R/dilp_functions.R

In dilp: Reconstruct Paleoclimate and Paleoecology with Leaf Physiognomy

#' Process raw leaf physiognomic data
#'
#' @description
#' `dilp_processing()` will typically only be called internally by `dilp()`.
#' However, it can be used on its own to generate and view a processed DiLP
#' dataset that includes raw and derived physiognomic values useful for DiLP and
#' other physiognomic analyses.  Returns a data frame.
#'
#' @param specimen_data A data frame containing specimen level leaf physiognomic
#' data. A good reference for how to put together the data: \code{\link{McAbeeExample}}
#' @return A data frame containing cleaned and processed specimen level leaf
#' physiognomic data.  New variables calculated are:
#' * Leaf area
#' * Feret diameter
#' * Feret diameter ratio (FDR)
#' * Raw blade perimeter corrected (Raw blade perimeter - length of cut perimeter)
#' * Internal raw blade perimeter corrected (Internal raw blade perimeter - length of cut perimeter)
#' * Total tooth count
#' * Total tooth count : internal perimeter (TC:IP)
#' * Perimeter ratio
#' * Petiole metric
#' * Aspect ratio
#' * Shape factor
#' * Compactness
#' * Tooth area
#' * Tooth area : perimeter (TA:P)
#' * Tooth area: internal perimeter (TA:IP)
#' * Tooth area : blade area (TA:BA)
#' * Average primary tooth area (Avg TA)
#' * Tooth count : blade area (TC:BA)
#' * Tooth count : perimeter (TC:P)
#' @export
#'
#' @examples
#' dilp_dataset <- dilp_processing(McAbeeExample)
#' dilp_dataset
dilp_processing <- function(specimen_data) {
  colnames(specimen_data) <- colnameClean(specimen_data)

  required_columns <- c(
    "site", "specimen_number", "morphotype", "margin", "feret", "blade_area",
    "raw_blade_perimeter", "internal_raw_blade_perimeter", "length_of_cut_perimeter",
    "no_of_primary_teeth", "no_of_subsidiary_teeth"
  )


  recommended_columns <- c(
    "petiole_width", "petiole_area", "blade_perimeter",
    "minimum_feret", "raw_blade_area", "internal_raw_blade_area"
  )
  missing_columns <- required_columns[!required_columns %in% colnames(specimen_data)]
  if (length(missing_columns) > 0) {
    stop(paste("specimen_data is missing required columns:", stringr::str_flatten(missing_columns, collapse = ", ")))
  } else {
    other_columns <- recommended_columns[!recommended_columns %in% colnames(specimen_data)]
    if (length(other_columns) > 0) {
      warning(paste("specimen_data is missing recommended columns and has filled them with NAs:", stringr::str_flatten(other_columns, collapse = ", ")), call. = FALSE)
      for (column in other_columns) {
        specimen_data[[column]] <- NA
      }
    }

    # Identify 0.5 margin species
    mixed_margins <- specimen_data %>%
      dplyr::group_by(.data$site, .data$morphotype) %>%
      dplyr::filter(1 %in% .data$margin & 0 %in% .data$margin) %>%
      dplyr::filter(.data$margin == 1) %>%
      dplyr::select("site", "morphotype", "specimen_number")


    if (length(mixed_margins$specimen_number) > 0) {
      for (i in 1:length(mixed_margins$specimen_number)) {
        specimen_data[which(specimen_data$specimen_number == mixed_margins$specimen_number[i]), ]$length_of_cut_perimeter <- 0
        specimen_data[which(specimen_data$specimen_number == mixed_margins$specimen_number[i]), ]$no_of_primary_teeth <- 0
        specimen_data[which(specimen_data$specimen_number == mixed_margins$specimen_number[i]), ]$no_of_subsidiary_teeth <- 0
      }
    }


    ## For specimens where blade area was measured, but there is an NA for petiole area, convert NA to 0 to permit the subsequent addition
    temp3 <- specimen_data$petiole_area
    specimen_data$petiole_area <- ifelse(specimen_data$blade_area > 0 & is.na(specimen_data$petiole_area), 0, specimen_data$petiole_area)
    # Sum petiole area and blade area
    specimen_data$leaf_area <- specimen_data$blade_area + specimen_data$petiole_area # if petiole area was measured but there is a NA for blade area, this addition will appropriately return a NA for leaf area

    # Feret Diameter Ratio
    specimen_data$feret_diameter <- 2 * sqrt(specimen_data$leaf_area / pi)
    specimen_data$fdr <- specimen_data$feret_diameter / specimen_data$feret

    # change NA's to zeros for length.of.cut.perimeter so that derived variables can be calculated
    temp1 <- specimen_data$length_of_cut_perimeter
    specimen_data$length_of_cut_perimeter <- ifelse(is.na(specimen_data$length_of_cut_perimeter), 0, specimen_data$length_of_cut_perimeter)

    # Corrected perimeters
    specimen_data$raw_blade_perimeter <- ifelse(is.na(specimen_data$raw_blade_perimeter) & !is.na(specimen_data$blade_perimeter) & specimen_data$margin == 0 & specimen_data$length_of_cut_perimeter == 0,
      specimen_data$blade_perimeter, specimen_data$raw_blade_perimeter
    )
    specimen_data$raw_blade_perimeter_corrected <- specimen_data$raw_blade_perimeter - specimen_data$length_of_cut_perimeter
    specimen_data$internal_raw_blade_perimeter_corrected <- specimen_data$internal_raw_blade_perimeter - specimen_data$length_of_cut_perimeter

    # Corrected raw blade area if there is no cut perimeter on a toothed leaf.
    specimen_data$raw_blade_area <- ifelse(is.na(specimen_data$raw_blade_area) & specimen_data$margin == 0 & specimen_data$length_of_cut_perimeter == 0,
      specimen_data$blade_area, specimen_data$raw_blade_area
    )

    ## Toothed variables
    # Total tooth count
    temp2 <- specimen_data$no_of_subsidiary_teeth
    specimen_data$no_of_subsidiary_teeth[is.na(specimen_data$no_of_subsidiary_teeth)] <- 0 # if it is left as NA then total tooth count will also return NA
    specimen_data$total_tooth_count <- ifelse(specimen_data$margin == 1, NA, specimen_data$no_of_primary_teeth + specimen_data$no_of_subsidiary_teeth)
    if (length(mixed_margins$specimen_number) > 0) {
      for (i in 1:length(mixed_margins$specimen_number)) {
        specimen_data[which(specimen_data$specimen_number == mixed_margins$specimen_number[i]), ]$total_tooth_count <- 0
      }
    }

    # Total tooth count : internal perimeter
    specimen_data$tc_ip <- specimen_data$total_tooth_count / specimen_data$internal_raw_blade_perimeter_corrected
    if (length(mixed_margins$specimen_number) > 0) {
      for (i in 1:length(mixed_margins$specimen_number)) {
        specimen_data[which(specimen_data$specimen_number == mixed_margins$specimen_number[i]), ]$tc_ip <- 0
      }
    }

    # Perimeter ratio
    specimen_data$perimeter_ratio <- specimen_data$raw_blade_perimeter_corrected / specimen_data$internal_raw_blade_perimeter_corrected
    if (length(mixed_margins$specimen_number) > 0) {
      for (i in 1:length(mixed_margins$specimen_number)) {
        specimen_data[which(specimen_data$specimen_number == mixed_margins$specimen_number[i]), ]$perimeter_ratio <- 1
      }
    }

    #### Apply required natural logs to appropriate variables
    specimen_data$ln_leaf_area <- log(100 * specimen_data$leaf_area) # Leaf area is expressed in mm2 in DiLP MLR and SLR models, so here also converting from cm2 to mm2
    specimen_data$ln_pr <- log(specimen_data$perimeter_ratio)
    specimen_data$ln_tc_ip <- log(specimen_data$tc_ip)
    if (length(mixed_margins$specimen_number) > 0) {
      for (i in 1:length(mixed_margins$specimen_number)) {
        specimen_data[which(specimen_data$specimen_number == mixed_margins$specimen_number[i]), ]$ln_tc_ip <- NA
      }
    }

    # Petiole metric for reconstructing leaf mass per area
    specimen_data$petiole_metric <- (specimen_data$petiole_width^2) / specimen_data$leaf_area
    # Aspect ratio
    specimen_data$aspect_ratio <- specimen_data$feret / specimen_data$minimum_feret
    # Shape factor
    specimen_data$shape_factor <- 4 * pi * (specimen_data$blade_area / (specimen_data$blade_perimeter^2))
    # Compactness
    specimen_data$compactness <- (specimen_data$blade_perimeter^2) / specimen_data$blade_area
    # Tooth area
    specimen_data$tooth_area <- specimen_data$raw_blade_area - specimen_data$internal_raw_blade_area
    if (length(mixed_margins$specimen_number) > 0) {
      for (i in 1:length(mixed_margins$specimen_number)) {
        specimen_data[which(specimen_data$specimen_number == mixed_margins$specimen_number[i]), ]$tooth_area <- 0
      }
    }

    # Tooth area : perimeter
    specimen_data$ta_p <- specimen_data$tooth_area / specimen_data$raw_blade_perimeter_corrected
    if (length(mixed_margins$specimen_number) > 0) {
      for (i in 1:length(mixed_margins$specimen_number)) {
        specimen_data[which(specimen_data$specimen_number == mixed_margins$specimen_number[i]), ]$ta_p <- 0
      }
    }

    # Tooth area : internal perimeter
    specimen_data$ta_ip <- specimen_data$tooth_area / specimen_data$internal_raw_blade_perimeter_corrected
    if (length(mixed_margins$specimen_number) > 0) {
      for (i in 1:length(mixed_margins$specimen_number)) {
        specimen_data[which(specimen_data$specimen_number == mixed_margins$specimen_number[i]), ]$ta_ip <- 0
      }
    }

    # Tooth area : blade area
    specimen_data$ta_ba <- specimen_data$tooth_area / specimen_data$raw_blade_area
    if (length(mixed_margins$specimen_number) > 0) {
      for (i in 1:length(mixed_margins$specimen_number)) {
        specimen_data[which(specimen_data$specimen_number == mixed_margins$specimen_number[i]), ]$ta_ba <- 0
      }
    }

    # Average primary tooth area
    specimen_data$avg_ta <- specimen_data$tooth_area / specimen_data$no_of_primary_teeth # double check
    if (length(mixed_margins$specimen_number) > 0) {
      for (i in 1:length(mixed_margins$specimen_number)) {
        specimen_data[which(specimen_data$specimen_number == mixed_margins$specimen_number[i]), ]$avg_ta <- 0
      }
    }

    # Tooth count : blade area
    specimen_data$tc_ba <- specimen_data$total_tooth_count / specimen_data$raw_blade_area
    if (length(mixed_margins$specimen_number) > 0) {
      for (i in 1:length(mixed_margins$specimen_number)) {
        specimen_data[which(specimen_data$specimen_number == mixed_margins$specimen_number[i]), ]$tc_ba <- 0
      }
    }

    # tooth count : perimeter
    specimen_data$tc_p <- specimen_data$total_tooth_count / specimen_data$raw_blade_perimeter_corrected
    if (length(mixed_margins$specimen_number) > 0) {
      for (i in 1:length(mixed_margins$specimen_number)) {
        specimen_data[which(specimen_data$specimen_number == mixed_margins$specimen_number[i]), ]$tc_p <- 0
      }
    }


    # Return length of cut perimeter and no subsidiary teeth back to original values to keep averages intact
    specimen_data$length_of_cut_perimeter <- temp1
    specimen_data$no_of_subsidiary_teeth <- temp2
    specimen_data$petiole_area <- temp3




    return(specimen_data)
  }
}

#' Check for common errors in DiLP measurements
#'
#' @description
#' `dilp_errors()` will typically only be called internally by [dilp()].
#' However, it can be used on its own to evaluate errors that commonly occur
#' during the data collection and processing steps.  A `dilp_errors()` call
#' will nearly always follow a [dilp_processing()] call.  Returns a data frame.
#'
#' @param specimen_data Processed specimen level leaf physiognomic data.  The
#' structure should match the structure of the output from [dilp_processing()]
#'
#' @return A 7 by X data frame.  Each row shows a common error, and which specimens
#' from the input dataset are tripping it.
#' @export
#'
#' @examples
#' # Check for errors in the provided McAbeeExample dataset.
#' dilp_dataset <- dilp_processing(McAbeeExample)
#' dilp_errors <- dilp_errors(dilp_dataset)
#' dilp_errors
dilp_errors <- function(specimen_data) {
  ### This data frame will be filled with the results of the following error checks
  errors <- data.frame()
  mixed_margins <- specimen_data %>%
    dplyr::group_by(.data$site, .data$morphotype) %>%
    dplyr::filter(1 %in% .data$margin & 0 %in% .data$margin) %>%
    dplyr::filter(.data$margin == 1) %>%
    dplyr::select("site", "morphotype", "specimen_number")

  dilp.check.1 <- specimen_data %>%
    dplyr::filter(.data$margin == 1)
  error <- specimen_data[which(dilp.check.1$total_tooth_count > -1 & !(dilp.check.1$specimen_number %in% mixed_margins$specimen_number)), 2]
  temp1 <- data.frame(Check = "Entire tooth count not NA", t(error))
  error <- specimen_data[which(dilp.check.1$tc_ip > -1 & !(dilp.check.1$specimen_number %in% mixed_margins$specimen_number)), 2]
  temp2 <- data.frame(Check = "Entire tooth count : IP not NA", t(error))
  error <- specimen_data[which(dilp.check.1$perimeter_ratio > -1 & !(dilp.check.1$specimen_number %in% mixed_margins$specimen_number)), 2]
  temp3 <- data.frame(Check = "Entire perimeter ratio not NA", t(error))
  dilp.check.2 <- tidyr::drop_na(specimen_data, "fdr")
  error <- specimen_data[which(dilp.check.2$fdr < 0 | dilp.check.2$fdr > 1), 2]
  temp4 <- data.frame(Check = "FDR not between 0-1", t(error))
  error <- specimen_data[which(specimen_data$internal_raw_blade_perimeter_corrected > specimen_data$raw_blade_perimeter_corrected), 2]
  temp5 <- data.frame(Check = "External perimeter not larger than internal perimeter", t(error))
  error <- specimen_data[which(specimen_data$minimum_feret > specimen_data$feret), 2]
  temp6 <- data.frame(Check = "Feret is not larger than minimum Feret", t(error))
  error <- as.data.frame(specimen_data[which(specimen_data$perimeter_ratio <= 1 & !(specimen_data$specimen_number %in% mixed_margins$specimen_number)), 2])
  temp7 <- data.frame(Check = "Perimeter ratio not greater than 1", t(error))
  errors <- dplyr::bind_rows(temp1, temp2, temp3, temp4, temp5, temp6, temp7)
  if (length(errors) == 1) {
    errors$Specimen1 <- "none"
  }
  names(errors) <- gsub(x = names(errors), pattern = "X", replacement = "Specimen")
  rownames(errors) <- NULL

  return(errors)
}

#' Identify outlier specimens
#'
#' @description
#' `dilp_outliers()` will typically only be called internally by [dilp()].
#' However, it can be used on its own to locate specimens that may have been
#' misreported or measured incorrectly.  `dilp_outliers()` returns a data frame
#' listing specimens that have unusually high or low values for the four key
#' parameters used in DiLP analyses.  If flagged, it may be worth taking a look at the
#' raw measurements and evaluating if the specimen should be used.
#'
#'
#' @param specimen_data Processed specimen level leaf physiognomic data.  The
#' structure should match the structure of the output from [dilp_processing()]
#'
#' @return A 4 by X data frame. Each row represents one of the DiLP parameters,
#' and the specimens that are outliers for that parameter.
#' @export
#'
#' @examples
#' # Check for outliers in the provided McAbeeExample dataset. Each
#' # of these outliers has been manually re-examined and was found acceptable.
#' dilp_dataset <- dilp_processing(McAbeeExample)
#' dilp_outliers <- dilp_outliers(dilp_dataset)
#' dilp_outliers
dilp_outliers <- function(specimen_data) {
  vars <- c("fdr", "tc_ip", "leaf_area", "perimeter_ratio") # DiLP variables
  outliers <- data.frame()
  index <- which(colnames(specimen_data) == "specimen_number")

  for (i in 1:length(vars)) {
    temp <- specimen_data
    colnames(temp)[colnames(temp) == vars[i]] <- "trait" # rename variable of focus
    temp.outliers <- grDevices::boxplot.stats(temp$trait)$out # check it for outliers
    temp.specimen <- temp[which(temp$trait %in% c(temp.outliers)), index] # determine specimen numbers for any outliers
    temp.specimen <- as.data.frame(t(temp.specimen))
    temp.output <- (trait <- vars[i])
    temp.output <- cbind(temp.output, temp.specimen) # create output table with variable name and any potential rows
    outliers <- dplyr::bind_rows(outliers, temp.output) # bind to summary table
  }
  # Rename column headers
  if (length(outliers) == 1) {
    outliers$Outlier1 <- "none"
  }
  names(outliers) <- gsub(x = names(outliers), pattern = "V", replacement = "Outlier")
  names(outliers) <- gsub(x = names(outliers), pattern = "temp.output", replacement = "Variable")
  rownames(outliers) <- NULL
  return(outliers)
}

#' Generate DiLP results
#'
#' @description
#' `dilp()` processes raw leaf physiognomic data, checks for common
#' errors/outliers, and returns the processed data, keys to finding potential
#' errors or outliers, and paleoclimate reconstructions.
#'
#' @param specimen_data A data frame containing specimen level leaf physiognomic
#' data. See Lowe et al. 2024 for more information on how to collect this data.
#' A good reference for how to put together the data: \code{\link{McAbeeExample}}
#'
#' Required columns:
#'    * site
#'    * specimen_number
#'    * morphotype
#'    * margin
#'    * feret
#'    * blade_area
#'    * raw_blade_perimeter
#'    * internal_raw_blade_perimeter
#'    * length_of_cut_perimeter
#'    * no_of_primary_teeth
#'    * no_of_subsidiary_teeth
#'
#'  Recommended columns:
#'    * petiole_width
#'    * petiole_area
#'    * blade_perimeter
#'    * minimum_feret
#'    * raw_blade_area
#'    * internal_raw_blade_area
#'
#' @param params Either a string referring to one of two preloaded parameter sets
#' of a list of custom parameters (same format as the list below).
#'
#' Preloaded parameter sets are "PeppeGlobal" and "PeppeNH" which are calibrated based on
#' global and northern hemisphere data respectively. Allen et al. (2020) illustrates a situation
#' in which the northern hemisphere parameters may be preferable.  The "PeppeNH" parameters
#' only estimate MAT.  Use "PeppeGlobal" for all MAP estimates. Defaults to "PeppeGlobal" as follows (Peppe et al. 2011):
#'
#'    * MAT.MLR.M = 0.21,
#'    * MAT.MLR.FDR = 42.296,
#'    * MAT.MLR.TC.IP = -2.609,
#'    * MAT.MLR.constant = -16.004,
#'    * MAT.MLR.error = 4,
#'    * MAT.SLR.M = 0.204,
#'    * MAT.SLR.constant = 4.6,
#'    * MAT.SLR.error = 4.9,
#'    * MAP.MLR.LA = 0.298,
#'    * MAP.MLR.TC.IP = 0.279,
#'    * MAP.MLR.PR = -2.717,
#'    * MAP.MLR.constant = 3.033,
#'    * MAP.MLR.SE = 0.6,
#'    * MAP.SLR.LA = 0.283,
#'    * MAP.SLR.constant = 2.92,
#'    * MAP.SLR.SE = 0.61
#'
#' @param subsite_cols A vector or list of columns present in `specimen_data` to calculate
#' paleoclimate estimates for.  A completely optional parameter - allows different groupings of
#' specimens to be tested, or comparisons of paleoclimate estimates at different levels of grouping.
#' Adds additional estimates to $results.
#'
#' @return A list of tables that includes all pertinent DiLP
#' information:
#'
#' * processed_leaf_data: the full set of cleaned and newly calculated leaf
#' physiognomic data that is necessary for DiLP analysis. See [dilp_processing()]
#' for more information.
#' * processed_morphotype_data: morphospecies-site pair means for all leaf
#' physiognomic data.
#' * processed_site_data: site means for all leaf physiognomic data.
#' * errors: lists any specimens that may be causing common errors in DiLP
#' calculations. See [dilp_errors()] for more information.
#' * outliers: flags outliers in variables used for DiLP analysis that may
#' represent incorrect data.  See [dilp_outliers()] for more information.
#' * results: climate reconstructions of MAT and MAP using single and multi-linear
#' regressions.
#'
#' @references
#' * Allen, S. E., Lowe, A. J., Peppe, D. J., & Meyer, H. W. (2020). Paleoclimate and paleoecology of the latest Eocene Florissant flora of central Colorado, USA. Palaeogeography, Palaeoclimatology, Palaeoecology, 551, 109678.
#' * Peppe, D.J., Royer, D.L., Cariglino, B., Oliver, S.Y., Newman, S., Leight, E., Enikolopov, G., Fernandez-Burgos, M., Herrera, F., Adams, J.M., Correa, E., Currano, E.D., Erickson, J.M., Hinojosa, L.F., Hoganson, J.W., Iglesias, A., Jaramillo, C.A., Johnson, K.R., Jordan, G.J., Kraft, N.J.B., Lovelock, E.C., Lusk, C.H., Niinemets, Ü., Peñuelas, J., Rapson, G., Wing, S.L. and Wright, I.J. (2011), Sensitivity of leaf size and shape to climate: global patterns and paleoclimatic applications. New Phytologist, 190: 724-739. https://doi.org/10.1111/j.1469-8137.2010.03615.x
#' * Lowe. A.J., Flynn, A.G., Butrim, M.J., Baumgartner, A., Peppe, D.J., and Royer, D.L. (2024), Reconstructing terrestrial paleoclimate and paleoecology with fossil leaves using Digital Leaf Physiognomy and leaf mass per area.  JoVE.
#' @export
#'
#' @examples
#' dilp_results <- dilp(McAbeeExample)
#' dilp_results$processed_leaf_data
#' dilp_results$processed_morphotype_data
#' dilp_results$processed_site_data
#' dilp_results$errors
#' dilp_results$outliers
#' dilp_results$results
dilp <- function(specimen_data, params = "PeppeGlobal", subsite_cols = NULL) {
  subsite_cols <- subsite_cols %>%
    stringr::str_trim() %>%
    stringr::str_to_lower() %>%
    stringr::str_replace_all("[.]", " ") %>%
    stringr::str_replace_all("[ ]", "_") %>%
    stringr::str_replace_all("__", "_")
  colnames(specimen_data) <- colnameClean(specimen_data)
  processed_specimen_data <- dilp_processing(specimen_data)
  errors <- dilp_errors(processed_specimen_data)
  outliers <- dilp_outliers(processed_specimen_data)
  if (is.list(params)) {

  } else {
    params <- grab_regression(params, "dilp")
  }

  ####### Morphotype average by site
  dilp_morphotype <- processed_specimen_data %>%
    dplyr::select(-"specimen_number") %>%
    dplyr::summarize(.by = c(.data$site, .data$morphotype), dplyr::across(dplyr::where(~ !is.character(.)), \(x) mean(x, na.rm = TRUE)))

  ##### Morphotypes that have variable leaf margin states require a margin state of 0.5

  # is just 1 and 0 listed? If so, the following finds margin state values between 0 and 1 and replaces them with 0.5
  if (length(unique(stats::na.omit(dilp_morphotype$margin))) > 2) {
    dilp_morphotype$margin[dilp_morphotype$margin > 0 & dilp_morphotype$margin < 1] <- 0.5
    if (length(unique(dilp_morphotype$margin)) > 2) {
      warning("Margin states outside the bounds of [0 - 1] present. Non 0 and 1 values converted to 0.5")
    }
  }

  ####### Site average
  dilp_site <- dilp_morphotype %>%
    dplyr::group_by(.data$site) %>%
    dplyr::select(-"morphotype") %>%
    dplyr::summarize(dplyr::across(dplyr::where(~ !is.character(.)), \(x) mean(x, na.rm = TRUE)))

  ### Convert site margin from proportion to percentage
  dilp_site$margin <- dilp_site$margin * 100

  ### Conditionally set site tc_ip to 0 and perimeter_ratio to 1 if all morphotypes are untoothed
  dilp_site$tc_ip <- ifelse(dilp_site$margin == 100, 0, dilp_site$tc_ip)
  dilp_site$perimeter_ratio <- ifelse(dilp_site$margin == 100, 1, dilp_site$perimeter_ratio)
  # dilp_site$ln_tc_ip <- ifelse(dilp_site$margin == 100, -20.7, dilp_site$ln_tc_ip)
  # dilp_site$ln_pr <- ifelse(dilp_site$margin == 100, 0, dilp_site$ln_pr)

  ## A loop is constructed to handle spreadsheets that contain either multiple sites or a single site. For the latter, the loop will run only once.
  sites <- c(unique(dilp_site$site))
  Results <- data.frame()

  for (i in 1:length(sites)) {
    temp <- dilp_site[dilp_site$site == sites[i], ] # isolate site

    #### MAT
    # MLR
    MAT.MLR <- (temp$margin * params$MAT.MLR.M) + (temp$fdr * params$MAT.MLR.FDR) + (temp$tc_ip * params$MAT.MLR.TC.IP) + params$MAT.MLR.constant

    # SLR
    MAT.SLR <- (temp$margin * params$MAT.SLR.M) + params$MAT.SLR.constant

    #### MAP
    # MLR
    MAP.MLR.exp <- (temp$ln_leaf_area * params$MAP.MLR.LA) + (temp$ln_tc_ip * params$MAP.MLR.TC.IP) + (temp$ln_pr * params$MAP.MLR.PR) + params$MAP.MLR.constant
    MAP.MLR <- exp(MAP.MLR.exp)

    MAP.MLR.error.plus <- (exp(MAP.MLR.exp + params$MAP.MLR.SE)) - MAP.MLR
    MAP.MLR.error.minus <- MAP.MLR - (exp(MAP.MLR.exp - params$MAP.MLR.SE))

    # SLR

    MAP.SLR.exp <- (temp$ln_leaf_area * params$MAP.SLR.LA) + params$MAP.SLR.constant
    MAP.SLR <- exp(MAP.SLR.exp)
    MAP.SLR.error.plus <- (exp(MAP.SLR.exp + params$MAP.SLR.SE)) - MAP.SLR
    MAP.SLR.error.minus <- MAP.SLR - (exp(MAP.SLR.exp - params$MAP.SLR.SE))

    # Results table

    temp_output <- data.frame(
      site = temp$site, margin = temp$margin, fdr = temp$fdr, tc_ip = temp$tc_ip, ln_leaf_area = temp$ln_leaf_area, ln_tc_ip = temp$ln_tc_ip, ln_pr = temp$ln_pr, MAT.MLR, MAT.MLR.error = params$MAT.MLR.error, MAT.SLR, MAT.SLR.error = params$MAT.SLR.error, MAP.MLR,
      MAP.MLR.error.plus, MAP.MLR.error.minus, MAP.SLR, MAP.SLR.error.plus, MAP.SLR.error.minus
    )
    Results <- rbind(Results, temp_output)
  }

  for (subsite in subsite_cols) {
    if (is.null(specimen_data[[subsite]])) {
      stop(paste("Subsite column:", subsite, "not found in specimen_data."))
    } else {
      subsite_data <- specimen_data %>%
        dplyr::select(-.data$site) %>%
        dplyr::mutate(site = .data[[subsite]])
      temp <- dilp(subsite_data)
      Results <- rbind(Results, temp$results)
    }
  }

  return(list(processed_leaf_data = processed_specimen_data, processed_morphotype_data = dilp_morphotype, processed_site_data = dilp_site, errors = errors, outliers = outliers, results = Results))
}