Transcript Biological Data Products Workshop

Obtaining diversity and abundance from OBIS
database – the way forward
Simon Claus & Klaas Deneudt
Flanders Marine Institute (VLIZ)
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Outline Presentation
1. Introduction
– Common biodiversity measurements & indices
– How to model biodiversity
2. EurOBIS and OBIS (Ocean Biogeographic Information System)
– Data scheme
– Data content
– Quality control
3. Applications OBIS data
– Global patterns of marine biodiversity (Tittensor et al, 2010 – Nature)
4. EMODnet data products
– Outcomes products from Biological data product workshop
– Visualisation abundance CPR data on EMODnet portal
5. Conclusions & way forward
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Biodiversity measurements (1)
•
Occurrence or presence/absence (or not detected)
–
When is a species absent?
• Absolute abundance or counts
– Total number of animals sampled, seen or counted
Positive: sample size is known
Negative: not “as is” comparable between different sites or samples
• Relative abundance or density
– Number of animals per surface, volume
– Derived from absolute abundance
Negative: Size of sample is unknown => limits possibilities for correct application of statistics and indices
Positive: comparable between different sites
• Biomass
–
Productivity
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Biodiversity indices (2)
• Species richness indices: Species richness is a measure for the total number of the
species in a community
• Evenness indices: Evenness expresses how evenly the individuals in a community
are distributed among the different species
•
Taxonomic indices: These indices take into account the taxonomic relation
between different organisms in a community
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Biodiversity indices(2)
• Examples of Biodiversity indices:
– Species richness S
– Simpson index
– Shannon index
– Hill Numbers
– Hulrbert index
– Taxonomic distinctness
– ...
http://www.coastalwiki.org/coastalwiki/Measurements_of_biodiversity
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Modelling biodiversity
•
Habitat suitability modelling
Delineating suitable area’s (likeliness of occurrence/absence) for certain species or
communities based on the environmental requirements of these species or
communities
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Predictions: environmental envelope modelling
Based on describing the environment encompassing the current distribution of a
species or ecosystem (the environmental envelope), then mapping the location of
this same envelope under a climate change scenario.
•
Predicting biodiversity indices
–
–
Predictability of marine nematode biodiversity (Merckx et al, 2009 - Ecological Modelling)
Predict biodiversity indices using Artifical Neural Networks ( a data driven modeling technique used
in ecological sciences as a tool for uncovering complex patterns in data)
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EurOBIS and OBIS – data scheme
• OBIS is a distributed system to integrate marine biogeographic information
•
The OBIS schema is an extension to the Darwin Core Version 2 standard
•
74 fields of which 6 requiered
•
Four fields to describe abundance/biomass
•
Link to metadata record which ISO 19115 Compliant
•
Link to World Register of Marine Species for taxonomy
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System overview EurOBIS & OBIS
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EurOBIS datacontent
Data contributions to EurOBIS
19%
eurobis tov OBIS
26%
OBIS tov GBIF
2%
2%
2%
24%
76%
3%
10%
10%
90%
12%
2%
16%
6%
CPR (plankton data)
WOD (plankton data)
JNCC (benthos data UK)
Marbef databases (benthos data)
ESAS (bird data)
Seaweed (algae data UK)
ICES EcoSystem (mixed data)
EurOBIS
OBIS
OBIS
GBIF
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EurOBIS datacontent
EMODnet
•
•
•
In total 13,601812 dr
In total 2,024359 abundance (14,8%) mainly benthic data
In total 67080 biomass (0.5%)
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EurOBIS datacontent
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EurOBIS quality control
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Taxonomy important, even crucial (WorMS)
– Integrate datasets
– correct estimates of biodiversity -> (Vandepitte et al, 2009 - Hydrobiology)
…In total, 6,172 unique taxon names were submitted to LargeNet. After a thorough quality
control, however, this number was reduced to 4,525, mostly due to spelling variations and
synonymy. Such quality control is highly needed, since a misspelled or obsolete name could be
compared to the introduction of a rare species, with adverse effects on further (biodiversity)
calculations.
•
•
Quality flags completeness of records
Quality procedures for biological data (sampling methods)
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EurOBIS data scheme & content:
issues
•
EurOBIS can store abundance/biomass data
•
However number of fields rather limited to capture these data
–
–
–
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How to store information on fractions (meiobenthos,phytoplankton...) – not linked to WoRMS ID
Link to additional metadata is important (with ISO 19115 compliant)
Link to WoRMS is key in order to compare datasets
Number of abundance/biomass data very limited
–
–
–
–
–
Not requiered (find the good trade off)
Not willing to provide the data (data protection)?
Not aware of possibilities to store the data in EurOBIS format
More labour intensive to provide absence/biomass data
More difficult to provide absence/biomass data
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Applications OBIS data
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•
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Global patterns and predictors of marine biodiversity across taxa
(Tittensor et al, 2010 – Nature)
examine global patterns and predictors of species richness across 13 major species
groups
For coastal fish: based on OBIS data
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Applications OBIS data
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SST only statistical factor
significant. Support to the
kinetic energy or
temperature hypothesis,
that is, that higher
metabolic rates or relaxed
thermal constraints promote
diversity.
•
Endothermic groups
(cetaceans and pinnipeds)
showed stronger positive
relationships with primary
productivity than SST
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Applications OBIS data
•
•
Hotspots for both oceanic
and coastal species
occurred in areas with
medium or higher human
impact
Such overlapping hotspots
of species richness and
human impact should be
further assessed and may
be an important focus for
marine management and
conservation efforts across
taxa
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Applications OBIS data
Gaps
•
Limited to taxa for which sufficient records were accessible to determine global
distribution (cells with less then 10 years of sampling removed)
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Distribution of deep-sea diversity, where data remain scarce
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Limited marine invertebrate data
•
Microbes or viruses not considered
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EMODnet data products
Outcomes products from Biological data product workshop
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Biological Data Products Workshop (25-26/02/2010)
Aim:
– To define a set of derived data products relevant for private bodies, public
authorities and researchers
– Discuss the marine biological (monitoring) data availability in Europe and gaps
– Present protoype portal to wide(r) community of European Biological Experts
and capture feedback
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EMODnet data products
Outcomes products from Biological data product workshop
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Biological Data Products Workshop (Participants)
– MARE, DG Research, OSPAR, ICES, HELCOM, Black Sea Commission, coordinators of the
other lots, project partners and advisory board
– Greece, France, UK, Italy, Belgium, Netherlands, Germany, Sweden, Ireland, Russia, Ukraine,
US, New Zealand
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EMODnet
EMODnet data products
Outcomes products from Biological data product workshop
– Workshop report available
– Data products:
– Species attributes (labelling) (functional groups, HAB’s, invasive species, red
list or protected species, BD and HD)
– Species distribution maps and trends
– Species sensitivity and vulnerability map
– Biodiversity indices
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EMODnet data products
Example: Visualisation abundance CPR data on EMODnet portal
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•
•
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CPR time series
Copepod abundance annual anomlies per CPR grid
A zooplankton time-series can be presented as a series of log-scale anomalies
relative to the long-term average of these data
Reveal interannual variability and trends in the time-series
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EMODnet data products
Example: Visualisation abundance CPR data on EMODnet portal
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EMODnet data products
Example: Visualisation abundance CPR data on EMODnet portal
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EMODnet data products
Example: Visualisation abundance CPR data on EMODnet portal
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EMODnet data products
Example: Visualisation abundance CPR data on EMODnet portal
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EMODnet data products
Example: Visualisation abundance CPR data on EMODnet portal
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EMODnet data products
Example: Visualisation abundance CPR data on EMODnet portal
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EMODnet data products
Example: Visualisation abundance CPR data on EMODnet portal
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Conclusions & way forward
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•
•
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Model & predict distributions & biodiversity: emerging area
OBIS scheme can handle most biodiversity measurment data but could be
improved
Abundance data at the moment underrepresented
Quality control is key aspect – especially taxonomic qc
EMODet Biological project making first steps on identification and visualisation of
biological dataproducts (indices, anomalies...)
Other types of biological data: biometry, stomach analyses, genes (growing
importance, possibly future projects with that focus)
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Thank you
References
Derek P. Tittensor, Camilo Mora, Walter Jetz, Heike K. Lotze, Daniel Ricard, Edward Vanden Berghe
& Boris Worm Global patterns and predictors of marine biodiversity across taxa, Nature 2010
doi:10.1038/nature09329
Merckx Bea , Peter Goethals, Maaike Steyaert, Ann Vanreusel, Magda Vincx, Jan Vanaverbeke
Predictability of marine nematode biodiversity, Ecological Modelling 220 (2009) 1449–1458
O’Brien, T. D., López-Urrutia, A., Wiebe, P. H., and Hay, S. (Eds). 2008. ICES Zooplankton Status Report
2006/2007
http://www.coastalwiki.org/coastalwiki/Measurements_of_biodiversity
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