In special-uor/smpds: The SPECIAL Modern Pollen Data Set for Climate Reconstructions
knitr::opts_chunk$set(echo = TRUE,
eval = FALSE,
comment = "#>",
fig.path = "man/figures/climate-pnv-recon-",
out.width = "100%",
dpi = 300,
fig.width = 7)
This appendix shows the details on how the climate and potential natural vegetation (PNV) reconstructions were obtained.
In addition to the functions described below, you need to installed the R package called smpds which can be downloaded from GitHub: http://github.com/special-uor/smpds
remotes::install_github("special-uor/smpds")
Climate reconstructions
Two functions were created to streamline the process:
reconstruct_climate
This function takes the following parametres:
.data: data frame with information of the sites/entities for which the reconstructions are required. It should contain at least the following three columns: elevation, latitude and longitude.dataset: string of characters to be used as the prefix for the output files (e.g. "smpdsv2").path: Path where the reference climate files are stored (check Appendix A1).output_path (optional): Path where the output files should be stored.... (optional): Additional parametres passed to the function smpds::gwr (including cpus, xy_buffer, z_buffer, etc.). Run the following command to get more details about this function, ?smpds::gwr
reconstruct_climate <- function(.data,
dataset,
path,
output_path = path,
...) {
if (!file.exists(file.path(
output_path,
paste0(dataset, "_climate_reconstructions_cld_", Sys.Date(), ".csv")
))) {
tictoc::tic("CLD")
.data_cld <- .data %>%
smpds::gwr(
varid = "cld",
.ref =
file.path(path, "cru_ts4.04.1901.2019.cld.dat-clim-1961-1990-int.nc"),
...)
.data_cld %>%
readr::write_excel_csv(
file.path(
output_path,
paste0(dataset, "_climate_reconstructions_cld_", Sys.Date(), ".csv")
),
na = "")
tictoc::toc()
} else {
print("Skipping CLD ...")
}
if (!file.exists(file.path(
output_path,
paste0(dataset, "_climate_reconstructions_pre_", Sys.Date(), ".csv")
))) {
tictoc::tic("PRE")
.data_pre <- .data %>%
smpds::gwr(
varid = "pre",
.ref =
file.path(path,
"cru_ts4.04.1901.2019.pre.dat-clim-1961-1990-int.nc"),
...)
.data_pre %>%
readr::write_excel_csv(
file.path(
output_path,
paste0(dataset, "_climate_reconstructions_pre_", Sys.Date(), ".csv")
),
na = "")
tictoc::toc()
} else {
print("Skipping PRE ...")
}
if (!file.exists(file.path(
output_path,
paste0(dataset, "_climate_reconstructions_tmp_", Sys.Date(), ".csv")
))) {
tictoc::tic("TMP")
.data_tmp <- .data %>%
smpds::gwr(
varid = "tmp",
.ref = file.path(path, "cru_ts4.04-clim-1961-1990-daily.tmp.nc"),
...)
.data_tmp %>%
readr::write_excel_csv(
file.path(
output_path,
paste0(dataset, "_climate_reconstructions_tmp_", Sys.Date(), ".csv")
),
na = "")
tictoc::toc()
} else {
print("Skipping TMP ...")
}
}
postprocessing
This function takes the following parametres:
.data: data frame with information of the sites/entities for which the reconstructions are required. It should contain at least the following three columns: elevation, latitude and longitude.dataset: string of characters used as the prefix for the output files (e.g. "smpdsv2").output_path: Path where the output files are store.CPUS (optional): Number of CPUs to be used on the computation of Moisture Index (MI).
postprocessing <- function(.data,
dataset,
output_path,
CPUS = 1) {
N_MAX <- nrow(.data)
# SPLASH is driven by daily temperature (tmp), precipitation (pre),
# cloud coverage (cld), and latitude.
suppressMessages({
.data_cld <-
readr::read_csv(
file.path(
output_path,
paste0(dataset, "_climate_reconstructions_cld_", Sys.Date(), ".csv")
),
n_max = N_MAX)
.data_cld2 <-
.data_cld %>%
smpds::pivot_data(varname = "cld")
# Calculate sunshine fraction from cloud cover
.data_sf2 <- .data_cld %>%
smpds::pivot_data(scale = -0.01, add = 1, varname = "sf")
.data_pre <-
readr::read_csv(
file.path(
output_path,
paste0(dataset, "_climate_reconstructions_pre_", Sys.Date(), ".csv")
),
n_max = N_MAX)
.data_pre2 <- .data_pre %>%
smpds::pivot_data(varname = "pre")
.data_tmp <-
readr::read_csv(
file.path(
output_path,
paste0(dataset, "_climate_reconstructions_tmp_", Sys.Date(), ".csv")
),
n_max = N_MAX)
.data_tmp2 <- .data_tmp %>%
smpds::pivot_data(varname = "tmp")
})
.data_all_vars <- .data_sf2 %>%
dplyr::left_join(.data_pre2) %>%
dplyr::left_join(.data_tmp2) %>%
dplyr::select(site_name:elevation, sf, pre, tmp)
.data_pre_map <- .data_pre %>%
dplyr::rowwise() %>%
dplyr::mutate(map = sum(dplyr::c_across(T1:T365), na.rm = TRUE), .before = T1)
## Reconstruct climate variables
.data_all_vars2 <- .data_all_vars %>%
smpds::mi(cpus = CPUS) %>%
smpds::gdd() %>%
smpds::mat() %>%
smpds::mtco() %>%
smpds::mtwa() %>%
dplyr::bind_cols(.data_pre_map %>% dplyr::select(map))
.data_all_vars2 %>%
dplyr::select(-sf, -tmp, -pre) %>%
readr::write_excel_csv(
file.path(
output_path,
paste0(dataset, "_climate_reconstructions_", Sys.Date(), ".csv")
),
na = ""
)
}
Example
Using the following toy dataset, we can obtain climate reconstructions.
set.seed(2022)
smpds::SMPDSv2 %>%
dplyr::select(site_name:elevation) %>%
.[sample(1:24649, 10), ] %>%
datapasta::dpasta()
`%>%` <- magrittr::`%>%`
smpdsv2_demo <-
tibble::tribble(
~site_name, ~entity_name, ~latitude, ~longitude, ~elevation,
"Madrid W", "Lopez_a385", 40.408461, -4.4047361, 550,
"Lya ljunghed", "Lya ljunghed", 59.585321, 16.727049, 27,
"Brandenburg", "Matthias_b17", 52.09969, 14.5223, 126,
"Dongling Mt", "Dongling Mt 02", 40.03, 115.45, 2280,
"SB-4, Santa Ana River, Seven Oaks", "SB-4", 34.17735, -116.92446, 1670,
"Western Siberia, southern taiga, N.Port-Tobolsk 83", "Ryabogina_a173", 59.478085, 69.141222, 56,
"Tianshan North", "Tianshan North 48", 43.9581, 88.0833, 1300,
"Dingbian", "Dingbian 09", 36.920222, 108.172222, 1325,
"Laguna Carimagua-Bosque", "CABOSQUE_10", 4.5865, -71.327135, 180,
"Stand 47 (Mack and Bryant 1974]", "RNM047", 46.9, -120.47, 610
)
reconstruct_climate(
.data = smpdsv2_demo,
dataset = "smpdsv2",
path = "/path/to/CRU/4.04/climatologies",
output_path = "/path/to/climate_reconstructions",
cpus = 10
)
postprocessing(
.data = smpdsv2_demo,
dataset = "smpdsv2",
output_path = "/path/to/climate_reconstructions",
cpus = 10
)
Output files
"smpdsv2_climate_reconstructions_YYYY-MM-dd.csv"
"smpdsv2_climate_reconstructions_cld_YYYY-MM-dd.csv"
"smpdsv2_climate_reconstructions_pre_YYYY-MM-dd.csv"
"smpdsv2_climate_reconstructions_tmp_YYYY-MM-dd.csv"
`%>%` <- magrittr::`%>%`
smpdsv2_demo <-
tibble::tribble(
~site_name, ~entity_name, ~latitude, ~longitude, ~elevation,
"Madrid W", "Lopez_a385", 40.408461, -4.4047361, 550,
"Lya ljunghed", "Lya ljunghed", 59.585321, 16.727049, 27,
"Brandenburg", "Matthias_b17", 52.09969, 14.5223, 126,
"Dongling Mt", "Dongling Mt 02", 40.03, 115.45, 2280,
"SB-4, Santa Ana River, Seven Oaks", "SB-4", 34.17735, -116.92446, 1670,
"Western Siberia, southern taiga, N.Port-Tobolsk 83", "Ryabogina_a173", 59.478085, 69.141222, 56,
"Tianshan North", "Tianshan North 48", 43.9581, 88.0833, 1300,
"Dingbian", "Dingbian 09", 36.920222, 108.172222, 1325,
"Laguna Carimagua-Bosque", "CABOSQUE_10", 4.5865, -71.327135, 180,
"Stand 47 (Mack and Bryant 1974]", "RNM047", 46.9, -120.47, 610
) %>%
dplyr::mutate(latitude = round(latitude, 6),
longitude = round(longitude, 6))
knitr::kable(smpdsv2_demo)
smpdsv2_climate <-
tibble::tribble(
~site_name, ~entity_name, ~latitude, ~longitude, ~elevation, ~ID_BIOME, ~PNV, ~mi, ~gdd0, ~mat, ~mtco, ~mtwa, ~map,
"Dingbian", "Dingbian 09", 36.9202222222222, 108.172222222222, 1325, 22, "steppe", 0.394949592723695, 3592.72302064678, 8.55092162490546, -6.61490292837552, 21.8971942186536, 436.062275230345,
"Dongling Mt", "Dongling Mt 02", 40.03, 115.45, 2280, 13, "temperate deciduous broadleaf forest", 0.390979730141493, 1573.68177363758, -2.17965933523764, -20.4553391763972, 13.749458265842, 363.91429531485,
"Tianshan North", "Tianshan North 48", 43.9581, 88.0833, 1300, 27, "desert", 0.142451940389485, 3283.31819345228, 5.40821518022873, -13.9987374998661, 21.7268906346751, 148.674522284724,
"Brandenburg", "Matthias_b17", 52.09969, 14.5223, 126, 9, "cool mixed forest", 0.806252444137485, 3263.46687219566, 8.79133963632887, -1.3051180853309, 18.0621638730891, 579.931754561202,
"Western Siberia, southern taiga, N.Port-Tobolsk 83", "Ryabogina_a173", 59.478085, 69.141222, 56, 15, "cold evergreen needleleaf forest", 1.02264793744194, 2046.97517727995, -0.611833301624974, -19.8340286819492, 18.4338271172047, 506.545585208792,
"Lya ljunghed", "Lya ljunghed", 59.585321, 16.727049, 27, 15, "cold evergreen needleleaf forest", 0.920452135545231, 2392.7633953845, 5.63467146736352, -4.01872443600383, 15.945166204496, 547.108706490404,
"SB-4, Santa Ana River, Seven Oaks", "SB-4", 34.17735, -116.92446, 1670, 20, "xerophytic woods/scrub", 0.394456557024853, 4309.16727557118, 11.8059377412909, 3.14813843297513, 23.3959895727668, 557.076435427559,
"Stand 47 (Mack and Bryant 1974]", "RNM047", 46.9, -120.47, 610, 22, "steppe", 0.57901181486918, 3472.61046927691, 9.33013859032236, -1.21107168650365, 19.9439175891367, 504.744316448146,
"Madrid W", "Lopez_a385", 40.4084611111111, -4.40473611111111, 550, 4, "warm-temperate evergreen and mixed forest", 0.348819036204376, 5356.98667283546, 14.6766758159876, 6.36787498222421, 24.8143271360429, 439.010745019972,
"Laguna Carimagua-Bosque", "CABOSQUE_10", 4.5865, -71.327135, 180, 3, "tropical deciduous broadleaf forest and woodland", 1.78953358913693, 9651.87135377917, 26.4434831610388, 25.0212021892405, 27.9689485746237, 2649.56673562742
) %>%
dplyr::mutate(latitude = round(latitude, 6),
longitude = round(longitude, 6))
smpdsv2_climate %>%
dplyr::select(-ID_BIOME, -PNV) %>%
knitr::kable()
Potential Natural Vegetation (PNV) reconstructions
The climate reconstructions are based on the work by (Hengl et al., 2018) and the package smpds includes a function to calculate the PNV for each site/entity.
`%>%` <- magrittr::`%>%`
smpdsv2_demo %>%
smpds::parallel_extract_biome(cpus = 2) %>%
smpds::biome_name() %>%
dplyr::rename(PND = description) %>%
dplyr::select(-colour) %>%
smpds::pb()
smpdsv2_climate %>%
dplyr::select(site_name:PNV) %>%
knitr::kable()
References
Hengl, T., Walsh, M.G., Sanderman, J., Wheeler, I., Harrison, S.P., Prentice, I.C., 2018. Global mapping of potential natural vegetation: an assessment of machine learning algorithms for estimating land potential. PeerJ 6, e5457. https://doi.org/10.7717/peerj.5457
special-uor/smpds documentation built on July 9, 2024, 5:39 p.m.
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knitr::opts_chunk$set(echo = TRUE, eval = FALSE, comment = "#>", fig.path = "man/figures/climate-pnv-recon-", out.width = "100%", dpi = 300, fig.width = 7)
This appendix shows the details on how the climate and potential natural vegetation (PNV) reconstructions were obtained.
In addition to the functions described below, you need to installed the R package called smpds which can be downloaded from GitHub: http://github.com/special-uor/smpds
remotes::install_github("special-uor/smpds")
Climate reconstructions
Two functions were created to streamline the process:
reconstruct_climate
This function takes the following parametres:
.data: data frame with information of the sites/entities for which the reconstructions are required. It should contain at least the following three columns:elevation,latitudeandlongitude.dataset: string of characters to be used as the prefix for the output files (e.g."smpdsv2").path: Path where the reference climate files are stored (check Appendix A1).output_path(optional): Path where the output files should be stored....(optional): Additional parametres passed to the functionsmpds::gwr(includingcpus,xy_buffer,z_buffer, etc.). Run the following command to get more details about this function,?smpds::gwr
reconstruct_climate <- function(.data, dataset, path, output_path = path, ...) { if (!file.exists(file.path( output_path, paste0(dataset, "_climate_reconstructions_cld_", Sys.Date(), ".csv") ))) { tictoc::tic("CLD") .data_cld <- .data %>% smpds::gwr( varid = "cld", .ref = file.path(path, "cru_ts4.04.1901.2019.cld.dat-clim-1961-1990-int.nc"), ...) .data_cld %>% readr::write_excel_csv( file.path( output_path, paste0(dataset, "_climate_reconstructions_cld_", Sys.Date(), ".csv") ), na = "") tictoc::toc() } else { print("Skipping CLD ...") } if (!file.exists(file.path( output_path, paste0(dataset, "_climate_reconstructions_pre_", Sys.Date(), ".csv") ))) { tictoc::tic("PRE") .data_pre <- .data %>% smpds::gwr( varid = "pre", .ref = file.path(path, "cru_ts4.04.1901.2019.pre.dat-clim-1961-1990-int.nc"), ...) .data_pre %>% readr::write_excel_csv( file.path( output_path, paste0(dataset, "_climate_reconstructions_pre_", Sys.Date(), ".csv") ), na = "") tictoc::toc() } else { print("Skipping PRE ...") } if (!file.exists(file.path( output_path, paste0(dataset, "_climate_reconstructions_tmp_", Sys.Date(), ".csv") ))) { tictoc::tic("TMP") .data_tmp <- .data %>% smpds::gwr( varid = "tmp", .ref = file.path(path, "cru_ts4.04-clim-1961-1990-daily.tmp.nc"), ...) .data_tmp %>% readr::write_excel_csv( file.path( output_path, paste0(dataset, "_climate_reconstructions_tmp_", Sys.Date(), ".csv") ), na = "") tictoc::toc() } else { print("Skipping TMP ...") } }
postprocessing
This function takes the following parametres:
.data: data frame with information of the sites/entities for which the reconstructions are required. It should contain at least the following three columns:elevation,latitudeandlongitude.dataset: string of characters used as the prefix for the output files (e.g."smpdsv2").output_path: Path where the output files are store.CPUS(optional): Number of CPUs to be used on the computation of Moisture Index (MI).
postprocessing <- function(.data, dataset, output_path, CPUS = 1) { N_MAX <- nrow(.data) # SPLASH is driven by daily temperature (tmp), precipitation (pre), # cloud coverage (cld), and latitude. suppressMessages({ .data_cld <- readr::read_csv( file.path( output_path, paste0(dataset, "_climate_reconstructions_cld_", Sys.Date(), ".csv") ), n_max = N_MAX) .data_cld2 <- .data_cld %>% smpds::pivot_data(varname = "cld") # Calculate sunshine fraction from cloud cover .data_sf2 <- .data_cld %>% smpds::pivot_data(scale = -0.01, add = 1, varname = "sf") .data_pre <- readr::read_csv( file.path( output_path, paste0(dataset, "_climate_reconstructions_pre_", Sys.Date(), ".csv") ), n_max = N_MAX) .data_pre2 <- .data_pre %>% smpds::pivot_data(varname = "pre") .data_tmp <- readr::read_csv( file.path( output_path, paste0(dataset, "_climate_reconstructions_tmp_", Sys.Date(), ".csv") ), n_max = N_MAX) .data_tmp2 <- .data_tmp %>% smpds::pivot_data(varname = "tmp") }) .data_all_vars <- .data_sf2 %>% dplyr::left_join(.data_pre2) %>% dplyr::left_join(.data_tmp2) %>% dplyr::select(site_name:elevation, sf, pre, tmp) .data_pre_map <- .data_pre %>% dplyr::rowwise() %>% dplyr::mutate(map = sum(dplyr::c_across(T1:T365), na.rm = TRUE), .before = T1) ## Reconstruct climate variables .data_all_vars2 <- .data_all_vars %>% smpds::mi(cpus = CPUS) %>% smpds::gdd() %>% smpds::mat() %>% smpds::mtco() %>% smpds::mtwa() %>% dplyr::bind_cols(.data_pre_map %>% dplyr::select(map)) .data_all_vars2 %>% dplyr::select(-sf, -tmp, -pre) %>% readr::write_excel_csv( file.path( output_path, paste0(dataset, "_climate_reconstructions_", Sys.Date(), ".csv") ), na = "" ) }
Example
Using the following toy dataset, we can obtain climate reconstructions.
set.seed(2022) smpds::SMPDSv2 %>% dplyr::select(site_name:elevation) %>% .[sample(1:24649, 10), ] %>% datapasta::dpasta()
`%>%` <- magrittr::`%>%` smpdsv2_demo <- tibble::tribble( ~site_name, ~entity_name, ~latitude, ~longitude, ~elevation, "Madrid W", "Lopez_a385", 40.408461, -4.4047361, 550, "Lya ljunghed", "Lya ljunghed", 59.585321, 16.727049, 27, "Brandenburg", "Matthias_b17", 52.09969, 14.5223, 126, "Dongling Mt", "Dongling Mt 02", 40.03, 115.45, 2280, "SB-4, Santa Ana River, Seven Oaks", "SB-4", 34.17735, -116.92446, 1670, "Western Siberia, southern taiga, N.Port-Tobolsk 83", "Ryabogina_a173", 59.478085, 69.141222, 56, "Tianshan North", "Tianshan North 48", 43.9581, 88.0833, 1300, "Dingbian", "Dingbian 09", 36.920222, 108.172222, 1325, "Laguna Carimagua-Bosque", "CABOSQUE_10", 4.5865, -71.327135, 180, "Stand 47 (Mack and Bryant 1974]", "RNM047", 46.9, -120.47, 610 ) reconstruct_climate( .data = smpdsv2_demo, dataset = "smpdsv2", path = "/path/to/CRU/4.04/climatologies", output_path = "/path/to/climate_reconstructions", cpus = 10 ) postprocessing( .data = smpdsv2_demo, dataset = "smpdsv2", output_path = "/path/to/climate_reconstructions", cpus = 10 )
Output files
"smpdsv2_climate_reconstructions_YYYY-MM-dd.csv" "smpdsv2_climate_reconstructions_cld_YYYY-MM-dd.csv" "smpdsv2_climate_reconstructions_pre_YYYY-MM-dd.csv" "smpdsv2_climate_reconstructions_tmp_YYYY-MM-dd.csv"
`%>%` <- magrittr::`%>%` smpdsv2_demo <- tibble::tribble( ~site_name, ~entity_name, ~latitude, ~longitude, ~elevation, "Madrid W", "Lopez_a385", 40.408461, -4.4047361, 550, "Lya ljunghed", "Lya ljunghed", 59.585321, 16.727049, 27, "Brandenburg", "Matthias_b17", 52.09969, 14.5223, 126, "Dongling Mt", "Dongling Mt 02", 40.03, 115.45, 2280, "SB-4, Santa Ana River, Seven Oaks", "SB-4", 34.17735, -116.92446, 1670, "Western Siberia, southern taiga, N.Port-Tobolsk 83", "Ryabogina_a173", 59.478085, 69.141222, 56, "Tianshan North", "Tianshan North 48", 43.9581, 88.0833, 1300, "Dingbian", "Dingbian 09", 36.920222, 108.172222, 1325, "Laguna Carimagua-Bosque", "CABOSQUE_10", 4.5865, -71.327135, 180, "Stand 47 (Mack and Bryant 1974]", "RNM047", 46.9, -120.47, 610 ) %>% dplyr::mutate(latitude = round(latitude, 6), longitude = round(longitude, 6)) knitr::kable(smpdsv2_demo)
smpdsv2_climate <- tibble::tribble( ~site_name, ~entity_name, ~latitude, ~longitude, ~elevation, ~ID_BIOME, ~PNV, ~mi, ~gdd0, ~mat, ~mtco, ~mtwa, ~map, "Dingbian", "Dingbian 09", 36.9202222222222, 108.172222222222, 1325, 22, "steppe", 0.394949592723695, 3592.72302064678, 8.55092162490546, -6.61490292837552, 21.8971942186536, 436.062275230345, "Dongling Mt", "Dongling Mt 02", 40.03, 115.45, 2280, 13, "temperate deciduous broadleaf forest", 0.390979730141493, 1573.68177363758, -2.17965933523764, -20.4553391763972, 13.749458265842, 363.91429531485, "Tianshan North", "Tianshan North 48", 43.9581, 88.0833, 1300, 27, "desert", 0.142451940389485, 3283.31819345228, 5.40821518022873, -13.9987374998661, 21.7268906346751, 148.674522284724, "Brandenburg", "Matthias_b17", 52.09969, 14.5223, 126, 9, "cool mixed forest", 0.806252444137485, 3263.46687219566, 8.79133963632887, -1.3051180853309, 18.0621638730891, 579.931754561202, "Western Siberia, southern taiga, N.Port-Tobolsk 83", "Ryabogina_a173", 59.478085, 69.141222, 56, 15, "cold evergreen needleleaf forest", 1.02264793744194, 2046.97517727995, -0.611833301624974, -19.8340286819492, 18.4338271172047, 506.545585208792, "Lya ljunghed", "Lya ljunghed", 59.585321, 16.727049, 27, 15, "cold evergreen needleleaf forest", 0.920452135545231, 2392.7633953845, 5.63467146736352, -4.01872443600383, 15.945166204496, 547.108706490404, "SB-4, Santa Ana River, Seven Oaks", "SB-4", 34.17735, -116.92446, 1670, 20, "xerophytic woods/scrub", 0.394456557024853, 4309.16727557118, 11.8059377412909, 3.14813843297513, 23.3959895727668, 557.076435427559, "Stand 47 (Mack and Bryant 1974]", "RNM047", 46.9, -120.47, 610, 22, "steppe", 0.57901181486918, 3472.61046927691, 9.33013859032236, -1.21107168650365, 19.9439175891367, 504.744316448146, "Madrid W", "Lopez_a385", 40.4084611111111, -4.40473611111111, 550, 4, "warm-temperate evergreen and mixed forest", 0.348819036204376, 5356.98667283546, 14.6766758159876, 6.36787498222421, 24.8143271360429, 439.010745019972, "Laguna Carimagua-Bosque", "CABOSQUE_10", 4.5865, -71.327135, 180, 3, "tropical deciduous broadleaf forest and woodland", 1.78953358913693, 9651.87135377917, 26.4434831610388, 25.0212021892405, 27.9689485746237, 2649.56673562742 ) %>% dplyr::mutate(latitude = round(latitude, 6), longitude = round(longitude, 6)) smpdsv2_climate %>% dplyr::select(-ID_BIOME, -PNV) %>% knitr::kable()
Potential Natural Vegetation (PNV) reconstructions
The climate reconstructions are based on the work by (Hengl et al., 2018) and the package smpds includes a function to calculate the PNV for each site/entity.
`%>%` <- magrittr::`%>%` smpdsv2_demo %>% smpds::parallel_extract_biome(cpus = 2) %>% smpds::biome_name() %>% dplyr::rename(PND = description) %>% dplyr::select(-colour) %>% smpds::pb()
smpdsv2_climate %>% dplyr::select(site_name:PNV) %>% knitr::kable()
References
Hengl, T., Walsh, M.G., Sanderman, J., Wheeler, I., Harrison, S.P., Prentice, I.C., 2018. Global mapping of potential natural vegetation: an assessment of machine learning algorithms for estimating land potential. PeerJ 6, e5457. https://doi.org/10.7717/peerj.5457
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