Download Models, predictions, and the fossil record of modern human origins

Evolutionary Anthropology 7
ISSUES
Models, Predictions, and the Fossil Record
of Modern Human Origins
JOHN H. RELETHFORD
I
t is clear from the recent contents
of this journal and others that the
debate about modern human origins continues unabated. My own foray
into this subject has dealt with some
of the genetic evidence. It has become
increasingly clear to me that much of
the genetic evidence is indeterminate
and that both African replacement and
multiregional models can explain observed patterns of genetic variation.1,2
Consider, for example, the finding that
many traits show higher genetic diversity within sub-Saharan African populations. While this finding can be interpreted as indicating a greater age for
African populations, thus supporting
a recent African origin, it can also be
explained by a larger long-term African population size, which is compatible with both a recent African origin
and multiregional evolution.
Population geneticists have long
known of the problems of unraveling
population history from genetic data.
Relationships between populations can
reflect either common ancestry or migration.3–7 When considering the predictions of different models, it is critical to make sure that the predictions
are unique to each model. I suggest
that the problem of indeterminate results is also characteristic of some
analyses of the fossil record. In particular, the results from several studies
proclaimed as proof of a recent African origin are also compatible with a
multiregional model.
John H. Relethford, Department of Anthropology, State University of New York College at Oneonta, Oneonta, NY 13820.
Phone: (607) 436-2017
Fax: (607) 436-2653
E-Mail: [email protected]
One approach to analyzing the fossil
record has been to compare fossil
samples from different geographic regions across time by using some form
of biological distance measure.8–11 A
useful comparison would be between
fairly recent modern human fossil
samples (⬍30 kya) and earlier samples
(35 to 100⫹ kya) across the major
geographic regions of the Old World.10,11
The most relevant distances are those
across time periods. Are the distances
within regions less than the distances
between regions? Several analyses
have shown that more recent modern
samples are morphologically more
similar to earlier samples from Africa
and the Middle East than to earlier
samples within their geographic region. For example, it has been suggested that recent modern samples
from Europe (e.g., Cro-Magnon) are
more similar to older samples from
Africa and the Skhul-Qafzeh samples
in the Middle East than to earlier
Europeans (Neandertals).8–11 These
findings are often taken as support for
a recent African origin because this is
the type of pattern we would expect to
see if all recent modern humans came
from Africa within the last 100,000
years.
I will not discuss here debates over
sample composition, measurements
used, or specifics of chronology. My
purpose is to examine the underlying
assumptions of such studies, and to
that end I will take the reported distances as given. Further study can
always help us refine our measurements and analyses, but this is of little
utility if we do not examine underlying
assumptions and make sure that our
interpretations are based on valid predictions of the models.
Assuming that the distances between fossil samples across time and
space are an accurate reflection of
past history, it is clear that such results
are compatible with a recent African
origin. This finding would reject a
multiregional model only if the results
do not agree with the predictions of a
multiregional model. What are these
predictions? It is common to see statements to the effect that multiregional
evolution predicts that the greatest
similarity across time will be within
geographic regions. According to this
prediction, recent Europeans should
be more similar to Neandertals than
are fossil samples from other regions
at roughly the same time period. This
assumption is apparent in Waddle’s
design matrix for the multiregional
model, in which she predicts that the
smallest biological distances will occur within geographic regions.10 The
assumption was made most recently
with reference to the extraction of
Neandertal mitochondrial DNA:12
Krings and colleagues stated that
‘‘whereas the Neandertals inhabited
the same geographic region as contemporary Europeans, the observed difference between the Neandertal sequence
as modern Europeans do not indicate
that it is more closely related to modern Europeans than to any other population of contemporary humans.’’
At first glance, this assumption
seems to make sense. After all, given
that multiregional evolution incorporates isolation by distance, we would
expect, in any given generation, a pattern of population endogamy. At an
aggregate level, this would translate
into regional endogamy, so that the
vast majority of a generation’s genes
would come from ancestors one generation earlier in the same geographic
region. It is then assumed that the
accumulated ancestry over many generations would reflect this pattern, so
8 Evolutionary Anthropology
that most of the genes in Europe would
derive from Europe, most genes in
Asia would derive from Asia, and so
on.
This last assumption is incorrect,
and that leads to an incorrect prediction of the multiregional model. The
problem arises from equating per-generation endogamy with accumulated
ancestry over many generations. The
easiest way to illustrate this problem
is with a simple example of gene flow.
Assume two populations, A and B,
where population A consists of 4,000
reproductive adults and population B
consists of 1,000 reproductive adults.
Further assume that these sizes are
constant over time. Now, let each population exchange 10 mates per generation. These numbers are easily visualized using a migration matrix, where
the columns refer to offspring and the
rows refer to their parents. In this
simple example, the migration matrix
is
A
B
A
3990
10
B
10
990.
A matrix of probabilities is obtained
by dividing each element of the matrix
by its corresponding column total. This
gives
A
B
0.9975
0.0100
B 0.0025
0.9900.
A
This matrix has a standard meaning in
population genetics. Each element represents the probability that a gene in
column j came from row i. In this
specific example, the probability of a
gene in population A coming from
population A (endogamy) is 0.9975,
while the probability of a gene in A
coming from population B (exogamy)
is 0.0025.
These hypothetical values show that
both populations are highly endogamous. Following the reasoning set
forth earlier, we might at first expect
that, given this endogamy, the greatest
similarities over time would occur
within each population. This is true
for short periods, but over many generations the total accumulated ancestry in each population will change.
Under a simple migration model, the
ISSUES
matrix of accumulated ancestry after t
generations can be derived by raising
the above matrix to the power t.13,14
For example, after 100 generations the
matrix of accumulated ancestry is
A
B
0.8569
0.5726
B 0.1431
0.4274.
A
After 100 generations of low gene flow,
population A would have derived
roughly 14% of its genes from population B, while population B would have
derived roughly 57% of its genes from
population A. In the latter case, we
would now expect that population B
would actually be closer to population
A as it was 100 generations earlier! As
the number of generations increases,
these numbers change even more. Af-
While archeological
evidence suggests times
during which parts of
Africa were relatively
depopulated,21 the
genetic evidence
supports the hypothesis
that the long-term
average population was
largest in Africa.
ter 200 generations, the matrix of accumulated ancestry would be
A
B
0.8162
0.7354
B 0.1838
0.2646.
A
It is clear that by this time both populations A and B would have derived
the majority of their ancestry from
population A. The matrix will continue to change until an equilibrium is
reached, as
A
B
0.8000
0.8000
B 0.2000
0.2000.
A
At equilibrium, both populations
would reflect 80% ancestry from popu-
lation A and 20% ancestry from population B. Note that these numbers are
equal to the relative sizes of the two
populations. Population A (N ⫽ 4,000)
accounts for 80% of the total population size (4,000 ⫹ 1,000 ⫽ 5,000), and
population B (N ⫽ 1,000) accounts for
20% of the total population size. This
is not coincidental; previous studies of
migration matrices show that, given
symmetric migrant numbers, the rows
of the equilibrium matrix will be equal
to the relative weights of the populations.15,16 The situation is more complex with asymmetric migrant numbers, but the same tendency still
applies. In any case, a symmetric
model seems appropriate as a simplifying model for describing most human
populations because the genetic effects of asymmetry are not very different from those expected under a symmetric model.17
What does this all mean? This simple
example shows clearly that, given
enough time, the accumulated ancestry of any population will be dominated by the largest population. This
is intuitive: The larger the population,
the greater the proportion of genes.
This principle has a major implication
for the analysis of hominid fossil
samples across time. Many genetic
studies have demonstrated that the
long-term population size of Africa is
larger than that of any other region.4,18
A larger African population is also
expected throughout most of prehistory, based on ecological arguments.19,20 It is important to keep in
mind that this model only makes the
assumption that, over time, Africa was
the largest. While archeological evidence suggests times during which
parts of Africa were relatively depopulated,21 the genetic evidence supports
the hypothesis that the long-term average population was largest in Africa.
If the African population was the
largest, then even under a model of
low-level gene flow it would exert the
greatest genetic impact. A comparison
across many generations would show
this effect. The more recent samples
would all be more similar to earlier
samples in Africa than to samples
from anywhere else. While these results might seem paradoxical, there is
no mystery. The common assumption
of greater similarity within regions
Evolutionary Anthropology 9
ISSUES
over long periods confuses per-generation endogamy with accumulated ancestry.
Of course, the above example relies
on a simple and unrealistic model in
which gene flow alone affects genetic
history. However, the same results apply with more complex models. Konigsberg22 has developed a general
model for examining biological distances across space and time. This
model incorporates gene flow, genetic
drift, and linear systematic pressure,
which includes mutation, long-range
migration, and selection. He found
that over time the biological distance
within a region will increase and the
biological distance between regions
will decrease. The overall pattern is
the same as illustrated in the simple
example I used earlier.
The implications for the fossil record of modern human origins are clear.
Given a larger long-term population
size in Africa, both recent African origin and multiregional models predict
that temporally recent fossil samples
across the Old World will more closely
resemble earlier populations in Africa.
While this demonstration does not
prove a multiregional model, it does
show that previous biological distance
analyses do not prove a recent African
origin. Both models produce the same
prediction. To further complicate matters, the same pattern could be produced by population expansion with
admixture as well as gene flow among
populations, depending on the rate of
admixture.
Resolution of the debate is therefore
not possible from analyses of overall
biological distance. One possibility is
to examine the relative occurrence of
regional continuity in individual traits.
My findings may seem contradictory
to the prediction of regional continuity under a multiregional model, which
is that the greatest similarity over time
will be within regions. However, this
prediction would be contradictory only
if we expect all traits to show a pattern
of regional continuity. However, proponents of the multiregional model do
not suggest that all traits will show
continuity. In reality, regional continuity is expected only for some traits as
the result of genetic drift and selection
acting to maintain high frequencies of
a trait within a region in opposition to
gene flow.20,23,24 My model provides a
prediction of the expected biological
distance between samples averaged
over many traits. We must also consider variation about these expected
averages. Individual traits could show
deviations from this expectation because of drift, selection, or both. Therefore, if the multiregional model is correct, we would expect to see regional
continuity for a small number of traits,
as well as greater similarity to earlier
African samples when considering the
average over many traits.
In this light, it is interesting to consider the findings of Lahr’s25 comprehensive analysis of continuity traits in
East Asian and Australasian samples.
Her analysis suggested continuity in
11 out of 30 (37%) traits. The remainder of the traits either did not show a
Given a larger long-term
population size in Africa,
both recent African
origin and multiregional
models predict that
temporally recent fossil
samples across the Old
World will more closely
resemble earlier
populations in Africa.
Population genetics models can provide us with valuable predictions for
the fossil record of modern human
origins. Two points are critical to future investigation. First, multiregional
models do not predict that the lowest
biological distance between time periods will be within geographic regions;
instead, the lowest distance will be to
the largest population. Second, this
expectation applies to biological distances averaged over many traits, but
not necessarily to each trait individually. By examining overall biological
distance we can address the issue of
accumulated ancestry and ancient
population size. By looking at individual traits and comparing them to
the average, we can assess claims of
regional continuity. Based on these
findings and the hypothesis of a larger
long-term African population, I suggest that the multiregional model predicts that biological distances based
on many traits will show that recent
modern fossil samples are more similar to earlier samples from Africa than
they are to samples from the same
geographic region. I also suggest that
regional continuity will be found in a
small number of traits, but not all
traits. Based on research to date, both
predictions appear to be confirmed.
Future resolution of the debate must
focus on regional continuity because
the first prediction also fits an African
replacement model.
ACKNOWLEDGMENTS
regional pattern or showed higher frequencies in other regions. Although
her analysis has been used to argue
against a multiregional model, I contend that the presence of some continuity traits is consistent with the gene
flow-drift model I have described.
Given the nature of accumulated ancestry, we would not expect most traits
to show regional continuity. The important finding is that some do, which is
more difficult to explain from the perspective of an African replacement
model. Further investigation is needed
to determine whether similar patterns
of continuity could be obtained under
a replacement model as a consequence
of recurrent mutation and drift. I am
currently planning a set of simulation
analyses to address these questions.
My thanks to Henry Harpending,
Lyle Konigsberg, and Milford Wolpoff
for their comments.
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