Isolation-By-Distance-and-Time in a stepping-stone model

Duforet-Frebourg, Nicolas; Slatkin, Montgomery

doi:10.1101/024133

Cited by 11 publications

(23 citation statements)

References 64 publications

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“…Here, we consider IBT as only isolation through time between population states rather than 'isolation by reproductive timing.' IBT, by this definition, arises due to the stochastic sorting of alleles within a population during coalescent processes and is directly tied to the effects of genetic drift (Skoglund et al 2014, Duforet-Frebourg andSlatkin 2016). Thus, under neutral processes on a stationary landscape, populations can diverge through both space and time, potentially leading to 'isolation by distance and time' (IBDT; Duforet-Frebourg and Slatkin 2016).…”

Section: Stationary Landscapes -Spatiotemporal Turnovermentioning

confidence: 99%

“…Thus, under neutral processes on a stationary landscape, populations can diverge through both space and time, potentially leading to 'isolation by distance and time' (IBDT; Duforet-Frebourg and Slatkin 2016). In this scenario, genetic covariance between populations decreases as a function of geographic distance and separation through time (Skoglund et al 2014, Duforet-Frebourg andSlatkin 2016). Just as IBD can be used as the null model for spatial structure, IBT can be used as the null model for temporal structure.…”

Section: Stationary Landscapes -Spatiotemporal Turnovermentioning

confidence: 99%

“…Predicting how species will respond to non-stationary environmental change is a major goal of evolutionary modeling (Hansen et al 2012, Duforet-Frebourg andSlatkin 2016). Scenarios that are non-stationary through time include global climate change, landscape alteration that affects population dynamics or demographics, and the introduction of novel selective regimes, like pests or pathogens.…”

Section: Genetic Variation On Non-stationary Landscapesmentioning

confidence: 99%

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The value of space‐for‐time substitution for studying fine‐scale microevolutionary processes

Wogan

Wang

2017

Ecography

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When the drivers of biological turnover in space are the same as those that drive turnover through time, space can be substituted for time to model how patterns of variation are predicted to change into the future. These space-for-time substitutions are widely used in ecological modeling but have only recently been applied to the study of microevolutionary processes, particularly over relatively fine spatial and temporal scales. Here, we review recent examples that have employed space-for-time substitution to study genetic patterns on stationary and non-stationary landscapes and examine whether space can reliably substitute for time in studies of population divergence, genetic structure, and adaptive evolution. Although there are only a relatively few examples, several recent studies were excellently crafted to provide valuable insights into the conditions governing the validity of space-for-time substitutions applied to population genetic data. We found that, although caution should be taken, spacefor-time substitutions appear valid for studying microevolutionary processes on both stationary and non-stationary landscapes. Further studies can help to evaluate the conditions under which space-for-time substitutions are reliable. When these methods are reliable, they will play an important role in modeling genetic responses to environmental change, population viability on non-stationary landscapes, and patterns of divergence and adaptation.

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Section: Stationary Landscapes -Spatiotemporal Turnovermentioning

confidence: 99%

Section: Stationary Landscapes -Spatiotemporal Turnovermentioning

confidence: 99%

Section: Genetic Variation On Non-stationary Landscapesmentioning

confidence: 99%

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The value of space‐for‐time substitution for studying fine‐scale microevolutionary processes

Wogan

Wang

2017

Ecography

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“…FST and related statistics have been widely used to characterize isolation by distance. Using the principles introduced by Skoglund et al (2014) and Duforet-Frebourg and Slatkin (2016), we will show how pairwise FST between archaic and present-day samples reflects both the distance and time separating samples in equilibrium populations and in non-equilibrium populations after a partial population replacement.…”

Section: Introductionmentioning

confidence: 99%

“…Skoglund et al (2014) developed the coalescent theory of samples of different age and showed that PCA analysis can reveal the time separation of spatially distributed samples. Duforet-Frebourg and Slatkin (2016) extended the classic Kimura-Weiss (1964) analysis of isolation by distance in a stepping-stone model to predict the decrease in identity by descent with increasing spatial and temporal separation of samples. Silva et al (2017) carried out an extensive simulation study that showed the importance of considering geographic structure when testing for population continuity.…”

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confidence: 99%

F_STbetween Archaic and Present-Day Samples

Vecchyo

Slatkin

2018

Preprint

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The increasing abundance of DNA sequences obtained from fossils calls for new population genetics theory that takes account of both the temporal and spatial separation of samples. Here we exploit the relationship between Wright's FST and average coalescence times to develop an analytic theory describing how FST depends on both the distance and time separating pairs of sampled genomes. We apply this theory to several simple models of population history. If there is a time series of samples, partial population replacement creates a discontinuity in pairwise FST values. The magnitude of the discontinuity depends on the extent of replacement. In stepping-stone models, pairwise FST values between archaic and present-day samples reflect both the spatial and temporal separation. At long distances, an isolation by distance pattern dominates. At short distances, the time separation dominates. Analytic predictions fit patterns generated by simulations. We illustrate our results with applications to archaic samples from European human populations.We compare present-day samples with a pair of archaic samples taken before and after a replacement event.. CC-BY 4.0 International license It is made available under a was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint (which . http://dx.doi.org/10.1101/362053 doi: bioRxiv preprint first posted online Jul. 4, 2018; 3 Genomic sequences obtained from fossils provide new information about the history of present-day species. Already, thousands of partial or complete genomic sequences have been obtained from modern humans and their extinct relatives, and DNA sequences from fossils of numerous other species have been obtained as well.Population genetics theory of ancient DNA (aDNA) has focused primarily on the time dimension. Several methods have been developed to test for natural selection and estimate selection coefficients in a time series of samples (Bollback, et al. 2008; Malaspinas, et al. 2012; Terhorst, et al. 2015;Schraiber, et al. 2016). Much less effort has gone into incorporating the spatial dimension. The usual approach to analyzing aDNA is to use methods such as principal components analysis (PCA) and f-statistics that were developed for contemporaneous populations and ignore the ages of the fossils from which sequences are obtained. (Slatkin 2016) There are three papers that have considered the spatial and temporal components of aDNA together. Skoglund et al. (2014) developed the coalescent theory of samples of different age and showed that PCA analysis can reveal the time separation of spatially distributed samples. Duforet-Frebourg and Slatkin (2016) extended the classic Kimura-Weiss (1964) analysis of isolation by distance in a stepping-stone model to predict the decrease in identity by descent with increasing spatial and temporal separation of samples. Silva et al. (2017) carried out an extensive simulation study that showed the importance of consi...

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