Colonization history of Galapagos giant tortoises: Insights from mitogenomes support the progression rule

Galapagos giant tortoises (Chelonoidis spp.) are a group of large, long-lived reptiles that includes 14 species, 11 of which are extant and threatened by human activities and introductions of non-native species. Here, we evaluated the phylogenetic relationships of all extant and two extinct species (Chelonoidis abingdonii from the island of Pinta and Chelonoidis niger from the island of Floreana) using Bayesian and maximum likelihood analysis of complete or nearly complete mitochondrial genomes. We also provide an updated phylogeographic scenario of their colonization of the Galapagos Islands using chrono-phylogenetic and biogeographic approaches. The resulting phylogenetic trees show three major groups of species: one from the southern, central, and western Galapagos Islands; the second from the northwestern islands; and the third group from the northern, central, and eastern Galapagos Islands. The time-calibrated phylogenetic and ancestral area reconstructions generally align with the geologic ages of the islands. The divergence of the Galapagos giant tortoises from their South American ancestor likely occurred in the upper Miocene. Their diversification on the Galapagos adheres to the island progression rule, starting in the Pleistocene with the dispersal of the ancestral form from the two oldest islands (San Cristóbal and Española) to Santa Cruz, Santiago, and Pinta, followed by multiple colonizations from different sources within the archipelago. Our work provides an example of how to reconstruct the history of endangered taxa in spite of extinctions and human-mediated dispersal events and provides a framework for evaluating the contribution of colonization and in situ speciation to the diversity of other Galapagos lineages.     

Congruent population genetic structures and divergence histories in anther‐smut fungi and their host plants Silene italica and the Silene nutans species complex

Study of the congruence of population genetic structure between hosts and pathogens gives important insights into their shared phylogeographical and coevolutionary histories. We studied the population genetic structure of castrating anther‐smut fungi (genus Microbotryum) and of their host plants, the Silene nutans species complex, and the morphologically and genetically closely related Silene italica, which can be found in sympatry. Phylogeographical population genetic structure related to persistence in separate glacial refugia has been recently revealed in the S. nutans plant species complex across Western Europe, identifying several distinct lineages. We genotyped 171 associated plant–pathogen pairs of anther‐smut fungi and their host plant individuals using microsatellite markers and plant chloroplastic single nucleotide polymorphisms. We found clear differentiation between fungal populations parasitizing S. nutans and S. italica plants. The population genetic structure of fungal strains parasitizing the S. nutans plant species complex mirrored the host plant genetic structure, suggesting that the pathogen was isolated in glacial refugia together with its host and/or that it has specialized on the plant genetic lineages. Using random forest approximate Bayesian computation (ABC‐RF), we found that the divergence history of the fungal lineages on S. nutans was congruent with that previously inferred for the host plant and probably occurred with ancient but no recent gene flow. Genome sequences confirmed the genetic structure and the absence of recent gene flow between fungal genetic lineages. Our analyses of individual host–pathogen pairs contribute to a better understanding of co‐evolutionary histories between hosts and pathogens in natural ecosystems, in which such studies remain scarce.     

Genetic diversity,structure, and demography of Pandanus boninensis (Pandanaceae) with sea drifted seeds,endemic to the Ogasawara Islands of Japan: Comparison between young and old islands

Pandanus boninensis, endemic to the Ogasawara Islands, Japan, is distributed on both the older Bonin and younger Volcano Islands. In this study, we conducted population genetic analyses of P. boninensis on these islands to examine the population diversity and structure across old and young islands, to assess potential differences in population demography with island age, and to collect any evidence of migration between old and young islands. We found that the genetic diversity of expressed sequence tag (EST)–based microsatellite (SSR) markers, the nucleotide diversity of nuclear DNA sequences, and the haplotype diversity of chloroplast DNA on young islands were lower than those on old islands. Clustering analyses of EST‐SSR indicated that populations on old islands were strongly diverged from those on young islands. Approximate Bayesian computation analysis of EST‐SSR suggested that population expansion occurred on old islands while population reduction occurred on young islands. We also found evidence of migration among old islands (mostly from south to north), while it appears that there have been very few migration events between old and young islands. These differences could be due to the fact that young islands tend to be geographically isolated and support smaller populations that began a shorter time ago from limited founders. The P. boninensis populations on the Volcano Islands are interesting from an evolutionary perspective as they constitute a classic example of the early stages of progressive colonization on oceanic islands with small effective population sizes and low genetic diversity.     

松栎林天然更新模拟与不确定性分析

森林天然更新的复杂性和不确定性是森林生态系统动态预测中的关键问题。本研究引入贝叶斯技术和全局敏感性分析,构建基于竞争、气候和地形3类因子的秦岭松栎林天然更新模型。备选模型形式以泊松(Poisson)模型、负二项(negative binomial,NB)模型、零膨胀泊松(zero-inflated Poisson,ZIP)模型和零膨胀负二项(zero-inflated negative binomial,ZINB)模型为基础。同时,根据模型参数传递的不确定性量化分析结果,阐释影响森林更新小概率事件的主导因子。结果表明: ZINB模型在油松和锐齿栎更新模拟中均优于其他模型。林分总断面积、光截留、坡位和生长季最低温是影响松栎林中油松天然更新的最关键因子;而林分总断面积、坡向与海拔的组合、年均温和最热季节降水量则是影响松栎林中锐齿栎天然更新的关键因子。油松更新模拟中,各类因子对模型输出的不确定性贡献率从小到大依次为: 竞争因子(25%)＜气候因子(29%)＜地形因子(46%);锐齿栎更新模拟中为: 气候因子(12%)＜竞争因子(24%)＜地形因子(64%)。油松天然更新数量对生长季最低温和最干季节降水量为正响应,对最干季节均温为负响应;锐齿栎天然更新数量对年均温、生长季最低温和最热季节降水量为正响应,对最干季节均温为负响应。基于贝叶斯技术的ZINB模型可以量化森林更新的影响因子,并解释参数传递的不确定性,是预测森林天然更新的有力工具。     

A layover in Europe: Reconstructing the invasion route of asexual lineages of a New Zealand snail to North America

Non‐native invasive species are threatening ecosystems and biodiversity worldwide. High genetic variation is thought to be a critical factor for invasion success. Accordingly, the global invasion of a few clonal lineages of the gastropod Potamopyrgus antipodarum is thus both puzzling and has the potential to help illuminate why some invasions succeed while others fail. Here, we used SNP markers and a geographically broad sampling scheme (N = 1617) including native New Zealand populations and invasive North American and European populations to provide the first widescale population genetic assessment of the relationships between and among native and invasive P. antipodarum. We used a combination of traditional and Bayesian molecular analyses to demonstrate that New Zealand populations harbour very high diversity relative to the invasive populations and are the source of the two main European genetic lineages. One of these two European lineages was in turn the source of at least one of the two main North American genetic clusters of invasive P. antipodarum, located in Lake Ontario. The other widespread North American group had a more complex origin that included the other European lineage and two New Zealand clusters. Altogether, our analyses suggest that just a small handful of clonal lineages of P. antipodarum were responsible for invasion across continents. Our findings provide critical information for prevention of additional invasions and control of existing invasive populations and are of broader relevance towards understanding the establishment and evolution of asexual populations and the forces driving biological invasion.     

Genetic variation in Plethodon cinereus and Plethodon hubrichti from in and around a contact zone

Climate change poses several challenges to biological communities including changes in the frequency of encounters between closely related congeners as a result of range shifts. When climate change leads to increased hybridization, hybrid dysfunction or genetic swamping may increase extinction risk—particularly in range‐restricted species with low vagility. The Peaks of Otter Salamander, Plethodon hubrichti, is a fully terrestrial woodland salamander that is restricted to ~18 km of ridgeline in the mountains of southwestern Virginia, and its range is surrounded by the abundant and widespread Eastern Red‐backed Salamander, Plethodon cinereus. In order to determine whether these two species are hybridizing and how their range limits may be shifting, we assessed variation at eight microsatellite loci and a 1,008 bp region of Cytochrome B in both species at allopatric reference sites and within a contact zone. Our results show that hybridization between P. hubrichti and P. cinereus either does not occur or is very rare. However, we find that diversity and differentiation are substantially higher in the mountaintop endemic P. hubrichti than in the widespread P. cinereus, despite similar movement ability for the two species as assessed by a homing experiment. Furthermore, estimation of divergence times between reference and contact zone populations via approximate Bayesian computation is consistent with the idea that P. cinereus has expanded into the range of P. hubrichti. Given the apparent recent colonization of the contact zone by P. cinereus, future monitoring of P. cinereus range limits should be a priority for the management of P. hubrichti populations.     

Predictor species: Improving assessments of rare species occurrence by modeling environmental co‐responses

Designing an effective conservation strategy requires understanding where rare species are located. Because rare species can be difficult to find, ecologists often identify other species called conservation surrogates that can help inform the distribution of rare species. Species distribution models typically rely on environmental data when predicting the occurrence of species, neglecting the effect of species' co‐occurrences and biotic interactions. Here, we present a new approach that uses Bayesian networks to improve predictions by modeling environmental co‐responses among species. For species from a European peat bog community, our approach consistently performs better than single‐species models and better than conventional multi‐species approaches that include the presence of nontarget species as additional independent variables in regression models. Our approach performs particularly well with rare species and when calibration data are limited. Furthermore, we identify a group of “predictor species” that are relatively common, insensitive to the presence of other species, and can be used to improve occurrence predictions of rare species. Predictor species are distinct from other categories of conservation surrogates such as umbrella or indicator species, which motivates focused data collection of predictor species to enhance conservation practices.     

New advances on the Aurignacian in the central Iberian Mediterranean basin

This study aims to obtain a chronological and cultural framework of the Evolved Aurignacian in the central Iberian Mediterranean basin and find agreement between this framework and other sequences of the Iberian southeast. Over the last few years, there has been remarkable progress in the research of the Evolved Aurignacian sites in the Valencian area, making a review of the main characteristics of the technocomplex on a regional scale necessary. The recent fieldwork carried out in Cova de les Malladetes (Valencia) and in Cova de les Cendres (Alicante) have been key to understanding the lithic, osseous and ornament assemblages ascribed to the Evolved Aurignacian. Several Bayesian modelled ages have been constructed from the large dataset of chronological dates obtained at Malladetes and Cendres, as well as in other sites. The Bayesian models have allowed us to chronologically place the characteristics of the analysed assemblages. The present research supports the importance of the Aurignacian as the first technocomplex of the Upper Palaeolithic in this area of the Iberian Peninsula.     

A Bayes factor approach with informative prior for rare genetic variant analysis from next generation sequencing data

The discovery of rare genetic variants through next generation sequencing is a very challenging issue in the field of human genetics. We propose a novel region‐based statistical approach based on a Bayes Factor (BF) to assess evidence of association between a set of rare variants (RVs) located on the same genomic region and a disease outcome in the context of case‐control design. Marginal likelihoods are computed under the null and alternative hypotheses assuming a binomial distribution for the RV count in the region and a beta or mixture of Dirac and beta prior distribution for the probability of RV. We derive the theoretical null distribution of the BF under our prior setting and show that a Bayesian control of the false Discovery Rate can be obtained for genome‐wide inference. Informative priors are introduced using prior evidence of association from a Kolmogorov‐Smirnov test statistic. We use our simulation program, sim1000G, to generate RV data similar to the 1000 genomes sequencing project. Our simulation studies showed that the new BF statistic outperforms standard methods (SKAT, SKAT‐O, Burden test) in case‐control studies with moderate sample sizes and is equivalent to them under large sample size scenarios. Our real data application to a lung cancer case‐control study found enrichment for RVs in known and novel cancer genes. It also suggests that using the BF with informative prior improves the overall gene discovery compared to the BF with noninformative prior.     

Bayesian modeling of skewed X inactivation in genetically diverse mice identifies a novel Xce allele associated with copy number changes

Female mammals are functional mosaics of their parental X-linked gene expression due to X chromosome inactivation (XCI). This process inactivates one copy of the X chromosome in each cell during embryogenesis and that state is maintained clonally through mitosis. In mice, the choice of which parental X chromosome remains active is determined by the X chromosome controlling element (Xce), which has been mapped to a 176-kb candidate interval. A series of functional Xce alleles has been characterized or inferred for classical inbred strains based on biased, or skewed, inactivation of the parental X chromosomes in crosses between strains. To further explore the function structure basis and location of the Xce, we measured allele-specific expression of X-linked genes in a large population of F1 females generated from Collaborative Cross (CC) strains. Using published sequence data and applying a Bayesian “Pólya urn” model of XCI skew, we report two major findings. First, inter-individual variability in XCI suggests mouse epiblasts contain on average 20–30 cells contributing to brain. Second, CC founder strain NOD/ShiLtJ has a novel and unique functional allele, Xce^g, that is the weakest in the Xce allelic series. Despite phylogenetic analysis confirming that NOD/ShiLtJ carries a haplotype almost identical to the well-characterized C57BL/6J (Xce^b), we observed unexpected patterns of XCI skewing in females carrying the NOD/ShiLtJ haplotype within the Xce. Copy number variation is common at the Xce locus and we conclude that the observed allelic series is a product of independent and recurring duplications shared between weak Xce alleles.