Author
Oliver L. Phillips
Other affiliations: University of York, University of Brasília, Center for Plant Conservation ...read more
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 published on a yearly basis
Papers
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University of Leeds1, Ghent University2, Université Paris-Saclay3, University of Paris4, Technische Universität München5, Potsdam Institute for Climate Impact Research6, National Institute for Space Research7, University of Exeter8, University of Edinburgh9, Los Alamos National Laboratory10, Wageningen University and Research Centre11, Australian National University12, Harvard University13, Cooperative Institute for Mesoscale Meteorological Studies14, Universidad Autónoma Gabriel René Moreno15, Florida International University16, Universidade Federal do Acre17, Institut national de la recherche agronomique18, Tropenbos International19, Paul Sabatier University20, Amazon.com21, Federal University of Pará22, University of Texas at Austin23, Museu Paraense Emílio Goeldi24, World Wide Fund for Nature25, James Cook University26, George Mason University27, Environmental Change Institute28, Universidade do Estado de Mato Grosso29, Duke University30, National University of Colombia31, University of Los Andes32, Georgetown University33, Federal University of Alagoas34, Naturalis35, University of Nottingham36
TL;DR: It is found that woody NPP is not correlated with stem mortality rates and is weakly positively correlated with AGB, and across the four models, basin‐wide average AGB is similar to the mean of the observations.
Abstract: Understanding the processes that determine above-ground biomass (AGB) in Amazonian forests is important for predicting the sensitivity of these ecosystems to environmental change and for designing and evaluating dynamic global vegetation models (DGVMs). AGB is determined by inputs from woody productivity [woody net primary productivity (NPP)] and the rate at which carbon is lost through tree mortality. Here, we test whether two direct metrics of tree mortality (the absolute rate of woody biomass loss and the rate of stem mortality) and/or woody NPP, control variation in AGB among 167 plots in intact forest across Amazonia. We then compare these relationships and the observed variation in AGB and woody NPP with the predictions of four DGVMs. The observations show that stem mortality rates, rather than absolute rates of woody biomass loss, are the most important predictor of AGB, which is consistent with the importance of stand size structure for determining spatial variation in AGB. The relationship between stem mortality rates and AGB varies among different regions of Amazonia, indicating that variation in wood density and height/diameter relationships also influences AGB. In contrast to previous findings, we find that woody NPP is not correlated with stem mortality rates and is weakly positively correlated with AGB. Across the four models, basin-wide average AGB is similar to the mean of the observations. However, the models consistently overestimate woody NPP and poorly represent the spatial patterns of both AGB and woody NPP estimated using plot data. In marked contrast to the observations, DGVMs typically show strong positive relationships between woody NPP and AGB. Resolving these differences will require incorporating forest size structure, mechanistic models of stem mortality and variation in functional composition in DGVMs.
129 citations
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Vitor Hugo Freitas Gomes1, Vitor Hugo Freitas Gomes2, Stéphanie D. Ijff3, Niels Raes3 +206 more•Institutions (69)
TL;DR: This pipeline provides a conservative estimate of a species’ area of occupancy, within an area slightly larger than its extent of occurrence, compatible to e.g. IUCN red list assessments.
Abstract: Species distribution models (SDMs) are widely used in ecology and conservation. Presence-only SDMs such as MaxEnt frequently use natural history collections (NHCs) as occurrence data, given their huge numbers and accessibility. NHCs are often spatially biased which may generate inaccuracies in SDMs. Here, we test how the distribution of NHCs and MaxEnt predictions relates to a spatial abundance model, based on a large plot dataset for Amazonian tree species, using inverse distance weighting (IDW). We also propose a new pipeline to deal with inconsistencies in NHCs and to limit the area of occupancy of the species. We found a significant but weak positive relationship between the distribution of NHCs and IDW for 66% of the species. The relationship between SDMs and IDW was also significant but weakly positive for 95% of the species, and sensitivity for both analyses was high. Furthermore, the pipeline removed half of the NHCs records. Presence-only SDM applications should consider this limitation, especially for large biodiversity assessments projects, when they are automatically generated without subsequent checking. Our pipeline provides a conservative estimate of a species’ area of occupancy, within an area slightly larger than its extent of occurrence, compatible to e.g. IUCN red list assessments.
128 citations
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Université libre de Bruxelles1, University of Oxford2, Amazon.com3, Smithsonian Institution4, Université d'Abobo-Adjamé5, Wageningen University and Research Centre6, University of Yaoundé I7, Missouri Botanical Garden8, University of Leeds9, Forestry Commission10, National Herbarium of the Netherlands11, Utrecht University12, University of Aberdeen13
TL;DR: In this paper, the authors explore the relationships between the tree α -diversity of small rain forest plots in Africa and in Amazonia and compare the diversity variation among and between regions.
Abstract: Summary 1. Comparative analyses of diversity variation among and between regions allow testing of alternative explanatory models and ideas. Here, we explore the relationships between the tree α -diversity of small rain forest plots in Africa and in Amazonia and climatic
128 citations
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TL;DR: In this article, a method to retrieve the top-of-canopy height from TomoSAR data was proposed, with an error of only 10% at 1.5-ha resolution.
122 citations
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TL;DR: It was found that stem height was the best predictor variable for arborescent palm biomass, but the relationship between stem height and biomass differed among species, and most species showed weak biomass–diameter relationships, but a significant relationship could be identified across all species.
120 citations
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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
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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: 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
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University of Melbourne1, Stony Brook University2, City University of New York3, Princeton University4, University of Lausanne5, University of California, Berkeley6, University of Alaska Fairbanks7, National Institute of Water and Atmospheric Research8, Commonwealth Scientific and Industrial Research Organisation9, University of São Paulo10, University of Missouri11, Consejo Nacional de Ciencia y Tecnología12, University of Kansas13, Landcare Research14, AT&T15, McGill University16, James Cook University17, Swiss Federal Institute for Forest, Snow and Landscape Research18
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
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Australian National University1, Stockholm Resilience Centre2, University of Copenhagen3, McGill University4, Stellenbosch University5, University of Wisconsin-Madison6, Wageningen University and Research Centre7, Stockholm University8, Royal Swedish Academy of Sciences9, Potsdam Institute for Climate Impact Research10, Commonwealth Scientific and Industrial Research Organisation11, International Livestock Research Institute12, University College London13, Stockholm Environment Institute14, The Energy and Resources Institute15, University of California, San Diego16, Royal Institute of Technology17
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