ct_fit_tte: Estimate abundance from Time-To-Event (TTE) Data

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

Estimate abundance from Time-To-Event (TTE) Data

Description

Estimate abundance from camera trap data using the Time-To-Event (TTE) model.

Usage

ct_fit_tte(
  data,
  deployment_data,
  viewshed_transit_time,
  periods_per_occasion,
  time_between_occasions,
  study_area,
  study_start = NULL,
  study_end = NULL,
  quiet = FALSE
)

Arguments

data	A tibble of camera trap detections. Must contain columns cam, datetime, and count.
deployment_data	A tibble of camera deployments. Must contain columns cam, start, end, and area.
viewshed_transit_time	Numeric. This is equal to the mean amount of time (in seconds) required for an animal to cross the average viewshed of a camera. It can be calculated in different ways depending on available information. For an animal with a movement speed of 30 m/hr passing through camera viewsheds of 300 m^2, 400 m^2, and 380 m^2, the sampling period can be approximated as: \frac{\sqrt{\frac{1}{n}\sum_{i=1}^{n} A_i}}{30/3600} where A_i represents the camera viewshed areas (in m^2) and n is the number of cameras. The denominator is the animal speed converted from meters/hour to meters/second.
periods_per_occasion	Numeric. Number of TTE sampling periods per sampling occasion.
time_between_occasions	Numeric. Length of time between sampling occasions (in seconds), allowing animals to re-randomize.
study_area	Numeric. The size of the total study area in the same units as the camera viewshed area.
study_start	POSIXct. The start of the study. Defaults to the minimum start time in deployment_data.
study_end	POSIXct. The end of the study. Defaults to the maximum end time in deployment_data.
quiet	Logical. Suppress status messages? Defaults to FALSE.

Value

A data.frame with the estimated abundance (N), its standard error (SE), and confidence intervals.

References

Moeller, A. K. and P. M. Lukacs. 2021. spaceNtime: an R package for estimating abundance of unmarked animals using camera-trap photographs. Mammalian Biology. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1007/s42991-021-00181-8")}

Moeller, A. K., P. M. Lukacs, and J. Horne. 2018. Three novel methods to estimate abundance of unmarked animals using remote cameras. Ecosphere 9(8): e02331. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1002/ecs2.2331")}

See Also

ct_fit_ste(), ct_fit_ise()

Examples

data <- dplyr::tibble(
  cam = c(1, 1, 2, 2, 2),
  datetime = as.POSIXct(
    c(
      "2026-01-02 12:00:00",
      "2026-01-03 13:12:00",
      "2026-01-02 12:00:00",
      "2026-01-02 14:00:00",
      "2026-01-03 16:53:42"
    ),
    tz = "Africa/Lagos"
  ),
  count = c(1, 0, 2, 1, 2)
)
deployment_data <- dplyr::tibble(
  cam = c(1, 2, 2, 2),
  start = as.POSIXct(
    c(
      "2025-12-01 15:00:00",
      "2025-12-08 00:00:00",
      "2026-01-01 00:00:00",
      "2026-01-02 00:00:00"
    ),
    tz = "Africa/Lagos"
  ),
  end = as.POSIXct(
    c(
      "2026-01-05 00:00:00",
      "2025-12-19 03:30:00",
      "2026-01-01 05:00:00",
      "2026-01-05 00:00:00"
    ),
    tz = "Africa/Lagos"
  ),
  area = c(300, 200, 200, 450)
)
ct_fit_tte(data,
       deployment_data,
       viewshed_transit_time = sqrt(mean(deployment_data$area))/(30/3600),
       periods_per_occasion = 24,
       time_between_occasions = 2 * 3600,
       study_area = 1e6)

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