## SECR : spatially explicit capture recapture |
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I've a couple of ideas for blog posts on SECR (spatially
explicit capture-recapture), and this post sets out the basic
concepts of SECR which I will need to refer to in later posts. Capture-recapture methods (also know as mark-recapture or
capture-mark-recapture) have been used to estimate the size of
animal populations for many years: the first software package
for analysis of this kind of data, CAPTURE (Otis Estimating the number of animals in a population would be
easy if you could be sure that all animals were captured in a
trapping exercise, ie, if capture probability, A major source of heterogeneity is due to individual animal's movement patterns and trap locations. A animal with several traps in its home range will have a higher capture probability than an animal with only one trap in its home range. More generally, the probability of capture in a specific trap depends on its location relative to the movement of the animal. SECR models incorporate this spatial element. ## How SECR worksSECR assumes that: - each animal has an Activity Centre (AC);
- capture probability is a function of the distance between the trap and the animal's AC.
Most models assume that the capture probability is highest when the trap is placed exactly at the animal's AC and declines as the distance between trap and AC increases. The half-normal function (see plot below) is usually a pretty good fit, but a variety of functions are available, including annular functions, where capture probability peaks at some distance from the AC. We'll see other detection functions in a later post.
To estimate the capture parameters, Provided More sophisticated models can be built,
allowing ## Assumptions- Animals do not lose their marks and animals are correctly identified (as with all mark-recapture methods).
- Trap locations are recorded accurately, as are any covariates used.
- Each animal has an Activity Centre and probability of capture depends on the distance between the trap and the AC.
- Closure: no births, deaths, immigration or emigration during the study, and activity centres do not change.
- Detectors are randomly placed with respect to the location of activity centres.
- Detections are independent.
- No unmodelled heterogeneity: for
the simplest models, that means that
*g0*and σ are the same for all animals and density is uniform.
## ImplementationMaximum likelihood methods are implemented in program DENSITY and in the R package secr, both authored by Murray Efford. The secr package has a wider range of options than DENSITY, and it provides a range of tools for simulating data in R. Bayesian analysis can be implemented in BUGS (ie, WinBUGS, OpenBUGS or JAGS). Until recently, BUGS models could only be written for rectangular areas of habitat, and the R package SPACECAP was developed to provide a Bayesian implementation for irregular patches of habitat. However, simple methods are now available to deal with irregular habitat patches in BUGS.
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Updated 1 Sept 2013 by Mike Meredith |