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What can ecosystems learn? Expanding evolutionary ecology with learning theory

What can ecosystems learn? Expanding evolutionary ecology with learning theory
What can ecosystems learn? Expanding evolutionary ecology with learning theory
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.
Theoretical ecology, Community
assembly, Network structures, Ecological memory, Associative learning, Regime shifts, Community matrix
*Correspondence:
1745-6150
1-24
Power, Daniel A.
83ab0f52-01b7-418f-9cbc-7a4a6962d0d6
Watson, Richard A.
ce199dfc-d5d4-4edf-bd7b-f9e224c96c75
Szathmáry, Eörs
7bac0db3-062e-4800-bafd-bc9b07076547
Mills, Rob
ffda467c-6a6e-4283-a468-5f0cfb733b4c
Powers, Simon T.
99f673bb-debc-4c1f-90d3-78724a6020bb
Doncaster, C. Patrick
0eff2f42-fa0a-4e35-b6ac-475ad3482047
Czapp, Blazej
b32f44d2-49a4-4920-b47f-af6d3a326b48
Power, Daniel A.
83ab0f52-01b7-418f-9cbc-7a4a6962d0d6
Watson, Richard A.
ce199dfc-d5d4-4edf-bd7b-f9e224c96c75
Szathmáry, Eörs
7bac0db3-062e-4800-bafd-bc9b07076547
Mills, Rob
ffda467c-6a6e-4283-a468-5f0cfb733b4c
Powers, Simon T.
99f673bb-debc-4c1f-90d3-78724a6020bb
Doncaster, C. Patrick
0eff2f42-fa0a-4e35-b6ac-475ad3482047
Czapp, Blazej
b32f44d2-49a4-4920-b47f-af6d3a326b48

Power, Daniel A., Watson, Richard A., Szathmáry, Eörs, Mills, Rob, Powers, Simon T., Doncaster, C. Patrick and Czapp, Blazej (2015) What can ecosystems learn? Expanding evolutionary ecology with learning theory. Biology Direct, 10 (69), 1-24. (doi:10.1186/s13062-015-0094-1).

Record type: Article

Abstract

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.
Theoretical ecology, Community
assembly, Network structures, Ecological memory, Associative learning, Regime shifts, Community matrix
*Correspondence:

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Power et al 2015 Biology Direct ~ What can ecosystems learn.pdf - Version of Record
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More information

Accepted/In Press date: 26 October 2015
Published date: 8 December 2015
Organisations: Electronics & Computer Science, Environmental

Identifiers

Local EPrints ID: 391398
URI: https://eprints.soton.ac.uk/id/eprint/391398
ISSN: 1745-6150
PURE UUID: 74a6d95f-3d08-42e1-a959-dacc0edb6b3b
ORCID for C. Patrick Doncaster: ORCID iD orcid.org/0000-0001-9406-0693

Catalogue record

Date deposited: 12 Apr 2016 10:36
Last modified: 06 Jun 2018 13:04

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Contributors

Author: Daniel A. Power
Author: Eörs Szathmáry
Author: Rob Mills
Author: Simon T. Powers
Author: Blazej Czapp

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