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  • 1.
    Etterlin, Pernille Engelsen
    et al.
    Swedish Univ Agr Sci SLU, Dept Biomed Sci & Vet Publ Hlth, Sect Pathol, S-75007 Uppsala, Sweden..
    Morrison, David A.
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Organismal Biology, Systematic Biology.
    Osterberg, Julia
    Natl Vet Inst SVA, S-75189 Uppsala, Sweden..
    Ytrehus, Bjornar
    Norwegian Inst Nat Res NINA, Terr Ecol Dept, N-7485 Trondheim, Norway..
    Heldmer, Eva
    Swedish Anim Hlth Serv SvDHV, S-46432 Mellerud, Sweden..
    Ekman, Stina
    Swedish Univ Agr Sci SLU, Dept Biomed Sci & Vet Publ Hlth, Sect Pathol, S-75007 Uppsala, Sweden..
    Osteochondrosis, but not lameness, is more frequent among free-range pigs than confined herd-mates2015In: ACTA VETERINARIA SCANDINAVICA, ISSN 0044-605X, Vol. 57, article id 63Article in journal (Refereed)
    Abstract [en]

    Background: Organic pig production is expanding and amongst the objectives of organic farming are enhancing animal health and welfare. However, some studies have reported a higher prevalence of lameness and joint condemnation at slaughter in free-range/organic pigs than in conventionally raised pigs. Organic slaughter pigs have free-range housing in which indoor and outdoor access is compulsory, while in conventional farming the pigs are commonly confined to indoor pens. The present study evaluated the effects of free-range and confined housing on lameness prevalence in a herd of 106 finisher pigs, and whether osteochondrosis and Erysipelothrix rhusiopathiae associated arthritis influences these effects. We also evaluated the association between clinical lameness during the rearing period and joint condemnations at slaughter. Results: Seventy free-range and 36 confined housed fattener pigs were scored for their gait twice during the rearing period and 848 joints were evaluated post mortem. Osteochondrosis was more frequent among free-range than confined pigs (P < 0.05), and when present it was also more severe (P < 0.001). Pigs with more numerous and more severe osteochondral lesions had their gait affected more than did pigs with fewer such lesions (P < 0.05). Hence it was a paradox that we did not detect more lameness among the free-range pigs than the confined pigs. E. rhusiopathiae associated arthritis was not diagnosed. The association between gait remarks/clinical lameness and joint condemnations at slaughter was not significant. Conclusions: The results indicate that free-range housing may have both positive and negative effects on locomotory traits. Free-range pigs may be less clinically affected by osteochondrosis than are confined pigs. One explanation for this effect may be strengthening of joint supportive tissue and pain relief promoted by exercise. Visual gait scoring missed serious joint lesions that probably were harmful to the pigs, and should therefore not be used as a sole indicator of joint/leg health in welfare inspection of pigs. The association between gait scores and joint condemnation appeared to be poor. This study was limited to one herd, and so more and larger studies on the effects of free-range housing on lameness severity and osteochondrosis development in pigs are recommended.

  • 2.
    Lidén, Magnus
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Organismal Biology, Systematic Biology.
    Morrison, David A.Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Organismal Biology, Systematic Biology.Baldauf, SandraUppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Organismal Biology, Systematic Biology.
    Contributions to Botany: Dedicated to Inga Hedberg2016Collection (editor) (Other academic)
  • 3.
    Morrison, David A.
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Organismal Biology, Systematic Biology.
    Aristotle’s Ladder, Darwin’s Tree: The Evolution of Visual Metaphors for Biological Order. — By J. David Archibald2015In: Systematic Biology, ISSN 1063-5157, E-ISSN 1076-836X, Vol. 64, no 5, p. 892-895Article, book review (Other academic)
  • 4.
    Morrison, David A.
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Organismal Biology, Systematic Biology.
    Biology of Evolution and Systematics: Cohesive, Concise, yet Comprehensive Introduction for Students and Professionals2016In: Systematic Biology, ISSN 1063-5157, E-ISSN 1076-836X, Vol. 65, no 1, p. 177-178Article, book review (Other academic)
  • 5.
    Morrison, David A.
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Organismal Biology, Systematic Biology.
    Biology of Evolution and Systematics: Cohesive, Concise, yet Comprehensive Introduction for Students and Professionals. By Paul Sanghera2016In: Systematic Biology, ISSN 1063-5157, E-ISSN 1076-836X, Vol. 65, no 1, p. 177-178Article, book review (Other academic)
  • 6.
    Morrison, David A.
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Organismal Biology, Systematic Biology.
    Genealogies: Pedigrees and Phylogenies are Reticulating Networks Not Just Divergent Trees2016In: Evolutionary biology, ISSN 0071-3260, E-ISSN 1934-2845, Vol. 43, no 4, p. 456-473Article in journal (Refereed)
    Abstract [en]

    Pedigrees illustrate the genealogical relationships among individuals, and phylogenies do the same for groups of organisms (such as species, genera, etc.). Here, I provide a brief survey of current concepts and methods for calculating and displaying genealogical relationships. These relationships have long been recognized to be reticulating, rather than strictly divergent, and so both pedigrees and phylogenies are correctly treated as networks rather than trees. However, currently most pedigrees are instead presented as “family trees”, and most phylogenies are presented as phylogenetic trees. Nevertheless, the historical development of concepts shows that networks pre-dated trees in most fields of biology, including the study of pedigrees, biology theory, and biology practice, as well as in historical linguistics in the social sciences. Trees were actually introduced in order to provide a simpler conceptual model for historical relationships, since trees are a specific type of simple network. Computationally, trees and networks are a part of graph theory, consisting of nodes connected by edges. In this mathematical context they differ solely in the absence or presence of reticulation nodes, respectively. There are two types of graphs that can be called phylogenetic networks: (1) rooted evolutionary networks, and (2) unrooted affinity networks. There are quite a few computational methods for unrooted networks, which have two main roles in phylogenetics: (a) they act as a generic form of multivariate data display; and (b) they are used specifically to represent haplotype networks. Evolutionary networks are more difficult to infer and analyse, as there is no mathematical algorithm for reconstructing unique historical events. There is thus currently no coherent analytical framework for computing such networks.

  • 7.
    Morrison, David A.
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Organismal Biology, Systematic Biology.
    Is Sequence Alignment an Art or a Science?2015In: Systematic Botany, ISSN 0363-6445, E-ISSN 1548-2324, Vol. 40, no 1, p. 14-26Article in journal (Refereed)
    Abstract [en]

    Aligning multiple nucleotide sequences is a prerequisite for many if not most comparative sequence analyses in evolutionary biology. These alignments are often recognized as representing the homology relations of the aligned nucleotides, but this is a necessary requirement only for phylogenetic analyses. Unfortunately, existing computer programs for sequence alignment are not based explicitly on detecting the homology of nucleotides, and so there is a notable gap in the existing bioinformatics repertoire. If homology is the goal, then current alignment procedures may be more art than science. To resolve this issue, I present a simple conceptual scheme relating the traditional criteria for homology to the features of nucleotide sequences. These relations can then be used as optimization criteria for nucleotide sequence alignments. I point out the way in which current computer programs for multiple sequence alignment relate to these criteria, noting that each of them usually implements only one criterion. This explains the apparent dissatisfaction with computerized sequence alignment in phylogenetics, as any program that truly tried to produce alignments based on homology would need to simultaneously optimize all of the criteria.

  • 8.
    Morrison, David A.
    Section for Parasitology, Swedish University of Agricultural Sciences.
    Is the tree of life the best metaphor, model, or heuristic for phylogenetics?2014In: Systematic Biology, ISSN 1063-5157, E-ISSN 1076-836X, Vol. 63, no 4, p. 628-638Article in journal (Refereed)
  • 9.
    Morrison, David A.
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Organismal Biology, Systematic Biology.
    Multiple Sequence Alignment Methods — Edited by David J. Russell2015In: Systematic Biology, ISSN 1063-5157, E-ISSN 1076-836X, Vol. 64, no 4, p. 690-692Article, book review (Other academic)
  • 10.
    Morrison, David A.
    Swedish University of Agricultural Sciences.
    Next generation sequencing and phylogenetic networks2014In: EMBnet.journal, ISSN 2226-6089, Vol. 20, no e760Article in journal (Refereed)
    Abstract [en]

    Networks are beginning to replace trees as the basic paradigm for data interpretation in phylogenetics. At the same time, next generation sequencing (NGS) offers the potential for generating suitable multilocus sequence data at a greater rate than ever before. Therefore, phylogeneticists now need to think about the relationship between NGS and phylogenetic networks, as there are a number of potential problems. These may not matter so much for tree-building algorithms, but it is a different matter for constructing networks. Each issue needs to be thought about to assess whether it is a serious problem or only of minor concern. Here, I introduce some of these issues, to start the discussion.

  • 11.
    Morrison, David A.
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Organismal Biology, Systematic Biology.
    Next Generation Systematics2017In: Systematic Biology, ISSN 1063-5157, EISSN 1076-836X, Vol. 66, no 1, p. 121-123Article, book review (Other academic)
  • 12.
    Morrison, David A.
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Organismal Biology, Systematic Biology.
    Pattern recognition in phylogenetics: trees and networks2016In: Pattern Recognition in Computational Molecular Biology: Techniques and Approaches / [ed] Mourad Elloumi, Costas S. Iliopoulos, Jason T. L. Wang, Albert Y. Zomaya, John Wiley & Sons, 2016, 1, p. 419-438Chapter in book (Refereed)
  • 13.
    Morrison, David A.
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Organismal Biology, Systematic Biology.
    Phylogenetic analysis of pathogens2017In: Genetics and Evolution of Infectious Diseases / [ed] Michel Tibayrenc, Amsterdam: Elsevier, 2017, 2, p. 167-193Chapter in book (Refereed)
  • 14.
    Morrison, David A.
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Organismal Biology, Systematic Biology. Department of Biomedical Sciences and Veterinary Public Health, Swedish University of Agricultural Sciences, Uppsala, Sweden.
    Phylogenetic networks: a new form of multivariate data summary for data mining and exploratory data analysis2014In: Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, ISSN 1942-4795, Vol. 4, no 4, p. 296-312Article in journal (Refereed)
    Abstract [en]

    Exploratory data analysis (EDA) involving both graphical displays and numerical summaries of data, is intended to evaluate the characteristics of the data as well as providing a form of data mining. For multivariate data, the best-known visual summaries include discriminant analysis, ordination, and clustering, particularly metric ordinations such as principal components analysis. However, these techniques have limiting mathematical assumptions that are not always realistic. Recently, network techniques have been developed in the biological field of phylogenetics that address some of these limitations. They are now widely used in biology under the name phylogenetic networks, but they are actually of general applicability to any multivariate dataset. Phylogenetic networks are fast and relatively easy to calculate, which makes them ideal as a tool for EDA. This review provides an overview of the field, with particular reference to the use of what are called splits graphs. There are several types of splits graph, which summarize the multivariate data in different ways. Example analyses are presented based on the neighbor-net graph, which seems to be the most generally useful of the available algorithms. This should encourage the more widespread use of these networks whenever a summary of a multivariate dataset is required.For further resources related to this article, please visit the WIREs website.Conflict of interest: The author has declared no conflicts of interest for this article.

  • 15.
    Morrison, David A.
    Department of Biomedical Sciences and Veterinary Public Health, Swedish University of Agricultural Sciences.
    Phylogenetic networks: a review of methods to display evolutionary history2014In: Annual Research & Review in Biology, ISSN 2347-565X, Vol. 4, no 10, p. 1518-1543Article in journal (Refereed)
    Abstract [en]

    Phylogenetic analysis attempts to reconstruct the genealogical history of evolutionary change in biological organisms. If the genealogy is complex, involving so-called horizontal evolutionary processes (such as recombination, hybridization, introgression and horizontal gene transfer) then an evolutionary network is required in order to graphically represent the history. Empirical examples of such networks have been used since the 1750s but only rarely. They fell out of favor from the late 1800s, when phylogenetic trees, which can represent only so-called vertical evolutionary processes (transfer of hereditary information directly from parent to offspring), were introduced to represent the Tree of Life. However, in the past 20 years there has been increased interest in using networks, as the evolutionary importance of horizontal processes has become increasingly more apparent. Unfortunately, there are currently few automated methods available, although this is an area of active algorithmic development. In this review, I discuss the development of both trees and networks as icons (or metaphors) for displaying phylogenetic relationships, to clarify some misunderstandings. I then provide an overview of the current approaches to using networks for the study of reticulate evolutionary relationships, explaining how the reticulation processes are detected based on the genetic patterns (or fingerprints) they produce. Finally, I review the current empirical use of evolutionary networks for displaying reticulate evolutionary histories. Due to the limitations of the current methods, many empirical networks have been produced manually or by modifying the output of a computer program.

  • 16.
    Morrison, David A.
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Organismal Biology, Systematic Biology.
    Taxonomy of Australian Mammals. By Stephen Jackson and Colin Groves2016In: Systematic Biology, ISSN 1063-5157, E-ISSN 1076-836X, Vol. 65, no 2, p. 346-348Article, book review (Other academic)
  • 17.
    Morrison, David A.
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Organismal Biology, Systematic Biology.
    The Biology and Identification of the Coccidia (Apicomplexa) of Marsupials of the World. Donald W. Duszynski.2016In: Systematic Biology, ISSN 1063-5157, E-ISSN 1076-836X, Vol. 65, no 4, p. 722-724Article, book review (Other academic)
  • 18.
    Morrison, David A.
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Organismal Biology, Systematic Biology.
    The Book of Trees: Visualizing Branches of Knowledge — By Manuel Lima. Design for Information: an Introduction to the Histories, Theories, and Best Practices Behind Effective Information Visualizations. — By Isabel Meirelles.2015In: Systematic Biology, ISSN 1063-5157, E-ISSN 1076-836X, Vol. 64, no 3, p. 363-365Article, book review (Other academic)
  • 19.
    Morrison, David A.
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Organismal Biology, Systematic Biology.
    The Invention of Nature: The Adventures of Alexander von Humboldt, the Lost Hero of Science (UK). The Invention of Nature: Alexander von Humboldt’s New World (USA). By Andrea Wulf2016In: Systematic Biology, ISSN 1063-5157, E-ISSN 1076-836X, Vol. 65, no 6, p. 1117-1119Article, book review (Other academic)
  • 20.
    Morrison, David A.
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Organismal Biology, Systematic Biology.
    The Monkey’s voyage: how improbable journeys shaped the history of life. €”by Alan de Queiroz.2014In: Systematic Biology, ISSN 1063-5157, E-ISSN 1076-836X, Vol. 63, no 5, p. 847-849Article in journal (Other academic)
  • 21.
    Morrison, David A.
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Organismal Biology, Systematic Biology.
    The Tree of Life: Evolution and Classification of Living Organisms .—Edited by Pablo Vargas and Rafael Zardoya; translated by Anne Louise2015In: Systematic Biology, ISSN 1063-5157, E-ISSN 1076-836X, Vol. 64, no 3, p. 546-548Article, book review (Other academic)
  • 22.
    Morrison, David A.
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Organismal Biology, Systematic Biology.
    Understanding Evolution .— By Kostas Kampourakis2015In: Systematic Biology, ISSN 1063-5157, E-ISSN 1076-836X, Vol. 64, no 6, p. 1121-1122Article, book review (Other academic)
  • 23.
    Morrison, David A.
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Organismal Biology, Systematic Biology.
    Morgan, Matthew J.
    CSIRO, Ecosyst Sci, Canberra, ACT 2601, Australia..
    Kelchner, Scot A.
    Utah State Univ, Dept Biol, Logan, UT 84322 USA..
    Molecular homology and multiple-sequence alignment: an analysis of concepts and practice2015In: Australian Systematic Botany, ISSN 1030-1887, E-ISSN 1446-5701, Vol. 28, no 1, p. 46-62Article in journal (Refereed)
    Abstract [en]

    Sequence alignment is just as much a part of phylogenetics as is tree building, although it is often viewed solely as a necessary tool to construct trees. However, alignment for the purpose of phylogenetic inference is primarily about homology, as it is the procedure that expresses homology relationships among the characters, rather than the historical relationships of the taxa. Molecular homology is rather vaguely defined and understood, despite its importance in the molecular age. Indeed, homology has rarely been evaluated with respect to nucleotide sequence alignments, in spite of the fact that nucleotides are the only data that directly represent genotype. All other molecular data represent phenotype, just as do morphology and anatomy. Thus, efforts to improve sequence alignment for phylogenetic purposes should involve a more refined use of the homology concept at a molecular level. To this end, we present examples of molecular-data levels at which homology might be considered, and arrange them in a hierarchy. The concept that we propose has many levels, which link directly to the developmental and morphological components of homology. Of note, there is no simple relationship between gene homology and nucleotide homology. We also propose terminology with which to better describe and discuss molecular homology at these levels. Our over-arching conceptual framework is then used to shed light on the multitude of automated procedures that have been created for multiple-sequence alignment. Sequence alignment needs to be based on aligning homologous nucleotides, without necessary reference to homology at any other level of the hierarchy. In particular, inference of nucleotide homology involves deriving a plausible scenario for molecular change among the set of sequences. Our clarifications should allow the development of a procedure that specifically addresses homology, which is required when performing alignment for phylogenetic purposes, but which does not yet exist.

  • 24.
    Nakhleh, Luay
    et al.
    Rice University, Houston, TX, USA.
    Morrison, David A.
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Organismal Biology, Systematic Biology.
    Phylogenetic networks2016In: Encyclopedia of Evolutionary Biology: Volume 3 / [ed] Richard M. Kliman, Elsevier, 2016, p. 264-269Chapter in book (Refereed)
  • 25.
    Podani, Janos
    et al.
    Eotvos Lorand Univ, Inst Biol, Dept Plant Systemat Ecol & Theoret Biol, Budapest, Hungary.;Hungarian Acad Sci, Ecol Res Grp, Budapest, Hungary..
    Morrison, David A.
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Organismal Biology, Systematic Biology.
    Categorizing ideas about systematics: alternative trees of trees, and related representations2017In: Rendiconti Lincei SCIENZE FISICHE E NATURALI, ISSN 2037-4631, E-ISSN 1720-0776, Vol. 28, no 1, p. 191-202Article in journal (Refereed)
    Abstract [en]

    This study is an attempt to expand a previous survey by Fisler and Lecointre (FL) for systematizing ideas on the use of the tree metaphor in classification, as expressed by various historically important figures in their writings. FL used a cladistic approach to analyze their data, as employed in biological classification. We supplement this analysis here using several methods of multivariate data exploration, producing a UPGMA dendrogram, a minimum spanning tree, a neighbor joining additive tree, a plexus graph, a phylogenetic network, and two multidimensional scaling ordinations of the same data used by FL. We confirm the validity of many of FL's smaller clusters of writings, and revealed a new 3-group categorization undetected by the previous study. These three groups largely correspond to Classifiers, who did not consider evolution for historical reasons or on purpose, Non-analytical evolutionists, who recognized evolution but with a more or less na < ve attitude towards the temporal change of life, and Modelers, with more explicit views on evolutionary processes, often applying objective mathematical tools for exploring the past and present of organismal diversity. Some scientists were difficult to assign to any group unambiguously, including J. W. von Goethe, who takes a unique position in the history of biology, and, to a lesser extent, E. Mayr and G. G. Simpson, the leaders of the gradist school of systematics. We argue that cladistic methods are insufficient by themselves, notably in situations where there are no obvious ancestor-descendant relationships underlying the development of the objects being analyzed.

  • 26.
    Schliep, Klaus
    et al.
    Univ Massachusetts, Boston, MA 02125 USA.
    Potts, Alastair J.
    Nelson Mandela Univ, Port Elizabeth, South Africa.
    Morrison, David A.
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Organismal Biology, Systematic Biology.
    Grimm, Guido W.
    Univ Vienna, Vienna, Austria.
    Intertwining phylogenetic trees and networks2017In: Methods in Ecology and Evolution, ISSN 2041-210X, E-ISSN 2041-210X, Vol. 8, no 10, p. 1212-1220Article in journal (Refereed)
    Abstract [en]

    1. The fields of phylogenetic tree and network inference have dramatically advanced in the past decade, but independently with few attempts to bridge them.

    2. Here we provide a framework, implemented in the phangorn library in R, to transfer information between trees and networks.

    3. This includes: (i) identifying and labelling equivalent tree branches and network edges, (ii) transferring tree branch support to network edges, and (iii) mapping bipartition support from a sample of trees (e.g. from bootstrapping or Bayesian inference) onto network edges.

    4. ability to readily combine tree and network information should lead to more comprehensive evolutionary comparisons and inferences.

1 - 26 of 26
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