5.2.1 Generic Issue
How well does the conceptual model capture the understanding of the ecological system and its stressors, both natural and anthropogenic? We, as cognitive beings, are constantly creating a variety of mental conceptualizations
(models, if you will) of our daily activities. We use these to represent
relationships among a variety of variables. The conceptual models used in
ecological assessments, like all models, are merely representations of reality
as we perceive it. As such, they are neither right nor wrong but a continuum of representations of reality with varying degrees of uncertainty. The
more information and understanding we have, the better our “model” will
capture the essential features of our perceived reality. The purpose of the
conceptual model is to capture, in general terms, our understanding of the
complexity of critical ecological systems and the potential causal relationships of ecological responses to environmental stressors. This approach
is nothing new; in fact, we intuitively develop conceptual models prior to
constructing analytical or numerical models. What is unique is that, in this
process, the conceptual model is explicit and developed from a consensus
of scientists and nonscientists and, thus, is totally transparent. If done well,
scientists and nonscientists alike will have increased confidence that the
essential elements and relationships of the ecosystem are captured in
the conceptual model, and that model will address successfully the goals
of the environmental problem at hand. It is at this early stage of the assessment process that one begins to identify the important sources of
uncertainty. If the conceptualization is not performed adequately, then
there is a high probability that the assessment will address the wrong questions and the results will be unreliable at best and irrelevant at worst.
5.2.2 Aggregation
Is the model developed at the appropriate level of aggregation or disaggregation? Again, because of the complexity of ecosystems, any model, be
it conceptual or predictive, must reduce the dimensionality of the problem
to a manageable level, thereby aggregating details into more synthetic state
variables or processes. However, too much aggregation can lead to loss of
critical information about stress–effect relationships, or may lose the ability
to address key ecological endpoints of concern. Conversely, too much disaggregation can lead to overwhelming information, unachievable data
needs, or losing the important results in a maze of details. Any of these cases
can lead to dismissal of a conceptual or simulation model as unrealistic.
5.2.3 Extrapolation
To what degree can the model be extrapolated? If a model was developed
for one system or one set of stressors, can it be used for another set of
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Mark A. Harwell and John H. Gentile
How well does the conceptual model capture the understanding of the ecological system and its stressors, both natural and anthropogenic? We, as cognitive beings, are constantly creating a variety of mental conceptualizations
(models, if you will) of our daily activities. We use these to represent
relationships among a variety of variables. The conceptual models used in
ecological assessments, like all models, are merely representations of reality
as we perceive it. As such, they are neither right nor wrong but a continuum of representations of reality with varying degrees of uncertainty. The
more information and understanding we have, the better our “model” will
capture the essential features of our perceived reality. The purpose of the
conceptual model is to capture, in general terms, our understanding of the
complexity of critical ecological systems and the potential causal relationships of ecological responses to environmental stressors. This approach
is nothing new; in fact, we intuitively develop conceptual models prior to
constructing analytical or numerical models. What is unique is that, in this
process, the conceptual model is explicit and developed from a consensus
of scientists and nonscientists and, thus, is totally transparent. If done well,
scientists and nonscientists alike will have increased confidence that the
essential elements and relationships of the ecosystem are captured in
the conceptual model, and that model will address successfully the goals
of the environmental problem at hand. It is at this early stage of the assessment process that one begins to identify the important sources of
uncertainty. If the conceptualization is not performed adequately, then
there is a high probability that the assessment will address the wrong questions and the results will be unreliable at best and irrelevant at worst.
5.2.2 Aggregation
Is the model developed at the appropriate level of aggregation or disaggregation? Again, because of the complexity of ecosystems, any model, be
it conceptual or predictive, must reduce the dimensionality of the problem
to a manageable level, thereby aggregating details into more synthetic state
variables or processes. However, too much aggregation can lead to loss of
critical information about stress–effect relationships, or may lose the ability
to address key ecological endpoints of concern. Conversely, too much disaggregation can lead to overwhelming information, unachievable data
needs, or losing the important results in a maze of details. Any of these cases
can lead to dismissal of a conceptual or simulation model as unrealistic.
5.2.3 Extrapolation
To what degree can the model be extrapolated? If a model was developed
for one system or one set of stressors, can it be used for another set of
96
Mark A. Harwell and John H. Gentile