secr-package              package:secr              R Documentation

_S_p_a_t_i_a_l_l_y _E_x_p_l_i_c_i_t _C_a_p_t_u_r_e-_R_e_c_a_p_t_u_r_e _M_o_d_e_l_s

_D_e_s_c_r_i_p_t_i_o_n:

     Analyse data from a spatially distributed animal population
     sampled with an array of passive detectors, such as traps.

_D_e_t_a_i_l_s:


       Package:  secr
       Type:     Package
       Version:  1.3.0
       Date:     2009-10-28
       License:  GNU General Public License Version 2 or later

     Warning: Version 1.3.0 should be viewed as a beta release: some
     functions may not work with all documented settings. Feedback is
     very welcome, including suggestions for additional documentation
     or new features consistent with the overall design.

     Data comprise the locations of detectors (traps) in an object of
     class 'traps' and the detection histories of individually marked
     animals in an object of class 'capthist'. Models for population
     density and detection are defined using symbolic formula notation.
     Possible predictors for detection probability include several
     pre-defined variables (t, b etc.) corresponding to 'time',
     'behaviour' and other effects. Habitat is distinguished from
     nonhabitat with an object of class 'mask'. Models are fitted by
     maximizing either the full likelihood or the likelihood
     conditional on the number of individuals (n). Conditional
     likelihood models, while limited to homogeneous Poisson density,
     allow continuous individual covariates for detection. Fitting
     creates an object of class 'secr'. Generic methods (plot, print,
     summary etc.) are available for each object class. 

     A more extensive overview can be got by typing

     RShowDoc ('secr-overview', package='secr') 

     at the R prompt after the package has been loaded.

     The analyses in 'secr' extend those available in the software
     Density (see <URL: www.otago.ac.nz/density> for the most recent
     version of Density).

_A_c_k_n_o_w_l_e_d_g_e_m_e_n_t_s:

     David Borchers made these methods possible with his work on the
     likelihood, and I'm grateful for his continuing advice. Jeff Laake
     provided encouragement and reviewed an early version. Ray
     Brownrigg got my Windows code running under Unix. Deanna Dawson
     editted some of the documentation (the cleaner bits!) and her
     support and collaboration were important throughout.

_A_u_t_h_o_r(_s):

     Murray Efford murray.efford@otago.ac.nz

_R_e_f_e_r_e_n_c_e_s:

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

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

     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., 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.

_S_e_e _A_l_s_o:

     'secr.fit',  'traps',  'capthist',  'mask'

_E_x_a_m_p_l_e_s:

     ## 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)

