Author
Alastair G. B. Simpson
Other affiliations: Canadian Institute for Advanced Research, Halifax, University of Sydney
Bio: Alastair G. B. Simpson is an academic researcher from Dalhousie University. The author has contributed to research in topics: Phylogenetic tree & Euglenozoa. The author has an hindex of 46, co-authored 136 publications receiving 11765 citations. Previous affiliations of Alastair G. B. Simpson include Canadian Institute for Advanced Research & Halifax.
Topics: Phylogenetic tree, Euglenozoa, Monophyly, Trimastix, Lineage (evolution)
Papers published on a yearly basis
Papers
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TL;DR: It is shown that the higher taxonomic classification of species follows a consistent and predictable pattern from which the total number of species in a taxonomic group can be estimated, and when applied to all domains of life, it predicts ∼8.7 million eukaryotic species globally.
Abstract: The diversity of life is one of the most striking aspects of our planet; hence knowing how many species inhabit Earth is among the most fundamental questions in science. Yet the answer to this question remains enigmatic, as efforts to sample the world's biodiversity to date have been limited and thus have precluded direct quantification of global species richness, and because indirect estimates rely on assumptions that have proven highly controversial. Here we show that the higher taxonomic classification of species (i.e., the assignment of species to phylum, class, order, family, and genus) follows a consistent and predictable pattern from which the total number of species in a taxonomic group can be estimated. This approach was validated against well-known taxa, and when applied to all domains of life, it predicts ∼8.7 million (±1.3 million SE) eukaryotic species globally, of which ∼2.2 million (±0.18 million SE) are marine. In spite of 250 years of taxonomic classification and over 1.2 million species already catalogued in a central database, our results suggest that some 86% of existing species on Earth and 91% of species in the ocean still await description. Renewed interest in further exploration and taxonomy is required if this significant gap in our knowledge of life on Earth is to be closed.
2,147 citations
Dalhousie University1, University of Georgia2, Bigelow Laboratory For Ocean Sciences3, Ontario Veterinary College4, New York State Department of Health5, Blaise Pascal University6, Bedford Institute of Oceanography7, University of Louisiana at Lafayette8, Duke University9, Pedagogical University10, Colorado State University11, University of Toronto12, University of Connecticut13, United States Forest Service14, University of Guelph15, Royal Botanic Garden Edinburgh16, Academy of Natural Sciences of Drexel University17, Michigan State University18, University of Copenhagen19, George Mason University20, University of Illinois at Urbana–Champaign21, Saint Petersburg State University22, University of Arkansas23, University of British Columbia24
TL;DR: This revision of the classification of unicellular eukaryotes updates that of Levine et al. (1980) for the protozoa and expands it to include other protists, and proposes a scheme that is based on nameless ranked systematics.
Abstract: This revision of the classification of unicellular eukaryotes updates that of Levine et al. (1980) for the protozoa and expands it to include other protists. Whereas the previous revision was primarily to incorporate the results of ultrastructural studies, this revision incorporates results from both ultrastructural research since 1980 and molecular phylogenetic studies. We propose a scheme that is based on nameless ranked systematics. The vocabulary of the taxonomy is updated, particularly to clarify the naming of groups that have been repositioned. We recognize six clusters of eukaryotes that may represent the basic groupings similar to traditional ''kingdoms.'' The multicellular lineages emerged from within monophyletic protist lineages: animals and fungi from Opisthokonta, plants from Archaeplastida, and brown algae from Stramenopiles.
1,620 citations
Dalhousie University1, University of Saskatchewan2, University of Rhode Island3, Sewanee: The University of the South4, Natural History Museum5, New York State Department of Health6, University of British Columbia7, Kaiserslautern University of Technology8, Charles University in Prague9, University of Guelph10, Le Moyne College11, Georgia College & State University12, University of Colorado Boulder13, University of Geneva14, Edinburgh Napier University15, University of Arkansas16, Saint Petersburg State University17
TL;DR: This revision of the classification of eukaryotes retains an emphasis on the protists and incorporates changes since 2005 that have resolved nodes and branches in phylogenetic trees.
Abstract: This revision of the classification of eukaryotes, which updates that of Adl et al. [J. Eukaryot. Microbiol. 52 (2005) 399], retains an emphasis on the protists and incorporates changes since 2005 that have resolved nodes and branches in phylogenetic trees. Whereas the previous revision was successful in re-introducing name stability to the classification, this revision provides a classification for lineages that were then still unresolved. The supergroups have withstood phylogenetic hypothesis testing with some modifications, but despite some progress, problematic nodes at the base of the eukaryotic tree still remain to be statistically resolved. Looking forward, subsequent transformations to our understanding of the diversity of life will be from the discovery of novel lineages in previously under-sampled areas and from environmental genomic information.
1,414 citations
Canadian Institute for Advanced Research1, University of British Columbia2, Monterey Bay Aquarium Research Institute3, Stony Brook University4, University of California, San Diego5, Marine Biological Laboratory6, Brown University7, University of Washington8, Dalhousie University9, National Center for Genome Resources10, Alfred Wegener Institute for Polar and Marine Research11, Rutgers University12, Texas A&M University13, University of Southern California14, University of Delaware15, Victoria University of Wellington16, University of Paris-Sud17, Columbia University18, University of Oslo19, University of Hawaii at Manoa20, University of Prince Edward Island21, University of Rhode Island22, Woods Hole Oceanographic Institution23, Max Planck Society24, Jacobs University Bremen25, Smith College26, University of South Alabama27, University of Geneva28, Saint Petersburg State University29, University of Connecticut30, Laval University31, University of Guelph32, University of North Carolina at Chapel Hill33, University of New Brunswick34, University of Camerino35, University of East Anglia36, Stazione Zoologica Anton Dohrn37, University of Georgia38, University of Technology, Sydney39, University of Puerto Rico40, University of Pisa41, Centre national de la recherche scientifique42, Colorado School of Mines43, Lund University44, University of Western Ontario45, California State University46, University of Texas at Austin47, Los Alamos National Laboratory48, Pierre-and-Marie-Curie University49, University of Oklahoma50, Plymouth Marine Laboratory51, Bigelow Laboratory For Ocean Sciences52, Princeton University53
TL;DR: In this paper, the authors describe a resource of 700 transcriptomes from marine microbial eukaryotes to help understand their role in the world's oceans and their biology, evolution, and ecology.
Abstract: Current sampling of genomic sequence data from eukaryotes is relatively poor, biased, and inadequate to address important questions about their biology, evolution, and ecology; this Community Page describes a resource of 700 transcriptomes from marine microbial eukaryotes to help understand their role in the world's oceans.
852 citations
University of Geneva1, Centre national de la recherche scientifique2, University of Saskatchewan3, Natural History Museum4, New York State Department of Health5, Charles University in Prague6, Kaiserslautern University of Technology7, University of Greifswald8, Dalhousie University9, Free University of Berlin10, Academy of Sciences of the Czech Republic11, University of British Columbia12, Sewanee: The University of the South13, Saint Petersburg State University14, Royal Botanic Garden Edinburgh15, University of Cologne16, Uppsala University17, University of New Brunswick18, Halifax19, National Institutes of Health20, University of Giessen21, National Museum of Natural History22
TL;DR: A group of protist experts proposes a two-step DNA barcoding approach, comprising a universal eukaryotic pre-barcode followed by group-specific barcodes, to unveil the hidden biodiversity of microbial Eukaryotes.
Abstract: Animals, plants, and fungi—the three traditional kingdoms of multicellular eukaryotic life—make up almost all of the visible biosphere, and they account for the majority of catalogued species on Earth [1]. The remaining eukaryotes have been assembled for convenience into the protists, a group composed of many diverse lineages, single-celled for the most part, that diverged after Archaea and Bacteria evolved but before plants, animals, or fungi appeared on Earth. Given their single-celled nature, discovering and describing new species has been difficult, and many protistan lineages contain a relatively small number of formally described species (Figure 1A), despite the critical importance of several groups as pathogens, environmental quality indicators, and markers of past environmental changes. It would seem natural to apply molecular techniques such as DNA barcoding to the taxonomy of protists to compensate for the lack of diagnostic morphological features, but this has been hampered by the extreme diversity within the group. The genetic divergence observed between and within major protistan groups greatly exceeds that found in each of the three multicellular kingdoms. No single set of molecular markers has been identified that will work in all lineages, but an international working group is now close to a solution. A universal DNA barcode for protists coupled with group-specific barcodes will enable an explosion of taxonomic research that will catalyze diverse applications.
458 citations
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TL;DR: The extensively curated SILVA taxonomy and the new non-redundant SILVA datasets provide an ideal reference for high-throughput classification of data from next-generation sequencing approaches.
Abstract: SILVA (from Latin silva, forest, http://www.arb-silva.de) is a comprehensive web resource for up to date, quality-controlled databases of aligned ribosomal RNA (rRNA) gene sequences from the Bacteria, Archaea and Eukaryota domains and supplementary online services. The referred database release 111 (July 2012) contains 3 194 778 small subunit and 288 717 large subunit rRNA gene sequences. Since the initial description of the project, substantial new features have been introduced, including advanced quality control procedures, an improved rRNA gene aligner, online tools for probe and primer evaluation and optimized browsing, searching and downloading on the website. Furthermore, the extensively curated SILVA taxonomy and the new non-redundant SILVA datasets provide an ideal reference for high-throughput classification of data from next-generation sequencing approaches.
18,256 citations
01 Jun 2012
TL;DR: SPAdes as mentioned in this paper is a new assembler for both single-cell and standard (multicell) assembly, and demonstrate that it improves on the recently released E+V-SC assembler and on popular assemblers Velvet and SoapDeNovo (for multicell data).
Abstract: The lion's share of bacteria in various environments cannot be cloned in the laboratory and thus cannot be sequenced using existing technologies. A major goal of single-cell genomics is to complement gene-centric metagenomic data with whole-genome assemblies of uncultivated organisms. Assembly of single-cell data is challenging because of highly non-uniform read coverage as well as elevated levels of sequencing errors and chimeric reads. We describe SPAdes, a new assembler for both single-cell and standard (multicell) assembly, and demonstrate that it improves on the recently released E+V-SC assembler (specialized for single-cell data) and on popular assemblers Velvet and SoapDeNovo (for multicell data). SPAdes generates single-cell assemblies, providing information about genomes of uncultivatable bacteria that vastly exceeds what may be obtained via traditional metagenomics studies. SPAdes is available online ( http://bioinf.spbau.ru/spades ). It is distributed as open source software.
10,124 citations
TL;DR: In this paper, the authors present a set of methods for soil sampling and analysis, such as: N.H.Hendershot, H.M.Hettiarachchi, C.C.De Freitas Arbuscular Mycorrhiza, Y.K.Soon and W.J.
Abstract: SOIL SAMPLING AND HANDLING, G.T. Patterson and M.R. Carter Soil Sampling Designs, D. Pennock, T. Yates, and J. Braidek Sampling Forest Soils, N. Belanger and K.C.J. Van Rees Measuring Change in Soil Organic Carbon Storage, B.H. Ellert, H.H. Janzen, A.J. VandenBygaart, and E. Bremer Soil Sample Handling and Storage, S.C. Sheppard and J.A. Addison Quality Control in Soil Chemical Analysis, C. Swyngedouw and R. Lessard DIAGNOSTIC METHODS for SOIL and ENVIRONMENTAL MANAGEMENT, J.J. Schoenau and I.P. O'Halloran Nitrate and Exchangeable Ammonium Nitrogen, D.G. Maynard, Y.P. Kalra, and J.A. Crumbaugh Mehlich 3 Extractable Elements, N. Ziadi and T. Sen Tran Sodium Bicarbonate Extractable Phosphorus, J.J. Schoenau and I. P. O'Halloran Boron, Molybdenum and Selenium, G. M. Hettiarachchi and U. C. Gupta Trace Element Assessment, W.H. Hendershot, H. Lalande, D. Reyes, and D. MacDonald Readily Soluble Aluminum and Manganese in Acid Soils, Y.K. Soon, N. Belanger, and W.H. Hendershot Lime Requirement, N. Ziadi and T. Sen Tran Ion Supply Rates Using Ion Exchange Resins, P. Qian, J.J. Schoenau, and N. Ziadi Environmental Soil Phosphorus Indices, A.N. Sharpley, P.J.A. Kleinman and J.L. Weld Electrical Conductivity and Soluble Ions, J.J. Miller and D. Curtin SOIL CHEMICAL ANALYSES, Y.K. Soon and W.H. Hendershot Soil Reaction and Exchangeable Acidity, W.H. Hendershot, H. Laland,e and M. Duquette Collection and Characterization of Soil Solutions, J.D. MacDonald, N. Belanger, S. Sauve, F. Courchesne, and W.H. Hendershot Ion Exchange and Exchangeable Cations, W.H. Hendershot, H. Lalande, and M. Duquette Non-Exchangeable Ammonium, Y.K. Soon and B.C. Liang Carbonates, T.B. Goh and A.R. Mermut Total and Organic Carbon, J.O. Skjemstad and J.A. Baldock Total Nitrogen, P.M. Rutherford, W.B. McGill, C.T. Figueiredo, and J.M. Arocena Chemical Characterization of Soil Sulphur, C.G. Kowalenko and M. Grimmett Total and Organic Phosphorus, I.P. O'Halloran and B.J. Cade-Menum Characterization of Available P by Sequential Extraction, H. Tiessen and J.O. Moir Extractable Al, Fe, Mn, and Si, F. Courchesne and M.C. Turmel Determining Nutrient Availability in Forest Soils, N. Belanger, David Pare, and W.H. Hendershot Chemical Properties of Organic Soils, A. Karam SOIL BIOLOGICAL ANALYSES, E. Topp and C.A. Fox Cultural Methods for Soil and Root Associated Microorganisms, J.J. Germida and J.R. de Freitas Arbuscular Mycorrhiza, Y. Dalpe and C. Hamel Root Nodule Bacteria and Symbiotic Nitrogen Fixation, D. Prevost and H. Antoun Microarthropods, J.P Winter and V.M. Behan-Pelletier Nematodes, T.A. Forge and J. Kimpinski Earthworms, M.J. Clapperton, G.H. Baker and C.A. Fox Enchytraeids, S.M. Adl Protozoa, S.M. Adl, D. Acosta-Mercado, and D.H. Lynn Denitrification Techniques for Soils, C.F. Drury, D.D. Myrold, E.G. Beauchamp, and W.D.Reynolds Nitrification Techniques in Soil Systems, C.F. Drury, S.C. Hart, and X.M. Yang Substrate-Induced Respiration and Selective Inhibition as Measures of Microbial Biomass in Soils, V.L. Bailey, J.L. Smith, and H. Bolton Jr. Assessment of Soil Biological Activity, R.P.Beyaert and C.A. Fox Soil ATP, R.P. Voroney, G. Wen, and R.P. Beyaert Lipid-Based Community Analysis, K.E. Dunfield Bacterial Community Analyses by Denaturing Gradient Gel Electrophoresis (DGGE), E. Topp, Y.-C. Tien, and A. Hartmann Indicators of Soil Food Web Properties, T.A. Forge and M. Tenuta SOIL ORGANIC MATTER ANALYSES, E.G. Gregorich and M.H. Beare Carbon Mineralization, D.W. Hopkins Mineralizable Nitrogen, Denis Curtin and C.A. Campbell Physically Uncomplexed Organic Matter, E.G. Gregorich and M.H. Beare Extraction and Characterization of Dissolved Organic Matter, M.H. Chantigny, D.A. Angers, K. Kaiser, and K. Kalbitz Soil Microbial Biomass C, N, P and S, R.P. Voroney, P.C. Brookes, and R.P. Beyaert Carbohydrates, M.H. Chantigny and D.A. Angers Organic Forms of Nitrogen, D.C. Olk Soil Humus Fractions, D.W. Anderson and J.J Schoenau Soil Organic Matter Analysis by Solid-State 13C Nuclear Magnetic Resonance Spectroscopy, M. J. Simpson and C. M. Preston Stable Isotopes in Soil and Environmental Research, B.H. Ellert and L. Rock SOIL PHYSICAL ANALYSES, D.A. Angers and F.J. Larney Particle Size Distribution, D. Kroetsch and C. Wang Soil Shrinkage, C.D. Grant Soil Density and Porosity, X. Hao, B.C. Ball, J.L.B. Culley, M.R. Carter, and G.W. Parkin Soil Consistency: Upper and Lower Plastic Limits, R.A. McBride Compaction and Compressibility, P. Defossez, T. Keller and G. Richard Field Soil Strength, G.C. Topp and D.R. Lapen Air Permeability, C.D. Grant and P.H. Groenevelt Aggregate Stability to Water, D.A. Angers, M.S. Bullock, and G.R. Mehuys Dry Aggregate Size Distribution, F.J. Larney Soil Air, R.E. Farrell and J.A. Elliott Soil-Surface Gas Emissions, P. Rochette and N. Bertrand Bulk Density Measurement in Forest Soils, D.G. Maynard and M.P. Curran Physical Properties of Organic Soils and Growing Media: Particle Size and Degree of Decomposition, L.E. Parent and J. Caron Physical Properties of Organic Soils and Growing Media: Water and Air Storage and Flow Dynamics, J. Caron, D.E. Elrick, J.C. Michel, and R. Naasz SOIL WATER ANALYSES, W.D. Reynolds and G.C. Topp Soil Water Analyses: Principles and Parameters, W.D. Reynolds and G.C. Topp Soil Water Content, G.C. Topp, G.W. Parkin, and Ty P.A Ferre Soil Water Potential, N.J. Livingston and G.C. Topp Soil Water Desorption and Imbibition: Tension and Pressure Techniques, W.D. Reynolds and G.C. Topp Soil Water Desorption and Imbibition: Long Column, W.D. Reynolds and G.C. Topp Soil Water Desorption and Imbibition: Psychrometry, W.D. Reynolds and G.C. Topp Saturated Hydraulic Properties: Laboratory Methods, W.D. Reynolds Saturated Hydraulic Properties: Well Permeameter, W.D. Reynolds Saturated Hydraulic Properties: Ring Infiltrometer, W.D. Reynolds Saturated Hydraulic Properties: Auger-Hole, G.C. Topp Saturated Hydraulic Properties: Piezometer, G.C. Topp Unsaturated Hydraulic Properties: Laboratory Tension Infiltrometer, F.J. Cook Unsaturated Hydraulic Properties: Laboratory Evaporation, O.O. B. Wendroth and N. Wypler Unsaturated Hydraulic Properties: Field Tension Infiltrometer, W.D. Reynolds Unsaturated Hydraulic Properties: Instantaneous Profile, W.D. Reynolds Estimation of Soil Hydraulic Properties, F.J. Cook and H.P. Cresswell Analysis of Soil Variability, B.C. Si, R.G. Kachanoski, and W.D. Reynolds APPENDIX Site Description, G.T. Patterson and J.A. Brierley General Safe Laboratory Operation Procedures, P. St-Georges INDEX
4,631 citations
Conrad L. Schoch1, Keith A. Seifert, Sabine M. Huhndorf2, Vincent Robert3 +157 more•Institutions (59)
TL;DR: Among the regions of the ribosomal cistron, the internal transcribed spacer (ITS) region has the highest probability of successful identification for the broadest range of fungi, with the most clearly defined barcode gap between inter- and intraspecific variation.
Abstract: Six DNA regions were evaluated as potential DNA barcodes for Fungi, the second largest kingdom of eukaryotic life, by a multinational, multilaboratory consortium. The region of the mitochondrial cytochrome c oxidase subunit 1 used as the animal barcode was excluded as a potential marker, because it is difficult to amplify in fungi, often includes large introns, and can be insufficiently variable. Three subunits from the nuclear ribosomal RNA cistron were compared together with regions of three representative protein-coding genes (largest subunit of RNA polymerase II, second largest subunit of RNA polymerase II, and minichromosome maintenance protein). Although the protein-coding gene regions often had a higher percent of correct identification compared with ribosomal markers, low PCR amplification and sequencing success eliminated them as candidates for a universal fungal barcode. Among the regions of the ribosomal cistron, the internal transcribed spacer (ITS) region has the highest probability of successful identification for the broadest range of fungi, with the most clearly defined barcode gap between inter- and intraspecific variation. The nuclear ribosomal large subunit, a popular phylogenetic marker in certain groups, had superior species resolution in some taxonomic groups, such as the early diverging lineages and the ascomycete yeasts, but was otherwise slightly inferior to the ITS. The nuclear ribosomal small subunit has poor species-level resolution in fungi. ITS will be formally proposed for adoption as the primary fungal barcode marker to the Consortium for the Barcode of Life, with the possibility that supplementary barcodes may be developed for particular narrowly circumscribed taxonomic groups.
4,116 citations
Journal Article•
TL;DR: FastTree as mentioned in this paper uses sequence profiles of internal nodes in the tree to implement neighbor-joining and uses heuristics to quickly identify candidate joins, then uses nearest-neighbor interchanges to reduce the length of the tree.
Abstract: Gene families are growing rapidly, but standard methods for inferring phylogenies do not scale to alignments with over 10,000 sequences. We present FastTree, a method for constructing large phylogenies and for estimating their reliability. Instead of storing a distance matrix, FastTree stores sequence profiles of internal nodes in the tree. FastTree uses these profiles to implement neighbor-joining and uses heuristics to quickly identify candidate joins. FastTree then uses nearest-neighbor interchanges to reduce the length of the tree. For an alignment with N sequences, L sites, and a different characters, a distance matrix requires O(N^2) space and O(N^2 L) time, but FastTree requires just O( NLa + N sqrt(N) ) memory and O( N sqrt(N) log(N) L a ) time. To estimate the tree's reliability, FastTree uses local bootstrapping, which gives another 100-fold speedup over a distance matrix. For example, FastTree computed a tree and support values for 158,022 distinct 16S ribosomal RNAs in 17 hours and 2.4 gigabytes of memory. Just computing pairwise Jukes-Cantor distances and storing them, without inferring a tree or bootstrapping, would require 17 hours and 50 gigabytes of memory. In simulations, FastTree was slightly more accurate than neighbor joining, BIONJ, or FastME; on genuine alignments, FastTree's topologies had higher likelihoods. FastTree is available at http://microbesonline.org/fasttree.
2,436 citations