Managing native vegetation - insights from longitudinal site management histories<br />Richard Thackway<br />ACEAS Sabbati...
Outline<br /><ul><li>TERN and ACEAS
Definitions and concepts
The method – vegetation transformations
A case study
How might this information be used?
Where to from here</li></li></ul><li>TERN facilities interaction<br />
Australia’s future landscapes – The big issues and questions<br />Biodiversity conservation, biodiverse carbon, biosequest...
A model of change in ecosystems<br />Reference<br />Settlement<br />Change in vegetation variable <br />1000<br />0<br />T...
Anthropogenic change   <br />Net impact<br />Relaxation <br />Occupation<br />Modification score <br />1850 <br />1900 <br...
Drivers for this information?<br /><ul><li>Public & private NRM agencies
reporting on the status of resource/s
developing policy & design programs
informing priorities for investment in NRM
monitoring and reporting and improvement following investment
Developing scenarios and planning
Researchers
Education
Wider community </li></li></ul><li>Land use and management – the primary agents of landscape transformation <br /><ul><li>...
For example 2010 Australia’s landscapes:
9 used for cropping
58 used for grazing sheep and cattle
0.2 plantations
12.8  in conservation reserves
Numerous studies have identified pressure metrics or indicators of the impacts of LU and LMP
Result in changes in vegetation structure, composition & function</li></li></ul><li>Solutions to date – snap shots<br /><u...
Scoring survey sites relative to benchmark sites e.g. BioCondition, Habitat Hectares etc
State and transition models
Whole of landscape assessments
Classifying mapping units relative to reference unmodified /least modified statee.g. VAST (Vegetation Assets States and Tr...
The problem<br />∆ VC score <br />∆ VC score<br />×<br />Vegetation transformation<br />∆ time<br />∆ space/extent<br />VC...
Why a project of transforming of Australia’s vegetated landscapes?<br />At the national level <br /><ul><li>No approach fo...
No consistent approach for assessing the response of vegetation communities to impacts/pressures over time and space
Regenerative capacity
Vegetation structure
Species composition
No infrastructure to compile a repository of where, when Australia’s vegetated landscapes were and are being transformed  ...
Develop and test a method for describing the transforming of Australia’s native vegetation by:
Documenting longitudinal site histories of LU) and LMP
Developing a system for scoring the responses of native vegetation communities to sequential changes in land use LU and LMP
Presenting interim results as transformation graphs
Contribute to developing guidelines for assessing and monitoring the transformation of vegetated landscapes</li></li></ul>...
Literature review and case studies<br /><ul><li>Review identified 22 indicators (pressure metrics, anthropogenic disturban...
Literature as a resource for case studies
More anecdotal stories than reliable observations /measurements
More two date than multi-temporal changes
More observations of coarse scale than fine scale changes
More binary/ single comparison of attributes than changes in multi-attribute states (e.g. regen capacity, structure and sp...
More remote sensing than ecological plot-based observations
More contemporary local than long term landscape change</li></li></ul><li>Data synthesis and hierarchy<br />Site<br />
Data synthesis and hierarchy<br />Site<br />22<br />Indicators<br />
Data synthesis and hierarchy<br />Site<br />10<br />Attribute groups<br />22<br />Indicators<br />
Data synthesis and hierarchy<br />Site<br />Diagnostic attributes<br />3<br />10<br />Attribute groups<br />22<br />Indica...
Data synthesis and hierarchy<br />Site<br />Transformation score/site /year<br />1<br />Diagnostic attributes<br />3<br />...
Scoring sites for each year<br />1<br />3<br />10<br />22<br />Diagnostic<br />attributes<br />Species<br />Composition<br...
Scoring sites for each year<br />1<br />3<br />10<br />22<br />Diagnostic<br />attributes<br />Vegetation<br />Structure<b...
Scoring sites for each year<br />1<br />3<br />10<br />22<br />Diagnostic<br />attributes<br />Vegetation<br />Structure<b...
Scoring sites for each year<br />1<br />3<br />10<br />22<br />Diagnostic<br />attributes<br />Vegetation<br />Structure<b...
Scoring sites for each year<br />1<br />Vegetation<br />Transformation<br />score<br />3<br />10<br />22<br />Diagnostic<b...
Case studies: <br />NSW Open Grassy Woodland<br />
How are longitudinal site histories compiled and transformation data derived for each site?<br />
Compiling and translating historical observations requires three core elements<br />Where<br />When<br />What<br />30<br />
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Thackway_MAY_presentation

  1. 1. Managing native vegetation - insights from longitudinal site management histories<br />Richard Thackway<br />ACEAS Sabbatical Fellow<br />CSIRO ES Seminar Series 19 May 2011<br />Canberra<br />
  2. 2. Outline<br /><ul><li>TERN and ACEAS
  3. 3. Definitions and concepts
  4. 4. The method – vegetation transformations
  5. 5. A case study
  6. 6. How might this information be used?
  7. 7. Where to from here</li></li></ul><li>TERN facilities interaction<br />
  8. 8. Australia’s future landscapes – The big issues and questions<br />Biodiversity conservation, biodiverse carbon, biosequestration, food security - agriculture moving to northern Australia etc<br />What has happened in this landscape over time e.g. <200yrs?<br />How might historic/ contemporary impacts of land use (LU) and land management practices (LMP) affect future land use options/ decisions?<br />
  9. 9. A model of change in ecosystems<br />Reference<br />Settlement<br />Change in vegetation variable <br />1000<br />0<br />Time <br />Source: Adamson and Fox (1982). <br />
  10. 10. Anthropogenic change <br />Net impact<br />Relaxation <br />Occupation<br />Modification score <br />1850 <br />1900 <br />1950 <br />2000 <br />1800 <br />Time <br />Transformation pathway <br />Reference<br />Based on Hamilton, Brown & Nolan (2008). FWPA PRO7.1050. pg 18Land use impacts on biodiversity and Life Cycle Assessment<br />
  11. 11. Drivers for this information?<br /><ul><li>Public & private NRM agencies
  12. 12. reporting on the status of resource/s
  13. 13. developing policy & design programs
  14. 14. informing priorities for investment in NRM
  15. 15. monitoring and reporting and improvement following investment
  16. 16. Developing scenarios and planning
  17. 17. Researchers
  18. 18. Education
  19. 19. Wider community </li></li></ul><li>Land use and management – the primary agents of landscape transformation <br /><ul><li>Management of native veg leads to modification, fragmentation, removal and replacement or enhancement
  20. 20. For example 2010 Australia’s landscapes:
  21. 21. 9 used for cropping
  22. 22. 58 used for grazing sheep and cattle
  23. 23. 0.2 plantations
  24. 24. 12.8 in conservation reserves
  25. 25. Numerous studies have identified pressure metrics or indicators of the impacts of LU and LMP
  26. 26. Result in changes in vegetation structure, composition & function</li></li></ul><li>Solutions to date – snap shots<br /><ul><li>Site-based assessments
  27. 27. Scoring survey sites relative to benchmark sites e.g. BioCondition, Habitat Hectares etc
  28. 28. State and transition models
  29. 29. Whole of landscape assessments
  30. 30. Classifying mapping units relative to reference unmodified /least modified statee.g. VAST (Vegetation Assets States and Transitions) and Vegmachine</li></ul>9<br />
  31. 31. The problem<br />∆ VC score <br />∆ VC score<br />×<br />Vegetation transformation<br />∆ time<br />∆ space/extent<br />VC = Benchmarked vegetation condition <br />
  32. 32. Why a project of transforming of Australia’s vegetated landscapes?<br />At the national level <br /><ul><li>No approach for compiling sequential land use and management histories
  33. 33. No consistent approach for assessing the response of vegetation communities to impacts/pressures over time and space
  34. 34. Regenerative capacity
  35. 35. Vegetation structure
  36. 36. Species composition
  37. 37. No infrastructure to compile a repository of where, when Australia’s vegetated landscapes were and are being transformed </li></li></ul><li>Project aims<br /><ul><li>Build on the ‘transitions’ component of the VAST framework
  38. 38. Develop and test a method for describing the transforming of Australia’s native vegetation by:
  39. 39. Documenting longitudinal site histories of LU) and LMP
  40. 40. Developing a system for scoring the responses of native vegetation communities to sequential changes in land use LU and LMP
  41. 41. Presenting interim results as transformation graphs
  42. 42. Contribute to developing guidelines for assessing and monitoring the transformation of vegetated landscapes</li></li></ul><li>
  43. 43.
  44. 44.
  45. 45.
  46. 46. Literature review and case studies<br /><ul><li>Review identified 22 indicators (pressure metrics, anthropogenic disturbances)
  47. 47. Literature as a resource for case studies
  48. 48. More anecdotal stories than reliable observations /measurements
  49. 49. More two date than multi-temporal changes
  50. 50. More observations of coarse scale than fine scale changes
  51. 51. More binary/ single comparison of attributes than changes in multi-attribute states (e.g. regen capacity, structure and species)
  52. 52. More remote sensing than ecological plot-based observations
  53. 53. More contemporary local than long term landscape change</li></li></ul><li>Data synthesis and hierarchy<br />Site<br />
  54. 54. Data synthesis and hierarchy<br />Site<br />22<br />Indicators<br />
  55. 55. Data synthesis and hierarchy<br />Site<br />10<br />Attribute groups<br />22<br />Indicators<br />
  56. 56. Data synthesis and hierarchy<br />Site<br />Diagnostic attributes<br />3<br />10<br />Attribute groups<br />22<br />Indicators<br />
  57. 57. Data synthesis and hierarchy<br />Site<br />Transformation score/site /year<br />1<br />Diagnostic attributes<br />3<br />10<br />Attribute groups<br />22<br />Indicators<br />
  58. 58. Scoring sites for each year<br />1<br />3<br />10<br />22<br />Diagnostic<br />attributes<br />Species<br />Composition<br />Attribute<br />groups<br />Understorey<br />Overstorey<br />(2)<br />(2)<br />Indicators<br />
  59. 59. Scoring sites for each year<br />1<br />3<br />10<br />22<br />Diagnostic<br />attributes<br />Vegetation<br />Structure<br />Species<br />Composition<br />Attribute<br />groups<br />Overstorey<br />Understorey<br />Overstorey<br />Understorey<br />(3)<br />(2)<br />(2)<br />(3)<br />Indicators<br />
  60. 60. Scoring sites for each year<br />1<br />3<br />10<br />22<br />Diagnostic<br />attributes<br />Vegetation<br />Structure<br />Species<br />Composition<br />Regenerative<br />Capacity<br />Attribute<br />groups<br />Reprodpotent<br />Fire<br />Soil<br />Overstorey<br />Understorey<br />Overstorey<br />Understorey<br />(3)<br />(2)<br />(2)<br />(3)<br />(2)<br />(2)<br />Biology<br />Structure<br />Chemistry<br />Hydrology<br />Indicators<br />(2)<br />(2)<br />(2)<br />(2)<br />
  61. 61. Scoring sites for each year<br />1<br />3<br />10<br />22<br />Diagnostic<br />attributes<br />Vegetation<br />Structure<br />Species<br />Composition<br />Regenerative<br />Capacity<br />Attribute<br />groups<br />Reprodpotent<br />Fire<br />Soil<br />Overstorey<br />Understorey<br />Overstorey<br />Understorey<br />(3)<br />(2)<br />(2)<br />(3)<br />(2)<br />(2)<br />Biology<br />Structure<br />Chemistry<br />Hydrology<br />Indicators<br />(2)<br />(2)<br />(2)<br />(2)<br />
  62. 62. Scoring sites for each year<br />1<br />Vegetation<br />Transformation<br />score<br />3<br />10<br />22<br />Diagnostic<br />attributes<br />Vegetation<br />Structure<br />Species<br />Composition<br />Regenerative<br />Capacity<br />Attribute<br />groups<br />Reprodpotent<br />Fire<br />Soil<br />Overstorey<br />Understorey<br />Overstorey<br />Understorey<br />(3)<br />(2)<br />(2)<br />(3)<br />(2)<br />(2)<br />Biology<br />Structure<br />Chemistry<br />Hydrology<br />Indicators<br />(2)<br />(2)<br />(2)<br />(2)<br />
  63. 63. Case studies: <br />NSW Open Grassy Woodland<br />
  64. 64. How are longitudinal site histories compiled and transformation data derived for each site?<br />
  65. 65. Compiling and translating historical observations requires three core elements<br />Where<br />When<br />What<br />30<br />
  66. 66. Sequencing historic & contemporary LU & LMP and responses of native vegetation<br />Final synthesised sequence<br />DNA matching <br />Multiple sources<br />Source ID: 1a<br />Source ID: 1b<br />2050<br />2000<br />1950<br />Year<br />1900<br />1850<br />1800<br />1750<br />
  67. 67. Workflow for deriving impacts of LU and LMP vegetation <br />Step 1: Compile primary data on LU and LMP histories for case study sites<br />Step 1C: Standardise site-based observation using national guidelines for LU & LMP. Fill gaps from regional records <br />Step 1B: Compile and check data on impacts of LU & LMP on native veg. <br />Step 1A: Compile and translate and check. Include major natural events e.g. droughts, floods, fires, cyclones <br />Step 2: Score impacts relative to a reference condition for each site and year<br />Step 2C: Score impacts of LU & LMP on attributes of vegetation composition<br />Step 2B: Score impacts of LU & LMP on attributes of vegetation structure<br />Step 2A: Score impacts of LU & LMP on attributes of regenerative capacity<br />Step 3: Calculate total scores of impacts of LU/LMP on themes for each site for each year<br />Step 4 – Graph total scores to illustrate transformation<br />Step 5– Model spatial and temporal extents of condition at a landscape level, using GIS, remote sensing , ecological models <br />Step 6 – Validate the results of the spatial and temporal models using independent datasets and peer review <br />
  68. 68. Step 1A & 1B: Compile and check disturbance histories<br />
  69. 69. Step 1C: Standardise LU & LMPs using national guidelines <br />34<br />
  70. 70. Step 2A: Derive scores for regen capacity<br />
  71. 71. Step 2B: Derive scores for vegetation structure<br />36<br />
  72. 72. Step 2C: Derive scores for species composition<br />
  73. 73. Step 3: Calculate total scores of impacts of LU/LMP for each site & year (benchmarked)<br />38<br />
  74. 74. Step 4: – Graph total scores to illustrate vegetation transformation<br />
  75. 75. Step 4: – Graph scores for diagnostic attributes<br />Benchmark scores <br />
  76. 76. How are this site-based scores validated?<br />
  77. 77. Certainty level standards for the LUMIS historical records <br />
  78. 78.
  79. 79.
  80. 80.
  81. 81.
  82. 82. Belconnen Naval Transmitter Station rainfall anomaly 1900-2010 <br />‘Good years’<br />Rainfall anomaly<br />11 year trend line <br />Drought years<br />Years<br />Source: BOM <br />
  83. 83. Assumptions of this approach<br /><ul><li>Changes in LU & LMP
  84. 84. result in predictable changes in structure, floristics & regen capacity
  85. 85. can be consistently and reliably differentiated from natural events
  86. 86. are adequately and reliably documented over time
  87. 87. can be reliably used to score changes in vegetation transformation
  88. 88. Sequential changes in veg transformation over time can be represented at sites and landscapes</li></ul>48<br />
  89. 89. Types of data and information<br />Mainly - text-based e.g. <br /><ul><li>Land use and land management history
  90. 90. Environmental history
  91. 91. Ecological history
  92. 92. Other </li></ul>Mainly spatial - maps and models incl. remotely images/GIS<br /><ul><li>ecological sources
  93. 93. land use and LMP sources
  94. 94. Geographical and historical sources
  95. 95. other</li></ul>Older & more qualitative <br />More recent & more quantitative<br />49<br />
  96. 96. How might this information be used to address the big issues?<br />Hypothetical<br />
  97. 97. How might this information be used to address the big issues?<br /><ul><li> Opportunities
  98. 98. Options
  99. 99. Tradeoffs </li></ul>Hypothetical<br />
  100. 100. Provides a basis for a conversation with a land manager<br />?<br />?<br />?<br />Benchmark scores<br />
  101. 101. 1<br />Provides a basis for a conversation with a land manager<br />Vegetation<br />Transformation<br />score<br />3<br />10<br />22<br />Diagnostic<br />attributes<br />Attribute<br />groups<br />
  102. 102. Provides a basis for a conversation with a land manager<br />1<br />Vegetation<br />Transformation<br />score<br />3<br />10<br />22<br />Diagnostic<br />attributes<br />Attribute<br />groups<br />
  103. 103. Provides a basis for a conversation with a land manager<br />1<br />Vegetation<br />Transformation<br />score<br />3<br />10<br />22<br />Diagnostic<br />attributes<br />Regenerative<br />Capacity<br />Attribute<br />groups<br />
  104. 104. Provides a basis for a conversation with a land manager<br />1<br />Vegetation<br />Transformation<br />score<br />3<br />10<br />22<br />Diagnostic<br />attributes<br />Regenerative<br />Capacity<br />Attribute<br />groups<br />Reprodpotent<br />Fire<br />Soil<br />(2)<br />(2)<br />Biology<br />Structure<br />Chemistry<br />Hydrology<br />Indicators<br />(2)<br />(2)<br />(2)<br />(2)<br />
  105. 105. Provides a basis for a conversation with a land manager<br />1<br />Vegetation<br />Transformation<br />score<br />3<br />10<br />22<br />Diagnostic<br />attributes<br />Vegetation<br />Structure<br />Regenerative<br />Capacity<br />Attribute<br />groups<br />Reprodpotent<br />Fire<br />Soil<br />Overstorey<br />Understorey<br />(3)<br />(3)<br />(2)<br />(2)<br />Biology<br />Structure<br />Chemistry<br />Hydrology<br />Indicators<br />(2)<br />(2)<br />(2)<br />(2)<br />
  106. 106. Provides a basis for a conversation with a land manager<br />1<br />Vegetation<br />Transformation<br />score<br />3<br />10<br />22<br />Diagnostic<br />attributes<br />Vegetation<br />Structure<br />Species<br />Composition<br />Regenerative<br />Capacity<br />Attribute<br />groups<br />Reprodpotent<br />Fire<br />Soil<br />Overstorey<br />Understorey<br />Overstorey<br />Understorey<br />(3)<br />(2)<br />(2)<br />(3)<br />(2)<br />(2)<br />Biology<br />Structure<br />Chemistry<br />Hydrology<br />Indicators<br />(2)<br />(2)<br />(2)<br />(2)<br />
  107. 107. Step 5 – Scaling up to landscape levels <br />Static layers<br /><ul><li>first contact by European explorers
  108. 108. slope & relief derived from 30m DEM
  109. 109. aspect classes derived from 30m DEM
  110. 110. weathering layer
  111. 111. digital atlas of soils+
  112. 112. pre-European vegetation types (NVIS)</li></ul>Time series response variables<br /><ul><li>rainfall anomaly (post 1900)
  113. 113. state-wide & national land tenure
  114. 114. FPC (post 1980s)*
  115. 115. ground cover (post 1980s)*
  116. 116. NDVI / EVI (post 1980s)*
  117. 117. native veg (tree) layers*
  118. 118. state-wide & national land use
  119. 119. sheep DSE
  120. 120. cattle DSE
  121. 121. cropping
  122. 122. urban areas
  123. 123. Plantations
  124. 124. nature conservation reserves
  125. 125. indigenous protected areas
  126. 126. Infrastructure
  127. 127. railways
  128. 128. roads
  129. 129. fire regime (fire area & No. fire starts)*
  130. 130. other</li></ul>TERN AusCover*<br />TERN Soils+<br />
  131. 131. Landform Pattern and Topographic Position Index. 30 m – DEM SRTM<br />Nass Valley - ACT<br />
  132. 132. Transformed native vegetation informing future land use options <br />Before 2010<br />1788<br />1800<br />1850<br />1900<br />1950<br />2000<br />Current<br />2010<br />Future scenarios – the big issues <br />2050 Scen 1<br />2050 Scen 2<br />2050 Scen 3<br />2050 Scen 4<br />61<br />
  133. 133. Vision for the future<br /><ul><li>Recognition of the benefits of compiling site-based contemporary LU & LMP
  134. 134. Greater awareness of consequences LU & LMP and the responses of native vegetation i.e. +ve and –ve
  135. 135. Discoverable and accessible data and info via a national repository
  136. 136. when and where landscapes were and are being transformed </li></li></ul><li>Conclusions<br /><ul><li>Process is tedious
  137. 137. Preliminary site-based results are promising
  138. 138. Independent datasets & peer review needed to validate results</li></li></ul><li>Acknowledgements<br /><ul><li>TERN ACEAS for funding my sabbatical fellowship at UQ in Brisbane
  139. 139. CSIRO Ecosystems Sciences, Canberra for hosting me in Canberra
  140. 140. ABARE-BRS, Greening Australia, Forestry NSW, CSIRO ES, John Ive and others for providing datasets </li></li></ul><li>Thank you<br />
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