Introduction
A progressive “thickening” of woody vegetation in grasslands and savannas is
a global phenomenon that has been widely documented
(Archer
et al., 2001; Boutton et al., 2009; Guillet et al., 2001; Krull et al.,
2005; Liao et al., 2006; Pessenda et al., 1998). Woody thickening is being
promoted by climate change, changes in fire regimes and other anthropogenic
land use activities (Jackson et al., 2000; Krull
et al., 2005; Silva et al., 2008) with increased woody (C3) plant
growth in response to continually increasing atmospheric CO2
concentrations a likely key driver (Bond and Midgley, 2012; Buitenwerf et
al., 2012; Donohue et al., 2013). The impact of these vegetation dynamics on
ecosystem biogeochemistry and the global carbon cycle may be highly
significant given the large extent of grass-dominated ecosystems, which
represent about 30 % of primary production of all terrestrial vegetation
and store 10–30 % of all soil organic carbon (SOC; Eswaran et al., 1993;
Grace et al., 2006; Hall and Scurlock, 1991). Moreover, despite an
increasing number of studies of terrestrial carbon dynamics, improved
predictions of the impacts of future climate-driven changes on the tropical
SOM pool requires a more detailed understanding of the interactions between
vegetation, climate, edaphic and disturbance effects than is currently
available
(Archer
et al., 2001; 2004; Boutton et al., 2009; Jackson et al., 2000).
The use of isotopic techniques in ecological studies broadens the
possibilities for better assessing soil carbon dynamics
(Bernoux et al., 1998; Bird and Pousai,
1997; Bird et al., 1996; Boutton, 1996; Leifeld and Fuhrer, 2009), the
documentation of past vegetation changes
(Krull et al., 2005; Liao et al., 2006;
Pessenda et al., 1998; Silva et al., 2008), and the reconstruction of
earlier environments (Bird et al., 1996; Cerling et al., 2011). The use
of the carbon isotope composition of SOM has proven to be a useful tool for
investigating the influence of C3 and C4 vegetation on SOM
dynamics (Wynn and Bird, 2007), and for identifying recent
(∼ 100 years) vegetation change patterns that in the past
could only be assessed by interpreting historical aerial photography or
satellite imagery (Krull et al., 2005). This approach relies on
the distinct carbon isotopic (δ13C) values of tropical grasses,
employing the C4 photosynthetic pathway (-14 ‰)
compared to the δ13C value of trees and shrubs that utilize the
C3 photosynthetic pathway (-27 ‰; average values
from Krull et al., 2007). It is feasible to obtain an integrated
measure of the carbon isotopic composition of regional vegetation using SOM,
given that this pool integrates the isotopic signature of the precursor
biomass, thus serving as a record of changes in the ratio of tree and
grass-derived organic matter input to the soil over different spatiotemporal
scales (Bird et al., 2004; Lloyd et al., 2008). However, the simple
interpretation of the δ13C value of SOM in a soil profile may
not be straightforward because of fractionation effects such as those
associated with microbial reprocessing of SOM, differential stabilization of
SOM compounds, and the terrestrial “Seuss effect” (Bird et al., 1996;
Blagodatskaya et al., 2011; Ehleringer et al., 2000; Rumpel and
Kögel-Knabner, 2011). Moreover, the specific characteristics of a soil
can fundamentally affect carbon isotopic dynamics through differential
physico-chemical protection of SOM as influenced by the chemical,
mineralogical, and textural properties of the soil (Krull et al.,
2003; Sollins et al., 2009; Veldkamp, 1994).
Physical fractionation of SOM is commonly employed to simplify the complex
soil matrix into discrete fractions of similar physico-chemical
characteristics, as defined by aggregation, particle size, density, or a
combination of these (Crow et al., 2007; Moni et al.,
2012; Zimmermann et al., 2007). Each individual technique will result in a
specific number of fractions with conceptually different carbon turnover
times, although a common feature shared by all methods is the
differentiation between mineral-associated organic matter and free
particulate organic matter. The latter fraction is predominantly composed of
plant-derived material in the early stages of decomposition and is generally
reported to mineralise more quickly than physically protected
mineral-associated fractions (Six and
Jastrow, 2002; Sollins et al., 1996; Zimmermann et al., 2007). The
determination of mean residence times (τ) of SOM by radiocarbon
(14C) age measurements, combined with δ13C
characterisation of vegetation change recorded in SOM has provided strong
evidence of the nature and timing of tropical vegetation shifts in the past
(Guillet et al., 2001; Krull et al., 2005;
2007; Pessenda et al., 1998). While the use of these techniques yields
useful information about SOM dynamics, there are a number of limitations and
biases that need to be taken into account when interpreting the results
(Creamer et al., 2011; Crow et al., 2007; Trumbore,
2009). Several authors have warned about possible biases related to deep SOM
14C dating, advising that single analyses of bulk SOM would fail to
take into account inputs of fresh organic matter from root decomposition and
solubilised SOC, which could distort the true age of stabilised SOM at depth
(Gaudinski et al., 2001; Herold et al., 2014; Krull et
al., 2005; Wurster et al., 2010). This artefact can be minimised by
implementing an SOM fractionation procedure, whereby distinct organic matter
fractions are independently analysed both for δ13C and 14C
at different depths. Nevertheless, it remains a challenge to assess past
tropical vegetation dynamics without the confounding effects posed by the
interaction of varying climatic and edaphic factors.
Environmental gradients offer great opportunities for both understanding
mechanisms of abiotic control on ecosystem processes and to study the potential
impacts of global change (Koch et al., 1995). A good example of such a
gradient is the sharp climatic gradient existing between the arid conditions
characteristic of West Africa's inner continental regions and the humid
environments predominant near its southern coast, which strongly influences
the distribution and functioning of the wide range of natural ecosystems
currently found across this expansive region (Domingues et
al., 2010; Saiz et al., 2012; Schrodt et al., 2015; Veenendaal et al.,
2015). However, in addition to climatic factors, SOM dynamics may vary
considerably depending on the specific physical and chemical characteristics
of the soil (Bruun et al., 2010; Paul et al.,
2008; Plante and Conant, 2014). Certainly, West Africa presents a wide
variety of soil types not directly related to contemporary climate or
vegetation (Pullan, 1969), which combined with increasing
anthropogenic pressure and the inherent natural heterogeneity of these
ecosystems, makes any attempt to generalise SOM dynamics challenging
(Saiz et al., 2012).
Field studies using the stable carbon isotopic composition of SOM can help
assess the influence of C3 and C4 vegetation on SOM dynamics,
enabling a test to determine whether there are differential patterns in
their mineralisation potential as previously reported in laboratory-based
studies (Wynn and Bird, 2007). The objectives of this study are
(1) delineate SOM dynamics across contrasting C3/C4 mixed
semi-natural tropical ecosystems; (2) investigate any potential variation in
tropical vegetation thickening along the precipitation transect; and (3)
unambiguously evaluate the effect of vegetation thickening on SOM dynamics
in two contiguous but structurally different woodland ecosystems.
Materials and methods
Characteristics of the sites
The description of the sites and sampling methodology used in the present
work have been provided in detail in a previous publication focusing on the
determinants of SOC stocks (Saiz et al., 2012), with further information
provided in Torello-Raventos et al. (2013) and Veenendaal et al. (2015). Hence, a short summary is provided here.
The study was conducted across a latitudinal transect encompassing a wide
range of semi-natural ecosystems and soils characteristic of West Africa
(Figs. 1, 2). The soil sampling campaign took place in Ghana, Burkina Faso,
and Mali from August to October 2006. Fourteen study sites comprised of a
total of ten 1 ha and four 0.5 ha plots, were established in locations
previously identified as representative of the potential natural vegetation
of the region. These included National Parks, Forest Reserves, and other
legally protected areas except for the Sahelian sites in Mali, which had no
specific conservation status and were subject to varying degrees of grazing
pressure, the latter also being the case for the most northern Sudan savanna
sites of Burkina Faso.
Geographical locations of the studied sites in West Africa.
The map interface is adapted from Globalis, a software tool developed by the
initiative of the United Nations Association of Norway.
Selected examples of ecosystem types and soil profiles
occurring over the precipitation gradient.
The transect was established on consistently flat terrain with less than 100 m of altitudinal variation between all sites. The northern end of the
transect was dominated by Sahelian ecosystems, consisting of grassland
savannas (Torello-Raventos et al., 2013) occurring on the relative nutrient-poor Arenosols on the southern border of the Sahara, receiving low
mean annual precipitation (PA ∼ 0.3 m a-1) and
subject to high rates of potential evaporation (Table A1). Further south
there is a natural progression into more woody-dominated savanna forms
heavily influenced by a gradual increase in PA. The southern end of the
transect corresponds to the more humid sites supporting semi-deciduous tall
forests (PA > 1.2 m a-1). The variation in mean annual
temperature is less than 4 ∘C across all sites. Saiz et al. (2012) report a wide range of soil types that are the result of the
interaction of contrasting geological, climatic, and vegetation factors over
extended time periods.
Estimates of the fractional vegetation cover of woody vegetation
(FCw) were obtained as described in Veenendaal et al. (2015), while
estimates of the fractional vegetation of the axylale vegetation
(FCa) are as in Torello-Raventos et al. (2013). In short, the canopy area
index (C), which is defined as the sum of individual canopy projected area
divided by the ground area, was estimated separately for three woody strata.
These strata are distinguished on the basis of stem diameter (D) at breast
height (1.3 m), and individual tree height (H). The upper (u) stratum consists
of trees with d > 0.1 m, all of which were individually measured at
each plot. The mid (m) stratum consists of woody vegetation of 0.1 > d > 0.025 m, which was quantified through measurements
made along ten 50 m long transects. The subordinate (s) or lower stratum is
made up of trees and shrubs with d < 0.025 m and H> 1.5 m,
which were quantified in the same way as the mid-stratum. Subsequently,
stand-level canopy projected area for each stratum (Cu, Cm, Cs) was
estimated according to site-specific allometric equations presented in
Torello-Raventos et al. (2013). Therefore, assuming a random distribution of
trees and/or shrubs, the fraction of ground covered by crowns (including
within-crown light gaps), which we refer here as the FC of woody vegetation
(FCw) can be estimated as
FCw=1-exp(-Cu-Cm-Cs),
FCa was visually recorded along a series of transects with a sampling
intensity of 110 × 1 m2 quadrants per plot.
Soil sampling
We made use of a stratified sampling strategy employed by Bird et al. (2004) and Wynn et al. (2006) that has been proven to be well suited for
both studying the inherent spatial heterogeneity of SOM that is typically
exhibited in mixed C3/C4 environments, and achieving robust
regional estimates of SOC inventories. This sampling approach consists of
taking samples in a stratified manner near trees “Tree” (-T) samples at half
canopy radius from trunks, and away from trees “Grass” (-G) samples at half
the maximum distance between trees.
Surface litter was removed when present at each sampling location and three
soil samples were taken at 0–0.05 m and one sample at 0–0.30 m with the aid
of a stainless steel corer 40 mm inner diameter (ø). All the samples
were placed in labelled zip-lock bags. Three replicate samples were
collected at 0–0.05 m to smooth out local heterogeneity, which is generally
more pronounced closer to the soil surface compared to deeper locations.
This procedure was replicated five times at each site (both for -T and -G
locations). Replicates were subsequently bulked according to location (-T
versus -G) and depth (0–0.05 and 0–0.30 m). In addition, a soil pit was
hand-dug up to 2 m depth at each plot to assess soil type, provide an
estimate of root biomass, and allow for the description of soil
characteristics (Quesada et al., 2011). Samples were taken at 0–0.05,
0.05–0.30, 0.30–0.50 m and then every 0.5 up to 2 m depth (impenetrable
layers permitting).
SOM fractionation procedure
Soil samples were fractionated using a combination of physical sieving and
density separation following a procedure that has been described in detail
by a number of publications (Dondini et
al., 2009; Wurster et al., 2010; Zimmermann et al., 2007). Briefly, thirty
grams of dry-sieved soil (< 2 mm) was added to 161 mL of
ultrapurified water (Milli-Q, Millipore Corp., Massachusetts, USA), and
dispersed using a calibrated ultrasonic probe-type (VC 750, Sonics &
Materials Inc., Newtown, CT, USA) with an output-energy of 22 J mL-1.
The soil solution was then wet sieved through a 53 µm mesh until the
rinsing water was clear, and the size fractions were subsequently dried at
40 ∘C. All the particulate material passing the 53 µm
mesh sieve corresponded to the silt and clay fraction (s+c). The material
> 53 µm containing sand and water stable aggregates (heavy
fraction - HF) was separated from the light fraction (LF) by means of static
dense media separation (Wurster et al., 2010) prepared at 1.87 g cm-3 using sodium polytungstate (Sometu-Europe™, Berlin,
Germany). All the resultant fractions were then washed and filtered at 0.45 µm with ultrapure water to remove any traces of salt, dried at
40 ∘C, and the weight of each fraction was determined before
further analysis.
Analytical methods and calculations
Stable isotope composition and elemental abundances of carbon and nitrogen
were determined in duplicate in powdered samples using a Costech Elemental
Analyzer fitted with a zero-blank auto-sampler coupled via a ConFloIII to a
ThermoFinnigan DeltaPlus-XL using Continuous-Flow Isotope Ratio Mass
Spectrometry (CF-IRMS) at the University of St Andrews Facility for Earth
and Environmental Analysis stable isotope laboratory (U.K.). Precisions
(S.D.) on internal standards for elemental carbon and nitrogen abundances
and stable carbon isotopic composition were better than 0.09 % and
0.2 ‰ respectively.
The relative change in C / N ratios (Rc) for the very stable (s+c) fraction in
relation to the relatively fresh particulate organic matter represented by
the light fraction (LF) was determined for each sampling location (-G and -T)
according to the following equations:
RcG=1-(C/Ns+cG/C/NLFG)RcT=1-(C/Ns+cT/C/NLFT),
where C/Ns+c and
C/NLF are the C / N ratios
of the s+c and LF fractions respectively, and the subscripts (-G or -T) indicate
the sampling location.
Subsequently, the difference in the relative change in C / N ratios for the
two contrasting SOM fractions between both sampling locations (ΔC/N*G-T) was calculated for each site according to Eq. (3):
ΔC/N*G-T=RcG-RcT.
Radiocarbon analyses were conducted at the Accelerator Mass Spectrometry
facility managed by the Australian Nuclear Science and Technology
Organisation (ANSTO) in Kirrawee, NSW, Australia. 14C measurement
efforts were concentrated at the two transitional sites (BFI-02 and 04) on
depth intervals where δ13C values of bulk SOM showed a
significant shift (0.3–0.5 m), and also in the deepest studied interval
(1.5–2.0 m) in order to determine the τ of mineral-bound deep SOM
fractions. Here we use radiocarbon age of each fraction as a proxy for its
average τ. Radiocarbon ages were calculated according to Stuiver
and Polach (1977). Calculation of τ for samples classified as
“Modern” (pMC > 100 %) was determined making use of the model
presented by Harkness et al. (1986). The reference 14CO2
atmospheric data for the Northern Hemisphere in 2006 (date of collection)
are those published in Hua and Barbetti (2004) and Levin et al. (2008).
Simple interpolation was used to quantify the τ where the data fell
between two points on the model. Furthermore, soil texture, pH, and
effective cation exchange capacity (ECEC) were determined for different
depth intervals to help explain potential variations in τ between the
two transitional sites (BFI-02 and 04). Particle size distribution was
determined gravimetrically as described by Reeuwijk (2002). Soil pH was
measured using a digital pH meter in a 2:1 water:soil solution. The CEC was
determined by inductively coupled plasma optical emission spectrometry
(ICP-OES) extraction of soils using dilute unbuffered Silver-Thiourea for
Al, K, Mg, Ca and Na as described by (Quesada et al., 2011), and ECEC was
calculated as the sum of these bases.
Results
Stable carbon isotopic composition of SOM across the
precipitation transect
Associated with the gradual shift in vegetation from the relatively open
savannas found in the interior of the continent in Mali to the dense forests
near the Atlantic coast of Ghana (Figs. 1, 2), were changes in the carbon
stable isotopic composition of SOM along the transect (Fig. 3a). Shallow
soil samples (0–0.05 m) showed distinctly high δ13C values in
grass-dominated environments of the north, in contrast with the lower
δ13C values observed in the forest ecosystems occurring in the
more moist, southern end of the transect. Within each sampling site, higher
δ13C values were consistently obtained at -G sampling locations.
The difference between -T and -G sampling locations at each site was
consistently larger at savanna sites compared to the difference observed in
forests (Fig. 3a). Nonetheless, it is worth noting the strong effect of tree
clumping in δ13C at sites BDA 01-02, as well as the relatively
large difference observed between the two sampling locations at the KOG-01
site, which reflects the vegetation composition of the southernmost savanna
site sampled. SOC contents and δ13C values ranged from 1.5 mg C g-1 and -13.7 ‰, respectively, in one of the Sahelian
sites (HOM-01), to 55.5 mg C g-1 and -28.5 ‰ in the
semideciduous dry forest (ASU-01) at the south end of the transect (Fig. 4).
Carbon contents were generally higher at -T locations, compared to -G locations,
while the opposite was true for δ13C values.
(a) Stable carbon isotope composition of soil samples taken
from the 0–0.05 m interval at different sampling locations (i.e. Grass,
Tree, and Clumps of trees). Sites are ordered by decreasing latitude;
(b) relationship between the weighted average stable carbon isotopic composition
of all sample locations and both the fractional vegetation cover (FC) of all
woody vegetation taller than 1.5 m, and the axylale vegetation (grass and
herbs). The gradation intensities of the axylale symbols correspond to the
relative contribution of C4 species over the total axylale vegetation.
Accordingly, white, grey and dark-grey symbols represent > 0.66,
0-66-0.33, and < 0.33 of that contribution respectively. Regressions
have the form FC =a+b× (δ13C) and the regression
coefficients (r2) are 0.81 and 0.93 for the woody (solid line) and
axylale (dotted line) vegetation respectively; p < 0.05 in both
regressions. The pure grassland stand (BDA-03) was not included in the
regressions.
Variation in δ13C values and C / N
ratios in bulk soil and SOM fractions
Analyses of bulk soil samples showed that dry forests and woody savannas
typically had lower C / N ratios than grass-dominated ecosystems (Fig. 5a).
Likewise, for the LF the relationship between δ13C and C / N ratios
showed lower C / N values being associated with woodier ecosystems (Fig. 5b);
though with the notable exception of the most northerly Sahelian grassland
savannas occurring in very sandy soils (HOM sites), which were characterised
by very low C / N ratios. Consistently higher C / N ratios were observed in
-T compared to -G sampling locations, with the differences between them becoming
larger as δ13C values became more positive.
Relationship between δ13C values and SOC content
for the 0–0.05 m depth interval at different sampling locations. The
regression has the form: δ13C =-4.536× ln (mg g-1
SOC) -9.981; r2= 0.61; p < 0.05.
The stable s+c fraction consistently exhibited lower C / N ratios compared to
LF at each site (data not shown). However, the relative difference in C / N
ratios for these two contrasting SOM fractions generally differed between
the (-G and -T) sampling locations at each site (Fig. 6a). Sites showing
positive ΔC/N*G-T values indicate that “Grass” sampling
locations had a greater relative reduction in C / N ratios than their
“Tree”
counterparts, while the opposite was true for negative ΔC/N*G-T values. The datum with the lowest
δ13C and a neutral ΔC/N*G-T value corresponds to a dry semideciduous forest with no
C4 vegetation (ASU-01). The relatively large variation in ΔC/N*G-T values across the transect appeared to be strongly
controlled by soil textural characteristics (Fig. 6a) with a function driven
by sand content and the weighted soil δ13C explaining 0.63 of
this variation (Fig. 6b). Here the weighted soil δ13C was
calculated for each site according to woody fractional vegetation cover
shown in Fig. 3b.
Relationship between δ13C and C / N values across
the gradient at both “Tree” and “Grass” sampling locations for (a) bulk soil
samples, and (b) the light fraction (LF). Solid and dotted regression lines
denote “Tree” and “Grass” sampling locations respectively. In red are the
Sahelian ecosystems (HOM sites), which have not been included in the
regressions. All samples derive from the 0–0.3 m depth interval. The
regressions have the form C / N =a+b× (δ13C) and the
regression coefficients (r2) are 0.3 and 0.37 for the Tree and Grass
locations respectively for bulk soil samples (5a); while r2 are 0.83
and 0.63 for the Tree and Grass locations respectively for LF samples (5b);
p < 0.05 in all regressions.
Stable carbon isotopic composition of SOM with depth across the
transect
The differences in δ13C values between samples collected within
the 0.05–0.3 m depth interval and the topmost soil layer (0–0.05 m) were
relatively small for most sites across the precipitation gradient (< 2 ‰ ; Fig. 7). The exception to this trend occurred at
transitional sites, which exist on the boundary between naturally occurring
forest and savannas at the wettest end of the transect (KOG-01 and BFI
sites). Similarly, the differences in δ13C between the 0.3–0.5 m depth interval and the topmost soil layer (0–0.05 m) were also the largest
in these transitional ecosystems. These differences were consistently
positive along the transect, except for the case of the Sahelian ecosystems
(HOM sites) and KOG-01, a savanna woodland occurring on a very sandy soil.
Figure 8 shows the variation of δ13C values in soil profiles
spanning 2 m depth for four selected sites, which illustrates the range of
past changes in vegetation that may have occurred at some sites across the
transect. A slight 13C enrichment with depth is observed in the
semideciduous forest at the wettest end of the transect (ASU-01, Fig. 1).
Both BFI-02 and BFI-04 show a very large increase in δ13C
values with depth. By contrast, a Sahelian site (HOM-01) showed a gradual
decrease in δ13C values with soil depth.
(a) Relationship between the weighted average soil δ13C calculated for each site and the difference in the relative change
in C / N values for two contrasting SOM fractions (see text for details)
between -G and -T sampling locations (ΔC / NG-T*). The size of each
data point is proportional to sand content. Grey dots denote transitional
sites (Saiz et al., 2012; Fig. 7). (b) Measured and predicted ΔC / NG-T* as a function of weighted average δ13C and sand
content (sc). The regression takes the form ΔC / N*G-T= 0.441+ 0.011 (δ13C) – 0.410 (sc); r2= 0.63,
P < 0.05, n= 13.
Shifts in δ13C and
14C with depth in SOM fractions in two contrasting
transitional ecosystems
Figure 9 shows δ13C values of bulk and fractionated SOM at
different depths for two contrasting transitional ecosystems (BFI-02 and
BFI-04). Both sites display varying degrees of increase in SOM δ13C values with soil depth. The low δ13C values observed
in the top soil layers (0–0.05 m) agree well with the current presence of
nearly closed canopy woodlands in which C4 vegetation is either absent
(BFI-04), or virtually absent (BFI-02). There were, however, differences in
the extent to which δ13C increased with soil depth at each
site, although both ecosystems showed a sharp increase in δ13C
values within the first 0.5 m. This trend was most acute for the woodland
savanna (BFI-02), which exhibited values as high as
-18.2 ‰ at 0.3–0.5 m, representing an absolute difference
of more than 4 ‰ for that depth interval between the two
sites. At deeper locations δ13C was relatively invariant for
the dry forest, while there was a gradual change in δ13C
towards lower values in the woodland savanna, nearly matching the trend
exhibited by the dry forest.
Differences in soil δ13C values between both the
0.05–0.3 and 0.3–0.5 m depth intervals and the topmost soil layer (0–0.05 m)
across the precipitation gradient. The dashed line represents mean annual
precipitation (PA). Sites are ordered by decreasing latitude. The shaded
region contains the transitional sites. The asterisk at BDA-3 denotes that
soil sampling was limited to 0.19 m only.
Our results show that SOC contents, root biomass, and C / N ratios decreased
with depth (Table 1). Moreover, radiocarbon analyses confirmed that high C / N
ratios observed near the surface represented relatively young (with three out of
four dates reflecting the contribution of modern carbon fixed after nuclear
weapons testing in the late 1950s; Table A2). Furthermore, as is shown in
Figure A1 there was a strong positive correlation between 14C activity
(pMC) and observed C / N ratios across the various SOM fractions. The
sand-size aggregate fraction (> 53 µm HF) consistently
presented younger 14C ages than the most comminuted fraction (< 53 µm s+c) within the same site and depth interval.
Soil properties determined for different depth intervals
at both studied sites. Numbers in brackets denote standard deviation from
the means (n= 5). Analyses were conducted on dry samples sieved to 2 mm.
Site
Depth
Sand
Clay
pH
eCEC
Root densitya
C
N
C / N
C / N soil fractionsb
(m)
(g g-1)
(g g-1)
(mmol kg-1)
(kg m-2)
(mg g-1)
(mg g-1)
Bulk
s+c
HF
LF
0.00–0.05
0.67
0.15
6.3
12.2 (6.4)
0.4 (0.2)
13.2 (2.1)
1.1 (0.2)
12.5 (1.8)
0.05–0.30
0.69
0.09
5.1
12.2 (5.7)
2.3 (3.6)
6.2 (1.5)
0.5 (0.2)
12.3 (2.0)
BFI 2
0.30–0.50
0.80
0.18
5.2
5.1
2.3 (3.3)
4.3 (1.1)
0.4 (0.1)
11.4 (2.1)
13.5
12.6
21.6
29.7
0.50–1.00
0.36
0.06
5.4
8.0
0.4 (0.3)
4.1 (0.7)
0.5 (0.1)
9.4 (1.6)
1.00–1.50
0.49
0.39
5.6
15.9
0.2 (0.2)
2.7 (0.8)
0.3 (0.1)
8.5 (2.0)
1.50–2.00
0.45
0.38
5.6
10.1
0.1 (0.1)
2.8 (0.8)
0.3 (0.1)
8.7 (2.8)
9.1
10.8
9.3
28.9
0.00–0.05
0.75
0.10
6.4
30.5 (15.7)
0.8 (0.5)
22.3 (12.2)
2.2 (0.9)
11.6 (3.1)
0.05–0.30
0.63
0.03
6.8
32.1 (8.8)
0.8 (0.7)
9.0 (7.0)
1.1 (0.6)
10.5 (4.5)
BFI 4
0.30–0.50
0.73
0.08
6.5
23.6
0.9
1.9
0.2
9.3
12.3
9.6
17.4
30.3
0.50–1.00
0.57
0.31
4.7
27.6
0.7
2.8
0.4
6.7
1.00–1.50
0.43
0.55
4.6
25.7
0.0
2.5
0.3
7.1
1.50–2.00
0.43
0.42
4.3
17.3
0.0
1.9
0.2
8.3
9.9
9.5
7.4
20.1
a Abundance of root fragments < 0.02 m was assessed over the
total dry mass of each individual sample prior to any sieving.
b Soil fractions were obtained from a composite sample made up from all
five individual samples at each depth interval. Bulk: composite sample subject
to fractionation; s+c: fraction < 53 µm associated to silt and
clay; HF: Heavy fraction > 53 µm and > 1.87 g cm-3; LF: Light fraction
< 1.87 g cm-3.
Discussion
Stable carbon isotopic composition of SOM across the
precipitation transect
The broad range of biotic and abiotic conditions present across the
precipitation gradient was reflected in a wide diversity of vegetation
structural formation types (Fig. 2). Besides climate, the inherent
characteristics of the soil play a dominant role in determining both plant
nutrient availability and the amount of water available for plant growth,
making it one of the most important factors controlling the type of
vegetation at a given site (Bloomfield et al., 2014; Quesada et
al., 2012; Saiz et al., 2012). In turn, vegetation type strongly controls
the quantity and quality of organic inputs returning to the soil, thus
exerting a strong influence on its carbon storage potential
(Post et al., 1982; Wynn and Bird, 2007). The gradual
depletion in 13C associated with increasing PA reflects current
vegetation patterns well, with high δ13C values corresponding
to grass-dominated savannas (Fig. 3a), showing an axylale (herbs and
grasses) layer containing a variable mixture of C3 and C4 species
(Fig. 3b; see also Torello-Raventos et al. 2013).
Variation in soil δ13C values with depth for
selected ecosystems demonstrating distinctive vegetation dynamics across the
precipitation gradient.
The relationship between δ13C and SOC content in mixed
C3/C4 systems has been reported to be linear by several local
studies (Bird et al., 2004, 2000), with this attributed to
differences in the input rates and turnover times of woody and grass-derived
carbon. In the present work the relationship was, however, non-linear (Fig. 4), suggesting that one or several additional factors may have been involved
in generating the observed pattern at a broad scale. In our study, the
northern arid sites are heavily limited by low water availability with a
rainy season of less than 4 months (Schrodt et al.,
2015) which undoubtedly not only
reduces organic inputs into the soil but likely the rate of OM decomposition
as well. The latter is confirmed by previous research conducted over broad
latitudinal gradients, which has shown SOM turnover rates to be heavily
controlled by climate (Bird and Pousai, 1997; Bird et al., 1996).
Besides climatic factors, the contrasting characteristics of the soils
studied have been shown to have a strong influence on the potential
preservation of SOM (Saiz et al., 2012). It is therefore plausible that the
contrasting textural and mineralogical characteristics of these soils may
have a major effect on both the physical protection of particulate carbon
and the chemical stabilization of 13C enriched microbial metabolites
(Dümig et al., 2013; Šantrůčková et al., 2000). Such
a strong influence would explain the comparatively high δ13C
and SOC values observed in the iron-rich, silty loams of the BDA sites in
particular (Saiz et al., 2012).
Stable carbon isotope composition of bulk and fractionated
SOM, and mean residence times (τ) for two transitional ecosystems in
central Ghana; (a) savanna woodland (BFI-02), (b) semi-deciduous dry forest
(BFI-04). Bulk soil is defined as < 2000 µm; < 53 µm is the fraction associated to silt and clay; HF is the heavy
fraction > 53 µm (specific density
> 1.87 g cm-3); LF is the light fraction > 53 µm (specific
density < 1.87 g cm-3). τ of selected SOM fractions are
shown adjacent to the fractions and denote conventional radiocarbon ages
and, in between brackets, calculated τ derived from radiocarbon
analyses presented in Table A2.
Differential patterns in SOM dynamics across contrasting
C3/C4 mixed ecosystems
Savannas in their natural state are generally less productive than forests
due to a myriad of reasons, which include characteristically lower
precipitation, the existence of periods of drought of varying severity,
discrete seasonal productivities, and an overall net reduction of annual
productivity due to fire (Grace et al., 2006). Studies conducted in West
African savannas show that more than half of the net primary productivity of
these ecosystems is attributable to C4 grasses (Menaut and Cesar,
1979), a fact further supported by the pantropical productivity estimates
for savannas reported by Lloyd et al. (2008).
In order to facilitate the study of heterogeneous C3/C4
environments, we adopted a soil sampling strategy that allowed for the
comparison of soil properties at systematically defined locations. This
approach allows for the relative unbiased accounting of the effect of woody
cover, thus minimising the potential confounding effects derived from
preferential sampling of either grass or woody-dominated areas across all
study sites. Indeed, there was considerable variation in carbon isotope
composition of surface soil samples at the individual plot scale, as
demonstrated by the large differences in δ13C values between
-T and -G sampling locations in ecosystems where C3/C4 species coexist
(Fig. 3). Similarly, carbon contents were generally higher in -T locations
than in their -G counterparts across all sites (Fig. 4). This, especially as
it has been reported by some earlier studies (Bird et al.,
2000; 2004; Wynn and Bird, 2007), suggests that such variation could be
mainly related to differences in the input rates and turnover times of tree
and grass-derived carbon. However, both the large temporal and spatial
variability in OM inputs and turnover rates, as well as the potentially
large time lag between production and decomposition processes, make any
comprehensive characterisation of SOM dynamics very challenging, especially
under field conditions. Indeed, it is characteristically difficult to make
sound generalizations of even the most fundamental properties of natural
ecosystems. Such is the case for plant biomass C / N ratios, a feature
considered to be highly relevant in SOM decomposition processes
(Brady and Weil, 2007; Kirschbaum et al., 2008; Tian et
al., 1992).
Plant C / N ratio is greatly dependent on a number of factors that include the
type and age of the tissue, sampling season, and the specific
characteristics of the species being sampled (i.e. drought tolerant,
contents of polyphenol and lignin, etc; Abbadie et al.,
2006). Large spatial variability in OM inputs is inherent in commonly
heterogeneous tropical environments (Mordelet and Menaut, 1995), and while
an adequate assessment of aboveground litter may be relatively
straightforward, it is certainly much more difficult to obtain accurate
information on the quality, quantity, and spatiotemporal variability of
belowground litter dynamics (Gignoux et al., 2006).
Hence, it is highly problematic to establish an unambiguous relationship
between the properties of the precursor biomass and that of SOM. Figure 5
shows that dry forests and woody savannas typically had lower C / N ratios
than grass-dominated ecosystems. The latter environments may also present
relatively high C / N values as influenced by their comparatively larger
amounts of pyrogenic carbon (Saiz et al., 2012). The exception to such a trend
were the Sahelian ecosystems (HOM-01-02) which had very low C / N ratios as a
result of their very low biomass (Aranibar et al., 2004), the occurrence of
dominant, albeit sparse, nitrogen fixing species (i.e. Acacia sp, Zornia
sp.; Hiernaux et al., 2009b), and the external nitrogen inputs from
domestic grazers (Saiz et al., 2012). The LF or particulate organic matter is
the soil fraction that best reflects recent organic inputs to the soil, as
it includes contributions from both aboveground and belowground biomass.
This fraction is widely referred to as a very labile SOM component with
characteristically high C / N ratios, mainly composed of recent OM inputs that
have not yet been physically protected by the soil matrix
(Marin-Spiotta et al., 2009; Zimmermann et al., 2007).
We therefore made use of the relative change in C / N ratios between two
contrasting SOM fractions (LF and s+c) in order to assess potential
differences in SOM dynamics in grass- and tree-dominated sampling locations.
Figure 6 shows a relatively large variation in ΔC / N*G-T values
across the transect, which appears to be strongly controlled by soil
textural characteristics. Negative ΔC / N*G-T values were
observed for relatively coarse-textured soils across the wide range of
ecosystems studied, indicating that “Tree” sampling locations exhibited a
greater relative change in C / N ratios between the two contrasting SOM
fractions than their `Grass' counterparts. This implies that, at those
sites, the processing of SOM was potentially faster in tree-dominated
locations compared to those dominated by grass. The role of trees in
preserving soil water on coarse-textured soils in semi-arid environments may
be very relevant at the canopy level due to their provision of shade and the
funnelling of precipitation with associated improvements in soil water
availability (Abbadie et al., 2006; Mordelet et al.,
1993; Ong and Leakey, 1999). Moreover, these ecosystems traditionally suffer
from a recurrent loss of herbaceous cover through fire and/or overgrazing
(Saiz et al., 2015), which have an adverse impact on the amount of fresh
organic inputs returned to the soil, an aspect that may be particularly
severe in grass-dominated locations (Abbadie and Bismarck
Nacro, 2006). These factors, together with the comparably higher SOM
contents observed in tree-dominated locations (Fig. 3a), may all result in
the persistence of suitable environmental conditions further promoting the
activities of SOM decomposers at these localities.
In addition to growing on relatively coarse-textured soils, the transitional
sites had a much lower abundance of C4 vegetation (Fig. 3b) which seems
is progressively being replaced by newly established C3 plants as a
result of woody thickening (the reader is referred to the next section for
more details). This might result in low C / N_LFG values,
and consequently an overall negative ΔC/N*G-T. On the other
hand, positive ΔC/N*G-T values were only evident in open
grass-dominated ecosystems occurring on fine-textured soils. Besides
maintaining the herbaceous cover for longer than Sahelian ecosystems (HOM
sites), these soils also have a greater water retention capacity compared to
sites on coarse-textured soils, which may diminish the role of trees as key
factors for maintaining suitable conditions for the activities of SOM
decomposers.
Further to site-specific soil stabilization mechanisms and differences in
the C / N composition of the precursor biomass, it is also likely that
differences in the organic biochemical composition of C3 and
C4-derived litter represent an inherent primary control on their
respective decomposition rates (Meentemeyer, 1978; Melillo et al., 1982).
Indeed, the recalcitrance of plant biomass to degradation is a function of
how polymers (e.g. lignin and cellulose) crosslink and aggregate within cell
walls (McCann and Carpita, 2008), and it is well recognised that the
composition of cell walls of grasses greatly differs from those of most
other angiosperms (Carpita and Gibeaut, 1993). Abbadie
and Bismarck Nacro (2006) showed the preponderant role that grass root
mineralisation plays in primary production for savannas where the axylale
layer was almost exclusively composed of C4 grass perennials, which
also provides support for the grass component being very dynamic. Recent
work suggests litter carbon chemistry is a key factor controlling litter
decay through its effect on microbial substrate use efficiency (Cotrufo et
al., 2013). While the latter is not the focus of the present work, our
findings broaden the possibilities for further research to be conducted on
preferential substrate utilisation of grass-derived carbon by microbial
communities in mixed C3/C4 ecosystems.
Laboratory-based work has reported different turnover times for tree and
grass-derived carbon in soils from mixed C3/C4 ecosystems
(Bird and Pousai, 1997; Wynn and Bird, 2007). However, the
findings of such studies cannot be universally extrapolated given that these
controlled experiments could not account for the contrasting environmental
conditions likely experienced by “Grass” and “Tree” sampling locations that
usually occur in real field settings. On the other hand, the work by
Wynn and Bird (2007) agrees well with our view that, under
comparable (site-scale) environmental conditions, C4-derived SOM
decomposes faster than SOM derived from woody biomass in mixed
C3/C4 ecosystems. Our results further suggest that soil textural
properties exert a strong influence on SOM dynamics across the ecosystems
examined along the transect. We postulate that the interdependence between
biotic and abiotic factors ultimately determine whether SOM dynamics of
C3- and C4-derived vegetation differ from each other in ecosystems
where both vegetation types coexist.
Vegetation shifts along the precipitation transect
The differences in δ13C values between the surface (0-0.05 m)
and the 0.3–0.5 m interval provide evidence of recent vegetation shifts
across the precipitation gradient (Fig. 7). Even after considering the
relatively small 13C enrichment with depth typically observed in
semi-arid soils (Bird et al., 2004; Krull et al., 2005), our
results suggest a significant increase in woody vegetation at the wetter
sites along the transect. The trend in isotope composition with depth is
also heavily dependant on the characteristics of the soils, which have also
been identified as one of the main determinants of vegetation type observed
at each site (Saiz et al., 2012). Vegetation thickening reached its maximum
in the transitional ecosystems (BFI sites; Fig. 2), which constitute the
natural border between savanna and dry forests on the transect. The
vegetation dynamics occurring at each end of the precipitation gradient were
quite different (Fig. 8). On the one hand, the wettest end of the transect
corresponded to a semideciduous dry forest (ASU-01) showing quite a stable
δ13C composition of SOM with depth, which agrees well with the
long-term persistence of a closed canopy ecosystem. On the other hand, the
arid Sahelian ecosystems studied (HOM sites) may have experienced a
relatively recent reduction in woody cover as evidenced from the large
enrichment in 13C towards the surface. There may be several reasons
behind this potential thinning of woody biomass at the driest sites, with a
combination of overgrazing, fuel harvesting, fires, and above all the severe
droughts suffered over the past few decades, being the most likely causes
(Krull et al., 2007). However, Sahelian ecosystems are known to
have a large resilience to drought and grazing, to the extent that woody
plant population dynamics can largely vary between nearby sites as a result
of contrasting substrates, grazing intensities, land use history, and
species composition (Hiernaux et al., 2009a; Mougin et al., 2009).
Vegetation shifts at a given site may alter the quantity and quality of OM
inputs into the soil, as different vegetation types are likely to have
distinct biochemical and physiological characteristics. Therefore, these
vegetation dynamics will have significant impacts on the total production
and allocation of biomass and on its mineralization potential
(Melillo et al., 1982; Wynn and Bird, 2007). Moreover,
fundamental differences in δ13C fractionation dynamics have
been reported for different soils and types of organic matter (Krull and
Skjemstad, 2003), and it is therefore of paramount importance to
unambiguously evaluate the impact that vegetation shifts may have exerted on
SOM dynamics in the past.
Effect of vegetation thickening on SOM dynamics in two
neighbouring transitional ecosystems
As has been previously discussed, it is difficult to assess past vegetation
dynamics in isolation from the confounding effects posed by the interactions
between climatic and edaphic factors. However, a Natural Reserve in central
Ghana provided the ideal conditions to study potential shifts in vegetation
in two structurally different ecosystems occurring under the same climatic
conditions given that the sites are < 1 km from each other. The two
sites are classified as transitional ecosystems, each with vegetation widely
divergent from the other (Table 1). BFI-02 is a savanna woodland with a
relatively sparse tree canopy cover (0.6) that allows the development of a
thin grass stratum. BFI-04 is a semideciduous dry tall forest with an almost
complete tree canopy closure (0.8). For a detailed floristic composition of
these sites, refer to Domingues et al. (2010) and
Torello-Raventos et al. (2013).
The detailed study of these two sites revealed a close concordance in
δ13C values along the soil profile between bulk SOM and the
fractions associated with silt and clay (s+c), and to a lesser degree to
sand-size aggregates (HF; Fig. 9). The only obvious exception to this general
trend was the surface layer (0–0.05 m), where a significant proportion of
the OM may not be associated with mineral phases. The observed decrease in
both SOC contents and C / N ratios with depth at both sites (Table 1) is a
commonly reported observation indicating that deep SOM is usually highly
processed by microbes (Rumpel and Kögel-Knabner, 2011). It is also worth
noting the large differences in C / N ratios between LF and the rest of the
fractions, which confirm their contrasting degrees of decomposition.
Fractionation of OM down soil profiles has previously been used to isolate
fresh OM inputs derived from current vegetation (Krull et al.,
2005; Marin-Spiotta et al., 2009). Indeed, the LF had significantly lower
δ13C values compared to the bulk soil, with this difference
increasing with depth at both sites. This is consistent with higher
contemporary C3 vegetation input throughout the soil profile than was
the case at some time in the past. On the other hand, the increasing δ13C values with soil depth is another commonly reported feature that
may be explained by a combined effect of several factors, which include OM
decomposition processes (i.e., progressive significance of microbial and
fungal decay to the SOM pool), the influence of carbon fixed from the higher
δ13C atmosphere that existed prior to significant fossil fuel
burning, and stabilization mechanisms influenced by specific properties of a
given soil (Bird et al., 1996; Ehleringer et al., 2000; Krull et
al., 2003). However, the magnitude of the increase in δ13C
values, and more specifically the large differences observed in δ13C between fresh organic matter (LF) and other fractions, are critical
indicators that the vegetation at these sites is undergoing significant
change (Krull et al., 2005).
It is worth reflecting on the very different τ observed between the
two sites. Both ecosystems present highly dynamic SOM processes, highlighted
by the relatively short τ of sand-size aggregates (> 53 µm HF) even at considerable depth (1.5–2.0 m; Fig. 9). Interestingly,
the most stable SOM fraction associated with silt and clay (< 53 µm s+c) has a shorter radiocarbon age in the savanna woodland (1450
y BP) than in the pure C3 dry forest site (3445 y BP). The main reason
for the contrasting τ observed in comparable fractions between the two
sites may be the different input rates of belowground OM. There is
increasing evidence that SOM turnover is mainly controlled by its
availability to decomposers (Don et al., 2013; Dungait et al., 2012). These
sites are characterised by a low abundance of SOM, particularly at depth,
and it is likely that woody encroachment has led to an increase in recent
organic matter inputs below the topmost soil as a result of the
larger/deeper root systems characteristic of woody vegetation
(Boutton et al., 2009). This process may have been
more pronounced in the savanna woodland (BFI-02 site) with the previously
more abundant grassland stratum being gradually replaced with woody
vegetation, as demonstrated by the large shift in δ13C values
towards the surface (Fig. 9). While both sites show a strong reduction in
root biomass with depth, this trend is more obvious in the case of the dry
tall forest (BFI-04), which shows a lower belowground biomass content over
the entire soil profile compared to the savanna woodland (BFI-02; Table 1).
This observation is in agreement with findings reported by Lawson et
al. (1970, 1968) in work conducted in a savanna and a tropical deciduous
forest in Ghana. Butler et al. (2012) also compared two structurally
contrasting savannas, and found that the more open canopy savanna had a
proportionally higher carbon investment belowground. Recent findings
reported by Don et al. (2013) show that any increase in the carbon
concentration down the soil profile decreases the distance between
decomposer and substrate, which may increase accessibility and SOM turnover.
Therefore, the enhanced contribution in belowground organic matter inputs in
the savanna woodland as a result of woody encroachment could largely explain
its comparatively shorter τ across all soil fractions compared to the
dry forest (Figs. 9, A1).
It is well established that the physical protection of SOM by aggregation
mechanisms plays a fundamental role in soil carbon stabilisation (Denef
et al., 2001; Six et al., 2004). Root-derived particulate organic matter has
a significant control on aggregate dynamics (Six et al., 2004) as the
stabilization and de-stabilization of macroaggregates in soils is strongly
linked to the incorporation and biodegradation of fresh plant debris (Puget
et al., 2000). This fresh OM is eventually redistributed among aggregate
classes through their destruction and re-formation. Compared to grasses, the
comparatively more recalcitrant OM input from woody vegetation can promote
lower microbial substrate use efficiency, which may have a negative effect
on aggregate stability (Cotrufo et al., 2013). Furthermore, a combination
of δ13C and 14C measurements has shown that even very
stable soil structures, such as iron nodules containing occluded carbon, do
not act as closed systems with respect to organic carbon exchange (Bird et
al., 1994). These observations together with the findings presented in this
study, further suggest that carbon transfers within the soil matrix are
highly dynamic, especially if impacted by recent shifts in vegetation type
(Guidi et al., 2014). It is therefore highly likely that the different τ observed between the two sites is strongly influenced by
vegetation-related factors, especially when considering that there are only
minor differences in soil physical properties (i.e. texture, mineralogy –
cf. Table 1 in Saiz et al., 2012), which are, by themselves, insufficient to explain such
contrasting τ between the two sites. Therefore, making the reasonable
assumption that both stands have been exposed to comparable deposition and
erosion regimes; our results strongly suggest that both ecosystems are
undergoing a rapid transition from open woodlands to denser canopy
formations. However, such vegetation thickening varies in intensity at each
site, and this exerts contrasting effects on their SOM dynamics (Fig. A1).
Conclusions
The first objective of this study was to assess the influence of C3 and
C4 vegetation on SOM dynamics in semi-natural tropical ecosystems
sampled along a precipitation gradient in West Africa. This work shows that
the interdependence between biotic and abiotic factors ultimately determine
whether SOM dynamics of C3- and C4-derived vegetation are at
variance in ecosystems where both vegetation types coexist. Our results
suggest that soil textural properties exert a strong influence on the
contrasting SOM dynamics observed across the precipitation gradient.
Accordingly, C4-derived SOM decomposes faster than SOM derived from
woody biomass in mixed C3/C4 ecosystems, provided comparable
(site-scale) environmental conditions exist. This is in agreement with
previous research conducted under controlled environmental conditions
(Wynn and Bird, 2007). Moreover, studies conducted in West
African savannas as well as pantropical productivity estimates both show
that more than half of the net primary productivity of these ecosystems is
attributable to C4 grasses (Lloyd et al., 2008; Menaut and Cesar,
1979). This agrees well with the notion that, at least in non-coarse
textured soils, SOC sequestration potential per unit productivity must be
inherently lower for C4 dominated locations, which is further confirmed
by the comparatively lower SOC concentrations observed at grass-dominated
locations (Saiz et al., 2012).
The second objective of our study was to investigate potential variations in
tropical vegetation shifts along the precipitation transect. Vegetation thickening was significant at the more humid sites, in the zones
of tension where forest and savanna formations coexist. Our findings reveal
that current environmental conditions favour the expansion of C3
species over their C4 counterparts in the more mesic savanna ecosystems
of West Africa. Such vegetation dynamics pose significant impacts not only
on the total production and allocation of biomass, but also on its
mineralization potential.
The third objective was to unambiguously evaluate the effect of vegetation
thickening on SOM dynamics in two contiguous but structurally contrasting
transitional ecosystems occurring on comparable soils. Such a setting was
chosen to minimise confounding effects posed by climatic and edaphic factors
as fundamental differences in δ13C fractionation dynamics have
been reported for different soils and types of organic matter (Krull and
Skjemstad, 2003). Radiocarbon dating of SOM fractions together with the
vertical variation in δ13C values strongly suggest that both
ecosystems are undergoing a rapid transition towards denser closed canopy
formations. However, vegetation thickening varied in intensity at each site
and exerted contrasting effects on SOM dynamics. This study further
highlights the far-reaching implications that vegetation thickening has for
the stability of deep SOC, which has been shown to be heavily controlled by
fresh organic inputs (Fontaine et al., 2007). It also confirms that SOM
pools that have been stabilized for centuries to millennia may be
susceptible to abrupt change when soils cross pedogenic thresholds
associated with rapid shifts in vegetation (Trumbore, 2009).
Our findings have significant implications for a lot of carbon cycle science
that exploits the carbon isotopic difference of C3/C4
photosynthetic pathways. This includes research work that uses the variation
in soil δ13C values to unravel past vegetation shifts and their
impact on SOC storage, paleoenvironmental interpretations, and modelling of
ecosystem carbon budgets (Lloyd et al., 2008; Saiz et al.,
2015; Wynn and Bird, 2007).