deplet {fishmethods} | R Documentation |
Variable and constant effort models for the estimation of abundance from catch-effort depletion data assuming a closed population.
deplet(catch = NULL, effort = NULL, method = c("l", "d", "ml", "hosc", "hesc", "hemqle", "sch"), nboot = 500)
catch |
the vector containing catches for each removal period (in sequential order). |
effort |
the vector containing effort associated with catch for each removal period. Rows must match those of catch. |
method |
the depletion method. Variable Effort Models: l = Leslie estimator, d = effort corrected
Delury estimator, ml = maximum likelihood estimator of Gould and Pollock (1997), hosc = sampling coverage
estimator for homogeneous model of Chao and Chang (1999), hesc = sampling coverage estimator for heterogeneous
model of Chao and Chang (1999), and hemqle = maximum quasi likelihood estimator for heterogeneous model of
Chao and Chang (1999). Constant Effort Models: sch = maximum likelihood models that test
for constant catchability.
|
nboot |
the number of bootstrap resamples for estimation of standard errors in the ml ,
hosc ,hesc , and hemqle methods |
The variable effort models include the Leslie-Davis (l
) estimator (Leslie and Davis, 1939), the effort-corrected Delury (d
) estimator (Delury,1947; Braaten, 1969),
the maximum likelihood (ml
) method of Gould and Pollock (1997), sample coverage estimator for the homogeneous model (hosc
) of Chao and Chang (1999),
sample coverage estimator for the heterogeneous model (hesc
) of Chao and Chang (1999), and the maximum quasi-likelihood estimator for the heterogeneous model (hemqle
) of Chao and Chang (1999).
The variable effort models can be applied to constant effort data by simply filling the effort
vector with 1s.
The constant effort models include only model 1 (constant catchability) and model 2 (different catchability in first period) (sch
) of Schnute (1983) which are equivalent to the generalized removal models for k=1 and k=2, respectively,
of White, Anderson, Burnham, and Otis (1982:p. 111-114). A vector of effort data is not required for the Schnute models. Note: Calculation of the standard error using the ml
method takes considerable time.
Separate output lists with the method name and extension .out
are created for each method and contain tables of various statistics associated with the method.
Gary A. Nelson, Massachusetts Division of Marine Fisheries gary.nelson@state.ma.us
Braaten, D. O. 1969. Robustness of the Delury population estimator. J. Fish. Res. Board Can. 26: 339-355.
Chao, A. and S. Chang. 1999. An estimating function approach to the inference of catch-effort models. Environ. Ecol. Stat. 6: 313-334.
Delury, D. B. 1947. On the estimation of biological populations. Biometrics 3: 145-167.
Gould, W. R. and H. H. Pollock. 1997. Catch-effort maximum likelihood estimation of important population parameters. Can. J. Fish. Aquat. Sci 54: 890-897.
Leslie, P. H. and D. H.S. Davis. 1939. An attempt to determine the absolute number of rats on a given area. J. Anim. Ecol. 9: 94-113.
Schnute, J. 1983. A new approach to estimating populations by the removal method. Can. J. Fish. Aquat. Sci. 40: 2153-2169.
White, G. C., D. R. Anderson, K. P. Burnham, and D. L. Otis. 1982. Capture-recapture and Removal Methods for Sampling Closed Populations. Los Alamos National Laboratory LA-8787-NERP. 235 p.
data(darter) deplet(catch=darter$catch,effort=darter$effort,method="hosc") hosc.out