Author
Thomas W. Crowther
Other affiliations: Cardiff University, University of Helsinki, Yale University ...read more
Bio: Thomas W. Crowther is an academic researcher from ETH Zurich. The author has contributed to research in topics: Ecosystem & Soil carbon. The author has an hindex of 42, co-authored 120 publications receiving 9264 citations. Previous affiliations of Thomas W. Crowther include Cardiff University & University of Helsinki.
Topics: Ecosystem, Soil carbon, Medicine, Ecology, Climate change
Papers
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TL;DR: There is room for an extra 0.9 billion hectares of canopy cover, which could store 205 gigatonnes of carbon in areas that would naturally support woodlands and forests, which highlights global tree restoration as one of the most effective carbon drawdown solutions to date.
Abstract: The restoration of trees remains among the most effective strategies for climate change mitigation. We mapped the global potential tree coverage to show that 4.4 billion hectares of canopy cover could exist under the current climate. Excluding existing trees and agricultural and urban areas, we found that there is room for an extra 0.9 billion hectares of canopy cover, which could store 205 gigatonnes of carbon in areas that would naturally support woodlands and forests. This highlights global tree restoration as our most effective climate change solution to date. However, climate change will alter this potential tree coverage. We estimate that if we cannot deviate from the current trajectory, the global potential canopy cover may shrink by ~223 million hectares by 2050, with the vast majority of losses occurring in the tropics. Our results highlight the opportunity of climate change mitigation through global tree restoration but also the urgent need for action.
1,052 citations
University of New South Wales1, Oregon State University2, Braunschweig University of Technology3, University of California, San Diego4, Norwegian University of Life Sciences5, University of Liverpool6, Max Planck Society7, University of Tasmania8, University of Vermont9, ETH Zurich10, Stazione Zoologica Anton Dohrn11, Montana State University12, University of Amsterdam13, University of Southern California14, Pacific Northwest National Laboratory15, University of Hawaii at Manoa16, University of California, Berkeley17, Marine Biological Laboratory18, University of California, Irvine19, University of Georgia20, California Institute of Technology21, University of Edinburgh22, Ohio State University23, University of Sydney24, University of Alberta25, Georgia Institute of Technology26, Australian Institute of Marine Science27, University of Melbourne28, University of Texas Medical Branch29, University of Queensland30
TL;DR: This Consensus Statement documents the central role and global importance of microorganisms in climate change biology and puts humanity on notice that the impact of climate change will depend heavily on responses of micro organisms, which are essential for achieving an environmentally sustainable future.
Abstract: In the Anthropocene, in which we now live, climate change is impacting most life on Earth. Microorganisms support the existence of all higher trophic life forms. To understand how humans and other life forms on Earth (including those we are yet to discover) can withstand anthropogenic climate change, it is vital to incorporate knowledge of the microbial 'unseen majority'. We must learn not just how microorganisms affect climate change (including production and consumption of greenhouse gases) but also how they will be affected by climate change and other human activities. This Consensus Statement documents the central role and global importance of microorganisms in climate change biology. It also puts humanity on notice that the impact of climate change will depend heavily on responses of microorganisms, which are essential for achieving an environmentally sustainable future.
963 citations
West Virginia University1, Yale University2, Food and Agriculture Organization3, Landcare Research4, University of Udine5, Max Planck Society6, University of Alaska Fairbanks7, Technische Universität München8, Université du Québec à Montréal9, University of the French West Indies and Guiana10, University of Freiburg Faculty of Biology11, Cornell University12, Wageningen University and Research Centre13, University of Sydney14, Polytechnic Institute of Viseu15, University of Trás-os-Montes and Alto Douro16, University of Göttingen17, Russian Academy of Sciences18, Oeschger Centre for Climate Change Research19, Lakehead University20, University of La Frontera21, Seoul National University22, Martin Luther University of Halle-Wittenberg23, University of Cambridge24, James Cook University25, Center for International Forestry Research26, University of Zurich27, University of Yaoundé I28, University of Wisconsin-Madison29, Queensland Government30, Florida International University31, Institut national de la recherche agronomique32, Forest Research Institute33, University of Minnesota34, Polish Academy of Sciences35, Warsaw University of Life Sciences36, Ştefan cel Mare University of Suceava37, University of Florence38, University of Warsaw39, Spanish National Research Council40, King Juan Carlos University41, National Scientific and Technical Research Council42, National University of Austral Patagonia43, International Trademark Association44, Wildlife Conservation Society45, College of African Wildlife Management46, University of York47, Durham University48, Ontario Ministry of Natural Resources49, Pontificia Universidad Católica del Ecuador50, Centre national de la recherche scientifique51, Museu Paraense Emílio Goeldi52, University College London53, University of Leeds54
TL;DR: A consistent positive concave-down effect of biodiversity on forest productivity across the world is revealed, showing that a continued biodiversity loss would result in an accelerating decline in forest productivity worldwide.
Abstract: The biodiversity-productivity relationship (BPR) is foundational to our understanding of the global extinction crisis and its impacts on ecosystem functioning. Understanding BPR is critical for the accurate valuation and effective conservation of biodiversity. Using ground-sourced data from 777,126 permanent plots, spanning 44 countries and most terrestrial biomes, we reveal a globally consistent positive concave-down BPR, showing that continued biodiversity loss would result in an accelerating decline in forest productivity worldwide. The value of biodiversity in maintaining commercial forest productivity alone-US$166 billion to 490 billion per year according to our estimation-is more than twice what it would cost to implement effective global conservation. This highlights the need for a worldwide reassessment of biodiversity values, forest management strategies, and conservation priorities.
889 citations
Pacific Northwest National Laboratory1, Yale University2, National Center for Atmospheric Research3, Marine Biological Laboratory4, Colorado State University5, Wageningen University and Research Centre6, University of California, Irvine7, Kansas State University8, University of Oregon9, Michigan Technological University10, University of Sydney11, University of Minnesota12, Duke University13, University of Tennessee14, University of Copenhagen15, Spanish National Research Council16, University of New Hampshire17, Northeast Normal University18, University of California, Berkeley19, University of Oklahoma20, Hungarian Academy of Sciences21, Swedish University of Agricultural Sciences22, University of Manchester23, Tsinghua University24, National University of Singapore25, Chinese Academy of Sciences26, University of Hohenheim27, University of Georgia28, Hampshire College29, Boston University30, University of Alaska Anchorage31
TL;DR: In this article, the authors present a comprehensive analysis of warming-induced changes in soil carbon stocks by assembling data from 49 field experiments located across North America, Europe and Asia, and provide estimates of soil carbon sensitivity to warming that may help to constrain Earth system model projections.
Abstract: The majority of the Earth's terrestrial carbon is stored in the soil. If anthropogenic warming stimulates the loss of this carbon to the atmosphere, it could drive further planetary warming. Despite evidence that warming enhances carbon fluxes to and from the soil, the net global balance between these responses remains uncertain. Here we present a comprehensive analysis of warming-induced changes in soil carbon stocks by assembling data from 49 field experiments located across North America, Europe and Asia. We find that the effects of warming are contingent on the size of the initial soil carbon stock, with considerable losses occurring in high-latitude areas. By extrapolating this empirical relationship to the global scale, we provide estimates of soil carbon sensitivity to warming that may help to constrain Earth system model projections. Our empirical relationship suggests that global soil carbon stocks in the upper soil horizons will fall by 30 ± 30 petagrams of carbon to 203 ± 161 petagrams of carbon under one degree of warming, depending on the rate at which the effects of warming are realized. Under the conservative assumption that the response of soil carbon to warming occurs within a year, a business-as-usual climate scenario would drive the loss of 55 ± 50 petagrams of carbon from the upper soil horizons by 2050. This value is around 12-17 per cent of the expected anthropogenic emissions over this period. Despite the considerable uncertainty in our estimates, the direction of the global soil carbon response is consistent across all scenarios. This provides strong empirical support for the idea that rising temperatures will stimulate the net loss of soil carbon to the atmosphere, driving a positive land carbon-climate feedback that could accelerate climate change.
787 citations
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TL;DR: Preface to the Princeton Landmarks in Biology Edition vii Preface xi Symbols used xiii 1.
Abstract: Preface to the Princeton Landmarks in Biology Edition vii Preface xi Symbols Used xiii 1. The Importance of Islands 3 2. Area and Number of Speicies 8 3. Further Explanations of the Area-Diversity Pattern 19 4. The Strategy of Colonization 68 5. Invasibility and the Variable Niche 94 6. Stepping Stones and Biotic Exchange 123 7. Evolutionary Changes Following Colonization 145 8. Prospect 181 Glossary 185 References 193 Index 201
14,171 citations
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TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).
13,246 citations
Journal Article•
TL;DR: In this article, the authors present a document, redatto, voted and pubblicato by the Ipcc -Comitato intergovernativo sui cambiamenti climatici - illustra la sintesi delle ricerche svolte su questo tema rilevante.
Abstract: Cause, conseguenze e strategie di mitigazione Proponiamo il primo di una serie di articoli in cui affronteremo l’attuale problema dei mutamenti climatici. Presentiamo il documento redatto, votato e pubblicato dall’Ipcc - Comitato intergovernativo sui cambiamenti climatici - che illustra la sintesi delle ricerche svolte su questo tema rilevante.
4,187 citations
TL;DR: A technique for automatically assigning a neuroanatomical label to each location on a cortical surface model based on probabilistic information estimated from a manually labeled training set is presented, comparable in accuracy to manual labeling.
Abstract: We present a technique for automatically assigning a neuroanatomical label to each location on a cortical surface model based on probabilistic information estimated from a manually labeled training set. This procedure incorporates both geometric information derived from the cortical model, and neuroanatomical convention, as found in the training set. The result is a complete labeling of cortical sulci and gyri. Examples are given from two different training sets generated using different neuroanatomical conventions, illustrating the flexibility of the algorithm. The technique is shown to be comparable in accuracy to manual labeling.
3,880 citations
TL;DR: This paper presented a unified account of two neural systems concerned with the development and expression of adaptive behaviors: a mesencephalic dopamine system for reinforcement learning and a generic error-processing system associated with the anterior cingulate cortex.
Abstract: The authors present a unified account of 2 neural systems concerned with the development and expression of adaptive behaviors: a mesencephalic dopamine system for reinforcement learning and a “generic” error-processing system associated with the anterior cingulate cortex The existence of the error-processing system has been inferred from the error-related negativity (ERN), a component of the event-related brain potential elicited when human participants commit errors in reaction-time tasks The authors propose that the ERN is generated when a negative reinforcement learning signal is conveyed to the anterior cingulate cortex via the mesencephalic dopamine system and that this signal is used by the anterior cingulate cortex to modify performance on the task at hand They provide support for this proposal using both computational modeling and psychophysiological experimentation
3,438 citations