load_pi: Load predictor importance (PI) rasters

In ebirdst: Access and Analyze eBird Status and Trends Data Products

Load predictor importance (PI) rasters

Description

The eBird Status models estimate the relative importance of each environmental predictor used in the model. These predictor importance (PI) data are converted to ranks (with a rank of 1 being the most important) relative to the full suite of environmental predictors. The ranks are summarized to a 27 km resolution raster grid for each predictor, where the cell values are the average across all models in the ensemble contributing to that cell. These data are available in raster format provided download_pis = TRUE was used when calling ebirdst_download_status(). PI estimates are available separately for both the occurrence and count sub-model and only the 30 most important predictors are distributed. Use list_available_pis() to see which predictors have PI data.

Usage

load_pi(
  species,
  predictor,
  response = c("occurrence", "count"),
  path = ebirdst_data_dir()
)

list_available_pis(species, path = ebirdst_data_dir())

Arguments

species	character; the species to load data for, given as a scientific name, common name or six-letter species code (e.g. "woothr"). The full list of valid species is in the ebirdst_runs data frame included in this package. To download the example dataset, use "yebsap-example".
predictor	character; the predictor that the PI data should be loaded for. The list of predictors that PI data are available for varies by species, use list_available_pis() to get the list for a given species.
response	character; the model (occurrence or count) that the PI data should be loaded for.
path	character; directory to download the data to. All downloaded files will be placed in a sub-directory of this directory named for the data version year, e.g. "2020" for the 2020 Status Data Products. Each species' data package will then appear in a directory named with the eBird species code. Defaults to a persistent data directory, which can be found by calling ebirdst_data_dir().

Value

A SpatRaster object with the PI ranks for the given predictor. For migrants, the estimates are weekly and the raster will have 52 layers, where the layer names are the dates (MM-DD format) of the midpoint of each week. For residents, a single year round layer is returned.

list_available_pis() returns a data frame listing the top 30 predictors for which PI rasters can be loaded. In addition to the predictor names, the mean range-wide rank (rangewide_rank) is given as well as the integer rank (rank) relative to the other 29 predictors.

Functions

• list_available_pis(): list the predictors that have PI information for this species.

Examples

## Not run: 
# download example data if hasn't already been downloaded
ebirdst_download_status("yebsap-example", download_pis = TRUE)

# identify the top predictor
top_preds <- list_available_pis("yebsap-example")
print(top_preds[1, ])

# load predictor importance raster of top predictor for occurrence
load_pi("yebsap-example", top_preds$predictor[1])

## End(Not run)

ebirdst documentation built on Nov. 16, 2023, 5:07 p.m.