同义反复解释了没有变异和选择的进化。评论没有变异和选择的进化过程"(2021 年)的评论,作者 Gabora 等人。

IF 3.7 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES Journal of The Royal Society Interface Pub Date : 2024-09-18 DOI:10.1098/rsif.2023.0579
István Zachar,Jakab Máté,Szabolcs Számadó
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引用次数: 0

摘要

加博拉和斯蒂尔(Gabora L, Steel M. 2021):没有变异和选择的进化过程。J. R. Soc. Interface 18, 20210334.[doi:10.1098/rsif.2021.0334])声称,没有自然选择,即没有变异和竞争,累积适应性进化是可能的。为了支持这一观点,作者模拟了一个名为 "自他重组"(SOR)的理论过程,该过程设想了一个由反射性自催化集合组成的种群,它可以在没有任何形式的出生、死亡或选择(即没有种群动态)的情况下积累有益的变化。作者声称,尽管 SOR 不是达尔文进化论,但适应性进化却发生在 SOR 中,并认为它与生命起源和文化进化有关。我们分析了 SOR 以及它在没有变异和选择的情况下实现进化的说法。我们发现,作者有意忽略了自催化集或其组成部分的增长和/或退化,假定所有影响都是有益的,而且 SOR 中的所有实体都是相同和不可改变的。我们证明,由于这些假设,SOR 是一个在静态种群中横向渗透有益效应的微不足道的模型。我们实施了一个扩展的 SOR 模型,其中包括更现实的假设,以证明考虑任何一个被忽略的过程都不可避免地会导致传统的达尔文动力学。我们的分析直接挑战了作者的说法,揭示了基础过于脆弱的证据。虽然作者错误地概括出的最佳情况可能在数学上是有效的,但撇开他们不切实际的假设,就会发现 SOR 并不代表真实的实体(如原细胞),而是模拟了快速水平扩散效应可以有效平衡种群的微不足道之处。SOR 中的适应性仅仅是因为作者只考虑了有益效应。由于忽略了死亡/增长动态和不适应效应,SOR 是不现实的,其与文化或生物进化的相关性也值得怀疑。
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Tautology explains evolution without variation and selection. A Comment on: 'An evolutionary process without variation and selection' (2021), by Gabora et al.
Gabora and Steel (Gabora L, Steel M. 2021 An evolutionary process without variation and selection. J. R. Soc. Interface 18, 20210334. [doi:10.1098/rsif.2021.0334]) claim that cumulative adaptive evolution is possible without natural selection, that is, without variation and competition. To support this claim, the authors modelled a theoretical process called self-other reorganization (SOR) that envisages a population of reflexively autocatalytic sets that can accumulate beneficial changes without any form of birth, death or selection, that is without population dynamics. The authors claim that despite being non-Darwinian, adaptive evolution happens in SOR, deeming it relevant to the origin of life and to cultural evolution. We analysed SOR and the claim that it implements evolution without variation and selection. We found that the authors, by design, ignore the growth and/or degradation of autocatalytic sets or their components, assuming all effects are beneficial and all entities in SOR are identical and immutable. We prove that due to these assumptions, SOR is a trivial model of horizontal percolation of beneficial effects over a static population. We implemented an extended model of SOR including more realistic assumptions to prove that accounting for any of the ignored processes inevitably leads to conventional Darwinian dynamics. Our analysis directly challenges the authors' claims, revealing evidence of an overly fragile foundation. While the best-case scenario the authors incorrectly generalize from may be mathematically valid, stripping away their unrealistic assumptions reveals that SOR does not represent real entities (e.g. protocells) but rather models the triviality that fast horizontal diffusion of effects can effectively equalize a population. Adaptation in SOR is solely because the authors only consider beneficial effects. The omission of death/growth dynamics and maladaptive effects renders SOR unrealistic and its relevance to evolution, cultural or biological, questionable.
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来源期刊
Journal of The Royal Society Interface
Journal of The Royal Society Interface 综合性期刊-综合性期刊
CiteScore
7.10
自引率
2.60%
发文量
234
审稿时长
2.5 months
期刊介绍: J. R. Soc. Interface welcomes articles of high quality research at the interface of the physical and life sciences. It provides a high-quality forum to publish rapidly and interact across this boundary in two main ways: J. R. Soc. Interface publishes research applying chemistry, engineering, materials science, mathematics and physics to the biological and medical sciences; it also highlights discoveries in the life sciences of relevance to the physical sciences. Both sides of the interface are considered equally and it is one of the only journals to cover this exciting new territory. J. R. Soc. Interface welcomes contributions on a diverse range of topics, including but not limited to; biocomplexity, bioengineering, bioinformatics, biomaterials, biomechanics, bionanoscience, biophysics, chemical biology, computer science (as applied to the life sciences), medical physics, synthetic biology, systems biology, theoretical biology and tissue engineering.
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