separation between technical assessments and the values and objectives of
the various parties.
2. What are the specific questions to be addressed in the assessment?
Risk of population extinction? Risk of decline in population size? Absolute numerical losses? Comparison of mortality from entrainment and
impingement and mortality from fishing? Given that AEI has yet to be
defined big EPA and given the site-specific differences in water bodies and
fish species, the choice of endpoints and associated measures and decision
criteria is fundamental. Some decision makers and stakeholders lack sufficient understanding of biological science to make sound choices. Hence,
modelers and other scientists need to help them define the objectives and
select the modeling approach, including the species selected for study and
useful endpoints and associated measures and decision criteria. This type
of open-ended and constructive process requires trust that scientists, regulators, and stakeholders are jointly striving to avoid or at least minimize
predictable bias.
3. What modeling approach will most likely be accepted? For any given
316(b) assessment, several modeling approaches of differing complexity
and designed to estimate different measures associated with the same
endpoint may prove helpful (i.e., the weight-of-evidence approach). As an
example, when the endpoint is fish populations, the minimum level of
complexity, beyond estimates of the number killed by entrainment and
impingement, might be the number of equivalent adults lost. [Equivalent
adults lost represents an extrapolation of the number of fish killed by
entrainment and/or impingement at younger life stages (e.g., eggs, larvae,
and juveniles) to the number of these fish that would otherwise have
survived to be adults (EPRI 1999).] Estimating a value for this measure
involves a model that is relatively easy for all parties to understand and that
can be used as a screening tool (EPRI 1999; EPRI in press). At the other
extreme of complexity, a probabilistic forecast of risk of population decline
that is made with a stochastic population-simulation model (e.g., Lohner
et al. 2000) is an attractive measure because it more directly addresses
the management objective of sustainability of fish populations. However,
even the simplest stochastic simulation models represent a relatively
high level of sophistication for many regulatory decisions, making the
science–policy bridge all the more important. The level of complexity is
almost limitless with the stochastic individual-based modeling approach,
where the effects on individual fish of daily changes in temperature, flow,
water velocity, food availability, competition, predation, ambient pollution,
extreme weather events, and anthropogenic impacts (such as fishing) can
be modeled explicitly to estimate population effects (Van Winkle et al.
1993).
As model complexity increases, understandability generally decreases. In
addition, most 316(b) decisions will not be helped by increasing levels of
model complexity that do not more clearly differentiate among PM&E
54
Webb Van Winkle and John Kadvany
the various parties.
2. What are the specific questions to be addressed in the assessment?
Risk of population extinction? Risk of decline in population size? Absolute numerical losses? Comparison of mortality from entrainment and
impingement and mortality from fishing? Given that AEI has yet to be
defined big EPA and given the site-specific differences in water bodies and
fish species, the choice of endpoints and associated measures and decision
criteria is fundamental. Some decision makers and stakeholders lack sufficient understanding of biological science to make sound choices. Hence,
modelers and other scientists need to help them define the objectives and
select the modeling approach, including the species selected for study and
useful endpoints and associated measures and decision criteria. This type
of open-ended and constructive process requires trust that scientists, regulators, and stakeholders are jointly striving to avoid or at least minimize
predictable bias.
3. What modeling approach will most likely be accepted? For any given
316(b) assessment, several modeling approaches of differing complexity
and designed to estimate different measures associated with the same
endpoint may prove helpful (i.e., the weight-of-evidence approach). As an
example, when the endpoint is fish populations, the minimum level of
complexity, beyond estimates of the number killed by entrainment and
impingement, might be the number of equivalent adults lost. [Equivalent
adults lost represents an extrapolation of the number of fish killed by
entrainment and/or impingement at younger life stages (e.g., eggs, larvae,
and juveniles) to the number of these fish that would otherwise have
survived to be adults (EPRI 1999).] Estimating a value for this measure
involves a model that is relatively easy for all parties to understand and that
can be used as a screening tool (EPRI 1999; EPRI in press). At the other
extreme of complexity, a probabilistic forecast of risk of population decline
that is made with a stochastic population-simulation model (e.g., Lohner
et al. 2000) is an attractive measure because it more directly addresses
the management objective of sustainability of fish populations. However,
even the simplest stochastic simulation models represent a relatively
high level of sophistication for many regulatory decisions, making the
science–policy bridge all the more important. The level of complexity is
almost limitless with the stochastic individual-based modeling approach,
where the effects on individual fish of daily changes in temperature, flow,
water velocity, food availability, competition, predation, ambient pollution,
extreme weather events, and anthropogenic impacts (such as fishing) can
be modeled explicitly to estimate population effects (Van Winkle et al.
1993).
As model complexity increases, understandability generally decreases. In
addition, most 316(b) decisions will not be helped by increasing levels of
model complexity that do not more clearly differentiate among PM&E
54
Webb Van Winkle and John Kadvany