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Schmalhausen's Law



            Ivan Ivanovich Schmalhausen1 was a Soviet evolutionary biologist working at the Academy of Sciences in Minsk.  In the 1940's his  book "Factors of Evolution" appeared and was denounced by T.D. Lysenko, whose neo-Lamarckian theories of genetics were then  on the ascendency. At the close of the 1948 Congress of the Timiryazev Academy of Agricultural Science it was revealed that Stalin had endorsed Lysenko's report to the Congress in which it was affirmed that the environment can alter the hereditary makeup of organisms in a directed way by altering their development. A number of opponents of Lysenko's views then took the floor to reverse themselves and support Lysenko. Schmalhausen was one of the few who reaffirmed his opposition and spent the rest of his life in his laboratory studying fish evolution and morphology.

            In the West, Lysenko's views were simply dismissed. An assistant professor of chemistry, Ralph Spitzer, lost his job for daring to suggest that Lysenko's ideas should at the least be examined and tested.  But Schmalhausen could not ignore the Lysenko agenda which insisted on a more complex  interpenetration of  heredity and environment than genetics generally recognized. Along with Marxist and progressive scientists in the west  such as C.D. Waddington in the UK, he accepted rather than ignored the challenge. As a result he developed a more sophisticated approach to these interactions which explained the observations of  the better studies cited by Lysenkoists.

            Schmalhausen argued that much of natural selection is stabilizing rather than directional. That is, if a species is more or less well adapted to its conditions "the tendency to vary" that Darwin invoked causes the population characteristics  to become more spread out about its average state, and selection eliminates these variants. Genes are selected which work together with the other more common genes in such a way that most of genetic combinations  produce more or less viable and similar offspring under more or less normal conditions. However under unusual or extreme conditions where selection has not had the opportunity to operate, these genetic differences show up as increased variation.  This claim provided an alternative explanation to the observation that  populations that are apparently uniform under normal conditions show a wide range of variation under new or extreme conditions. Whereas Lysenko argued "that these populations were uniform and that the environment created new genetic variation, Schmalhausen argued that the environment revealed latent genetic differences which could then be selected.

            Waddington developed this line of reasoning further with his idea of genetic assimilation: suppose that there is some threshold condition in the environment for the development of a particular trait. Much below threshold none of the individuals show it, much above threshold they all do. But under some intermediate conditions some will be above and some below threshold. If those whose threshold is lowest are selected, we can select for low threshold and eventually produce organisms whose threshold is so low that the trait always appears under any conditions in which the organism can survive. Then the trait had become "assimilated": an environmentally induced condition had become fully genetic.

The 1950's and '60s was a time of great interest in the variability of traits in populations.  In Dobzhansky's laboratory at Columbia University, where both of us raised and counted fruit flies, there was interest in showing that genetically heterozygous individuals could tolerate a wider range of conditions than those that were genetically homozygous. Attempts were made to link population genetics with physiologcal homeostasis and the stability of development.. Some researchers in the same tradition examined the differences in the number bristles on two sides of the same fly to determine the ease with which differences in conditions on a microscopic scale could influence development. (When the scale is small enough, these microscopic events are designated "random" since we cannot influence them.)

            Schmalhausen's law is more general than the consequence of stabilizing selection and has implications in many areas.

            In biogeography: At almost any location on the earth, the ecological community is made up of species near the boundary of their distribution and also species that are in the middle of their range. When the environment changes, this has a major impact on the species near their boundary. Some may become locally extinct, other may experience great expansions of their abundance and their range, while others will remain more or less as they have been. Further, populations near their boundaries are especially sensitive to changing conditions and are more likely to show big differences from year to year. Therefore simple predictions about the effect of climate change are bound to err if they take into account only the direct physiological impact of the environmental change on species one at a time.

            The thresholds of toxicity: Tolerable levels of toxic substances are often set on the basis of experiments with animals. Usually the work is done with standardized healthy animals under well controlled conditions so as to minimize "error" due to individual differences or variation in the environment. However this methodology underestimates the impact of a toxin for a number of reasons. If an organism is exposed to a toxic substance of external or internal origin, it has various mechanisms to detoxify that substance. But thetoxin is still present., If there is a constant level of exposure, the toxin will reach some level of balance between new aborption of toxin and the rate of removal. This equilibrium depends on the level of exposure and the maximum capacity of the detoxification system to remove the poison.

            But of course we know that the environmental exposure is not constant for all members of a population or even for any one individual over time. And we also know that different members of the population differ in their detoxification capacity and that it may vary over time for the same person. Furthermore, this variability matters and cannot be averaged away.

             What good is a model that assumes constant conditions? Here we see one of the powerful ways in which models are useful in science. In physical and engineering sciences it is often possible to isolate a problem sufficiently to ignore external influences, assume that all switches are the same in what is relevant, that all salt molecules are interchangeable and so on. Then we can measure accurately and get equations that are as exact as we need. But in ecological and social sciences this is not possible. The populations are not uniform, conditions change and there is always an outside impinging on the system of interest. We cannot even believe the equations too literally. But we can still study these systems. First  we find the consequences of models under unrealistic conditions that are easily studied and give precise results. Then we ask, how do departures from those assumptions affect the expected outcomes? In this case, the standing level of toxicity, a measure of damage done to organism, is a mathematical function of v-s, the maximum detoxification capacity minus the exposure.  The maximum removal rate has to be greater than the exposure or according to the math the toxicity will accumulate without limit.  In reality, it will accumulate to the point where other processes which were negligible in the original model, take over. These might involve any of the consequences of toxicity such as cell deaths. When v is greater than s the graph of  toxicity plotted against v-s  decreases from zero as capacity exceeds exposure by greater and greater amounts. Furthermore, it is concave upward. That is, it is steeper the closer we are to v=s and flattens out when capacity is much greater than exposure.  If we measure the dose response curve in the range where capacity is much bigger than exposure then the results will show little effect of the poison and we will be reassured by claims that there is no detectable effect. Testing is often carried under optimal conditions on uniform populations of experimental animals in order to get uniform results, reduce the error, and avoid "confounding factors".

            If different stressors are confronted by the same detox pathways, they can be added at the level of  exposure and act synergistically at the level of toxicity. Therefore if we look at only one insult at a time, the other "confounding factors" increase the damage.

            In the United States, exposure varies with location and occupation. The poor, excluded and marginalized communities such as inner cities, colonias and reservations are often subject to multiple exposures due to incinerators, maquiladoras, poor water quality, malnutrition and unsafe jobs. Therefore even toxics which meet EPA standards will prove more harmful than expected. But these effects will be hard to detect since we wil observe an array of health impairments rather than a single harm appearing to different degrees.

            Similar arguments hold if the capacity to detoxify varies among individuals: the average toxicity in the population is greater than the toxicity at average detox capacity. Once again, if detox capacities are reduced then each unit of insult has a bigger effect than expected.

            We suspect that detox capacities are undermined in the course of life for all of us after the first two decades, but that adverse conditions accelerate this erosion so that vulnerability increases more rapidly and life expectancy is reduced by some [fill in] years for African-American women and [].

The variability of results: Since, when v-s is small, small differences in either one can have big effects, a population which is at a disadvantage will show big differences among people for reasons we cannot explain, and different poor communities will differ widely in the rates of adverse outcomes. This can easily be misinterpreted: it appears as if under the "same" conditions some do well and others badly, and we can then blame those who do badly. But what really is happening is that under conditions of any kind of stress, small differences have big effects.




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