Sherwin William Bruce
Evolution & Ecology Research Center, School of Biological Earth and Environmental Science, UNSW Sydney, Sydney 2052, Australia.
Entropy (Basel). 2018 Sep 21;20(10):727. doi: 10.3390/e20100727.
This article discusses how entropy/information methods are well-suited to analyzing and forecasting the four processes of innovation, transmission, movement, and adaptation, which are the common basis to ecology and evolution. Macroecologists study assemblages of differing species, whereas micro-evolutionary biologists study variants of heritable information within species, such as DNA and epigenetic modifications. These two different modes of variation are both driven by the same four basic processes, but approaches to these processes sometimes differ considerably. For example, macroecology often documents patterns without modeling underlying processes, with some notable exceptions. On the other hand, evolutionary biologists have a long history of deriving and testing mathematical genetic forecasts, previously focusing on entropies such as heterozygosity. Macroecology calls this Gini-Simpson, and has borrowed the genetic predictions, but sometimes this measure has shortcomings. Therefore it is important to note that predictive equations have now been derived for molecular diversity based on Shannon entropy and mutual information. As a result, we can now forecast all major types of entropy/information, creating a general predictive approach for the four basic processes in ecology and evolution. Additionally, the use of these methods will allow seamless integration with other studies such as the physical environment, and may even extend to assisting with evolutionary algorithms.
本文讨论了熵/信息方法如何非常适合分析和预测创新、传播、迁移和适应这四个过程,而这四个过程是生态学和进化的共同基础。宏观生态学家研究不同物种的集合,而微观进化生物学家研究物种内可遗传信息的变体,如DNA和表观遗传修饰。这两种不同的变异模式都由相同的四个基本过程驱动,但对这些过程的研究方法有时有很大差异。例如,宏观生态学常常记录模式,却不对潜在过程进行建模,不过也有一些显著的例外。另一方面,进化生物学家在推导和测试数学遗传预测方面有着悠久的历史,以前主要关注杂合性等熵。宏观生态学称其为基尼-辛普森指数,并借鉴了遗传预测,但有时这种度量存在缺陷。因此,重要的是要注意到,现在已经基于香农熵和互信息推导出了分子多样性的预测方程。结果,我们现在可以预测所有主要类型的熵/信息,为生态学和进化中的四个基本过程创建一种通用的预测方法。此外,使用这些方法将允许与物理环境等其他研究无缝整合,甚至可能扩展到辅助进化算法。