Tutorials

This vignette provides a series of additional tutorials to help you with using wildRtrax.

Webinars

wildRtrax 1.1.0 Public Webinar

In this webinar, Alex MacPhail, Marcus Becker and Elly Knight, take you on a tour of the main components of the package, including authenticating into WildTrax directly in R, downloading data reports as data frames, and an introduction to various functions that aid in downstream analyses of both ARU and remote camera data.

Acoustic data

Detect missing species using BirdNET

BirdNET is an avian multi-species classifier. WildTrax utilizes the BirdNET API to automatically detect species in recordings uploaded to projects once the task is Transcribed. The results can then be retrieved using wt_download_report():

library(wildRtrax)

Sys.setenv(WT_USERNAME = 'guest', WT_PASSWORD = 'Apple123')
wt_auth()
data <- wt_download_report(620,'ARU',c('birdnet','main'),F)

When downloading multiple reports, the data object then becomes a list of tibbles. The results from the main report can then be anti-joined against the birdnet report to determine if any species were detected than a human did not find. It’s recommended to use high threshold values from BirdNET and species that should occur or be allowed in the project.

birdnet_report <- data[[1]] |>
  dplyr::filter(is_species_allowed_in_project == 't',
         confidence > 50) |>
  tibble::add_column(observer = 'BirdNET') |>
  dplyr::select(location, recording_date_time, observer, species_code) |>
  dplyr::distinct()

main_report <- data[[2]] |>
  wt_tidy_species(remove = c("mammal", "amphibian", "abiotic", "insect"), zerofill = F) |>
  dplyr::select(location, recording_date_time, observer, species_code) |>
  dplyr::distinct()

merged_data <- dplyr::bind_rows(birdnet_report, main_report) |>
  dplyr::arrange(location, recording_date_time) |>
  dplyr::group_by(location, recording_date_time, observer, species_code) |>
  dplyr::mutate(presence = dplyr::row_number()) |>
  dplyr::ungroup() |>
  tidyr::pivot_wider(names_from = species_code, values_from = presence, values_fill = 0) |>
  tidyr::pivot_longer(cols = -c(location, recording_date_time, observer), names_to = "species_code", values_to = "presence")

missing_detections <- merged_data |>
  dplyr::filter(observer == "BirdNET" & presence == 1) |>
  dplyr::select(location, recording_date_time, species_code) |>
  dplyr::anti_join(
    merged_data |>
      dplyr::filter(observer != "BirdNET") |>
      dplyr::select(location, recording_date_time, species_code, presence),
    by = c("location", "recording_date_time", "species_code")
  )

nrow(missing_detections)
#> [1] 0

Therefore, BirdNET did not detect any additional species.