|
|
Workshop 5 Description:
Workshop 5: Uncertainty in Ecological Analysis
The field of ecology is becoming increasingly aware of the importance
of accurately accounting for multiple
sources of uncertainty when modeling ecological phenomena and making
forecasts. This development is
motivated in part by the desire to provide an accurate picture of
the state of knowledge of ecosystems and
to be able to better assess the quality of predictions of local
and global change. However, accounting for
various sources of uncertainty is by no means a simple task. Ecological
data are almost always observed
incompletely with large and unknown amounts of measurement error
or data uncertainty, and often the
expense of data collection prohibits collecting as much data as
might be desirable. In addition, most ecological phenomena of interest
can only be studied by combining various sources of data; aligning
these data properly presents interesting statistical challenges.
While data plays a large role in most ecological analyses, incorporating
scientific knowledge into the analyses through substantive modeling
of ecological processes is essential. Often such theoretical contributions
are based on competing scientific theories and simplifications of
reality. This results in an additional source of uncertainty termed
model or process uncertainty. Finally, substantive models must acknowledge
parameter uncertainty. For example, more realistic descriptions
of ecosystems might allow parameters to vary over space and time.
The aim of this workshop is to present a thorough investigation
and discussion of these various sources of
uncertainty that typically play a role in ecological analyses and
of the statistical techniques that enable
proper inferences and predictions to be made in light of these uncertainties.
These concepts will be illustrated
using new data sources and sophisticated modeling tools developed
for studying a diverse collection
of ecological phenomena. In addition, there will be a discussion
of strategies for reducing some of the sources
of uncertainty including improved design of monitoring networks.
This discussion will promote increased
communication between the theoretical and empirical communities
as to prioritizing data collection efforts.
One of the largest communities to use these methods for important
decision-making is state and federal
governments, and they will be involved in the workshop as well.
In summary, this workshop will provide
an opportunity for the ecological science community to interact
with the statistical and abstract-modeling
communities and will promote novel, interdisciplinary research developments
on complex models, inference,
and design in the face of various sources of uncertainty.
|
|
|