American geneticist and statistician Alan R. Tempeleton (b. 1947), at Washington University in St. Louis, U.S., has been an outspoken and vocal critic of the use of Bayesian statitstics to support the Out Of African theory, and the replacement of ancient humans in Asia by our more recent African ancestors.
He holds a master's degree in statistics, so he is an expert in that field, unlike most geneticists (by the way, he also holds a doctorate in human genetics). This places him in the unique position to understand the genetics underlying the black-box algorithms used by researchers. He knows the statistical tools.
Templeton has criticized the incorrect use of Bayesian calculations and the mathematical and formal errors reproduced in different research publications.
His arguments sound solid, and professional. He disputes the missuse of specific statistical tools, as only an expert can. Below are some excerpts from his work.
One paper published in 2023 (Templeton A. The importance of gene flow in human evolution. Hum Popul Genet Genom 2023; 3(3):0005. https://doi.org/10.47248/hpgg2303030005) states the following:
"By the 1980’s CE, the paleontological record had convinced most scientists that the ancestors of humans had first evolved in Africa and then spread out into Eurasia in the early Pleistocene as Homo erectus. However, there was no consensus on what happened next. Three major models emerged by the latter half of the 20th century: the out-of-Africa replacement (OAR) model [1], the candelabra model of racial isolates [2], and the multiregional model [3,4]. Both the OAR and candelabra models posit that the expansion of Homo erectus into Eurasia results in independently evolving populations with no or extremely little genetic interchange. The OAR model in addition assumes a more modern form of humans, Homo sapiens, first evolved in Africa followed by an expansion into Eurasia, where the more modern humans completely replaced the archaic inhabitants of Eurasia. In both of these models, human evolution is dominated by splits into isolated lineages, followed by mostly independent evolution within the isolates. The OAR model in addition posits that the African isolate evolved into a form that expanded into Eurasia where it drove to extinction all the archaic Eurasians without genetic interchange with them. There is no or little role for gene flow in human evolution under these two models: rather, human evolution is dominated by splits, isolation, and extinction of lineages. Weidenreich’s multiregional model takes an opposite position on the importance of these evolutionary forces. There are no splits or isolates in his model because all human populations are interconnected by gene flow.
...
Despite the extensive evidence for gene flow and the lack of evidence of highly isolated evolutionary lineages, much of the human evolutionary literature is still full of “splits’, “divergence times of populations”, and pictures of human evolutionary trees showing separate branches leading to modern day Europeans, Asians, and Africans. These “splits”, “divergence times”, and “trees” are typically estimated with computer programs that will automatically yield a population tree regardless of whether or not the underlying data has a tree-like structure."
This is a clear description of the OOA hypothesis, and the expansion of hominins from Africa into Eurasia. Then he mentions the artifacts and models created with computer programs (trees, splits and dats for the forks of the branches of those trees). This is a novel point of view. It shows how we frame our ideas in models that then restrict how our thoughts can evolve in the future. A generation of geneticists thinks in terms of trees, splits, divergences, and coalescence dates.
In 1995, Templeton developed a statistical method called Nested Clade Phylogeographic Analysis or (NCPA) which uses genetic and geographic data to study how a population evolved (more about it in this paper). He mentions this method in his 2023 article, which continues below:
"... aDNA studies have confirmed the most controversial conclusion from NCPA that there was limited genetic interchange between the expanding out-of-Africa population with Eurasian populations. Moreover, genomic studies have revealed much genetic interchange and movement of human populations over the last 100,000 years, as reviewed in. One common method for achieving such inferences is to assume an evolutionary tree for the populations being sampled, and then calculate from the sequence data various statistical tests such as ABBA/BABA or several other alternatives... These statistics are tests of the null hypothesis that the underlying data do indeed come from a tree of populations. Rejection of this null hypothesis indicates that genetic interchange occurred that violated the assumed tree-like structure. When these tests reject a tree-like structure, often an admixture event of genetic interchange is assumed to have occurred to explain the rejection of the null hypothesis of a tree. For example, Figure 4... presents a typical visualization of this type of analysis."
Figure 4 is reproduced below.
Regarding the term "null hypothesis" used by Templeton, it is a statistical term used when one analyzes if the difference between two features of a population are due to chance, sampling errors, or some unknown real variable. To do so, one defines a null hypothesis (in this case, the differences between populations are due to a tree-like structure) and then performs rigorous statistical tests to compare the samples to see if chance or a real effect is responsible for any differences (usually a Student's t-test, created by English statistician William Sealy Gosset (1876–1937), aka Student), one also specifies a probability (the significance level or p), usually 5%, which sets the rejection threshold. If the calculated probability value from the t-test is lower than the significance level, one can reject the null hypothesis and accept that the results are not caused by random chance alone. In Templeton's example the null hypothesis was that tree populations explain human populations' genetics. But statistical analysis rejects this hypothesis meaning that some other genetic factors have intervened that are not tree-like. To save the day, geneticists have concocted admixture events into their trees to explain away the null hypothesis rejection.
Figure 4. A simplified version of Figure 8 from [51] that shows the estimated gene flow between populations of modern humans and various archaic populations. Gene flow from modern humans into archaic hominins was not estimated.
Source
Tempelton rejects these admixture patches and the concept itself:
"There are two serious problems with this analytical approach. First, these test statistics have an identifiability problem as they cannot distinguish between a single, virtually instantaneous admixture event, versus multiple, recurring admixture events, versus continuous gene flow, or versus gene flow with isolation by distance.
Hence, figures such as Figure 4 are visually misleading as they imply a degree of knowledge that is not truly available from the test results. Drawing a trellis between populations with gene flow would have been equally justified for Figure 4. Other workers have used fossils from different time periods and/or analytical techniques that make use of the length of introgressed genomic segments to infer recurrent and frequent genetic interchange between Neanderthals and modern humans in Eurasia from 100,000 ybp to 37,000 ybp, and perhaps as far back as 270,000 ybp.
Hence, Figure 4 is not only visually misleading, it displays a false narrative for Neanderthals and modern humans. The only conclusion that is justifiable from these ABBA/BABA and similar analyses is the falsification of the null hypothesis that these populations are interrelated through an evolutionary tree. The null hypothesis of human population evolutionary trees has been falsified again and again since the mid-Pleistocene (as reviewed in Chapter 7 in [19]), and this is not surprising as NCPA already indicated that since the mid-Pleistocene human population structure has been dominated by population movements and/or individual dispersal coupled with interbreeding, with no significant role for splits and isolation"
Then he attacks the statistical logic behind the analysis that leads to these trees, and points out how researchers use computing tools to suit the outcome they are searching for, and don't quite grasp the meaning of the output of these programs:
"The second and more serious reason why figures such as Figure 4 are misleading is that the analytical method of starting with a tree and then adding connections to reflect gene flow is that this approach is statistically inconsistent when the actual relationship of the populations is a more complicated network than a simple tree.
Statistical inconsistency means that the estimators do not converge to the true answer with increasing amounts of data; indeed, the more data you have, the more likely you will have the wrong answer. Patently, inconsistency, just like incoherence, is a highly undesirable statistical property. This inconsistency is illustrated by the work of Pugach et al. They analyzed genomic data from Siberian populations for which some prior demographic historical information was available. Using the TreeMix program that starts with an evolutionary tree of the populations followed by adding on admixture events as needed, they found that the “TreeMix results were not easy to interpret and seem to contradict well-accepted aspects of human population history.” They then analyzed the same data with SpaceMix, a program that does not assume an underlying evolutionary tree. In contrast to TreeMix, the SpaceMix results fit the genomic data better and without contradictions to well-accepted aspects of the population history. SpaceMix indicated a history that included isolation by distance, long-distance dispersals, and multiple admixture events – all of which violate the assumption of a population evolutionary tree. Because of inconsistency, the credibility of figures such as Figure 4 is highly questionable in human evolutionary studies."
Templeton then attacks the use of trees and the lack statistical knowledge or interpretation by scholars:
"Despite the almost universal rejection of tree-like structures in human evolution since the mid-Pleistocene, some workers in this area still construct population trees using programs that will always generate a tree no matter how bad the fit is to a tree, treating each human population as an isolate on its own branch of the tree without any indication of any genetic interchange between branches... These population trees are typically presented without any statistical assessment of how well the tree fits the underlying genetic data. I tested the tree given in [63], and rejected a tree-like structure with a p-value < 10-200. To say the least, this is an abysmal fit, and the utility of such poor-fitting trees to gain insight into human evolution and population structure is highly questionable."
I agree with Templeton, and in the past posted on these tools (Some thoughts about the tools used in genetic admixture analysis), remarking that a craftsman is only as good as his tools.
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