RichSamp: Estimate richness and sampling completeness

In jivelasquezt/WhereNext-Pkg: Biological Survey Recommending System Based on General Dissimilarity Modeling

Description

This code will estimate richness using non-parametric estimators for sampled cells, defined by using the raster provided in the arguments. Completeness will be estimated based on the proportion of estimated species that are actually recorded.

Usage

RichSamp(occ.table, in.raster, field.names)

Arguments

occ.table	data frame with species occurrence and sampling information. Must contain coordinates x, coordinates y, species, a sampling event id (e.g. date) and count field.
in.raster	a raster in the same projection of coordinates provided in the occurrence table and with the desired cell size for analyses.
field.names	a vector of field names in the occurrence table in the following order: coordinates x, coordinates y, event id, species name, count and cell field.

Value

A data frame of observed species richness(Species), number of surveys (n), estimated species richness (chao, jack1, jack2, boot) and completeness of sampling (C_chao, C_jack1, C_jack2, C_boot) for each sampled cell. XY coordinates of each cell and standard errors (suffix .se) are also provided.

Examples

## Not run: 
#To compute cell stats for colombian bats
library(lubridate)
library(raster)
library(rgbif)
library(WhereNext)

#Get occurrence data
gbif.key <- name_backbone(name = "Chiroptera")
gbif.res <- DownloadGBIF(gbif.key$orderKey, "your username", "your email", "your password", "CO") #Enter your GBIF credentials here

#Get environmental data
col <- getData("GADM", country="COL",level=0)
env.vars <- getData("worldclim", var="bio", res=5)
env.vars <- crop(env.vars, col)
env.vars <- mask(env.vars, col)
env.vars <- Normalize(env.vars) #Normalize environmental variables
env.vars <- RemCorrLayers(env.vars, 0.8) #Remove variables correlated more than r=0.8.

#Do minimal occ.table cleaning
occ.table <- gbif.res$occ.table
occ.table$eventDate <- as_date(occ.table$eventDate)
occ.table$individualCount <- 1 #Data is presence only
occ.table.clean <- subset(occ.table, !is.na(eventDate) & taxonRank=="SPECIES")
row.names(occ.table.clean) <- 1:nrow(occ.table.clean)
occ.table.clean <- CoordinateCleaner::clean_coordinates(occ.table.clean,
                                                        lon="decimalLongitude",
                                                        lat="decimalLatitude",
                                                        species="species",
                                                        countries = "countryCode",
                                                        value="clean",
                                                        tests=c("capitals","centroids", "equal", "gbif",
                                                                "institutions", "outliers", "seas","zeros"))

#Estimate cell sampling stats & filter occurrence data
occ.table.clean$cell <- cellFromXY(env.vars, occ.table.clean[, c("decimalLongitude","decimalLatitude")])
cell.stats <- RichSamp(occ.table.clean, env.vars, c("decimalLongitude","decimalLatitude","eventDate","species","individualCount","cell"))
selected.cells <- cell.stats$cell[which(cell.stats$n>3&cell.stats$Species>5)] #Consider places with at least 3 sampling events and 5 species recorded as well sampled
occ.table.sel <- occ.table.clean[which(occ.table.clean$cell %in% selected.cells), ] #Use only ocurrences of well sampled cells

## End(Not run)

jivelasquezt/WhereNext-Pkg documentation built on Oct. 11, 2021, 9:46 p.m.