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TL;DR: This work combines available data on solid waste with a model that uses population density and economic status to estimate the amount of land-based plastic waste entering the ocean, which is estimated to be 275 million metric tons.
Abstract: Plastic debris in the marine environment is widely documented, but the quantity of plastic entering the ocean from waste generated on land is unknown. By linking worldwide data on solid waste, population density, and economic status, we estimated the mass of land-based plastic waste entering the ocean. We calculate that 275 million metric tons (MT) of plastic waste was generated in 192 coastal countries in 2010, with 4.8 to 12.7 million MT entering the ocean. Population size and the quality of waste management systems largely determine which countries contribute the greatest mass of uncaptured waste available to become plastic marine debris. Without waste management infrastructure improvements, the cumulative quantity of plastic waste available to enter the ocean from land is predicted to increase by an order of magnitude by 2025.
6,689 citations
TL;DR: It is concluded that cox1 sequencing, or ‘barcoding’, can be used to identify fish species.
Abstract: GC than sharks (44.7% versus 41.0%), again largely due to higher GC in the 3rd codon position in the former (36.3% versus 26.8%). Average within-species, genus, family, order and class Kimura two parameter (K2P) distances were 0.39%, 9.93%, 15.46%, 22.18% and 23.27%, respectively. All species could be differentiated by their cox1 sequence, although single individuals of each of two species had haplotypes characteristic of a congener. Although DNA barcoding aims to develop species identification systems, some phylogenetic signal was apparent in the data. In the neighbourjoining tree for all 754 sequences, four major clusters were apparent: chimaerids, rays, sharks and teleosts. Species within genera invariably clustered, and generally so did genera within families. Three taxonomic groups—dogfishes of the genus Squalus, flatheads of the family Platycephalidae, and tunas of the genus Thunnus—were examined more closely. The clades revealed after bootstrapping generally corresponded well with expectations. Individuals from operational taxonomic units designated as Squalus species B through F formed individual clades, supporting morphological evidence for each of these being separate species. We conclude that cox1 sequencing, or ‘barcoding’, can be used to identify fish species.
3,212 citations
American Museum of Natural History1, University of Tartu2, University of Colombo3, Royal Netherlands Academy of Arts and Sciences4, University of Florida5, University of Palermo6, Goethe University Frankfurt7, Hobart Corporation8, Nakhon Phanom University9, University of Bamenda10, University of Gothenburg11, Naturalis12, Swedish University of Agricultural Sciences13, Royal Botanic Gardens14, Universiti Malaysia Sabah15, United States Department of Agriculture16, Forest Research Institute Malaysia17, Humboldt State University18, Chinese Academy of Sciences19, Landcare Research20, University of Western Australia21, Estonian University of Life Sciences22, University of Southern Queensland23, Botanic Garden Meise24, Manchester Metropolitan University25, James Cook University26
TL;DR: Diversity of most fungal groups peaked in tropical ecosystems, but ectomycorrhizal fungi and several fungal classes were most diverse in temperate or boreal ecosystems, and manyfungal groups exhibited distinct preferences for specific edaphic conditions (such as pH, calcium, or phosphorus).
Abstract: Fungi play major roles in ecosystem processes, but the determinants of fungal diversity and biogeographic patterns remain poorly understood. Using DNA metabarcoding data from hundreds of globally distributed soil samples, we demonstrate that fungal richness is decoupled from plant diversity. The plant-to-fungus richness ratio declines exponentially toward the poles. Climatic factors, followed by edaphic and spatial variables, constitute the best predictors of fungal richness and community composition at the global scale. Fungi show similar latitudinal diversity gradients to other organisms, with several notable exceptions. These findings advance our understanding of global fungal diversity patterns and permit integration of fungi into a general macroecological framework.
2,346 citations
Hobart Corporation1, Spanish National Research Council2, University of Évora3, University of Copenhagen4, Conservation International5, University of Wollongong6, University of Hong Kong7, National Cheng Kung University8, Umeå University9, James Cook University10, Commonwealth Scientific and Industrial Research Organisation11, University of Cape Town12, Stellenbosch University13, National Oceanic and Atmospheric Administration14, Monash University15, Yale University16, University of Tasmania17, University of Picardie Jules Verne18, Southern Cross University19, University of Western Australia20, University of Eastern Finland21, University of Queensland22, Zoological Society of London23, National Oceanography Centre24, University of Florida25, University of California, Irvine26, La Trobe University27, University of British Columbia28, Academia Sinica29, University of New South Wales30
TL;DR: The negative effects of climate change cannot be adequately anticipated or prepared for unless species responses are explicitly included in decision-making and global strategic frameworks, and feedbacks on climate itself are documented.
Abstract: Distributions of Earth’s species are changing at accelerating rates, increasingly driven by human-mediated climate change. Such changes are already altering the composition of ecological communities, but beyond conservation of natural systems, how and why does this matter? We review evidence that climate-driven species redistribution at regional to global scales affects ecosystem functioning, human well-being, and the dynamics of climate change itself. Production of natural resources required for food security, patterns of disease transmission, and processes of carbon sequestration are all altered by changes in species distribution. Consideration of these effects of biodiversity redistribution is critical yet lacking in most mitigation and adaptation strategies, including the United Nation’s Sustainable Development Goals.
1,917 citations
École Normale Supérieure1, University of Exeter2, Norwich Research Park3, Wageningen University and Research Centre4, University of Groningen5, Max Planck Society6, Ludwig Maximilian University of Munich7, Commonwealth Scientific and Industrial Research Organisation8, Université Paris-Saclay9, Stanford University10, National Oceanic and Atmospheric Administration11, National Institute for Space Research12, Bermuda Institute of Ocean Sciences13, University of Southampton14, PSL Research University15, National Institute for Environmental Studies16, Japan Agency for Marine-Earth Science and Technology17, University of Maryland, College Park18, University of Leeds19, International Institute of Minnesota20, Flanders Marine Institute21, ETH Zurich22, University of East Anglia23, German Aerospace Center24, Woods Hole Research Center25, University of Illinois at Urbana–Champaign26, University of Toulouse27, Japan Meteorological Agency28, Plymouth Marine Laboratory29, University of Paris30, Hobart Corporation31, Oeschger Centre for Climate Change Research32, Tsinghua University33, National Center for Atmospheric Research34, Appalachian State University35, University of Colorado Boulder36, University of Washington37, Atlantic Oceanographic and Meteorological Laboratory38, Princeton University39, Met Office40, Leibniz Institute of Marine Sciences41, Auburn University42, University of Tasmania43, VU University Amsterdam44, Oak Ridge National Laboratory45, Sun Yat-sen University46, Nanjing University47
TL;DR: In this paper, the authors describe and synthesize data sets and methodology to quantify the five major components of the global carbon budget and their uncertainties, including emissions from land use and land-use change data and bookkeeping models.
Abstract: Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere in a changing climate – the “global carbon budget” – is important to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe and synthesize data sets and methodology to quantify the five major components of the global carbon budget and their uncertainties. Fossil CO2 emissions (EFOS) are based on energy statistics and cement production data, while emissions from land-use change (ELUC), mainly deforestation, are based on land use and land-use change data and bookkeeping models. Atmospheric CO2 concentration is measured directly and its growth rate (GATM) is computed from the annual changes in concentration. The ocean CO2 sink (SOCEAN) and terrestrial CO2 sink (SLAND) are estimated with global process models constrained by observations. The resulting carbon budget imbalance (BIM), the difference between the estimated total emissions and the estimated changes in the atmosphere, ocean, and terrestrial biosphere, is a measure of imperfect data and understanding of the contemporary carbon cycle. All uncertainties are reported as ±1σ. For the last decade available (2010–2019), EFOS was 9.6 ± 0.5 GtC yr−1 excluding the cement carbonation sink (9.4 ± 0.5 GtC yr−1 when the cement carbonation sink is included), and ELUC was 1.6 ± 0.7 GtC yr−1. For the same decade, GATM was 5.1 ± 0.02 GtC yr−1 (2.4 ± 0.01 ppm yr−1), SOCEAN 2.5 ± 0.6 GtC yr−1, and SLAND 3.4 ± 0.9 GtC yr−1, with a budget imbalance BIM of −0.1 GtC yr−1 indicating a near balance between estimated sources and sinks over the last decade. For the year 2019 alone, the growth in EFOS was only about 0.1 % with fossil emissions increasing to 9.9 ± 0.5 GtC yr−1 excluding the cement carbonation sink (9.7 ± 0.5 GtC yr−1 when cement carbonation sink is included), and ELUC was 1.8 ± 0.7 GtC yr−1, for total anthropogenic CO2 emissions of 11.5 ± 0.9 GtC yr−1 (42.2 ± 3.3 GtCO2). Also for 2019, GATM was 5.4 ± 0.2 GtC yr−1 (2.5 ± 0.1 ppm yr−1), SOCEAN was 2.6 ± 0.6 GtC yr−1, and SLAND was 3.1 ± 1.2 GtC yr−1, with a BIM of 0.3 GtC. The global atmospheric CO2 concentration reached 409.85 ± 0.1 ppm averaged over 2019. Preliminary data for 2020, accounting for the COVID-19-induced changes in emissions, suggest a decrease in EFOS relative to 2019 of about −7 % (median estimate) based on individual estimates from four studies of −6 %, −7 %, −7 % (−3 % to −11 %), and −13 %. Overall, the mean and trend in the components of the global carbon budget are consistently estimated over the period 1959–2019, but discrepancies of up to 1 GtC yr−1 persist for the representation of semi-decadal variability in CO2 fluxes. Comparison of estimates from diverse approaches and observations shows (1) no consensus in the mean and trend in land-use change emissions over the last decade, (2) a persistent low agreement between the different methods on the magnitude of the land CO2 flux in the northern extra-tropics, and (3) an apparent discrepancy between the different methods for the ocean sink outside the tropics, particularly in the Southern Ocean. This living data update documents changes in the methods and data sets used in this new global carbon budget and the progress in understanding of the global carbon cycle compared with previous publications of this data set (Friedlingstein et al., 2019; Le Quere et al., 2018b, a, 2016, 2015b, a, 2014, 2013). The data presented in this work are available at https://doi.org/10.18160/gcp-2020 (Friedlingstein et al., 2020).
1,764 citations
Authors
Showing all 3980 results
| Name | H-index | Papers | Citations |
|---|---|---|---|
| Paul M. Thompson | 183 | 2271 | 146736 |
| Carl Folke | 133 | 360 | 125990 |
| David Smith | 129 | 2184 | 100917 |
| Thomas H. Marwick | 121 | 1063 | 58763 |
| Paul Turner | 114 | 1099 | 61390 |
| Qiang Yang | 112 | 1117 | 71540 |
| Henry F. Schaefer | 111 | 1611 | 68695 |
| Terence P. Speed | 102 | 461 | 96770 |
| Gerald F. Watts | 100 | 889 | 43807 |
| Timothy Clark | 95 | 1137 | 53665 |
| John Ross | 93 | 578 | 31543 |
| David H. Green | 92 | 288 | 30311 |
| David K. Gardner | 92 | 398 | 25301 |
| David Couper | 89 | 345 | 34699 |
| Gavin Giovannoni | 89 | 852 | 38443 |