What can ecosystems learn? Expanding evolutionary ecology with learning theory

Biology Direct

Daniel A. Power⁶  Richard A. Watson⁵  Eörs Szathmáry³  Rob Mills¹  Simon T. Powers⁴  C. Patrick Doncaster²  BłaŻej Czapp² 

[1] Department of Informatics, Faculty of Sciences, University of Lisbon, Lisbon, Portugal

[2] School of Biological Sciences, University of Southampton, Southampton, UK

[3] The Parmenides Found, Center for the Conceptual Foundations of Science, Pullach, Germany

[4] Department of Ecology & Evolution, University of Lausanne, Lausanne, Switzerland

[5] Institute for Life Sciences/Electronics and Computer Science, University of Southampton, Southampton, UK

[6] Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, UK

关键词: Community matrix;    Regime shifts;    Associative learning;    Ecological memory;    Network structures;    Community assembly;    Theoretical ecology;    Lotka-Volterra dynamics;    Alternative stable states;    Evolutionary ecology;   

Others  :  1234986
DOI  :  10.1186/s13062-015-0094-1

 received in 2015-04-08, accepted in 2015-10-26,  发布年份 2015

【 摘 要 】

Background

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?

Results

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.

Conclusions

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.

Reviewers

This article was reviewed by Prof. Ricard V Solé, Universitat Pompeu Fabra, Barcelona and Prof. Rob Knight, University of Colorado, Boulder.

【 授权许可】

   
2015 Power et al.

【 图 表 】

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