R/Composite.R
In USFWS/AKaerial: Analysis of Alaska Region Aerial Survey Data
Defines functions
CompositeIndices
Documented in
CompositeIndices
#' Compile index estimates for Pacific Flyway data book
#'
#' CompositeIndices compiles appropriate estimates from multiple surveys for Taverner's geese, Pacific whitefronts, mid-continent whitefronts, western tundra and Pacific coast trumpeter swans, and lesser Canada geese.
#'
#' USFWS-MBM-Alaska Region surveys are used in harvest management decisions by the Pacific Flyway. Although management plans generally use a single survey index to decide
#' appropriate management actions, 6 species require a composite index across 2 or more Alaska Region surveys. These species and their composite indices are: \enumerate{
#' \item Taverner's geese (TAVS) - All strata from the Arctic Coastal Plain (ACPHistoric), CCGO in the "low" stratum or at latitudes > 63 on the Yukon-Kuskokwim Delta (YKGHistoric), strata 10, 11, and 99 (clipped section of 9) on the WBPHS (WBPHSHistoric)
#' \item Pacific white-fronted geese - GWFG on the Yukon-Kuskokwim Delta (YKGHistoric) or in strata 8 or 99 on the WBPHS (WBPHSHistoric)
#' \item Western tundra swans (TUSW) - TUSW or SWAN on the Yukon-Kuskokwim Delta (YKGHistoric) or in strata 8, 99, 10, or 11 on the WBPHS (WBPHSHistoric)
#' \item Lesser Canada geese - CCGO in strata 1, 2, 3, 4, or 12 on the WBPHS (WBPHSHistoric)
#' \item Mid-continent white-fronted geese - GWFG in strata 3, 4, 5, 6, 10, or 11 on the WBPHS (WBPHSHistoric) and all GWFG on the Arctic Coastal Plain (ACPHistoric)
#' \item Pacific coast trumpeter swans (TRSW) - TRSW or SWAN in strata 1, 2, 3, 4, 6, or 7 on the WBPHS (WBPHSHistoric)
#' }
#' Note that WBPHS stratum 9 is clipped and renamed 99 to avoid double counting birds in an area surveyed on the Yukon-Kuskokwim Delta surveys.
#' The default range of years for each species is set to the earliest year in which all surveys that make up the index were conducted.
#'
#' @author Charles Frost, \email{charles_frost@@fws.gov}
#' @references \url{https://github.com/USFWS/AKaerial}
#'
#' @param tavs.year range of years for the Taverner's geese composite table, defaults to 1985-2022
#' @param pw.year range of years for the Pacific white-fronted goose composite table, defaults to 1985-2022
#' @param tusw.year range of years for the western tundra swan composite table, defaults to 1985-2022
#' @param lesser.year range of years for the lesser Canada goose composite table, defaults to 1964-2022
#' @param mcw.year range of years for the mid-continent white-fronted goose composite table, defaults to 1964-2022
#' @param trsw.year range of years for the Pacific coast trumpeter swan composite table, defaults to 1964-2022
#' @param versioning TRUE/FALSE should the time stamp of the data tables used be included with the composite table?
#'
#' @return Six .csv files are written to the current working directory (1 for each species) and optionally a .csv of the current version of the data files used
#'
#' @export
CompositeIndices = function(tavs.year = c(1985:2024),
pw.year = c(1985:2024),
tusw.year = c(1985:2024),
lesser.year = c(1964:2024),
mcw.year = c(1964:2024),
trsw.year = c(1964:2024),
versioning=FALSE){
# TAVERNERS
#
#Estimates and se’s for 1) indicated total birds, 2) indicated singles and paired
#
#1) ACP: all strata combined
#2) YKG: whatever you identified as Taverners (“Low”, Lat >63)
#3) WBPHS:
# a) clipped stratum 99
# b) stratum 10
# c) stratum 11
# 4) Total and se for all surveys combined
acp.tavs = ACPHistoric$combined %>%
filter(Species == "CCGO", Year %in% tavs.year) %>%
select(Year, itotal, itotal.se, ibb, ibb.se) %>%
rename(itotal_ACP=itotal, itotal.se_ACP = itotal.se, ibb_ACP=ibb, ibb.se_ACP=ibb.se)
ykg.tavs = YKGHistoric$combined %>%
filter(Species == "TAVS", Year %in% tavs.year) %>%
select(Year, itotal, itotal.se, ibb, ibb.se) %>%
rename(itotal_YKG=itotal, itotal.se_YKG = itotal.se, ibb_YKG=ibb, ibb.se_YKG=ibb.se)
wbphs.tavs = WBPHSHistoric %>%
filter(Species == "CCGO", Year %in% tavs.year, Stratum %in% c(99, 10, 11)) %>%
select(Year, Stratum, itotal.est, itotal.se, ibb.est, ibb.se) %>%
rename(itotal=itotal.est, ibb=ibb.est) %>%
pivot_wider(names_from=Stratum, values_from = c(itotal, itotal.se, ibb, ibb.se)) %>%
select(Year, itotal_99, itotal.se_99, itotal_10, itotal.se_10, itotal_11, itotal.se_11,
ibb_99, ibb.se_99, ibb_10, ibb.se_10, ibb_11, ibb.se_11, )
tavs.table = data.frame("Year"=tavs.year) %>%
merge(acp.tavs, by="Year", all.x=TRUE) %>%
merge(ykg.tavs, by="Year", all.x=TRUE) %>%
merge(wbphs.tavs, by="Year", all.x=TRUE) %>%
group_by(Year) %>%
mutate(itotal.all = sum(itotal_ACP, itotal_YKG, itotal_10, itotal_11, itotal_99),
itotal.se = sqrt(sum(itotal.se_ACP^2, itotal.se_YKG^2, itotal.se_10^2, itotal.se_11^2, itotal.se_99^2)),
ibb.all = sum(ibb_ACP, ibb_YKG, ibb_10, ibb_11, ibb_99),
ibb.se = sqrt(sum(ibb.se_ACP^2, ibb.se_YKG^2, ibb.se_10^2, ibb.se_11^2, ibb.se_99^2))) %>%
relocate(starts_with("ibb"), .after=starts_with("itotal"))
# PACIFIC WHITEFRONTS
#
#Estimates and se’s for 1) indicated total birds, 2) indicated singles and paired
#
# 1) YKG: all strata combined
# 2) WBPHS:
# a) stratum 8
# b) clipped stratum 99
# 3) Total and se for all surveys combined
ykg.pw = YKGHistoric$combined %>%
filter(Species == "GWFG", Year %in% pw.year) %>%
select(Year, itotal, itotal.se, ibb, ibb.se) %>%
rename(itotal_YKG=itotal, itotal.se_YKG = itotal.se, ibb_YKG=ibb, ibb.se_YKG=ibb.se)
wbphs.pw = WBPHSHistoric %>%
filter(Species == "GWFG", Year %in% pw.year, Stratum %in% c(99, 8)) %>%
select(Year, Stratum, itotal.est, itotal.se, ibb.est, ibb.se) %>%
rename(itotal=itotal.est, ibb=ibb.est) %>%
pivot_wider(names_from=Stratum, values_from = c(itotal, itotal.se, ibb, ibb.se)) %>%
select(Year, itotal_8, itotal.se_8, itotal_99, itotal.se_99,
ibb_8, ibb.se_8, ibb_99, ibb.se_99 )
pw.table = data.frame("Year"=pw.year) %>%
merge(ykg.pw, by="Year", all.x=TRUE) %>%
merge(wbphs.pw, by="Year", all.x=TRUE) %>%
group_by(Year) %>%
mutate(itotal.all = sum(itotal_YKG, itotal_8, itotal_99),
itotal.se = sqrt(sum(itotal.se_YKG^2, itotal.se_8^2, itotal.se_99^2)),
ibb.all = sum(ibb_YKG, ibb_8, ibb_99),
ibb.se = sqrt(sum(ibb.se_YKG^2, ibb.se_8^2, ibb.se_99^2))) %>%
relocate(starts_with("ibb"), .after=starts_with("itotal"))
#WESTERN TUNDRA SWANS
#
#Estimates and se’s for 1) total birds, 2) singles and paired (note these are NOT indicated)
#
#1) YKG: all strata combined
#2) WBPHS:
# a) stratum 8
# b) clipped stratum 99
# c) stratum 10
# d) stratum 11
#3) Total and se for all surveys combined
ykg.tusw = YKGHistoric$combined %>%
filter(Species == "TUSW", Year %in% tusw.year) %>%
select(Year, total, total.se, sing1pair2, sing1pair2.se) %>%
rename(total_YKG=total, total.se_YKG = total.se, sing1pair2_YKG=sing1pair2, sing1pair2.se_YKG=sing1pair2.se)
wbphs.tusw = WBPHSHistoric %>%
filter(Species == "SWAN", Year %in% tusw.year, Stratum %in% c(8, 99, 10, 11)) %>%
select(Year, Stratum, total.est, total.se, sing1pair2.est, sing1pair2.se) %>%
rename(total=total.est, sing1pair2=sing1pair2.est) %>%
pivot_wider(names_from=Stratum, values_from = c(total, total.se, sing1pair2, sing1pair2.se)) %>%
select(Year, total_8, total.se_8, total_99, total.se_99, total_10, total.se_10, total_11, total.se_11,
sing1pair2_8, sing1pair2.se_8, sing1pair2_99, sing1pair2.se_99, sing1pair2_10, sing1pair2.se_10, sing1pair2_11, sing1pair2.se_11)
tusw.table = data.frame("Year"=tusw.year) %>%
merge(ykg.tusw, by="Year", all.x=TRUE) %>%
merge(wbphs.tusw, by="Year", all.x=TRUE) %>%
group_by(Year) %>%
mutate(total.all = sum(total_YKG, total_8, total_99, total_10, total_11),
total.se = sqrt(sum(total.se_YKG^2, total.se_8^2, total.se_99^2, total.se_10^2, total.se_11^2)),
sing1pair2.all = sum(sing1pair2_YKG, sing1pair2_8, sing1pair2_99, sing1pair2_10, sing1pair2_11),
sing1pair2.se = sqrt(sum(sing1pair2.se_YKG^2, sing1pair2.se_8^2, sing1pair2.se_99^2, sing1pair2.se_10^2, sing1pair2.se_11^2))) %>%
relocate(starts_with("sing1pair2"), .after=starts_with("total"))
# LESSERS
#
#Estimates and se’s for 1) indicated total birds, 2) indicated singles and paired
#
#1) WBPHS:
#a) stratum 1
#b) stratum 2
#c) stratum 3
#d) stratum 4
#e) stratum 12
#2) Total and se for all of the above strata combined
wbphs.lesser = WBPHSHistoric %>%
filter(Species == "CCGO", Year %in% lesser.year, Stratum %in% c(1, 2, 3, 4, 12)) %>%
select(Year, Stratum, itotal.est, itotal.se, ibb.est, ibb.se) %>%
rename(itotal=itotal.est, ibb=ibb.est) %>%
pivot_wider(names_from=Stratum, values_from = c(itotal, itotal.se, ibb, ibb.se)) %>%
select(Year, itotal_1, itotal.se_1, itotal_2, itotal.se_2, itotal_3, itotal.se_3, itotal_4, itotal.se_4, itotal_12, itotal.se_12,
ibb_1, ibb.se_1, ibb_2, ibb.se_2, ibb_3, ibb.se_3, ibb_4, ibb.se_4, ibb_12, ibb.se_12 )
lesser.table = data.frame("Year"=lesser.year) %>%
merge(wbphs.lesser, by="Year", all.x=TRUE) %>%
group_by(Year) %>%
mutate(itotal.all = sum(itotal_1, itotal_2, itotal_3, itotal_4, itotal_12),
itotal.se = sqrt(sum(itotal.se_1^2, itotal.se_2^2, itotal.se_3^2, itotal.se_4^2, itotal.se_12^2)),
ibb.all = sum(ibb_1, ibb_2, ibb_3, ibb_4, ibb_12),
ibb.se = sqrt(sum(ibb.se_1^2, ibb.se_2^2, ibb.se_3^2, ibb.se_4^2, ibb.se_12^2))) %>%
relocate(starts_with("ibb"), .after=starts_with("itotal"))
#MID CONTINENT WHITEFRONTS
#
# Estimates and se’s for 1) indicated total birds, 2) indicated singles and paired
#
#1) WBPHS:
# a) stratum 3
# b) stratum 4
# c) stratum 5
# d) stratum 6
# e) stratum 10
# f) stratum 11
#2) Total and se for all of the above WBPHS strata combined
#3) ACP
acp.mcw = ACPHistoric$combined %>%
filter(Species == "GWFG", Year %in% mcw.year) %>%
select(Year, itotal, itotal.se, ibb, ibb.se) %>%
rename(itotal_ACP=itotal, itotal.se_ACP = itotal.se, ibb_ACP=ibb, ibb.se_ACP=ibb.se)
wbphs.mcw = WBPHSHistoric %>%
filter(Species == "GWFG", Year %in% mcw.year, Stratum %in% c(3, 4, 5, 6, 10, 11)) %>%
select(Year, Stratum, itotal.est, itotal.se, ibb.est, ibb.se) %>%
rename(itotal=itotal.est, ibb=ibb.est) %>%
pivot_wider(names_from=Stratum, values_from = c(itotal, itotal.se, ibb, ibb.se)) %>%
select(Year, itotal_3, itotal.se_3, itotal_4, itotal.se_4, itotal_5, itotal.se_5, itotal_6, itotal.se_6, itotal_10, itotal.se_10, itotal_11, itotal.se_11,
ibb_3, ibb.se_3, ibb_4, ibb.se_4, ibb_5, ibb.se_5, ibb_6, ibb.se_6, ibb_10, ibb.se_10, ibb_11, ibb.se_11 )
mcw.table = data.frame("Year"=mcw.year) %>%
merge(acp.mcw, by="Year", all.x=TRUE) %>%
merge(wbphs.mcw, by="Year", all.x=TRUE) %>%
group_by(Year) %>%
mutate(itotal.wbphs = sum(itotal_3, itotal_4, itotal_5, itotal_6, itotal_10, itotal_11),
itotal.wbphs.se = sqrt(sum(itotal.se_3^2, itotal.se_4^2, itotal.se_5^2, itotal.se_6^2, itotal.se_10^2, itotal.se_11^2)),
ibb.wbphs = sum(ibb_3, ibb_4, ibb_5, ibb_6, ibb_10, ibb_11),
ibb.wbphs.se = sqrt(sum(ibb.se_3^2, ibb.se_4^2, ibb.se_5^2, ibb.se_6^2, ibb.se_10^2, ibb.se_11^2))) %>%
relocate(starts_with("ibb"), .after=starts_with("itotal"))
#PACIFIC COAST TRUMPETER SWAN
#
#Estimates and se’s for 1) total birds, 2) singles and paired (note these are NOT indicated)
#
#1) WBPHS:
#a) stratum 1
#b) stratum 2
#c) stratum 3
#d) stratum 4
#e) stratum 6
#f) stratum 7
#2) Total and se for all of the above strata combined
wbphs.trsw = WBPHSHistoric %>%
filter(Species == "SWAN", Year %in% trsw.year, Stratum %in% c(1, 2, 3, 4, 6, 7)) %>%
select(Year, Stratum, total.est, total.se, sing1pair2.est, sing1pair2.se) %>%
rename(total=total.est, sing1pair2=sing1pair2.est) %>%
pivot_wider(names_from=Stratum, values_from = c(total, total.se, sing1pair2, sing1pair2.se)) %>%
select(Year, total_1, total.se_1, total_2, total.se_2, total_3, total.se_3, total_4, total.se_4, total_6, total.se_6, total_7, total.se_7,
sing1pair2_1, sing1pair2.se_1, sing1pair2_2, sing1pair2.se_2, sing1pair2_3, sing1pair2.se_3, sing1pair2_4, sing1pair2.se_4, sing1pair2_6, sing1pair2.se_6, sing1pair2_7, sing1pair2.se_7)
trsw.table = data.frame("Year"=trsw.year) %>%
merge(wbphs.trsw, by="Year", all.x=TRUE) %>%
group_by(Year) %>%
mutate(total.all = sum(total_1, total_2, total_3, total_4, total_6, total_7),
total.se = sqrt(sum(total.se_1^2, total.se_2^2, total.se_3^2, total.se_4^2, total.se_6^2, total.se_7^2)),
sing1pair2.all = sum(sing1pair2_1, sing1pair2_2, sing1pair2_3, sing1pair2_4, sing1pair2_6, sing1pair2_7),
sing1pair2.se = sqrt(sum(sing1pair2.se_1^2, sing1pair2.se_2^2, sing1pair2.se_3^2, sing1pair2.se_4^2, sing1pair2.se_6^2, sing1pair2.se_7^2))) %>%
relocate(starts_with("sing1pair2"), .after=starts_with("total"))
write.csv(tavs.table, "CompositeTAVS.csv", row.names = FALSE, quote=FALSE)
write.csv(pw.table, "CompositePGWFG.csv", row.names = FALSE, quote=FALSE)
write.csv(tusw.table, "CompositeTUSW.csv", row.names = FALSE, quote=FALSE)
write.csv(lesser.table, "CompositeLECG.csv", row.names = FALSE, quote=FALSE)
write.csv(mcw.table, "CompositeMCGWFG.csv", row.names = FALSE, quote=FALSE)
write.csv(trsw.table, "CompositeTRSW.csv", row.names = FALSE, quote=FALSE)
if(versioning==TRUE){
acp.version = file.info("./data/ACPHistoric.rda")$mtime
ykg.version = file.info("./data/YKGHistoric.rda")$mtime
wbphs.version = file.info("./data/WBPHSHistoric.rda")$mtime
versions = data.frame("Survey"=c("ACP", "WBPHS", "YKG"), "Version"=c(acp.version, wbphs.version, ykg.version))
write.csv(versions, "Versioning.csv", row.names=FALSE, quote=FALSE)
}
}
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Defines functions CompositeIndices
Documented in CompositeIndices
#' Compile index estimates for Pacific Flyway data book
#'
#' CompositeIndices compiles appropriate estimates from multiple surveys for Taverner's geese, Pacific whitefronts, mid-continent whitefronts, western tundra and Pacific coast trumpeter swans, and lesser Canada geese.
#'
#' USFWS-MBM-Alaska Region surveys are used in harvest management decisions by the Pacific Flyway. Although management plans generally use a single survey index to decide
#' appropriate management actions, 6 species require a composite index across 2 or more Alaska Region surveys. These species and their composite indices are: \enumerate{
#' \item Taverner's geese (TAVS) - All strata from the Arctic Coastal Plain (ACPHistoric), CCGO in the "low" stratum or at latitudes > 63 on the Yukon-Kuskokwim Delta (YKGHistoric), strata 10, 11, and 99 (clipped section of 9) on the WBPHS (WBPHSHistoric)
#' \item Pacific white-fronted geese - GWFG on the Yukon-Kuskokwim Delta (YKGHistoric) or in strata 8 or 99 on the WBPHS (WBPHSHistoric)
#' \item Western tundra swans (TUSW) - TUSW or SWAN on the Yukon-Kuskokwim Delta (YKGHistoric) or in strata 8, 99, 10, or 11 on the WBPHS (WBPHSHistoric)
#' \item Lesser Canada geese - CCGO in strata 1, 2, 3, 4, or 12 on the WBPHS (WBPHSHistoric)
#' \item Mid-continent white-fronted geese - GWFG in strata 3, 4, 5, 6, 10, or 11 on the WBPHS (WBPHSHistoric) and all GWFG on the Arctic Coastal Plain (ACPHistoric)
#' \item Pacific coast trumpeter swans (TRSW) - TRSW or SWAN in strata 1, 2, 3, 4, 6, or 7 on the WBPHS (WBPHSHistoric)
#' }
#' Note that WBPHS stratum 9 is clipped and renamed 99 to avoid double counting birds in an area surveyed on the Yukon-Kuskokwim Delta surveys.
#' The default range of years for each species is set to the earliest year in which all surveys that make up the index were conducted.
#'
#' @author Charles Frost, \email{charles_frost@@fws.gov}
#' @references \url{https://github.com/USFWS/AKaerial}
#'
#' @param tavs.year range of years for the Taverner's geese composite table, defaults to 1985-2022
#' @param pw.year range of years for the Pacific white-fronted goose composite table, defaults to 1985-2022
#' @param tusw.year range of years for the western tundra swan composite table, defaults to 1985-2022
#' @param lesser.year range of years for the lesser Canada goose composite table, defaults to 1964-2022
#' @param mcw.year range of years for the mid-continent white-fronted goose composite table, defaults to 1964-2022
#' @param trsw.year range of years for the Pacific coast trumpeter swan composite table, defaults to 1964-2022
#' @param versioning TRUE/FALSE should the time stamp of the data tables used be included with the composite table?
#'
#' @return Six .csv files are written to the current working directory (1 for each species) and optionally a .csv of the current version of the data files used
#'
#' @export
CompositeIndices = function(tavs.year = c(1985:2024),
pw.year = c(1985:2024),
tusw.year = c(1985:2024),
lesser.year = c(1964:2024),
mcw.year = c(1964:2024),
trsw.year = c(1964:2024),
versioning=FALSE){
# TAVERNERS
#
#Estimates and se’s for 1) indicated total birds, 2) indicated singles and paired
#
#1) ACP: all strata combined
#2) YKG: whatever you identified as Taverners (“Low”, Lat >63)
#3) WBPHS:
# a) clipped stratum 99
# b) stratum 10
# c) stratum 11
# 4) Total and se for all surveys combined
acp.tavs = ACPHistoric$combined %>%
filter(Species == "CCGO", Year %in% tavs.year) %>%
select(Year, itotal, itotal.se, ibb, ibb.se) %>%
rename(itotal_ACP=itotal, itotal.se_ACP = itotal.se, ibb_ACP=ibb, ibb.se_ACP=ibb.se)
ykg.tavs = YKGHistoric$combined %>%
filter(Species == "TAVS", Year %in% tavs.year) %>%
select(Year, itotal, itotal.se, ibb, ibb.se) %>%
rename(itotal_YKG=itotal, itotal.se_YKG = itotal.se, ibb_YKG=ibb, ibb.se_YKG=ibb.se)
wbphs.tavs = WBPHSHistoric %>%
filter(Species == "CCGO", Year %in% tavs.year, Stratum %in% c(99, 10, 11)) %>%
select(Year, Stratum, itotal.est, itotal.se, ibb.est, ibb.se) %>%
rename(itotal=itotal.est, ibb=ibb.est) %>%
pivot_wider(names_from=Stratum, values_from = c(itotal, itotal.se, ibb, ibb.se)) %>%
select(Year, itotal_99, itotal.se_99, itotal_10, itotal.se_10, itotal_11, itotal.se_11,
ibb_99, ibb.se_99, ibb_10, ibb.se_10, ibb_11, ibb.se_11, )
tavs.table = data.frame("Year"=tavs.year) %>%
merge(acp.tavs, by="Year", all.x=TRUE) %>%
merge(ykg.tavs, by="Year", all.x=TRUE) %>%
merge(wbphs.tavs, by="Year", all.x=TRUE) %>%
group_by(Year) %>%
mutate(itotal.all = sum(itotal_ACP, itotal_YKG, itotal_10, itotal_11, itotal_99),
itotal.se = sqrt(sum(itotal.se_ACP^2, itotal.se_YKG^2, itotal.se_10^2, itotal.se_11^2, itotal.se_99^2)),
ibb.all = sum(ibb_ACP, ibb_YKG, ibb_10, ibb_11, ibb_99),
ibb.se = sqrt(sum(ibb.se_ACP^2, ibb.se_YKG^2, ibb.se_10^2, ibb.se_11^2, ibb.se_99^2))) %>%
relocate(starts_with("ibb"), .after=starts_with("itotal"))
# PACIFIC WHITEFRONTS
#
#Estimates and se’s for 1) indicated total birds, 2) indicated singles and paired
#
# 1) YKG: all strata combined
# 2) WBPHS:
# a) stratum 8
# b) clipped stratum 99
# 3) Total and se for all surveys combined
ykg.pw = YKGHistoric$combined %>%
filter(Species == "GWFG", Year %in% pw.year) %>%
select(Year, itotal, itotal.se, ibb, ibb.se) %>%
rename(itotal_YKG=itotal, itotal.se_YKG = itotal.se, ibb_YKG=ibb, ibb.se_YKG=ibb.se)
wbphs.pw = WBPHSHistoric %>%
filter(Species == "GWFG", Year %in% pw.year, Stratum %in% c(99, 8)) %>%
select(Year, Stratum, itotal.est, itotal.se, ibb.est, ibb.se) %>%
rename(itotal=itotal.est, ibb=ibb.est) %>%
pivot_wider(names_from=Stratum, values_from = c(itotal, itotal.se, ibb, ibb.se)) %>%
select(Year, itotal_8, itotal.se_8, itotal_99, itotal.se_99,
ibb_8, ibb.se_8, ibb_99, ibb.se_99 )
pw.table = data.frame("Year"=pw.year) %>%
merge(ykg.pw, by="Year", all.x=TRUE) %>%
merge(wbphs.pw, by="Year", all.x=TRUE) %>%
group_by(Year) %>%
mutate(itotal.all = sum(itotal_YKG, itotal_8, itotal_99),
itotal.se = sqrt(sum(itotal.se_YKG^2, itotal.se_8^2, itotal.se_99^2)),
ibb.all = sum(ibb_YKG, ibb_8, ibb_99),
ibb.se = sqrt(sum(ibb.se_YKG^2, ibb.se_8^2, ibb.se_99^2))) %>%
relocate(starts_with("ibb"), .after=starts_with("itotal"))
#WESTERN TUNDRA SWANS
#
#Estimates and se’s for 1) total birds, 2) singles and paired (note these are NOT indicated)
#
#1) YKG: all strata combined
#2) WBPHS:
# a) stratum 8
# b) clipped stratum 99
# c) stratum 10
# d) stratum 11
#3) Total and se for all surveys combined
ykg.tusw = YKGHistoric$combined %>%
filter(Species == "TUSW", Year %in% tusw.year) %>%
select(Year, total, total.se, sing1pair2, sing1pair2.se) %>%
rename(total_YKG=total, total.se_YKG = total.se, sing1pair2_YKG=sing1pair2, sing1pair2.se_YKG=sing1pair2.se)
wbphs.tusw = WBPHSHistoric %>%
filter(Species == "SWAN", Year %in% tusw.year, Stratum %in% c(8, 99, 10, 11)) %>%
select(Year, Stratum, total.est, total.se, sing1pair2.est, sing1pair2.se) %>%
rename(total=total.est, sing1pair2=sing1pair2.est) %>%
pivot_wider(names_from=Stratum, values_from = c(total, total.se, sing1pair2, sing1pair2.se)) %>%
select(Year, total_8, total.se_8, total_99, total.se_99, total_10, total.se_10, total_11, total.se_11,
sing1pair2_8, sing1pair2.se_8, sing1pair2_99, sing1pair2.se_99, sing1pair2_10, sing1pair2.se_10, sing1pair2_11, sing1pair2.se_11)
tusw.table = data.frame("Year"=tusw.year) %>%
merge(ykg.tusw, by="Year", all.x=TRUE) %>%
merge(wbphs.tusw, by="Year", all.x=TRUE) %>%
group_by(Year) %>%
mutate(total.all = sum(total_YKG, total_8, total_99, total_10, total_11),
total.se = sqrt(sum(total.se_YKG^2, total.se_8^2, total.se_99^2, total.se_10^2, total.se_11^2)),
sing1pair2.all = sum(sing1pair2_YKG, sing1pair2_8, sing1pair2_99, sing1pair2_10, sing1pair2_11),
sing1pair2.se = sqrt(sum(sing1pair2.se_YKG^2, sing1pair2.se_8^2, sing1pair2.se_99^2, sing1pair2.se_10^2, sing1pair2.se_11^2))) %>%
relocate(starts_with("sing1pair2"), .after=starts_with("total"))
# LESSERS
#
#Estimates and se’s for 1) indicated total birds, 2) indicated singles and paired
#
#1) WBPHS:
#a) stratum 1
#b) stratum 2
#c) stratum 3
#d) stratum 4
#e) stratum 12
#2) Total and se for all of the above strata combined
wbphs.lesser = WBPHSHistoric %>%
filter(Species == "CCGO", Year %in% lesser.year, Stratum %in% c(1, 2, 3, 4, 12)) %>%
select(Year, Stratum, itotal.est, itotal.se, ibb.est, ibb.se) %>%
rename(itotal=itotal.est, ibb=ibb.est) %>%
pivot_wider(names_from=Stratum, values_from = c(itotal, itotal.se, ibb, ibb.se)) %>%
select(Year, itotal_1, itotal.se_1, itotal_2, itotal.se_2, itotal_3, itotal.se_3, itotal_4, itotal.se_4, itotal_12, itotal.se_12,
ibb_1, ibb.se_1, ibb_2, ibb.se_2, ibb_3, ibb.se_3, ibb_4, ibb.se_4, ibb_12, ibb.se_12 )
lesser.table = data.frame("Year"=lesser.year) %>%
merge(wbphs.lesser, by="Year", all.x=TRUE) %>%
group_by(Year) %>%
mutate(itotal.all = sum(itotal_1, itotal_2, itotal_3, itotal_4, itotal_12),
itotal.se = sqrt(sum(itotal.se_1^2, itotal.se_2^2, itotal.se_3^2, itotal.se_4^2, itotal.se_12^2)),
ibb.all = sum(ibb_1, ibb_2, ibb_3, ibb_4, ibb_12),
ibb.se = sqrt(sum(ibb.se_1^2, ibb.se_2^2, ibb.se_3^2, ibb.se_4^2, ibb.se_12^2))) %>%
relocate(starts_with("ibb"), .after=starts_with("itotal"))
#MID CONTINENT WHITEFRONTS
#
# Estimates and se’s for 1) indicated total birds, 2) indicated singles and paired
#
#1) WBPHS:
# a) stratum 3
# b) stratum 4
# c) stratum 5
# d) stratum 6
# e) stratum 10
# f) stratum 11
#2) Total and se for all of the above WBPHS strata combined
#3) ACP
acp.mcw = ACPHistoric$combined %>%
filter(Species == "GWFG", Year %in% mcw.year) %>%
select(Year, itotal, itotal.se, ibb, ibb.se) %>%
rename(itotal_ACP=itotal, itotal.se_ACP = itotal.se, ibb_ACP=ibb, ibb.se_ACP=ibb.se)
wbphs.mcw = WBPHSHistoric %>%
filter(Species == "GWFG", Year %in% mcw.year, Stratum %in% c(3, 4, 5, 6, 10, 11)) %>%
select(Year, Stratum, itotal.est, itotal.se, ibb.est, ibb.se) %>%
rename(itotal=itotal.est, ibb=ibb.est) %>%
pivot_wider(names_from=Stratum, values_from = c(itotal, itotal.se, ibb, ibb.se)) %>%
select(Year, itotal_3, itotal.se_3, itotal_4, itotal.se_4, itotal_5, itotal.se_5, itotal_6, itotal.se_6, itotal_10, itotal.se_10, itotal_11, itotal.se_11,
ibb_3, ibb.se_3, ibb_4, ibb.se_4, ibb_5, ibb.se_5, ibb_6, ibb.se_6, ibb_10, ibb.se_10, ibb_11, ibb.se_11 )
mcw.table = data.frame("Year"=mcw.year) %>%
merge(acp.mcw, by="Year", all.x=TRUE) %>%
merge(wbphs.mcw, by="Year", all.x=TRUE) %>%
group_by(Year) %>%
mutate(itotal.wbphs = sum(itotal_3, itotal_4, itotal_5, itotal_6, itotal_10, itotal_11),
itotal.wbphs.se = sqrt(sum(itotal.se_3^2, itotal.se_4^2, itotal.se_5^2, itotal.se_6^2, itotal.se_10^2, itotal.se_11^2)),
ibb.wbphs = sum(ibb_3, ibb_4, ibb_5, ibb_6, ibb_10, ibb_11),
ibb.wbphs.se = sqrt(sum(ibb.se_3^2, ibb.se_4^2, ibb.se_5^2, ibb.se_6^2, ibb.se_10^2, ibb.se_11^2))) %>%
relocate(starts_with("ibb"), .after=starts_with("itotal"))
#PACIFIC COAST TRUMPETER SWAN
#
#Estimates and se’s for 1) total birds, 2) singles and paired (note these are NOT indicated)
#
#1) WBPHS:
#a) stratum 1
#b) stratum 2
#c) stratum 3
#d) stratum 4
#e) stratum 6
#f) stratum 7
#2) Total and se for all of the above strata combined
wbphs.trsw = WBPHSHistoric %>%
filter(Species == "SWAN", Year %in% trsw.year, Stratum %in% c(1, 2, 3, 4, 6, 7)) %>%
select(Year, Stratum, total.est, total.se, sing1pair2.est, sing1pair2.se) %>%
rename(total=total.est, sing1pair2=sing1pair2.est) %>%
pivot_wider(names_from=Stratum, values_from = c(total, total.se, sing1pair2, sing1pair2.se)) %>%
select(Year, total_1, total.se_1, total_2, total.se_2, total_3, total.se_3, total_4, total.se_4, total_6, total.se_6, total_7, total.se_7,
sing1pair2_1, sing1pair2.se_1, sing1pair2_2, sing1pair2.se_2, sing1pair2_3, sing1pair2.se_3, sing1pair2_4, sing1pair2.se_4, sing1pair2_6, sing1pair2.se_6, sing1pair2_7, sing1pair2.se_7)
trsw.table = data.frame("Year"=trsw.year) %>%
merge(wbphs.trsw, by="Year", all.x=TRUE) %>%
group_by(Year) %>%
mutate(total.all = sum(total_1, total_2, total_3, total_4, total_6, total_7),
total.se = sqrt(sum(total.se_1^2, total.se_2^2, total.se_3^2, total.se_4^2, total.se_6^2, total.se_7^2)),
sing1pair2.all = sum(sing1pair2_1, sing1pair2_2, sing1pair2_3, sing1pair2_4, sing1pair2_6, sing1pair2_7),
sing1pair2.se = sqrt(sum(sing1pair2.se_1^2, sing1pair2.se_2^2, sing1pair2.se_3^2, sing1pair2.se_4^2, sing1pair2.se_6^2, sing1pair2.se_7^2))) %>%
relocate(starts_with("sing1pair2"), .after=starts_with("total"))
write.csv(tavs.table, "CompositeTAVS.csv", row.names = FALSE, quote=FALSE)
write.csv(pw.table, "CompositePGWFG.csv", row.names = FALSE, quote=FALSE)
write.csv(tusw.table, "CompositeTUSW.csv", row.names = FALSE, quote=FALSE)
write.csv(lesser.table, "CompositeLECG.csv", row.names = FALSE, quote=FALSE)
write.csv(mcw.table, "CompositeMCGWFG.csv", row.names = FALSE, quote=FALSE)
write.csv(trsw.table, "CompositeTRSW.csv", row.names = FALSE, quote=FALSE)
if(versioning==TRUE){
acp.version = file.info("./data/ACPHistoric.rda")$mtime
ykg.version = file.info("./data/YKGHistoric.rda")$mtime
wbphs.version = file.info("./data/WBPHSHistoric.rda")$mtime
versions = data.frame("Survey"=c("ACP", "WBPHS", "YKG"), "Version"=c(acp.version, wbphs.version, ykg.version))
write.csv(versions, "Versioning.csv", row.names=FALSE, quote=FALSE)
}
}
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