scholarly journals Artificial selection of communities drives the emergence of structured interactions

2021 ◽  
Author(s):  
Jules Fraboul ◽  
Giulio Biroli ◽  
Silvia De Monte
Keyword(s):  
Artificial Selection ◽  
Evolutionary Dynamics ◽  
Interspecific Interactions ◽  
Evolutionary Process ◽  
Ecological Resilience ◽  
Interaction Matrix ◽  
Volterra Equations ◽  
Theoretical Understanding ◽  
Multiple Parameters ◽  
Selection Of

Species-rich communities, such as the microbiota or environmental microbial assemblages, provide key functions for human health and ecological resilience. Increasing effort is being dedicated to design experimental protocols for selecting community-level functions of interest. These experiments typically involve selection acting on populations of communities, each of which is composed of multiple species. Numerical explorations allowed to link the evolutionary dynamics to the multiple parameters involved in this complex, multi-scale evolutionary process. However, a comprehensive theoretical understanding of artificial selection of communities is still lacking. Here, we propose a general model for the evolutionary dynamics of species-rich communities, each described by disordered generalized Lotka-Volterra equations, that we study analytically and by numerical simulations. Our results reveal that a generic response to selection for larger total community abundance is the emergence of an isolated eigenvalue of the interaction matrix that can be understood as an effective cross-feeding term. In this way, selection imprints a structure on the community, which results in a global increase of both the level of mutualism and the diversity of interactions. Our approach moreover allows to disentangle the role of intraspecific competition, interspecific interactions symmetry and number of selected communities in the evolutionary process, and can thus be used as a guidance in optimizing artificial selection protocols.

scholarly journals Steering ecological-evolutionary dynamics during artificial selection of microbial communities

2018 ◽  
Author(s):  
Li Xie ◽  
Wenying Shou
Keyword(s):  
Microbial Communities ◽  
Artificial Selection ◽  
Evolutionary Dynamics ◽  
Ecological Interactions ◽  
Evolutionary Trajectory ◽  
Community Function ◽  
Selection Strategies ◽  
Effective Selection ◽  
Orthogonal Components ◽  
Selection Of

AbstractMicrobial communities often perform important functions that arise from interactions among member species. Community functions can be improved via artificial selection: Many communities are repeatedly grown, mutations arise, and communities with the highest desired function are chosen to reproduce where each is partitioned into multiple offspring communities for the next cycle. Since selection efficacy is often unimpressive in published experiments and since multiple experimental parameters need to be tuned, we sought to use computer simulations to learn how to design effective selection strategies. We simulated community selection to improve a community function that requires two species and imposes a fitness cost on one of the species. This simplified case allowed us to distill community function down to two fundamental and orthogonal components: a heritable determinant and a nonheritable determinant. We then visualize a “community function landscape” relating community function to these two determinants, and demonstrate that the evolutionary trajectory on the landscape is restricted along a path designated by ecological interactions. This path can prevent the attainment of maximal community function, and trap communities in landscape locations where community function has low heritability. Exploiting these observations, we devise a species spiking approach to shift the path to improve community function heritability and consequently selection efficacy. We show that our approach is applicable to communities with complex and unknown function landscapes.

scholarly journals The quantum mitochondrion and optimal health

2016 ◽  
Vol 44 (4) ◽  
pp. 1101-1110 ◽  
Author(s):  
Alistair V.W. Nunn ◽  
Geoffrey W. Guy ◽  
Jimmy D. Bell
Keyword(s):  
Natural Selection ◽  
Energy Production ◽  
Healthy Aging ◽  
Evolutionary Process ◽  
Emergent Behaviour ◽  
Whole Process ◽  
Optimal Health ◽  
Response To Stress ◽  
Will Self ◽  
Selection Of

A sufficiently complex set of molecules, if subject to perturbation, will self-organize and show emergent behaviour. If such a system can take on information it will become subject to natural selection. This could explain how self-replicating molecules evolved into life and how intelligence arose. A pivotal step in this evolutionary process was of course the emergence of the eukaryote and the advent of the mitochondrion, which both enhanced energy production per cell and increased the ability to process, store and utilize information. Recent research suggest that from its inception life embraced quantum effects such as ‘tunnelling’ and ‘coherence’ while competition and stressful conditions provided a constant driver for natural selection. We believe that the biphasic adaptive response to stress described by hormesis–a process that captures information to enable adaptability, is central to this whole process. Critically, hormesis could improve mitochondrial quantum efficiency, improving the ATP/ROS ratio, whereas inflammation, which is tightly associated with the aging process, might do the opposite. This all suggests that to achieve optimal health and healthy aging, one has to sufficiently stress the system to ensure peak mitochondrial function, which itself could reflect selection of optimum efficiency at the quantum level.

scholarly journals Breeding for chemical camouflage the case of salmon kairomones and resistance to salmon lice infectivity

2021 ◽  
Author(s):  
Gareth Difford ◽  
John-Erik Haugen ◽  
Muhammad Luqman Aslam ◽  
Lill-Heidi Johansen ◽  
Mette Breiland ◽  
...  
Keyword(s):  
Artificial Selection ◽  
Farmed Salmon ◽  
Chemical Camouflage ◽  
Salmon Lice ◽  
Selection Of

Abstract Salmon lice are ectoparasites that threaten wild and farmed salmonids. Artificial selection of salmon for resistance to the infectious copepodid lice stage currently relies on in vivo challenge trials on thousands of salmon a year. We found that salmon emit a bouquet of kairomones which the lice use to find and infect the salmon. Some of these compounds vary between families and could be used as a more direct and ethical measurements of lice resistance for breeding farmed salmon.

The Ecology and Evolution of Model Microbial Mutualisms

2021 ◽  
Vol 52 (1) ◽  
Author(s):  
Jeremy M. Chacón ◽  
Sarah P. Hammarlund ◽  
Jonathan N.V. Martinson ◽  
Leno B. Smith ◽  
William R. Harcombe
Keyword(s):  
Future Study ◽  
Evolutionary Dynamics ◽  
Interspecific Interactions ◽  
Natural World ◽  
Annual Review ◽  
Publication Date ◽  
Controlled Experiments ◽  
Evolutionary Origins ◽  
Ecological Features ◽  
Open Questions

Mutually beneficial interspecific interactions are abundant throughout the natural world, including between microbes. Mutualisms between microbes are critical for everything from human health to global nutrient cycling. Studying model microbial mutualisms in the laboratory enables highly controlled experiments for developing and testing evolutionary and ecological hypotheses. In this review, we begin by describing the tools available for studying model microbial mutualisms. We then outline recent insights that laboratory systems have shed on the evolutionary origins, evolutionary dynamics, and ecological features of microbial mutualism. We touch on gaps in our current understanding of microbial mutualisms, note connections to mutualism in nonmicrobial systems, and call attention to open questions ripe for future study. Expected final online publication date for the Annual Review of Ecology, Evolution, and Systematics, Volume 52 is November 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

An Optimal Fuzzy Load Balanced Adaptive Gateway Discovery for Ubiquitous Internet Access in MANET

Fuzzy Systems ◽  
2017 ◽  
pp. 663-681 ◽  
Author(s):  
Prakash Srivastava ◽  
Rakesh Kumar
Keyword(s):  
Fuzzy Logic ◽  
Natural Disaster ◽  
Ad Hoc ◽  
Mobile Ad Hoc Network ◽  
Internet Access ◽  
The Internet ◽  
Optimal Selection ◽  
Multiple Parameters ◽  
Gateway Discovery ◽  
Selection Of

A mobile ad hoc network (MANET) is an autonomous collection of independent nodes cooperating together to form an infrastructure less network spontaneously. For increasing usability of MANET domain which finds application in natural disaster such as earthquake, floods etc. it is also desired to be connected with Internet through Internet gateways. Therefore, an efficient gateway discovery mechanism is required for MANET-Internet integration. Existing schemes use one or multiple parameters for optimal selection of gateway which causes a particular gateway to be selected many times which results in higher delay latency and packet drops due to prevailing congestion at a particular gateway. To avoid this situation, the authors have utilized the potential of fuzzy logic to ascertain the decision of load balancing at the Internet gateway. Besides this, their scheme also incorporates an effective adaptive gateway discovery mechanism. Consequently, enhanced performance is achieved as compared to existing state-of-the-art related schemes. The proposed approach is evaluated by simulation and analytical validation.

scholarly journals A Novel Process Model of Ship Rust Removal by Premixed Abrasive Jet based on Neural Network

2019 ◽  
Vol 257 ◽  
pp. 02007
Author(s):  
Qing Guo ◽  
Shuzhen Yang ◽  
Tao Yu
Keyword(s):  
Neural Network ◽  
Process Parameters ◽  
Process Model ◽  
Test Results ◽  
Network Modelling ◽  
Multiple Parameters ◽  
System Pressure ◽  
Artificial Neural Network Modelling ◽  
Selection Of ◽  
Moving Speed

In view of the technological requirements of the development of green shipbuilding technology on the effect of ship surface rust removal, the premixed abrasive jet technology is used to remove rust. Because the rust removal of ships with premixed abrasive jet is influenced by multiple parameters and has a high nonlinear relationship between various parameters, the accurate process model of it is difficult to establish. On the basis of artificial neural network modelling technology, the model of ship rust removal with premixed abrasive jet is built. The model takes the system pressure, the target distance, the moving speed of the spray gun and the particle size of the abrasive as input parameters, and the score which can most reflect the effect of the rust removal as output parameter. The test results show that the prediction error of the model is small, and it can better reflect the process rule between the effect of the premixed abrasive jet and the process parameters. We can guide the selection of process parameters according to the model.

scholarly journals Artificial selection of reaction norms of wing pattern elements in Bicyclus anynana

Heredity ◽  
1995 ◽  
Vol 74 (1) ◽  
pp. 91-99 ◽  
Author(s):  
Graham J Holloway ◽  
Paul M Brakefield
Keyword(s):  
Artificial Selection ◽  
Reaction Norms ◽  
Wing Pattern ◽  
Bicyclus Anynana ◽  
Selection Of

How Not to Use Evolutionary Theory in Politics: A Critique of Peter John

2000 ◽  
Vol 2 (1) ◽  
pp. 72-80 ◽  
Author(s):  
Keith Dowding
Keyword(s):  
Social Sciences ◽  
Evolutionary Process ◽  
Policy Formation ◽  
Evolutionary Explanation ◽  
Evolutionary Account ◽  
The Social ◽  
Poor Understanding ◽  
Selection Of ◽  
Poll Tax

In a recent issue of this journal Peter John (1999) suggests we can use an evolutionary account to explain policy change. In particular he suggests we should see the battle of ideas about policy formation as an evolutionary process and gives as an example the introduction and abolition of the poll tax. John is correct in two claims in his article. First, traditional models of policy-generation tend to ignore the role of ideas, concentrating attention upon the bargaining and power struggles between different sets of competing interests. Secondly, he is right that evolutionary explanation has a place in the social sciences. But these two thoughts are best kept apart and the way he packages them suggests a poor understanding of evolutionary explanation and of the role ideas may play within it. There are at least three problems with his account. First, the object at which he directs explanation —in his example the poll tax—is misspecified. Secondly, he fails to specify a mechanism for the natural selection of ideas, leaving his claim about the promise of evolutionary accounts vague and unsatisfactory. Finally, he fails to distinguish learning as an intentional process from selection as an evolutionary one.

scholarly journals Optimalisasi Penyelesaian Knapsack Problem Dengan Algoritma Genetika

2016 ◽  
pp. 182
Author(s):  
I Wayan Supriana
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Everyday Life ◽  
Knapsack Problem ◽  
Selection Process ◽  
Optimization Algorithms ◽  
Evolutionary Process ◽  
Knapsack Problems ◽  
Theory Of Evolution ◽  
Selection Of

Knapsack problems is a problem that often we encounter in everyday life. Knapsack problem itself is a problem where a person faced with the problems of optimization on the selection of objects that can be inserted into the container which has limited space or capacity. Problems knapsack problem can be solved by various optimization algorithms, one of which uses a genetic algorithm. Genetic algorithms in solving problems mimicking the theory of evolution of living creatures. The components of the genetic algorithm is composed of a population consisting of a collection of individuals who are candidates for the solution of problems knapsack. The process of evolution goes dimulasi of the selection process, crossovers and mutations in each individual in order to obtain a new population. The evolutionary process will be repeated until it meets the criteria o f an optimum of the resulting solution. The problems highlighted in this research is how to resolve the problem by applying a genetic algorithm knapsack. The results obtained by the testing of the system is built, that the knapsack problem can optimize the placement of goods in containers or capacity available. Optimizing the knapsack problem can be maximized with the appropriate input parameters.

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