Understanding tree-grass
dynamics in Australian
savanna
Caitlin Moore
Supervisory Team: Jason Beringer, Nigel Tapper, Lindsay Hutley,
Bradley Evans
Outline
Research Rationale
Aims and Objectives
Scientific Background
Proposed Methods
Some Results
Why are savannas important?
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Savannas dominate 25 % of the global land surface and 20-25 % of Australia.
•
They sequester c. 31.3 Pg C y-1 & account for c. 25 % of global GPP
•
They support 20 % of the world’s human population
•
In Australia, savanna accounts for 33 % of terrestrial carbon stores: they are a sink
for carbon
•
Savanna structure and productivity is influenced by annual rainfall, soil nutrients,
CO2 fertilisation, herbivory and fire.
•
The Northern Territory comprises c. 33 % of total savanna cover in Australia.
•
Australian savannas are unique due to their minimal fragmentation, low human
population density and vegetation structure.
Information sourced from: Hutley & Beringer 2012, Scholes & Archer1997, Beer et al., 2010, House & Hall 200, Grace et al., 20061
Rationale & Significance
Changing
rainfall
regimes
Fire, herbivory,
land-use
change
A changing
climate
Altered
savanna
dynamics
↑ atmospheric
CO2
CO2
fertilization
Woody
encroachment
in savanna
Altered
savanna
productivity
Trees
Grass
Research Aim and Objectives
How does the grassy understory of an Australian woody savanna contribute to annual
productivity?
1. To quantify the temporal dynamics of GPP of an open woodland savanna in Australia and how
it is partitioned between trees and grass
2. To understand how grass eco-physiological characteristics such as green biomass, chlorophyll
and nitrogen content relate to GPP of the savanna as well as how they can be related to
spectral reflectance
3. To develop and test tools using moderate resolution remote sensing of savanna GPP to
partition between trees and grass
4.
To identify the annual productivity/biomass of the grasses in the savanna and relate this to
historical climate data in the context of a changing climatic regime
The Savanna
Carbon
Cycle
Image Source: Hutley & Beringer 2010
Research Approach
Study Site: Howard Springs
•
•
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Long-term (1982-2006) rainfall = 1782 mm
Open savanna woodland dominated by eucalyptus
woody overstory and C4 grassy understory
Canopy height 14-16 m and coverage 50-60 %
•
•
•
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Wet Season: Dec-Apr, ~95 % rain falls
Dry Season: May-Sep
Transition: Oct-Nov
Site is a listed OzFlux site
(http://www.ozflux.org.au/)
Map Source: www.maps.google.com
Leaf Level Observations
• Biomass Harvest
• Leaf morphology
• Leaf chemistry:
Chlorophyll &
Nitrogen
• Leaf spectra
Courtesy of J.Pettigrew
Plot Level Observations
• Photosynthetically Active
Radiation (PAR) sensors
• 4-channel Light sensors
• Digital cover photography
(DCP) cameras
• Flux towers
Hemispheric
photography LAI Image
Digital Cover
Photography Image
Courtesy J. Beringer
Courtesy L. Hutley
The mini towers tell a story…
1800
180
1600
160
140
1400
120
1200
Above
Grass
PAR_ABVGRS_IN
Downward
PAR_ABVGRS_OUT
Facing
PAR_BLWGRS_IN
100
1000
80080
60060
40040
20020
0
0
5/28/2013 0:00
5/30/2013 0:00
6/1/2013 0:00
6/3/2013 0:00
6/5/2013 0:00
5/28/2013
-20 0:00 5/30/2013 0:00 6/1/2013 0:00 6/3/2013 0:00 6/5/2013 0:00 6/7/2013 0:00
-200
2000
1800
1600
1400
1200
1000
800
600
400
200
0
5/28/2013
0:00
5/30/2013 0:00
6/1/2013 0:00
6/3/2013 0:00
6/5/2013 0:00
-200
6/7/2013 0:00
6/9/2013 0:00
6/9/2013 0:00 6/11/2013 0:00
6/11/2013 0:00
Above Grass
Upward Facing
6/7/2013 0:00
6/9/2013 0:00
6/11/2013 0:00
Flux Towers on Site
• 2 x flux towers measure fluxes from
the ecosystem (at 23 m) and the
understory (at 10 m) to record actual
GPP of trees and grass.
• Core instrumentation on each tower
includes a 3D sonic anemometer and
an infra-red gas analyser.
• Supported by range of meteorological
instrumentation
• Data collected every 30 mins and sent
to us via the internet in real time.
Validating the understory data
Averaged Spectra -Velocity components
0
Averaged Spectra -Scalar components
0
10
10
STsonic
SCO2
SH2O
0.35
U
V
W
0.3
-1
10
0.25
0.2
0.15
0.1
-2
10
-4
10
0.05
0
-3
10
-3
-2
10
-2
10
-1
0
10
10
Natural Frequency Hz
-1
10
0
10
1
10
10
Normalized frequency
With help (lots) from James Kathilan & Israel Begashaw
1
10
2
10
3
10
(Spectra x Frequency)/ Variance
Frequency x Cospectra/Frequency
(Spectra x Frequency)/ Variance
Frequency x Cospectra/Frequency
Su
Sv
Sw
0.8
WTv
WCO2
-1
10
0.7
WH2O
0.6
0.5
-2
10
0.4
0.3
0.2
-3
10
-4
10
0.1
0
-3
10
-3
-2
10
-2
10
-1
0
10
10
Natural Frequency Hz
-1
10
0
1
10
1
10
10
Normalized frequency
10
2
10
3
10
Ecosystem Scale
• Moderate resolution
remote sensing: To add a
wider spatial context to
the study → Link
observations with RS
• Powerful, long-term
MODIS satellite record.
• MODIS satellites pass over
the NT area twice daily,
the best time being the
11:00 day pass.
www.fsl.orst.edu/larse/bigfoot
Remote sensing data
www.arts.monash.edu.au/ges/research/climate/howard/
•
•
•
•
•
Partition savanna GPP from observations
Compare observations to MODIS
Test algorithms
Extrapolate grass productivity back in time using long-term records
Identify long-term drivers of savanna productivity
Thanks to my helpers so far…
References
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BEER, C., REICHSTEIN, M., TOMELLERI, E., CIAIS, P., JUNG, M., CARVALHAIS, N.,
RÖDENBECK, C., ARAIN, M. A., BALDOCCHI, D., BONAN, G. B., BONDEAU, A.,
CESCATTI, A., LASSLOP, G., LINDROTH, A., LOMAS, M., LUYSSAERT, S., MARGOLIS,
H., OLESON, K. W., ROUPSARD, O., VEENENDAAL, E., VIOVY, N., WILLIAMS, C.,
WOODWARD, F. I. & PAPALE, D. 2010. Terrestrial gross carbon dioxide uptake:
Global distribution and covariation with climate. Science, 329, 834-838.
GRACE, J., JOSÉ, J. S., MEIR, P., MIRANDA, H. S. & MONTES, R. A. 2006. Productivity
and carbon fluxes of tropical savannas. Journal of Biogeography, 33, 387-400.
HOUSE, J. I. & HALL, D. O. 2001. Productivity of Tropical Savannas and Grasslands.
In: SAUGIER, B., ROY, J. & MOONEY, H. A. (eds.) Terrestrial Global Productivity. San
Diego: Academic Press.
HUTLEY, L. B. & BERINGER, J. 2010. Disturbance and Climatic Drivers of Carbon
Dynamics of a North Australian Tropical Savanna. In: HILL, M. J. & HANAN, N. P.
(eds.) Ecosystem Function in Savannas: Measurement and modelling at landscape
to global scales. Boca Raton, FL, USA: CRC Press.
SCHOLES, R. J. & ARCHER, S. R. 1997. Tree-grass interactions in Savannas. Annual
Review of Ecology and Systematics, 28, 517-544.
Questions…