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Toninha_2015_2020_08_02_2021.R
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# Code for:
## Fransiscana dolphin 'Pontoporia blainvillei' stranding patterns in
## southeast-south Brazil, Fransiscana Management Area (FMA) II,
## between 2015-2020
##
## Code by Jonatas H F do Prado, Jessica L Schattschneider, Nicholas W Daudt
##
### General tidy rules - - - - -
# variable_names <- write with lower case and underlines
# dataframeNames <- cammelCase style (startLowerCaseAndEveryNewWordStartWithCapital)
# package::function notation, unless from 'base' packages
# Sessions delimited by "# Session name ####"
# Every step within the Session, use ## and a quick comment
###- - - - - - - - - - - - - - -
rm(list = ls())
# Libraries ####
library(plyr)
library(reshape2)
library(tidyverse)
library(mapview)
library(sf)
library(ggspatial)
library(KernSmooth)
library(raster)
################################################################################
############################## Dataset manipulation ############################
################################################################################
##----------------------------------------------------------------------------##
## Stranding dataset ##
##----------------------------------------------------------------------------##
## Open stranding dataset
pontoporia <- as.data.frame(
readxl::read_xlsx("./data/Pontoporia PMP 2015_08_24 a 2020_06_11 SC_PR_SP_RJ.xlsx",
sheet = 2))
## Filter columns
pontoporia <-
pontoporia %>%
dplyr::select(`Identificador do indivíduo`, Estado, Praia, Trecho,
`Estratégia do trecho`, `Tipo do monitoramento`, `Data/Hora`,
`Ponto - Lat`, `Ponto - Long`, `Condição da carcaça`, `OFAI - Sexo`)
## Rename columns and levels
names(pontoporia) <- c("id_individual", "state", "beach",
"stretch", "stretch_scheme", "monitoring_type",
"date_hour", "lat", "long", "cod_decomposition", "sex")
pontoporia$stretch_scheme <- as.factor(pontoporia$stretch_scheme)
levels(pontoporia$stretch_scheme) <- list(daily = "Diário",
weekly = "Semanal",
fortnightly = "Diário 15",
call = "Acionamento")
pontoporia$monitoring_type <- as.factor(pontoporia$monitoring_type)
levels(pontoporia$monitoring_type) <- list(regular = "Regular",
call = "Acionamento")
pontoporia$sex <- as.factor(pontoporia$sex)
levels(pontoporia$sex) <- list(female = "Fêmea",
male = "Macho",
unkwown = "Indefinido")
pontoporia$state <- as.factor(pontoporia$state)
## Remove stretch_scheme == "call", monitoring_type == "call" & stretch_scheme == "weekly",
## monitoring_type == "call" & stretch_scheme == "fortnightly" and "date_hour" column;
## Create columns date, year, month, day, week, year_n, season and
## fortnight_month;
## Filter survey effort from date > "September2015" and <= "2020-06-07".
pontoporia <-
pontoporia %>%
dplyr::filter(stretch_scheme!= "call") %>%
dplyr::filter(!((monitoring_type == "call" & stretch_scheme == "weekly") |
(monitoring_type == "call" & stretch_scheme == "fortnightly"))) %>%
dplyr::mutate(date = lubridate::as_date(date_hour)) %>%
dplyr::select(- date_hour) %>%
dplyr::filter(date > "2015-08-31" & date <= "2020-06-07") %>%
dplyr::mutate(year = lubridate::year(date),
month = lubridate::month(date),
day = lubridate::day(date),
week = lubridate::week(date)) %>%
dplyr::mutate(season =
ifelse(month >= 1 & month <=3, "summer",
ifelse(month >= 4 & month <= 6, "autumn",
ifelse(month >= 7 & month <= 9, "winter", "spring")))) %>%
dplyr::mutate(year_n =
ifelse(date > "2015-08-31" & date < "2016-09-01", "Year1",
ifelse(date > "2016-08-31" & date < "2017-09-01", "Year2",
ifelse(date > "2017-08-31" & date < "2018-09-01", "Year3",
ifelse(date > "2018-08-31" & date < "2019-09-01", "Year4",
"Year5"))))) %>%
dplyr::mutate(fortnight_month = as.numeric(
ifelse(day <= 15, "1", "2")))
## Create a continuous id for fortnight periods through the year
fortnight_df <-
pontoporia %>%
dplyr::select(id_individual, month, fortnight_month)
fortnight_df <-
fortnight_df %>%
dplyr::group_by(month, fortnight_month) %>%
dplyr::mutate(fortnight_id = dplyr::cur_group_id()) %>%
dplyr::ungroup() %>%
dplyr::select(id_individual, fortnight_id)
## Join 'fortnight_df' into 'pontoporia'
pontoporia <-
dplyr::left_join(pontoporia, fortnight_df, by = "id_individual")
## Create column back_date and zone for environment variables gathering
pontoporia <-
pontoporia %>%
dplyr::mutate(back_date = lubridate::as_date(ifelse(cod_decomposition == 2,
date - 1,
date - 6))) %>%
dplyr::mutate(zone =
ifelse(lat > -23.75, "1",
ifelse(lat < -23.75 & lat > -26, "2", "3")))
#- - - - - - -
## PS - zones were defined based on summaries related to mean drifting
## distances from the 'drift_experiment'.
## See in general results, summary 'table5'.
#- - - - - - -
## Create column "macroregions" defined as in Cantor et al. (2020).
## Remove records from state == "Rio de Janeiro", cod_decomposition == "5",
## and date == "NA".
pontoporia <-
pontoporia %>%
dplyr::filter(state != "Rio de Janeiro") %>%
dplyr::filter(cod_decomposition != 5) %>%
dplyr::filter(!is.na(date))
## Remove records distant more than 300 m from the coast line
# Open shapefiles (shp) of beach monitoring lines
# Create a list with all subdirectories containing the shp
ff <- as.list(list.files(path = ".", pattern = "linha.shp$",
recursive = TRUE, full.names = TRUE))
## Function to open the shp in subdirectories
open_shp <- function(ff){
linha <- sf::read_sf(ff[i])
linha
}
## Loop creating a list with all shp
lines <- list()
for (i in 1:length(ff)) {
lines[[i]] <- open_shp(ff)
}
## Merge all beach monitoring lines into one shp
originalStretches <- do.call(rbind, lines)
## Remove streches with strategy == "noneeffort", compriment == "0",
## and beach_name == "Ponta do Itaguá";
## Create an id for streches.
originalStretches <-
originalStretches %>%
dplyr::filter(stretch_st != "noneffort") %>%
dplyr::filter(compriment != 0) %>%
dplyr::rename(original_length = compriment) %>%
dplyr::filter(beach_name != "Ponta do Itaguá") %>%
dplyr::rename(beach = beach_name) %>%
dplyr::rename(stretch = stretch_na) %>%
dplyr::mutate(id_original = dplyr::row_number()) %>%
sf::st_cast("MULTILINESTRING")
## Create a buffer of 300 m around the beach monitoring lines
originalStretches <- sf::st_transform(originalStretches, crs = 31982)
buffer <- sf::st_buffer(originalStretches, dist = 300)
## Transform 'pontoporia' into a geospatial feature
pontoporia <-
pontoporia %>%
dplyr::mutate(long1 = long)%>%
dplyr::mutate(lat1 = lat)
pontoporiaSpatial <-
pontoporia %>%
sf::st_as_sf(coords = c("long1","lat1"), crs = 4326)
## Trasform pontoporiaSpatial crs equal to originalStreches
pontoporiaSpatial <- sf::st_transform(pontoporiaSpatial, crs = 31982)
## Remove records outside the buffer
pontoporiaSpatial$on_buffer <- lengths(sf::st_within(pontoporiaSpatial, buffer))
pontoporiaSpatial <-
pontoporiaSpatial %>%
dplyr::filter(on_buffer > 0) %>%
dplyr::select(- on_buffer)
# mapview::mapview(buffer) + pontoporiaSpatial
##----------------------------------------------------------------------------##
## Survey effort dataset ##
##----------------------------------------------------------------------------##
## Open survey effort dataset
ef_SP <- read.csv2("data/Effort_SP_aug2019_jul_2020.csv",
header = TRUE, encoding = "UTF-8")
ef_SC_PR <- read.csv2("data/Effort_SC_PR_aug2019_jul_2020.csv",
header = TRUE, encoding = "UTF-8")
ef_SP_PR_SC <- read.csv2("data/Effort_SC_PR_SP_aug2015_aug_2019.csv",
header = TRUE, encoding = "UTF-8")
eff <- rbind(ef_SP, ef_SC_PR, ef_SP_PR_SC)
rm(list = ls(pattern = "ef_"))
## Split data and hour
eff$date <-
lubridate::dmy(
sapply(strsplit(as.character(eff$Data.Hora.início), " "), "[", 1))
## Removing unused columns
eff[c(2:3, 10:12, 15:16)] <- list(NULL)
## Rename columns and levels
colnames(eff) <- c("code", "state", "city", "beach", "stretch", "type",
"stretch_scheme", "initial_lat", "initial_long", "complete",
"date")
eff$stretch_scheme <- as.factor(eff$stretch_scheme)
levels(eff$stretch_scheme) <- list(daily = "Diário",
weekly = "Semanal",
fortnightly = "Diário 15",
call = "Acionamento")
eff$type <- as.factor(eff$type)
levels(eff$type) <- list(boat = "Embarcado",
land = "Terrestre")
eff$complete <- as.factor(eff$complete)
levels(eff$complete) <- list(yes = "Sim",
no = "Não")
## Filter survey effort from date > "September2015" and <= "2020-06-07";
## Remove stretch_scheme == "call" and complete == "NA";
## Create columns year, month, day, week, season and year_n.
eff <-
eff %>%
dplyr::filter(date > "2015-08-31" & date <= "2020-06-07") %>%
dplyr::filter(stretch_scheme != "call") %>%
dplyr::filter(complete != "NA") %>%
dplyr::mutate(year = lubridate::year(date),
month = lubridate::month(date),
day = lubridate::day(date),
week = lubridate::week(date)) %>%
dplyr::mutate(season =
ifelse(month >= 1 & month <= 3, "summer",
ifelse(month >= 4 & month <= 6, "autumn",
ifelse(month >= 7 & month <= 9, "winter", "spring")))) %>%
dplyr::mutate(year_n =
ifelse(date > "2015-08-31" & date < "2016-09-01", "Year1",
ifelse(date > "2016-08-31" & date < "2017-09-01", "Year2",
ifelse(date > "2017-08-31" & date < "2018-09-01", "Year3",
ifelse(date > "2018-08-31" & date < "2019-09-01", "Year4",
"Year5"))))) %>%
dplyr::mutate(fortnight_month = ifelse(day <= 15, "1", "2"))
## Create a continuous id for fortnight periods through the year
fortnight_df_eff <-
eff %>%
dplyr::select(code, month, fortnight_month)
fortnight_df_eff <-
fortnight_df_eff %>%
dplyr::group_by(month, fortnight_month) %>%
dplyr::mutate(fortnight_id = dplyr::cur_group_id()) %>%
dplyr::ungroup() %>%
dplyr::select(code, fortnight_id)
## Join 'fortnight_df_eff' into 'eff'
eff <- dplyr::left_join(eff, fortnight_df_eff, by = "code")
##----------------------------------------------------------------------------##
## Standardize the numbers and names of beach surveyed ##
## in eff and originalStretches ##
##----------------------------------------------------------------------------##
## Removing excess of spaces between caracters
eff$beach <- gsub("\\s+", " ", eff$beach)
eff$stretch <-gsub("\\s+", " ", eff$stretch)
originalStretches$beach <- gsub("\\s+", " ", originalStretches$beach)
originalStretches$stretch <- gsub("\\s+", " ", originalStretches$stretch)
## Identify beaches and stretches in 'eff'
## that are not in 'originalStretches' and vice verse
identify1 <- dplyr::setdiff(eff$beach,originalStretches$beach)
identify2 <- dplyr::setdiff(originalStretches$beach,eff$beach)
identify3 <- dplyr::setdiff(eff$stretch,originalStretches$stretch)
identify4 <- dplyr::setdiff(originalStretches$stretch,eff$stretch)
## Select and view the beaches and stretches in the above conditions
## Transform 'eff' into a geospatial feature
effSpatial <-
eff %>%
sf::st_as_sf(coords = c("initial_long", "initial_lat"), crs = 4326)
select1 <-
effSpatial %>%
dplyr::filter(beach == "Bal. Barra do Sul" | beach == "Itararé")
select3 <-
effSpatial %>%
dplyr::filter(stretch == "antigo D15- Ilha Comprida" |
stretch == "Antigo Superagui Sul e trapiche-Rio" |
stretch == "Ipanema" | stretch == "Matinhos 1" |
stretch == "Matinhos 2" | stretch == "Pontal do Sul" |
stretch == "Praia da Barra N" | stretch == "Praia da Barra S" |
stretch == "Praia de Garopaba N" | stretch=="Praia de Garopaba S" |
stretch == "Praia de Leste")
# mapview::mapview(originalStretches) + select1
# mapview::mapview(originalStretches) + select3
## Verify duplicated beach names
eff_BeachNames <-
effSpatial %>%
dplyr::group_by(beach, stretch, city, state) %>%
dplyr::summarise(n = n()) %>%
dplyr::group_by(beach) %>%
dplyr::filter(n() > 1)
## Select and view the duplicated beaches
select_duplicated_BeachNames <-
originalStretches %>%
dplyr::filter(beach == "...") # insert the beach name in "..." to visualize
# mapview::mapview(select_duplicated_BeachNames)
#---------------
# Conclusions:
# C1: Same beaches with different names in 'eff' and 'originalStretches':
# "Bal. Barra do Sul" == ""Barra do Sul - 2 - 395 - 396 - 67" and
# "Itararé" == "Praia do José Menino, Praia do Gonzaga, Praia do Boqueirão, Praia
# do Embaré, Praia Aparecida, Ponta da Praia".
# C2: Different beaches identified with the same name in 'eff' and 'originalStretches':
#"Armação", "Brava","Camburizinho","Estaleiro","Pereque","Praia Brava","Praia Grande".
# C3: Beaches with two or more stretches in 'eff' and one in 'originalStretches':
# "Praia da Barra N" and "Praia da Barra S" == "Praia da Barra";
# "Praia de Garopaba N" and "Praia de Garopaba S" == "Praia de Garopara";
# "antigo D15- Ilha Comprida" and "Ilha Comprida" == "Ilha Comprida";
# "Ipanema","Matinhos,Matinhos 2", "Pontal do Sul","Pontal do Sul/ Flamingo" and
# "Praia de Leste" == "Pontal do Sul/ Flamingo".
# C3.1: The stretch name "antigo D15- Ilha Comprida" was the unique stretch used
# for "Ilha Comprida" beach until 2017/03/23. Since then it was
# rename to "Ilha Comprida".
# C3.2: The stretch "Pontal do Sul/ Flamingo" was used from spetember2015 to 2019/08/19.
# Since then it was split in five new strecthes: "Ipanema","Matinhos,Matinhos 2",
# "Pontal do Sul" and "Praia de Leste".
# C4:The stretch name "Antigo Superagui Sul e trapiche-Rio" was the unique stretch used
# for "Ilha do Superagui" beach until 2017/10/21. Since then it was split in two new stretches:
# "Superagui Trapiche-Rio Novo" and "Superagui Sul Novo.
#---------------
## Standardize beaches and stretches in 'eff' and 'originalStretches' according
## to above conclusions
## C1: rename beaches "Bal. Barra do Sul" and "Itararé" as in 'originalStreches'.
effStandardize <-
eff %>%
dplyr::mutate(beach = recode_factor(beach,
"Bal. Barra do Sul" = "Barra do Sul - 2 - 395 - 396 - 67",
"Itararé" = "Praia do José Menino, Praia do Gonzaga, Praia do Boqueirão, Praia do Embaré, Praia Aparecida, Ponta da Praia"))
## C2: rename different beaches identified with the same name in 'eff' and 'originalStreches'
effStandardize$beach <- as.character(effStandardize$beach)
effStandardize$city <- as.character(effStandardize$city)
effStandardize <-
effStandardize %>%
dplyr::mutate(beach =
ifelse(beach == "Armação" & city == "Florianópolis", "ArmaçãoFloripa",
ifelse(beach == "Armação" & city == "Ilhabela", "ArmaçãoIlhaBela",
ifelse(beach == "Armação" & city == "Penha", "ArmaçãoPenha",
ifelse(beach == "Brava" & city == "Florianópolis", "BravaFloripa",
ifelse(beach == "Brava" & city == "Balneário Camboriú", "BravaItajaí",
ifelse(beach == "Brava" & city == "Balneário Camboriú, Itajaí", "BravaItajaí",
ifelse(beach == "Camburizinho" & city == "Guarujá", "CamburizinhoGuarujá",
ifelse(beach == "Camburizinho" & city == "São Sebastião", "CamburizinhoSebastião",
ifelse(beach == "Estaleiro" & city == "Balneário Camboriú", "EstaleiroCamburiú",
ifelse(beach == "Estaleiro" & city == "Ubatuba", "EstaleiroUbatuba",
ifelse(beach == "Pereque" & city == "Guarujá", "PerequeGarujá",
ifelse(beach == "Pereque" & city == "Ilhabela", "PerequeIlhaBela",
ifelse(beach == "Pereque" & city == "Porto Belo", "PerequePortoBelo",
ifelse(beach == "Praia Brava" & city == "Ilhabela", "Praia BravaIlhaBela",
ifelse(beach == "Praia Brava" & city == "Matinhos","Praia BravaMatinhos",
ifelse(beach == "Praia Grande" & city == "Praia Grande","Praia Grande",
ifelse(beach == "Praia Grande" & city == "Penha","Praia GrandePenha",
beach))))))))))))))))))
originalStretchesStandardize <-
originalStretches %>%
dplyr::mutate(beach =
ifelse(beach == "Armação" & id_original == 282, "ArmaçãoFloripa",
ifelse(beach == "Armação" & id_original == 89, "ArmaçãoIlhaBela",
ifelse(beach == "Armação" & id_original == 339, "ArmaçãoPenha",
ifelse(beach == "Brava" & id_original == 287, "BravaFloripa",
ifelse(beach == "Brava" & id_original == 334, "BravaItajaí",
ifelse(beach == "Brava" & id_original == 371, "BravaItajaí",
ifelse(beach == "Camburizinho" & id_original == 234, "CamburizinhoGuarujá",
ifelse(beach == "Camburizinho" & id_original == 75, "CamburizinhoSebastião",
ifelse(beach == "Estaleiro" & id_original == 338, "EstaleiroCamburiú",
ifelse(beach == "Estaleiro" & id_original == 146, "EstaleiroUbatuba",
ifelse(beach == "Pereque" & id_original == 254, "PerequeGarujá",
ifelse(beach == "Pereque" & id_original == 77, "PerequeIlhaBela",
ifelse(beach == "Pereque" & id_original == 359, "PerequePortoBelo",
ifelse(beach == "Praia Brava" & id_original == 202, "Praia BravaIlhaBela",
ifelse(beach == "Praia Brava" & id_original == 312, "Praia BravaMatinhos",
ifelse(beach == "Praia Grande" & id_original == 222, "Praia Grande",
ifelse(beach == "Praia Grande" & id_original == 352, "Praia GrandePenha",
beach))))))))))))))))))
## C3: For those beaches with multiple stretches in the same period, choose one
## stretches, rename it as in 'originalStretches' and remove the remain stretches.
## For those beaches with multiple stretches, but in different periods
## (e.g. "Ilha Comprida"), just rename the stretch as in 'originalStretches'.
## Step1: remove the stretches
effStandardize <-
effStandardize %>%
dplyr::filter(stretch != "Praia da Barra N") %>%
dplyr::filter(stretch !="Praia de Garopaba N") %>%
dplyr::filter(stretch != "Matinhos 1") %>%
dplyr::filter(stretch != "Matinhos 2") %>%
dplyr::filter(stretch != "Ipanema") %>%
dplyr::filter(stretch != "Praia de Leste")
## Step2: rename the stretches as in originalStretches
effStandardize <-
effStandardize %>%
dplyr::mutate(stretch =
ifelse(beach == "Praia da Barra" & stretch == "Praia da Barra S",
"Praia da Barra",
ifelse(beach == "Praia de Garopaba" & stretch == "Praia de Garopaba S",
"Praia de Garopaba",
ifelse(beach == "Pontal do Sul/ Flamingo" & stretch == "Pontal do Sul",
"Pontal do Sul/ Flamingo",
ifelse(beach == "Ilha Comprida" & stretch =="antigo D15- Ilha Comprida",
"Ilha Comprida",
stretch)))))
## C4: split "Antigo Superagui Sul e trapiche-Rio" in two stretches: "Superagui Sul Novo" and
## "Superagui Trapiche-Rio Novo".
## Step1: remove repeted date for stretch == "Antigo Superagui Sul e trapiche-Rio" in 'eff'
Superagui_df <-
effStandardize %>%
dplyr::filter(stretch == "Antigo Superagui Sul e trapiche-Rio")
Superagui_df <- Superagui_df[!duplicated(Superagui_df$date), ]
## Step2: rename "Antigo Superagui Sul e trapiche-Rio" as "Superagui Sul Novo"
Superagui_df$stretch <- as.factor(Superagui_df$stretch)
Superagui_df_SulNovo <-
Superagui_df %>%
dplyr::mutate(stretch = recode_factor(stretch,
"Antigo Superagui Sul e trapiche-Rio" = "Superagui Sul Novo"))
## Step3: rename "Antigo Superagui Sul e trapiche-Rio" as "Superagui Trapiche-Rio Novo"
Superagui_df_TrapicheNovo <-
Superagui_df %>%
dplyr::mutate(stretch = recode_factor(stretch,
"Antigo Superagui Sul e trapiche-Rio" = "Superagui Trapiche-Rio Novo"))
## Step4: combine "Superagui_df_TrapicheNovo" and "Superagui_df_SulNovo"
Superagui_df <- rbind(Superagui_df_SulNovo, Superagui_df_TrapicheNovo)
## Step5: combine "dfSuperagui" and "effStandardize"
effStandardize <-
effStandardize %>%
dplyr::filter(stretch != "Antigo Superagui Sul e trapiche-Rio")
effStandardize <- rbind(effStandardize, Superagui_df)
## Create a id for 'effStandardize' and 'originaStrechesStandardize' based on
## "beach" and "stretch" columns.
eff2 <-
effStandardize %>%
dplyr::mutate(beach_id = paste(beach, stretch,sep = "/")) %>%
dplyr::relocate(stretch, .before = beach) %>%
dplyr::relocate(beach_id, .after = beach)
originalStretches2 <-
originalStretchesStandardize %>%
dplyr::select(-c(beach_id, pmp_id, stretch_id, executing_, geometry, executin_1,
OBJECTID, stretch__1)) %>%
dplyr::mutate(beach_id = paste(beach, stretch, sep = "/")) %>%
dplyr::select(id_original, executing1, stretch_st, stretch_ty,
stretch, beach, beach_id, original_length, geometry)
## Identify beaches and stretches in 'eff2'
## that are not in 'originalStretches2' and vice verse
identify1 <- dplyr::setdiff(eff2$beach_id,
originalStretches2$beach_id)
identify2 <- dplyr::setdiff(originalStretches2$beach_id,
eff2$beach_id)
##----------------------------------------------------------------------------##
## Split monitored beach segments based on polygon sectors ##
##----------------------------------------------------------------------------##
## Create a 30 latitudinal-band Sector polygon,
## which will be the basis for final analysis
## Trasform crs
originalStretches <- sf::st_transform(originalStretches2, crs = 4326)
# mapview(originalStretches)
## Latitudinal definition
n <- diff(sf::st_bbox(originalStretches)[c(2, 4)])/30
## Longitudinal definition
m <- diff(sf::st_bbox(originalStretches)[c(1, 3)])
## Create the 'sectorsPolygon' based on 'originalStretches' extend
sectorsPolygon <- sf::st_make_grid(originalStretches, cellsize = c(m, n))
## Set the correct spatial attributes for 'sectorsPolygon' layer
## Define 'sectorsPolygon' as single feature
sectorsPolygon <- sf::st_sf(sectorsPolygon)
## Create an identifier for each sector
sectorsPolygon$id_polygon <- 1:nrow(sectorsPolygon)
## Define 'sectorsPolygon' as a multipolygon
sectorsPolygon <-
sectorsPolygon %>%
sf::st_cast("MULTIPOLYGON")
# mapview::mapview(sectorsPolygon)+originalStretches
## Create a multiline feature based on 'sectorsPolygon' id limits
sectorsPolygon_lines <- sf::st_cast(sectorsPolygon, "MULTILINESTRING",
group_or_split = FALSE)
# mapview::mapview(sectorsPolygon_lines)
#------------
# Look for intersections between the 30 latitudinal-band 'sectorsPolygon' and
# the monitored beaches 'originalStretches'
# Cut features and calculate each segment length, for calculate effort (m)
#------------
## Calculate intersection between 'originalStretches' and 'sectorsPolygon'
newStretches <-
sf::st_intersection(originalStretches, sectorsPolygon)
## Add a new id for each segment line, called "id_newStretches"
newStretches$id_newStretches <- 1:nrow(newStretches)
## Calculate individual segment lengths
newStretches$new_length <- sf::st_length(newStretches)
## Set "new_length" and "original_length" in kilometers and Obtained the
## difference between them.
newStretches <-
newStretches %>%
dplyr::mutate(new_length = as.numeric(new_length/1000)) %>%
dplyr::mutate(original_length = as.numeric(original_length/1000)) %>%
dplyr::mutate_if(is.numeric, round, digits = 2) %>%
dplyr::mutate(difference = original_length - new_length)
difference <-
newStretches %>%
dplyr::filter(difference > 0)
# mapview::mapView(difference) + sectorsPolygon_lines
## Clean 'newStretches' and and rename some columns.
newStretches <-
newStretches %>%
dplyr::select(id_original, id_newStretches,
id_polygon, executing1, beach,
stretch, beach_id, stretch_st,
new_length, geometry) %>%
dplyr::rename(stretch_scheme = stretch_st) %>%
dplyr::rename(new_length_km = new_length) %>%
sf::st_as_sf() %>%
sf::st_set_crs(4326)
# mapview::mapview(sectorsPolygon) + newStretches2
##----------------------------------------------------------------------------##
## Join'pontoporia' and eff with 'newStretches' ##
##----------------------------------------------------------------------------##
## Trasform crs
pontoporiaSpatial <- sf::st_transform(pontoporiaSpatial, crs = 4326)
## Join attributes from 'newStretches' into 'pontoporiaSpatial'
pontoporiaSpatialJoin <-
sf::st_join(pontoporiaSpatial, newStretches,
join = sf::st_nearest_feature)
## Return to df format; clean, rename and reorganize the columns.
pontoporia <-
pontoporiaSpatialJoin %>%
as.data.frame() %>%
dplyr::rename(beach = beach.x) %>%
dplyr::rename(stretch = stretch.x) %>%
dplyr::rename(stretch_scheme = stretch_scheme.x) %>%
dplyr::select(id_original, id_newStretches, id_polygon, zone,
id_individual, state, beach, stretch, beach_id, new_length_km,
stretch_scheme, monitoring_type, lat, long,
date, year, year_n, month, season, fortnight_id, week, day,
back_date, cod_decomposition, sex)
## Join attributes from 'newStretches' into 'eff2'
effJoin <- merge(eff2, newStretches, by= "beach_id", all = T)
## Clean, rename and reorganize the columns
eff <-
effJoin %>%
dplyr::rename(beach = beach.x) %>%
dplyr::rename(stretch = stretch.x) %>%
dplyr::rename(stretch_scheme = stretch_scheme.x) %>%
dplyr::select(code, id_original, id_newStretches, id_polygon, executing1,
state, beach, stretch, beach_id, new_length_km,
stretch_scheme, type, initial_lat, initial_long, complete,
date, year, month, season, fortnight_id, week, day, year_n) %>%
dplyr::arrange(date)
##----------------------------------------------------------------------------##
## Stranding records collected in complete-made stretches ##
##----------------------------------------------------------------------------##
## Create 'eff_i' and "date_id_newStretches" column
eff_i <-
eff %>%
dplyr::filter(complete != "yes") %>%
dplyr::mutate(date_id_newStretches = paste(date, id_newStretches))
## Create "date_id_newStretches" column in 'pontoporia'
pontoporia <-
pontoporia %>%
dplyr::mutate(date_id_newStretches = paste(date, id_newStretches))
## Remove strandings in 'pontoporia' recorded during incomplet beach surveys.
pontoporia <- dplyr::anti_join(pontoporia, eff_i, by = "date_id_newStretches")
## Remove "date_id_newStretches" from 'pontoporia'
pontoporia <-
pontoporia %>%
dplyr::select(- date_id_newStretches)
# write.csv2(pontoporia,"./data_out/pontoporia_stranding.csv")
##----------------------------------------------------------------------------##
## Join environment data with pontoporia df ##
##----------------------------------------------------------------------------##
## The enviromental data was collected from ERA5
## through a Python routine files = {apiRequest.py} + {requirements.txt}
## Open environment variables dataset
envVariables <- as.data.frame(
utils::read.csv("./data/environmental_data/envVariables.csv"))
## Remove id column
envVariables <- envVariables %>% dplyr::select(-1)
## Melt env variables
envVariables_melt <- reshape2::melt(envVariables, id.vars = "fileName")
## Extract only the info of interest (numeric part)
envVariables_melt <-
envVariables_melt %>%
dplyr::mutate(value = gsub("dtype=float32", "", value)) %>%
dplyr::mutate(value = gsub("<xarray.Variable", "", value)) %>%
dplyr::mutate(value = gsub("[^0-9.-]", "", value)) %>%
dplyr::mutate(value = as.numeric(value)) %>%
dplyr::mutate(fileName = gsub("WTUVP", "", fileName)) %>%
dplyr::mutate(fileName = gsub(".nc", "", fileName))
## Return df to wide format
envVariablesNew <- tidyr::pivot_wider(envVariables_melt,
names_from = variable,
values_from = value)
## Set equal ID's prior to merge
pontoporia$id_individual <- as.character(as.numeric(pontoporia$id_individual))
envVariablesNew$fileName <- as.character(as.numeric(envVariablesNew$fileName))
## Merge
pontoporiaAndEnv <- merge(pontoporia, envVariablesNew,
by.x = "id_individual", by.y = "fileName")
## Renaming Env columns
pontoporia <-
pontoporiaAndEnv %>%
dplyr::rename(u_wind = u10mean,
u_wind_min = u10min,
u_wind_max = u10max,
v_wind = v10mean,
v_wind_min = v10min,
v_wind_max = v10max,
mean_wave_dir = mwdmean,
mean_wave_dir_max = mwdmax,
mean_wave_dir_min = mwdmin,
mean_wave_period = mwpmean,
mean_wave_period_min = mwpmin,
mean_wave_peiod_max = mwpmax,
sig_height_wind_wave = shwwmean,
sig_height_wind_wave_min = shwwmin,
sig_height_wind_wave_max = shwwmax)
##----------------------------------------------------------------------------##
## Summarize by polygon, month, fortnight and week ##
##----------------------------------------------------------------------------##
## Obtain completed survey effort df
eff_c <-
eff %>%
dplyr::filter(complete != "no")
## Summarise 'pontoporia' and 'eff_c':
#-------------------
#categorical<- pontoporia[18:20]
#vnames <- colnames(categorical)
#pon <- list()
#j = 1
#for (i in categorical) {
# pon[[j]] <-
# pontoporia %>%
# group_by(year_n,id_polygon, {{i}}) %>%
# summarise(y = n())%>%
# dplyr::mutate_at(c(1:3),as.factor)
# pon[[j]]$vnames = vnames[j]
# j = j + 1
#}
#for (i in seq(pon))
# assign(paste0("pon", i), pon[[i]])
#----------------------
## Per "week"
ponWeek <-
pontoporia %>%
dplyr::group_by(year_n, week, id_polygon) %>%
dplyr::summarise(y = n()) %>%
dplyr::mutate_at(c(1:3), as.factor)
effWeek <-
eff_c %>%
dplyr::group_by(year_n, id_polygon, week) %>%
dplyr::summarise(effort_km = sum(new_length_km)) %>%
dplyr::mutate_at(c(1:3), as.factor)
ponWeekFinal <-
dplyr::left_join(effWeek, ponWeek,
by = c("id_polygon", "year_n", "week")) %>%
replace(., is.na(.), 0)
## Per "fortnight"
ponFortnight <-
pontoporia %>%
dplyr::group_by(year_n, id_polygon, fortnight_id) %>%
dplyr::summarise(y = n()) %>%
dplyr::mutate_at(c(1:3), as.factor)
effFortnight <-
eff_c %>%
dplyr::group_by(year_n, id_polygon, fortnight_id) %>%
dplyr::summarise(effort_km = sum(new_length_km)) %>%
dplyr::mutate_at(c(1:3), as.factor)
ponFortnightFinal <-
dplyr::left_join(effFortnight, ponFortnight,
by = c("id_polygon", "year_n", "fortnight_id")) %>%
replace(., is.na(.), 0)
## Per "month"
ponMonth <-
pontoporia %>%
dplyr::group_by(id_polygon, year_n, month) %>%
dplyr::summarise(y = n()) %>%
dplyr::mutate_at(c(1:3), as.factor)
effMonth <-
eff_c %>%
dplyr::group_by(year_n, id_polygon, month) %>%
dplyr::summarise(effort_km = sum(new_length_km)) %>%
dplyr::mutate_at(c(1:3), as.factor)
ponMonthFinal <-
dplyr::left_join(effMonth, ponMonth,
by = c("id_polygon", "year_n", "month")) %>%
replace(., is.na(.), 0)
## Per "season"
ponSeason <-
pontoporia %>%
dplyr::group_by(id_polygon, year_n, season) %>%
dplyr::summarise(y = n()) %>%
dplyr::mutate_at(c(1:3), as.factor)
effSeason <-
eff_c %>%
dplyr::group_by(year_n, id_polygon, season) %>%
dplyr::summarise(effort_km = sum(new_length_km)) %>%
dplyr::mutate_at(c(1:3), as.factor)
ponSeasonFinal <-
dplyr::left_join(effSeason, ponSeason,
by = c("id_polygon","year_n", "season")) %>%
replace(., is.na(.), 0)
# write.csv2(ponWeekFinal,"./data_out/ponWeekFinal.csv")
# write.csv2(ponFortnightFinal,"./data_out/ponFortnightFinal.csv")
# write.csv2(ponMonthFinal,"./data_out/ponMonthFinal.csv")
# write.csv2(ponSeasonFinal,"./data_out/ponSeasonFinal.csv")
##----------------------------------------------------------------------------##
## Drift dataset ##
##----------------------------------------------------------------------------##
## Open drift experiment dataset
drift <- utils::read.csv("./data/drift_experiment_data_2.csv", sep = ";")
# drift <- ### Jony
# drift %>%
# dplyr::rename(campaign=ï..campaign)
## Create "zoneExp" column
drift <-
drift %>%
dplyr::mutate(zoneExp =
ifelse(lat_r > -23.8, "1",
ifelse(lat_r < -23.8 & lat_r > -24.4,"2",
ifelse(lat_r < -24.4 & lat_r > -26, "3", "4"))))
drift$zoneExp <- as.factor(drift$zoneExp)
#------------
# The limits of the zoneExp was defined based on the distance between the
# release station and the coast line using the 20 m isobath as a reference.
#------------
## View the zoneExp
## Transform df into a geospatial feature
driftSpatial <-
drift %>% sf::st_as_sf(coords = c("long_r", "lat_r"), crs = 4326)
## Create the 'sectorsZoneExp' based on 'DriftSpatial' extend
# mapview(sectorsZoneExp)
## Filter and set up date columns
drift_i <-
drift %>%
dplyr::filter(lat_s != is.na(lat_s)) %>%
dplyr::filter(date_s != is.na(date_s))
drift_i$date_r <- lubridate::dmy(drift_i$date_r)
drift_i$date_s <- lubridate::dmy(drift_i$date_s)
################################################################################
############################ Figures and Tables ################################
########################### Material and Methods ###############################
################################################################################
## Open the Brazilian coast shapefile
brazil <-
sf::read_sf(dsn = "./data/shp_brasil", layer = "brasil") %>%
sf::st_set_crs(4326)
## Figure 1. Study area
figure1 <-
ggplot() +
ggspatial::geom_sf(data = brazil) +
geom_sf(data = originalStretches, color = "red", show.legend = F) +
labs(x = "Longitude", y = "Latitude") +
coord_sf(xlim = c(-50.5, -44), ylim = c(-28.5, -23)) +
theme_bw() +
theme(axis.title = element_text(colour = "black", size = 16),
axis.text = element_text(colour = "black", size = 10),
axis.line = element_line(colour = "black")) +
annotation_scale(location = "br", width_hint = 0.3) +
annotation_north_arrow(location = "br", which_north = "true",
pad_x = unit(0.45, "in"), pad_y = unit(0.3, "in"),
style = north_arrow_fancy_orienteering) +
annotate(geom = "text", x = -46, y = -26, label = "Atlantic Ocean",
color = "grey22", size = 5) +
annotate(geom = "text", x = -47.5, y = -23.58, label = "São Paulo",
color = "grey22", size = 5) +
annotate(geom = "text", x = -49.8, y = -25, label = "Paraná",
color = "grey22", size = 5) +
annotate(geom = "text", x = -49.8, y = -27.3, label = "Santa Catarina",
color = "grey22", size = 5)
## Figure 2. Latitudinal division of the study area.
figure2 <-
ggplot() +
ggspatial::geom_sf(data = brazil) +
geom_sf(data = originalStretches,
color = "red", show.legend = F) +
geom_sf(data = sectorsPolygon_lines,
color = "black", show.legend = F) +
labs(x = "Longitude", y = "Latitude") +
coord_sf(xlim = c(-50.5, -44), ylim = c(-28.5, -23)) +
theme_bw() +
theme(axis.title = element_text(colour = "black", size = 16),
axis.text = element_text(colour = "black", size = 10),
axis.line = element_line(colour = "black")) +
annotation_scale(location = "br", width_hint = 0.3,
pad_x = unit(0.1, "in"), pad_y = unit(0.05, "in")) +
annotation_north_arrow(location = "br", which_north = "true",
pad_x = unit(0.10, "in"), pad_y = unit(0.2, "in"),
style = north_arrow_fancy_orienteering) +
annotate(geom = "text", x = -46, y = -26, label = "Atlantic Ocean",
color = "grey22", size = 5) +
annotate(geom = "text", x = -47.5, y = -23.20, label = "São Paulo",
color = "grey22", size = 5) +
annotate(geom = "text", x = -49.8, y = -25, label = "Paraná",
color = "grey22", size = 5) +
annotate(geom = "text", x = -49.8, y = -27.3, label = "Santa Catarina",
color = "grey22", size = 5)
# Figure 3. Release station map
driftSpatial <-
drift %>%
dplyr::group_by(campaign, id, zoneExp) %>%
dplyr::filter(!duplicated(lat_r)) %>%
sf::st_as_sf(coords = c("long_r", "lat_r"), crs = 4326) %>%
dplyr::mutate(campaign = as.factor(campaign))
figure3 <- #### Conferir, nao esta plotando os eixos 'Lat/Long'
ggplot() +
ggspatial::geom_sf(data = brazil) +
ggspatial::geom_sf(data = driftSpatial, aes(color = campaign),
show.legend = T) +
coord_sf(xlim = c(-50.5, -44), ylim = c(-28.5, -23)) +
labs(x = "Longitude", y = "Latitude") +
theme_bw() +
theme(axis.title = element_text(colour = "black", size = 0),
axis.text = element_text(colour = "black", size = 10),
axis.line = element_line(colour = "black")) +
annotation_scale(location = "br", width_hint = 0.3) +
annotation_north_arrow(location = "br", which_north = "true",
pad_x = unit(0.45, "in"), pad_y = unit(0.3, "in"),
style = north_arrow_fancy_orienteering) +
annotate(geom = "text", x = -46, y = -26, label = "Atlantic Ocean",
color = "grey22", size = 5) +
annotate(geom = "text", x = -47.5, y = -23.58, label = "São Paulo",
color = "grey22", size = 5) +
annotate(geom = "text", x = -49.8, y = -25, label = "Paraná",
color = "grey22", size = 5) +
annotate(geom = "text", x = -49.8, y = -27.3, label = "Santa Catarina",
color = "grey22", size = 5)
## Figure 4. Create zoneExp map
## Figure 5. Create zone map
## Table1. Release station by "campaign", "id", "zoneExp".
table1 <-
drift %>%
dplyr::select(c(1:7, zoneExp)) %>%
dplyr::group_by(campaign, id, zoneExp) %>%
dplyr::filter(!duplicated(lat_r)) %>%
dplyr::ungroup() %>%
dplyr::select(- hour_r) %>%
dplyr::mutate(release = c(rep(33, 18), 30, rep(33, 8))) %>% # number of drifters released
dplyr::relocate(release, .after = id)
names(table1) <- c("Campign", "State", "Release station"," No.drifters",
"Date", "Lat", "Long","Drift zone")
################################################################################
############################## General Results #################################
################################################################################
##----------------------------------------------------------------------------##
## Drift experiment - Mean distance & time between release and strandings ##
##----------------------------------------------------------------------------##