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isd_crete_analysis_summary.Rmd
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---
title: "Crete soil microbiome summary"
date: "`r format(Sys.time(), '%d %B, %Y')`"
output:
pdf_document:
toc: yes
toc_depth: '2'
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
# Load the data
```{r, warning=FALSE, message=FALSE, echo=T, results='hide'}
library(tidyverse)
library(ggnewscale)
library(sf)
library(terra)
library(raster)
library(knitr)
library(kableExtra)
source("functions.R")
```
```{r, warning=FALSE, message=FALSE, echo=T, results='hide'}
## biodiversity
community_matrix_l <- read_delim("../results/community_matrix_l.tsv",delim="\t")
crete_biodiversity <- read_delim("../results/crete_biodiversity_asv.tsv",delim="\t")
asv_metadata <- read_delim("../results/asv_metadata.tsv",delim="\t")
genera_phyla_samples <- read_delim("../results/genera_phyla_samples.tsv",delim="\t")
# Metadata
metadata <- read_delim("../results/sample_metadata.tsv", delim="\t")
metadata$elevation_bin <- factor(metadata$elevation_bin,
levels=unique(metadata$elevation_bin)[order(sort(unique(metadata$elevation_bin)))])
samples_ucie_nmds_genera <- read_delim("../results/samples_ucie_nmds_genera.tsv")
## spatial
locations_spatial <- metadata %>%
st_as_sf(coords=c("longitude", "latitude"),
remove=F,
crs="WGS84")
crete_shp <- sf::st_read("../spatial_data/crete/crete.shp")
crete_peaks <- read_delim("../spatial_data/crete_mountain_peaks.csv", delim=";", col_names=T) %>%
st_as_sf(coords=c("X", "Y"),
remove=F,
crs="WGS84")
clc_crete_shp <- st_read("../spatial_data/clc_crete_shp/clc_crete_shp.shp")
crete_geology <- st_read("../spatial_data/crete_geology/crete_geology.shp")
natura_crete <- sf::st_read("../spatial_data/natura2000/natura2000_crete.shp")
wdpa_crete <- sf::st_read("../spatial_data/wdpa_crete/wdpa_crete.shp") %>% filter(DESIG_ENG=="Wildlife Refugee") %>%
mutate(DESIG_ENG = gsub("Wildlife Refugee", "Wildlife Refuge", DESIG_ENG))
natura_crete_land <- st_intersection(natura_crete, crete_shp)
natura_crete_land_sci <- natura_crete_land %>% filter(SITETYPE=="B")
# raster DEM hangling
dem_crete <- raster("../spatial_data/dem_crete/dem_crete.tif")
dem_crete_pixel <- as(dem_crete, "SpatialPixelsDataFrame")
dem_crete_df <- as.data.frame(dem_crete_pixel) %>% filter(dem_crete>0)
# raster bioclim 1 Annual Mean Temperature
bioclim1_crete <- raster("../spatial_data/world_clim_crete/crete_wc2.1_30s_bio_1.tif")
bioclim1_crete_pixel <- as(bioclim1_crete, "SpatialPixelsDataFrame")
bioclim1_crete_df <- as.data.frame(bioclim1_crete_pixel)
# raster bioclim 12 Annual Mean Precipitation
bioclim12_crete <- raster("../spatial_data/world_clim_crete/crete_wc2.1_30s_bio_12.tif")
bioclim12_crete_pixel <- as(bioclim12_crete, "SpatialPixelsDataFrame")
bioclim12_crete_df <- as.data.frame(bioclim12_crete_pixel)
# raster global aridity index
aridity_crete <- rast("../spatial_data/crete_aridity_index.tif")
aridity_crete[aridity_crete[] == 0 ] = NA
aridity_crete_df <- as.data.frame(aridity_crete, cells=TRUE)
aridity_crete_df$aridity <- aridity_crete_df$awi_pm_sr_yr*0.0001
aridity_crete_df$aridity_class <- cut(aridity_crete_df$aridity,
breaks=c(0,0.03,0.2,0.5, 0.65,0.9),
labels=c("Hyper Arid", "Arid", "Semi-Arid", "Dry sub-humid", "Humid"))
# raster desertification and Environmental Sensitive Areas Index
desertification_crete <- rast("../spatial_data/crete_desertification_risk/esa3rdp_crete.tif")
desertification_crete_cat <- read_delim("../spatial_data/crete_desertification_risk/esa3rdp_crete.tsv", delim="\t")
desertification_crete_df <- as.data.frame(desertification_crete,xy=T, cells=T)
# harmonised world soil database v2
hwsd2 <- rast("../spatial_data/hwsd2_crete/hwsd2_crete.tif")
# hswd metadata
# with trimws the leading spaces are removed for the values.
HWSD2_wrb4 <- read_delim("../spatial_data/hwsd2_crete/HWSD2_D_WRB4.tsv", delim="\t") |>
mutate(VALUE=trimws(VALUE)) |>
distinct(VALUE, CODE)
HWSD2_SMU <- read_delim("../spatial_data/hwsd2_crete/HWSD2_SMU.tsv", delim="\t") |>
distinct(HWSD2_SMU_ID, WRB4) |>
left_join(HWSD2_wrb4, by=c("WRB4"="CODE"))
```
# Crete data cube maps
Here are some maps of Crete with different layers.
![ISD Crete sampling sites]("../figures/map_crete_blank.png")
![Crete digital elevation model]("../figures/map_fig1a.png")
![Corine Land Cover, EEA Copernicus]("../figures/map_fig1b.png")
![Geology of Crete, EMERIC - CRINNO]("../figures/map_crete_geology.png")
![Desertification from risk, ESDAC]("../figures/map_crete_crete_desertification.png")
![Annual precipitation, WorldClim 2.0]("../figures/map_bio_12_crete.png")
![Mean annual temperature, WorldClim 2.0]("../figures/map_bio_1_crete.png")
![Global Aridity index v3 classes]("../figures/map_crete_aridity_class.png")
![Harmonised World Soil database v2, Crete map]("../figures/map_crete_hwsd2.png")
# Table summary
A summary table of each order.
```{r}
# the number of samples per layer
classes <- c("LABEL2", "geology_na", "ESA_12CL", "aridity_class" )
classes_samples <- list()
for (i in seq_along(classes)){
print(i)
classes_samples[[i]] <- metadata |>
group_by(metadata[[classes[i]]]) |>
summarise(samples=n(),
taxa_richness=sum(taxa),
asv_richness=sum(asvs),
mean_shannon=mean(shannon),
sd_shannon=sd(shannon))
}
samples_total <- do.call(rbind, classes_samples)
colnames(samples_total)[1] <- c("class")
# the area of different layers in Crete
clc_label2_area <- clc_crete_shp |>
mutate(area_polygon=units::set_units(st_area(geometry),km^2)) |>
st_drop_geometry() |>
group_by(LABEL2) |>
rename(class=LABEL2) |>
group_by(class) |>
summarise(area=sum(area_polygon)) |>
mutate(category="CLC LABEL2")
geology_area <- crete_geology |>
st_make_valid() |>
mutate(area_polygon=units::set_units(st_area(geometry),km^2)) |>
st_drop_geometry() |>
rename(class=geology_na) |>
group_by(class) |>
summarise(area=sum(area_polygon)) |>
mutate(category="Geology")
desertification_crete_area <- terra::freq(desertification_crete) |>
as_tibble() |>
mutate(area=units::set_units(count*4.4,km^2)) |>
rename(class=value) |>
dplyr::select(class,area) |>
mutate(category="Desertification Risk")
aridity_area <- aridity_crete_df |>
as_tibble() |>
rename(class=aridity_class) |>
group_by(class) |>
summarise(cells=n()) |>
mutate(area=units::set_units(cells*0.69,km^2)) |>
dplyr::select(class,area) |>
mutate(category="Aridity class")
area_total <- do.call(rbind,
list(clc_label2_area,
geology_area,
desertification_crete_area,
aridity_area)) |>
left_join(samples_total) |>
mutate(area=round(area, digits=0), mean_shannon=round(mean_shannon, digits=2), sd_shannon=round(sd_shannon, digits=2))
write_delim(area_total, "../results/data_cube_summary_table.tsv", delim="\t")
area_total |> arrange(category, class, area) |> kbl() |> kable_styling(latex_options = "scale_down")
```
```{r}
#order_taxa <- endemic_species |>
# group_by(order) |>
# summarise(taxa=n())
#
#order_sites <- locations_shp |>
# distinct(order,decimalLatitude,decimalLongitude) |>
# group_by(order) |>
# summarise(sites=n())
#
#order_occ <- locations_shp |>
# group_by(order) |>
# summarise(occurrences=n()) |>
# st_drop_geometry()
#
#order_locations <- locations_grid |>
# distinct(CELLCOD, order) |>
# group_by(order) |>
# summarise(locations=n())
#
#order_iucn <- endemic_species_s_i |>
# pivot_wider(names_from=iucn,
# values_from=c(n_species,proportion),
# id_cols=order) |>
# group_by(order) |>
# rowwise() |>
# mutate(threatened=sum(n_species_EN,n_species_VU,n_species_CR,na.rm=TRUE),
# proportion_threatened=threatened/sum(n_species_EN,n_species_VU,n_species_CR,`n_species_NT/LC`, na.rm=TRUE))
#
#order_paca <- endemic_species_s_o |>
# pivot_wider(names_from=paca,
# values_from=c(n_species,proportion),
# id_cols=order)
#
#order_total_l <- list(order_taxa,order_sites,order_occ,order_locations,order_iucn,order_paca)
#
#order_total <- order_total_l |> reduce(full_join, by="order")
#
#total_iucn <- endemic_species_i |>
# pivot_wider(names_from=iucn,
# values_from=c(n_species,proportion)) |>
# dplyr::select(-method) |>
# rowwise() |>
# mutate(threatened=sum(n_species_EN,n_species_VU,n_species_CR,na.rm=TRUE),
# proportion_threatened=threatened/sum(n_species_EN,n_species_VU,n_species_CR,`n_species_NT/LC`, na.rm=TRUE))
#
#total_paca <- endemic_species_p |>
# pivot_wider(names_from=paca,
# values_from=c(n_species,proportion)) |>
# dplyr::select(-method)
#
#
#total_data <- data.frame(order="total",
# taxa = length(unique(endemic_species$scientificName)),
# sites = nrow(unique(st_coordinates(locations_shp))),
# occurrences = nrow(locations_shp),
# locations = length(unique(locations_grid$CELLCOD)))
#
#total_row <- cbind(total_data, total_paca, total_iucn)
#
#total_summary <- rbind(total_row, order_total)
#write_delim(total_summary, "../results/total_summary.tsv", delim="\t")
```