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Evaluate independent detections

mm_independence.Rd

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

mm_independence(
  data = NULL,
  species_column,
  site_column,
  datetime,
  format,
  threshold = 30 * 60,
  only = FALSE
)

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).

only

A logical value indicating whether to return only the rows of data that are identified as independent events. If TRUE, only independent events are returned. If FALSE, the entire data frame is returned with an additional column indicating the independence status. The default is TRUE.

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.

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.

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. https://doi.org/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. https://doi.org/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. https://doi.org/10.3390/biology13050341

Examples


library(dplyr)

# Load example dataset
cam_data <- read.csv(system.file("penessoulou_season1.csv", package = "maimer"))

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

sprintf("Independent observations: %s", nrow(indep1))
#> [1] "Independent observations: 177"

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

sprintf("Independent observations: %s", nrow(indep2))
#> [1] "Independent observations: 183"

# Use a standalone vector of datetime values
dtime <- cam_data$datetimes
mm_independence(datetime = dtime, format = "%Y-%m-%d %H:%M:%S", only = TRUE)
#> # A tibble: 177 × 1
#>    datetime           
#>    <dttm>             
#>  1 2019-01-20 15:24:41
#>  2 2019-01-21 15:23:38
#>  3 2019-01-23 15:35:20
#>  4 2019-01-24 06:01:07
#>  5 2023-09-20 16:15:28
#>  6 2023-09-21 13:32:45
#>  7 2024-03-02 22:32:10
#>  8 2024-03-10 10:21:56
#>  9 2024-03-10 15:04:38
#> 10 2024-03-10 17:28:11
#> # ℹ 167 more rows