1) The document outlines a method for documenting longitudinal site histories of land use and land management practices and scoring the responses of native vegetation communities over time. 2) A case study applying this method examines the transformation of open grassy woodlands in New South Wales through compiling historical land use and disturbance records and scoring changes in vegetation structure, composition, and regenerative capacity. 3) The method aims to provide insights on landscape transformation over decades and centuries that can inform future land use and conservation decisions by making visible the impacts of past and present land management practices.

1. Managing native vegetation - insights from longitudinal site management historiesRichard ThackwayACEAS Sabbatical FellowCSIRO ES Seminar Series 19 May 2011Canberra

2. OutlineTERN and ACEAS

3. Definitions and concepts

4. The method – vegetation transformations

5. A case study

6. How might this information be used?

7. Where to from hereTERN facilities interaction

8. Australia’s future landscapes – The big issues and questionsBiodiversity conservation, biodiverse carbon, biosequestration, food security - agriculture moving to northern Australia etcWhat has happened in this landscape over time e.g. <200yrs?How might historic/ contemporary impacts of land use (LU) and land management practices (LMP) affect future land use options/ decisions?

9. A model of change in ecosystemsReferenceSettlementChange in vegetation variable 10000Time Source: Adamson and Fox (1982).

10. Anthropogenic change Net impactRelaxation OccupationModification score 1850 1900 1950 2000 1800 Time Transformation pathway ReferenceBased on Hamilton, Brown & Nolan (2008). FWPA PRO7.1050. pg 18Land use impacts on biodiversity and Life Cycle Assessment

11. Drivers for this information?Public & private NRM agencies

12. reporting on the status of resource/s

13. developing policy & design programs

14. informing priorities for investment in NRM

15. monitoring and reporting and improvement following investment

16. Developing scenarios and planning

17. Researchers

18. Education

19. Wider community Land use and management – the primary agents of landscape transformation Management of native veg leads to modification, fragmentation, removal and replacement or enhancement

20. For example 2010 Australia’s landscapes:

21. 9 used for cropping

22. 58 used for grazing sheep and cattle

23. 0.2 plantations

24. 12.8 in conservation reserves

25. Numerous studies have identified pressure metrics or indicators of the impacts of LU and LMP

26. Result in changes in vegetation structure, composition & functionSolutions to date – snap shotsSite-based assessments

27. Scoring survey sites relative to benchmark sites e.g. BioCondition, Habitat Hectares etc

28. State and transition models

29. Whole of landscape assessments

30. Classifying mapping units relative to reference unmodified /least modified statee.g. VAST (Vegetation Assets States and Transitions) and Vegmachine9

31. The problem∆ VC score ∆ VC score×Vegetation transformation∆ time∆ space/extentVC = Benchmarked vegetation condition

32. Why a project of transforming of Australia’s vegetated landscapes?At the national level No approach for compiling sequential land use and management histories

33. No consistent approach for assessing the response of vegetation communities to impacts/pressures over time and space

34. Regenerative capacity

35. Vegetation structure

36. Species composition

37. No infrastructure to compile a repository of where, when Australia’s vegetated landscapes were and are being transformed Project aimsBuild on the ‘transitions’ component of the VAST framework

38. Develop and test a method for describing the transforming of Australia’s native vegetation by:

39. Documenting longitudinal site histories of LU) and LMP

40. Developing a system for scoring the responses of native vegetation communities to sequential changes in land use LU and LMP

41. Presenting interim results as transformation graphs

42. Contribute to developing guidelines for assessing and monitoring the transformation of vegetated landscapes

46. Literature review and case studiesReview identified 22 indicators (pressure metrics, anthropogenic disturbances)

47. Literature as a resource for case studies

48. More anecdotal stories than reliable observations /measurements

49. More two date than multi-temporal changes

50. More observations of coarse scale than fine scale changes

51. More binary/ single comparison of attributes than changes in multi-attribute states (e.g. regen capacity, structure and species)

52. More remote sensing than ecological plot-based observations

53. More contemporary local than long term landscape changeData synthesis and hierarchySite

54. Data synthesis and hierarchySite22Indicators

55. Data synthesis and hierarchySite10Attribute groups22Indicators

56. Data synthesis and hierarchySiteDiagnostic attributes310Attribute groups22Indicators

57. Data synthesis and hierarchySiteTransformation score/site /year1Diagnostic attributes310Attribute groups22Indicators

58. Scoring sites for each year131022DiagnosticattributesSpeciesCompositionAttributegroupsUnderstoreyOverstorey(2)(2)Indicators

59. Scoring sites for each year131022DiagnosticattributesVegetationStructureSpeciesCompositionAttributegroupsOverstoreyUnderstoreyOverstoreyUnderstorey(3)(2)(2)(3)Indicators

60. Scoring sites for each year131022DiagnosticattributesVegetationStructureSpeciesCompositionRegenerativeCapacityAttributegroupsReprodpotentFireSoilOverstoreyUnderstoreyOverstoreyUnderstorey(3)(2)(2)(3)(2)(2)BiologyStructureChemistryHydrologyIndicators(2)(2)(2)(2)

61. Scoring sites for each year131022DiagnosticattributesVegetationStructureSpeciesCompositionRegenerativeCapacityAttributegroupsReprodpotentFireSoilOverstoreyUnderstoreyOverstoreyUnderstorey(3)(2)(2)(3)(2)(2)BiologyStructureChemistryHydrologyIndicators(2)(2)(2)(2)

62. Scoring sites for each year1VegetationTransformationscore31022DiagnosticattributesVegetationStructureSpeciesCompositionRegenerativeCapacityAttributegroupsReprodpotentFireSoilOverstoreyUnderstoreyOverstoreyUnderstorey(3)(2)(2)(3)(2)(2)BiologyStructureChemistryHydrologyIndicators(2)(2)(2)(2)

63. Case studies: NSW Open Grassy Woodland

64. How are longitudinal site histories compiled and transformation data derived for each site?

65. Compiling and translating historical observations requires three core elementsWhereWhenWhat30

66. Sequencing historic & contemporary LU & LMP and responses of native vegetationFinal synthesised sequenceDNA matching Multiple sourcesSource ID: 1aSource ID: 1b205020001950Year1900185018001750

67. Workflow for deriving impacts of LU and LMP vegetation Step 1: Compile primary data on LU and LMP histories for case study sitesStep 1C: Standardise site-based observation using national guidelines for LU & LMP. Fill gaps from regional records Step 1B: Compile and check data on impacts of LU & LMP on native veg. Step 1A: Compile and translate and check. Include major natural events e.g. droughts, floods, fires, cyclones Step 2: Score impacts relative to a reference condition for each site and yearStep 2C: Score impacts of LU & LMP on attributes of vegetation compositionStep 2B: Score impacts of LU & LMP on attributes of vegetation structureStep 2A: Score impacts of LU & LMP on attributes of regenerative capacityStep 3: Calculate total scores of impacts of LU/LMP on themes for each site for each yearStep 4 – Graph total scores to illustrate transformationStep 5– Model spatial and temporal extents of condition at a landscape level, using GIS, remote sensing , ecological models Step 6 – Validate the results of the spatial and temporal models using independent datasets and peer review

68. Step 1A & 1B: Compile and check disturbance histories

69. Step 1C: Standardise LU & LMPs using national guidelines 34

70. Step 2A: Derive scores for regen capacity

71. Step 2B: Derive scores for vegetation structure36

72. Step 2C: Derive scores for species composition

73. Step 3: Calculate total scores of impacts of LU/LMP for each site & year (benchmarked)38

74. Step 4: – Graph total scores to illustrate vegetation transformation

75. Step 4: – Graph scores for diagnostic attributesBenchmark scores

76. How are this site-based scores validated?

77. Certainty level standards for the LUMIS historical records

82. Belconnen Naval Transmitter Station rainfall anomaly 1900-2010 ‘Good years’Rainfall anomaly11 year trend line Drought yearsYearsSource: BOM

83. Assumptions of this approachChanges in LU & LMP

84. result in predictable changes in structure, floristics & regen capacity

85. can be consistently and reliably differentiated from natural events

86. are adequately and reliably documented over time

87. can be reliably used to score changes in vegetation transformation

88. Sequential changes in veg transformation over time can be represented at sites and landscapes48

89. Types of data and informationMainly - text-based e.g. Land use and land management history

90. Environmental history

91. Ecological history

92. Other Mainly spatial - maps and models incl. remotely images/GISecological sources

93. land use and LMP sources

94. Geographical and historical sources

95. otherOlder & more qualitative More recent & more quantitative49

96. How might this information be used to address the big issues?Hypothetical

97. How might this information be used to address the big issues? Opportunities

98. Options

99. Tradeoffs Hypothetical

100. Provides a basis for a conversation with a land manager???Benchmark scores

101. 1Provides a basis for a conversation with a land managerVegetationTransformationscore31022DiagnosticattributesAttributegroups

102. Provides a basis for a conversation with a land manager1VegetationTransformationscore31022DiagnosticattributesAttributegroups

103. Provides a basis for a conversation with a land manager1VegetationTransformationscore31022DiagnosticattributesRegenerativeCapacityAttributegroups

104. Provides a basis for a conversation with a land manager1VegetationTransformationscore31022DiagnosticattributesRegenerativeCapacityAttributegroupsReprodpotentFireSoil(2)(2)BiologyStructureChemistryHydrologyIndicators(2)(2)(2)(2)

105. Provides a basis for a conversation with a land manager1VegetationTransformationscore31022DiagnosticattributesVegetationStructureRegenerativeCapacityAttributegroupsReprodpotentFireSoilOverstoreyUnderstorey(3)(3)(2)(2)BiologyStructureChemistryHydrologyIndicators(2)(2)(2)(2)

106. Provides a basis for a conversation with a land manager1VegetationTransformationscore31022DiagnosticattributesVegetationStructureSpeciesCompositionRegenerativeCapacityAttributegroupsReprodpotentFireSoilOverstoreyUnderstoreyOverstoreyUnderstorey(3)(2)(2)(3)(2)(2)BiologyStructureChemistryHydrologyIndicators(2)(2)(2)(2)

107. Step 5 – Scaling up to landscape levels Static layersfirst contact by European explorers

108. slope & relief derived from 30m DEM

109. aspect classes derived from 30m DEM

110. weathering layer

111. digital atlas of soils+

112. pre-European vegetation types (NVIS)Time series response variablesrainfall anomaly (post 1900)

113. state-wide & national land tenure

114. FPC (post 1980s)*

115. ground cover (post 1980s)*

116. NDVI / EVI (post 1980s)*

117. native veg (tree) layers*

118. state-wide & national land use

119. sheep DSE

120. cattle DSE

121. cropping

122. urban areas

123. Plantations

124. nature conservation reserves

125. indigenous protected areas

126. Infrastructure

127. railways

128. roads

129. fire regime (fire area & No. fire starts)*

130. otherTERN AusCover*TERN Soils+

131. Landform Pattern and Topographic Position Index. 30 m – DEM SRTMNass Valley - ACT

132. Transformed native vegetation informing future land use options Before 2010178818001850190019502000Current2010Future scenarios – the big issues 2050 Scen 12050 Scen 22050 Scen 32050 Scen 461

133. Vision for the futureRecognition of the benefits of compiling site-based contemporary LU & LMP

134. Greater awareness of consequences LU & LMP and the responses of native vegetation i.e. +ve and –ve

135. Discoverable and accessible data and info via a national repository

136. when and where landscapes were and are being transformed ConclusionsProcess is tedious

137. Preliminary site-based results are promising

138. Independent datasets & peer review needed to validate resultsAcknowledgementsTERN ACEAS for funding my sabbatical fellowship at UQ in Brisbane

139. CSIRO Ecosystems Sciences, Canberra for hosting me in Canberra

140. ABARE-BRS, Greening Australia, Forestry NSW, CSIRO ES, John Ive and others for providing datasets Thank you