Am Nat. 2020 Feb;195(2):300-314. doi: 10.1086/705992. Epub 2019 Dec 27.
The history of a trait within a lineage may influence its future evolutionary trajectory, but macroevolutionary theory of this process is not well developed. For example, consider the simplified binary trait of living in cave versus surface habitat. The longer a species has been cave dwelling, the more accumulated loss of vision, pigmentation, and defense may restrict future adaptation if the species encounters the surface environment. However, the Markov model of discrete trait evolution that is widely adopted in phylogenetics does not allow the rate of cave-to-surface transition to decrease with longer duration as a cave dweller. Here we describe three models of evolution that remove this memoryless constraint, using a renewal process to generalize beyond the typical Poisson process of discrete trait macroevolution. We then show how the two-state renewal process can be used for inference, and we investigate the potential of phylogenetic comparative data to reveal different influences of trait duration, or memory in trait evolution. We hope that such approaches may open new avenues for modeling trait evolution and for broad comparative tests of hypotheses that some traits become entrenched.
一个谱系内特征的历史可能会影响其未来的进化轨迹,但这一过程的宏观进化理论还没有得到很好的发展。例如,考虑一个简化的二元特征,即生活在洞穴与地面栖息地。如果一个物种遇到地面环境,那么它在洞穴中生活的时间越长,可能会导致视力、色素沉着和防御能力的累积丧失,从而限制未来的适应能力。然而,在系统发育学中广泛采用的离散特征进化的马尔可夫模型并不允许洞穴到表面的转换率随着洞穴居住时间的延长而降低。在这里,我们描述了三种去除这种无记忆约束的进化模型,使用更新过程来推广超越离散特征宏观进化的典型泊松过程。然后,我们展示了如何使用两状态更新过程进行推断,并研究了系统发育比较数据揭示特征持续时间或特征进化中记忆的不同影响的潜力。我们希望这些方法可能为模型化特征进化和广泛检验某些特征变得根深蒂固的假说提供新的途径。