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1
Drahosova, Michaela, Lukas Sekanina, and Michal Wiglasz. "Adaptive Fitness Predictors in Coevolutionary Cartesian Genetic Programming." Evolutionary Computation 27, no. 3 (September 2019): 497–523. http://dx.doi.org/10.1162/evco_a_00229.
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In genetic programming (GP), computer programs are often coevolved with training data subsets that are known as fitness predictors. In order to maximize performance of GP, it is important to find the most suitable parameters of coevolution, particularly the fitness predictor size. This is a very time-consuming process as the predictor size depends on a given application, and many experiments have to be performed to find its suitable size. A new method is proposed which enables us to automatically adapt the predictor and its size for a given problem and thus to reduce not only the time of evolution, but also the time needed to tune the evolutionary algorithm. The method was implemented in the context of Cartesian genetic programming and evaluated using five symbolic regression problems and three image filter design problems. In comparison with three different CGP implementations, the time required by CGP search was reduced while the quality of results remained unaffected.
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Giosan, C., and K. Wyka. "FC01-03 - High-K reproductive strategy as a negative predictor of PTSD." European Psychiatry 26, S2 (March 2011): 1812. http://dx.doi.org/10.1016/s0924-9338(11)73516-7.
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This study examined the associations between a high-K fitness strategy (i.e., a Darwinian reproductive strategy where the individual invests significantly in a small number of offspring) and PTSD on a sample of 1400 disaster workers who had exposure to a singular traumatic event (the 9/11 attack on the World Trade Center in New York City). The participants underwent psychological evaluations consisting of1) Clinician-Administered PTSD Scale (CAPS) and2) PTSD Checklist (PCL). The participants were also administered the3) High-K Strategy Scale (HKSS) (Giosan, 2006).Other factors, such as demographics, prior trauma, prior psychiatric history, and the severity of exposure to 9/11 were also captured. The results showed that HKSS score was an important negative predictor of PTSD, accounting for 10% of the variance in the PCL and 5% of the variance in the CAPS, after controlling for demographics, prior trauma, prior psychiatric history and the severity of exposure to 9/11. The findings speak against the evolutionary view that PTSD symptoms are adaptive while strengthening the evolutionary view that PTSD is the expression of an overlearned survival response in vulnerable individuals (Silove, 1998), which, when activated, can have significant negative effects on fitness.
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Cheney, Dorothy L., Joan B. Silk, and Robert M. Seyfarth. "Network connections, dyadic bonds and fitness in wild female baboons." Royal Society Open Science 3, no. 7 (July 2016): 160255. http://dx.doi.org/10.1098/rsos.160255.
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In many social mammals, females who form close, differentiated bonds with others experience greater offspring survival and longevity. We still know little, however, about how females' relationships are structured within the social group, or whether connections beyond the level of the dyad have any adaptive value. Here, we apply social network analysis to wild baboons in order to evaluate the comparative benefits of dyadic bonds against several network measures. Results suggest that females with strong dyadic bonds also showed high eigenvector centrality, a measure of the extent to which an individual's partners are connected to others in the network. Eigenvector centrality was a better predictor of offspring survival than dyadic bond strength. Previous results have shown that female baboons derive significant fitness benefits from forming close, stable bonds with several other females. Results presented here suggest that these benefits may be further augmented if a female's social partners are themselves well connected to others within the group rather than being restricted to a smaller clique.
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Nigenda-Morales, Sergio F., Ryan J. Harrigan, and Robert K. Wayne. "Playing by the rules? Phenotypic adaptation to temperate environments in an American marsupial." PeerJ 6 (March 27, 2018): e4512. http://dx.doi.org/10.7717/peerj.4512.
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Phenotypic variation along environmental gradients can provide evidence suggesting local adaptation has shaped observed morphological disparities. These differences, in traits such as body and extremity size, as well as skin and coat pigmentation, may affect the overall fitness of individuals in their environments. The Virginia opossum (Didelphis virginiana) is a marsupial that shows phenotypic variation across its range, one that has recently expanded into temperate environments. It is unknown, however, whether the variation observed in the species fits adaptive ecogeographic patterns, or if phenotypic change is associated with any environmental factors. Using phenotypic measurements of over 300 museum specimens of Virginia opossum, collected throughout its distribution range, we applied regression analysis to determine if phenotypes change along a latitudinal gradient. Then, using predictors from remote-sensing databases and a random forest algorithm, we tested environmental models to find the most important variables driving the phenotypic variation. We found that despite the recent expansion into temperate environments, the phenotypic variation in the Virginia opossum follows a latitudinal gradient fitting three adaptive ecogeographic patterns codified under Bergmann’s, Allen’s and Gloger’s rules. Temperature seasonality was an important predictor of body size variation, with larger opossums occurring at high latitudes with more seasonal environments. Annual mean temperature predicted important variation in extremity size, with smaller extremities found in northern populations. Finally, we found that precipitation and temperature seasonality as well as low temperatures were strong environmental predictors of skin and coat pigmentation variation; darker opossums are distributed at low latitudes in warmer environments with higher precipitation seasonality. These results indicate that the adaptive mechanisms underlying the variation in body size, extremity size and pigmentation are related to the resource seasonality, heat conservation, and pathogen-resistance hypotheses, respectively. Our findings suggest that marsupials may be highly susceptible to environmental changes, and in the case of the Virginia opossum, the drastic phenotypic evolution in northern populations may have arisen rapidly, facilitating the colonization of seasonal and colder habitats of temperate North America.
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Ferguson, Moira M., and Peter E. Ihssen. "Distribution and Phenotypic Correlates of Variation at Enzyme Coding Loci in Rainbow Trout (Oncorhynchus mykiss) from the Lower Laurentian Great Lakes." Canadian Journal of Fisheries and Aquatic Sciences 48, no. 7 (July 1, 1991): 1308–15. http://dx.doi.org/10.1139/f91-157.
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The distribution of variation at 37 enzyme coding loci was determined in six samples of rainbow trout (Oncorhynchus mykiss) from hatchery and natural sources. Significant heterogeneity in allele frequencies was detected among samples of Lower Laurentian Great Lakes rainbow trout. Geographical proximity was a poor predictor of genetic similarity among samples. The adaptive significance of two loci was examined in 16 experimental families. Rainbow trout homozygous for an allele at a phosphoglucomutase structural gene (Pgm1-s), which results in no detectable activity of phosphoglucomutase (PGM1) in all tissues where this locus is normally expressed (e.g. muscle), were larger than those heterozygous or homozygous for the active allele. However, no significant differences in asymmetry of four bilateral meristic traits were detected between fish with different PGM1 phenotypes. Rainbow trout with PGM1 in liver because of a regulatory gene, Pgm1-t(b), were marginally larger than those without the allele. These data suggest a correlation between biochemical genetic variation and phenotypic characteristics associated with fitness.
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Major, John E., Debby C. Barsi, Alex Mosseler, Om P. Rajora, and Moira Campbell. "Predominant paternal inheritance pattern of light-energy processing adaptive traits in red and black spruce hybrids." Canadian Journal of Forest Research 37, no. 2 (February 2007): 293–305. http://dx.doi.org/10.1139/x06-228.
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Ecophysiological traits related to light-energy processing and freezing tolerance are important adaptive traits in plants. Our goal was to investigate the pattern of inheritance of these traits in hybrids using controlled intra- and inter-specific crosses of red spruce ( Picea rubens Sarg.) (RS) and black spruce ( Picea mariana (Mill.) BSP) (BS). Our initial working hypothesis was that expected hybrid index categories could be a predictor of adaptive traits. Species results of dark-adapted photochemical efficiency (Fv/Fm) and light-adapted light-energy processing traits, quantum yield (YLD), thermal dissipation efficiency (qN), and chlorophyll fluorescence (Fpc), were consistent with previously published open-pollinated, species provenance results. Initially, YLD, qN, and Fpc, showed an additive inheritance pattern, evident by average hybrid index 50 having a mid-parent value. Because of various crosstypes of the hybrid families, parental analysis, testing male, female, and interaction effects, and having three categories (pure RS, pure BS, and hybrid spruce), revealed significant male and nonsignificant female and interactive effects. Underlying the averaged additive results was a significant species-specific paternal inheritance pattern. Crosses with BS males had 13.7% higher YLD (P = 0.001), 15.4% lower qN (P = 0.008), and 43.0% higher Fpc (P = 0.096) than crosses with either RS or hybrid males. Fv/Fm showed a nonadditive or parental species pattern. Parental analysis of Fv/Fm showed significant male, female, and male × female interaction effects, and further analysis supports a largely species-specific and paternally inherited trait. Freezing tolerance revealed a mixed model of inheritance dominated by species effects. Total dry mass was positively correlated with YLD, and negatively correlated with Fv/Fm and qN, suggesting a biological tradeoff. We know of no other studies in trees demonstrating paternal inheritance of ecophysiological processes that affect adaptation and fitness.
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Nicolaus, Marion, Joost M. Tinbergen, Karen M. Bouwman, Stephanie P. M. Michler, Richard Ubels, Christiaan Both, Bart Kempenaers, and Niels J. Dingemanse. "Experimental evidence for adaptive personalities in a wild passerine bird." Proceedings of the Royal Society B: Biological Sciences 279, no. 1749 (October 24, 2012): 4885–92. http://dx.doi.org/10.1098/rspb.2012.1936.
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Individuals of the same species differ consistently in risky actions. Such ‘animal personality’ variation is intriguing because behavioural flexibility is often assumed to be the norm. Recent theory predicts that between-individual differences in propensity to take risks should evolve if individuals differ in future fitness expectations: individuals with high long-term fitness expectations (i.e. that have much to lose) should behave consistently more cautious than individuals with lower expectations. Consequently, any manipulation of future fitness expectations should result in within-individual changes in risky behaviour in the direction predicted by this adaptive theory. We tested this prediction and confirmed experimentally that individuals indeed adjust their ‘exploration behaviour’, a proxy for risk-taking behaviour, to their future fitness expectations. We show for wild great tits ( Parus major ) that individuals with experimentally decreased survival probability become faster explorers (i.e. increase risk-taking behaviour) compared to individuals with increased survival probability. We also show, using quantitative genetics approaches, that non-genetic effects (i.e. permanent environment effects) underpin adaptive personality variation in this species. This study thereby confirms a key prediction of adaptive personality theory based on life-history trade-offs, and implies that selection may indeed favour the evolution of personalities in situations where individuals differ in future fitness expectations.
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Liu, Ding. "Research and Application of Hybrid Adaptive Forecasting Method Based on SOA." E3S Web of Conferences 213 (2020): 02007. http://dx.doi.org/10.1051/e3sconf/202021302007.
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The current network security situation is becoming more and more severe. In order to improve the accuracy of network security situation prediction, a network security situation prediction method based on crowd search algorithm optimized BP neural network is proposed. This algorithm uses the four characteristics of egoism, altruism, pre-action and uncertain reasoning unique to the crowd search algorithm to determine the search strategy, finds the best fitness individual, obtains the optimal weights and thresholds, and then performs random initialization of the BP neural network The threshold and weight are assigned, and the predicted value is obtained after neural network training. Finally, it is compared with the predicted value obtained by the other two optimization algorithms. Experiments show that the algorithm used in network security situation prediction has higher accuracy, smaller errors, and better stability.
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McFarlane, S. Eryn, Jamieson C. Gorrell, David W. Coltman, Murray M. Humphries, Stan Boutin, and Andrew G. McAdam. "The nature of nurture in a wild mammal's fitness." Proceedings of the Royal Society B: Biological Sciences 282, no. 1806 (May 7, 2015): 20142422. http://dx.doi.org/10.1098/rspb.2014.2422.
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Genetic variation in fitness is required for the adaptive evolution of any trait but natural selection is thought to erode genetic variance in fitness. This paradox has motivated the search for mechanisms that might maintain a population's adaptive potential. Mothers make many contributions to the attributes of their developing offspring and these maternal effects can influence responses to natural selection if maternal effects are themselves heritable. Maternal genetic effects (MGEs) on fitness might, therefore, represent an underappreciated source of adaptive potential in wild populations. Here we used two decades of data from a pedigreed wild population of North American red squirrels to show that MGEs on offspring fitness increased the population's evolvability by over two orders of magnitude relative to expectations from direct genetic effects alone. MGEs are predicted to maintain more variation than direct genetic effects in the face of selection, but we also found evidence of maternal effect trade-offs. Mothers that raised high-fitness offspring in one environment raised low-fitness offspring in another environment. Such a fitness trade-off is expected to maintain maternal genetic variation in fitness, which provided additional capacity for adaptive evolution beyond that provided by direct genetic effects on fitness.
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Wilson, David Sloan, and Steven Jay Lynn. "Adaptive misbeliefs are pervasive, but the case for positive illusions is weak." Behavioral and Brain Sciences 32, no. 6 (December 2009): 539–40. http://dx.doi.org/10.1017/s0140525x09991543.
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AbstractIt is a foundational prediction of evolutionary theory that human beliefs accurately approximate reality only insofar as accurate beliefs enhance fitness. Otherwise, adaptive misbeliefs will prevail. Unlike McKay & Dennett (M&D), we think that adaptive belief systems rely heavily upon misbeliefs. However, the case for positive illusions as an example of adaptive misbelief is weak.
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Ab. Aziz, Nor Azlina, Tasiransurini Ab Rahman, and Nor Hidayati Abdul Aziz. "Fitness-evaluated Adaptive Switching Simulated Kalman Filter Algorithm with Randomness." Mekatronika 1, no. 2 (July 1, 2019): 45–65. http://dx.doi.org/10.15282/mekatronika.v1i2.4986.
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The original Simulated Kalman Filter (SKF) is an optimizer that employs synchronous update mechanism. The agents in SKF update their solutions after all fitness calculations, prediction process, and measurement process are completed. An alternative to synchronous update is asynchronous update. In asynchronous update, only one agent does fitness calculation, prediction, measurement, and estimation processes at one time. A recent study shows that the asynchronous SKF outperforms synchronous SKF. In this study, synchronous and asynchronous mechanisms are combined in SKF. At first, the SKF starts with either synchronous or asynchronous update. By evaluating the fitness, if no improved solution is found, the SKF changes its update mechanism. The decision to switch from synchronous to asynchronous or vice versa is made randomly. Using the CEC2014 benchmark test suite, experimental results indicate that the proposed adaptive switching SKF randomness outperforms the original SKF algorithm.
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Tian, Xiaoxia, Jingwen Yan, and Chi Xiao. "Parameter Identification of the Vortex-Induced Vertical Force Model Using a New Adaptive PSO." Mathematical Problems in Engineering 2018 (October 28, 2018): 1–9. http://dx.doi.org/10.1155/2018/2028196.
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The paper proposes a new adaptive PSO (NAPSO) that adaptively adjust the inertial weight of every particle according to its own current fitness. In NAPSO, the searching ability of each particle is controlled by the inertial weight. In pursuit of the optimal solution, if a particle has a rather small value of normalized fitness, it has a small inertia weight so as to increase local searching ability; on the contrary, it has a large inertia weight to increase global searching ability. Simulation results include three parts: the NAPSO shows fast convergence and good stability compared with other PSOs; the NAPSO shows good fit and short run-time compared with GA and GALMA; according to the identified parameters, the time history of predicted vertical displacement is quite in accordance with the time history of measured displacement. As far as the nonlinear VIVF model is concerned, the NAPSO is a simple and effective identification method.
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Arias, Mónica, Yann le Poul, Mathieu Chouteau, Romain Boisseau, Neil Rosser, Marc Théry, and Violaine Llaurens. "Crossing fitness valleys: empirical estimation of a fitness landscape associated with polymorphic mimicry." Proceedings of the Royal Society B: Biological Sciences 283, no. 1829 (April 27, 2016): 20160391. http://dx.doi.org/10.1098/rspb.2016.0391.
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Characterizing fitness landscapes associated with polymorphic adaptive traits enables investigation of mechanisms allowing transitions between fitness peaks. Here, we explore how natural selection can promote genetic mechanisms preventing heterozygous phenotypes from falling into non-adaptive valleys. Polymorphic mimicry is an ideal system to investigate such fitness landscapes, because the direction of selection acting on complex mimetic colour patterns can be predicted by the local mimetic community composition. Using more than 5000 artificial butterflies displaying colour patterns exhibited by the polymorphic Müllerian mimic Heliconius numata , we directly tested the role of wild predators in shaping fitness landscapes. We compared predation rates on mimetic phenotypes (homozygotes at the supergene controlling colour pattern), intermediate phenotypes (heterozygotes), exotic morphs (absent from the local community) and palatable cryptic phenotypes. Exotic morphs were significantly more attacked than local morphs, highlighting predators' discriminatory capacities. Overall, intermediates were attacked twice as much as local homozygotes, suggesting the existence of deep fitness valleys promoting strict dominance and reduced recombination between supergene alleles. By including information on predators' colour perception, we also showed that protection on intermediates strongly depends on their phenotypic similarity to homozygous phenotypes and that ridges exist between similar phenotypes, which may facilitate divergence in colour patterns.
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Gordo, Isabel, and Paulo R. A. Campos. "Evolution of clonal populations approaching a fitness peak." Biology Letters 9, no. 1 (February 23, 2013): 20120239. http://dx.doi.org/10.1098/rsbl.2012.0239.
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Populations facing novel environments are expected to evolve through the accumulation of adaptive substitutions. The dynamics of adaptation depend on the fitness landscape and possibly on the genetic background on which new mutations arise. Here, we model the dynamics of adaptive evolution at the phenotypic and genotypic levels, focusing on a Fisherian landscape characterized by a single peak. We find that Fisher's geometrical model of adaptation, extended to allow for small random environmental variations, is able to explain several features made recently in experimentally evolved populations. Consistent with data on populations evolving under controlled conditions, the model predicts that mean population fitness increases rapidly when populations face novel environments and then achieves a dynamic plateau, the rate of molecular evolution is remarkably constant over long periods of evolution, mutators are expected to invade and patterns of epistasis vary along the adaptive walk. Negative epistasis is expected in the initial steps of adaptation but not at later steps, a prediction that remains to be tested. Furthermore, populations are expected to exhibit high levels of phenotypic diversity at all times during their evolution. This implies that populations are possibly able to adapt rapidly to novel abiotic environments.
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Faridah, Sh Ismail, and Abu Bakar Nordin. "Adaptive GA-NN for MDF Prediction Model." Advanced Materials Research 980 (June 2014): 214–18. http://dx.doi.org/10.4028/www.scientific.net/amr.980.214.
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This research presents a hybrid Genetic Algorithm Neural Network (GA-NN) model to replace the physical tests procedures of Medium Density Fiberboard (MDF). Data included in the model is MDF properties and its fiber characteristics. Multilayer Perceptron (MLP) NN model is reliable to learn from seven inputs fed to the network to produce prediction of three targets. In order to avoid result from local optimum scenario, GA optimizes synaptic weights of the network towards reducing prediction error. The research used a fixed probability rates for crossover and mutation for hybrid GA-NN model. GA-NN model is further improved using adaptive mechanism to help identify the best probability rates. The fitness value refers to Sum of Squared Error. Performance comparisons are among three models; namely NN with Back Propagation (BP), hybrid GA-NN and hybrid GA-NN with adaptive mechanism. Results show the hybrid GA-NN model perform much better than NN model used with back propagation optimizer. Adaptive mechanism in GA helps increase capability to converge at zero sooner than the ordinary GA.
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Burch, Christina L., and Lin Chao. "Evolution by Small Steps and Rugged Landscapes in the RNA Virus ϕ6." Genetics 151, no. 3 (March 1, 1999): 921–27. http://dx.doi.org/10.1093/genetics/151.3.921.
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Abstract Fisher’s geometric model of adaptive evolution argues that adaptive evolution should generally result from the substitution of many mutations of small effect because advantageous mutations of small effect should be more common than those of large effect. However, evidence for both evolution by small steps and for Fisher’s model has been mixed. Here we report supporting results from a new experimental test of the model. We subjected the bacteriophage ϕ6 to intensified genetic drift in small populations and caused viral fitness to decline through the accumulation of a deleterious mutation. We then propagated the mutated virus at a range of larger population sizes and allowed fitness to recover by natural selection. Although fitness declined in one large step, it was usually recovered in smaller steps. More importantly, step size during recovery was smaller with decreasing size of the recovery population. These results confirm Fisher’s main prediction that advantageous mutations of small effect should be more common. We also show that the advantageous mutations of small effect are compensatory mutations whose advantage is conditional (epistatic) on the presence of the deleterious mutation, in which case the adaptive landscape of ϕ6 is likely to be very rugged.
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Pfaender, Jobst, Renny K. Hadiaty, Ulrich K. Schliewen, and Fabian Herder. "Rugged adaptive landscapes shape a complex, sympatric radiation." Proceedings of the Royal Society B: Biological Sciences 283, no. 1822 (January 13, 2016): 20152342. http://dx.doi.org/10.1098/rspb.2015.2342.
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Strong disruptive ecological selection can initiate speciation, even in the absence of physical isolation of diverging populations. Species evolving under disruptive ecological selection are expected to be ecologically distinct but, at least initially, genetically weakly differentiated. Strong selection and the associated fitness advantages of narrowly adapted individuals, coupled with assortative mating, are predicted to overcome the homogenizing effects of gene flow. Theoretical plausibility is, however, contrasted by limited evidence for the existence of rugged adaptive landscapes in nature. We found evidence for multiple, disruptive ecological selection regimes that have promoted divergence in the sympatric, incipient radiation of ‘sharpfin’ sailfin silverside fishes in ancient Lake Matano (Sulawesi, Indonesia). Various modes of ecological specialization have led to adaptive morphological differences between the species, and differently adapted morphs display significant but incomplete reproductive isolation. Individual fitness and variation in morphological key characters show that disruptive selection shapes a rugged adaptive landscape in this small but complex incipient lake fish radiation.
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Capblancq, Thibaut, Matthew C. Fitzpatrick, Rachael A. Bay, Moises Exposito-Alonso, and Stephen R. Keller. "Genomic Prediction of (Mal)Adaptation Across Current and Future Climatic Landscapes." Annual Review of Ecology, Evolution, and Systematics 51, no. 1 (November 2, 2020): 245–69. http://dx.doi.org/10.1146/annurev-ecolsys-020720-042553.
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Signals of local adaptation have been found in many plants and animals, highlighting the heterogeneity in the distribution of adaptive genetic variation throughout species ranges. In the coming decades, global climate change is expected to induce shifts in the selective pressures that shape this adaptive variation. These changes in selective pressures will likely result in varying degrees of local climate maladaptation and spatial reshuffling of the underlying distributions of adaptive alleles. There is a growing interest in using population genomic data to help predict future disruptions to locally adaptive gene-environment associations. One motivation behind such work is to better understand how the effects of changing climate on populations’ short-term fitness could vary spatially across species ranges. Here we review the current use of genomic data to predict the disruption of local adaptation across current and future climates. After assessing goals and motivationsunderlying the approach, we review the main steps and associated statistical methods currently in use and explore our current understanding of the limits and future potential of using genomics to predict climate change (mal)adaptation.
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Steele, Ariel L., and Daniel A. Warner. "Sex-specific effects of developmental temperature on morphology, growth and survival of offspring in a lizard with temperature-dependent sex determination." Biological Journal of the Linnean Society 130, no. 2 (April 4, 2020): 320–35. http://dx.doi.org/10.1093/biolinnean/blaa038.
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Abstract The developmental environment plays a pivotal role in shaping fitness-relevant phenotypes of all organisms. Phenotypes are highly labile during embryogenesis, and environmental factors experienced early in development can have profound effects on fitness-relevant traits throughout life. Many reptiles exhibit temperature-dependent sex determination (TSD), whereby temperature during embryonic development permanently determines offspring sex. The leading hypothesis for the adaptive significance of TSD posits that egg incubation temperature differentially affects the fitness of males vs. females so that each sex is produced at its optimal temperature. The goal of this research is to address this hypothesis by quantifying the sex-specific effects of incubation temperature on phenotypes and survival in a lizard (Agama picticauda) with TSD. By incubating eggs under constant and fluctuating temperatures, we demonstrated that incubation temperature affects fitness-relevant phenotypes in A. picticauda; but males and females had similar reaction norms. However, females produced from female-biased incubation temperatures had greater survival than those from male-biased temperatures, and male survival was lowest for individuals produced from a female-biased temperature. In addition, eggs incubated at male-biased temperatures hatched earlier than those incubated at female-biased temperatures, which may have sex-specific consequences later in life as predicted by models for the adaptive significance of TSD.
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Le, Minh Nghia, Yew Soon Ong, Stefan Menzel, Yaochu Jin, and Bernhard Sendhoff. "Evolution by Adapting Surrogates." Evolutionary Computation 21, no. 2 (May 2013): 313–40. http://dx.doi.org/10.1162/evco_a_00079.
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To deal with complex optimization problems plagued with computationally expensive fitness functions, the use of surrogates to replace the original functions within the evolutionary framework is becoming a common practice. However, the appropriate datacentric approximation methodology to use for the construction of surrogate model would depend largely on the nature of the problem of interest, which varies from fitness landscape and state of the evolutionary search, to the characteristics of search algorithm used. This has given rise to the plethora of surrogate-assisted evolutionary frameworks proposed in the literature with ad hoc approximation/surrogate modeling methodologies considered. Since prior knowledge on the suitability of the data centric approximation methodology to use in surrogate-assisted evolutionary optimization is typically unavailable beforehand, this paper presents a novel evolutionary framework with the evolvability learning of surrogates (EvoLS) operating on multiple diverse approximation methodologies in the search. Further, in contrast to the common use of fitness prediction error as a criterion for the selection of surrogates, the concept of evolvability to indicate the productivity or suitability of an approximation methodology that brings about fitness improvement in the evolutionary search is introduced as the basis for adaptation. The backbone of the proposed EvoLS is a statistical learning scheme to determine the evolvability of each approximation methodology while the search progresses online. For each individual solution, the most productive approximation methodology is inferred, that is, the method with highest evolvability measure. Fitness improving surrogates are subsequently constructed for use within a trust-region enabled local search strategy, leading to the self-configuration of a surrogate-assisted memetic algorithm for solving computationally expensive problems. A numerical study of EvoLS on commonly used benchmark problems and a real-world computationally expensive aerodynamic car rear design problem highlights the efficacy of the proposed EvoLS in attaining reliable, high quality, and efficient performance under a limited computational budget.
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Fernandez-de-Cossio-Diaz, Jorge, Guido Uguzzoni, and Andrea Pagnani. "Unsupervised Inference of Protein Fitness Landscape from Deep Mutational Scan." Molecular Biology and Evolution 38, no. 1 (August 8, 2020): 318–28. http://dx.doi.org/10.1093/molbev/msaa204.
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Abstract The recent technological advances underlying the screening of large combinatorial libraries in high-throughput mutational scans deepen our understanding of adaptive protein evolution and boost its applications in protein design. Nevertheless, the large number of possible genotypes requires suitable computational methods for data analysis, the prediction of mutational effects, and the generation of optimized sequences. We describe a computational method that, trained on sequencing samples from multiple rounds of a screening experiment, provides a model of the genotype–fitness relationship. We tested the method on five large-scale mutational scans, yielding accurate predictions of the mutational effects on fitness. The inferred fitness landscape is robust to experimental and sampling noise and exhibits high generalization power in terms of broader sequence space exploration and higher fitness variant predictions. We investigate the role of epistasis and show that the inferred model provides structural information about the 3D contacts in the molecular fold.
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Kingsolver, Joel G., and Lauren B. Buckley. "Climate variability slows evolutionary responses of Colias butterflies to recent climate change." Proceedings of the Royal Society B: Biological Sciences 282, no. 1802 (March 7, 2015): 20142470. http://dx.doi.org/10.1098/rspb.2014.2470.
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How does recent climate warming and climate variability alter fitness, phenotypic selection and evolution in natural populations? We combine biophysical, demographic and evolutionary models with recent climate data to address this question for the subalpine and alpine butterfly, Colias meadii , in the southern Rocky Mountains. We focus on predicting patterns of selection and evolution for a key thermoregulatory trait, melanin (solar absorptivity) on the posterior ventral hindwings, which affects patterns of body temperature, flight activity, adult and egg survival, and reproductive success in Colias . Both mean annual summer temperatures and thermal variability within summers have increased during the past 60 years at subalpine and alpine sites. At the subalpine site, predicted directional selection on wing absorptivity has shifted from generally positive (favouring increased wing melanin) to generally negative during the past 60 years, but there is substantial variation among years in the predicted magnitude and direction of selection and the optimal absorptivity. The predicted magnitude of directional selection at the alpine site declined during the past 60 years and varies substantially among years, but selection has generally been positive at this site. Predicted evolutionary responses to mean climate warming at the subalpine site since 1980 is small, because of the variability in selection and asymmetry of the fitness function. At both sites, the predicted effects of adaptive evolution on mean population fitness are much smaller than the fluctuations in mean fitness due to climate variability among years. Our analyses suggest that variation in climate within and among years may strongly limit evolutionary responses of ectotherms to mean climate warming in these habitats.
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Streby, Henry M., Jeanine M. Refsnider, Sean M. Peterson, and David E. Andersen. "Retirement investment theory explains patterns in songbird nest-site choice." Proceedings of the Royal Society B: Biological Sciences 281, no. 1777 (February 22, 2014): 20131834. http://dx.doi.org/10.1098/rspb.2013.1834.
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When opposing evolutionary selection pressures act on a behavioural trait, the result is often stabilizing selection for an intermediate optimal phenotype, with deviations from the predicted optimum attributed to tracking a moving target, development of behavioural syndromes or shifts in riskiness over an individual's lifetime. We investigated nest-site choice by female golden-winged warblers, and the selection pressures acting on that choice by two fitness components, nest success and fledgling survival. We observed strong and consistent opposing selection pressures on nest-site choice for maximizing these two fitness components, and an abrupt, within-season switch in the fitness component birds prioritize via nest-site choice, dependent on the time remaining for additional nesting attempts. We found that females consistently deviated from the predicted optimal behaviour when choosing nest sites because they can make multiple attempts at one fitness component, nest success, but only one attempt at the subsequent component, fledgling survival. Our results demonstrate a unique natural strategy for balancing opposing selection pressures to maximize total fitness. This time-dependent switch from high to low risk tolerance in nest-site choice maximizes songbird fitness in the same way a well-timed switch in human investor risk tolerance can maximize one's nest egg at retirement. Our results also provide strong evidence for the adaptive nature of songbird nest-site choice, which we suggest has been elusive primarily due to a lack of consideration for fledgling survival.
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Heckmann, David. "Modelling metabolic evolution on phenotypic fitness landscapes: a case study on C4 photosynthesis." Biochemical Society Transactions 43, no. 6 (November 27, 2015): 1172–76. http://dx.doi.org/10.1042/bst20150148.
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How did the complex metabolic systems we observe today evolve through adaptive evolution? The fitness landscape is the theoretical framework to answer this question. Since experimental data on natural fitness landscapes is scarce, computational models are a valuable tool to predict landscape topologies and evolutionary trajectories. Careful assumptions about the genetic and phenotypic features of the system under study can simplify the design of such models significantly. The analysis of C4 photosynthesis evolution provides an example for accurate predictions based on the phenotypic fitness landscape of a complex metabolic trait. The C4 pathway evolved multiple times from the ancestral C3 pathway and models predict a smooth ‘Mount Fuji’ landscape accordingly. The modelled phenotypic landscape implies evolutionary trajectories that agree with data on modern intermediate species, indicating that evolution can be predicted based on the phenotypic fitness landscape. Future directions will have to include structural changes of metabolic fitness landscape structure with changing environments. This will not only answer important evolutionary questions about reversibility of metabolic traits, but also suggest strategies to increase crop yields by engineering the C4 pathway into C3 plants.
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Selim, Kamal Samy, Ahmed Okasha, and Heba M. Ezzat. "Loss Aversion, Adaptive Beliefs, and Asset Pricing Dynamics." Advances in Decision Sciences 2015 (October 8, 2015): 1–18. http://dx.doi.org/10.1155/2015/971269.
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We study asset pricing dynamics in artificial financial markets model. The financial market is populated with agents following two heterogeneous trading beliefs, the technical and the fundamental prediction rules. Agents switch between trading rules with respect to their past performance. The agents are loss averse over asset price fluctuations. Loss aversion behaviour depends on the past performance of the trading strategies in terms of an evolutionary fitness measure. We propose a novel application of the prospect theory to agent-based modelling, and by simulation, the effect of evolutionary fitness measure on adaptive belief system is investigated. For comparison, we study pricing dynamics of a financial market populated with chartists perceive losses and gains symmetrically. One of our contributions is validating the agent-based models using real financial data of the Egyptian Stock Exchange. We find that our framework can explain important stylized facts in financial time series, such as random walk price behaviour, bubbles and crashes, fat-tailed return distributions, power-law tails in the distribution of returns, excess volatility, volatility clustering, the absence of autocorrelation in raw returns, and the power-law autocorrelations in absolute returns. In addition to this, we find that loss aversion improves market quality and market stability.
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Vila-Aiub. "Fitness of Herbicide-Resistant Weeds: Current Knowledge and Implications for Management." Plants 8, no. 11 (November 1, 2019): 469. http://dx.doi.org/10.3390/plants8110469.
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Herbicide resistance is the ultimate evidence of the extraordinary capacity of weeds to evolve under stressful conditions. Despite the extraordinary plant fitness advantage endowed by herbicide resistance mutations in agroecosystems under herbicide selection, resistance mutations are predicted to exhibit an adaptation cost (i.e., fitness cost), relative to the susceptible wild-type, in herbicide untreated conditions. Fitness costs associated with herbicide resistance mutations are not universal and their expression depends on the particular mutation, genetic background, dominance of the fitness cost, and environmental conditions. The detrimental effects of herbicide resistance mutations on plant fitness may arise as a direct impact on fitness-related traits and/or coevolution with changes in other life history traits that ultimately may lead to fitness costs under particular ecological conditions. This brings the idea that a “lower adaptive value” of herbicide resistance mutations represents an opportunity for the design of resistance management practices that could minimize the evolution of herbicide resistance. It is evident that the challenge for weed management practices aiming to control, minimize, or even reverse the frequency of resistance mutations in the agricultural landscape is to “create” those agroecological conditions that could expose, exploit, and exacerbate those life history and/or fitness traits affecting the evolution of herbicide resistance mutations. Ideally, resistance management should implement a wide range of cultural practices leading to environmentally mediated fitness costs associated with herbicide resistance mutations.
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Rezvani Nejad, Saeed, Ahmad Borjali, Mahdi Khanjani, and Daniel J. Kruger. "Belief in an Afterlife Influences Altruistic Helping Intentions in Alignment With Adaptive Tendencies." Evolutionary Psychology 19, no. 2 (April 1, 2021): 147470492110117. http://dx.doi.org/10.1177/14747049211011745.
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Evolutionary definitions of altruism are only concerned with reproductive consequences and not motives or other psychological mechanisms, making them ideal for generalization to all forms of organisms. Hamilton’s inclusive fitness theory explains altruistic behavior toward genetic relatives and has generated extensive empirical support. Trivers’ theory of reciprocal altruism helps explain patterns of helping among non-kin, and other research has demonstrated that human helping intentions follow fitness consequences from age-based reproductive value on altruism. The current study examines a novel psychological factor, belief in the afterlife, which may influence altruistic helping intentions. Belief in the afterlife was incorporated into a previous study design assessing the effects of a target’s genetic relatedness and age-based reproductive value. The influences of inclusive fitness and target age were reproduced in a non-Western sample of participants ( N = 300) in Iran. Belief in the afterlife predicted the overall confidence of risking one’s life to save another across all targets, and also moderated the effects of genetic relatedness and target age. Rather than promoting altruism equitably or advantaging those favored by adaptive tendencies, higher belief in an afterlife aligned with these tendencies in promoting further favoritism toward close kin and younger targets with higher reproductive value.
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Zhang, Junxi, and Shiru Qu. "Optimization of Backpropagation Neural Network under the Adaptive Genetic Algorithm." Complexity 2021 (July 6, 2021): 1–9. http://dx.doi.org/10.1155/2021/1718234.
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This study is to explore the optimization of the adaptive genetic algorithm (AGA) in the backpropagation (BP) neural network (BPNN), so as to expand the application of the BPNN model in nonlinear issues. Traffic flow prediction is undertaken as a research case to analyse the performance of the optimized BPNN. Firstly, the advantages and disadvantages of the BPNN and genetic algorithm (GA) are analyzed based on their working principles, and the AGA is improved and optimized. Secondly, the optimized AGA is applied to optimize the standard BPNN, and the optimized algorithm is named as OAGA-BPNN. Finally, three different cases are proposed based on the actual scenario of traffic flow prediction to analyse the optimized algorithm on the matrix laboratory (MATLAB) platform by simulation. The results show that the average error distribution of the GA-BPNN algorithm is about 1% with small fluctuation range, better calculation accuracy, and generalization performance in contrast to the BPNN. The average output error of the AGA-BPNN fluctuates around 0 and remains in a relatively stable range as a whole in contrast to that of GA-BPNN; the maximum fitness level keeps increasing during the evolution process but approaches the average value in later process, so the population diversity is hard to be guaranteed. The output error of the OAGA-BPNN fluctuates little compared with that of AGA-BPNN, and its maximum fitness continues to increase in the evolution process with guaranteed population diversity. In short, the OAGA-BPNN algorithm can achieve the best performance in terms of calculation accuracy, generalization performance, and population evolution.
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Moutinho, Ana Filipa, Thomas Bataillon, and Julien Y. Dutheil. "Variation of the adaptive substitution rate between species and within genomes." Evolutionary Ecology 34, no. 3 (December 14, 2019): 315–38. http://dx.doi.org/10.1007/s10682-019-10026-z.
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AbstractThe importance of adaptive mutations in molecular evolution is extensively debated. Recent developments in population genomics allow inferring rates of adaptive mutations by fitting a distribution of fitness effects to the observed patterns of polymorphism and divergence at sites under selection and sites assumed to evolve neutrally. Here, we summarize the current state-of-the-art of these methods and review the factors that affect the molecular rate of adaptation. Several studies have reported extensive cross-species variation in the proportion of adaptive amino-acid substitutions (α) and predicted that species with larger effective population sizes undergo less genetic drift and higher rates of adaptation. Disentangling the rates of positive and negative selection, however, revealed that mutations with deleterious effects are the main driver of this population size effect and that adaptive substitution rates vary comparatively little across species. Conversely, rates of adaptive substitution have been documented to vary substantially within genomes. On a genome-wide scale, gene density, recombination and mutation rate were observed to play a role in shaping molecular rates of adaptation, as predicted under models of linked selection. At the gene level, it has been reported that the gene functional category and the macromolecular structure substantially impact the rate of adaptive mutations. Here, we deliver a comprehensive review of methods used to infer the molecular adaptive rate, the potential drivers of adaptive evolution and how positive selection shapes molecular evolution within genes, across genes within species and between species.
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Ichimura, Takumi, Hiroshi Inoue, Akira Hara, Tetsuyuki Takahama, and Kenneth J. Mackin. "A Proposal of Memory and Prediction Based Genetic Algorithm Using Speciation in Dynamic Multimodal Function Optimization." Journal of Advanced Computational Intelligence and Intelligent Informatics 15, no. 8 (October 20, 2011): 1082–94. http://dx.doi.org/10.20965/jaciii.2011.p1082.
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It is a difficult problem for evolutionary algorithms to search an optimal solution in multimodal functions with dynamic environments, where individuals searchmore than one optimum and their fitness values change over time. In this paper, we propose a method of Memory and Prediction Based Genetic Algorithm Using Speciation. This method is extended with a case based memory and a meta learner for precise prediction of environmental change. Especially, the individuals in a memory consist of 4 kinds of predictors and they can adjust to the change of dynamic environment adaptively. Speciation has shown to be an effective technique for multimodal optimization. A niching method based on speciation can be used to classify a population into groups according to their similarity measured by a distance. In this paper, each group by speciation has a memory and the individuals stored in the memory can respond to the situation according to the dynamic environment. In order to verify the effectiveness, the method is examined to search for an optimal solutions in multimodal functions.
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Zhou, Huiyu, Shingo Mabu, Wei Wei, Kaoru Shimada, and Kotaro Hirasawa. "Traffic Flow Prediction with Genetic Network Programming (GNP)." Journal of Advanced Computational Intelligence and Intelligent Informatics 13, no. 6 (November 20, 2009): 713–25. http://dx.doi.org/10.20965/jaciii.2009.p0713.
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In this paper, a method for traffic flow prediction has been proposed to obtain prediction rules from the past traffic data using Genetic Network Programming (GNP). GNP is an evolutionary approach which can evolve itself and find the optimal solutions. It has been clarified that GNP works well especially in dynamic environments since GNP is consisted of directed graph structures, creates quite compact programs and has an implicit memory function. In this paper, GNP is applied to create a traffic flow prediction model. And we proposed the spatial adjacency model for the prediction and two kinds of models forN-step prediction. Additionally, the adaptive penalty functions are adopted for the fitness function in order to alleviate the infeasible solutions containing loops in the training process. Furthermore, the sharing function is also used to avoid the premature convergence.
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Su, LiYun, and Fan Yang. "Prediction of Chaotic Time Series Based on BEN-AGA Model." Complexity 2021 (September 15, 2021): 1–16. http://dx.doi.org/10.1155/2021/6656958.
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Aiming at the prediction problem of chaotic time series, this paper proposes a brain emotional network combined with an adaptive genetic algorithm (BEN-AGA) model to predict chaotic time series. First, we improve the emotional brain learning (BEL) model using the activation function to change the two linear structures the amygdala and the orbitofrontal cortex into the nonlinear structure, and then we establish the brain emotional network (BEN) model. The brain emotional network model has stronger nonlinear calculation ability and generalization ability. Next, we use the adaptive genetic algorithm to optimize the parameters of the brain emotional network model. The weights to be optimized in the model are coded as chromosomes. We design the dynamic crossover probability and mutation probability to control the crossover process and the mutation process, and the optimal parameters are selected through the fitness function to evaluate the chromosome. In this way, we increase the approximation capability of the model and increase the calculation speed of the model. Finally, we reconstruct the phase space of the observation sequence based on the short-term predictability of the chaotic time series; then we establish a brain emotional network model and optimize its parameters with an adaptive genetic algorithm and perform a single-step prediction on the optimized model to obtain the prediction error. The model proposed in this paper is applied to the prediction of Rossler chaotic time series and sunspot chaotic time series. The experimental results verify the effectiveness of the BEN-AGA model and show that this model has higher prediction accuracy and more stability than other methods.
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Fabozzi, Michael, and Alessandro Dama. "Field study on thermal comfort in naturally ventilated and air-conditioned university classrooms." Indoor and Built Environment 29, no. 6 (November 12, 2019): 851–59. http://dx.doi.org/10.1177/1420326x19887481.
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Maintaining a satisfactory thermal environment is of primary importance, especially when the goal is to maximize learning such as in schools or universities. This paper presents a field study conducted in Milan during summer 2017 in 16 classrooms of Politecnico di Milano, including both naturally ventilated (NV) and air-conditioned (AC) environments. This study asked 985 students to report their thermal perception and their responses were evaluated according to the measured thermal comfort parameters to assess the prediction as given by Fanger and adaptive models, according to ANSI/ASHRAE 55-2017 and EN 15251:2007 standards. Furthermore, an analysis regarding potential effects of gender in comfort perception was performed. The results confirmed the fitness of Fanger’s model for the prediction of occupants’ thermal sensations in AC classrooms with a reasonable accuracy. In NV classrooms, the Adaptive model was proven to be suitable for predicting students’ comfort zone according to ASHRAE 55 Standard, while the adaptive comfort temperatures recommended by EN 15251 were not acceptable for a large number of students. No significant differences in thermal comfort perception between genders have been observed, except for two NV classrooms in which females’ thermal sensation votes had resulted closer to neutrality in comparison to males, who expressed a warmer thermal sensation.
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Popova, Anfisa V., Ksenia R. Safina, Vasily V. Ptushenko, Anastasia V. Stolyarova, Alexander V. Favorov, Alexey D. Neverov, and Georgii A. Bazykin. "Allele-specific nonstationarity in evolution of influenza A virus surface proteins." Proceedings of the National Academy of Sciences 116, no. 42 (October 2, 2019): 21104–12. http://dx.doi.org/10.1073/pnas.1904246116.
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Influenza A virus (IAV) is a major public health problem and a pandemic threat. Its evolution is largely driven by diversifying positive selection so that relative fitness of different amino acid variants changes with time due to changes in herd immunity or genomic context, and novel amino acid variants attain fitness advantage. Here, we hypothesize that diversifying selection also has another manifestation: the fitness associated with a particular amino acid variant should decline with time since its origin, as the herd immunity adapts to it. By tracing the evolution of antigenic sites at IAV surface proteins, we show that an amino acid variant becomes progressively more likely to become replaced by another variant with time since its origin—a phenomenon we call “senescence.” Senescence is particularly pronounced at experimentally validated antigenic sites, implying that it is largely driven by host immunity. By contrast, at internal sites, existing variants become more favorable with time, probably due to arising contingent mutations at other epistatically interacting sites. Our findings reveal a previously undescribed facet of adaptive evolution and suggest approaches for prediction of evolutionary dynamics of pathogens.
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Pietrzak, Barbara, Max Rabus, Maciej Religa, Christian Laforsch, and Maciej J. Dańko. "Phenotypic plasticity of senescence in Daphnia under predation impact: no ageing acceleration when the perceived risk decreases with age." Royal Society Open Science 7, no. 2 (February 2020): 191382. http://dx.doi.org/10.1098/rsos.191382.
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Recognising the nature of the predation risk, and responding to it accurately, is crucial to fitness. Yet, even the most accurate adaptive responses to predation risk usually entail costs, both immediate and lifelong. Rooting in life-history theory, we hypothesize that an animal can perceive the nuances of prey size and age selectivity by the predator and modulate its life history accordingly. We test the prediction that—contrary to the faster or earlier senescence under predation risk that increases with prey size and age—under predation risk that decreases with prey size and age either no senescence acceleration or even its deceleration is to be observed. We use two species of indeterminate growers, small crustaceans of the genus Daphnia , Daphnia Pulex and Daphnia magna , as the model prey, and their respective gape-limited invertebrate predators, a dipteran, midge larva Chaoborus flavicans , and a notostracan, tadpole shrimp Triops cancriformis . We analyse age-specific survival, mortality and fertility rates, and find no senescence acceleration, as predicted. With this study, we complete the picture of the expected non-consumptive phenotypic effects of perceived predation pressure of different age-dependence patterns.
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Nguyen, Thanh, Abbas Khosravi, Douglas Creighton, and Saeid Nahavandi. "Multi-Output Interval Type-2 Fuzzy Logic System for Protein Secondary Structure Prediction." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 23, no. 05 (October 2015): 735–60. http://dx.doi.org/10.1142/s0218488515500324.
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A new multi-output interval type-2 fuzzy logic system (MOIT2FLS) is introduced for protein secondary structure prediction in this paper. Three outputs of the MOIT2FLS correspond to three structure classes including helix, strand (sheet) and coil. Quantitative properties of amino acids are employed to characterize twenty amino acids rather than the widely used computationally expensive binary encoding scheme. Three clustering tasks are performed using the adaptive vector quantization method to construct an equal number of initial rules for each type of secondary structure. Genetic algorithm is applied to optimally adjust parameters of the MOIT2FLS. The genetic fitness function is designed based on the Q3 measure. Experimental results demonstrate the dominance of the proposed approach against the traditional methods that are Chou-Fasman method, Garnier-Osguthorpe-Robson method, and artificial neural network models.
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Stevens, Jeffrey R. "Evolutionary pressures on primate intertemporal choice." Proceedings of the Royal Society B: Biological Sciences 281, no. 1786 (July 7, 2014): 20140499. http://dx.doi.org/10.1098/rspb.2014.0499.
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From finding food to choosing mates, animals must make intertemporal choices that involve fitness benefits available at different times. Species vary dramatically in their willingness to wait for delayed rewards. Why does this variation across species exist? An adaptive approach to intertemporal choice suggests that time preferences should reflect the temporal problems faced in a species's environment. Here, I use phylogenetic regression to test whether allometric factors relating to body size, relative brain size and social group size predict how long 13 primate species will wait in laboratory intertemporal choice tasks. Controlling for phylogeny, a composite allometric factor that includes body mass, absolute brain size, lifespan and home range size predicted waiting times, but relative brain size and social group size did not. These findings support the notion that selective pressures have sculpted intertemporal choices to solve adaptive problems faced by animals. Collecting these types of data across a large number of species can provide key insights into the evolution of decision making and cognition.
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Berg, Elena C., and Alexei A. Maklakov. "Sexes suffer from suboptimal lifespan because of genetic conflict in a seed beetle." Proceedings of the Royal Society B: Biological Sciences 279, no. 1745 (August 22, 2012): 4296–302. http://dx.doi.org/10.1098/rspb.2012.1345.
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Males and females have different routes to successful reproduction, resulting in sex differences in lifespan and age-specific allocation of reproductive effort. The trade-off between current and future reproduction is often resolved differently by males and females, and both sexes can be constrained in their ability to reach their sex-specific optima owing to intralocus sexual conflict. Such genetic antagonism may have profound implications for evolution, but its role in ageing and lifespan remains unresolved. We provide direct experimental evidence that males live longer and females live shorter than necessary to maximize their relative fitness in Callosobruchus maculatus seed beetles. Using artificial selection in a genetically heterogeneous population, we created replicate long-life lines where males lived on average 27 per cent longer than in short-life lines. As predicted by theory, subsequent assays revealed that upward selection on male lifespan decreased relative male fitness but increased relative female fitness compared with downward selection. Thus, we demonstrate that lifespan-extending genes can help one sex while harming the other. Our results show that sexual antagonism constrains adaptive life-history evolution, support a novel way of maintaining genetic variation for lifespan and argue for better integration of sex effects into applied research programmes aimed at lifespan extension.
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Kurenbach, Brigitta, Amy M. Hill, William Godsoe, Sophie van Hamelsveld, and Jack A. Heinemann. "Agrichemicals and antibiotics in combination increase antibiotic resistance evolution." PeerJ 6 (October 12, 2018): e5801. http://dx.doi.org/10.7717/peerj.5801.
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Antibiotic resistance in our pathogens is medicine’s climate change: caused by human activity, and resulting in more extreme outcomes. Resistance emerges in microbial populations when antibiotics act on phenotypic variance within the population. This can arise from either genotypic diversity (resulting from a mutation or horizontal gene transfer), or from differences in gene expression due to environmental variation, referred to as adaptive resistance. Adaptive changes can increase fitness allowing bacteria to survive at higher concentrations of antibiotics. They can also decrease fitness, potentially leading to selection for antibiotic resistance at lower concentrations. There are opportunities for other environmental stressors to promote antibiotic resistance in ways that are hard to predict using conventional assays. Exploiting our previous observation that commonly used herbicides can increase or decrease the minimum inhibitory concentration (MIC) of different antibiotics, we provide the first comprehensive test of the hypothesis that the rate of antibiotic resistance evolution under specified conditions can increase, regardless of whether a herbicide increases or decreases the antibiotic MIC. Short term evolution experiments were used for various herbicide and antibiotic combinations. We found conditions where acquired resistance arises more frequently regardless of whether the exogenous non-antibiotic agent increased or decreased antibiotic effectiveness. This is attributed to the effect of the herbicide on either MIC or the minimum selective concentration (MSC) of a paired antibiotic. The MSC is the lowest concentration of antibiotic at which the fitness of individuals varies because of the antibiotic, and is lower than MIC. Our results suggest that additional environmental factors influencing competition between bacteria could enhance the ability of antibiotics to select antibiotic resistance. Our work demonstrates that bacteria may acquire antibiotic resistance in the environment at rates substantially faster than predicted from laboratory conditions.
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Hill, Geoffrey E. "Reconciling the Mitonuclear Compatibility Species Concept with Rampant Mitochondrial Introgression." Integrative and Comparative Biology 59, no. 4 (April 27, 2019): 912–24. http://dx.doi.org/10.1093/icb/icz019.
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Abstract The mitonuclear compatibility species concept defines a species as a population that is genetically isolated from other populations by uniquely coadapted mitochondrial (mt) and nuclear genes. A key prediction of this hypothesis is that the mt genotype of each species will be functionally distinct and that introgression of mt genomes will be prevented by mitonuclear incompatibilities that arise when heterospecific mt and nuclear genes attempt to cofunction to enable aerobic respiration. It has been proposed, therefore, that the observation of rampant introgression of mt genotypes from one species to another constitutes a strong refutation of the mitonuclear speciation. The displacement of a mt genotype from a nuclear background with which it co-evolved to a foreign nuclear background will necessarily lead to fitness loss due to mitonuclear incompatibilities. Here I consider two potential benefits of mt introgression between species that may, in some cases, overcome fitness losses arising from mitonuclear incompatibilities. First, the introgressed mt genotype may be better adapted to the local environment than the native mt genotype such that higher fitness is achieved through improved adaptation via introgression. Second, if the mitochondria of the recipient taxa carry a high mutational load, then introgression of a foreign, less corrupt mt genome may enable the recipient taxa to escape its mutational load and gain a fitness advantage. Under both scenarios, fitness gains from novel mt genotypes could theoretically compensate for the fitness that is lost via mitonuclear incompatibility. I also consider the role of endosymbionts in non-adaptive rampant introgression of mt genomes. I conclude that rampant introgression is not necessarily evidence against the idea of tight mitonuclear coadaptation or the mitonuclear compatibility species concept. Rampant mt introgression will typically lead to erasure of species but in some cases could lead to hybrid speciation.
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Xiao, Yi, John J. Liu, Yi Hu, and Yingfeng Wang. "Time Series Forecasting Using a Hybrid Adaptive Particle Swarm Optimization and Neural Network Model." Journal of Systems Science and Information 2, no. 4 (August 25, 2014): 335–44. http://dx.doi.org/10.1515/jssi-2014-0335.
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AbstractFor time series forecasting, the problem that we often encounter is how to increase the prediction accuracy as much as possible with the irregular and noise data. This study proposes a novel multilayer feedforward neural network based on the improved particle swarm optimization with adaptive genetic operator (IPSO- MLFN). In the proposed IPSO, inertia weight is dynamically adjusted according to the feedback from particles’ best memories, and acceleration coefficients are controlled by a declining arccosine and an increasing arccosine function. Further, a crossover rate which only depends on generation and does not associate with the individual fitness is designed. Finally, the parameters of MLFN are optimized by IPSO. The empirical results on the container throughput forecast of Shenzhen Port show that forecasts with IPSO-MLFN model are more conservative and credible.
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Markham, A. Catherine, and Laurence R. Gesquiere. "Costs and benefits of group living in primates: an energetic perspective." Philosophical Transactions of the Royal Society B: Biological Sciences 372, no. 1727 (July 3, 2017): 20160239. http://dx.doi.org/10.1098/rstb.2016.0239.
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Group size is a fundamental component of sociality, and has important consequences for an individual's fitness as well as the collective and cooperative behaviours of the group as a whole. This review focuses on how the costs and benefits of group living vary in female primates as a function of group size, with a particular emphasis on how competition within and between groups affects an individual's energetic balance. Because the repercussions of chronic energetic stress can lower an animal's fitness, identifying the predictors of energetic stress has important implications for understanding variation in survivorship and reproductive success within and between populations. Notably, we extend previous literature on this topic by discussing three physiological measures of energetic balance—glucocorticoids, c-peptides and thyroid hormones. Because these hormones can provide clear signals of metabolic states and processes, they present an important complement to field studies of spatial and temporal changes in food availability. We anticipate that their further application will play a crucial role in elucidating the adaptive significance of group size in different social and ecological contexts. This article is part of the themed issue ‘Physiological determinants of social behaviour in animals'.
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Neumann, Rainer, Nicole Ruppel, and Jutta M. Schneider. "Fitness implications of sex-specific catch-up growth in Nephila senegalensis, a spider with extreme reversed SSD." PeerJ 5 (November 15, 2017): e4050. http://dx.doi.org/10.7717/peerj.4050.
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Background Animal growth is often constrained by unfavourable conditions and divergences from optimal body size can be detrimental to an individual’s fitness, particularly in species with determinate growth and a narrow time-frame for life-time reproduction. Growth restriction in early juvenile stages can later be compensated by means of plastic developmental responses, such as adaptive catch-up growth (the compensation of growth deficits through delayed development). Although sex differences regarding the mode and degree of growth compensation have been coherently predicted from sex-specific fitness payoffs, inconsistent results imply a need for further research. We used the African Nephila senegalensis, representing an extreme case of female-biased sexual size dimorphism (SSD), to study fitness implications of sex-specific growth compensation. We predicted effective catch-up growth in early food-restricted females to result in full compensation of growth deficits and a life-time fecundity (LTF) equivalent to unrestricted females. Based on a stronger trade-off between size-related benefits and costs of a delayed maturation, we expected less effective catch-up growth in males. Methods We tracked the development of over one thousand spiders in different feeding treatments, e.g., comprising a fixed period of early low feeding conditions followed by unrestricted feeding conditions, permanent unrestricted feeding conditions, or permanent low feeding conditions as a control. In a second experimental section, we assessed female fitness by measuring LTF in a subset of females. In addition, we tested whether compensatory development affected the reproductive lifespan in both sexes and analysed genotype-by-treatment interactions as a potential cause of variation in life-history traits. Results Both sexes delayed maturation to counteract early growth restriction, but only females achieved full compensation of adult body size. Female catch-up growth resulted in equivalent LTF compared to unrestricted females. We found significant interactions between experimental treatments and sex as well as between treatments and family lineage, suggesting that family-specific responses contribute to the unusually large variation of life-history traits in Nephila spiders. Our feeding treatments had no effect on the reproductive lifespan in either sex. Discussion Our findings are in line with predictions of life-history theory and corroborate strong fecundity selection to result in full female growth compensation. Males showed incomplete growth compensation despite a delayed development, indicating relaxed selection on large size and a stronger trade-off between late maturation and size-related benefits. We suggest that moderate catch-up growth in males is still adaptive as a ‘bet-hedging’ strategy to disperse unavoidable costs between life-history traits affected by early growth restriction (the duration of development and adult size).
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Gao, Tingwei, and Yueting Chai. "Improving Stock Closing Price Prediction Using Recurrent Neural Network and Technical Indicators." Neural Computation 30, no. 10 (October 2018): 2833–54. http://dx.doi.org/10.1162/neco_a_01124.
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This study focuses on predicting stock closing prices by using recurrent neural networks (RNNs). A long short-term memory (LSTM) model, a type of RNN coupled with stock basic trading data and technical indicators, is introduced as a novel method to predict the closing price of the stock market. We realize dimension reduction for the technical indicators by conducting principal component analysis (PCA). To train the model, some optimization strategies are followed, including adaptive moment estimation (Adam) and Glorot uniform initialization. Case studies are conducted on Standard & Poor's 500, NASDAQ, and Apple (AAPL). Plenty of comparison experiments are performed using a series of evaluation criteria to evaluate this model. Accurate prediction of stock market is considered an extremely challenging task because of the noisy environment and high volatility associated with the external factors. We hope the methodology we propose advances the research for analyzing and predicting stock time series. As the results of experiments suggest, the proposed model achieves a good level of fitness.
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Fournier-Level, Alexandre, Emily O. Perry, Jonathan A. Wang, Peter T. Braun, Andrew Migneault, Martha D. Cooper, C. Jessica E. Metcalf, and Johanna Schmitt. "Predicting the evolutionary dynamics of seasonal adaptation to novel climates in Arabidopsis thaliana." Proceedings of the National Academy of Sciences 113, no. 20 (May 2, 2016): E2812—E2821. http://dx.doi.org/10.1073/pnas.1517456113.
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Predicting whether and how populations will adapt to rapid climate change is a critical goal for evolutionary biology. To examine the genetic basis of fitness and predict adaptive evolution in novel climates with seasonal variation, we grew a diverse panel of the annual plant Arabidopsis thaliana (multiparent advanced generation intercross lines) in controlled conditions simulating four climates: a present-day reference climate, an increased-temperature climate, a winter-warming only climate, and a poleward-migration climate with increased photoperiod amplitude. In each climate, four successive seasonal cohorts experienced dynamic daily temperature and photoperiod variation over a year. We measured 12 traits and developed a genomic prediction model for fitness evolution in each seasonal environment. This model was used to simulate evolutionary trajectories of the base population over 50 y in each climate, as well as 100-y scenarios of gradual climate change following adaptation to a reference climate. Patterns of plastic and evolutionary fitness response varied across seasons and climates. The increased-temperature climate promoted genetic divergence of subpopulations across seasons, whereas in the winter-warming and poleward-migration climates, seasonal genetic differentiation was reduced. In silico “resurrection experiments” showed limited evolutionary rescue compared with the plastic response of fitness to seasonal climate change. The genetic basis of adaptation and, consequently, the dynamics of evolutionary change differed qualitatively among scenarios. Populations with fewer founding genotypes and populations with genetic diversity reduced by prior selection adapted less well to novel conditions, demonstrating that adaptation to rapid climate change requires the maintenance of sufficient standing variation.
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Bushell, Francesca, John M. J. Herbert, Thippeswamy H. Sannasiddappa, Daniel Warren, A. Keith Turner, Francesco Falciani, and Peter A. Lund. "Mapping the Transcriptional and Fitness Landscapes of a Pathogenic E. coli Strain: The Effects of Organic Acid Stress under Aerobic and Anaerobic Conditions." Genes 12, no. 1 (December 31, 2020): 53. http://dx.doi.org/10.3390/genes12010053.
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Several methods are available to probe cellular responses to external stresses at the whole genome level. RNAseq can be used to measure changes in expression of all genes following exposure to stress, but gives no information about the contribution of these genes to an organism’s ability to survive the stress. The relative contribution of each non-essential gene in the genome to the fitness of the organism under stress can be obtained using methods that use sequencing to estimate the frequencies of members of a dense transposon library grown under different conditions, for example by transposon-directed insertion sequencing (TraDIS). These two methods thus probe different aspects of the underlying biology of the organism. We were interested to determine the extent to which the data from these two methods converge on related genes and pathways. To do this, we looked at a combination of biologically meaningful stresses. The human gut contains different organic short-chain fatty acids (SCFAs) produced by fermentation of carbon compounds, and Escherichia coli is exposed to these in its passage through the gut. Their effect is likely to depend on both the ambient pH and the level of oxygen present. We, therefore, generated RNAseq and TraDIS data on a uropathogenic E. coli strain grown at either pH 7 or pH 5.5 in the presence or absence of three SCFAs (acetic, propionic and butyric), either aerobically or anaerobically. Our analysis identifies both known and novel pathways as being likely to be important under these conditions. There is no simple correlation between gene expression and fitness, but we found a significant overlap in KEGG pathways that are predicted to be enriched following analysis of the data from the two methods, and the majority of these showed a fitness signature that would be predicted from the gene expression data, assuming expression to be adaptive. Genes which are not in the E. coli core genome were found to be particularly likely to show a positive correlation between level of expression and contribution to fitness.
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Firman, Renée C. "Exposure to high male density causes maternal stress and female-biased sex ratios in a mammal." Proceedings of the Royal Society B: Biological Sciences 287, no. 1926 (May 6, 2020): 20192909. http://dx.doi.org/10.1098/rspb.2019.2909.
Full textAbstract:
A shift from the traditional perspective that maternal stress is invariably costly has instigated recent interest into its adaptive role in offspring sex allocation. Stress generated by social instability has been linked to offspring sex ratio biases that favour the production of female offspring, which converges with the theoretical prediction that mothers in the poor condition are better off investing in daughters rather than sons. However, previous research has failed to disentangle two different processes: the passive consequence of maternal stress on sex-specific mortality and the adaptive effect of maternal stress at the time of conception. Here, I show that exposure to high male density social conditions leads to elevated stress hormone levels and female-biased in utero offspring sex ratios in house mice ( Mus musculus domesticus ), and identify that sex-specific offspring production—not sex-specific mortality—is the mechanism accounting for these sex ratio skews. This outcome reflects the optimal fitness scenario for mothers in a male-dominated environment: the production of daughters, who are guaranteed high mate availability, minimizes male–male competition for their sons. Overall, this study supports the idea that maternal stress has the potential to be adaptive and advances our understanding of how exposure to different social conditions can influence sex allocation in mammals.
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Miner, Brooks E., and Benjamin Kerr. "Adaptation to local ultraviolet radiation conditions among neighbouring Daphnia populations." Proceedings of the Royal Society B: Biological Sciences 278, no. 1710 (October 13, 2010): 1306–13. http://dx.doi.org/10.1098/rspb.2010.1663.
Full textAbstract:
Understanding the historical processes that generated current patterns of phenotypic diversity in nature is particularly challenging in subdivided populations. Populations often exhibit heritable genetic differences that correlate with environmental variables, but the non-independence among neighbouring populations complicates statistical inference of adaptation. To understand the relative influence of adaptive and non-adaptive processes in generating phenotypes requires joint evaluation of genetic and phenotypic divergence in an integrated and statistically appropriate analysis. We investigated phenotypic divergence, population-genetic structure and potential fitness trade-offs in populations of Daphnia melanica inhabiting neighbouring subalpine ponds of widely differing transparency to ultraviolet radiation (UVR). Using a combination of experimental, population-genetic and statistical techniques, we separated the effects of shared population ancestry and environmental variables in predicting phenotypic divergence among populations. We found that native water transparency significantly predicted divergence in phenotypes among populations even after accounting for significant population structure. This result demonstrates that environmental factors such as UVR can at least partially account for phenotypic divergence. However, a lack of evidence for a hypothesized trade-off between UVR tolerance and growth rates in the absence of UVR prevents us from ruling out the possibility that non-adaptive processes are partially responsible for phenotypic differentiation in this system.
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Velicer, Gregory J., Richard E. Lenski, and Lee Kroos. "Rescue of Social Motility Lost during Evolution of Myxococcus xanthus in an Asocial Environment." Journal of Bacteriology 184, no. 10 (May 15, 2002): 2719–27. http://dx.doi.org/10.1128/jb.184.10.2719-2727.2002.
Full textAbstract:
ABSTRACT Replicate populations of the social bacterium Myxococcus xanthus underwent extensive evolutionary adaptation to an asocial selective environment (liquid batch culture). All 12 populations showed partial or complete loss of their social (S) motility function after 1,000 generations of evolution. Mutations in the pil gene cluster (responsible for type IV pilus biogenesis and function) were found to be at least partially responsible for the loss of S motility in the majority of evolved lines. Restoration (partial or complete) of S motility in the evolved lines by genetic complementation with wild-type pil genes positively affected their fruiting body development and sporulation while negatively affecting their competitive fitness in the asocial regime. This genetic tradeoff indicates that mutations in the pil region were adaptive in the asocial selective environment. This finding was confirmed by experiments showing that defined deletions of pil gene regions conferred a competitive advantage under asocial conditions. Moreover, an amino acid substitution in an evolved genotype was located in a region predicted by genetic complementation analysis to bear an adaptive mutation.
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Takou, Margarita, Tuomas Hämälä, Evan M. Koch, Kim A. Steige, Hannes Dittberner, Levi Yant, Mathieu Genete, et al. "Maintenance of Adaptive Dynamics and No Detectable Load in a Range-Edge Outcrossing Plant Population." Molecular Biology and Evolution 38, no. 5 (January 22, 2021): 1820–36. http://dx.doi.org/10.1093/molbev/msaa322.
Full textAbstract:
Abstract During range expansion, edge populations are expected to face increased genetic drift, which in turn can alter and potentially compromise adaptive dynamics, preventing the removal of deleterious mutations and slowing down adaptation. Here, we contrast populations of the European subspecies Arabidopsis lyrata ssp. petraea, which expanded its Northern range after the last glaciation. We document a sharp decline in effective population size in the range-edge population and observe that nonsynonymous variants segregate at higher frequencies. We detect a 4.9% excess of derived nonsynonymous variants per individual in the range-edge population, suggesting an increase of the genomic burden of deleterious mutations. Inference of the fitness effects of mutations and modeling of allele frequencies under the explicit demographic history of each population predicts a depletion of rare deleterious variants in the range-edge population, but an enrichment for fixed ones, consistent with the bottleneck effect. However, the demographic history of the range-edge population predicts a small net decrease in per-individual fitness. Consistent with this prediction, the range-edge population is not impaired in its growth and survival measured in a common garden experiment. We further observe that the allelic diversity at the self-incompatibility locus, which ensures strict outcrossing and evolves under negative frequency-dependent selection, has remained unchanged. Genomic footprints indicative of selective sweeps are broader in the Northern population but not less frequent. We conclude that the outcrossing species A. lyrata ssp. petraea shows a strong resilience to the effect of range expansion.