Chart and table of population level and growth rate for the Montpellier, France metro area from 1950 to 2025.

Cette donnée renseigne le nombre de décès depuis 2000. Elle est divisée entre le nombre de transcriptions de décès (montpelliérains de naissance décédés hors Montpellier), le nombre de décès sur la commune et le nombre de décès d’enfants sans vie. Pour plus d'informations : http://www.legifrance.gouv.fr/affichTexte.do?cidTexte=JORFTEXT000019350025 . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

This data contains the socio-demographic diagnoses of the City of Montpellier, by large district (7 large districts) and district (30 districts). These diagnoses, carried out by the territorial coordination department of the Territorial Action Department, are based on data collected from INSEE, CAF and employment center. These are data from 2009 to 2012 relating to population, family, education, diplomas, employment, social benefits, housing, business travel and economic activities. Each diagnosis is available in xlsx, xls and ods format. Previous diagnostics are archived in the VilleMTP_MTP_DiagSocioDemo_archive file.

Cette statistique présente l'évolution du nombre de personnes vivant à Montpellier depuis 1968, jusqu'en 2020. En 50 ans, la population montpelliéraine a augmenté d'environ 37.000 habitants. En 2020, la ville compte près de 299.096 habitants.

Data from the 'Population evolution observatory' (EVOPOP): Monitoring the evolution of animal and plant populations: genetic evolution, demographic, anthropization, .... This observatory is part of the Montpellier Research Observatory of Environment (OSU OREME, oreme.org). It consists in different observing tasks: Brassica insularis ; Clape Centauree ; Common trout ; Insecticide resistance of Culex pipiens ; Wild vine.

Data from the 'Population ecology observatory' (ECOPOP): Individual monitoring of wild animals: physical, biological, social environment, genetic, physiological, immunological, behavioral data.. This observatory is part of the Montpellier Research Observatory of Environment (OSU OREME, oreme.org). It consists in different observing tasks: African ungulates ; Albatross ; Arctic marine ecosystems ; Black-headed Gull ; Black-legged Kittiwake ; Cape gannets ; Contact areas between striated mouse species ; Dynamics of Camargue vertebrate populations ; Ecology and sociality in chacma baboons ; Griffon Vultures ; Kestrel falcon ; Larus michahellis Yellow-legged Gull ; Mandrillus project ; Northern gannets ; Scopoli's Shearwater ; Sociable weavers ; Tits.

Les données représentées ici, révèlent que la densité de population varie sensiblement d'une zone à l’autre. On distingue très clairement ou sont les zones les plus peuplées. Dans cette carte, les zones de plus forte densité dépassent 30.000 habitants par Km². Celles à très forte densité dépassent 7.000 habitants par Km². La forte densité est au-delà de 5.200 habitants par Km². Les dernières catégories sont à 3.330 habitants par Km², et 1.500 habitants par Km².

Les données utilisées proviennent du produit FranceIRIS® développé par Esri France, les informations de population utilisées sont de 2011.

Unveiling the implications of hybridization on fitness stands as a primary focus in the realms of ecology and evolution. Numerous investigations elucidate how evolutionary mechanisms regulate the intricate pattern of introgression across genomes, yet few have examined the consequential impact of genetic admixture on fitness attributes. Leveraging the Western Mediterranean population of the European seabass (Dicentrarchus labrax), a population formed through hybridization of the Atlantic and Mediterranean lineages in the Alboran Sea, we utilized the Axiom Sea Bass 57k SNP DlabChip array to genotype 1850 hybrid individuals. This enabled us to evaluate the correlation between individual admixture levels and fitness traits under varying thermal conditions (19°C, 21°C, 23°C, and 25°C). Our initial findings unveil a male-biased sex ratio and high temperature sensitivity among admixed individuals with a greater proportion of Atlantic ancestry. Subsequently, our analysis demonstrates that individuals with a higher Atlantic genetic background also exhibit reduced body weight (a parameter linked to fecundity in fish) compared to those with lower Atlantic ancestry. These outcomes underscore the disadvantageous nature of Atlantic ancestry introgression in the Mediterranean region, aligning with previous observations of the elimination of Atlantic ancestry segments subsequent to hybridization.

Les données représentées ici, montrent l'évolution de la population en 2025 par rapport au dernier recensement de population de 2011.Les Iris rouges montre une très forte augmentation de la population, de plus de 20% par rapport à 2011. Les Iris oranges présentent une forte augmentation de la population de 10 à 20%. En jaune l'évolution est de 5 à 10% et en vert clair de 0 à 5%.A l'inverse, le bleu clair représente une faible diminution de 0 à -2%, le bleu moyen une diminution de -2 à -5%, le bleu une forte diminution de -5 à -10% et le bleu foncé caractérise la plus forte diminution au delà de -10%.Les données utilisées proviennent du produit France2025® développé par Esri France.

Each dataset consists of three classes of entities: * the refined distribution of the population to the plot (polygons) * the refined distribution of the population to the built (polygons) * the refined distribution of the population to the centroid of the structure (punctual) The treatment is carried out from several data sources: * the IRIS GE INSEE population — 2018 (Latest year, production is n-3 compared to the current year) * CEREMA land files — 2021 (the vintage of the Cerema land files of year n is based on the MAJIC of year n and the MAJIC data of this vintage returns the data from year n) * the BDTopo IGN — 2021 * the Parcellaire Express IGN — 2021 We we worked with the most recent vintage on the population (2018) which does not match the IGN and Cerema data vintage (2021). It should therefore be borne in mind that this data does not show the actual distribution in 2018 but a potential distribution on the plots and buildings of the year 2020 (FF n-1). The sub-communal population is distributed in proportion to the living area of the plots. It is then ventilated on the significant buildings of each plot. The entire processing is described in the methodology that accompanies the data.OPenIG, the co-producer of the data, participated in the specifications, the industrialisation of the processing chain and the dissemination of the classes of entities. ** Warning** The operations that may be made of this data will be the sole responsibility of the user. It does not in any way provide the actual, exact or legal distribution of the population at the level of the building or parcel. Thus, use on this scale makes no sense (e.g. distribution of the population on the buildings of the same plot) as well as possible calculations of evolution between two dates. Authors: Montpellier Méditerranée Métropole, City of Montpellier, OPenIG ** Covered territory:** The data is delivered on a department-wide basis. To have the data on your department in the Occitanie region, do not hesitate to request it: webmestre@openig.org

Répartition de la population (valeur 2008 suite au "nouveau recensement") à partir du carroyage de 200m x 200m de l'INSEE. Montpellier Méditerranée Métropole.

Répartition de la population (valeur 2008 suite au "nouveau recensement") à partir du carroyage de 200m x 200m de l'INSEE, mais réparti plus précisément en fonction de l'occupation des sols. Montpellier Méditerranée Métropole.

Each dataset consists of 2 classes of entities: * the refined distribution of the population to the built (polygons) * the refined distribution of the population to the centroid of the building (puctual) The treatment is carried out from several data sources: * the IRIS GE INSEE population – 2020 (the production is n-3 compared to the current year) * the CEREMA – 2021 land files (the vintage of the Cerema land files of year n is based on the MAJIC vintage of year n and the MAJIC data of this vintage returns the data of year n-1) * the BDTopo IGN – 2021 * the product Parcellaire Express IGN – 2023 We have worked with the most recent vintage on the population (2020) which does not correspond with the vintage of the IGN and Cerema data (2021). It must therefore be taken into account that this data does not present the actual distribution in 2020 but a potential distribution on the plots and buildings of the year 2020 (FF n-1). The sub-communal population is distributed in proportion to the living area of the plots. It is then ventilated on the significant buildings of each plot. The entire treatment is described in the methodology that accompanies the data. OPenIG, co-producer of the data, participated in the specifications, the industrialisation of the processing chain and the dissemination of the classes of entities. ** Warning** The operations that may be made of this data will be the sole responsibility of the user. Under no circumstances does it provide the actual, exact or legal distribution of the population at the level of the building or plot. Thus a use on this scale makes no sense (e.g.: distribution of the population on the buildings of the same plot) as well as possible calculations of evolution between two dates. Authors: Montpellier Méditerranée Métropole, City of Montpellier, OPenIG covered territory: Data is delivered at the scale of the Occitanie region.

Data from the 'Biodiversity, structure and disturbance populations of southern french common trout (Salmo trutta)' Observing Task, part of the Population evolution observatory (EVOPOP) Observation Service of the Montpellier Research Observatory of Environment (OSU OREME, oreme.org).

Data from the 'Dispersion and operation of subdivided populations: Black-headed Gull model' Observing Task, part of the Population ecology observatory (ECOPOP) Observation Service of the Montpellier Research Observatory of Environment (OSU OREME, oreme.org).

Each dataset consists of three classes of entities: * the refined distribution of the population to the plot (polygons) * the refined distribution of the population to the built (polygons) * the refined distribution of the population to the centroid of the building (puctual) The treatment is carried out from several data sources: * the IRIS GE INSEE population – 2020 (the most recent millennium, the production is n-3 compared to the current year) * the CEREMA – 2021 land files (the vintage of the Cerema land files of the year n is based on the MAJIC Millesime of year n and the MAJIC data of this vintage returns the data of year n-1) * the BDTopo IGN – 2021 * the product Parcellaire Express IGN – 2023 We have worked with the most recent vintage on the population (2020) which does not match the vintage of the IGN and Cerema data (2023 and 2021). It must therefore be taken into account that this data does not present the actual distribution in 2020 but a potential distribution on the plots and buildings of the year 2020 (FF n-1). The sub-communal population is distributed in proportion to the living area of the plots. It is then ventilated on the significant buildings of each plot. The entire processing is described in the methodology that accompanies the data.OPenIG, co-producer of the data, participated in the specifications, the industrialisation of the processing chain and the dissemination of the classes of entities. ** Warning** The operations that may be made of this data will be the sole responsibility of the user. Under no circumstances does it provide the actual, exact or legal distribution of the population at the level of the building or plot. Thus a use on this scale makes no sense (e.g.: distribution of the population on the buildings of the same plot) as well as possible calculations of evolution between two dates. Authors: Montpellier Méditerranée Métropole, City of Montpellier, OPenIG

Estimating the proportion of adaptive substitutions (α) is of primary importance to uncover the determinants of adaptation in comparative genomic studies. Several methods have been proposed to estimate α from patterns polymorphism and divergence in coding sequences. However, estimators of α can be biased when the underlying assumptions are not met. Here we focus on a potential source of bias, i.e., variation through time in the long term population size (N) of the considered species. We show via simulations that ancient demographic fluctuations can generate severe overestimations of α, and this irrespective of the recent population history.

Data from the 'In situ dynamics of populations and diversity of Brassica insularis Moris' Observing Task, part of the Population evolution observatory (EVOPOP) Observation Service of the Montpellier Research Observatory of Environment (OSU OREME, oreme.org).

Data from the ‘Movement and population dynamics of griffon vultures in response to changes in rendering practices in the Causses’ Observing Task, part of the Population ecology observatory (ECOPOP) Observation Service of the Montpellier Research Observatory of Environment (OSU OREME, oreme.org).

Data from the 'Population spatial ecology: Black-legged Kittiwake model and parasites 'Observing Task, part of the Population ecology observatory (ECOPOP) Observation Service of the Montpellier Research Observatory of Environment (OSU OREME, oreme.org).