abapToOccuR: ABAP to occuR

In AfricaBirdData/ABAP: Access to African Bird Atlas Project data from R

ABAP to occuR

Description

This function transforms a raw ABAP data frame (returned by getAbapData) into an list which can be used to fit single-species occupancy models using the package occuR. This package can fit non-linear effects, including spatial, temporal and spatio-temporal effects using splines.

Usage

abapToOccuR(abap_data, occasion, pentads = NULL, proj_coords = TRUE)

Arguments

abap_data	ABAP data downloaded using getAbapData.
occasion	A character string indicating the variable in abap_data that informs about the season (or occasion, as referred to in occuR). This must always be supplied, although it is really only important in the case of multi-season data. It is typically "year" but could be something else.
pentads	Optional, An sf object returned by getRegionPentads. Defaults to NULL.
proj_coords	logical value indicating whether pentad coordinates are projected (TRUE) or kept in decimal degree format (FALSE). Defaults to TRUE. See details below for coordinate reference system used during transformation.

Details

The occuR package can fit spatial effects, for which we need the spatial location of our sites. Within the context of ABAP, these locations are the centroid of each sampling pentad. In order to provide these spatial data to this function, simply use getRegionPentads and provide the same inputs for .region_type and .region that are specified in corresponding getAbapData call. If proj_coords is set to TRUE then the coordinates will be transformed using the African Albers Equal Area coordinate system (see here for details). This projection is best suited for land masses extending in an east-to-west orientation at mid-latitudes making it suitable for projecting pentads in Southern Africa.

In addition to reformatting the detection/non-detection ABAP data for use in occuR occupancy models, this function also extracts two survey-level covariates and adds them to the output list: hours and jday. The hours variable is the total number of hours spent atlassing which is recorded on the pentad card and jday is the Julian day corresponding to the first day of atlassing for that card.

Value

A list containing data necessary for model fitting in occuR. List elements are: visit_data, a data frame containing information about individual visits; site_data, a data frame containing information about sites and seasons. If pentads are given then the coordinates of the centroid of the pentads will be included in site_data.

Examples

## Not run: 
library(ABAP)
library(rgee)
library(ABDtools)
library(dplyr)

## Download single-season ABAP data
abap_data <- getAbapData(.spp_code = 212,
                         .region_type = "province",
                         .region = "Eastern Cape",
                         .years = 2012)

# Download ABAP site spatial data
abap_pentads <- getRegionPentads(.region_type = "province",
                                 .region = "Eastern Cape")

# We will use years as occupancy reference seasons
abap_data$year <- format(abap_data$StartDate, "%Y")

## Return list for spatial occupancy model
occur_data <- abapToOccuR(abap_data, occasion = "year", abap_pentads)
str(occur_data)

## Return list for spatial occupancy model (without coordinate projection)
occur_data <- abapToOccuR(abap_data, "year", abap_pentads, proj_coords = FALSE)
str(occur_data)

## List for non-spatial occupancy model
occur_data <- abapToOccuR(abap_data, "year")
str(occur_data)

## Transform multi-season ABAP data into occuR data is just as easy
abap_data <- getAbapData(.spp_code = 212,
                         .region_type = "province",
                         .region = "Eastern Cape",
                         .years = 2012:2015)

# We will use years as occupancy reference seasons
abap_data$year <- format(abap_data$StartDate, "%Y")

## Return list for spatial occupancy model
occur_data <- abapToOccuR(abap_data, occasion = "year", abap_pentads)
str(occur_data)

### ADD SITE-YEAR VARIABLES ###

## Start up GEE
ee_check()
ee_Initialize(drive = TRUE)

## Create assetId for pentads of interest
assetId <- file.path(ee_get_assethome(), 'EC_pentads')

## Upload to pentads to GEE (only run this once per asset)
uploadFeaturesToEE(pentads = abap_pentads,
                   asset_id = assetId,
                   load = FALSE)

## Load the remote asset into R session
pentads <- ee$FeatureCollection(assetId)

## Create a multi-band image with mean NDVI for each year
ndvi_multiband <- EEcollectionToMultiband(collection = "MODIS/006/MOD13A2",
                                          dates = c("2012-01-01", "2015-12-31"),
                                          band = "NDVI",
                                          group_type = "year",
                                          groups = 2012:2015,
                                          reducer = "mean",
                                          unmask = FALSE)

## Find mean and standard deviation of NDVI value for each pentad and year from the multi-band image
ndvi_mean <- addVarEEimage(pentads, ndvi_multiband, "mean")
ndvi_sd <- addVarEEimage(pentads, ndvi_multiband, "stdDev")

## Format the data to include the pentad column and GEE values for each year
ndvi_mean <- ndvi_mean %>%
    select(pentad, paste0("NDVI_", as.character(2012:2015)))

ndvi_sd <- ndvi_sd %>%
    select(pentad, paste0("NDVI_", as.character(2012:2015)))

## occuR data have the structure of regular data frames (they are data tables, but
## behave very similarly), so there are many ways we can transfer covariate values from
## other data frame-like objects. Here, we will transfer the value of these variables
## with dplyr::left_join(), but first we need to give covariates a long format with tidyr
ndvi_mean_long <- ndvi_mean %>%
    sf::st_drop_geometry() %>%
    tidyr::pivot_longer(-pentad, names_to = "year", values_to = "NDVI_mean") %>%
    mutate(year = gsub("NDVI_", "", year))

ndvi_sd_long <- ndvi_sd %>%
    sf::st_drop_geometry() %>%
    tidyr::pivot_longer(-pentad, names_to = "year", values_to = "NDVI_sd") %>%
    mutate(year = gsub("NDVI_", "", year))

# Transfer variables via join
occur_data_ee <- occur_data$site %>%
    dplyr::left_join(ndvi_mean_long, by = c("pentad", "year")) %>%
    dplyr::left_join(ndvi_sd_long, by = c("pentad", "year"))

summary(occur_data_ee)


## End(Not run)

AfricaBirdData/ABAP documentation built on Aug. 4, 2024, 4:41 p.m.