1. Harmonizing Measurements for Marine Biodiversity Observation Networks
Margaret O’Brien
Santa Barbara Channel MBON
ESIP, Winter 2017

2. MBON Goals

3. MBON Data Processing

4. MBON Data Processing

5. GOOS Timeline
Timeline from Group on Ocean Observing.

6. MBON Partnerships - example
Source: Jennifer Brown - MBNMS, CINMS

7. CINMS Data Needs - Draft
Source: Jennifer Brown - MBNMS, CINMS

8. Data in Preparation

9. DM Workshop, 2016 UCSB
GOALS
Understand the needs of potential MBON data users
Initiate a coordinated approach for the three demonstration MBONs

10. DM Workshop, 2016 UCSB
If vocabularies are complete, structured, well known and broadly used, then recommendations for their adoption can be handled.
Current vocabulary efforts are conceptual and need to be fully operationalized
Accommodate
primary observations (e.g., organism spatial abundance) and
derived variables (e.g., indices of evenness, dominance, diversity)
Unambiguous
Meet needs of all MBON data
Structure, content
Basic approach for the MBON community should be to adopt one or more existing vocabularies as is possible, and to augment those with missing terms or contribute definitions using established.

11. Adopt one or more existing vocabularies as is possible
DM Workshop, 2016 UCSB
Basic approach:
Adopt one or more existing vocabularies as is possible
Augment those with missing terms or contribute definitions using established.
Process is likely to be complex because the biological data to which these variables must apply are often hand-collected, ad hoc and idiosyncratic
Basic approach for the MBON community should be to adopt one or more existing vocabularies as is possible, and to augment those with missing terms or contribute definitions using established.

12. DM Workshop, 2016 UCSB
Assemble existing vocabularies which could be applied to MBON data
Examine and evaluate candidate vocabularies for MBON use
Adopt groups of terms deemed appropriate and adequate
Suggest additions to vocabularies which are incomplete
Outline additional work and funding needs, if appropriate

13. Biodiversity Variables
Essential Biodiversity Variables
GOOS
IOOC

14. Measurement Vocabularies
Darwin Core (DwC)
IndividualCount, OrganismQuantity, OrganismQuantityType (Occurrence)
Extensions, e.g., “Fish Abundance”
CF Conventions
Mostly physical, a few related to biomass
BODC
Parameter codes
Taxonomic Database Working Group (TDWG)
The status of vocabularies for diversity-related variables can be compared to that for physical measurements from instrument data, where a considerable number of fairly formal descriptions are available (e.g., CF Conventions) and communities have well-established recommendations and processes for new contributions.
biodiversity measurements will require formal measurement descriptions at least as complex as are used by the CF Conventions community.

15. Ontologies Population and Communities Ontology (PCO)
Phenotypic Traits Ontology (PATO)
Ecosystem Ontology (ECSO)
Ontlolgies are a step more complex than controlled lists, with structure that allows machine processing. But along with that, you get a class and CONTEXT structure that means there are other terms that can now be associated with that dataset, not just the measurement. That advantage means they are something we should explore using.
There are some ontolgies out there now that may be able to help us:
An ontology called “PCO”….
PATO ….
ECSO is one that I am working on, with several from a much broader ontology and repository community. Introduce that.

16. Ecosystem Ontology (ECSO)
Imports:
PATO
– phenotypic traits
ENVO
– environment, context
CHEBI
– elements, chemicals UO
– units, dimensions
Using OBO Foundry recommendations and practices
ecso supported by d1, for data discovery at the measurement level (other efforts working at the higher levels, eg, GeoLink).
examined many existing ontologies before proceeding.
IMPORTS: components of several stable ontlogies.
Created an ID system that can be permanent, plan for updates.
Complex process. So started with one class of data, Carbon Cycling. Limited number of datasets (several 100s), but still a corpus that was complex enough to expose potential problems. LTER data was a major use case. Also modeling data from the MSTIMIP (model intercomparison project)

17. Data Diversity http://portal.lternet.edu knb-lter-hfr.103.27
knb-lter-sbc.37.4
Methods vary widely
scope (organism, community, ecosystem),
scale (temporal and spatial).
Why would we go to all this trouble?
Above, a satellite image depicting NPP values from the Harvard Forest (image from ORNL DAAC
Below, a chamber for measuring in situ NPP in a benthic algal community at the Santa Barbara Coastal LTER.
These would both use the same STANDARD NAME – that is, net_primary_production.
But is that enough for a user to be able to tell them apart?
Both datasets have values for “net_primary_production”, with rich metadata & units of “mass per area per time”

18. Observation Model https://github.com/NCEAS/oboe/ Measurement based
Entity
ENVO, CHEBI
Characteristic
PATO, OBOE
Standard UO
Protocol
Precision
Built on a basic observational model. Extensions.
Model is compatible with other high level observation models like O&M
The imported ontologies

19. Local Dictionary -> EML Metadata
Cut to the chase: can put the ECSO measurement ID directly into metadata.
See poster for more on dataset annotations, testing and implementation

20. ECSO - Biomass
Some ECSO classes are ready for use in population studies
Plus, it is mature enough to add another group of measurements. Looking for a candidate.

21. Input, Discussion
Assemble existing vocabularies which could be applied to MBON data
Examine and evaluate candidate vocabularies for MBON use
Adopt groups of terms deemed appropriate and adequate
Suggest additions to vocabularies which are incomplete
Outline additional work and funding needs, if appropriate
Back to the list of tasks.