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

CO2 utilization in the perspective of industrial ecology, an overview

Carbon dioxide emissions from anthropic activities have accumulated in the atmosphere in excess of 800 Gigatons since preindustrial times, and are continuously increasing. Among other strategies, CO2 capture and storage is one option to mitigate the emissions from large point sources. In addition, carbon dioxide extraction from ambient air is assessed to reduce the atmospheric concentration of CO2. Both direct and indirect (through photosynthesis) pathways are possible. Geological sequestration has significant disadvantages (high cost, low public acceptance, long term uncertainty) whereas carbon dioxide recycling (or utilization) is more consistent with the basic principle of industrial ecology, almost closing material cycles. In this article, a series of technologies for CO2 capture and valorization is described as integrated and optimized pathways. This integration increases the environmental and economic benefits of each technology. Depending on the source of carbon dioxide, appropriate capture and valorization processes are evaluated based on material and energy constraints.

Importance of amoebae as a tool to isolate amoeba-resisting microorganisms and for their ecology and evolution: the Chlamydia paradigm.

Free-living amoebae are distributed worldwide and are frequently in contact with humans and animals. As cysts, they can survive in very harsh conditions and resist biocides and most disinfection procedures. Several microorganisms, called amoeba-resisting microorganisms (ARMs), have evolved to survive and multiply within these protozoa. Among them are many important pathogens, such as Legionella and Mycobacteria, and also several newly discovered Chlamydia-related bacteria, such as Parachlamydia acanthamoebae, Estrella lausannensis, Simkania negevensis or Waddlia chondrophila whose pathogenic role towards human or animal is strongly suspected. Amoebae represent an evolutionary crib for their resistant microorganisms since they can exchange genetic material with other ARMs and develop virulence traits that will be further used to infect other professional phagocytes. Moreover, amoebae constitute an ideal tool to isolate strict intracellular microorganisms from complex microbiota, since they will feed on other fast-growing bacteria, such as coliforms potentially present in the investigated samples. The paradigm that ARMs are likely resistant to macrophages, another phagocytic cell, and that they are likely virulent towards humans and animals is only partially true. Indeed, we provide examples of the Chlamydiales order that challenge this assumption and suggest that the ability to multiply in protozoa does not strictly correlate with pathogenicity and that we should rather use the ability to replicate in multiple and diverse eukaryotic cells as an indirect marker of virulence towards mammals. Thus, cell-culture-based microbial culturomics should be used in the future to try to discover new pathogenic bacterial species.