@article{11640, abstract = {Spatially explicit population genetic models have long been developed, yet have rarely been used to test hypotheses about the spatial distribution of genetic diversity or the genetic divergence between populations. Here, we use spatially explicit coalescence simulations to explore the properties of the island and the two-dimensional stepping stone models under a wide range of scenarios with spatio-temporal variation in deme size. We avoid the simulation of genetic data, using the fact that under the studied models, summary statistics of genetic diversity and divergence can be approximated from coalescence times. We perform the simulations using gridCoal, a flexible spatial wrapper for the software msprime (Kelleher et al., 2016, Theoretical Population Biology, 95, 13) developed herein. In gridCoal, deme sizes can change arbitrarily across space and time, as well as migration rates between individual demes. We identify different factors that can cause a deviation from theoretical expectations, such as the simulation time in comparison to the effective deme size and the spatio-temporal autocorrelation across the grid. Our results highlight that FST, a measure of the strength of population structure, principally depends on recent demography, which makes it robust to temporal variation in deme size. In contrast, the amount of genetic diversity is dependent on the distant past when Ne is large, therefore longer run times are needed to estimate Ne than FST. Finally, we illustrate the use of gridCoal on a real-world example, the range expansion of silver fir (Abies alba Mill.) since the last glacial maximum, using different degrees of spatio-temporal variation in deme size.}, author = {Szep, Eniko and Trubenova, Barbora and Csilléry, Katalin}, issn = {1755-0998}, journal = {Molecular Ecology Resources}, number = {8}, pages = {2941--2955}, publisher = {Wiley}, title = {{Using gridCoal to assess whether standard population genetic theory holds in the presence of spatio-temporal heterogeneity in population size}}, doi = {10.1111/1755-0998.13676}, volume = {22}, year = {2022}, } @article{6637, abstract = {The environment changes constantly at various time scales and, in order to survive, species need to keep adapting. Whether these species succeed in avoiding extinction is a major evolutionary question. Using a multilocus evolutionary model of a mutation‐limited population adapting under strong selection, we investigate the effects of the frequency of environmental fluctuations on adaptation. Our results rely on an “adaptive‐walk” approximation and use mathematical methods from evolutionary computation theory to investigate the interplay between fluctuation frequency, the similarity of environments, and the number of loci contributing to adaptation. First, we assume a linear additive fitness function, but later generalize our results to include several types of epistasis. We show that frequent environmental changes prevent populations from reaching a fitness peak, but they may also prevent the large fitness loss that occurs after a single environmental change. Thus, the population can survive, although not thrive, in a wide range of conditions. Furthermore, we show that in a frequently changing environment, the similarity of threats that a population faces affects the level of adaptation that it is able to achieve. We check and supplement our analytical results with simulations.}, author = {Trubenova, Barbora and Krejca, Martin and Lehre, Per Kristian and Kötzing, Timo}, journal = {Evolution}, number = {7}, pages = {1356--1374}, publisher = {Wiley}, title = {{Surfing on the seascape: Adaptation in a changing environment}}, doi = {10.1111/evo.13784}, volume = {73}, year = {2019}, } @article{6795, abstract = {The green‐beard effect is one proposed mechanism predicted to underpin the evolu‐tion of altruistic behavior. It relies on the recognition and the selective help of altruists to each other in order to promote and sustain altruistic behavior. However, this mechanism has often been dismissed as unlikely or uncommon, as it is assumed that both the signaling trait and altruistic trait need to be encoded by the same gene or through tightly linked genes. Here, we use models of indirect genetic effects (IGEs) to find the minimum correlation between the signaling and altruistic trait required for the evolution of the latter. We show that this correlation threshold depends on the strength of the interaction (influence of the green beard on the expression of the altruistic trait), as well as the costs and benefits of the altruistic behavior. We further show that this correlation does not necessarily have to be high and support our analytical results by simulations.}, author = {Trubenova, Barbora and Hager, Reinmar}, issn = {20457758}, journal = {Ecology and Evolution}, number = {17}, pages = {9597--9608}, publisher = {Wiley}, title = {{Green beards in the light of indirect genetic effects}}, doi = {10.1002/ece3.5484}, volume = {9}, year = {2019}, } @article{723, abstract = {Escaping local optima is one of the major obstacles to function optimisation. Using the metaphor of a fitness landscape, local optima correspond to hills separated by fitness valleys that have to be overcome. We define a class of fitness valleys of tunable difficulty by considering their length, representing the Hamming path between the two optima and their depth, the drop in fitness. For this function class we present a runtime comparison between stochastic search algorithms using different search strategies. The (1+1) EA is a simple and well-studied evolutionary algorithm that has to jump across the valley to a point of higher fitness because it does not accept worsening moves (elitism). In contrast, the Metropolis algorithm and the Strong Selection Weak Mutation (SSWM) algorithm, a famous process in population genetics, are both able to cross the fitness valley by accepting worsening moves. We show that the runtime of the (1+1) EA depends critically on the length of the valley while the runtimes of the non-elitist algorithms depend crucially on the depth of the valley. Moreover, we show that both SSWM and Metropolis can also efficiently optimise a rugged function consisting of consecutive valleys.}, author = {Oliveto, Pietro and Paixao, Tiago and Pérez Heredia, Jorge and Sudholt, Dirk and Trubenova, Barbora}, journal = {Algorithmica}, number = {5}, pages = {1604 -- 1633}, publisher = {Springer}, title = {{How to escape local optima in black box optimisation when non elitism outperforms elitism}}, doi = {10.1007/s00453-017-0369-2}, volume = {80}, year = {2018}, } @article{1336, abstract = {Evolutionary algorithms (EAs) form a popular optimisation paradigm inspired by natural evolution. In recent years the field of evolutionary computation has developed a rigorous analytical theory to analyse the runtimes of EAs on many illustrative problems. Here we apply this theory to a simple model of natural evolution. In the Strong Selection Weak Mutation (SSWM) evolutionary regime the time between occurrences of new mutations is much longer than the time it takes for a mutated genotype to take over the population. In this situation, the population only contains copies of one genotype and evolution can be modelled as a stochastic process evolving one genotype by means of mutation and selection between the resident and the mutated genotype. The probability of accepting the mutated genotype then depends on the change in fitness. We study this process, SSWM, from an algorithmic perspective, quantifying its expected optimisation time for various parameters and investigating differences to a similar evolutionary algorithm, the well-known (1+1) EA. We show that SSWM can have a moderate advantage over the (1+1) EA at crossing fitness valleys and study an example where SSWM outperforms the (1+1) EA by taking advantage of information on the fitness gradient.}, author = {Paixao, Tiago and Pérez Heredia, Jorge and Sudholt, Dirk and Trubenova, Barbora}, issn = {01784617}, journal = {Algorithmica}, number = {2}, pages = {681 -- 713}, publisher = {Springer}, title = {{Towards a runtime comparison of natural and artificial evolution}}, doi = {10.1007/s00453-016-0212-1}, volume = {78}, year = {2017}, } @article{1111, abstract = {Adaptation depends critically on the effects of new mutations and their dependency on the genetic background in which they occur. These two factors can be summarized by the fitness landscape. However, it would require testing all mutations in all backgrounds, making the definition and analysis of fitness landscapes mostly inaccessible. Instead of postulating a particular fitness landscape, we address this problem by considering general classes of landscapes and calculating an upper limit for the time it takes for a population to reach a fitness peak, circumventing the need to have full knowledge about the fitness landscape. We analyze populations in the weak-mutation regime and characterize the conditions that enable them to quickly reach the fitness peak as a function of the number of sites under selection. We show that for additive landscapes there is a critical selection strength enabling populations to reach high-fitness genotypes, regardless of the distribution of effects. This threshold scales with the number of sites under selection, effectively setting a limit to adaptation, and results from the inevitable increase in deleterious mutational pressure as the population adapts in a space of discrete genotypes. Furthermore, we show that for the class of all unimodal landscapes this condition is sufficient but not necessary for rapid adaptation, as in some highly epistatic landscapes the critical strength does not depend on the number of sites under selection; effectively removing this barrier to adaptation.}, author = {Heredia, Jorge and Trubenova, Barbora and Sudholt, Dirk and Paixao, Tiago}, issn = {00166731}, journal = {Genetics}, number = {2}, pages = {803 -- 825}, publisher = {Genetics Society of America}, title = {{Selection limits to adaptive walks on correlated landscapes}}, doi = {10.1534/genetics.116.189340}, volume = {205}, year = {2017}, } @inproceedings{1349, abstract = {Crossing fitness valleys is one of the major obstacles to function optimization. In this paper we investigate how the structure of the fitness valley, namely its depth d and length ℓ, influence the runtime of different strategies for crossing these valleys. We present a runtime comparison between the (1+1) EA and two non-elitist nature-inspired algorithms, Strong Selection Weak Mutation (SSWM) and the Metropolis algorithm. While the (1+1) EA has to jump across the valley to a point of higher fitness because it does not accept decreasing moves, the non-elitist algorithms may cross the valley by accepting worsening moves. We show that while the runtime of the (1+1) EA algorithm depends critically on the length of the valley, the runtimes of the non-elitist algorithms depend crucially only on the depth of the valley. In particular, the expected runtime of both SSWM and Metropolis is polynomial in ℓ and exponential in d while the (1+1) EA is efficient only for valleys of small length. Moreover, we show that both SSWM and Metropolis can also efficiently optimize a rugged function consisting of consecutive valleys.}, author = {Oliveto, Pietro and Paixao, Tiago and Heredia, Jorge and Sudholt, Dirk and Trubenova, Barbora}, booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference 2016 }, location = {Denver, CO, USA}, pages = {1163 -- 1170}, publisher = {ACM}, title = {{When non-elitism outperforms elitism for crossing fitness valleys}}, doi = {10.1145/2908812.2908909}, year = {2016}, } @inproceedings{1430, abstract = {Evolutionary algorithms (EAs) form a popular optimisation paradigm inspired by natural evolution. In recent years the field of evolutionary computation has developed a rigorous analytical theory to analyse their runtime on many illustrative problems. Here we apply this theory to a simple model of natural evolution. In the Strong Selection Weak Mutation (SSWM) evolutionary regime the time between occurrence of new mutations is much longer than the time it takes for a new beneficial mutation to take over the population. In this situation, the population only contains copies of one genotype and evolution can be modelled as a (1+1)-type process where the probability of accepting a new genotype (improvements or worsenings) depends on the change in fitness. We present an initial runtime analysis of SSWM, quantifying its performance for various parameters and investigating differences to the (1+1) EA. We show that SSWM can have a moderate advantage over the (1+1) EA at crossing fitness valleys and study an example where SSWM outperforms the (1+1) EA by taking advantage of information on the fitness gradient.}, author = {Paixao, Tiago and Sudholt, Dirk and Heredia, Jorge and Trubenova, Barbora}, booktitle = {Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation}, location = {Madrid, Spain}, pages = {1455 -- 1462}, publisher = {ACM}, title = {{First steps towards a runtime comparison of natural and artificial evolution}}, doi = {10.1145/2739480.2754758}, year = {2015}, } @article{1542, abstract = {The theory of population genetics and evolutionary computation have been evolving separately for nearly 30 years. Many results have been independently obtained in both fields and many others are unique to its respective field. We aim to bridge this gap by developing a unifying framework for evolutionary processes that allows both evolutionary algorithms and population genetics models to be cast in the same formal framework. The framework we present here decomposes the evolutionary process into its several components in order to facilitate the identification of similarities between different models. In particular, we propose a classification of evolutionary operators based on the defining properties of the different components. We cast several commonly used operators from both fields into this common framework. Using this, we map different evolutionary and genetic algorithms to different evolutionary regimes and identify candidates with the most potential for the translation of results between the fields. This provides a unified description of evolutionary processes and represents a stepping stone towards new tools and results to both fields. }, author = {Paixao, Tiago and Badkobeh, Golnaz and Barton, Nicholas H and Çörüş, Doğan and Dang, Duccuong and Friedrich, Tobias and Lehre, Per and Sudholt, Dirk and Sutton, Andrew and Trubenova, Barbora}, journal = { Journal of Theoretical Biology}, pages = {28 -- 43}, publisher = {Elsevier}, title = {{Toward a unifying framework for evolutionary processes}}, doi = {10.1016/j.jtbi.2015.07.011}, volume = {383}, year = {2015}, } @article{1809, abstract = {Background: Indirect genetic effects (IGEs) occur when genes expressed in one individual alter the expression of traits in social partners. Previous studies focused on the evolutionary consequences and evolutionary dynamics of IGEs, using equilibrium solutions to predict phenotypes in subsequent generations. However, whether or not such steady states may be reached may depend on the dynamics of interactions themselves. Results: In our study, we focus on the dynamics of social interactions and indirect genetic effects and investigate how they modify phenotypes over time. Unlike previous IGE studies, we do not analyse evolutionary dynamics; rather we consider within-individual phenotypic changes, also referred to as phenotypic plasticity. We analyse iterative interactions, when individuals interact in a series of discontinuous events, and investigate the stability of steady state solutions and the dependence on model parameters, such as population size, strength, and the nature of interactions. We show that for interactions where a feedback loop occurs, the possible parameter space of interaction strength is fairly limited, affecting the evolutionary consequences of IGEs. We discuss the implications of our results for current IGE model predictions and their limitations.}, author = {Trubenova, Barbora and Novak, Sebastian and Hager, Reinmar}, journal = {PLoS One}, number = {5}, publisher = {Public Library of Science}, title = {{Indirect genetic effects and the dynamics of social interactions}}, doi = {10.1371/journal.pone.0126907}, volume = {10}, year = {2015}, } @misc{9772, author = {Trubenova, Barbora and Novak, Sebastian and Hager, Reinmar}, publisher = {Public Library of Science}, title = {{Description of the agent based simulations}}, doi = {10.1371/journal.pone.0126907.s003}, year = {2015}, } @misc{9715, author = {Trubenova, Barbora and Novak, Sebastian and Hager, Reinmar}, publisher = {Public Library of Science}, title = {{Mathematical inference of the results}}, doi = {10.1371/journal.pone.0126907.s001}, year = {2015}, }