This is a presentation made by Richard to CSIRO on May 19 2011. It is very large.

1. Managing native vegetation - insights from longitudinal site management histories Richard Thackway ACEAS Sabbatical Fellow CSIRO ES Seminar Series 19 May 2011 Canberra

3. Definitions and concepts

4. The method – vegetation transformations

5. A case study

6. How might this information be used?

8. Australia’s future landscapes – The big issues and questions Biodiversity conservation, biodiverse carbon, biosequestration, food security - agriculture moving to northern Australia etc What 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 ecosystems Reference Settlement Change in vegetation variable 1000 0 Time Source: Adamson and Fox (1982).

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

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

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

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/extent VC = Benchmarked vegetation condition

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

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

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

54. Data synthesis and hierarchy Site 22 Indicators

55. Data synthesis and hierarchy Site 10 Attribute groups 22 Indicators

56. Data synthesis and hierarchy Site Diagnostic attributes 3 10 Attribute groups 22 Indicators

57. Data synthesis and hierarchy Site Transformation score/site /year 1 Diagnostic attributes 3 10 Attribute groups 22 Indicators

58. Scoring sites for each year 1 3 10 22 Diagnostic attributes Species Composition Attribute groups Understorey Overstorey (2) (2) Indicators

59. Scoring sites for each year 1 3 10 22 Diagnostic attributes Vegetation Structure Species Composition Attribute groups Overstorey Understorey Overstorey Understorey (3) (2) (2) (3) Indicators

60. Scoring sites for each year 1 3 10 22 Diagnostic attributes Vegetation Structure Species Composition Regenerative Capacity Attribute groups Reprodpotent Fire Soil Overstorey Understorey Overstorey Understorey (3) (2) (2) (3) (2) (2) Biology Structure Chemistry Hydrology Indicators (2) (2) (2) (2)

61. Scoring sites for each year 1 3 10 22 Diagnostic attributes Vegetation Structure Species Composition Regenerative Capacity Attribute groups Reprodpotent Fire Soil Overstorey Understorey Overstorey Understorey (3) (2) (2) (3) (2) (2) Biology Structure Chemistry Hydrology Indicators (2) (2) (2) (2)

62. Scoring sites for each year 1 Vegetation Transformation score 3 10 22 Diagnostic attributes Vegetation Structure Species Composition Regenerative Capacity Attribute groups Reprodpotent Fire Soil Overstorey Understorey Overstorey Understorey (3) (2) (2) (3) (2) (2) Biology Structure Chemistry Hydrology Indicators (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 elements Where When What 30

66. Sequencing historic & contemporary LU & LMP and responses of native vegetation Final synthesised sequence DNA matching Multiple sources Source ID: 1a Source ID: 1b 2050 2000 1950 Year 1900 1850 1800 1750

67. Workflow for deriving impacts of LU and LMP vegetation Step 1: Compile primary data on LU and LMP histories for case study sites Step 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 year Step 2C: Score impacts of LU & LMP on attributes of vegetation composition Step 2B: Score impacts of LU & LMP on attributes of vegetation structure Step 2A: Score impacts of LU & LMP on attributes of regenerative capacity Step 3: Calculate total scores of impacts of LU/LMP on themes for each site for each year Step 4 – Graph total scores to illustrate transformation Step 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 structure 36

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 attributes Benchmark 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 anomaly 11 year trend line Drought years Years Source: BOM

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

90. Environmental history

91. Ecological history

93. land use and LMP sources

94. Geographical and historical sources

95. otherOlder & more qualitative More recent & more quantitative 49

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

98. Options

99. Tradeoffs Hypothetical

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

101. 1 Provides a basis for a conversation with a land manager Vegetation Transformation score 3 10 22 Diagnostic attributes Attribute groups

102. Provides a basis for a conversation with a land manager 1 Vegetation Transformation score 3 10 22 Diagnostic attributes Attribute groups

103. Provides a basis for a conversation with a land manager 1 Vegetation Transformation score 3 10 22 Diagnostic attributes Regenerative Capacity Attribute groups

104. Provides a basis for a conversation with a land manager 1 Vegetation Transformation score 3 10 22 Diagnostic attributes Regenerative Capacity Attribute groups Reprodpotent Fire Soil (2) (2) Biology Structure Chemistry Hydrology Indicators (2) (2) (2) (2)

105. Provides a basis for a conversation with a land manager 1 Vegetation Transformation score 3 10 22 Diagnostic attributes Vegetation Structure Regenerative Capacity Attribute groups Reprodpotent Fire Soil Overstorey Understorey (3) (3) (2) (2) Biology Structure Chemistry Hydrology Indicators (2) (2) (2) (2)

106. Provides a basis for a conversation with a land manager 1 Vegetation Transformation score 3 10 22 Diagnostic attributes Vegetation Structure Species Composition Regenerative Capacity Attribute groups Reprodpotent Fire Soil Overstorey Understorey Overstorey Understorey (3) (2) (2) (3) (2) (2) Biology Structure Chemistry Hydrology Indicators (2) (2) (2) (2)

108. slope & relief derived from 30m DEM

109. aspect classes derived from 30m DEM

110. weathering layer

111. digital atlas of soils+

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 SRTM Nass Valley - ACT

132. Transformed native vegetation informing future land use options Before 2010 1788 1800 1850 1900 1950 2000 Current 2010 Future scenarios – the big issues 2050 Scen 1 2050 Scen 2 2050 Scen 3 2050 Scen 4 61

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

137. Preliminary site-based results are promising

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