Transcript BiodiversityData - Cardiff Biodiversity Informatics

Biodiversity Data
Richard White
Outline
• Biodiversity: what is it?
– Definitions: is biodiversity:
• A resource?
• Something which can be measured?
• How to measure it
– Who is it for?
• Data providers
• Researchers
• Users
• Biodiversity Informatics
– Research into techniques for handling data
Threats to
the planet
Ecology
Ecological
diversity
Evolution
Genetics
Molecular
biology
Species
diversity
Genetic
diversity
Human
activities
Habitat
conservation
Legal
issues
Conservation
Management
Species
conservation
Information
services
Genetic
resources
Economics
Exploitation
Biodiversity data types
• Kinds of biodiversity information:
– Data about areas, habitats, etc.
– Data about individual specimens
– Data about species
• Biodiversity data dimensions
– Species
– Diverse information types
• Descriptive, geographical, chemical, genomic etc.
Data about areas, habitats, etc.
• Species lists, for
– Conservation
– Management
– Legal obligations
• Ecological processes
– Modelling ecosystems
– Predicting impacts
Information about individual
specimens
• Curatorial data about the management of each
specimen
• Data describing characteristics of the specimen itself
(which can also describe an entire species)
Curatorial information
• Collection event
– Date and place of collection
– Collector’s name
• Identifications (determinations)
– Species name (see data about individual specimens and
species)
– Who identified it, date, etc.
• Management information
– Location within the specimen collection (storage)
– Treatments given to specimen, etc.
Data about specimens and species
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curatorial data
nomenclatural data
descriptive data
geographical data, maps
images
bibliographic data
Data describing specimens and
species (1)
• Genetic diversity
– Allele and chromosome frequencies
• Molecular bioinformatics
– Molecular data – enzyme properties, etc.
– Molecular sequences – DNA, protein,
polysaccharides, etc.
• “Traditional” data used in taxonomy etc.
– See next slide
Data describing specimens and
species (2)
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Nomenclature – accepted name, synonyms
Taxonomy – higher taxa
Geographical data – distribution (range)
Chemical constituents (especially in plants)
Behavioural information (animals)
Descriptive data
– Anatomical and morphological descriptors
– Images
• Bibliographic data (source references, especially for
species data)
Geographical data - storage
• Database may store:
– Individual locations of specimens or sightings
– Status in an area based on a number of
specimens or sightings: (present, absent,
introduced, etc.)
• Locations may be stored as
– Area names (languages, synonyms,
hierarchies, overlaps)
– Grid coordinates (various systems)
Geographical data - use
• May be used to generate
– summary distributions (e.g. for species
distribution from specimen data)
– Maps (point locations or shaded areas)
• May be used to allow searching by location or area –
user may specify a point or an area name
Descriptive data
• Should be carefully designed, because it is complex
and may be used for many purposes
• It should be
– Structured
– Consistently applied
• It may include data types suitable for statistical and
multivariate analysis
• Special problems exist
Descriptive data
Structured, for
• Querying
• Classification, phylogenetic analysis
• Identification
• Documentation and dissemination
Descriptive data
Consistency and comparability:
• Consistent terminology (c.f. attempts to standardise
terms for indexing purposes, as in BioCASE
Thesaurus)
• Same characters for all specimens or taxa
• Characters precisely defined
– Discontinuous - set of character states
– Continuous – units, precision
Descriptive data – special
problems
• Variability
– specimens within a species
– repeated structures within a specimen
• Character dependence (inapplicable characters)
• Taxonomic hierarchy issues, e.g.
– Is the data for a species in agreement with the data
for a genus?
– Can the data be stored at the appropriate taxonomic
level only?
Images
• Type
– Bitmap files, e.g. JPEG
– Vector graphics, e.g. drawings, diagrams
• Location
– In the local database
– Elsewhere in a separate image bank
• The Web makes the latter option easy – just store the
URL in the database
End