scholarly journals Population Genomics Reveals Gene Flow and Adaptive Signature in Invasive Weed Mikania micrantha

Genes ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 1279
Author(s):  
Xiaoxian Ruan ◽  
Zhen Wang ◽  
Yingjuan Su ◽  
Ting Wang
Keyword(s):  
Gene Flow ◽  
Environmental Variables ◽  
Mixed Model ◽  
Population Genomics ◽  
Southern China ◽  
Defense Responses ◽  
Economic Losses ◽  
Functional Genes ◽  
Invasive Weed ◽  
Mikania Micrantha

A long-standing and unresolved issue in invasion biology concerns the rapid adaptation of invaders to nonindigenous environments. Mikania micrantha is a notorious invasive weed that causes substantial economic losses and negative ecological consequences in southern China. However, the contributions of gene flow, environmental variables, and functional genes, all generally recognized as important factors driving invasive success, to its successful invasion of southern China are not fully understood. Here, we utilized a genotyping-by-sequencing approach to sequence 306 M. micrantha individuals from 21 invasive populations. Based on the obtained genome-wide single nucleotide polymorphism (SNP) data, we observed that all the populations possessed similar high levels of genetic diversity that were not constrained by longitude and latitude. Mikania micrantha was introduced multiple times and subsequently experienced rapid-range expansion with recurrent high gene flow. Using FST outliers, a latent factor mixed model, and the Bayesian method, we identified 38 outlier SNPs associated with environmental variables. The analysis of these outlier SNPs revealed that soil composition, temperature, precipitation, and ecological variables were important determinants affecting the invasive adaptation of M. micrantha. Candidate genes with outlier signatures were related to abiotic stress response. Gene family clustering analysis revealed 683 gene families unique to M. micrantha which may have significant implications for the growth, metabolism, and defense responses of M. micrantha. Forty-one genes showing significant positive selection signatures were identified. These genes mainly function in binding, DNA replication and repair, signature transduction, transcription, and cellular components. Collectively, these findings highlight the contribution of gene flow to the invasion and spread of M. micrantha and indicate the roles of adaptive loci and functional genes in invasive adaptation.

scholarly journals Evaluation of redundancy analysis to identify signatures of local adaptation

2018 ◽  
Author(s):  
Thibaut Capblancq ◽  
Keurcien Luu ◽  
Michael G.B. Blum ◽  
Eric Bazin
Keyword(s):  
Environmental Variables ◽  
Statistical Power ◽  
Redundancy Analysis ◽  
Mixed Model ◽  
Population Genomics ◽  
Environmental Data ◽  
Biological Information ◽  
Adaptive Variation ◽  
Data Set ◽  
Genome Scans

ABSTRACTOrdination is a common tool in ecology that aims at representing complex biological information in a reduced space. In landscape genetics, ordination methods such as principal component analysis (PCA) have been used to detect adaptive variation based on genomic data. Taking advantage of environmental data in addition to genotype data, redundancy analysis (RDA) is another ordination approach that is useful to detect adaptive variation. This paper aims at proposing a test statistic based on RDA to search for loci under selection. We compare redundancy analysis to pcadapt, which is a nonconstrained ordination method, and to a latent factor mixed model (LFMM), which is a univariate genotype-environment association method. Individual-based simulations identify evolutionary scenarios where RDA genome scans have a greater statistical power than genome scans based on PCA. By constraining the analysis with environmental variables, RDA performs better than PCA in identifying adaptive variation when selection gradients are weakly correlated with population structure. Additionally, we show that if RDA and LFMM have a similar power to identify genetic markers associated with environmental variables, the RDA-based procedure has the advantage to identify the main selective gradients as a combination of environmental variables. To give a concrete illustration of RDA in population genomics, we apply this method to the detection of outliers and selective gradients on an SNP data set of Populus trichocarpa (Geraldes et al., 2013). The RDA-based approach identifies the main selective gradient contrasting southern and coastal populations to northern and continental populations in the northwestern American coast.

scholarly journals Development of the first georeferenced map of Rhipicephalus (Boophilus) spp. in Mexico from 1970 to date and prediction of its spatial distribution

2018 ◽  
Vol 13 (1) ◽  
Author(s):  
Yazmin Alcala-Canto ◽  
Juan Antonio Figueroa-Castillo ◽  
Froylán Ibarra-Velarde ◽  
Yolanda Vera-Montenegro ◽  
María Eugenia Cervantes-Valencia ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Environmental Variables ◽  
Control Strategies ◽  
Integrated Control ◽  
Area Under The Curve ◽  
Entropy Method ◽  
Distribution Model ◽  
Economic Losses ◽  
Precipitation Seasonality ◽  
Epidemiological Risk

The tick genus Ripicephalus (Boophilus), particularly R. microplus, is one of the most important ectoparasites that affects livestock health and considered an epidemiological risk because it causes significant economic losses due, mainly, to restrictions in the export of infested animals to several countries. Its spatial distribution has been tied to environmental factors, mainly warm temperatures and high relative humidity. In this work, we integrated a dataset consisting of 5843 records of Rhipicephalus spp., in Mexico covering close to 50 years to know which environmental variables mostly influence this ticks’ distribution. Occurrences were georeferenced using the software DIVA-GIS and the potential current distribution was modelled using the maximum entropy method (Maxent). The algorithm generated a map of high predictive capability (Area under the curve = 0.942), providing the various contribution and permutation importance of the tested variables. Precipitation seasonality, particularly in March, and isothermality were found to be the most significant climate variables in determining the probability of spatial distribution of Rhipicephalus spp. in Mexico (15.7%, 36.0% and 11.1%, respectively). Our findings demonstrate that Rhipicephalus has colonized Mexico widely, including areas characterized by different types of climate. We conclude that the Maxent distribution model using Rhipicephalus records and a set of environmental variables can predict the extent of the tick range in this country, information that should support the development of integrated control strategies.

Population genomics reveals high gene flow in grass rockfish ( Sebastes rastrelliger )

2017 ◽  
Vol 33 ◽  
pp. 57-63 ◽  
Author(s):  
Edith Martinez ◽  
Vincent Buonaccorsi ◽  
John R. Hyde ◽  
Andres Aguilar
Keyword(s):  
Gene Flow ◽  
Population Genomics ◽  
High Gene Flow ◽  
High Gene

scholarly journals Spatial population genomics of a recent mosquito invasion

2020 ◽  
Author(s):  
Thomas L Schmidt ◽  
T. Swan ◽  
Jessica Chung ◽  
Stephan Karl ◽  
Samuel Demok ◽  
...  
Keyword(s):  
Gene Flow ◽  
Population Genomics ◽  
Isolation By Distance ◽  
Spatial Genetic Structure ◽  
Nucleotide Polymorphisms ◽  
Discrete Events ◽  
Long Distance ◽  
Population Genomic ◽  
Asian Tiger ◽  
Study Designs

AbstractPopulation genomic approaches can characterise dispersal across a single generation through to many generations in the past, bridging the gap between individual movement and intergenerational gene flow. These approaches are particularly useful when investigating dispersal in recently altered systems, where they provide a way of inferring long-distance dispersal between newly established populations and their interactions with existing populations. Human-mediated biological invasions represent such altered systems which can be investigated with appropriate study designs and analyses. Here we apply temporally-restricted sampling and a range of population genomic approaches to investigate dispersal in a 2004 invasion of Aedes albopictus (the Asian tiger mosquito) in the Torres Strait Islands (TSI) of Australia. We sampled mosquitoes from 13 TSI villages simultaneously and genotyped 373 mosquitoes at genome-wide single nucleotide polymorphisms (SNPs): 331 from the TSI, 36 from Papua New Guinea (PNG), and 4 incursive mosquitoes detected in uninvaded regions. Within villages, spatial genetic structure varied substantially but overall displayed isolation by distance and a neighbourhood size of 232–577. Close kin dyads revealed recent movement between islands 31–203 km apart, and deep learning inferences showed incursive Ae. albopictus had travelled to uninvaded regions from both adjacent and non-adjacent islands. Private alleles and a coancestry matrix indicated direct gene flow from PNG into nearby islands. Outlier analyses also detected four linked alleles introgressed from PNG, with the alleles surrounding 12 resistance-associated cytochrome P450 genes. By treating dispersal as both an intergenerational process and a set of discrete events, we describe a highly interconnected invasive system.

scholarly journals Speciation in the presence of gene flow: population genomics of closely related and diverging Eucalyptus species

Heredity ◽  
2018 ◽  
Vol 121 (2) ◽  
pp. 126-141 ◽  
Author(s):  
Susan Rutherford ◽  
Maurizio Rossetto ◽  
Jason G. Bragg ◽  
Hannah McPherson ◽  
Doug Benson ◽  
...  
Keyword(s):  
Gene Flow ◽  
Population Genomics ◽  
Eucalyptus Species

scholarly journals Using linear mixed model and dummy variable  model approaches to construct compatible single-tree biomass equations at different scales – A case study for Masson pine in Southern China

2012 ◽  
Vol 58 (No. 3) ◽  
pp. 101-115 ◽  
Author(s):  
L.Y. Fu ◽  
W.S. Zeng ◽  
S.Z. Tang ◽  
R.P. Sharma ◽  
H.K. Li
Keyword(s):  
Mixed Model ◽  
Linear Mixed Model ◽  
Southern China ◽  
Forest Biomass ◽  
Tree Biomass ◽  
Variable Model ◽  
Masson Pine ◽  
Dummy Variable ◽  
Single Tree ◽  
Biomass Models

The estimation of forest biomass is important for practical issues and scientific purposes in forestry. The estimation of forest biomass on a large-scale level would be merely possible with the application of generalized single-tree biomass models. The aboveground biomass data on Masson pine (Pinus massoniana) from nine provinces in southern China were used to develop generalized single-tree biomass models using both linear mixed model and dummy variable model methods. An allometric function requiring only diameter at breast height was used as a base model for this purpose. The results showed that the aboveground biomass estimates of individual trees with identical diameters were different among the forest origins (natural and planted) and geographic regions (provinces). The linear mixed model with random effect parameters and dummy model with site-specific (local) parameters showed better fit and prediction performance than the population average model. The linear mixed model appears more flexible than the dummy variable model for the construction of generalized single-tree biomass models or compatible biomass models at different scales. The linear mixed model method can also be applied to develop other types of generalized single-tree models such as basal area growth and volume models.  

Evaluating the effects of ground-level O3 on rice yield and economic losses in Southern China

2020 ◽  
Vol 267 ◽  
pp. 115694
Author(s):  
Jiachen Cao ◽  
Xuemei Wang ◽  
Hui Zhao ◽  
Mingrui Ma ◽  
Ming Chang
Keyword(s):  
Rice Yield ◽  
Southern China ◽  
Ground Level ◽  
Economic Losses

Allopolyploid Speciation Accompanied by Gene Flow in a Tree Fern

2020 ◽  
Vol 37 (9) ◽  
pp. 2487-2502 ◽  
Author(s):  
Jing Wang ◽  
Shiyong Dong ◽  
Lihua Yang ◽  
Aj Harris ◽  
Harald Schneider ◽  
...  
Keyword(s):  
Gene Flow ◽  
East Asian Monsoon ◽  
Southern China ◽  
Evolutionary Process ◽  
Data Sets ◽  
Hybrid Speciation ◽  
Tree Fern ◽  
Hybrid Species ◽  
Allopolyploid Speciation ◽  
New Perspective

Abstract Hybridization in plants may result in hybrid speciation or introgression and, thus, is now widely understood to be an important mechanism of species diversity on an evolutionary timescale. Hybridization is particularly common in ferns, as is polyploidy, which often results from hybrid crosses. Nevertheless, hybrid speciation as an evolutionary process in fern lineages remains poorly understood. Here, we employ flow cytometry, phylogeny, genomewide single nucleotide polymorphism data sets, and admixture and coalescent modeling to show that the scaly tree fern, Gymnosphaera metteniana is a naturally occurring allotetraploid species derived from hybridization between the diploids, G. denticulata and G. gigantea. Moreover, we detected ongoing gene flow between the hybrid species and its progenitors, and we found that G. gigantea and G. metteniana inhabit distinct niches, whereas climatic niches of G. denticulata and G. metteniana largely overlap. Taken together, these results suggest that either some degree of intrinsic genetic isolation between the hybrid species and its parental progenitors or ecological isolation over short distances may be playing an important role in the evolution of reproductive barriers. Historical climate change may have facilitated the origin of G. metteniana, with the timing of hybridization coinciding with a period of intensification of the East Asian monsoon during the Pliocene and Pleistocene periods in southern China. Our study of allotetraploid G. metteniana represents the first genomic-level documentation of hybrid speciation in scaly tree ferns and, thus, provides a new perspective on evolution in the lineage.

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