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National Vegetation Information System Taxonomic Review

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Report Contacts: Anthony Whalen or Jim Croft Centre for Plant Biodiversity Research GPO Box 1600 Canberra ACT 2601 April 2004 The views and opinions expressed in this report are those of the authors and do not necessarily reflect those of the Commonwealth Government or the Minister for the Environment and Heritage.


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ACKNOWLEDGEMENTS The CPBR would like to thank the following people for their input and assistance in compiling this report:

• Brendan Lepschi, Jessie Szigethy–Gyula, Laurie Adams for assessing the NVIS database, making recommendations and assistance with the report compilation;

• Matt Bolton, Leo Berzins, Gaston Rozenbilds, from ERIN for providing report guidance and technical support;

• Rosemary Purdie and Simon Bennett for advice on SPRAT.


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Contents ACKNOWLEDGEMENTS ii CONTENTS iii TABLES v FIGURES v EXECUTIVE SUMMARY vii RECOMMENDATIONS SUMMARY viii 1.0 INTRODUCTION.............................................................................................. 1

1.1 Project Brief 1 1.11 Aims and Objectives 1 1.12 Deliverables 2

1.2 Rationale 2 1.3 Background 3

1.31 NVIS 2000 database 4 1.32 Restructured NVIS database 4 1.33 Taxonomic issues in the restructured NVIS database 5 1.34 Taxon Lists in the restructured NVIS database 5

2.0 METHODOLOGY ............................................................................................ 7

2.1 Database preparation and early review 7 2.2 Database annotation of immediate taxonomic issues 8

2.21 MASTER column 8 2.22 CPBR_PROBLEM column 8 2.23 CPBR_SOLUTION column 8 2.24 CPBR_WIN_CURR column 9 2.25 CPBR_WIN_NAME column 9 2.26 CPBR_CHECKED_BY column 10 2.27 CPBR_CHECKED column 10

2.3 Structural review of the NVIS database 10 2.31 NVIS Taxon_Lists structural assessment 10 2.32 Interlinking NVIS with other databases 10 2.33 State jurisdictional lists 11

2.4 Development of test measures for future NVIS assessment 11 2.5 Development of guidance material for NVIS collaborator manuals 11

3.0 RESULTS ......................................................................................................... 12

3.1 Taxonomy 12 3.11 Record duplication 13 3.12 Infraspecific name issues 13 3.13 Synonymy 14

3.131 Nomenclatural synonyms 15 3.132 Taxonomic synonyms 15

3.14 Other taxonomic problems 16 3.141 Alternate family name 16 3.142 Missing Family Name 18 3.143 Name Qualifier Present 18 3.144 Double epithets 18 3.145 Status Unknown 19


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3.146 Name misspelt 20 3.147 Phrase names 20 3.148 Non-plant taxon 21

3.2 Author issues 21 3.21 Authors missing 22 3.22 Authors incorrect 22 3.23 Author in wrong field 22

3.3 Large group case studies 23 3.31 Family Proteaceae 23 3.32 The wattles (Acacia - family Mimosaceae) 25 3.33 The eucalypts 26

4.0 DISCUSSION & RECOMMENDATIONS................................................... 28

4.01 General principles 28 4.011 The taxonomic process 28 4.012 The ‘Consensus Census’ 28 4.013 Reference to a common taxonomic standard 28 4.014 Other national databases 29 4.015 Common names 29 4.016 Linking managers and custodians of taxonomic data 29

4.1 Taxonomic issues and database structure 30 4.11 Record duplication 30 4.12 Infraspecific name issues 31 4.13 Synonymy 32 4.14 Other taxonomic problems 32

4.141 Alternate family name 32 4.142 Missing Family Name 33 4.143 Name Qualifier Present 33 4.144 Double epithets 33 4.145 Status Unknown 33 4.146 Name misspelt 34 4.147 Phrase names 34 4.148 Non-plant taxon 35

4.15 Author Name issues 35 4.16 Large group case studies 35

4.2 NVIS in relation to other botanical databases 36 4.21 NVIS relationship with Commonwealth and national databases 36 4.22 The role of State and Territory botanical databases 37 4.23 HISPID standards 38

4.3 Future test measures 39 4.4 Guidance material for incorporation in future NVIS manuals 47

4.41 Provision of data to NVIS 47 4.42 Field data collection and herbarium vouchers 48

5.0 CONCLUSION ................................................................................................ 50 6.0 REFERENCES................................................................................................. 51


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7.0 APPENDIXES .................................................................................................. 52 Appendix A: Improving the comparability and consistency of native vegetation information across Australia 52 Appendix B: NVIS database structure 55 Appendix C: Family priorities 57 Appendix D: NVIS synonymic (non_current) names 59 Appendix E: Families assessed for author accuracy 63 Appendix F: Total NVIS Taxon_Lists 64 LIST OF ACRONYMS ......................................................................................... 65

Tables Table 1.31 The NVIS Information Hierarchy 4 Table 1.34 Counts of taxa (species and infraspecies) in the NVIS Taxon_Lists table 6 Table 2.22 Summary of taxonomic and nomenclatural issues 9 Table 2.23 Taxonomic problem and solution relationships 9 Table 2.25 Name currency 10 Table 3.1 Summary of the NVIS records with taxonomic issues 12 Table 3.11 Example of NVIS taxon record duplication 13 Table 3.12 Examples of misspelt rank identifiers 14 Table 3.13 Synonyms by States 15 Table 3.14 Other taxonomic issues by State 16 Table 3.141a Legume family name variation 17 Table 3.141b Erroneous family allocation 17 Table 3.141c Family name typographic errors 17 Table 3.145 Examples of “status unknown” records 19 Table 3.147 Examples of taxa with phrase names 21 Table 3.2 Nomenclatural examination of selected families 21 Table 3.31 Family Proteaceae by States 24 Table 3.32 Acacia by States 25 Table 3.33 Eucalypts by States 26 Table 4.145 Species qualifier correction 34 Table 4.23 Atomizing of nomenclatural data elements 38 Table 4.3 Summary of discrepancy handling options 41

Figures Figure 4.3a Option 1: Species Taxonomy in NVIS Vegetation Descriptions (short term arrangement) 43 Figure 4.3b Option 2: Species Taxonomy in NVIS Vegetation Descriptions (mid-term arrangement) 44 Figure 4.3c Option 3: Species Taxonomy in NVIS Vegetation Descriptions (long term arrangement) 45


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EXECUTIVE SUMMARY Floristic data is the key input used to standardise National Vegetation Information System (NVIS) vegetation descriptions. The Environmental Resources Information Network (ERIN) funded the Centre for Plant Biodiversity Research (CPBR) to provide a taxonomic review of this database. This report is for the consideration of ERIN managers and NVIS collaborators. CPBR assessed the taxonomic accuracy and currency of the 5,447 species used in the NVIS Taxon_Lists table to create plant communities in the Veg_Description table. It should be noted that ERIN had previously validated most of these records; this review was partly conducted to assess the accuracy of current ERIN validation procedures. This review marked 896 (16.45%) records as having issues that needed correction. Name duplication, synonymy and inconsistent use of family names were the main taxonomic issues encountered. Just over 50% of records involved some form of name duplication resulting from the database design; for example, the 5,447 taxonomic records relate to 3,684 individual taxa. Simple data errors relating to infraspecific names were also common, 42% of infraspecific taxa were found to have the rank component of the name misspelt. Some issues encountered related to inconsistencies in marrying the data provided by multiple agencies that have alternate taxonomic viewpoints. Significant widespread plant groups such as eucalypts, wattles and the family Proteaceae were examined in greater detail, including an assessment of author name accuracy. For the purposes of this review, ERIN set up new columns within the Taxon_Lists dataset to allow for individual record level commentary. Every record with a taxonomic issue was provided with problem identification and a recommended solution. Comment is also provided on the overall database architecture, including recommendations that involve the removal of existing Taxon_Lists columns (authors, family) and the addition of a new one (qualifier). Discussion is provided on existing and future links between NVIS and other Australian botanical databases such as SPRAT, APNI/WIN and State censuses. These links should help to create a synergy that will benefit NVIS product users and develop closer ties between Commonwealth, State and Territory collaborators. Future developments such as the “Consensus Census” project (as part of the AVH) are discussed with comment on the potential benefits to NVIS management. These include a moving towards a consensus view of taxonomy between Commonwealth and State government institutions whilst maintaining existing access to State data custodian’s local knowledge. Independent database advancements (APNI/WIN, SPRAT) should reduce the need for NVIS managers to keep on top of current taxonomic opinion. It should also reduce the need to dedicate staff and resources to maintaining up-to-date taxonomic (family, genus, species and infraspecies) and associated data (e.g. author name). Guidance is also provided to data collaborators to help improve the accuracy of data they manage. Field collection techniques and appropriate vouchering procedures are detailed to encourage data collaborators to increase the number of NVIS records that are vouchered in permanent herbarium collections.


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RECOMMENDATIONS SUMMARY

Recommendations on taxonomic issues, database linkages and database structure are summarised below. It should be noted that many of these recommendations involve work that is already being effectively done by NVIS collaborators. The recommendations are more an attempt to develop a consistent framework that NVIS managers and collaborators can use for developing and sharing data. Database linkages Recommendation 1 - NVIS collaborators form closer relationships with their relevant State herbaria (Sections 4.012, 4.016, 4.13, 4.22, 4.42). Recommendation 2 - NVIS adopt APNI/WIN and State censuses as the common/standard databases for plant nomenclature and taxonomic validation. NVIS should align its taxonomy with CHAH’s national ‘Consensus Census’ project (Sections 4.013, 4.21, 4.22). Recommendation 3 - NVIS periodically use the SPRAT taxonomy files for data validation. Also that links between SPRAT and APNI/WIN be improved to allow SPRAT to be updated rapidly in response to taxonomic changes. It is recommended that these links be enhanced and that formal arrangements established (Sections 4.014, 4.13, 4.21). Recommendation 4 - Common names should have no role in the NVIS Veg_Description table; if they are required for presentational or interpretative purposes, they can be imported from APNI/WIN and other taxonomic databases (Section 4.015). Recommendation 5 - For future NVIS data exchange, it is recommended that NVIS managers and collaborators adopt the use of the relevant HISPID standards (Sections 4.016, 4.23, see also recommendations 14 and 22). Taxonomic issues and database structure Recommendation 6 - Duplication of names should be consolidated. Vegetation descriptions (Veg_Description table) that use marked duplicate records should be shifted to marked master records. Unique, unmarked records should not be changed (Section 4.11). Recommendation 7 - Duplicate records should not be immediately discarded as they represent unique contributions from each of the States (Section 4.11). Recommendation 8 - Infraspecific name rank identifier typographic errors should be corrected following the instructions provided in the CPBR_SOLUTION field (Section 4.12).


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Recommendation 9 - Records with infraspecific rank identifier, but no infraspecific name should be referred back to their data source. Where possible the data provider should check what data is missing and provide the correct infraspecific name. If no name can be provided, the rank identifier should be removed (Section 4.12). Recommendation 10 - Infraspecific level taxa should be used where possible and appropriate (Sections 4.12, 4.41). Recommendation 11 - Synonyms used to generate vegetation descriptions (Veg_Description table) should be changed to the name written in the CPBR_WIN_NAME field (Section 4.13). Recommendation 12 (short term) - Alternate family names should be replaced with names provided in the CPBR_SOLUTION field. A consensus view of family names should be adopted (Sections 4.141, 4.142). Recommendation 13 (long term) - future data provision from collaborators should not include family names. NVIS data import routines should ignore family names provided by collaborators, which can be retrofitted from taxonomic databases as discussed in recommendation 2 (Sections 4.141, 4.142, 4.22, 4.41). Recommendation 14 - Name qualifiers (e.g. sens. lat., sp. agg.) should be moved into a new Taxon_Lists QUALIFIER field to increase the consistency with HISPID standards (Sections 4.016, 4.143, 4.23, 4.41). Whilst outside the scope of this project it is recommended that the SPRAT database also adopt a separated QUALIFIER field. Recommendation 15 - Double epithets should be referred back to the host institution to enable the appropriate checking and correction of the original data (Section 4.144). Recommendation 16 (short term) Status Unknown qualification - remove the qualification in the SPECIES field, leaving the identification at the genus (or family) level (Section 4.145). Recommendation 17 (long term) Status Unknown qualification - refer records back to data provider to try and determine what the species being referred to actually are (Section 4.145). Recommendation 18 - Misspelt names should be replaced with the correct spellings found in the relevant CPBR_WIN_NAME fields (Section 4.146). Recommendation 19 - Non-plant taxon records should be removed for the Taxon_Lists dataset (Section 4.148). Recommendation 20 - The two Taxon_Lists author columns be removed from the NVIS database and from NVIS data interchange formats (Sections 4.15, 4.22, 4.23, 4.41).


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Future Test Measures Recommendation 21 - ERIN adopt a multi-stage approach to the checking of taxonomic data, with iterative checks on the databases against which NVIS is being checked (Sections 4.21, 4.3):

• National plant list used by ERIN be updated regularly from ‘Consensus Census’ information sourced through APNI/WIN (quarterly or ad hoc preload updates may be sufficient);

• Changes or updates detected or required at the national level be communicated to NVIS custodians. Dialogue to scope and resolve differences to be initiated;

• NVIS abandon the concept of maintaining its own taxonomic authority file for internal and incoming data checking and validation;

• APNI/WIN and the evolving ‘Consensus Census’ be used as the taxonomic resource for NVIS; for reasons of pragmatism, expediency and practicality, use of this resource need not be direct if there is an appropriate up to date local gateway to views of this information; SPRAT fulfill these requirements;

• NVIS adopts an existing accessible taxonomic authority file for this purpose. From a practical point of view, the taxon tables of the SPRAT database are well-placed for this role;

• NVIS enters into a MOU or Service Level Agreement with SPRAT establishing a clear understanding of the use(s) SPRAT taxonomic data will be put, NVIS requirements and expectations of SPRAT taxonomic data and mechanisms for dealing with inconsistencies and unexpected events;

• Given that NVIS data providers have not empowered the NVIS database to change provided taxon names, NVIS will need map provided names to an NVIS endorsed name; in most cases the mapping will be 1:1with no change involved; a small percentage will be either errors or inconsistencies which will require communication between NVIS, SPRAT and the data provider;

• Existing NVIS data will be compared against SPRAT and inconsistencies will be rectified; changes will need to be communicated with NVIS data providers and agreements reached on how errors and legitimate differences will be flagged and handled, both within NVIS and within the supplying databases; and

• NVIS data supplied for amalgamation will be compared (and updated) on load with the national NVIS plant list, stored as fields in SPRAT, which will reflect the taxonomy of APNI/WIN and the ‘Consensus Census’.


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Advice to collaborators Recommendation 22 - NVIS collaborators should follow the nomenclatural database structure outlined in the HISPID standards, atomizing data into single elements (or fields) (Sections 4.016, 4.23). Recommendation 23 - NVIS collaborators, in the interests of providing more consistent data, become more closely familiar with the 2003 Australian Vegetation Attributes Manual’s (ESCAVI 2003) guidelines (Sections 4.12, 4.4, 4.41). Recommendation 24 - Data providers need to take greater care with the quality of infraspecific rank data provided. Preferred abbreviations are recommended which match those used in the ESCAVI (2003) manual (Sections 4.12, 4.41). Recommendation 25 - Taxon fields such as family name and taxon authors (if used) should be sourced from an authoritative taxonomic database such as APNI/WIN or SPRAT (Section 4.41). Recommendation 26 - future data incorporated in NVIS, where possible, should be based on herbarium vouchers lodged with recognised Australian state and/or national herbaria (Section 4.42).


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1.0 INTRODUCTION Floristic data is the key input used to standardise National Vegetation Information System (NVIS) vegetation descriptions. The Department of the Environment and Heritage (DEH), through the Environmental Resources Information Network (ERIN), has funded this project, which aims to assess taxonomic issues in NVIS and provide recommendations regarding improvement of its floristic data. The Centre for Plant Biodiversity Research (CPBR) was approached by ERIN to provide this taxonomic assessment. The CPBR assessed the taxonomic profile of the NVIS database and made recommendations in the form of a report. This report is for the consideration of ERIN management and NVIS collaborators. 1.1 Project Brief 1.11 Aims and objectives The four main outcomes in brief that ERIN was looking for from the CPBR were:

1. to assess the extent of taxonomic and nomenclatural issues in NVIS and recommend changes;

2. to develop and test measures of taxonomic and nomenclatural data quality in the NVIS database;

3. review the structure of taxonomic parts of the NVIS database and provide advice and options on protocols and procedures for the continuous improvement of taxonomic data in NVIS; and

4. provide guidance material for incorporation in future NVIS manuals. In greater detail (see also Appendix A) this would involve: 1. Assess the extent of any immediate taxonomic and nomenclatural issues in NVIS

and recommend changes for the approx. 3,600 unique species in NVIS. Priority would be on, but not limited to, major/widespread groups such as: Eucalyptus, Acacia, Chenopodiaceae, Casuarinaceae, etc (see Appendix C).

It was envisaged that the proposed changes would be presented to NVIS collaborators for adoption. 2. Develop and test measures to periodically assess the quality of taxonomic and

nomenclatural data in NVIS overall and subdivided by State/Territory and major groups, for example: • Extent and nature of taxonomic inconsistencies; and • Extent of uncertainties to be solved by: (a) simple name change, (b) re-

identification of voucher and (c) field visit. It was envisaged that these measures could be applied to the re-supplied NVIS dataset, in the future – i.e. after completion of this project.

3. Review the structure of taxonomic parts of the NVIS database and provide advice and options on protocols and procedures for the continuous improvement of taxonomic data in NVIS:


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• Review the measures developed in #2 and suggest a reporting frequency and thresholds for action;

• Specify corrective actions that might be needed; • Examine the costs and benefits of identifying and recording infra-species for

vegetation surveys; • Review the existing NVIS structure and content with respect to taxonomic

data. Examine the role of separate jurisdictional lists (which typically hold many more species than are used in the NVIS vegetation descriptions) and how these should be maintained to support updates of NVIS descriptions;

• Examine whether the Commonwealth needs to maintain a separate list of unresolved taxonomic issues between jurisdictions, to ensure the operational capability of NVIS; and

• Review technical options for cooperative linkages between State and Territory survey agencies with national biodiversity information initiatives – such as: the Australia’s Virtual Herbarium, SPRAT and NVIS.

The project report would focus on identifying issues and discussing the pros and cons of various possible solutions. 4. Provide guidance material for incorporation in future NVIS manuals:

• Advise and provide details of current standards/best practice for databasing and interchange of species data, e.g. the Herbarium Information Standards and Protocols for Interchange of Data (HISPID); and

• Provide details of current standards and/or best practice for the taxonomic aspects of vegetation survey – especially the use of vouchers.

1.12 Deliverables

1. Document proposed changes to each taxonomic record (in newly-created fields so that existing State/Territory data are not changed) in the NVIS database, with clear comments as to reasons;

2. A final report addressing the Aims and Objectives (Section 1.11). The report

would be for circulation to NVIS collaborators; and

3. Provide a briefing on the project to the NVIS collaborators at a national workshop and/or ESCAVI meeting, as appropriate.

1.2 Rationale A recent ERIN review of species names in the NVIS 2000 database indicated that approximately 5% of the 3,600 unique species names used in NVIS to be non-current. The problems may have one or more sources, ranging from simple misspellings to the use of different taxonomic concepts by vegetation surveyors. Australia’s Virtual Herbarium (AVH), a consortium of State, Territory and Commonwealth herbaria, is considering similar issues in creating equivalent taxa across Australia. It is timely to scope the options for beneficial interaction between herbaria and NVIS, including the development of sustainable systems and processes to manage taxonomic issues in NVIS.


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Further rationale for the project is given in Section 1.3 below and in Appendix A. 1.3 Background 1.31 NVIS 2000 database The NVIS 2000 database (Thackway et al. 2001) was collated by the Bureau of Rural Sciences for the National Land and Water Resources Audit (Audit) with collaboration of Commonwealth, State and Territory agencies across Australia. NVIS provides a framework which:

• specifies guidelines for the collection, compilation and monitoring of Australia's vegetation;

• stores data on type and extent of vegetation; • provides an Australia-wide geographic and attribute information system for

vegetation data that facilitates analysis and reporting; • provides access to a range of detailed and compatible mapped data sets; and • provides and maintains the technical infrastructure to support these activities.

This data set comprises two themes - Pre-European and Present (circa 1997) Native vegetation type and extent. The NVIS database contains vegetation data from about 100 source datasets collected and mapped by States and Territories using a variety of methods over several decades. It is compiled from source data at a variety of scales and with varying attribution (Thackway et al. 2001; NLWRA 2001). The NVIS database is the result of the translating these data sets into the nationally consistent NVIS vegetation classification system (NLWRA 2000). The NVIS vegetation classification system contains information on vegetation structure (growth form, height, cover) and floristics (genus and species). The NVIS information hierarchy summarises detailed vegetation association data at six levels of description. Level 1 (Class) is the most general description describing a single vegetation type (e.g. tree, tussock grass, etc.) while Level 6 (Sub-association) is the most complex, describing up to 5 vegetation strata/layers, 5 growth forms and 5 species per layer (ESCAVI 2003; NLWRA 2000). Table 1.31 provides details as to what structural and floristic components make up each of the NVIS hierarchical levels. The complexity of vegetation descriptions supplied to NVIS varies between Levels 4 and 6. Most data in the NVIS 2000 database contain descriptions to Level 4, and some validation was completed across Australia for Levels 1 to 4.


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Table 1.31 The NVIS Information Hierarchy (ESCAVI 2003) Hierarchical

Level Description NVIS structural/floristic components required

L1 Class Dominant growth form for the ecologically or structurally dominant stratum

L2 Structural Formation

Dominant growth form, cover and height for the ecologically or structurally dominant stratum.

L3 Broad Floristic Formation

Dominant growth form, cover, height and dominant land cover genus for the upper most or the ecologically or structurally dominant stratum.

L4 Sub-Formation Dominant growth form, cover, height and dominant genus for each of the three traditional strata. (i.e. Upper, Mid and Ground)

L5 Association Dominant growth form, height, cover and species (3 species) for the three traditional strata. (i.e. Upper, Mid and Ground)

L6 Sub-Association Dominant growth form, height, cover and species (5 species) for all layers/sub-strata.

The Levels below the dark line are the “complex” levels recommended for data compilation. 1.32 Restructured NVIS database During 2001 to 2003, the NVIS 2000 database was restructured to make it much more efficient, in terms of streamlining data updates and data validation, reducing the volume of data and ensuring faster query times for users. The main idea was to reduce redundancy in the database through normalisation and simplification; to ensure map units referenced a unique list of vegetation descriptions, rather than many variations on the same description. The NVIS collaborators have revised and updated the NVIS Framework to reflect the restructure and other agreed improvements (ESCAVI 2003). Much work has also been done to validate Levels 5 and 6 in the Veg_Description table and lower level (Stratum, Taxon_Data and Growth_form) tables (see Appendix B). The emphasis was on ensuring consistent vegetation descriptions at all levels of the NVIS Information Hierarchy, as shown in Table 1.31. The full NVIS database is stored as two components - a set of GIS spatial coverages in ArcInfo format and a full set of attribute information stored in an Oracle database. This is essential due to the complexity of both the line-work in the spatial coverages and the volume of attribute information. The relationship between the two is maintained by NVIS_ID codes identifying unique vegetation associations in both the spatial coverage and the attribute database. Vegetation mapping can be quite complex at the scales employed and a single polygon may contain up to six vegetation associations referred to as mosaics in vegetation mapping.


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1.33 Taxonomic issues in the restructured NVIS database Prior to this project, very little work has been done on validating the entries in the Taxon_Lists and Taxon_List_Origin tables (Appendix B). What validation had been done included comparison of species names in NVIS with the taxonomic lists in the DEH’s Species Profile and Threat (SPRAT) database. The SPRAT taxonomic module contains listings of Australia’s plant names that have been validly published. These published name lists are kept current by manual updates from the CPBR’s Australian Plant Name Index/What’s Its Name (APNI/WIN) database. The SPRAT database has fields to note whether taxa are “current”; this facility is used by DEH for implementation of the threatened species component of the Environment Protection and Biodiversity Conservation Act 1999 (Commonwealth), but has no other status. The validation thus checked:

• spelling errors; and • indicative taxonomic “currency”.

Early validation work by ERIN indicated that the SPRAT database needed updating. Changes were made for approximately 50 species. ERIN then re-ran the validation and made the necessary spelling corrections in the vegetation descriptions, lower tables and in the Taxon_Lists tables. During this process statistics of spelling errors by State were not recorded. These (and other) suggested corrections have been agreed by most States and Territories and form a key basis for this review. The comparison of NVIS species names with SPRAT “current” flag indicated that 206 (or approximately 5%) of the 3,916 unique taxon names used in NVIS to be non-current. In any particular case, this is probably the result of one or more of the following reasons:

• nomenclatural inconsistencies, such as annotations to indicate doubtful identifications;

• old vegetation survey data, using outdated species concepts; • differing species concepts between vegetation surveyors; • observer differences in their ability to identify plant specimens; and • errors in SPRAT.

The NVIS collaborators have not, however, given permission for the Commonwealth to change taxon names, because there is no one agreed national taxonomy for vascular plants in Australia. So no changes were made to the database as a result of the currency check. The error rate can be taken as indicative of taxonomic issues that need to be resolved by NVIS collaborators. 1.34 Taxon Lists in the restructured NVIS database The Taxon_List_Origin table (see Appendix B) contains a description of the source of each taxon list, of which there are 20 in the NVIS 2000 database. The Taxon_Lists table (Appendix B) contains the detailed lists, with one row per taxon. Neither the structure nor contents of the Taxon_Lists table have been changed since the NVIS 2000 database was collated. The original purpose of this table was as a lookup table to convert each taxon’s Source_Code in other parts of NVIS to a full scientific name. The table contains lists of taxa supplied by each State and Territory. In some States, this comprises a list of taxa for each NVIS input dataset and, in other States, the table contains multiple versions of a complete State list.


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The end result is massive duplication and redundancy at the national level as shown in Table 1.34. Of the 59,507 rows (taxa) in the Taxon_Lists table, there are only 23,198 distinct taxa, thus representing considerable duplication. A total of only 5,159 taxa (rows) in the Taxon_Lists table are used in the vegetation descriptions, meaning that 54,348 (59,507-5,159) taxa are redundant. Of course some of these may be used to lookup taxa for new vegetation descriptions, but that is a lot of maintenance for a “just- in-case” scenario. It should also be noted that a total of only approximately 18,000 vascular taxa are present in Australian flora (Orchard, A. 2003, pers. comm., 31 October). Table 1.34 Counts of taxa (species and infraspecies) in the NVIS Taxon_Lists table. State/Source No of

Vegetation Descriptions

No of Unique Taxa Used in Vegetation Descriptions

Total Number of Taxa

Commonwealth 73 ACT 343 78 564 NSW 969 843 4,776 NT 1,005 467 5,674 QLD 921 1,880 32,071 SA 538 694 5,444 TAS 169 341 2,766 VIC 568 856 5,829 WA 0* 2,306 Total 5,159 59,513 * WA data were provided at NVIS Level 4, which does not use species data.

When the total of 5,159 taxa were further investigated, only 3,916 were found to be distinct. This represents further dup lication within the taxonomic component of the NVIS database and arises from inherent inefficiency in the database design and update protocols. Even further efficiencies might be gained from examining content issues for each taxon, for example:

• standardising (or removing) notations for doubtful identifications and qualifiers - e.g. sp. aff., sens. lat., complex, etc.;

• standardising taxonomy to an agreed national set of current names - meaning that some of the 3,916 species names would be “synonymised”; and

• standardising (or removing) other attributes in the Taxon_Lists table. CPBR is well equipped to advise ERIN and the NVIS partners on the above taxonomic content issues and some of the structural issues. It should be noted that at the time of the CPBR review, the number of records used to generate vegetation descriptions rose from 5,159 to 5,447. All CPBR analysis thus relates to this latter figure.


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2.0 METHODOLOGY The CPBR was asked to assess the extent and nature of taxonomic inconsistencies found in the NVIS Taxon_Lists dataset and what measures can be taken to address them. ERIN asked for guidance as to whether these inconsistencies could be solved by a simple name change, re- identification of herbarium vouchers or by future field assessment. In order to do this, a structured method of assessment needed to be developed. It was decided that the structure of the database would be assessed to determine what additional database columns would be needed, these are outlined in Section 2.1. The types of annotations were standardised and written in the format outlined in Section 2.2. The APNI/WIN database was agreed as the standard to be used for checking the currency of scientific names. Any difference in the scientific names used in NVIS as compared to APNI/WIN was commented on in the appropriate new fields. Once completed, the results of this assessment were presented in Section 3. All taxonomic issues encountered are discussed in Section 3.1; authority problems in Section 3.2 and large group findings are discussed in Section 3.3. 2.1 Database preparation and early review Access was set up to the Restructured NVIS database. PL/SQL Developer was used to access and annotate this dataset. It was decided in the early stages of the project that CPBR reviewers would not alter the pre-existing NVIS data. As taxonomic assessment of the Taxon_Lists dataset was required and annotations needed for records that displayed anomalies, ERIN set up two new columns within the dataset to allow for these comments. The new columns added were:

• CPBR_PROBLEM • CPBR_SOLUTION

These two columns were set up to allow for preliminary assessment of records in the Taxon_Lists dataset. CPBR staff then assessed records, making annotations in the CPBR_PROBLEM field with what problem is apparent. The recommended solution to the problem was then added to the CPBR_SOLUTION field. After early analysis it was clear duplication of names was an issue and several other columns would be needed to give proper review accountability. These additional columns were:

• MASTER • CPBR_WIN_CURR • CPBR_WIN_NAME • CPBR_CHECKED_BY • CPBR_CHECKED


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2.2 Database annotation of immediate taxonomic issues Once the new fields were added, CPBR staff assessed all records for taxonomic accuracy. Plant name authority was also assessed for approximately half the records. The agreed to methodology for what would be added to each of the new fields is detailed in the following subsections. A complete listing of all records and their CPBR annotations is shown in Appendix F. 2.21 MASTER column With initial analysis of the dataset, it was apparent many of the taxa present in the list were duplicated multiple times. The Master column was set up to help quantify the extent of duplication. Single records for a taxon are left blank and considered to be unique taxon records. Where multiple records relating to one taxon are present, one record (usually the lowest TAXDSC_ID number) is chosen as the Master record, indicated with an “M”. All other records relating to the same name are considered duplicate records and this was marked with a “D”. Duplicate records are also coupled with a reference to the Master record’s TAXDSC_ID number, which is added to the CPBR_SOLUTION field as shown below: Eg. Refer to 12345 2.22 CPBR_PROBLEM column Early assessment of records gave a greater understanding of the taxonomic and nomenclatural issues that were present in the Taxon_Lists dataset. The issues encountered were gradually qualified into a number of different categories. Table 2.22 below details all categories encountered. While taxonomic and nomenclatural assessment was provided for all records, the validity of plant name authors was only surveyed for selected records. Some records had multiple problems; these were displayed in the CPBR_PROBLEM field with a forward slash separating the issues, as shown below: Eg. Wrong family name/Name misspelt in INFRA_SPECIES 2.23 CPBR_SOLUTION column For records that had comments added to the CPBR_PROBLEM field, a solution was in most circumstances provided in the CPBR_SOLUTION field. Most occasions where comment was added to CPBR_SOLUTION field and no comment was made in the CPBR_PROBLEM field related to duplicate records, as outlined in Section 2.21. Other occasions relate to where the solution is intuitive. Table 2.23 below shows the format for a single-issue entry, the format for a record that has multiple issues and a record that has an issue where the solution is intuitive.


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Table 2.22 Summary of taxonomic and nomenclatural issues For all records For selected records Rank misspelt Author in wrong field Infra_name in wrong field Author incorrect Infra_name missing Author incorrect in INFRA_AUTHOR Nomenclatural synonym Author incorrect in SP_AUTHOR Taxonomic synonym Author incorrect in two fields Alternate family name Author missing Family missing Author missing in INFRA_AUTHOR Name misspelt in SPECIES Author missing in SP_AUTHOR Name misspelt in INFRA_SPECIES Phrase name Non-plant taxon Double epithet Status unknown s.l. qualifier present s.s. qualifier present sensu qualifier present sp. agg. qualifier present complex qualifier present Table 2.23 Taxonomic problem and solution relationships CPBR_PROBLEM CPBR_SOLUTION Rank misspelt replace with subsp. Rank misspelt replace with var. Rank misspelt/Author incorrect in INFRA_AUTHOR replace with subsp./L.A.S.Johnson & K.D.Hill Author in wrong field 2.24 CPBR_WIN_CURR column This field relates to the currency of scientific names as compared to the APNI/WIN database. Where the name used in a record matched that shown in APNI/WIN, a “Y” is used; where the name does not match, such in the case of synonyms, an “N” is used. For duplicate records, no listing was provided, this listing being provided for the duplicate’s master record. A “U” was listed for incomplete names and where the status of the name was unknown. Different examples for each category are shown in Table 2.25. 2.25 CPBR_WIN_NAME column This column is directly dependent on the CPBR_WIN_CURR column (Section 2.24). NVIS names compared to the APNI/WIN database that are found to be non-current have an “N” placed in the CPBR_WIN_CURR field. Where a name is found to be non-current, the current name, as used in APNI/WIN, is added to the CPBR_WIN_NAME field. NVIS names that are found to be current have a “Y” listed in the CPBR_WIN_CURR field and consequently have no need for a name added to the CPBR_WIN_NAME field. Table 2.25 gives examples of NVIS names and how their name currency is displayed.


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Table 2.25 Name currency

GENUS SPECIES RANK INFRA_SPECIES CPBR_PROBLEM CPBR_WIN_CURR CPBR_WIN_NAME

Xanthorrhoea glauca Y

Syncarpia glomulifera subsp. glomulifera Y

Acacia burkittii Nomenclatural synonym N Acacia acuminata subsp. burkittii

Elymus scabrus var. scabrus Name misspelt N Elymus scaber var. scaber

Acacia myrtifolia var. Infra_name missing U

Vittadinia sp. Status unknown U

2.26 CPBR_CHECKED_BY column This column is where the initials of the three CPBR staff involved in record checking were recorded. This field provided assurance that a record had been checked even in cases where no annotations were required. 2.27 CPBR_CHECKED column This column links with CPBR_CHECKED_BY and relates to the date the record was checked. 2.3 Structural review of the NVIS database 2.31 NVIS Taxon_Lists structural assessment The second part of the NVIS taxonomic review by the CPBR was to provide an assessment of the structure of the Taxon_Lists dataset. It was agreed that the assessment of immediate taxonomic issues as discussed in Sections 2.1 and 2.2 would need to be completed before this could happen. Once these results were completed (see Section 3), meaningful comment on the database structure is possible. These comments will be presented in Section 4.1. The main aim of the structural review is to assess the existing Taxon_Lists architecture and the data content. Assessment was expected to result in the recommendation of the removal of some existing columns or possibly the addition of new ones. It was decided that recommendations on the structure and content was too difficult to separate and they would be dealt with together in Section 4.1. ERIN as part of this specifically requested an examination of the costs and benefits of identifying and recording to infraspecies level for vegetation surveys. This will be discussed with the recommendations made on the INRFA_SPECIES column in Section 4.12. 2.32 Interlinking NVIS with other databases ERIN also sought comment to the role of other botanical databases play in relation to NVIS. The CPBR was asked to review the technical options for cooperative linkages between other national and/or Commonwealth databases such as such as the Australia’s Virtual Herbarium (AVH), APNI/WIN and the SPRAT databases. All these databases have different strengths and emphases that could potentially add value to the NVIS database. Section 4.21 discusses the potential linkages between these databases.


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2.33 State jurisdictional lists Separate State jurisdictional lists are maintained outside of the NVIS database. These lists typically hold many more species than are used in NVIS vegetation descriptions. ERIN sought comment as to how these lists should be maintained to support updates of NVIS descriptions. This discussion is presented in Section 4.22. For many plant groups differences of taxonomic opinion exist between botanical institutions. These institutions provide the data that combined, forms the NVIS database. Part of the review of this State data was to examine whether the Commonwealth needs to maintain a separate list of these unresolved taxonomic issues, to ensure the operational capability of NVIS. The issue of finding consensus on these issues will also be discussed in Section 4.22. 2.4 Development of test measures for future NVIS assessment The CPBR review outlined in Section 2.2 was a one off assessment of the Taxon_Lists dataset. While comprehensive in its assessment, the NVIS database is periodically updated with new data provided by the States. With these updates in mind, the CPBR was asked to develop ways for ERIN managers to periodically assess the quality of taxonomic data. Periodic assessment would help keep the Taxon_Lists dataset taxonomically and nomenclaturally current. The frequency of these assessments was also to be examined. This frequency is very much tied in with the structural review discussed in Section 4.1 and the interlinking of NVIS to the other national databases as discussed in Section 4.21. The recommendations developed by the CPBR relating to periodic test measures are discussed in Section 4.3. 2.5 Development of guidance material for NVIS collaborator manuals The final advice sought from the CPBR related to the provision guidance material for incorporation in future NVIS collaborator manuals. These manua ls provide recommendations to State data contributors, helping them to provide botanical records that are consistent with other contributors. The CPBR was asked to provide comment on the current standards used for data entry and interchange of botanical information. The Herbarium Information Standards and Protocols for Interchange of Data (HISPID) are seen by ERIN to have particular relevance to NVIS and comment on these standards is invited. Similarly, guidance material was requested relating to the best practice for the taxonomic aspects of vegetation survey, especially the collection of herbarium vouchers. Recommendations relating to future collaborator manuals and collection techniques are detailed in Section 4.4.


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3.0 RESULTS The CPBR assessed the taxonomic accuracy and currency of the 5,447 species used in Taxon_Lists table to create plant community descriptions in the NVIS Veg_Description table. Priority was on major, widespread groups such as eucalypts (Eucalyptus, Corymbia and Angophora), Acacia (Mimosaceae), the grasses (Poaceae) and dominant shrub families such as Proteaceae and Epacridaceae. Priorities were set (see Appendix C) as it was unclear at the start of the project as to whether all records could be examined in the time frame. All records were examined for taxonomy and comments were added in the new columns (Sections 2.1 and 2.2) where appropriate. Along with taxonomic assessment, comment on the accuracy of authors was also provided for several major plant groups, family Proteaceae, Acacia and the eucalypts. This author assessment was carried out to check the accuracy and consistency of information between different State data providers. The issue of plant name authors is discussed in Section 3.2. The major plant groups are discussed in detail in Section 3.3. 3.1 Taxonomy Of the 5,447 records assessed a number of taxonomic issues were apparent. 896 (16.45%) records need to be altered to some degree. Table 3.1 shows record numbers, the types of taxonomic issues and their frequency within the Taxon_Lists dataset. Table 3.1 Summary of the NVIS records with taxonomic issues

NO. RECORDS % RECORDS RECORD NUMBERS 5447 100 Single (unique) occurrence names 2704 49.6 Master records 980 18 Duplicate records 1763 32.4 Total taxa 3684 67.63 INFRASPECIFIC NAME ISSUES Rank misspelt 227 4.2 Infra name in wrong field 1 0.02 Infra name missing 37 0.7 SYNONYMY Nomenclatural synonym 54 1 Taxonomic synonym 15 0.3 OTHER TAXONOMIC ISSUES Alternate family name 415 7.6 Missing family name 1 0.02 Name misspelt 6 0.1 Phrase name 12 0.2 Non-plant taxon 1 0.02 Double epithet 4 0.07 Status unknown 72 1.3 Name qualifier present 51 0.9 Total taxonomic issues 896 16.45


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The issues shown in Table 3.1 will be discussed in greater detail in the following sections. 3.11 Record duplication As discussed in Section 2.21 there was early evidence of many of the records present in the Taxon_Lists dataset being duplicates of other records. All taxa were assessed to separate those that had unique records from those with multiple records. Taxa with multiple records were marked with either “M” for master records and “D” for duplicate records, following the procedure outlined in Section 2.21. Overall, just over 50% of Taxon_List records involved some form of name duplication. The identification of name duplication helped provide an accurate picture of how many taxa are listed in the Taxon_Lists dataset. The 5,447 records present relate to 3,684 individual taxa. Duplication was based on the identification of multiple occurrences of an individual taxon, not necessarily an exact match of the taxon’s name. While an exact match of name was often the case, there were many examples of a taxon with multiple records with slight variations in the way authors were written. Despite these minor variations, records identified as duplicates were marked accordingly, with a CPBR_SOLUTION reference to the master record. Table 3.11 shows examples of how duplication was presented in the Taxon_Lists dataset. Table 3.11 Example of NVIS taxon record duplication

Tax

dsc

Id

Gen

us

Sp

ecie

s

Sp

ecie

s A

utho

r

Ran

k

Infr

a S

pec

ies

Infr

a A

utho

r

Mas

ter

reco

rd

Cp

br

Sol

utio

n

117982 Carpobrotus glaucescens M

118726 Carpobrotus glaucescens D Refer to 117982

137137 Carpobrotus rossii (Haw.) N.E.Br. D Refer to 2041

2041 Carpobrotus rossii (Haw.) Schwantes M

15723 Carpobrotus rossii (Haw.)Schwantes D Refer to 2041

2520 Disphyma crassifolium ssp. clavellatum (Haw.) Chinnock M

16418 Disphyma crassifolium (L.)L.Bolus ssp. clavellatum (Haw.) Chinn. D Refer to 2520

3.12 Infraspecific name issues Of the 5,447 NVIS taxon records, 538 (9.9%) relate to infraspecific taxa, typically subspecies and varieties. The NVIS database structure separates each name element ; this includes a separate column for the infraspecific rank component of the name. This “rank identifier” was populated by a considerable amount of typographic error. Of the 538 infraspecific names, 227 records (42%) had the rank component of the infraspecific name misspelt. Table 3.12 below shows examples of typographic errors found with reference to State data sources.


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Table 3.12 Examples of misspelt rank identifiers Misspelt rank identifier

Correct spelling Source State

Number of misspellings

nothossp. nothosubsp. SA 3

ssp subsp. NSW 12

ssp. subsp. ACT, NSW, SA, Vic 146

subsp subsp. NSW 47

subsp . subsp. NSW 1

var var. NSW 29

TOTAL No. 227 The most common misspelt rank found was for subspecies, being abbreviated to “ssp.” as opposed to the more usual form of “subsp.” Other simple errors included the dropping of full stops at the end of abbreviations. The high degree of variation in rank spelling is a concern when trying to match up taxon records between States. The example below demonstrates how a minor variation in the spelling of the species rank identifier could potentially see the same taxon treated as two separate taxa. Eg. Lomandra confertifolia subsp pallida NSW

Lomandra confertifolia subsp. pallida Qld The other major infraspecific name related to some records possessing a rank identifier with no infraspecific name. Nomenclaturally this makes no sense; typically if the infraspecific taxon is unknown the rank identifier is not added. Two examples of this are shown in the example below: Eg. Lomandra multiflora ssp. Dianella revoluta var. All 37 cases of this relate to SA provided records probably from the same single source. It seems likely that the infraspecific names were lost during the data transfer. 3.13 Synonymy A total of 69 records were found to have synonymous (non-current) names. It should be noted that these names are considered non-current as compared to the APNI/WIN database. It should also be noted that in some cases, there were differences in names when comparing between States. These were scored as two synonym types:

• Nomenclatural synonyms • Taxonomic synonyms

The types and frequency of synonyms for each State contributor are shown in Table 3.13 below.


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Table 3.13 Synonyms by States State Nomenclatural Taxonomic Total % of

synonyms NSW 10 2 12 17.4 NT 13 1 14 20.3 QLD 10 5 15 21.7 SA 7 4 11 15.9 TAS 5 0 5 7.2 VIC 9 3 12 17.4 TOTAL No. 54 15 69 See Appendix D for a full list of synonyms and what is listed as the correct name on APNI/WIN. 3.131 Nomenclatural synonyms The majority of synonymous names proved to be nomenclatural synonyms. A nomenclatural synonym is a non-current name, which is based on the same type (specimen) as the current name concerned. Most nomenclatural synonyms are a simple case of a change in generic limits (see example below). In some cases, changes in taxonomy in recent publications has been picked up in APNI/WIN but not updated by the States in their respective databases. Eg. Danthonia caespitosa to Austrodanthonia caespitosa Cyathodes juniperina to Leptecophylla juniperina In other cases, publication of new taxa results in the published name replacing a temporary phrase name. E.g. Leptorhynchos squamatus Leptorhynchos squamatus

subsp. A to subsp. squamatus 3.132 Taxonomic synonyms A total of fifteen taxonomic synonyms were found, spread fairly evenly between the States. A taxonomic synonym is a non-current name, which is based on a different type (specimen) to the current name concerned. Most taxonomic synonyms encountered in NVIS are the result of changed taxonomic concepts, where for example, two related taxa are reassessed and considered to represent one taxon, the older of the two names then assuming priority (see Greuter et al. 2000). E.g. Hakea muelleriana to Hakea mitchellii In other cases, taxonomic synonyms may be generated by the reduction of published infraspecific taxa to synonymy, where these are found to be unworkable or poorly defined. E.g. Atriplex nummularia subsp. omissa to Atriplex nummularia


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Taxonomic synonyms may also be generated where there is a difference of opinion as to the taxonomic limits of a particular taxon. In the example below, there is a difference in taxonomic opinion between NSW and other State institutions. E.g. Themeda australis (NSW) to Themeda triandra (other institutions) Most institutions consider the Australian Kangaroo Grass to be part of a cosmopolitan species known as Themeda triandra. NSW recognises differences between the Australian populations and those found in Asia and Africa, recognising a separate species, Themeda australis. For the purposes of this example, APNI/WIN is following the consensus view and treats Themeda australis as synonym. 3.14 Other taxonomic problems A wide range of other taxonomic issues were encountered, these included:

• Alternate family name • Missing family name • Name qualifier present • Double epithet • Status unknown • Name misspelt • Phrase name • Non-plant taxon

These issues are presented in Table 3.14 along with a State breakdown. Table 3.14 Other taxonomic issues by State NSW NT QLD SA TAS VIC Total Alternate family name 154 2 18 224 10 7 415 Missing family name 1 1 Name qualifier present 2 49 51 Double epithet 1 2 1 4 Status unknown 4 4 1 24 26 13 72 Name misspelt 2 3 1 6 Phrase name 12 12 Non-plant taxon 1 1 Total 162 6 35 252 37 70 562

3.141 Alternate family name 415 records possessed family names that were different to those found in APNI/WIN. In accordance with the principles of taxonomic hierarchy, each family can be made up of many genera and many more species. However, each species can only belong to one family. As our understanding of plant evolution progresses, there is considerable debate and discussion as to the limits of higher taxa, especially families, and changes in family composition are frequent and rarely stable. Some differences in family names are due to different institutions following different family treatments, some of which differ again to those adopted by APNI/WIN. The high total for NSW can be accounted for in this way, particularly with the legumes. APNI/WIN recognises three separate families for the legumes, whereas NSW


17

recognises one family, divided into three subfamilies. South Australia on the other hand recognises yet another alternative for the legumes. Table 3.141a shows the variability in family names for some of the legume genera as compared to APNI/WIN. Table 3.141a Legume family name variation State Genus NVIS family name APNI/WIN family name NSW Daviesia Fabaceae (Faboideae) Fabaceae NSW Acacia Fabaceae (Mimosoideae) Mimosaceae NSW Acacia Mimosoideae Mimosaceae SA Acacia Leguminosae Mimosaceae SA Senna Leguminosae Caesalpiniaceae Whilst such differences of systematic opinion are common amongst botanical institutions, it does make it difficult to group families in the NVIS database. Table 3.141a shows the use by South Australia of the alternate legume family name Leguminosae. South Australia data includes many other alternate family names, including Umbelliferae (Apiaceae), Gramineae (Poaceae), Compositae (Asteraceae), Labiatae (Lamiaceae) and Cruciferae (Brassicaceae). The International Code of Botanical Nomenclature, Section 18.5 (Greuter et al. 2000) makes it clear that such alternate family names are validly published and nomenclaturally acceptable. However for the purposes of linking different State’s data for genera such as Acacia, alternate family names such as Leguminosae make the process difficult. Other occurrences of alternate family names in the Taxon_Lists dataset seem to be simple errors, with species being linked to families that they clearly have no relation to. Table 3.141b below shows examples of some of these family errors, all such occurrences relate to NSW data. Table 3.141b Erroneous family allocation Species NVIS family APNI/WIN family Polystichum proliferum Araliaceae Dryopteridaceae Podolobium ilicifolium Asteraceae Fabaceae Pultenaea daphnoides Cyperaceae Fabaceae Phragmites australis Rutaceae Poaceae Pteridium esculentum Rubiaceae Dennstaedtiaceae Two other records seem to have simple typographic errors in the FAMILY field; these are shown in Table 3.141c below. Table 3.141c Family name typographic errors Species NVIS family APNI/WIN family State Polystichum proliferum Dryopteridophyta Dryopteridaceae TAS Eucalyptus cordata Myrtaceaea Myrtaceae TAS


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3.142 Missing Family Name There was one record that had no information present in the FAMILY field. This seems to have been a simple oversight with one of the NSW records, Linum marginale (Linaceae). 3.143 Name Qualifier Present The process of trying to name and describe organisms is often problematic, living entities not easily fitting into simple hierarchical structures. Delimiting plant species from one another and naming variations has historically been difficult, particularly for closely related taxa. Some species, such as Acacia aneura are apomictic, that is, possessing an ability to reproduce asexually through seed. Apomixis and other biological complexities often result in complex clusters of microspecies, not easily dealt with in traditional taxonomic and nomenclatural systems. One method that is often used to help make allowances for such complexity is the use of name qualifiers. These qualifiers help provide additional information about the delimitation of a particular taxon. Unfortunately botanists have through time used different forms of qualifiers to describe similar forms of complexity. This is apparent in the Taxon_Lists datset, with “complex”, “sp. agg.” (species aggregate), “s.l.” (sensu lato = in the broad sense) and “nothossp.” (notho-subspecies) all being used to describe species complexes of different kinds. As can be seen in Table 3.14, the NVIS dataset has 51 records that have some form of qualification. Table 2.22 lists all types of qualifiers present, generally occurring in the same field as the species epithet. The example below showing how qualifiers typically appear in the Taxon_Lists dataset: E.g. Triodia irritans complex Pellaea falcata s.l. In general the State contributors have avoided using name qualification, preferring to keep species names as simple as possible. Table 3.14 shows that name qualification is used almost exclusively by Victoria; with two “complex” qualifiers also used by South Australia. While qualifiers are a valid descriptor in plant taxonomy, they can be problematic as far as databases are concerned. The fact that different data providers choose to use them and others avoid their use makes it difficult to compare records, even where the same taxon is concerned. Similarly, duplication could not be assigned to taxa with qualified names, the presence of the qualifier resulting in a name different enough to not be considered a true duplicate. 3.144 Double epithets There were four of examples of what for the purposes of this project described as “double epithets”. These are cases where records do not have the typical single species epithet, but two epithets separated by a slash. The example below shows how these are displayed in the NVIS database:


19

Eg. Eucalyptus saligna/botryoides Sclerolaena diacantha/uniflora

Similar to species complexes described with qualified names in Section 3.143, these double epithets describe the complexity found with closely related or unrelated, but morphologically similar, taxa. One case of likely hybridisation is shown in the example above. Eucalyptus botryoides and Eucalyptus saligna are known to form hybrid swarms (Harden 1991), with hybrid plants in these populations often showing features of both parents. This may account for the use of the double epithet in the case of this NSW record. Known hybrids are typically written as shown in the example below: Eg. Eucalyptus saligna x botryoides Where a hybrid is sufficiently frequent or well known, it may be formally named, for example Adenanthos x cunninghamii (Proteaceae) a hybrid between A. cuneatus and A. sericeus. These are comparatively rare and none were noted in the NVIS dataset. Another explanation for the use of double epithets is difficulties with identification. For example, two related (or sometimes unrelated) species may be identical in many of their shared characters, but differ significantly in one character (for example fruit shape or flower colour), characters which may not be apparent for part of the year. This probably explains the presence of the Sclerolaena diacantha/uniflora record. These species are separated in Harden (1990) by fruit features, but are otherwise indistinguishable. 3.145 Status Unknown There were 72 records in the Taxon_Lists that related to taxa without identification to species level. This has been represented in the database by some form of “species qualifier”. For the purposes of editing the database, “status unknown” was added to the CPBR_PROBLEM field and “U” was added to the CPBR_WIN_CURR field. Whilst the status of these qualifiers is understood, there is no equivalent to these species qualifier names within precise name databases such as APNI/WIN. They are thus difficult to compare and were marked as unknown. Table 3.145 shows some examples of these records, starting with records identified to genus level, and then followed by two examples identified to family level. All States use some form of these species qualifiers records as shown in Table 3.14. Table 3.145 Examples of “status unknown” records Family Genus Species State Myrtaceae Leptospermum spp. ACT Poaceae Poa sp. ACT Nelumbonaceae Nelumbo species NT Juncaceae Juncus spp. VIC Cyperaceae Cyperaceae sp. SA Asteraceae Asteraceae spp. QLD


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As can be seen in Table 3.145, the qualifier present in the SPECIES field is quite variable, with “species”, “sp.” and “spp.” used. It is difficult to determine whether the use of these qualifiers is referring to a single species or multiple species of that genus or family. The correct use of the abbreviation “sp.” relates to a single species, however it seems likely the use of “Cyperaceae sp.” in Table 3.145 refers to multiple sedge species and should be written as Cyperaceae spp. The Nelumbo species example shown in this table could be referring to a single taxon or multiple taxa. There is considerable confusion with the use of the species qualifier as to exactly what the original data recorder was referring to. There are a number of reasons that the species qualifier may be present in the Taxon_Lists. Table 3.145 above shows some of the more traditionally difficult taxa to identify, such as Poa, Juncus and Cyperaceae. Whilst these groups are easily recognisable, they can be difficult to identify to species level and surveyors often add the genus or family name to species lists with the species qualifier. Another possibility may be a surveyed site included numerous species of a family, which individually were occasional, but collectively the family is dominant. The example below shows how this can describe the character of a landscape fairly simply: E.g. Eucalyptus spp. woodland Asteraceae spp. dominated herb field 3.146 Name misspelt There were six examples of records with names featuring minor typographic errors in either the SPECIES or INFRA_SPECIES fields. These were identified in the CPBR_PROBLEM field and listed as “N” (not current) in the CPBR_WIN_CURR field. The correct spelling of the name, according to the APNI/WIN database, was then added to the CPBR_WIN_NAME. 3.147 Phrase names Twelve examples of phrase names were encountered in the Taxon_Lists dataset, all from Queensland sources, see Table 3.14. Phrase names are temporary names given to taxa that may warrant formal description at a later date. Providing phrase names helps distinguish between multiple undescribed taxa within the same genus (as can be seen with Eucalyptus in Table 3.147 below) and also provides a means of referring to a known, undescribed taxon, formal description of which may be some years away. The examples listed shown the typical way modern phrase names are presented, including some indication of where a taxon occurs (e.g. Buckland Tableland) and a single herbarium collection (e.g. A.R. Bean 799) used for reference. The phrase names encountered were highlighted, as they are not present within APNI/WIN; these records thus received a “U” in the CPBR_WIN_CURR field. There appears to be no problem with the presentation of these phrase names and one could assume that Queensland data providers have a good understanding of what undescribed taxa are present within their State. The APNI/WIN database in this case should be updated with these phrase names, which it has the capability of listing.


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Table 3.147 Examples of NVIS records with phrase names FAMILY GENUS SPECIES RANK INFRA_SPECIES Myrtaceae Austromyrtus sp. (Main Range P.R.Sharpe 4877) Myrtaceae Callistemon sp. (Boulia L.Pedley 5297) Myrtaceae Eucalyptus fibrosa subsp. (Glen Geddes M.I.Brooker 10230) Myrtaceae Eucalyptus sp. (Buckland Tableland A.R.Bean 799) Myrtaceae Eucalyptus sp. (Caldervale D.Jermyn AQ582304) 3.148 Non-plant taxon The presence of a fungi taxon from a Queensland data source was part of the Taxon_Lists dataset. This was highlighted in the CPBR_PROBLEM field with a “Non-plant taxon” identifier and listed as “U” in the CPBR_WIN_CURR field as the APNI/WIN database does not include fungal families. 3.2 Author issues As mentioned in Sections 2.22 and 3.0, a portion of the Taxon_Lists dataset was examined for the third component of plant scientific names, authors. Authors are the names (frequently abbreviated) of botanists who are involved in the publication of a particular plant name. A standard list of authors is maintained with the International Plant Name Index (IPNI 2003) based at Kew Botanic Gardens. The IPNI author list is used by APNI/WIN as the standard for all plant names. Authors for species and infraspecies were examined, however authors for genera and families were not relevant to the NVIS database and thus were ignored. Records from 29 selected families (approx. 2,200 records, 50% of total records) were examined for author accuracy as per APNI/WIN name authors. Appendix E lists the families examined for author accuracy. The two relevant Taxon_Lists fields examined were SP_AUTHOR and INFRA_AUTHOR. Three types of author issues were encountered:

• author missing • author incorrect • author name in wrong field

Table 3.2 below presents the author issue types, their numbers and proportions for the selected families examined. From this table it can be seen that of the records examined, one in four had author inconsistencies or errors. Authors as they relate to some of the major plant groups are discussed in Section 3.3. Table 3.2 Nomenclatural examination of selected families

Author issues Record numbers Proportion of records* Author missing 301 13.7 Author incorrect 240 10.9

Author in wrong field 14 0.6 Total 555 25.2

* proportion of 2,200 records


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3.21 Authors missing The records found in the Taxon_Lists provided by State contributors were inconsistent when it came to the provision of author data. Most providers included some form of author; however NSW omitted authors with many of their records. In these cases, the CPBR_PROBLEM field was annotated with one of three messages:

• Author missing • Author missing in INFRA_AUTHOR • Author missing in SP_AUTHOR

“Author missing” was added to taxa without infraspecific ranks. The other two messages were added where infraspecific ranks were present to help indicate which of the two authors needed correction. Whilst the problem with these records is a simple presence/absence issue, it does result in an inconsistent dataset. It also makes it difficult to compare a taxon listed by one State with records from other States. 3.22 Authors incorrect These records all had some form of author present, but what was present was inconsistent with the author listed in APNI/WIN. Just over 10% of records examined had incorrect author data. This may take the form of typographic errors, the wrong author cited, missing base names, misplaced punctuation or incorrect author abbreviations. In these cases, the CPBR_PROBLEM field was annotated with one of four messages:

• Author incorrect • Author incorrect in INFRA_AUTHOR • Author incorrect in SP_AUTHOR • Author incorrect in two fields

“Author incorrect” was added to taxa without infraspecific ranks. The other three messages were added where infraspecific ranks were present to help indicate which of the two authors was in error, if not both. 3.23 Author in wrong field This error relates the positioning of the author in the wrong field. All fourteen of these cases were Victorian records and all related to autonyms. When an author names a infraspecific taxon, such as Blechnum patersonii subsp. queenslandicum T.C.Chambers & P.A.Farrant, it is because they have recognised features that distinguish the newly described infraspecific taxon from the typical or “type” form of the species. This type subspecies (or variety) is known as an autonym; when an autonym is created the species epithet is duplicated at the subspecies rank, as shown in the example below: Eg. Blechnum patersonii subsp. patersonii The author for an autonym is the same as it is for the species as it relates to the same taxon. The author error encountered with these autonyms relates to its positioning. An example of one of these erroneous records is shown below:


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Eg. Blechnum patersonii subsp. patersonii (R. Br.) Mett. As is shown in this example above, the authority is in the wrong position, appearing in the INFRA_AUTHOR field. Autonym authors are positioned after the specific epithet. For the context of the Taxon_Lists dataset this would appear in the SP_AUTHOR field, resulting in the correct order as shown below: Eg. Blechnum patersonii (R. Br.) Mett. subsp. patersonii Authors should be placed after the infraspecific epithet in all non-autonymic names, as in Blechnum patersonii subsp. queenslandicum T.C.Chambers & P.A.Farrant above. 3.3 Large group case studies Along with assessing all records present in Taxon_Lists, it was decided that it would be useful to examine some of the more prominent Australian plant groups in more detail. The groups chosen were family Proteaceae, the wattles (Acacia) and the eucalypts (Eucalyptus, Corymbia and Angophora). It was felt that as these groups are important land cover taxa in all contributing States, it would be useful to compare the data provided by each of these contributors, particularly with regard to duplication of records. As for all Taxon_Lists records, these large groups were assessed for taxonomic accuracy. Most taxa were also assessed for author accuracy, making up three of the 29 families mentioned in Section 3.2. Each of these three groups in the following subsections is presented in tables broken down by the taxonomic and authority issues encountered and by State. 3.31 Family Proteaceae Proteaceae includes many genera and species that make up important components of many Australian vegetation communities. Genera such as Banksia, Hakea and Grevillea are typically present in the shrub strata of many of these communities. Table 3.31 shows the details of taxonomic and nomenclatural issues encountered with Proteaceae records in the Taxon_Lists dataset. In this it can be seen that all States provided records, NSW contributing the most records. Overall 186 records are present representing 112 individual taxa, which indicates a record duplication rate of 37%. While record duplication between States was expected, it was interesting to note that there was a 63% duplication of records for NSW alone, clearly reflecting multiple data providers for that state. Authors were completely checked for Proteaceae, the main issue relating to missing authors for most of the NSW records. Incorrect author citation also accompanied 9% of the Proteaceae records. Misspelling of infraspecific rank was the next most common error, with ten records. Other issues were encountered in relatively low proportions.


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It should be noted that Western Australian species were not assessed for this study due to data from that State being unavailable (see Table 1.34). South-west Western Australia has a very high diversity of Proteaceae with an equally high level of endemism. This should result in low duplication of records when added to the other records in the Taxon_Lists dataset. Table 3.31 Family Proteaceae by States NSW NT QLD SA TAS VIC TOTAL RECORD NUMBERS 62 15 46 20 13 30 186 Single occurrence names 11 5 24 12 10 12 74 Master records 7 8 8 4 1 15 43 Duplicate records 44 2 14 4 2 3 69 Total taxa 39 15 43 20 13 30 112 AUTHOR ISSUES Author missing 59 59 Author incorrect 2 1 6 1 7 17 Author in wrong field INFRA SPECIES Rank misspelt 6 3 1 10 Infra name in wrong field Infra name missing SYNONYMS Nomenclatural synonym 1 1 2 Taxonomic synonym 1 1 2 OTHER TAXONOMIC ISSUES Wrong family name Missing family name Name misspelt Phrase name Non-plant taxon Double epithet Status unknown 1 1 Name qualifier present 1 1


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3.32 The wattles (Acacia - family Mimosaceae) The wattles (Acacia) comprise the most diverse plant genus in Australia, with species distributed across the continent in nearly all vegetation communities, especially in arid Australia. Table 3.32 shows the details of taxonomic and nomenclatural issues encountered with wattle records in the Taxon_Lists dataset. High levels of record duplication were encountered with the wattles (33.3%), reflecting perhaps the nature of many species possessing a distribution crossing several state boundaries. New South Wales once again showed high levels of duplication between its data providers. Author accuracy was examined for many of the Acacia records. Authors missing and incorrect citations were found with many of the records. Table 3.32 Acacia by States NSW NT QLD SA TAS VIC TOTAL RECORD NUMBERS 81 51 140 42 11 32 357 Single occurrence names 23 30 83 21 3 8 168 Master records 3 12 24 9 2 20 70 Duplicate records 55 9 33 12 6 4 119 Total taxa 59 51 130 38 10 32 231 AUTHOR ISSUES Author missing 27 2 29 Author incorrect 7 3 6 3 11 30 Author in wrong field INFRA SPECIES Rank misspelt 3 1 2 6 Infra name in wrong field Infra name missing 1 SYNONYMS Nomenclatural synonym 2 1 1 2 6 Taxonomic synonym 1 1 OTHER TAXONOMIC ISSUES Wrong family name 80 42 122 Missing family name Name misspelt Phrase name Non-plant taxon Double epithet Status unknown 1 1 2 Name qualifier present 1 1 2


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There were relatively few problems encountered with the infraspecific names or currency of names (synonymy) with the wattles. The greatest issue of concern was the inconsistency with the family name. As discussed in Section 3.141, there were several different family names used for the legumes in the Taxon_Lists dataset. Table 3.32 shows that NSW and SA provided alternate names to those used by other States and the APNI/WIN database. 3.33 The eucalypts The eucalypts for the purposes of this study relate three genera, Eucalyptus, Corymbia and Angophora. Eucalypts are of similar importance as land cover species as the wattles, and dominate the tree strata of most Australian woodland and forest communities. Perhaps reflecting the importance of this group, 13.5% of all Taxon_Lists records are eucalypts. Table 3.33 shows the details of taxonomic and nomenclatural issues encountered with wattle records in the Taxon_Lists dataset. Table 3.33 Eucalypts by States ACT NSW NT QLD SA TAS VIC TOTAL RECORD NUMBERS 17 294 55 207 54 28 78 733 Single occurrence names 3 41 36 101 34 17 27 259 Master records 13 29 12 48 9 1 34 146 Duplicate records 1 224 7 58 11 10 17 328 Total taxa 17 150 55 172 54 28 75 381 AUTHOR ISSUES Author missing 57 2 1 60 Author incorrect 2 1 5 1 19 28 Author in wrong field 2 2 INFRA SPECIES Rank misspelt 3 18 10 10 41 Infra name in wrong field Infra name missing 4 4 SYNONYMS Nomenclatural synonym 4 11 15 Taxonomic synonym 1 1 OTHER TAXONOMIC ISSUES Wrong family name 1 1 Missing family name Name misspelt Phrase name 5 5 Non-plant taxon Double epithet 1 1 2 Status unknown 1 1 2 Name qualifier present 10 10


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The highest level of record duplication was encountered with the eucalypts, out of the three large groups profiled. 44.7% of the 733 eucalypt records were duplicates, when consolidated the records relate to a total of 381 taxa. Duplication was also encountered when comparing individual contributors data within a state. New South Wales and Queensland columns are shown in Table 3.33 as representing fewer taxa than there are actual records. Approximately half the eucalypt records were checked for author accuracy. From this sample it was apparent that many NSW records were missing authors, showing consistency with the findings outlined in Section 3.31 and 3.32. One trend that did stand out as being somewhat different was the high numbers of incorrect author citations that accompanied records from Victoria. Infraspecific rank misspellings were also relatively high, relating to nearly 6% of all eucalypt records. The high numbers of nomenclatural synonyms encountered with the Northern Territory records relates to alternate views on taxonomy. The Northern Territory maintains a broad view of the eucalypts, retaining the Bloodwoods and Ghostgums within Eucalyptus, as opposed to other States and the APNI/WIN database, which recognise the generic segrega te Corymbia for these groups. The only other issue of note for the eucalypts was the ten records from Victoria that possessed name qualifiers. These qualifiers are discussed in Section 3.143 in greater detail.


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4.0 DISCUSSION & RECOMMENDATIONS Discussion and recommendations on taxonomic issues, database linkages and database structure are provided. It should be noted that many of these recommendations involve work that is already being effectively done by NVIS collaborators. The recommendations are more an attempt to develop a consistent framework that NVIS managers and collaborators can use for developing and sharing data. 4.01 General principles 4.011 The taxonomic process The complex nature of botanical taxonomy has resulted in problems over time that knowledge managers find difficult to address. There is no magic bullet for taxonomic problems found in biodiversity databases and the NVIS database is no exception. The dynamic nature of the taxonomic process means that data improvement in this area will take many years to complete and will never finish completely. The taxonomic data within NVIS will improve over time with the gradual improvement of data quality in State-based projects and between the States and the NVIS database. Improvement of data quality will depend on constant and on-going feedback cycles between data custodians, stakeholders and clients. Thus taxonomic maintenance will need to be an on-going and integral part of NVIS. The following sections outline some of the developments that should assist NVIS managers and collaborators in this process. 4.012 The ‘Consensus Census’ As part of the Australia’s Virtual Herbarium (AVH) project, the Council of the Heads of Australia Herbarium (CHAH) has initiated a project to develop an agreed national consensus taxonomy of Australian plants against which State-based censuses can be mapped. With most plant names (estimated to be c. 95%) there will be agreement between States, but there will be instances of genuine differences of interpretation and NVIS will have to deal with these. It is recommended as a priority, that NVIS participants form close relationships with their State herbaria so that they may receive regular and timely notification of changes in taxonomy (recommendation 1). The Australian Plant Name Index (APNI) is to be a foundation for this ‘Consensus Census’. 4.013 Reference to a common taxonomic standard It is also recommended that NVIS adopt a common/standard database for plant nomenclature and taxonomic validation and that this be applied at all levels from data gathering to data integration. To take advantage of the taxonomic work taking place as part of the national ‘Consensus Census’, it is recommended that the Australian Plant Name Index/What’s Its Name (APNI/WIN) database and its contributing State censuses should be used for this purpose (recommendation 2).


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4.014 Other national databases The Federal Department of the Environment and Heritage (DEH) runs a number of national databases (including plant names databases) to serve various purposes. Of particular relevance is the SPRAT database, which is found on the same Oracle backbone as NVIS. The plant data in SPRAT is sourced from APNI/WIN, although there are still some issues of discrepancies between the two databases. Ideally both SPRAT and NVIS should not hold taxonomic information, but pick this up through direct links to APNI/WIN. In reality, for operational reasons, it is more efficient to store a local extract of relevant information and update this regularly. To avoid proliferation of redundant information and problems of inconsistency, it is recommended that NVIS use the SPRAT taxonomy files for validation and that links between SPRAT and APNI/WIN be improved to allow SPRAT to be updated rapidly in response to changes in taxonomy (recommendation 3). This linkage is discussed in greater detail in Section 4.3. 4.015 Common names While useful at a local level, common names are problematic when employed at a national level as the same common name may not be used across all States or the same common name may be applied to different taxa in different States. Common names should have no role in the NVIS Veg_Description table (recommendation 4); if they are required for presentational or interpretative purposes, they can be picked up from APNI/WIN and other taxonomic databases. Despite some taxonomic problems (see Section 4.011), the use of scientific names will provide greater consistency between NVIS stakeholders. 4.016 Linking managers and custodians of taxonomic data State and Commonwealth herbaria are custodians and managers of plant taxonomic information. These herbaria have a mandate, the staff, expertise and resources to manage this data effectively. In many instances, NVIS surveys and data management is undertaken by agencies other than those responsible for maintaining the census of plants in the State. In all cases it is recommended that strong links be forged between NVIS data custodians and local State or Territory herbaria to minimise the risks of working with outdated or inappropriate taxonomy (recommendation 1). Sections 4.21 and 4.22 will discuss these linkages in greater detail. A more consistent approach to database structure and data exchange is also recommended (recommendation 5) by following the standards and protocols outlined in HISPID (Conn 1996). Section 4.23 outlines the benefits to NVIS managers and collaborators in adopting a more structured, uniform approach to nomenclatural data. The atomizing of plant name data into single elements is also recommended (recommendation 22), to simply the comparison of data. This recommendation also has the benefit or separating taxonomic opinion from the simple nomenclatural elements, the separation of qualification from the end of specific epithets being a case in point (recommendation 14) (see Sections 4.143, 4.41).


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4.1 Taxonomic issues and database structure Section 3 and Table 3.1 details a wide range of taxonomic issues encountered in the Taxon_Lists dataset. Discussion and recommendations on each of these issues will be detailed. For the most part, these solutions involve a simple editing of existing records. Most of these solutions are presented as a brief set of instructions in the CPBR_SOLUTION field as described in Section 2.23 and as shown in Appendix F. Apart from examination of the data, comment was sought on the database architecture. As discussed in Section 2.31, an understanding of the nature and extent of taxonomic issues was needed before useful advice could be provided on the database structure. It is impossible to talk about the taxonomic issues and the database structure separately as they are so strongly interlinked. Thus, recommendations relating to many of the taxonomic issues will include recommendations relating to the structure of the dataset. A number of the solutions relate to different ways of storing information and by enhancing structural links to other databases. 4.11 Record duplication There was considerable duplication of taxa within the Taxon_Lists dataset as discussed in Section 3.11. 3,684 taxa are represented by 5,447 records in Taxon_Lists; these records used to generate NVIS Vegetation Descriptions. Each record was assessed for duplication, marked in the MASTER_RECORD field with “M” for master records, “D” for duplicates and blank for unique records. The recommendation for dealing with this duplication is consolidation of the records used to generate the records in the Veg_Descriptions table (recommendation 6). Duplicate records, apart form the “D” marking, were also accompanied by referral statements in the CPBR_SOLUTION field (see Section 3.11). All duplicate records that are linked to Vegetation Descriptions should be shifted to records marked as master records. The referral statement provides the TAXDSC_ID number to help find the master record. Unique, unmarked records can stay as is, no consolidation being needed. Most duplication was found when comparing data provided by one State with that of another. This problem could be reduced, even eliminated, by comparing NVIS data at the custodial level with a nationally agreed data set, such as APNI/WIN and the developing ‘Consensus Census’ (see Section 4.012). Comparison against official State censuses would also help reduce duplication, and the effectiveness of this would increase as each State and national census resolved their inconsistencies. The large group case studies (Section 3.3) also showed that there was duplication of names when examining the data of a single State. This problem could be reduced, even eliminated, if NVIS custodians were to compare their data against a standard list or State (or national) census. It is not recommended in the short term that duplicate records be discarded as they represent unique contributions from each of the States (recommendation 7). Although the names may appear to be the same, they may in fact apply to different taxonomic concepts (e.g. a species in a broad or in a narrow sense). In most cases it will be quite acceptable to combine records with the same name, but in some cases it


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may not. The resolution of these inconsistencies can be quite complex and rely on specialist knowledge and database structures. Databases are being developed to track taxonomic concepts and it may be possible in the future to link taxa in NVIS with databases such as APNI/WIN; unique identifiers exist in both databases and may help to facilitate this. In rare cases involving historical taxonomy, the same name has been given to two completely different and distinct plant species. These duplicate names are known as homonyms and are unlikely to occur in NVIS data (see Section 4.15). 4.12 Infraspecific name issues If was found of the 538 records that related to infraspecific taxa, 42% of them had rank identifiers that were misspelt. Considerable varia tion was encountered, largely relating to how “subspecies” and “variety” are abbreviated in the INFRA_SPECIES_RANK field. Table 3.12 documents this variation along with the recommended forms of abbreviation. The short term solution for these records is to correct the typographic errors following the instructions written in the CPBR_SOLUTION field (recommendation 8). Greater care is needed with the quality of data provided by NVIS collaborators. By checking the spelling of the rank identifier is consistent, future data transfers will be more easily linked with other states records. The abbreviations recommended (recommendation 24) are the same as used in the Australian Vegetation Attribute Manual (ESCAVI 2003) and we recommend State data providers refer to this publication for further guidance (recommendation 23). Section 3.12 also discussed the presence of records form South Australia that possessed an infraspecific rank identifier, but no infraspecific name. It was speculated that these names were lost during the data transfer process. The advice provided in the CPBR_SOLUTION field is to refer these records back to their data source, in this case South Australia. The data provider should be able to check what data was lost and provide the correct infraspecific name. If no name can be provided, the rank identifier should be removed, taking the record back to a species level identification (recommendation 9). Overall the presence of records identified to infraspecific level in the NVIS database is considered to be worthwhile. These records are informative to a finer level than records identified only to species level. Furthermore, well-recognised taxa at the species level are routinely reclassified as varieties or subspecies of another species. The reverse is often true where varieties and subspecies are elevated to the rank of species as a result of further taxonomic research or new evidence. This is part of the normal taxonomic process and many examples exist within current NVIS data. For NVIS the important issue is that a taxon is recognisable and distinct and that the correct name is applied to it, not the rank of the name. To choose arbitrarily not to use taxa below a certain rank cannot be justified scientifically and seriously limits the value and utility of NVIS. It is recommended that were possible and appropriate, infraspecific level taxa be used (recommendation 10). Linking to State and national censuses will be extremely beneficial in this regard.


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4.13 Synonymy A total of 69 records were found to possess synonymous or non-current names when compared to the APNI/WIN database, as discussed in Section 3.13. The immediate solution to the synonymy found in Taxon_Lists is to change the records used to generate Vegetation Descriptions from the synonymous names to the name written in the CPBR_WIN_NAME field (recommendation 11). To improve the NVIS taxonomic currency it is recommended that a closer linkage to the APNI/WIN database be established (recommendation 3). When a change in the name of a taxon is accepted in APNI/WIN, this change could be communicated to NVIS. APNI/WIN attempts to keep track of the consensus view of what is a plant’s current name. As taxonomic understanding changes and is published in botanical journals, APNI/WIN is updated and this new change is made available online (www.anbg.gov.au/win). Alternate names and synonymy are real issues when comparing datasets between herbaria. APNI/WIN whilst being a good nomenclatural database, it currently represents the taxonomic opinion of only one institution, the CPBR. It is hoped that the ‘Consensus Census’ (Section 4.012) will go to the next level, establishing closer ties between all government herbaria (recommendation 1), resulting in a consensus view of plant name currency, with synonymy being part of this. 4.14 Other taxonomic problems Apart from issues relating to record duplication, infraspecific taxa and synonymy, eight other taxonomic issues were identified in the Taxon_Lists dataset. Tables 3.1 and 3.14 provide details of these issues and their frequency. The solutions to addressing these issues are outlined in the following subsections. 4.141 Alternate family name There were 415 cases where the family name found in Taxon_Lists did not match what is used in the APNI/WIN database. The three reasons for this mismatch are described in Section 3.141. Some cases related to a valid alternate taxonomic view, others related to simple typographic errors. While the reasons for variation are mixed, the short term solution recommended for each of these issues is the same; the alternate names should be replaced with the family names written in the CPBR_SOLUTION field (recommendation 12). While some of the family names are valid alternate taxonomic viewpoints, in the interests of simplifying the dataset, a consensus view should be adopted. The advantages of this should be self evident for groups such as Acacia, where the species for this genus listed in the NVIS Taxon_Lists table are currently linked to several family names (see Table 3.141a). As for the long term solution, it is recommended as a priority, that NVIS data providers ignore family names and that NVIS data import routines discard family name data on import (recommendation 13). Family names will be retrofitted after import from a standard database such as APNI/WIN when/if they are need for a particular purpose. NVIS data custodians can continue to use family names locally if


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they feel a particular local need, but need to be aware that these will be discarded and replaced as part of data integration at the national level. 4.142 Missing Family Name There was only one record found that had the FAMILY field blank. For the short term, the correct family name should be added to this record, as written in the CPBR_SOLUTIONS field (recommendation 12). Longer term, recommendation 13 outlined in Section 4.141 for handling family names will accommodate completely those situations where a family name is missing. 4.143 Name Qualifier Present Section 3.143 provided commentary on some of the difficulties found in plant taxonomy. Five different qualifiers were found in the Taxon_Lists dataset (see Table 2.22) to attempt to account for this complexity. Records originating from Victorian and South Australian data providers had qualifiers following the species epithet in the SPECIES field. Herbarium specimen databases such as ANHSIR (http://www.anbg.gov.au/ cgi-bin/anhsir) provides separate fields for name qualification, in accordance with HISPID standards (Conn 1996), see Section 4.23 for more details. The recommendation for qualifiers is that they be moved into a new QUALIFIER field (recommendation 14). The present lumping of name and qualifier in the same field makes it difficult to compare data sourced from different States. This separation of species name from the qualification will allow for easier comparison, qualification can then also be ignored where appropriate. The establishment of this new field will help NVIS Taxon_Lists become more consistent with the fields outlined in the HISPID standards (Conn 1996), which are used by all government herbaria. This will allow for a more streamlined data exchange process between NVIS collaborators and managers (see Section 4.23). It should also be noted that SPRAT currently has no qualifier field, for consistency it is recommended that this field also be added to this database. 4.144 Double epithets Records that possessed double epithets in the SPECIES field are discussed in Section 3.144. Hybridisation and identification difficulties were speculated as possible reasons for double epithets. “Refer to origin” was added to the CPBR_SOLUTION column, as source clarification is needed. It is recommended that referral back to the host institution be made to enable the appropriate checking of the original data source (recommendation 15). This would likely result in a change in the SPECIES field data, hopefully with a reduction to one epithet. 4.145 Status Unknown Taxon_Lists was found to have 72 records identified to either genus or family level; followed by some form of species qualifier in the SPECIES field (see Section 3.145). With this qualification it was difficult to determine whether a single species or multiple species were being referred to. The currency in the CPBR_WIN_CURR field was listed as being “unknown”. APNI/WIN relates to definitive names, not vague, qualified taxa.


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There are a number of options with these qualified names, keeping them as is would be considered an unsatisfactory solution. The preferred short term option would be to remove the qualification in the SPECIES field, leaving the identification at the genus (or family) level as shown in Table 4.145 (recommendation 16). This option will help such records better compare to other States data and other botanical databases like APNI/WIN. Table 4.145 Species qualifier correction Family Genus Species Before Juncaceae Juncus spp. After Juncaceae Juncus The more difficult but more informative solution would be to try and determine what the species being referred to, actually are. These records would need to be referred back to the State data providers, who in most cases are Tasmania, South Australia or Victoria (see Table 3.14). This might involve rechecking of herbarium specimens or resurveying sites in the attempt to improve the quality of past species lists. Future data provided by State collaborators will hopefully be of a superior quality, possessing fewer of these vague qualified records. It is recommended where possible these records be referred back to the host institut ion (recommendation 17). Another, less desirable option would be to change all the qualifiers to make them more consistent. The variation found in Table 3.145 could be rationalised, all “species” qualifiers being abbreviated to a standard “spp.”. It should be noted that this abbreviation relates only to multiple species within a genus (or family) and only abbreviated in this way for these cases. 4.146 Name misspelt The six examples of typographic errors discussed in Section 3.146 were listed in the CPBR_WIN_CURR field as being “not current”. The recommendation for these errors is to replace these names with the correct spellings found in the relevant CPBR_WIN_NAME fields (recommendation 18). 4.147 Phrase names Twelve examples were found within the Taxon_Lists dataset as being correctly cited phrase names (Section 3.147). These names were not found in the APNI/WIN database they were marked in the CPBR_WIN_CURR field as being unknown. This exercise has helped CPBR database managers to realise that it has a number of gaps in APNI/WIN when it comes to phrase names. No editing of these names is required in Taxon_Lists until the taxa are formally published; however APNI/WIN needs updating with the twelve NVIS phrase names. There were other cases where phrase names have subsequently been formally described and published. These for the purposes of this assessment were treated as synonyms, being linked in the CPBR_WIN_NAME to the current names.


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4.148 Non-plant taxon There was one fungal record identified and discussed in Section 3.148. As the NVIS database relates to plant profiles, the recommendation is that this record be removed for the Taxon_Lists dataset (recommendation 19). 4.15 Author Name issues The Taxon_Lists dataset possesses two columns that relate to plant name authors, AUTHOR and INFRA_AUTHOR. These fields when surveyed were found to possess three types of authority issues; authority missing, author incorrect and author name in wrong field. These issues were discussed in Section 3.2 where it was revealed 25% of the records assessed had some form of author inconsistency or error. The correction of these issues and keeping author data accurate in the future is considered to be a high maintenance task. The recommendation is that the two author columns be removed from the NVIS database and from NVIS data interchange formats (recommendation 20). Specialist plant name databases such as APNI/WIN are dedicated to keeping such nomenclatural information current and can be used as sources of this information if it is needed. For NVIS to maintain a separate list of authors in parallel to APNI/WIN would be double handling and a poor use of resources. The general recommendation is that author names are not needed for databases such as NVIS and that to use, check and maintain them is an unnecessary and expensive overhead. Author names serve little purpose outside the arcane world of historical taxonomy where they constitute a flag to indicate the same name has been used for different taxa. The only risk or removing authors from NVIS is nomenclatural homonyms would be harder to detect. Homonyms are cases where a botanist describes a new species using a name that is already preoccupied, applied to another species. Authors are useful in detecting these cases, as they will identify the different botanists involved. The risk of missing homonyms in the NVIS context is considered to be negligible, as they were more an issue is the early years of plant taxonomy. Today homonyms are rare; the tools (i.e. online databases) for checking if a name is preoccupied are more advanced and accessible. 4.16 Large group case studies Recommendations relating to issues uncovered with the large group case studies (Proteaceae, the wattles and the eucalypts) have been sufficiently dealt with by Sections 4.1 through to 4.15. The greater detail shown with these groups included a State-by-State comparison to help data providers develop a greater awareness of the inconsistencies encountered when attempting to marry taxonomic data from multiple sources.


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4.2 NVIS in relation to other botanical databases 4.21 NVIS relationship with Commonwealth and national databases As a general rule, NVIS is a vegetation and ecological database and as such should not seek to duplicate taxonomic effort taking place in other institutions dedicated to this task. NVIS should endeavour to use the results of these other projects, freeing up NVIS resources for other activities. There are numerous taxonomic database projects at national, state and even local level competing for maintenance resources and NVIS should not add to this list. Many of these projects are seeking ways to collaborate, reduce duplication and achieve higher levels of consistency. NVIS should position itself to take advantage of this by linking in with them rather than maintaining its own independent taxonomy. At the Commonwealth level, the CPBR and Australian Biological Resources Study (ABRS) compile and maintain the Australian Plant Name Index (APNI) as a list of all names of plants known to occur in Australia, including details of published synonymy. An overlay to this database, What’s Its Name (WIN) attempts to crystallise contemporary taxonomic thought from the alternatives offered in the botanical literature. APNI/WIN has links to and contributes to the International Plant Name Index (IPNI), ensuring that names used in Australia and in line with those used by the international botanical community. Acknowledging the confusion and difficulties caused by differences in taxonomy used by different State herbaria and in different State censuses, CHAH have embarked on a ‘Consensus Census’ project that will be striving for a single national view of Australian plant taxonomy. The WIN interface to APNI has been offered as the platform for the ‘Consensus Census’ and will reflect the combined and compromise view of all Australian government herbaria. The expectation is that over time there will be a gradual convergence and agreement of taxonomy in all states. The recommendation from this report is that NVIS align its taxonomy with the CHAH national ‘Consensus Census’ project when it becomes available (recommendation 2). This can be done both directly, through APNI/WIN and indirectly through links to State herbarium plant census projects. A closer alliance between NVIS taxonomy and national botanical projects like the CHAH endorsed ‘Consensus Census’ will remove the perception of a Commonwealth imposed taxonomy since the content of the ‘Consensus Census’ is being provided by the States themselves, with the Commonwealth as just another equal partner. Furthermore, this association is likely to foster greater collaboration between NVIS and the botanical resources of State and Commonwealth herbaria and provide greater opportunity for NVIS to take advantage of, have input to, and influence the direction and priorities of the AVH project. Acknowledging practical difficulties in establishing direct database links between APNI/WIN and NVIS, this report recommends interim solutions involving periodic updates of taxonomic information from APNI/WIN into the immediate NVIS database environment (see Section 4.3). The logical place for this is DEH’s SPRAT database in the same Oracle RDBMS as NVIS. SPRAT contains taxonomic tables of all plant and animal names of interest to DEH and already has direct data links APNI/WIN.


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The Taxon_Lists table is made up of 59,513 records (Section 1.34) of which 5,447 records (3,684 taxa) are used to create plant community profiles in the NVIS Veg_Description table (Section 3.0). After a clean up of the data following the Recommendations of Section 4.1, it is recommended (recommendation 21, in part ) that these linked records be migrated from NVIS into SPRAT as described in greater detail in Section 4.3. It is recommended that these links be enhanced and that formal arrangements be established to ensure timely updates of SPRAT from APNI/WIN (recommendation 3). 4.22 The role of State and Territory botanical databases It is recommended that survey agencies contributing to NVIS as a priority build active working links with State herbaria who are custodians of census information about plants occurring in each State (recommendation 1). Taxonomic lists for each State/Territory (which typically hold many more species than are used in NVIS vegetation descriptions) such as the State and Territory censuses maintained by herbaria should be used to support updates of NVIS taxonomic data. Examples of these State censuses include The National Herbarium of NSW’s PlantNet (http://plantnet.rbgsyd.gov.au/P lantNet/NSWplants/nswplants.htm) and Perth Herbarium’s FloraBase (http://florabase.calm.wa.gov.au). Links to all available State censuses can be found on Australia’s Virtual Herbarium web site (http://www.chah.gov.au/avh). The Commonwealth Government has a requirement for a separate combined national list of plant names, including indications of unresolved taxonomic issues between jurisdictions. The CHAH endorsed ‘Consensus Census’ is seen at the appropriate vehicle for this and this report recommends that NVIS align itself with the taxonomy that will be contained in this census (recommendation 2). State and Territory custodians of NVIS are responsible for maintaining the content of this database, and it is highly advisable that they consider adopting a ‘Consensus Census’ view of taxonomy before data is dispatched to the national NVIS database as illustrated in Figure 4.3c. This will radically reduce the amount of work required in the data compilation stage. Comparing the initial NVIS taxon data against the local State census would be a valuable first step in this process. Discrepancies and inconsistencies that are detected during the NVIS data loading process will be fed back to NVIS data custodians. This will initiate a dialogue towards agreement on an acceptable taxonomy. A simpler data structure, with no families (recommendation 13) or authors (recommendation 20), will help future transfers of data pass validation get through to “validation successful” without the need for human intervention.


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4.23 HISPID standards Managers of Australian Commonwealth, State and Territory government herbaria, collectively form the Council of Heads of Australian Herbaria (CHAH) with a technical subcommittee, the Herbarium Information Systems Committee (HISCOM). This committee develops and maintains the Herbarium Information Standards and Protocols for Interchange of Data (HISPID), which specifies how herbarium data is to be recorded and transferred between databases. Computer operating systems and database platforms vary between herbaria and HISPID attempts to store herbarium specimen data in consistent or compatible fields. Coupled with this were consistent protocols and data delivery mechanisms to allow for a simplified exchange of data between institutions. This has been essential to the AVH project, and specimen exchange accompanied by electronic field data, reduces data entry duplication. The current HISPID standards (Version 3 - Conn 1996) (http://plantnet.rbgsyd.nsw.gov.au/HISCOM/HISPID/HISPID3/hispidright.html) are actively used by the major Australian government herbaria, key collaborators in the NVIS database. For future NVIS data exchange, it is recommended that NVIS managers and collaborators adopt the use of the relevant HISPID conventions and standards for the representation of botanical data (recommendation 5). By adopting these standards, data can made more consistent between NVIS stakeholders, allowing for simpler uploading of data from collaborators to NVIS managers. One current area of difference between the HISPID standards and data provided to NVIS by some collaborators is the combining of multiple nomenclatural elements into one string (or field). It is our recommendation (recommendation 22) that collaborators should follow the nomenclatural database structure outlined in the HISPID standards, effectively atomizing data into single elements (or fields). These name elements can be recombined later if necessary for presentational or reporting purposes. Table 4.23 below shows a hypothetical example of how combined data could be broken up to match HISPID standards. Table 4.23 Atomizing of nomenclatural data elements Before

Genus name Species epithet Infraspecific epithet Acacia excelsa Benth. subsp. angusta Pedley

After Genus name Species epithet Infraspecific rank Infraspecific epithet Acacia excelsa subsp. angusta Note: In this example the authors have be omitted in accordance with recommendation 20. Whilst the before picture appears to be simpler, with fewer fields, the multiple data elements in a single field complicate comparisons to other records. As mentioned in Section 4.143, the presents of name qualifiers at the end of some records made comparison with records without qualification difficult, even when multiple records of the same taxon were compared. Moving the qualification, or other data, into a separate field (recommendation 14) simplifies the data elements and allows users greater control.


39

4.3 Future Test Measures The on-going validity of NVIS will depend on the reliability of occurrence records in the database to which an identification based on correct and consistent taxonomy is critical. This can only be achieved by regular checking of data and comparison against reliable data sets serving as a standard for the project and the updating of NVIS and contributing databases to these standards. Databases against which NVIS can and should be checked are themselves being checked, enhanced and updated – taxonomy is governed by contemporary acceptance and best practice rather than legislation and continual change is the only constant. This report recommends (recommendation 21) a multi-stage approach to the checking of taxonomic data, with iterative checks on the databases against which NVIS is being checked:

• NVIS data to be compared (and updated) at source against local State censuses which in turn will be compared against a combined national ‘Consensus Census’;

• National plant list used by ERIN be updated regularly from ‘Consensus Census’ information sourced through APNI/WIN (quarterly or ad hoc preload updates may be sufficient);

• Changes or updates detected or required at the national level be communicated to NVIS custodians. Dialogue to scope and resolve differences to be initiated.

• NVIS abandon the concept of maintaining its own taxonomic authority file for internal and incoming data checking and validation;

• APNI/WIN and the evolving ‘Consensus Census’ be used as the taxonomic resource for NVIS; for reasons of pragmatism, expediency and practicality, use of this resource need not be direct if there is an appropriate up to date local gateway to views of this information; SPRAT fulfill these requirements;

• NVIS adopts an existing accessible taxonomic authority file for this purpose. From a practical point of view, the taxon tables of the SPRAT database are well-placed for this role:

o The SPRAT taxon tables are pre-populated and available o SPRAT taxon data is a reduced subset of the unnecessarily complex

(for NVIS purposes) APNI/WIN data o They are Oracle database tables on the same database server as NVIS

and can be made accessible as part of NVIS o They are managed by the same database unit which manages the NVIS

database o SPRAT uses APNI/WIN as a source of taxonomic and nomenclatural

data o Mechanisms are already in place to provide SPRAT with direct

database access to APNI/WIN data o APNI/WIN is managed by DEH and is continually maintained and

updated in response to the contemporary taxonomic literature and input from the Australian botanical community

o APNI/WIN is a key dataset of the peak body, CHAH and is the foundation of their ‘Consensus Census’ project to build and maintain an agreed contemporary taxonomy for Australian plants


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o Through its links to APNI/WIN and APNI’s links to CHAH and the State Herbaria, SPRAT is likely to be a reasonable reflection of names and taxonomy employed by NVIS State and Territory contributing agencies;

• NVIS enters into a MOU or Service Level Agreement with SPRAT establishing a clear understanding of the use(s) SPRAT taxonomic data will be put, NVIS requirements and expectations of SPRAT taxonomic data and mechanisms for dealing with inconsistencies and unexpected events;

• Given that NVIS data providers have not empowered the NVIS database to change provided taxon names, NVIS will need map provided names to an NVIS endorsed name; in most cases the mapping will be 1:1with no change involved; a small percentage will be either errors or inconsistencies which will require communication between NVIS, SPRAT and the data provider;

• Existing NVIS data will be compared against SPRAT and inconsistencies will be rectified; changes will need to be communicated with NVIS data providers and agreements reached on how errors and legitimate differences will be flagged and handled, both within NVIS and within the supplying databases; and

• NVIS data supplied for amalgamation will be compared (and updated) on load with the national NVIS plant list, stored as fields in SPRAT, which will reflect the taxonomy of APNI/WIN and the ‘Consensus Census’. As previously mentioned, changes will need to be communicated with NVIS data providers and agreements reached on how errors and legitimate differences will be flagged and handled by both NVIS and the data providers.

From time to time it might be advisable to conduct an independent audit or focused evaluation of taxonomic data in the NVIS database, with a scope similar to this report. While this could be conducted annually or with each major data re-supply or a significant database restructure, a detailed evaluation of NVIS taxonomy will probably not be necessary for another 2-3 years. In this time the CHAH ‘Consensus Census’ is likely to have been implemented at the national level; this would provide an appropriate and useful trigger for a new evaluation and updated on NVIS taxonomic data. Schematics of how the checking and validation process might be implemented and progressed over time have been developed in discussion with ERIN and are outlined in Figures 4.3a, 4.3b and 4.3c. Option 1 and Option 2 are initial and intermediate transition configurations, leading to a fairly simple final stage in Option 3 when links between State and national censuses and the NVIS database are more mature. Table 4.3 indicates actions that might be taken by NVIS to address inconsistencies, incompatibilities, differences and errors in NVIS data files supplied by NVIS data providers. In line with the Figures mentioned above, it also details what issues may be detected and measured as part of an automated process.


41

Table 4.3 Summary of discrepancy handling options

DISCREPANCY Action Reported Automatically

Reported quantitatively

RECORD NUMBERS Single (unique) occurrence names No action; all should

match an entry in taxon name table

no In summary report

Master records No action; not relevant as all master entries are in external taxon name table (SPRAT)

n/a n/a

Duplicate records n/a in the context of supplied data

n/a n/a

Total taxa yes In summary report INFRASPECIFIC NAME ISSUES Rank misspelled Correct manually and

reload yes In summary report

Infra name in wrong field Correct manually and reload

yes In summary report

Infra name missing Refer to data supplier, correct and reload


SYNONYMY Nomenclatural synonym Map to current name;

notify data supplier yes In summary report

Taxonomic synonym Map to current name; notify data supplier


OTHER TAXONOMIC ISSUES Alternate family name n/a - family names to

be ignored and supplied through SPRAT

no In summary report

Missing family name n/a - family names to be ignored and supplied through SPRAT


Name misspelt Correct manually and reload; notify data supplier


Phrase name Treat as normal names; must match existing name in names table


Non-plant taxon Refer to data supplier, correct and reload if appropriate


Double epithet Correct and reload manually, notify data supplier


Status unknown Refer to data supplier; correct and reload if appropriate


Name qualifier present yes In summary report


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Figure 4.3a Option 1: Species Taxonomy in NVIS Vegetation Descriptions (short term arrangement)

State NVIS

Database

Installation

National NVIS

DatabaseXML

Import

XML

XML

Export

New

Spelling

Update

Fails Validation

Holding Area

Outcome

Report

Refine Record

New NVIS_Id

Val

idat

ion

NVIS spatial (polygon) coverage

Lookup Table (taxonomy as supplied)

• Spelling resolved • Taxonomy/Currency not

resolved • Doubtful Ids not resolved

Species Profile & Threats (SPRAT) taxon list • spelling

Apply other agreed NVIS rules (via XML, XSLT and PL/SQL, etc.) • Not shown on other

diagrams for simplicity.

Convert State_ Source_ Code to Taxon Name

Figure 4.3b Option 2: Species Taxonomy in NVIS Vegetation Descriptions (mid-term arrangement)

State NVIS

Database

Installation

National NVIS

DatabaseXML

Import

XML

XML

Export

New

Spelling

Update

Fails Validation

Holding Area

Outcome

Report

Refine Record

New NVIS_Id

Val

idat

ion


Lookup Table (resolved to AVH Consensus Census)

• Spelling resolved • Taxonomy/ Currency

resolved • Doubtful Ids partly

resolved

Lookup Table (taxonomy as supplied)

Duplicated fields or tables: NVIS Veg. Description Table Taxon_Data table.

What’s Its Name (WIN) database

Species Profile & Threats (SPRAT) taxon list • spelling

Regular updates

Apply “current” flag as per AVH Consensus Census

STATE CENSUS Convert State_Source_Code to Taxon Name/

Figure 4.3c Option 3: Species Taxonomy in NVIS Vegetation Descriptions (long term arrangement)

State NVIS

Database

Installation

National NVIS

DatabaseXML

Import

XML

XML

Export

New

Spelling

Update

Fails Validation

Holding Area

Outcome

Report

Refine Record

New NVIS_Id

Val

idat

ion


AVH’s Consensus Census

STATE CENSUS

Lookup Table

• Spelling resolved • Taxonomy/Currency

resolved • Doubtful Ids resolved


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4.4 Guidance material for incorporation in future NVIS manuals The data that is amalgamated to make up the NVIS Taxon_Lists table comes from multiple sources at different times from collaborators with differing backgrounds and research emphases. With this variation in mind, the Executive Steering Committee for Australian Vegetation Information developed the 2003 Australian Vegetation Attributes Manual (ESCAVI manual). This manual provides “nationally agreed guidelines for translating and compiling mapped vegetation datasets into the NVIS database through describing the NVIS attribute framework and links to the NVIS database” (ESCAVI 2003: 3). It is the recommendation of this report that collaborators, in the interests of providing NVIS more consistent data, become well familiar with this manual’s guidelines (recommendation 23). Data is periodically added to the NVIS database and existing data regularly reviewed. Guidance is provided (Section 4.41) to data collaborators to help simplify and improve the accuracy of data to which they are custodians, data that ultimately is incorporated into NVIS. Field collection techniques and appropriate vouchering procedures are also outlined (Section 4.42) to encourage data collaborators to increase the number of vouchered NVIS records. 4.41 Provision of data to NVIS In terms of the taxonomic data provided by the NVIS data custodians, this report concurs generally with the data structure of the Taxon_Lists table of the NVIS database. In this instance, simplicity is key, and all that is really required are fields for the essential elements of a taxon name. All other details of taxonomy can be obtained from other databases of plant names (i.e. SPRAT) or specialist taxonomic databases (i.e. APNI/WIN) as outlined in Section 4.21. This supplementary information can be obtained though a dynamic link between databases or can be imported into appropriate database fields if necessary. The long term recommendation (recommendation 21) to simply the process even further is to migrate this Taxon_Lists data into SPRAT, see Section 4.3. If NVIS accepts the recommendation of this report to ignore family names and author names (recommendations 13 and 20) for data transfer, and adds a new Qualifier field (recommendation 14) then the Taxon_List dataset will be much simpler:

• Genus • Species • Infraspecies rank • Infraspecies name • Qualifier (new field)

This will provide a unique taxon match for the purposes of data loading and for feedback of problems to NVIS data custodians. This will also mirror the fields and standards outlined under HISPID, allowing greater consistency of data exchange between key stakeholders, such as herbaria (see Section 4.23). Under the International Code of Botanical Nomenclature (ICBN) (Greuter et al. 2000), it is not necessary to include all ranks, but it is recommended that data be supplied to the finest possible


48

level, such as subspecies and varieties, for greater information clarity, as per Section 4.12 (recommendation 10). NVIS database managers may choose to manage additiona l taxon fields such as family name (sources from SPRAT or APNI/WIN) and taxon authors. If so, these should be sourced from an authoritative taxonomic database such as APNI/WIN or SPRAT (recommendation 25). See comments in Section 4.15 on the limited utility of author names. Whilst simplification of NVIS data is recommended above, the data present in the remaining fields will need to be improved. Section 4.4 stressed the importance of data consistency and the role of the ESCAVI manual. One example of where greater familiarity with the ESCAVI manual will benefit managers and data collaborators is with infraspecific names. As Section 3.12 shows, State providers need to take greater care with the quality of infraspecific rank data they provide. Section 4.12 makes recommendation (recommendation 24) as to what preferred rank abbreviations are (e.g. subsp., var.). These abbreviations match those recommended in the ESCAVI manual. If collaborators follow these guidelines, data will be much more consistent allowing for improved correlation of information in NVIS (recommendation 23). 4.42 Field data collection and herbarium vouchers It is the recommendation of this report that future data incorporated in NVIS be based on herbarium vouchers lodged with recognised Australian state and national herbaria wherever possible. However, it is understood that full vouchering of all vegetation survey data is impractical, both from a field and herbarium standpoint. Material in the field may be inadequate or insufficient for herbarium specimens, and vouchering all taxa encountered in a field survey is essentially an impossible task. Nonetheless, the judicious collection of vouchers, where possible, is strongly recommended (recommendation 26). For NVIS collaborators not associated with herbaria, we would encourage the development of closer ties to the State or Territory herbarium that is most appropriate (recommendation 1). Most herbaria will accept specimen donations and manage them indefinitely for relatively little cost. There are definite advantages in data that is linked to a tangible voucher specimen. Views on taxonomy change over time and it is not uncommon for names associated with herbarium specimens to change. As herbarium specimens are each given a unique numerical identifier (HISPID Accession number, for details see http://plantnet.rbgsyd.nsw.gov.au/HISCOM/default.htm), this allows specimen name changes to be rapidly checked and communicated, for example to NVIS. Data provided to NVIS from non-vouchered sources cannot be so readily updated and over time may come to represent out-dated taxonomic concepts, which in turn may lessen the value of the data.


49

Most data providers will be familiar with the process of collecting herbarium vouchers and associated data. For collaborators who need guidance on these techniques, a number of useful references are available:

Australian National Botanic Gardens (2003) How to Collect Plants (http://www.anbg.gov.au/projects/collecting/collecting.html) Royal Botanic Gardens Sydney (1995) Collection, Preparation and Preservation of Plant Specimens, 2nd edn., Royal Botanic Gardens, Sydney. Bridson, D. & Forman, L. (eds.) (1998) The Herbarium Handbook, 3rd edn., Royal Botanic Gardens, Kew.


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5.0 CONCLUSION The CPBR assessment of the 5,447 species used in the NVIS Taxon_Lists table identified 896 (16.45%) records as having issues that needed correction. Overall the quality of information was high, reflecting the time and energy spent by data providers and NVIS managers in maintaining this dataset. Issues and their solutions have been added to the review fields in the Taxon_Lists table to allow ERIN to make corrections where appropriate. Many of the issues encountered, such as name duplication and alternate family names, were clearly the result of trying to marry similar information from multiple datasets. It is hoped with improved collaborator coordination, an emphasis on accurate vouchered information and a re-examination of the types of data that make up the NVIS database, will go a long way towards simplifying the long term management of NVIS. Along with internal restructuring and data correction, links to external projects such as the SPRAT database and the developing ‘Consensus Census’ project (as part of the AVH) will help reduce the need for NVIS managers to keep on top of current taxonomic opinion. The eventual migration of key NVIS data into SPRAT has also been recommended to prevent duplication of effort in maintaining taxonomic and nomenclatural data. Closer links between NVIS and these other databases should help to create a synergy that will benefit NVIS product users and develop closer ties between Commonwealth, State and Territory collaborators. Greater knowledge and understanding of Australia’s flora is being advanced all the time and with this the NVIS database clearly has an important role.


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6.0 REFERENCES Australian National Botanic Gardens (2003) How to Collect Plants. Available from: http://www.anbg.gov.au/projects/collecting/collecting.html Bridson, D. & Forman, L. (eds.) (1998) The Herbarium Handbook, 3rd edn., Kew: Royal Botanic Gardens. Conn, B.J. (ed.). (1996) HISPID3: Herbarium Information Standards and Protocols for Interchange of Data Version 3, National Herbarium of New South Wales: Sydney. Available at: http://plantnet.rbgsyd.nsw.gov.au/HISCOM/HISPID/HISPID3/H3.html [Accessed 2004, 31 March] ESCAVI (2003) Australian Vegetation Attribute Manual, Version 6.0. Executive Steering Committee for Australian Vegetation Information, Dept of Environment and Heritage, Canberra.

Greuter, W. et al. (eds.) (2000) International Code of Botanical Nomenclature (Saint Louis Code), Regnum Vegetabile, vol. 138, Koeltz Scientific Books, Königstein, Germany. Harden, G.J. (ed.) (1990) Flora of New South Wales, Volume 1, NSW University Press. Harden, G.J. (ed.) (1991) Flora of New South Wales, Volume 2, NSW University Press. International Plant Name Index (IPNI) (2003) International Plant Name Index. Available from: http://www.ipni.org/index.html National Land and Water Resources Audit (NLWRA) (2000). Australian Vegetation Attributes: National Vegetation Information System Version 5.0. Audit Canberra. National Land and Water Resources Audit (NLWRA) (2001). Australian Native Vegetation Assessment, 2001. Audit Canberra. Royal Botanic Gardens Sydney (1995) Collection, Preparation and Preservation of Plant Specimens, 2nd edn., Royal Botanic Gardens, Sydney Sun, D., Hnatiuk, R. and Neldner, V.J. (1996) Vegetation classification and mapping systems for Australian forest management. Bureau of Resource Sciences/Australian Nature Conservation Agency, Canberra. Thackway, R, Sonntag, S. and Donohue, R. (2001) Compilation of the National Vegetation Information System (NVIS) Vegetation 2000 dataset . Final Report BRR13, National Land & Water Resources Audit, Canberra. Walker J. and Hopkins, M.S. (1990). Vegetation. In: McDonald, R.C., Isbell, R.F., Speight, J.G., Walker, J. and Hopkins, M.S. (Eds) Australian Soil and Land Survey. Field Handbook. 2nd edn Melbourne: Inkata Press.


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APPENDIX A: IMPROVING THE COMPARABILITY AND CONSISTENCY OF NATIVE VEGETATION INFORMATION

ACROSS AUSTRALIA

Project to Assess Taxonomic Issues in the National Vegetation Information System (NVIS): Project Summary

Introduction Floristic data will be a key input to resolving the equivalence of NVIS vegetation descriptions. Environment Australia is funding this project to assess taxonomic issues in NVIS and to develop options for the establishment of measures, systems and processes for the continuous improvement of floristic data in NVIS. The report will be for consideration by NVIS collaborators. Project Aims

1. To assess the extent of taxonomic and nomenclatural issues in NVIS and recommend changes;

2. To develop and test measures of taxonomic and nomenclatural data quality in the NVIS database;

3. To provide advice and options on protocols and procedures for the continuous improvement of taxonomic data in NVIS; and

4. Provide guidance material for incorporation in future NVIS manuals. Rationale The Australian Native Vegetation Assessment 2001 identified the need to address edge matching and equivalence issues arising from the compilation of disparate vegetation datasets. The Executive Steering Committee for Australian Vegetation Information (ESCAVI) has recognised its high priority through its endorsement of the Commonwealth NVIS Business Plan and recent annual Environment Australia workplans. Over the last 2 years, NVIS collaborators have addressed attribute consistency and structural issues in the NVIS (2000) database. NVIS now has a standardised terminology for comparison of vegetation descriptions. In addressing equivalence between vegetation types, key inputs to understand include the floristic composition, vegetation structure, remote sensing sources and the methods used to classify and map vegetation types. With respect to floristic composition, it is now timely to understand the various species concepts used by vegetation mappers at the time of data collection. Some species names may need to be standardised to ensure comparability between vegetation types from different datasets. A recent review of NVIS species names in relation to the Species Profile and Threats (SPRAT) database indicated that approximately 5% of the 3600 species names used in NVIS to be non-current. In any particular case, this is probably the result of one or more of the following reasons:

• Spelling errors and other nomenclatural inconsistencies; • Old vegetation survey data, using outdated species concepts;


53

• Differing species concepts between vegetation surveyors; and • Observer differences in their ability to identify plant specimens.

The above are similar issues to those being addressed in the development of the Australia’s Virtual Herbarium (AVH), a consortium of State, Territory and Commonwealth herbaria. Through the AVH online query tool, a map of the distribution of each species will be generated from a distributed database of plant specimen data. There would be considerable duplication of effort for NVIS collaborators to independently consider these issues for the species in NVIS. The challenge, in general, will be to ensure that the herbaria in each jurisdiction are able to meet the information needs of vegetation surveyors. In particular, there is a need to scope the options for beneficial interaction between the herbaria and NVIS, including the development of sustainable systems and processes to manage taxonomic issues in NVIS. Whilst this project will only tackle one out of the five or six key aspects of equivalence, it is a very important component. It seems to be a relatively tractable and low-cost component, compared with (say) re-mapping vegetation, which may ultimately be necessary to fully resolve edge-matching issues. There may also be wider lessons to be learned for solving NVIS vegetation-equivalence from the way that the AVH is tackling species-equivalence. Project Description 1. Advise on solutions to any immediate taxonomic issues for circa 3,600 unique

species in NVIS. Priority would be on, but not limited to, major/widespread groups such as: Eucalyptus, Acacia, Chenopodiaceae, Casuarinaceae, etc.

It is envisaged that the proposed changes would be presented to NVIS collaborators for adoption. 2. Develop and test measures to periodically assess the quality of taxonomic data in

NVIS overall and subdivided by State/Territory and major groups, for example: Extent and nature of taxonomic inconsistencies; and Extent of uncertainties to be solved by, say: (a) simple name change, (b) re-identification of voucher and (c) field visit.

It is envisaged that these measures could be applied to the re-supplied NVIS dataset, in the future – i.e. after completion of this project.

3. Review the structure of taxonomic parts of the NVIS database and provide an

options report for continuous improvement of taxonomic data in NVIS: • Review the measures developed in #2 and suggest a reporting frequency and

thresholds for action; • Specify corrective actions that might be needed; • Examine the costs and benefits of identifying and recording infra-species for

vegetation surveys; • Review the existing NVIS structure and content with respect to taxonomic

data. Examine the role of separate jurisdictional lists (which typically hold


54

many more species than are used in the NVIS vegetation descriptions) and how these should be maintained to support updates of NVIS descriptions;

• Examine whether the Commonwealth needs to maintain a separate list of unresolved taxonomic issues between jurisdictions, to ensure the operational capability of NVIS; and

• Review technical options for cooperative linkages between State and Territory survey agencies with national biodiversity information initiatives – such as: the Australia’s Virtual Herbarium, SPRAT and NVIS.

The project report will focus on identifying issues and discussing the pros and cons of various possible solutions. 4. Provide guidance material for incorporation in future NVIS Manuals:

• Advise and provide details of current standards/best practice for databasing and interchange of species data, e.g. the Herbarium Information Standards and Protocols for Interchange of Data (HISPID)

• Provide details of current standards and/or best practice for the taxonomic aspects of vegetation survey – especially the use of vouchers.

Deliverables 1. Document proposed changes to each taxonomic record (in newly-created fields so

that existing State/Territory data are not changed) in the NVIS development database, with clear comments as to reasons.

2. A final report incorporating the items in the Project Description, above. The report

would be for circulation to NVIS collaborators; 3. Provide a briefing on the project to the NVIS collaborators at a national workshop

and/or ESCAVI meeting, as appropriate. Personnel

• ERIN, EA - Matt Bolton, Leo Berzins & Simon Bennett • CPBR (Centre for Plant Biodiversity Research) – Anthony Whalen, Laurie

Adams, Jessie Szigethy-Gyula, Brendan Lepschi & Jim Croft • State and Territory herbaria would be contacted by CPBR for advice on

AVH and established linkages with vegetation mappers in each jurisdiction

• State and Territory survey agencies may also be contacted for advice. Timing June 2003 to 31 October 2003.

Vegetation Descriptions (NVIS Hierarchy)

Dataset Information

APPENDIX B: NVIS DATABASE STRUCTURE

AGENCY

#* AGENCY_ID --------------------- AGENCY_NAME AGENCY_ADDRESS AGENCY_LOCATION STATE

MAP_UNIT #* MAP_UNIT_ID * VEG_ID * DATA_SET_ID -------------------------------------------- MAPUNT_IDENTIFIER >( NVIS_ID) SPATIAL_MIX NUMBER_OF_VEG_DESCRIPTIONSVEG_DESC_POSITION VEG_DESC_PROPORTION >> SOURCE_CODE >> SOURCE_DESCRIPTION >>> SOURCE_CODE_COMPONENT ----------------------------------------------- > Optional for state – only if required for operational purposes >> Only until mosaics are resolved, when fields moved to Veg table >>> Field to be dropped after mosaics resolved

VEG_ DESCRIPTION #* VEG_ID ----------------------------------------------- > NVIS_ID >> SOURCE_CODE LEVEL_OF_DETAIL NUMBER_OF_STRATA L1_CLASS L2_STRUCTURAL_FORMATION L3_BROAD_FLORISTIC_FORMATION L4_SUB_FORMATION L5_ASSOCIATION L6_SUB_ASSOCIATION >> SOURCE_DESCRIPTION ENVIRONMENTAL_DESCRIPTION ----------------------------------------------- > Table index is VEG_ID in State; NVIS_ID in C/W. NVIS_ID is applied by C/W and stored by state as separate field. >> When all mosaics have been resolved Optimal location for these fields

STRATUM

#* STRATUM_ID * VEG_ID ------------------------------------------------STRATUM_CODE SUB_STRATUM_RANK NUMBER_OF_GROWTH_FORMS NUMBER_OF_TAXA COVER_TYPE COVER_TYPE_DERIV_METHOD COVER_MINIMUM_VALUE COVER_MAXIMUM_VALUE COVER_MEDIAN_VALUE COVER_MEAN_VALUE COVER_CODE HEIGHT_TYPE HEIGHT_TYPE_DERIV_METHOD HEIGHT_MINIMUM_VALUE HEIGHT_MAXIMUM_VALUE HEIGHT_MEAN_VALUE HEIGHT_MEDIAN_VALUE HEIGHT_CLASS DOMINANT_STRATUM_FLAG

GROWTH_FORM #* GROWTH_FORM_ID * STRATUM_ID --------------------------------------------- GROWTH_FORM,_RANK GROWTH_FORM_CODE COVER_TYPE COVER_TYPE_DERIV_METHOD COVER_MINIMUM_VALUE COVER_MAXIMUM_VALUE COVER_MEDIAN_VALUE COVER_MEAN_VALUE GR_FORM_DOMINANCE_QUALIFIER GROWTH_FORM_FREQUENCY GROWTH_FORM_ALWAYS_THERE GROWTH_FORM_SUMMARY_FLAG

CONTACT #* CONTACT_ID * AGENCY_ID

----------------------- CONTACT_TYPE CONTACT_NAME CONTACT_ADDRESS CONTACT_PHONE_NUMBERS CONTACT_EMAIL

DATA_SET #* DATA_SET_ID (* AGENCY_ID) -------- (Attribute Information) ----------- DATA_SET_NAME > (DATA_SET_NUMBER) VEGETATION_THEME_CODE VEGETATION_THEME_CONSTRAINTS ANZLIC_METADATA_IDENTIFIER ANZLIC_METADATA_NAME ANZLIC_METADATA_URL STRUCTURAL_CLASSFN_SYSTEM FLORISTIC_GROUP_TYPE CLASSIFICATION_METHOD SAMPLING_TYPE BOTANICAL_EXPERTISE POSITIONAL_ACCURACY POSITIONAL_ACCURACY_DETERM POSITIONAL_ACCURACY_MEASURE MAP_PUBLICATION_SCALE FINEST_SCALE BROADEST_SCALE SURVEY_AND_MAP_RELIABILITY START_DATE_ATTRIBUTE END_DATE_ATTRIBUTE START_DATE_SPATIAL END_DATE_SPATIAL ---------------------------------------------------- > Optional – numbers applied by C/W

REFERENCE #* REFERENCE_ID * DATA_SET_ID ----------------------------- CITATION FORMAT STORAGE_LOCATION

MAPPING_SOURCE #* MAPPING_SOURCE_ID * DATA_SET_ID ----------------------------------- MAPPING_SOURCE_NUMBER MAPPING_METHOD MAPPING_EXPERTISE IMAGERY_SOURCE IMAGERY_SCALE IMAGERY_RESOLUTION MAPPING_SOURCE_EXTENT DELINEATION_MEDIUM START_DATE_SOURCE END_DATE_SOURCE MAP_BASE

TAXON_DATA

#* TAXON_DATA_ID * STRATUM_ID * TAXON_LISTS_ID

---------------------------------------------- TAXON_DATA_RANK TAXON_DATA_DESCRIPTION TAXON_DATA_SOURCE_CODE COVER_TYPE COVER_TYPE_DERIV_METHOD COVER_MINIMUM_VALUE COVER_MAXIMUM_VALUE COVER_MEDIAN_VALUE COVER_MEAN_VALUE TAXON_DATA_DOMINANCE_QUALIFIER TAXON_DATA__FREQUENCY TAXON_DATA_ALWAYS_THERE TAXON_DATA_SUMMARY_FLAG

TAXON LISTS

#* TAXON_LISTS_ID * TAXON_LIST_ORIGIN_ID ----------------------------------- TAXON_LISTS_SOURCE_CODE TAXON_LISTS_FAMILY TAXON_LISTS_GENUS TAXON_LISTS_SPECIES TAXON_LISTS_AUTHOR TAXON_LISTS_INFRA_SPECIES_RANK TAXON_LISTS_INFRA_SPECIES TAXON_LISTS_INFRASP_AUTHOR TAXON_LISTS_COMMON_NAME TAXON_LISTS_REFERENCE

Detailed Source Component Attributes

Spatial

SPATIAL_COVERAGE (Attribute Table Format)

State - C/W

------------------------- MAPUNT_IDENTIFIER VEGDSC1 - NVISDSC1

> VEGDSC2 - NVISDSC2 > > VEGDSC3 - NVISDSC3 > > VEGDSC4 - NVISDSC4 > > VEGDSC5 - NVISDSC5 > > VEGDSC6 - NVISDSC6 >

VEGPROP1 VEGPROP2 * VEGPROP3 * VEGPROP4 * VEGPROP5 * VEGPROP6 *

NO_VEG_DESC SPATIAL_MIX

(> Only completed where valid description exists)

TAXON_LIST _ORIGIN

#* TAXON_ TAXON_LIST_ORIGIN_ID --------------------------------------- TAXON_LIST_ORIGIN_CUSTODIAN TAXON_LIST_ORIGIN_STATE TAXON_LIST_ORIGIN_DETAILS

Inter-convertible

Note: The spatial coverage is not part

of the NVIS database, but is an integral part of the

LEGEND

Many to One

Vegetation Stratum & Lower Level Info (Secondary Database)

Dataset Information

NVIS Spatial Coverage – (Not necessarily SDE)

Unshaded Table: Not reqd for state NVIS D/B or state should have own equivalent

#* Primary Key; * Foreign Key


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APPENDIX C: FAMILY PRIORITIES Below is a list of the families found in the NVIS database in order of records numbers. This list more or less equates with the family priorities identified by ERIN for assessment. Family Records Myrtaceae 8883 Poaceae 4252 Mimosaceae 2363 Chenopodiaceae 956 Cyperaceae 939 Proteaceae 899 Asteraceae 696 Epacridaceae 612 Fabaceae 538 Caesalpiniaceae 495 Casuarinaceae 462 Euphorbiaceae 457 Myoporaceae 446 Xanthorrhoeaceae 443 Sapindaceae 381 Rubiaceae 281 Liliaceae 266 Rutaceae 263 Cupressaceae 238 Rhamnaceae 202 Dennstaedtiaceae 194 Apocynaceae 166 Pittosporaceae 164 Restionaceae 161 Combretaceae 158 Apiaceae 150 Rosaceae 148 Haloragaceae 136 Dilleniaceae 121 Santalaceae 119 Adiantaceae 110 Sterculiaceae 110 Violaceae 109 Malvaceae 105 Convolvulaceae 103 Lauraceae 101 Blechnaceae 98 Capparaceae 98 Goodeniaceae 95 Aizoaceae 93 Polygonaceae 84 Arecaceae 78 Verbenaceae 72 Dryopteridaceae 70 Lamiaceae 66 Juncaceae 62 Solanaceae 62 Monimiaceae 57 Moraceae 57 Oleaceae 55 Pandanaceae 54 Amaranthaceae 53 Meliaceae 53 Caryophyllaceae 53 Lecythidaceae 51 Campanulaceae 50 Anacardiaceae 47 Zygophyllaceae 47 Thymelaeaceae 47 Brassicaceae 46 Scrophulariaceae 45 Burseraceae 43 Dicksoniaceae 43 Iridaceae 41 Geraniaceae 39 Ranunculaceae 38 Sapotaceae 38 Fagaceae 37 Rhizophoraceae 37

Araliaceae 35 Orchidaceae 35 Cunoniaceae 35 Araucariaceae 33 Clusiaceae 31 Ebenaceae 30 Tiliaceae 30 Menyanthaceae 30 Winteraceae 30 Acanthaceae 29 Bignoniaceae 27 Bixaceae 27 Eucryphiaceae 26 Asclepiadaceae 25 Stylidiaceae 25 Myrsinaceae 25 Oxalidaceae 24 Portulacaceae 24 Polypodiaceae 24 Elaeocarpaceae 23 Urticaceae 23 Smilacaceae 23 Juncaginaceae 23 Primulaceae 23 Cyatheaceae 22 Centrolepidaceae 21 Droseraceae 21 Gleicheniaceae 21 Chrysobalanaceae 20 Zamiaceae 20 Lindsaeaceae 20 Crassulaceae 19 Vitaceae 18 Taxodiaceae 17 Tremandraceae 17 Aspleniaceae 16 Bombacaceae 15 Celastraceae 15 Commelinaceae 14 Phyllocladaceae 14 Boraginaceae 13 Marsileaceae 12 Zingiberaceae 12 Erythroxylaceae 11 Selaginellaceae 11 Typhaceae 11 Nyctaginaceae 11 Melastomataceae 11 Hernandiaceae 11 Loganiaceae 11 Araceae 10 Potamogetonaceae 10 Grossulariaceae 10 Frankeniaceae 10 Annonaceae 9 Flagellariaceae 9 Ulmaceae 9 Sphagnaceae 9 Podocarpaceae 9 Brunoniaceae 8 Loranthaceae 8 Gyrostemonaceae 8 Culcitaceae 7 Xyridaceae 7 Plantaginaceae 7 Myristicaceae 7 Agavaceae 6 Plumbaginaceae 6 Caprifoliaceae 6 Lythraceae 6 Cycadaceae 5

Schizaeaceae 5 Surianaceae 5 Pteridaceae 5 Onagraceae 5 Eupomatiaceae 5 Hymenophyllaceae 5 Eriocaulaceae 5 Grammitidaceae 4 Stackhousiaceae 4 (no name) 4 Polygalaceae 4 Azollaceae 3 Taccaceae 3 Phytolaccaceae 3 Osmundaceae 3 Menispermaceae 3 Leeaceae 3 Flacourtiaceae 3 Dichapetalaceae 3 Cucurbitaceae 3 Alangiaceae 2 Gesneriaceae 2 Linaceae 2 Zannichelliaceae 2 Simaroubaceae 2 Ruppiaceae 2 Pontederiaceae 2 Pinaceae 2 Passifloraceae 2 Nymphaeaceae 2 Nelumbonaceae 2 Marattiaceae 2 Lentibulariaceae 2 Icacinaceae 2 Datiscaceae 2 Davalliaceae 2 Alismataceae 2 Aquifoliaceae 1 Aristolochiaceae 1 Zosteraceae 1 Vittariaceae 1 Tricholomataceae 1 Stemonaceae 1 Olacaceae 1 Nepenthaceae 1 Molluginaceae 1 Malpighiaceae 1 Lycopodiaceae 1 Lomariopsidaceae 1 Hydrocharitaceae 1 Haemodoraceae 1 Gunneraceae 1 Salviniaceae 1 Psilotaceae 1 Posidoniaceae 1 Piperaceae 1 Ericaceae 1 Donatiaceae 1 Dioscoreaceae 1 Coralinaceae 1 Burmanniaceae 1 Balanophoraceae 1 Bataceae 1


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APPENDIX D: NVIS SYNONYMIC (NON_CURRENT) NAMES FAMILY GENUS SPECIES RANK INFRA_SPECIES CPBR_WIN_NAME STATE SYNONYM TYPE Amaranthaceae Alternanthera dentata Alternanthera brasiliana Qld Taxonomic Amaranthaceae Alternanthera micrantha Alternanthera denticulata var. micrantha Qld Nomenclatural Apiaceae Apium prostratum var prostratum Apium prostratum subsp. prostratum NSW Nomenclatural Araliaceae Polyscias sambucifolia subsp B Polyscias sambucifolia NSW Taxonomic Araliaceae Polyscias sambucifolia ssp. C Polyscias sambucifolia Vic Taxonomic Asteraceae Bracteantha bracteata Xerochrysum bracteanthum Vic Nomenclatural Asteraceae Bracteantha viscosa Xerochrysum viscosum Vic Nomenclatural Asteraceae Leptorhynchos squamatus subsp A Leptorhynchos squamatus subsp. squamatus NSW Nomenclatural Avicenniaceae Avicennia marina var. resinifera Avicennia marina SA Taxonomic Chenopodiaceae Atriplex nummularia ssp. omissa Atriplex nummularia SA Taxonomic Chenopodiaceae Sclerochlamys brachyptera Sclerolaena brachyptera Vic Nomenclatural Epacridaceae Cyathodes juniperina Leptecophylla juniperina Vic Nomenclatural Epacridaceae Cyathodes parvifolia Leptecophylla juniperina subsp. parvifolia Tas Nomenclatural Gramineae Critesion murinum ssp. leporinum Hordeum leporinum SA Nomenclatural Gramineae Danthonia caespitosa Austrodanthonia caespitosa SA Nomenclatural Gramineae Danthonia setacea var. setacea Austrodanthonia setacea var. setacea SA Nomenclatural Gramineae Eragrostis dielsii var. dielsii Eragrostis dielsii SA Nomenclatural Leguminosae Acacia myrtifolia var. myrtifolia Acacia myrtifolia SA Nomenclatural Mimosaceae Acacia bivenosa subsp. wayi Acacia cupularis Qld Taxonomic Mimosaceae Acacia julifera subsp. curvinervia Acacia curvinervia Qld Nomenclatural Mimosaceae Acacia longifolia var. longifolia Acacia longifolia subsp. longifolia Qld Nomenclatural Mimosaceae Acacia longifolia var. sophorae Acacia longifolia subsp. sophorae Vic Nomenclatural Mimosaceae Acacia mucronata var. longifolia Acacia mucronata subsp. longifolia Vic Nomenclatural Mimosaceae Acacia sophorae Acacia longifolia subsp. sophorae Tas Nomenclatural Myrtaceae Austromyrtus bidwillii Gossia bidwillii Qld Nomenclatural Myrtaceae Austromyrtus floribunda Gossia floribunda Qld Nomenclatural Myrtaceae Austromyrtus minutiflora Gossia myrsinocarpa Qld Taxonomic

FAMILY GENUS SPECIES RANK INFRA_SPECIES CPBR_WIN_NAME STATE SYNONYM TYPE Myrtaceae Baeckea behrii Babingtonia behrii SA Nomenclatural Myrtaceae Baeckea densifolia Babingtonia densifolia NSW Nomenclatural Myrtaceae Baeckea virgata Babingtonia virgata NSW Nomenclatural Myrtaceae Eucalyptus 'anceps' Eucalyptus phenax SA Taxonomic Myrtaceae Eucalyptus arnhemensis Corymbia arnhemensis NT Nomenclatural Myrtaceae Eucalyptus confertiflora Corymbia confertiflora NT Nomenclatural Myrtaceae Eucalyptus eremaea subsp. eremaea Corymbia eremaea NT Nomenclatural Myrtaceae Eucalyptus eximia Corymbia eximia NSW Nomenclatural Myrtaceae Eucalyptus ferruginea Corymbia ferruginea NT Nomenclatural Myrtaceae Eucalyptus gummifera Corymbia gummifera NSW Nomenclatural Myrtaceae Eucalyptus maculata Corymbia maculata NSW Nomenclatural Myrtaceae Eucalyptus nesophila Corymbia nesophila NT Nomenclatural Myrtaceae Eucalyptus pachycarpa Corymbia pachycarpa NT Nomenclatural Myrtaceae Eucalyptus papuana Corymbia papuana NT Nomenclatural Myrtaceae Eucalyptus polycarpa var. polycarpa Corymbia polycarpa NT Nomenclatural Myrtaceae Eucalyptus polysciada Corymbia polysciada NT Nomenclatural Myrtaceae Eucalyptus setosa Corymbia setosa NT Nomenclatural Myrtaceae Eucalyptus terminalis Corymbia terminalis NT Nomenclatural Myrtaceae Eucalyptus trachyphloia Corymbia tracyphloia NSW Nomenclatural Poaceae Agrostis avenacea Lachnagrostis filiformis Vic Taxonomic Poaceae Agrostis billardierei Lachnagrostis billardierei Vic Nomenclatural Poaceae Chionochloa pallida Joycea pallida NSW Nomenclatural Poaceae Critesion secalinum Hordeum secalinum Vic Nomenclatural Poaceae Ehrharta distichophylla Tetrarrhena distichophylla Tas Nomenclatural Poaceae Ehrharta juncea Tetrarrhena juncea Tas Nomenclatural Poaceae Ehrharta tasmanica Microlaena tasmanica Tas Nomenclatural Poaceae Oplismenus imbecillis Oplismenus hirtellus subsp. imbecillis NSW Nomenclatural Poaceae Paspalidium basicladum Setaria basiclada Qld Nomenclatural Poaceae Paspalidium caespitosum Setaria brigalow Qld Taxonomic

FAMILY GENUS SPECIES RANK INFRA_SPECIES CPBR_WIN_NAME STATE SYNONYM TYPE Poaceae Paspalidium constrictum Setaria constricta Qld Nomenclatural Poaceae Paspalidium criniforme Setaria criniformis Qld Nomenclatural Poaceae Paspalidium distans Setaria distans Qld Nomenclatural Poaceae Paspalidium gracile Setaria spartella Qld Taxonomic Poaceae Paspalidium jubiflorum Setaria jubiflora Qld Nomenclatural Poaceae Paspalidium rarum Setaria rara NT Nomenclatural Poaceae Sporobolus virginicus var. minor Sporobolus virginicus NT Nomenclatural Poaceae Themeda australis Themeda triandra NSW Taxonomic Proteaceae Banksia integrifolia var. integrifolia Banksia integrifolia subsp. integrifolia Vic Nomenclatural Proteaceae Grevillea rogersii Grevillea lavandulacea subsp. rogersii SA Nomenclatural Proteaceae Hakea muelleriana Hakea mitchellii SA Taxonomic Proteaceae Hakea suberea Hakea lorea subsp. lorea NT Taxonomic


63

APPENDIX E: FAMILIES ASSESSED FOR AUTHOR

ACCURACY Acanthaceae Adiantaceae Aizoaceae Amaranthaceae Anthericaceae Apiaceae Apocynaceae Araliaceae Arecaceae Asclepiadaceae Asphodelaceae Aspleniaceae Asteraceae Avicenniaceae Bignoniaceae Blechnaceae Casuarinaceae Chenopodiaceae Cupressaceae Epacridaceae Euphorbiaceae Fabaceae sens. lat. Liliaceae Malvaceae Mimosaceae Myrsinaceae Myrtaceae Proteaceae Rhamnaceae Rosaceae Violaceae


64

APPENDIX F: NVIS TAXON_LISTS TAXONOMIC REVIEW The CD-ROM found on the inside backcover of this report contains an excel spreadsheet that is a complete listing of the 5,447 records used to generate NVIS Veg_Description table profiles. This spreadsheet contains the additional columns used by the CPBR to assess the quality of taxonomic and nomenclatural data. The CPBR comments include all issues identified and their suggested solutions. This was used to generate all the statistics found in this report. Overall this spreadsheet is a snapshot of the NVIS Taxon_Lists table at the completion of the taxonomic review in August 2003.


65

LIST OF ACRONYMS ABRS Australian Biological Resources Study

ACT Australian Capital Territory

ANHSIR Australian National Herbarium Specimen Information Register database

APNI Australian Plant Name Index database

AVH Australia’s Virtual Herbarium

CHAH Council of Heads of Australian Herbaria

CPBR Centre for Plant Biodiversity Research

DEH Department of the Environment & Heritage (Federal)

ERIN Earth Resources Information Network

ESCAVI Executive Steering Committee for Australian Vegetation Information

HISCOM Herbarium Information Systems Committee

HISPID Herbarium Information Standards and Protocols for Interchange of Data

ICBN International Code of Botanical Nomenclature

IPNI International Plant Name Index database

NLWRA National Land and Water Resources Audit

NSW New South Wales

NT Northern Territory

NVIS National Vegetation Information System

QLD Queensland

RDBMS Relational Database Management System

SA South Australia

SPRAT Species Profile and Threats database

TAS Tasmania

VIC Victoria

WA Western Australia

WIN What’s Its Name database

XML Extensible Markup Language


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national vegetation information system taxonomic …taxonomic issue was provided with problem...

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