NVS

1. NVS New Zealand National Vegetation Survey

2. What is NVS? • NVS (National Vegetation Survey) – New Zealand’s largest archive facility for plot-based vegetation data • NVS is both a physical (field data sheets, maps, photographs) and electronic archive (database)

3. NVS - coverage • Best in grassland and indigenous forest • Collection intensity has varied over 50+ years • 14 000 permanent and 52 000 relevé plots

4. Who uses NVS? Four main types of user: • biodiversity management practitioners, • researchers, • database-to-database, • users and policy developers and IT-level users.

5. How is NVS used? Users rely on NVS as an archive and as a primary source of quantitative vegetation biodiversity data Traditional uses • monitoring environmental change • assessing and monitoring impacts of introduced animals • inventory and description of plant communities New activities • guiding the design of national-level biodiversity inventory and monitoring programmes • providing empirical data for validating predictive models • deriving carbon storage estimates.

6. NVS data management • NVS database consists of two distinct parts:

7. NVS data management • NVS database consists of two distinct parts: 1. Metadata for projects and datasets (>3000) • Databank organisation and management • Search and locate datasets • Assess suitability for use • Constraints- Permissions • Xml Schema – Web delivery

8. NVS data management • NVS database consists of two distinct parts: 1. Metadata for projects and datasets (> 3000) 2. Plot-based vegetation survey data • Mainly standardised survey’s (20x20m plots) • Plot/site descriptors • Relevé, Repeat measures of individual trees, Understory composition • Also a wide range of other data types collected by various means (ranks, CWD, browse, etc) • Relational database – Desktop management system (new system currently being developed)

9. Metadata application

10. Species present

11. Species distribution

12. Dataset request

13. Plot vegetation application

14. Sample methods

15. Tree data

16. Data model overview

17. Future uses • What do users want to do in the future: • Use NVS data in concert with Land Environments New Zealand classification (LENZ) toidentify priority sites forconservation management • Comparative analysis of vegetation communities in different regions of New Zealand • Address monitoring requirements supporting a number of national and internationalreporting objectives, e.g. NZ Carbon Monitoring System, Montreal Process Indicatorreporting, Natural Heritage AssetManagement reporting • Large-scale ecological analyses using pooled data at both national and global scales • What they require: • Tools for entering data into a format suitable for NVS-specific analysis packages • Formalised and automatic mechanism for uploading data into NVS and returning data corrections and additions to datasets already stored in NVS • An unrestrictive format to handle data collected using non-standard methods or miscellaneous associated data

18. Future plans • Complete new system • Database loaded with historic data • Standard data exchange format • Analysis tools

19. Goals for this workshop • Draft vegetation schema is developed • Ensure that our specific requirements fit into a schema that is usable for all • Our effort is aligned with that of the larger vegetation science community

20. What LCR can offer • Experience in management and recording of individual tree data • Users protocols, end-users • New tools being developed • TDWG/GBiF, TCS, LSID expertise

21. Funded by NZ Foundation for Research Science & Technology - FRST NZ Department of Conservation - Terrestrial & Freshwater Biodiversity Information System - TFBIS

22. LCR requirements • Snapshot/single dataset oriented schema • Link to project and dataset level metadata • Capture methods, units, constraints for data • A format for transferring data between end-users and NVS • A portable format that is easily read by humans, but also suitable for machine processing

23. Design requirements (LCR) • Meet best accepted approach for schema design and naming conventions • Modular approach to enable flexibility and reuse • Probably a plot centric view • Developed as a standard export format • A portable format that can be consumed by other tools • Design that enables large scale data analysis • Possibly a schema that can be used in either a simple or complex way

24. Plot tables

25. Method tables

26. Relevé tables

27. Individual tree tables