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VegBank and the ESA Cyber-infrastructure for Vegetation Science

VegBank and the ESA Cyber-infrastructure for Vegetation Science . R.K. Peet, Don Faber-Langendoen, Michael Jennings, & Michael Lee Ecological Society of America Vegetation Panel. Ecological Society of America. National Science Foundation. Gap Analysis Program.

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VegBank and the ESA Cyber-infrastructure for Vegetation Science

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  1. VegBank and the ESA Cyber-infrastructure for Vegetation Science • R.K. Peet, Don Faber-Langendoen, Michael Jennings, & Michael Lee • Ecological Society of America Vegetation Panel

  2. Ecological Society of America National Science Foundation Gap Analysis Program National Center for Ecological Analysis and Synthesis National Biological Information Infrastructure Federal Geographic Data Committee We are pleased to acknowledge the support and cooperation of

  3. The new community ecology Intersection of 3 data types • Site data: climate, soils, topography, etc. • Taxon attribute data: identification, phylogeny, distribution, life-history, functional attributes ... • Co-occurrence data: attributes of individuals (e.g., size, age, growth rate) and taxa (e.g., cover, biomass) that co-occur at a site.

  4. The Vegetation Plot • The primary unit of vegetation observation. • Universal attributes: date, location, area, species list, species importance • Optional attributes: environment, soil, disturbance • Protocols and formats: many & flexible • Available data: > 106 plot records containing > 5x107 species occurrence records.

  5. VegBank • VegBank – a public archive for vegetation plot observations (http://vegbank.org). • VegBank functions in a manner analogous to GenBank. • Plot data can be deposited, cited, discovered, referenced, viewed, shared, annotated, updated, & downloaded. • Plot data can be used for documentation validation and reanalysis.

  6. VegBank strategies • Standard exchange format • Supports multiple protocols. • Flexible and expandable • Tools for data discovery, integration, and summarization. • Generalizable to most types of species co-occurrence data. • Incentives to participate.

  7. Background The ESA Vegetation Classification Panel was established in 1993 with a mandate to support the emerging U.S. Vegetation Classification.

  8. ESA Guidelines forvegetationclassification The ESA Vegetation Panel has developed guidelines for vegetation classification covering requirements for: • Vegetation field plots. • Documentation & description of floristic types. • Submission & peer review of proposed types. • Management, citation, & archiving of vegetation data.

  9. North American Vegetation Classification • Ecological Society of America – Standards, peer review & publication. • US Federal Geographic Data Committee –US government standards. • NatureServe – Maintenance and distribution of the Classification. • USDA & ITIS – Taxonomic standards for organisms

  10. NatureServe Explorer US-NVC--- Proposed data flow Extraction NatureServe Biotics Classification Mgt. NVC Proceedings US-NVC Panel Peer Review Proposal submission Legend External Action Analysis & Synthesis Internal Action VegBank & other plot archives Software Entity

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  14. Biodiversity data structure Locality Observation/ Collection Event Observation database Specimen or Object Occurrence database Bio-Taxon Taxonomic database Observation or Community Type Observation type database

  15. Core elements of VegBank Project Plot Plot Observation Taxon / Individual Observation Taxon Interpretation Plot Interpretation

  16. www.vegbank.org

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  20. Requirements: exchange standards for plot data • Standard data structure (draft by VegBank team) in implementation. • XML Schema (draft by VegBank team, modification proposed by the German team). • International standards and compatibility (Active Working Group within the International Association for Vegetation Science).

  21. Taxonomic database challenge:Standardizing organisms and communities The problem: Integration of data potentially representing different times, places, investigators and taxonomic standards. The traditional solution: A standard list of organisms / communities.

  22. Standardized taxon lists fail • to allow dataset integration • The reasons include: • Taxonomic concepts are not defined (just lists), • Relationships among concepts are not defined • The user cannot reconstruct the database as viewed at an arbitrary time in the past, • Multiple party perspectives on taxonomic concepts and names cannot be supported or reconciled.

  23. One concept ofAbieslasiocarpa USDA Plants & ITIS Abies lasiocarpa var. lasiocarpa var. arizonica

  24. A narrow concept of Abies lasiocarpa Flora North America Abies lasiocarpa Abies bifolia Partnership with USDA plants to provide plant concepts for data integration

  25. Relationships among conceptsallow comparisons and conversions • Congruent, equal (=) • Includes (>) • Included in (<) • Overlaps (><) • Disjunct (|) • and others …

  26. High-elevation fir trees of western US AZ NM CO WY MT AB eBC wBC WA OR Distribution Abies lasiocarpa var. arizonica var. lasiocarpa USDA & ITIS Abies bifolia Abies lasiocarpa Flora North America A. lasiocarpasec USDA > A. lasiocarpasecFNA A. lasiocarpasecUSDA > A. bifoliasecFNA A. lasiocarpa v. lasiocarpasecUSDA > A. lasiocarpasecFNA A. lasiocarpa v. lasiocarpasecUSDA >< A. bifoliasecFNA A. lasiocarpa v. arizonicasecUSDA < A. bifoliasecFNA

  27. Party Perspective • VegBank supports selection of Party perspective at an arbitrary date by tracking: • Status – Standard, Nonstandard, Undetermined • Correlation with other concepts – Equal, Greater, Lesser, Overlap, Undetermined • Start & Stop dates.

  28. Taxon/community interpretationDocumenting the user’s informal working concept • Multiple concepts can be linked simultaneously by concept relationship notation. • Degree of fit for each can be indicated by fuzzy logic notation • Subsequent interpretations supported.

  29. Scale for concept fit 1 = Absolutely wrong. Unambiguously incorrect. 2 = Understandable but wrong. Doesn't fit but is close. Not a good answer. 3 = Reasonable or acceptable answer 4 = Good answer. Unambiguously correct 5 = Absolutely correct. Perfect fit.

  30. Documenting identifications Always show the concept – not just the name!! Relationships added for identification = Indicates identification ~ (or aff.) Indicates similarity >,<,><,| As with concept relationships Example of complex identification < Potentilla sec. Cronquist 1991 + ~ Potentilla simplex sec Cronquist 1991 + ~ Potentilla canadensis sec Cronquist 1991

  31. Conclusion: The new community ecology depends on standards and connectivity • Standard for co-occurrence data • Standards for data exchange • Public data archives (functions for deposit, discovery, withdrawal, citation, annotation) • Standards for data archiving • Standards for reference to taxonomic data

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