Appendix I: Vegetation species list
Table 5. A list of dominant species at each dune system of the Cooloola chronosequence (adapted
from Thompson and Moore, 1984).
Dune
Soil Series
Vegetation
Mutyi
Colonising patches of shrubland:
System
1
Pultenaea, Banksia, Acacia, Casuarina and Callitris.
2
Chalambar
Layered woodlands to grassy open forests:
Upper storey: Eucalyptus pilularis (20-30m);
Midstorey: Allocasuarina torulosa (18-20m);
Open to dense understorey: Angophora woodsiana and Casuarina;
Ground cover: Pteridium, Themeda, Imperata and Xanthorrhoea
macronema
4a
Warrawonga
Simple or complex notophyll vine forests and microphyll vine forests (2040m). Also included are minor areas of secondary low meophyll vine forest
with sclerophyll emergents. The main areas of vine forest are in this
landscape, minor areas occupy sheltered sites in other soil landscapes.
4b
Warrawonga
Woodlands and forests:
Upperstorey: Eucalyptus Pilularis (30-50m), Corymbia Intermedia,
Lophostemon confertus, Syncarpia Hillii, Allocasuarina Torulosa, Angophora
Woodsiana and Banksia Aemula;
Open to dense understorey: Acacia, Leptospermum and Casuarina.
Ground cover: Pteridium, Themeda, Imperata, Xanthorrhoea macronema
6a
Kabali
Shrubby woodlands (8-12m):
Upperstorey: Eucalyptus signata, Angophora costata, Corymbia intermedia,
6b
Banksia Aemula, Acacia flavescens and Leptospermum trinervium;
Understorey: Xanthorrhoea johnsonii and heath genera.
Areas of dwarf shrubby woodland (1-3m) where soil depth to B horizon is
>20m
Appendix II: Lag time
The lag time, an integral part of the steady-state model, is the period taken between fixation
of atmospheric CO2 and input into the soil pool. It is important to emphasize that the 0.1-0.2
m depth range will likely have inputs that vary in lag time from fine root turnover with
almost zero year lag time to diffusive and adjective vertical fluxes down the profile which
can have decadal lag time. To start, 1000 years was assigned to the passive fraction TT
(Baisden et al. 2013). We solved the model for the proportion of passive fraction and the TT
of the remaining proportion of SOC. The varying periods of lag at Cooloola dune system 4b
were established in Fig. 4.
With a 7 year lag period, the modelled allocation to the passive fraction climaxes at 20%
which is close to the MIR-predicted measure of 21% purported passive carbon properties
(data not shown; following the methods of Baldock et al., (2013)). We have chosen the
average of lag times of all sites 6.2 years (Table 5) since the lag effect should be assigned a
minimum of at least several years (Sanderman and Amundson 2009) and lag times at the
higher end of the figure give somewhat unreasonable main pool TT and passive fractions
(Fig. 5).
Table 6. Peak proportion of passive fraction with associated main pool SOC TT and lag time
calculated at each dune system and average of all dune systems.
Dune
Main pool TT
Lag time (years)
Proportion passive
system
(years)
1
17
5
0.07
2
17
6
0.14
4a
20
7
0.14
4b
18
7
0.20
6b
16
6
0.22
Average
18
6.2
0.15
fraction
A sensitivity analysis of modelling SOC TT with differing lag times was conducted and
described here. Soil radiocarbon values from Hawai’i and Mattole were modelled on their
respective date of sampling (1996 and 2003) with a range of lag times 1, 5 and 10 years (lag
times greater than 15 years could not be solved) (Table 6). The results show that the fastcycling SOC (<100 year TT) only changed by 0.1 years between lag times of 1 and 10 years.
The radiocarbon values considered in the “middle range” of this study (+24 to -35 ‰)
demonstrated the most sensitivity to changing lag times with a difference of up to 16 years
between 1 and 10 years lag time. Considering this makes up approximately 2 to 6 % of SOC
TT at these sites, we considered this sensitivity to be considerably unimportant for the
purpose of comparing vastly different soil radiocarbon values. Radiocarbon values older than
this range showed decreasing sensitivity to changing lag time, which is expected given that
this century to millennial-scale SOC would not have received fresh inputs of modern
radiocarbon thus lag time of transcending C would become increasingly irrelevant.
Table 7. Sensitivity of modelled SOC turnover times to changing lag times at sites in Hawai’i
and Mattole chronosequence.
SOC ∆14C
SOC TT (years) with changing lag
∆SOC turnover time
period 1 to 10 years
(from lag periods 1 to 10)
‰
1
5
10
Years
%
144
59
60
59
<1
<1
24
249
244
234
14
6
0
354
348
338
16
4
-35
561
556
546
15
3
-63
771
768
763
8
1
-121a
1260
1258
1254
6
<1
Hawai’i
Mattole
aValues
from this site were not included in this study but were considered relevant for this modelling exercise