endpoints are those decision-making attributes of the ecosystem that relate
to the goals. Thus, for a model to be used successfully, it must relate to the
environmental goals and address one or more specific endpoints; otherwise,
the model is simply irrelevant to the decision-making process, a situation
that occurs all too frequently. Similarly, the models must be able to relate
the results to the reference and benchmark conditions (see Figure 5.3);
otherwise, there is no way to evaluate the consequences or significance of
the results. Issues involved here include having a database that covers the
baseline ecological conditions (e.g., historical, reference, and pristine conditions), a model construct that captures phase shifts in system states under
certain stress regimes, or models that can distinguish among alternate
benchmark conditions. If the model cannot reflect these characteristics, then
there is a significant likelihood that the model results will be misleading.
For example, if a model of an estuarine community does not include
the discontinuous shift from one dominant benthic habitat to another (e.g.,
seagrass to hard bottom) associated with an environmental gradient, such
as sediment depth, then the model cannot reliably to assess impacts to that
ecosystem from a stressor that affects that gradient.
5.2.6 Model Complexity, Communication to Different
Audiences, and Terminology
How well do the models communicate complexity in understandable terms
to different audiences? Communication can be an important barrier to the
acceptance of models. There are two facets to the communication issue:
appropriately targeting the discussion to the audience and adequately communicating the complexity of the model basis and outputs. Audiences for
models may include scientists who are knowledgeable in modeling, scientists and other stakeholders who are not familiar with models, and decision
makers. Similarly, models span a continuum of complexities from those that
are so complex that the details can only be understood by other modelers,
to those that are more accessible and understandable to decision makers,
to those designed to be policy friendly. The policy-friendly models often
have such user-friendly characteristics as (1) ease of changing inputs to
reflect different management options or scenarios and (2) outputs that are
visual and synthetic.
A critical facet of communicating models is to explain adequately the
basis or overall construct of the model and its component elements. In
addition, one needs to explain the sources of data, their limitations,
the range of applicability, the important relationships built into the model,
etc. The results or outputs of the models should be transparent. One technique is to use visualization tools to make output relationships clear. In
general, the more attention placed on these communication issues, the more
the model will be used, and the more confidence will be generated in its
results.
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Mark A. Harwell and John H. Gentile
to the goals. Thus, for a model to be used successfully, it must relate to the
environmental goals and address one or more specific endpoints; otherwise,
the model is simply irrelevant to the decision-making process, a situation
that occurs all too frequently. Similarly, the models must be able to relate
the results to the reference and benchmark conditions (see Figure 5.3);
otherwise, there is no way to evaluate the consequences or significance of
the results. Issues involved here include having a database that covers the
baseline ecological conditions (e.g., historical, reference, and pristine conditions), a model construct that captures phase shifts in system states under
certain stress regimes, or models that can distinguish among alternate
benchmark conditions. If the model cannot reflect these characteristics, then
there is a significant likelihood that the model results will be misleading.
For example, if a model of an estuarine community does not include
the discontinuous shift from one dominant benthic habitat to another (e.g.,
seagrass to hard bottom) associated with an environmental gradient, such
as sediment depth, then the model cannot reliably to assess impacts to that
ecosystem from a stressor that affects that gradient.
5.2.6 Model Complexity, Communication to Different
Audiences, and Terminology
How well do the models communicate complexity in understandable terms
to different audiences? Communication can be an important barrier to the
acceptance of models. There are two facets to the communication issue:
appropriately targeting the discussion to the audience and adequately communicating the complexity of the model basis and outputs. Audiences for
models may include scientists who are knowledgeable in modeling, scientists and other stakeholders who are not familiar with models, and decision
makers. Similarly, models span a continuum of complexities from those that
are so complex that the details can only be understood by other modelers,
to those that are more accessible and understandable to decision makers,
to those designed to be policy friendly. The policy-friendly models often
have such user-friendly characteristics as (1) ease of changing inputs to
reflect different management options or scenarios and (2) outputs that are
visual and synthetic.
A critical facet of communicating models is to explain adequately the
basis or overall construct of the model and its component elements. In
addition, one needs to explain the sources of data, their limitations,
the range of applicability, the important relationships built into the model,
etc. The results or outputs of the models should be transparent. One technique is to use visualization tools to make output relationships clear. In
general, the more attention placed on these communication issues, the more
the model will be used, and the more confidence will be generated in its
results.
98
Mark A. Harwell and John H. Gentile