Laboratoire Population Environnement Développement

Statistical and machine learning theory has developed several conditions ensuring that popular estimators such as the Lasso or the Dantzig selector perform well in high-dimensional sparse regression, including the restricted eigenvalue, compatibility, and [Formula: see text] sensitivity properties. However, some of the central aspects of these conditions are not well understood. For instance, it is unknown if these conditions can be checked efficiently on any given data set. This is problematic, because they are at the core of the theory of sparse regression. Here we provide a rigorous proof that these conditions are NP-hard to check. This shows that the conditions are computationally infeasible to verify, and raises some questions about their practical applications. However, by taking an average-case perspective instead of the worst-case view of NP-hardness, we show that a particular condition, [Formula: see text] sensitivity, has certain desirable properties. This condition is weaker and more general than the others. We show that it holds with high probability in models where the parent population is well behaved, and that it is robust to certain data processing steps. These results are desirable, as they provide guidance about when the condition, and more generally the theory of sparse regression, may be relevant in the analysis of high-dimensional correlated observational data.

Thanks to genome-scale diversity data, present-day studies can provide a detailed view of how natural and cultivated species adapt to their environment and particularly to environmental gradients. However, due to their sensitivity, up-to-date studies might be more sensitive to undocumented demographic effects such as the pattern of migration and the reproduction regime. In this study, we provide guidelines for the use of popular or recently developed statistical methods to detect footprints of selection. We simulated 100 populations along a selective gradient and explored different migration models, sampling schemes and rates of self-fertilization. We investigated the power and robustness of eight methods to detect loci potentially under selection: three designed to detect genotype-environment correlations and five designed to detect adaptive differentiation (based on F(ST) or similar measures). We show that genotype-environment correlation methods have substantially more power to detect selection than differentiation-based methods but that they generally suffer from high rates of false positives. This effect is exacerbated whenever allele frequencies are correlated, either between populations or within populations. Our results suggest that, when the underlying genetic structure of the data is unknown, a number of robust methods are preferable. Moreover, in the simulated scenario we used, sampling many populations led to better results than sampling many individuals per population. Finally, care should be taken when using methods to identify genotype-environment correlations without correcting for allele frequency autocorrelation because of the risk of spurious signals due to allele frequency correlations between populations.

Abstract This paper synthesizes insights from new global data on the effectiveness of migration policies. It investigates the complex links between migration policies and migration trends to disentangle policy effects from structural migration determinants. The analysis challenges two central assumptions underpinning the popular idea that migration restrictions have failed to curb migration. First, post‐WWII global migration levels have not accelerated, but remained relatively stable while most shifts in migration patterns have been directional. Second, post‐WWII migration policies have generally liberalized despite political rhetoric suggesting the contrary. While migration policies are generally effective, “substitution effects” can limit their effectiveness, or even make them counterproductive, by geographically diverting migration, interrupting circulation, encouraging unauthorized migration, or prompting “now or never” migration surges. These effects expose fundamental policy dilemmas and highlight the importance of understanding the economic, social, and political trends that shape migration in sometimes counterintuitive, but powerful, ways that largely lie beyond the reach of migration policies.

There is a growing interest in identifying ecological factors that influence adaptive genetic diversity patterns in both model and nonmodel species. The emergence of large genomic and environmental data sets, as well as the increasing sophistication of population genetics methods, provides an opportunity to characterize these patterns in relation to the environment. Landscape genetics has emerged as a flexible analytical framework that connects patterns of adaptive genetic variation to environmental heterogeneity in a spatially explicit context. Recent growth in this field has led to the development of numerous spatial statistical methods, prompting a discussion of the current benefits and limitations of these approaches. Here we provide a review of the design of landscape genetics studies, the different statistical tools, some important case studies, and perspectives on how future advances in this field are likely to shed light on important processes in evolution and ecology.

Understanding the genetic basis of species adaptation in the context of global change poses one of the greatest challenges of this century. Although we have begun to understand the molecular basis of adaptation in those species for which whole genome sequences are available, the molecular basis of adaptation is still poorly understood for most non-model species. In this paper, we outline major challenges and future research directions for correlating environmental factors with molecular markers to identify adaptive genetic variation, and point to research gaps in the application of landscape genetics to real-world problems arising from global change, such as the ability of organisms to adapt over rapid time scales. High throughput sequencing generates vast quantities of molecular data to address the challenge of studying adaptive genetic variation in non-model species. Here, we suggest that improvements in the sampling design should consider spatial dependence among sampled individuals. Then, we describe available statistical approaches for integrating spatial dependence into landscape analyses of adaptive genetic variation.

Hybrid zones are fascinating systems to investigate the structure of genetic barriers. Marine hybrid zones deserve more investigation because of the generally high dispersion potential of planktonic larvae which allows migration on scales unrivalled by terrestrial species. Here we analyse the genetic structure of the mosaic hybrid zone between the marine mussels Mytilus edulis and M. galloprovincialis, using three length-polymorphic PCR loci as neutral and diagnostic markers on 32 samples along the Atlantic coast of Europe. Instead of a single genetic gradient from M. galloprovincialis on the Iberian Peninsula to M. edulis populations in the North Sea, three successive transitions were observed in France. From South to North, the frequency of alleles typical of M. galloprovincialis first decreases in the southern Bay of Biscay, remains low in Charente, then increases in South Brittany, remains high in most of Brittany, and finally decreases again in South Normandy. The two enclosed patches observed in the midst of the mosaic hybrid zone in Charente and Brittany, although predominantly M. edulis-like and M. galloprovincialis-like, respectively, are genetically original in two respects. First, considering only the various alleles typical of one species, the patches show differentiated frequencies compared to the reference external populations. Second, each patch is partly introgressed by alleles of the other species. When introgression is taken into account, linkage disequilibria appear close to their maximum possible values, indicating a strong genetic barrier within all transition zones. Some pre- or postzygotic isolation mechanisms (habitat specialization, spawning asynchrony, assortative fertilization and hybrid depression) have been documented in previous studies, although their relative importance remains to be evaluated. We also provided evidence for a recent migratory 'short-cut' connecting M. edulis-like populations of the Charente patch to an external M. edulis population in Normandy and thought to reflect artificial transfer of spat for aquaculture.

Recently, techniques available for identifying clusters of individuals or boundaries between clusters using genetic data from natural populations have expanded rapidly. Consequently, there is a need to evaluate these different techniques. We used spatially-explicit simulation models to compare three spatial Bayesian clustering programs and two edge detection methods. Spatially-structured populations were simulated where a continuous population was subdivided by barriers. We evaluated the ability of each method to correctly identify boundary locations while varying: (i) time after divergence, (ii) strength of isolation by distance, (iii) level of genetic diversity, and (iv) amount of gene flow across barriers. To further evaluate the methods’ effectiveness to detect genetic clusters in natural populations, we used previously published data on North American pumas and a European shrub. Our results show that with simulated and empirical data, the Bayesian spatial clustering algorithms outperformed direct edge detection methods. All methods incorrectly detected boundaries in the presence of strong patterns of isolation by distance. Based on this finding, we support the application of Bayesian spatial clustering algorithms for boundary detection in empirical datasets, with necessary tests for the influence of isolation by distance.

A major challenges facing landscape geneticists studying adaptive variation is to include all the environmental variables that might be correlated with allele frequencies across the genome. One way of identifying loci that are possibly under selection is to see which ones are associated with environmental gradient or heterogeneity. Since it is difficult to measure all environmental variables, one may take advantage of the spatial nature of environmental filters to incorporate the effect of unaccounted environmental variables in the analysis. Assuming that the spatial signature of these variables is broad-scaled, broad-scale Moran's eigenvector maps (MEM) can be included as explanatory variables in the analysis as proxies for unmeasured environmental variables. We applied this approach to two data sets of the alpine plant Arabis alpina. The first consisted of 140 AFLP loci sampled at 130 sites across the European Alps (large scale). The second one consisted of 712 AFLP loci sampled at 93 sites (regional scale) in three mountain massifs (local scale) of the French Alps. For each scale, we regressed the frequencies of each AFLP allele on a set of eco-climatic and MEM variables as predictors. Twelve (large scale) and 11% (regional scale) of all loci were detected as significantly correlated to at least one of the predictors ( > 0.5), and, except for one massif, 17% at the local scale. After accounting for spatial effects, temperature and precipitation were the two major determinants of allele distributions. Our study shows how MEM models can account for unmeasured environmental variation in landscape genetics models.

The community involved in modeling radiation transfer over terrestrial surfaces designed and implemented the first phase of a radiation transfer model intercomparison (RAMI) exercise. This paper discusses the rationale and motivation for this endeavor, presents the intercomparison protocol as well as the evaluation procedures, and describes the principal results. Participants were asked to simulate the transfer of radiation for a variety of precisely defined terrestrial environments and illumination conditions. These were abstractions of typical terrestrial systems and included both homogeneous and heterogeneous scenes. The differences between the results generated by eight different models, including both one‐dimensional and three‐dimensional approaches, were then documented and analyzed. RAMI proposed a protocol to quantitatively assess the consequences of the model discrepancies with respect to application, such as those motivating the development of physically based inversion procedures. This first phase of model intercomparison has already proved useful in assessing the ability of the modeling community to generate similar radiation fields despite the large panoply of models that were tested. A detailed analysis of the results also permitted to identify apparent “outliers” and their main deficiencies. Future undertakings in this intercomparison framework must be oriented toward an expansion of RAMI into other and more complex geophysical systems as well as the focusing on actual inverse problems.

Deforestation is a major cause of biodiversity loss with a negative impact on human health. This study explores at global scale whether the loss and gain of forest cover and the rise of oil palm plantations can promote outbreaks of vector-borne and zoonotic diseases. Taking into account the human population growth, we find that the increases in outbreaks of zoonotic and vector-borne diseases from 1990 to 2016 are linked with deforestation, mostly in tropical countries, and with reforestation, mostly in temperate countries. We also find that outbreaks of vector-borne diseases are associated with the increase in areas of palm oil plantations. Our study gives new support for a link between global deforestation and outbreaks of zoonotic and vector-borne diseases as well as evidences that reforestation and plantations may also contribute to epidemics of infectious diseases. The results are discussed in light of the importance of forests for biodiversity, livelihoods and human health and the need to urgently build an international governance framework to ensure the preservation of forests and the ecosystem services they provide, including the regulation of diseases. We develop recommendations to scientists, public health officers and policymakers who should reconcile the need to preserve biodiversity while taking into account the health risks posed by lack or mismanagement of forests.

Species range shifts in response to climate and land use change are commonly forecasted with species distribution models based on species occurrence or abundance data. Although appealing, these models ignore the genetic structure of species, and the fact that different populations might respond in different ways because of adaptation to their environment. Here, we introduced ancestry distribution models, that is, statistical models of the spatial distribution of ancestry proportions, for forecasting intra-specific changes based on genetic admixture instead of species occurrence data. Using multi-locus genotypes and extensive geographic coverage of distribution data across the European Alps, we applied this approach to 20 alpine plant species considering a global increase in temperature from 0.25 to 4 °C. We forecasted the magnitudes of displacement of contact zones between plant populations potentially adapted to warmer environments and other populations. While a global trend of movement in a north-east direction was predicted, the magnitude of displacement was species-specific. For a temperature increase of 2 °C, contact zones were predicted to move by 92 km on average (minimum of 5 km, maximum of 212 km) and by 188 km for an increase of 4 °C (minimum of 11 km, maximum of 393 km). Intra-specific turnover-measuring the extent of change in global population genetic structure-was generally found to be moderate for 2 °C of temperature warming. For 4 °C of warming, however, the models indicated substantial intra-specific turnover for ten species. These results illustrate that, in spite of unavoidable simplifications, ancestry distribution models open new perspectives to forecast population genetic changes within species and complement more traditional distribution-based approaches.

Identifying adaptive genetic variation is a challenging task, in particular in non-model species for which genomic information is still limited or absent. Here, we studied distribution patterns of amplified fragment length polymorphisms (AFLPs) in response to environmental variation, in 13 alpine plant species consistently sampled across the entire European Alps. Multiple linear regressions were performed between AFLP allele frequencies per site as dependent variables and two categories of independent variables, namely Moran's eigenvector map MEM variables (to account for spatial and unaccounted environmental variation, and historical demographic processes) and environmental variables. These associations allowed the identification of 153 loci of ecological relevance. Univariate regressions between allele frequency and each environmental factor further showed that loci of ecological relevance were mainly correlated with MEM variables. We found that precipitation and temperature were the best environmental predictors, whereas topographic factors were rarely involved in environmental associations. Climatic factors, subject to rapid variation as a result of the current global warming, are known to strongly influence the fate of alpine plants. Our study shows, for the first time for a large number of species, that the same environmental variables are drivers of plant adaptation at the scale of a whole biome, here the European Alps.

INTRODUCTION: The objective of this study was to measure the rate of asymptomatic carriage of plasmodium in the Dakar region two years after the implementation of new strategies in clinical malaria management. METHODOLOGY: Between October and December 2008, 2952 households selected in 50 sites of Dakar area, were visited for interviews and blood sampling. Giemsa-stained thick blood smears (TBS) were performed for microscopy in asymptomatic adult women and children aged 2 to 10 years. To ensure the quality of the microscopy, we performed a polymerase chain reaction (PCR) with real time qPCR in all positive TBS by microscopy and in a sample of negative TBS and filter paper blood spots. RESULTS: The analysis has concerned 2427 women and 2231 children. The mean age of the women was 35.6 years. The mean age of the children was 5.4 years. The parasite prevalence was 2.01% (49/2427) in women and 2.15% (48/2231) in children. Parasite prevalence varied from one study site to another, ranging from 0 to 7.41%. In multivariate analysis, reporting a malaria episode in 2008 was associated with plasmodium carriage (OR = 2.57, P = 0.002) in women; in children, a malaria episode (OR = 6.19, P<0.001) and a travel out of Dakar during last 3 months (OR = 2.27, P = 0.023) were associated with plasmodium carriage. Among the positive TBS, 95.8% (93/97) were positive by plasmodium PCR. Among the negative TBS, 13.9% (41/293) were positive by PCR. In blood spots, 15.2% (76/500) were positive by PCR. We estimated at 16.5% the parasite prevalence if PCR were performed in 4658 TBS. CONCLUSION: Parasite prevalence in Dakar area seemed to be higher than the rate found by microscopy. PCR may be the best tool for measuring plasmodium prevalence in the context of low transmission. Environmental conditions play a major role in the heterogeneity of parasite prevalence within sites.

Understanding the genetic basis of adaptation in response to environmental variation is fundamental as adaptation plays a key role in the extension of ecological niches to marginal habitats and in ecological speciation. Based on the assumption that some genomic markers are correlated to environmental variables, we aimed to detect loci of ecological relevance in the alpine plant Arabis alpina L. sampled in two regions, the French (99 locations) and the Swiss (109 locations) Alps. We used an unusually large genome scan [825 amplified fragment length polymorphism loci (AFLPs)] and four environmental variables related to temperature, precipitation and topography. We detected linkage disequilibrium among only 3.5% of the considered AFLP loci. A population structure analysis identified no admixture in the study regions, and the French and Swiss Alps were differentiated and therefore could be considered as two independent regions. We applied generalized estimating equations (GEE) to detect ecologically relevant loci separately in the French and Swiss Alps. We identified 78 loci of ecological relevance (9%), which were mainly related to mean annual minimum temperature. Only four of these loci were common across the French and Swiss Alps. Finally, we discuss that the genomic characterization of these ecologically relevant loci, as identified in this study, opens up new perspectives for studying functional ecology in A. alpina, its relatives and other alpine plant species.

et de quelque manire que ce soit, est interdite sauf accord pralable et crit de l'diteur, en dehors des cas prvus par la lgislation en vigueur en France. Il est prcis que son stockage dans une base de donnes est galement interdit.

Marine protected areas (MPAs) are major tools to protect biodiversity and sustain fisheries. For species with a sedentary adult phase and a dispersive larval phase, the effectiveness of MPA networks for population persistence depends on connectivity through larval dispersal. However, connectivity patterns between MPAs remain largely unknown at large spatial scales. Here, we used a biophysical model to evaluate connectivity between MPAs in the Mediterranean Sea, a region of extremely rich biodiversity that is currently protected by a system of approximately a hundred MPAs. The model was parameterized according to the dispersal capacity of the dusky grouper Epinephelus marginatus, an archetypal conservation-dependent species, with high economic importance and emblematic in the Mediterranean. Using various connectivity metrics and graph theory, we showed that Mediterranean MPAs are far from constituting a true, well-connected network. On average, each MPA was directly connected to four others and MPAs were clustered into several groups. Two MPAs (one in the Balearic Islands and one in Sardinia) emerged as crucial nodes for ensuring multi-generational connectivity. The high heterogeneity of MPA distribution, with low density in the South-Eastern Mediterranean, coupled with a mean dispersal distance of 120 km, leaves about 20% of the continental shelf without any larval supply. This low connectivity, here demonstrated for a major Mediterranean species, poses new challenges for the creation of a future Mediterranean network of well-connected MPAs providing recruitment to the whole continental shelf. This issue is even more critical given that the expected reduction of pelagic larval duration following sea temperature rise will likely decrease connectivity even more.

Mitochondrial DNA (mtDNA) is one of the most popular population genetic markers. Its relevance as an indicator of population size and history has recently been questioned by several large-scale studies in animals reporting evidence for recurrent adaptive evolution, at least in invertebrates. Here we focus on mammals, a more restricted taxonomic group for which the issue of mtDNA near neutrality is crucial. By analyzing the distribution of mtDNA diversity across species and relating it to allozyme diversity, life-history traits, and taxonomy, we show that (i) mtDNA in mammals does not reject the nearly neutral model; (ii) mtDNA diversity, however, is unrelated to any of the 14 life-history and ecological variables that we analyzed, including body mass, geographic range, and The World Conservation Union (IUCN) categorization; (iii) mtDNA diversity is highly variable between mammalian orders and families; (iv) this taxonomic effect is most likely explained by variations of mutation rate between lineages. These results are indicative of a strong stochasticity of effective population size in mammalian species. They suggest that, even in the absence of selection, mtDNA genetic diversity is essentially unpredictable, knowing species biology, and probably uncorrelated to species abundance.

RESUME Loin de constituer une figure nouvelle dans l’histoire de la migration africaine, l’aventurier se pose en figure récurrente et connaît un regain de visibilité, dès lors que les politiques migratoires se durcissent un peu partout sur la planète et que la libre circulation des hommes est rendue de plus en plus problématique. À l’heure où les principes du salariat et de la fonction publique sont sérieusement contestés sur le continent africain, des itinéraires d’accumulation inédits prospèrent, tout comme de nouveaux modèles de réussite fondés sur la ruse, la bravoure et l’exploit sont célébrés. L’aventure migratoire s’identifie totalement aux risques encourus et à l’intensité de la vie vécue ; elle permet à l’homme d’advenir et de s’aguerrir. Si elle a un commencement, elle a aussi une fin : au temps des projets aventureux doit succéder la construction de sa carrière. L’aventure doit être aussi interrogée dans ses déterminations imaginaires. Pour les uns, c’est l’imaginaire de la prédation qui prime, pour d’autres encore c’est celui de la contestation, pour d’autres enfin l’aspiration à l’Ailleurs s’apparente à la geste épique.

BACKGROUND: In Africa, women tested for HIV during antenatal care are counselled to share with their partner their HIV test result and to encourage partners to undertake HIV testing. We investigate, among women tested for HIV within a prevention of mother-to-child transmission of HIV (PMTCT) programme, the key moments for disclosure of their own HIV status to their partner and the impact on partner HIV testing. METHODS AND FINDINGS: Within the Ditrame Plus PMTCT project in Abidjan, 546 HIV-positive and 393 HIV-negative women were tested during pregnancy and followed-up for two years after delivery. Circumstances, frequency, and determinants of disclosure to the male partner were estimated according to HIV status. The determinants of partner HIV testing were identified according to women's HIV status. During the two-year follow-up, disclosure to the partner was reported by 96.7% of the HIV-negative women, compared to 46.2% of HIV-positive women (chi(2) = 265.2, degrees of freedom [df] = 1, p < 0.001). Among HIV-infected women, privileged circumstances for disclosure were just before delivery, during early weaning (at 4 mo to prevent HIV postnatal transmission), or upon resumption of sexual activity. Formula feeding by HIV-infected women increased the probability of disclosure (adjusted odds ratio 1.54, 95% confidence interval 1.04-2.27, Wald test = 4.649, df = 1, p = 0.031), whereas household factors such as having a co-spouse or living with family reduced the probability of disclosure. The proportion of male partners tested for HIV was 23.1% among HIV-positive women and 14.8% among HIV-negative women (chi(2) = 10.04, df = 1, p = 0.002). Partners of HIV-positive women who were informed of their wife's HIV status were more likely to undertake HIV testing than those not informed (37.7% versus 10.5%, chi(2) = 56.36, df = 1, p < 0.001). CONCLUSIONS: In PMTCT programmes, specific psychosocial counselling and support should be provided to women during the key moments of disclosure of HIV status to their partners (end of pregnancy, weaning, and resumption of sexual activity). This support could contribute to improving women's adherence to the advice given to prevent postnatal and sexual HIV transmission.

The increasing urbanization of rural areas leads to a strong development of horticultural flora, which is the main source of alien and invasive plants. In order to assess the pool of cultivated species under different urbanization pressures, the diversity and distribution of horticultural flora were studied in 120 Mediterranean gardens belonging to three housing density types. The results showed a great richness and heterogeneity of this flora, and similarities in species composition between gardens of the same housing density types. Twenty-four percent of the cultivated species are well adapted to the Mediterranean climate, and 21 species known to be invasive on the French territory have emanated from gardens. Inventorying areas adjoining gardens would be useful in identifying escaped garden plants and to assess the associated risks for biological diversity. The results also suggested a detailed analysis of the influence of social, economic and regional factors on planting practices, in order to identify the drivers of these original floral patterns.