Thursday, October 22, 2026

Can We Redesign the Scientific Paper Before We Know the Answer?

Imagine submitting a scientific paper before doing the experiment.

The reviewers evaluate the question.

They examine the experimental design.

They inspect the planned statistical analysis.

And the journal agrees in advance to publish the study if it is conducted properly, regardless of whether the final result is positive, negative or inconvenient.

That is the logic behind registered reports.

The National Academies report discusses this approach as one possible response to publication bias. By judging the importance of the question and the quality of the methodology before the result is known, registered reports attempt to separate scientific quality from whether the experiment produces an exciting outcome.

The attraction is obvious.

Researchers have less incentive to reshape an analysis around the result.

Null findings remain publishable.

Unexpected outcomes cannot quietly transform a confirmatory study into an apparently preplanned discovery.

But the report does not present preregistration as a magic wand.

Critics worry about administrative burden and about discouraging exploratory research. And a badly designed study remains badly designed even if its bad design was meticulously preregistered.

This reveals a broader lesson running throughout the report.

No single reform can manufacture reliable science.

Open data do not guarantee good analysis.

Shared code can faithfully reproduce a programming mistake.

Preregistration cannot rescue a poor hypothesis.

Replication cannot automatically tell us which of two conflicting studies is correct.

Peer review cannot detect every error.

Each reform addresses a different failure mode.

The goal should therefore be an ecosystem of practices: better documentation, transparent analysis, appropriate statistics, publication of negative results, data and code sharing where possible, replication, training and incentives that reward careful work.

Scientific reliability is unlikely to arrive through one magnificent reform.

It is more likely to be assembled from many rather unglamorous habits done consistently well.

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Further reading: This post is based on Reproducibility and Replicability in Science, a consensus study report of the National Academies of Sciences, Engineering, and Medicine. National Academies of Sciences, Engineering, and Medicine. 2019. Reproducibility and Replicability in Science. Washington, DC: The National Academies Press. DOI: 10.17226/25303.


Wednesday, October 21, 2026

The P-Value Was Never Supposed to Be a Truth Machine

Few numbers in science have acquired as much psychological power as 0.05.

A result at p = 0.049 can feel publishable.

A result at p = 0.051 can feel disappointing.

Yet nature does not know that somebody drew a line at 0.05.

The National Academies report devotes considerable attention to the misuse of statistical significance.

A p-value does not tell us the probability that a scientific hypothesis is true.

It is calculated under an assumption about the null hypothesis and describes the compatibility of the observed data with that framework.

Reducing an entire study to significant versus non-significant therefore throws away enormous amounts of information.

What was the effect size?

How precise was the estimate?

How plausible was the hypothesis beforehand?

Were multiple hypotheses tested?

How large was the sample?

What assumptions were built into the model?

What uncertainty accompanies the result?

The report notes that judging replication simply by asking whether both studies cross the same statistical-significance threshold can be unreliable. Researchers should instead examine distributions, estimated effects, variability and uncertainty.

There is another fascinating tension.

A scientific field could achieve excellent replication rates by studying only extremely predictable phenomena.

But science also exists to discover things we do not already know.

The report points out that bold, low-prior-probability hypotheses are naturally less likely to replicate than safe hypotheses. Yet some transformative discoveries necessarily begin as improbable ideas.

The goal, therefore, cannot be to engineer science so that every experiment succeeds.

A perfectly predictable scientific enterprise would be reliable.

It might also be spectacularly boring.

Good science needs both exploration and confirmation.

The challenge is knowing which mode we are in.

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Further reading: This post is based on Reproducibility and Replicability in Science, a consensus study report of the National Academies of Sciences, Engineering, and Medicine. National Academies of Sciences, Engineering, and Medicine. 2019. Reproducibility and Replicability in Science. Washington, DC: The National Academies Press. DOI: 10.17226/25303.


Tuesday, October 20, 2026

The File Drawer Has a Distorting Mirror

Scientific publishing loves a dramatic result.

A treatment works.

A gene has an effect.

A relationship is statistically significant.

A surprising mechanism appears.

But what happens to the experiment in which nothing happens?

Historically, quite often, very little.

The National Academies report identifies “publication bias” as an important source of distortion in the scientific literature.

Researchers and journals face incentives favouring novel, statistically significant and apparently important results. Null findings, replications and inconclusive results can be much harder to publish.

That creates a peculiar problem.

Suppose 20 laboratories test a weak effect.

Eighteen find nothing interesting and the studies disappear into drawers or abandoned folders.

Two obtain positive results and publish them.

A reader searching the literature encounters two papers supporting the effect and zero contradicting it.

The literature is technically real.

But it is not representative.

The report provides a powerful example involving tamoxifen. Twenty-two of 23 individual trials discussed in a systematic review did not individually cross the conventional p < 0.05 threshold, yet the cumulative analysis demonstrated an important treatment effect.

That example reveals the opposite danger too.

Scientific understanding cannot be reduced to counting individually significant papers.

The entire distribution of evidence matters.

This is why negative results are scientifically valuable.

They constrain hypotheses.

They improve estimates.

They prevent future researchers from unknowingly repeating dead ends.

And they make systematic reviews more representative of reality.

The report discusses emerging attempts to publish null results, replications and registered reports, while acknowledging that changing journals alone will not solve the problem if academic incentives continue rewarding novelty more strongly than reliability.

The scientific literature should be a record of what we learned.

Not merely a highlight reel.

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Further reading: This post is based on Reproducibility and Replicability in Science, a consensus study report of the National Academies of Sciences, Engineering, and Medicine. National Academies of Sciences, Engineering, and Medicine. 2019. Reproducibility and Replicability in Science. Washington, DC: The National Academies Press. DOI: 10.17226/25303.


Monday, October 19, 2026

A Failed Replication Is Not Automatically Bad Science

Few phrases sound more fatal today than:

The result failed to replicate.

It sounds like a scientific obituary.

The National Academies report strongly resists that simplistic interpretation.

Some failures to replicate result from poor design, inadequate reporting, mistakes or misconduct.

But others can be scientifically productive.

Complex natural systems contain variation. A relationship may depend on environmental conditions, population structure, developmental stage, cultural context, measurement precision or variables nobody thought to record.

The report therefore distinguishes potentially useful sources of non-replicability from avoidable ones.

Imagine that Study A reports an effect.

Study B finds none.

That discrepancy could eventually reveal that the effect occurs only within a certain age range.

Study C might then demonstrate the mechanism responsible for that age dependence.

What initially looked like failure has uncovered biology.

This is why the report argues that the overall frequency of non-replication is a poor measure of the health of science.

The important task is diagnosing why results differed.

The report identifies characteristics that make some phenomena intrinsically more difficult to replicate: complex systems, large numbers of interacting variables, noisy measurements, poor control of variables, changing conditions across space or time, and imperfect measurement of the underlying phenomenon.

There is therefore an important distinction between:

productive disagreement, which exposes previously unknown complexity,

and

avoidable disagreement, which results from weak design, inadequate reporting, mistakes or bias.

Science benefits from the first.

It should work aggressively to reduce the second.

The difference cannot be discovered by attaching the label failed replication and walking away.

Investigation begins precisely where the disagreement starts.

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Further reading: This post is based on Reproducibility and Replicability in Science, a consensus study report of the National Academies of Sciences, Engineering, and Medicine. National Academies of Sciences, Engineering, and Medicine. 2019. Reproducibility and Replicability in Science. Washington, DC: The National Academies Press. DOI: 10.17226/25303.


Sunday, October 18, 2026

Why Two Competent Scientists Can Analyse the Same Data and Disagree

There is a comforting fantasy that data speak for themselves.

They do not.

Researchers make decisions before the data ever reach the final graph.

Which observations should be excluded?

How should missing values be handled?

How should categories be defined?

Which statistical model is appropriate?

Should an outlier remain?

Should variables be transformed?

Where should a threshold be placed?

The report describes a remarkable study in which almost 30 independent research teams received the same raw dataset and the same research question concerning red cards in football.

The outcomes differed substantially.

Sixty-nine percent of teams reported a significant positive relationship, while 31 percent did not. Differences in analytical approaches and data-grouping choices contributed to the divergent conclusions.

Nobody had secretly swapped the dataset.

The analytical pathways diverged.

This exposes an underappreciated feature of modern research: a dataset may contain a branching tree of defensible analytical choices.

The report similarly notes that scientific experiments contain hundreds or thousands of seemingly minor decisions. Something as mundane as reagent storage temperature, mixing procedure, survey wording or data-cleaning choices may alter outcomes.

Some decisions matter.

Others do not.

The difficult part is that we may not know which are consequential beforehand.

This is one reason transparency matters so much.

A researcher does not merely need to tell readers what result emerged. They need to reveal enough of the analytical route that somebody else can understand how the result emerged.

That idea becomes particularly important in genomics and other data-rich sciences.

A pipeline is not just a neutral pipe carrying truth from raw reads to final figure.

Every filter, threshold, reference database and parameter is a junction.

Reproducible science leaves signposts at those junctions.

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Further reading: This post is based on Reproducibility and Replicability in Science, a consensus study report of the National Academies of Sciences, Engineering, and Medicine. National Academies of Sciences, Engineering, and Medicine. 2019. Reproducibility and Replicability in Science. Washington, DC: The National Academies Press. DOI: 10.17226/25303.


Saturday, October 17, 2026

Can Chromosomes Create New Species? How Genome Rearrangements Shape Evolution

From microscopic chromosome changes to reproductive isolation, a century of research reveals that evolution is not only about changes in genes, but also about how genomes are organized.

When we think about evolution, we often imagine small genetic mutations accumulating over thousands or millions of generations. These mutations alter proteins, change observable traits, and sometimes help organisms adapt to new environments. Eventually, populations become sufficiently different to form new species.

But what if evolution can also proceed through much larger changes? What if the rearrangement of entire chromosomes could alter how genes interact, affect fertility, and even help establish reproductive barriers between populations?

This is the question explored by I. Yu. Bakloushinskaya in the 2016 review “Chromosomal Rearrangements, Genome Reorganization, and Speciation,” published in Biology Bulletin.

The article brings together more than a century of discoveries in cytogenetics, evolutionary biology, and genome organization. Its central argument is that chromosomes are not passive containers of genes. Their structure, arrangement, spatial organization, and patterns of inheritance can influence evolutionary processes, sometimes contributing directly to speciation.

The review also emphasizes an important qualification: chromosomal differences do not automatically create species, and demonstrating that a rearrangement initiated speciation is often difficult.

1. Beyond genes: Why chromosome organization matters

Every organism inherits genetic information arranged into chromosomes. Yet the significance of chromosomes extends beyond the DNA sequences they contain.

Two organisms can possess largely similar collections of genes while differing considerably in chromosome number, chromosome shape, or the relative positions of genetic regions.

Such differences arise through chromosomal rearrangements, including inversions, translocations, fusions, and fissions.

An inversion reverses the orientation of a chromosomal segment. A translocation relocates genetic material between chromosomal regions or chromosomes. A chromosome fusion joins formerly separate chromosomes, while a fission divides one chromosome into two.

These structural changes need not alter the total DNA content dramatically. Nevertheless, they can reorganize linkage relationships, modify recombination, affect gene expression, and interfere with chromosome pairing during meiosis.

Bakloushinskaya highlights a longstanding debate: are chromosomal rearrangements merely consequences of evolutionary divergence, or can they help initiate the formation of new species?

The distinction is fundamental.

Under one interpretation, populations first diverge through conventional genetic processes, and their chromosomes subsequently accumulate differences. Under the alternative, chromosomal restructuring itself can help establish reproductive isolation, enabling further evolutionary divergence.

The review argues that the second pathway is biologically possible and supported by certain natural systems.

2. A historical journey from chromosomes to evolutionary theory

The article begins by tracing how the study of chromosomes transformed biological thinking.

During the nineteenth century, chromosomes were recognized primarily as structures visible during cell division. Their role in heredity became clearer through the work of researchers including Sutton, Stevens, Wilson, Morgan, and their contemporaries.

The chromosomal theory of inheritance established that genes are carried by chromosomes and transmitted through the processes of cell division and reproduction.

The concept of the karyotype, developed in the early twentieth century, added another dimension. Instead of examining individual genes alone, researchers could characterize organisms by the number, size, shape, and organization of their chromosomes.

Chromosome sets became useful tools for distinguishing species, identifying hybrids, and investigating evolutionary relationships.

A major early demonstration came from plant hybridization studies. The artificial hybrid Raphanobrassica, produced through intergeneric hybridization followed by chromosome doubling, helped establish that changes in chromosome complements could contribute to the origin of new reproductive lineages.

The implications were especially clear for plants, where polyploidy can establish reproductive differences relatively rapidly.

In animals, however, the relationship between chromosome evolution and speciation proved more complicated.

The twentieth century therefore witnessed the development of several competing theories, seeking to explain how chromosome changes might arise, persist, and ultimately influence the formation of species.

3. Chromosomal speciation: Can a structural mutation start evolutionary divergence?

One of the review's central themes is chromosomal speciation, the hypothesis that chromosome rearrangements can contribute to the establishment of reproductive isolation.

Traditional population genetics emphasized changes in allele frequencies, natural selection, migration, genetic drift, and geographic separation. Chromosomal rearrangements were often treated as secondary events.

But several evolutionary biologists proposed a more active role for chromosome restructuring.

Wallace's work in the 1950s emphasized inversions and their potential consequences when geographically differentiated populations come into secondary contact. Mayr's ideas about changes in the genetic environment of isolated populations provided another framework for understanding rapid evolutionary divergence.

The review also discusses Vorontsov's distinction between gradualistic speciation and a more abrupt pathway initiated by changes in the chromosome complement.

In a gradualistic scenario, populations become geographically or ecologically separated, accumulate genetic differences, and eventually develop reproductive barriers.

In a chromosome-initiated scenario, structural changes can precede or help establish reproductive isolation, after which additional genetic and phenotypic divergence accumulates.

These are not necessarily mutually exclusive alternatives.

Chromosome restructuring may interact with geographic isolation, ecological divergence, demographic history, and conventional genetic changes.

The difficult question is determining causality: did the chromosome rearrangement help create the reproductive barrier, or did it accumulate after reproductive isolation had already developed?

4. Robertsonian fusions and the remarkable role of chromosome arms

One of the most compelling mechanisms discussed in the review involves Robertsonian, or centric, fusions.

These rearrangements typically join two acrocentric chromosomes, whose centromeres lie near one end, to form a metacentric chromosome.

An organism may therefore undergo a reduction in chromosome number without losing much of its major gene-containing sequence.

A simple fusion need not immediately cause severe reproductive problems. In some heterozygotes, the chromosomes can pair and segregate sufficiently well for fertility to remain relatively high.

The evolutionary consequences become more dramatic when independent populations acquire different fusions.

Consider three ancestral chromosomes, A, B, and C.

One population develops a fusion between A and B. Another develops a fusion between A and C.

Each new arrangement may function effectively within its own population. However, hybrids between the two populations inherit chromosomes that share only one homologous arm in common.

This condition is known as monobrachial homology.

During meiosis, the chromosomes may form complex pairing configurations rather than ordinary bivalents. These configurations can interfere with proper segregation, generate unbalanced gametes, and reduce hybrid fertility.

The result can be reproductive isolation even when each chromosomal arrangement was relatively harmless within its original population.

Bakloushinskaya discusses examples involving house mice, common shrews, mole voles, bats, rats, and rock-wallabies.

The broader evolutionary lesson is striking: two chromosome arrangements that are individually compatible with reproduction can become incompatible when brought together in hybrids.

This offers a plausible mechanism through which chromosome evolution contributes to speciation.

Importantly, the review also discusses cases in which hybrid fertility is retained despite substantial chromosomal differences. Such examples demonstrate that the consequences of rearrangements depend on chromosome structure, meiotic behavior, and genetic background.

5. Why chromosomal evolution may be nonrandom

Chromosomal rearrangements are not distributed uniformly across evolutionary lineages or genomic regions.

Why should certain rearrangements repeatedly occur in particular groups while others remain rare?

The review revisits the concept of karyotypic orthoselection, associated with M. J. D. White.

This concept addresses the tendency for particular chromosome configurations or rearrangement types to recur or become established in certain evolutionary groups.

Several explanations are considered.

Chromosome size and shape may influence which rearrangements are mechanically possible. The distribution of heterochromatin, satellite DNA, and ribosomal DNA may make particular regions more susceptible to structural changes. The locations of meiotic exchanges may affect which rearrangements can arise or persist.

Another important factor is the three-dimensional organization of chromosomes inside the nucleus.

In this interpretation, chromosome evolution is constrained by the physical and functional architecture of the genome.

Mutations may arise through stochastic processes, but the probability that a given rearrangement occurs, survives, or becomes fixed need not be uniform.

This provides a bridge between cytogenetics and modern genome biology: chromosome structure can influence evolutionary outcomes without requiring every rearrangement to be directly adaptive.

6. The nucleus is not a bag of chromosomes

One of the most forward-looking sections of the review concerns the spatial organization of the genome.

During interphase, chromosomes occupy distinct but interacting regions called chromosome territories.

Their arrangement is not completely random. Chromosome size, gene density, transcriptional activity, and cell type can influence their positions within the nucleus.

Bakloushinskaya discusses evidence that gene-rich chromosomal regions may preferentially occupy more central nuclear positions in certain cell types, while other regions are found closer to the nuclear periphery.

These patterns matter because genes are regulated within a three-dimensional nuclear environment.

Chromosomal rearrangements can change the spatial relationships among genes and regulatory sequences. A fusion may bring previously separated regions into closer proximity, while a translocation may relocate a gene into a different chromatin environment.

Such changes could influence transcription, replication timing, or recombination.

The article therefore encourages readers to move beyond the idea of chromosomes as one-dimensional strings of genes.

Their arrangement inside the nucleus may itself be evolutionarily consequential.

The author connects this perspective with evolutionary breakpoint regions, genomic locations at which chromosome rearrangements are inferred to have occurred during evolution.

These regions are not uniformly distributed. Their relationship with chromatin organization suggests that nuclear architecture may help influence the genomic locations of structural change.

The review presents this as an important research direction rather than a universal explanation for all evolutionary breakpoints.

7. Centromeres: Small chromosome regions with enormous evolutionary consequences

The centromere is essential for chromosome segregation. It provides the chromosomal site at which the kinetochore assembles, allowing the spindle apparatus to move chromosomes during mitosis and meiosis.

Traditionally, centromeres might appear to be stable structural features.

However, their evolutionary history is remarkably dynamic.

The review emphasizes that there is no single universal DNA sequence specifying centromere identity in all organisms. Centromere function depends on a combination of sequence context, chromatin organization, and epigenetic mechanisms.

A particularly intriguing phenomenon is the emergence of evolutionarily new centromeres, or neocentromeres.

These occur when centromere activity becomes established at a new genomic location.

Such changes can alter chromosome morphology and contribute to differences between species, sometimes without an obvious rearrangement of the surrounding gene order.

Bakloushinskaya discusses neocentromere evolution in mammals, including primates and mole voles.

The implication is that chromosome evolution does not depend exclusively on DNA breakage and reunion. Changes in the functional identity of chromosomal regions can also contribute to karyotype evolution.

This adds another layer of complexity to speciation research: differences in chromosome function may matter even when their gene content remains conserved.

8. Heterochromatin: From supposedly inert DNA to an evolutionary participant

Another major theme involves heterochromatin.

Historically, heterochromatic regions were often characterized as transcriptionally inactive and dominated by repetitive sequences. They were frequently considered less important than the gene-rich euchromatic portions of chromosomes.

The review challenges that simplistic view.

Heterochromatin contributes to chromosome structure and stability, while differences in its organization may influence interactions between chromosomes and the processes of cell division.

Evidence from Drosophila is discussed in connection with hybrid incompatibility, illustrating how species-specific heterochromatic differences can interfere with normal chromosome behavior.

The article also examines chromosomal races of the Ural field mouse, Sylvaemus uralensis.

These populations differ in their pericentromeric heterochromatin. Cytogenetic analyses indicate that variation in these regions is associated with differences in repetitive DNA content.

Such observations suggest that evolutionary divergence can involve changes in highly repetitive parts of the genome, not only conventional protein-coding genes.

The central message is that genomic regions once dismissed as relatively unimportant can influence chromosome organization and potentially reproductive compatibility.

However, the author acknowledges that the role of heterochromatin variation in speciation remains incompletely understood.

9. Polyploidy and the duplication of entire genomes

The review next turns to one of the most dramatic forms of genome reorganization: polyploidy.

Polyploid organisms possess additional complete sets of chromosomes.

Whole-genome duplication has played a major role in evolutionary history, particularly in plants and several vertebrate lineages.

The article discusses ancient genome duplication events in vertebrates and additional duplication histories in certain fishes.

Polyploidy can provide extra genetic material, allowing duplicated genes to acquire new functions, divide ancestral functions, or undergo loss.

Yet doubling an entire genome also introduces problems.

It changes gene dosage, complicates chromosome pairing, and may disrupt sex determination or other regulatory systems.

The author discusses unusual mammals with large chromosome complements, including the red viscacha rat, Tympanoctomys barrerae, and the golden viscacha rat, Pipanacoctomys aureus, whose genome sizes and chromosome characteristics have generated debate about polyploid evolution.

These examples illustrate that genome duplication in animals is a complex phenomenon requiring evidence from DNA content, chromosome organization, and meiotic behavior.

Polyploidy is thus both an important evolutionary mechanism and a challenge for maintaining a functional reproductive system.

10. Transposable elements and noncoding RNA: Additional forces shaping chromosomes

The paper also considers mobile genetic elements and noncoding RNAs.

Transposable elements are DNA sequences capable of changing their positions or producing additional copies within genomes.

Their activity can generate mutations, promote recombination between repetitive sequences, and contribute to structural rearrangements.

Building on Barbara McClintock's work, the review discusses the possibility that environmental or genetic stress can influence transposable element activity.

Hybrid dysgenesis in certain Drosophila groups provides an example in which mobile elements, genome instability, and reproductive abnormalities are connected.

The review also considers noncoding RNA as a potential participant in chromosome regulation and rearrangement.

These mechanisms broaden the picture of chromosome evolution.

Instead of viewing structural changes only as occasional accidental chromosome breaks, the article points to interactions among repetitive DNA, transposable elements, RNA-mediated regulation, chromatin structure, and cellular stress.

Nevertheless, it does not establish that transposable elements or noncoding RNAs initiate most speciation events. Their possible roles are presented as components of a complex evolutionary process.

11. How do chromosome rearrangements become established in populations?

A chromosome rearrangement cannot influence species evolution unless it survives and is transmitted across generations.

This raises a fundamental population-genetic problem.

Many rearrangements can reduce fertility in heterozygotes. If so, why are they not immediately eliminated by natural selection?

Bakloushinskaya explores several mechanisms that can facilitate their persistence.

Meiotic drive

Under ordinary Mendelian inheritance, the two alternatives of a genetic variant are expected to have equal transmission probabilities.

Meiotic drive occurs when transmission becomes biased, allowing one variant to be inherited more frequently than expected.

The review discusses how chromosomal variants may benefit from preferential segregation into functional gametes, particularly under circumstances involving asymmetric female meiosis.

Even a rearrangement with some negative fitness consequences might spread if its transmission advantage is sufficiently strong.

Genetic drift

In small populations, random changes in variant frequencies can have strong evolutionary effects.

A chromosomal rearrangement may therefore become established without providing an adaptive advantage.

Inbreeding and population subdivision

Small, partially isolated groups may experience elevated inbreeding and stronger drift.

These conditions can sometimes facilitate the fixation of chromosome arrangements that would struggle to become common in a large, freely mixing population.

The review discusses the potential significance of small demes and restricted dispersal in chromosome evolution.

The overarching message is that chromosome rearrangements become evolutionarily consequential through interactions between their biological effects and the demographic structure of populations.

12. Are there special genes that cause speciation?

The review also addresses the possibility of identifying individual genes whose divergence contributes directly to reproductive isolation.

A central example is PRDM9, a gene involved in directing meiotic recombination in mammals.

PRDM9 influences the locations at which recombination-associated chromatin marks are established. Changes in its function can alter recombination patterns and contribute to meiotic abnormalities.

The author highlights studies connecting PRDM9 divergence with hybrid sterility in mice.

This example is particularly significant because it connects a specific molecular mechanism with chromosome behavior during meiosis.

However, the review resists reducing speciation to the action of one or a few special genes.

Instead, it emphasizes that chromosome organization, recombination, genomic conflict, gene regulation, and reproductive isolation are interconnected.

The evolutionary emergence of species may result from changes to an entire genetic system rather than from isolated mutations acting independently.

PRDM9 provides a strong example of a gene contributing to reproductive incompatibility in a particular system, not proof that one universal speciation gene exists.

13. Speciation with gene flow: Chromosomal barriers need not be absolute

An important subtlety in the article concerns the relationship between reproductive isolation and gene flow.

It is tempting to imagine speciation as a clean separation in which two populations stop exchanging genes entirely.

In reality, evolutionary divergence can be more complicated.

Some chromosome regions may resist introgression while others continue to move between populations through hybridization.

Consequently, gene flow does not automatically imply that two populations have failed to diverge.

The review discusses examples involving chromosome races of shrews, house mice, and wallabies, where reproductive barriers and genetic exchange may coexist.

This leads to a more flexible understanding of speciation.

Chromosomal rearrangements may reduce effective gene flow in particular genomic regions, facilitate the maintenance of divergent genetic combinations, or interact with other incompatibilities.

Complete hybrid sterility is only one possible outcome.

14. What does this mean for modern evolutionary genomics?

Although published in 2016, the article provides an important conceptual foundation for contemporary comparative genome research.

Whole-genome sequencing allows researchers to identify conserved syntenic blocks, ancestral chromosome arrangements, fusion and fission events, inversion boundaries, segmental deletions, and differences in gene repertoires.

But detecting a genomic difference is only the beginning.

Understanding its evolutionary significance requires determining when it arose, whether it was fixed before or after population divergence, and whether it influenced fertility, recombination, gene regulation, or adaptation.

The review suggests that several levels of genome organization should be considered together: the sequence of individual genes, their arrangement along chromosomes, the architecture of chromatin, and the spatial organization of chromosomes within nuclei.

This perspective is especially relevant when evolutionary studies identify nonrandom genomic patterns, such as concentrations of deletions near rearrangement boundaries or repeated losses of genes within particular chromosomal regions.

Such patterns may reflect structural constraints, recurrent mutation mechanisms, selection, drift, or combinations of these processes.

Importantly, the article does not demonstrate that genes near evolutionary breakpoint regions are necessarily more likely to be lost. Nor does it establish that chromosome rearrangements inevitably produce gene deletions.

Those relationships require direct comparative genomic evidence.

15. The larger message: Evolution can reorganize genetic systems

The deepest insight of Bakloushinskaya's review is that evolution operates on multiple organizational levels.

Individual DNA mutations matter, but so do chromosome number, chromosome structure, chromatin composition, nuclear positioning, and the mechanisms governing inheritance.

A chromosome fusion may change meiotic compatibility. An inversion may alter recombination. A translocation may change a gene's regulatory environment. A neocentromere may modify chromosome behavior. Differences in heterochromatin may contribute to reproductive incompatibility.

These processes can interact with natural selection, genetic drift, inbreeding, and genomic conflict.

None provides a universal explanation for speciation.

The author ultimately presents chromosomal speciation as one biologically credible pathway among several, while recognizing that proving its initiating role in particular evolutionary events remains challenging.

Conclusion: Sometimes evolution rewrites the genome's architecture

A genome is more than its list of genes.

Its chromosomes determine how genetic information is packaged, arranged, recombined, regulated, and inherited. Changes to that architecture can have evolutionary consequences extending far beyond the individual rearranged segment.

Chromosomal Rearrangements, Genome Reorganization, and Speciation reminds us that the emergence of species cannot always be understood simply by counting mutations or measuring differences in DNA sequence.

To understand how species arise, we must also ask how their genomes are organized, how those organizations change, and what happens when distinct chromosomal systems meet during reproduction.

The review's most enduring message is not that every chromosome rearrangement creates a new species. It is that the organization of the genome can itself be a participant in evolutionary change, rather than merely a record of it.


Article discussed

Bakloushinskaya, I. Yu. (2016). Chromosomal Rearrangements, Genome Reorganization, and Speciation. Biology Bulletin, 43(8). DOI: 10.1134/S1062359016080057.

This blog post summarizes and interprets the attached 2016 review. It is not an independent systematic review of subsequent research, and the scientific examples reflect the evidence and historical framing presented in that article.

Why Your Methods Section Is No Longer Enough

Modern science has quietly undergone a technological revolution.

Sequencing machines generate terabytes of data.

Telescopes produce enormous digital archives.

Climate scientists run simulations on supercomputers.

Machine-learning models may contain billions of parameters.

And researchers routinely construct analytical pipelines containing dozens of programs, scripts, transformations and intermediate datasets.

Yet we often try to describe all of this in a few paragraphs labelled Methods.

The National Academies report argues that this model is increasingly inadequate. When scientific results arise from complex computations and large datasets, a conventional methods section may not contain enough information to reproduce the analysis.

A computational experiment contains many hidden ingredients.

Which version of the program was used?

Which reference genome?

Which parameters?

Which libraries?

Which operating system?

How were missing values treated?

Which samples were discarded?

Was a random seed fixed?

What intermediate filtering occurred?

The final figure may look wonderfully clean while sitting on top of a computational jungle.

One passage cited in the report makes the point sharply, referring to “all details of the computation” being made available.

That means reproducibility increasingly requires more than prose.

It may require data.

Code.

Exact software versions.

Dependency information.

Workflow descriptions.

Intermediate files.

Parameter configurations.

And sometimes the computational environment itself.

This is particularly important because the report notes that many scientists use software without formal training in practices such as version control, workflow management and software testing.

The transformation required is conceptual.

Code is not merely machinery operating behind the research.

In computational science, the code is part of the scientific method.

And if an important part of the method disappears when a student's laptop dies, then part of the scientific record disappears with it.

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Further reading: This post is based on Reproducibility and Replicability in Science, a consensus study report of the National Academies of Sciences, Engineering, and Medicine. National Academies of Sciences, Engineering, and Medicine. 2019. Reproducibility and Replicability in Science. Washington, DC: The National Academies Press. DOI: 10.17226/25303.


Friday, October 16, 2026

Science Is a Team Sport Across Generations

The popular image of science still loves the solitary genius.

Newton under his apple tree.

Darwin with his notebooks.

Einstein at his desk.

Real science is much noisier.

It involves students, technicians, statisticians, reviewers, software developers, competing laboratories, databases, previous generations of researchers and people who notice that something in somebody else's paper does not quite add up.

The National Academies report puts this directly:

“Science is a communal enterprise.”

Scientific claims become valuable partly because other scientists can inspect them.

That requires communication.

Another researcher should be able to understand what was measured, how samples were chosen, how data were processed, which observations were excluded, what statistical assumptions were made and how much uncertainty accompanies the result.

Without those details, a published result becomes strangely isolated.

Others may cite it.

They may even believe it.

But they cannot properly interrogate it.

This is why transparency is not merely a fashionable add-on to science. It is part of the mechanism through which science becomes collective knowledge.

The report describes science as a process in which researchers confirm, dispute and refine one another's findings. The ability to scrutinize claims allows confidence to accumulate over time.

That also explains why criticism in science should not automatically be interpreted as hostility.

When another laboratory tries to replicate your result, it is subjecting the claim to one of the processes through which scientific knowledge becomes stronger.

When somebody finds a limitation, they may have discovered the boundary of the phenomenon.

And boundaries are scientifically interesting.

A biological effect that exists only in juveniles, only under nutritional stress, only within a certain genetic background or only at a particular temperature tells us more about the underlying mechanism than the original claim that the effect happens everywhere.

Science therefore advances not only when someone says yes.

It also advances when someone asks:

Under exactly what conditions is that yes actually true?

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Further reading: This post is based on Reproducibility and Replicability in Science, a consensus study report of the National Academies of Sciences, Engineering, and Medicine. National Academies of Sciences, Engineering, and Medicine. 2019. Reproducibility and Replicability in Science. Washington, DC: The National Academies Press. DOI: 10.17226/25303.


Thursday, October 15, 2026

Reproducibility and Replicability Are Not the Same Thing

Scientists frequently use reproducibility and replicability as though they mean the same thing.

The National Academies report deliberately separates them.

That distinction is extremely useful, particularly now that so much research depends on computation.

Suppose I publish a genomic analysis.

I provide the sequencing data, scripts, software versions and parameter settings. You download exactly those materials, run exactly my analysis and obtain the same results.

That is reproducibility.

Now imagine that you collect a completely new biological dataset to test the same scientific question. You analyse those new data and obtain results consistent with mine.

That is replicability.

The report defines reproducibility as obtaining consistent results using the same input data and computational procedures, whereas replication involves another study addressing the same scientific question using newly obtained data.

There is a third concept too:

generalizability.

A finding may reproduce perfectly and replicate in another experiment, but does it hold in another population, species, laboratory, climate, time period or experimental context?

That is a different question.

These distinctions matter because each tests a different layer of scientific reliability.

Reproducibility asks:

Can I reconstruct what you actually did?

Replicability asks:

Does the phenomenon appear again when we gather new evidence?

Generalizability asks:

How far beyond the original circumstances does the finding extend?

And crucially, they need not succeed or fail together.

A computational result can be perfectly reproducible yet scientifically wrong. If a programming error exists in the original code, rerunning the same erroneous code can faithfully reproduce the same erroneous answer.

Conversely, a meticulously performed study may fail to replicate because the biological or social system being studied contains previously unidentified variation.

So asking simply, Did it replicate? can be far too crude.

The better questions are:

What exactly was tested?

What changed?

How large was the uncertainty?

And what does the disagreement teach us?

Those questions lead to better science than a binary stamp reading PASS or FAIL.

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Further reading: This post is based on Reproducibility and Replicability in Science, a consensus study report of the National Academies of Sciences, Engineering, and Medicine. National Academies of Sciences, Engineering, and Medicine. 2019. Reproducibility and Replicability in Science. Washington, DC: The National Academies Press. DOI: 10.17226/25303.


Wednesday, October 14, 2026

Science Is Supposed to Change Its Mind

One of the easiest ways to misunderstand science is to imagine it as a warehouse of facts. A result enters the warehouse, acquires the label scientifically proven, and remains there forever.

That is not how science works.

The National Academies report Reproducibility and Replicability in Science presents a much more interesting picture. Scientific knowledge develops through discovery, confirmation, criticism and correction. New evidence can strengthen an existing explanation, reveal its limits, or occasionally force scientists to replace it.

The report captures this beautifully in three words: scientific knowledge is “durable and mutable.”

Those two qualities might seem contradictory.

Durable means that well-supported scientific knowledge does not evaporate every time a new paper appears. Newtonian mechanics did not become useless when relativity was developed. Existing knowledge survives because it has accumulated evidence behind it.

Mutable means that no scientific claim receives immunity from new evidence.

Better instruments can reveal things that older instruments could not detect. Larger datasets can uncover patterns hidden in small samples. New experiments can identify conditions under which an apparently universal relationship no longer applies.

That ability to change is not a defect in science. It is one of its central strengths.

The report's preface uses changes in nutritional and medical advice to illustrate the danger of speaking too confidently about what we currently believe to be true. Scientific conclusions generally arrive with some degree of uncertainty, and repeated confirmation increases confidence rather than magically transforming a claim into absolute truth.

This is why a paper that overturns an earlier result does not necessarily mean that someone previously failed at science.

Sometimes the new study is wrong.

Sometimes the original study was wrong.

Sometimes both studies are correct under different conditions.

And sometimes the disagreement reveals a variable nobody previously knew mattered.

The scientific process is designed to sort among these possibilities.

The deeper lesson is therefore surprisingly optimistic: science progresses not because scientists never make mistakes, but because scientific claims remain exposed to interrogation.

A scientific result is not carved into stone.

It enters a conversation.

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Further reading: This post is based on Reproducibility and Replicability in Science, a consensus study report of the National Academies of Sciences, Engineering, and Medicine. National Academies of Sciences, Engineering, and Medicine. 2019. Reproducibility and Replicability in Science. Washington, DC: The National Academies Press. DOI: 10.17226/25303.


Tuesday, October 13, 2026

Is It Still Science If It Isn't New?

 We often talk about science as if its defining feature were novelty.

A scientist discovers something nobody has seen before. A new species is found. A new gene function is reported. A new mechanism is proposed. A new theory explains an old observation.

And because scientific papers are expected to make a "novel contribution," it is tempting to equate science with discovering something new.

But that raises a deceptively simple question:

If an experiment tells us nothing new, is it still science?

And an even more interesting one:

If an experiment is perfectly reproducible, but the interpretation of its results is wrong, is it still science?

The answer to both questions is essentially yes—but with important distinctions.

Science is not the same thing as novelty. Novelty describes a contribution to knowledge. Science describes a way of generating, testing, communicating, and correcting knowledge about the world.

That distinction changes how we think about scientific research.


Science is more than discovery

Imagine that a researcher performs an experiment and obtains a result that has already been reported ten times.

At first glance, it might seem that nothing scientific has happened. There is no discovery. No new phenomenon. No new fact.

But suppose the researcher carefully repeats the experiment with a different population, laboratory, instrument, organism, or environmental condition.

The result is again obtained.

The experiment has not necessarily produced a novel fact. But it has done something scientifically valuable: it has provided confirmation.

Science does not consist only of asking:

"What has nobody seen before?"

It also asks:

"Is what we think we know actually reliable?"

That is why replication matters.

A replicated result may be completely unsurprising, yet still contribute to the reliability of scientific knowledge.


Discovery is science—but science is not only discovery

The U.S. National Academies describe scientific knowledge as developing through discovery, confirmation, and correction.

That three-part description is useful because it immediately takes us beyond the popular image of the lone scientist making a spectacular discovery.

Consider three researchers.

Researcher 1: Discovery

They investigate a poorly studied organism and discover that a particular gene is completely absent from its genome.

Nobody had previously reported this.

If the observation is supported by appropriate genomic evidence, this is a scientific discovery.

It adds something previously unknown to our knowledge of nature.

Researcher 2: Confirmation

Another scientist investigates the same gene in independently generated genomes and finds the same absence.

They haven't discovered a new fact.

But they have strengthened the evidence that the original observation was genuine.

That is also science.

Researcher 3: Correction

A third scientist discovers that both studies overlooked a highly divergent copy of the gene.

The apparent "gene loss" was actually an annotation problem.

Again, science has happened.

The third study has not merely failed to discover something new. It has corrected an existing scientific claim.

And correction is not an embarrassment that sits outside science.

It is one of the ways science works.


What about a wrong interpretation?

This is where things become particularly interesting.

Suppose an experiment is beautifully designed.

The measurements are reliable.

The experiment is independently replicated.

Everyone agrees on the observed result.

But the researchers interpret the result incorrectly.

Does that make the experiment non-science?

No.

We need to separate the observation from the interpretation.

Consider this simplified chain:

Experiment → Data → Observation → Interpretation → Conclusion

Each step makes a different claim.

Suppose an experiment shows that treatment X causes gene Y to become more highly expressed.

That observation may be completely reproducible.

The researchers then conclude:

"Therefore, gene Y causes cell proliferation."

But their experiment never manipulated gene Y. It only measured its expression.

The causal interpretation may therefore be wrong.

The experiment can still be scientifically sound.

The data can still be scientifically useful.

The conclusion can still be wrong.

And, importantly, discovering that the conclusion was wrong can itself become part of science.


Reproducibility does not make an interpretation true

This distinction is easy to miss.

We sometimes talk about reproducibility as though it were a guarantee of truth.

It isn't.

Imagine that ten laboratories independently reproduce an experimental observation.

That gives us increasing confidence that the observation is real.

But it does not necessarily establish that we understand what the observation means.

You can therefore have:

Highly reproducible data + highly reproducible analysis + an incorrect interpretation.

Consider a diagnostic assay that consistently produces a particular molecular signal.

The reproducibility tells us that the signal is reliably generated.

But we still have to establish what the signal represents.

Is it:

  • the presence of a particular protein?
  • a cross-reacting molecule?
  • a degradation product?
  • a related paralog?
  • an artifact of the assay?
  • or genuinely the biological entity being claimed?

Reproducibility answers one question:

"Does this observation reliably occur?"

It does not necessarily answer:

"Why does it occur?"

And certainly not:

"Does our preferred explanation for it have to be true?"


An experiment can be scientific even when its conclusion is wrong

This is an uncomfortable but fundamental feature of science.

Science is not a magical procedure that converts experiments into truth.

Scientists can make mistakes.

They can misunderstand mechanisms.

They can overinterpret correlations.

They can mistake artifacts for biological signals.

They can construct explanations that later turn out to be wrong.

What distinguishes science is not that scientists are always correct.

It is that scientific claims are supposed to remain open to empirical challenge and correction.

A wrong scientific hypothesis can therefore be perfectly legitimate science.

The problem arises when the evidence is presented as establishing something it does not establish, particularly when alternative explanations are ignored or contradictory evidence is suppressed.

The experiment may remain scientific.

The claim about what the experiment demonstrates may not.


Does science require novelty?

This is where scientific research and scientific publishing sometimes get confused.

There are at least two different questions:

Question 1

Is this science?

Question 2

Is this a sufficiently novel contribution to publish in this particular journal?

They are not the same question.

A replication study can be science even if a journal decides that it lacks sufficient novelty.

A careful reanalysis of existing data can be science.

A correction of an existing result can be science.

A negative result can be science.

A methodological validation can be science.

A failure to reproduce an influential result can be science.

A careful measurement that confirms something we already strongly suspected can be science.

None necessarily requires discovering a previously unknown fact.

Novelty is therefore a property of the contribution, not the defining property of science itself.


What kinds of science can lack novelty?

Consider several possibilities.

1. Replication

"We performed the experiment again and obtained the same result."

No new phenomenon may have been discovered.

But the reliability of the existing observation has been tested.

Science? Yes.


2. Confirmation

"Using an independent method, we obtain evidence consistent with the existing conclusion."

Again, there may be little or no novelty in the phenomenon itself.

But independent evidence can strengthen scientific knowledge.

Science? Yes.


3. Falsification or refutation

"We tested a previously accepted prediction and found that it does not hold."

This may not discover a new phenomenon.

Instead, it removes support from an existing explanation.

Science? Absolutely.


4. Correction

"The earlier observation was real, but the proposed explanation was incorrect."

The new study may not discover anything fundamentally new.

It may simply establish that our interpretation of existing evidence needs revision.

Science? Yes.


5. Methodological validation

"We tested whether this experimental technique actually measures what researchers think it measures."

The study may produce no new biological discovery whatsoever.

But it can establish whether a tool is reliable.

Science? Yes.


6. Negative results

"We predicted that X would happen, but under these conditions it does not."

The absence of an expected effect can be scientifically informative.

Indeed, a scientific framework in which only positive results count would systematically distort our understanding of nature.

Science? Yes.


7. Reanalysis of existing data

Suppose all the data already exist.

A researcher develops a new statistical analysis and asks whether the original conclusion follows from the data.

No new experiment is performed.

No new organism is collected.

No new molecule is discovered.

Yet the analysis may reveal that the original conclusion was unsupported—or that an overlooked pattern exists in the existing data.

Science? It can be.

Science is not defined by whether new data were personally generated. It is defined by what is being done with evidence and how claims about the world are evaluated.


But then what is novelty for?

If novelty isn't what makes something science, why do scientists care so much about it?

Because science is not only about producing reliable knowledge.

It is also about extending the frontier of knowledge.

A research paper generally needs to answer:

"What does this work add?"

That addition might be a new observation.

Or a new explanation.

Or a new method.

Or stronger evidence.

Or a correction.

Or a previously unrecognized limitation.

Or a demonstration that a supposedly general phenomenon does not generalize.

Thus, novelty is enormously important for scientific progress and publication, even though it is not the defining characteristic of scientific activity.

A useful distinction is:

Science asks whether a claim is empirically justified.

Scientific novelty asks whether the resulting knowledge adds something not already known.

Those are different axes.


And what about an incorrect scientific paper?

This brings us to perhaps the most uncomfortable conclusion.

A paper can be:

  • scientifically conducted,
  • carefully executed,
  • reproducible,
  • transparently reported,

and nevertheless contain a wrong interpretation.

That does not necessarily mean the work was "not science."

Instead, we should ask which component is wrong.

Perhaps the measurement was wrong.

Perhaps the experiment was incapable of distinguishing competing hypotheses.

Perhaps the statistical inference was inappropriate.

Perhaps the causal interpretation went beyond the evidence.

Perhaps the biological mechanism was misunderstood.

Perhaps a later study revealed an overlooked alternative explanation.

These distinctions matter because otherwise we fall into a binary way of thinking:

Correct = science
Incorrect = not science

But science doesn't work that way.

If it did, there would be no need for scientific correction.


The deeper idea: science is a process, not a truth machine

Perhaps the most useful way to think about science is not:

Science = discovering new facts.

Nor:

Science = producing correct answers.

Rather:

Science is a systematic, evidence-based process through which claims about the natural world are generated, tested, challenged, communicated, confirmed, refined, and corrected.

Within that process, discovery is one activity.

Replication is another.

Confirmation is another.

Refutation is another.

Correction is another.

And explanation is another.

Some produce novelty.

Some don't.

Some ultimately produce correct conclusions.

Some don't.

Yet all can participate in science.


The paradox

There is therefore a fascinating paradox at the heart of science:

A study can be scientifically valuable without discovering anything new.

And:

A study can discover something genuinely new while getting its interpretation wrong.

The first tells us that novelty is not necessary for something to be science.

The second tells us that being new is not sufficient for something to be scientifically established.

What matters is the relationship between evidence and claims.

A new observation requires evidence.

An old observation can be tested again.

An interpretation must be justified.

A conclusion must not exceed what the evidence supports.

And when we get something wrong, science has a mechanism—at least in principle—for finding out that we were wrong.

Perhaps that is a more useful definition of science than the popular image of scientists constantly discovering spectacular new things.

Science is not simply the business of finding what nobody knew before.

It is the collective process of figuring out what we have good reason to believe about the world—and continuously finding out where we were wrong.

Sunday, October 11, 2026

Forgotten Crops May Be Part of Agriculture's Future

The final section of the article turns archaeology toward the future.

This is perhaps its most important message.

Studying ancient agriculture is not merely about discovering where wheat came from.

The archaeological record represents thousands of years of experiments in agricultural resilience.

And many of those experiments have been forgotten.

Diversity repeatedly rises and falls

Early domestication often incorporated variation from multiple populations.

But isolation can gradually erode that diversity.

The same process occurs at the level of farming systems.

Agricultural communities once cultivated many crop combinations that later disappeared.

Some species became marginal crops.

Others vanished from large parts of their former ranges.

A crop can go from important to nearly invisible

The paper highlights Triticum timopheevi.

This wheat was once cultivated across a large region.

Archaeological evidence places it in parts of Turkey before 6000 BC.

It eventually spread from central Europe toward Turkmenistan.

Yet during later periods its range contracted dramatically.

By the twentieth century it had become largely associated with Georgia.

The crop did not cease being biologically viable.

Agricultural history moved around it.

“Orphan crops” are evolutionary libraries

Many under-studied crops may contain useful characteristics:

drought tolerance,

disease resistance,

adaptation to poor soils,

nutritional qualities,

unusual perennial growth,

or compatibility with local farming systems.

Modern agriculture is heavily concentrated around a relatively small number of globally dominant crops.

That concentration creates efficiency, but also vulnerability.

A changing climate makes those vulnerabilities harder to ignore.

Archaeology can become agricultural research

Usually we imagine modern agricultural science helping us understand ancient farming.

The authors propose reversing the direction.

Ancient agriculture can help us imagine future agricultural systems.

Archaeobotany tells us which crops survived particular climatic fluctuations.

Genomics reveals where ancient populations acquired new genetic diversity.

Archaeology documents agricultural systems that persisted for centuries or millennia.

Historical crop distributions reveal species that were once important but later abandoned.

Together, these records form a gigantic natural experiment conducted across thousands of years.

The final lesson

The history of domestication is not a story of humans progressively perfecting a few plants.

It is a history of experimentation, hybridisation, ecological engineering, accidental selection, migration, loss and renewal.

Agricultural diversity has repeatedly contracted.

But it has also repeatedly been rebuilt when people moved crops, exchanged seeds and brought previously separated populations together.

The future of agriculture may therefore depend partly on recovering something that the past possessed in abundance:

options. 🌾

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Further reading: This post is inspired by and draws on Dorian Q. Fuller, Tim Denham, and Robin Allaby, “Plant domestication and agricultural ecologies,” Current Biology 33, no. 11 (2023): R636–R649, doi:10.1016/j.cub.2023.04.038. Read the original article in Current Biology
For a classic and highly readable perspective on why plant domestication arose in some regions but not others, see Jared Diamond, Chapter 8, “Apples or Indians: Why Did Peoples of Some Regions Fail to Domesticate Plants?”, in Guns, Germs, and Steel: The Fates of Human Societies (W. W. Norton, 1997), pp. 131–156. Read “Apples or Indians” View Guns, Germs, and Steel on Google Books

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Saturday, October 10, 2026

Why Some Crops Conquered Continents While Others Stayed Home

Some domesticated plants spread astonishingly far.

Wheat and barley moved across huge portions of Eurasia.

Rice spread from China across much of Asia.

Maize moved far beyond Mesoamerica.

Pearl millet expanded beyond West Africa.

Other crops remained geographically restricted.

Why?

The article highlights two especially important variables:

time and productivity.

Older crops had longer to travel

A crop domesticated several thousand years earlier simply had more opportunities to spread through migration and exchange.

But age is not enough.

Agricultural productivity influences human demography.

Productivity changes population movement

A highly productive crop can support relatively high population densities.

Under lower productivity, communities may reach the effective carrying capacity of a local environment more quickly.

This can encourage group fission.

Some people establish new settlements.

Crops travel with them.

Paradoxically, under certain circumstances a less productive agricultural system might therefore promote faster territorial expansion.

Carrying capacity is not fixed

The paper wisely treats carrying capacity as “notional.”

A landscape does not possess one permanent human carrying capacity.

It changes with:

technology,

labour,

crop choice,

soil management,

climate,

water availability,

and cultivation strategy.

Humans continually modify the ecological ceiling they are approaching.

Some forgotten crops may simply have missed history's transport network

A crop remaining local does not necessarily mean it is agronomically inferior.

Historical circumstances matter.

Enset in Ethiopia, oca in the Andes and browntop millet in India are examples discussed in the paper of crops whose geographical spread remained limited.

Their unrealised agricultural potential may therefore be considerable.

History's winners are not automatically biology's best candidates.

Sometimes they were simply the crops standing nearest the door when human populations began moving.

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Further reading: This post is inspired by and draws on Dorian Q. Fuller, Tim Denham, and Robin Allaby, “Plant domestication and agricultural ecologies,” Current Biology 33, no. 11 (2023): R636–R649, doi:10.1016/j.cub.2023.04.038. Read the original article in Current Biology
For a classic and highly readable perspective on why plant domestication arose in some regions but not others, see Jared Diamond, Chapter 8, “Apples or Indians: Why Did Peoples of Some Regions Fail to Domesticate Plants?”, in Guns, Germs, and Steel: The Fates of Human Societies (W. W. Norton, 1997), pp. 131–156. Read “Apples or Indians” View Guns, Germs, and Steel on Google Books

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Friday, October 9, 2026

Domestication Did Not Necessarily Begin with a Genetic Bottleneck

A classic genetic model of domestication predicts a bottleneck.

A small subset of a wild population is selected.

Those individuals become the founders of the crop.

Genetic diversity collapses.

Strong selection then fixes important domestication alleles.

This certainly can happen.

But the review argues that it is not an adequate general model for early plant domestication.

Archaeology predicts weak selection

If domestication took thousands of generations, selection could have been relatively weak.

Under weak selection, more individuals remain within the reproducing population.

That preserves diversity.

Genomes reveal multiple contributors

Ancient and modern genomic data increasingly indicate that several crops incorporated genetic material from multiple wild populations.

Barley, emmer, maize and bananas all provide examples of complex histories.

Instead of:

wild population → bottleneck → crop,

the history may resemble:

multiple wild populations ↔ cultivated populations ↔ migration ↔ hybridisation → crop metapopulation.

Gene flow can rescue crops

One of the most important examples discussed in the paper concerns sorghum.

Ancient sorghum genomes from Nubia suggest that genetic load increased through time.

Isolation can allow harmful variation to accumulate or useful diversity to disappear.

But contact with other sorghum populations can introduce new variation.

This process is known as genomic rescue.

Hybridisation, far from contaminating a pure crop lineage, can therefore restore resilience.

Purity may be the wrong metaphor

Modern breeding often emphasises uniformity.

Ancient domestication frequently appears to have benefited from mixture.

The history of successful crops may therefore be partly a history of repeatedly reconnecting populations.

Agricultural resilience sometimes grows from genetic messiness.

That lesson has obvious consequences for modern crop conservation.

Wild relatives, traditional landraces and geographically isolated crop populations should not be viewed simply as museum pieces.

They represent reservoirs of evolutionary options.

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Further reading: This post is inspired by and draws on Dorian Q. Fuller, Tim Denham, and Robin Allaby, “Plant domestication and agricultural ecologies,” Current Biology 33, no. 11 (2023): R636–R649, doi:10.1016/j.cub.2023.04.038. Read the original article in Current Biology
For a classic and highly readable perspective on why plant domestication arose in some regions but not others, see Jared Diamond, Chapter 8, “Apples or Indians: Why Did Peoples of Some Regions Fail to Domesticate Plants?”, in Guns, Germs, and Steel: The Fates of Human Societies (W. W. Norton, 1997), pp. 131–156. Read “Apples or Indians” View Guns, Germs, and Steel on Google Books

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