Mathematical Biology Seminar
Friday, April 6, 2012, 12:00pm, 119 Physics
Leah Johnson (U Chicago)
Inference for Mechanistic Models in Ecology: From Individuals to Populations
Abstract:- Mechanistic mathematical models are important tools for understanding
the processes that shape ecological systems. Models have been used to
describe life cycles of individuals, population dynamics, behavior,
and more. However, in order for these models to reach their full
potential as both tools for understanding and for prediction we must
be able to link modeled quantities to data and infer model parameters.
However, general methods of parameter inference for many of these
models, including Individual Based Models (IBMs) or their component
models, are not available. In addition, some models include components
that are unmeasurable or poorly known, which can impact parameter
inference and thus prediction. Here I discuss two examples of
ecological models of these types. First is a bioenergetic model of
individual growth and reproduction in a dynamic environment. This
example highlights how input mis-specification can affect inference,
and the consequences for prediction for both individuals and
populations. The second example uses an example of an IBM developed to
describe the spread of Chytridiomycosis in a population of frogs. This
case study shows how one can perform inference for IBMs that exhibit
certain characteristics with a traditional likelihood-based approach. [video]
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