eBird Status models are evaluated against a test set of eBird data not used
during model training and a suite of predictive performance metrics (PPMs)
are calculated. The PPMs for each base model are summarized to a 27 km
resolution raster grid, 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_ppms = TRUE was used when calling
ebirdst_download_status().
load_ppm(
species,
ppm = c("binary_f1", "binary_pr_auc", "occ_bernoulli_dev", "count_spearman",
"log_count_pearson", "abd_poisson_dev", "abd_spearman", "log_abd_pearson"),
path = ebirdst_data_dir()
)A SpatRaster object with the PPM data. For
migrants, rasters are weekly with 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.
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".
character; the name of a single metric to load data for. See Details for definitions of each metric.
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().
Eight predictive performance metrics are provided:
binary_f1: F1-score comparing the model predictions converted to binary
with the observed detection/non-detection for the test checklists.
binary_pr_auc: the area on the precision-recall curve generated by
comparing the model predictions converted to binary with the observed
detection/non-detection for the test checklists.
occ_bernoulli_dev: Bernoulli deviance comparing the predicted occurrence
with the observed detection/non-detection for the test checklists.
count_spearman: Spearman's rank correlation coefficient comparing the
predicted count with the observed count for the subset of test checklists
on which the species was detected.
log_count_pearson: Pearson correlation coefficient comparing the
logarithm of the predicted count with the logarithm of the observed count
for the subset of test checklists on which the species was detected.
abd_poisson_dev: Poisson deviance comparing the predicted relative
abundance with the observed count for the full set of test checklists.
abd_spearman: Spearman's rank correlation coefficient comparing the
predicted relative abundance with the observed count for the full set of
test checklists.
log_abd_pearson: Pearson correlation coefficient comparing the logarithm
of the predicted relative abundance with the logarithm of the observed
count for the full set of test checklists.
if (FALSE) {
# download example data if hasn't already been downloaded
ebirdst_download_status("yebsap-example", download_ppms = TRUE)
# load area under the precision-recall curve PPM raster
load_ppm("yebsap-example", ppm = "binary_pr_auc")
}
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