The United Nations recently released population projections based on data until 2012 and a Bayesian probabilistic methodology. Analysis of these data reveals that, contrary to previous literature, world population is unlikely to stop growing this century. There is an 80% probability that world population, now 7.2 billion, will increase to between 9.6 and 12.3 billion in 2100. This uncertainty is much smaller than the range from the traditional UN high and low variants. Much of the increase is expected to happen in Africa, in part due to higher fertility and a recent slowdown in the pace of fertility decline. Also, the ratio of working age people to older people is likely to decline substantially in all countries, even those that currently have young populations.The United Nations (UN) is the leading agency that projects world population into the future on a regular basis (2). Every two years it publishes revised data of the populations of all countries by age and sex, as well as fertility, mortality and migration rates, in a biennial publication called the World Population Prospects (WPP). In July 2014, probabilistic projections for individual countries to 2100 were released Unlike previous projections, they allow us to quantify our confidence in projected future trends using established methods of statistical inference. They are based on recent data, including the results of the 2010 round of censuses and recent surveys until 2012, as well as the most recent data on incidence, ‡

The fractal or Hausdorff dimension is a measure of roughness (or smoothness) for time series and spatial data. The graph of a smooth, differentiable surface indexed in R d has topological and fractal dimension d. If the surface is nondifferentiable and rough, the fractal dimension takes values between the topological dimension, d, and d + 1. We review and assess estimators of fractal dimension by their large sample behavior under infill asymptotics, in extensive finite sample simulation studies, and in a data example on arctic sea-ice profiles. For time series or line transect data, box-count, Hall-Wood, semi-periodogram, discrete cosine transform and wavelet estimators are studied along with variation estimators with power indices 2 (variogram) and 1 (madogram), all implemented in the R package fractaldim. Considering both efficiency and robustness, we recommend the use of the madogram estimator, which can be interpreted as a statistically more efficient version of the Hall-Wood estimator. For two-dimensional lattice data, we propose robust transect estimators that use the median of variation estimates along rows and columns. Generally, the link between power variations of index p > 0 for stochastic processes, and the Hausdorff dimension of their sample paths, appears to be particularly robust and inclusive when p = 1. Fig. 5. Log-log regression for the standard version of the box-count estimator and the datasets in the lower row of Figure 1. Only the points marked with filled circles are used when fitting the regression line.

Projections of countries' future populations, broken down by age and sex, are widely used for planning and research. They are mostly done deterministically, but there is a widespread need for probabilistic projections. We propose a Bayesian method for probabilistic population projections for all countries. The total fertility rate and female and male life expectancies at birth are projected probabilistically using Bayesian hierarchical models estimated via Markov chain Monte Carlo using United Nations population data for all countries. These are then converted to age-specific rates and combined with a cohort component projection model. This yields probabilistic projections of any population quantity of interest. The method is illustrated for five countries of different demographic stages, continents and sizes. The method is validated by an out of sample experiment in which data from 1950-1990 are used for estimation, and applied to predict 1990-2010. The method appears reasonably accurate and well calibrated for this period. The results suggest that the current United Nations high and low variants greatly underestimate uncertainty about the number of oldest old from about 2050 and that they underestimate uncertainty for high fertility countries and overstate uncertainty for countries that have completed the demographic transition and whose fertility has started to recover towards replacement level, mostly in Europe. The results also indicate that the potential support ratio (persons aged 20-64 per person aged 65þ) will almost certainly decline dramatically in most countries over the coming decades.double logistic function | Lee-Carter method | life expectancy at birth | predictive distribution | United Nations World Population Prospects P rojections of countries' future populations, broken down by age and sex, are used by governments for social, economic, and infrastructure planning by international organizations for development planning and monitoring and global modeling, by the private sector for strategic and marketing decisions, and by academic and other researchers as inputs to social and health research.Most population projections are currently done deterministically, using the cohort component method (1, 2). This is an ageand sex-structured version of the basic demographic identity that the population of a country at the next time point is equal to the population at the current time point, plus the number of births, minus the number of deaths, plus the number of immigrants minus the number of emigrants. It was formulated in matrix form by Leslie (3) and is described in detail in ref. (4, chap. 6).Population projections are currently produced by many organizations, including national and local governments and private companies. The main organizations that have produced population projections for all or most of the world's countries are the United Nations (UN) (5), the World Bank (6), and the United States Census Bureau (7), all of which use the standard deterministic approach. Among these, the UN produces ...

We propose a Bayesian hierarchical model for producing probabilistic forecasts of male period life expectancy at birth for all the countries of the world from the present to 2100. Such forecasts would be an input to the production of probabilistic population projections for all countries, which is currently being considered by the United Nations. To evaluate the method, we did an out-of-sample cross-validation experiment, fitting the model to the data from 1950–1995, and using the estimated model to forecast for the subsequent ten years. The ten-year predictions had a mean absolute error of about 1 year, about 40% less than the current UN methodology. The probabilistic forecasts were calibrated, in the sense that (for example) the 80% prediction intervals contained the truth about 80% of the time. We illustrate our method with results from Madagascar (a typical country with steadily improving life expectancy), Latvia (a country that has had a mortality crisis), and Japan (a leading country). We also show aggregated results for South Asia, a region with eight countries. Free publicly available software packages called and are available to implement the method.

The social cost of carbon dioxide (SC-CO2) measures the monetized value of the damages to society caused by an incremental metric tonne of CO2 emissions and is a key metric informing climate policy. Used by governments and other decision-makers in benefit–cost analysis for over a decade, SC-CO2 estimates draw on climate science, economics, demography and other disciplines. However, a 2017 report by the US National Academies of Sciences, Engineering, and Medicine1 (NASEM) highlighted that current SC-CO2 estimates no longer reflect the latest research. The report provided a series of recommendations for improving the scientific basis, transparency and uncertainty characterization of SC-CO2 estimates. Here we show that improved probabilistic socioeconomic projections, climate models, damage functions, and discounting methods that collectively reflect theoretically consistent valuation of risk, substantially increase estimates of the SC-CO2. Our preferred mean SC-CO2 estimate is $185 per tonne of CO2 ($44–$413 per tCO2: 5%–95% range, 2020 US dollars) at a near-term risk-free discount rate of 2%, a value 3.6 times higher than the US government’s current value of $51 per tCO2. Our estimates incorporate updated scientific understanding throughout all components of SC-CO2 estimation in the new open-source Greenhouse Gas Impact Value Estimator (GIVE) model, in a manner fully responsive to the near-term NASEM recommendations. Our higher SC-CO2 values, compared with estimates currently used in policy evaluation, substantially increase the estimated benefits of greenhouse gas mitigation and thereby increase the expected net benefits of more stringent climate policies.

We produce probabilistic projections of population for all countries based on probabilistic projections of fertility, mortality, and migration. We compare our projections to those from the United Nations' Probabilistic Population Projections, which uses similar methods for fertility and mortality but deterministic migration projections. We find that uncertainty in migration projection is a substantial contributor to uncertainty in population projections for many countries. Prediction intervals for the populations of Northern America and Europe are over 70% wider, whereas prediction intervals for the populations of Africa, Asia, and the world as a whole are nearly unchanged. Out-of-sample validation shows that the model is reasonably well calibrated.Bayesian hierarchical model | international migration | predictive distribution | United Nations | World Population Prospects I n this paper we describe a method for probabilistic projection of population for all countries, with a focus on accounting for uncertainty in projections of international migration. In particular, we are motivated by the needs of the United Nations (UN) Population Division in producing population projections for all countries until 2100 based on projections of fertility, mortality, and migration.A variety of forces contribute to the ebb and flow of international migration. Economic theories at varying levels of granularity indicate that migration flows can arise from individual attempts to maximize income (1, 2), household-level mitigation of risk (3, 4), or differences in global supply and demand for labor (5, 6). Individuals decide to migrate based on an assessment of push and pull factors (7), which may include migration policy (8), geopolitical conflict (9), and quality of the natural environment (10, 11). Networks of migrants provide a feedback mechanism such that migration flows tend to perpetuate themselves over time (12,13). Bijak (14) gives a thorough overview of theories and models of international migration. Despite their acknowledged role in driving migration, our model does not make use of push and pull factors, economic or otherwise, as covariates. Such factors are largely too difficult to predict in the long term to be of use. Instead, we appeal to the inertia of selfperpetuating migration patterns by modeling migration as an autoregressive process.Historically, most methods for projecting population have been deterministic. If the current population is known, broken down by age and sex, and future age-and sex-specific rates are projected for fertility, mortality, and migration, then the cohortcomponent method produces population projections (15). However, the UN Population Division now produces probabilistic projections of population, fertility, and mortality for all countries, but these projections still condition on deterministic migration projections (16, 17). The current methodology in the UN's World Population Prospects (WPP) differs from country to country but typically projects that net migration counts will remain cons...

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Novel species of fungi described in this study include those from various countries as follows: Australia, Austroboletus asper on soil, Cylindromonium alloxyli on leaves of Alloxylon pinnatum, Davidhawksworthia quintiniae on leaves of Quintinia sieberi, Exophiala prostantherae on leaves of Prostanthera sp., Lactifluus lactiglaucus on soil, Linteromyces quintiniae (incl. Linteromyces gen. nov.) on leaves of Quintinia sieberi, Lophotrichus medusoides from stem tissue of Citrus garrawayi, Mycena pulchra on soil, Neocalonectria tristaniopsidis (incl. Neocalonectria gen. nov.)and Xyladictyochaeta tristaniopsidis on leaves of Tristaniopsis collina, Parasarocladium tasmanniae on leaves of Tasmannia insipida, Phytophthora aquae-cooljarloo from pond water, Serendipita whamiae as endophyte from roots of Eriochilus cucullatus, Veloboletus limbatus (incl. Veloboletus gen. nov.)onsoil. Austria, Cortinarius glaucoelotus onsoil. Bulgaria, Suhomyces rilaensis from the gut of Bolitophagus interruptus found on a Polyporus sp. Canada, Cantharellus betularum among leaf litter of Betula, Penicillium saanichii from house dust. Chile, Circinella lampensis on soil, Exophiala embothrii from rhizosphere of Embothrium coccineum. China, Colletotrichum cycadis on leaves of Cycas revoluta. Croatia, Phialocephala melitaea on fallen branch of Pinus halepensis. Czech Republic, Geoglossum jirinae on soil, Pyrenochaetopsis rajhradensis from dead wood of Buxus sempervirens. Dominican Republic, Amanita domingensis on litter of deciduous wood, Melanoleuca dominicana on forest litter. France, Crinipellis nigrolamellata (Martinique) on leaves of Pisonia fragrans, Talaromyces pulveris from bore dust of Xestobium rufovillosum infesting floorboards. French Guiana, Hypoxylon hepaticolor on dead corticated branch. Great Britain, Inocybe ionolepis on soil. India, Cortinarius indopurpurascens among leaf litter of Quercus leucotrichophora. Iran, Pseudopyricularia javanii on infected leaves of Cyperus sp., Xenomonodictys iranica (incl. Xenomonodictys gen. nov.) on wood of Fagus orientalis. Italy, Penicillium vallebormidaense from compost. Namibia, Alternaria mirabibensis on plant litter, Curvularia moringae and Moringomyces phantasmae (incl. Moringomyces gen. nov.) on leaves and flowers of Moringa ovalifolia, Gobabebomyces vachelliae (incl. Gobabebomyces gen. nov.) on leaves of Vachellia erioloba, Preussia procaviae on dung of Procavia capensis. Pakistan, Russula shawarensis from soil on forest floor. Russia, Cyberlindnera dauci from Daucus carota. South Africa, Acremonium behniae on leaves of Behnia reticulata, Dothiora aloidendri and Hantamomyces aloidendri (incl. Hantamomyces gen. nov.) on leaves of Aloidendron dichotomum, Endoconidioma euphorbiae on leaves of Euphorbia mauritanica , Eucasphaeria proteae on leaves of Protea neriifolia , Exophiala mali from inner fruit tissue of Malus sp., Graminopassalora geissorhizae on leaves of Geissorhiza splendidissima, Neocamarosporium leipoldtiae on leaves of Leipoldtia schultzii, Neocladosporium osteospermi on leaf spots of Osteospermum moniliferum, Neometulocladosporiella seifertii on leaves of Combretum caffrum, Paramyrothecium pituitipietianum on stems of Grielum humifusum, Phytopythium paucipapillatum from roots of Vitis sp., Stemphylium carpobroti and Verrucocladosporium carpobroti on leaves of Carpobrotus quadrifolius, Suttonomyces cephalophylli on leaves of Cephalophyllum pilansii. Sweden, Coprinopsis rubra on cow dung, Elaphomyces nemoreus fromdeciduouswoodlands. Spain, Polyscytalum pini-canariensis on needles of Pinus canariensis, Pseudosubramaniomyces septatus from stream sediment, Tuber lusitanicum on soil under Quercus suber. Thailand, Tolypocladium flavonigrum on Elaphomyces sp. USA, Chaetothyrina spondiadis on fruits of Spondias mombin, Gymnascella minnisii from bat guano, Juncomyces patwiniorum on culms of Juncus effusus, Moelleriella puertoricoensis on scale insect, Neodothiora populina (incl. Neodothiora gen. nov.) on stem cankers of Populus tremuloides, Pseudogymnoascus palmeri fromcavesediment. Vietnam, Cyphellophora vietnamensis on leaf litter, Tylopilus subotsuensis on soil in montane evergreen broadleaf forest. Morphological and culture characteristics are supported by DNA barcodes.