods

Figure 2 in Data In Brief manuscript - aboveground pools ---------------------------------------

3. Acknowledgements

4. References

Try the NitrogenUptake2016 package in your browser

R Package Documentation

Browse R Packages

We want your feedback!

NitrogenUptake2016 Data and Source Code From: Nitrogen Uptake and Allocation Estimates for Spartina Alterniflora and Distichlis Spicata

Nitrogen and carbon concentrations and stable isotope ratios: Data from a ^15^N tracer study in short-form _Spartina alterniflora_ and _Distichlis spicata_' In NitrogenUptake2016: Data and Source Code From: Nitrogen Uptake and Allocation Estimates for Spartina Alterniflora and Distichlis Spicata

Abstract

Specifications Table

Value of the data

1. Data

2. Experimental Design, Materials and Methods

Figure 2 in Data In Brief manuscript - aboveground pools ---------------------------------------

3. Acknowledgements

4. References

Try the NitrogenUptake2016 package in your browser

R Package Documentation

Browse R Packages

We want your feedback!

NitrogenUptake2016
Data and Source Code From: Nitrogen Uptake and Allocation Estimates for Spartina Alterniflora and Distichlis Spicata

Nitrogen and carbon concentrations and stable isotope ratios: Data from a ^15^N tracer study in short-form _Spartina alterniflora_ and _Distichlis spicata_'
In NitrogenUptake2016: Data and Source Code From: Nitrogen Uptake and Allocation Estimates for Spartina Alterniflora and Distichlis Spicata