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Thackway_aceas_v1.4

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Richard Thackway's presentation to CSIRO March 2011

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Thackway_aceas_v1.4

  1. 1. A model for depicting transformations of Australia’s vegetated landscapes Richard Thackway ACEAS Sabbatical Fellow CSIRO ES Discussion 22 March 2011 Canberra
  2. 2. Outline• Context• Project outline• Approach• Case studies• Who needs this information?• How might this information be used?
  3. 3. Australia’s future landscapes – The big issues and questionsBiodiversity conservation, biodiverse carbon, biosequestration, foodsecurity - agriculture moving to northern Australia etc1. What happened in this landscape over time <200yrs?2. How might historic/ contemporary impacts of land use and land management practices affect future land use decisions?
  4. 4. TERN’s facilities Visiting fellow
  5. 5. Aims• To develop and test a method for describing & mapping the transforming of Australia’s native vegetation ̵ Based on the responses of native vegetation communities to land use (LU) and land management practices (LMP)
  6. 6. Transformed native vegetation informing future land use optionsBefore 2010 1788 1800 1850 1900 1950 2000Current 2010Future scenarios – the big issues2050 Scen 1 2050 Scen 2 2050 Scen 3 2050 Scen 4 6
  7. 7. X, Y Tas Midlands 0 1 2 X, Y Tas Midlands 3 X, Y Tas Midlands 40 0 51 6 22 73 4 1750 1800 1850 1900 1950 2000 205045 667 1750 1800 1850 1900 1950 2000 2050 1750 1800 1850 1900 1950 2000 2050 X, Y Tas Midlands X, Y Tas Midlands 0 0 • Opportunities 1 1 2 2 3 3 4 • Options 4 5 5 6 6 7 • Tradeoffs 7 1750 1800 1850 1900 1950 2000 2050 1750 1800 1850 1900 1950 2000 2050 X, Y Tas Midlands 0 1 2 3 4 X, Y Tas Midlands 5 X, Y Tas Midlands 6 0 7 1 0 2 1750 1800 1850 1900 1950 2000 2050 3 2 4 5 4 6 6 7 1750 1800 1850 1900 1950 2000 2050 1750 1800 1850 1900 1950 2000 2050 Hypothetical
  8. 8. The problem ∆ VC score (site) ∆ VC score (site) Vegetationtransformation ∆ time × ∆ space (site) (site and landscape) VC = Benchmarked vegetation condition
  9. 9. Vegetation transformations time and space Increasing vegetation modification 0 I II III IV V VISite & patch - changes in score /class (time is implicit) Increasing vegetation modificationLandscape - changes in score/ class (time is implicit) Fragmentationand modification Modification Increasing Site – changes in score over time (space is implicit) Time Reference / benchmark
  10. 10. A framework for compiling & reporting vegetation condition Increasing vegetation modification 0 I II III IV V VI Naturally Residual Modified Transformed Replaced - Replaced - Replaced - bare Adventive managed removed Vegetation thresholds Condition states Transitions = trend Benchmark Native vegetation Non-native vegetation for each veg type (NVIS) cover cover Diagnostic attributes of states: • Vegetation structure • Species composition • Regenerative capacity Thackway & Lesslie (2008)Vegetation States Assets and Transitions (VAST) framework Environmental Management, 42, 572-90
  11. 11. Vegetation condition – a snapshotThackway & Lesslie (2008)Environmental Management, 42, 572-90
  12. 12. VAST and Landscapealteration levelsFragmentation Intact Variegated Fragmented Relictual >90% 60-90% retained 10-60% retained <10% retainedModification VAST I Residual Unmodified VAST III Transformed Highly modified VAST 0 Naturally Bare Modified and retained VAST II Modified VAST IV Replaced – Adventive, Destroyed VAST V Replaced – Managed VAST VI RemovedMcIntyre & Hobbs (1999) Thackway & Lesslie (2008)Cons. Biology 13, 1282-92 Environmental Management, 42, 572-90
  13. 13. Landscapealteration levels – a snapshot Continental 2.5k Moving Window Radius 100 Residual* 90 Modified Average Proportion (%) of VAST Condition State Transformed 80 Managed Removed 70 60 50 40 30 20 10 0 Intact Variegated Fragmented Relictual Landscape Alteration Level LALs derived using a 2.5 km Input VAST national 1 kmMutendeudzi and ThackwayBRS 2010
  14. 14. Way forward - generalized model of vegetation transformation Anthropogenic change Reference Vegetation modification score Net impact Relaxation Occupation 1800 1850 1900 1950 2000 TimeBased on Hamilton, Brown & Nolan 2008. FWPA PRO7.1050. pg 18Land use impacts on biodiversity and Life Cycle Assessment
  15. 15. Primary agents of veg transformation are LU & LMP• Veg is managed for private & public benefit/s & services by changing vegetation structure, composition & function• Impacts of LU and LMP have +ve & -ve outcomes – When and where and to what degree were vegetated landscapes transformed? – What are the consequences of these transformations for delivering cost effective solutions for the big issues in the future?
  16. 16. A new approach is needed for reportingtransformation of vegetated landscapes Aim: To represent change and trend over space and time – site & landscape scales 16
  17. 17. Assumptions• Changes in LU & LMP – result in predictable changes in structure, floristics & regen capacity – are adequately and reliably documented over time – can be used to simulate changes in vegetation condition – can be consistently and reliably differentiated from natural events• Sequential changes in veg condition at sites over time can be represented as transformations of vegetated landscapes 17
  18. 18. Compiling and translating historicalobservations requires three elements Where When What 18
  19. 19. Sources of data and information1. Published text-based observation i.e. mainly aspatial • Environmental history • Ecological research Older & more • Other qualitative2. Published maps and models including remotely images and GIS • Ecological research • Land use and LMP sources • Geographical and historical sources More recent & more3. Plot /site-based data (once, short & long-term) quantitative • Ecological research • Impacts of LU and LMP
  20. 20. LU & LMP & impacts Data and information sources Very Very Detailed Coursedetailed coarse Gen. public NGOs Government Industry Land manager Researchers Other
  21. 21. Literature on responses of native veg to LU & LMP is diverse• More stories than maps and models• More two date than multi-temporal changes• More coarse scale than fine scale changes• More binary/ single attributes than changes in multi-attribute states (e.g. state and transition models)• More examples use remote sensing than ecological models• More examples of recent local than long term landscape histories
  22. 22. Sequencing responses of native veg to LU & LMP LU & LMPDNA matching matching Final synthesised Source Source Multiple sources sequence ID: 1a ID: 1b 2050 2000 1950 Year 1900 1850 1800 1750
  23. 23. Simulating responses of native veg to LU & LMP i.e. vegetation transformations• Information on impacts is derived from local and published sources• Change is simulated relative to a reference state for a vegetation type – Structure – Composition – Regenerative capacity/potential• Change is recorded at sites• Transformation will be simulated over time and across landscapes
  24. 24. Data synthesis and hierarchy SiteTransformation score /site /year 1Diagnostic attributes 3Attribute groups 9Vegetationresponse 20Indicators
  25. 25. Diagnostic Attribute Score 20 Indicators of vegetation condition attributes groups 1. Spatial patterns – fire areas Fire regime 2. Aspatialprocesses - Departure from natural fire frequency, intensity or seasonality Change in key abiotic and Hydrological 3. Reduction natural surface water entering the soil i.e. more run-off physicochemical state 4. Increase in natural ground water (e.g. rising water table, irrigation)Vegetation Transformation score processes Soil physical 5. Reduction or addition to the depth of A horizon (e.g. erosion or deposition) affecting state 6. Reduction of soil structure (e.g. compaction, cultivation) REGENERATIVE 7. Reduction of natural fertility CAPACITY Soil chemical (100% = 400 points) (20% = Maximum state 8. Addition of industrial fertilisers (e.g. NPK and/or trace elements) 80 points) 9. Reduction of invertebrate recyclers Soil biological state 10. Reduction of locally indigenous surface organic matter 11. Mean top height (seven modification states) Change in Overstorey 12. Mean foliage projective cover (seven modification states) VEGETATION structure 13. Structural diversity of growth form age classes(seven modification states) STRUCTURE 14. Mean top height (seven modification states) (60% = Maximum Understorey 15. Mean ground cover (seven modification states) 240 points) structure 16. Structural diversity of growth form age classes (seven modification states) 17. Density of functional species groups Change in Overstorey e.g. weeds, invasive native species, firewood vs non-firewood, dominant composition millablevsnon-millable, fodder vs non-fodder structuring 18. Relative number of species (richness) species affecting SPECIES 19. Density of functional species groups COMPOSITION Understorey e.g. woody vs non-woody, weeds, invasive native species, palatable vs non-palatable (20% = Maximum composition 80 points) 20. Relative number of species (richness)
  26. 26. Approach – to develop & test a methodSelect 25 case studies across agro-climatic regions1. Compile LU and LMP histories for site & landscape scales and impacts on native vegetation2. Simulate temporal impacts of LU and LMP on native vegetation3. Model landscape transformations by integrating site data with remote sensing, GIS, ground surveys and ecological models 26
  27. 27. Case studies:NSW Open Grassy Woodland Click on red symbol
  28. 28. Workflow for simulating impacts of land use and land management on native vegetation Step 1: Compile primary data on LU and LMP histories for case study sitesStep 1A: Compile and translate and check. Step 1B: Compile and check Step 1C: Standardise site-based observationInclude major natural events e.g. data on impacts of LU & LMP using national guidelines for LU & LMP. Fill gapsdroughts, floods, fires, cyclones on native veg. from regional records Step 2: Simulate impacts relative to a reference condition for vegetation response indicators each site and year Step 2A: simulate impacts of LU & Step 2B: simulate impacts of LU & Step 2C: simulate impacts of LU & LMP on attributes of regenerative LMP on attributes of vegetation LMP on attributes of vegetation capacity structure composition Step 3: Calculate total transformation scores of impacts of LU/LMP on themes for each site for each year Step 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
  29. 29. List of LU and LMP history nsw_talaheni_murrumbateman:Year 34,58,1.94S,,149,10,41.15E1788 Indigenous land management, 17881825 First explorers in the district1830 Grazing of native vegetation, 1830 (shepherds)1850 Fencing and set stocking with sheep commenced1860 Pre-clearing of overstorey set stocking with sheep continues1900 Overstorey cleared1962 Overstorey thinned to promote grazing1980 Commenced rehabilitation toward native vegetation1983 Area grazed using pulse grazing in drought1986 Area continues to be used for pulse grazing in drought1997 Manage the stand composition and structure to meet multiple outcomes2004 Continuing to light graze with sheep in droughts
  30. 30. Estimated change in physicochemical factors affecting regenerative capacity relative to 1800 Fire regime Soil hydrology Soil physical state 120 120 120 Per cent change Per cent chnage Per cent change 100 100 100 80 80 80 60 60 60 40 40 40 20 20 20 0 0 0 1700 1800 1900 2000 2100 1700 1800 1900 2000 2100 1700 1800 1900 2000 2100 Year Year Year Soil chemistry Soil biological state 120 120 per cent chnage Per cent change 100 100 80 80 60 60 40 40 20 20 0 0 1700 1800 1900 2000 2100 1700 1800 1900 2000 2100 Axis Title Year Estimated change in regenetative capacity 100 Benchmarked score 80 60 40 20 0 1750 1800 1850 1900 1950 2000 2050 Year
  31. 31. Estimated change in species composition relative to 1800 Estimated change in species Estimated change in relative number functional groups of species 120 120 Per cent changePer cent change 100 100 80 80 60 60 40 40 20 20 0 0 1700 1800 1900 2000 2100 1700 1800 1900 2000 2100 Year Axis Title Estimated change in species composition 90 80 Benchmarked score 70 60 50 40 30 20 10 0 1750 1800 1850 1900 1950 2000 2050 Axis Title
  32. 32. Estimated change in vegetation structure relative to 1800 Estimated change in the structure of the overstorey Estimated change structure of the understorey 120per cent change 100 120 Per cent Change 80 100 80 60 60 40 40 20 20 0 0 1750 1800 1850 1900 1950 2000 2050 1750 1800 1850 1900 1950 2000 2050 Year Year Estimated change in the vegetation structure 300 250 200 Benchmarked score 150 100 50 0 1750 1800 1850 1900 1950 2000 2050 Year
  33. 33. Vegetation structure Regenerative capacitySpecies composition
  34. 34. 1962 Fencing and Commenced set stocking Overstorey Lightly restoration commenced cleared grazing toward native commenced vegetation
  35. 35. • 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
  36. 36. Vision for the future• Improved understanding of consequences LU & LMP over time & space in transforming vegetated landscapes• Recognition of the benefits of compiling LU & LMP using a consistent approach between key researchers, institutions and agencies• Discoverable and accessible data and info – a national repository
  37. 37. Step 5 – spatial and temporal modelling Static layers Time series response variables•first contact by European explorers •rainfall anomaly (post 1900)•slope classes derived from 30m DEM •state-wide & national land tenure•aspect classes derived from 30m DEM •FPC (post 1980s)•elevation classes derived from 30m DEM •ground cover (post 1980s)•digital atlas of soils •NDVI / EVI (post 1980s)•pre-European vegetation types •native veg (tree) layers •state-wide & national land use • sheep DSE • cattle DSE • cropping • urban areas • Plantations • nature conservation reserves • indigenous protected areas •Infrastructure • railways • roads •fire regime (fire area & No. fire starts) •other
  38. 38. • Preliminary site-based results are promising• Independent datasets & peer review needed to validate results• Modelling of landscape change will involve continuous environmental data layers e.g. remote sensing, DEM, soils, climate etc
  39. 39. Acknowledgements• TERN ACEAS for funding my sabbatical at UQ in Brisbane• CSIRO Ecosystems Sciences, Canberra for hosting me in Canberra• ABARE-BRS, Greening Australia, Forestry NSW, CSIRO ES, John Ive for providing datasets

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