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2
The Empirical and Conceptual
Foundations of Indicators
' ndicators describe and summarize: they can be used for diagnosis and
warning, and they can be used to monitor change. No indicator needs
~ to do all these things, but if one wants to know whether an indicator is
of value, its intended use must be clear.
Some indicators are more oriented to describing the state of a system,
others to predicting its future state (NRC 1995a). Both description and
prediction have their uses. However, it is impossible to imagine a suc-
cessful set of indicators that fails to describe current conditions or fails to
facilitate prediction. We need to know both where we are and where we
are going.
Good indicators have three key features. First, they quantify infor-
mation so that its significance is more apparent. Second, they simplify
information about complex phenomena to improve communication
(Hammond et al. 1995~. Finally, indicators are used based on the assump-
tion that doing so is a cost-effective and accurate alternative to monitor-
ing many individual processes, species, and so on (Landres 1992~. The
most difficult conceptual problem in developing indicators is to ensure
that they are complete enough to capture the dynamics of key processes
without being so complex that their meaning what they indicate is
unclear. Not all indicators need to have immediate policy implications,
but if they are to be policy-relevant, the relationships between them and
the issues relevant to public policy choices should be clear. In addition, so
many ecological indicators have been proposed and used that the costs of
monitoring all of these indicators would be prohibitive.
27
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28
ECOLOGICAL INDICATORS FOR THE NATION
Most input data to indicators are generated by monitoring environ-
mental states over time, either retrospectively or prospectively. The
distinction between retrospective and prospective monitoring has been
described by the NRC in its review of the EPA's Environmental Monitor-
ing and Assessment Program (EMAP) (NRC 1995a):
Retrospective or effects-oriented monitoring is monitoring that
seeks to find effects by detecting changes in status or condition of some
organism, population, or community. Examples include monitoring the
body temperature of a person, monitoring the productivity of a lake,
monitoring the condition of foliage in forests, and so on. It is retrospec-
tive in that it is based on detecting an effect after it has occurred. It does
not assume any knowledge of cause-effect relationships, although the
intention is usually to try to establish a cause if an effect is found. It is
EMAP's general approach.
Predictive or stress-oriented monitoring is monitoring that seeks to
detect the known or suspected cause of an undesirable effect (a stressor)
before the effect has had a chance to occur or to become serious. Exam-
ples include monitoring the cholesterol level in a person's blood, moni-
toring the stress level along a geological fault, monitoring animal tissues
for the presence of known carcinogens or other disease-causing com-
pounds, and monitoring with a canary the toxic gas level in a mine. It is
predictive in that the cause-effect relationship is known, so that if the
cause can be detected early, the effect can be predicted before it occurs.
Both retrospective and prospective monitoring are of value in devel-
oping ecological indicators. An indicator based on retrospective monitor-
ing, which describes the state of an ecosystem, could be useful in assessing
the need for environmental management or the effectiveness of environ-
mental programs. For example, an indicator of the condition of popula-
tions of anadromous fishes, such as salmon and shad, is the degree to
which a river is regulated by dams and the kinds of dams that are present.
Such an indicator, if developed quantitatively, could be used to trigger
action to prevent deterioration, assess future prospects, and suggest
appropriate mitigation.
Prospective monitoring is common and often required by laws. For
example, monitoring concentrations of various gases in the atmosphere
and contaminants in water is required by the Clean Air Act and the Clean
Water Act. The nation monitors for the presence of microorganisms in
drinking water, community composition in some fresh waters (IBI), and
the presence of various introduced and native pests.
Identifying useful indicators of stressors rather than the stressors
themselves is straightforward if the likely stressor and its probable effect
are known. Monitoring birds' eggs for DOT by looking at the thickness of
their shells (Buckley 1986) is a good example. The stressor DDT and its
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THE EMPIRICAL AND CONCEPTUAL FOUNDATIONS OF INDICATORS 29
metabolite DDE was known; at least one of its effects interference with
avian reproduction was known; and a good indicator eggshell thick-
ness was relevant and useful. Such indicators must be developed on a
case-by-case basis. No systematic way exists to predict the existence and
effects of stressors before they become a problem.
In many cases, however, an adverse effect is identified whose cause is
not known (e.g., recent observations of deformed frogs), or a variety of
adverse effects are known to be caused by a mixture of factors whose
individual roles are unknown (e.g., declining salmon populations in the
Pacific Northwest, various ecosystem changes in Chesapeake Bay). In
these cases, stressor-indicators cannot be developed until the stressors are
known. Several possible stressors could be monitored, but there are so
many that this approach is unlikely to be cost-effective or efficient. For
example, although the developmental condition of frogs might well be a
good indicator of some as-yet unidentified stressor, and it might be a
good retrospective indicator of something important beyond the valuable
information it provides about frogs themselves, the value of using
deformed frogs as prospective indicators cannot be assessed without
knowing why they are deformed.
Some factors are both stressors and effects. For example, soil erosion
is a cause of stream sedimentation, and so erosion rate is a stressor-
indicator of the condition of stream communities. But soil erosion also is
an indicator of the effects of overgrazing or deforestation on terrestrial
ecosystems. For these reasons, the committee focuses its recommenda-
tions on national indicators that inform about the status and trends in
ecosystem extent, condition, and functioning rather than focusing specifi-
cally on indicators of the stressors themselves.
Useful ecological indicators are based on clear conceptual models of
the structure and functioning of the ecosystems to which they apply. The
models can be empirical or theoretical, quantitative or qualitative, but, as
the following discussion emphasizes, some type of model is essential to
ground and rationalize all indicators.
SCIENTIFIC UNDERPINNINGS OF INDICATORS
All indicators are grounded in substantive knowledge and some sort
of scientific logic. Consideration of the major varieties of such logical
arguments helps assess the degree to which any given indicator is reliable.
Natural History
All indicators begin with data taken from the world. Whether these
data concern the nesting season of ravens or the ozone concentration of
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30
ECOLOGICAL INDICATORS FOR THE NATION
the upper atmosphere, they are needed because it is impossible to under-
stand the environment without observing it. Indicators based on natural
history are descriptive. Even when they measure a rate, such as stream
flow, they are not capable, by themselves, of predicting future conditions.
To become predictive, natural history observations must be incorporated
into some model of how the relevant environmental processes operate.
Paleabiology
By taking a long view of things one can determine whether current
conditions and trends are within the range of variations that have
occurred in the past or whether they are in some way anomalous. Are
current changes in temperature and precipitation unusual? Is the current
relationship between diversity and land area similar to that prevailing
millions of years ago? Patterns that are repeated many times during
geological history can be incorporated into the tools used to predict the
future. The past can be a key to the future if the Earth has performed
experiments that have yielded reasonably consistent results.
Experiments
Based on observations of the environment, scientists develop hypoth-
eses about the relationships and processes that influence it. These
hypotheses are tested experimentally in the field and in the laboratory
and modified appropriately. Where they can be used, experiments are
powerful tools in the development and testing of hypotheses.
Analytical Predictions
Mathematical models of environmental processes generate predic-
tions that can be tested observationally and experimentally in the field.
They also may generate predictions of future events that cannot be tested
directly, but these predictions may suggest which events should be moni-
tored to determine whether the predictions are borne out. Analytical
models have been used to explain the existence of the ozone hole over the
Southern Hemisphere and to predict future ranges of species under dif-
ferent scenarios of global climate change.
Computer Simulations
The properties of many complex ecological systems cannot be deter-
mined analytically, but they can be studied by using computer simulation
models. These models allow investigators to manipulate key variables to
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THE EMPIRICAL AND CONCEPTUAL FOUNDATIONS OF INDICATORS 31
assess the sensitivity of the ecosystem's behavior to changes in the values
of these different inputs. Global climate models and models of the
dynamics of complex ecosystems, which are examples of such models,
have played important roles in assessing the likely consequences of
human-induced changes in the environment.
USING MULTIPLE APPROACHES
The most important point to make about these different sources of
scientific data is that no one source by itself is sufficient to guide the
design and implementation of useful ecological indicators or to formulate
sound environmental policies. Information from all these sources is
needed, and the richer the array of information, the better. From a scien-
tific perspective, indicators are most useful if they are reliable, that is, if
the measurements on which they are based are repeatable and do not
vary significantly depending on who gathers them.
Indicators increase in value with the time period over which their
supporting data are gathered. Because the value of long-term data depends
strongly on the consistency of the methods used to gather them, changes
in measurement methods need to be implemented carefully. Technologi-
cal advances regularly improve the speed, reliability, and accuracy with
which data can be gathered. To fail to incorporate such advances is gen-
erally undesirable. Therefore, good indicators should be robust to changes
in measurement technology, so that long-term data sets are internally
consistent even if measurement methods have changed. The calibration
of methods during periods of technological change is an essential compo-
nent of integrating indicators across technological boundaries.
HISTORICAL AND PALEOECOLOGICAL DATA AS AIDS
TO INDICATOR DEVELOPMENT
Paleoecology, taken here to encompass also paleontology, paleo-
limnology, paleogeochemistry, and paleoclimatology, is concerned with
describing past ecological communities and their environments. Records
of temporal change in the distribution of fossilized organisms, and of
physical and chemical properties of the environment, can provide a very
useful background to assess the influence on ecosystems of diverse natural
and anthropogenic disturbances. These records are an extremely valu-
able complement to environmental monitoring, and can suggest whether
the causes of past changes have been natural or anthropogenic (Charles et
al. 1994~. Such records, which can be gathered for a wide range of
temporal and spatial scales, can help determine requirements for sampling
frequency and duration.
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32
ECOLOGICAL INDICATORS FOR THE NATION
In conjunction with monitoring programs, paleoecological indicators
can help answer five main questions:
· What were communities and ecosystems like before they were subjected to
natural or human disturbances? Evidence from pollen and seeds preserved
in dated cores of lake sediments, along with other sources of information,
helps to answer this question for regional and local vegetation (Brush
1986, Davis 1985, McAndrews and Boyko-Diakonow 1989~. Diatoms and
fossilized chlorophylls and carotenoids can be used to answer this ques-
tion for lakes (Engstrom et al. 1985~.
· What have been the patterns of recovery from disturbances, and have the
initial conditions been regained? Foster (1995) used records of fossilized
pollen to show that forests in central Massachusetts did not return to their
composition before the arrival of European settlers following abandon-
ment of agriculture. Recovery of lakes from severe anthropogenic acidifi-
cation has been assessed by analysis of past diatom and chrysophyte
species in sediment cores (Dixit et al. 1992~.
· What has been the nature and degree of variability in the past, especially
the frequency of extreme events? Heinselman (1996), using fire scars and
tree-ring analysis, reconstructed the fire history of the Boundary Waters
Wilderness in northern Minnesota. His analysis showed that between
1681 and 1894, the intervals between major fires ranged from 11 to 42
years. Clark (1988a, b) showed that fire frequency in this area was greater
during the dry medieval warm period than during the subsequent Little
Ice Age.
· Were communities and ecosystems relatively stable, were they following
trajectories of gradual change, or did they exhibit sudden fluctuations or transi-
tions to another state? The postglacial vegetation history of North America,
established by numerous regional studies of pollen records (Wright 1971),
offers extensive evidence concerning this question. In the midwestern
United States, for instance, deciduous forest changed to prairie and back
again during the period from 8,000 to 4,000 years ago. Renberg (1990)
determined the pH history of a Swedish Lake, Lilla Oresjon, by studying
its diatom stratigraphy. Many diatoms have rather narrow pH toler-
ances, and their sculptured shells (frustules) can be identified to species,
making them excellent indicators of lake acidification. Renberg showed
that Lilla Oresjon acidified slowly from neutrality 12,000 years ago to pH
5.2 2,300 years ago. It then became much less acid owing to land clear-
ances by humans and the consequent leaching of bases into the lake. It re-
acidified to about pH 4.5 during the present century owing to acid
deposition.
· Have anthropogenic perturbations been different, in degree or kind, from
natural perturbations? The history of Lilla Oresjon provides evidence that
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THE EMPIRICAL AND CONCEPTUAL FOUNDATIONS OF INDICATORS 33
recent anthropogenic acidification exceeded the natural acidification that
preceded it for thousands of years. A difference in kind of change is
illustrated by the observed eggshell thinning in birds caused by exposure
to chlorinated hydrocarbons (Ratcliffe 1967), which appears to have no
natural analog.
Environmental Problems for Which Paleoecological Data
Have Been Useful
Paleoecological data have been used to study a wide range of environ-
mental problems, including climate change, acid deposition, eutrophica-
tion, losses of biodiversity, fish declines and introductions, fire frequency,
soil erosion, silting of lakes and estuaries, and pollution by heavy metals
(e.g., lead and mercury) and trace organic molecules (e.g., DDT and
toxaphene). Indeed, the understanding of most environmental problems
can be improved by access to Paleoecological information.
Paleoecological data have often revealed major unsuspected environ-
mental changes, over time scales ranging from the mass extinctions
observed in the fossil record to the sudden catastrophic loss of submerged
macrophytes in Chesapeake Bay in the early 1970s (Brush and Davis 1984,
Davis 1985~. Paleoecological data have been especially important in alter-
ing views of the operation of the global climate system. Tree-ring records
from the southwestern United States reveal numerous abrupt changes in
regional precipitation between 800 and 2,000 years ago (Graumlich 1993,
Hughes and Graumlich 1996~. Likewise, close-interval paleoclimatic stud-
ies of the Greenland ice core have shown that mean annual temperatures
there have changed by as much as 10°C in a few years (Grootes et al.1993,
Alley et al. 1993), and that sudden "jumps" in regional climate may be
likely as changing conditions lead to a warmer climate globally (Broecker
1987, Overpeck 1996~.
To anticipate "surprise" events, possible hazards will need to be moni-
tored in a variety of environments. Attention will need to be paid to
outliers in the data, which may reflect not errors in measurement but
unusual, rare phenomena (Kates and Clark 1996~. Thus, programs that
monitor environments subject to a wide range of natural and anthropo-
genic stresses can benefit greatly from complementary studies of paleo-
ecological indicators. Indeed, much useful information, such as long-
term baseline records, can be gathered in no other way. Retrospective
Paleoecological indicators can be used prospectively to identify trends
that may be useful in evaluating monitoring results.
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ECOLOGICAL INDICATORS FOR THE NATION
SOURCES OF INFORMATION ABOUT CURRENT
ECOLOGICAL PROCESSES
Regularly gathered information can provide the broad spatial cover-
age and substantial time series necessary to detect environmental trends.
Monitoring key ecological information is important because the value of
an indicator is reduced considerably if new baseline data must be gathered
before it can be used. Because ecosystems are so variable in space and
time, gathering enough data for national ecological indicators will be
difficult. Fortunately, recent developments in remote-sensing technology
offer new opportunities for measurements at extensive spatial scales.
Because the committee recommends indicators that depend on remotely
sensed information, we describe these developments in some detail.
Remote Sensing From Satellites
There are many reasons for using remotely sensed data as inputs to
indicators. For many of the indicators that the committee recommends,
especially for information on terrestrial ecosystem processes and some
aspects of ecosystem status, remote sensing offers rapid and relatively
accurate sampling. For a few remotely sensed measurements, there
already is an approximately 25-year time series of reasonably well cali-
brated data of nearly global coverage. In addition, for measurements
over large spatial areas, remote sensing offers the only affordable means
of sampling. Operational costs for satellite systems are not necessarily
higher than the personnel costs of large in situ monitoring efforts, but the
capital costs of developing and launching satellite missions are extremely
large.
The availability of time series of satellite measurements with well-
established precision and accuracy is important in using remotely sensed
data. Satellite-generated data are especially valuable if all instruments,
both within and among satellite platforms, are fully calibrated and if the
data are accessible and affordable. Fortunately, considerable attention
has been paid to these issues.
The two types of satellite imagery most widely used by terrestrial
ecologists are Landsat TM (Thematic Mapper) and AVHRR (Advanced
Very High Resolution Radiometer). Both cover most of the Earth's sur-
face, TM imagery at 30 m resolution and AVHRR imagery at 1 km resolu-
tion. Each records the intensity of radiation reflected from the planet in
five to seven spectral bands, in the visible, near-infrared (NIR), and
thermal infrared (JR) parts of the spectrum.
The Landsat data record, which starts in 1972, has continued through
the use of three primary instruments. The first provided video images,
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THE EMPIRICAL AND CONCEPTUAL FOUNDATIONS OF INDICATORS 35
some of which are still archived. The video system was accompanied by
the Multi-Spectral Scanner, which very quickly became the preferred
instrument for quantitative analysis. Since 1982, the primary instrument
has been the Thematic Mapper, with six spectral bands in the visible and
short-wave near-infrared, all of which have 30 m spatial resolution, and
one thermal band with 120 m spatial resolution. A Landsat scene is
approximately 185 km on a side; the combination of swath width and
orbital characteristics of the satellite means that any spot on the Earth is
revisited at about 16- to 18-day intervals. Six Landsat missions were
launched, of which five achieved orbit. Landsat 5 is currently in the 13th
year of its planned three-year lifetime.
The United States holds in its archives at the U.S. Geological Survey
(USGS) Eros Data Center nearly complete global data from the Landsat
system for the years 1972 to 1980, but the dates of cloud-free acquisitions
vary geographically. Less comprehensive coverage is held in U.S. archives
since 1980 because of the U.S. policy of commercializing the Landsat
system (although the record for the United States itself remains fairly
complete). However, 10 to 17 international ground stations, which were
operating during the period of the Landsat missions, have maintained
accessible data archives. Thus, in principle, global data sets could be
derived for the 1980s and 1990s. The tape recorders on Landsat 5 failed
several years ago, restricting its data downlink capabilities to direct trans-
mission to ground stations (without relays to other communication satel-
lites). This failure created holes in global coverage where ground stations
are not operating, most notably Siberia, Alaska, and central Africa.
The AVHRR a standard instrument on the National Oceanic and
Atmospheric Administration (NOAA) polar-orbiting meteorological sat-
ellites has several spectral bands in the visible, NIR, and IR parts of the
spectrum. However, the AVHRR collects data with very different spatial
characteristics from Landsat data, reflecting its origin as a meteorological
instrument. Global-area coverage data, which are resampled on board
the satellite before being transmitted, have a 4 km spatial resolution.
Local-area coverage data, which are directly transmitted to the ground,
have a 1 km spatial resolution. The combination of swath width of the
sensor (about 1000 km) and orbital characteristics of the satellite platform
achieve daily to twice-daily coverage. However, unlike Landsat TM,
whose radiometric properties are known and monitored with a high de-
gree of accuracy, the AVHRR has no provision for on-orbit calibration,
and the satellite orbits have drifted substantially over the years. The data
themselves are less than ideal because the instruments are poorly cali-
brated, and intercalibration of instruments on different platforms is also
poor. Therefore, although roughly decade-long time series of truly global
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ECOLOGICAL INDICATORS FOR THE NATION
data exist, they must be extensively processed before they can be used for
quantitative analyses of ecosystem properties and processes.
Ecosystem Processes. The most widely used satellite-derived indicator
of processes is the normalized difference vegetation index (NDVI). NDVI
is a direct measure of absorbed photosynthetically active radiation,
because chlorophyll absorbs in the satellite's NIR band and reflects in the
IR band, whereas rocks, stems, and soil reflect in both NIR and IR. The
numerator of NDVI is IR minus NIR, which is large if abundant chloro-
phyll reduces the NIR. To compensate for the variable transparency of
the atmosphere, this difference is divided by the sum NIR plus IR. Global
NDVI measurements at a variety of resolutions are now available through
the Internet (Kidwell 1997~.
As a result of an extensive effort by the National Aeronautics and
Space Administration (NASA) and NOAA, a decade-long record of 4 km
resolution NDVI data with consistently processed and documented tech-
niques is available through the AVHRR Pathfinder Program. Many indi-
vidual research groups have collected and maintained archives of AVHRR
and NDVI. For example, S. Los and colleagues have compiled a one-
degree aggregation of 4 km resolution data for 1981 through 1990 (Los et
al. in press). A complete global record at 1 km resolution is now available
for the period 1993 to 1994, and continues to be collected and processed
by NASA and the USGS. Collaborating ground receiving stations around
the world have recently developed a more precise measure of absorbed
photosynthetically active radiation than is possible with NDVI by using
multiple scenes with the sun and sensor in different positions and invert-
ing a radiative transfer model.
Once one knows the amount of leaf area in a location, it is a relatively
easy matter to predict the region's net primary production (NPP). The
simplest approach is to regress NDVI against measures of NPP. Gener-
ally this is done separately for each biome (e.g., Fung et al. 1987~. More
precise information can be obtained by using the vegetation index to
parameterize a biogeochemical model, especially if there are data about
the weather, topography, and soil type (which are widely available for
the United States and to a lesser degree globally from NOAA and Depart-
ment of Energy Web sites and on CD-ROM from these agencies). The best
example of an ecosystem model driven by NDVI is the CASA model of
Potter et al. (1993~. This model translates monthly NDVI and climate data
into predictions of primary production, carbon storage, net ecosystem
production, and nitrogen mineralization. It contains only a single fitted
parameter (one value for the entire globe). Although the model is rela-
tively simple, it has an accuracy approaching direct-measurement accu-
racy of NPP and nitrogen mineralization about 25 percent.
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THE EMPIRICAL AND CONCEPTUAL FOUNDATIONS OF INDICATORS 37
Several widely used land-surface models, initially developed to
improve long-range weather forecasts, also rely heavily on satellite imag-
ery. These models predict the transfer of matter, energy, and momentum
between the land surface, vegetation, and the atmosphere at short (e.g., 20
minute) time scales. The models typically contain an enormous amount
of detail at a fine scale, including the biochemistry of photosynthesis, the
physiological control of stomates, the distribution of leaves in the canopy,
and the vertical distribution of water in the soil. The best example of this
type of model is the SiB model (SIB I, II, and III) of P. Sellers and col-
leagues (Sellers et al. 1997~.
Because these models contain so much detail, they rely heavily on
satellite imagery to fix the properties of vegetation at each location (veg-
etation type, albedo, leaf area, etc.~. Even so, a relatively large number of
parameters and functions are little more than educated guesses. Despite
these limitations, models such as SiB II have a remarkable capacity to
predict diurnal and seasonal patterns of production, respiration, and
transpiration. SiB II also improves the predictions of those weather
models that incorporate it.
The first international comparison of model simulations of NPP has
recently been conducted under the auspices of the International
Geosphere and Biosphere Program (IGBP). The study, which included
models ranging from empirically fitted regressions to the most complex
SiB II-type model, attempted to isolate the reasons for differences in esti-
mates of ecosystem productivity and NPP. Results (Cramer et al. 1999,
Cramer and Field 1999) indicate a fair degree of spread in the simulated
NPP values, with some of the variation certainly attributable to differ-
ences in the underlying data sets used. However, such comparison studies
are likely to lead to a greater understanding of the limitations of both the
underlying data and the models themselves. The result should be better
and more quantitative documentation of NPP patterns.
Vegetation Characterization and Classification. Classifying and mapping
land cover and vegetation, the most common use of aerial photography,
is served well by satellite imagery. The number of vegetation categories
used is limited by the number of spectral bands, the resolution of the most
commonly used imagery, and the experience of the interpreter. Although
automated statistical techniques are used to cluster most data, there is no
fully automated technique for land-cover classification.
Even simple classifications are useful as indicators of ecosystem
status. For example, the rate of loss of closed-canopy humid tropical
forest in Brazil was measured from the late 1970s to the late 1980s using
Landsat data (Skole and Tucker, 1993) and independently verified by
scientists at the Brazilian Space Agency, INPE. INPE now uses analogous
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40
ECOLOGICAL INDICATORS FOR THE NATION
geographic coverage of the nation's aquatic resources cost-effectively
remains largely untapped. Satellite data have been used to monitor the
effects of drought on the area of shallow lakes (Brown et al. 1977a), and
they could be used to monitor wetland hydrology.
Most satellite applications to lake and reservoir monitoring involve
use of spectral-reflectance data to estimate water clarity and related con-
ditions. Reflectance of a water body is a function of optically active sub-
stances, primarily algae (and their pigments), dissolved humic matter,
and suspended minerals, each of which have different spectral patterns.
Water clarity (and thus spectral reflectance) in most lakes depends prima-
rily on phytoplankton abundance in the water column, which depends
generally on the level of nutrient enrichment. Reflectance data can be
used to estimate chlorophyll concentrations in water (Mittenzwey et al.
1992) and thus the trophic status of lakes. Low water clarity in reservoirs
often results from nonalgal turbidity (clay-like suspended solids) caused
by soil erosion, and so different relationships must be developed between
reflectance and trophic status in such systems (e.g., Gallie and Murtha
1993~. Satellite data have been used to assess the trophic status and water
quality of individual lakes (e.g., Sudhakar and Pal 1993, Chacon-Torres et
al. 1992) and in a few cases the status of groups of lakes or all lakes in a
region (Brown et al. 1977b, Lillesand et al. 1983, Lathrop 1992~.
Landsat Thematic Mapper data are obtained at 16-day intervals across
the United States. As many as five to six images are thus available for a
given region or lake during the critical summer growth period (approxi-
mately the end of tune to mid-September in northern states). Partial or
complete cloud cover at the time of a Landsat overpass decreases the
number of images available for processing, often to only one or two
images per season. This frequency is insufficient for detailed assessment
of time trends in individual lakes, but should be adequate for long-term
monitoring of lake conditions on a regional basis. Other satellite plat-
forms that will become available in the near future will provide more
frequent coverage; the Moderate Resolution Imaging Spectroradiometer
(MODIS) system, described below, will provide daily coverage of indi-
vidual sites, if at lower spatial resolution (250 m, compared with 10 m
resolution for Landsat). MODIS may be useful for intraseasonal monitor-
ing of water quality in medium to large lakes.
Future Developments. Satellite-based observation systems will soon
improve dramatically. With the successful launch of Landsat 7 in April
1999, satellite-based observation systems have improved dramatically.
Landsat 7 has an Enhanced Thematic Mapper (ETM) on board. The ETM
maintains the same spectral bands as previous Landsats, but it also has a
15 m black-and-white band, useful both in its own right and for sharpen-
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THE EMPIRICAL AND CONCEPTUAL FOUNDATIONS OF INDICATORS 41
ing the imagery from the 30 m bands. The ETM also has better calibra-
tion. Most importantly, the system design for Landsat 7, which again is
under government control, ensures that the U.S. archive will again be
truly global. The mission is designed to update the U.S.-held global
archive, and the archives held by international ground stations, on ap-
proximately a seasonal basis. Data will be more affordable because the
federal government will seek only to cover the costs of responding to data
requests.
The Earth Observing System (EOS) AM platform Terra was
launched in late 1999. Terra will operate five instruments, including
MODIS, which is of particular relevance for monitoring ecosystem pro-
cesses. MODIS will have 32 spectral channels, with stringently defined
radiometric calibration. Most of the spectral channels will have 1 km
spatial resolution, although a few will have 250 to 500 m resolution. With
this system, a global data set can be acquired roughly every few days. In
addition, the combination of this system's spectral and spatial character-
istics will enable scientists to accurately calculate a variety of vegetation
indices, land-cover products, fire detection products, and land-cover
change indices.
In about two years, NASA will launch the Vegetation Canopy Lidar
(VCL), an instrument capable of measuring foliage-height profiles every-
where on the Earth. The technology has already been tested on the Space
Shuttle, and the imager itself is currently on a research aircraft. The
satellite-mounted VCL will allow the monitoring of harvesting and
regrowth, measuring above-ground biomass, and assessing structural
habitat features important to animal species.
The commercial sector is increasingly active in providing remote-
sensing information. Most of their proposed missions will seek very high
spatial resolution data (1 to 10 m), either black-and-white or with a few
spectral bands. Because these data provide little spectral information,
they are of little use for assessing ecosystem processes. However, they
have great utility for vegetation classification and analysis of land-cover
changes. Recently declassified data provide an opportunity to analyze
time series of land cover in some locations back into the 1960s.
Remote Sensing from Aircraft
Aerial photographs are available for most developed countries for
most of the current century. Aerial photographs can provide all of the
measurements available from satellites and at higher resolution, but usu-
ally in a form that is more difficult to digest. Photo interpreters are
routinely employed by timber-producing industries and the U. S. Forest
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ECOLOGICAL INDICATORS FOR THE NATION
Service to assess the sizes, densities, and species identities of trees in
aerial photographs.
Ground-Based Measurements
Despite their great value, remote-sensing techniques do not eliminate
the need for ground-based measurements. Such measurements record
processes that are not detectable from afar and they are needed to
"ground-truth" measurements from aircraft and especially from satel-
lites. The most extensive and systematic system of ecological research
sites is the National Science Foundation-funded network of 21 Long Term
Ecological Research Sites (LTER). Several LTER sites have been in con-
tinuous operation for 15 to 20 years. The LTER sites span a range of
ecosystem types on U.S. territory from arctic tundra at Toolik Lake in
Alaska's Brooks Range to tropical rainforest at the Luquillo Experimental
Forest near San Juan, Puerto Rico (see http://lternet.edu/network/sites/~.
Although each site has a different investigator-driven mission, several
measurements are made at each site every year. The results, which
include estimates of primary production, nitrogen mineralization rates,
standing crop, abundances of most soil cations, detritus production, and
censuses of dominant plant species, are available in standard form.
A second source of information that has been collected systematically
for more than 50 years is the U. S. Forest Service's Continuous Forest
Inventory and Analysis (FIA), which is used by the Forest Service to set
timber-management policy. This system represents several thousand
plots on which every tree greater than 10 cm in diameter is measured
every ten years. These data obviously could be used to detect trends in
the abundances of species and information about primary production.
However, the usefulness of the data is reduced by the large number of
errors in the archived data and the archaic format of the magnetic tape on
which most of the data are stored. Precise knowledge of plot locations is
not widely available to avoid the possibility that someone would manipu-
late the plots to affect future forest policy.
With a few exceptions, formal censuses of animal species are local
and short-term. The amateur (National Audubon Society) Christmas Bird
Count (CBC), begun in the winter of 1900-1901, and the North American
Breeding Bird Survey (BBS), launched in 1966 by the Migratory Bird Popu-
lation Station in Laurel, Maryland (now the USGS Patuxent Environmen-
tal Science Center), are conducted annually and cover the continental
United States and parts of Canada (Root and McDaniel 1995, Peterjohn et
al. 1995, Saner et al. 1997~. Data from both the CBC and BBS are now
compiled and are available on the Internet at USGS and Cornell Labora-
tory of Ornithology Web sites (BBS at http://www.mbr-pwrc.usgs.gov/
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THE EMPIRICAL AND CONCEPTUAL FOUNDATIONS OF INDICATORS 43
bbs.html; CBC at http://birdsource.tc.cornell.edu/cbcdata/~. Root (1993)
and her coworkers have shown that these data are sufficiently reliable to
detect temporal and spatial trends. In 1975, the Xerxes Society started the
annual Fourth of fuly Butterfly Count (FIC), now administered by the
North American Butterfly Association. The FIC is modeled after the CBC
and provides data that, when used carefully, are valuable for the study of
status and trends of rare and widely distributed species (Swengel 1995~.
The U.S. Department of Agriculture, in cooperation with other federal
agencies, funds systematic studies of crop and timber pests through such
programs as the Forest Health Monitoring Program (FHM) (USDA Forest
Service 1994, see http: / /willow.ncfes.umn.edu/fhm/publicat.htm). Most
Fish and Game departments census game fish, birds, and mammals. Fi-
nally, all officially endangered species are periodically censused and their
current ranges are known by county (Dobson et al. 1997~.
MODELS TO ASSESS ECOSYSTEM FUNCTIONING
For many years, ecosystems have been studied to determine patterns
(community structure, biogeography) and processes (energy flow, nutri-
ent cycling, stream flow, and oxygen content), but the measurements and
accompanying models focus on relatively small scales. More recently,
attention has also turned to processes operating at watershed scales that
link upstream and downstream communities and terrestrial and aquatic
. .
communities.
Conceptual Models
Conceptual models have been significant in the development of eco-
system ecology, especially in limnology, where they have been used
extensively for more than a century. For example, the concept of a lake as
a microcosm (Forbes 1887) introduced the ecosystem approach to ecology
and identified the processes that are still the primary foci of ecosystem
ecology: energy flow, elemental cycling, production and decomposition
of organic matter, and food web interactions. Lindeman's (1942) trophic-
dynamic model of energy flow through the food web of Cedar Lake intro-
duced to ecology such important topics as energy transfer efficiency,
relationships between production and decomposition, and physical and
chemical constraints on biological production.
Conceptual models help identify key links between ecosystem com-
ponents and serve as the basis for developing quantitative models. Flow-
diagram models are widely used to describe nutrient cycles, food webs,
and energy flows in ecosystems. The river-continuum model is the most
important conceptual model for river ecosystems. It is based on the fact
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ECOLOGICAL INDICATORS FOR THE NATION
that stream ecosystems undergo predictable physical and biological changes
from headwaters to mouth (Vannote et al. 1980~. In headwaters, terres-
trial ecosystems are the principal source of the organic matter that pro-
vides energy for stream organisms. Further downstream, these extrinsic
sources are increasingly replaced by instream primary production. Thus,
headwaters are dominated by organisms capable of processing leaves
and wood, whereas most of the consumers in downstream communities
depend on instream photosynthetic plants.
The river-continuum model provides qualitative predictions about
the kinds of species expected in a particular stream reach and region. The
model links the physical structure of streams and expected biota, and
provides a conceptual foundation for many biologically based stream
monitoring approaches. Extensive data are available for mid-reaches of
wadable streams, but few data are available for rapidly changing head-
waters and large rivers (but see Patrick et al. [1967] for a valiant attempt to
survey large rivers).
Other conceptual models have been developed to assist the design of
biotic indicators and to evaluate the effects of oxygen-demanding wastes
on stream ecosystems (Metcalfe 1989, Cairns and Pratt 1993~. These wastes
increase concentrations of fine organic particles and decrease oxygen con-
centrations. Among such indices are the Saprobien system (Kolkwitz and
Marsson 1909) and models based on the tolerances of species to changes
in turbidity and oxygen concentrations (Hilsenhoff 1982, 1987~.
Conceptual models that focus on specific groups of organisms (e.g.,
fishes) have been used to determine minimum conditions for survival of
recreationally or commercially important species. Models based on the
instream flow increment method (IFIM) seek to determine stream flows
necessary to support species of concern (Bovee 1996~. IFIM models com-
pare the proportional use of habitats by a species with the proportional
availability of particular water velocities. Other habitat-suitability models
have been developed by the U.S. Fish and Wildlife Service.
Recently, biogeographic models, based on expectations of regional
distributions of species in intact habitats (Kerr et al. 1985, see Chapter 1),
have been used to assess the condition of stream biotas. The best known
model of this type is the earlier-mentioned Index of Biotic Integrity (IBI,
discussed further in chapters 4 and 5), which was first applied to fish
communities and later extended to stream macrobenthos and diatom com-
munities. IBI models have been developed and used in the upper Mid-
west (Illinois and Ohio), the South (Arkansas), and the West (Oregon).
Many state agencies are using IBI analyses to develop biological criteria
for evaluating the status of stream ecosystems (e.g., Whittier and Rankin
1992~. IBI models and the analytical procedures that support them are
robust and capable of detecting many changes in community composi-
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THE EMPIRICAL AND CONCEPTUAL FOUNDATIONS OF INDICATORS 45
lion, although often they cannot distinguish among causes of degrada-
tion. Individual IBI models are region-specific and depend on availability
of extensive data on species distributions in the focal region.
Empirical Models
Empirical models are used to describe quantitative associations be-
tween variables or sets of variables and to predict the values of variables
from measured values of other variables. These models are capable of
making useful predictions even when cause-effect relationships between
predictor and predicted variables are poorly understood.
Examples of useful empirically determined relationships are the posi-
tive correlation between chlorophyll a levels (a measure of algal biomass)
and total phosphorus concentration (the nutrient assumed to be limiting
algal growth) (Sakamoto 1966, Dillon and Rigler 1974a); the negative cor-
relation between Secchi-disk transparency (a simple measure of water
clarity) and chlorophyll a levels (Carlson 1977~; the positive correlation
between mercury concentrations in fish and their size (Lathrop 1992~; and
the correlation between log KoW (the octanol-water partition coefficient)
for various synthetic organic compounds and various measures of bio-
accumulation and microbial degradability of such compounds (see
Brezonik 1994 for an extensive review).
Semi-empirical models typically employ major simplifying assump-
tions to portray process mechanisms. Short-term variations and detailed
spatial patterns in ecosystem conditions are not generated by the outputs
of these models. Often called reactor models, they have been most exten-
sively developed for lakes, which are treated as completely mixed tank
reactors. Both inputs and losses of the substance being modeled are
assumed to be constant or to be simple first-order processes. Reactor
models were first applied by Vollenweider (1969, 1975) to analyze lake
responses to phosphorus inputs. Coupled with empirical relationships
between average concentrations of total phosphorus in the water column,
summertime chlorophyll a levels, and Secchi-disk transparency measures,
these models were successful in describing gross features of lake eutrophi-
cation. They were also used to develop phosphorus-loading criteria, at
values above which the water quality of a lake would be expected to
degrade (Vollenweider 1975, 1976; Dillon and Rigler 1974b, Baker et al.
1981~. The model BATHTUB (Walker 1987) is a computerized version of
the reactor model for lakes and reservoirs.
Reactor models have also been used to describe sulfate reduction and
alkalinity generation in acid-sensitive lakes (Baker and Brezonik 1988,
Kelly et al. 1988), retention of humic matter in bog lakes (Engstrom et al.
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ECOLOGICAL INDICATORS FOR THE NATION
1988), and concentrations of various synthetic organic contaminants in
lakes and reservoirs (Schnoor 1981~.
The advantages of reactor models include their simplicity and low
data requirements. They require few coefficients and small amounts of
physical information on the system being modeled. Their disadvantages,
which also stem from their simplicity, are that their coefficients lack simple
physical meaning and must be determined empirically, that they are
unable to capture short-term dynamics, and that they lack explicit ties
between modeled output of substances and biotic responses.
Compartment models are similar to reactor models and use many of
the same mathematical formalisms (Brezonik 1994~. The compartments
in such models generally represent mass quantities (or reservoirs) of sub-
stances or elements within discrete biotic and abiotic components. Flows
of substances between compartments are expressed as simple first- or
second-order differential equations. Such models have been used to
describe in-lake dynamics of phosphorus cycling, including regeneration
rates of inorganic P from organic P by zooplankton and microbial decom-
posers (Lyche et al.1996~. On a much broader scale, compartment models
have been used to describe global cycling of carbon and phosphorus
among major biotic and abiotic reservoirs (Lasaga 1985~.
The forerunner of all deterministic water quality models is the
Streeter-Phelps model for dissolved oxygen in rivers (Streeter and Phelps
1925~. Developed in the mid-1920s, long before the advent of computers,
the model expressed changes in oxygen concentration in a river as the
difference between a loss term representing biochemical oxygen demand,
caused by microbial degradation of organic matter, and a source term
representing atmospheric re-aeration. Other source and sink terms, such
as planktonic primary production and sediment oxygen demand, later
were added to the model; it was computerized in the 1960s. Currently it
can be applied to complicated river-estuarine systems and can produce
time-varying output at any desired distance along the stream.
Simulation Models
The most advanced simulation models for water quality (e.g.,
HydroQual, Inc. 1991, 1998; fin et al. 1998) are capable of modeling river-
ine-lake and riverine-estuarine ecosystems in three spatial dimensions at
integration times on the order of minutes. They can produce output as
daily averages over a year or several-year period for a wide range of
physical, chemical, and biological variables, including water elevation
(lakes) or flow (rivers), temperature, concentrations of inorganic nitrogen
forms, dissolved organic phosphate, dissolved organic N and P. particu-
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THE EMPIRICAL AND CONCEPTUAL FOUNDATIONS OF INDICATORS 47
late N and P. silica, inorganic carbon, pH, dissolved and particulate or-
ganic carbon, and phyto- and zooplankton biomass.
Although some early simulation models included fishes, these
models' outputs failed to accurately portray fish population dynamics.
Also, current simulation models of aquatic ecosystems typically exclude
benthic invertebrate and macrophyte populations. These components of
aquatic ecosystems are difficult to model for several reasons: these popu-
lations are influenced by many factors other than food availability (the
major driver in the models); the models simplify life cycles of organisms;
and the rates at which the ecosystem populations fluctuate are often much
slower than the rates of changes in other processes in the models.
Models that do not attempt to simulate variations in all three spatial
dimensions have much simpler data requirements and are more practical
for long-term assessments of biological processes. The one-dimensional
lake simulation model MINLAKE (Riley and Stefan 1987) ignores areal
variability, but treats important vertical variations, and is suitable for
modeling relatively small lakes. It accurately simulates seasonal patterns
of thermal stratification in temperate lakes and is fairly successful in simu-
lating temporal trends in vertical profiles for a variety of chemicals,
including dissolved oxygen. The model has been used to predict the
effects of climate warming on cold-water and warm-water fish communi-
ties in Midwest lakes (Stefan et al. 1995, 1996~.
A number of process models provide well-tested empirical estimates
of changes based on point or diffuse source inputs of oxygen-demanding
wastes (Beck 1987, Thomann and Mueller 1987~. They require only mea-
sures of stream discharge and estimates of degradation and improvement
parameters; no biological data are used.
Landscape Models
Spatial models of landscapes using geographic information systems
(GIS) are used to make a variety of predictions about landscape changes
and to indicate how those changes may affect stream ecosystems. For
example, soil erosion from landscapes is predicted by the Universal Soil
Loss Equation (USLE, Wischmeier and Smith 1978~. The Area Nonpoint
Source Watershed Environmental Response Simulation (ANSWERS;
Beasley and Huggins 1982) simulates surface runoff and erosion in agri-
cultural watersheds. This model incorporates data on overland flows that
are not included in USLE. The Agricultural Nonpoint Source (AGNPS)
model simulates runoff and sediment and nutrient transport across a
range of watershed sizes (Young et al. 1989~. This model uses grid cells to
evaluate hydrology and material transport, and incorporates data on
streambanks, eroded gullies, and nutrient sources. Flowpath models,
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ECOLOGICAL INDICATORS FOR THE NATION
such as TOPMODEL (Beven and Kirkby 1979, Beven 1997), make pixel-
by-pixel estimates of conditions using land-surface data inputs. The
Regional HydroEcological Simulation System (RHESSys; Band et al. 1991,
1993) and CENTURY model (Parson et al. 1992, 1994) are also widely used
ecosystem models, the former including hydrology and the latter with a
focus on soil organic matter (SOM). Despite the extensive development
of these and many other ecological simulation and process models, the
linkages between landscape processes and stream biota remain poorly
understood. Understanding these connections is difficult because time
lags between landscape changes and instream responses are highly variable.
THE COMMITTEE'S CONCEPTUAL MODEL FOR
CHOOSING INDICATORS
To guide its selection of national ecological indicators, the committee
assessed the current status of empirical and conceptual knowledge of the
factors that most strongly influence ecosystem functioning. With a few
local exceptions, terrestrial and freshwater ecosystems are open systems
powered by sunlight. Solar energy is incorporated into ecosystems by
photosynthesis, which is carried out by green plants, protists, and photo-
synthetic bacteria. The goods and services that ecosystems provide to
humans depend directly or indirectly on ecosystem productivity, i.e., their
ability to capture solar energy and store it as carbon-based molecules.
Therefore, the committee recommends several indicators of ecosystem
productivity.
The rate of capture of solar energy by photosynthesis is called primary
productivity. Primary productivity is strongly influenced by temperature,
moisture, soil fertility, and the structure and composition of ecological
communities. Information on these factors can be used as the basis for
accurate estimates of the primary productivity of most ecosystems. There-
fore, useful indicators of ecological conditions and the productivity of
ecosystems are based on data about these factors.
Extensive climatic data are already being gathered and are available
to be incorporated into models of ecosystem performance. Rates of photo-
synthesis are measured locally for different types of ecosystems. To cal-
culate the overall status and productivity of the nation's ecosystems,
information is needed on the extent of each of the major types of eco-
system used to determine photosynthetic rates.
The condition for maintenance of soil fertility, a key determinant of
productivity, is that inputs and losses of nutrients must be balanced. In
natural ecosystems, new nutrients are made available by weathering of
rocks and soils and by atmospheric deposition. In most agroecosystems,
natural nutrient inputs are supplemented, sometimes massively, by
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THE EMPIRICAL AND CONCEPTUAL FOUNDATIONS OF INDICATORS 49
application of fertilizers. Indicators of fertility can use data from direct
measurements of nutrient concentrations in soils and of nutrient exports
to other ecosystems.
Rates of photosynthesis are strongly influenced by the structure and
composition of the species in the ecosystem. Structure is important
because more complex vegetation may be able to intercept more sunlight
than structurally simpler vegetation. The species composition of biologi-
cal communities influences primary production in part because species
differ in their abilities to photosynthesize under different weather condi-
tions. Although relationships between ecosystem productivity and the
number of species in the system are as yet poorly understood, it is clear
that without some minimal number of species, ecosystems would func-
tion poorly (Grime 1997, Tilman 1996~. Therefore, although relationships
between species richness and ecosystem functioning cannot yet be quan-
tified, the loss of species is a cause for concern. If one discovers that a
species had great ecological or economic importance after it has dis-
appeared, it is too late to do something about it. In addition, species are
valued by societies for moral, aesthetic, and cultural reasons (Sagoff 1996),
as expressed in international treaties and national laws (NRC l999b).
Species composition also influences ecosystem performance by influ-
encing the frequency and severity of diseases and pest outbreaks
(Gunderson et al. 1995, Mooney et al. 1996~. In addition, exotic species,
many of which have escaped from their natural enemies, often achieve
higher abundances than in their native lands and hence cause ecological
problems (Drake et al. 1989~. Therefore, measures of the presence of native
and exotic species are important inputs to national ecological indicators.
How the committee used this conceptual model is described in
Chapter 4, where we recommend a set of indicators that use data on the
key factors that influence ecosystem functioning. These indicators are
intended to provide the basis for a comprehensive national assessment of
the current state and trends in the nation's ecosystems.
Policy Perspectives on Indicators
Indicators are most likely to be useful if they are understandable,
quantifiable, and broadly applicable. They are likely to command atten-
tion if they capture changes of significance to many people in many places.
Although indicators of local effects are not without value, they must be
aggregated into some composite indicator if they are to serve broad policy
purposes. Indicators are most policy-relevant if they are easily inter-
preted in terms of environmental trends or progress toward clearly
articulated policy goals, and if their relevance is made clear (Landres
1992~. In other words, indicators that convey information meaningful to
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ECOLOGICAL INDICATORS FOR THE NATION
decision makers and in a form these decision makers and the public can
understand are more likely to be observed and acted on. Indicators are
also more likely to be influential if they are few in number and capture
key features of environmental systems in a highly condensed but under-
standable way. The manner in which data are aggregated to yield a small
number of general indicators should be clear, especially to those who
wish to understand how the indicators were developed. The reasons for
choosing indicators, and the selection criteria, should also be clear
(Landres 1992~.
Any objective ecological indicators should be expressed numerically,
so that results can be compared with those of indicators in other places
and times. For the indicators to command attention and be used, the data
and calculations they are based on must be credible. The choice of what
indicators to use and how to define them is necessarily somewhat subjec-
tive, but the procedures for measurement and calculations associated with
a particular indicator, once defined, must be clearly specified, repeatable,
and as free of subjective judgments as possible. Where they are unavoid-
able, the sources of subjectivity should be defined and identified (Landres
1992, Susskind and Dunlap 1981~. For example, the Consumer Price Index
and the percent of people unemployed are calculated by well-defined
rules that have been agreed on, regardless of a person's view about the
value of full employment or low inflation or even the validity of these
indices. Debates about these numbers do not involve who calculated
them. Similarly, ecological indicators need to be based on calculations
that are well defined and agreed on.
In addition to being based on credible measurements and calcula-
tions, the choice, motivation, and interpretation of indicators should be
publicly trusted for them to be of greatest use. That means that the people
and organizations who produce the indicators should be generally trusted
(Greenwalt 1992~. The committee cannot specify the best methods for
achieving this goal, but notes that in at least some cases separating the
responsibility for preparing indicators from responsibility for carrying
out policies based on them seems to enhance trust in the indicators. For
example, the Bureau of the Census has no policy-making responsibility;
so, despite recent political arguments about the validity of sampling as
opposed to counting everyone, the population estimates produced by the
Bureau are usually trusted. Similarly, the National Weather Service has
no responsibility for environmental policies, and so, beyond some scien-
tific questions about the nature and placement of its instruments, its
statistics are generally widely respected and trusted. The importance of
public trust in the indicators is even more critical if ecological indicators
are to be used as input for a national assessment of the state of the nation's
ecosystems, as we hope they will be.
Representative terms from entire chapter:
conceptual foundations
