Genomics and conservation: Guidance from training to analyses and applications.

Environmental change is intensifying the biodiversity crisis and threatening species across the tree of life. Conservation genomics can help inform conservation actions and slow biodiversity loss. However, more training, appropriate use of novel genomic methods and communication with managers are needed. Here, we review practical guidance to improve applied conservation genomics. We share insights aimed at ensuring effectiveness of conservation actions around three themes: (1) improving pedagogy and training in conservation genomics including for online global audiences, (2) conducting rigorous population genomic analyses properly considering theory, marker types and data interpretation and (3) facilitating communication and collaboration between managers and researchers. We aim to update students and professionals and expand their conservation toolkit with genomic principles and recent approaches for conserving and managing biodiversity. The biodiversity crisis is a global problem and, as such, requires international involvement, training, collaboration and frequent reviews of the literature and workshops as we do here.

Genetic swamping of the critically endangered Scottish wildcat was recent and accelerated by disease.

The European wildcat population in Scotland is considered critically endangered as a result of hybridization with introduced domestic cats,1,2 though the time frame over which this gene flow has taken place is unknown. Here, using genome data from modern, museum, and ancient samples, we reconstructed the trajectory and dated the decline of the local wildcat population from viable to severely hybridized. We demonstrate that although domestic cats have been present in Britain for over 2,000 years,3 the onset of hybridization was only within the last 70 years. Our analyses reveal that the domestic ancestry present in modern wildcats is markedly over-represented in many parts of the genome, including the major histocompatibility complex (MHC). We hypothesize that introgression provides wildcats with protection against diseases harbored and introduced by domestic cats, and that this selection contributes to maladaptive genetic swamping through linkage drag. Using the case of the Scottish wildcat, we demonstrate the importance of local ancestry estimates to both understand the impacts of hybridization in wild populations and support conservation efforts to mitigate the consequences of anthropogenic and environmental change.

Rapid homoploid hybrid speciation in British gardens: The origin of Oxford ragwort (Senecio squalidus).

Hybridisation can lead to homoploid hybrid speciation, i.e., the origin of new species without change in chromosome number between parents and offspring. Central to homoploid hybrid speciation is the role of hybridisation in the establishment of reproductive isolation between the hybrid and the parental species in the early stages of speciation, when typically all species occur at least partly in sympatry. In this work we analyse genome-wide polymorphism data obtained by transcriptome sequencing of the British hybrid species Oxford ragwort (Senecio squalidus, Asteraceae), its two Italian parental species (S. aethnensis and S. chrysanthemifolius) and their naturally occurring hybrids on Mt Etna (Italy). We show that Oxford ragwort most likely originated from de novo hybridisation between its two Italian parental species whilst they were in cultivation in British gardens at the turn of the 18th century. Reproductive isolation between the new hybrid species and its parental species probably resulted from inheritance of genetic incompatibilities between the two parental species and subsequent ecological segregation - both of which have been shown in previous studies. Our results imply that S. squalidus meets the most stringent criteria set forth to identify homoploid hybrid speciation, and call attention to the creative role of hybridisation in responding to novel environmental conditions.

Performance of A Priori and A Posteriori Calibration Strategies in Divergence Time Estimation.

Relaxed molecular clock methods allow the use of genomic data to estimate divergence times across the tree of life. This is most commonly achieved in Bayesian analyses where the molecular clock is calibrated a priori through the integration of fossil information. Alternatively, fossil calibrations can be used a posteriori, to transform previously estimated relative divergence times that were inferred without considering fossil information, into absolute divergence times. However, as branch length is the product of the rate of evolution and the duration in time of the considered branch, the extent to which a posteriori calibrated, relative divergence time methods can disambiguate time and rate, is unclear. Here, we use forward evolutionary simulations and compare a priori and a posteriori calibration strategies using different molecular clock methods and models. Specifically, we compare three Bayesian methods, the strict clock, uncorrelated clock and autocorrelated clock, and the non-Bayesian algorithm implemented in RelTime. We simulate phylogenies with multiple, independent substitution rate changes and show that correct timescales cannot be inferred without the use of calibrations. Under our simulation conditions, a posteriori calibration strategies almost invariably inferred incorrect rate changes and divergence times. The a priori integration of fossil calibrations is fundamental in these cases to improve the accuracy of the estimated divergence times. Relative divergence times and absolute timescales derived by calibrating relative timescales to geological time a posteriori appear to be less reliable than a priori calibrated, timescales.

Climate-induced phenology shifts linked to range expansions in species with multiple reproductive cycles per year.

Advances in phenology (the annual timing of species' life-cycles) in response to climate change are generally viewed as bioindicators of climate change, but have not been considered as predictors of range expansions. Here, we show that phenology advances combine with the number of reproductive cycles per year (voltinism) to shape abundance and distribution trends in 130 species of British Lepidoptera, in response to ~0.5 °C spring-temperature warming between 1995 and 2014. Early adult emergence in warm years resulted in increased within- and between-year population growth for species with multiple reproductive cycles per year (n = 39 multivoltine species). By contrast, early emergence had neutral or negative consequences for species with a single annual reproductive cycle (n = 91 univoltine species), depending on habitat specialisation. We conclude that phenology advances facilitate polewards range expansions in species exhibiting plasticity for both phenology and voltinism, but may inhibit expansion by less flexible species.

Looking for Local Adaptation: Convergent Microevolution in Aleppo Pine (Pinus halepensis).

Finding outlier loci underlying local adaptation is challenging and is best approached by suitable sampling design and rigorous method selection. In this study, we aimed to detect outlier loci (single nucleotide polymorphisms, SNPs) at the local scale by using Aleppo pine (Pinus halepensis), a drought resistant conifer that has colonized many habitats in the Mediterranean Basin, as the model species. We used a nested sampling approach that considered replicated altitudinal gradients for three contrasting sites. We genotyped samples at 294 SNPs located in genomic regions selected to maximize outlier detection. We then applied three different statistical methodologies-Two Bayesian outlier methods and one latent factor principal component method-To identify outlier loci. No SNP was an outlier for all three methods, while eight SNPs were detected by at least two methods and 17 were detected only by one method. From the intersection of outlier SNPs, only one presented an allelic frequency pattern associated with the elevational gradient across the three sites. In a context of multiple populations under similar selective pressures, our results underline the need for careful examination of outliers detected in genomic scans before considering them as candidates for convergent adaptation.

CodABC: a computational framework to coestimate recombination, substitution, and molecular adaptation rates by approximate Bayesian computation.

The estimation of substitution and recombination rates can provide important insights into the molecular evolution of protein-coding sequences. Here, we present a new computational framework, called "CodABC," to jointly estimate recombination, substitution and synonymous and nonsynonymous rates from coding data. CodABC uses approximate Bayesian computation with and without regression adjustment and implements a variety of codon models, intracodon recombination, and longitudinal sampling. CodABC can provide accurate joint parameter estimates from recombining coding sequences, often outperforming maximum-likelihood methods based on more approximate models. In addition, CodABC allows for the inclusion of several nuisance parameters such as those representing codon frequencies, transition matrices, heterogeneity across sites or invariable sites. CodABC is freely available from http://code.google.com/p/codabc/, includes a GUI, extensive documentation and ready-to-use examples, and can run in parallel on multicore machines.

Detecting and measuring selection from gene frequency data.

The recent advent of high-throughput sequencing and genotyping technologies makes it possible to produce, easily and cost effectively, large amounts of detailed data on the genotype composition of populations. Detecting locus-specific effects may help identify those genes that have been, or are currently, targeted by natural selection. How best to identify these selected regions, loci, or single nucleotides remains a challenging issue. Here, we introduce a new model-based method, called SelEstim, to distinguish putative selected polymorphisms from the background of neutral (or nearly neutral) ones and to estimate the intensity of selection at the former. The underlying population genetic model is a diffusion approximation for the distribution of allele frequency in a population subdivided into a number of demes that exchange migrants. We use a Markov chain Monte Carlo algorithm for sampling from the joint posterior distribution of the model parameters, in a hierarchical Bayesian framework. We present evidence from stochastic simulations, which demonstrates the good power of SelEstim to identify loci targeted by selection and to estimate the strength of selection acting on these loci, within each deme. We also reanalyze a subset of SNP data from the Stanford HGDP-CEPH Human Genome Diversity Cell Line Panel to illustrate the performance of SelEstim on real data. In agreement with previous studies, our analyses point to a very strong signal of positive selection upstream of the LCT gene, which encodes for the enzyme lactase-phlorizin hydrolase and is associated with adult-type hypolactasia. The geographical distribution of the strength of positive selection across the Old World matches the interpolated map of lactase persistence phenotype frequencies, with the strongest selection coefficients in Europe and in the Indus Valley.

A novel approach for choosing summary statistics in approximate Bayesian computation.

The choice of summary statistics is a crucial step in approximate Bayesian computation (ABC). Since statistics are often not sufficient, this choice involves a trade-off between loss of information and reduction of dimensionality. The latter may increase the efficiency of ABC. Here, we propose an approach for choosing summary statistics based on boosting, a technique from the machine-learning literature. We consider different types of boosting and compare them to partial least-squares regression as an alternative. To mitigate the lack of sufficiency, we also propose an approach for choosing summary statistics locally, in the putative neighborhood of the true parameter value. We study a demographic model motivated by the reintroduction of Alpine ibex (Capra ibex) into the Swiss Alps. The parameters of interest are the mean and standard deviation across microsatellites of the scaled ancestral mutation rate (θ(anc) = 4N(e)u) and the proportion of males obtaining access to matings per breeding season (ω). By simulation, we assess the properties of the posterior distribution obtained with the various methods. According to our criteria, ABC with summary statistics chosen locally via boosting with the L(2)-loss performs best. Applying that method to the ibex data, we estimate θ(anc)≈ 1.288 and find that most of the variation across loci of the ancestral mutation rate u is between 7.7 × 10(-4) and 3.5 × 10(-3) per locus per generation. The proportion of males with access to matings is estimated as ω≈ 0.21, which is in good agreement with recent independent estimates.

Mcheza: a workbench to detect selection using dominant markers.

MOTIVATION: Dominant markers (DArTs and AFLPs) are commonly used for genetic analysis in the fields of evolutionary genetics, ecology and conservation of genetic resources. The recent prominence of these markers has coincided with renewed interest in detecting the effects of local selection and adaptation at the level of the genome. RESULTS: We present Mcheza, an application for detecting loci under selection based on a well-evaluated F(ST)-outlier method. The application allows robust estimates to be made of model parameters (e.g. genome-wide average, neutral F(ST)), provides data import and export functions, iterative contour smoothing and generation of graphics in an easy to use graphical user interface with a computation engine that supports multicore processors for enhanced performance. Mcheza also provides functionality to mitigate common analytical errors when scanning for loci under selection. AVAILABILITY: Mcheza is freely available under GPL version 3 from http://popgen.eu/soft/mcheza.

Multiple approaches to detect outliers in a genome scan for selection in ocellated lizards (Lacerta lepida) along an environmental gradient.

Identification of loci with adaptive importance is a key step to understand the speciation process in natural populations, because those loci are responsible for phenotypic variation that affects fitness in different environments. We conducted an AFLP genome scan in populations of ocellated lizards (Lacerta lepida) to search for candidate loci influenced by selection along an environmental gradient in the Iberian Peninsula. This gradient is strongly influenced by climatic variables, and two subspecies can be recognized at the opposite extremes: L. lepida iberica in the northwest and L. lepida nevadensis in the southeast. Both subspecies show substantial morphological differences that may be involved in their local adaptation to the climatic extremes. To investigate how the use of a particular outlier detection method can influence the results, a frequentist method, DFDIST, and a Bayesian method, BayeScan, were used to search for outliers influenced by selection. Additionally, the spatial analysis method was used to test for associations of AFLP marker band frequencies with 54 climatic variables by logistic regression. Results obtained with each method highlight differences in their sensitivity. DFDIST and BayeScan detected a similar proportion of outliers (3-4%), but only a few loci were simultaneously detected by both methods. Several loci detected as outliers were also associated with temperature, insolation or precipitation according to spatial analysis method. These results are in accordance with reported data in the literature about morphological and life-history variation of L. lepida subspecies along the environmental gradient.

The confounding effects of population structure, genetic diversity and the sampling scheme on the detection and quantification of population size changes.

The idea that molecular data should contain information on the recent evolutionary history of populations is rather old. However, much of the work carried out today owes to the work of the statisticians and theoreticians who demonstrated that it was possible to detect departures from equilibrium conditions (e.g., panmictic population/mutation-drift equilibrium) and interpret them in terms of deviations from neutrality or stationarity. During the last 20 years the detection of population size changes has usually been carried out under the assumption that samples were obtained from populations that can be approximated by a Wright-Fisher model (i.e., assuming panmixia, demographic stationarity, etc.). However, natural populations are usually part of spatial networks and are interconnected through gene flow. Here we simulated genetic data at mutation and migration-drift equilibrium under an n-island and a stepping-stone model. The simulated populations were thus stationary and not subject to any population size change. We varied the level of gene flow between populations and the scaled mutation rate. We also used several sampling schemes. We then analyzed the simulated samples using the Bayesian method implemented in MSVAR, the Markov Chain Monte Carlo simulation program, to detect and quantify putative population size changes using microsatellite data. Our results show that all three factors (genetic differentiation/gene flow, genetic diversity, and the sampling scheme) play a role in generating false bottleneck signals. We also suggest an ad hoc method to counter this effect. The confounding effect of population structure and of the sampling scheme has practical implications for many conservation studies. Indeed, if population structure is creating "spurious" bottleneck signals, the interpretation of bottleneck signals from genetic data might be less straightforward than it would seem, and several studies may have overestimated or incorrectly detected bottlenecks in endangered species.

Evaluating nested clade phylogeographic analysis under models of restricted gene flow.

Nested clade phylogeographic analysis (NCPA) is a widely used method that aims to identify past demographic events that have shaped the history of a population. In an earlier study, NCPA has been fully automated, allowing it to be tested with simulated data sets generated under a null model in which samples simulated from a panmictic population are geographically distributed. It was noted that NCPA was prone to inferring false positives, corroborating earlier findings. The present study aims to evaluate both single-locus and multilocus NCPA under the scenario of restricted gene flow among spatially distributed populations. We have developed a new program, ANeCA-ML, which implements multilocus NCPA. Data were simulated under 3 models of gene flow: a stepping stone model, an island model, and a stepping stone model with some long-distance dispersal. Results indicate that single-locus NCPA tends to give a high frequency of false positives, but, unlike the random-mating scenario presented previously, inferences are not limited to restricted gene flow with isolation by distance or contiguous range expansion. The proportion of single-locus data sets that contained false inferences was 76% for the panmictic case, 87% for the stepping stone model, 79% for the stepping stone model with long-distance dispersal, and more than 99% for the island model. The frequency of inferences is inversely related to the amount of gene flow between demes. We performed multilocus NCPA by grouping the simulated loci into data sets of 5 loci. The false-positive rate was reduced in multilocus NCPA for some inferences but remained high for others. The proportion of multilocus data sets that contained false inferences was 17% for the panmictic case, 30% for the stepping stone model, 4% for the stepping stone model with long-distance dispersal, and 54% for the island model. Multilocus NCPA reduces the false-positive rate by restricting the sensitivity of the method but does not appear to increase the accuracy of the approach. Three classical tests-the analysis of molecular variance method, Fu's Fs, and the Mantel test-show that there is information in the data that gives rise to explicable results using these standard approaches. In conclusion, for the scenarios that we have examined, our simulation study suggests that the NCPA method is unreliable and its inferences may be misleading. We suggest that the NCPA method should not be used without objective simulation-based testing by independent researchers.

Likelihood-free inference of population structure and local adaptation in a Bayesian hierarchical model.

We address the problem of finding evidence of natural selection from genetic data, accounting for the confounding effects of demographic history. In the absence of natural selection, gene genealogies should all be sampled from the same underlying distribution, often approximated by a coalescent model. Selection at a particular locus will lead to a modified genealogy, and this motivates a number of recent approaches for detecting the effects of natural selection in the genome as "outliers" under some models. The demographic history of a population affects the sampling distribution of genealogies, and therefore the observed genotypes and the classification of outliers. Since we cannot see genealogies directly, we have to infer them from the observed data under some model of mutation and demography. Thus the accuracy of an outlier-based approach depends to a greater or a lesser extent on the uncertainty about the demographic and mutational model. A natural modeling framework for this type of problem is provided by Bayesian hierarchical models, in which parameters, such as mutation rates and selection coefficients, are allowed to vary across loci. It has proved quite difficult computationally to implement fully probabilistic genealogical models with complex demographies, and this has motivated the development of approximations such as approximate Bayesian computation (ABC). In ABC the data are compressed into summary statistics, and computation of the likelihood function is replaced by simulation of data under the model. In a hierarchical setting one may be interested both in hyperparameters and parameters, and there may be very many of the latter--for example, in a genetic model, these may be parameters describing each of many loci or populations. This poses a problem for ABC in that one then requires summary statistics for each locus, which, if used naively, leads to a consequent difficulty in conditional density estimation. We develop a general method for applying ABC to Bayesian hierarchical models, and we apply it to detect microsatellite loci influenced by local selection. We demonstrate using receiver operating characteristic (ROC) analysis that this approach has comparable performance to a full-likelihood method and outperforms it when mutation rates are variable across loci.

Combining genetic, historical and geographical data to reconstruct the dynamics of bioinvasions: application to the cane toad Bufo marinus.

We developed a spatially explicit model of a bioinvasion and used an approximate Bayesian computation (ABC) framework to make various inferences from a combination of genetic (microsatellite genotypes), historical (first observation dates) and geographical (spatial coordinates of introduction and sampled sites) information. Our method aims to discriminate between alternative introduction scenarios and to estimate posterior densities of demographically relevant parameters of the invasive process. The performance of our landscape-ABC method is assessed using simulated data sets differing in their information content (genetic and/or historical data). We apply our methodology to the recent introduction and spatial expansion of the cane toad, Bufo marinus, in northern Australia. We find that, at least in the context of cane toad invasion, historical data are more informative than genetic data for discriminating between introduction scenarios. However, the combination of historical and genetic data provides the most accurate estimates of demographic parameters. For the cane toad, we find some evidence for a strong bottleneck prior to introduction, a small initial number of founder individuals (about 15), a large population growth rate (about 400% per generation), a standard deviation of dispersal distance of 19 km per generation and a high invasion speed at equilibrium (50 km per year). Our approach strengthens the application of the ABC method to the field of bioinvasion by allowing statistical inferences to be made on the introduction and the spatial expansion dynamics of invasive species using a combination of various relevant sources of information.

Saving Darwin's muse: evolutionary genetics for the recovery of the Floreana mockingbird.

The distribution of mockingbird species among the Galápagos Islands prompted Charles Darwin to question, for the first time in writing, the 'stability of species'. Some 50 years after Darwin's visit, however, the endemic Floreana mockingbird (Mimus trifasciatus) had become extinct on Floreana Island and, today, only two small populations survive on two satellite islets. As Darwin noted, rarity often precedes extinction. To avert extinction, plans are being developed to reintroduce M. trifasciatus to Floreana. Here, we integrate evolutionary thinking and conservation practice using coalescent analyses and genetic data from contemporary and museum samples, including two collected by Darwin and Robert Fitzroy on Floreana in 1835. Our microsatellite results show substantial differentiation between the two extant populations, but our coalescence-based modelling does not indicate long, independent evolutionary histories. One of the populations is highly inbred, but both harbour unique alleles present on Floreana in 1835, suggesting that birds from both islets should be used to establish a single, mixed population on Floreana. Thus, Darwin's mockingbird specimens not only revealed to him a level of variation that suggested speciation following geographical isolation but also, more than 170 years later, return important information to their place of origin for the conservation of their conspecifics.

In defence of model-based inference in phylogeography.

Recent papers have promoted the view that model-based methods in general, and those based on Approximate Bayesian Computation (ABC) in particular, are flawed in a number of ways, and are therefore inappropriate for the analysis of phylogeographic data. These papers further argue that Nested Clade Phylogeographic Analysis (NCPA) offers the best approach in statistical phylogeography. In order to remove the confusion and misconceptions introduced by these papers, we justify and explain the reasoning behind model-based inference. We argue that ABC is a statistically valid approach, alongside other computational statistical techniques that have been successfully used to infer parameters and compare models in population genetics. We also examine the NCPA method and highlight numerous deficiencies, either when used with single or multiple loci. We further show that the ages of clades are carelessly used to infer ages of demographic events, that these ages are estimated under a simple model of panmixia and population stationarity but are then used under different and unspecified models to test hypotheses, a usage the invalidates these testing procedures. We conclude by encouraging researchers to study and use model-based inference in population genetics.

Rapid radiation in spiny lobsters (Palinurus spp) as revealed by classic and ABC methods using mtDNA and microsatellite data.

BACKGROUND: Molecular tools may help to uncover closely related and still diverging species from a wide variety of taxa and provide insight into the mechanisms, pace and geography of marine speciation. There is a certain controversy on the phylogeography and speciation modes of species-groups with an Eastern Atlantic-Western Indian Ocean distribution, with previous studies suggesting that older events (Miocene) and/or more recent (Pleistocene) oceanographic processes could have influenced the phylogeny of marine taxa. The spiny lobster genus Palinurus allows for testing among speciation hypotheses, since it has a particular distribution with two groups of three species each in the Northeastern Atlantic (P. elephas, P. mauritanicus and P. charlestoni) and Southeastern Atlantic and Southwestern Indian Oceans (P. gilchristi, P. delagoae and P. barbarae). In the present study, we obtain a more complete understanding of the phylogenetic relationships among these species through a combined dataset with both nuclear and mitochondrial markers, by testing alternative hypotheses on both the mutation rate and tree topology under the recently developed approximate Bayesian computation (ABC) methods. RESULTS: Our analyses support a North-to-South speciation pattern in Palinurus with all the South-African species forming a monophyletic clade nested within the Northern Hemisphere species. Coalescent-based ABC methods allowed us to reject the previously proposed hypothesis of a Middle Miocene speciation event related with the closure of the Tethyan Seaway. Instead, divergence times obtained for Palinurus species using the combined mtDNA-microsatellite dataset and standard mutation rates for mtDNA agree with known glaciation-related processes occurring during the last 2 my. CONCLUSION: The Palinurus speciation pattern is a typical example of a series of rapid speciation events occurring within a group, with very short branches separating different species. Our results support the hypothesis that recent climate change-related oceanographic processes have influenced the phylogeny of marine taxa, with most Palinurus species originating during the last two million years. The present study highlights the value of new coalescent-based statistical methods such as ABC for testing different speciation hypotheses using molecular data.

PopABC: a program to infer historical demographic parameters.

UNLABELLED: PopABC is a computer package for inferring the pattern of demographic divergence of closely related populations and species. The software performs coalescent simulation in the framework of approximate Bayesian computation (ABC). PopABC can also be used to perform Bayesian model choice to discriminate between different demographic scenarios. The program can be used either for research or for education and teaching purposes. AVAILABILITY AND IMPLEMENTATION: Source code and binaries are freely available at http://www.reading.ac.uk/ approximately sar05sal/software.htm. The program was implemented in C and can run on UNIX, MacOSX and Windows operating systems.

The origins of lactase persistence in Europe.

Lactase persistence (LP) is common among people of European ancestry, but with the exception of some African, Middle Eastern and southern Asian groups, is rare or absent elsewhere in the world. Lactase gene haplotype conservation around a polymorphism strongly associated with LP in Europeans (-13,910 C/T) indicates that the derived allele is recent in origin and has been subject to strong positive selection. Furthermore, ancient DNA work has shown that the--13,910*T (derived) allele was very rare or absent in early Neolithic central Europeans. It is unlikely that LP would provide a selective advantage without a supply of fresh milk, and this has lead to a gene-culture coevolutionary model where lactase persistence is only favoured in cultures practicing dairying, and dairying is more favoured in lactase persistent populations. We have developed a flexible demic computer simulation model to explore the spread of lactase persistence, dairying, other subsistence practices and unlinked genetic markers in Europe and western Asia's geographic space. Using data on--13,910*T allele frequency and farming arrival dates across Europe, and approximate Bayesian computation to estimate parameters of interest, we infer that the--13,910*T allele first underwent selection among dairying farmers around 7,500 years ago in a region between the central Balkans and central Europe, possibly in association with the dissemination of the Neolithic Linearbandkeramik culture over Central Europe. Furthermore, our results suggest that natural selection favouring a lactase persistence allele was not higher in northern latitudes through an increased requirement for dietary vitamin D. Our results provide a coherent and spatially explicit picture of the coevolution of lactase persistence and dairying in Europe.