Joseph Felsenstein Research Articles

Fisher’s Fundamental Theorem of Natural Selection Isn’t Fundamental After All

Fisher’s Fundamental Theorem of Natural Selection (FTNS) was called “biology’s central theorem” (Fisher, 1930, pgs. 36–37; Brockman, 2011; Royal Society, 2020). FTNS might possibly have been accorded this status for decades because Fisher himself declared his own theorem to be fundamental to biology (Fisher, 1930, pgs. 36–37). However, the idea that Fisher’s theorem is biology’s central theorem is by-and-large a myth promoted by popu- lar science writers like Richard Dawkins (Brockman, 2011). Joseph Felsenstein, when delivering the 2018 Fisher Memorial Lecture declared that FTNS was “alas, not so fundamental” (Felsenstein, 2018; Felsenstein, 2017, pg. 94. One may be hard-pressed to find a biology textbook or biology student who can explain how FTNS helps them understand biology. Even the meaning and proof of the FTNS have re- mained contentious even to this day (Price, 1972; Basener and Sanford, 2018).
 Not only does FTNS do little to nothing to explain biological evolution, but like most population genetic and evolutionary literature, FTNS relies on a definition of fit- ness in terms of population growth rates rather than the biophysical notions of fitness which are more in line with the common-sense intuitions of the medical and engineering communities.
 From the perspective of the biophysical (rather than the population growth) notion of fitness, natural selection might be more accurately described as an agent against the increase of complexity rather than an agent for it. Thus, metaphorically speaking, some sort of anti-Weasel model of natural selection might better describe how selection actu- ally works in nature rather than Dawkins’ Weasel or other man-made genetic algorithms.
 However, the main focus of this communication is to pro- vide some pedagogical insights through simple numerical illustrations of Fisher’s Theorem. The hope is that this will show the general irrelevance of FTNS to the question of the evolution of complexity by means of natural selection, and thus show that Fisher’s Theorem is not so fundamental after all.

Special Issue: Mathematical and Computational Evolutionary Biology-2015.

This special issue of Systematic Biology contains review articles contributed by keynote speakers at the “Mathematical and Computational EvolutionaryBiology” conference (MCEB; see web sites below), held in 2015. This is the seventh conference in this series, the first being held in Paris in 2003 with keynote addresses from such notables as Joe Felsenstein, Ziheng Yang, and Walter Fitch. Oxford University Press published two books based on the 2003 and 2005 Paris meetings (Gascuel 2005; Gascuel and Steel 2007).

"The Theory was Beautiful Indeed": Rise, Fall and Circulation of Maximizing Methods in Population Genetics (1930-1980).

Describing the theoretical population geneticists of the 1960s, Joseph Felsenstein reminisced: "our central obsession was finding out what function evolution would try to maximize. Population geneticists used to think, following Sewall Wright, that mean relative fitness, W, would be maximized by natural selection" (Felsenstein 2000). The present paper describes the genesis, diffusion and fall of this "obsession", by giving a biography of the mean fitness function in population genetics. This modeling method devised by Sewall Wright in the 1930s found its heyday in the late 1950s and early 1960s, in the wake of Motoo Kimura's and Richard Lewontin's works. It seemed a reliable guide in the mathematical study of deterministic effects (the study of natural selection in populations of infinite size, with no drift), leading to powerful generalizations presenting law-like properties. Progress in population genetics theory, it then seemed, would come from the application of this method to the study of systems with several genes. This ambition came to a halt in the context of the influential objections made by the Australian mathematician Patrick Moran in 1963. These objections triggered a controversy between mathematically- and biologically-inclined geneticists, with affected both the formal standards and the aims of population genetics as a science. Over the course of the 1960s, the mean fitness method withered with the ambition of developing the deterministic theory. The mathematical theory became increasingly complex. Kimura re-focused his modeling work on the theory of random processes; as a result of his computer simulations, Lewontin became the staunchest critic of maximizing principles in evolutionary biology. The mean fitness method then migrated to other research areas, being refashioned and used in evolutionary quantitative genetics and behavioral ecology.

Using combinatorial bioinformatics methods to analyze annual perspective changes of influenza viruses and to accelerate development of effective vaccines.

The standard World Health Organization procedure for vaccine development has provided a guideline for influenza viruses, but no systematic operational model. We recently designed a systemic analysis method to evaluate annual perspective sequence changes of influenza virus strains. We applied dnaml of PHYLIP 3.69, developed by Joseph Felsenstein of Washington University, and ClustalX2, developed by Larkin et al, for calculating, comparing, and localizing the most plausible vaccine epitopes. This study identified the changes in biological sequences and associated alignment alterations, which would ultimately affect epitope structures, as well as the plausible hidden features to search for the most conserved and effective epitopes for vaccine development. Addition our newly designed systemic analysis method to supplement the WHO guidelines could accelerate the development of urgently needed vaccines that might concurrently combat several strains of viruses within a shorter period.

Mathematical and Computational Evolutionary Biology (2013)

This special issue of Systematic Biology contains review articles, contributed by keynote speakers after the fifth edition of the “Mathematical and Computational Evolutionary Biology” conference (MCEB; see web sites below) conference, held in 2013. We started in 2003 (under a slightly different name: “Mathematics of Evolution and Phylogeny”) at the Mathematics Research Center Henri Poincare, Paris, with well-known speakers like Walter Fitch and Joe Felsenstein. We had the feeling that the considerable efforts of the keynote speakers to synthesize and present their research in an extensive but understanding manner would deserve to be known by a large audience. Two books containing review articles corresponding to keynote presentations were published, following the 2003 and 2005 conference edition (Gascuel 2005; Gascuel and Steel 2007).

Secretary’s Report, 2014

Secretary’s Report, 2014

WHERE HAVE ALL THE CLADES GONE? A SYSTEMATIST'S TAKE ON INFERRING PHYLOGENIES1

Joseph Felsenstein published four papers between 1978 and 1985 that enabled phylogenetic biology to take giant steps forward. In chronological order, Felsenstein showed that parsimony could be misleading (1978), derived maximum likelihood methods for DNA phylogenies (1981), constructed a method for establishing significance of trees (bootstrapping: 1985a), and invented a phylogenetic correction to the comparative method (independent contrasts: 1985b). With the publication of Inferring Phylogenies 20 years later, he reviews a monumental portion of the increasingly difficult literature on phylogenetic methods that he helped foster. His book provides insights into extraordinarily difficult problems that few biologists are trained to understand and exposes the reader to the bleeding edge of a discipline undergoing rapid change. His emphasis on the very latest theoretical results is tempered by a consistently scholarly, if occasionally controversial, account of the history of the field. It is not much use as a beginner's guide to phylogenetic inference-one can search in vain through its nearly 600 pages for the most basic of definitions-nor does it adequately treat a large literature on parsimony-based phylogenetics (the reader is better off with Kitching et al.'s [1998] book), but no one with a serious interest in the quantitative aspects of the subject will be able to live without it. How quantitative the field has become will be apparent to the reader quickly. Something has happened to phylogenetic theory in the last 10 years. Previously, a few biologists with real expertise in math, like Felsenstein, together with a very small number of applied mathematicians-professional theorists, let's call them-were joined by a larger cadre of enthusiastic amateurs in moving the field forward. Recently, a New Wave of mathematicians and computer scientists has begun to play a much more dominant role. If you think Inferring Phylogenies is mathematically daunting, pick up a copy of Semple and Steel's Phylogenetics (2003), or the recently published edited volume by Olivier Gascuel on Mathematics of Evolution and Phylogeny (2005). Felsenstein's book builds a bridge between biologists and this New Wave. If you don't happen to recall Laplace or Fourier transforms, appreciate the utility of a uniformly most powerful statistical test, or enjoy the beauty of a good generating function, a few chapters will contain stumbling blocks. However, more often than not, he offers what may be the last best hope for those with persistence and at least a passing familiarity with basic calculus and probability theory. As arcane as the Had-

Inferring phylogenies: an instant classic

Joe Felsenstein has had more positive influence on the statistical revolution of phylogenetics than any other researcher in the field. For that reason, many biologists view him as the father of statistical phylogenetics. It was with this in mind that I finally got my hands on his long-awaited book, Inferring Phylogenies. The short answer is: it delivers. In a narrow sense, Felsenstein's book could be viewed as old-fashioned, with a number of chapters devoted to 'old' methods such as parsimony and distance-based approaches. This view misses the point. No other book gives as clear and comprehensive a picture of the major problems, solved or otherwise, in phylogenetic inference. In fact, it is perhaps precisely because Felsenstein follows the progress of statistical phylogenetics from its early beginnings that the book will be so enduring. It is enjoyable to read and is written in an uncluttered way that invites the reader to understand the details, and to work them out with pen and paper when the answer is not explicitly given. Scientists should know their tools, and Felsenstein does his best to explain to tree-builders what in fact they are actually doing when they run an analysis using popular phylogeny programs like PAUP* or PHYLIP. But this book is not just about building trees. There are also chapters devoted to modeling changes in gene frequencies, genealogy-based population genetics (the coalescent) and quantitative genetics, to mention just a few. Not only is this an unrivalled reference book for users of phylogenetic methods, but it also serves as a foundation stone on which developers of phylogenetic methods can build. The nature of the material demands that the difficulty level is a bit uneven from chapter to chapter, but an effort is always made to convey the essential features of the discipline discussed. The book is well balanced, containing enough detail to suit most readers, and providing ample references for those that require more.

A Broad Look at Tree-Building

Inferring Phylogenies . by Joseph Felsenstein. Sinauer, Sunderland, MA, 2004. 684 pp. Paper, $59.95, £33.99, €48.87. ISBN 0-87893-177-5. In a work that can serve both as a text and a reference, Felsenstein explains the assumptions and logic that underlie the reconstruction and use of evolutionary trees.

Getting it right for the wrong reason

A statistical framework is necessary for almost every scientific field, but few disciplines had as much trouble as phylogenetics did in establishing one. The long and frustrating arguments over different methodologies and philosophies (cladistics versus phenetics versus evolutionary taxonomy in the earlier years – cladistics versus statistical inference more recently) might have waned but their echo still reverberates. In a short historical essay1, Joe Felsenstein, one of the principal architects of the ‘statistical revolution’ in phylogenetics, tells the story of the growth of a much-troubled field with characteristic clarity and vigour. Those interested in the way that science (and scientists) proceeds and advances will find the essay illuminating, not least because it offers a nice illustration of a paradigm-shift in the making.

A Bayesian guide to tree felling

To most evolutionary biologists, building phylogenetic trees is a bore. Comparative approaches to understanding patterns as diverse as the correlation of body size and brain size in primates, and the evolution of extended phenotypes in gallwasps, require a robust phylogeny on which to place changes in character state. But, for every question you are really interested in there are a thousand petty arguments concerning tree building. Which method should you use? Which parameter values should you plug in? How much can you trust the results? As Joe Felsenstein wrote, ‘Comparative biologists may understandably feel frustrated upon being told that they need to know the phylogenies of their groups in great detail, when this is not something they had much interest in knowing.’ However, he also goes on to say ‘Nevertheless phylogenies are fundamental to comparative biology; there is no doing it without taking them into account.’ Phylogenetic trees might be a nuisance, but until now you could not do without them. And, if your ‘favourite’ tree was wrong, so were your conclusions. However, a recent paper by Huelsenbeck et al.1xAccommodating phylogenetic uncertainty in evolutionary studies. Huelsenbeck, J.P. et al. Science. 2000; 288: 2349–2350Crossref | PubMed | Scopus (203)See all References1 has raised an exciting new possibility: evolution without trees.The authors do not suggest that you don’t do your phylogenetic tree-building homework, but that you can eliminate the uncertainty involved in tree building by considering all possible phylogenies and parameter values, weighting according to the probability that each is correct. The idea is just one more application of a field that is becoming increasingly common in evolutionary analysis: Bayesian statistics. In practice, this means that you replace parameters, such as tree topologies, biases in DNA mutation and rates of change of characters, by distributions representing your certainty, or otherwise, about them. These can be based on previous analyses, current data or even biological intuition. The probability of a given hypothesis being correct given the observed data is then summed over all possible realizations, weighting each by their relative probability. As Huelsenbeck et al.1xAccommodating phylogenetic uncertainty in evolutionary studies. Huelsenbeck, J.P. et al. Science. 2000; 288: 2349–2350Crossref | PubMed | Scopus (203)See all References1 demonstrate, this can lead to considerably different conclusions from what popular methods of tree reconstruction, such as parsimony, would suggest. More importantly, it gives you a good feel for how much faith to have in your conclusions.Performing this task requires some serious computational effort, but there is new software appearing that can actually do the number crunching in reasonable time. Perhaps, more importantly, Bayesian analysis relies exclusively on the likelihood framework for tree building, and die-hard opponents of such model-based methods could argue that unless you are sure that the model of sequence evolution you are using is correct, the likelihood value is meaningless. Of course, you can always include more parameters, but this is a path that can spiral rapidly into absurdity. Nevertheless, tree felling the Bayesian way is going to make a large difference to everyone working with the comparative method because it eliminates the potential nightmare of using the wrong tree. As the lumberjacks say, if you can’t see the wood for the trees, chop them down.

Sewall Wright and Evolutionary Biology

Provine's thorough and thoroughly admirable examination of Wright's life and influence, which is accompanied by a very useful collection of Wright's papers on evolution, is the best we have for any recent figure in evolutionary biology.--Joe Felsenstein, Nature In Sewall Wright and Evolutionary Biology . . . Provine has produced an intellectual biography which serves to chart in considerable detail both the life and work of one man and the history of evolutionary theory in the middle half of this century. Provine is admirably suited to his task. . . . The resulting book is clearly a labour of love which will be of great interest to those who have a mature interest in the history of evolutionary theory.-John Durant,; ITimes Higher Education Supplement; X

Joseph felsenstein (ed.) Numerical taxonomy NATO advanced science institutes series, ecological sciences No. 1

Joseph felsenstein (ed.) Numerical taxonomy NATO advanced science institutes series, ecological sciences No. 1

Numerical Taxonomy. Proceedings of the NATO Advanced Study Institute Held at Bad Windsheim, Germany, July 4-16, 1982.Joseph Felsenstein

Previous articleNext article No AccessNew Biological BooksNumerical Taxonomy. Proceedings of the NATO Advanced Study Institute Held at Bad Windsheim, Germany, July 4-16, 1982. Joseph Felsenstein David L. SwoffordDavid L. Swofford Search for more articles by this author PDFPDF PLUS Add to favoritesDownload CitationTrack CitationsPermissionsReprints Share onFacebookTwitterLinkedInRedditEmail SectionsMoreDetailsFiguresReferencesCited by The Quarterly Review of Biology Volume 60, Number 3Sep., 1985 Published in association with Stony Brook University Article DOIhttps://doi.org/10.1086/414459 Copyright 1985 Stony Brook Foundation, Inc.PDF download Crossref reports no articles citing this article.

Conceptual Issues in Evolutionary Biology.

These essays by leading scientists and philosophers address conceptual issues that arise in the theory and practice of evolutionary biology. The third edition of this widely used anthology has been substantially revised and updated. Four new sections have been added: on women in the evolutionary process, evolutionary psychology, laws in evolutionary theory, and race as social construction or biological reality. Other sections treat fitness, units of selection, adaptationism, reductionism, essentialism, species, phylogenetic inference, cultural evolution, and evolutionary ethics.Each of the twelve sections contains two or three essays that develop different views of the subject at hand. For example, the section on evolutionary psychology offers one essay by two founders of the field and another that questions its main tenets. One sign that a discipline is growing is that there are open questions, with multiple answers still in competition; the essays in this volume demonstrate that evolutionary biology and the philosophy of evolutionary biology are living, growing disciplines. ContributorsRobin O. Andreasen, Kwame Anthony Appiah, David A. Baum, John H. Beatty, David J. Buller, Leda Cosmides, James Donoghue, Steven J. Farris, Joseph Felsenstein, Susan K. Finsen, Joseph Fracchia, Stephen Jay Gould, Sarah Blaffer Hrdy, David L. Hull, Philip Kitcher, R. C. Lewontin, Elisabeth Lloyd, Ernst Mayr, Michael Ruse, John Maynard Smith, Elliott Sober, John Tooby, C. Kenneth Waters, George C. Williams, David Sloan Wilson, E. O. Wilson

A THEME THAT WILL BE REPEATED

EvolutionVolume 36, Issue 1 p. 206-206 Book ReviewFree Access A THEME THAT WILL BE REPEATED† Joe Felsenstein, Joe Felsenstein Department of Genetics, University of Washington, Seattle, Washington, 98195Search for more papers by this author Joe Felsenstein, Joe Felsenstein Department of Genetics, University of Washington, Seattle, Washington, 98195Search for more papers by this author First published: January 1982 https://doi.org/10.1111/j.1558-5646.1982.tb05029.x †Evolution and Variation in Multigene Families. Tomoko Ohta. Lecture Notes in Biomathematics, Number 37. Springer-Verlag, Berlin, Heidelberg, and New York. 1980. viii + 131 p.; ill.; index. $11.50. AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume36, Issue1January 1982Pages 206-206 RelatedInformation

Haploid Populations Subject to Varying Environment: The Characteristic Values and the Rate of Loss of Alleles

Previous article Next article Haploid Populations Subject to Varying Environment: The Characteristic Values and the Rate of Loss of AllelesKeith GladstienKeith Gladstienhttps://doi.org/10.1137/0132065PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstractA class of stochastic matrices, arising in genetics, are considered. The characteristic values are obtained and related to the genetic process.[1] C. Cannings, The latent roots of certain Markov chains arising in genetics: a new approach. I. Haploid models, Advances in Appl. Probability, 6 (1974), 260–290 MR0343949 0284.60064 CrossrefGoogle Scholar[2] C. Cannings, The latent roots of certain Markov chains arising in genetics: a new approach. II. Further haploid models, Advances in Appl. Probability, 7 (1975), 264–282 MR0371430 0339.60067 CrossrefISIGoogle Scholar[3] A. B. Chia, Random mating in a population of cyclic size, J. Appl. Probability, 5 (1968), 21–30 MR0225583 0162.51901 CrossrefISIGoogle Scholar[4] William Feller, Diffusion processes in genetics, Proceedings of the Second Berkeley Symposium on Mathematical Statistics and Probability, 1950, University of California Press, Berkeley and Los Angeles, 1951, 227–246 MR0046022 0045.09302 Google Scholar[5] Joseph Felsenstein, The rate of loss of multiple alleles in finite haploid populations, Theoret. Population Biology, 2 (1971), 391–403 10.1016/0040-5809(71)90028-1 MR0332215 0234.92010 CrossrefGoogle Scholar[6] Keith Gladstien, Loss of alleles in a haploid population with varying environment, Theoret. Population Biology, 10 (1976), 383–394 10.1016/0040-5809(76)90025-3 MR0496833 0346.92014 CrossrefISIGoogle Scholar[7] Keith Gladstien, The characteristic values and vectors for a class of stochastic matrices arising in genetics, SIAM J. Appl. Math., 34 (1978), 630–642 10.1137/0134050 MR0475977 0386.92004 LinkISIGoogle Scholar[8] Samuel Karlin, Equilibrium behavior of population genetic models with non-random mating. II. Pedigrees, homozygosity and stochastic models, J. Appl. Probability, 5 (1968), 487–566 MR0246657 0205.49601 CrossrefISIGoogle Scholar[9] S. Karlin, Rates of approach to homozygosity for finite stochastic models with variable population size, Amer. Naturalist, 102 (1968), 443–455 10.1086/282557 CrossrefISIGoogle Scholar[10] S. Wright, Evolution in Mendelain populations, Genetics, 16 (1931), 97–159 Google Scholar Previous article Next article FiguresRelatedReferencesCited ByDetails Looking Forwards and Backwards in the Multi-Allelic Neutral Cannings Population ModelJournal of Applied Probability, Vol. 47, No. 03 | 14 July 2016 Cross Ref Looking Forwards and Backwards in the Multi-Allelic Neutral Cannings Population ModelJournal of Applied Probability, Vol. 47, No. 3 | 14 July 2016 Cross Ref The time back to the most recent common ancestor in exchangeable population modelsAdvances in Applied Probability, Vol. 36, No. 01 | 1 July 2016 Cross Ref The time back to the most recent common ancestor in exchangeable population modelsAdvances in Applied Probability, Vol. 36, No. 1 | 1 July 2016 Cross Ref Robustness results for the coalescentJournal of Applied Probability, Vol. 35, No. 02 | 14 July 2016 Cross Ref Robustness results for the coalescentJournal of Applied Probability, Vol. 35, No. 2 | 14 July 2016 Cross Ref Line-of-descent and genealogical processes, and their applications in population genetics modelsTheoretical Population Biology, Vol. 26, No. 2 | 1 Oct 1984 Cross Ref The Characteristic Values and Vectors for a Class of Stochastic Matrices Arising in GeneticsKeith GladstienSIAM Journal on Applied Mathematics, Vol. 34, No. 4 | 12 July 2006AbstractPDF (1212 KB) Volume 32, Issue 4| 1977SIAM Journal on Applied Mathematics707-895 History Submitted:29 April 1976Published online:12 July 2006 InformationCopyright © 1977 Society for Industrial and Applied MathematicsPDF Download Article & Publication DataArticle DOI:10.1137/0132065Article page range:pp. 778-783ISSN (print):0036-1399ISSN (online):1095-712XPublisher:Society for Industrial and Applied Mathematics