Experimental evolution of competing bean beetle species reveals long‐term reversals of short‐term evolution, but no consistent character displacement

Kinetic equations are widely used in many branches of science to describe the evolution of random wave spectra. To examine the validity of these equations, we study numerically the long-term evolution of water wave spectra without wind input using three different models. The first model is the classical kinetic (Hasselmann) equation (KE). The second model is the generalised kinetic equation (gKE), derived employing the same statistical closure as the KE but without the assumption of quasistationarity. The third model, which we refer to as the DNS-ZE, is a direct numerical simulation algorithm based on the Zakharov integrodifferential equation, which plays the role of the primitive equation for a weakly nonlinear wave field. It does not employ any statistical assumptions. We perform a comparison of the spectral evolution of the same initial distributions without forcing, with/without a statistical closure and with/without the quasistationarity assumption. For the initial conditions, we choose two narrow-banded spectra with the same frequency distribution and different degrees of directionality. The short-term evolution ($O(10^{2})$ wave periods) of both spectra has been previously thoroughly studied experimentally and numerically using a variety of approaches. Our DNS-ZE results are validated both with existing short-term DNS by other methods and with available laboratory observations of higher-order moment (kurtosis) evolution. All three models demonstrate very close evolution of integral characteristics of the spectra, approaching with time the theoretical asymptotes of the self-similar stage of evolution. Both kinetic equations give almost identical spectral evolution, unless the spectrum is initially too narrow in angle. However, there are major differences between the DNS-ZE and gKE/KE predictions. First, the rate of angular broadening of initially narrow angular distributions is much larger for the gKE and KE than for the DNS-ZE, although the angular width does appear to tend to the same universal value at large times. Second, the shapes of the frequency spectra differ substantially (even when the nonlinearity is decreased), the DNS-ZE spectra being wider than the KE/gKE ones and having much lower spectral peaks. Third, the maximal rates of change of the spectra obtained with the DNS-ZE scale as the fourth power of nonlinearity, which corresponds to the dynamical time scale of evolution, rather than the sixth power of nonlinearity typical of the kinetic time scale exhibited by the KE. The gKE predictions fall in between. While the long-term DNS show excellent agreement with the KE predictions for integral characteristics of evolving wave spectra, the striking systematic discrepancies for a number of specific spectral characteristics call for revision of the fundamentals of the wave kinetic description.

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Long-term evolution is surprisingly predictable in lattice proteins

Journal of The Royal Society Interface ◽

10.1098/rsif.2013.0026 ◽

2013 ◽

Vol 10 (82) ◽

pp. 20130026 ◽

Cited By ~ 4

Author(s):

Michael E. Palmer ◽

Arnav Moudgil ◽

Marcus W. Feldman

Keyword(s):

Natural Selection ◽

Basins Of Attraction ◽

Long Term Outcomes ◽

Short Term ◽

Term Selection ◽

Term Evolution ◽

Evolutionary Success ◽

Lattice Proteins

It has long been debated whether natural selection acts primarily upon individual organisms, or whether it also commonly acts upon higher-level entities such as lineages. Two arguments against the effectiveness of long-term selection on lineages have been (i) that long-term evolutionary outcomes will not be sufficiently predictable to support a meaningful long-term fitness and (ii) that short-term selection on organisms will almost always overpower long-term selection. Here, we use a computational model of protein folding and binding called ‘lattice proteins’. We quantify the long-term evolutionary success of lineages with two metrics called the k -fitness and k -survivability. We show that long-term outcomes are surprisingly predictable in this model: only a small fraction of the possible outcomes are ever realized in multiple replicates. Furthermore, the long-term fitness of a lineage depends only partly on its short-term fitness; other factors are also important, including the ‘evolvability’ of a lineage—its capacity to produce adaptive variation. In a system with a distinct short-term and long-term fitness, evolution need not be ‘short-sighted’: lineages may be selected for their long-term properties, sometimes in opposition to short-term selection. Similar evolutionary basins of attraction have been observed in vivo , suggesting that natural biological lineages will also have a predictive long-term fitness.

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Growth and business cycle in Argentina. A long-run approach, 1870–2015

Applied Economic Analysis ◽

10.1108/aea-03-2020-0024 ◽

2020 ◽

Vol 28 (84) ◽

pp. 197-220

Author(s):

María Dolores Gadea ◽

Isabel Sanz-Villarroya

Keyword(s):

Long Term Evolution ◽

Growth Potential ◽

Short Term ◽

Content Type ◽

Developed Economies ◽

Term Evolution ◽

Long Run ◽

High Volatility ◽

The Moment

Purpose The purpose of this study is to focus deeply on the short term to explain the relative long-term evolution of the Argentinian economy in the long and the short term. Design/methodology/approach The study of the long-term evolution of the Argentine economy and identifying the moment in which it began to lose ground compared to other developed economies, such as Australia and Canada, constitutes the central axis of the historiography of this country. However, an additional problem presented by the Argentine economy is its high volatility. For this reason, the long term should be influenced by the short term, an issue that requires a more detailed study of the cyclical behavior and a deep analysis of the relationship between the long and the short term. Findings The results obtained point to a cyclical development that influences the long-term evolution and, therefore, explains Argentina’s convergence process with Australia and Canada. Frequent deep busts and short booms characterize the Argentine cycle, offsetting its long-term growth potential. Originality/value Although the long term has been profusely studied in Argentina, the short term has not been analyzed to the same extent, which is surprising given the extreme volatility of this economy (Prebisch, 1950). The studies performed on economic cycles have always been partial, disconnected from the long term and carried out without much technical rigor.

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Selection maintains protein interactome resilience in the long-term evolution experiment with Escherichia coli

10.1101/2021.01.20.427477 ◽

2021 ◽

Author(s):

Rohan Maddamsetti

Keyword(s):

Experimental Evolution ◽

Long Term Evolution ◽

Rate Of Change ◽

Network Resilience ◽

Protein Protein Interaction ◽

Ppi Networks ◽

Term Evolution ◽

Protein Interactome ◽

Evolution Experiment

AbstractMost cellular functions are carried out by a dynamic network of interacting proteins. An open question is whether the network properties of protein interactomes represent phenotypes under natural selection. One proposal is that protein interactomes have evolved to be resilient, such that they tend to maintain connectivity when proteins are removed from the network. This hypothesis predicts that interactome resilience should be maintained during long-term experimental evolution. I tested this prediction by modeling the evolution of protein-protein interaction (PPI) networks in Lenski’s long-term evolution experiment with Escherichia coli (LTEE). In this test, I removed proteins affected by nonsense, insertion, deletion, and transposon mutations in evolved LTEE strains, and measured the resilience of the resulting networks. I compared the rate of change of network resilience in each LTEE population to the rate of change of network resilience for corresponding randomized networks. The evolved PPI networks are significantly more resilient than networks in which random proteins have been deleted. Moreover, the evolved networks are generally more resilient than networks in which the random deletion of proteins was restricted to those disrupted in LTEE. These results suggest that evolution in the LTEE has favored PPI networks that are, on average, more resilient than expected from the genetic variation across the evolved populations. My findings therefore support the hypothesis that selection maintains protein interactome resilience over evolutionary time.Significance StatementUnderstanding how protein-protein interaction (PPI) networks evolve is a central goal of evolutionary systems biology. One property that has been hypothesized to be important for PPI network evolution is resilience, which means that networks tend to maintain connectivity even after many nodes (proteins in this case) have been removed. This hypothesis predicts that PPI network resilience should be maintained during long-term experimental evolution. Consistent with this prediction, I found that the PPI networks that evolved over 50,000 generations of Lenski’s long-term evolution experiment with E. coli are more resilient than expected by chance.

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Long-term Evolution of the Short-term X-Ray Variability of the Jetted TDE Swift J1644+57

The Astrophysical Journal ◽

10.3847/1538-4357/ac1674 ◽

2021 ◽

Vol 920 (1) ◽

pp. 60

Author(s):

Chichuan Jin

Keyword(s):

Long Term Evolution ◽

Short Term ◽

X Ray ◽

Term Evolution

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Long-Term Experimental Evolution in Escherichia coli. IX. Characterization of Insertion Sequence-Mediated Mutations and Rearrangements

Genetics ◽

10.1093/genetics/156.2.477 ◽

2000 ◽

Vol 156 (2) ◽

pp. 477-488

Author(s):

Dominique Schneider ◽

Esther Duperchy ◽

Evelyne Coursange ◽

Richard E Lenski ◽

Michel Blot

Keyword(s):

Escherichia Coli ◽

Experimental Evolution ◽

Insertion Sequence ◽

Term Evolution ◽

Molecular Events ◽

Evolution Experiment ◽

Two Populations ◽

Escherichia Coli B

Abstract As part of a long-term evolution experiment, two populations of Escherichia coli B adapted to a glucose minimal medium for 10,000 generations. In both populations, multiple IS-associated mutations arose that then went to fixation. We identify the affected genetic loci and characterize the molecular events that produced nine of these mutations. All nine were IS-mediated events, including simple insertions as well as recombination between homologous elements that generated inversions and deletions. Sequencing DNA adjacent to the insertions indicates that the affected genes are involved in central metabolism (knockouts of pykF and nadR), cell wall synthesis (adjacent to the promoter of pbpA-rodA), and ill-defined functions (knockouts of hokB-sokB and yfcU). These genes are candidates for manipulation and competition experiments to determine whether the mutations were beneficial or merely hitchhiked to fixation.

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Evolutionary forecasting of phenotypic and genetic outcomes of experimental evolution in Pseudomonas

10.1101/342261 ◽

2018 ◽

Cited By ~ 5

Author(s):

Peter A. Lind

Keyword(s):

Experimental Evolution ◽

Negative Regulation ◽

Mutation Rates ◽

Short Term ◽

Promoter Regions ◽

Regulatory Pathways ◽

Term Evolution ◽

Upstream Promoter ◽

Successful Prediction ◽

Underlying Causes

AbstractExperimental evolution is often highly repeatable, but the underlying causes are generally unknown, which prevents extension of evolutionary forecasts to related species. Data on adaptive phenotypes, mutation rates and targets from the Pseudomonas fluorescens SBW25 Wrinkly Spreader system combined with mathematical models of the genotype-to-phenotype map allowed evolutionary forecasts to be made for several related Pseudomonas species. Predicted outcomes of experimental evolution in terms of phenotype, types of mutations, relative rates of pathways and mutational targets were then tested in Pseudomonas protegens Pf-5. As predicted, most mutations were found in three specific regulatory pathways resulting in increased production of Pel exopolysaccharide. Mutations were, as predicted, mainly found to disrupt negative regulation with a smaller number in upstream promoter regions. Mutated regions in proteins could also be predicted, but most mutations were not identical to those previously found. This study demonstrates the potential of short-term evolutionary forecasting in experimental populations.Impact statementConservation of genotype-to-phenotype maps allows successful prediction of short-term evolution in P. protegens Pf-5 and lays the foundation for evolutionary forecasting in other Pseudomonas.

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Empirical model for short-time prediction of COVID-19 spreading

PLoS Computational Biology ◽

10.1371/journal.pcbi.1008431 ◽

2020 ◽

Vol 16 (12) ◽

pp. e1008431

Author(s):

Martí Català ◽

Sergio Alonso ◽

Enrique Alvarez-Lacalle ◽

Daniel López ◽

Pere-Joan Cardona ◽

...

Keyword(s):

Health Workers ◽

Gompertz Model ◽

Control Measures ◽

Short Term ◽

Spreading Velocity ◽

Current State ◽

Term Evolution ◽

Public Health Workers ◽

Short Time

The appearance and fast spreading of Covid-19 took the international community by surprise. Collaboration between researchers, public health workers, and politicians has been established to deal with the epidemic. One important contribution from researchers in epidemiology is the analysis of trends so that both the current state and short-term future trends can be carefully evaluated. Gompertz model has been shown to correctly describe the dynamics of cumulative confirmed cases, since it is characterized by a decrease in growth rate showing the effect of control measures. Thus, it provides a way to systematically quantify the Covid-19 spreading velocity and it allows short-term predictions and longer-term estimations. This model has been employed to fit the cumulative cases of Covid-19 from several European countries. Results show that there are systematic differences in spreading velocity among countries. The model predictions provide a reliable picture of the short-term evolution in countries that are in the initial stages of the Covid-19 outbreak, and may permit researchers to uncover some characteristics of the long-term evolution. These predictions can also be generalized to calculate short-term hospital and intensive care units (ICU) requirements.

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