tdwg annual conference 2013 florence n.
Skip this Video
Loading SlideShow in 5 Seconds..
TDWG Annual Conference 2013, Florence PowerPoint Presentation
Download Presentation
TDWG Annual Conference 2013, Florence

Loading in 2 Seconds...

play fullscreen
1 / 18
Download Presentation

TDWG Annual Conference 2013, Florence - PowerPoint PPT Presentation

Download Presentation

TDWG Annual Conference 2013, Florence

- - - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript

  1. Integrating observation and survey data for production of the Essential Biodiversity Variables – the EU BON approach TDWG Annual Conference 2013, Florence Hannu Saarenmaa University of Eastern Finland

  2. Main objective of EU BON • building a European contribution to GEO BON • A key feature of EU BON • delivery of relevant biodiversity information and analysis – from on-ground / in-situ observation and remote sensing – to various stakeholders and end users, ranging from local to global levels The new, integrative EU BON approach will facilitate (political) decisions in different sectors concerned with biodiversity for human well-being at different levels, ranging from local park management to national governments, and IPBES.

  3. EU BON outputsand products (1) • gap analysis for available data layers at different scales, mainly in/for Europe (WP1) • strategies for targeted data mobilization (WP1) • new and improved data standardsfor advancing interoperability and new generation of data provider tools (WP2) • new, scalable/customized European Biodiversity Portal (WP2 / WP8) • software tools for improved recording / mapping of habitats, species distributions and patterns (WP3) • Improved models for impacts of different drivers on abundance & distribution, applicable at different scales (WP4) • guidelines for improved, integrated monitoring schemes at different scales / levels (WP4)

  4. EU BON and GEO BON: Integration of biodiversity data – across realms • Collections • Observations • Surveys • Remote sensing • Statistics • Biologic / socioeconomic

  5. Conceived by GEO BON Collaborators (Pereira (2013) “Essential Biodiversity Variables”, Science, Vol. 339, 18 Jan 2013) EBVs facilitate data integration by providing an intermediate abstraction layer between primary observations and indicators. EBVs aim to help observation communities harmonise monitoring, by identifying how variables should be sampled and measured. EBVs standardise an ontology for biodiversity and harmonise measurements, observations, and protocols. Endorsed by Convention on Biological Diversity (CBD) and in line with the 2020 Aichi Targets Provide focus for GEO BON and hence for the interoperability thrust within GEO BON A Use Case for EU BON to focus on Essential Biodiversity Variables

  6. Is this the reality in biodiversity monitoring? Achilleamillefolium According to GBIF visualising data gaps…

  7. Coordination of biodiversity observation • CBD AdequacyReport: • Observation systems related to the state of biodiversity all have significant global-scale observation systems, typically with national or better resolution, already in place. There are deficiencies in the evenness of global coverage and data quality, and some of the observations are too narrow in scope, but in the opinion of the experts, fit-for-purpose adequacy is technically achievable in all cases if sufficient resources are made available. EU BON description of work: The fragmentation and heterogeneity of environmental datasets and biodiversity observation systems remains a major challenge ... Data-collection and observation systems are unbalanced in terms of geographic, temporal, topical, and taxonomic coverage. Information currently available differs across countries and continents due to their different traditions in, and societal frameworks for biodiversity monitoring, and is often heavily biased towards easily recognizable and high profile taxa. Terrestrial, freshwater, and marine environments are studied and monitored by largely different independent communities, rarely sharing concepts, data or infrastructures.

  8. Gap analysis • EU BON is carrying out a gapanalysis • Data gap is agaponly in context of data use. • Notsame as data quality. • In Europethereareabout 2000 biodiversityobservationnetworks (643 listed in EUMON) • There is a massiveduplication of effort in data management, and lack of data sharing

  9. Change the way we are dealing with data Interdisciplinary challenges Data generator User Data generator User Data generators User Data generators User Support services Tool Tool Tool Tool Data storage Data storage Data storage Data storage Data infrastructure Slide by courtesy of Wouter Los

  10. Develop trust Domestic storage Bring it to a Bank Direct transfer To a Bank Slide by courtesy of Wouter Los

  11. European vision of a collaborative Data Infrastructure Data Generators Users User functionalities, data capture and transfer, virtual research environments Trust & Curation Community Support Services Data discovery & navigation, workflow generation, annotation, interpretability Persistant storage, identification, authencity, workflow execution Common Data Services Slide by courtesy of Wouter Los

  12. LifeWatch architecture Virtual laboratories for scientific cooperation Select the data, software, computing power Integrate resources Linking to resources (databases, sensors, software, computing power) Slide by courtesy of Wouter Los

  13. Need to reorganise our data standards to fit in common data services Collection or Experimental Site (shared, external) Project or Survey (EML) -Protocol • Sampling Event (DC) • Date Time • Agents • Methods Locality (GML, shared, external) -UUID Taxon (DwC, shared external checklist) -UUID Sampling Object – popular fields from DwC, VegCore, O&M which are not practical to put in MeasurementOrFacts, in classes such as: -Organism occurrence, vouchered specimen, image -Plot, subplot, transect -Instrument, machine MeasurementOrFact; DwC) -Attribute (examples: identification, quantity) -Value (examples: Aus beus, 1000) -Unit (examples: species, count) -Range (examples: certain, 200)

  14. New generation of data sharing tools • Common data serviceswillbebased on networked data repositories and fewportals. • Repostoriesneed to supportbasicbiodiversity data, AND ecologicalmeasurements, AND [what?] • Based on existingtools • GBIF IPT: Beyonda fixed ”starschema” to a flexiblerelationalmodel • Metacat: Startrequiringuse of standardterms in data • Bothneed to implementan extended Darwin Corestandard • EU BON is working on a review of standards

  15. Thank you very much for your attention