ct_independence: Evaluate independent detections

In ct: Camera Trap Data Management and Multi-Method Ecological Analysis

Evaluate independent detections

Description

Filters camera trap data to ensure temporal independence between detections, removing consecutive entry of the same species at the same location within a specified time window.

Usage

ct_independence(
  data = NULL,
  species_column,
  site_column,
  datetime,
  format,
  threshold = 30 * 60
)

Arguments

data	A data.frame, tbl_df, or tbl containing the event data. This should include a column with datetime values. If NULL, the function will use the datetime argument instead of the data argument.
species_column	An optional column name specifying the species grouping. If provided, independence will be assessed separately within each species group.
site_column	An optional column name specifying the site/camera grouping. If provided, independence will be assessed separately within each site group.
datetime	A character string specifying the name of the column in data that contains the datetime values. This argument is required if data is provided.
format	A character string defining the format used to parse the datetime values in the datetime column.
threshold	A numeric value representing the time difference threshold (in seconds) to determine whether events are independent. Events are considered independent if the time difference between them is greater than or equal to this threshold. The default is 30 minutes (1800 seconds).

Details

Following Ridout & Linkie (2009), consecutive photos of the same species at the same location within 30 minutes are considered non-independent and removed.

The approach mirrors the methodology applied by Linkie & Ridout (2011) for Sumatran tiger-prey interactions study and Ahmad et al. (2024) to calculate activity levels where such filtering is essential for:

• Avoiding autocorrelation in activity pattern data

• Ensuring each record represents an independent observation

• Creating a random sample from the underlying activity distribution

The filtered data can then be used to estimate probability density functions of daily activity patterns, assuming animals are equally detectable during their active periods.

Value

• If data is provided and only is TRUE, a tibble of events identified as independent.

• If data is provided and only is FALSE, a tibble of the original data with additional columns indicating the independent status and deltatime differences (in second).

• If data is not provided, a tibble of the deltatime values with independent status.

References

Ridout, M.S., & Linkie, M. (2009). Estimating overlap of daily activity patterns from camera trap data. Journal of Agricultural, Biological, and Environmental Statistics, 14(3), 322-337. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1198/jabes.2009.08038")}

Linkie, M., & Ridout, M.S. (2011). Assessing tiger-prey interactions in Sumatran rainforests. Journal of Zoology, 284(3), 224-229.\Sexpr[results=rd]{tools:::Rd_expr_doi("10.1111/j.1469-7998.2011.00801.x")}

Ahmad, F., Mori, T., Rehan, M., Bosso, L., & Kabir, M. (2024). Applying a Random Encounter Model to Estimate the Asiatic Black Bear (Ursus thibetanus) Density from Camera Traps in the Hindu Raj Mountains, Pakistan. Biology, 13(5), 341. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.3390/biology13050341")}

Examples

library(dplyr)
data(penessoulou)

# Load example dataset
cam_data <- penessoulou %>%
  dplyr::filter(project == "Last")

# Independence without considering species
indep1 <- cam_data %>%
  ct_independence(data = ., datetime = datetimes, format = "%Y-%m-%d %H:%M:%S")

sprintf("Independent observations: %s", nrow(indep1))

# Independence considering species
indep2 <- cam_data %>%
  ct_independence(data = ., datetime = datetimes,
                  format = "%Y-%m-%d %H:%M:%S",
                  species_column = "species")

sprintf("Independent observations: %s", nrow(indep2))

# Use a standalone vector of datetime values
dtime <- cam_data$datetimes
ct_independence(datetime = dtime, format = "%Y-%m-%d %H:%M:%S")

ct documentation built on Oct. 22, 2026, 1:07 a.m.