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evl_nitro.R
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# LOAD PACKAGES ----
if(!isTRUE(require(tidyverse, quietly = TRUE))) {
install.packages("tidyverse", dependencies = TRUE); library(tidyverse)
} else {
require(tidyverse)}
if(!isTRUE(require(sf, quietly = TRUE))) {
install.packages("sf", dependencies = TRUE); library(sf)
} else {
require(sf)}
if(!isTRUE(require(sp, quietly = TRUE))) {
install.packages("sp", dependencies = TRUE); library(sp)
} else {
require(sp)}
if(!isTRUE(require(proj4, quietly = TRUE))) {
install.packages("proj4", dependencies = TRUE); library(proj4)
} else {
require(proj4)}
if(!isTRUE(require(ggradar, quietly = TRUE))) {
install.packages("ggradar", dependencies = TRUE); library(ggradar)
} else {
require(ggradar)}
if(!isTRUE(require(leaflet, quietly = TRUE))) {
install.packages("leaflet", dependencies = TRUE); library(leaflet)
} else {
require(leaflet)}
# LOAD DATA ----
# VRSTVA EVL
evl <- st_read("//bali.nature.cz/du/OchranaPrirody/Natura 2000/EvVyzLok_440_2021.shp")
evl_sjtsk <- st_transform(evl, CRS("+init=epsg:5514"))
# VMB
vmb_shp_sjtsk_22_read <- sf::st_read("//bali.nature.cz/du/Mapovani/Biotopy/CR_2022/20220531_Segment.shp")
vmb_hab_dbf_22 <- sf::st_read("//bali.nature.cz/du/Mapovani/Biotopy/CR_2022/Biotop/HAB_BIOTOP.dbf")
vmb_pb_dbf_22 <- sf::st_read("//bali.nature.cz/du/Mapovani/Biotopy/CR_2022/Biotop/PB_BIOTOP.dbf") %>%
dplyr::filter(!OBJECTID %in% vmb_hab_dbf_22$OBJECTID)
vmb_hab_pb_dbf_22 <- dplyr::bind_rows(vmb_hab_dbf_22, vmb_pb_dbf_22) %>%
dplyr::group_by(SEGMENT_ID) %>%
dplyr::mutate(moz_num = n(),
FSB_EVAL_prep = dplyr::case_when(sum(STEJ_PR, na.rm = TRUE) < 50 ~ "X",
sum(STEJ_PR, na.rm = TRUE) >= 50 &
sum(STEJ_PR, na.rm = TRUE) < 200 ~ "moz.",
sum(STEJ_PR, na.rm = TRUE) == 200 ~ NA_character_)) %>%
dplyr::ungroup() %>%
dplyr::select(SEGMENT_ID,
FSB_EVAL_prep) %>%
distinct()
vmb_shp_sjtsk_22 <- vmb_shp_sjtsk_22_read %>%
dplyr::left_join(vmb_hab_dbf_22, by = "SEGMENT_ID") %>%
dplyr::left_join(vmb_hab_pb_dbf_22, by = "SEGMENT_ID") %>%
dplyr::mutate(FSB_EVAL = dplyr::case_when(FSB_EVAL_prep == "moz." ~ "moz.",
FSB_EVAL_prep == "X" ~ "X",
TRUE ~ FSB),
HABITAT = dplyr::case_when(BIOTOP %in% c("T3.3C", "3.4A", "T3.4C", "T3.5A") ~ "6210p",
TRUE ~ HABITAT))
# DEPOZICE DUSÍKU ----
results_habitats_read <- read.csv2("S:/Gaigr/hodnoceni_stanovist_grafy/results_habitats_fin.csv")
results_habitats_read[is.na(results_habitats_read)] <- 0
habitat_hodnoceni_vyber <- results_habitats_read %>%
group_by(HABITAT_CODE, SITECODE) %>%
mutate(HABITAT_NAZEV = find.habitat.NAME_CZ(unique(HABITAT_CODE)))
write.csv2(habitat_hodnoceni_vyber,
"S:/Gaigr/hodnoceni_stanovist_grafy/habitat_hodnoceni_vyber.csv",
row.names = FALSE)
nempir <- raster::raster("Nempir.tif")
ntotal2017 <- stars::read_stars("ntot2017.tif")
ndepo2017 <- sf::st_as_sf(ntotal2017)
ndepo_semin <- st_intersection(filter(evl_sjtsk, NAZEV == "Semínský přesyp"), ndepo2017)
ndepo_semin <- ndepo_semin %>%
mutate(depo_segment = ntot2017.tif*st_area(geometry)/sum(st_area(geometry)))
ndepo_semin$depo_segment %>% sum()
karpaty_6210p_depo <- vmb_shp_sjtsk %>%
st_intersection(filter(evl_sjtsk, NAZEV == "Bílé Karpaty")) %>%
filter(HABITAT == "6210p") %>%
st_intersection(., ndepo2017) %>%
mutate(AREA_real = units::drop_units(st_area(geometry))) %>%
mutate(PLO_BIO_M2_EVL = PLO_BIO_M2*AREA_real/SHAPE_AREA) %>%
mutate(depo_segment = ntot2017.tif*PLO_BIO_M2_EVL/sum(PLO_BIO_M2_EVL))
plot(karpaty_6210p_depo$geometry)
krkonose_6520_depo <- vmb_shp_sjtsk %>%
st_intersection(filter(evl_sjtsk, NAZEV == "Krkonoše")) %>%
filter(HABITAT == "6520") %>%
st_intersection(., ndepo2017) %>%
mutate(AREA_real = units::drop_units(st_area(geometry))) %>%
mutate(PLO_BIO_M2_EVL = PLO_BIO_M2*AREA_real/SHAPE_AREA) %>%
mutate(depo_segment = ntot2017.tif*PLO_BIO_M2_EVL/sum(PLO_BIO_M2_EVL))
oblik_depo <- vmb_shp_sjtsk_22 %>%
st_intersection(filter(evl_sjtsk, NAZEV == "Oblík - Srdov - Brník")) %>%
dplyr::filter(grepl("T3.3", BIOTOP) | grepl("T3.4", BIOTOP)) %>%
st_intersection(., ndepo2017) %>%
sf::st_cast(., "POLYGON") %>%
mutate(AREA_real = units::drop_units(st_area(geometry))) %>%
mutate(PLO_BIO_M2_EVL = PLO_BIO_M2*AREA_real/SHAPE_AREA) %>%
mutate(depo_segment = ntot2017.tif*PLO_BIO_M2_EVL/sum(PLO_BIO_M2_EVL))
krkonose_6520_depo %>%
mutate(TYP_DRUHY_SEG = case_when(TD == "N" ~ 0, # PŘEPOČET TYPICKÝCH DRUHŮ NA SEGMENTU
TD == "MP" ~ 5,
TD == "P" ~ 10),
TD_SEG = TYP_DRUHY_SEG*PLO_BIO_M2_EVL/sum(PLO_BIO_M2_EVL),
QUAL = case_when(KVALITA == 1 ~ 1,
KVALITA == 2 ~ 1,
KVALITA == 3 ~ 2,
KVALITA == 4 ~ 2)) %>%
group_by(QUAL)
habdepo <- function(hab_code, evl_site) {
vmb_target_sjtsk <- vmb_shp_sjtsk %>%
st_intersection(filter(evl_sjtsk, NAZEV == evl_site)) %>%
filter(HABITAT == hab_code) %>%
st_intersection(., ndepo2017) %>%
mutate(AREA_real = units::drop_units(st_area(geometry))) %>%
mutate(PLO_BIO_M2_EVL = PLO_BIO_M2*AREA_real/SHAPE_AREA) %>%
mutate(depo_segment = ntot2017.tif*PLO_BIO_M2_EVL/sum(PLO_BIO_M2_EVL))
return(as.numeric(sum(vmb_target_sjtsk$depo_segment)))
}
biodepo <- function(bio_code, evl_site) {
vmb_target_sjtsk <- vmb_shp_sjtsk_22 %>%
sf::st_intersection(filter(evl_sjtsk, NAZEV == evl_site)) %>%
dplyr::filter(BIOTOP == bio_code) %>%
sf::st_intersection(., ndepo2017) %>%
dplyr::mutate(AREA_real = units::drop_units(st_area(geometry))) %>%
dplyr::mutate(PLO_BIO_M2_EVL = PLO_BIO_M2*AREA_real/SHAPE_AREA) %>%
dplyr::mutate(depo_segment = ntot2017.tif*PLO_BIO_M2_EVL/sum(PLO_BIO_M2_EVL))
return(as.numeric(sum(vmb_target_sjtsk$depo_segment)))
}
oblik_depo <- biodepo("T3.3A", "Oblík-Srdov-Brník")
habdepo("6210", "Bílé Karpaty")
find.habitat.NAME_CZ(6520)
find.evl.TARGETS("Šumava")
sites_habitats_ndepo <- sites_habitats %>%
mutate(nitrodepo = habdepo(Kód.fenoménu, Název.lokality))
conpal <- colorNumeric(palette = "Spectral",
domain = oblik_depo$ntot2017.tif,
na.color = "black")
conpal
oblik_depo[130,]
leaflet() %>%
addProviderTiles(providers$Esri.WorldTopoMap, group = "Esri WorldTopoMap") %>%
addProviderTiles(providers$Esri.WorldImagery, group = "Esri WorldImagery") %>%
addPolygons(data = sf::st_transform(oblik_depo, CRS("+init=epsg:4326")) %>%
slice(c(1:129,131:430)) %>%
sf::st_make_valid(),
color = ~conpal(ntot2017.tif),
fill = ~conpal(ntot2017.tif),
weight = 1,
opacity = 1,
label = ~format(round(ntot2017.tif, 3), nsmall = 3),
group = "Potential habitat patches") %>%
addLayersControl(baseGroups = c("Esri WorldTopoMap", "Esri WorldImagery"),
options = layersControlOptions(collapsed = FALSE)) %>%
addMeasure(primaryLengthUnit = "meters") %>%
addScaleBar(position = "bottomleft")
# ANALYSIS ----
vmb_T <- vmb_shp_sjtsk_22 %>%
dplyr::filter(grepl("T", BIOTOP)) %>%
dplyr::mutate(TYP_DRUHY_SEG = dplyr::case_when(DG == "W" ~ 0,
TD == "N" ~ 0,
TD == "MP" ~ 5,
TD == "P" ~ 10)) %>%
sf::st_intersection(., ndepo2017) %>%
dplyr::mutate(AREA_real = units::drop_units(st_area(geometry)),
PLO_BIO_M2_SEG = PLO_BIO_M2*AREA_real/SHAPE_AREA) %>%
dplyr::group_by(SEGMENT_ID) %>%
dplyr::mutate(depo_segment = ntot2017.tif*PLO_BIO_M2_SEG/sum(PLO_BIO_M2_SEG)) %>%
dplyr::ungroup()
vmb_L <- vmb_shp_sjtsk_22 %>%
dplyr::filter(grepl("L", BIOTOP)) %>%
dplyr::mutate(TYP_DRUHY_SEG = dplyr::case_when(DG == "W" ~ 0,
TD == "N" ~ 0,
TD == "MP" ~ 5,
TD == "P" ~ 10)) %>%
sf::st_intersection(., ndepo2017) %>%
dplyr::mutate(AREA_real = units::drop_units(st_area(geometry)),
PLO_BIO_M2_SEG = PLO_BIO_M2*AREA_real/SHAPE_AREA) %>%
dplyr::group_by(SEGMENT_ID) %>%
dplyr::mutate(depo_segment = ntot2017.tif*PLO_BIO_M2_SEG/sum(PLO_BIO_M2_SEG)) %>%
dplyr::ungroup()
plot(vmb_T %>% slice(1:500) %>% dplyr::select(depo_segment, geometry))
ndepo_T <- vmb_T %>%
sf::st_drop_geometry() %>%
dplyr::mutate(BIOTOP_CAT = stringr::str_sub(BIOTOP, 1, 4)) %>%
dplyr::group_by(SEGMENT_ID, BIOTOP_SEZ, BIOTOP) %>%
dplyr::slice(1) %>%
dplyr::ungroup() %>%
dplyr::mutate(TYP_DRUHY_SEG = dplyr::case_when(is.na(TD) ~ 0,
DG == "W" ~ 0,
TD == "N" ~ 0,
TD == "MP" ~ 5,
TD == "P" ~ 10),
TD_CAT = dplyr::case_when(TYP_DRUHY_SEG == 0 ~ "N",
TYP_DRUHY_SEG == 5 ~ "MP",
TYP_DRUHY_SEG == 10 ~ "P"),
TD_TWO = dplyr::case_when(TYP_DRUHY_SEG == 0 ~ "B",
TYP_DRUHY_SEG == 5 ~ "S",
TYP_DRUHY_SEG == 10 ~ "S"),
DG_CAT = NA,
QUAL = dplyr::case_when(is.na(KVALITA) ~ 0,
DG == "W" ~ 0,
KVALITA == 1 ~ 1,
KVALITA == 2 ~ 1,
KVALITA == 3 ~ 2,
KVALITA == 4 ~ 2,
TRUE ~ 0),
REDLIST = dplyr::case_when(BIOTOP == "T3.3A" ~ "NT",
BIOTOP == "T3.3B" ~ "NT",
BIOTOP == "T3.3C" ~ "EN",
BIOTOP == "T3.3D" ~ "NT",
BIOTOP == "T3.4A" ~ "EN",
BIOTOP == "T3.4B" ~ "NT",
BIOTOP == "T3.4C" ~ "VU",
BIOTOP == "T3.4D" ~ "NT",
BIOTOP == "T3.5A" ~ "VU",
BIOTOP == "T3.5B" ~ "VU"),
REDLIST = as.factor(REDLIST))
write.csv(ndepo_T,
"S:/Gaigr/hodnoceni_stanovist_grafy/ndepo_T.csv",
row.names = FALSE,
fileEncoding = "UTF-8")
source("https://gist.githubusercontent.com/benmarwick/2a1bb0133ff568cbe28d/raw/fb53bd97121f7f9ce947837ef1a4c65a73bffb3f/geom_flat_violin.R")
ggplot(data = ndepo_T %>%
dplyr::filter(grepl("T1.4", BIOTOP)),
aes(x = as.factor(TD_CAT), y = ntot2017.tif, fill = as.factor(TD_CAT))) +
geom_flat_violin(position = position_nudge(x = 0.2, y = 0), alpha = 0.8) +
geom_point(aes(y = ntot2017.tif, color = as.factor(TD_CAT)),
position = position_jitter(width = 0.15), size = 1, alpha = 0.5) +
geom_boxplot(width = 0.2, outlier.shape = NA, alpha = 0.8) +
xlab("typické druhy\n") +
ylab("\ncelková depozice N (kg/ha)") +
scale_fill_viridis_d() +
scale_colour_viridis_d() +
coord_flip() +
guides(fill=guide_legend(title="New Legend Title"),
color = "none") +
theme_classic()
ggplot(data = ndepo_T %>%
dplyr::filter(grepl("T3.3", BIOTOP)),
aes(x = reorder(as.factor(TD_CAT), -desc(TYP_DRUHY_SEG)),
y = ntot2017.tif,
fill = BIOTOP)) +
geom_boxplot(alpha = 0.8) +
xlab("typical species' value\n") +
ylab("\nnitrogen deposition (kg/ha)") +
scale_x_discrete(labels = c('poor', 'moderate', 'good')) +
scale_fill_viridis_d(option = "magma") +
scale_colour_viridis_d(option = "magma") +
coord_flip() +
guides(fill=guide_legend(title="habitat type"),
color = "none") +
theme_classic() +
theme(text = element_text(family = "Arial"),
axis.title.x = element_text(color = "black", size = 18, face = "bold"),
axis.title.y = element_text(color = "black", size = 18, face = "bold"),
axis.text = element_text(color = "black", size = 16, face = "bold"),
legend.title = element_text(color = "black", size = 18, face = "bold"),
legend.text = element_text(color = "black", size = 16, face = "bold"),
legend.text.align = 0.5)
ggplot(data = ndepo_T %>%
dplyr::filter(grepl("T3.3", BIOTOP)),
aes(x = as.factor(TD_CAT),
y = ntot2017.tif,
fill = BIOTOP)) +
geom_boxplot(alpha = 0.8) +
xlab("typical species' presence\n") +
ylab("\nnitrogen deposition (kg/ha)") +
scale_fill_viridis_d(option = "magma") +
scale_colour_viridis_d(option = "magma") +
coord_flip() +
guides(fill=guide_legend(title="habitat type"),
color = "none") +
theme_classic() +
theme(text = element_text(family = "Arial"),
axis.title.x = element_text(color = "black", size = 18, face = "bold"),
axis.title.y = element_text(color = "black", size = 18, face = "bold"),
axis.text = element_text(color = "black", size = 16, face = "bold"),
legend.title = element_text(color = "black", size = 18, face = "bold"),
legend.text = element_text(color = "black", size = 16, face = "bold"),
legend.text.align = 0.5)
ggplot(data = ndepo_T %>%
dplyr::filter(grepl("T3.3", BIOTOP)),
aes(x = reorder(as.factor(TD_TWO), -desc(TYP_DRUHY_SEG)),
y = ntot2017.tif,
fill = BIOTOP)) +
geom_boxplot(alpha = 0.8) +
xlab("typické druhy\n") +
ylab("\ncelková depozice N (kg/ha)") +
scale_fill_viridis_d() +
scale_colour_viridis_d() +
coord_flip() +
theme_classic()
model_n <- glm(data = ndepo_T %>%
dplyr::filter(grepl("T3.3", BIOTOP) |
grepl("T3.4", BIOTOP) |
grepl("T3.5", BIOTOP) |
grepl("T3.2", BIOTOP)),
TYP_DRUHY_SEG ~ ntot2017.tif * BIOTOP,
family = "poisson")
summary(model_n)
anova(model_n)
one_way <- aov(data = ndepo_T %>%
dplyr::filter(grepl("T3.4", BIOTOP)),
TYP_DRUHY_SEG ~ ntot2017.tif)
two_way <- aov(data = ndepo_T %>%
dplyr::filter(grepl("T3.4", BIOTOP)),
TYP_DRUHY_SEG ~ ntot2017.tif + BIOTOP)
interaction <- aov(data = ndepo_T %>%
dplyr::filter(grepl("T3.4", BIOTOP)),
TYP_DRUHY_SEG ~ ntot2017.tif*BIOTOP)
library(AICcmodavg)
model.set <- list(one_way, two_way, interaction)
model.names <- c("one.way", "two.way", "interaction")
aictab(model.set, modnames = model.names)
TukeyHSD(interaction)