Lounès Chikhi

Effects of chemical contaminants on genetic diversity in natural populations: implications for biomonitoring and ecotoxicology.

The conservation of genetic diversity has emerged as one of the central issues in conservation biology. Although researchers in the areas of evolutionary biology, population management, and conservation biology routinely investigate genetic variability in natural populations, only a handful of studies have addressed the effects of chemical contamination on population genetics. Chemical contamination can cause population reduction by the effects of somatic and heritable mutations, as well as non-genetic modes of toxicity. Stochastic processes in small populations, increased mutation load, and the phenomenon of mutational meltdown are compounding factors that cause reduced fitness and accelerate the process of population extirpation. Although the original damage caused by chemical contaminants is at the molecular level, there are emergent effects at the level of populations, such as the loss of genetic diversity, that are not predictable based solely on knowledge of the mechanism of toxicity of the chemical contaminants. Therefore, the study of evolutionary toxicology, which encompasses the population-genetic effects of environmental contaminants, should be an important focus of ecotoxicology. This paper reviews the issues surrounding the genetic effects of pollution, summarizes the technical approaches that can be used to address these issues, and provides examples of studies that have addressed some of them.

Genetic Signature of Anthropogenic Population Collapse in Orang-utans

Great ape populations are undergoing a dramatic decline, which is predicted to result in their extinction in the wild from entire regions in the near future. Recent findings have particularly focused on African apes, and have implicated multiple factors contributing to this decline, such as deforestation, hunting, and disease. Less well-publicised, but equally dramatic, has been the decline in orang-utans, whose distribution is limited to parts of Sumatra and Borneo. Using the largest-ever genetic sample from wild orang-utan populations, we show strong evidence for a recent demographic collapse in North Eastern Borneo and demonstrate that this signature is independent of the mutation and demographic models used. This is the first demonstration that genetic data can detect and quantify the effect of recent, human-induced deforestation and habitat fragmentation on an endangered species. Because current demographic collapses are usually confounded by ancient events, this suggests a much more dramatic decline than demographic data alone and emphasises the need for major conservation efforts.

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.

The Confounding Effect of Population Structure on Bayesian Skyline Plot Inferences of Demographic History

Many coalescent-based methods aiming to infer the demographic history of populations assume a single, isolated and panmictic population (i.e. a Wright-Fisher model). While this assumption may be reasonable under many conditions, several recent studies have shown that the results can be misleading when it is violated. Among the most widely applied demographic inference methods are Bayesian skyline plots (BSPs), which are used across a range of biological fields. Violations of the panmixia assumption are to be expected in many biological systems, but the consequences for skyline plot inferences have so far not been addressed and quantified. We simulated DNA sequence data under a variety of scenarios involving structured populations with variable levels of gene flow and analysed them using BSPs as implemented in the software package BEAST. Results revealed that BSPs can show false signals of population decline under biologically plausible combinations of population structure and sampling strategy, suggesting that the interpretation of several previous studies may need to be re-evaluated. We found that a balanced sampling strategy whereby samples are distributed on several populations provides the best scheme for inferring demographic change over a typical time scale. Analyses of data from a structured African buffalo population demonstrate how BSP results can be strengthened by simulations. We recommend that sample selection should be carefully considered in relation to population structure previous to BSP analyses, and that alternative scenarios should be evaluated when interpreting signals of population size change.

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.

Patterns of genetic diversity and migration in increasingly fragmented and declining orang-utan ( Pongo pygmaeus ) populations from Sabah, Malaysia

We investigated the genetic structure within and among Bornean orang‐utans (Pongo pygmaeus) in forest fragments of the Lower Kinabatangan flood plain in Sabah, Malaysia. DNA was extracted from hair and faecal samples for 200 wild individuals collected during boat surveys on the Kinabatangan River. Fourteen microsatellite loci were used to characterize patterns of genetic diversity. We found that genetic diversity was high in the set of samples (mean HE = 0.74) and that genetic differentiation was significant between the samples (average FST = 0.04, P < 0.001) with FST values ranging from low (0.01) to moderately large (0.12) values. Pairwise FST values were significantly higher across the Kinabatangan River than between samples from the same river side, thereby confirming the role of the river as a natural barrier to gene flow. The correlation between genetic and geographical distance was tested by means of a series of Mantel tests based on different measures of geographical distance. We used a Bayesian method to estimate immigration rates. The results indicate that migration is unlikely across the river but cannot be completely ruled out because of the limited FST values. Assignment tests confirm the overall picture that gene flow is limited across the river. We found that migration between samples from the same side of the river had a high probability indicating that orang‐utans used to move relatively freely between neighbouring areas. This strongly suggests that there is a need to maintain migration between isolated forest fragments. This could be done by restoring forest corridors alongside the river banks and between patches.

Ancient DNA from an Early Neolithic Iberian population supports a pioneer colonization by first farmers

The Neolithic transition has been widely debated particularly regarding the extent to which this revolution implied a demographic expansion from the Near East. We attempted to shed some light on this process in northeastern Iberia by combining ancient DNA (aDNA) data from Early Neolithic settlers and published DNA data from Middle Neolithic and modern samples from the same region. We successfully extracted and amplified mitochondrial DNA from 13 human specimens, found at three archaeological sites dated back to the Cardial culture in the Early Neolithic (Can Sadurní and Chaves) and to the Late Early Neolithic (Sant Pau del Camp). We found that haplogroups with a low frequency in modern populations—N* and X1—are found at higher frequencies in our Early Neolithic population (∼31%). Genetic differentiation between Early and Middle Neolithic populations was significant (FST∼0.13, P < 10−5), suggesting that genetic drift played an important role at this time. To improve our understanding of the Neolithic demographic processes, we used a Bayesian coalescence‐based simulation approach to identify the most likely of three demographic scenarios that might explain the genetic data. The three scenarios were chosen to reflect archaeological knowledge and previous genetic studies using similar inferential approaches. We found that models that ignore population structure, as previously used in aDNA studies, are unlikely to explain the data. Our results are compatible with a pioneer colonization of northeastern Iberia at the Early Neolithic characterized by the arrival of small genetically distinctive groups, showing cultural and genetic connections with the Near East.

A multi-samples, multi-extracts approach for microsatellite analysis of faecal samples in an arboreal ape

We investigated the effect of the number of faecal samples, ofextracts per sample and of PCRs per extract on the reliability ofgenotypes for a microsatellite locus in free-living orang-utans.For each individual 36 PCRs were performed using DNA extractionsfrom up to four faecal samples. We found a very largeinter-individual variation in positive PCRs (P+) (36/36 for oneindividual and 0/36 for another). As many as 30% of the cases ledto erroneous genotypes when only one P+ was obtained. It ispreferable to use at least 4 P+ per extract to reduce thisproportion to less than 1%. With 3 P+ results, erroneousgenotypes were still observed in 26% of the cases together. Theseresults indicate that it is necessary to do a minimum of 4 PCRsper extract. In order to have a chance to observe 4 P+, threeextracts should be ideally analysed for each sample. We alsorecommend that when possible two or more samples should becollected in the field to increase the chance of having extractscontaining DNA and to provide independent replicates. While werecognise the difficulty of working with faecal samples, weadvocate the use of faecal material for genetic studies ofcertain wild animal populations where the advantages of avoidingdisturbance, stress and injury are deemed of critical importance.

Landscape genetics of an endangered lemur (Propithecus tattersalli) within its entire fragmented range

Habitat fragmentation may strongly reduce individuals’ dispersal among resource patches and hence influence population distribution and persistence. We studied the impact of landscape heterogeneity on the dispersal of the golden‐crowned sifaka (Propithecus tattersalli), an endangered social lemur species living in a restricted and highly fragmented landscape. We combined spatial analysis and population genetics methods to describe population units and identify the environmental factors which best predict the rates and patterns of genetic differentiation within and between populations. We used non‐invasive methods to genotype 230 individuals at 13 microsatellites in all the main forest fragments of its entire distribution area. Our analyses suggest that the Manankolana River and geographical distance are the primary structuring factors, while a national road crossing the region does not seem to impede gene flow. Altogether, our results are in agreement with a limited influence of forest habitat connectivity on gene flow patterns (except for North of the species’ range), suggesting that dispersal is still possible today among most forest patches for this species. Within forest patches, we find that dispersal is mainly among neighbouring social groups, hence confirming previous behavioural observations.

Approximate Bayesian Computation Without Summary Statistics: The Case of Admixture

In recent years approximate Bayesian computation (ABC) methods have become popular in population genetics as an alternative to full-likelihood methods to make inferences under complex demographic models. Most ABC methods rely on the choice of a set of summary statistics to extract information from the data. In this article we tested the use of the full allelic distribution directly in an ABC framework. Although the ABC techniques are becoming more widely used, there is still uncertainty over how they perform in comparison with full-likelihood methods. We thus conducted a simulation study and provide a detailed examination of ABC in comparison with full likelihood in the case of a model of admixture. This model assumes that two parental populations mixed at a certain time in the past, creating a hybrid population, and that the three populations then evolve under pure drift. Several aspects of ABC methodology were investigated, such as the effect of the distance metric chosen to measure the similarity between simulated and observed data sets. Results show that in general ABC provides good approximations to the posterior distributions obtained with the full-likelihood method. This suggests that it is possible to apply ABC using allele frequencies to make inferences in cases where it is difficult to select a set of suitable summary statistics and when the complexity of the model or the size of the data set makes it computationally prohibitive to use full-likelihood methods.