Download Problems of Macroevolution (Molecular Evolution, Phenotype

AMER. ZOOL., 15:295-314 (1975).
Problems of Macroevolution (Molecular Evolution, Phenotype Definition, and
Canalization) as Seen from a Hierarchical Viewpoint
S. N. SALTHE
Department of Biology, Brooklyn College, C.U.N.Y., Brooklyn, New York 11210
SYNOPSIS. As seen from a hierarchical viewpoint, macroevolution is neither a functional
process nor a series of events in the past. It is a record only. For this reason macroevolutionary laws are all statistical laws.
Natural selection is a process that operates from one generation to the next at the
population level in the hierarchy. Yet structures at the organism level are found to "evolve."
It is possible to formulate only a tautological form of the concept of natural selection at the
population level alone; the bridge between levels in this case is the phenotype. The
phenotype (i) exists at the boundary between the organismic and population levels of the
hierarchy; (ii) is a functional manifestation of the interaction between the genotype and the
local environment only during the period of a single generation; (iii) should ideally be
defined so as to exclude traits not reviewed by natural selection; (iv) is factorable into many
individual functional traits if one views viability selection as being instituted by a sequence of
environmental catastrophes, each of which emphasizes a particular set of traits as being
temporarily important to survival.
It is reemphasized that the action of natural selection on continuously distributed, nonpolymorphic traits curtails variability in proportion to the intensity of selection. The necessity for coadaptation within the organism imposes a bell-shaped curve upon surviving
variability. Canalizing selection is proposed as the process that modifies these bell-shaped
curves into lognormal parametric distributions. It is also proposed that the per cent variability of the sample populations can serve as a measure of the intensity of natural selection
(normalizing and directional together) that has most recently been acting upon the traits in
question in the populations used to establish the parametric distributions.
What we now call macroevolution was
what Darwin hoped to explain using his
theory of natural selection. Macroevolution
can be defined as phenotypic transformations occurring over periods of time which
are long with respect to generation times.
There are at least three problems concerning the relationship between macroevolution and neo-Darwinian evolutionary
I would like to thank the following persons for helpful criticisms of an earlier version of the last portion of
this paper: C. Cans of the University of Michigan; W.
C. Kerfoot of the University of Washington; D. C.
Simberloff of the Florida State University; D. W.
Tinkle of the University of Michigan; L. Van Valen of
the University of Chicago. P. F. A. Maderson of Brooklyn College has been contributing to the development
of this model since its inception. I have had valuable
suggestions and help from R. H. Kaplan and J. Steinberg, both of Brooklyn College. I would like to thank
S. J. Gould, Harvard University, for his comments on
the manuscript. This work was carried out during the
time the author had the following research grants:
GB-7749 from N.S.F.; and 1108 and 1594 from
CUNY.
theory, which has for the most part been
concerned with genetic changes at single
loci over a period of relatively few generations. First, it is clear that the simple Mendelian "traits" which have served as model loci
in the development of evolutionary theory
in the Twentieth Century are not typical of
those usually dealt with in considering
long-term transformations, which are generally multigenetic traits. Attempts at combining the adaptive values of separate
Mendelian loci to build up a total adaptive
value for an individual have not been quite
successful, and some work has been devoted to sidestepping this problem by
proposing instead, for example, to view individuals in terms of the proportion of their
loci that are heterozygous (Sved et al.,
1967). Even more formidable problems
have appeared in attempting to understand
the effects on population genetic models of
tight linkage or of the differential fitnesses
of different combinations of alleles at
linked Mendelian loci (Kojima and Lewon-
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S. N. SALTHE
tin, 1970). Only very few genes can be
handled together analytically without overtaxing existing computational technology.
A second major problem arising when a
link is sought between current evolutionary
theory and macroevolution is that of a
definition of the "phenotype." This is related to the logical and practical problems
mentioned above but those problems can
be dealt with on the genetic level without
ever explicitly asking, "What is a useable
definition of phenotype?" Thus far, the
implicit answer to this question appears to
have been "anything we can measure." The
reason this answer is not adequate, as I
hope to suggest in the course of this paper,
is that we are capable of measuring properties of living systems that have little or no
relationship to their teleological nature—to
their functioning and adaptive needs. A
third problem arising from an attempt to
extend microevolutionary theory over the
area of macroevolution derives from differences in the absolute duration of the
events involved, on the one hand, in populations, and on the other, in lineages. This
problem can be best formulated by reference to a hierarchy of natural phenomena.
A NATURAL HIERARCHY
Table 1 depicts an incomplete hierarchy
of natural phenomena as viewed by
biologists. Clearly this is not the only
hierarchy that could be constructed traversing some of the same levels of organization.
However, as one moves up into higher
levels of organization, that statement becomes less and less true. Thus, there are
many molecular phenomena not involved
in living systems; there are only some organisms (asexual) not integrated into populations; there are a very few populations
(for example, lichens living on bare rocks in
boreal regions) that are not parts of communities; there are probably no communities not forming parts of an ecosystem, which is presumably the next level of
organization in the hierarchy in Table 1.
That would imply that the ecosystem level is
the final level in the hierarchy depicted; at
that level different communities would interact, for example, at ecotones. A
geologist, however, could construct a partly
different hierarchy of nature, deriving
from a different viewpoint. Furthermore,
one might look for hierarchical organization,
for example, within the organismic level,
but that organization would of necessity extend downward into the molecular and
nuclear levels below. Prigogine and Nicolis
(1971) have made some interesting comments concerning the origin of hierarchies
by the emergence of higher levels from
lower ones as spontaneous thermodynamic
processes (see also Simon, 1969).
I believe the hierarchy in Table 1 represents the viewpoints of biologists in many
fields. The principle of organization at each
level is clearly reductive in that the functional processes at each level are processes
which occur over time periods shorter than
the duration of the entities or elements
found at that level. The lower limit for the
inclusion of a process in a given level is a
threshold value of absolute time which, if a
process occurs to completion in a shorter
time, is a process functioning at the next
lower level of organization in the hierarchy.
Thus, each level of the hierarchy is viewed
as a functioning domain. One can make
complete and scientifically satisfying
explanatory statements and predictions at
each level separately. One may do experiments at each level by manipulating the
constraints and boundary conditions surrounding it. In order to analyze the processes at work at each level, I have found it
useful to work within the following syntax:
"given these constraints and boundary conditions, this force drives these mechanisms to
produce these processes measurable as
. . . . " Constraints are naturally imposed by
adjacent levels of organization while
boundary conditions are imposed by more
distant levels. It is only by altering constraints and boundary conditions that one
level may affect another in a hierarchical
organization (Pattee, 1973).
For the purpose of this paper, I would
like to focus upon the population level of
organization and its relationship to the organismic. A direct result of the organizational principle of this hierarchy is that
macroevolution (in the usual sense of long-
TABLE 1. A hierarchy of nature from the xriewpoint of the biologist."
Molecular
Cellularorganismic
Population
Community
Arbitrary
measurement
changes in
concentration of
reactants
change of stage of
development or in
degree of
differentiation
changes in gene frequencies or in means,
modes, and variances
of measurements
changes in the differentiation and number
of ecological niches
found in, and in biomass
Descriptive
mode
(process)
rearrangement of
bonds
epigenesis and
homeostasis
natural selection
niche diversification,
increase in coadaptations, efficiency,
and stability
Mechanisms
redistributions
of bond energies
enzyme kinetics,
differential synthesis,
diffusion, active transport, biological
rhythms, etc.
intraspecific (including
intragenotypic) competition—e.g., differential
reproduction
interspecific
competition,
speciation,
adaptation, predation
unstable chemical
bonds—e.g., "free
energy"
dissipation of energy
in structure
resource utilization
energy flow through,
entropic changes in
genetic information,
etc.
critical variable
concentration or
distribution,
protein sequences,
temperature
critical variable
concentration or
distribution,
which alleles are
present at various loci
Driving
force
Constraints
(and boundary
conditions)
-a
m
\
•a
o
primary generation of
variability,
population density,
limited resources,
rates of environmental
change and population
growth, etc.
Pi
•n
Z
H
5
land mass size,
habitat, temperature,
preadaptations,
informational capacities of genomes
a
Constraints originate in adjacent levels, boundary conditions from more distant ones. The next level below the molecular would be the nuclear or
elementary particle level, the next level above the community would be the ecosystem level. In principle the number of levels is not fixed, although the
upper level does become fixed when a particular viewpoint is chosen. For the biological viewpoint the upper level is seemingly the ecosystem. I have not
included these other levels here because this paper explores only the organismic and population levels.
NO
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S. N. SALTHE
term transformations of structure at the
organismic level) is not a functional process
in nature. Precisely, the evolutionary transformation of structure, as found in the fossil record, is a record only and not a series of
events "in the past." One consequence of
this circumstance is that there can be no
causal laws of macroevolution. Those few
generalities about macroevolution that appear to be broadly valid—Dollo's Law
(Gould, 1970; Salthe, 1972) and Van Valen's new Law of Extinction (Van Valen,
1973; see also Raup et al., 1973), for
example—are clearly seen to be statistical
laws. Other interesting but less general patterns in the fossil record, such as parallel
evolution, can also be most easily viewed as
the outcome of the interaction of many
events with fairly clear probability distributions as opposed to the more unique results
of evolutionary transformations in more
"specialized" lineages. In the latter the
probability distributions are unclear and
the term "capricious" (Lewontin, 1967) best
describes our lack of feeling for the probabilities that were involved in these events.
Within the context of the natural hierarchy, then, evolutionary change is a functional property of the population level
(microevolution) and, in some as yet unclear ways, of the community and ecosystem levels of organization. It is not a property of the organismic level, where ontogenetic change (in the broadest sense, including aging and learning) is the name for
those events occurring at the appropriate
rates. The interesting relationship is the
one between the population and organismic levels. Formally, that relationship can
only be one where microevolutionary
events, guided largely by the process of
natural selection, impose constraints upon
the outcome of epigenetic and homeostatic
processes working at the organismic level.
The community level establishes boundary
conditions on these ontogenetic events because it constrains the outcome of the workings of natural selection. (It should be
noted in passing that constraints may work
upward from lower levels upon higher ones
too, but the question we are concerned with
here would only be obscured by considering this added complexity.)
In the remainder of this paper I would
like to do two things. First, I will attempt to
demonstrate that, given appropriate data,
the non-functional nature of long-term
change at the organismic level (macroevolution) is a property of great value in
dealing with historical questions precisely
because the record of events viewed in this
way is a statistical accumulation. Because
only very limited sorts of data can be
treated in this statistical manner over any
but the shortest geological time spans, this
approach will have limited applicability to
evolutionary problems. Therefore, I will
subsequently attempt to begin obtaining
some practical grip upon the problem
posed by the fact that evolutionary change
takes place at the population level, while
measurements pertinent to macroevolution refer to the organismic level.
NON-DARWINIAN EVOLUTION
It is possible to measure various
parameters of fossil remains and, in the
presence of dating techniques, to construct
series from which one can induce rates of
change (Simpson, 1949, 1953). One can
find that an organ dimension in such and
such a lineage was transformed in time at
such and such a rate (Lerman, 1965).
Non-Darwinian evolution (King and Jukes,
1969) might serve as a handy phrase to
cover all considerations of "evolution" at
the organismic level even though it was invented specifically in connection with protein evolution. I would like to attempt to
delineate some of the sorts of information
that studies of non-Darwinian evolution
can contribute, and it will be convenient, as
well as timely, to use data from those dealing indirectly with the evolution of primary
gene products for this purpose. Anatomical
transformations could presumably be
treated in the same ways but the restricted
range of taxa that could be covered, and
therefore the small time spans to which one
would be restricted, make them less suitable.
Figure 1 shows the relationship between
the number of amino acid sequence differences (normalized per 100) in some proteins from several living species in different
PHENOTYPE DEFINITION
- -
10O
MILLION YEARS
cytochronwc
200
SINCE
300
DIVERGENCE
FIG. 1. The relationship between number of amino
acid sequence differences in several proteins and the
time since divergence of the lineages from which the
proteins were sampled. The data are taken from Dayhoff (1972). A similar graph can be found in Dickerson
(1971). The open circle data represent insulin, and the
wide variability of these data in more recent times of
divergence seems to be due to the fact that one of the
species sampled has a very atypical molecule—i.e., it is
a sampling problem. The closed circles are alpha and
beta hemoglobins together.
taxa and the estimated time since their
lineages diverged in the past (based on the
fossil record). The slopes of these lines are
characteristically different for different
proteins, presumably reflecting differences
in the overall evolutionary conservatism of
the loci in question over time (Dickerson,
1971). Fitch and Markowitz (1970) have
presented evidence that when codons
showing no substitutions between taxa
(very conservative codons) are eliminated
from these comparisons, the different proteins will tend to line up on the same slope.
It has been suggested that these relationships will allow retrodiction of dates of divergence of various lineages for which
there are no fossil records (Zuckerkandl
and Pauling, 1962, 1965). For this possibility to be realized it will be necessary to know
the true shapes of these curves. A linear
relationship between difference and time
since divergence has been assumed so far,
presumably as a simplifying working assumption; Figure 1 does not give me
confidence in that assumption, and, indeed, the obvious requirement that, when
two things are being compared as they become more different with time, there must
299
be an assymptotic approach to the
maximum possible difference, clearly prevents the relationship from being linear. I
have performed some simple simulations in
order to test whether simple assumptions
about the specific rate of substitution could
generate difference curves of the sort in
Figure 1. The simulations were essentially
those used by Kirsch (1969), on a sequence
of 50 elements, with the added stipulation
that after every generation the probabilities
of change for each "codon" or element are
changed randomly except that half of the
elements that had a probability of zero
change in the last generation would retain
that probability in the present one, thus
building in a concept of conservative codons. The two simple assumptions about
the specific rate of substitution (AY I At. \IY)
were (i) constantly decreasing (kit), and (ii)
constant (k). As in Kirsch (1969), if a "codon" once becomes different between the
two lineages, it is not allowed to change so as
to reacquire identity, i.e., no convergence is
allowed (see Jukes and Holmquist, 1972,
for a discussion of the minor effects of convergence at this level).
Figures 2 and 3 show the results of these
simulations. For the present purposes the
important curves are those showing the accumulation of difference (Z) with time since
these are directly comparable with Figure
1. On the assumption of a constantly decreasing specific rate of change (after the
suggestion of Goodman—see, for example,
Goodman etal., 1971), we obtain a sigmoid,
in fact a logistic, mean curve for the accumulation of difference. On the assumption of constant specific rate of substitution
we obtain a hyperbolic curve for the mean
accumulation of difference. While it is possible that either of these simplifying assumptions could explain the curves in Figure 1 (although I feel the hyperbolic curve
is closer), the important point is that the
empirical curves are consistent with
stochastic models of change. In order to
expand the data base somewhat, I have
utilized some calculations from Goodman
et al (1971). Using the sequence data available for various globins they reconstructed
primitive globins for two periods in the
past. Figure 4 shows the sequence differ-
300
S. N. SALTHE
o CHANGES ACCUMULATI1
ALONG Y WITH
DECREASING RATE OF
CHANGE
Y
- ACCUMULATION O F DIFFERENCE ( Z )
W I T H O U T SIZE R E S T R I C T I O N
(EXPONENTIAL)
ACCUMULATION ( Z ) WITH
S I Z E RESTRICTION
(LOGISTIC)
z
UJ
IT
Hi SO
a.
UJ
t
TIME
-«—TIME
°
••
SINCE D I V E R G E N C E
CHANGES ACCUMULATING
ALONG Y AT CONSTANT
RATE
ACCUMULATION OF
DIFFERENCE ( Z ) WITHOUT
SIZE RESTRICTION
(CONSTANT)
ACCUMULATION (Z) WITH
SIZE RESTRICTION
TIME SINCE DIVERGENCE
FIGS. 2, 3. The results of a simulation of molecular
evolution as a stochastic process with the specific rate
of substitution (dY/dt • \IY = kit) constantly decreasing (Fig. 2), and (dK/d( • \IY = k) constant (Fig. 3). The
accumulation of difference (Z) with time in the presence of size restrictions is the curve that simulates
those in Figure 1.
ences between modern globins and both of
these projected ancestral sequences plotted
against time since divergence. Again, both
of the specific rates of change utilized in the
simulations could explain the data; Goodman et al. favor a constantly decreasing rate
model. Note that in Figures 1 and 4 the
dates of divergence are uncertain enough
so that no real statistical test of the goodness
of fit of the two theoretical curves is possible. Again, the point to be established is that
purely stochastic processes working within
roughly known constraints are sufficient to
generate curves very much like the empirical ones. In Figure 4 I have shown the per
cent variability of the data at the two times
in the past. The data from the more recent
date of divergence are more variable. In the
simulations the per cent variability decreases as the sequences approach the assymptote of maximum difference, and so
this aspect of the empirical data (Stebbins
and Lewontin, 1972, p. 34) are also consistent with stochastic patterns of change.
At this juncture it becomes important to
point out that I am not moving toward a
suggestion that "evolution is random."
Much debate has occurred over whether or
not these regular patterns of change are
consistent with natural selection being the
main process in evolution. It has been
suggested that in order to explain these
regularities we must suppose that most
amino acid substitutions are carried in by
genetic drift at selectively neutral or near
neutral codons (for example, Kimura and
Ohta, I97la,b; Ohta and Kimura, 1971).
Since this question bears upon whether or
not it is reasonable to suppose that we could
have constant or constantly decreasing
specific rates of amino acid substitutions in
our models, I would like to show in two
different ways that such constancy is possible even if every substitution is mediated by
selection. First, keeping the discussion in
the same gaming field as previous ones, we
may note that there is ample documentation for the fact that there is no simple
one-to-one relationship between phenotype and genotype; for any given
phenotype there are a number of equivalent
genotypes possible (one could refer to a
301
PHENOTYPE DEFINITION
i:
or
. . • -> "£V= 13.4
!J
1
LU
^
uj
IONAL
6OODM/
Qz
-
»-
RECONSTRUCTED
PRIMITIVE
EUTHERIAN
HEMOGLOBIN
/
. "
/
•
'
•"
RECONSTRUCTED
ANCESTRAL GLOBIN
(GOODMAN ETAL.I97I)
•
s
TIME SINCE
FIG. 4. The relationship between the number of
amino acid sequence differences/100 between modern
and reconstructed ancestral globin sequences (data
1
1
1
1
1800
500
DIVERGENCE (M.Y,)
from Goodman et al., 1971) and time since divergence
of the modern groups from the ancestor. CV is the
coefficient of variability.
polygene model for continuously distributed traits or to selectively equivalent alleles at
some Mendelian locus). Suppose we have a
locus producing an enzyme in a
poikilotherm: the environment is warm;
the rate of activity of the enzyme is at an
optimum. Now the environment becomes
colder and the rate of activity of the enzyme
is reduced to a suboptimum level. Any mutation that is capable of restoring the rate of
activity to an optimal level is favored provided it has no untoward pleiotropic effects. Since more than one allotype is capable of showing the same kinetic rate, there
are several to many possible, selectively
equivalent alleles that can solve the adaptive problem. Furthermore, since altered
rates of activity of other enzymes can compensate for a reduced rate in our enzyme,
the adaptive solution is not restricted to
mutations at a single locus. Later the environment becomes warmer again: now the
enzyme in question has a superoptimal rate
that disrupts homeostasis. Will a back muta-
tion be most likely to restore the old allele?
Or is it not more reasonable to invoke a
more probable forward mutation to still a
third allele whose product will be
phenotypically like the original warmadapted allotype? (It need not be exactly
alike since other aspects of the intracellular
milieu will have changed in the meantime.)
We may observe this locus through a long
period of time during which the environmental temperatures fluctuate and never
once see a back mutation fixed so that, while
the environment oscillates, the gene steadily
becomes more and more different from its
original, ancestral form—that is, it evolves.
Recalling that most selectively relevant
phenotypic expressions are multigenetic
we must suppose that our locus could be
brought to bear upon solutions to quite different physiological problems stemming
from many different environmental challenges whose primary effects are "at other
loci." Since the functional genotype, like
the niche, is an n-dimensional concept, it is
^
1
1
1
1
100
302
S. N. SALTHE
possible that our locus might be called upon be derived from Hennig [1966] on the one
to evolve in response to selection pressures hand, and from Eldredge and Gould
at any time in what is now an effectively [1972] on the other). If we are dealing with
continually changing (or deteriorating) en- populations of two species, we find ourvironment, and the same is true for all selves no longer at the population level of
other loci. Under these circumstances any organization, but at the community level
locus has some probability of being called (and not even there unless certain other
upon to solve any given adaptive problem conditions are met). Thus, no process is
generated by the continually deteriorating mediating macroevolution at the organisenvironment, and so the probability of any mic or any lower level.
one mutation becoming fixed by selection is
Note that I am not claiming that natural
roughly the same as the probability of any selection is not involved in the substitution
other mutation becoming fixed by selec- of one allele for another. It may be, or getion. Under these circumstances divergent netic drift may be. I am claiming that the
evolution of the genotype between differ- accumulation of these substitutions over
ent lineages would go on constantly, while long periods of time is not viewed by
the difference in magnitudes between for- natural selection. I am suggesting that the
ward and back mutations would suppress number of amino acid sequence differences
convergence at individual loci. Because this between two taxa, like electrophoretic momodel invokes large numbers of selectively bility differences or immunological differequivalent alleles at any locus (Maynard ences between taxa, are a species of nonSmith, 1970), it is only a contextually richer adaptive character (Simpson, 1961) or
or more filled-out (but less elegant) version selectively neutral trait. I am furthermore
of the neutral allele drift theory. For this suggesting by extension of this idea that any
reason we are fortunate in having a com- comparative macroevolutionary observapletely different approach to this problem tion at the organismic level—for example,
deriving from considerations of the hierar- the transformation of an anatomical strucchy of nature (Table 1).
ture from one form to another in some
lineage
(Bock and Von Wahlert, 1965)—is
It is clear from the relationships depicted
in Table 1 that macroevolution at the or- not mediated as such by natural selection, or
ganismic (or any lower) level, including pri- by any other process. Such series are the
mary gene products, is not a result of a result of comparative operations by the scifunctional process in nature. Furthermore, entist and have no ontological status of
natural selection cannot be the process me- their own in nature.
diating macroevolution at any level of the
Thus, transformations of structure at the
hierarchy, since it is a process operating organismic level are merely post hoc results
only at the population level. Natural selec- of meaningful changes occurring at other
tion operates from one generation to the levels of organization. As such, one might
next, does not skip generations, and cannot, predict that macroevolutionary changes at
in any sense, "detect" the future. Any at- this level will occur at random with respect
tempt to bring natural selection into the to the teleological requirements of evolving
macroevolutionary range by looking for populations except to the degree that estabthe upper limit of duration of a population lished structure at the organismic level
in time and then trying to connect two pop- produces (preadaptive) constraints upon
ulations in a lineage that have a measurable the directions and rates of change that can
difference at some trait is illogical (because occur at the population level of organizamore than a single generation is being view- tion. This last "except" clause is what preed). Furthermore it is easily faulted by not- vents any further elaboration of this idea at
ing that the populations being thus viewed the level of phenotypic structure, but no
are easily considered to be in different spe- such limitation appears at the genetic or
cies if they have enough genetic difference to primary gene product level because of the
make a measurable phenotypic difference loose connection between genotype and
(the logical structure of this argument can phenotype. This question as to whether a
PHENOTYPE DEFINITION
303
random with respect to the adaptive requirements of organisms because the process that operates on these adaptive requirements (natural selection) is not the
process, nor is there any process, mediating
the accummulating changes making up
macroevolution. Of course, adaptively or
teleologically meaningless data are not
necessarily useless. For example, measurements of protein sequence differences
between lineages lacking fossil records have
permitted the retrodiction of their possible
dates of divergence. Indeed, in some sense
data collected at the molecular and nuclear
levels of organization by chemists and
physicists represent just such collections
unrelated to the forms of the macrostructures built hierarchically upon those levels,
and these data have not been useless.
Interestingly, the well-known law of the
irreversibility of evolution proposed by
Dollo is deducible as a corollary (proceeding as in Chapter 5, Salthe, 1972) from what
may be presented as a fundamental statistical law of evolutionary biology: for any time
period, if a lineage accumulates a mean
amount (X) of genomic difference per locus
from an ancestral population, then any
other lineage descending from this ancestral population will also accumulate an average of X genomic differences from that ancestral population during the same time
period. However, this law will be irrelevant
to most observations that have been made
concerning macroevolution, namely observations about phenotypic expressions.
Concerning these, the hierarchical viewpoint revives in even more urgent form
(and then allows resolution of) the problem
of the possible tautological nature of the
concept of natural selection by placing various, presumably adaptive, phenotypic expressions (antler tine number, scale
number, heart beat rate after exercise, performance on some test, fur thickness, etc.)
at one level, the organismic, while placing
comparisons between individuals in a
The inappropriate viewpoint engen- population in terms of reproductive success
dered by counting ideologically informative at another, the population level. This
events converts them to noise. The provi- means that no phenotypic attributes of orsional conclusion from all of this, then, is ganisms exist at the population level. Indithat molecular evolution is non-Darwinian viduals in populations are effectively black
because all macroevolution at the organis- boxes with varying adaptive values that are
mic level is non-Darwinian, that is, occurs at
series of events involving some sytem that
are not mediated by a mechanism-driven
process will occur at random with respect to
the adaptive needs or functional requirements of the system is too large to be pursued at length here. Instead, I will attempt
to partially justify it by reference to several
examples from the realm of human artefacts, where in each case we know the
"selective" meaning of each choice. Each
series of artefacts was examined in such a
way as to deliberately measure parameters
which probably could not reflect the adaptive significance of the choices made in
order to test the hypothesis that aggregate
data about such events would be randomly
distributed. I believe that the following
analysis will show that they are. Thus, the
number of moves made by the white queen
in the first 18 moves in 185 master chess
games (Horowitz, 1956) forms a Poisson
distribution (x2 = 2.67; 6df; P = 0.90
> 0.80), as does the number of times in 120
master games (Horowitz, 1956) that a white
knight move is followed by a black pawn
move in the first 18 moves (\2 = 5.65; 4df;
P = 0.30 > 0.20). The number of times the
word "in" appears per 20 words in a long
major poem (Little Gidding by T. S. Eliot),
scanning 96 segments of 20, is again described by a Poisson distribution
(X2 = 0.010; 2df; P > 0.99). Finally, the
number of times the note A appears per bar
of music in a G-major violin sonatina
by Telemann was also found to be randomly distributed (x2 = 7.27; 3df;
P = 0.10 > 0.05). It appears as though
data collected from a viewpoint unrelated
to the teleological aspects of a system will, at
least often, be found to be randomly distributed (or to change at random) with respect to the teleological requirements of
that system, or with respect to regularities
which we would project (or predict) if the
system were working according to the
forces and mechanisms known to us.
304
S. N. SALTHE
non-factorable. Fisher's influential fundamental theorem of natural selection
(Fisher, 1929) makes no reference to anything other than the gestalt notion of adaptive value or Darwinian fitness. Yet, Darwin
himself wanted to use the notion of natural
selection to explain transformations over
time of phenotypic traits (e.g., macroevolution), and he was eager to relate fitness to
the environment in terms of adaptation
(Williams, 1974). From the vantage point of
the hierarchy, we see that the environment,
working through the ecosystem and community levels of organization, contrains the
results of natural selection to within certain
values for any given phenotypic attribute,
and this in turn constrains the epigenetic
and homeostatic processes of any organism
sampled as an individual from that population, including those processes that produce one-time structures such as a tooth.
Thus, we could sample a population repeatedly, obtaining organismic data from
many individuals and feel confident that
the statistical population before us then,
representing a model of some aspect of the
real population we sampled, will be rdatable to the wider environment. This is the
way out of the tautology provided by the
hierarchical approach, and this without
contesting the operational fact that individuals in populations are non-factorable"
black boxes without discernible phenotypic
attributes. Thus, in order to avoid a
tautological statement of natural selection,
we note the interplay of constraints between the population and community levels
on the one hand and between the population and organismic levels on the other.
This paper will continue to examine the
latter interaction only.
THE NATURE OF THE PHENOTYPE
Duration of existence of the phenotype
In order to clarify the relationship between evolutionary change at the organismic and population levels, we can use an
imaginary experiment. An individual organism is drawn at random from a population of haploids, and one gene, or its pro-
duct, is sequenced. In actual fact at the
population level there are at this locus two
alternative alleles, one (the one we sequenced) a common wild type and the
other a rare allele. Now the environment
changes in such a way that the gene product
of the rare allele is better at producing some
adaptation than is the previous wild type.
This comparison is associated with a reproductive difference so that the old wild type
becomes selected against, and this process
will register as changing gene frequencies
each generation. After many generations
the previously rare allele reaches fixation.
We again sample an individual organism
from this population and note that the gene
in question has "evolved." At the population level the gene frequencies have become altered. Natural selection monitors
the probabilities of existence of various alleles in gene pools from one generation to
the next. These probabilities constrain the
kind of organism you can pick up at any
given moment. But long-term evolution is
no more ^functional process at the population level than it is at the organism level. A
comparison of gene frequencies between
populations separated by many generations is no more than a scorecard tally kept
by evolutionary biologists. The organism,
and its genes and gene products viewed
separately, cannot evolve in any functional
sense at all. The population can evolve, by
natural selection, only from generation to
generation, and we must note here that the
functional time period of the population
level of organization is precisely and only
one generation. Recall that the functional
time period for the organismic level is the
length of life of an individual organism. If
the phenotype is to be a functional concept,
its existence must be located, then, somewhere between the fastest events that may
occur in an organism without crossing the
threshold into the molecular level of organization (nerve impulse transmission,
rate of production of functional amounts of
ATP, etc.) and events that may take a
lifetime of an organism to be completed
(additions and repairs to a family nesting
place, the total number of offspring left,
etc.).
PHENOTYPE DEFINITION
305
ous randomly chosen traits during artificial
selection experiments demonstrates only
Does the phenotype exist only in indi- that those traits are potentially parts of the
vidual, sampled, organisms? Or do some functioning phenotype. Of course, traits
aspects of the phenotype emerge from the that are not currently heritable or modifiinterface between the organismic and able by artificial selection could in the fupopulation levels of organization? (I will ture come under the view of natural selectake the position that the phenotype cannot tion if they became important in the lives of
be a property of populations themselves, the organisms involved.
since that would place the concept too far
The genotype of an organism is clearly
outside traditional notions to be useful.) located at the organismic level. The
Since evolutionary decisions made at the phenotype, if we use the definition just
population level can never be reduced in proposed (which is really not new, only
toto to the organismic level because of vari- more clearly stated), is located at the boundous emergent properties of populations ary between the organismic and population
(density, for example), the phenotype must levels, and forms, in fact, the bridge bebe both an organismic property and a tween them, the means by which conproperty of the interaction of individuals in straints from each level impinge upon the
populations. But does any aspect of a func- other. On the one hand, individuals with
tional phenotype really occur in a sampled genetic diseases (disruption of homeostatic
individual organism? In other words, is a and/or epigenetic processes by the presence
tooth height measurement a phenotypic of non-wild type allotypes) never appear
datum? The phenotype is usually viewed as functionally at the population level, thus
a product of the interaction of the genotype constraining the kinds of individuals that
with the local environment. This rules out can compete at that level. On the other
any properties of organisms that do not hand the results of competition between
have a measurable heritability as, for individuals at the population level conexample, the spotting patterns on the backs strains the frequencies of various alleles,
of leopard frogs (Casler, 1967). On the thus establishing probabilities concerning
other hand, it also rules out properties that the rates of enzymatically mediated procould never be elicited in any possible local cesses, for example, in organisms sampled
environment—for example, the length of as individuals from some population. In the
time an arctic fish can survive in water of first case we have selective elimination contropical temperature, or the nature of cerning absolute fitness, and in the second
courtship behavior elicited out of season by we are concerned with relative fitness,
hormone injections in a laboratory. Not ev- wherein an individual is "compared" with
erything we can measure is usefully consi- other organisms, or with population statisdered as part of a phenotype—not the elec- tics, in respect to its reproductive success.
trophoretic mobility of some protein at a
given pH in a given temperature on a given
substratum, for example, nor the im- Is the phenotype divisible?
munological difference between two orThe next problem is to understand in
ganisms as viewed by a third (Williams,
what
manner it is possible to observe the
1964). If we take these restrictions seriously
we arrive at an interesting definition of interactions between individual genotypes
phenotype as any property of organisms and their local environment. The main
that could be involved in intraspecific problem here is that relative reproductive
competition—because we have eliminated success arises finally out of all sequential
all other properties with our restrictions. and simultaneous interactions between the
Thus, we can define as aspects of the genotype and the local environment. As a
phenotype only those traits that are visible limiting case, we can note that artificial
to natural selection. The fact that we can selection experiments have demonstrated
alter the population measurements of vari- that a single dimension of a single trait can
Location of the phenotype
be detected by selection, and this observa-
306
S. N. SALTHE
tion touches upon natural selection conceptually when we imagine isogenic
individuals—say, a female cladoceran starts
a population in a pond, and these accumulate a few single gene differences by mutation during the summer. In this limiting
case we might be near the real situation
when we ascribe differences in reproductive success to some single phenotypic attribute, possibly temperature tolerance. That
being so, if we could measure the temperature tolerances of each individual in the
population and compare these measurements with the normal temperature regime
in the pond, we could predict which individuals will out-reproduce others. In one
case any individual with tolerance for more
than a certain amount of heat in its environment will reproduce maximally; those
below this threshold level will not contribute as many individuals to the next generation. Adaptive value is here a simple function of temperature tolerance. In most real
cases, however, individuals differ from
each other at more than a single trait, thereby having the consequence that more than
a single dimension of the environment will
be important in determining reproductive
success. When a very large number of
traits contribute to Darwinian fitness, can
any one of them be considered to be visible to natural selection? In order to examine this, imagine many functionally independent normally and unimodally distributed traits. Suppose that on the average
any individual whose measurement is
within a standard deviation of the mean
would be capable of realizing its maximum
reproductive potential as far as that trait is
concerned. Then the probability of being
within a standard deviation of the mean for
each trait is 0.66, and the probability of
being within a standard deviation of the
mean for n traits = P(trait 1) x P (trait
2) X P (traiti).. .P (traitn). Reasoning this
way, it can be shown that in a large population with a large number of genes very few
individuals (< 1%) can have a large
number of their traits (> 50%) measure
within a standard deviation of the mean if
all the traits are continuously (and therefore simultaneously) visible to selection.
This is analogous to Haldane's substitu-
tional load problem (Kimura and Ohta,
19716), but there is a way out of this one
that is not available to Haldane's dilemma.
Consider the effective environment to be
constituted by a succession of ecological
catastrophes. Some will be highly predictable seasonal phenomena, others more capricious events with longer term
periodicities. Each catastrophe emphasizes
from one to a few traits (those most important in coping) temporarily. If one were to
total the "instantaneous" contributions of
various traits to survival as above (Pi x?2 X
Pi . .. P„), then these few traits would come
"first," "second," "third," and so on, in the
calculation at this time, in order of importance, until, after about five traits had been
combined, the rest of them would contribute very little each and together towards
survival for the moment—i.e., they would
not be visible at that time as individual traits.
Although the quantitative picture does not
change from one catastrophe to the next,
the qualitative picture does; there are different traits being considered in different
ranking each time. As the intensity of the
selection instituted by the catastrophe increases, the number of traits visible (or factorable) at that time will increase and the
number of survivors will be correspondingly less (it will take adequacy for more
traits to survive an exceptionally cold spell
than it takes to survive a milder one—see a
similar idea in O'Donald, 1973). Traits relatively unimportant most of the time become
more important under the onslaught of an
exceptionally severe deterioration of the
environment when the required number of
adequate traits for survival increases. Selection intensities being equal, traits reviewed
more often by catastrophic events (heart
beat rate, tibia length) will undergo more
rigorous selection than will those that are
reviewed fewer times (some factor involved
in surviving a relatively rare event). In this
model very many, and perhaps most, traits
will be scanned at least once by selection
during the duration of a cohort, but there
will be a ranking of traits by importance
based upon both the intensity of the selective episode a trait is typically involved in
and the number of such episodes the trait is
called upon to function in. The ranking
PHENOTYPE DEFINITION
may change from generation to generation
as the environment changes. As the last few
members of a cohort die out, one could in
principle rank traits over the whole life of
the cohort, but this would be highly artefactual. In this case again it would seem that
natural selection scanned very few traits individually (perhaps wing color in industrial
melanism would stand out), and one would
feel forced to conclude that the organism is
a black box. But, if one analyzed the cohort
from moment to moment, one would see
the intermittent functioning of traits, a
block of a few at a time. (If epistasis were to
be taken into consideration, more traits
could be visible at any given time because
many of the probabilities of being within a
standard deviation of the mean would be
conditionals—
P trait 2
P trait 1 X
P trait 1
Also, as will be explored below, canalization
increases the probability of an individual
being found under the area of the [lognormal] curve between plus and minus one
standard deviation.) Thus, I conclude that
natural selection can view any given trait as
a separate or near separate entity. How are
we to measure it.?
MEASURING THE PHENOTYPE AND THE INTENSITY
OF NATURAL SELECTION
In order to be clear about what is to be
measured, I will begin with a recapitulation
and gradually work into a concrete mode.
Neo-Darwinian theory has as perhaps its
most important axiom: Natural selection
works to weed out the ecologically unfit from the
307
a model of natural selection proposed by the
early biometricians: Natural selection tends on
the whole (at least in multicellular organisms
with moderate intrinsic rates of natural increase)
to disfavor individuals to the degree that the
magnitudes of their trait dimensions depart from
the mean for the population (Bumpus, 1898;
Weldon, 1901). This can be connected with
the observation that such dimensions are
typically normally or lognormally distributed (Simpson et al., 1960). These bellshaped distributions are explainable in
general as resulting from the requirement
for coadaptation among the various
phenotypic attributes of organisms, as
suggested by the use of the word "unfit"
above. Any evolving dimension is constrained by this requirement to within quite
small allowable changes in the modal measurement per generation (Lerner, 1954).
Put another way, natural selection cannot
foresee the future. All modes of selection
are constrained by this requirement, including directional. Figure 5 shows how
this works in regard to directional selection.
Thus, the mode can always be taken to represent the value most recently favored by
natural selection, regardless of whether the
selection is mostly directional or stabilizing.
In other words, it is not possible on this view
to distinguish between these modes, and I
will make no such distinctions in this paper.
Furthermore, in the hierarchical view selection works over the period of time that is
found within a single generation. Selectional modes are described on the basis of what
a trend in some dimension might be over
several generations, and thus represent
records of events rather than descriptions
of functional events.
population (informal paraphrase of axiom D
The fact of the bell-shaped curves of trait
4; Williams, 1970). A modern interpreta- dimensions can be used as a premise,
tion of the term unfit that I will use here is: which, in combination with another premunfitness arises in individual organisms when ise (to be noted shortly) and with the axiom
their traits are not sufficiently harmoniously in- stated above, provide the basis for deriving
tegrated to produce adequate responses to all en- a theorem that will form the framework of
countered environmental onslaughts (for one the remainder of this paper. The second
expression of this idea see Lerner, 1954). premise is that the intensity of selection in reThe import of this is that natural selection spect to some trait being experimentally followed
tends always to disfavor extreme phenotyp- is not at all times or under all conditions the same.
ic expressions relative to what is common, In one sense this simply means that selecaverage, or normal for the kind of organ- tion coefficients can have many values. The
ism in question. This points immediately to justification for this statement can be taker
308
S. N. SALTHE
DIRECTIONAL
SELECTION
FIG. 5. A comparison of directional natural selection with directional artificial selection. The shaded
area covers the proportion of the population favored
ARTIFICIAL
SELECTION
by the selective elimination of those in the unshaded
portion.
from any recent general work, for example, well (Rothstein, 1973), thereby bringing in
Dobzhansky (1970). Since continuously dis- an entirely separate and usually unknown
tributed dimensions of traits are usually element. Secondly, some provision must be
distributed as bell-shaped curves, and made for factoring out and estimating the
therefore must be reviewed by selection (by degree of canalization (Waddington, 1957;
the axiom) despite the theoretical limita- Rendel, 1967) that has taken place with
tions on the number of dimensions that can each dimension observed. This will be atbe simultaneously viewed by selection, the tempted below. Finally, the model assumes
theorem is that natural selection must scan that viability selection (all selection directed
individual traits sequentially in time. It can do at non-reproductive traits) is more or less
this because the intensity of selection faithfully recorded by natural selection as a
operating upon any given trait can vary. whole, even though there can be wide difThe detailed argument for this proposal ferences in the reproductive success of viawas presented in the section above entitled ble (ecologically adequate) individuals. Dif"Is the phenotype divisible?" The work of ferences in reproductive success of indithe rest of this section is to set this theorem viduals prescreened by viability selection,
into an explicit model, and to explore the but not reflecting the results of that selecgeneral conditions under which the model tion, will be adjusted each successive genershould hold. Extending the biometrical ation by viability selection again. Thus,
model of the axiom as stated above, on the even if at one point in time there comes to
basis of this theorem, we can derive the be an association between high fertility and
observable consequence that if in one popula- values a full standard deviation away from
tion, selection has been more intense in connec- the mean for some given dimension, that
tion with trait X than it has been with trait Y, then association would not lead to any significant
the intrinsic variability of dimensions of trait Y change in the mean dimension when comwill exceed that of those of trait X.
paring two or more generations unless the
This model can only hold if certain con- environment changes. In other words, such
ditions are met. First, polymorphic traits an association would not be likely to lead to
must be excluded because for them varia- the formation of a supergene complex unbility can be connected to niche breadth as less the environment changed so as to
309
PHENOTYPE DEFINITON
change the optimum value for the dimension in question. If this were not true overall and most of the time, living systems
could not maintain their teleological nature, which they patently do.
Any dimensions of a trait can be represented by a bell-shaped curve of value
distribution. There are, however, three different possible bell-shaped distributions
(Fig. 6). The curve may be symmetrical
(normal), skewed to the left (positively
skewed lognormal), or skewed to the right
(negatively skewed lognormal) (Aitchison
and Brown, 1957). Which shape one is dealing with is determined by plotting the standard deviations versus the means for all the
samples drawn from the parametric domain (Kerfoot, 1969). If the relationship is
linear with positive slope, the parametric
distribution is a positively skewed lognormal one; if there is no systematic relationship between the means and standard deviations, the distribution can be taken to be
PARAMETRIC
DISTRIBUTIONS
^
•
•
,
normal; if the relationship is linear with
negative slope, the parametric distribution
is a negatively skewed lognormal. Cases
where the relationship is non-linear will not
be discussed in this paper; they clearly represent only complications that can be
examined elsewhere. The domain of the
parametric distribution is determined arbitrarily, like the domains of adaptive
zones. One of them might represent scale
counts in different populations of a species
of lizard; another might be scale counts in a
genus of lizards; still another might be scale
counts in a family of lizards. The sample
populations would be of different
taxonomic rank in these three cases. The
upper limit upon possible domains is set by
various biological factors, the earliest to
come into play of which might be differences in age structure of the populations.
Such differences would tend to increase the
dispersion of the points on the mean versus
standard deviation plot, increasingly so as
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T—~
•
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•
MAXIMUM
MINIMUM
SAMPLE
STATISTICS
CANALIZED'
FIG. 6. A comparison of parametric distributions for
some trait dimensions and the statistical parameters
(standard deviation and the mean) of sample popula-
tions drawn from such distributions. See text for
further explication.
310
S. N. SALTHE
the sample variances increase. At some
level of comparison that dispersion could
become so great that the usefulness of the
model is curtailed. This is so because different survivorship patterns will affect the
number, intensity, and spacing of environmental catastrophes so that random
samples including all age groups in characteristic proportions from two populations
very different in this regard will not contain
similar proportions of cohorts that have
undergone viability selection for given
lengths of time.
The lower limit to variate values in the
positively skewed parametric distribution is
zero; there is no lower limit in the unskewed and negatively skewed cases. On
the other hand the latter has an upper limit,
while the other two do not. In any case in
real biological examples the curves are always truncated, the values never being
found over more than a restricted portion
of the curve. When the distribution goes to
zero in the positively skewed case, the slope
of the line is the coefficient of variability,
%V =
cv
a
where CV is the coefficient of variability the
line would have if it went through zero, and
a is the sample mean minus r (Aitchison
and Brown, 1957).
If we take canalization to be a selective
process the result of which is to restrict the
possible values a dimensional measurement
can take, we can fit the concept into the
model being developed here as follows.
Suppose we have a near-normal parametric
distribution going to the origin. The actual
values found in nature occur over a portion
of the ascending limb of the distribution.
We can view canalization as a process that
changes this parametric distribution into a
more skewed lognormal one by establishing
a threshold larger than zero and then
gradually moving it to higher and higher
values. Imposed upon this process will be
the constraint that the modal value favored
by selection can not change, so that the result of changing the threshold will not be
simply to displace the original curve to the
right. Instead, the curve will become dean accepted measure of intrinsic variability formed, with a larger and larger portion of
(Simpson et al., 1960). The problem is to the variability centering around the mode
find a way to use validly this coefficient, or (Fig. 7). In fact, the proportion of the dissomething equivalent, in all cases; other- tribution contained between the quantiles
wise we can have no general measure of X - SD and X + SD will increase, and,
intrinsic variability for all traits. The nega- clearly, the distribution will have become
tively skewed curve can be treated simply as "canalized." (Changing the threshold alone
a mirror image of the positively skewed one will not reflect the natural canalizing pro(Aitchison and Brown, 1957), thereby cess adequately because in that case the
eliminating that as a theoretical problem. curve will simply be deflected to the right
The problem arises in those cases where the without changing its shape so as to incorpominimum value of the parametric distribu- rate more variability between the mean plus
tion is not zero, but another "threshold" and minus a standard deviation). In terms
value greater than zero. In this case the per of the sample statistics, what occurs is that
cent variability is not a single coefficient of the line relating the standard deviations to
variability (or is not equivalent to the slope the means will move to the right away from
of the line over the whole of the plot of the origin and its slope will decrease in value
standard deviation versus the mean). (curve C versus curve A in the lower left
Rather, per cent variability forms an as- hand plot in Fig. 6).
symptotic function approaching the value
Now, while this manipulation of the
of the slope of the line as the mean increases model is as close to representing canalizain value, as in Figure 10. If T is the amount tion as I think possible, it lacks definitional
by which the curve has been displaced from clarity because two operations are being
the position it would have if its minimum carried out upon the parametric distribuvalue were zero, the per cent variability tion simultaneously—that is, the threshold
function is:
of the distribution is being moved off zero
PHENOTYPE DEFINITION
311
change of the mean. In the sample standard
deviation versus the mean plots, this is
MODE
reflected
by a decreased slope only. Thus,
I
line B in Figure 6 represents a less variable
dimension than does line ,4. The intrinsic
variability in these two particular cases is
represented by the coefficients of variability
of the sample statistics (because the curves
go to the origin), and that of line A is larger
than that of line B. If two lines like these
originated at a threshold value greater than
zero, the intrinsic variability of the distributions would be represented by the
coefficients of variability of two other lines
of identical slope originating at the origin,
which, incidentally, would represent the
CV's of the parametric distribution themFIG. 7. Comparison of a nearly-normal distribution selves. Thus, canalization alone does not afcurve (A) with a curve derived from it (B, a lognormal fect the parametric distribution. From this
distribution) by moving the threshold (T) away from it is concluded that, within the scope of this
the origin while at the same time maintaining the orig- model, canalization itself does not necessarinal modal value. This process mimics canalization.
ily decrease the intrinsic variability of a diSee text.
mension; however, it can be the process
that
results in such a decrease when certain
and further and further to the right, and at
the same time the variance of the paramet- constraints are in effect, as they probably
ric distribution is being decreased (because mostly are—for example, selection mainof the constraint that the mode should not taining the previous mode. On the other
change). It will be more useful to define as hand, normalizing selection always dethe results of canalization only the chang- creases intrinsic variability.
ing theshold. This process alone will result
All that has been said so far can be taken
in increasingly smaller values of per cent to apply directly to anatomical traits. Exvariability since the standard deviation will tending the discussion to physiological (and
not change even though the mean is in- presumably behavioral) traits is begun by
creasing. It will not, however, affect the as- noting that lognormal parametric distribusymptotic value of the coefficient of varia- tions with negative slope are frequently enbility that the distribution would have if it countered among them, and even,
went to the origin. I am proposing that that perhaps, almost completely symmetrical
value be taken as the magnitude of the in- distributions. Figure 8 is a schematic sumtrinsic variability for any given trait dimen- mary of the kinds of situations I have found
sion instead of the per cent variability that in the physiological literature. As environcan be calculated from any given sample mental conditions change, the parametric
population. Therefore, only if the distributions change their character at cerparametric distribution goes to the origin, tain points, as shown. Sometimes there is a
will the per cent variability of any sample very abrupt transition from a positively
represent the intrinsic variability of the di- skewed to a negatively skewed distribution
mension in question. This strategy allows us with no plateau between them, as in a study
to factor out the effects of canalization in showing breathing rates in rats under difcomparing the variabilities of two dimen- ferent conditions of oxygen availability
sions, which can now be considered to be (Clegg and Ainsworth Harrison, 1967).
caused by normalizing selection alone.
Figure 9 shows some sample statistics from
a
study (Stevens and Randall, 1967) that
Normalizing selection is then defined as
can
serve as an example of the kind of
the process that results in diminished variance of the sample populations with no analysis proposed. First, both blood pres-
312
S. N. SALTHE
Sd
X
BREATHING RATE
FIG. 8. Comparison of parametric distributions with
sample statistics for a representative physiological
trait, showing the presence of negatively skewed
parametric distributions (at low oxygen concentration). See text.
sure parametric distributions appear to
have been canalized (see also Fig. 10). The
intrinsic variability of the ventral aortic systolic blood pressure is less than the intrinsic
variability of the "subintestinal" vein
maximum blood pressure (see also Fig. 10).
This is biologically a reasonable result when
it is realized that during swimming the
amount of venous return via this vessel is
not as great (or as important) as other returns. Therefore, we can provisionally conclude that natural selection is much more
"interested" in the ventral aorta under
these conditions than it is in the "subintestinal" vein. Alternatively, "subintestinal"
vein maximum blood pressure during
swimming is a relatively less important trait
than is systolic blood pressure in the ventral
aorta. This is not to say that these relationships would be maintained under all conditions. After feeding the relationships might
be quite different. It must be realized that
in defining physiological traits the local environmental conditions bearing must be
taken explicitly into account. Thus, in
Anolis carolinensis, seminiferous tubule
diameter is extremely variable in the winter
compared with the breeding season (data
from Fox and Dessauer, 1958). Presumably
this trait is not up for possible review by
selection except during the breeding season. Out of season it may be essentially a
selectively neutral trait.
In Figure 9 it is shown that the heart rate
has a negatively skewed parametric distribution. This is also true for heart rate in
Limulus (data from Pax and Sanborn, 1964)
and in the chicken (data from Sturkle et al.,
1953). The presence of such curves in
physiological traits and their evident absence in anatomical traits deserves consideration. Put another way, the seemingly
exclusive presence of positively skewed
curves in anatomical traits needs to be
explained. I suspect the answer will involve
canalization, as follows. The negatively
skewed curves in the physiological context
can be interpreted to indicate that selection
has been pushing toward maximum values
(as of heart beat rate) resulting in decreased
variability as the assymptote is approached
(see Fig. 4), and so the parametric distributions are skewed towards the maximum
values possible for the trait in question.
Conversely, then, positively skewed
0
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HEART RATE,
beots/min,
v
<> X
14
"SUBINTESTINAL",
V E I N MAXIMUM ,
BLOOD PRESSURE,
x'xx
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XX
12
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10
Sd
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TROUT,SWIMMING IN
DIFFERENT WATER
VELOCITIES
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6
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VENTRAL AORTA
SYSTOLIC BLOOD
PRESSURE, mm.Hg
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2
10
20
7^
30
40
60
FIG. 9. Statistical parameters of three physiological
traits monitored simultaneously in fishes swimming
under different conditions of water velocity. Each
point is a sample from a different water velocity.
Notice that the units of measurement differ in one
case; this is really two figures combined because the
absolute values of the statistical parameters happened
to be in a similar range. We are interested only in the
slopes and X-origin for each curve. (Data from Stevens
and Randall, 1967.)
PHENOTYPE DEFINITION
313
to this one can find many subtraits—blood
pressure, breathing rates, oxygen loading
capacity of hemoglobin, and so on. Each of
these subtraits can compensate for a deficiency in any of the others, but presumably
to different degrees. The more crucially
cv
important subcomponents of traits may be
identified by appropriate studies from the
present viewpoint. Those components that
are more often visible to selection during
catastrophic events, or that cannot be as
easily compensated for by other subcomponents, will undergo more intense selection and consequently be less intrinsically
FIG. 10. Some of the same data from Figure 9, this variable than other traits. We could then
time plotting the dimensionless per cent of variability (C V) against the sample means. See text for explica- view the phenotype of the organism as a
composite of elements that are under different degrees of constraint decided at the
population
level.
parametric distributions may be representative of traits that have had their values
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