Global fire emissions and the contribution of deforestation, savanna, forest, agricultural, and peat fires (1997-2009)

New burned area datasets and top-down constraints from atmospheric concentration measurements of pyrogenic gases have decreased the large uncertainty in fire emissions estimates. However, significant gaps remain in our understanding of the contribution of deforestation, savanna, forest, agricultural waste, and peat fires to total global fire emissions. Here we used a revised version of the Carnegie-Ames-Stanford-Approach (CASA) biogeochemical model and improved satellite-derived estimates of area burned, fire activity, and plant productivity to calculate fire emissions for the 1997–2009 period on a 0.5° spatial resolution with a monthly time step. For November 2000 onwards, estimates were based on burned area, active fire detections, and plant productivity from the MODerate resolution Imaging Spectroradiometer (MODIS) sensor. For the partitioning we focused on the MODIS era. We used maps of burned area derived from the Tropical Rainfall Measuring Mission (TRMM) Visible and Infrared Scanner (VIRS) and Along-Track Scanning Radiometer (ATSR) active fire data prior to MODIS (1997–2000) and estimates of plant productivity derived from Advanced Very High Resolution Radiometer (AVHRR) observations during the same period. Average global fire carbon emissions according to this version 3 of the Global Fire Emissions Database (GFED3) were 2.0 Pg C year⁻¹ with significant interannual variability during 1997–2001 (2.8 Pg C year⁻¹ in 1998 and 1.6 Pg C year⁻¹ in 2001). Globally, emissions during 2002–2007 were relatively constant (around 2.1 Pg C year⁻¹) before declining in 2008 (1.7 Pg C year⁻¹) and 2009 (1.5 Pg C year⁻¹) partly due to lower deforestation fire emissions in South America and tropical Asia. On a regional basis, emissions were highly variable during 2002–2007 (e.g., boreal Asia, South America, and Indonesia), but these regional differences canceled out at a global level. During the MODIS era (2001–2009), most carbon emissions were from fires in grasslands and savannas (44%) with smaller contributions from tropical deforestation and degradation fires (20%), woodland fires (mostly confined to the tropics, 16%), forest fires (mostly in the extratropics, 15%), agricultural waste burning (3%), and tropical peat fires (3%). The contribution from agricultural waste fires was likely a lower bound because our approach for measuring burned area could not detect all of these relatively small fires. Total carbon emissions were on average 13% lower than in our previous (GFED2) work. For reduced trace gases such as CO and CH₄, deforestation, degradation, and peat fires were more important contributors because of higher emissions of reduced trace gases per unit carbon combusted compared to savanna fires. Carbon emissions from tropical deforestation, degradation, and peatland fires were on average 0.5 Pg C year⁻¹. The carbon emissions from these fires may not be balanced by regrowth following fire. Our results provide the first global assessment of the contribution of different sources to total global fire emissions for the past decade, and supply the community with an improved 13-year fire emissions time series

Fungal diversity notes 1512-1610: taxonomic and phylogenetic contributions on genera and species of fungal taxa

This article is the 14th in the Fungal Diversity Notes series, wherein we report 98 taxa distributed in two phyla, seven classes, 26 orders and 50 families which are described and illustrated. Taxa in this study were collected from Australia, Brazil, Burkina Faso, Chile, China, Cyprus, Egypt, France, French Guiana, India, Indonesia, Italy, Laos, Mexico, Russia, Sri Lanka, Thailand, and Vietnam. There are 59 new taxa, 39 new hosts and new geographical distributions with one new combination. The 59 new species comprise Angustimassarina kunmingense, Asterina lopi, Asterina brigadeirensis, Bartalinia bidenticola, Bartalinia caryotae, Buellia pruinocalcarea, Coltricia insularis, Colletotrichum flexuosum, Colletotrichum thasutense, Coniochaeta caraganae, Coniothyrium yuccicola, Dematipyriforma aquatic, Dematipyriforma globispora, Dematipyriforma nilotica, Distoseptispora bambusicola, Fulvifomes jawadhuvensis, Fulvifomes malaiyanurensis, Fulvifomes thiruvannamalaiensis, Fusarium purpurea, Gerronema atrovirens, Gerronema flavum, Gerronema keralense, Gerronema kuruvense, Grammothele taiwanensis, Hongkongmyces changchunensis, Hypoxylon inaequale, Kirschsteiniothelia acutisporum, Kirschsteiniothelia crustaceum, Kirschsteiniothelia extensum, Kirschsteiniothelia septemseptatum, Kirschsteiniothelia spatiosum, Lecanora immersocalcarea, Lepiota subthailandica, Lindgomyces guizhouensis, Marthe asmius pallidoaurantiacus, Marasmius tangerinus, Neovaginatispora mangiferae, Pararamichloridium aquisubtropicum, Pestalotiopsis piraubensis, Phacidium chinaum, Phaeoisaria goiasensis, Phaeoseptum thailandicum, Pleurothecium aquisubtropicum, Pseudocercospora vernoniae, Pyrenophora verruculosa, Rhachomyces cruralis, Rhachomyces hyperommae, Rhachomyces magrinii, Rhachomyces platyprosophi, Rhizomarasmius cunninghamietorum, Skeletocutis cangshanensis, Skeletocutis subchrysella, Sporisorium anadelphiae-leptocomae, Tetraploa dashaoensis, Tomentella exiguelata, Tomentella fuscoaraneosa, Tricholomopsis lechatii, Vaginatispora flavispora and Wetmoreana blastidiocalcarea. The new combination is Torula sundara. The 39 new records on hosts and geographical distribution comprise Apiospora guiyangensis, Aplosporella artocarpi, Ascochyta medicaginicola, Astrocystis bambusicola, Athelia rolfsii, Bambusicola bambusae, Bipolaris luttrellii, Botryosphaeria dothidea, Chlorophyllum squamulosum, Colletotrichum aeschynomenes, Colletotrichum pandanicola, Coprinopsis cinerea, Corylicola italica, Curvularia alcornii, Curvularia senegalensis, Diaporthe foeniculina, Diaporthe longicolla, Diaporthe phaseolorum, Diatrypella quercina, Fusarium brachygibbosum, Helicoma aquaticum, Lepiota metulispora, Lepiota pongduadensis, Lepiota subvenenata, Melanconiella meridionalis, Monotosporella erecta, Nodulosphaeria digitalis, Palmiascoma gregariascomum, Periconia byssoides, Periconia cortaderiae, Pleopunctum ellipsoideum, Psilocybe keralensis, Scedosporium apiospermum, Scedosporium dehoogii, Scedosporium marina, Spegazzinia deightonii, Torula fici, Wiesneriomyces laurinus and Xylaria venosula. All these taxa are supported by morphological and multigene phylogenetic analyses. This article allows the researchers to publish fungal collections which are important for future studies. An updated, accurate and timely report of fungus-host and fungus-geography is important. We also provide an updated list of fungal taxa published in the previous fungal diversity notes. In this list, erroneous taxa and synonyms are marked and corrected accordingly

A century of trends in adult human height

Being taller is associated with enhanced longevity, and higher education and earnings. We reanalysed 1472 population-based studies, with measurement of height on more than 18.6 million participants to estimate mean height for people born between 1896 and 1996 in 200 countries. The largest gain in adult height over the past century has occurred in South Korean women and Iranian men, who became 20.2 cm (95% credible interval 17.5-22.7) and 16.5 cm (13.3-19.7) taller, respectively. In contrast, there was little change in adult height in some sub-Saharan African countries and in South Asia over the century of analysis. The tallest people over these 100 years are men born in the Netherlands in the last quarter of 20th century, whose average heights surpassed 182.5 cm, and the shortest were women born in Guatemala in 1896 (140.3 cm; 135.8-144.8). The height differential between the tallest and shortest populations was 19-20 cm a century ago, and has remained the same for women and increased for men a century later despite substantial changes in the ranking of countries

Rising rural body-mass index is the main driver of the global obesity epidemic in adults

Body-mass index (BMI) has increased steadily in most countries in parallel with a rise in the proportion of the population who live in cities 1,2 . This has led to a widely reported view that urbanization is one of the most important drivers of the global rise in obesity 3�6 . Here we use 2,009 population-based studies, with measurements of height and weight in more than 112 million adults, to report national, regional and global trends in mean BMI segregated by place of residence (a rural or urban area) from 1985 to 2017. We show that, contrary to the dominant paradigm, more than 55 of the global rise in mean BMI from 1985 to 2017�and more than 80 in some low- and middle-income regions�was due to increases in BMI in rural areas. This large contribution stems from the fact that, with the exception of women in sub-Saharan Africa, BMI is increasing at the same rate or faster in rural areas than in cities in low- and middle-income regions. These trends have in turn resulted in a closing�and in some countries reversal�of the gap in BMI between urban and rural areas in low- and middle-income countries, especially for women. In high-income and industrialized countries, we noted a persistently higher rural BMI, especially for women. There is an urgent need for an integrated approach to rural nutrition that enhances financial and physical access to healthy foods, to avoid replacing the rural undernutrition disadvantage in poor countries with a more general malnutrition disadvantage that entails excessive consumption of low-quality calories. © 2019, The Author(s)

Requirements and specifications for adaptive security: concepts and analysis

In an adaptive security-critical system, security mechanisms change according to the type of threat posed by the environment. Specifying the behavior of these systems is difficult because conditions of the environment are difficult to describe until the system has been deployed and used for a length of time. This paper defines the problem of adaptation in security-critical systems, and outlines the RELAIS approach for expressing requirements and specifying the behavior in a way that helps identify the need for adaptation, and the appropriate adaptation behavior at runtime. The paper introduces the notion of adaptation via input approximation and proposes statistical machine learning techniques for realizing it. The approach is illustrated with a running example and is applied to a realistic security example from a cloud-based file-sharing application. Bayesian classification and logistic regression methods are used to implement adaptive specifications and these methods offer different levels of adaptive security and usability in the file-sharing application