Richard Thackway's presentation to CSIRO March 2011

1. A model for depicting transformations of
Australia’s vegetated landscapes
Richard Thackway
ACEAS Sabbatical Fellow
CSIRO ES Discussion 22 March 2011
Canberra

2. Outline
• Context
• Project outline
• Approach
• Case studies
• Who needs this information?
• How might this information be used?

3. Australia’s future landscapes –
The big issues and questions
Biodiversity conservation, biodiverse carbon, biosequestration, food
security - agriculture moving to northern Australia etc
1. 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. TERN’s facilities
Visiting fellow

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. 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
6

7. X, Y Tas Midlands
0
1
2
X, Y Tas Midlands 3 X, Y Tas Midlands
4
0
0
5
1 6
2
2 7
3
4
1750 1800 1850 1900 1950 2000 2050
4
5 6
6
7
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. The problem
∆ VC score (site) ∆ VC score (site)
Vegetation
transformation ∆ time
× ∆ space
(site) (site and landscape)
VC = Benchmarked vegetation condition

9. Vegetation transformations
time and space
Increasing vegetation modification
0 I II III IV V VI
Site & patch - changes in score /class
(time is implicit)
Increasing vegetation modification
Landscape - changes in score/ class
(time is implicit) Fragmentationand modification
Modification
Increasing
Site – changes in score over time
(space is implicit)
Time
Reference / benchmark

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. Vegetation condition – a snapshot
Thackway & Lesslie (2008)
Environmental Management, 42, 572-90

12. VAST and Landscapealteration levels
Fragmentation Intact Variegated Fragmented Relictual
>90% 60-90% retained 10-60% retained <10% retained
Modification
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 Removed
McIntyre & Hobbs (1999) Thackway & Lesslie (2008)
Cons. Biology 13, 1282-92 Environmental Management, 42, 572-90

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 km
Mutendeudzi and Thackway
BRS 2010

14. Way forward - generalized model of
vegetation transformation
Anthropogenic change Reference
Vegetation modification score
Net impact
Relaxation
Occupation
1800 1850 1900 1950 2000
Time
Based on Hamilton, Brown & Nolan 2008. FWPA PRO7.1050. pg 18
Land use impacts on biodiversity and Life Cycle Assessment

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. A new approach is needed for reporting
transformation of vegetated landscapes
Aim: To represent change and trend over space and
time – site & landscape scales
16

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. Compiling and translating historical
observations requires three elements
Where
When What
18

19. Sources of data and information
1. Published text-based observation i.e. mainly aspatial
• Environmental history
• Ecological research Older &
more
• Other
qualitative
2. Published maps and models including remotely images and GIS
• Ecological research
• Land use and LMP sources
• Geographical and historical sources
More recent
& more
3. Plot /site-based data (once, short & long-term) quantitative
• Ecological research
• Impacts of LU and LMP

20. LU & LMP & impacts
Data and information sources
Very Very
Detailed Course
detailed coarse
Gen. public
NGOs
Government
Industry
Land manager
Researchers
Other

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

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. Data synthesis and hierarchy
Site
Transformation score /site /year
1
Diagnostic attributes
3
Attribute groups 9
Vegetation
response 20
Indicators

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. Approach – to develop & test a method
Select 25 case studies across agro-climatic regions
1. Compile LU and LMP histories for site & landscape scales and
impacts on native vegetation
2. Simulate temporal impacts of LU and LMP on native vegetation
3. Model landscape transformations by integrating site data with
remote sensing, GIS, ground surveys and ecological models
26

27. Case studies:
NSW Open Grassy Woodland
Click on
red
symbol

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 sites
Step 1A: Compile and translate and check. Step 1B: Compile and check Step 1C: Standardise site-based observation
Include major natural events e.g. data on impacts of LU & LMP using national guidelines for LU & LMP. Fill gaps
droughts, 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 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

29. List of LU and LMP history nsw_talaheni_murrumbateman:
Year
34,58,1.94S,,149,10,41.15E
1788 Indigenous land management, 1788
1825 First explorers in the district
1830 Grazing of native vegetation, 1830 (shepherds)
1850 Fencing and set stocking with sheep commenced
1860 Pre-clearing of overstorey set stocking with sheep continues
1900 Overstorey cleared
1962 Overstorey thinned to promote grazing
1980 Commenced rehabilitation toward native vegetation
1983 Area grazed using pulse grazing in drought
1986 Area continues to be used for pulse grazing in drought
1997 Manage the stand composition and structure to meet multiple outcomes
2004 Continuing to light graze with sheep in droughts

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. 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 change
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
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. Estimated change in vegetation structure relative to 1800
Estimated change in the structure of the
overstorey Estimated change structure of the
understorey
120
per 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. Vegetation structure
Regenerative capacity
Species composition

34. 1962
Fencing and Commenced
set stocking Overstorey Lightly restoration
commenced cleared grazing toward native
commenced vegetation

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. 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. 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. • 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. 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