RetoSchmucki
commented
9 months ago @ptg732 Thank you for your help! I will update the website with this code once I tested everything. 😃 🚀
Open ptg732 opened 9 months ago
RetoSchmucki
commented
9 months ago @ptg732 Thank you for your help! I will update the website with this code once I tested everything. 😃 🚀
ptg732
commented
8 months ago Hi Reto One question: the code uses data stored locally. Is it possible to add the ability to search the API to provide data stored directly in the database? Or is there a database limitation preventing this kind of use?
I'm a registered user of the EBMS and would like to explore the data that we are providing for further analysis
regards
Pedro
RetoSchmucki @.***> escreveu (segunda, 26/02/2024 à(s) 15:47):
@ptg732 https://github.com/ptg732 Thank you for your help! I will update the website with this code once I tested everything. 😃 🚀
— Reply to this email directly, view it on GitHub https://github.com/RetoSchmucki/rbms/issues/23#issuecomment-1964462427, or unsubscribe https://github.com/notifications/unsubscribe-auth/ABSXYPPNN2CUU6VPCXG546TYVSU7NAVCNFSM6AAAAABDNNTDWCVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMYTSNRUGQ3DENBSG4 . You are receiving this because you were mentioned.Message ID: @.***>
RetoSchmucki
commented
7 months ago Hi Pedro, Unfortunately, we do not have an API to query the eBMS database. This is partly due to the data agreement we have with the National BMS partners (the data owners). Also, the eBMS online portal only covers some of the data available across the partner schemes. Many countries use their own system (database). We maintain a database that collates all the data up to 2021, but access to these data needs to be granted by the contributing schemes. A data request can be done through the website https://butterfly-monitoring.net/ebms-data%20access
ptg732
commented
7 months ago Thank you for your answer. regards
Pedro
RetoSchmucki @.***> escreveu (quarta, 10/04/2024 à(s) 11:30):
Hi Pedro, Unfortunately, we do not have an API to query the eBMS database. This is partly due to the data agreement we have with the National BMS partners (the data owners). Also, the eBMS online portal only covers some of the data available across the partner schemes. Many countries use their own system (database). We maintain a database that collates all the data up to 2021, but access to these data needs to be granted by the contributing schemes. A data request can be done through the website https://butterfly-monitoring.net/ebms-data%20access
— Reply to this email directly, view it on GitHub https://github.com/RetoSchmucki/rbms/issues/23#issuecomment-2047164152, or unsubscribe https://github.com/notifications/unsubscribe-auth/ABSXYPNYWPFVF2INWWWXF53Y4UIFPAVCNFSM6AAAAABDNNTDWCVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDANBXGE3DIMJVGI . You are receiving this because you were mentioned.Message ID: @.***>
Hi
I did update the code from your workshop (https://butterfly-monitoring.github.io/bms_workshop/index.html) to use teh new terra package and discard the use of raster and rasterVis. I did also correct some small issues in your code taht were causing errors. Hope this is usefull
regards
if (!require("data.table")){ install.packages("data.table") library(data.table)} if (!require("speedglm")){ install.packages("speedglm") library(speedglm)} if (!require("devtools")){ install.packages("devtools") library(devtools)} if (!require("ggplot2")){ install.packages("ggplot2") library(ggplot2)} if (!require("reshape2")){ install.packages("reshape2") library(reshape2)} if (!require("plyr")){ install.packages("plyr") library(plyr)} if (!require("sf")){ install.packages("sf") library(sf)} if (!require("terra")) { install.packages("terra") library(terra)} if (!require("tmap")){ remotes::install_github('r-tmap/tmap')
install.packages("tmap")
if (!require("DT")){ install.packages("DT") library(DT)}
rbms from GitHub
if(!requireNamespace("devtools")) { install.packages("devtools") devtools::install_github("RetoSchmucki/rbms") library(rbms)}
library(data.table)
load .rds data, we use the function readRDS()
b_count_sub <- readRDS("bms_workshop_data/work_count.rds") # species data with counts m_visit_sub <- readRDS("bms_workshop_data/work_visit.rds") # date and time of transects/stations m_site_sub <- readRDS("bms_workshop_data/work_site.rds") # km coordinates of sites
to read csv into a data.table we use fread()
bcz_class <- data.table::fread("bms_workshop_data/GEnS_v3_classification.csv") # Bioclimate classes b_count_sub
Extract columns, to store only 'bms_id', the reference to samples, teh year of the record and the full date
my_new_dt <- b_count_sub[ , .(bms_id, transect_id_serial, year, visit_date)] my_new_dt
Extract unique
unique(my_new_dt$bms_id) # store the unique 'bms_id' unique(my_new_dt[, .(bms_id, year)]) # store 'bms_id' and 'year'
Subset
my_new_dt[bms_id == "NLBMS", ]
Work with date
my_new_dt[ , month := month(visit_date)][ , c("day", "year") := .(mday(visit_date), year(visit_date))] my_new_dt
Count object
my_new_dt[ , .N, by = bms_id]
rename column
change names
setnames(b_count_sub, "transect_id_serial", "SITE_ID") setnames(b_count_sub, c("species_name", "bms_id"), c("SPECIES", "BMS_ID")) names(b_count_sub) <- toupper(names(b_count_sub)) b_count_sub
merge data set
merge species count data with site coordinate data based on a common identifier (SITE_ID),
demonstrating both inner and left joins.
setnames(m_site_sub, "transect_id_serial", "SITE_ID")
setkey(b_count_sub, SITE_ID) setkey(m_site_sub, SITE_ID)
merge(b_count_sub, m_site_sub[bms_id == "FRBMS" , .(SITE_ID, transect_lon_1km, transect_lat_1km)]) merge(b_count_sub, m_site_sub[bms_id == "FRBMS" , .(SITE_ID, transect_lon_1km, transect_lat_1km)], all.x = TRUE) data_m <- merge(b_count_sub, m_site_sub[ , .(SITE_ID, transect_lon_1km, transect_lat_1km)], all.x = TRUE) data_m
Mapping BMS sites
library(data.table) library(sf) library(tmap)
b_count_sub <- readRDS("bms_workshop_data/work_count.rds") m_visit_sub <- readRDS("bms_workshop_data/work_visit.rds") m_site_sub <- readRDS("bms_workshop_data/work_site.rds")
Mapping
bcz <- terra::rast("bms_workshop_data/metzger_bcz_genz3.grd") bb <- sf::st_read("bms_workshop_data/bbox.shp") bcz_class <- data.table::fread("bms_workshop_data/GEnS_v3_classification.csv")
sf transect location
site_sf <- st_as_sf(m_site_sub, coords = c("transect_lon_1km", "transect_lat_1km"), crs = 3035) mapview::mapview(site_sf)
To produce a map with the Bioclimate Region, we use the function levelplot() in the rasterVis package
plot point on raster
site_sf_4326 <- st_transform(site_sf, crs = 4326) bb_4326 <- st_transform(bb, crs = 4326)
Begin the tmap visualization
tm_layout(frame = FALSE) + # Optional: Adjust layout settings as needed tm_shape(bcz) + tm_raster(style = "cont", palette = "viridis", title = "Bioclimate") + tm_shape(site_sf_4326) + tm_dots(col = "cyan4", size = 0.1) + # Adjust size as needed tm_shape(bb_4326) + tm_borders(col = "magenta", lwd = 2, lty = 2)
plot point on raster using tmap
tm_shape(bcz) + tm_raster(palette = "viridis", alpha = 1) + # Example using a viridis palette tm_shape(st_geometry(st_transform(site_sf, 4326))) + tm_symbols(col = "dodgerblue4", size = 0.1, shape = 20) + tm_shape(st_geometry(st_transform(bb, 4326))) + tm_borders(lty = 2, col= 'magenta', lwd=2)
Extracting spatial information
site_dt <- data.table(site_sf_4326)
Convert 'site_sf' to 'data.table', ensuring to include all attributes
site_dt <- data.table::setDT(sf::st_drop_geometry(site_sf_4326)) print(names(site_sf_4326))
Assuming 'bcz' is a SpatRaster object loaded with terra
Transform 'site_sf' to match the CRS of 'bcz'
site_sf_4326 <- st_transform(site_sf_4326, crs = crs(bcz))
Assuming 'site_sf_4326' is correctly transformed
Extract values
extracted_values <- terra::extract(bcz, site_sf_4326, exact=TRUE)
Ensure extracted_values is correctly structured for assignment
If bcz has a single layer, extracted_values should be a vector or a single-column data.frame
if (is.data.frame(extracted_values)) {
Assuming we're interested in the first layer or there's only one
} else {
If it's not a data.frame, it should be directly assignable, but this condition is just for illustration
}
----
setkey(site_dt, GEnZ_seq) setkey(bcz_class, GEnZ_seq) site_dt <- merge(site_dt, unique(bcz_class[, .(GEnZ_seq, GEnZname)]), all.x=TRUE)
After extraction and before merge
print(names(site_dt))
Exploring results
library(DT) datatable(site_dt[ ,.(bms_id, transect_id_serial, GEnZ_seq, GEnZname)], rownames = FALSE, filter = "top", options = list(pageLength = 5, scrollX = T))
---
mapview::mapview(bcz) + mapview::mapview(st_transform(site_sf, 4326))
Phenology in BMS data - flight curve
if(!requireNamespace("devtools")) install.packages("devtools") devtools::install_github("RetoSchmucki/rbms") library(data.table) library(rbms)
b_count_sub <- readRDS("bms_workshop_data/work_count.rds") m_visit_sub <- readRDS("bms_workshop_data/work_visit.rds")
Explore the data
library(DT) datatable(b_count_sub[1:50, ], rownames = FALSE, filter = "top", options = list(pageLength = 5, scrollX = T)) datatable(m_visit_sub[1:50, ], rownames = FALSE, filter = "top", options = list(pageLength = 5, scrollX = T)) unique(b_count_sub[ , .(bms_id, species_name)]) unique(b_count_sub[order(year), year])
1. Select a single species
s_sp <- "Polyommatus icarus" region_bms <- c("NLBMS", "FRBMS") data(m_visit) data(m_count)
2. Arrange data set name to fit the functions requirements
setnames(m_visit_sub, c("transect_id_serial", "visit_date"), c("SITE_ID", "DATE")) names(m_visit_sub) <- toupper(names(m_visit_sub))
setnames(b_count_sub, c("transect_id_serial", "visit_date", "species_name"), c("SITE_ID", "DATE", "SPECIES")) names(b_count_sub) <- toupper(names(b_count_sub))
3. Prepare a full time-series to align count, visit, monitoring and flight curve estimate,
using the function ts_dwmy_table(), with 'InitYear', 'LastYear' and 'WeekDay1'
ts_date <- rbms::ts_dwmy_table(InitYear = 2008, LastYear = 2017, WeekDay1 = "monday")
4. Define the monitoring season with the function ts_monit_season()(), using ts_date, StartMonth, EndMonth, StartDay,
EndDay, CompltSeason, Anchor, AnchorLength, AnchorLag, TimeUnit (“d” or “w”) as arguments
ts_season <- rbms::ts_monit_season(ts_date, StartMonth = 4, EndMonth = 9, StartDay = 1, EndDay = NULL, CompltSeason = TRUE, Anchor = TRUE, AnchorLength = 2, AnchorLag = 2, TimeUnit = "w")
5. Align the site visit in the region of interest with your monitoring season
MY_visit_region <- m_visit_sub[BMS_ID %in% region_bms, ] ts_season_visit <- rbms::ts_monit_site(ts_season, MY_visit_region)
6. Align the butterfly counts recorded for the species and region of interest with your visit and monitoring season
MY_count_region <- b_count_sub[BMS_ID %in% region_bms, ] ts_season_count <- rbms::ts_monit_count_site(ts_season_visit, MY_count_region, sp = s_sp)
7. Estimate the flight curve for a species and region of interest,
using cubic spline smoother available in the mgcv package and considering
ts_flight_curve <- rbms::flight_curve(ts_season_count, NbrSample = 500, MinVisit = 3, MinOccur = 2, MinNbrSite = 5, MaxTrial = 4, GamFamily = "nb", SpeedGam = FALSE, CompltSeason = TRUE, SelectYear = NULL, TimeUnit = "w" ) saveRDS(ts_flight_curve, file.path("bms_workshopdata", paste(gsub(" ", "", s_sp), paste(regionbms, collapse=""), "pheno.rds", sep = "_")))
8. Extract and plot the estimated phenology
ts_flight_curve <- readRDS(file.path("bms_workshopdata", paste(gsub(" ", "", s_sp), paste(regionbms, collapse=""), "pheno.rds", sep = "_"))) datatable(ts_flight_curve$pheno[1:370, .(SPECIES, M_YEAR, MONTH, trimWEEKNO, WEEK_SINCE, ANCHOR, NM)], rownames = FALSE, filter = "top", options = list(pageLength = 5, scrollX = T))
Plot using base R
Extract phenology part
pheno <- ts_flight_curve$pheno
add the line of the first year
yr <- unique(pheno[order(M_YEAR), as.numeric(as.character(M_YEAR))])
if("trimWEEKNO" %in% names(pheno)){ plot(pheno[M_YEAR == yr[1], trimWEEKNO], pheno[M_YEAR == yr[1], NM], type = 'l', ylim = c(0, max(pheno[, NM])), xlab = 'Monitoring Week', ylab = 'Relative Abundance') } else { plot(pheno[M_YEAR == yr[1], trimDAYNO], pheno[M_YEAR == yr[1], NM], type = 'l', ylim = c(0, max(pheno[, NM])), xlab = 'Monitoring Day', ylab = 'Relative Abundance')
}
add individual curves for additional years
if(length(yr) > 1) { i <- 2 for(y in yr[-1]){ if("trimWEEKNO" %in% names(pheno)){ points(pheno[M_YEAR == y , trimWEEKNO], pheno[M_YEAR == y, NM], type = 'l', col = i) } else { points(pheno[M_YEAR == y, trimDAYNO], pheno[M_YEAR == y, NM], type = 'l', col = i) } i <- i + 1 } }
add legend
legend('topright', legend = c(yr), col = c(seq_along(c(yr))), lty = 1, bty = 'n')
Computing site and collated indices
Generalized Abundance Index
library(data.table) library(rbms) library(ggplot2)
s_sp <- "Maniola jurtina"
s_sp <- "Polyommatus icarus" region_bms <- c("NLBMS", "FRBMS")
We will use the count and visit data
b_count_sub <- readRDS("bms_workshop_data/work_count.rds") m_visit_sub <- readRDS("bms_workshop_data/work_visit.rds")
setnames(m_visit_sub, c("transect_id_serial", "visit_date"), c("SITE_ID", "DATE")) names(m_visit_sub) <- toupper(names(m_visit_sub))
setnames(b_count_sub, c("transect_id_serial", "visit_date", "species_name"), c("SITE_ID", "DATE", "SPECIES")) names(b_count_sub) <- toupper(names(b_count_sub))
ts_date <- rbms::ts_dwmy_table(InitYear = 2008, LastYear = 2017, WeekDay1 = "monday")
ts_season <- rbms::ts_monit_season(ts_date, StartMonth = 4, EndMonth = 9, StartDay = 1, EndDay = NULL, CompltSeason = TRUE, Anchor = TRUE, AnchorLength = 2, AnchorLag = 2, TimeUnit = "w")
MY_visit_region <- m_visit_sub[BMS_ID %in% region_bms, ] ts_season_visit <- rbms::ts_monit_site(ts_season, MY_visit_region)
MY_count_region <- b_count_sub[BMS_ID %in% region_bms, ] ts_season_count <- rbms::ts_monit_count_site(ts_season_visit, MY_count_region, sp = s_sp)
Impute & Site index
impute values for missing week counts
and adequately compute sites abundance index over the entire monitoring season
impt_counts <- rbms::impute_count(ts_season_count = ts_season_count, ts_flight_curve = pheno, YearLimit = NULL, TimeUnit = "w")
sindex <- rbms::site_index(butterfly_count = impt_counts, MinFC = 0.10)
saveRDS(sindex, file.path("bms_workshopdata", paste( gsub(" ", "", s_sp), paste(regionbms, collapse=""), "sindex.rds", sep="_")))
Collated index, using the collated_index function in rbms to estimate annual abundance indices for a wider region
we will focus on the sites monitored in the Netherlands (NLBMS) and compute a collated index for Polyommatus icarus
accross the site monitored in that region over the 10-year period.
bms_id <- "NLBMS" sindex <- readRDS(file.path("bms_workshopdata", paste( gsub(" ", "", s_sp), paste(regionbms, collapse=""), "sindex.rds", sep="_"))) sindex[substr(SITE_ID, 1, 5) == bms_id, ]
co_index <- collated_index(data = sindex[substr(SITE_ID, 1, 5) == bms_id, ], s_sp = s_sp, sindex_value = "SINDEX", glm_weights = TRUE, rm_zero = TRUE) co_index$col_index
Bootstrap CI
bms_id <- "NLBMS" set.seed(125361)
bootsample <- rbms::boot_sample(sindex[substr(SITE_ID, 1, 5) == bms_id, ], boot_n = 500) co_index <- list()
for progression bar, uncomment the following
pb <- txtProgressBar(min = 0, max = dim(bootsample$boot_ind)[1], initial = 0, char = "*", style = 3)
for (i in c(0, seq_len(dim(bootsample$boot_ind)[1]))) { co_index[[i + 1]] <- rbms::collated_index(data = sindex[substr(SITE_ID, 1, 5) == bms_id, ], s_sp = s_sp, sindex_value = "SINDEX", bootID = i, boot_ind = bootsample, glm_weights = TRUE, rm_zero = TRUE)
}
collate and append all the result in a data.table format
co_index <- rbindlist(lapply(co_index, FUN = "[[", "col_index")) co_index$BMS_ID <- bms_id co_index$SPECIES <- s_sp
co_index_boot <- co_index saveRDS(co_index_boot, file.path("bms_workshopdata", paste( gsub(" ", "", s_sp), bms_id, "co_indexboot.rds", sep="")))
Figure with CI
bms_id <- "NLBMS"
co_index_boot <- readRDS(file.path("bms_workshopdata", paste( gsub(" ", "", s_sp), bms_id, "co_indexboot.rds", sep="")))
boot0 <- co_index_boot[ , mean(COL_INDEX, na.rm=TRUE), by = M_YEAR] boot0[, LOGDENSITY0 := log(V1) / log(10)] boot0[, meanlog0 := mean(LOGDENSITY0)]
Add mean of BOOTi == 0 (real data) to all data
boot_ests <- merge(co_index_boot, boot0[, .(M_YEAR, meanlog0)], by = c("M_YEAR") )
boot_ests[, LOGDENSITY := log(COL_INDEX) / log(10)] boot_ests[, TRMOBSCI := LOGDENSITY - meanlog0 + 2]
boot_ests[, TRMOBSCILOW := quantile(TRMOBSCI, 0.025), by = M_YEAR] boot_ests[, TRMOBSCIUPP := quantile(TRMOBSCI, 0.975), by = M_YEAR]
ylim_ <- c(min(floor(range(boot_ests[TRMOBSCI <= TRMOBSCIUPP & TRMOBSCI >= TRMOBSCILOW, TRMOBSCI]))), max(ceiling(range(boot_ests[TRMOBSCI <= TRMOBSCIUPP & TRMOBSCI >= TRMOBSCILOW, TRMOBSCI]))))
plot_col <- ggplot(data = boot_ests[TRMOBSCI <= TRMOBSCIUPP & TRMOBSCI >= TRMOBSCILOW, ], aes(x = M_YEAR, y = TRMOBSCI)) + geompoint(colour = "orange", alpha = 0.2) + ylim(ylim) + geom_line(data = boot_ests[TRMOBSCI <= TRMOBSCIUPP & TRMOBSCI >= TRMOBSCILOW, median(TRMOBSCI, na.rm=TRUE), by = M_YEAR], aes(x = M_YEAR, y = V1), linewidth = 1, colour = "dodgerblue4") + geom_hline(yintercept = 2, linetype = "dashed", color = "grey50") + scale_x_continuous(minor_breaks = waiver(), limits = c(2005, 2020), breaks = seq(2005, 2020, by = 5)) + xlab("Year") + ylab("Abundance Incices (Log/Log(10))") + labs(subtitle = paste(s_sp, bms_id, sep = " "))
plot_col
Calculating species trends
source("workshop_functions.R")
Read in collated indices for species and BMS of interest
Specify the species - note the underscore
spp <- "Maniola_jurtina"
Specify the BMS
bms <- "UKBMS"
Read in the bootstrapped collated indices
co_index <- readRDS(paste0("./bms_workshop_data/", spp, "_co_index_boot.rds"))
Filter to a single BMS to focus upon
co_index <- co_index[BMS_ID == bms] co_index
Convert to log 10 collated indices (TRMOBS)
Only use COL_INDEX larger then 0 for calculation or logdensity and trmobs
co_index[COL_INDEX > 0, LOGDENSITY:= log(COL_INDEX)/log(10)][, TRMOBS := LOGDENSITY - mean(LOGDENSITY) + 2, by = .(BOOTi)] co_index
Estimate and classify species trends with a confidence interval based on the bootstraps
sp_trend <- estimate_boot_trends(co_index) sp_trend
Plot the species log collated index with bootstrapped 95% confidence interval and linear trend line (in red)
Calculate mean log index for original data
co_index0_mean <- mean(co_index[BOOTi == 0]$LOGDENSITY, na.rm = TRUE)
Derive interval from quantiles
co_index_ci <- merge(co_index[BOOTi == 0, .(M_YEAR, TRMOBS, BMS_ID)], co_index[BOOTi != 0, .( LOWER = quantile(LOGDENSITY - co_index0_mean + 2, 0.025, na.rm = TRUE), UPPER = quantile(LOGDENSITY - co_index0_mean + 2, 0.975, na.rm = TRUE)), by = .(BMS_ID, M_YEAR)], by=c("BMS_ID","M_YEAR"))
ggplot(co_index_ci, aes(M_YEAR, TRMOBS))+ theme(text = element_text(size = 14))+ geom_line()+ geom_point()+ geom_ribbon(aes(ymin = LOWER, ymax = UPPER), alpha = .3)+ geomsmooth(method="lm", se=FALSE, color="red")+ xlab("Year")+ylab(expression('log '['(10)']*' Collated Index'))+ ggtitle(paste("Collated index for", gsub("", " ", spp), "in", bms))
Multi-species indicators
Read in collated indices for two species and filter to one BMS
bms <- "UKBMS" co_index <- rbind(readRDS("./bms_workshop_data/Maniola_jurtina_co_index_boot.rds"), readRDS("./bms_workshop_data/Polyommatus_icarus_co_index_boot.rds")) co_index <- co_index[BMS_ID == bms] co_index
Convert to log 10 collated indices (TRMOBS)
co_index[COL_INDEX > 0, LOGDENSITY:= log(COL_INDEX)/log(10)][,TRMOBS := LOGDENSITY - mean(LOGDENSITY) + 2, by = .(SPECIES, BOOTi)] co_index
calculate and plot the indicator, where the confidence interval is based on the bootstraps
First we generate the indicator for real data
msi <- produce_indicator0(co_index[BOOTi == 0]) msi
Then generate an indicator for each bootstrap
msi_boot <- produce_indicators_boot(co_index[BOOTi > 0]) str(msi_boot) msi_boot[,1:5]
add a confidence interval to the indicator based on quantiles from the bootstrapped indicators
msi <- add_indicator_CI(msi, msi_boot) msi
Plot the indicator
ggplot(msi, aes(year, indicator))+ theme(text = element_text(size = 14))+ geom_ribbon(aes(ymin = LOWsmooth1, ymax = UPPsmooth1), alpha=.2, fill = "blue")+ geom_point(color = "blue")+ geom_line(aes(y = SMOOTH), color = "blue")+ ylim(c(0, max(200,max(msi$UPPsmooth1))))+ ggtitle(paste("Example indicator for", bms, "based on two species"))
Calculate the indicator trend including confidence interval from the bootstraps
msi_trend <- estimate_ind_trends(msi, msi_boot) msi_trend