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.

Knowledge Management diskurssina suomalaisissa terveydenhuoltoalan tieteellisissä ja ammatillisissa lehdissä

Abstract: Knowledge Management as a discourse in Finnish scientific and professional journals on the health care field

Predicting the future effectiveness of protected areas for bird conservation in Mediterranean ecosystems under climate change and novel fire regime scenarios

AimGlobal environmental changes challenge traditional conservation approaches based on the selection of static protected areas due to their limited ability to deal with the dynamic nature of driving forces relevant to biodiversity. The Natura 2000 network (N2000) constitutes a major milestone in biodiversity conservation in Europe, but the degree to which this static network will be able to reach its long-term conservation objectives raises concern. We assessed the changes in the effectiveness of N2000 in a Mediterranean ecosystem between 2000 and 2050 under different combinations of climate and land cover change scenarios. LocationCatalonia, Spain. MethodsPotential distribution changes of several terrestrial bird species of conservation interest included in the European Union's Birds Directive were predicted within an ensemble-forecasting framework that hierarchically integrated climate change and land cover change scenarios. Land cover changes were simulated using a spatially explicit fire-succession model that integrates fire management strategies and vegetation encroachment after the abandonment of cultivated areas as the main drivers of landscape dynamics in Mediterranean ecosystems. ResultsOur results suggest that the amount of suitable habitats for the target species will strongly decrease both inside and outside N2000. However, the effectiveness of N2000 is expected to increase in the next decades because the amount of suitable habitats is predicted to decrease less inside than outside this network. Main conclusionsSuch predictions shed light on the key role that the current N2000may play in the near future and emphasize the need for an integrative conservation perspective wherein agricultural, forest and fire management policies should be considered to effectively preserve key habitats for threatened birds in fire-prone, highly dynamic Mediterranean ecosystems. Results also show the importance of considering landscape dynamics and the synergies between different driving forces when assessing the long-term effectiveness of protected areas for biodiversity conservation.

Informe d'Autors UOC a ISI Web of Knowledge. Octubre 2014

UOC

The knowledge about diagnostic imaging methods among primary care and medical emergency physicians

Objective To evaluate the knowledge about diagnostic imaging methods among primary care and medical emergency physicians. Materials and Methods Study developed with 119 primary care and medical emergency physicians in Montes Claros, MG, Brazil, by means of a structured questionnaire about general knowledge and indications of imaging methods in common clinical settings. A rate of correct responses corresponding to ≥ 80% was considered as satisfactory. The Poisson regression (PR) model was utilized in the data analysis. Results Among the 81 individuals who responded the questionnaire, 65% (n = 53) demonstrated to have satisfactory general knowledge and 44% (n = 36) gave correct responses regarding indications of imaging methods. Respectively, 65% (n = 53) and 51% (n = 41) of the respondents consider that radiography and computed tomography do not use ionizing radiation. The prevalence of a satisfactory general knowledge about imaging methods was associated with medical residency in the respondents' work field (PR = 4.55; IC 95%: 1.18-16.67; p-value: 0.03), while the prevalence of correct responses regarding indication of imaging methods was associated with the professional practice in primary health care (PR = 1.79; IC 95%: 1.16-2.70; p-value: 0.01). Conclusion Major deficiencies were observed as regards the knowledge about imaging methods among physicians, with better results obtained by those involved in primary health care and by residents.

Informe d'Autors UOC a ISI Web of Knowledge. Novembre 2014

UOC

Informe d'Autors UOC a ISI Web of Knowledge. Desembre 2014

UOC

Online Volunteers : Knowledge Managers in Nonprofits

Peer-reviewed

An ontology to automated learning scenarios? an approach to its knowledge domain

Automation or semi-automation of learning scenariospecifications is one of the least exploredsubjects in the e-learning research area. There isa need for a catalogue of learning scenarios and atechnique to facilitate automated retrieval of stored specifications. This requires constructing anontology with this goal and is justified inthis paper. This ontology must mainlysupport a specification technique for learning scenarios. This ontology should also be useful in the creation and validation of new scenarios as well as in the personalization of learning scenarios or their monitoring. Thus, after justifying the need for this ontology, a first approach of a possible knowledge domain is presented. An example of a concrete learning scenario illustrates some relevant concepts supported by this ontology in order to define the scenario in such a way that it could be easy to automate.

Fostering Collaborative Knowledge Building by the Effective Provision of Knowledge about the Discussion Process

Peer-reviewed