Lynx distribution in the French Alps (1995-1999)

<strong>Abstract</strong> From 1995 to 1999, 69 data were recorded on lynx presence in the French Alps, in an area of 3,636 km². Lynx presence was recorded in the major forested regions of the pré-Alpes (Chablais, Glière/Aravis, Bauges, Chartreuse, Vercors, Diois/Beauchène), in the Chamonix and Maurienne valleys and the Briançon region, but no large continuous area of presence was shown. Lynx have probably been permanently present in certain locations during the past years, but the presence of a large lynx population in the Alps is improbable in the northern French Alps. In the future, we recommend that habitat suitability for lynx in the northern French-Alps should be assessed, together with possibilities of connection between alpine regions and possible bias in the monitoring system

Isotopic evidence of individual specialization toward free-ranging chickens in a rural population of red foxes

International audienc

Breaking down population density into different components to better understand its spatial variation

International audienceAbstract Background Population size and densities are key parameters in both fundamental and applied ecology, as they affect population resilience to density-dependent processes, habitat changes and stochastic events. Efficient management measures or species conservation programs thus require accurate estimates of local population densities across time and space, especially for continuously distributed species. For social species living in groups, population density depends on different components, namely the number of groups and the group size, for which relative variations in space may originate from different environmental factors. Whether resulting spatial variations in density are mostly triggered by one component or the other remains poorly known. Here, we aimed at determining the magnitude of the spatial variation in population densities of a social, group-living species, i.e. the European badger Meles meles , in 13 different sites of around 50 km 2 across France, to decipher whether sett density, group size or proportion of occupied sett variation is the main factor explaining density variation. Besides the intrinsic factors of density variation, we also assessed whether habitat characteristics such as habitat fragmentation, urbanisation, and resource availability, drove both the spatial variation of density components and local population densities. Results We proposed a new standardised approach combining use of multiple methods, namely distance sampling for estimating the density of occupied sett clusters, i.e. group density, and camera and hair trapping for genetic identification to determine the mean social group size. The density of adult badgers was on average 3.8 per km 2 (range 1.7–7.9 per km 2 ) and was positively correlated with the density of sett clusters. The density of adult badgers per site was less related to the social group size or to the proportion of occupied sett clusters. Landscape fragmentation also explained the spatial variation of adult badger density, with highly fragmented landscapes supporting lower adult densities. Density components were linked differently to environmental variables. Conclusions These results underline the need to break down population density estimates into several components in group-living species to better understand the pattern of temporal and spatial variation in population density, as different components may vary due to different ecological factors

Modélisation de l'expression des gènes à partir de données de séquence ADN

International audienceGene expression is tightly controlled to ensure a wide variety of cell types and functions. The development of diseases, particularly cancers, is invariably related to deregulations of these controls. Our objective is to model the link between gene expression and nucleotide composition of different regulatory regions in the genome. We propose to address this problem in a regression framework using a Lasso approach coupled to a regression tree. We use exclusively sequence data and we fit a different model for each cell type. We show that (i) different regulatory regions provide particular and complementary information and that (ii) the only information contained in the nucleotide compositions allows predicting gene expression with an error comparable to that obtained using experimental data. Moreover, the fitted linear model is not as powerful for all genes, but better fit certain groups of genes with particular nucleotides compositions.L'expression des gènes est étroitement contrôlée pour assurer une grande variété de fonctions et de types cellulaires. Le développement des maladies, en particulier les cancers, est invariablement lié à la dérégulation de ces contrôles. Notre objectif est de modéliser le lien entre l'expression des gènes et la composition nucléotidique des différentes régions régulatrices du génome. Nous proposons d'aborder ce problème dans un cadre de régression avec une approche Lasso couplée à un arbre de régression. Nous utilisons exclusivement des données de séquences et nous apprenons un modèle différent pour chaque type cellulaire. Nous montrons (i) que les différentes régions régulatrices apportent des informations diffé-rentes et complémentaires et (ii) que la seule information de leur composition nucléotidique permet de prédire l'expression des gènes avec une erreur comparable à celle obtenue en utilisant des données expérimentales. En outre, le modèle linéaire appris n'est pas aussi performant pour tous les gènes, mais modélise mieux certaines classes de gènes avec des compositions nucléotidiques particulières

Modélisation de l'expression des gènes à partir de données de séquence ADN

International audienceGene expression is tightly controlled to ensure a wide variety of cell types and functions. The development of diseases, particularly cancers, is invariably related to deregulations of these controls. Our objective is to model the link between gene expression and nucleotide composition of different regulatory regions in the genome. We propose to address this problem in a regression framework using a Lasso approach coupled to a regression tree. We use exclusively sequence data and we fit a different model for each cell type. We show that (i) different regulatory regions provide particular and complementary information and that (ii) the only information contained in the nucleotide compositions allows predicting gene expression with an error comparable to that obtained using experimental data. Moreover, the fitted linear model is not as powerful for all genes, but better fit certain groups of genes with particular nucleotides compositions.L'expression des gènes est étroitement contrôlée pour assurer une grande variété de fonctions et de types cellulaires. Le développement des maladies, en particulier les cancers, est invariablement lié à la dérégulation de ces contrôles. Notre objectif est de modéliser le lien entre l'expression des gènes et la composition nucléotidique des différentes régions régulatrices du génome. Nous proposons d'aborder ce problème dans un cadre de régression avec une approche Lasso couplée à un arbre de régression. Nous utilisons exclusivement des données de séquences et nous apprenons un modèle différent pour chaque type cellulaire. Nous montrons (i) que les différentes régions régulatrices apportent des informations diffé-rentes et complémentaires et (ii) que la seule information de leur composition nucléotidique permet de prédire l'expression des gènes avec une erreur comparable à celle obtenue en utilisant des données expérimentales. En outre, le modèle linéaire appris n'est pas aussi performant pour tous les gènes, mais modélise mieux certaines classes de gènes avec des compositions nucléotidiques particulières

Modélisation de l'expression des gènes à partir de données de séquence ADN

International audienceGene expression is tightly controlled to ensure a wide variety of cell types and functions. The development of diseases, particularly cancers, is invariably related to deregulations of these controls. Our objective is to model the link between gene expression and nucleotide composition of different regulatory regions in the genome. We propose to address this problem in a regression framework using a Lasso approach coupled to a regression tree. We use exclusively sequence data and we fit a different model for each cell type. We show that (i) different regulatory regions provide particular and complementary information and that (ii) the only information contained in the nucleotide compositions allows predicting gene expression with an error comparable to that obtained using experimental data. Moreover, the fitted linear model is not as powerful for all genes, but better fit certain groups of genes with particular nucleotides compositions.L'expression des gènes est étroitement contrôlée pour assurer une grande variété de fonctions et de types cellulaires. Le développement des maladies, en particulier les cancers, est invariablement lié à la dérégulation de ces contrôles. Notre objectif est de modéliser le lien entre l'expression des gènes et la composition nucléotidique des différentes régions régulatrices du génome. Nous proposons d'aborder ce problème dans un cadre de régression avec une approche Lasso couplée à un arbre de régression. Nous utilisons exclusivement des données de séquences et nous apprenons un modèle différent pour chaque type cellulaire. Nous montrons (i) que les différentes régions régulatrices apportent des informations diffé-rentes et complémentaires et (ii) que la seule information de leur composition nucléotidique permet de prédire l'expression des gènes avec une erreur comparable à celle obtenue en utilisant des données expérimentales. En outre, le modèle linéaire appris n'est pas aussi performant pour tous les gènes, mais modélise mieux certaines classes de gènes avec des compositions nucléotidiques particulières

Population genetic structures at multiple spatial scales: importance of social groups in European badgers

International audienceAbstract Population viability and metapopulation dynamics are strongly affected by gene flow. Identifying ecological correlates of genetic structure and gene flow in wild populations is therefore a major issue both in evolutionary ecology and species management. Studying the genetic structure of populations also enables identification of the spatial scale at which most gene flow occurs, hence the scale of the functional connectivity, which is of paramount importance for species ecology. In this study, we examined the genetic structure of a social, continuously distributed mammal, the European badger (Meles meles), both at large spatial scales (among populations) and fine (within populations) spatial scales. The study was carried out in 11 sites across France utilizing a noninvasive hair trapping protocol at 206 monitored setts. We identified 264 badgers genotyped at 24 microsatellite DNA loci. At the large scale, we observed high and significant genetic differentiation among populations (global Fst = 0.139; range of pairwise Fst [0.046–0.231]) that was not related to the geographic distance among sites, suggesting few large-scale dispersal events. Within populations, we detected a threshold value below which badgers were genetically close (< 400 m), highlighting that sociality is the major structuring process within badger populations at the fine scale

Probing instructions for expression regulation in gene nucleotide compositions - Fig 7

<p><b>A: Nucleotide compositions of resident genes distinguish TADs.</b> For each TAD and for each region considered, the percentage of each nucleotide and dinucleotide associated to the embedded genes were compared to that of all other genes using a Kolmogorov-Smirnov test. Red indicates FDR-corrected p-value ≥ 0.05 and yellow FDR-corrected p-value < 0.05. TAD clustering was made using this binary information. Only TADs with at least one p-value < 0.05 are shown (i.e. 87% of the TADs containing at least 10 genes). y-axis from top to bottom: G_INTR, GpC_INTR, CpC_INTR, CpC_3UTR, GpC_3UTR, G_3UTR, GpC_CDS, CpC_CDS, G_CDS, G_DFR, CpC_DFR, GpC_DFR, CpG_INTR, CpG_3UTR, CpG_CDS, CpG_DFR, G_DU, GpC_DD, CpG, DU, CpG_DD, GpC_DU, CpC_DU, CpC_DD, G_DD, GpC_5UTR, CpG_5UTR, G_5UTR, GpC_CORE, CpG_CORE, CpC_CORE, G_CORE, CpC_5UTR, CpT_3UTR, CpT_CDS, CpT_INTR, ApT_INTR, TpA_INTR, A_INTR, ApA_INTR, TpA_3UTR, ApT_3UTR, A_3UTR, ApA_3UTR, ApA_CDS, A_CDS, ApT_CDS, TpA_CDS, A_DD, ApA_DD, ApT_DD, TpA_DD, TpA_DU, ApT_DU, ApA_DU, A_DU, TpA_DFR, ApT_DFR, A_DFR, ApA_DFR, ApA_CORE, A_CORE, ApT_CORE, TpA_CORE, ApA_5UTR, ApT_5UTR, A_5UTR, TpA_5UTR, ApC_DFR, ApC_DD, ApC_DU, TpC_DU, TpC_DFR, ApC_CORE, CpA_DU, CpA_DFR, CpA_CDS, ApC_CDS, ApC_3UTR, TpC_CDS, TpC_CORE, CpT_5UTR, TpC_5UTR, CpT_CORE, TpC_DD, CpA_CORE, ApC_5UTR, CpA_5UTR, ApC_INTR, CpA_DD, CpT_DFR, CpT_DD, CpT_DU, TpC_3UTR, TpC_INTR, CpA_INTR, CpA_3UTR. <b>B: TAD enrichment within groups of genes whose expression is accurately predicted by our model.</b> The enrichment for each TAD (containing more than 10 genes) in each gene group accurately predicted by our model (i.e. groups with mean error < mean errors of the 1st quartile) was evaluated using an hypergeometric test. The fraction of groups with enriched TADs (p-value < 0.05) is represented.</p