What can ecosystems learn? Expanding evolutionary ecology with learning theory.
What can ecosystems learn? Expanding evolutionary ecology with learning theory.
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DOI：
10.1186/s13062-015-0094-1
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发表时间：
2015-12-08
期刊：
影响因子：
5.5
通讯作者：
Czapp B
中科院分区：
文献类型：
--
作者：
Power DA;Watson RA;Szathmáry E;Mills R;Powers ST;Doncaster CP;Czapp B
生态系统内生态相互作用的结构和组织是由它所包含的个体物种的进化和共同进化所改变的。了解历史条件如何塑造这种结构对于理解系统对微生物以上尺度变化的反应至关重要。然而，在没有群体选择过程的情况下，整个群落所表现出的集体行为和生态系统功能无法在达尔文的意义上组织或适应。因此，一个长期悬而未决的问题仍然存在：是否存在其他组织原则，使我们能够理解和预测组成物种的共同进化如何创造和维持整个生态系统所表现出的复杂集体行为？在这里，我们通过结合联结主义学习的原理来回答这个问题，联结主义学习是一门先前不相关的学科，它已经使用了关于简单网络中涌现行为如何产生的成熟理论。具体而言，我们展示了自然选择生态相互作用的条件，在功能上相当于一种简单的连接主义学习，“无监督学习”，众所周知的认知系统的神经网络模型，产生许多非平凡的集体行为。因此，我们发现，一个社区可以自我组织在一个明确的和非平凡的意义上没有选择在社区水平;它的组织可以由过去的经验在同一意义上的连接主义的学习模型习惯于刺激。这种条件作用驱使群落形成多个过去状态的分布式生态记忆，使群落：a）从任何随机的初始组成收敛到这些状态; B）从小片段准确地恢复历史组成; c）在干扰之后恢复状态组成;以及d）根据模糊的初始组成与学习到的组成的相似性来正确地分类模糊的初始组成。我们研究了替代稳定状态的形成如何改变社区对不断变化的环境强迫的反应，并确定了生态系统表现出滞后性的条件下，潜在的灾难性政权转移。这项工作突出了联结理论的潜力，以扩大我们的进化生态动力学和集体生态行为的理解。在这个框架内，我们发现，尽管不是一个达尔文的单位，生态社区可以表现得像连接主义的学习系统，创造内部条件，习惯于过去的环境条件，并积极回忆这些条件。这篇文章由巴塞罗那庞培法布拉大学的Ricard V Solé教授和博尔德科罗拉多大学的Rob Knight教授审查。
The structure and organisation of ecological interactions within an ecosystem is modified by the evolution and coevolution of the individual species it contains. Understanding how historical conditions have shaped this architecture is vital for understanding system responses to change at scales from the microbial upwards. However, in the absence of a group selection process, the collective behaviours and ecosystem functions exhibited by the whole community cannot be organised or adapted in a Darwinian sense. A long-standing open question thus persists: Are there alternative organising principles that enable us to understand and predict how the coevolution of the component species creates and maintains complex collective behaviours exhibited by the ecosystem as a whole? Here we answer this question by incorporating principles from connectionist learning, a previously unrelated discipline already using well-developed theories on how emergent behaviours arise in simple networks. Specifically, we show conditions where natural selection on ecological interactions is functionally equivalent to a simple type of connectionist learning, ‘unsupervised learning’, well-known in neural-network models of cognitive systems to produce many non-trivial collective behaviours. Accordingly, we find that a community can self-organise in a well-defined and non-trivial sense without selection at the community level; its organisation can be conditioned by past experience in the same sense as connectionist learning models habituate to stimuli. This conditioning drives the community to form a distributed ecological memory of multiple past states, causing the community to: a) converge to these states from any random initial composition; b) accurately restore historical compositions from small fragments; c) recover a state composition following disturbance; and d) to correctly classify ambiguous initial compositions according to their similarity to learned compositions. We examine how the formation of alternative stable states alters the community’s response to changing environmental forcing, and we identify conditions under which the ecosystem exhibits hysteresis with potential for catastrophic regime shifts. This work highlights the potential of connectionist theory to expand our understanding of evo-eco dynamics and collective ecological behaviours. Within this framework we find that, despite not being a Darwinian unit, ecological communities can behave like connectionist learning systems, creating internal conditions that habituate to past environmental conditions and actively recalling those conditions. This article was reviewed by Prof. Ricard V Solé, Universitat Pompeu Fabra, Barcelona and Prof. Rob Knight, University of Colorado, Boulder.