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We* develop a unified framework for jointly defining population dynamics models and
measurements taken on a population. The framework is a state-space model where the
population processes are modelled by the state process and measurements are modelled
by the observation process. In many cases, the expected value for the state process
can be represented as a generalization of the standard population projection matrix:
each sub-process within the state process may be modelled by a separate matrix and
the product of these matrices is a generalized Leslie matrix. By selecting
appropriate matrices and their ordering, a wide range of models may be specified.
The method is fully flexible for allowing stochastic variation in the processes.
Process parameters may themselves be modelled as functions of covariates. The
structure accommodates effects such as density dependence, competition and
predator-prey relationships, and metapopulations are readily modelled. For
inference, we show how likelihood functions can be built that reflect both
demographic stochasticity (which appears in the state process) and stochastic
errors in the observations. Parameter estimation and estimation of state process
variables can be conducted using monte Carlo procedures. An application to the
British grey seal metapopulation is presented.
* The speaker with Steve Buckland, Len Thomas, and Nils Kösters at Centre for Research into ecological
and Environmental Modelling, University of St. Andrews, Scotland.
Key words and phrases: Generalized Leslie matrix; Open population models; Population dynamics models;
recursive filtering algorithms; State-space models
All interested faculty, staff, and graduate students are invited to attend.
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