In RS-eco/climateNiche: R package for analysing the climatic niche of a species

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  eval=F
)

Set file directory and load required packages

#Automatically install required packages, which are not yet installed
packages <- c("raster", "dplyr", "tidyr", "remotes")
new.packages <- packages[!(packages %in% installed.packages()[,"Package"])]
if(length(new.packages)) install.packages(new.packages); rm(new.packages)

# Load packages
l <- sapply(packages, require, character.only = TRUE, quietly=TRUE); rm(packages, l)

# Instal missing packages from GitHub
packages_github <- c("rasterSp", "climateNiche")
new.packages <- packages_github[!(packages_github %in% installed.packages()[,"Package"])]
if(length(new.packages)) remotes::install_github(paste0("RS-eco/", new.packages))
rm(new.packages)

Get species names

# Load small range data
data("amphibians_dist_smallrange", package="rasterSp")
data("ter_birds_dist_smallrange", package="rasterSp")
data("ter_mammals_dist_smallrange", package="rasterSp")

# Get species names
amphinames <- as.character(unique(amphibians_dist_smallrange$species))
avesnames <- as.character(unique(ter_birds_dist_smallrange$species))
mamanames <- as.character(unique(ter_mammals_dist_smallrange$species))

Retrieve GBIF data

For information on how to create the gbif_database, see the vignette("create-gbif-db") vignette.

# Connect to GBIF database
con <- DBI::dbConnect(RSQLite::SQLite(), 
                      dbname=paste0(filedir,"/gbif_database.sqlite"))
gbif <- tbl(con, "gbif")

# Run loop for all species of each of the three taxa
dataG <- lapply(1:3, function(j){
  taxa <- list(amphinames, mamanames, avesnames)[[j]]
  dataG <- list()
  for(i in 1:length(taxa)){
    #collect data from gbif
    data <- gbif %>% filter(species == taxa[i]) %>% collect() %>% data.frame()
    #get rid of data missing long or lat
    data <- data[which(!is.na(data$decimallatitude) & !is.na(data$decimallongitude)),]

    dataG[[taxa[[i]]]] <- data

    #I added a break-signal after reaching x species for testing
    #if(i == 50){break}
  }
  return(dataG)
})

# Disconnect from database
DBI::dbDisconnect(con); rm(gbif,con)

# Assign correct filename to data
for(j in 1:3){assign(x=paste0(c("amphibians", "ter_mammals", "ter_birds")[j], "_smallrange_GBIF"), value=dataG[[j]])}

Rasterize Data

data <- lapply(1:3, function(j){
  r_dataG <- list()
  dataLess2G <- character()
  group <- c("amphibians", "ter_mammals", "ter_birds")[j]
  dataG <- get(load(paste0("data/", group, "_smallrange_GBIF.rda")))
  for(i in 1:length(dataG)){
    #collect data from List
    data <- dataG[[i]]
    #data should have at least 2 data points
    if(length(unique(data$decimallatitude))+length(unique(data$decimallongitude))>=4){
      #rasterizePoints
      r_data <- rasterSp::rasterizePoints(data,long = "decimallongitude", 
                                          lat = "decimallatitude", res = 0.25); rm(data)
      names(r_data) <- names(dataG)[[i]]
      r_dataG[[i]] <- r_data; rm(r_data)
    } else{
      #  #if there are less than 2 data-points the species name is saved in another list, to have
      #  #the opportunity to check for errors in the data-set like spelling-errors etc.
      dataLess2G[[i]] <- names(dataG)[[i]]; rm(data)
    }
    #I added a break-signal after reaching x species for testing
    #if(i == 50){break}
  }
  #save the Data as rasterStack
  r_dataG <- Filter(Negate(is.null), r_dataG)
  r_dataG <- raster::stack(r_dataG)
  return(r_dataG)
})

Data summary

Get data, turn into dataframe and combine all three taxa

#convert to one data.frame
amphiSmall_GBIF <- do.call("rbind", amphibians_smallrange_GBIF)
mamaSmall_GBIF <- do.call("rbind", ter_mammals_smallrange_GBIF)
avesSmall_GBIF <- do.call("rbind", ter_birds_smallrange_GBIF)

amphiSmall_GBIF$taxa <- "amphibians"
amphiSmall_GBIF$total <- length(amphinames)
mamaSmall_GBIF$taxa <- "ter_mammals"
mamaSmall_GBIF$total <- length(mamanames)
avesSmall_GBIF$taxa <- "ter_birds"
avesSmall_GBIF$total <- length(avesnames)

alltaxa_small_GBIF <- rbind(amphiSmall_GBIF, mamaSmall_GBIF, avesSmall_GBIF)

Check number of species with GBIF data

alltaxa_small_GBIF %>% select(taxa, species, total) %>% tidyr::drop_na() %>% 
  group_by(taxa) %>% summarise(n = n_distinct(species), total=mean(total)) %>% 
  mutate(perc = round(n/total*100, digits=2)) %>% knitr::kable()

Check number of species with GBIF data and more than 10 occurrences

alltaxa_small_GBIF %>% select(taxa, species, total) %>% tidyr::drop_na() %>%
  group_by(species) %>% filter(n() > 10) %>% ungroup() %>% 
  group_by(taxa) %>% summarise(n = n_distinct(species), total=mean(total)) %>% 
  mutate(perc = round(n/total*100, digits=2)) %>% knitr::kable()

Plot species occurrence

group <- c("amphibians", "ter_mammals", "ter_birds")[1]

# Create plot
climateNiche::plotSp(r_dataG[[1:5]])

RS-eco/climateNiche documentation built on Feb. 21, 2023, 5:25 a.m.