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Oliver L. Phillips

Bio: Oliver L. Phillips is an academic researcher from University of Leeds. The author has contributed to research in topics: Biodiversity & Amazon rainforest. The author has an hindex of 98, co-authored 336 publications receiving 50569 citations. Previous affiliations of Oliver L. Phillips include University of York & University of Brasília.


Papers
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Journal ArticleDOI
08 Jan 2004-Nature
TL;DR: Estimates of extinction risks for sample regions that cover some 20% of the Earth's terrestrial surface show the importance of rapid implementation of technologies to decrease greenhouse gas emissions and strategies for carbon sequestration.
Abstract: Climate change over the past approximately 30 years has produced numerous shifts in the distributions and abundances of species and has been implicated in one species-level extinction. Using projections of species' distributions for future climate scenarios, we assess extinction risks for sample regions that cover some 20% of the Earth's terrestrial surface. Exploring three approaches in which the estimated probability of extinction shows a power-law relationship with geographical range size, we predict, on the basis of mid-range climate-warming scenarios for 2050, that 15-37% of species in our sample of regions and taxa will be 'committed to extinction'. When the average of the three methods and two dispersal scenarios is taken, minimal climate-warming scenarios produce lower projections of species committed to extinction ( approximately 18%) than mid-range ( approximately 24%) and maximum-change ( approximately 35%) scenarios. These estimates show the importance of rapid implementation of technologies to decrease greenhouse gas emissions and strategies for carbon sequestration.

7,089 citations

Journal ArticleDOI
19 Aug 2011-Science
TL;DR: The total forest sink estimate is equivalent in magnitude to the terrestrial sink deduced from fossil fuel emissions and land-use change sources minus ocean and atmospheric sinks, with tropical estimates having the largest uncertainties.
Abstract: The terrestrial carbon sink has been large in recent decades, but its size and location remain uncertain. Using forest inventory data and long-term ecosystem carbon studies, we estimate a total forest sink of 2.4 ± 0.4 petagrams of carbon per year (Pg C year–1) globally for 1990 to 2007. We also estimate a source of 1.3 ± 0.7 Pg C year–1 from tropical land-use change, consisting of a gross tropical deforestation emission of 2.9 ± 0.5 Pg C year–1 partially compensated by a carbon sink in tropical forest regrowth of 1.6 ± 0.5 Pg C year–1. Together, the fluxes comprise a net global forest sink of 1.1 ± 0.8 Pg C year–1, with tropical estimates having the largest uncertainties. Our total forest sink estimate is equivalent in magnitude to the terrestrial sink deduced from fossil fuel emissions and land-use change sources minus ocean and atmospheric sinks.

4,948 citations

Journal ArticleDOI
Jens Kattge1, Sandra Díaz2, Sandra Lavorel3, Iain Colin Prentice4, Paul Leadley5, Gerhard Bönisch1, Eric Garnier3, Mark Westoby4, Peter B. Reich6, Peter B. Reich7, Ian J. Wright4, Johannes H. C. Cornelissen8, Cyrille Violle3, Sandy P. Harrison4, P.M. van Bodegom8, Markus Reichstein1, Brian J. Enquist9, Nadejda A. Soudzilovskaia8, David D. Ackerly10, Madhur Anand11, Owen K. Atkin12, Michael Bahn13, Timothy R. Baker14, Dennis D. Baldocchi10, Renée M. Bekker15, Carolina C. Blanco16, Benjamin Blonder9, William J. Bond17, Ross A. Bradstock18, Daniel E. Bunker19, Fernando Casanoves20, Jeannine Cavender-Bares7, Jeffrey Q. Chambers21, F. S. Chapin22, Jérôme Chave3, David A. Coomes23, William K. Cornwell8, Joseph M. Craine24, B. H. Dobrin9, Leandro da Silva Duarte16, Walter Durka25, James J. Elser26, Gerd Esser27, Marc Estiarte28, William F. Fagan29, Jingyun Fang, Fernando Fernández-Méndez30, Alessandra Fidelis31, Bryan Finegan20, Olivier Flores32, H. Ford33, Dorothea Frank1, Grégoire T. Freschet34, Nikolaos M. Fyllas14, Rachael V. Gallagher4, Walton A. Green35, Alvaro G. Gutiérrez25, Thomas Hickler, Steven I. Higgins36, John G. Hodgson37, Adel Jalili, Steven Jansen38, Carlos Alfredo Joly39, Andrew J. Kerkhoff40, Don Kirkup41, Kaoru Kitajima42, Michael Kleyer43, Stefan Klotz25, Johannes M. H. Knops44, Koen Kramer, Ingolf Kühn16, Hiroko Kurokawa45, Daniel C. Laughlin46, Tali D. Lee47, Michelle R. Leishman4, Frederic Lens48, Tanja Lenz4, Simon L. Lewis14, Jon Lloyd49, Jon Lloyd14, Joan Llusià28, Frédérique Louault50, Siyan Ma10, Miguel D. Mahecha1, Peter Manning51, Tara Joy Massad1, Belinda E. Medlyn4, Julie Messier9, Angela T. Moles52, Sandra Cristina Müller16, Karin Nadrowski53, Shahid Naeem54, Ülo Niinemets55, S. Nöllert1, A. Nüske1, Romà Ogaya28, Jacek Oleksyn56, Vladimir G. Onipchenko57, Yusuke Onoda58, Jenny C. Ordoñez59, Gerhard E. Overbeck16, Wim A. Ozinga59, Sandra Patiño14, Susana Paula60, Juli G. Pausas60, Josep Peñuelas28, Oliver L. Phillips14, Valério D. Pillar16, Hendrik Poorter, Lourens Poorter59, Peter Poschlod61, Andreas Prinzing62, Raphaël Proulx63, Anja Rammig64, Sabine Reinsch65, Björn Reu1, Lawren Sack66, Beatriz Salgado-Negret20, Jordi Sardans28, Satomi Shiodera67, Bill Shipley68, Andrew Siefert69, Enio E. Sosinski70, Jean-François Soussana50, Emily Swaine71, Nathan G. Swenson72, Ken Thompson37, Peter E. Thornton73, Matthew S. Waldram74, Evan Weiher47, Michael T. White75, S. White11, S. J. Wright76, Benjamin Yguel3, Sönke Zaehle1, Amy E. Zanne77, Christian Wirth58 
Max Planck Society1, National University of Cordoba2, Centre national de la recherche scientifique3, Macquarie University4, University of Paris-Sud5, University of Western Sydney6, University of Minnesota7, VU University Amsterdam8, University of Arizona9, University of California, Berkeley10, University of Guelph11, Australian National University12, University of Innsbruck13, University of Leeds14, University of Groningen15, Universidade Federal do Rio Grande do Sul16, University of Cape Town17, University of Wollongong18, New Jersey Institute of Technology19, Centro Agronómico Tropical de Investigación y Enseñanza20, Lawrence Berkeley National Laboratory21, University of Alaska Fairbanks22, University of Cambridge23, Kansas State University24, Helmholtz Centre for Environmental Research - UFZ25, Arizona State University26, University of Giessen27, Autonomous University of Barcelona28, University of Maryland, College Park29, Universidad del Tolima30, University of São Paulo31, University of La Réunion32, University of York33, University of Sydney34, Harvard University35, Goethe University Frankfurt36, University of Sheffield37, University of Ulm38, State University of Campinas39, Kenyon College40, Royal Botanic Gardens41, University of Florida42, University of Oldenburg43, University of Nebraska–Lincoln44, Tohoku University45, Northern Arizona University46, University of Wisconsin–Eau Claire47, Naturalis48, James Cook University49, Institut national de la recherche agronomique50, Newcastle University51, University of New South Wales52, Leipzig University53, Columbia University54, Estonian University of Life Sciences55, Polish Academy of Sciences56, Moscow State University57, Kyushu University58, Wageningen University and Research Centre59, Spanish National Research Council60, University of Regensburg61, University of Rennes62, Université du Québec à Trois-Rivières63, Potsdam Institute for Climate Impact Research64, Technical University of Denmark65, University of California, Los Angeles66, Hokkaido University67, Université de Sherbrooke68, Syracuse University69, Empresa Brasileira de Pesquisa Agropecuária70, University of Aberdeen71, Michigan State University72, Oak Ridge National Laboratory73, University of Leicester74, Utah State University75, Smithsonian Institution76, University of Missouri77
01 Sep 2011
TL;DR: TRY as discussed by the authors is a global database of plant traits, including morphological, anatomical, physiological, biochemical and phenological characteristics of plants and their organs, which can be used for a wide range of research from evolutionary biology, community and functional ecology to biogeography.
Abstract: Plant traits – the morphological, anatomical, physiological, biochemical and phenological characteristics of plants and their organs – determine how primary producers respond to environmental factors, affect other trophic levels, influence ecosystem processes and services and provide a link from species richness to ecosystem functional diversity. Trait data thus represent the raw material for a wide range of research from evolutionary biology, community and functional ecology to biogeography. Here we present the global database initiative named TRY, which has united a wide range of the plant trait research community worldwide and gained an unprecedented buy-in of trait data: so far 93 trait databases have been contributed. The data repository currently contains almost three million trait entries for 69 000 out of the world's 300 000 plant species, with a focus on 52 groups of traits characterizing the vegetative and regeneration stages of the plant life cycle, including growth, dispersal, establishment and persistence. A first data analysis shows that most plant traits are approximately log-normally distributed, with widely differing ranges of variation across traits. Most trait variation is between species (interspecific), but significant intraspecific variation is also documented, up to 40% of the overall variation. Plant functional types (PFTs), as commonly used in vegetation models, capture a substantial fraction of the observed variation – but for several traits most variation occurs within PFTs, up to 75% of the overall variation. In the context of vegetation models these traits would better be represented by state variables rather than fixed parameter values. The improved availability of plant trait data in the unified global database is expected to support a paradigm shift from species to trait-based ecology, offer new opportunities for synthetic plant trait research and enable a more realistic and empirically grounded representation of terrestrial vegetation in Earth system models.

2,017 citations

Journal ArticleDOI
06 Mar 2009-Science
TL;DR: Records from multiple long-term monitoring plots across Amazonia are used to assess forest responses to the intense 2005 drought, a possible analog of future events that may accelerate climate change through carbon losses and changed surface energy balances.
Abstract: Amazon forests are a key but poorly understood component of the global carbon cycle. If, as anticipated, they dry this century, they might accelerate climate change through carbon losses and changed surface energy balances. We used records from multiple long-term monitoring plots across Amazonia to assess forest responses to the intense 2005 drought, a possible analog of future events. Affected forest lost biomass, reversing a large long-term carbon sink, with the greatest impacts observed where the dry season was unusually intense. Relative to pre-2005 conditions, forest subjected to a 100-millimeter increase in water deficit lost 5.3 megagrams of aboveground biomass of carbon per hectare. The drought had a total biomass carbon impact of 1.2 to 1.6 petagrams (1.2 × 1015 to 1.6 × 1015 grams). Amazon forests therefore appear vulnerable to increasing moisture stress, with the potential for large carbon losses to exert feedback on climate change.

1,545 citations


Cited by
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28 Jul 2005
TL;DR: PfPMP1)与感染红细胞、树突状组胞以及胎盘的单个或多个受体作用,在黏附及免疫逃避中起关键的作�ly.
Abstract: 抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1(PfPMP1)与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用,在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员,通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

18,940 citations

Journal ArticleDOI
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

Journal ArticleDOI
TL;DR: In this paper, the use of the maximum entropy method (Maxent) for modeling species geographic distributions with presence-only data was introduced, which is a general-purpose machine learning method with a simple and precise mathematical formulation.

13,120 citations

Journal ArticleDOI
TL;DR: This work compared 16 modelling methods over 226 species from 6 regions of the world, creating the most comprehensive set of model comparisons to date and found that presence-only data were effective for modelling species' distributions for many species and regions.
Abstract: Prediction of species' distributions is central to diverse applications in ecology, evolution and conservation science. There is increasing electronic access to vast sets of occurrence records in museums and herbaria, yet little effective guidance on how best to use this information in the context of numerous approaches for modelling distributions. To meet this need, we compared 16 modelling methods over 226 species from 6 regions of the world, creating the most comprehensive set of model comparisons to date. We used presence-only data to fit models, and independent presence-absence data to evaluate the predictions. Along with well-established modelling methods such as generalised additive models and GARP and BIOCLIM, we explored methods that either have been developed recently or have rarely been applied to modelling species' distributions. These include machine-learning methods and community models, both of which have features that may make them particularly well suited to noisy or sparse information, as is typical of species' occurrence data. Presence-only data were effective for modelling species' distributions for many species and regions. The novel methods consistently outperformed more established methods. The results of our analysis are promising for the use of data from museums and herbaria, especially as methods suited to the noise inherent in such data improve.

7,589 citations

Journal ArticleDOI
13 Feb 2015-Science
TL;DR: An updated and extended analysis of the planetary boundary (PB) framework and identifies levels of anthropogenic perturbations below which the risk of destabilization of the Earth system (ES) is likely to remain low—a “safe operating space” for global societal development.
Abstract: The planetary boundaries framework defines a safe operating space for humanity based on the intrinsic biophysical processes that regulate the stability of the Earth system. Here, we revise and update the planetary boundary framework, with a focus on the underpinning biophysical science, based on targeted input from expert research communities and on more general scientific advances over the past 5 years. Several of the boundaries now have a two-tier approach, reflecting the importance of cross-scale interactions and the regional-level heterogeneity of the processes that underpin the boundaries. Two core boundaries—climate change and biosphere integrity—have been identified, each of which has the potential on its own to drive the Earth system into a new state should they be substantially and persistently transgressed.

7,169 citations