Erratum to: 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

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

Learning Two-input Linear and Nonlinear Analog Functions with a Simple Chemical System

The current biochemical information processing systems behave in a predetermined manner because all features are defined during the design phase. To make such unconventional computing systems reusable and programmable for biomedical applications, adaptation, learning, and self-modification baaed on external stimuli would be highly desirable. However, so far, it haa been too challenging to implement these in real or simulated chemistries. In this paper we extend the chemical perceptron, a model previously proposed by the authors, to function as an analog instead of a binary system. The new analog asymmetric signal perceptron learns through feedback and supports MichaelisMenten kinetics. The results show that our perceptron is able to learn linear and nonlinear (quadratic) functions of two inputs. To the best of our knowledge, it is the first simulated chemical system capable of doing so. The small number of species and reactions allows for a mapping to an actual wet implementation using DNA-strand displacement or deoxyribozymes. Our results are an important step toward actual biochemical systems that can learn and adapt

Migration strategies of skuas in the southwest Atlantic Ocean revealed by stable isotopes

Stable isotope ratios of carbon (δ13C) and nitrogen (δ15N) were measured in feathers to compare the non-breeding distributions and habitat use of adult brown skuas Stercorarius antarcticus lönnbergi from high-latitude colonies at Esperanza/Hope Bay (Antarctic Peninsula, 63°S) and Signy Island (South Orkneys, 60°S), with those from Bird Island (South Georgia, 54°S), which have also been tracked previously using geolocators. Breeding colony, but not sex, had a significant effect on feather δ13C and δ15N values. Feather stable isotope data from South Georgia birds mostly corresponded to oceanic, mixed subtropical–subantarctic to subantarctic waters, which agrees with the tracking data, as did a subset of the birds from the two higher latitude populations. However, other individuals displayed feather stable isotope ratios that were consistent with continental shelf or shelf-slope waters, suggesting that unlike the vast majority of brown skuas from South Georgia, many birds from higher latitude colonies spend the non-breeding season on, or near, the Patagonian Shelf. These population-level differences may have implications for exposure to anthropogenic threats or have carryover effects on subsequent breeding behaviour or performance

Power in Transition: An Interdisciplinary Framework to Study Power in Relation to Structural Change

This article conceptualizes power in the context of long-term process of structural change. First, it discusses the field of transition studies, which deals with processes of structural change in societal systems on the basis of certain presumptions about power relations, but still lacks an explicit conceptualization of power. Then the article discusses some prevailing points of contestation in debates on power. It is argued that for the context of transition studies, it is necessary to develop an interdisciplinary framework in which power is explicitly conceptualized in relation to change. Subsequently, such a framework is presented, with reference to existing literature on power. Starting with a philosophical and operational definition of power, a typology is developed of the different ways in which power can be exercised, explicitly including innovative power and transformative power. Finally, the presented power framework is applied to transition studies, redefining pivotal transition concepts in terms of power and formulating hypotheses on the role of power in transitions. By doing so, the article not only offers an interdisciplinary framework to study power in the context of transition studies, but also contributes to power debates more generally by including innovation and transformation as acts of power, and thereby proposes a re-conceptualization of the relation between power and structural change

Factors influencing success in quality-improvement collaboratives: development and psychometric testing of an instrument

Contains fulltext : 88630.pdf (publisher's version ) (Open Access)ABSTRACT: BACKGROUND: To increase the effectiveness of quality-improvement collaboratives (QICs), it is important to explore factors that potentially influence their outcomes. For this purpose, we have developed and tested the psychometric properties of an instrument that aims to identify the features that may enhance the quality and impact of collaborative quality-improvement approaches. The instrument can be used as a measurement instrument to retrospectively collect information about perceived determinants of success. In addition, it can be prospectively applied as a checklist to guide initiators, facilitators, and participants of QICs, with information about how to perform or participate in a collaborative with theoretically optimal chances of success. Such information can be used to improve collaboratives. METHODS: We developed an instrument with content validity based on literature and the opinions of QIC experts. We collected data from 144 healthcare professionals in 44 multidisciplinary improvement teams participating in two QICs and used exploratory factor analysis to assess the construct validity. We used Cronbach's alpha to ascertain the internal consistency. RESULTS: The 50-item instrument we developed reflected expert-opinion-based determinants of success in a QIC. We deleted nine items after item reduction. On the basis of the factor analysis results, one item was dropped, which resulted in a 40-item questionnaire. Exploratory factor analysis showed that a three-factor model provided the best fit. The components were labeled 'sufficient expert team support', 'effective multidisciplinary teamwork', and 'helpful collaborative processes'. Internal consistency reliability was excellent (alphas between .85 and .89). CONCLUSIONS: This newly developed instrument seems a promising tool for providing healthcare workers and policy makers with useful information about determinants of success in QICs. The psychometric properties of the instrument are satisfactory and warrant application either as an objective measure or as a checklist

First Observation of Coherent π0\pi^0 Production in Neutrino Nucleus Interactions with Eν<E_{\nu}< 2 GeV

The MiniBooNE experiment at Fermilab has amassed the largest sample to date of $\pi^0$s produced in neutral current (NC) neutrino-nucleus interactions at low energy. This paper reports a measurement of the momentum distribution of $\pi^0$s produced in mineral oil (CH$_2$) and the first observation of coherent $\pi^0$ production below 2 GeV. In the forward direction, the yield of events observed above the expectation for resonant production is attributed primarily to coherent production off carbon, but may also include a small contribution from diffractive production on hydrogen. Integrated over the MiniBooNE neutrino flux, the sum of the NC coherent and diffractive modes is found to be (19.5 $\pm$1.1 (stat) $\pm$2.5 (sys))% of all exclusive NC $\pi^0$ production at MiniBooNE. These measurements are of immediate utility because they quantify an important background to MiniBooNE's search for $\nu_{\mu} \to \nu_e$ oscillations.Comment: Submitted to Phys. Lett.

Adsorption of mono- and multivalent cat- and anions on DNA molecules

Adsorption of monovalent and multivalent cat- and anions on a deoxyribose nucleic acid (DNA) molecule from a salt solution is investigated by computer simulation. The ions are modelled as charged hard spheres, the DNA molecule as a point charge pattern following the double-helical phosphate strands. The geometrical shape of the DNA molecules is modelled on different levels ranging from a simple cylindrical shape to structured models which include the major and minor grooves between the phosphate strands. The densities of the ions adsorbed on the phosphate strands, in the major and in the minor grooves are calculated. First, we find that the adsorption pattern on the DNA surface depends strongly on its geometrical shape: counterions adsorb preferentially along the phosphate strands for a cylindrical model shape, but in the minor groove for a geometrically structured model. Second, we find that an addition of monovalent salt ions results in an increase of the charge density in the minor groove while the total charge density of ions adsorbed in the major groove stays unchanged. The adsorbed ion densities are highly structured along the minor groove while they are almost smeared along the major groove. Furthermore, for a fixed amount of added salt, the major groove cationic charge is independent on the counterion valency. For increasing salt concentration the major groove is neutralized while the total charge adsorbed in the minor groove is constant. DNA overcharging is detected for multivalent salt. Simulations for a larger ion radii, which mimic the effect of the ion hydration, indicate an increased adsorbtion of cations in the major groove.Comment: 34 pages with 14 figure