Building Science for Environmental Protection in the 21st Century - Science For Environmental Protection: The Road Ahead - page 111

BOX 4-1 Continued
 Develop indicators that are robust and reliable. A robust indicator is
relatively insensitive to expected sources of disturbance and yields reliable
and useful numbers in the face of inevitable external perturbations. A robust
indicator is based on measurements that can be continued in compatible form
when measurement technologies change.
 Understand each indicator's statistical properties so that changes in its
values will have clear and unambiguous meanings. The indicator should be
sensitive enough to detect real and important changes but not so sensitive
that its signals are masked by natural variability.
 Clarify the spatial and temporal scales over which each indicator is
relevant. If indicator data is to be aggregated to yield measures on larger spa-
tial scales, consistency in how the data are gathered in different places is
vital.
 Identify the skills needed at all stages of indicator development and
use. Acceptance of the indicator requires that potential users have confidence
in the skills and integrity of the people that gather, store, and report the data.
 Separate the entities that compute and report status and trends in in-
dicators from management and enforcement agencies. Confidence in the
numbers being reported requires the belief that the numbers do not depend
on who gathers and reports them. Actual or perceived conflicts of interest are
likely to arise if the gatherers and interpreters of data also establish and en-
force regulations based on trends in the data.
More detail on the reasoning behind the principles above can be found in
NRC (2000) and Orians and Policansky (2009).
Long-Term Data Collection
Once indicators have been established, there is a need to measure them
over time. To meet its mission, EPA needs an understanding of long-term
changes in the environment and trends in rates at which pollutants enter the en-
vironment. In the absence of trend and duration data, it is often hard to know
whether any specific pollutant load—particularly the load of a nontoxic pollut-
ant, such as nitrogen—is of concern. Long-term monitoring is essential for
tracking changes in ecosystems and populations to identify, at the earliest stage,
emerging changes and challenges. Without long-term data, it is difficult to know
whether current variations fall within the normal range of variation or are truly
unprecedented. It is also essential for knowing whether EPA's management in-
terventions are having their intended effect. Monitoring is a fundamental com-
ponent of hypothesis-testing. All management interventions are based on ex-
plicit or implicit hypotheses that justify them and explain why they should yield
the desired results. The hypothesis may focus on physical and biologic processes
or on expected human behavioral responses. If the hypothesis is made explicit