Plant Systematics

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Plant Systematics databases Users perspectives
Robert K. Peet, University of North Carolina In
collaboration with The National Center for
Ecological Analysis SynthesisVegBank
Development TeamEcological Society of America
Vegetation PanelScience Environment for
Ecological Knowledge International Association
for Vegetation Science
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Biodiversity data structure
Community Type
Community type database
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New data sources EcoInformatics

• Site data climate, soils, topography, etc.
• Taxon attribute data identification, phylogeny,
  distribution, life-history, functional
  attributes, etc.
• Occurrence data attributes of individuals
  (e.g., size, age, growth rate) and taxa (e.g.,
  cover, biomass) that occur or co-occur at a site.

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How do we get there?Standards, tools access

• Standard protocols.
• Standard data structures exchange formats.
• Public data archives and databases
• Tools for data discovery semantic mediation.

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• VegBank
• The ESA Vegetation Panel is developingVegBank
  (www.vegbank.org) as a public vegetation plot
  archive
• VegBank is expected to function for vegetation
  data in a manner analogous to GenBank.
• Data deposited for storage preservation,
  references documentation, access
  identification, novel synthesis reanalysis.
• Millions of co-occurrence records

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SEEK-Taxon

• Tools for data discovery and semantic mediation
• Tools and standards for data markup
• Methods and tools for describing more precisely
  the meaning of concepts associated with organism
  names
• Demonstration databases that maintain mappings of
  taxonomic concepts

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Types of systematics databases

• Comprehensive lists (compilers e.g. IPNI, Zoo
  Record)
• Authoritative checklists (aggregators e.g.
  ITIS/USDA, Species2000)
• Concepts and perspectives (e.g. EuroMed, VegBank)
• Taxon attributes (e.g. USDA, BioFlor, LEDA, IRIS,
  TreeBase)
• Specimens (distributed, various standards and
  protocols)

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Compilations (e.g. IPNI)

• Semi-comprehensive no registration requirement
• Duplications of names no rectification?
• Inconsistencies between names in the list and in
  references (names or protonyms?)
• Web services needed for validating names
• No standard for exchange or unique identification
  of names or references

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Standard checklists for taxa Representative
examples for North American higher plants
USDA Plants http//plants.usda.gov
ITIS http//www.itis.usda.gov
NatureServe Biotics http//www.natureserve.org
BONAP http//www.bonap.org/
Flora North America http//hua.huh.harvard.edu/FN
A/ These are intended to be checklists wherein
the taxa recognized perfectly partition all
plants. Most of the lists are dynamic.
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Taxonomic database challengeStandardizing
organism names The problem Integration of
data potentially representing different times,
places, investigators and taxonomic
standards The traditional solution A standard
checklists of organisms
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• Most taxon checklists fail to allow effective
  dataset integration
• The reasons include
• The user cannot reconstruct the database as
  viewed at an arbitrary time in the past,
• Taxonomic concepts are not defined (just lists),
• Multiple party perspectives on taxonomic concepts
  and names cannot be supported or reconciled.

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Taxonomic theory A taxon concept represents a
unique combination of a name and a
reference Taxon concept roughly equivalent to
Potential taxon assertion
Name
Reference
Concept
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Three concepts of shagbark hickory Splitting one
species into two illustrates the ambiguity often
associated with scientific names.
Carya carolinae-septentrionalis (Ashe) Engler
Graebner
Carya ovata (Miller) K. Koch
Carya ovata (Miller) K. Koch
sec. FNA 1997
sec. Kartesz 1999
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A usage represents an association of a concept
with a name.
Name
Concept
Usage

• Usage does not appear in the IOPI model, but
  instead is a special case of concept
• Desirable for stability in recognized concepts
  when strictly nomenclatural synonyms are created.
• Usage can be used to apply multiple name systems
  to a concept

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Six shagbark hickory concepts Possible synonyms
are listed together
Names Carya ovata Carya carolinae-septentrion
alis Carya ovata v. ovata Carya ovata v.
australis
Concept groups (One shagbark) C. ovata sec
Gleason 52 C. ovata sec FNA 97 (Southern
shagbark) C. carolinae-s. sec Kartesz 99 C.
ovata v. australis sec FNA 97 (Northern
shagbark) C. ovata sec Kartesz 99 C. ovata
(v. ovata) sec FNA 97
References Gleason 1952. Britton Brown
Kartesz 1999. Synthesis Stone 1997. Flora North
America 3
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Data relationshipsVegBank taxonomic data model
Concept
Name
Usage Start, Stop NameStatus Name system
Status Start, Stop ConceptStatus Level, Parent
Reference
Single party, dynamic perspective
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• Party Perspective
• The Party Perspective on a concept includes
• Status Standard, Nonstandard, Undetermined
• Correlation with other concepts e.g. Equal,
  Greater, Lesser, Overlap, Undetermined
• Start Stop dates for tracking changes

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Data relationshipsVegBank taxonomic data model
Name
Concept
Usage Start, Stop NameStatus Name system
Correlation
Party
Status Start, Stop ConceptStatus Level, Parent
Reference
With party correlations and lineages
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Plot Observation
Some core elements of VegBank
Taxon Observation
Taxon Interpretation
Taxon Assignment
Taxon Concept
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Plant systematics databases What do we need?

• What has been done?
• What is going on?
• What additional work is needed?

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General data model and data exchange standard

• Numerous data models incorporate concepts. The
  IOPI, VegBank, and Taxonomer models are optimized
  for different uses.
• Jessie Kennedy, representing SEEK, GBIF, and
  TDWG, is seeking a consensus model to be
  presented in May 2004 and revised for TDWG
• A unique opportunity to build on other efforts.
  Kennedys results will need to be reviewed prior
  to TDWG in October.

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True concept-based checklists

• Equivalent of ITIS but with concept documentation
  and including how other concepts map onto the
  concepts accepted by the party.
• Fully archived so that can be viewed as existed
  at any given time.
• Several are operative or in development including
  EuroMed, IOPI-GPC, Biotics, VegBank. Planned for
  IT IS/USDA.

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Population of concept-based checklists

• For concept-based taxonomy to be widely adopted
  an initial set of accepted concepts must be
  identified.
• VegBank and NatureServe are collaborating to
  develop concepts for the 2004 revision of the
  Kartesz list. The concepts will be used to
  populate VegBank, Biotics, ITIS and USDA PLANTS.
• The IOPI Global Plant Checklist is gradually
  incorporating concepts.

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Registration system and standard identifiers for
names, references, and concepts

• Essential for data exchange
• SEEK is in the early design stages for a
  identifier system and central database.
• IPNI and GBIF would be ideal parties to host a
  names registry.

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Tools to develop and map concepts

• Taxonomists need mapping and visualization tools
  for relating concepts of various authors. SEEK
  will build prototypes for review and possible
  adoption.
• Aggregators need tools for mapping relationships
  among concepts.
• Users need tools for entering legacy concepts.
  Several are in development

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Publishers, curators and data managers need to
tag taxon interpretations with concepts

• Precedence exists with tagging literature
  citations and GenBank accessions
• Allen Press is linking scientific names in many
  ejournals to ITIS (e.g. Evolution, Ecology)
• Much work to be done here. SEEK is developing
  recommendations

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Standard protocols for recording plant traits and
exchanging plant trait data.

• TDWG standards.
• European ecological initiativesBioFlor
  www.ufz.ed/bioflor/index.jspLEDA -
  www.leda-traitbase.orgIRIS -
  www.synbiosys.alterra.nl/IRIS/

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Where are we?

• Standards, tools and databases are essential for
  advancement of our fields
• Much is going on
• Much needs to be done
• Resources are scarce
• Collaboration is essential

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• Primary differences between the VegBank model and
  the IOPI(Berendsohn) models
• The VB model is optimized for
• stability in accepted concepts,
• support of multiple dynamic party perspectives,
• support of multiple name systems.
• The IOPI model is optimized for
• Describing taxonomic decisions represented in
  literature.

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Core elements of theIOPI (Berendsohn) model
Name
Interpretation
Assertion
Rank
Correlation
Reference
Source
Assertion Status
Author