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1. Detecting deviations from Kingman coalescence using 2-site frequency spectra

   https://doi.org/10.1093/genetics/iyaf023  

   Fenton, Eliot F; Rice, Daniel P; Novembre, John; Desai, Michael M (February 2025, GENETICS)

   Lohse, K (Ed.)

   Abstract Demographic inference methods in population genetics typically assume that the ancestry of a sample can be modeled by the Kingman coalescent. A defining feature of this stochastic process is that it generates genealogies that are binary trees: no more than 2 ancestral lineages may coalesce at the same time. However, this assumption breaks down under several scenarios. For example, pervasive natural selection and extreme variation in offspring number can both generate genealogies with “multiple-merger” events in which more than 2 lineages coalesce instantaneously. Therefore, detecting violations of the Kingman assumptions (e.g. due to multiple mergers) is important both for understanding which forces have shaped the diversity of a population and for avoiding fitting misspecified models to data. Current methods to detect deviations from Kingman coalescence in genomic data rely primarily on the site frequency spectrum (SFS). However, the signatures of some non-Kingman processes (e.g. multiple mergers) in the SFS are also consistent with a Kingman coalescent with a time-varying population size. Here, we present a new statistical test for determining whether the Kingman coalescent with any population size history is consistent with population data. Our approach is based on information contained in the 2-site joint frequency spectrum (2-SFS) for pairs of linked sites, which has a different dependence on the topologies of genealogies than the SFS. Our statistical test is global in the sense that it can detect when the genome-wide genetic diversity is inconsistent with the Kingman model, rather than detecting outlier regions, as in selection scan methods. We validate this test using simulations and then apply it to demonstrate that genomic diversity data from Drosophila melanogaster is inconsistent with the Kingman coalescent. 

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2. Genetic decline and recovery of a demographically rebuilt fishery species

   https://doi.org/10.1111/mec.16697  

   Hoey, Jennifer A.; Able, Kenneth W.; Pinsky, Malin L. (November 2022, Molecular Ecology)

   Abstract The demographic history of a population is important for conservation and evolution, but this history is unknown for many populations. Methods that use genomic data have been developed to infer demography, but they can be challenging to implement and interpret, particularly for large populations. Thus, understanding if and when genetic estimates of demography correspond to true population history is important for assessing the performance of these genetic methods. Here, we used double‐digest restriction‐site associated DNA (ddRAD) sequencing data from archived collections of larval summer flounder (Paralichthys dentatus,n = 279) from three cohorts (1994–1995, 1997–1998 and 2008–2009) along the U.S. East coast to examine how contemporary effective population size and genetic diversity responded to changes in abundance in a natural population. Despite little to no detectable change in genetic diversity, coalescent‐based demographic modelling from site frequency spectra revealed that summer flounder effective population size declined dramatically in the early 1980s. The timing and direction of change corresponded well with the observed decline in spawning stock census abundance in the late 1980s from independent fish surveys. Census abundance subsequently recovered and achieved the prebottleneck size. Effective population size also grew following the bottleneck. Our results for summer flounder demonstrate that genetic sampling and site frequency spectra can be useful for detecting population dynamics, even in species with large effective sizes. 

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3. Pitfalls and windfalls of detecting demographic declines using population genetics in long‐lived species

   https://doi.org/10.1111/eva.13754  

   Clark, Meaghan I; Fitzpatrick, Sarah W; Bradburd, Gideon S (July 2024, Evolutionary Applications)

   Abstract Detecting recent demographic changes is a crucial component of species conservation and management, as many natural populations face declines due to anthropogenic habitat alteration and climate change. Genetic methods allow researchers to detect changes in effective population size (N_e) from sampling at a single timepoint. However, in species with long lifespans, there is a lag between the start of a decline in a population and the resulting decrease in genetic diversity. This lag slows the rate at which diversity is lost, and therefore makes it difficult to detect recent declines using genetic data. However, the genomes of old individuals can provide a window into the past, and can be compared to those of younger individuals, a contrast that may help reveal recent demographic declines. To test whether comparing the genomes of young and old individuals can help infer recent demographic bottlenecks, we use forward‐time, individual‐based simulations with varying mean individual lifespans and extents of generational overlap. We find that age information can be used to aid in the detection of demographic declines when the decline has been severe. When average lifespan is long, comparing young and old individuals from a single timepoint has greater power to detect a recent (within the last 50 years) bottleneck event than comparing individuals sampled at different points in time. Our results demonstrate how longevity and generational overlap can be both a hindrance and a boon to detecting recent demographic declines from population genomic data. 

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4. Inference of Historical Population-Size Changes with Allele-Frequency Data

   https://doi.org/10.1534/g3.119.400854  

   Lynch, Michael; Haubold, Bernhard; Pfaffelhuber, Peter; Maruki, Takahiro (January 2020, G3 Genes|Genomes|Genetics)

   null (Ed.)

   Abstract With up to millions of nearly neutral polymorphisms now being routinely sampled in population-genomic surveys, it is possible to estimate the site-frequency spectrum of such sites with high precision. Each frequency class reflects a mixture of potentially unique demographic histories, which can be revealed using theory for the probability distributions of the starting and ending points of branch segments over all possible coalescence trees. Such distributions are completely independent of past population history, which only influences the segment lengths, providing the basis for estimating average population sizes separating tree-wide coalescence events. The history of population-size change experienced by a sample of polymorphisms can then be dissected in a model-flexible fashion, and extension of this theory allows estimation of the mean and full distribution of long-term effective population sizes and ages of alleles of specific frequencies. Here, we outline the basic theory underlying the conceptual approach, develop and test an efficient statistical procedure for parameter estimation, and apply this to multiple population-genomic datasets for the microcrustacean Daphnia pulex. 

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5. Optimising Sampling Design for Landscape Genomics

   https://doi.org/10.1111/1755-0998.14052  

   Bishop, Anusha P; Terasaki_Hart, Drew E; Wang, Ian J (April 2025, Molecular Ecology Resources)

   ABSTRACT Landscape genomic approaches for detecting genotype‐environment associations (GEA), isolation by distance (IBD) and isolation by environment (IBE) have seen a dramatic increase in use, but there have been few thorough analyses of the influence of sampling strategy on their performance under realistic genomic and environmental conditions. We simulated 24,000 datasets across a range of scenarios with complex population dynamics and realistic landscape structure to evaluate the effects of the spatial distribution and number of samples on common landscape genomics methods. Our results show that common analyses are relatively robust to sampling scheme as long as sampling covers enough environmental and geographic space. We found that for detecting adaptive loci and estimatingIBE, sampling schemes that were explicitly designed to increase coverage of available environmental space matched or outperformed sampling schemes that only considered geographic space. When sampling does not cover adequate geographic and environmental space, such as with transect‐based sampling, we detected fewer adaptive loci and had higher error when estimatingIBDandIBE. We found thatIBDcould be detected with as few as nine sampling sites, while large sample sizes (e.g., greater than 100 individuals) were crucial for detecting adaptive loci andIBE. We also demonstrate that, even with optimal sampling strategies, landscape genomic analyses are highly sensitive to landscape structure and migration—when spatial autocorrelation and migration are weak, commonGEAmethods fail to detect adaptive loci. 

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