Description Details Author(s) References See Also Examples

Functions to estimate the density and size of a spatially distributed animal population sampled with an array of passive detectors, such as traps, or by searching polygons or transects.

Package: | secr |

Type: | Package |

Version: | 4.4.5 |

Date: | 2021-07-12 |

License: | GNU General Public License Version 2 or later |

Spatially explicit capture–recapture is a set of methods for studying marked animals distributed in space. Data comprise the locations of detectors (traps, searched areas, etc. described in an object of class ‘traps’), and the detection histories of individually marked animals. Individual histories are stored in an object of class ‘capthist’ that includes the relevant ‘traps’ object.

Models for population density (animals per hectare) and detection are defined in secr using symbolic formula notation. Density models may include spatial or temporal trend. Possible predictors for detection probability include both pre-defined variables (t, b, etc.) corresponding to ‘time’, ‘behaviour’ and other effects), and user-defined covariates of several kinds. Habitat is distinguished from nonhabitat with an object of class ‘mask’.

Models are fitted in secr by maximizing either the full likelihood
or the likelihood conditional on the number of individuals observed
(*n*). Conditional likelihood models are limited to homogeneous
Poisson density, but allow continuous individual covariates for
detection. A model fitted with `secr.fit`

is an object
of class `secr`

. Generic methods (plot, print, summary, etc.) are
provided for each object class.

A link at the bottom of each help page takes you to the help index. Several vignettes complement the help pages:

General interest | |

secr-overview.pdf | general introduction |

secr-datainput.pdf | data formats and input functions |

secr-version4.pdf | changes in secr 4.0 |

secr-manual.pdf | consolidated help pages |

secr-tutorial.pdf | introductory tutorial |

secr-habitatmasks.pdf | buffers and habitat masks |

secr-models.pdf | linear models in secr |

secr-troubleshooting.pdf | problems with secr.fit, including speed issues |

More specialised topics | |

secr-densitysurfaces.pdf | modelling density surfaces |

secr-finitemixtures.pdf | mixture models for individual heterogeneity |

secr-markresight.pdf | mark-resight data and models |

secr-multisession.pdf | multi-session capthist objects and models |

secr-noneuclidean.pdf | non-Euclidean distances |

secr-parameterisations.pdf | alternative parameterisations sigmak, a0 |

secr-polygondetectors.pdf | using polygon and transect detector types |

secr-sound.pdf | analysing data from microphone arrays |

secr-varyingeffort.pdf | variable effort in SECR models |

The datasets `captdata`

and `ovenbird`

include examples of fitted
models. For models fitted to other datasets see secr-version4.pdf Appendix 2.

Two add-on packages extend the capability of secr and are documented separately. secrlinear enables the estimation of linear density (e.g., animals per km) for populations in linear habitats such as stream networks (secrlinear-vignette.pdf). secrdesign enables the assessment of alternative study designs by Monte Carlo simulation; scenarios may differ in detector (trap) layout, sampling intensity, and other characteristics (secrdesign-vignette.pdf).

The analyses in secr extend those available in the software Density (see www.otago.ac.nz/density/ for the most recent version of Density). Help is available on the ‘DENSITY | secr’ forum at www.phidot.org and the Google group secrgroup. Feedback on the software is also welcome, including suggestions for additional documentation or new features consistent with the overall design.

Murray Efford murray.efford@otago.ac.nz

Borchers, D. L. and Efford, M. G. (2008) Spatially explicit maximum
likelihood methods for capture–recapture studies. *Biometrics*
**64**, 377–385.

Borchers, D. L. and Fewster, R. M. (2016) Spatial capture–recapture models.
*Statistical Science* **31**, 219–232.

Efford, M. G. (2004) Density estimation in live-trapping studies.
*Oikos* **106**, 598–610.

Efford, M. G. (2011) Estimation of population density by spatially
explicit capture–recapture with area searches. *Ecology*
**92**, 2202–2207.

Efford, M. G., Borchers D. L. and Byrom, A. E. (2009) Density estimation
by spatially explicit capture-recapture: likelihood-based methods. In:
D. L. Thomson, E. G. Cooch and M. J. Conroy (eds) *Modeling
Demographic Processes in Marked Populations*. Springer, New York. Pp.
255–269.

Efford, M. G., Borchers D. L. and Mowat, G. (2013) Varying effort in
capture–recapture studies. *Methods in Ecology and Evolution*
**4**, 629–636.

Efford, M. G., Dawson, D. K. and Borchers, D. L. (2009) Population
density estimated from locations of individuals on a passive detector
array. *Ecology* **90**, 2676–2682.

Efford, M. G., Dawson, D. K. and Robbins C. S. (2004) DENSITY: software
for analysing capture-recapture data from passive detector arrays.
*Animal Biodiversity and Conservation* **27**,
217–228.

Efford, M. G. and Fewster, R. M. (2013) Estimating population
size by spatially explicit capture–recapture. *Oikos*
**122**, 918–928.

Efford, M. G. and Hunter, C. M. (2017) Spatial capture–mark–resight
estimation of animal population density. *Biometrics* **74**, 411–420.

Efford, M. G. and Mowat, G. (2014) Compensatory heterogeneity in
capture–recapture data.*Ecology* **95**, 1341–1348.

Royle, J. A., Chandler, R. B., Sollmann, R. and Gardner, B. (2014)
*Spatial capture–recapture.* Academic Press.

Royle, J. A. and Gardner, B. (2011) Hierarchical spatial
capture–recapture models for estimating density from trapping
arrays. In: A.F. O'Connell, J.D. Nichols and K.U. Karanth (eds)
*Camera Traps in Animal Ecology: Methods and Analyses*. Springer,
Tokyo. Pp. 163–190.

`read.capthist`

,
`secr.fit`

,
`traps`

,
`capthist`

,
`mask`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | ```
## Not run:
## generate some data & plot
detectors <- make.grid (nx = 10, ny = 10, spacing = 20,
detector = "multi")
plot(detectors, label = TRUE, border = 0, gridspace = 20)
detections <- sim.capthist (detectors, noccasions = 5,
popn = list(D = 5, buffer = 100),
detectpar = list(g0 = 0.2, sigma = 25))
session(detections) <- "Simulated data"
plot(detections, border = 20, tracks = TRUE, varycol = TRUE)
## generate habitat mask
mask <- make.mask (detectors, buffer = 100, nx = 48)
## fit model and display results
secr.model <- secr.fit (detections, model = g0~b, mask = mask)
secr.model
## End(Not run)
``` |

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