The quantitative study of marked individuals in ecology, evolution ...

EURING 2003 Radolfzell
strated with data from a hen clam pollution experiment and compared with results
from previous analysis by Anderson, Burnham, and White, using MARK. For noninformative
priors, the Bayesian and frequentist statistical results are comparable. For
informative priors, however, results may differ. Bayesian inference offers a sequential
approach to analysis, based upon multiple datasets obtained from population monitoring,
providing periodic reassessments of parameters along with estimates of risk.
Such reassessments are useful for adaptive management decision-making. Bayesian
models can also be compared, analogous to frequentist models, using informationtheoretic
methods based upon AIC and DIC weights, for their relative competitiveness
at fitting population sample data, and model averaging techniques can be applied to
provide robust estimates of parameters.
10:55 AM - 11:20 AM
Evaluation of ultrastructure and random effects band recovery models for estimating
relationships between survival and harvest rates in exploited populations
Dave Otis & Gary White
The functional relationship between vital rates and harvest rates of exploited populations
is a fundamental interest of population biologists. Despite the development by
many authors of a collection of density- dependent population models and functional
representations of the relationship between annual survival and harvest rates, statistical
analysis techniques for empirical investigation of these phenomena are extremely
limited. In the case of band recovery data from harvested species, standard practice
has been to incorporate ultrastructure functions of the form Si = S0 ( 1 - b*Ki) or Si =
S0 ( 1 - Ki)b into band recovery models and use the estimated parameter b as a index
for the relative evidence for additive or compensatory harvest mortality. Satisfactory
performance of this approach has been inconsistent, and limited Monte Carlo simulations
of the statistical performance of the estimator have revealed some problematic
distributional and bias properties. Furthermore, the sensitivity of the estimator for detecting
annual survival rate and harvest rate relationships is unknown.
An alternative approach to the use of fixed effect ultrastructure models is possible if
we consider annual harvest rates and survival rates as random effects. We envision
an underlying process covariation between these 2 rates, which represents the parameter
of interest, that is randomly perturbed by a collection of additional biotic and
abiotic factors. The perturbation could be temporal, as in the case of released banded
cohorts from single population for a series of years. Alternatively, if banding is done in
multiple populations, we might consider survival and harvest as fixed effects in a given
population, and assume random spatial perturbation in the parameters.
We construct underlying models that incorporate specified additive, linear compensatory,
and nonlinear compensatory functional relationships between harvest and
natural mortality in a seasonally exploited population, and use Monte Carlo simulation
to generate annual samples of band recovery data. These datasets are then analyzed
using fixed effect ultrastructure models and random effects models. Parameter estimation
is accomplished by using customized SAS code in PROC NLMIXED. Summary
statistics of performance the alternative techniques are presented and compared,
design considerations are discussed, and recommendations are made for further development.
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