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1. Population genomics of the eastern cottonwood ( Populus deltoides )

   https://doi.org/10.1002/ece3.3466  

   Fahrenkrog, Annette M. ; Neves, Leandro G. ; Resende, Jr, Márcio F. R. ; Dervinis, Christopher ; Davenport, Ruth ; Barbazuk, W. Brad ; Kirst, Matias ( October 2017 , Ecology and Evolution)

   Despite its economic importance as a bioenergy crop and key role in riparian ecosystems, little is known about genetic diversity and adaptation of the eastern cottonwood (Populus deltoides). Here, we report the first population genomics study for this species, conducted on a sample of 425 unrelated individuals collected in 13 states of the southeastern United States. The trees were genotyped by targeted resequencing of 18,153 genes and 23,835 intergenic regions, followed by the identification of single nucleotide polymorphisms (SNPs). This naturalP. deltoidespopulation showed low levels of subpopulation differentiation (F_ST = 0.022–0.106), high genetic diversity (θ_W = 0.00100, π = 0.00170), a large effective population size (N_e ≈ 32,900), and low to moderate levels of linkage disequilibrium. Additionally, genomewide scans for selection (Tajima'sD), subpopulation differentiation (X^TX), and environmental association analyses with eleven climate variables carried out with two different methods (LFMMandBAYENV2) identified genes putatively involved in local adaptation. Interestingly, many of these genes were also identified as adaptation candidates in another poplar species,Populus trichocarpa, indicating possible convergent evolution. This study constitutes the first assessment of genetic diversity and local adaptation inP. deltoidesthroughout the southern part of its range, information we expect to be of use to guide management and breeding strategies for this species in future, especially in the face of climate change.

    

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2. Spatial population genomics of the brown rat ( Rattus norvegicus ) in New York City

   https://doi.org/10.1111/mec.14437  

   Combs, Matthew ; Puckett, Emily E. ; Richardson, Jonathan ; Mims, Destiny ; Munshi‐South, Jason ( December 2017 , Molecular Ecology)

   Human commensal species such as rodent pests are often widely distributed across cities and threaten both infrastructure and public health. Spatially explicit population genomic methods provide insights into movements for cryptic pests that drive evolutionary connectivity across multiple spatial scales. We examined spatial patterns of neutral genomewide variation in brown rats (Rattus norvegicus) across Manhattan, New York City (NYC), using 262 samples and 61,401SNPs to understand (i) relatedness among nearby individuals and the extent of spatial genetic structure in a discrete urban landscape; (ii) the geographic origin ofNYCrats, using a large, previously published data set of global rat genotypes; and (iii) heterogeneity in gene flow across the city, particularly deviations from isolation by distance. We found that rats separated by ≤200 m exhibit strong spatial autocorrelation (r = .3,p = .001) and the effects of localized genetic drift extend to a range of 1,400 m. Across Manhattan, rats exhibited a homogeneous population origin from rats that likely invaded from Great Britain. While traditional approaches identified a single evolutionary cluster with clinal structure across Manhattan, recently developed methods (e.g., fineSTRUCTURE,sPCA,EEMS) provided evidence of reduced dispersal across the island's less residential Midtown region resulting in fine‐scale genetic structuring (F_ST = 0.01) and two evolutionary clusters (Uptown and Downtown Manhattan). Thus, while some urban populations of human commensals may appear to be continuously distributed, landscape heterogeneity within cities can drive differences in habitat quality and dispersal, with implications for the spatial distribution of genomic variation, population management and the study of widely distributed pests.

    

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3. Species‐level view of population structure and gene flow for a critically endangered primate ( Varecia variegata )

   https://doi.org/10.1002/ece3.1119  

   Baden, Andrea L. ; Holmes, Sheila M. ; Johnson, Steig E. ; Engberg, Shannon E. ; Louis, Jr, Edward E. ; Bradley, Brenda J. ( June 2014 , Ecology and Evolution)

   Lemurs are among the world's most threatened mammals. The critically endangered black‐and‐white ruffed lemur (Varecia variegata), in particular, has recently experienced rapid population declines due to habitat loss, ecological sensitivities to habitat degradation, and extensive human hunting pressure. Despite this, a recent study indicates that ruffed lemurs retain among the highest levels of genetic diversity for primates. Identifying how this diversity is apportioned and whether gene flow is maintained among remnant populations will help to diagnose and target conservation priorities. We sampled 209 individuals from 19 sites throughout the remainingV. variegatarange. We used 10 polymorphic microsatellite loci and ~550 bp of mtDNAsequence data to evaluate genetic structure and population dynamics, including dispersal patterns and recent population declines. Bayesian cluster analyses identified two distinct genetic clusters, which optimally partitioned data into populations occurring on either side of theMangoro River. Localities north of the Mangoro were characterized by greater genetic diversity, greater gene flow (lower genetic differentiation) and higher mtDNAhaplotype and nucleotide diversity than those in the south. Despite this, genetic differentiation across all sites was high, as indicated by high averageF_ST(0.247) and Φ_ST(0.544), and followed a pattern of isolation‐by‐distance. We use these results to suggest future conservation strategies that include an effort to maintain genetic diversity in the north and restore connectivity in the south. We also note the discordance between patterns of genetic differentiation and current subspecies taxonomy, and encourage a re‐evaluation of conservation management units moving forward.

    

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4. Ecological genomics of local adaptation in Cornus florida L. by genotyping by sequencing

   https://doi.org/10.1002/ece3.2623  

   Pais, Andrew L. ; Whetten, Ross W. ; Xiang, Qiu‐Yun ( December 2016 , Ecology and Evolution)

   Discovering local adaptation, its genetic underpinnings, and environmental drivers is important for conserving forest species. Ecological genomic approaches coupled with next‐generation sequencing are useful means to detect local adaptation and uncover its underlying genetic basis in nonmodel species. We report results from a study on flowering dogwood trees (Cornus florida L.) using genotyping by sequencing (GBS). This species is ecologically important to easternUSforests but is severely threatened by fungal diseases. We analyzed subpopulations in divergent ecological habitats within North Carolina to uncover loci under local selection and associated with environmental–functional traits or disease infection. At this scale, we tested the effect of incorporating additional sequencing before scaling for a broader examination of the entire range. To test for biases ofGBS, we sequenced two similarly sampled libraries independently from six populations of three ecological habitats. We obtained environmental–functional traits for each subpopulation to identify associations with genotypes via latent factor mixed modeling (LFMM) and gradient forests analysis. To test whether heterogeneity of abiotic pressures resulted in genetic differentiation indicative of local adaptation, we evaluatedF_stper locus while accounting for genetic differentiation between coastal subpopulations and Piedmont‐Mountain subpopulations. Of the 54 candidate loci with sufficient evidence of being under selection among both libraries, 28–39 were Arlequin–BayeScanF_stoutliers. ForLFMM, 45 candidates were associated with climate (of 54), 30 were associated with soil properties, and four were associated with plant health. Reanalysis of combined libraries showed that 42 candidate loci still showed evidence of being under selection. We conclude environment‐driven selection on specific loci has resulted in local adaptation in response to potassium deficiencies, temperature, precipitation, and (to a marginal extent) disease. High allele turnover along ecological gradients further supports the adaptive significance of loci speculated to be under selection.

    

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5. A novel quantitative framework for riverscape genetics

   https://doi.org/10.1002/eap.2147  

   White, Shannon L. ; Hanks, Ephraim M. ; Wagner, Tyler ( June 2020 , Ecological Applications)

   Riverscape genetics, which applies concepts in landscape genetics to riverine ecosystems, lack appropriate quantitative methods that address the spatial autocorrelation structure of linear stream networks and account for bidirectional geneflow. To address these challenges, we present a general framework for the design and analysis of riverscape genetic studies. Our framework starts with the estimation of pairwise genetic distance at sample sites and the development of a spatially structured ecological network (SSEN) on which riverscape covariates are measured. We then introduce the novel bidirectional geneflow in riverscapes (BGR) model that uses principles of isolation‐by‐resistance to quantify the effects of environmental covariates on genetic connectivity, with spatial covariance defined using simultaneous autoregressive models on the SSEN and the generalized Wishart distribution to model pairwise distance matrices arising through a random walk model of geneflow. We highlight the utility of this framework in an analysis of riverscape genetics for brook trout (Salvelinus fontinalis) in north central Pennsylvania, USA. Using the fixation index (F_ST) as the measure of genetic distance, we estimated the effects of 12 riverscape covariates on geneflow by evaluating the relative support of eight competing BGR models. We then compared the performance of the top‐ranked BGR model to results obtained from comparable analyses using multiple regression on distance matrices (MRM) and the program STRUCTURE. We found that the BGR model had more power to detect covariate effects, particularly for variables that were only partial barriers to geneflow and/or uncommon in the riverscape, making it more informative for assessing patterns of population connectivity and identifying threats to species conservation. This case study highlights the utility of our modeling framework over other quantitative methods in riverscape genetics, particularly the ability to rigorously test hypotheses about factors that influence geneflow and probabilistically estimate the effect of riverscape covariates, including stream flow direction. This framework is flexible across taxa and riverine networks, is easily executable, and provides intuitive results that can be used to investigate the likely outcomes of current and future management scenarios.

    

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