Whereas the population is expected to decrease somewhat until 2100 in Asia, Europe, and South America, it is predicted to grow significantly in Africa. While there were 1.5 billion inhabitants on the continent at the beginning of 2024, the number of inhabitants is expected to reach 3.8 billion by 2100. In total, the global population is expected to reach nearly 10.4 billion by 2100. Worldwide population In the United States, the total population is expected to steadily increase over the next couple of years. In 2024, Asia held over half of the global population and is expected to have the highest number of people living in urban areas in 2050. Asia is home to the two most populous countries, India and China, both with a population of over one billion people. However, the small country of Monaco had the highest population density worldwide in 2021. Effects of overpopulation Alongside the growing worldwide population, there are negative effects of overpopulation. The increasing population puts a higher pressure on existing resources and contributes to pollution. As the population grows, the demand for food grows, which requires more water, which in turn takes away from the freshwater available. Concurrently, food needs to be transported through different mechanisms, which contributes to air pollution. Not every resource is renewable, meaning the world is using up limited resources that will eventually run out. Furthermore, more species will become extinct which harms the ecosystem and food chain. Overpopulation was considered to be one of the most important environmental issues worldwide in 2020.

Australia Population Projection: Mid Year: Growth data was reported at 0.300 % in 2100. This stayed constant from the previous number of 0.300 % for 2099. Australia Population Projection: Mid Year: Growth data is updated yearly, averaging 0.750 % from Jun 1986 (Median) to 2100, with 115 observations. The data reached an all-time high of 2.230 % in 2008 and a record low of 0.300 % in 2100. Australia Population Projection: Mid Year: Growth data remains active status in CEIC and is reported by U.S. Census Bureau. The data is categorized under Global Database’s Australia – Table AU.US Census Bureau: Demographic Projection.

This statistic shows the median age of the population in Australia from 1950 to 2100. The median age of a population is an index that divides the population into two equal groups: half of the population is older than the median age and the other half younger. In 2020, the median age of Australia's population was 36.9 years.

In the middle of 2023, about 60 percent of the global population was living in Asia.The total world population amounted to 8.1 billion people on the planet. In other words 4.7 billion people were living in Asia as of 2023. Global populationDue to medical advances, better living conditions and the increase of agricultural productivity, the world population increased rapidly over the past century, and is expected to continue to grow. After reaching eight billion in 2023, the global population is estimated to pass 10 billion by 2060. Africa expected to drive population increase Most of the future population increase is expected to happen in Africa. The countries with the highest population growth rate in 2024 were mostly African countries. While around 1.47 billion people live on the continent as of 2024, this is forecast to grow to 3.9 billion by 2100. This is underlined by the fact that most of the countries wit the highest population growth rate are found in Africa. The growing population, in combination with climate change, puts increasing pressure on the world's resources.

Anthropogenic climate change has caused widespread loss of species biodiversity and ecosystem productivity across the globe, particularly on tropical coral reefs. Predicting the future vulnerability of reef-building corals, the foundation species of coral reef ecosystems, is crucial for cost-effective conservation planning in the Anthropocene. In this study, we combine regional population genetic connectivity and seascape analyses to explore patterns of genetic offset (the mismatch of gene-environmental associations under future climate conditions) in Acropora digitifera across 12 degrees of latitude in Western Australia. Our data revealed a pattern of restricted gene flow and limited genetic connectivity among geographically distant reef systems. Environmental association analyses identified a suite of loci strongly associated with the regional temperature variation. These loci helped forecasting future genetic offset in random forest and generalised dissimilarity models. These analyses predicted pronounced differences in the response of different reef systems in Western Australia to rising temperatures. Under the most optimistic future warming predictions (RCP 2.6), we observed a general pattern of increasing genetic offset with latitude. Under the most extreme climate scenario (RCP 8.5 in 2090-2100), coral populations at the Ningaloo World Heritage Area were predicted to experience a higher mismatch in genetic composition, compared to populations in the inshore Kimberley region. The study suggest complex and spatially heterogeneous patterns of climate-change vulnerability in coral populations across Western Australia, reinforcing the notion that regionally tailored conservation efforts will be most effective at managing coral reef resilience into the future. Methods 1. DArT sequencing Population samples were collected from five reef systems (see Figure 1): 1) The oceanic reef systems of Ashmore Reef and 2) the Rowley Shoals; 3) the turbid and macro-tidal inshore Kimberley reef system (Adele Island, Beagle Reef and the nearshore fringing reefs within the Lalang-Garram Marine Park; 4) the fringing reefs of Gnaraloo, Quobba and Ningaloo Stations within the Ningaloo Coast World Heritage Area; and 5) Pelorus Island, midshelf central Great Barrier Reef (GBR). GBR samples were included to provide broad evolutionary and geographic context to the levels of diversity and divergence detected among reef systems in Western Australia. A total of 756 A. digitifera samples (~ 1-6 cm3) were collected from 31 sites across the four aforementioned reef systems in Western Australia (see Figure 1, Table S1), along with an additional 33 samples collected from Pelorus Island (Great Barrier Reef). Samples were identified in the field according to the morphological description provided by (Wallace, 1999). Samples were stored in 100% ethanol, subsampled and sent to Diversity Array Technology Pty Ltd. (DArT P/L) for DNA extraction, library prep, sequencing and SNP calling using the same protocol as in Thomas et al., 2020. 2. Genetic offset to climate change Genetic offset is a term used to describe the mismatch of gene-environmental associations (GEAs) under future climate conditions (Bay, Harrigan, Underwood, et al., 2018; Fitzpatrick & Keller, 2015). This is usually characterised by the Euclidean distance between present and future biological space (Ellis et al., 2012). Under this framework, we used two model algorithms, gradient forest (GF) and generalised dissimilarity models (GDM) in the R packages ‘gradientforest’ (Ellis et al., 2012) and ‘gdm’ (Fitzpatrick et al., 2021), respectively, to describes patterns of observed genetic variation under specified climate conditions at the 26 sample sites in WA (excluding the Lalang-Garram sites). In contrast to GF which partitions the genotype data along the gradient of environmental data, GDMs are not based on machine learning and integrate distance matrices to fit gene-environmental responses using I-splines, which inform on the magnitude and slope of variables when explaining genetic turnover (Fitzpatrick & Keller, 2015; Fitzpatrick et al., 2013; Gibson et al., 2017). Once gene-environmental responses are identified at sample site locations, the models were then used to estimate regional spatial similarities in gene-environmental associations in site-neighbouring regions to predict future mismatches in these associations under climate change conditions (genetic offset) across reef systems in Western Australia, following the approach described in Fitzpatrick & Keller, 2015. Before running ‘gradientforest’ and ‘gdm’, we identified outliers with significant GEAs using BayeScEnv (as above and excluding samples from the GBR due to high genetic dissimilarity to WA samples), which is an adapted Bayesian approach that combines FST differentiation at loci level with the selective pressure on allele frequencies driven by environmental and geomorphological conditions (de Villemereuil & Gaggiotti, 2015; Stucki et al., 2017). Loci outside the 95% false discovery rate threshold were considered outliers possibly under directional selection, and these were included in the gradient forest analysis. Environmental variables were selected based on their importance in delineating coral growth, settlement and survival (see Table 2) (Maina et al., 2011) and can be classified into five groups; sea surface temperature (SST), SST anomalies (Zinke et al., 2018), water column optical parameters, geomorphological variables, and physical water column parameters. All variables were downscaled to the 250 m bathymetry resolution of Australia (Whiteway, 2009) using nearest neighbour resampling (Gogina & Zettler, 2010) after smoothing and completing missing environmental data using kriging interpolation (Assis et al., 2018). Once downscaled, all variables were clipped to the 0-40 m bathymetry mask, representing the zone that most photic hard corals occupy (Veron & Marsh, 1988). Prior to running BayeScEnv, variables that were correlated ³ |0.80| (Mateo et al., 2013; Senaviratna & Cooray, 2019) (Pearson correlation) with other variables at site locations were excluded to avoid overfitting, whilst retaining at least one variable from each group (see Table 2). Values of the remaining less correlated variable were extracted at each site (see Table S5) and standardised to absolute environmental distances following the BayeScEnv developers’ recommendation (Villemereuil & Gaggiotti, 2015). When extracted site variable data returned NA, values at the closest neighbouring pixel were used in further analyses. Transformed variables in association with allele frequencies of the SNP genotype data were integrated in BayeScEnv, applying default chain and model parameter settings (5,000 iterations, 20 pilot runs and 5,000 burn-in length). Posterior error probability incorporating the environmental factor (PEP g) < 0.05 was applied as recommended threshold to identify potential outlier loci or putative adaptive loci. Putatively adaptive loci were selected for the GF analysis if they were polymorphic in more than 20% of sampled populations (Fitzpatrick & Keller, 2015) while all adaptive loci, identified using BayeScEnv are used for GDM analysis. The ‘gradientforest’ algorithm was based on 2,000 regression trees per SNP and constructed with a depth of conditional permutation adjusted to the number of variables (Fitzpatrick & Keller, 2015) and a variable correlation threshold of 0.8. GF model performance was calculated and variable importance was visualised using cumulative importance plots across individual and overall SNP’s with positive R2 value. For the GDM, the default model setting of three I-splines was used. GDM performance was assessed based on % deviance explained and the relative variable importance which was represented by the sum of I-spline coefficients (Fitzpatrick et al., 2013). To identify the regional variation in GEA patterns and assess the future genetic offset of A. digitifera populations in WA, the study area of the 26 sites in WA was extended with a radius of 50 km {very few larvae disperse farther than 50 km (Graham et al., 2011; Jones et al., 2009; Underwood, 2009)}. The similarity in GEAs within this 50 km radius was assessed in both models based on similarities with the environmental conditions at the sampled sites (Bay, Harrigan, Underwood, et al., 2018) and visualised in Principle Component Analysis (PCA) as described in Fitzpatrick and Keller (2015). As a complementary method to determine if the spatial variable importance from the gradient forest were robust, we carried out a Sambada analysis (Sambada method and results are described in the supplementary text in the supplementary data). Finally, we used the GF and GDM to assess the future genetic offset across the different reef systems in WA. Projected SST data from four different climate change scenarios, which we extracted from three Atmosphere-Ocean General Circulation Models (AOGCM), CCSM4, HADGEM2-ES and MIROC 5 from the CMIP 5 database (Taylor et al., 2012). We resampled future SST data to 4 km resolution using the NASA/OB.DAAC Data Analysis Software (NASA SeaDAS V 7.5.3). SST data from 2040-2050 and 2090-2100 data under RCP 2.6 (mildest scenario) and RCP 8.5 (extreme case scenario) were averaged to account for variability in future SST data. Buffer zones with a radius of 50 km of future SST data were constructed using the same downscaling and masking procedures as used for the present environmental conditions. Significant differences in genetic offset was tested between reef systems across the four climate change conditions using Kruskal-wallis non-parametric test with posthoc Dunn test with Bonferroni correction or two-way Anova with posthoc Tukey test based on the extracted Euclidean distance values within a 50 km radius around the site locations.

AU：UCB预测：人口：年中：增长在06-01-2100达0.300%，相较于06-01-2099的0.300%保持不变。AU：UCB预测：人口：年中：增长数据按年更新，06-01-1986至06-01-2100期间平均值为0.750%，共115份观测结果。该数据的历史最高值出现于06-01-2008，达2.230%，而历史最低值则出现于06-01-2100，为0.300%。CEIC提供的AU：UCB预测：人口：年中：增长数据处于定期更新的状态，数据来源于U.S. Census Bureau，数据归类于全球数据库的澳大利亚 – Table AU.US Census Bureau: Demographic Projection。

This dataset comes from a study which sought to forecast the future risk of Barmah Forest virus disease (BFV) under climate change scenarios.

The study was conducted in Queensland, Australia, an area covering 1,727,200 km² (22·5% of the country) with 7,400 km of continental coastline and 9,800 km including islands. The estimated population was 4,580,725 on 30 June, 2011 for Queensland, Australia.

To complete the forecast several existing datasets were drawn upon: data on notified BFV cases covering the period 2000–2008 were obtained from Queensland Health. Gridded (5 km×5 km) climate data with complete records of annual average maximum and minimum temperature and rainfall were provided by Australian Bureau of Meteorology for the same period. Data on socio-economic indicator (i.e., SEIFA index) and tides were obtained from Australian Bureau of Statistics and Queensland Transport, respectively.

This data was used to model the current geographical distribution of BFV disease transmission across Queensland coastal regions and to project the potential changes in the risk of geographical distribution of BFV disease transmission for the years 2025, 2050 and 2100 in Queensland, Australia, using the medium level A1B climate change scenario.

This dataset presents the odds ratios of climate, socio-economic and tidal variables associated with Barmah Forest virus disease outbreaks in the entire coastal region in Queensland.

Cette statistique présente l'évolution de la population mondiale de 1950 à 2024, ainsi que les prévisions de l'ONU jusqu'à 2100. La population mondiale s'élevait à environ 8,16 milliards de personnes en 2024. Comme présenté ci-dessus, le nombre total de personnes dans le monde a au moins doublé depuis les années 1950, et cette augmentation se poursuit. Un coup d’œil à l'évolution de la population mondiale depuis le début de l'ère commune permet de constater que cette augmentation est sans précédent. La première hausse importante de la population a eu lieu au 14ème siècle, après les périodes de peste qui entrainèrent la mort d'environ 25 millions de personnes dans le monde. Par la suite, la population mondiale a lentement mais régulièrement progressé pour finalement atteindre les valeurs records recensées entre 1950 et 2000. La majorité de la population mondiale vit sur le continent asiatique, comme le montre la statistique sur la population mondiale par continent. Dans environ cent ans, on estime que la population du continent africain devrait atteindre des niveaux similaires à ceux de l'Asie aujourd'hui. En prévision du développement de la population mondiale, on estime que les chiffres devraient atteindre les 10 milliards d'habitants au 22ème siècle. L'augmentation de la population présente un risque grandissant pour la planète, puisque cette montée en flèche correspond à une augmentation équivalente de consommation en nourriture et en ressources. Les scientifiques s'interrogent quant à raréfaction des ressources naturelles comme le pétrole et la nourriture, qui met en danger le genre humain et l'écosystème mondial. De nos jours, le nombre de personnes sous-alimentées/en situation de famine dans le monde est légèrement à la baisse, mais les prévisions annoncent un avenir plus sombre.

Ce graphique montre l'âge médian de la population résidente au Qatar de 1950 à 2100. L’âge médian est l’âge qui divise une population en deux groupes numériquement égaux, c’est-à-dire que la moitié des gens sont plus jeunes que cet âge et la moitié sont plus âgés. Il s’agit d’un indice unique qui résume la distribution par âge d’une population. En 2020, l’âge médian de la population qatarie était de 32 ans.

Au milieu de l'année 2023, environ 60 % de la population mondiale vivait en Asie. La population mondiale totale s'élevait à 8,1 milliards de personnes sur la planète.Population mondiale Grâce aux progrès médicaux, de meilleures conditions de vie et l'augmentation de la productivité agricole, la population mondiale devrait augmenter. Les taux de mortalité sont en baisse et l'âge médian de la population mondiale a augmenté lors des dernières années de 24 ans en 1995 à 30,9 ans en 2020. Selon le Département des affaires économiques et sociales des Nations unies, le nombre de personnes devrait régulièrement augmenter et en 2100, la population mondiale devrait atteindre environ 10,87 milliards de personnes. Les pays avec le taux de croissance de population le plus élevé en 2021 étaient principalement des pays africains. Comparé à l'année précédente, la population du Soudan du Sud a augmenté d'environ 5,05% en 2021. Le pays avec le taux de déclin de population le plus élevé en 2021 était les îles Cook. Aux îles Cook, la population a diminué d'environ 2,46% par rapport à l'année précédente. L'Asie est le continent le plus peuplé de la Terre. La Chine était le pays avec la plus grande population. À la mi-2021, environ 1,4 milliard de personnes vivaient en Chine.

Cette statistique présente la population mondiale par continent au milieu de l'année 2022. Au milieu de l'année 2022, environ 4,73 milliards de personnes vivaient en Asie. Population mondiale et pays les plus peuplés

Il y a environ 8 milliards de personnes dans le monde aujourd'hui, chiffre souligne l'accroissement spectaculaire de la population depuis le début de la révolution industrielle.. Depuis les années 1950, la population mondiale a également au moins doublé. On estime que la population mondiale devrait atteindre les environs de 9,75 milliards de personnes en 2050.

En ce qui concerne le nombre d'habitants, le continent asiatique arrive largement tête. Au milieu de l'année 2022, plus de 4,73 milliards de personnes vivaient uniquement en Asie, soit plus de la moitié de la population mondiale. Comme pour les autres régions du monde (mise à part l'Europe) la majorité de la population asiatique a moins de 15 ans. Depuis 2022, environ 8 % de la population mondiale est âgée de 64 ans ou plus.

Cette même année, les pays les plus peuplés du monde étaient la Chine, puis l'Inde, deux pays situés sur le continent asiatique et deux pays comptant environ quatre fois plus d'habitants que le troisième pays : les États-Unis. On estime que la population totale du pays le plus peuplé, la Chine, était d’environ 1,411 milliards en 2014. Non seulement la population totale de la Chine augmente de façon continue, mais l'âge médian a également augmenté rapidement au cours des six dernières décennies. On estime que, grâce à l'amélioration des services de santé et des conditions de vie, l'âge médian en Chine en 2025 sera de 40ans, c'est à dire que la moitié de la population chinoise sera plus jeune et l'autre moitié, plus âgée.

Il est intéressant de remarque que les 20 pays ayant le taux de croissance démographique le plus élevé en 2014 étaient principalement des pays du continent africain. Cette croissance est due au manque de contraceptions et à la baisse de la mortalité infantile. La population d'Asie et d'Afrique devrait augmenter rapidement au cours des 100 prochaines années. La population africaine devrait ainsi dépasser les 4,1 milliards de personnes en 2100. Avec ces deux continents menant le développement démographique de l'humanité, la croissance démographique mondiale est extrêmement rapide.

Ce graphique présente le taux de natalité de la Chine entre 2000 et 2021, pour 1.000 habitants. En l'espace de vingt ans, le taux de natalité chinois a ainsi reculé de 39 % pour atteindre moins de 8 naissances pour 1.000 habitants en 2021. Une population vieillissante et proche du déclin démographique La situation de la Chine s’inscrit dans la tendance mondiale du ralentissement démographique. L’ancien pays le plus peuplé au monde, avec plus d’1,425 milliards d’habitants en mi-2023, voit sa population vieillir et devrait plonger à 767 millions d’habitants en 2100, tandis que l’Inde et le Nigeria culmineront respectivement à 1530 et 546 millions d’habitants. Alors que l’espérance de vie en Chine a augmenté de presque 7 ans de 2000 à 2023, l’âge médian de la population chinoise ne cesse lui aussi d’augmenter, passant de 29 ans en 2000 à 39 ans en 2023. Ce vieillissement est lié au déclin démographique que traverse la Chine. En effet, après de légers pics de croissance en 2012 et 2017, respectivement de 0,68% et 0,61%, la population chinoise n’a augmenté que de 0,09% en 2021. Le déclin démographique chinois a de nombreuses sources : le frein qu’a été la politique de l’enfant unique lancée au début des années 80 et ses conséquences sur la parité hommes femmes, l’éducation, le rejet progressif du mariage et de la maternité par les jeunes générations, la crise du marché du travail en sont quelques exemples. Une cause de l’essoufflement de l’économie chinoise Malgré son rang de deuxième puissance économique mondiale et son PIB estimé à 18.000 milliards de dollars en 2022, la Chine pâtit d’un ralentissement économique en partie dû à ce déclin démographique. Ce déclin est d’autant plus préoccupant pour les Chinois que le pays reste inégalitaire sur les revenus. Malgré des régions plus riches comme la Chine de l’Est, le PIB par habitant chinois était presque 6 fois inférieur à celui des Etats-Unis en 2023. Le manque de renouvellement de la population amène l’état chinois à assumer une partie des frais médicaux de la population vieillissante, tout en augmentant les versements d’allocations familiales afin de relancer la natalité. D’autres facteurs comme l’affaiblissement de secteurs clefs du PIB chinois tels que l’immobilier, la réduction des exportations en 2023 et les tensions géopolitiques contribuent à fragiliser l’économie chinoise.