R/species2unit.R
In ptompalski/MISTR: Implementation of Ontario's forest growth and yield models.
Defines functions
species2unit_region3
species2unit_region2
species2unit_region1
species2unit
Documented in
species2unit
#' Assign standard forest unit based on species composition and additional criteria
#'
#' @description This function uses a set of rules to assign standard forest unit (SFU or just FU) based on
#' forest composition, region and additional parameters that depend on the selected region.
#' @param species_composition This can be: (1) A data frame with two columns - first column has to contain species code, the second has to
#' contain species proportion (species proportions should sum up to 1); (2) a string containing information on species composition.
#' Note that this string has to be formated as follows: PW 60PR 40, or PW 60 PR 40, or PW60 PR40.
#' @param region Region number (1-3)
#' @param ecosite Id of ecosite, required for Region 1. Eg.: NW17
#' @param WG Working group, required for Region 1.
#' @param age Stand age, required for Region 1.
#' @param site_class. Site class (1-4), required for Region 2 and 3.
#' @param stocking Stocking, required for Region 3. Default = 1
#' @return Standard forest unit code
#' @export
#'
#' @examples
#'
#' species2unit(species_composition = "PR 90 BW 10", region = 2, site_class = 1)
#'
#' species2unit(species_composition = "SB 80SW 5LA 5BW 5BF 5", region = 2, site_class = 2)
species2unit <- function(species_composition, region, ecosite=NA, WG=NA, age=NA, site_class=NA, stocking=1) {
#species composition needs to be a data frame with two columns:
# $species_code and
# $species_proportion
if (is.character(species_composition)) {
species_composition <- MISTR:::species_comp_string2df(species_composition)
} else if (is.data.frame(species_composition)) {
if (ncol(species_composition) != 2) {
stop("If species_composition is a data.frame() it needs to contain two columns, first column: species code, second column: species proportion")
} else {
names(species_composition) <- c("species_code", "species_proportion")
}
}
s <- species_composition
WG <- toupper(WG)
funit <- NA
if (sum(s[,2])!=1) {
warning("Species proportions do not sum up to 1.0")
}
if (region == 1) {
if(is.na(ecosite) | is.na(age) | is.na(WG)) {
stop("Identification of SFU in region 1 requires information on ecosite, age, and WG.")
}
funit <- MISTR:::species2unit_region1(s=s, ecosite = ecosite, age = age, WG = WG)
}
if (region == 2) {
if(is.na(site_class)) {
stop("Identification of SFU in region 2 requires information on site class.")
}
funit <- MISTR:::species2unit_region2(s=s,site_class = site_class)
}
if (region == 3) {
if(is.na(site_class) | is.na(stocking)) {
stop("Identification of SFU in region 3 requires information on site_class and stocking.")
}
funit <-MISTR::: species2unit_region3(s=s,site_class = site_class, stocking = stocking)
}
return(funit)
}
#internal function for region 1
species2unit_region1 <- function(s, ecosite, age, WG) {
if (sum(dplyr::filter(s, species_code=="PW")[,2]) >= 0.4) {
funit <- "PwDom"
} else if (sum(dplyr::filter(s, species_code=="PR")[,2]) >= 0.7) {
funit <- "PrDom"
} else if (sum(dplyr::filter(s,species_code %in% c("PW", "PR"))[,2], na.rm = T) >= 0.4 ) {
funit <- "PrwMx"
} else if (sum(dplyr::filter(s, species_code=="CE")[,2]) >= 0.2 & stringr::str_detect(ecosite, "NW17.") ) {
funit <- "UplCe"
} else if ((sum(dplyr::filter(s,species_code %in% c("CE", "LA"))[,2], na.rm = T) >= 0.5 | WG == "CE" | WG == "LA") &
sum(dplyr::filter(s,species_code %in% c("PR", "PW", "PJ","SW","BW"))[,2], na.rm = T) < 0.1) {
funit <- "OCLow"
} else if ((stringr::str_detect(ecosite, "NW34.") & sum(dplyr::filter(s,species_code %in% c("PR", "PW", "PJ", "SW", "BF"))[,2], na.rm = T) <= 0.2) |
(stringr::str_detect(ecosite, "NW35.") | stringr::str_detect(ecosite, "NW36.") | stringr::str_detect(ecosite, "NW37.")) |
( stringr::str_detect(ecosite, "NW38.") & WG %in% c("SX", "SB", "CE", "LA")) ) {
funit <- "SbLow"
} else if (sum(dplyr::filter(s, species_code=="SB")[,2]) >= 0.7 &
sum(dplyr::filter(s, species_code %in% c("PO", "BW"))[,2]) <= 0.2 &
(stringr::str_detect(ecosite, "NW12.") | stringr::str_detect(ecosite, "NW11."))) {
funit <- "SbSha"
} else if (sum(dplyr::filter(s, species_code=="SB")[,2]) >= 0.7 &
sum(dplyr::filter(s, species_code %in% c("PO", "BW"))[,2]) <= 0.2) {
funit <- "SbDee"
} else if ( ((sum(dplyr::filter(s, species_code=="PJ")[,2]) >= 0.7 &
sum(dplyr::filter(s, species_code %in% c("PO", "BW"))[,2]) <= 0.2) |
(sum(dplyr::filter(s, species_code=="PJ")[,2]) >= 0.5 &
sum(dplyr::filter(s, species_code %in% c("PO", "BW"))[,2]) <= 0.2 & age >= 120)) &
(stringr::str_detect(ecosite, "NW12.") | stringr::str_detect(ecosite, "NW11."))) {
funit <- "PjSha"
} else if (
(sum(dplyr::filter(s, species_code=="PJ")[,2]) >= 0.7 &
sum(dplyr::filter(s, species_code %in% c("PO", "BW"))[,2]) <= 0.2) |
(sum(dplyr::filter(s, species_code=="PJ")[,2]) >= 0.5 & sum(dplyr::filter(s, species_code %in% c("PO", "BW"))[,2]) <= 0.2 & age >= 120) |
(sum(dplyr::filter(s, species_code=="PJ")[,2]) >= 0.7 & (stringr::str_detect(ecosite, "NW13.") | stringr::str_detect(ecosite, "NW14."))) ) {
funit <- "PjDee"
} else if ( sum(dplyr::filter(s, species_code=="PO")[,2]) >= 0.7 & (stringr::str_detect(ecosite, "NW12.") | stringr::str_detect(ecosite, "NW11."))) {
funit <- "PoSha"
} else if ( sum(dplyr::filter(s, species_code=="PO")[,2]) >= 0.7 ) {
funit <- "PoDee"
} else if ( sum(dplyr::filter(s, species_code=="BW")[,2]) >= 0.6 &
sum(dplyr::filter(s, species_code %in% c("PO", "BW"))[,2]) >= 0.7 &
( stringr::str_detect(ecosite, "NW12.") | stringr::str_detect(ecosite, "NW11.") )) {
funit <- "BwSha"
} else if (sum(dplyr::filter(s, species_code=="BW")[,2]) >= 0.6 &
sum(dplyr::filter(s, species_code %in% c("PO", "BW"))[,2]) >= 0.7) {
funit <- "BwDee"
} else if ( sum(dplyr::filter(s, species_code %in% c("YB","MH","MS","AB","AW","BD","BE","CH","EW","IW","QR","OB","OW","OH"))[,2]) >= 0.3) {
funit <- "OthHd"
} else if (
sum(dplyr::filter(s, species_code %in% c("PR","SB","PJ","SW","BF"))[,2]) >= 0.7 &
sum(dplyr::filter(s, species_code=="BF")[,2]) <= 0.1 &
sum(dplyr::filter(s, species_code %in% c("PO","BW"))[,2]) <= 0.2 &
sum(dplyr::filter(s, species_code %in% c("SB","SW"))[,2]) > sum(dplyr::filter(s, species_code=="PJ")[,2])
) {
funit <- "SbMx1"
} else if (
sum(dplyr::filter(s, species_code %in% c("PR","SB","PJ","SW","BF"))[,2]) >= 0.7 &
sum(dplyr::filter(s, species_code=="BF")[,2]) <= 0.1 &
sum(dplyr::filter(s, species_code %in% c("PO","BW"))[,2]) <= 0.2 &
sum(dplyr::filter(s, species_code %in% c("SB","SW"))[,2]) <= sum(dplyr::filter(s, species_code=="PJ")[,2])
) {
funit <- "PjMx1"
} else if ( sum(dplyr::filter(s, species_code=="BF")[,2]) >= 0.7 ) {
funit <- "BfPur"
} else if (
sum(dplyr::filter(s, species_code %in% c("PW","PR","PJ","SB","SW","BF","CE","LA","OC","HE"))[,2]) >= 0.7 &
sum(dplyr::filter(s, species_code=="BF")[,2]) > 0.1 &
sum(dplyr::filter(s, species_code %in% c("BF","SW"))[,2]) >= 0.3
) {
funit <- "BfMx1"
} else if (
sum(dplyr::filter(s, species_code %in% c("PO","PL","PB","BW","YB","MH","MS","AB","AW","BD","BE","CH","EW","IW","QR","OB","OW","OH"))[,2]) >= 0.7 ) {
funit <- "HrDom"
} else if (
sum(dplyr::filter(s, species_code %in% c("PO","PL","PB","BW","YB","MH","MS","AB","AW","BD","BE","CH","EW","IW","QR","OB","OW","OH"))[,2]) >= 0.5 ) {
funit <- "HrdMw"
} else if ( sum(dplyr::filter(s, species_code %in% c("PW","PR","PJ","SB","SW","BF","CE","LA","OC","HE"))[,2]) >= 0.5) {
funit <- "ConMx"
} else {
funit <- "Fudge"
}
return(funit)
}
species2unit_region2 <- function(s, site_class) {
if (sum(dplyr::filter(s, species_code=="PR")[,2]) >= 0.7) {
funit <- "PR1"
} else if ((sum(dplyr::filter(s,species_code %in% c("PW", "PR", "SW","HE"))[,2], na.rm = T) >= 0.4 & sum(dplyr::filter(s, species_code=="PW")[,2]) >= 0.3) ) {
funit <- "PW1"
} else if (sum(dplyr::filter(s,species_code %in% c("PW", "PR"))[,2], na.rm = T) >= 0.4) {
funit <- "PRW"
} else if (sum(dplyr::filter(s,species_code %in% c("MS", "AB", "EW"))[,2], na.rm = T) >= 0.4 | sum(dplyr::filter(s, species_code=="PB")[,2]) >= 0.3 ) {
funit <- "LH1"
} else if (sum(dplyr::filter(s,species_code %in% c("MS","AB","EW","MH","YB","AW","BD","BE","CH","IW","QR","OB","OW","OH"))[,2], na.rm = T) >= 0.3) {
funit <- "TH1"
} else if (sum(dplyr::filter(s,species_code %in% c("SB", "LA"))[,2], na.rm = T) >= 0.7 & sum(dplyr::filter(s, species_code=="PW")[,2]) == 0 & site_class == 4) {
funit <- "BOG"
} else if (sum(dplyr::filter(s, species_code=="SB")[,2]) >= 0.8 &
sum(dplyr::filter(s,species_code %in% c("MH","YB","AW","BD","BE","CH","IW","QR","OB","OW","OH","PR"))[,2], na.rm = T) == 0 &
sum(dplyr::filter(s,species_code %in% c("PW", "PJ"))[,2], na.rm = T) <= 0.1) {
funit <- "SB1"
} else if (sum(dplyr::filter(s, species_code=="PJ")[,2]) >= 0.7 &
sum(dplyr::filter(s, species_code %in% c("PO","BW","MH","YB","AW","BD","BE","CH","IW","QR","OB","OW","OH","MS","AB","EW"))[,2], na.rm = T) <= 0.2) {
funit <- "PJ1"
} else if (sum(dplyr::filter(s,species_code %in% c("CW", "LA","SB"))[,2], na.rm = T) >= 0.8 &
sum(dplyr::filter(s,species_code %in% c("MH","YB","AW","BD","BE","CH","IW","QR","OB","OW","OH","PR"))[,2], na.rm = T) == 0 &
sum(dplyr::filter(s,species_code %in% c("PW", "PJ"))[,2], na.rm = T) <= 0.1) {
funit <- "LC1"
} else if ( sum(dplyr::filter(s,species_code %in% c("PJ", "SB","PR"))[,2], na.rm = T) >= 0.7 |
(sum(dplyr::filter(s,species_code %in% c("PJ"))[,2], na.rm = T) >= 0.5 &
sum(dplyr::filter(s,species_code %in% c("PW","PR","PJ","SB","BF","SW","HE","CE","LA","OC"))[,2], na.rm = T) >= 0.7 &
sum(dplyr::filter(s,species_code %in% c("BF","SW","HE","PW","CE","LA"))[,2], na.rm = T) <= 0.2) &
sum(dplyr::filter(s,species_code %in% c("PJ"))[,2], na.rm = T) >= sum(dplyr::filter(s,species_code %in% c("SB"))[,2], na.rm = T)
) {
funit <- "PJ2"
} else if (sum(dplyr::filter(s,species_code %in% c("SB", "SW","BF","CE","LA","PW","PJ","PR","HE"))[,2], na.rm = T) >= 0.7 &
(sum(dplyr::filter(s,species_code %in% c("BF","CE","PW","LA","SW","HE"))[,2], na.rm = T) <= 0.2 |
sum(dplyr::filter(s,species_code %in% c("PJ","PR"))[,2], na.rm = T) >= 0.3)) {
funit <- "SP1"
} else if (sum(dplyr::filter(s,species_code %in% c("SB","SW","BF","CE","LA","PW","PJ","HE"))[,2], na.rm = T) >= 0.7) {
funit <- "SF1"
} else if (sum(dplyr::filter(s,species_code %in% c("PO","BW","MH","YB","AW","BD","BE","CH","IW","QR","OB","OW","OH","MS","AB","EW"))[,2], na.rm = T) >= 0.7 &
sum(dplyr::filter(s, species_code=="PO")[,2]) >= 0.5) {
funit <- "PO1"
} else if (sum(dplyr::filter(s,species_code %in% c("PO","BW","MH","YB","AW","BD","BE","CH","IW","QR","OB","OW","OH","MS","AB","EW"))[,2], na.rm = T) >= 0.7) {
funit <- "BW1"
} else if (sum(dplyr::filter(s,species_code %in% c("PJ", "PR"))[,2], na.rm = T) >= 0.2 ) {
funit <- "MW1"
} else {
funit <- "MW2"
message("no condition matched")
}
return(funit)
}
species2unit_region3 <- function(s, site_class, stocking) {
if (sum(dplyr::filter(s, species_code=="PR")[,2]) >= 0.7 &
sum(dplyr::filter(s, species_code=="PW")[,2]) < 0.3) {
funit <- "PR1"
} else if (sum(dplyr::filter(s,species_code %in% c("PW","PR"))[,2], na.rm = T) >= 0.5 &
sum(dplyr::filter(s, species_code=="PW")[,2]) > sum(dplyr::filter(s, species_code=="PR")[,2]) &
sum(dplyr::filter(s,species_code %in% c("PW","PR"))[,2], na.rm = T) * stocking >= 0.3 &
sum(dplyr::filter(s,species_code %in% c("QR","OW"))[,2], na.rm = T) < 0.2
) {
funit <- "PWUS4"
} else if (
sum(dplyr::filter(s,species_code %in% c("PW","PR", "QR", "OW"))[,2], na.rm = T) >= 0.5 &
sum(dplyr::filter(s,species_code %in% c("PW"))[,2], na.rm = T) >= sum(dplyr::filter(s,species_code %in% c("QR","OW"))[,2], na.rm = T) &
sum(dplyr::filter(s,species_code %in% c("PW","PR", "QR", "OW"))[,2], na.rm = T) * stocking >= 0.3 &
sum(dplyr::filter(s,species_code %in% c("QR","OW"))[,2], na.rm = T) >= 0.2
) {
funit <- "PWOR"
} else if (
(sum(dplyr::filter(s,species_code %in% c("PW","PR"))[,2], na.rm = T) >= 0.3 &
sum(dplyr::filter(s,species_code %in% c("PW","PR"))[,2], na.rm = T) * stocking >= 0.3 ) |
(
sum(dplyr::filter(s, species_code=="PW")[,2]) >= sum(dplyr::filter(s, species_code=="HE")[,2]) &
sum(dplyr::filter(s, species_code=="PW")[,2]) >= sum(dplyr::filter(s, species_code=="SW")[,2]) &
sum(dplyr::filter(s, species_code=="PW")[,2]) >= sum(dplyr::filter(s, species_code=="CE")[,2]) &
sum(dplyr::filter(s, species_code=="PW")[,2]) >= sum(dplyr::filter(s, species_code=="QR")[,2]) &
sum(dplyr::filter(s,species_code %in% c("PW","PR"))[,2], na.rm = T) >= 0.3 &
sum(dplyr::filter(s,species_code %in% c("PW","PR","SW","HE","QR","PJ","CE"))[,2], na.rm = T) * stocking >= 0.3
) & (
sum(dplyr::filter(s,species_code %in% c("PW","PR","PJ","SW","SB","HE","BF","CE","LA"))[,2], na.rm = T) >= 0.8
)
) {
funit <- "PWUSC"
} else if (
(
sum(dplyr::filter(s, species_code=="PW")[,2]) >= sum(dplyr::filter(s, species_code=="PR")[,2]) &
sum(dplyr::filter(s,species_code %in% c("PW","PR"))[,2], na.rm = T) >= 0.3 &
sum(dplyr::filter(s,species_code %in% c("PW","PR"))[,2], na.rm = T) * stocking >= 0.3
) | (
sum(dplyr::filter(s, species_code=="PW")[,2]) >= sum(dplyr::filter(s, species_code=="PR")[,2]) &
sum(dplyr::filter(s, species_code=="PW")[,2]) >= sum(dplyr::filter(s, species_code=="HE")[,2]) &
sum(dplyr::filter(s, species_code=="PW")[,2]) >= sum(dplyr::filter(s, species_code=="SW")[,2]) &
sum(dplyr::filter(s, species_code=="PW")[,2]) >= sum(dplyr::filter(s, species_code=="CE")[,2]) &
sum(dplyr::filter(s, species_code=="PW")[,2]) >= sum(dplyr::filter(s, species_code=="QR")[,2]) &
sum(dplyr::filter(s,species_code %in% c("PW","PR"))[,2], na.rm = T) >= 0.3 &
sum(dplyr::filter(s,species_code %in% c("PW","PR","SW","HE","QR","PJ","CE"))[,2], na.rm = T) * stocking >= 0.3
) & (
sum(dplyr::filter(s,species_code %in% c("PW","PR","PJ","SW","SB","HE","BF","CE","LA"))[,2], na.rm = T) < 0.8
)
) {
funit <- "PWUSH"
} else if (
sum(dplyr::filter(s,species_code %in% c("PW","PR"))[,2], na.rm = T) >= 0.3 &
sum(dplyr::filter(s,species_code %in% c("PW","PR"))[,2], na.rm = T) >= sum(dplyr::filter(s, species_code=="HE")[,2]) &
sum(dplyr::filter(s,species_code %in% c("PW","PR"))[,2], na.rm = T) >= sum(dplyr::filter(s, species_code=="SW")[,2]) &
sum(dplyr::filter(s,species_code %in% c("PW","PR"))[,2], na.rm = T) >= sum(dplyr::filter(s, species_code=="SB")[,2]) &
sum(dplyr::filter(s,species_code %in% c("PW","PR"))[,2], na.rm = T) >= sum(dplyr::filter(s, species_code=="CE")[,2]) &
sum(dplyr::filter(s,species_code %in% c("PW","PR"))[,2], na.rm = T) >= sum(dplyr::filter(s, species_code=="QR")[,2])
) {
funit <- "PWST"
} else if (
sum(dplyr::filter(s, species_code=="PJ")[,2]) >= 0.7 &
sum(dplyr::filter(s,species_code %in% c("MH","AB","AW","BD","BE","CH","EW","IW","QR","YB","OW","PO","BW","MS"))[,2], na.rm = T) <=0.2
) {
funit <- "PJ1"
} else if (
(
sum(dplyr::filter(s,species_code %in% c("PJ","SB","PR"))[,2], na.rm = T) >= 0.7 |
(
sum(dplyr::filter(s, species_code=="PJ")[,2]) >= 0.5 &
sum(dplyr::filter(s,species_code %in% c("PJ","SB","BF","SW","HE","PW","PR","CE","LA"))[,2], na.rm = T) >= 0.7 &
sum(dplyr::filter(s,species_code %in% c("BF","SW","HE","PW","CE","LA"))[,2], na.rm = T) <= 0.2
)
) & sum(dplyr::filter(s, species_code=="PJ")[,2]) >= sum(dplyr::filter(s, species_code=="SB")[,2])
) {
funit <- "PJ2"
} else if (
sum(dplyr::filter(s, species_code=="HE")[,2]) >= 0.4
) {
funit <- "HE1"
} else if (
sum(dplyr::filter(s, species_code=="CE")[,2]) >= 0.4 &
sum(dplyr::filter(s, species_code=="HE")[,2]) >= sum(dplyr::filter(s,species_code %in% c("SB","LA","BF"))[,2], na.rm = T) &
sum(dplyr::filter(s,species_code %in% c("OW","EW","IW","CH","MH","AB","AW","BD","BE","QR","YB","PO","BW","MS"))[,2], na.rm = T) < 0.3
) {
funit <- "CE1"
} else if (
sum(dplyr::filter(s, species_code=="SB")[,2]) >= 0.8 &
sum(dplyr::filter(s,species_code %in% c("MH","AW","BD","BE","CH","IW","QR","OW","YB","PR"))[,2], na.rm = T) == 0 &
sum(dplyr::filter(s,species_code %in% c("PW","PJ"))[,2], na.rm = T) <= 0.1
) {
funit <- "SB1"
} else if (
sum(dplyr::filter(s,species_code %in% c("SB","CE","LA"))[,2], na.rm = T) >= 0.8 &
sum(dplyr::filter(s,species_code %in% c("MH","AW","BD","BE","CH","IW","QR","OW","YB","PR"))[,2], na.rm = T) == 0 &
sum(dplyr::filter(s,species_code %in% c("PW","PJ"))[,2], na.rm = T) <= 0.1
) {
funit <- "LC1"
} else if (
sum(dplyr::filter(s,species_code %in% c("SB","SW","BF","CE","LA","PW","PJ","PR","HE"))[,2], na.rm = T) >= 0.7 &
(
sum(dplyr::filter(s,species_code %in% c("BF","CE","PW","LA","SW","HE"))[,2], na.rm = T) <= 0.2 |
sum(dplyr::filter(s, species_code=="PJ")[,2]) >= 0.3
)
) {
funit <- "SP1"
} else if (
sum(dplyr::filter(s,species_code %in% c("SW","SB","PW","PR","PJ","BF","CE","LA","HE"))[,2], na.rm = T) >= 0.7
) {
funit <- "SF1"
} else if (
sum(dplyr::filter(s, species_code=="YB")[,2]) >= 0.4
) {
funit <- "BY1"
} else if (
sum(dplyr::filter(s, species_code=="QR")[,2]) >= sum(dplyr::filter(s,species_code %in% c("MH","BE"))[,2], na.rm = T) &
sum(dplyr::filter(s, species_code=="QR")[,2]) >= 0.3 &
sum(dplyr::filter(s,species_code %in% c("QR","MH","AW","AB","BE","BD","YB","PW","PR","SW","HE"))[,2], na.rm = T) >= 0.4
) {
funit <- "OAK"
} else if (
sum(dplyr::filter(s,species_code %in% c("BD","AW","CH","QR","OW"))[,2], na.rm = T) >= 0.3 |
sum(dplyr::filter(s,species_code %in% c("BE","QR","OW"))[,2], na.rm = T) >= 0.3 |
sum(dplyr::filter(s, species_code=="BE")[,2]) >= 0.2
) {
funit <- "HDSL2"
} else if (
sum(dplyr::filter(s,species_code %in% c("MH","AW","BD","BE","CH","EW","IW","QR","YB","OW","HE"))[,2], na.rm = T) >= 0.5 &
sum(dplyr::filter(s,species_code %in% c("PO","BW","BF"))[,2], na.rm = T) <= 0.3 &
site_class %in% c(0,1,2)
) {
funit <- "HDSL1"
} else if (
sum(dplyr::filter(s,species_code %in% c("CE","AB","LA","SB"))[,2], na.rm = T) >= 0.3 &
(sum(dplyr::filter(s, species_code=="AB")[,2]) >= 0.2 |
sum(dplyr::filter(s,species_code %in% c("AB","MS","YB"))[,2], na.rm = T) >= 0.3)
) {
funit <- "LWMW"
} else if (
sum(dplyr::filter(s,species_code %in% c("MH","AW","BD","BE","CH","EW","IW","QR","YB","OW","HE"))[,2], na.rm = T) >= 0.5
) {
funit <- "HDUS"
} else if (
sum(dplyr::filter(s, species_code=="PO")[,2]) >= 0.5 &
sum(dplyr::filter(s,species_code %in% c("MH","AB","AW","BD","BE","CH","EW","IW","QR","YB","OW","PO","BW","MS"))[,2], na.rm = T) >= 0.7
) {
funit <- "PO1"
} else if (
sum(dplyr::filter(s,species_code %in% c("PO","BW"))[,2], na.rm = T) >= 0.5 &
sum(dplyr::filter(s,species_code %in% c("MH","AB","AW","BD","BE","CH","EW","IW","QR","YB","OW","PO","BW","MS"))[,2], na.rm = T) >= 0.7
) {
funit <- "BW1"
} else if (
sum(dplyr::filter(s,species_code %in% c("SW","PW","PR","CE","MH","YB","AW","CH","BD","QR","OW","IW","BE","HE"))[,2], na.rm = T) * stocking >= 0.3
) {
funit <- "MWUS"
} else if (
sum(dplyr::filter(s,species_code %in% c("PJ","PW","PR"))[,2], na.rm = T) >= 0.2
) {
funit <- "MWD"
} else {
funit <- "MWR"
}
return(funit)
}
ptompalski/MISTR documentation built on March 18, 2024, 10:13 p.m.
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Defines functions species2unit_region3 species2unit_region2 species2unit_region1 species2unit
Documented in species2unit
#' Assign standard forest unit based on species composition and additional criteria
#'
#' @description This function uses a set of rules to assign standard forest unit (SFU or just FU) based on
#' forest composition, region and additional parameters that depend on the selected region.
#' @param species_composition This can be: (1) A data frame with two columns - first column has to contain species code, the second has to
#' contain species proportion (species proportions should sum up to 1); (2) a string containing information on species composition.
#' Note that this string has to be formated as follows: PW 60PR 40, or PW 60 PR 40, or PW60 PR40.
#' @param region Region number (1-3)
#' @param ecosite Id of ecosite, required for Region 1. Eg.: NW17
#' @param WG Working group, required for Region 1.
#' @param age Stand age, required for Region 1.
#' @param site_class. Site class (1-4), required for Region 2 and 3.
#' @param stocking Stocking, required for Region 3. Default = 1
#' @return Standard forest unit code
#' @export
#'
#' @examples
#'
#' species2unit(species_composition = "PR 90 BW 10", region = 2, site_class = 1)
#'
#' species2unit(species_composition = "SB 80SW 5LA 5BW 5BF 5", region = 2, site_class = 2)
species2unit <- function(species_composition, region, ecosite=NA, WG=NA, age=NA, site_class=NA, stocking=1) {
#species composition needs to be a data frame with two columns:
# $species_code and
# $species_proportion
if (is.character(species_composition)) {
species_composition <- MISTR:::species_comp_string2df(species_composition)
} else if (is.data.frame(species_composition)) {
if (ncol(species_composition) != 2) {
stop("If species_composition is a data.frame() it needs to contain two columns, first column: species code, second column: species proportion")
} else {
names(species_composition) <- c("species_code", "species_proportion")
}
}
s <- species_composition
WG <- toupper(WG)
funit <- NA
if (sum(s[,2])!=1) {
warning("Species proportions do not sum up to 1.0")
}
if (region == 1) {
if(is.na(ecosite) | is.na(age) | is.na(WG)) {
stop("Identification of SFU in region 1 requires information on ecosite, age, and WG.")
}
funit <- MISTR:::species2unit_region1(s=s, ecosite = ecosite, age = age, WG = WG)
}
if (region == 2) {
if(is.na(site_class)) {
stop("Identification of SFU in region 2 requires information on site class.")
}
funit <- MISTR:::species2unit_region2(s=s,site_class = site_class)
}
if (region == 3) {
if(is.na(site_class) | is.na(stocking)) {
stop("Identification of SFU in region 3 requires information on site_class and stocking.")
}
funit <-MISTR::: species2unit_region3(s=s,site_class = site_class, stocking = stocking)
}
return(funit)
}
#internal function for region 1
species2unit_region1 <- function(s, ecosite, age, WG) {
if (sum(dplyr::filter(s, species_code=="PW")[,2]) >= 0.4) {
funit <- "PwDom"
} else if (sum(dplyr::filter(s, species_code=="PR")[,2]) >= 0.7) {
funit <- "PrDom"
} else if (sum(dplyr::filter(s,species_code %in% c("PW", "PR"))[,2], na.rm = T) >= 0.4 ) {
funit <- "PrwMx"
} else if (sum(dplyr::filter(s, species_code=="CE")[,2]) >= 0.2 & stringr::str_detect(ecosite, "NW17.") ) {
funit <- "UplCe"
} else if ((sum(dplyr::filter(s,species_code %in% c("CE", "LA"))[,2], na.rm = T) >= 0.5 | WG == "CE" | WG == "LA") &
sum(dplyr::filter(s,species_code %in% c("PR", "PW", "PJ","SW","BW"))[,2], na.rm = T) < 0.1) {
funit <- "OCLow"
} else if ((stringr::str_detect(ecosite, "NW34.") & sum(dplyr::filter(s,species_code %in% c("PR", "PW", "PJ", "SW", "BF"))[,2], na.rm = T) <= 0.2) |
(stringr::str_detect(ecosite, "NW35.") | stringr::str_detect(ecosite, "NW36.") | stringr::str_detect(ecosite, "NW37.")) |
( stringr::str_detect(ecosite, "NW38.") & WG %in% c("SX", "SB", "CE", "LA")) ) {
funit <- "SbLow"
} else if (sum(dplyr::filter(s, species_code=="SB")[,2]) >= 0.7 &
sum(dplyr::filter(s, species_code %in% c("PO", "BW"))[,2]) <= 0.2 &
(stringr::str_detect(ecosite, "NW12.") | stringr::str_detect(ecosite, "NW11."))) {
funit <- "SbSha"
} else if (sum(dplyr::filter(s, species_code=="SB")[,2]) >= 0.7 &
sum(dplyr::filter(s, species_code %in% c("PO", "BW"))[,2]) <= 0.2) {
funit <- "SbDee"
} else if ( ((sum(dplyr::filter(s, species_code=="PJ")[,2]) >= 0.7 &
sum(dplyr::filter(s, species_code %in% c("PO", "BW"))[,2]) <= 0.2) |
(sum(dplyr::filter(s, species_code=="PJ")[,2]) >= 0.5 &
sum(dplyr::filter(s, species_code %in% c("PO", "BW"))[,2]) <= 0.2 & age >= 120)) &
(stringr::str_detect(ecosite, "NW12.") | stringr::str_detect(ecosite, "NW11."))) {
funit <- "PjSha"
} else if (
(sum(dplyr::filter(s, species_code=="PJ")[,2]) >= 0.7 &
sum(dplyr::filter(s, species_code %in% c("PO", "BW"))[,2]) <= 0.2) |
(sum(dplyr::filter(s, species_code=="PJ")[,2]) >= 0.5 & sum(dplyr::filter(s, species_code %in% c("PO", "BW"))[,2]) <= 0.2 & age >= 120) |
(sum(dplyr::filter(s, species_code=="PJ")[,2]) >= 0.7 & (stringr::str_detect(ecosite, "NW13.") | stringr::str_detect(ecosite, "NW14."))) ) {
funit <- "PjDee"
} else if ( sum(dplyr::filter(s, species_code=="PO")[,2]) >= 0.7 & (stringr::str_detect(ecosite, "NW12.") | stringr::str_detect(ecosite, "NW11."))) {
funit <- "PoSha"
} else if ( sum(dplyr::filter(s, species_code=="PO")[,2]) >= 0.7 ) {
funit <- "PoDee"
} else if ( sum(dplyr::filter(s, species_code=="BW")[,2]) >= 0.6 &
sum(dplyr::filter(s, species_code %in% c("PO", "BW"))[,2]) >= 0.7 &
( stringr::str_detect(ecosite, "NW12.") | stringr::str_detect(ecosite, "NW11.") )) {
funit <- "BwSha"
} else if (sum(dplyr::filter(s, species_code=="BW")[,2]) >= 0.6 &
sum(dplyr::filter(s, species_code %in% c("PO", "BW"))[,2]) >= 0.7) {
funit <- "BwDee"
} else if ( sum(dplyr::filter(s, species_code %in% c("YB","MH","MS","AB","AW","BD","BE","CH","EW","IW","QR","OB","OW","OH"))[,2]) >= 0.3) {
funit <- "OthHd"
} else if (
sum(dplyr::filter(s, species_code %in% c("PR","SB","PJ","SW","BF"))[,2]) >= 0.7 &
sum(dplyr::filter(s, species_code=="BF")[,2]) <= 0.1 &
sum(dplyr::filter(s, species_code %in% c("PO","BW"))[,2]) <= 0.2 &
sum(dplyr::filter(s, species_code %in% c("SB","SW"))[,2]) > sum(dplyr::filter(s, species_code=="PJ")[,2])
) {
funit <- "SbMx1"
} else if (
sum(dplyr::filter(s, species_code %in% c("PR","SB","PJ","SW","BF"))[,2]) >= 0.7 &
sum(dplyr::filter(s, species_code=="BF")[,2]) <= 0.1 &
sum(dplyr::filter(s, species_code %in% c("PO","BW"))[,2]) <= 0.2 &
sum(dplyr::filter(s, species_code %in% c("SB","SW"))[,2]) <= sum(dplyr::filter(s, species_code=="PJ")[,2])
) {
funit <- "PjMx1"
} else if ( sum(dplyr::filter(s, species_code=="BF")[,2]) >= 0.7 ) {
funit <- "BfPur"
} else if (
sum(dplyr::filter(s, species_code %in% c("PW","PR","PJ","SB","SW","BF","CE","LA","OC","HE"))[,2]) >= 0.7 &
sum(dplyr::filter(s, species_code=="BF")[,2]) > 0.1 &
sum(dplyr::filter(s, species_code %in% c("BF","SW"))[,2]) >= 0.3
) {
funit <- "BfMx1"
} else if (
sum(dplyr::filter(s, species_code %in% c("PO","PL","PB","BW","YB","MH","MS","AB","AW","BD","BE","CH","EW","IW","QR","OB","OW","OH"))[,2]) >= 0.7 ) {
funit <- "HrDom"
} else if (
sum(dplyr::filter(s, species_code %in% c("PO","PL","PB","BW","YB","MH","MS","AB","AW","BD","BE","CH","EW","IW","QR","OB","OW","OH"))[,2]) >= 0.5 ) {
funit <- "HrdMw"
} else if ( sum(dplyr::filter(s, species_code %in% c("PW","PR","PJ","SB","SW","BF","CE","LA","OC","HE"))[,2]) >= 0.5) {
funit <- "ConMx"
} else {
funit <- "Fudge"
}
return(funit)
}
species2unit_region2 <- function(s, site_class) {
if (sum(dplyr::filter(s, species_code=="PR")[,2]) >= 0.7) {
funit <- "PR1"
} else if ((sum(dplyr::filter(s,species_code %in% c("PW", "PR", "SW","HE"))[,2], na.rm = T) >= 0.4 & sum(dplyr::filter(s, species_code=="PW")[,2]) >= 0.3) ) {
funit <- "PW1"
} else if (sum(dplyr::filter(s,species_code %in% c("PW", "PR"))[,2], na.rm = T) >= 0.4) {
funit <- "PRW"
} else if (sum(dplyr::filter(s,species_code %in% c("MS", "AB", "EW"))[,2], na.rm = T) >= 0.4 | sum(dplyr::filter(s, species_code=="PB")[,2]) >= 0.3 ) {
funit <- "LH1"
} else if (sum(dplyr::filter(s,species_code %in% c("MS","AB","EW","MH","YB","AW","BD","BE","CH","IW","QR","OB","OW","OH"))[,2], na.rm = T) >= 0.3) {
funit <- "TH1"
} else if (sum(dplyr::filter(s,species_code %in% c("SB", "LA"))[,2], na.rm = T) >= 0.7 & sum(dplyr::filter(s, species_code=="PW")[,2]) == 0 & site_class == 4) {
funit <- "BOG"
} else if (sum(dplyr::filter(s, species_code=="SB")[,2]) >= 0.8 &
sum(dplyr::filter(s,species_code %in% c("MH","YB","AW","BD","BE","CH","IW","QR","OB","OW","OH","PR"))[,2], na.rm = T) == 0 &
sum(dplyr::filter(s,species_code %in% c("PW", "PJ"))[,2], na.rm = T) <= 0.1) {
funit <- "SB1"
} else if (sum(dplyr::filter(s, species_code=="PJ")[,2]) >= 0.7 &
sum(dplyr::filter(s, species_code %in% c("PO","BW","MH","YB","AW","BD","BE","CH","IW","QR","OB","OW","OH","MS","AB","EW"))[,2], na.rm = T) <= 0.2) {
funit <- "PJ1"
} else if (sum(dplyr::filter(s,species_code %in% c("CW", "LA","SB"))[,2], na.rm = T) >= 0.8 &
sum(dplyr::filter(s,species_code %in% c("MH","YB","AW","BD","BE","CH","IW","QR","OB","OW","OH","PR"))[,2], na.rm = T) == 0 &
sum(dplyr::filter(s,species_code %in% c("PW", "PJ"))[,2], na.rm = T) <= 0.1) {
funit <- "LC1"
} else if ( sum(dplyr::filter(s,species_code %in% c("PJ", "SB","PR"))[,2], na.rm = T) >= 0.7 |
(sum(dplyr::filter(s,species_code %in% c("PJ"))[,2], na.rm = T) >= 0.5 &
sum(dplyr::filter(s,species_code %in% c("PW","PR","PJ","SB","BF","SW","HE","CE","LA","OC"))[,2], na.rm = T) >= 0.7 &
sum(dplyr::filter(s,species_code %in% c("BF","SW","HE","PW","CE","LA"))[,2], na.rm = T) <= 0.2) &
sum(dplyr::filter(s,species_code %in% c("PJ"))[,2], na.rm = T) >= sum(dplyr::filter(s,species_code %in% c("SB"))[,2], na.rm = T)
) {
funit <- "PJ2"
} else if (sum(dplyr::filter(s,species_code %in% c("SB", "SW","BF","CE","LA","PW","PJ","PR","HE"))[,2], na.rm = T) >= 0.7 &
(sum(dplyr::filter(s,species_code %in% c("BF","CE","PW","LA","SW","HE"))[,2], na.rm = T) <= 0.2 |
sum(dplyr::filter(s,species_code %in% c("PJ","PR"))[,2], na.rm = T) >= 0.3)) {
funit <- "SP1"
} else if (sum(dplyr::filter(s,species_code %in% c("SB","SW","BF","CE","LA","PW","PJ","HE"))[,2], na.rm = T) >= 0.7) {
funit <- "SF1"
} else if (sum(dplyr::filter(s,species_code %in% c("PO","BW","MH","YB","AW","BD","BE","CH","IW","QR","OB","OW","OH","MS","AB","EW"))[,2], na.rm = T) >= 0.7 &
sum(dplyr::filter(s, species_code=="PO")[,2]) >= 0.5) {
funit <- "PO1"
} else if (sum(dplyr::filter(s,species_code %in% c("PO","BW","MH","YB","AW","BD","BE","CH","IW","QR","OB","OW","OH","MS","AB","EW"))[,2], na.rm = T) >= 0.7) {
funit <- "BW1"
} else if (sum(dplyr::filter(s,species_code %in% c("PJ", "PR"))[,2], na.rm = T) >= 0.2 ) {
funit <- "MW1"
} else {
funit <- "MW2"
message("no condition matched")
}
return(funit)
}
species2unit_region3 <- function(s, site_class, stocking) {
if (sum(dplyr::filter(s, species_code=="PR")[,2]) >= 0.7 &
sum(dplyr::filter(s, species_code=="PW")[,2]) < 0.3) {
funit <- "PR1"
} else if (sum(dplyr::filter(s,species_code %in% c("PW","PR"))[,2], na.rm = T) >= 0.5 &
sum(dplyr::filter(s, species_code=="PW")[,2]) > sum(dplyr::filter(s, species_code=="PR")[,2]) &
sum(dplyr::filter(s,species_code %in% c("PW","PR"))[,2], na.rm = T) * stocking >= 0.3 &
sum(dplyr::filter(s,species_code %in% c("QR","OW"))[,2], na.rm = T) < 0.2
) {
funit <- "PWUS4"
} else if (
sum(dplyr::filter(s,species_code %in% c("PW","PR", "QR", "OW"))[,2], na.rm = T) >= 0.5 &
sum(dplyr::filter(s,species_code %in% c("PW"))[,2], na.rm = T) >= sum(dplyr::filter(s,species_code %in% c("QR","OW"))[,2], na.rm = T) &
sum(dplyr::filter(s,species_code %in% c("PW","PR", "QR", "OW"))[,2], na.rm = T) * stocking >= 0.3 &
sum(dplyr::filter(s,species_code %in% c("QR","OW"))[,2], na.rm = T) >= 0.2
) {
funit <- "PWOR"
} else if (
(sum(dplyr::filter(s,species_code %in% c("PW","PR"))[,2], na.rm = T) >= 0.3 &
sum(dplyr::filter(s,species_code %in% c("PW","PR"))[,2], na.rm = T) * stocking >= 0.3 ) |
(
sum(dplyr::filter(s, species_code=="PW")[,2]) >= sum(dplyr::filter(s, species_code=="HE")[,2]) &
sum(dplyr::filter(s, species_code=="PW")[,2]) >= sum(dplyr::filter(s, species_code=="SW")[,2]) &
sum(dplyr::filter(s, species_code=="PW")[,2]) >= sum(dplyr::filter(s, species_code=="CE")[,2]) &
sum(dplyr::filter(s, species_code=="PW")[,2]) >= sum(dplyr::filter(s, species_code=="QR")[,2]) &
sum(dplyr::filter(s,species_code %in% c("PW","PR"))[,2], na.rm = T) >= 0.3 &
sum(dplyr::filter(s,species_code %in% c("PW","PR","SW","HE","QR","PJ","CE"))[,2], na.rm = T) * stocking >= 0.3
) & (
sum(dplyr::filter(s,species_code %in% c("PW","PR","PJ","SW","SB","HE","BF","CE","LA"))[,2], na.rm = T) >= 0.8
)
) {
funit <- "PWUSC"
} else if (
(
sum(dplyr::filter(s, species_code=="PW")[,2]) >= sum(dplyr::filter(s, species_code=="PR")[,2]) &
sum(dplyr::filter(s,species_code %in% c("PW","PR"))[,2], na.rm = T) >= 0.3 &
sum(dplyr::filter(s,species_code %in% c("PW","PR"))[,2], na.rm = T) * stocking >= 0.3
) | (
sum(dplyr::filter(s, species_code=="PW")[,2]) >= sum(dplyr::filter(s, species_code=="PR")[,2]) &
sum(dplyr::filter(s, species_code=="PW")[,2]) >= sum(dplyr::filter(s, species_code=="HE")[,2]) &
sum(dplyr::filter(s, species_code=="PW")[,2]) >= sum(dplyr::filter(s, species_code=="SW")[,2]) &
sum(dplyr::filter(s, species_code=="PW")[,2]) >= sum(dplyr::filter(s, species_code=="CE")[,2]) &
sum(dplyr::filter(s, species_code=="PW")[,2]) >= sum(dplyr::filter(s, species_code=="QR")[,2]) &
sum(dplyr::filter(s,species_code %in% c("PW","PR"))[,2], na.rm = T) >= 0.3 &
sum(dplyr::filter(s,species_code %in% c("PW","PR","SW","HE","QR","PJ","CE"))[,2], na.rm = T) * stocking >= 0.3
) & (
sum(dplyr::filter(s,species_code %in% c("PW","PR","PJ","SW","SB","HE","BF","CE","LA"))[,2], na.rm = T) < 0.8
)
) {
funit <- "PWUSH"
} else if (
sum(dplyr::filter(s,species_code %in% c("PW","PR"))[,2], na.rm = T) >= 0.3 &
sum(dplyr::filter(s,species_code %in% c("PW","PR"))[,2], na.rm = T) >= sum(dplyr::filter(s, species_code=="HE")[,2]) &
sum(dplyr::filter(s,species_code %in% c("PW","PR"))[,2], na.rm = T) >= sum(dplyr::filter(s, species_code=="SW")[,2]) &
sum(dplyr::filter(s,species_code %in% c("PW","PR"))[,2], na.rm = T) >= sum(dplyr::filter(s, species_code=="SB")[,2]) &
sum(dplyr::filter(s,species_code %in% c("PW","PR"))[,2], na.rm = T) >= sum(dplyr::filter(s, species_code=="CE")[,2]) &
sum(dplyr::filter(s,species_code %in% c("PW","PR"))[,2], na.rm = T) >= sum(dplyr::filter(s, species_code=="QR")[,2])
) {
funit <- "PWST"
} else if (
sum(dplyr::filter(s, species_code=="PJ")[,2]) >= 0.7 &
sum(dplyr::filter(s,species_code %in% c("MH","AB","AW","BD","BE","CH","EW","IW","QR","YB","OW","PO","BW","MS"))[,2], na.rm = T) <=0.2
) {
funit <- "PJ1"
} else if (
(
sum(dplyr::filter(s,species_code %in% c("PJ","SB","PR"))[,2], na.rm = T) >= 0.7 |
(
sum(dplyr::filter(s, species_code=="PJ")[,2]) >= 0.5 &
sum(dplyr::filter(s,species_code %in% c("PJ","SB","BF","SW","HE","PW","PR","CE","LA"))[,2], na.rm = T) >= 0.7 &
sum(dplyr::filter(s,species_code %in% c("BF","SW","HE","PW","CE","LA"))[,2], na.rm = T) <= 0.2
)
) & sum(dplyr::filter(s, species_code=="PJ")[,2]) >= sum(dplyr::filter(s, species_code=="SB")[,2])
) {
funit <- "PJ2"
} else if (
sum(dplyr::filter(s, species_code=="HE")[,2]) >= 0.4
) {
funit <- "HE1"
} else if (
sum(dplyr::filter(s, species_code=="CE")[,2]) >= 0.4 &
sum(dplyr::filter(s, species_code=="HE")[,2]) >= sum(dplyr::filter(s,species_code %in% c("SB","LA","BF"))[,2], na.rm = T) &
sum(dplyr::filter(s,species_code %in% c("OW","EW","IW","CH","MH","AB","AW","BD","BE","QR","YB","PO","BW","MS"))[,2], na.rm = T) < 0.3
) {
funit <- "CE1"
} else if (
sum(dplyr::filter(s, species_code=="SB")[,2]) >= 0.8 &
sum(dplyr::filter(s,species_code %in% c("MH","AW","BD","BE","CH","IW","QR","OW","YB","PR"))[,2], na.rm = T) == 0 &
sum(dplyr::filter(s,species_code %in% c("PW","PJ"))[,2], na.rm = T) <= 0.1
) {
funit <- "SB1"
} else if (
sum(dplyr::filter(s,species_code %in% c("SB","CE","LA"))[,2], na.rm = T) >= 0.8 &
sum(dplyr::filter(s,species_code %in% c("MH","AW","BD","BE","CH","IW","QR","OW","YB","PR"))[,2], na.rm = T) == 0 &
sum(dplyr::filter(s,species_code %in% c("PW","PJ"))[,2], na.rm = T) <= 0.1
) {
funit <- "LC1"
} else if (
sum(dplyr::filter(s,species_code %in% c("SB","SW","BF","CE","LA","PW","PJ","PR","HE"))[,2], na.rm = T) >= 0.7 &
(
sum(dplyr::filter(s,species_code %in% c("BF","CE","PW","LA","SW","HE"))[,2], na.rm = T) <= 0.2 |
sum(dplyr::filter(s, species_code=="PJ")[,2]) >= 0.3
)
) {
funit <- "SP1"
} else if (
sum(dplyr::filter(s,species_code %in% c("SW","SB","PW","PR","PJ","BF","CE","LA","HE"))[,2], na.rm = T) >= 0.7
) {
funit <- "SF1"
} else if (
sum(dplyr::filter(s, species_code=="YB")[,2]) >= 0.4
) {
funit <- "BY1"
} else if (
sum(dplyr::filter(s, species_code=="QR")[,2]) >= sum(dplyr::filter(s,species_code %in% c("MH","BE"))[,2], na.rm = T) &
sum(dplyr::filter(s, species_code=="QR")[,2]) >= 0.3 &
sum(dplyr::filter(s,species_code %in% c("QR","MH","AW","AB","BE","BD","YB","PW","PR","SW","HE"))[,2], na.rm = T) >= 0.4
) {
funit <- "OAK"
} else if (
sum(dplyr::filter(s,species_code %in% c("BD","AW","CH","QR","OW"))[,2], na.rm = T) >= 0.3 |
sum(dplyr::filter(s,species_code %in% c("BE","QR","OW"))[,2], na.rm = T) >= 0.3 |
sum(dplyr::filter(s, species_code=="BE")[,2]) >= 0.2
) {
funit <- "HDSL2"
} else if (
sum(dplyr::filter(s,species_code %in% c("MH","AW","BD","BE","CH","EW","IW","QR","YB","OW","HE"))[,2], na.rm = T) >= 0.5 &
sum(dplyr::filter(s,species_code %in% c("PO","BW","BF"))[,2], na.rm = T) <= 0.3 &
site_class %in% c(0,1,2)
) {
funit <- "HDSL1"
} else if (
sum(dplyr::filter(s,species_code %in% c("CE","AB","LA","SB"))[,2], na.rm = T) >= 0.3 &
(sum(dplyr::filter(s, species_code=="AB")[,2]) >= 0.2 |
sum(dplyr::filter(s,species_code %in% c("AB","MS","YB"))[,2], na.rm = T) >= 0.3)
) {
funit <- "LWMW"
} else if (
sum(dplyr::filter(s,species_code %in% c("MH","AW","BD","BE","CH","EW","IW","QR","YB","OW","HE"))[,2], na.rm = T) >= 0.5
) {
funit <- "HDUS"
} else if (
sum(dplyr::filter(s, species_code=="PO")[,2]) >= 0.5 &
sum(dplyr::filter(s,species_code %in% c("MH","AB","AW","BD","BE","CH","EW","IW","QR","YB","OW","PO","BW","MS"))[,2], na.rm = T) >= 0.7
) {
funit <- "PO1"
} else if (
sum(dplyr::filter(s,species_code %in% c("PO","BW"))[,2], na.rm = T) >= 0.5 &
sum(dplyr::filter(s,species_code %in% c("MH","AB","AW","BD","BE","CH","EW","IW","QR","YB","OW","PO","BW","MS"))[,2], na.rm = T) >= 0.7
) {
funit <- "BW1"
} else if (
sum(dplyr::filter(s,species_code %in% c("SW","PW","PR","CE","MH","YB","AW","CH","BD","QR","OW","IW","BE","HE"))[,2], na.rm = T) * stocking >= 0.3
) {
funit <- "MWUS"
} else if (
sum(dplyr::filter(s,species_code %in% c("PJ","PW","PR"))[,2], na.rm = T) >= 0.2
) {
funit <- "MWD"
} else {
funit <- "MWR"
}
return(funit)
}
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