ct_fit_ise: Estimate abundance from Instantaneous Sampling (ISE) Data

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

Estimate abundance from Instantaneous Sampling (ISE) Data

Description

Estimate abundance from camera trap data using Instantaneous Sampling / point counts.

Usage

ct_fit_ise(
  data,
  deployment_data,
  sampling_frequency,
  sampling_length,
  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.
sampling_frequency	Numeric. The number of seconds between the start of each sampling occasion.
sampling_length	Numeric. The number of seconds to sample at each sampling occasion.
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_tte(), ct_fit_ste()

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_ise(data, deployment_data,
       sampling_frequency = 3600,
       sampling_length = 10,
       study_area = 1e6)

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