Unlocking evolutionary mechanisms with Excel — An introduction to evolutionary biologyBook Review
Daisuke Kyogoku [Author] Maruzen Publishing, Published November 2025, 2,700 yen (excluding tax)
Having once participated in the translation of a bulky textbook on evolutionary biology, I believe I do not hold any major misconceptions about biological evolution. However, I am terrible at mathematics and have a habit of unconsciously skipping over mathematical formulas whenever they appear. Although I own several textbooks on population genetics (including two different editions of Hartl and Clark, which is repeatedly introduced in the "For Further Reading" section of this book), I was intimidated by the sheer number of formulas and would only selectively read parts of them when the need arose. Therefore, even if the direction of my understanding was not wrong, discussions such as how many generations are required for a predicted situation to occur were completely beyond me.
From the title "Unraveling with Excel," I intuitively felt that this book would teach me the approach I had always avoided out of laziness and dispel my lingering doubts of many years, and I was not wrong. In the afterword, the author is modest, stating that the content of the book is population genetics and that the title is not entirely accurate, but I think it is an exquisite title. It begins with simulating how gene frequencies change due to differences in fitness with one locus and two alleles by entering formulas into Excel (Chapter 2), and proceeds to frequency-dependent selection (Chapter 3). Here, the hawk-dove game and the evolution of sex ratios appear. These topics frequently appear in behavioral ecology, but are unfamiliar in population genetics textbooks. I did not know that these could be simulated as changes in gene frequency. At this point, it is already no ordinary book on population genetics. Chapter 4 deals with mutation and migration-dispersion, Chapter 5 deals with genetic drift and assortative mating, and Chapter 6 elaborates on the Hardy-Weinberg law. This law appears in high school biology textbooks, but its significance is generally not very well understood. I suspect this is because the exercise of using this law to calculate the expected values of each genotype from the actual observed values of each genotype and comparing the two is rarely introduced, or even when it is, examples are often used where the expected values do not deviate (such as the frequencies of each blood type in the ABO blood system). Isn't it only natural for the observed and expected values to be the same, since you calculate the gene frequency from the actual observed values and then derive the expected value from there—is probably the feeling of an ordinary person. To make the explanation easier to understand, I think it would be good to use overdominance or heterozygote inferiority as examples. For instance, someone like me who has investigated chromosome hybrid zones for many years (where observed values of heterozygous karyotype individuals are almost invariably lower than expected values) and has become accustomed to Hardy-Weinberg tests cannot help but think that it would have further aided understanding if they had included a box showing things like the table counting genotypes of the β-globin allele including the sickle-cell gene in West Africa from page 171 of Hartl and Clark's 2nd edition (though this table is a bit complicated because normal β-globin is distinguished into A and C making it three alleles, the observed and expected values for each genotype indeed deviate significantly; unfortunately, that table is not in the 4th edition), along with the calculation of gene frequencies, Hardy-Weinberg expected values, and how to perform significance tests between observed and expected values.
Now, in Chapter 7, as cases where multiple factors influence outcomes, simulations of three examples are first introduced: natural selection and migration, natural selection and mutation, and natural selection and genetic drift. Chapter 8 then details the neutral theory, introducing simulations that examine allele frequencies under neutral mutation and genetic drift. However, since this is said to be difficult to express in Excel, only R language programs and their results are presented. Yet, the discussion progresses to how population sizes can be estimated from this, introducing how we can understand the changes in population sizes of modern humans, Neanderthals, and Denisovans. Furthermore, Chapter 9 introduces three simulations where multiple loci are involved: recombination and linkage disequilibrium, genetic incompatibility and speciation, and the evolution of mate preferences and ornamental traits in sexual selection. Chapter 10 features numerical simulations of the evolution of altruistic behavior incorporating inclusive fitness. Explanations of sexual selection and inclusive fitness through such an approach were fresh and interesting to me. Finally, the book concludes with the connection between evolutionary biology and human society (Chapter 11). Here, topics such as selective breeding, the history and problems of eugenics, the naturalistic fallacy, problems that biological evolution brings to medicine, agriculture, and fisheries, the evolution seen in various human traits, and evolutionary explanations for the delayed development of evolutionary biology are discussed concisely yet with solid persuasiveness. I would go so far as to say that this book is worth purchasing just to read Chapter 11.
There are 15 Excel sheets to be completed in this book. Although the instructions on how to create them are easy to understand and it takes time, it is not frustrating. Each one includes a list of formulas to be entered into each cell (which only use basic arithmetic and are not complicated), along with a completed visual mockup showing numerical values up to the third generation, and a graph of the simulation results (a scatter plot depicting generations and gene frequencies). Therefore, even if you make an input error in some cell, you can spot the mistake by comparing it with the numbers in the visual mockup and the completed scatter plot. While it is satisfying just to complete the graphs after painstakingly entering the data, the process of changing parameter values in the completed sheets and seeing how the results change is even more enjoyable. The completed Excel sheets can also be downloaded from the publisher's website, but I strongly recommend taking the trouble to enter them yourself from scratch while verifying what is being calculated in each cell. Furthermore, although there are numerous formulas to be entered into the cells of the tables, there was not a single mistake among them. However, there were a few minor typos in the cell numbers. On page 125, E6 in the leftmost column of Table 9.5 should likely be D6; on page 136, H12 in the leftmost column of the table should likely be H11, and I12 should likely be I11. Both are typos that anyone would notice, but since they can be a bit unsettling, I hope they will be corrected in the second printing.
The content of this book far surpassed what I had first imagined from its title. Despite being called an introductory text, the subjects it covers are both wide-ranging and quite deep. It is a well-crafted book, and as I read through it, I was left speechless by the author's remarkable talent and capability. Whether it's a sequel, a full-fledged textbook, or even a different theme, I am already looking forward to the author's next work.
Nobuhiro Tsurusaki (Professor Emeritus, Tottori University)
