Harmonizing Measurements for Marine Biodiversity Observation Networks Margaret O’Brien Santa Barbara Channel MBON http://sbc.marinebon.org ESIP, Winter 2017

MBON Goals

MBON Data Processing

MBON Data Processing

GOOS Timeline Timeline from Group on Ocean Observing.

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

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

Data in Preparation

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

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.

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.

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

Biodiversity Variables Essential Biodiversity Variables http://geobon.org/essential-biodiversity-variables/ebv-classes-2/ GOOS http://goosocean.org/index.php?option=com_content&view=article&id=79&Itemid=273 IOOC http://www.iooc.us/activities/biological-integration-observation-task-team/

Measurement Vocabularies Darwin Core (DwC) IndividualCount, OrganismQuantity, OrganismQuantityType (Occurrence) Extensions, e.g., “Fish Abundance” http://rs.gbif.org/sandbox/extension/mbg-fish-abundance.xml CF Conventions Mostly physical, a few related to biomass BODC Parameter codes Taxonomic Database Working Group (TDWG) http://www.tdwg.org/activities/osr/ 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.

Ontologies Population and Communities Ontology (PCO) http://www.ontobee.org/ontology/PCO Phenotypic Traits Ontology (PATO) http://www.ontobee.org/ontology/PATO Ecosystem Ontology (ECSO) http://bioportal.bioontology.org/ontologies/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.

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)

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”

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

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

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. https://github.com/DataONEorg/sem-prov-ontologies/

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.