AI 中文总结
研究生物复杂性，提出语义复杂性概念，通过简单数学模型说明其与句法复杂性区别，发现总句法复杂性进化具路径依赖性，有驱动和熵两种模式，早期特化偶然性与多最优景观决定谱系在各模式停留时间及适应性达成方式。
AI 中文摘要
试图量化生物复杂性的研究通常考虑选定层次水平和分辨率下的内在结构特性，如身体部位数量及其分化程度，这些措施本质上是‘句法的’，与指定排列所需信息有关，而非生物功能。我们提出研究‘语义’复杂性，即结构特征中其变化对生物体适应性有可测量影响的子集。我们在具有功能约束、对称性破缺和特化的简单体节形成数学模型中说明了这种区别。我们发现，随着选择驱使谱系趋向全局最优适应性，总句法复杂性的进化是路径依赖的，揭示了两种进化模式：一种是驱动模式，语义和句法复杂性共同上升；另一种是熵模式，句法复杂性在近中性进化下向上漂移。早期特化中的历史偶然性与多最优适应性景观共同决定谱系在每种模式下停留的时间。那些未直接通向最高适应性状态的路径上的谱系在驱动模式下停留更长时间，最终可达到相当的适应性，但只能通过进化出句法复杂性更高的形态来实现。
英文摘要
Attempts to quantify biological complexity often consider intrinsic structural properties at a chosen hierarchical level and resolution, such as counts of body parts and their degree of differentiation. These measures are inherently \emph{syntactic}, being concerned with the information needed to specify an arrangement rather than the biological functions performed. Syntactic complexity alone is therefore not sophisticated enough of a measure to fully address the role of complexity as either a driver or consequence of evolution. We propose to study the counterpart, \emph{semantic} complexity: the subset of structural features whose variation has a measurable effect on organismal fitness. We illustrate this distinction in a simple mathematical model of tagmosis with functional constraints, symmetry breaking, and specialisation. We find that the total syntactic complexity evolved as selection drives lineages toward globally optimal fitness is path-dependent, revealing two evolutionary modes: a driven mode, in which semantic and syntactic complexity rise together, and an entropic mode, in which syntactic complexity drifts upward under a near-neutral evolution. Historical contingencies in early specialisation, combined with multi-optima fitness landscapes, govern how long lineages stay in each mode. Those on paths that do not lead directly to the highest-fitness states remain in the driven mode for longer and can eventually reach comparable fitness, but only by evolving morphologies with greater syntactic complexity.
