Defining Phylogenetic Relationships of Ochrophyta Using 18S Rrna
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New Zealand's Genetic Diversity
1.13 NEW ZEALAND’S GENETIC DIVERSITY NEW ZEALAND’S GENETIC DIVERSITY Dennis P. Gordon National Institute of Water and Atmospheric Research, Private Bag 14901, Kilbirnie, Wellington 6022, New Zealand ABSTRACT: The known genetic diversity represented by the New Zealand biota is reviewed and summarised, largely based on a recently published New Zealand inventory of biodiversity. All kingdoms and eukaryote phyla are covered, updated to refl ect the latest phylogenetic view of Eukaryota. The total known biota comprises a nominal 57 406 species (c. 48 640 described). Subtraction of the 4889 naturalised-alien species gives a biota of 52 517 native species. A minimum (the status of a number of the unnamed species is uncertain) of 27 380 (52%) of these species are endemic (cf. 26% for Fungi, 38% for all marine species, 46% for marine Animalia, 68% for all Animalia, 78% for vascular plants and 91% for terrestrial Animalia). In passing, examples are given both of the roles of the major taxa in providing ecosystem services and of the use of genetic resources in the New Zealand economy. Key words: Animalia, Chromista, freshwater, Fungi, genetic diversity, marine, New Zealand, Prokaryota, Protozoa, terrestrial. INTRODUCTION Article 10b of the CBD calls for signatories to ‘Adopt The original brief for this chapter was to review New Zealand’s measures relating to the use of biological resources [i.e. genetic genetic resources. The OECD defi nition of genetic resources resources] to avoid or minimize adverse impacts on biological is ‘genetic material of plants, animals or micro-organisms of diversity [e.g. genetic diversity]’ (my parentheses). -
University of Oklahoma
UNIVERSITY OF OKLAHOMA GRADUATE COLLEGE MACRONUTRIENTS SHAPE MICROBIAL COMMUNITIES, GENE EXPRESSION AND PROTEIN EVOLUTION A DISSERTATION SUBMITTED TO THE GRADUATE FACULTY in partial fulfillment of the requirements for the Degree of DOCTOR OF PHILOSOPHY By JOSHUA THOMAS COOPER Norman, Oklahoma 2017 MACRONUTRIENTS SHAPE MICROBIAL COMMUNITIES, GENE EXPRESSION AND PROTEIN EVOLUTION A DISSERTATION APPROVED FOR THE DEPARTMENT OF MICROBIOLOGY AND PLANT BIOLOGY BY ______________________________ Dr. Boris Wawrik, Chair ______________________________ Dr. J. Phil Gibson ______________________________ Dr. Anne K. Dunn ______________________________ Dr. John Paul Masly ______________________________ Dr. K. David Hambright ii © Copyright by JOSHUA THOMAS COOPER 2017 All Rights Reserved. iii Acknowledgments I would like to thank my two advisors Dr. Boris Wawrik and Dr. J. Phil Gibson for helping me become a better scientist and better educator. I would also like to thank my committee members Dr. Anne K. Dunn, Dr. K. David Hambright, and Dr. J.P. Masly for providing valuable inputs that lead me to carefully consider my research questions. I would also like to thank Dr. J.P. Masly for the opportunity to coauthor a book chapter on the speciation of diatoms. It is still such a privilege that you believed in me and my crazy diatom ideas to form a concise chapter in addition to learn your style of writing has been a benefit to my professional development. I’m also thankful for my first undergraduate research mentor, Dr. Miriam Steinitz-Kannan, now retired from Northern Kentucky University, who was the first to show the amazing wonders of pond scum. Who knew that studying diatoms and algae as an undergraduate would lead me all the way to a Ph.D. -
Number of Living Species in Australia and the World
Numbers of Living Species in Australia and the World 2nd edition Arthur D. Chapman Australian Biodiversity Information Services australia’s nature Toowoomba, Australia there is more still to be discovered… Report for the Australian Biological Resources Study Canberra, Australia September 2009 CONTENTS Foreword 1 Insecta (insects) 23 Plants 43 Viruses 59 Arachnida Magnoliophyta (flowering plants) 43 Protoctista (mainly Introduction 2 (spiders, scorpions, etc) 26 Gymnosperms (Coniferophyta, Protozoa—others included Executive Summary 6 Pycnogonida (sea spiders) 28 Cycadophyta, Gnetophyta under fungi, algae, Myriapoda and Ginkgophyta) 45 Chromista, etc) 60 Detailed discussion by Group 12 (millipedes, centipedes) 29 Ferns and Allies 46 Chordates 13 Acknowledgements 63 Crustacea (crabs, lobsters, etc) 31 Bryophyta Mammalia (mammals) 13 Onychophora (velvet worms) 32 (mosses, liverworts, hornworts) 47 References 66 Aves (birds) 14 Hexapoda (proturans, springtails) 33 Plant Algae (including green Reptilia (reptiles) 15 Mollusca (molluscs, shellfish) 34 algae, red algae, glaucophytes) 49 Amphibia (frogs, etc) 16 Annelida (segmented worms) 35 Fungi 51 Pisces (fishes including Nematoda Fungi (excluding taxa Chondrichthyes and (nematodes, roundworms) 36 treated under Chromista Osteichthyes) 17 and Protoctista) 51 Acanthocephala Agnatha (hagfish, (thorny-headed worms) 37 Lichen-forming fungi 53 lampreys, slime eels) 18 Platyhelminthes (flat worms) 38 Others 54 Cephalochordata (lancelets) 19 Cnidaria (jellyfish, Prokaryota (Bacteria Tunicata or Urochordata sea anenomes, corals) 39 [Monera] of previous report) 54 (sea squirts, doliolids, salps) 20 Porifera (sponges) 40 Cyanophyta (Cyanobacteria) 55 Invertebrates 21 Other Invertebrates 41 Chromista (including some Hemichordata (hemichordates) 21 species previously included Echinodermata (starfish, under either algae or fungi) 56 sea cucumbers, etc) 22 FOREWORD In Australia and around the world, biodiversity is under huge Harnessing core science and knowledge bases, like and growing pressure. -
Protocols for Monitoring Harmful Algal Blooms for Sustainable Aquaculture and Coastal Fisheries in Chile (Supplement Data)
Protocols for monitoring Harmful Algal Blooms for sustainable aquaculture and coastal fisheries in Chile (Supplement data) Provided by Kyoko Yarimizu, et al. Table S1. Phytoplankton Naming Dictionary: This dictionary was constructed from the species observed in Chilean coast water in the past combined with the IOC list. Each name was verified with the list provided by IFOP and online dictionaries, AlgaeBase (https://www.algaebase.org/) and WoRMS (http://www.marinespecies.org/). The list is subjected to be updated. Phylum Class Order Family Genus Species Ochrophyta Bacillariophyceae Achnanthales Achnanthaceae Achnanthes Achnanthes longipes Bacillariophyta Coscinodiscophyceae Coscinodiscales Heliopeltaceae Actinoptychus Actinoptychus spp. Dinoflagellata Dinophyceae Gymnodiniales Gymnodiniaceae Akashiwo Akashiwo sanguinea Dinoflagellata Dinophyceae Gymnodiniales Gymnodiniaceae Amphidinium Amphidinium spp. Ochrophyta Bacillariophyceae Naviculales Amphipleuraceae Amphiprora Amphiprora spp. Bacillariophyta Bacillariophyceae Thalassiophysales Catenulaceae Amphora Amphora spp. Cyanobacteria Cyanophyceae Nostocales Aphanizomenonaceae Anabaenopsis Anabaenopsis milleri Cyanobacteria Cyanophyceae Oscillatoriales Coleofasciculaceae Anagnostidinema Anagnostidinema amphibium Anagnostidinema Cyanobacteria Cyanophyceae Oscillatoriales Coleofasciculaceae Anagnostidinema lemmermannii Cyanobacteria Cyanophyceae Oscillatoriales Microcoleaceae Annamia Annamia toxica Cyanobacteria Cyanophyceae Nostocales Aphanizomenonaceae Aphanizomenon Aphanizomenon flos-aquae -
The Plankton Lifeform Extraction Tool: a Digital Tool to Increase The
Discussions https://doi.org/10.5194/essd-2021-171 Earth System Preprint. Discussion started: 21 July 2021 Science c Author(s) 2021. CC BY 4.0 License. Open Access Open Data The Plankton Lifeform Extraction Tool: A digital tool to increase the discoverability and usability of plankton time-series data Clare Ostle1*, Kevin Paxman1, Carolyn A. Graves2, Mathew Arnold1, Felipe Artigas3, Angus Atkinson4, Anaïs Aubert5, Malcolm Baptie6, Beth Bear7, Jacob Bedford8, Michael Best9, Eileen 5 Bresnan10, Rachel Brittain1, Derek Broughton1, Alexandre Budria5,11, Kathryn Cook12, Michelle Devlin7, George Graham1, Nick Halliday1, Pierre Hélaouët1, Marie Johansen13, David G. Johns1, Dan Lear1, Margarita Machairopoulou10, April McKinney14, Adam Mellor14, Alex Milligan7, Sophie Pitois7, Isabelle Rombouts5, Cordula Scherer15, Paul Tett16, Claire Widdicombe4, and Abigail McQuatters-Gollop8 1 10 The Marine Biological Association (MBA), The Laboratory, Citadel Hill, Plymouth, PL1 2PB, UK. 2 Centre for Environment Fisheries and Aquacu∑lture Science (Cefas), Weymouth, UK. 3 Université du Littoral Côte d’Opale, Université de Lille, CNRS UMR 8187 LOG, Laboratoire d’Océanologie et de Géosciences, Wimereux, France. 4 Plymouth Marine Laboratory, Prospect Place, Plymouth, PL1 3DH, UK. 5 15 Muséum National d’Histoire Naturelle (MNHN), CRESCO, 38 UMS Patrinat, Dinard, France. 6 Scottish Environment Protection Agency, Angus Smith Building, Maxim 6, Parklands Avenue, Eurocentral, Holytown, North Lanarkshire ML1 4WQ, UK. 7 Centre for Environment Fisheries and Aquaculture Science (Cefas), Lowestoft, UK. 8 Marine Conservation Research Group, University of Plymouth, Drake Circus, Plymouth, PL4 8AA, UK. 9 20 The Environment Agency, Kingfisher House, Goldhay Way, Peterborough, PE4 6HL, UK. 10 Marine Scotland Science, Marine Laboratory, 375 Victoria Road, Aberdeen, AB11 9DB, UK. -
(1 → 4)-Β-D-Glucan Is a Component of Cell Walls in Brown Algae
Insoluble (13), (14)--D-glucan is a component of cell walls in brown algae (Phaeophyceae) and is masked by alginates in tissues Asunción Salmeán, Armando; Duffieux, Delphine; Harholt, Jesper; Qin, Fen; Michel, Gurvan; Czjzek, Mirjam; Willats, William George Tycho; Hervé, Cécile Published in: Scientific Reports DOI: 10.1038/s41598-017-03081-5 Publication date: 2017 Document version Publisher's PDF, also known as Version of record Citation for published version (APA): Asunción Salmeán, A., Duffieux, D., Harholt, J., Qin, F., Michel, G., Czjzek, M., Willats, W. G. T., & Hervé, C. (2017). Insoluble (13), (14)--D-glucan is a component of cell walls in brown algae (Phaeophyceae) and is masked by alginates in tissues. Scientific Reports, 7, [2880]. https://doi.org/10.1038/s41598-017-03081-5 Download date: 23. Sep. 2021 www.nature.com/scientificreports OPEN Insoluble (1 → 3), (1 → 4)-β-D- glucan is a component of cell walls in brown algae (Phaeophyceae) and Received: 28 October 2016 Accepted: 24 April 2017 is masked by alginates in tissues Published: xx xx xxxx Armando A. Salmeán 1, Delphine Duffieux2,3, Jesper Harholt4, Fen Qin4, Gurvan Michel2,3, Mirjam Czjzek2,3, William G. T. Willats1,5 & Cécile Hervé2,3 Brown algae are photosynthetic multicellular marine organisms. They belong to the phylum of Stramenopiles, which are not closely related to land plants and green algae. Brown algae share common evolutionary features with other photosynthetic and multicellular organisms, including a carbohydrate-rich cell-wall. Brown algal cell walls are composed predominantly of the polyanionic polysaccharides alginates and fucose-containing sulfated polysaccharides. These polymers are prevalent over neutral and crystalline components, which are believed to be mostly, if not exclusively, cellulose. -
Tracing the Origin of Planktonic Protists in an Ancient Lake
microorganisms Article Tracing the Origin of Planktonic Protists in an Ancient Lake Nataliia V. Annenkova 1,* , Caterina R. Giner 2,3 and Ramiro Logares 2,* 1 Limnological Institute Siberian Branch of the Russian Academy of Sciences 3, Ulan-Batorskaya St., 664033 Irkutsk, Russia 2 Institute of Marine Sciences (ICM), CSIC, Passeig Marítim de la Barceloneta, 37-49, ES08003 Barcelona, Spain; [email protected] 3 Institute for the Oceans and Fisheries, University of British Columbia, 2202 Main Mall, Vancouver, BC V6T 1Z4, Canada * Correspondence: [email protected] (N.V.A.); [email protected] (R.L.) Received: 26 February 2020; Accepted: 7 April 2020; Published: 9 April 2020 Abstract: Ancient lakes are among the most interesting models for evolution studies because their biodiversity is the result of a complex combination of migration and speciation. Here, we investigate the origin of single celled planktonic eukaryotes from the oldest lake in the world—Lake Baikal (Russia). By using 18S rDNA metabarcoding, we recovered 1414 Operational Taxonomic Units (OTUs) belonging to protists populating surface waters (1–50 m) and representing pico/nano-sized cells. The recovered communities resembled other lacustrine freshwater assemblages found elsewhere, especially the taxonomically unclassified protists. However, our results suggest that a fraction of Baikal protists could belong to glacial relicts and have close relationships with marine/brackish species. Moreover, our results suggest that rapid radiation may have occurred among some protist taxa, partially mirroring what was already shown for multicellular organisms in Lake Baikal. We found 16% of the OTUs belonging to potential species flocks in Stramenopiles, Alveolata, Opisthokonta, Archaeplastida, Rhizaria, and Hacrobia. -
High-Throughput Sequencing for Algal Systematics
High-throughput sequencing for algal systematics Mariana C. Oliveiraa, Sonja I. Repettib, Cintia Ihaab, Christopher J. Jacksonb, Pilar Díaz-Tapiabc, Karoline Magalhães Ferreira Lubianaa, Valéria Cassanoa, Joana F. Costab, Ma. Chiela M. Cremenb, Vanessa R. Marcelinobde, Heroen Verbruggenb a Department of Botany, Biosciences Institute, University of São Paulo, São Paulo 05508-090, Brazil b School of BioSciences, University of Melbourne, Victoria 3010, Australia c Coastal Biology Research Group, Faculty of Sciences and Centre for Advanced Scientific Research (CICA), University of A Coruña, 15071, A Coruña, Spain d Marie Bashir Institute for Infectious Diseases and Biosecurity and Sydney Medical School, University of Sydney, New South Wales 2006, Australia e Westmead Institute for Medical Research, Westmead, New South Wales 2145, Australia Abstract In recent years, the use of molecular data in algal systematics has increased as high-throughput sequencing (HTS) has become more accessible, generating very large data sets at a reasonable cost. In this perspectives paper, our goal is to describe how HTS technologies can advance algal systematics. Following an introduction to some common HTS technologies, we discuss how metabarcoding can accelerate algal species discovery. We show how various HTS methods can be applied to generate datasets for accurate species delimitation, and how HTS can be applied to historical type specimens to assist the nomenclature process. Finally, we discuss how HTS data such as organellar genomes and transcriptomes can be used to construct well resolved phylogenies, leading to a stable and natural classification of algal groups. We include examples of bioinformatic workflows that may be applied to process data for each purpose, along with common programs used to achieve each step. -
Benthic and Planktonic Microalgal Community Structure and Primary Productivity in Lower Chesapeake Bay Matthew Reginald Semcheski Old Dominion University
Old Dominion University ODU Digital Commons Biological Sciences Theses & Dissertations Biological Sciences Spring 2014 Benthic and Planktonic Microalgal Community Structure and Primary Productivity in Lower Chesapeake Bay Matthew Reginald Semcheski Old Dominion University Follow this and additional works at: https://digitalcommons.odu.edu/biology_etds Part of the Biology Commons, Ecology and Evolutionary Biology Commons, Environmental Sciences Commons, and the Marine Biology Commons Recommended Citation Semcheski, Matthew R.. "Benthic and Planktonic Microalgal Community Structure and Primary Productivity in Lower Chesapeake Bay" (2014). Doctor of Philosophy (PhD), dissertation, Biological Sciences, Old Dominion University, DOI: 10.25777/j7nz-k382 https://digitalcommons.odu.edu/biology_etds/79 This Dissertation is brought to you for free and open access by the Biological Sciences at ODU Digital Commons. It has been accepted for inclusion in Biological Sciences Theses & Dissertations by an authorized administrator of ODU Digital Commons. For more information, please contact [email protected]. BENTHIC AND PLANKTONIC MICROALGAL COMMUNITY STRUCTURE AND PRIMARY PRODUCTIVITY IN LOWER CHESAPEAKE BAY by Matthew Reginald Semcheski B.S. May 2003, East Stroudsburg University M.S. August 2008, Old Dominion University A Dissertation Submitted to the Faculty of Old Dominion University in Partial Fulfillment of the Requirements for the Degree of DOCTOR OF PHILOSOPHY ECOLOGICAL SCIENCES OLD DOMINION UNIVERSITY MAY 2014 Approved by: Harold G. Marshall Kneeland K. Nesius (Member) John R. McConaugha (Member) ABSTRACT BENTHIC AND PLANKTONIC MICROALGAL COMMUNITY STRUCTURE AND PRIMARY PRODUCTIVITY IN LOWER CHESAPEAKE BAY Matthew Reginald Semcheski Old Dominion University, 2014 Director: Dr. Harold G. Marshall Microalgal populations are trophically important to a variety of micro- and macroheterotrophs in marine and estuarine systems. -
Phylogeny of ‘Araphid’ Diatoms Inferred from SSU and LSU Rdna, RBCL and PSBA Sequences Linda K
Phylogeny of ‘araphid’ diatoms inferred from SSU and LSU rDNA, RBCL and PSBA sequences Linda K. Medlin, Yves Desdevises To cite this version: Linda K. Medlin, Yves Desdevises. Phylogeny of ‘araphid’ diatoms inferred from SSU and LSU rDNA, RBCL and PSBA sequences. Vie et Milieu / Life & Environment, Observatoire Océanologique - Laboratoire Arago, 2016. hal-02144540 HAL Id: hal-02144540 https://hal.archives-ouvertes.fr/hal-02144540 Submitted on 26 Jul 2019 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. VIE ET MILIEU - LIFE AND ENVIRONMENT, 2016, 66 (2): 129-154 PHYLOGENY OF ‘arapHID’ DIatoms INFErrED From SSU and LSU RDNA, RBCL AND PSBA SEQUENCES L. K. MEDLIN 1*, Y. DESDEVISES 2 1 Marine Biological Association of the UK, the Citadel, Plymouth, PL1 2PB UK Royal Botanic Gardens, Edinburgh, Scotland, UK 2 Sorbonne Universités, UPMC Univ Paris 06, CNRS, Biologie Intégrative des Organismes Marins (BIOM), Observatoire Océanologique, F-66650, Banyuls/Mer, France * Corresponding author: [email protected] ARAPHID ABSTRACT. – Phylogenies of the diatoms have largely been inferred from SSU rDNA sequenc- DIATOMS DIVERGENCE TIME ESTIMATION es. Because previously published SSU rDNA topologies of araphid pennate diatoms have var- LSU ied, a supertree was constructed in order to summarize those trees and used to guide further PINNATE analyses where problems arose. -
Towards a Digital Diatom: Image Processing and Deep Learning Analysis of Bacillaria Paradoxa Dynamic Morphology
bioRxiv preprint doi: https://doi.org/10.1101/2019.12.21.885897; this version posted December 23, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. Towards a Digital Diatom: image processing and deep learning analysis of Bacillaria paradoxa dynamic morphology Bradly Alicea1,2, Richard Gordon3,4, Thomas Harbich5, Ujjwal Singh6, Asmit Singh6, Vinay Varma7 Abstract Recent years have witnessed a convergence of data and methods that allow us to approximate the shape, size, and functional attributes of biological organisms. This is not only limited to traditional model species: given the ability to culture and visualize a specific organism, we can capture both its structural and functional attributes. We present a quantitative model for the colonial diatom Bacillaria paradoxa, an organism that presents a number of unique attributes in terms of form and function. To acquire a digital model of B. paradoxa, we extract a series of quantitative parameters from microscopy videos from both primary and secondary sources. These data are then analyzed using a variety of techniques, including two rival deep learning approaches. We provide an overview of neural networks for non-specialists as well as present a series of analysis on Bacillaria phenotype data. The application of deep learning networks allow for two analytical purposes. Application of the DeepLabv3 pre-trained model extracts phenotypic parameters describing the shape of cells constituting Bacillaria colonies. Application of a semantic model trained on nematode embryogenesis data (OpenDevoCell) provides a means to analyze masked images of potential intracellular features. -
Vertical Distribution of Expansive, Bloom-Forming Algae Gonyostomum Semen Vs
Knowl. Manag. Aquat. Ecosyst. 2018, 419, 28 Knowledge & © W. Pęczuła et al., Published by EDP Sciences 2018 Management of Aquatic https://doi.org/10.1051/kmae/2018017 Ecosystems www.kmae-journal.org Journal fully supported by Onema RESEARCH PAPER Vertical distribution of expansive, bloom-forming algae Gonyostomum semen vs. plankton community and water chemistry in four small humic lakes Wojciech Pęczuła1,*, Magdalena Grabowska2, Piotr Zieliński3, Maciej Karpowicz2 and Mateusz Danilczyk2,4 1 Department of Hydrobiology and Protection of Ecosystems, University of Life Sciences in Lublin, Lublin, Poland 2 Department of Hydrobiology, Institute of Biology, University of Białystok, Białystok, Poland 3 Department of Environmental Protection, Institute of Biology, University of Białystok, Białystok, Poland 4 Wigry National Park, Krzywe, Poland Abstract – One of the features of Gonyostomum semen, a bloom-forming and expansive flagellate, is uneven distribution in the vertical water column often observed in humic lakes. In this paper, we analysed vertical distribution of the algae in four small (0.9–2.5 ha) and humic (DOC: 7.4–16.5 mg dmÀ3) lakes with similar morphometric features with the aim to test the hypothesis that vertical distribution of G. semen may be shaped by zooplankton structure and abundance. In addition, we wanted to check whether high biomass of this flagellate has any influence on the chemical composition as well as on planktonic bacteria abundance of the water column. The results of the study showed that vertical distribution of the algae during the day varied among all studied lakes. Our most important finding was that (a) the abundance and structure of zooplankton community (especially in case of large bodied daphnids Daphnia pulicaria, D.