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Estimate abundance from Space-To-Event (STE) Data

Source: R/ct_fit_ste.R
ct_fit_ste.Rd

Estimate abundance from camera trap data using the Space-To-Event (STE) model.

Usage

ct_fit_ste(
  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. 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. doi:10.1002/ecs2.2331

See also

ct_fit_ise(), ct_fit_tte()

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_ste(data,
       deployment_data,
       sampling_frequency = 3600,
       sampling_length = 10,
       study_area = 1e6)
#> 
#> ── Space-To-Event (STE) Estimation ─────────────────────────────────────────────
#> ℹ Running data checks
#> [37ms]
#> 
#> ℹ Building sampling occasions...
#> ℹ Building encounter history...
#> ℹ Running data checks
#> ✔ Running data checks [39ms]
#> 
#> ℹ Building effort for each camera
#> ✔ Building effort for each camera [33ms]
#> 
#> ℹ Calculating censors
#> ✔ Calculating censors [21ms]
#> 
#> ℹ Calculating STE at each occasion
#> ✔ Calculating STE at each occasion [116ms]
#> 
#> ℹ Fitting model...
#> ✔ Estimation complete!
#> # A tibble: 1 × 4
#>       N    SE   LCI   UCI
#>   <dbl> <dbl> <dbl> <dbl>
#> 1  5.97  4.22  1.71  20.8