Unravelling the invasion history of the Asian tiger mosquito in Europe

Multiple introductions are key features for the establishment and persistence of introduced species. However, little is known about the contribution of genetic admixture to the invasive potential of populations. To address this issue, we studied the recent invasion of the Asian tiger mosquito (Aedes albopictus) in Europe. Combining genome‐wide single nucleotide polymorphisms and historical knowledge using an approximate Bayesian computation framework, we reconstruct the colonization routes and establish the demographic dynamics of invasion. The colonization of Europe involved at least three independent introductions in Albania, North Italy and Central Italy that subsequently acted as dispersal centres throughout Europe. We show that the topology of human transportation networks shaped demographic histories with North Italy and Central Italy being the main dispersal centres in Europe. Introduction modalities conditioned the levels of genetic diversity in invading populations, and genetically diverse and admixed populations promoted more secondary introductions and have spread farther than single‐source invasions. This genomic study provides further crucial insights into a general understanding of the role of genetic diversity promoted by modern trade in driving biological invasions.     

The evolution of microendemism in a reef fish (Hypoplectrus maya)

Marine species tend to have extensive distributions, which are commonly attributed to the dispersal potential provided by planktonic larvae and the rarity of absolute barriers to dispersal in the ocean. Under this paradigm, the occurrence of marine microendemism without geographic isolation in species with planktonic larvae poses a dilemma. The recently described Maya hamlet (Hypoplectrus maya, Serranidae) is exactly such a case, being endemic to a 50‐km segment of the Mesoamerican Barrier Reef System (MBRS). We use whole‐genome analysis to infer the demographic history of the Maya hamlet and contrast it with the sympatric and pan‐Caribbean black (H. nigricans), barred (H. puella) and butter (H. unicolor) hamlets, as well as the allopatric but phenotypically similar blue hamlet (H. gemma). We show that H. maya is indeed a distinct evolutionary lineage, with genomic signatures of inbreeding and a unique demographic history of continuous decrease in effective population size since it diverged from congeners just ~3,000 generations ago. We suggest that this case of microendemism may be driven by the combination of a narrow ecological niche and restrictive oceanographic conditions in the southern MBRS, which is consistent with the occurrence of an unusually high number of marine microendemics in this region. The restricted distribution of the Maya hamlet, its decline in both census and effective population sizes, and the degradation of its habitat place it at risk of extinction. We conclude that the evolution of marine microendemism can be a fast and dynamic process, with extinction possibly occurring before speciation is complete.     

Spatiotemporally explicit demographic modelling supports a joint effect of historical barriers to dispersal and contemporary landscape composition on structuring genomic variation in a red‐listed grasshopper

Inferring the processes underlying spatial patterns of genomic variation is fundamental to understand how organisms interact with landscape heterogeneity and to identify the factors determining species distributional shifts. Here, we use genomic data (restriction site‐associated DNA sequencing) to test biologically informed models representing historical and contemporary demographic scenarios of population connectivity for the Iberian cross‐backed grasshopper Dociostaurus hispanicus, a species with a narrow distribution that currently forms highly fragmented populations. All models incorporated biological aspects of the focal taxon that could hypothetically impact its geographical patterns of genomic variation, including (a) spatial configuration of impassable barriers to dispersal defined by topographic landscapes not occupied by the species; (b) distributional shifts resulting from the interaction between the species bioclimatic envelope and Pleistocene glacial cycles; and (c) contemporary distribution of suitable habitats after extensive land clearing for agriculture. Spatiotemporally explicit simulations under different scenarios considering these aspects and statistical evaluation of competing models within an Approximate Bayesian Computation framework supported spatial configuration of topographic barriers to dispersal and human‐driven habitat fragmentation as the main factors explaining the geographical distribution of genomic variation in the species, with no apparent impact of hypothetical distributional shifts linked to Pleistocene climatic oscillations. Collectively, this study supports that both historical (i.e., topographic barriers) and contemporary (i.e., anthropogenic habitat fragmentation) aspects of landscape composition have shaped major axes of genomic variation in the studied species and emphasizes the potential of model‐based approaches to gain insights into the temporal scale at which different processes impact the demography of natural populations.     

Population genomic evidence for plant glacial survival in Scandinavia

Quaternary glaciations have played a major role in shaping the genetic diversity and distribution of plant species. Strong palaeoecological and genetic evidence supports a postglacial recolonization of most plant species to northern Europe from southern, eastern and even western glacial refugia. Although highly controversial, the existence of small in situ glacial refugia in northern Europe has recently gained molecular support. We used genomic analyses to examine the phylogeography of a species that is critical in this debate. Carex scirpoidea Michx subsp. scirpoidea is a dioecious, amphi‐Atlantic arctic–alpine sedge that is widely distributed in North America, but absent from most of Eurasia, apart from three extremely disjunct populations in Norway, all well within the limits of the Weichselian ice sheet. Range‐wide population sampling and variation at 5,307 single nucleotide polymorphisms show that the three Norwegian populations comprise unique evolutionary lineages divergent from Greenland with high between‐population divergence. The Norwegian populations have low within‐population genetic diversity consistent with having experienced genetic bottlenecks in glacial refugia, and host private alleles that probably accumulated in long‐term isolated populations. Demographic analyses support a single, pre‐Weichselian colonization into Norway from East Greenland, and subsequent divergence of the three populations in separate refugia. Other refugial areas are identified in North‐east Greenland, Minnesota/Michigan, Colorado and Alaska. Admixed populations in British Columbia and West Greenland indicate postglacial contact. Taken together, evidence from this study strongly indicates in situ glacial survival in Scandinavia.     

Noncancer risk assessment of arsenic safe and unsafe water uses: Bayesian estimation on cohort study

Cancer and noncancer risk of arsenic exposure depends on arsenic intake through drinking water and diets. The present study evaluated the probability of noncancer effects of arsenic exposure from drinking water and diets in a cohort of 82 participants in arsenic-endemic rural areas, considering arsenic-safe and arsenic-unsafe water uses for three consecutive years. The risk assessment included the collection of last 24 hours' diet replica and urine of the participants followed by total arsenic analysis of the same. Toxic dose emerging from exposure duration is a nonlinear variable. So, Bayesian estimation of the data for noncancer risk assessment of the variable arsenic consumption was performed. In spite of using arsenic-safe water, we observed arsenic consumption and release. Participants with skin lesions had more arsenic in urine than participants without skin lesions. Future risk for participants without skin lesions was twice due to less arsenic release in urine. For the first time, Bayesian simulation was used to assess noncancer risk on a cohort for a consecutive three-year study. A significant finding was the higher assessed noncancer risk of the participants without skin lesions than the participants with skin lesions.     

Integration of ground survey and remote sensing derived data: Producing robust indicators of habitat extent and condition

The availability of suitable habitat is a key predictor of the changing status of biodiversity. Quantifying habitat availability over large spatial scales is, however, challenging. Although remote sensing techniques have high spatial coverage, there is uncertainty associated with these estimates due to errors in classification. Alternatively, the extent of habitats can be estimated from ground‐based field survey. Financial and logistical constraints mean that on‐the‐ground surveys have much lower coverage, but they can produce much higher quality estimates of habitat extent in the areas that are surveyed. Here, we demonstrate a new combined model which uses both types of data to produce unified national estimates of the extent of four key habitats across Great Britain based on Countryside Survey and Land Cover Map. This approach considers that the true proportion of habitat per km² (Z_i) is unobserved, but both ground survey and remote sensing can be used to estimate Z_i. The model allows the relationship between remote sensing data and Z_i to be spatially biased while ground survey is assumed to be unbiased. Taking a statistical model‐based approach to integrating field survey and remote sensing data allows for information on bias and precision to be captured and propagated such that estimates produced and parameters estimated are robust and interpretable. A simulation study shows that the combined model should perform best when error in the ground survey data is low. We use repeat surveys to parameterize the variance of ground survey data and demonstrate that error in this data source is small. The model produced revised national estimates of broadleaved woodland, arable land, bog, and fen, marsh and swamp extent across Britain in 2007.     

WHAT DO WE KNOW ABOUT THE HERITABILITY OF DEVELOPMENTAL INSTABILITY? ANSWERS FROM A BAYESIAN MODEL

In spite of over half a century of research, little is known about the genetic basis of developmental instability (DI). The estimation of the heritability of DI is seriously hampered by the fact that fluctuating asymmetry-FA, that is, the observable outcome of DI-only poorly reflects DI. This results in an underestimation of the heritability of DI. Current methods transforming heritabilities in FA into those of DI fail to take all sources of variation into account and yield incorrect confidence bands that are usually based on unrealistic assumptions. Therefore, a Bayesian latent variable model is developed and explored. Simulations show that with sample sizes currently applied in empirical studies, extremely wide posterior distributions are obtained and data do not allow to distinguish between high (0.5) and low (0.1) heritabilities of DI at all. Even sample sizes of 5000 result in very wide posteriors in many cases. Furthermore, for smaller samples (250 and 1250), up to 70% of the estimates of the heritability of DI were below the mean expected value because of the high skewness of its distribution. Knowing that in only one study, sample sizes were above 5000, there is a need for larger studies to evaluate the evolutionary potential of DI. Designs with relatively low numbers of sires (1-2% of total number of offspring) appear most efficient. Because such high sample sizes are hard to obtain for many study organisms, more insights are required about how data from different traits can be combined in a single analysis. In addition, new designs and methods, such as QTL analyses and micro-array techniques, should be applied to gain a better understanding of the genetic basis of DI.     

Contrasting patterns of genetic structure and phylogeography in the marine agarophytes Gelidiophycus divaricatus and G. freshwateri (Gelidiales,Rhodophyta) from East Asia

The evolutionary and population demographic history of marine red algae in East Asia is poorly understood. Here, we reconstructed the phylogeographies of two upper intertidal species endemic to East Asia, Gelidiophycus divaricatus and G. freshwateri. Phylogenetic and phylogeographic inferences of 393 mitochondrial cox1, 128 plastid rbcL, and 342 nuclear ITS2 sequences were complemented with ecological niche models. Gelidiophycus divaricatus, a southern species adapted to warm water, is characterized by a high genetic diversity and a strong geographical population structure, characteristic of stable population sizes and sudden reduction to recent expansion. In contrast, G. freshwateri, a northern species adapted to cold temperate conditions, is genetically relatively homogeneous with a shallow population structure resulting from steady population growth and recent equilibrium. The overlap zone of the two species roughly matches summer and winter isotherms, indicating that surface seawater temperature is a key feature influencing species range. Unidirectional genetic introgression was detected at two sites on Jeju Island where G. divaricatus was rare while G. freshwateri was common, suggesting the occurrence of asymmetric natural hybrids, a rarely reported event for rhodophytes. Our results illustrate that Quaternary climate oscillations have left strong imprints on the current day genetic structure and highlight the importance of seawater temperature and sea level change in driving speciation in upper intertidal seaweed species.     

Living with observational data in biological anthropology

The establishment of cause and effect relationships is a fundamental objective of scientific research. Many lines of evidence can be used to make cause–effect inferences. When statistical data are involved, alternative explanations for the statistical relationship need to be ruled out. These include chance (apparent patterns due to random factors), confounding effects (a relationship between two variables because they are each associated with an unmeasured third variable), and sampling bias (effects due to preexisting properties of compared groups). The gold standard for managing these issues is a controlled randomized experiment. In disciplines such as biological anthropology, where controlled experiments are not possible for many research questions, causal inferences are made from observational data. Methods that statisticians recommend for this difficult objective have not been widely adopted in the biological anthropology literature. Issues involved in using statistics to make valid causal inferences from observational data are discussed.