load_trends: Load eBird Trends estimates for a set of species

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

Load eBird Trends estimates for a set of species

Description

Load the relative abundance trend estimates for a single species or a set of species. Trends are estimated on a 27 km by 27 km grid for a single season per species (breeding, non-breeding, or resident). Note that data must be download using ebirdst_download_trends() prior to loading it using this function.

Usage

load_trends(species, fold_estimates = FALSE, path = ebirdst_data_dir())

Arguments

species	character; one or more species given as scientific names, common names or six-letter species codes (e.g. "woothr"). The full list of valid species can be viewed in the ebirdst_runs data frame included in this package; species with trends estimates are indicated by the has_trends column. To access the example dataset, use "yebsap-example".
fold_estimates	logical; by default, the trends summarized across the 100-fold ensemble are returned; however, by setting fold_estimates = TRUE the individual fold-level estimates are returned.
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().

Details

The trends in relative abundance are estimated using a double machine learning model. To quantify uncertainty, an ensemble of 100 estimates is made at each location, each based on a random subsample of eBird data. The estimated trend is the median across the ensemble, and the 80% confidence intervals are the lower 10th and upper 90th percentiles across the ensemble. To access estimates from the individual folds making up the ensemble use fold_estimates = TRUE. These fold-level estimates can be used to quantify uncertainty, for example, when calculating the trend for a given region. For further details on the methodology used to estimate trends consult Fink et al. 2023.

Value

A data frame containing the trends estimates for a set of species. The following columns are included:

• species_code: the alphanumeric eBird species code uniquely identifying the species.

• season: season that the trend was estimated for: breeding, nonbreeding, or resident.

• start_year/end_year: the start and end years of the trend time period.

• start_date/end_date: the start and end dates (MM-DD format) of the season for which the trend was estimated.

• srd_id: unique integer identifier for the grid cell.

• longitude/latitude: longitude and latitude of the grid cell center.

• abd: relative abundance estimate for the middle of the trend time period (e.g. 2014 for a 2007-2021 trend).

• abd_ppy: the median estimated percent per year change in relative abundance.

• abd_ppy_lower/abd_ppy_upper: the 80% confidence interval for the estimated percent per year change in relative abundance.

• abd_ppy_nonzero: a logical (TRUE/FALSE) value indicating if the 80% confidence limits overlap zero (FALSE) or don't overlap zero (TRUE)

• abd_trend: the median estimated cumulative change in relative abundance over the trend time period.

• abd_trend_lower/abd_trend_upper: the 80% confidence interval for the estimated cumulative change in relative abundance over the trend time period.

If fold_estimates = TRUE, a data frame of fold-level trend estimates is returned with the following columns:

• species_code: the alphanumeric eBird species code uniquely identifying the species.

• season: season that the trend was estimated for: breeding, nonbreeding, or resident.

• srd_id: unique integer identifier for the grid cell.

• abd: relative abundance estimate for the middle of the trend time period (e.g. 2014 for a 2007-2021 trend).

• abd_ppy: the estimated percent per year change in relative abundance.

References

Fink, D., Johnston, A., Strimas-Mackey, M., Auer, T., Hochachka, W. M., Ligocki, S., Oldham Jaromczyk, L., Robinson, O., Wood, C., Kelling, S., & Rodewald, A. D. (2023). A Double machine learning trend model for citizen science data. Methods in Ecology and Evolution, 00, 1–14. https://doi.org/10.1111/2041-210X.14186

Examples

## Not run: 
# download example trends data if it hasn't already been downloaded
ebirdst_download_trends("yebsap-example")

# load trends
trends <- load_trends("yebsap-example")

# load fold-level estimates
trends_folds <- load_trends("yebsap-example", fold_estimates = TRUE)

## End(Not run)

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