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With the genetic health of many plant and animal populations deteriorating due to climate change outpacing adaptation, interventions, such as assisted gene flow (AGF), may provide genetic variation necessary for populations to adapt to climate change. We ran genetic simulations to mimic different AGF scenarios in large populations and measured their outcomes on population-level fitness to determine circumstances in which it is worthwhile to perform AGF. In the absence of inbreeding depression, AGF was beneficial within a few generations only when introduced genotypes had much higher fitness than local individuals and traits affecting fitness were controlled by a few genes of large effect. AGF was harmful over short periods (e.g., first ∼10-20 generations) if there was strong outbreeding depression or introduced deleterious genetic variation. When the adaptive trait was controlled by many loci of small effect, the benefits of AGF took over 10 generations to realize-potentially too long for most climate-related management scenarios. The genomic integrity of the recipient population typically remained intact following AGF; the amount of genetic material from the donor population usually constituted no more of the recipient population's genome than the fraction of the population introduced. Significant genomic turnover (e.g., >50% replacement) only occurred when the selective advantage of the adaptive trait and translocation fraction were extremely high. Our results will be useful when adaptive management is used to maintain the genetic health and productivity of large populations under climate change.

Con el deterioro de la salud genética de muchas poblaciones de plantas y animales debido a la ventaja que le lleva el cambio climático a la adaptación, algunas intervenciones, como el flujo génico asistido (FGA), pueden proporcionar la variación genética necesaria para que las poblaciones se adapten al cambio climático. Simulamos diferentes escenarios de FGA aplicado en poblaciones grandes y medimos los resultados en la aptitud a nivel poblacional para determinar las circunstancias en las que merece la pena realizar FGA. Cuando no hubo depresión endogámica, el FGA produjo un beneficio en pocas generaciones sólo cuando se introdujeron genotipos que tenían una aptitud mucho mayor que los individuos locales y cuando unos cuantos genes de gran efecto controlaron los rasgos que afectaban a la aptitud. El flujo génico asistido fue dañino en periodos cortos (p.ej.: las primeras 10-20 generaciones) si existía una fuerte depresión exogámica o una variación genética deletérea introducida. Cuando muchos loci de pequeño efecto controlaron el rasgo adaptativo, los beneficios del FGA tardaron más de 10 generaciones en aparecer - un tiempo potencialmente muy largo para la mayoría de la gestión relacionada con el clima. La integridad genómica de la población receptora casi siempre permaneció intacta después del FGA; es decir, la cantidad de material genético de la población donante generalmente no constituyó más que la fracción de población introducida en el genoma de la población receptora. La rotación genómica significativa (p.ej.: reemplazos >50%) sólo ocurrió cuando la ventaja selectiva del rasgo adaptativo y la fracción de reubicación fueron extremadamente elevadas. Nuestros resultados serán útiles cuando se use la gestión adaptativa para mantener la salud genética y la productividad de las poblaciones grandes bajo el cambio climático.

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MOTIVATION: fastsimcoal2 extends fastsimcoal, a continuous time coalescent-based genetic simulation program, by enabling the estimation of demographic parameters under very complex scenarios from the site frequency spectrum under a maximum-likelihood framework. RESULTS: Other improvements include multi-threading, handling of population inbreeding, extended input file syntax facilitating the description of complex demographic scenarios, and more efficient simulations of sparsely structured populations and of large chromosomes. AVAILABILITY AND IMPLEMENTATION: fastsimcoal2 is freely available on http://cmpg.unibe.ch/software/fastsimcoal2/. It includes console versions for Linux, Windows and MacOS, additional scripts for the analysis and visualization of simulated and estimated scenarios, as well as a detailed documentation and ready-to-use examples.

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SimBit is a general purpose, high performance forward-in-time population genetics simulator. SimBit can simulate a wide variety of selection scenarios (any selection and dominance coefficients variation, any epistatic interaction, any spatial and temporal changes of selection scenario, etc.), demographic scenarios (any changes in patch sizes, migration rates, realistic demography dependent on fecundity, hard vs. soft selection, exponential vs. logistic growth, gametic or zygotic dispersion, etc.) and mating systems (cloning and selfing rates, hermaphrodites or males and females). SimBit can also track QTLs (with hyperdimensional phenotypes, explicit fitness landscape, plasticity, developmental noise, etc.). Finally, SimBit can simulate multiple species with their ecological relationships. SimBit comes with a R wrapper that simplifies the management of an entire research project from the creation of a grid of parameters and corresponding inputs, running simulations and gathering outputs for analysis. SimBit's performance was extensively benchmarked in comparison to SLiM, Nemo and SFS_CODE, varying population size, recombination rate, mutation rate, and the number of loci. I also reproduced simulations from previous studies, benchmarked QTLs and coalescent tree recording techniques. SimBit was most often the highest performing program with the only notable exception of SLiM outperforming SimBit in scenarios with few loci and low genetic diversity.

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Costs of plasticity are thought to have important physiological and evolutionary consequences. A commonly predicted cost to plasticity is that plastic genotypes are likely to suffer from developmental instability. Adaptive plasticity requires that the developing organism can in some way sense what environment it is in or how well it is performing in that environment. These two information pathways-an "environmental signal" or a "performance signal" that indicates how well a developing phenotype matches the optimum in the current environment-can differ in their consequences for the organism and its evolution. Here, we consider how developmental instability might emerge as a side-effect of these two distinct mechanisms. Because a performance cue allows a regulatory feedback loop connecting a trait to a feedback signal, we hypothesized that plastic genotypes using a performance signal would be more developmentally robust compared to those using a purely environmental signal. Using a numerical model of a network of gene interactions, we show that plasticity comes at a cost of developmental instability when the plastic response is mediated via an environmental signal, but not when it is mediated via a performance signal. We also show that a performance signal mechanism can evolve even in a constant environment, leading to genotypes preadapted for plasticity to novel environments even in populations without a history of environmental heterogeneity.

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Background selection is a process whereby recurrent deleterious mutations cause a decrease in the effective population size and genetic diversity at linked loci. Several authors have suggested that variation in the intensity of background selection could cause variation in FST across the genome, which could confound signals of local adaptation in genome scans. We performed realistic simulations of DNA sequences, using recombination maps from humans and sticklebacks, to investigate how variation in the intensity of background selection affects FST and other statistics of population differentiation in sexual, outcrossing species. We show that, in populations connected by gene flow, Weir and Cockerham's (1984; Evolution, 38, 1358) estimator of FST is largely insensitive to locus-to-locus variation in the intensity of background selection. Unlike FST , however, dXY is negatively correlated with background selection. Moreover, background selection does not greatly affect the false-positive rate in FST outlier studies in populations connected by gene flow. Overall, our study indicates that background selection will not greatly interfere with finding the variants responsible for local adaptation.

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Invasion success of species introduced to novel environments may be facilitated by adaptive evolution and by phenotypic plasticity. Here we investigate the independent and joint contribution of both mechanisms as drivers of invasiveness in the perennial sunflower Helianthus tuberosus. We show that invasive genotypes have multiple origins, and that invasive spread was facilitated by the repeated evolution of extreme values in a single trait, clonality. In line with genetic accommodation theory, we establish that this evolutionary transition occurred by refining a preexisting plastic response of clonality to water availability. Further, we demonstrate that under the non-drought conditions typically experienced by this plant in its introduced range, invasive spread is mediated by hybrid vigour and/or two major additive-effect loci, and that these mechanisms are complementary. Thus, in H. tuberosus, evolution of invasiveness was facilitated by phenotypic plasticity, and involved the use of multiple genetic solutions to achieve the same invasiveness trait.

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The biotic and abiotic factors that facilitate or hinder species range expansions are many and complex. We examine the impact of two genetic processes and their interaction on fitness at expanding range edges: local maladaptation resulting from the presence of an environmental gradient and expansion load resulting from increased genetic drift at the range edge. Results from spatially explicit simulations indicate that the presence of an environmental gradient during range expansion reduces expansion load; conversely, increasing expansion load allows only locally adapted populations to persist at the range edge. Increased maladaptation reduces the speed of range expansion, resulting in less genetic drift at the expanding front and more immigration from the range center, therefore reducing expansion load at the range edge. These results may have ramifications for species being forced to shift their ranges because of climate change or other anthropogenic changes. If rapidly changing climate leads to faster expansion as populations track their shifting climatic optima, populations may suffer increased expansion load beyond previous expectations.

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