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Validating models of ecosystem response to global change
For the dificulty in geting full text, here I just give the full text of this great paper.
Validating models of ecosystem response to global change., By: Rastetter, Edward B., Bioscience,
00063568, Mar1996, Vol. 46, Issue 3
How can we best assess models of long-term global change? Short-term test are unable to address the
slow-responding mechanisms likely to dominate long-term responses to climate and carbon dioxide change
Models are an essential component of any assessment of ecosystem response to changes in global climate
and elevated atmospheric carbon dioxide concentration (Reynolds et al. 1996). The problem with these
models is that their long-term predictions are impossible to test unambiguously except by allowing enough
time for the full ecosystem response to develop. Unfortunately, when one must assess potentially
devastating changes in the global environment, time becomes a luxury. Therefore, confidence in these
models has to be built through the accumulation of fairly weak corroborating evidence rather than through a
few crucial and unambiguous tests. The criteria employed to judge the value of these models are thus likely
to differ greatly from those used to judge finer scale models, which are more amenable to the scientific
tradition of hypothesis formulation and testing.
A recent exchange in the literature has rekindled the debate over model testing in the earth sciences
(Oreskes et al. 1994, Rykiel 1994). The essence of the debate is the so-called problem of induction, that is,
the problem of extrapolating from the specific to the general (Popper 1959). Because of the problem of
induction, no specific test or series of tests can establish the general validity of any model. Modern solutions
to the problem of induction all rely on severe model tests. For example, Popper's (1979) falsification solution
builds confidence in the validity of a hypothesis (model) through repeated attempts to falsify it with severe
and crucial tests. Platt's (1964) method of strong inference also depends on an unequivocal test capable of
eliminating one or more hypotheses from among several alternatives.
The unequivocal tests required by modern scientific methods cannot be used to evaluate models of
ecosystem response to changes in climate and carbon dioxide concentration (ERCC models). All such tests
prove either irrelevant or ambiguous unless enough time is allowed for the full ecosystem response to
develop. Because of the impossibility of rigorous tests, Weinberg (1972) would classify the questions
addressed by ERCC models as "trans-scientific." That is, "though they are, epistemologically speaking,
questions of fact and can be stated in the language of science, they are unanswerable by science; they
transcend science" (Weinberg 1972, p. 209). The resolution of trans-scientific questions requires the
synthesis of indirect scientific information from diverse sources. I believe that the major role of ERCC
models is to provide this synthesis.
Four categories of tests potentially could be used to evaluate ERCC models: tests against short-term data,
space-for-time substitutions, reconstruction of past responses to climate change, and comparison with other
models. I believe that these four categories cover all possible tests except the ultimate test of waiting the
requisite time for the full response to global climate change to develop. I address each category in turn and
illustrate why not one presents a means to construct severe and crucial tests. I then discuss the synthesis
role of ERCC models and why they are vital to any assessment of long-term responses of ecosystems to
changes in global climate and carbon dioxide concentration. Tests against short-term data
Data derived from short-term (less than 10 years) experiments are often used to test models designed to
simulate long-term (20-200years) ecosystem behavior. Two critical questions emerge as to the relevance of
these tests: Is corroboration by short-term data sufficient to justify confidence in long-term projections
made with the model? Is corroboration by short-term data necessary to justify confidence in long-term
projections made with the model? The answer to both questions is no.
The arguments for both aspects of relevance are based on an analogy to a well-corroborated photosynthesis
model--the Farquhar model (Farquhar and von Caemmerer 1982). In each case, the analogy is developed
by redefining short term and long term. By applying the Farquhar model at three different time scales
(milliseconds, seconds to minutes, and weeks), I illustrate the problems inherent in testing a model at a
scale other than that at which it was intended to be applied.
In the Farquhar model, carbon assimilation is calculated as the minimum of the rate of ribulose
bisphosphate (RuBP) carboxylation and the rate of RuBP regeneration. Variations on this model have dealt
either with alternate equations to calculate the effect of quantum flux density (e.g., Harley et al. 1992,
McMurtrie et al. 1992), ways to couple it to a stomatal model (e.g., Ball et al. 1987), or with additional
constraints imposed by other factors, such as phosphorus concentration (e.g., Harley et al. 1992).
Nevertheless, the Farquhar model is a well-tested, well-corroborated model of the responses of C3
plants(n1) to changes in carbon dioxide concentration and light on a temporal scale of seconds to minutes
(e.g., Collatz et al. 1990, Farquhar and yon Caemmerer 1982, Harley et al. 1992).
Sufficiency. Short-term tests are never sufficient to test long-term models, because slow-responding
mechanisms that dominate long-term behavior can often be neglected in the short term. To illustrate this
problem, I use the corroboration of the Farquhar model at the time scale of seconds to minutes as the
analog for corroboration of ERCC models against data from experiments lasting less than ten years. I use
three-week projections made with the Farquhar model as the analog to 20-to 200-year projections made
with ERCC models. Is this strong corroboration on a seconds-to-minutes time scale (short term) sufficient to
justify confidence in using the model to project responses of leaves to changes in carbon dioxide and light
on a scale of weeks (long term)?
Consider the results of a doubled carbon dioxide experiment conducted by Tissue and Oechel (1987). When
leaves of Eriophorum vaginatum that had been grown at 340 ppmv carbon dioxide were suddenly subjected
to 680 ppmv carbon dioxide, net photosynthesis increased just as the Farquhar model predicts (Figure 1a).
However, after approximately three weeks of exposure to 680 ppmv carbon dioxide, net photosynthesis
decreased back to its original rate. The Farquhar model cannot simulate this decrease in net photosynthesis
because the mechanisms responsible for the acclimation to higher carbon dioxide, concentrations (e.g., a
decrease in the maximal rate of carboxylation) are not represented.
This application of the Farquhar model on a time scale of weeks is unfair--it was never intended to be
applied at that scale. However, it does illustrate the point: Corroboration of a model against short-term data
(seconds to minutes in this case) does not justify confidence in longer term projections with the model
(weeks). The potential always exists that long-term behavior is affected by slow-responding mechanisms
whose effects can be neglected in the short term. Short-term data are never sufficient for testing how well
these slow-responding mechanisms are represented in the model (Reynolds and Leadley 1992). Thus, even
if the Farquhar model were coupled to an acclimation model, the short-term (minutes) data would not be
sufficient to test how adequately the acclimation mechanisms were represented in the model.
Necessity. Short-term tests are not necessary to test long-term models because the fast-responding
mechanisms that dominate short-term behavior are often not important to long-term behavior. To illustrate
this case, let us use manipulations at a time scale of milliseconds as the analog for less-than-10-year
experiments and the seconds-to-minutes projections made with the Farquhar model as the analog to the 20to 200-year ERCC model projections.
Emerson and Arnold (1932) found that the rate of photosynthesis for Chlorella pyrenoidosa cultures exposed
to a bright light that flashed once every 20 msec was no slower than that of cultures exposed to continuous
bright light. By adjusting the relative lengths of the light flashes and the interspersed dark periods, they
found that they could change the efficiency of photosynthesis per unit light quanta. With a 4-millisecond
flash followed by a 16-millisecond dark period the efficiency increased fourfold over that of continuous light
(Figure 1b). The Farquhar model is unable to simulate this increase in photosynthetic efficiency because,
again, its structure does not incorporate the short-term mechanisms controlling the rapid charge and
discharge of chlorophyll and the short delay as energy is transferred between the light and dark
photosynthetic reactions. However, does this falsification of the Farquhar model with short-term data
(milliseconds) preclude its use to model the long-term responses for which it was designed (seconds to
minutes)? Again, applying the Farquhar model on this fine time scale is unfair, but it illustrates that
falsification of a model against short-term data does not mean that the model is unreliable for making longer
term projections. The Farquhar model can still be used reliably to predict photosynthetic responses to
changes in carbon dioxide and light on time scales of seconds to minutes.
Clearly corroboration of a model with short-term data is neither sufficient nor necessary for making reliable
longer term projections. This conclusion was obvious for the Farquhar model, because in each case I had
access to both the short-term and long-term data. In the case of ERCC models, the long-term data are not
available, but the conclusion nevertheless holds. Data are relevant for testing a model only if the time scale
of the mechanisms represented in the model and the time scale of the data are the same. It is likely that
ERCC models have both short-term (less than 10 years) and long-term (20-200years) mechanisms. Shortterm experimental data currently available are applicable only for testing the short-term mechanisms in
ERCC models. The long-term mechanisms (e.g., those involving slow turnover of woody tissues and soil)
cannot be tested in this way. Unfortunately, the long-term mechanisms are likely to dominate the long-term
responses of ecosystems to changes in global climate and atmospheric carbon dioxide concentration.
To place this conclusion in the context of ERCC models, consider a model (Marine Biological Laboratory's
General Ecosystem Model, MBL-GEM; Rastetter et al. 1991) calibrated and tested using data from a nineyear experimental manipulation of temperature, nutrients, and light (Chapin et al. 1995).(n2) I applied this
model to make 50-year projections, assuming that the 9-year experimental data were sufficient to constrain
longer term mechanisms represented in the model. Simulations were run to examine the responses of arctic
moist tundra to the combined effects of a 5 Celsius increase in temperature and a 10% decrease in soil
moisture (Rastetter et al. in press). Both treatments were expected to increase the rate of decomposition in
this waterlogged ecosystem (Oechel et al. 1993). Thus the initial effect in the simulation was a release of
soil carbon as carbon dioxide. However, increased decomposition also releases mineral nitrogen, which
becomes available to vegetation and stimulates productivity in this strongly nitrogen-limited ecosystem
(Chapin et al. 1995, Shaver and Chapin 1991, Shaver et al. 1992). The simulations indicate that for
approximately 20 years the rate of nitrogen uptake by vegetation is approximately equal to the
mineralization rate of nitrogen from soils. Thus, nitrogen lost from the soils is accumulated in vegetation,
and little of the mineralized soil nitrogen is lost from the ecosystem. However, after approximately 20 years,
the vegetation becomes dense enough to intercept all usable light. Further increases in vegetation are
therefore limited by the availability of light needed for photosynthesis. Nitrogen can then no longer be used
by vegetation at the same rate that it is mineralized in the soil, and the loss rate of nitrogen from the
ecosystem increases markedly (Figure 2).
The ramifications of this so-called nitrogen breakthrough for terrestrial ecosystems downslope, aquatic
ecosystems downstream, and nitrogen-based greenhouse gases in the atmosphere could be important in the
context of global change. However, can short-term experiments validate the result? Clearly tests can be
made on the short-term responses of the model like the initial increase in decomposition and nitrogen
mineralization and the increase in productivity and rate of nitrogen uptake by vegetation (e.g., Chapin et al.
1995).(n3) However, the key to the predicted result is the decoupling of soil nitrogen release and vegetation
nitrogen uptake, which, according to the model, does not happen for approximately 20 years. Unanticipated
slow-responding mechanisms could easily change the predicted result. No short-term data could uncover the
omission of such mechanisms from the model. Even if all the important mechanisms are in the model, the
short-term data are not sufficient to constrain the parameters controlling long-term responses, because
errors in these parameters would have little effect on short-term predictions. Therefore, the result, although
important and deserving of due consideration when assessing responses of tundra to global climate change,
cannot be corroborated with short-term data. Space-for-time substitutions
Space-for-time substitutions are among the most powerful means available for studying succession (i.e., the
progressive change in plant communities at a site following a major disturbance). Studies employing this
method have, for example, examined ecosystem characteristics of a series of plots in old agricultural fields
abandoned 75, 42, 34, 22, 11, 3, 2, and i year(s) ago (e.g., Oosting 1942); plots from which a glacier
retreated 180, 120, 100, 70, 35, 31, 29, 23, and 11 years ago (e.g., Crocker and Major 1955); or lava flows
that are 47, 137, 300, 400, and 3000 years old (e.g., Drake and Mueller-Dombois 1993). In this way, space
(location of the plot) is substituted for time (time since abandonment or glacial retreat or age of lava flow).
Fastie (1995) has pointed out the weaknesses of this space-for-time substitution, but the approach has
nonetheless been powerful in the development of theories of succession. Responses of ecosystems to
changes in global climate and atmospheric carbon dioxide concentration also can be viewed as a type of
succession. Therefore, it seems reasonable to expect that a similar approach could address climate change
issues and, in particular, test ERCC models.
If the climate at one location is expected to change so that it resembles the climate at another location, then
the ecosystem characteristics (e.g., species composition and biogeochemistry) at the original location might
be expected to change so that they resemble the present-day characteristics of the second ecosystem. Of
course, this hypothesis must itself be tested. However, for the present purposes, I assume that the
hypothesis is valid.
There are two problems with space-for-time substitutions in the context of testing ERCC models. First, it is
difficult to find regions with carbon dioxide levels as high as those projected for the next century. If the
direct response to carbon dioxide is an important aspect of ecosystem response to changes in climate and
carbon dioxide, then the space-for-time substitution is difficult to apply.
Second, unlike the case for succession, applying space-for-time substitutions to changes in climate involves
a comparison with ecosystems that have come into balance with the local climate. Consider the Amazon
basin. With a doubling of carbon dioxide, it is projected that temperatures in this region could increase by as
much as 4 Celsius within 60-80 years, that soil moisture could decrease by more than 2 cm, and that rainfall
(and cloudiness) could change substantially (Bretherton et al. 1990). Although these changes are large, the
present-day range of climatic conditions within the Amazon are of approximately the same magnitude. We
have used this range in present-day climatic conditions to constrain and test the equilibrium predictions of
MBL-GEM (McKane et al. 1995). However, unlike the abandoned fields or the retreating glacier, there are no
places within the Amazon or elsewhere where the climate has changed by this magnitude within the last 20200 years. The ecosystems across the climatic gradient have had hundreds or thousands of years to adjust
to the respective climates, and the available data can therefore only be used to test the long-term,
equilibrium predictions of the model. Even if the underlying assumption that ecosystems eventually come to
resemble one another if placed in similar climates is valid, there is no information in this space-for-time
substitution to indicate how the ecosystem characteristics change through time. The data only indicate the
final state.
Responses of terrestrial ecosystems to climate change involve slow changes in species composition, the
growth of long-lived tree species, and the slow turnover of soil organic matter. These responses can
therefore take centuries. If the goal is to model the 20-200 year responses, then the space-for-time
substitution does not yield the necessary information. There could be many ways by which the ecosystem
might arrive at a new equilibrium state under a new climate. Thus, the second problem with space-for-time
substitutions is exactly the opposite of that of the short-term tests. Therefore, the same arguments can be
applied in reverse to the relevance of very-long-term (i.e., equilibrium) tests of long-term models. That is,
there easily could be unanticipated mechanisms acting on a 20- to 200-year time scale that would not be
obvious from an examination of equilibrium data. No equilibrium data could uncover the omission of such
mechanisms from the model. Reconstruction of the past
There have been significant changes in climate and carbon dioxide concentration in the recent past (last 200
years; e.g., Boden et al. 1994, Fritts and Lough 1985, Graumlich and Brubaker 1986). On first inspection,
these changes present the ideal opportunity for testing ERCC models. Not only would such tests involve
responses to many of the same factors that are of concern for the future, but the responses would be at the
appropriate time scale. However, other problems with the data preclude their use for severe and crucial
tests of ERCC models. The most obvious problem is the potential for nonlinear responses to changes in
climate and carbon dioxide. Because of these nonlinearities, there is no guarantee that tests against the
range of climate and carbon dioxide changes in the past will apply to the range expected in the future.
Short-term tests can be used to gain confidence that the nonlinearities in fast-responding processes are well
represented, but the slow-responding processes remain problematic.
A second problem is that the magnitude of the response of ecosystems to changes in climate and carbon
dioxide thus far is too small to be discernible given the quality of available data (Mooney et al. 1991). That
is, there is a signal to noise problem. This problem is exacerbated by the inherently stochastic nature of
many ecosystem processes, such as seed dispersal and tree mortality. Models that incorporate these
stochastic processes have correspondingly broad confidence ranges in their predictions (e.g., Shugart and
West 1980).
A consideration of the atmospheric carbon budget illustrates the magnitude of the signal-to-noise problem.
Each year approximately 7.0 +/- 1.1 x 1015g carbon is released to the atmosphere through fossil fuel
burning, cement manufacturing, and changes in land use (Watson et al. 1990). Of this amount, 3.4 +/- 0.2
x 1015g accumulates in the atmosphere, 2.0 +/- 0.8 x 1015g is taken up by ocean water, and 1.6 +/- 1.4 x
1015 g is removed from the atmosphere by some unknown process. The 1.6 x 1015g carbon that is removed
by an unknown process is often called the missing carbon (Watson et al. 1990) and is often thought to be
stored in terrestrial ecosystems as a result of an imbalance between net primary productivity and soil
respiration (e.g., Kauppi et al. 1992).
If the missing carbon is entering terrestrial ecosystems, it represents a storage rate equal to only
approximately 3% of the net primary production of all terrestrial ecosystems (Melillo et al. 1993). Similarly,
the cumulative missing carbon since 1850 is only 70-95 x 1015g carbon (Houghton 1993, 1995), or
approximately 4% of the total carbon in soils and vegetation around the world (Schlesinger 1991). If this
rate of carbon accumulation is distributed among terrestrial ecosystems in proportion to their respective
rates of net primary production, then this rate is far too small to be detected as either the difference
between net primary production and soil respiration or as a cumulative increase in ecosystem carbon stocks.
Even if all the storage were restricted to temperate and boreal forests (Francey et al. 1995), it would equal
only approximately 15% of net primary production in these ecosystems and would be difficult to detect. The
detection problem is exacerbated by past disturbances (e.g., fire or logging) in many ecosystems. Past
disturbances make it difficult to discern how much of the present-day carbon accumulation rate is
attributable to recovery from disturbance and how much is attributable to changes in climate and carbon
dioxide concentration (e.g., Rastetter and Houghton 1992).
Even if the present-day rate of carbon accumulation associated with changing climate and carbon dioxide
could be measured, it would not be enough to test a model designed to simulate long-term carbon
accumulation. To test the model, information would also be needed to initialize it for preindustrial conditions.
Any error in this initial estimate would propagate through the simulation and corrupt predictions throughout.
The problems with estimating present-day carbon stocks and flux rates are amplified for estimates of these
quantities in the past. I know of no reliable estimates of preindustrial organic matter stocks in soils and
vegetation. To get the timing of ecosystem response correct, information would also be needed at several
times during the reconstruction, but such records of changes in ecosystem structure and function through
time are poor (Mooney et al. 1991).
In addition, time-series data on climate and carbon dioxide concentration would be needed to drive the
model. The carbon dioxide data are readily available from Mauna Loa measurements (Keeling and Whorf
1994) and the Sieple (Neftel et al. 1994) and Greenland (Wahlen et al. 1991) ice-core records and are of
high quality. Temperature records, on the other hand, are only available for some locations and often do not
extend back more than a few decades or are reported from fairly broad regions (e.g., Karl et al. 1994,
Wilson and Hansen 1994). Temperature records can also be inferred from tree rings, but the correlation
coefficients (r2) for even high-quality tree ring temperature reconstructions are typically less than 50%
(e.g., Graumlich and Brubaker 1986). Clear-day irradiance is readily calculated from latitude and time of
year (e.g., McMurtrie 1993), but climatic changes in cloudiness can greatly influence both photosynthetically active and total radiation. I know of no way to reliably reconstruct cloudiness. Soil moisture
models require not only precipitation data, which might not be available in all areas, but radiation and
temperature data as well, with the associated difficulties just mentioned. Thus there are major problems
with the input data needed to model ecosystem responses to changes in climate over the last few hundred
years. Any errors in these data would propagate through the model and corrupt the resulting reconstruction.
In the case of reconstructed responses to past changes in climate and carbon dioxide concentration, there
could easily be either unanticipated mechanisms or poorly constrained representations of known
mechanisms acting at a 20- to 200-year time scale. Available data could not uncover these problems in the
model because the responses thus far have been too small to measure. Even if these responses could be
measured, however, discrepancies between model predictions and the measurements could easily be
attributed to errors in the initialization data and the data used to drive the model rather than to an inherent
error in the model structure or parametrization. The best that can be done with the available data is to put
an upper limit on the magnitude of responses in the reconstruction. Little can be said about the timing or
direction of these responses. Comparison with other models
Because of the uncertainty associated with global circulation models, it has become commonplace to publish
side-by-side climate predictions from several global circulation models (e.g., Mitchell et al. 1990). The
implication is that the uncertainty is reduced if a prediction from one global circulation model agrees with
the predictions from other models. It is only a small logical step from this implied decrease in uncertainty to
an implication of corroboration among models. Of course, there is the problem of independence to resolve:
If the models are based on the same underlying concepts, would they not be expected to make similar
predictions? The flip side of the issue is more difficult: If two models do not agree, which of the two is false,
or are both?
Most empiricists and many modelers are rightly uncomfortable with this logic of testing models against
models. It clearly should not be considered a severe and crucial test. However, there is some merit in the
logic. Consider two cases:
If a tentative model is compared with an established model about which there is a high degree of
confidence, then surely some of that confidence is transferred if the two models' predictions agree. In this
case, the established model is simply acting as a synthesis of the data with which it had already been
compared and from which the high degree of confidence is derived. If the tentative model agrees with the
established model, then it should also agree with the data against which the established model was
corroborated.
Within the realm of global climate and carbon dioxide change, it is more common to compare models about
which there is equal skepticism (e.g., global circulation models). Each model in such a comparison is likely
to have been developed from an underlying set of established or hypothesized principles (e.g., mass
balance, resource limitation, competitive interactions). Agreement among models in this case means that
the results of one model do not conflict with the principles underlying the other models. For example, one
model might be based on biogeochemical principles of mass balance and the interactions among carbon and
nitrogen cycles (e.g., MBL-GEM; Rastetter et al. 1991). Another model might be based on the principles of
competition among individual trees for light and soil resources (e.g., Shugart and West 1980). If the two
models agree on the accumulation rate of carbon in the vegetation, then the biogeochemical model has not
conflicted with the constraints of tree-to-tree interactions in the competition model, and the competition
model has not conflicted with the constraints of mass balance and carbon-nitrogen interactions in the
biogeochemical model.
The degree of confidence that emerges from such a comparison depends on how well the underlying
principles in each model have been established and how independent these principles are among the
models. That confidence should build as the predictions are confirmed by more and more models with widely
different underlying principles. For example, confidence that carbon is likely to accumulate in terrestrial
ecosystems under projected changes in climate and carbon dioxide concentration should be greater if
predicted by ten different models than if predicted by only one or two models based on similar underlying
principles. The important property of this consensus-building approach is that the models be independent
and based on widely different underlying principles. It makes little sense to compare a model with another
that is based on the same underlying principles, because the model is essentially being compared with itself.
A weakness of the consensus-building approach is that it is not clear what a lack of consensus means. For
example, if the biogeochemically based model and the competition-based model disagree on the rate of
carbon accumulation, neither can be said to be falsified. There is no way of telling from an analysis of the
models whether the long-term carbon dynamics of the ecosystem are likely to be governed by the
biogeochemical constraints or the constraints imposed by tree-to-tree interactions. Similarly, if six out of
seven models agree on a particular prediction, there is no way to tell whether the principles underlying the
one nonconforming model are the ones that actually govern the long-term dynamics of the ecosystem.
However, such a comparison of models and an analysis of their underlying differences should suggest
productive lines for further inquiry. In particular, the comparisons can identify mechanisms that were
overlooked or omitted from the model but nevertheless have been found to be important in other models
(Oreskes et al. 1994).
Only a few formal comparisons have been made among ERCC models (e.g., Ryan et al. in press, VEMAP
members 1995). For example, Ryan et al. (in press) compared seven models based on their predictions of
long-term responses of pine plantations (Pinus radiata and Pinus sylvestris) to increased temperature and
carbon dioxide concentration. The models were classified into two groups based on their predictions. The
physiologically based models predicted a decrease in productivity with increased temperature because of
increased water stress and consequent stomatal closure. In contrast, the nutrient-oriented models predicted
increased productivity with increased temperature because of increased rates of nutrient mineralization and
consequent increases in nutrient availability. Because none of the seven models incorporated both the
physiological aspects of stomatal control and the recycling of nutrients from soil organic matter, there was
no way to evaluate which of these two mechanisms would dominate the long-term responses of the forests.
Ryan et al. (in press) call for "experiments ... designed to resolve this issue" and models linking the
pertinent aspects of both classes of model to "help explore the trade-offs." Inasmuch as the exercise
reported by Ryan et al. (in press) identified a potentially important omission in both sets of models, this
comparison constituted an important test. However, the test could not falsify or validate any of the models.
Synthetic value of ERCC models
The true value of ERCC models is in their synthesis of available information. An analogy to Weinberg's
(1972) discussion of engineering as trans-science illustrates that value.
Weinberg (1972) used large engineering projects, such as the design of a major dam, as an example of a
trans-scientific activity, because the design "typically involves decisions made on the basis of incomplete
data." An assessment of ecosystem response to global change has much in common with a large
engineering project. In both cases, tests on a full-scale prototype are impractical, long-term performance of
the system must be evaluated, and decisions must be made within a rigid time schedule based on "whatever
scientific data are at hand" (Weinberg 1972). The scientific data at hand encompass a great deal of
knowledge about the components of the systems being analyzed. Those components are often small enough
and respond rapidly enough that their independent performance can be evaluated using traditional scientific
tests. The scientific data at hand also include observations on similar systems in other situations, past and
present. The assessment must synthesize all this information so that well-informed decisions can be made.
The role of ERCC models is to provide this synthesis, a role that is often overlooked. A great deal is already
known about how ecosystems function, at least qualitatively. For example, the metabolic responses of plants
and soil microorganisms to temperature are known; the response of photosynthesis to light and carbon
dioxide concentration for most species is known; that plants compete for light and nutrients is known. As
skeptics, scientists tend to concentrate on the conjectural content of ERCC models and do not give enough
credit to their synthetic content.
Synthesis is the most important aspect of ERCC models. Because of this synthesis, ERCC models surpass
any other method of projecting responses to changes in climate and carbon dioxide concentration. For
example, projections could be based directly on the results of short-term experiments. However, this
method would be unreliable for longer term projections for exactly the same reasons that the short-term
experiments cannot be used as severe tests of ERCC models. The same is true of projections based on
space-for-time substitutions and assessments based on past responses to climate and carbon dioxide
change. However, a model can be constrained to be simultaneously consistent with all these empirical
sources of relevant evidence. That is, it can be used to build confidence through the synthesis of many lines
of empirical evidence, each of which might not be compelling in and of itself.
The synthesis within an ERCC model is more than just a compilation of independent information. Because
the model places the available information within the same context, the synthesis provides a test of the
"internal consistency of the system [model]" (Popper 1959). That is, do the pieces logically fit together? For
example, can the increased rates of growth predicted by greenhouse carbon dioxide experiments be
maintained by the rates of nutrient supply predicted by experiments on the turnover of soil organic matter?
The formal structure of a model is conducive to the resolution of this type of question. Because of the
complexity of interactions among the many components of an ecosystem, it would be difficult to develop a
self-consistent synthesis of the available information without a model. Passing this test of internal
consistency validates the model as a synthetic tool, if not as a predictive tool. Conclusion
I have examined four ways to evaluate ERCC models and illustrated why none of them can provide severe
and crucial tests of those models. Short-term empirical tests are unable to address the slow-responding
mechanisms likely to dominate long-term responses to climate and carbon dioxide change. Space-for-time
substitutions can only address the equilibrium responses to climate change and cannot address responses to
changes in carbon dioxide concentration. Reconstruction of responses to past climate change are ambiguous
because of the uncertainties in the data needed both to drive the model and to test it. Comparisons among
models can be used to evaluate the relative importance of various processes in determining the long-term
responses to global climate and carbon dioxide change, but they can never substitute for tests against realworld data. The unfortunate conclusion from this analysis is that ERCC models cannot be rigorously tested.
The whole topic of ecosystem responses to global climate and carbon dioxide change is therefore transscientific.
There are two potential ways the topic might be removed from the trans-scientific realm. First,
methodological or technical breakthroughs could improve the precision of data needed to drive and test
ERCC models through a comparison with past responses to climate and carbon dioxide. I believe that the
order of magnitude improvement that would be needed is unlikely. Second, responses to continued changes
in climate and carbon dioxide in the future are likely to be strong enough to make these responses
detectable even with currently obtainable precision. However, by the time the changes are detected in many
ecosystems, it may be too late for effective mitigation efforts. Therefore, the focus of both experimental and
monitoring efforts should be on ecosystems that are expected to respond rapidly. Identification of these
ecosystems should be a high priority and could be aided by the analysis of ERCC models.
Although the prospect for testing ERCC models is grim, these models are likely to remain a vital part of any
evaluation of the responses to changes in global climate and carbon dioxide because the alternatives are
worse. It would be foolhardy to plunge blindly into the future without some attempt to evaluate the global
consequences of human activities.
Evaluations should not be based solely on data from short-term experiments, space-for-time analogs, or
reconstructions of the past. Such evaluations would suffer from the same shortcomings that limit the use of
these data for testing long-term model predictions. The advantage that a model-based evaluation has over
all of these empirically based extrapolations is that it can be used to synthesize all of the empirical evidence.
The synthesis in the model provides a means of testing the self-consistency of the interpretations of
empirical evidence. Therefore, the model is a vital part of any evaluation of the responses of ecosystems to
global climate and carbon dioxide change. Acknowledgments
This article is derived from work supported by the National Science Foundation, the US Environmental
Protection Agency, and the National Aeronautics and Space Administration. I would also like to thank Gus
Shaver, Goren Agren, David Ford, Terry Chapin, Chuck Hopkinson, Dave McGuire, Bob McKane, Jerry Melillo,
John Hobbie, Dave Fernandes, and Mat Williams for their helpful comments.
(n1) Plants using the most common photosynthetic pathway, in which carbon dioxide is incorporated to form
the three-carbon compound 3-phosphoglyceric acid.
(n2) R. B. McKane, E. B. Rastetter, G. R. Shaver, K. J. Nadelhoffer, A. E. Giblin, and J. A. Laundre, 1995,
submitted manuscript.
(n3) See footnote 2.
GRAPH: Figure 1. Short-term tests are neither sufficient nor necessary to validate long-term model
predictions. (a) The model of Farquhar and von Caemmerer (1982) applied to the data of Tissue and Oechel
(1987). Although the model predicts changes in net photosynthesis minutes after carbon dioxide levels are
increased from 340 to 680 ppmv, this prediction is not sufficient to validate the model for predicting
responses weeks in the future. (b) The Farquhar and yon Caemmerer (1982) model applied to the data of
Emerson and Arnold (1932). Although the model is unable to predict increased rates of photosynthesis per
unit quantum flux (efficiency) as the ratio of light to dark periods decreases in a rapidly flashing light (20millisecond cycle), agreement at this fine time scale is not necessary to validate the model for use at the
time scale of seconds to minutes.
GRAPH: Figure 2. Simulated 50-year response of the nitrogen cycle in arctic moist tundra to a 5 Celsius
increase in temperature and a 10% decrease in soil moisture based on simulations reported in Rastetter et
al. (in press). The rates of nitrogen mineralization and nitrogen uptake by vegetation are approximately
equal for 20 years after the change in temperature and moisture. However, after 20 years the two processes
become decoupled and the loss of nitrogen from the ecosystem increases. There are no short-term data that
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~~~~~~~~
By Edward B. Rastetter
Edward B. Rastetter is an associate scientist at The Ecosystem Center of the Marine Biological Laboratory in
Woods Hole, MA 02543. His major research interests are in modeling carbon and nitrogen interactions in
terrestrial ecosystems and in the rates of carbon sequestration by terrestrial ecosystems. Copyright 1996
American Institute of Biological Sciences.