soil carbon storage controlled by interactions between ... · carbon, the stability of organic...
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The role of geochemistry-climate interactions on soil carbon storage
Sebastian Doetterl, Antoine Stevens, Johan Six, Roel Merckx, Kristof Van Oost, Manuel Casanova
Pinto, Angélica Casanova-Katny, Cristina Muñoz, Mathieu Boudin, Erick Zagal Venegas, Pascal
Boeckx
Supplemented information
SI 1 Supplementary Discussion
1.1 Interpretation of the partial correlation analysis
The variable importance assessment on the best predicting models (Tables 1, S5) shows very clearly
that precipitation, as a predictor variable, plays a minor role when compared to geochemical predictors
when building prediction models for SOC response variables.
However, this does not mean that the importance of precipitation as a driving factor for SOC dynamics
can be dismissed. But from our data we conclude that geochemistry has higher information content.
Partial correlations support the conclusions of this analysis, showing that the correlations of SOC
response variables with precipitation drop to zero when the correlation is controlled for the
geochemical variables (Figures 2 & S2). One reason for this is that precipitation does not include
information on the geochemical features of the parent material. On the other hand, precipitation has a
similar effect on the correlation of Si and BS with SOC variables (extended data figure S2, extended
data table S8). This is due to their high dependency on precipitation, as precipitation is the main driver
for the chemical weathering, and hence loss and alteration of minerals (see for example 37).
In summary, the data indicate a strong interrelation between geochemical soil properties and climatic
conditions along the investigated transect, predominantly through precipitation patterns and not
through temperature patterns. The control of temperature on geochemical predictors was not
significant along the investigated transect. However, temperature does have an influence on the
Soil carbon storage controlled by interactions between geochemistry and climate
1
The role of geochemistry-climate interactions on soil carbon storage
Sebastian Doetterl, Antoine Stevens, Johan Six, Roel Merckx, Kristof Van Oost, Manuel Casanova
Pinto, Angélica Casanova-Katny, Cristina Muñoz, Mathieu Boudin, Erick Zagal Venegas, Pascal
Boeckx
Supplemented information
SI 1 Supplementary Discussion
1.1 Interpretation of the partial correlation analysis
The variable importance assessment on the best predicting models (Tables 1, S5) shows very clearly
that precipitation, as a predictor variable, plays a minor role when compared to geochemical predictors
when building prediction models for SOC response variables.
However, this does not mean that the importance of precipitation as a driving factor for SOC dynamics
can be dismissed. But from our data we conclude that geochemistry has higher information content.
Partial correlations support the conclusions of this analysis, showing that the correlations of SOC
response variables with precipitation drop to zero when the correlation is controlled for the
geochemical variables (Figures 2 & S2). One reason for this is that precipitation does not include
information on the geochemical features of the parent material. On the other hand, precipitation has a
similar effect on the correlation of Si and BS with SOC variables (extended data figure S2, extended
data table S8). This is due to their high dependency on precipitation, as precipitation is the main driver
for the chemical weathering, and hence loss and alteration of minerals (see for example 37).
In summary, the data indicate a strong interrelation between geochemical soil properties and climatic
conditions along the investigated transect, predominantly through precipitation patterns and not
through temperature patterns. The control of temperature on geochemical predictors was not
significant along the investigated transect. However, temperature does have an influence on the
SUPPLEMENTARY INFORMATIONDOI: 10.1038/NGEO2516
NATURE GEOSCIENCE | www.nature.com/naturegeoscience 1
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availability of water for biochemical reactions and is non-linearly correlated to SOC storage (See
interactive figure). Although precipitation has a strong influence on the correlation between SOC
response variables and geochemical predictors, some relationships are unaffected by climatic
conditions (i.e. Al or clay content) which are predominately related to the mineralogy of the parent
material and its weathering status.
Indirect and direct control on soil carbon storage
We defined a predictor as a direct control if a change in the predictor triggers a direct response of
the concerning SOC variable. A predictor is defined as an indirect control if change in the predictor
triggers change in another factor that in turn influences the investigated SOC variable directly. For
example, increased precipitation leads to higher soil reactivity and net primary productivity (NPP),
hence stimulating the association of carbon with minerals resulting in higher SOC stocks and lower
specific respiration rates (Figure 1).
Naturally, precipitation affects our geochemical indices as a control on chemical weathering and also
indirectly through influencing plant (NPP) and microbial activity, which could induce biological
driven mineral weathering, producing biogenic silica to stabilize C, etc. All variables in our model
have to be seen as proxies for the key environmental conditions controlling SOC dynamics. However,
these proxies are either directly related to the actual processes controlling SOC dynamics, and hence
more exact (geochemical variables), or further removed from these processes and rather integrative
across a wide range of factors (climate variables).
1.2 Caveats and other influences on soil carbon stabilization
In this section we discuss further factors controlling SOC stabilization in soils: this discussion covers
the main elements of our current knowledge on the factors influencing SOC stability in soils, i.e. (I)
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Temperature sensitivity of different carbon pools, (II) physical and geochemical protection
mechanisms of SOC in soils, (III) molecular structure and recalcitrance of molecules to
decomposition, (IV) potential sources for SOC priming or nutrient limitation in soils, (V) microbial
community structure and enzyme activity as well as (VI) environmental controls (soil temperature,
soil moisture and oxygen limitation).
Chemically, one of the most important factors for stabilization of carbon in soils is availability of
mineral surfaces for sorptive protection of SOC38. Among the factors that determine the availability
of mineral surfaces for sorptive stabilization are soil texture and soil mineralogy, which control the
chemical interactions that SOC forms with the mineral phase10. Reactive soil minerals and availability
of sorptive surfaces also contribute towards the formation of soil aggregates that provide physical
stabilization of SOC, by allowing the SOC to become encapsulated inside aggregate spaces where the
diffusion of oxygen, water, and enzymes needed for breakdown of organic matter is limited39. Clay-
sized minerals tend to be the most important mineral constituents of organo-mineral associations given
their high specific surface area and hydroxylated reactive surfaces40. Reactivity of silicate minerals
with SOC depends on their type (expandable 2:1 versus non-expandable 2:1 or 1:1 phyllosilicate clay
minerals) and size (i.e. specific surface area). However, there is an increasing number of studies that
show that the relationship between surface area/clay content and organic matter accumulation is not
as straight-forward as previously assumed. Vogel et al.41 showed that carbon is preferentially
stabilized in certain hot-spot zones where stabilized C is already present, and that only a limited
portion of the clay-sized surfaces contribute to SOC sequestration. This is in line with studies on
grassland showing the limited importance of clay content for explaining C accumulation42. In andic
soils, the accumulation of reactive Fe-, Mn-, and Al-oxy-hydroxides rather than silt or clay content
and climate conditions, was identified as the most important factor that controls organic matter (OM)
levels43,44.
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In addition, certain C compounds in soil, such as fire-altered C (also known as black carbon (BC) or
pyrogenic C (PyC)), lignin, and lipids are assumed to be ‘inherently recalcitrant’ and remain stable in
soils over centuries to millennia45,46. However, recent studies indicate that this may only hold true for
short-term changes (< 10 yrs), while environmental variables of SOC decomposition become more
important on longer time scales. Carbon quality can further influence decomposition rates as fresh,
undecomposed material can be an essential source of energy for decomposers to volatize more
recalcitrant organic matter, an effect commonly described as priming47. In the absence of fresh organic
carbon, the stability of organic carbon might be maintained. Similarly, soil organic matter turnover is
affected by the size and the diversity of microbial populations, which is in return influenced by the
supply of energy-rich litter compounds48. Hence, carbon stability in soils depends on a complex
interplay of bio-geochemical factors, constrained by environmental conditions3,49.
The environmental controls on stability of SOC19 ,50,51,52 include availability of optimal temperature
and moisture conditions for C sequestration through plants photosynthesis and C release through
decomposition by microorganisms. For example, studies have shown that soil humidity53,54,
aeration55,56 and soil temperature3 are key controls on microbial activity and hence SOC stability.
Physical inaccessibility as dictated by aggregation, and spatial separation of decomposers and SOC in
soils19,57,58 are further environmental controls on SOC persistence. Finally, it is likely that each of
these variables interact in a complex manner affecting SOC stability49,59. For example, organo-mineral
associations can, in addition, be physically separated from decomposers or placed in a low oxygen,
water saturated environment. Similarly, the temperature sensitivity of C decay is dependent on its
chemical properties. More biogeochemically recalcitrant organic matter has greater temperature
sensitivity than more labile material59.
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1.3 Interpreting the role of clay for stabilizing SOC.
Despite the well-investigated relationship found between higher clay content19, or short range ordered
minerals60 and higher SOC stocks and slower turnover, clay content did not emerge as an important
correlate in our analysis for SOC storage (Table S5). First, our dataset does not span the full range of
values needed to identify clay as a strong driver for SOC stocks. Studies illustrating the importance
of clay content are usually spanning 0-80% clay19. In our study, except for one observation, clay
content is between 8-24%.
Finally, by integrating geochemical soil properties such as Si and soil texture into the same analysis,
clay content will not pop-up as an important predictor if its explanatory power is covered already by
Si or another variable, which contains more information than the clay content alone. Furthermore,
Percival et al.42 could show that in New Zealand grassland soils, clay content relates only poorly to
long-term soil organic C accumulation, while other geochemical characteristics had a much higher
explanatory power. In addition, recent studies61 indicate that the type of clay minerals (expandable vs.
non-expandable clay minerals) and the presence of very reactive Al and Fe-hydroxides are more
important parameters to explain correlations of SOC with minerals than clay content by itself.
1.4 The time-scale of the soil mineral reactivity response to climatic changes and its effect on SOC
dynamics
The alteration of minerals can act very rapidly in areas where the degree of former weathering is low
and both water and heat are sufficiently available to fuel (bio)chemical weathering15,62 of minerals.
Hence, weathering rates can increase sharply under certain conditions, bringing them very close to
those of C stabilization. Empirical data from chronosequence studies63,64 and current soil weathering
models65 suggest exponential weathering curves, meaning that less weathered material at the surface
will change very rapidly in the beginning and this rates decrease with time or soil depth. Examples of
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an increase in weathering rates due to global warming are arctic soils or forefields of retreating
glaciers, where organic matter is stabilized by association with minerals. A series of studies on recent
warming include arctic regions66 or areas of glacial retreat67 showed that a less weathered soil,
experiencing suddenly higher chemical weathering can respond very rapidly (annual to decadal
timescales) in stabilizing C with minerals. The mineralogy in areas with low amounts of available
water and/or low temperatures will then react particularly strongly to climatic changes. For example,
in arctic soils temperature increase can lead to higher chemical reactivity of these soils, which might
enhance the stabilization of SOC with the mineral phase and outbalance to some extent higher
decomposition rates66. In soils from hot arid areas, C dynamics will most likely not be affected by a
temperature increase, but higher amounts of precipitation might stimulate the weathering cycle and
the biologic activity. In tropical areas, reactive minerals have been washed out and altered to less
reactive forms due to millions of years of weathering. Hence, temperature or precipitation changes
will likely not lead to significant changes in mineral reactivity. In humid temperate zones, where
mineral weathering is not as advanced as in the tropics due to rejuvenation of the soil landscape during
glacial cycles, the weathering cycle might be stimulated by higher temperatures and precipitation.
However, the effect of this on SOC dynamics cannot be fully detangled from NPP increases, as
conditions for higher chemical weathering are also conditions where plant life can thrive68,69. SOC
stocks in permafrost soils, for example, restructure very rapidly by losing unprotected POM after
thawing, but at the same time increase C stabilization with minerals and experience higher NPP after
warming takes place66. Furthermore, a system with low NPP can still have high SOC stocks if
conditions for stabilization are favorable in soils and vice versa. Support for this claim is shown in
Figure 1: Areas with presumably high NPP are at the same time areas where most of the C is stabilized
with minerals, as soil reactivity/weathering is high under these conditions. Potential specific
respiration rates (normalized for SOC content) deliver further support for our claim showing that the
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potential specific respiration rate in areas with mineral stabilized C is very low, indicating high
stability and, hence, tendency to accumulate. The opposite can be observed for the profiles in the hot
arid and cold arid climate zones.
Some of our sites in climatically extreme conditions might face rapid changes in SOC stocks if
chemical weathering rates increase, given the high amount of primary minerals and the low amount
of weathering they have been confronted with in the past. The existing research on SOC dynamics in
these areas suggests that C stabilization mechanisms like aggregation and association of C with clay
particles are likely to respond very rapidly to increased C input and chemical weathering.
1.5 Distribution of SOC fractions
Similar to SOCStock and SPR, geochemical variables are of great importance for predicting SOC
fractions (Table S5) and resulted generally in good predictions (R2 = 0.39 - 0.79), while precipitation
or temperature hold little prediction power (Table S4; Extended Figure 2). In contrast to the main C
response variables described above, soil texture (silt and clay content) was identified as an important
predictor for the CPOM (silt negatively correlated to CPOM) and the s+c associated fraction (clay
positively correlated to s+c). However, soil texture was not identified as an important variable for the
aggregate associated C fraction. One possible explanation could be that aggregate C consist of both
particulate organic matter and silt and clay19 associated C. As a consequence, the formation of
aggregates is dependent on high C input rates and a suitable mineralogical set-up in a chemically
highly reactive system. This is supported by our observations on the spatial distribution of the three
investigated fractions: silt and clay associated C does not show a distinct latitudinal pattern and is
likely the fraction with the biggest dependency on geochemical factors. However, it can contribute
significantly to total SOCStock (up to 47%), both in areas where CPOM or aggregate associated C are
high. Similar to the SOCMicrobial and SOCMineral pools, the CPOM and aggregate-associated C fractions
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8
follow opposing spatial trends. The CPOM associated C fraction is largest (up to 57% of SOCStock) in
climatically extreme regions (hot or cold arid) with low chemical weathering rates, small SOCStock
and presumably small C input rates. In contrast, the microaggregate associated C fractions is largest
(up to 79% of SOCStock) where chemical weathering rates and SOCStock are high, with presumably
high C input rates to soils (warm-humid) (see Table S1; S3 and interactive graph). In conclusion,
similar as for the investigated main SOC response variables, the distribution of SOC fractions reflects
the complex interplay of climatic and geochemical factors, (Figure S2, Table S8) resulting in a
complex spatial pattern. In conclusion, changes in some of the identified controls might lead to a
strong differential response of these fractions, but not necessarily affect SOC storage, respiration or
turnover in the same way.
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Geoderma 247-248, 73-87 (2015).
62. Song, Z., Müller, K. & Wang, H. Biogeochemical silicon cycle and carbon sequestration in agricultural
ecosystems. Earth-Sci. Rev. 139, 268-278 (2014).
63. Cornelis, J.T. et al. Silicon isotopes record dissolution and re-precipitation of pedogenic clay minerals
in a podzolic soil chronosequence. Geoderma 235-236, 19-29 (2014).
64. Alexandrovskiy, A.L. Rates of soil-forming processes in three main models of pedogenesis. Rev. Mex.
Cienc. Geol. 24, 283-292 (2007).
65. Finke, P. & Hutson, J. Modelling soil genesis in calcaerous löss. Geoderma 145, 462-479 (2008).
66. Sistla, S.A. et al. Long-term warming restructures Arctic tundra without changing net soil carbon
storage. Nature 497, 615-618 (2013).
67. Duemig, A., Smittenberg, R. & Koegel-Knabner, I. Concurrent evolution of organic and mineral
components during initial soil development after retreat of the Damma glacier, Switzerland. Geoderma,
163, 83-94 (2011).
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10
68. Elmendorf, S.C. et al. Plot-scale evidence of tundra vegetation change and links to recent summer
warming. Nat. Clim. Chang. 2, 453-457 (2012).
69. Pearson, R.G. et al. Shifts in Arctic vegetation and associated feedbacks under climate change. Nat.
Clim. Chang. 3, 673-677 (2013).
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11
Extended data
SI 3 Extended data tables
Table S1. Basic characteristics of selected and sampled sites.
(- = no data available, STR = soil temperature regime, SMR= soil moisture regime, MAP= mean annual precipitation, MAT= mean annual temperature [Period: 1950-
2000]).
Site Local name Soil Series Chile Region WGS1984 X WGS1984 Y Elevation STR SMR MAP MAT
(ddd,ddd) (ddd,ddd) m asl mm yr-1 °C Soil Taxonomy (USDA) WRB (ISRIC)
1 Las Cardas Tambillo IV -71.25071 -30.20157 242 Thermic Aridic 98 15.2 Cambidic Haplodurid Petric Durisols
2 Los Vilos Los Vilos IV -71.50008 -31.81284 95 Isothermic Aridic 208 16.4 Torric Psamment Arenosol (Eutric)
3 Los Andes Calle Larga V -70.52162 -32.87609 768 Thermic Xeric 349 14.6 Typic Argixeroll Luvic Kastanozem / Luvic
Chernozem
4 Alhué Pudahuel MR -71.14047 -33.99911 194 Thermic Xeric 450 16.6 Vitrandic Durixeroll Duric Kastanozem / Duric
Chernozem
5 Maipú Cuesta Barriga MR -70.83242 -33.47780 489 Thermic Xeric 401 13.8 Typic Haploxerolls Kastanozem/Chernozem
6 San Antonio Bochinche V -71.61243 -33.33901 159 Isothermic Ustic 580 15.1 Typic Haploxeroll Kastanozem/Chernozem
7 Matanzas Matanzas VI -71.87564 -33.96865 124 Isothermic Ustic 568 16.9 Oxic Haplustoll Chernozem / Kastanozem /
Phaeozem
8 Chillán Santa Bárbara VIII -71.69721 -36.45816 452 Thermic Xeric 1321 12.0 Typic Haploxerand Andosol
9 Arauco Carampangue VIII -73.26686 -37.25344 13 Isomesic Udic 1431 13.4 Fluvaquentic Cambisol (Dystric)
10 Puerto Saavedra Peule IX -73.38968 -38.77412 3 Isomesic Udic 1205 13.0 Typic Endoaquept Gleysol
11 Panguipulli Choshuenco XIV -72.11120 -39.85941 263 Isomesic Udic 2108 11.0 Andic Dystrudept Cambisol (Dystric)
12 Corral Hueicoya XIV -73.41283 -39.93245 6 Isomesic Udic 2174 11.6 Typic Haplohumult Acrisol (Humic)
13 Purranque Corte Alto X -73.15404 -40.90340 118 Isomesic Udic 1456 10.9 Typic Hapludand Andosol
14 Chiloé Island Pachabrán X -73.82429 -42.42186 204 Isomesic Perudic 2233 9.9 Histic Duraquand Petroduric Histic Andosol
15 Chiloé Island Aituí X -73.61712 -43.05791 30 Isomesic Perudic 2232 10.8 Hydric Fulvudand Fulvic Andosol
16 La Junta La Junta XI -72.39653 -43.96393 43 Isomesic Perudic 2308 10.3 Thaptic Hapludand Thaptic Andosol
17 Chacabuco Aisén XI -72.81482 -45.48736 33 Mesic Udic 2120 6.7 ~Andic Dystrudept Cambisol (Dystric)
18 Coyhaique Simpson XI -72.90757 -45.78639 462 Mesic Udic 1524 3.2 Andic Dystrudept Cambisol (Dystric)
19 Puerto Sánchez Murta XI -72.61276 -46.56823 330 Mesic Udic 1048 7.0 Typic Hapludand Andosol
20 Puerto Natales Ultima Esperanza XII -72.16488 -51.80696 67 Isomesic Udic 394 6.5 ~Mollisol Kastanozem
21 Punta Arenas Agua Fresca XII -70.98860 -53.43267 58 Cryic Udic 620 6.2 ~Entisol Leptosol
22 Porvenir Santa Olga XII -70.36106 -53.31478 59 Cryic Udic 483 6.3 ~Entisol Leptosol
23 Admiralty Bay King George Island - -58.46611 -62.16196 52 Cryic Ustic 797 -2.9 Gelisol Cryosol
24 Byers Peninsula Livingstone Island - -61.08332 -62.65007 5 Cryic Udic 648 -2.3 Gelisol Cryosol
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12
Table S2. Description of dominant and secondary vegetation for all sampling sites.
Values for biomass dry weight are taken from Ruiz13 for Chile (Sites 1-22) and from Barcikowski et al.14 for the
Antarctic Peninsula (Sites 23-24).
Site Local name Biomass
Dry weight kg
ha-1 yr-1
Vegetation
Dominant + (secondary)
1 Las Cardas 300-600
Woody plants : Colliguaya odorífera, Trichocereus chilensis, Fluorensia thurifera, Acacia
caven Grasses: Erodium cicutarium, Adesmia tenella, Vulpia dertonensis, Plantago hispidula,
Erodium moschatum, Stipa lacchnophylla, Pectocarya dimorpha,
2 Los Vilos 400- 800 Woody plants: Bahia ambrosioides, Puya chilensis, Oxalis gigantean, Fuchsia lysioides. Grasses: Nassella chilensis, Piptochaetium stipoides, Dichondra repens, Trifolium
megalanthum
3 Los Andes 800-2000 Woody plants: Acacia Caven
Grasses: Plantago firma, Adesmia angustifolia, Adesmia tenella , Medicago polymorpha , Plantago hispidula, Aira caryophillea , Avena barbata
4 Maipú-Alhué 1000-2500 Woody plants: Acacia caven
Erodium moschatum , Avena barbata, Bromus hordeasus, Vulpia dertonensis, Raphanus sativus + Chaetanthera chilensis, Aira caryophyllea
5 Maipú 1000-2500 Woody plants: Acacia caven, Baccharis linearis
Grasses: Trisetobromus hirtus, Erodium moschatum, Avena barbata, Bromus hordeasus,
Vulpia dertonensis, Raphanus sativus, Chaetanthera chilensis and Aira caryophyllea )
6 San Antonio 1200-3500 Woody plants: Baccharis linearis
Grasses: Lolium multiflorum, Medicago polymorpha, Bromus hordeasus, Leontodon
taraxacoides, Nasella chilensis
7 Matanzas 1500-3500 Grasses: Lolium multiflorum, Briza máxima, Briza minor, Medicago polymorpha, Trifolium subterraneum, Hordeum murinum
8 Chillán 2000-5000 Grasses: Stipa neesiana, Piptochaetium stipoides, Lolium multiflorum, Briza máxima Bromus
hordeasus Hordeum murinum , Plantago lanceolata, Medicago polymorpha
9 Arauco 3000-6000 Grasses: Holcus lanatus, Trifolium repens, Agrostis tenuis, , Lotus uliginosus, Taraxacum
officinalis, Cynosurus echinatus
10 Puerto Saavedra 3000-6500 Trifolium repens, Holcus lanatus, Hypochoeris radicata, Plantago lanceolada,
11 Panguipulli 4000-7000 Holcus lanatus, Taraxacum officinale, Deschampsia Antarctica and Leucanthemum vulgare
12 Corral 5000-8000 Bromus valdivianus, Trifolium repens, Lotus uliginosus, Lolium perenne .Taraxacum officinale
13 Purranque 4000-6000 Grasses: Dactylis glomerata, Paspalum dilatatum, Trifolium repens
14 Chiloé Island 6000-8000 Grasses: Lotus uliginosus, Holcus lanatus, Hypochoeris radicata, Poa pratensis,
Leucanthemum vulgare.
15 Chiloé Island 6000-8000 Grasses: Trifolium repens, Dactylis glomerata, Agrostis sp., Taraxacum officinale.
16 La Junta 6000-8000 Grasses: Holcus lanatus, Lotus uliginosus s, Hypochoeris radicata, Plantago lanceolada +
Agrostis tenuis
17 Chacabuco ≈4000 Grasses: Lotus uliginosus, Holcus lanatus, Deschampsia berteroanum, Hypochoeris radicata, Dactylis glomerata, Arrhenatherum elatius,
18 Coyhaique 2000-5000 Grasses: Dactylis glomerata, Trifolium repens, Holcus lanatus, Agrostis tenuis, Acaena
pinnatifida, Geranium magellanicum
19 Puerto Sánchez 3000-5000 Grasses: Arrhenatherum elatius, Dactylis glomerata Geum magellanicum, Plantago lanceolata, Acaena pinnatifida, Anemone multifida, Rumex
acetosella 20 Puerto Natales 500-1000 Grasses: Poa pratensis, Acaena magellanica
Holcus lanatus, Deschampsia flexuosa, Agrostis sp., Festuca , Hypochoeris incana (Rumex
crispus + Achillea millefolium+ Dactylis glomerata), Agrostis capillaris 21 Punta Arenas 350-850 Grasses: Festuca pallescens, Hieracium aurantiacum, Trifolium repens, Agrostis spp.,
Festuca arundicnacea, Berberis buxifolia, Deschampsia Antarctica
22 Porvenir 350-850 Grasses: Chiliotrichioum diffussum, Baccharis magellanicum, Festuca pallescens, Poa
pratensis, Trifolium repens, Dactylis glomerata, Taraxacum officinale
23 Admiralty Bay, King George Island
500-2000 Grasses: Deschampsia antarctica + (Moss Herb Tundra)
24 Byers Peninsula,
Livingstone Island
500-2000 Grasses: Deschampsia antarctica + (Polytrichastrum alpinum)
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13
Table S3. Soil mineral and organic carbon parameters.
(CECpot = Potential cation exchange capacity; BStotal = Total base saturation of CECpot; TEC = Total elemental content; TRB = Total reserve in base cations; BD = Bulk
density; MD = Mineral density; SOC% = Soil organic carbon concentration; SOCStock = Soil organic carbon stock; Respiration = Specific potential CO2-C respiration
(SPR); SOCmineral = Mineral associated SOC; SOCmicrobial = Microbially available SOC; CPOM = Coarse particulate organic matter; s+c = Free silt and clay associated
C; m = (Micro-)aggregate associated C).
Site CECpot BStotal Texture TEC Al/Si TRB pHKCl BD MD SOC% SOCStock Respiration SOCMicrobial SOCMineral CPOM s+c m
2000-50 50-2 ˂2 µm Si Al Fe Mn P
0-10cm depth (SPR)
cmolc kg-1 % Vol % g kg-1 cmolc kg-1 g cm-3 g cm-3 g C kg-1 kg m-2 µg C-CO2 g-1 SOC h-1 % SOCStock (± rel. SD)
1 11.6 89 35 41 24 254 90 72 3.2 0.9 0.35 366 5.8 1.7 2.7 6.6±2.3 1.1±0.4 25.4±3.3 47±32 58±0.5 42±0.3 40±0.2 18±0.1
2 4.4 94 78 15 7 317 74 25 0.6 0.6 0.23 331 5.4 1.4 2.7 7.4±1.4 1.0±0.2 26.5±3.9 41±15 58±4.5 42±2.7 14±9.6 43±5.6
3 28.7 120 20 57 23 243 92 56 1.3 0.9 0.38 422 5.9 1.1 2.8 18.4±3.1 2.0±0.3 17.9±2.6 21±19 75±1.2 25±1.2 38±2.8 37±4.0
4 11.4 95 46 36 18 259 90 53 1.0 0.9 0.35 465 6.0 1.1 3.0 14.3±2.5 1.6±0.3 23.1±1.8 60±16 67±0.8 33±3.2 23±1.4 44±1.3
5 14.4 147 25 52 23 240 89 70 1.7 1.5 0.37 423 5.4 1.7 2.8 21.7±1 3.6±0.2 20.2±3.2 41±5 95±0.2 5±0.2 47±2.4 48±2.6
6 9.1 60 41 45 16 359 51 19 0.7 0.4 0.14 160 4.9 1.3 2.6 12.9±2.4 1.7±0.3 25.9±5.7 52±15 86±4.3 14±3.6 44±9.6 42±6.0
7 21.7 90 16 65 19 265 63 78 1.7 0.9 0.24 270 5.0 1.1 2.9 32.0±1.1 3.4±0.1 12.1±0.6 ≈1 64±2.2 36±3.2 16±1.6 48±1.6
8 33.9 15 21 66 13 203 101 58 1.8 2.2 0.49 197 5.0 0.8 2.4 60.4±4.8 5.0±0.4 3.8±0.4 8±7 97±2.4 3±2.3 46±1.2 51±1.1
9 16.4 32 16 68 16 287 84 32 0.5 0.7 0.29 185 4.1 1.6 2.6 26.7±7.4 4.3±1.2 12.2±3 28±24 64±0.4 3±0.8 46±6.1 51±7.3
10 28.2 58 10 67 23 249 90 35 0.4 1.1 0.36 278 4.5 1.1 2.4 43.9±1.7 4.9±0.2 15±2.3 89±3 96±0.6 36±0.5 23±0.3 41±0.9
11 25.7 14 61 31 7 221 80 50 1.1 1.7 0.36 429 4.5 0.8 2.6 74.7±9.3 5.8±0.7 6.8±0.6 5±10 70±2.4 4±1.5 38±1.2 59±6.4
12 19.4 7 38 57 6 265 74 35 0.5 0.9 0.28 248 3.9 0.8 2.6 57.0±1.6 4.6±0.1 9±1.2 45±2 76±2.9 30±9.3 13±1.8 57±9.3
13 46.6 30 15 45 40 179 103 70 2.0 2.2 0.57 128 4.5 0.8 2.5 77.3±2.4 6.3±0.2 7.7±3.1 5±3 90±4.0 10±3.6 38±1.0 52±6.5
14 46 11 15 62 23 200 60 48 0.4 1.2 0.30 114 4.2 0.6 1.7 123.4±9.8 7.3±0.6 7.8±0.4 4±7 89±1.8 11±1.6 37±0.2 52±1.8
15 50.4 20 32 60 8 174 48 38 0.5 2.2 0.27 117 4.6 0.6 1.7 148.3±8.5 8.2±0.5 6±2.3 ≈1 98±0.4 2±1.4 19±1.4 79±6.4
16 41.5 49 24 64 12 198 52 31 0.9 2.8 0.27 199 4.9 0.5 2.1 127.0±4.5 6.7±0.2 6.7±1.4 4±3 91±1.0 9±0.9 43±0.5 48±1.3
17 35.3 11 36 56 8 179 63 45 0.7 2.6 0.35 260 4.5 0.5 2.0 116.9±6.7 5.6±0.3 5.6±1 ≈1 88±0.6 12±9.6 11±2.5 77±9.2
18 27.4 40 24 52 19 227 85 59 1.2 2.5 0.38 445 5.3 0.7 2.6 43.4±6.5 3.2±0.5 9±1.7 28±13 91±3.0 9±2.8 43±1.3 48±1.6
19 38.6 45 41 45 14 184 99 58 1.9 3.2 0.54 301 5.3 0.6 2.4 74.4±13.5 4.7±0.9 7.6±1.8 8±16 65±5.9 35±1.1 11±1.9 54±1.9
20 27.6 88 43 45 12 273 63 28 1.2 1.4 0.23 247 5.1 0.7 2.6 70.2±5.5 5.0±0.4 18.7±1.4 11±7 69±1.0 31±4.9 10±1.7 59±1.1
21 38.3 93 41 39 20 259 59 25 1.6 1.5 0.23 223 5.5 0.7 2.5 94.9±5.4 6.7±0.4 9.1±1.8 39±5 94±0.1 6±0.1 30±1.7 64±1.8
22 29.3 72 28 51 21 282 62 28 0.9 1.5 0.22 213 4.6 0.8 2.5 67.1±2.2 5.3±0.2 14.8±1.9 36±3 94±2.7 6±2.5 24±1.2 70±1.3
23 35.2 87 45 37 19 229 98 61 1.1 3.3 0.43 555 4.2 1.7 2.4 13.2±1.3 2.3±0.2 10.7±2 19±24 43±2.3 57±1.1 14±6.6 30±4.6
24 3.2 158 81 11 8 273 75 48 0.8 1.4 0.27 438 3.8 1.7 2.5 7.3±3.5 1.2±0.6 27.5±2.7 28±18 54±0.5 46±0.3 13±2.7 40±2.9
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14
Table S4. RMSE and R2 of cross-validation for the different applied modeling strategies.
(LARS = Linear least angle regression; BSS = Linear best subset; CUBIST = non linear model tree; for SOC response
variable abbreviations see caption of Table S3).
Unit RMSE Rcv2 Dependent Model
g C kg-1
11.54 0.95
SOC%
LARS
11.24 0.96 BSS
17.91 0.87 CUBIST
kg SOC m2
1.30 0.75
SOCStock
LARS
1.22 0.71 BSS
1.21 0.77 CUBIST
µg CO2-C g-1 SOC h-1
5.00 0.76
Respiration (SPR)
LARS
3.13 0.86 BSS
4.99 0.74 CUBIST
% of SOCStock
15 0.63
SOCMineral
LARS
13 0.69 BSS
17 0.44 CUBIST
% of SOCStock
21 0.41
SOCMicrobial
LARS
18 0.54 BSS
20 0.39 CUBIST
% of SOCStock
13 0.54
CPOM
LARS
11 0.59 BSS
14 0.43 CUBIST
% of SOCStock
11 0.58
s+c
LARS
10 0.65 BSS
12 0.50 CUBIST
% of SOCStock
12 0.48
m
LARS
8 0.79 BSS
14 0.47 CUBIST
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15
Table S5. Relative contributions of geo-climatic variables in predicting SOC response variables.
Standardized coefficients indicating their importance in the models. R2 and RMSE are computed by cross-validation of
the LARS (Least Angle regression) and BSS (Best Subset) models (see methods for details) (MAP = Mean annual
precipitation; MAT = Mean annual temperature; Clay & Silt = Soil clay & silt content; TRB = Total reserve in “base”
cations; BS = Base saturation of potential cation exchange capacity; Si, Al, Fe, K, P, Mn = soil total content of tested
elements; pHKCl = soil pH measured in KCl solution; blanks = not selected; for SOC response variable abbreviations see
caption of Table S3).
Least Angle Linear Regression (LARS)
MAP MAT Clay Silt Si BS Al TRB pHKCl Fe K P Mn R2cv RMSEcv RMSE Unit
SOCMineral 7.61 0.63 15
% of
total SOCStock
SOCMicrobial 101 0.41 21
CPOM -15 0.54 13
s+c 17 5 1 0.58 11
m 5 -0.05 -4 -16 0.48 12
Best Subset Selection Linear Regression (BSS)
MAP MAT Clay Silt Si BS Al TRB pHKCl Fe K P Mn R2cv RMSEcv RMSE Unit
SOCMineral 0.54 -0.33 0.69 13
% of
total SOCStock
SOCMicrobial 0.32 0.72 0.54 18
CPOM -0.54 0.33 0.59 11
s+c 0.57 0.37 0.65 10
m -0.71 -0.30 -0.61 0.79 8
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16
Table S6. Correlations (Pearson’s r) between predictors and SOC response variables.
SOC% SOCStock Respiration (SPR) SOCMicrobial SOCMineral CPOM m s+c
MAP 0.74 0.72 -0.77 -0.42 0.40 -0.40 0.59 -0.14
MAT -0.15 -0.10 0.21 0.25 0.18 -0.18 -0.11 0.34
Clay -0.13 0.02 0.02 0.18 0.25 -0.25 -0.31 0.65
Silt 0.40 0.50 -0.56 0.08 0.63 -0.63 0.27 0.49
Sand -0.27 -0.42 0.47 -0.13 -0.63 0.63 -0.09 -0.69
Si -0.67 -0.62 0.75 0.62 -0.41 0.42 -0.37 -0.12
BS -0.62 -0.63 0.75 0.28 -0.34 0.34 -0.48 0.10
Al -0.49 -0.36 0.01 0.00 -0.26 0.26 -0.51 0.23
Al/Si ratio 0.02 0.09 -0.40 -0.34 0.03 -0.04 -0.16 0.21
TRB -0.62 -0.66 0.36 0.06 -0.53 0.53 -0.51 -0.11
pHKCl -0.26 -0.35 0.33 0.02 0.02 -0.01 -0.25 0.29
Fe -0.24 -0.21 -0.10 -0.14 -0.02 0.02 -0.48 0.50
K -0.68 -0.67 0.70 0.42 -0.49 0.49 -0.65 0.09
P 0.46 0.38 -0.64 -0.52 0.11 -0.12 0.25 -0.13
Mn -0.21 -0.19 0.07 -0.02 -0.06 0.06 -0.44 0.41
For variable abbreviations see captions of Tables S3 and S5.
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17
Table S7. Correlations (Pearson’s r) between predictors for SOC response variables.
MAP MAT Clay Silt Sand Si BS Al Al/Si ratio TRB pHKCl Ca Fe K Mn P
MAP 1 -0.12 -0.26 0.43 -0.25 -0.61 -0.81 -0.24 0.15 -0.40 -0.58 -0.24 -0.12 -0.61 -0.42 0.43
MAT -0.12 1 0.13 0.31 -0.29 0.24 -0.08 -0.01 -0.13 -0.27 0.43 -0.34 0.04 0.33 0.12 -0.60
Clay -0.26 0.13 1 0.21 -0.59 -0.12 0.14 0.39 0.39 -0.1 0.22 -0.12 0.44 0.02 0.44 -0.06
Silt 0.43 0.31 0.21 1 -0.91 -0.32 -0.48 -0.09 0.09 -0.5 -0.09 -0.43 0.06 -0.28 -0.1 0.07
Sand -0.25 -0.29 -0.59 -0.91 1 0.33 0.34 -0.1 -0.25 0.43 -0.02 0.38 -0.25 0.23 -0.11 -0.06
Si -0.61 0.24 -0.12 -0.32 0.33 1 0.44 -0.22 -0.67 0.1 0.1 -0.04 -0.44 0.56 -0.18 -0.77
BS -0.81 -0.08 0.14 -0.48 0.34 0.44 1 0.11 -0.21 0.55 0.41 0.43 0.16 0.49 0.23 -0.28
Al -0.24 -0.01 0.39 -0.09 -0.1 -0.22 0.11 1 0.83 0.49 0.19 0.46 0.6 0.18 0.44 0.21
Al/Si ratio 0.15 -0.13 0.39 0.09 -0.25 -0.67 -0.21 0.83 1 0.21 0.07 0.28 0.65 -0.23 0.45 0.59
TRB -0.40 -0.27 -0.10 -0.50 0.43 0.10 0.55 0.49 0.21 1 0.29 0.94 0.41 0.34 0.17 0.13
pHKCl -0.58 0.43 0.22 -0.09 -0.02 0.10 0.41 0.19 0.07 0.29 1 0.23 0.21 0.35 0.53 -0.13
Ca -0.24 -0.34 -0.12 -0.43 0.38 -0.04 0.43 0.46 0.28 0.94 0.23 1 0.35 0.06 0.02 0.30
Fe -0.12 0.04 0.44 0.06 -0.25 -0.44 0.16 0.6 0.65 0.41 0.21 0.35 1 0.11 0.65 0.28
K -0.61 0.33 0.02 -0.28 0.23 0.56 0.49 0.18 -0.23 0.34 0.35 0.06 0.11 1 0.38 -0.52
Mn -0.42 0.12 0.44 -0.1 -0.11 -0.18 0.23 0.44 0.45 0.17 0.53 0.02 0.65 0.38 1 0.14
P 0.43 -0.6 -0.06 0.07 -0.06 -0.77 -0.28 0.21 0.59 0.13 -0.13 0.30 0.28 -0.52 0.14 1
For variable abbreviations see captions of Tables S3 and S5.
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18
Table S8. Partial correlations between SOC response and selected geochemical variables (GCV),
controlled for temperature (MAT) and precipitation (MAP).
SPR SOCStock SOC%
GCV Zero-
order MAP MAT Climate GCV
Zero-
order MAP MAT Climate GCV
Zero-
order MAP MAT Climate
Clay 0.02 -0.30 -0.01 -0.32 Clay 0.02 0.31 0.04 0.32 Clay -0.13 0.10 -0.11 0.11
Silt -0.56 * -0.40 -0.67 * -0.52 * Silt 0.50 * 0.30 0.56 * 0.34 Silt 0.40 0.13 0.47 * 0.19
Si 0.75 * 0.55 * 0.73 * 0.53 * Si -0.62 * -0.33 -0.62 * -0.34 Si -0.67 * -0.41 -0.66 * -0.4
BS 0.75 * 0.33 0.78 * 0.41 BS -0.63 * -0.12 -0.65 * -0.13 BS -0.62 * -0.04 -0.64 * -0.07
Al 0.01 -0.28 0.01 -0.28 Al -0.36 -0.27 -0.36 -0.27 Al -0.49 * -0.48 * -0.5 * -0.49 *
TRB 0.36 0.09 0.44 0.16 TRB -0.66 * -0.57 * -0.71 * -0.62 * TRB -0.62 * -0.51 * -0.69 * -0.58 *
pHKCL 0.33 -0.22 0.27 -0.33 pHKCL -0.35 0.12 -0.34 0.14 pHKCL -0.26 0.31 -0.22 0.39
Fe -0.10 -0.30 -0.11 -0.31 Fe -0.21 -0.17 -0.2 -0.17 Fe -0.24 -0.22 -0.24 -0.22
K 0.7 * 0.47 * 0.69 * 0.44 K -0.67 * -0.42 -0.67 * -0.44 K -0.68 * -0.43 -0.67 * -0.42
P -0.64 * -0.53 * -0.66 * -0.54 * P 0.38 0.11 0.40 0.13 P 0.46 * 0.23 0.47 * 0.23
Mn 0.07 -0.43 0.05 -0.45 Mn -0.19 0.17 -0.18 0.17 Mn -0.21 0.16 -0.20 0.17
SOCMineral SOCMicrobial
GCV Zero-
order MAP MAT Climate GCV
Zero-
order MAP MAT Climate
Clay 0.25 0.40 0.23 0.39 Clay 0.18 0.08 0.15 0.06
Silt 0.63 * 0.55 * 0.61 * 0.51 * Silt 0.08 0.31 0.00 0.25
Si -0.41 * -0.23 -0.48 * -0.30 Si 0.62 * 0.51 * 0.6 * 0.49 *
BS -0.34 -0.02 -0.33 0.06 BS 0.28 -0.1 0.32 -0.04
Al -0.26 -0.19 -0.27 -0.18 Al 0.00 -0.11 0.00 -0.11
TRB -0.53 * -0.44 -0.51 * -0.39 TRB 0.06 -0.13 0.14 -0.06
pHKCL 0.02 0.33 -0.07 0.25 pHKCL 0.02 -0.3 -0.1 -0.45
Fe -0.02 0.03 -0.03 0.02 Fe -0.14 -0.21 -0.15 -0.22
K -0.49 * -0.34 -0.59 * -0.46 K 0.42 * 0.23 0.37 0.17
P 0.11 -0.07 0.28 0.11 P -0.52 * -0.42 -0.48 * -0.37
Mn -0.06 0.13 -0.08 0.11 Mn -0.02 -0.24 -0.06 -0.26
CPOM m s+c
GCV Zero-
order MAP MAT Climate GCV
Zero-
order MAP MAT Climate GCV
Zero-
order MAP MAT Climate
Clay -0.25 -0.4 -0.23 -0.39 Clay -0.31 -0.21 -0.30 -0.20 Clay 0.65 * 0.64 * 0.65 * 0.65 *
Silt -0.63 * -0.55 * -0.62 * -0.51 * Silt 0.27 0.03 0.32 0.05 Silt 0.49 * 0.61 * 0.43 0.55 *
Si 0.42 * 0.24 0.48 * 0.31 Si -0.37 -0.02 -0.36 -0.01 Si -0.12 -0.25 -0.22 -0.35
BS 0.34 0.03 0.33 -0.06 BS -0.48 * -0.01 -0.50 * -0.03 BS 0.10 -0.02 0.14 0.09
Al 0.26 0.18 0.26 0.18 Al -0.51 * -0.47 * -0.52 * -0.48 * Al 0.23 0.21 0.25 0.24
TRB 0.53 * 0.44 0.51 * 0.39 TRB -0.51 * -0.37 -0.56 * -0.41 TRB -0.11 -0.18 -0.02 -0.07
pHKCL -0.01 -0.33 0.07 -0.25 pHKCL -0.25 0.13 -0.23 0.17 pHKCL 0.29 0.26 0.16 0.13
Fe 0.02 -0.03 0.03 -0.03 Fe -0.48 * -0.51 * -0.48 * -0.51 * Fe 0.50 * 0.49 * 0.51 * 0.51 *
K 0.49 * 0.33 0.59 * 0.46 K -0.65 * -0.45 -0.65 * -0.46 K 0.09 0.02 -0.02 -0.10
P -0.12 0.07 -0.28 -0.11 P 0.25 0 0.24 -0.03 P -0.13 -0.08 0.09 0.16
Mn 0.06 -0.14 0.08 -0.12 Mn -0.44 * -0.27 -0.43 -0.26 Mn 0.41 * 0.39 0.39 0.39
Differences between zero-order and partial correlations indicate the level of dependency of a geochemical predictor to
climatic predictors: MAP = Mean annual precipitation, MAT = Mean annual temperature or the combined effect of both
indicated as “Climate”); significance of the correlations (*) is evaluated at the 0.05 level. For variable abbreviations see
captions of Tables S3 and S5.
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19
SI 4 Extended data figures
Figure S1. SOC fractionation scheme. Applied SOC fractionation scheme following 31 and its
interpretation in terms of functional SOC pools and protection mechanisms.
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Figure S2. Partial correlations between SOC response and climatic variables. Change of correlation
between SOC response and climatic variables controlled for geochemical variables separately and
all combined (column “Geochem”). Difference between zero-order and partial correlations indicate
the level of dependency of a given predictor and the SOC response. Color and displayed numbers
indicate the strength and sign of the correlation. (No change in color between controlled variable
and zero-order = no dependency; decrease/increase of color intensity = loss of / gain of correlation).
Significance of the correlations (*) is evaluated at the 0.05 level. Abbreviations are explained in
caption of Tables S3 and S5.
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Figure S3. Geographical location of the study plots in relation to mean annual temperature and
precipitation. Annual temperature and precipitation are averages of the period 1950-2000 taken from
the worldclim dataset24 for Chile and from the Bellingshausen and Esperanza research stations for
the Antarctic Peninsula.
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