sampling_method = data.insect$sampling_method,
abundance = data.insect$abundance,
total_sampled_area = data.insect$total_sampled_area,
total_sampled_time = data.insect$total_sampled_time,
total_sampled_flowers = data.insect$total_sampled_flowers,
Description = data.insect$Description
)
setwd("C:/Users/USUARIO/Desktop/OBservData/Datasets_storage")
write_csv(insect_sampling, "insect_sampling_Katherine_LW_Burns_Malus_domestica_Ireland_2018.csv")
setwd(dir_ini)
field_level_data <- tibble(
study_id=data.site$study_id,
site_id=data.site$site_id,
crop=data.site$crop,
variety=data.site$variety,
management=data.site$management,
country=data.site$country,
latitude=data.site$latitude,
longitude=data.site$longitude,
X_UTM=NA,
Y_UTM=NA,
zone_UTM=NA,
sampling_start_month=data.site$sampling_start_month,
sampling_end_month=data.site$sampling_end_month,
sampling_year=data.site$sampling_year,
field_size=data.site$field_size,
yield=data.site$yield,
yield_units=data.site$yield_units,
yield2=data.site$yield2,
yield2_units=data.site$yield2_units,
yield_treatments_no_pollinators=data.site$yield_treatments_no_pollinators,
yield_treatments_pollen_supplement=data.site$yield_treatments_pollen_supplement,
yield_treatments_no_pollinators2=data.site$yield_treatments_no_pollinators2,
yield_treatments_pollen_supplement2=data.site$yield_treatments_pollen_supplement2,
fruits_per_plant=data.site$mean_fruits_per_plant,
fruit_weight=data.site$fruit_weight,
plant_density=data.site$plant_density,
seeds_per_fruit=data.site$seeds_per_fruit,
seeds_per_plant=data.site$seeds_per_plant,
seed_weight=data.site$seed_weight,
observed_pollinator_richness=data.site$observed_pollinator_richness,
other_pollinator_richness=data.site$other_pollinator_richness,
other_richness_estimator_method=data.site$other_richness_estimator_method,
abundance=data.site$total,
ab_honeybee=data.site$honeybees,
ab_bombus=data.site$bumblebees,
ab_wildbees=data.site$wild_bees,
ab_syrphids=data.site$syrphids,
ab_humbleflies=data.site$humbleflies,
ab_other_flies=data.site$other_flies,
ab_beetles=data.site$beetles,
ab_lepidoptera=data.site$lepidoptera,
ab_nonbee_hymenoptera=data.site$non_bee_hymenoptera,
ab_others=data.site$other,
total_sampled_area=data.site$total_sampled_area.y,
total_sampled_time=data.site$total_sampled_time.y,
visitation_rate_units="visits per 100 flowers and hour",
visitation_rate=data.site$visitation_rate,
visit_honeybee=data.site$visit_honeybee,
visit_bombus=data.site$visit_bombus,
visit_wildbees=data.site$visit_wildbees,
visit_syrphids=data.site$visit_syrphids,
visit_humbleflies=data.site$visit_humbleflies,
visit_other_flies=data.site$visit_other_flies,
visit_beetles=data.site$visit_beetles,
visit_lepidoptera=data.site$visit_lepidotera,
visit_nonbee_hymenoptera=data.site$visit_nonbee_hymenoptera,
visit_others=data.site$visit_others,
Publication=data.site$Publication,
Credit=data.site$Credit,
Email_contact=data.site$email
)
setwd("C:/Users/USUARIO/Desktop/OBservData/Datasets_storage")
write_csv(field_level_data, "field_level_data_Katherine_LW_Burns_Malus_domestica_Ireland_2018.csv")
setwd(dir_ini)
View(insect_sampling)
View(field_level_data)
View(insect_sampling)
View(field_level_data)
library(tidyverse)
library(openxlsx)
library("iNEXT")
dir_ini <- getwd()
data.site <- read.xlsx("globalcrop.APPLE.BurnsStanley.xlsx",
sheet = "field_level_data", startRow = 1)
data.site <- as_tibble(data.site)
data.site$study_id <- "Katherine_LW_Burns_Malus_domestica_Ireland_2018"
data.site$Publication <- NA
data.site$Credit <- data.site$Credit[1]
data.site$email <- data.site$email[1]
# Sanity check names
data.site %>% group_by(site_id) %>% count() #All site_id are unique
data.insect <- read.xlsx("globalcrop.APPLE.BurnsStanley.xlsx",
sheet = "insect_sampling", startRow = 1)
data.insect <- as_tibble(data.insect)
data.insect$study.id <- "Katherine_LW_Burns_Malus_domestica_Ireland_2018"
data.insect <- data.insect %>% rename(
study_id = study.id,
site_id = site.id,
sampling_method = sampling.method,
total_sampled_area = total.sampled.area,
total_sampled_time = total.sampled.time,
total_sampled_flowers = total.sampled.flowers,
Description = `Description.(Fee.text)`
)
data.insect$guild[data.insect$guild == "non-bee hymenoptera"]  <- "non_bee_hymenoptera"
data.insect$guild[data.insect$guild == "other flies"]  <- "other_flies"
data.insect$guild[data.insect$guild == "other wild bees"]  <- "wild_bees"
abundance_aux <- data.insect %>% filter(sampling_method =="transects") %>%
group_by(site_id,guild) %>% count(wt=abundance) %>%
spread(key=guild, value=n) %>% rename()
names(abundance_aux)
abundance_aux <- abundance_aux %>% mutate(other=0, humbleflies=0,total=0)
abundance_aux[is.na(abundance_aux)] <- 0
abundance_aux$total <- rowSums(abundance_aux[,c(2:ncol(abundance_aux))])
data.site <- data.site %>% left_join(abundance_aux, by = "site_id")
abundace_field <- data.insect %>% filter(sampling_method=="transects") %>%
select(site_id,pollinator,abundance)%>%
group_by(site_id,pollinator) %>% count(wt=abundance)
abundace_field <- abundace_field %>% spread(key=pollinator,value=n)
abundace_field[is.na(abundace_field)] <- 0
abundace_field$r_obser <-  0
abundace_field$r_chao <-  0
for (i in 1:nrow(abundace_field)) {
x <- as.numeric(abundace_field[i,2:(ncol(abundace_field)-2)])
chao  <-  ChaoRichness(x, datatype = "abundance", conf = 0.95)
abundace_field$r_obser[i] <-  chao$Observed
abundace_field$r_chao[i] <-  chao$Estimator
}
tax_res <- read_csv("taxon_table_burn01.csv")
#Mutate pollinator labels to match those of taxon table
tax_estimation <- data.insect %>%
left_join(tax_res, by="pollinator")
tax_estimation %>% group_by(rank) %>% count()
percentage_species_morphos <-
sum(tax_estimation$rank %in% c("morphospecies","species"))/nrow(tax_estimation)
View(tax_estimation)
richness_aux <- abundace_field %>% select(site_id,r_obser,r_chao)
richness_aux <- richness_aux %>% rename(observed_pollinator_richness=r_obser,
other_pollinator_richness=r_chao) %>%
mutate(other_richness_estimator_method="Chao1")
if (percentage_species_morphos < 0.8){
richness_aux[,2:ncol(richness_aux)] <- NA
}
data.site <- data.site %>% left_join(richness_aux, by = "site_id")
sampling_aux <- data.insect %>% group_by(site_id,sampling_method) %>%
summarize(total_sampled_area=mean(total_sampled_area),
total_sampled_time=mean(total_sampled_time)
) %>%
group_by(site_id) %>%
summarize(total_sampled_area=sum(total_sampled_area),
total_sampled_time=sum(total_sampled_time)
)
data.site <- data.site %>% left_join(sampling_aux, by = "site_id")
insect_sampling <- tibble(
study_id = data.insect$study_id,
site_id = data.insect$site_id,
pollinator = data.insect$pollinator,
guild = data.insect$guild,
sampling_method = data.insect$sampling_method,
abundance = data.insect$abundance,
total_sampled_area = data.insect$total_sampled_area,
total_sampled_time = data.insect$total_sampled_time,
total_sampled_flowers = data.insect$total_sampled_flowers,
Description = data.insect$Description
)
View(insect_sampling)
field_level_data <- tibble(
study_id=data.site$study_id,
site_id=data.site$site_id,
crop=data.site$crop,
variety=data.site$variety,
management=data.site$management,
country=data.site$country,
latitude=data.site$latitude,
longitude=data.site$longitude,
X_UTM=NA,
Y_UTM=NA,
zone_UTM=NA,
sampling_start_month=data.site$sampling_start_month,
sampling_end_month=data.site$sampling_end_month,
sampling_year=data.site$sampling_year,
field_size=data.site$field_size,
yield=data.site$yield,
yield_units=data.site$yield_units,
yield2=data.site$yield2,
yield2_units=data.site$yield2_units,
yield_treatments_no_pollinators=data.site$yield_treatments_no_pollinators,
yield_treatments_pollen_supplement=data.site$yield_treatments_pollen_supplement,
yield_treatments_no_pollinators2=data.site$yield_treatments_no_pollinators2,
yield_treatments_pollen_supplement2=data.site$yield_treatments_pollen_supplement2,
fruits_per_plant=data.site$mean_fruits_per_plant,
fruit_weight=data.site$fruit_weight,
plant_density=data.site$plant_density,
seeds_per_fruit=data.site$seeds_per_fruit,
seeds_per_plant=data.site$seeds_per_plant,
seed_weight=data.site$seed_weight,
observed_pollinator_richness=data.site$observed_pollinator_richness,
other_pollinator_richness=data.site$other_pollinator_richness,
other_richness_estimator_method=data.site$other_richness_estimator_method,
abundance=data.site$total,
ab_honeybee=data.site$honeybees,
ab_bombus=data.site$bumblebees,
ab_wildbees=data.site$wild_bees,
ab_syrphids=data.site$syrphids,
ab_humbleflies=data.site$humbleflies,
ab_other_flies=data.site$other_flies,
ab_beetles=data.site$beetles,
ab_lepidoptera=data.site$lepidoptera,
ab_nonbee_hymenoptera=data.site$non_bee_hymenoptera,
ab_others=data.site$other,
total_sampled_area=data.site$total_sampled_area.y,
total_sampled_time=data.site$total_sampled_time.y,
visitation_rate_units="visits per 100 flowers and hour",
visitation_rate=data.site$visitation_rate,
visit_honeybee=data.site$visit_honeybee,
visit_bombus=data.site$visit_bombus,
visit_wildbees=data.site$visit_wildbees,
visit_syrphids=data.site$visit_syrphids,
visit_humbleflies=data.site$visit_humbleflies,
visit_other_flies=data.site$visit_other_flies,
visit_beetles=data.site$visit_beetles,
visit_lepidoptera=data.site$visit_lepidotera,
visit_nonbee_hymenoptera=data.site$visit_nonbee_hymenoptera,
visit_others=data.site$visit_others,
Publication=data.site$Publication,
Credit=data.site$Credit,
Email_contact=data.site$email
)
View(field_level_data)
library(tidyverse)
library(openxlsx)
library("iNEXT")
dir_ini <- getwd()
data.site <- read.xlsx("globalcrop.APPLE.BurnsStanley.xlsx",
sheet = "field_level_data", startRow = 1)
data.site <- as_tibble(data.site)
data.site$study_id <- "Katherine_LW_Burns_Malus_domestica_Ireland_2018"
data.site$Publication <- NA
data.site$Credit <- data.site$Credit[1]
data.site$email <- data.site$email[1]
# Sanity check names
data.site %>% group_by(site_id) %>% count() #All site_id are unique
data.insect <- read.xlsx("globalcrop.APPLE.BurnsStanley.xlsx",
sheet = "insect_sampling", startRow = 1)
data.insect <- as_tibble(data.insect)
data.insect$study.id <- "Katherine_LW_Burns_Malus_domestica_Ireland_2018"
data.insect <- data.insect %>% rename(
study_id = study.id,
site_id = site.id,
sampling_method = sampling.method,
total_sampled_area = total.sampled.area,
total_sampled_time = total.sampled.time,
total_sampled_flowers = total.sampled.flowers,
Description = `Description.(Fee.text)`
)
data.insect$guild[data.insect$guild == "non-bee hymenoptera"]  <- "non_bee_hymenoptera"
data.insect$guild[data.insect$guild == "other flies"]  <- "other_flies"
data.insect$guild[data.insect$guild == "other wild bees"]  <- "wild_bees"
abundance_aux <- data.insect %>% filter(sampling_method =="transects") %>%
group_by(site_id,guild) %>% count(wt=abundance) %>%
spread(key=guild, value=n) %>% rename()
names(abundance_aux)
abundance_aux <- abundance_aux %>% mutate(other=0, humbleflies=0,total=0)
abundance_aux[is.na(abundance_aux)] <- 0
abundance_aux$total <- rowSums(abundance_aux[,c(2:ncol(abundance_aux))])
data.site <- data.site %>% left_join(abundance_aux, by = "site_id")
abundace_field <- data.insect %>% filter(sampling_method=="transects") %>%
select(site_id,pollinator,abundance)%>%
group_by(site_id,pollinator) %>% count(wt=abundance)
abundace_field <- abundace_field %>% spread(key=pollinator,value=n)
abundace_field[is.na(abundace_field)] <- 0
abundace_field$r_obser <-  0
abundace_field$r_chao <-  0
for (i in 1:nrow(abundace_field)) {
x <- as.numeric(abundace_field[i,2:(ncol(abundace_field)-2)])
chao  <-  ChaoRichness(x, datatype = "abundance", conf = 0.95)
abundace_field$r_obser[i] <-  chao$Observed
abundace_field$r_chao[i] <-  chao$Estimator
}
tax_res <- read_csv("taxon_table_burn01.csv")
#Mutate pollinator labels to match those of taxon table
tax_estimation <- data.insect %>%
left_join(tax_res, by="pollinator")
tax_estimation %>% group_by(rank) %>% count()
sum(tax_estimation$rank %in% c("morphospecies","species"))
nrow(tax_estimation)
library(tidyverse)
library(openxlsx)
library("iNEXT")
dir_ini <- getwd()
data.site <- read.xlsx("globalcrop.APPLE.BurnsStanley_REVISED.xlsx",
sheet = "field_level_data", startRow = 1)
data.site <- as_tibble(data.site)
data.site$study_id <- "Katherine_LW_Burns_Malus_domestica_Ireland_2018"
data.site$Publication <- NA
data.site$Credit <- data.site$Credit[1]
data.site$email <- data.site$email[1]
# Sanity check names
data.site %>% group_by(site_id) %>% count() #All site_id are unique
# Sanity check names
data.site %>% group_by(site_id) %>% count() %>%
filter(n>1)#All site_id are unique
data.insect <- read.xlsx("globalcrop.APPLE.BurnsStanley_REVISED.xlsx",
sheet = "insect_sampling", startRow = 1)
data.insect <- as_tibble(data.insect)
data.insect$study.id <- "Katherine_LW_Burns_Malus_domestica_Ireland_2018"
data.insect <- data.insect %>% rename(
study_id = study.id,
site_id = site.id,
sampling_method = sampling.method,
total_sampled_area = total.sampled.area,
total_sampled_time = total.sampled.time,
total_sampled_flowers = total.sampled.flowers,
Description = `Description.(Fee.text)`
)
data.insect %>% group_by(guild) %>% count()
data.insect$guild[data.insect$guild == "non-bee hymenoptera"]  <- "non_bee_hymenoptera"
data.insect$guild[data.insect$guild == "other flies"]  <- "other_flies"
data.insect$guild[data.insect$guild == "other wild bees"]  <- "wild_bees"
abundance_aux <- data.insect %>% filter(sampling_method =="transects") %>%
group_by(site_id,guild) %>% count(wt=abundance) %>%
spread(key=guild, value=n) %>% rename()
names(abundance_aux)
abundance_aux <- abundance_aux %>% mutate(other=0, humbleflies=0,total=0)
abundance_aux[is.na(abundance_aux)] <- 0
abundance_aux$total <- rowSums(abundance_aux[,c(2:ncol(abundance_aux))])
data.site <- data.site %>% left_join(abundance_aux, by = "site_id")
abundace_field <- data.insect %>% filter(sampling_method=="transects") %>%
select(site_id,pollinator,abundance)%>%
group_by(site_id,pollinator) %>% count(wt=abundance)
abundace_field <- abundace_field %>% spread(key=pollinator,value=n)
abundace_field[is.na(abundace_field)] <- 0
abundace_field$r_obser <-  0
abundace_field$r_chao <-  0
for (i in 1:nrow(abundace_field)) {
x <- as.numeric(abundace_field[i,2:(ncol(abundace_field)-2)])
chao  <-  ChaoRichness(x, datatype = "abundance", conf = 0.95)
abundace_field$r_obser[i] <-  chao$Observed
abundace_field$r_chao[i] <-  chao$Estimator
}
tax_res <- read_csv("taxon_table_burn01.csv")
#Mutate pollinator labels to match those of taxon table
tax_estimation <- data.insect %>%
left_join(tax_res, by="pollinator")
tax_estimation %>% group_by(rank) %>% count()
percentage_species_morphos <-
sum(tax_estimation$rank %in% c("morphospecies","species"))/nrow(tax_estimation)
richness_aux <- abundace_field %>% select(site_id,r_obser,r_chao)
richness_aux <- richness_aux %>% rename(observed_pollinator_richness=r_obser,
other_pollinator_richness=r_chao) %>%
mutate(other_richness_estimator_method="Chao1")
if (percentage_species_morphos < 0.8){
richness_aux[,2:ncol(richness_aux)] <- NA
}
data.site <- data.site %>% left_join(richness_aux, by = "site_id")
sampling_aux <- data.insect %>% group_by(site_id,sampling_method) %>%
summarize(total_sampled_area=mean(total_sampled_area),
total_sampled_time=mean(total_sampled_time)
) %>%
group_by(site_id) %>%
summarize(total_sampled_area=sum(total_sampled_area),
total_sampled_time=sum(total_sampled_time)
)
data.site <- data.site %>% left_join(sampling_aux, by = "site_id")
insect_sampling <- tibble(
study_id = data.insect$study_id,
site_id = data.insect$site_id,
pollinator = data.insect$pollinator,
guild = data.insect$guild,
sampling_method = data.insect$sampling_method,
abundance = data.insect$abundance,
total_sampled_area = data.insect$total_sampled_area,
total_sampled_time = data.insect$total_sampled_time,
total_sampled_flowers = data.insect$total_sampled_flowers,
Description = data.insect$Description
)
setwd("C:/Users/USUARIO/Desktop/OBservData/Datasets_storage")
write_csv(insect_sampling, "insect_sampling_Katherine_LW_Burns_Malus_domestica_Ireland_2018.csv")
setwd(dir_ini)
field_level_data <- tibble(
study_id=data.site$study_id,
site_id=data.site$site_id,
crop=data.site$crop,
variety=data.site$variety,
management=data.site$management,
country=data.site$country,
latitude=data.site$latitude,
longitude=data.site$longitude,
X_UTM=NA,
Y_UTM=NA,
zone_UTM=NA,
sampling_start_month=data.site$sampling_start_month,
sampling_end_month=data.site$sampling_end_month,
sampling_year=data.site$sampling_year,
field_size=data.site$field_size,
yield=data.site$yield,
yield_units=data.site$yield_units,
yield2=data.site$yield2,
yield2_units=data.site$yield2_units,
yield_treatments_no_pollinators=data.site$yield_treatments_no_pollinators,
yield_treatments_pollen_supplement=data.site$yield_treatments_pollen_supplement,
yield_treatments_no_pollinators2=data.site$yield_treatments_no_pollinators2,
yield_treatments_pollen_supplement2=data.site$yield_treatments_pollen_supplement2,
fruits_per_plant=data.site$mean_fruits_per_plant,
fruit_weight=data.site$fruit_weight,
plant_density=data.site$plant_density,
seeds_per_fruit=data.site$seeds_per_fruit,
seeds_per_plant=data.site$seeds_per_plant,
seed_weight=data.site$seed_weight,
observed_pollinator_richness=data.site$observed_pollinator_richness,
other_pollinator_richness=data.site$other_pollinator_richness,
other_richness_estimator_method=data.site$other_richness_estimator_method,
abundance=data.site$total,
ab_honeybee=data.site$honeybees,
ab_bombus=data.site$bumblebees,
ab_wildbees=data.site$wild_bees,
ab_syrphids=data.site$syrphids,
ab_humbleflies=data.site$humbleflies,
ab_other_flies=data.site$other_flies,
ab_beetles=data.site$beetles,
ab_lepidoptera=data.site$lepidoptera,
ab_nonbee_hymenoptera=data.site$non_bee_hymenoptera,
ab_others=data.site$other,
total_sampled_area=data.site$total_sampled_area.y,
total_sampled_time=data.site$total_sampled_time.y,
visitation_rate_units="visits per 100 flowers and hour",
visitation_rate=data.site$visitation_rate,
visit_honeybee=data.site$visit_honeybee,
visit_bombus=data.site$visit_bombus,
visit_wildbees=data.site$visit_wildbees,
visit_syrphids=data.site$visit_syrphids,
visit_humbleflies=data.site$visit_humbleflies,
visit_other_flies=data.site$visit_other_flies,
visit_beetles=data.site$visit_beetles,
visit_lepidoptera=data.site$visit_lepidotera,
visit_nonbee_hymenoptera=data.site$visit_nonbee_hymenoptera,
visit_others=data.site$visit_others,
Publication=data.site$Publication,
Credit=data.site$Credit,
Email_contact=data.site$email
)
setwd("C:/Users/USUARIO/Desktop/OBservData/Datasets_storage")
write_csv(field_level_data, "field_level_data_Katherine_LW_Burns_Malus_domestica_Ireland_2018.csv")
setwd(dir_ini)
View(field_level_data)
field_level_data <- tibble(
study_id=data.site$study_id,
site_id=data.site$site_id,
crop=data.site$crop,
variety=data.site$variety,
management=data.site$management,
country=data.site$country,
latitude=data.site$latitude,
longitude=data.site$longitude,
X_UTM=NA,
Y_UTM=NA,
zone_UTM=NA,
sampling_start_month=data.site$sampling_start_month,
sampling_end_month=data.site$sampling_end_month,
sampling_year=data.site$sampling_year,
field_size=data.site$field_size,
yield=data.site$yield,
yield_units=data.site$yield_units,
yield2=data.site$yield2,
yield2_units=data.site$yield2_units,
yield_treatments_no_pollinators=data.site$yield_treatments_no_pollinators,
yield_treatments_pollen_supplement=data.site$yield_treatments_pollen_supplement,
yield_treatments_no_pollinators2=data.site$yield_treatments_no_pollinators2,
yield_treatments_pollen_supplement2=data.site$yield_treatments_pollen_supplement2,
fruits_per_plant=data.site$mean_fruits_per_plant,
fruit_weight=data.site$fruit_weight,
plant_density=data.site$plant_density,
seeds_per_fruit=data.site$seeds_per_fruit,
seeds_per_plant=data.site$seeds_per_plant,
seed_weight=data.site$seed_weight,
observed_pollinator_richness=data.site$observed_pollinator_richness,
other_pollinator_richness=data.site$other_pollinator_richness,
other_richness_estimator_method=data.site$other_richness_estimator_method,
richness_restriction = NA,
abundance=data.site$total,
ab_honeybee=data.site$honeybees,
ab_bombus=data.site$bumblebees,
ab_wildbees=data.site$wild_bees,
ab_syrphids=data.site$syrphids,
ab_humbleflies=data.site$humbleflies,
ab_other_flies=data.site$other_flies,
ab_beetles=data.site$beetles,
ab_lepidoptera=data.site$lepidoptera,
ab_nonbee_hymenoptera=data.site$non_bee_hymenoptera,
ab_others=data.site$other,
total_sampled_area=data.site$total_sampled_area.y,
total_sampled_time=data.site$total_sampled_time.y,
visitation_rate_units="visits per 100 flowers and hour",
visitation_rate=data.site$visitation_rate,
visit_honeybee=data.site$visit_honeybee,
visit_bombus=data.site$visit_bombus,
visit_wildbees=data.site$visit_wildbees,
visit_syrphids=data.site$visit_syrphids,
visit_humbleflies=data.site$visit_humbleflies,
visit_other_flies=data.site$visit_other_flies,
visit_beetles=data.site$visit_beetles,
visit_lepidoptera=data.site$visit_lepidotera,
visit_nonbee_hymenoptera=data.site$visit_nonbee_hymenoptera,
visit_others=data.site$visit_others,
Publication=data.site$Publication,
Credit=data.site$Credit,
Email_contact=data.site$email
)
setwd("C:/Users/USUARIO/Desktop/OBservData/Datasets_storage")
write_csv(field_level_data, "field_level_data_Katherine_LW_Burns_Malus_domestica_Ireland_2018.csv")
setwd(dir_ini)
