Each of the eBird Status and Trends raster products is packaged as a GeoTIFF file representing predictions on a regular grid. The core products are occurrence, count, relative abundance, and percent of population. This function loads one of the available data products into R as a RasterStack object.

load_raster(
path,
product = c("abundance", "count", "occurrence", "percent-population"),
period = c("weekly", "seasonal", "full-year"),
metric = NULL,
resolution = c("hr", "mr", "lr")
)

## Arguments

path

character; directory that the Status and Trends data for a given species was downloaded to. This path is returned by ebirdst_download() or get_species_path().

product

character; Status and Trends product to load: occurrence, count, relative abundance, or percent of population. See Details for a detailed explanation of each of these products.

period

character; temporal period of the estimation. The Status and Trends models make predictions for each week of the year; however, as a convenience, data are also provided summarized at the seasonal or annual ("full-year") level.

metric

character; by default, the weekly products provide estimates of the median value (metric = "median") and the summarized products are the cell-wise mean across the weeks within the season (metric = "mean"). However, additional variants exist for some of the products. For the weekly relative abundance, confidence intervals are provided: specify metric = "lower" to get the 10th quantile or metric = "upper" to get the 90th quantile. For the seasonal and annual products, the cell-wise maximum values across weeks can be obtained with metric = "max".

resolution

character; the resolution of the raster data to load. The default is to load the native ~3 km resolution ("hr"); however, for some applications 9 km ("mr") or 27 km ("lr") data may be suitable.

## Value

For the weekly cubes, a RasterStack with 52 layers for the given product, labeled by week. Seasonal cubes will have up to four layers labeled according to the seasons. The full-year products will have a single layer.

## Details

The core Status and Trends data products provide weekly estimates across a regular spatial grid. They are packaged as rasters with 52 layers, each corresponding to estimates for a week of the year, and we refer to them as "cubes" (e.g. the "relative abundance cube"). All estimates are the median expected value for a standard 1km, 1 hour eBird Traveling Count by an expert eBird observer at the optimal time of day and for optimal weather conditions to observe the given species. These products are:

• occurrence: the expected probability (0-1) of occurrence a species.

• count: the expected count of a species, conditional on its occurrence at the given location.

• abundance: the expected relative abundance of a species, computed as the product of the probability of occurrence and the count conditional on occurrence.

• percent-population: the percent of the total relative abundance within each cell. This is a derived product calculated by dividing each cell value in the relative abundance raster with the total abundance summed across all cells.

In addition to these weekly data cubes, this function provides access to data summarized over different periods. Seasonal cubes are produced by taking the cell-wise mean or max across the weeks within each season. The boundary dates for each season are species specific and are available in ebirdst_runs, and if a season failed review no associated layer will be included in the cube. In addition, full-year summaries provide the mean or max across all weeks of the year that fall within a season that passed review. Note that this is not necessarily all 52 weeks of the year. For example, if the estimates for the non-breeding season failed expert review for a given species, the full-year summary for that species will not include the weeks that would fall within the non-breeding season.

## Examples

if (FALSE) {
path <- get_species_path("example_data")

# weekly relative abundance
# note that only low resolution (lr) data are available for the example data
abd_weekly <- load_raster(path, "abundance", resolution = "lr")
# identify the weeks for each layer
parse_raster_dates(abd_weekly)

# max seasonal abundance
period = "seasonal", metric = "max",
resolution = "lr")
# available seasons in stack
names(abd_seasonal)
# subset to just breeding season abundance
abd_seasonal[["breeding"]]
}