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Climate control on terrestrial biospheric carbon turnover


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Terrestrial vegetation and soils hold three times more carbon than the atmosphere. Much debate concerns how anthropogenic activity will perturb these surface reservoirs, potentially exacerbating ongoing changes to the climate system. Uncertainties specifically persist in extrapolating point-source observations to ecosystem-scale budgets and fluxes, which require consideration of vertical and lateral processes on multiple temporal and spatial scales. To explore controls on organic carbon (OC) turnover at the river basin scale, we present radiocarbon ( ¹⁴ C) ages on two groups of molecular tracers of plant-derived carbon—leaf-wax lipids and lignin phenols—from a globally distributed suite of rivers. We find significant negative relationships between the ¹⁴ C age of these biomarkers and mean annual temperature and precipitation. Moreover, riverine biospheric-carbon ages scale proportionally with basin-wide soil carbon turnover times and soil ¹⁴ C ages, implicating OC cycling within soils as a primary control on exported biomarker ages and revealing a broad distribution of soil OC reactivities. The ubiquitous occurrence of a long-lived soil OC pool suggests soil OC is globally vulnerable to perturbations by future temperature and precipitation increase. Scaling of riverine biospheric-carbon ages with soil OC turnover shows the former can constrain the sensitivity of carbon dynamics to environmental controls on broad spatial scales. Extracting this information from fluvially dominated sedimentary sequences may inform past variations in soil OC turnover in response to anthropogenic and/or climate perturbations. In turn, monitoring riverine OC composition may help detect future climate-change–induced perturbations of soil OC turnover and stocks.
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Climate control on terrestrial biospheric
carbon turnover
Timothy I. Eglinton
, Valier V. Galy
, Jordon D. Hemingway
, Xiaojuan Feng
, Hongyan Bao
Thomas M. Blattmann
, Angela F. Dickens
, Hannah Gies
, Liviu Giosan
, Negar Haghipour
, Pengfei Hou
Maarten Lupker
, Cameron P. McIntyre
, Daniel B. Montluçon
, Bernhard Peucker-Ehrenbrink
, Camilo Ponton
Enno Schefuß
, Melissa S. Schwab
, Britta M. Voss
, Lukas Wacker
, Ying Wu
, and Meixun Zhao
Department of Earth Sciences, ETH Zurich, 8092, Switzerland;
Department of Marine Chemistry and Geochemistry, Woods Hole Oceanographic
Institution, Woods Hole, MA 02543;
Department of Earth and Planetary Sciences, Harvard University, Cambridge, MA 02138;
State Key Laboratory of
Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China;
State Key Laboratory of Estuarine and
Coastal Research, East China Normal University, Shanghai 200062, China;
Department of Geology and Geophysics, Woods Hole Oceanographic Institution,
Woods Hole, MA 02543;
Laboratory for Ion Beam Physics, Department of Physics, ETH Zurich, 8093 Zurich, Switzerland;
Frontiers Science Center for Deep
Ocean Multispheres and Earth System, Key Laboratory of Marine Chemistry Theory and Technology, Ministry of Education, Ocean University China, Qingdao
266100, China; and
Center for Marine Environmental Sciences, University of Bremen, Bremen 28359, Germany
Edited by Susan E. Trumbore, Max Planck Institute for Biogeochemistry, Jena, Germany, and approved December 29, 2020 (received for review June 5, 2020)
Terrestrial vegetation and soils hold three times more carbon than
the atmosphere. Much debate concerns how anthropogenic activ-
ity will perturb these surface reservoirs, potentially exacerbating
ongoing changes to the climate system. Uncertainties specifically
persist in extrapolating point-source observations to ecosystem-
scale budgets and fluxes, which require consideration of vertical
and lateral processes on multiple temporal and spatial scales. To
explore controls on organic carbon (OC) turnover at the river basin
scale, we present radiocarbon (
C) ages on two groups of molec-
ular tracers of plant-derived carbonleaf-wax lipids and lignin
phenolsfrom a globally distributed suite of rivers. We find sig-
nificant negative relationships between the
C age of these bio-
markers and mean annual temperature and precipitation. Moreover,
riverine biospheric-carbon ages scale proportionally with basin-
wide soil carbon turnover times and soil
C ages, implicating OC
cycling within soils as a primary control on exported biomarker
ages and revealing a broad distribution of soil OC reactivities.
The ubiquitous occurrence of a long-lived soil OC pool suggests
soil OC is globally vulnerable to perturbations by future tempera-
ture and precipitation increase. Scaling of riverine biospheric-
carbon ages with soil OC turnover shows the former can constrain
the sensitivity of carbon dynamics to environmental controls on
broad spatial scales. Extracting this information from fluvially
dominated sedimentary sequences may inform past variations in
soil OC turnover in response to anthropogenic and/or climate per-
turbations. In turn, monitoring riverine OC composition may help
detect future climate-changeinduced perturbations of soil OC
turnover and stocks.
plant biomarkers
carbon turnover times
fluvial carbon
carbon cycle
Terrestrial biospheric carbon residing in vegetation and soils
may moderate or exacerbate ongoing buildup of atmospheric
greenhouse gases on timescales that are of direct relevance to
humankind (1). Much current debate surrounds the response
and potential contributions of terrestrial ecosystems to climate
change, with large uncertainties concerning the magnitudeand
even the signof change in response to different environmental
forcing factors such as temperature and hydrology (2, 3). Be-
cause of their large organic carbon (OC) stocks and potential to
stabilize carbon on a range of timescales, soils are thought to
regulate overall terrestrial ecosystem carbon storage (4, 5). Glob-
ally, soil carbon turnover time (τ
) (i.e., the ratio of soil carbon
stock to input flux) is estimated via remote sensing approaches
(4, 5) and Earth-system models (3) that are calibrated using
numerous observational and experimental studies investigating
controls on soil OC turnover in a range of ecosystems and soil types
(6, 7). However, findings from such studies are often relevant only
to a specific experiment, plot, or environment, thus hindering
extrapolation and regional validation of remote sensing and model
products (8, 9).
One major reason for this limitation is our lack of constraints
regarding the importance of erosion and lateral transport, de-
spite a growing realization that these processes are pervasive on
diverse landscapes (10) and link terrestrial and aquatic components
Terrestrial organic-carbon reservoirs (vegetation, soils) cur-
rently consume more than a third of anthropogenic carbon
emitted to the atmosphere, but the response of this terrestrial
sinkto future climate change is widely debated. Rivers export
organic carbon sourced over their watersheds, offering an
opportunity to assess controls on land carbon cycling on broad
spatial scales. Using radiocarbon ages of biomolecular tracer
compounds exported by rivers, we show that temperature and
precipitation exert primary controls on biospheric-carbon
turnover within river basins. These findings reveal large-scale
climate control on soil carbon stocks, and they provide a
framework to quantify responses of terrestrial organic-carbon
reservoirs to past and future change.
Author contributions: T.I.E. and V.V.G. designed research; T.I.E., V.V.G., J.D.H., and X.F.
performed research; H.B., T.M.B., A.F.D., H.G., L.G., N.H., P.H., M.L., C.P.M., D.B.M., B.P.-E.,
C.P., E.S., M.S.S., B.M.V., L.W., Y.W., and M.Z. contributed new reagents/analytic tools;
T.I.E., V.V.G., J.D.H., and X.F. analyzed data; and T.I.E., V.V.G., and J.D.H. wrote the paper.
The authors declare no competing interest.
This article is a PNAS Direct Submission.
This open access article is distributed under Creative Commons Attribution-NonCommercial-
NoDeriv atives Lic ense 4.0 (CC B Y-NC-ND) .
T.I.E. and V.V.G. contributed equally to this work.
To whom correspondence may be addressed. Email: or
Present address : State Key Laborator y of Marine Environmen tal Science, Colle ge of
Ocean and Earth Sciences, Xiamen University, Xiamen 361102, China.
Present address: Biogeochemistry Research Center, Japan Agency for Marine-Earth Sci-
ence and Technology, Yokosuka 237-0061, Japan.
Present address: Wisconsin Department of Natural Resources, Bureau of Air
Management, Madison, WI 53707.
Present address: Accelerator Mass Spectrometry Laboratory, Scottish Universities Envi-
ronmental Research Centre, East Kilbride G75 0QF, United Kingdom.
Present address : Geology Departm ent, Western Washi ngton University, Bellingham,
WA 98225.
Present address: Environmental Assessment Program, Washington State Department of
Ecology, Lacey, WA 98503.
This article contains supporting information online at
Published February 15, 2021.
PNAS 2021 Vol. 118 No. 8 e2011585118
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of the carbon cycle (11). Investigations of lateral transport of
terrestrial biospheric carbon have thus far focused on small
spatial scales (e.g., hillslopes), which do not fully encompass
heterogeneous landscape mosaics (10, 12, 13) and cannot be
readily extrapolated to assess processes relevant at the ecosystem
or biome scale. In particular, there is a paucity of information on
how carbon turnover observed at individual plots relates to
landscape- and basin-scale biospheric carbon dynamics. Depend-
ing on the mode of carbon turnover, mobilization, and transport,
lateral processes can either export freshly synthesized carbon (e.g.,
via surface runoff) or exhume carbon stocks sequestered in deeper
soils and wetlands (14, 15); the relative importance of these pro-
cesses likely exerts a strong influence on carbon stocks and dy-
namics but is currently underrepresented in large-scale models.
Given that climate change, as well as direct anthropogenic per-
turbations (e.g., land-use practices), may potentially modify and
amplify such carbon fluxes and trajectories (3), establishing the
underlying drivers and pacing of carbon cycling on appropriate
spatial and temporal scales is of key importance.
Insight into the factors that control ecosystem-scale carbon
turnover times can be determined using the
C activity (repor-
ted as age in
C years) of OC laterally exported by rivers. Rivers
integrate processes within their watersheds, thus enabling in-
vestigation of biogeochemical processes at the basin scale, fa-
cilitating observational extrapolation, and linking terrestrial and
marine realms. Fluvial systems form the major conduit that
transfers OC from the continents to the ocean, exporting a
combined 4.5 ×10
g of dissolved organic carbon (DOC) and
particulate organic carbon (POC) annually (16, 17). The majority
of this OC exported by rivers is in dissolved form, but much of
this is rapidly mineralized (16). Although POC is also subject to
extensive degradation (16) and riverine POC export is estimated
to account for less than 0.2% of net primary production (NPP,
17), the export of terrestrial biospheric POC and its subsequent
burial in marine sediments is important in modulating atmo-
spheric CO
on a range of timescales (18) and provides some of
the most continuous and long-term records of past climate and
carbon-cycle dynamics on the continents.
Prior studies have examined the nature and magnitude of
carbon transfer via rivers to the ocean (ref. 17 and references
therein) and have shown that soil OC represents a dominant
component of the terrestrial POC exported by many fluvial sys-
tems (19, 20). Strong contrasts in POC yield (i.e., carbon flux per
unit catchment area) and composition relate to geomorphic and
climatic factors influencing mobilization and retention of OC
within drainage basins, as well as the proportions and fluxes of
biospheric versus rock-derived (petrogenic) carbon inputs (14,
21). Bulk OC radiocarbon measurements in both particulate and
dissolved phases reveal a wide variety of
C ages (22); however,
interpretations in terms of biospheric-carbon dynamics are con-
founded by diverse OC contributions (e.g., petrogenic OC; ref.
17 and references therein) and secondary overprinting (within-river
autotrophy and heterotrophy) (22). Moreover, for most modern
river systems, anthropogenic activities influence
C ages through
the introduction of organic contaminants that may be either rel-
atively modern (e.g., domestic sewage) or fossil (e.g., petroleum or
petrochemical contamination) in age (23).
These interferences can be obviated by determining ages of
organic compounds unique to vascular land-plant biomass (24).
To explore controls on the age of terrestrial biospheric carbon
exported from river basins, here we compile previously reported
(n=95) and report additional (n=28)
C age measurements of
source-specific biomarkertracer compounds measured on 36
fluvial systems representing diverse watersheds and collectively
accounting for 42, 29, and 20% of the global riverine water,
sediment, and POC discharge, respectively (Fig. 1 and SI Ap-
pendix, Fig. S1 and Tables S1 and S2) (17). We focus on two well-
established classes of terrestrial higher plant biomarkersplant-
wax lipids (25) and lignin-derived phenols (26). Because of their
hydrophobic nature, plant waxes reside in the particulate
phaseparticularly via association with mineral surfaces (27)
and persist in soils and downstream environments (24). The
abundances, distributions, and stable isotopic compositions (
reported as δ
C) of these compounds preserved in sedimentary
and soil sequences carry information on past vegetation inputs,
plant productivity, and environmental conditions (24). We spe-
cifically analyze n-alkanoic acids (fattyacids [FAs]) since
n-alkanes can be influenced by contributions from bedrock- or
fossil-fuelderived sources that impact corresponding
C ages
(19). Lignin imparts structural support for the plant; phenolic
monomers liberated by chemical hydrolysis of this biopolymer
and its corresponding residues in soils and sediments carry in-
formation on plant and tissue type and extent of degradation
(ref. 26 and references therein). Assessments of lignin stability
and turnover in soils vary (7); however, like plant waxes, lignin
signatures are present in fluvial sediments and deposits (15). We
therefore treat measured
C ages of these two biomarker classes
60º N
60º S
60º N
60º S
180º E180º W
biomarker 14C age
absolute latitude at river mouth (º)
fatty acids
lignin phenols
14C age (yr)
0 5,000 10,000
Fig. 1. Riverine biomarker
C ages. The catchment areas of all rivers ana-
lyzed in this study are color coded by (A) plant-wax fatty-acid and (B) lignin-
C ages (SI Appendix, Table S1). Rivers with catchment areas smaller
than 30,000 km
are shown as colored circles for clarity. The legend above
(A) applies to both panels. (C) Biomarker ages as a function of the absolute
latitude at the river mouth, showing both fatty acids (black circles) and lignin
phenols (white squares).
PNAS Eglinton et al. Climate control on terrestrial biospheric carbon turnover
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as independent yet complementary estimates of the mean age of
fluvially exported biospheric OC.
Results and Discussion
Relationships with Basin Properties. We examined
C and, in se-
lected cases, δ
C variations in bulk OC, lignin phenols, and fatty
acids from fluvial sediment samples (Materials and Methods and
SI Appendix, Tables S1 and S2). Bulk OC
C ages and δ
values in the investigated rivers encompass much of the vari-
ability that has been observed in riverine POC worldwide (SI
Appendix, Fig. S2) (22), indicating that our sample set accurately
captures global trends. Bulk POC ages range from modern to
almost 11,000
C y in our sample set (n=43), whereas bio-
marker ages range from above modern (reflecting incorporation
of nuclear-bombderived
C) to more than 10,000
C y for fatty
acids (n=44) and from above modern to almost 5,000
C y for
lignin phenols (n=16) (SI Appendix, Tables S1 and S2). Ob-
served age offsets between fatty acids and lignin phenols collected
from the same river basins may reflect differences in their degree
of mineral stabilization (28) and/or pathways of mobilization (15).
To identify key parameters governing bulk OC and biomarker
C ages and δ
C values, we performed an ordinary least
squares (OLS) multivariate linear regression (MLR) using a
suite of climatic, geomorphic, and anthropogenic properties as
control variables (Materials and Methods;SI Appendix, Table S3).
This approach implicitly treats results calculated using modern
datasets as representative of control variable values over the
timescales of biomarker
C ages (i.e., centuries to millennia).
Because this assumption does not strictly hold for all control
variables (e.g., anthropogenic land use), correlations calculated
herein may deviate from steady-state results. Many control var-
iables are spatially resolved and must be averaged across each
river basin. However, it has been shown that fluvially exported
POC and, in particular, biomarker isotope signatures exhibit a
downstream bias and do not represent a uniformly integrated
basin signal (29). We therefore weight spatial values by a
factorthe e-folding distancethat decays exponentially with
upstream flow distance from each sampling location (Materials
and Methods). We choose as the optimal e-folding distance the
value that maximizes the fraction of total bulk OC and bio-
marker isotope variance that is explained by our MLR analysis
(average adjusted r
=0.72; SI Appendix, Fig. S3). This optimal
value of 500 km agrees with previous estimates of biomarker
spatial integration in large river basins (29, 30). For all statistical
analyses, spatially resolved control variables are thus weighted by
upstream flow distance with an e-folding distance of 500 km
when calculating catchment averages (SI Appendix, Table S3).
For all bulk and biomarker isotope measurements, MLR-predicted
values show no bias (measured versus predicted slopes are always
statistically identical to unity) and explain the majority of ob-
served sample variance (measured versus predicted adjusted r
always 0.35 and typically 0.64; SI Appendix, Fig. S4), indi-
cating the robustness of the chosen control variables.
Catchment-weighted geomorphic characteristics such as basin
area, relief (mean basin slope), and relative floodplain extent
showed no significant relationship with biomarker age (SI Ap-
pendix, Table S4). Instead, FA and lignin-phenol
C ages are
significantly correlated with climate variableschiefly, catchment-
weighted mean annual temperature (MAT) and precipitation
(MAP) (Fig. 2Aand SI Appendix, Table S4). Globally, biomarker
C ages decrease (become younger) with increasing MAT and
MAP; this phenomenon manifests as a relationship between
C age and latitude because of the strong covariation
of the latter with climate (Fig. 1C). A significant, albeit weaker,
positive correlation is also observed between biomarker
C ages
and the fraction of catchment area that is impacted by anthro-
pogenic land use (i.e., agriculture and urbanization; Fig. 2Aand
SI Appendix, Table S4), suggesting such perturbations might
mobilize old OC that would otherwise be stable under natural
conditions. However, directly ascribing land-usechange impacts
on biomarker
C ages is challenging since the extent of anthro-
pogenically perturbed area additionally exhibits strong covariance
with climate variables, particularly MAT and MAP. Finally,
lignin-phenol and, to a lesser extent, plant-wax fatty-acid
ages display a significant negative correlation with runoff (Fig. 2).
While other variablesfor example, soil properties such as clay
content (31, 32)may contribute to these relationships, their
strength implies that climate constitutes the dominant direct or
indirect driver of biospheric-carbon ages. Bulk OC
play systematically weaker correlations with the set of tested
control variables (e.g., MAT and MAP), as well as with latitude
(SI Appendix, Fig. S5), reflecting the influence of additional OC
sources (e.g., petrogenic carbon, in situ aquatic productivity) on
bulk OC isotopic signatures.
To further assess underlying natural variables controlling bulk
POC and biomarker isotope compositions, we additionally per-
formed a redundancy analysis (RDA) (Materials and Methods).
The results show that two orthogonal axes explain a combined
-4 -2 0 42
RDA1 (35 % variance)
RDA2 (24 % variance)
12 13
FA 14C (i)
lignin 14C (ii)
POC 14C (iii)
FA δ13C (iv)
POC δ13C (v)
runoff (3)
elevation (2)
sample type (1)
log TSS yield (4)
MAT (6)
temp. CV (7)
log MAP (8)
precip. CV (9)
cont. perm. (10)
soil C stock (12)
NPP (13)
τecosystem (14)
τsoil (15)
frac. anthro. (16)
log POC yield (5)
discont. perm. (11)
15 16
correlation coefficient (r)
Fig. 2. Multivariate statistical analysis. (A) Matrix of Pearson correlation coefficients (rvalues) between environmental control variables (x-axis) and POC and
C and δ
C responses (y-axis) (SI Appendix, Table S4). Box sizes and colors correspond to the strength of the correlation (sizes: magnitude only;
colors: magnitude and sign). Correlations that are significant at the P=0.05 level are outlined with a thick, black border. Sample typerefers to the fol-
lowing: suspended sediment, bank/bedload sediment, or shelf-deposit sediment. (B) RDA triplot showing the RDA1 and RDA2 canonical axes (SI Appendix,
Table S5); labels show the percent of total sample variance explained by each axis. Environmental control variable loadings are plotted as gray arrows, POC
and biomarker
C and δ
C response variable loadings are plotted as red arrows, and individual sample scores are plotted as black circles. Environmental and
response variable loadings are scaled for visual clarity. Numbers and roman numerals correspond to control and response variables, respectively, as listed in
(A). Only control variables that are statistically significantly correlated with at least one response variable are included in the analysis (SI Appendix, Table S4).
TSS, total suspended sediment; POC, particulate organic carbon; CV, coefficient of variation; cont. perm., continuous permafrost cover; discont. perm., dis-
continuous permafrost cover; MAP, mean annual precipitation; NPP, net primary production.
Eglinton et al. PNAS
Climate control on terrestrial biospheric carbon turnover
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57% of the total sample variance (Fig. 2Band SI Appendix, Table
S5). The first axis (RDA1) loads closely with catchment-weighted
MAT, MAP, and associated highly correlated variables (e.g.,
permafrost cover, land use), whereas the second axis (RDA2)
loads closely with precipitation seasonality (coefficient of vari-
ability). All
C metrics, particularly fatty-acid
C age, load al-
most exclusively onto RDA1, indicating that they are controlled
most strongly by MAT and MAP (and/or all highly correlated
variables). In contrast, δ
C values of fatty acids and bulk POC,
broadly reflecting biome structure within the basin (25), load
almost exclusively on RDA2, revealing that they are controlled
most strongly by precipitation seasonality (and/or all highly
correlated variables). Specifically, lower δ
C values correspond
to lower seasonal variation in precipitation, as observed in other
regional studies (e.g., 33).
Relationships with τ
and Mean Age. Plant-wax fatty-acid and, to a
lesser extent, lignin-phenol
C ages display positive relationships
with catchment-weighted τ
estimated from remote-sensingderived
soil carbon stocks and NPP (Fig. 3 Aand C). These linear relation-
ships imply the age of fluvially exported biospheric OC reflects soil
carbon turnover at the basin scale, the latter being controlled by
MAT and MAP (4, 5). Still, fatty-acid and lignin-phenol
C ages
are on average 43.6- and 16.6-fold greater than the corresponding
. To further probe these relationships, we additionally con-
sidered recent spatially resolved estimates of soil
C ages (34).
C ages display similar positive relationships with
catchment-weighted soil
C ages integrated from 0 to 100 cm
depth, further demonstrating the strong imprint of soil OC aging
processes on fluvial biomarker radiocarbon ages (Fig. 3 Band D,
Materials and Methods, and SI Appendix, Table S4). Unlike τ
however, fatty-acid and lignin-phenol
C ages are on average
2.5- and 5.0-fold lower than corresponding soil
C ages, sug-
gesting that lateral transport processes do not fully capture older,
deeper soil horizons. This interpretation is supported by corre-
lations between biomarker
C ages and catchment-weighted soil
C ages integrated from 0 to 30 cm depth, in which biomarkers
display similar or slightly older
C ages than corresponding soils,
and integrated from 30 to 100 cm depth, in which soils display
significantly older
C ages than corresponding biomarkers (SI
Appendix, Fig. S6).
Nonetheless, the observed discrepancy between biomarker
ages and τ
contrasts with the much closer agreement between
C ages and soil mean carbon age, regardless of soil
integration depth (Fig. 3 and SI Appendix, Fig. S6). This offset
between both biomarker
C ages and catchment-weighted soil
C ages on one hand and τ
on the other hand likely reflects
the fundamental principles governing organic matter degrada-
tion and aging. Natural organic matter is compositionally het-
erogenous, with age heterogeneity evident even within individual
compound populations (20, 35). Complex interplay between
environmental properties and chemical composition results in
widely variable OC degradation rates (36). We therefore attribute
discrepancies between riverine biospheric-carbon age and τ
a heavy-tailed distribution of OC degradation, as has been hy-
pothesized previously (37). Assuming degradation rate constants
follow a lognormal distribution with given variance (38), the ratio
between mean age and turnover time is proportional to the
natural exponential function of the variance (39). Accepting that
mean ages can be approximated using
C ages (Materials and
0 10050
= 0.65
slope = 40.1 ± 3.9
= 0.47
slope = 24.4 ± 5.2
150 200
0 10,0005,000
= 0.61
slope = 0.62 ± 0.07
= 0.58
slope = 0.34 ± 0.06
soil mean carbon age (yr)
fatty acid
C age (yr)
C age (yr)
Fig. 3. Relationships between τ
, soil mean carbon age, and biomarker
C age. (Aand B) Plant-wax fatty-acid and (Cand D) lignin-phenol
C ages as a
function of weighted-catchment τ
(Left, ref. 5), and soil mean carbon age (0 to 100 cm, Right, ref. 34). Solid and dashed black lines are reduced major-axis
regression lines; reported values are the corresponding reduced major-axis regression slopes and r
values (Materials and Methods). Uncertainty (±1σ)is
always smaller than marker points.
PNAS Eglinton et al. Climate control on terrestrial biospheric carbon turnover
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Methods), the biomarker
C age versus τ
slopes (Fig. 3 Aand
C) provide estimates of the globally averaged ratio between
mean age and turnover time. These ratios correspond to log-
normal degradation rate-constant variances of 3.6 ±0.7 using
fatty acids (μ±1σ,n=44) and 2.7 ±0.8 using lignin phenols
(μ±1σ,n=16), consistent with experimentally determined plant
OC degradation rate-constant variances of 1.0 to 4.0 (38, 39).
The scaling of biomarker
C ages (and soil mean carbon age)
with ecosystem turnover time reveals the heterogenous nature of
organic matter and the associated wide distribution of OC deg-
radation rates. This is particularly relevant in the context of
potential soil OC destabilization upon increases in temperature
and precipitation since OC pools characterized by low decay
rates (i.e., long turnover) are proportionally more vulnerable to
destabilization. Specifically, the theory of temperature-dependent
activation energies predicts that turnover of long-lived OC pools
(characterized by low decay rates) will increase proportionally
more than that of short-lived OC pools (characterized by high
decay rates) for a given increase in temperature or precipitation
(40). As such, the prevalence of wide OC degradation rate dis-
tributions observed here implies that some fraction of the global
soil OC reservoir is characterized by low decay rates and is thus
susceptible to destabilization upon increases in temperature and
precipitationa positive climate feedback.
Interpreting Exported Carbon Ages. Lignin-phenol
C ages, fatty-
C ages, and τ
all exhibit significant negative power-law
relationships with MAT and the logarithm of MAP (Fig. 4). For both
fatty acids and lignin phenols, this implies a 0.4 order-of-magnitude
decrease in mean age for each 10-degree increase in MAT. Inter-
estingly, this power-law exponent is statistically identical for τ
both biomarker classes despite differences in their absolute
ages and the fact that they likely reflect different biospheric-
carbon provenance, transport pathways, and/or degradation
rates within the drainage basin (15). Meanwhile, sediment yield
does not appear to correlate with either τ
or the ages of
biospheric OC exported by rivers (SI Appendix, Fig. S7). This
contrasts with biospheric OC yield (i.e., the annual riverine flux
of biospheric OC normalized to catchment area), which is pri-
marily controlled by erosion rates (17), suggesting a decoupling
between biospheric OC yield and age. Soil carbon turnover is
primarily driven by respiration flux, which is linked to climate
variables (4), whereas the comparatively minor riverine bio-
spheric OC export flux is controlled by geomorphic properties
such as catchment slope and runoff (14).
Overall, our observations suggest that the age of vascular plant
biomarkers exported by rivers echo organic-matter dynamics at
the basin scale and are primarily controlled by τ
. This implies
that basin-scale information on the latter can be gleaned from
C investigations of biospheric-carbon components exported by
rivers and that past changes in ecosystem dynamics in response
to climate and anthropogenic forcing can be deduced using
sedimentary archives (41), although additional preaged carbon
sources such as permafrost or peat deposits must be carefully
considered (16). Furthermore, this study provides a global as-
sessment and mechanistic understanding of biospheric-carbon
age in modern river basins, thus contextualizing any observed
future perturbations in biospheric-carbon turnover. Ongoing
temperature increases and accompanying changes to the hydro-
logical cycle are likely to influence ecosystem turnover times and
vulnerability of previously stable carbon stocks. Potential shifts in
the balance of degradation versus lateral transport of these car-
bon stocks may influence the distribution of carbon in Earths
active reservoirs. In-depth investigations of carbon dynamics
in river basins are needed to assess the large-scale impact of
environmental change on terrestrial ecosystems and the manner
and efficiency by which rivers act as carbon conveyorsor
Materials and Methods
Sample Selection.
Rationale for selection of river systems. We focus on 36 globally distributed river
systems that collectively account for one-third of the global exorheic land
surface and for 42, 29, and 20% of the global riverine water, sediment, and
POC discharge, respectively (SI Appendix, Table S5). The majority of these
rivers have been the subject of prior in-depth biogeochemical studies. The 36
rivers included in this study nearly cover the full range of intrinsic basin
properties (e.g., catchment area, latitude, relief, physical erosion rate, dis-
charge, and POC flux) and are evenly distributed across the continuum of
geomorphic characteristics (SI Appendix, Fig. S1). This ensures that our re-
sults are not biased toward any particular set of basin properties.
Rationale for focus on POC as opposed to DOC. We focus on organic matter that is
associated with fluvial, fluvially derived, or fluvially influenced sediments
either transported as suspended particles or recently deposited near the
terminus of the river system. The translocation and sequestration of POC in
marine sediments is considered to influence atmospheric CO
over millennial
and longer timescales, whereas DOC is efficiently remineralized in coastal
waters (16, 18). This POC leaves a legacy of terrestrial carbon fixation that
can be traced in the sediment record and can be used to reconstruct conti-
nental carbon cycling over a wide range of timescales. Diagnostic bio-
markers of higher plant productivity (including lignin and plant-leaf waxes)
reside in the particulate fraction because of their physiological role (e.g.,
structural polymers) or their hydrophobic nature (e.g., lipids), enabling bio-
spheric carbon to be directly traced from plant source to sedimentary sink.
Sample Congruency. We consider potential complications and variability that
may arise from heterogeneities within the overall sample suite. Because these
measurements result from field and analytical work that span a range of
sampling dates, modes of collection, and settings, we assess potential biases
that may be introduced. Specifically, the samples have been collected over a
period spanning more than three decades, during which the atmospheric
signature, and hence the
C content of coeval produced biomass, has
changed significantly (43). Samples have also been collected in differing
locations with respect to the terminus of the river under different flow
conditions (e.g., high discharge events versus low flow conditions) and also
span a range of sample types (e.g., suspended sediments, river flood de-
posits, offshore depocenters, etc.). Our statistical analysis did not identify
any obvious biases related to the heterogenous nature of the sample set.
Still, below we discuss the influence of a set of individual parameters in the
context of the overall observed variability and trends.
Sample type. Our sample set includes suspended sediments, bed sediments,
recently deposited bank deposits (i.e., flood deposits), and floodplain sedi-
ments, as well as fluvially dominated coastal sediments collected at or close to
-20 -10 0 10 3020 1.2 1.6 2.0 2.4
MAT (ºC) log
MAP (cm yr
C age or τ
Fig. 4. Environmental controls on τ
and biomarker
C ages. Logarithmic
plant-wax fatty-acid
C ages (black circles), lignin-phenol
C ages (white
squares), and catchment τ
[gray triangles (5)] as functions of (A) MAT and
(B) logarithmic MAP (SI Appendix, Tables S1 and S3). Solid black, dashed
black, and gray lines are fatty-acid, lignin-phenol, and τ
-reduced major-
axis regression lines. Relationship slopes and r
values are as follows: (A)
C ages: slope =0.036 ±0.004, r
=0.62; lignin-phenol
C ages:
slope =0.036 ±0.006, r
=0.67; and τ
: slope =0.030 ±0.002, r
(B) Fatty-acid
C ages: slope =1.46 ±0.17, r
=0.55; lignin-phenol
C ages:
slope =1.44 ±0.30, r
=0.56; and τ
: slope =1.13 ±0.11, r
Uncertainty (±1σ) is always smaller than marker points.
Eglinton et al. PNAS
Climate control on terrestrial biospheric carbon turnover
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the river terminus. In general, biospheric OC tends to be concentrated in the
finer grain-size fractions, likely reducing potential impact from sorting
processes. Moreover, where possible, poorly sorted bed and bank/flank
sediments were presieved to concentrate finer-grained (<63 μm) material, as
well as to exclude coarse-grained detritus that may have been locally in-
troduced (e.g., from riverbank erosion) to the river. Nevertheless, given
potential differences in grain size distribution and associated hydrodynamic
characteristics, it is important to assess associated variability in
C charac-
teristics. In the Yellow River, for example, plant-wax
C ages have been
found to vary among grain-size fractions of suspended sediments (44). For
systems for which more than one sample type is available (i.e., Brahmaputra,
Danube, Ganges, and Mississippi), a comparison of
C characteristics reveals
no systematic offsets between sample type. In the Danube River, vertical
water-column profiles of plant-wax δ
C and
C ages showed no systematic
trend with depth (45), suggesting plant waxes are relatively homogenous
within the water column and rather insensitive to hydrodynamic sorting.
This likely results from the association of plant-wax compounds with mineral
surfaces (especially clay minerals) and the uniform distribution of fine-grain
clays in the water column (46, 47). We nonetheless include sample type in
the statistical analysis since it is significantly correlated with lignin
C age
(Fig. 2).
Seasonal and interannual variability. Most rivers included in this study exhibit
large seasonal variations in water and sediment discharge, which has been
linked to variations in the composition of POC (29, 48). Our sampling gen-
erally does not capture seasonal variations in the
C age of biomarkers. The
limited number of investigations of temporal variability in rivers does not
reveal marked seasonal variations in biomarker
C age [e.g., plant waxes in
the Yellow River (49); lignin phenols in the Mekong River (50)]. Furthermore,
in many cases, the samples investigated in this study were collected during
or shortly following maximum discharge, which accounts for much of the
annual sediment and POC load. As such, we argue that overall, our sampling
is not affected by a strong seasonal bias. Interannual variability of riverine
sediment load and bulk POC composition has been reported for several river
systems (51, 52). Our dataset includes samples collected over different years
for several river systems (Brahmaputra, Danube, Ganges, Yangtze, and
Yellow River). Compared to the observed overall range of variability, none
of these rivers shows large interannual variations in fatty-acid
C ages. This
suggest that interannual variations are not a significant driver of the ob-
served trends in biomarker
C ages.
Sample collection date. Samples have been collected between 1976 and 2016, a
period of time over which atmospheric
C, and hence freshly produced
terrestrial plant organic matter, has exhibited a marked decline in response
to the redistribution of bomb
C(from above-ground thermonuclear
weapons testing in the late 1950s and early 1960s) in the earths surface
carbon reservoirs (43, 53). Recently synthesized biospheric carbon then
transmits this bomb
C signal through river basins, inducing time-variable
deviations from natural
C levels. We have examined this potential influ-
ence on observed signals for selected river systems in two different ways: 1)
through comparison of data from samples from the same river system but
collected at different times over the last few decades; and 2) through
analysis of rapidly accumulating and well-dated fluvially influenced sedi-
mentary sequences with well-defined chronologies that span a time interval
encompassing the bomb spike (20, 35). Although the influence of the bomb
C spike clearly manifests itself in plant-wax fatty acids, the induced vari-
ability is small relative to the overall
C variability observed within the
global dataset presented in this study. These muted changes in plant-wax
C ages require the presence of at least two different aged
populationsone reflecting rapid (i.e., within years to decades) transfer of
these tracer molecules from biological source to sedimentary sink and a
second, substantially preaged (i.e., hundreds to several thousand years)
component that implies substantial retention prior to export and deposition.
In both of the above studies, model results suggest that the majority (49 to
83%) of the plant-wax signal derives from a preaged pool that likely cor-
responds to a mineral-bound soil OC component. Hence, sensitivity to bomb
C is relatively small, and the date of sampling does not impart a large bias
on our results.
Anthropogenic influences. A key consideration in any study of modern rivers is
the extent to which modern observations of fluvial-sediment flux and
composition have been impacted by anthropogenic activity, both within the
catchment area (e.g., land-use change) and on the hydrological network
(e.g., damming, channelization, etc.). As a proxy for anthropogenic influ-
ences, here we include in our statistical analysis the fraction of each river
basin that is impacted by anthropogenic land-use change (i.e., agriculture
and urbanization) (SI Appendix, Table S3). This approach implicitly assumes
the following: 1) Land use is an accurate proxy for all anthropogenic
disturbances, and 2) modern land-use change extent is representative of
perturbations over the timescale of biomarker
C ages (i.e., centuries to
millennia). Neither of these assumptions is strictly true in all studied river
basins. For example, several of the rivers included in the current study are
greatly impacted by recent human activity, particularly since the industrial
era (e.g., Yellow, Yangtze, and Danube) (49, 51). In contrast, other fluvial
systems have been subject to human modification over millennia (e.g.,
Godavari) (54). Nonetheless, our statistical correlation results indicate that
C ages are more strongly controlled by climate as opposed to
anthropogenic variables (SI Appendix, Table S4), suggesting that differences
in the type and duration of anthropogenic disturbance likely impart only a
small impact on resulting biomarker
C ages.
Sample Collection and Processing. Sample collection.
Samples include suspended sediments obtained via filtration of river
water, riverbed sediments sampled via a grab or bedload sampler, recently
deposited river flank and floodplain sediments, and fluvially influenced
coastal sediments deposited proximal to the mouth of the river. For river
flank, floodplain, and coastal sediments, emphasis was placed on sampling
deposits where fine-grained sediments accumulate, such as quiescent areas
characterized by weak river and coastal currents.
Sample processing and analysis procedures vary somewhat between field
campaigns. Detailed procedures are provided in previous publications and are
briefly summarized below. In general, exposed or slightly submerged river-
bank/flood-deposit sediments were collected using a shovel, whereas river-
bed samples used a bedload sampler (55), and suspended river samples were
obtained by large-volume filtration (typically 100 L) over either poly-
ethersulfone membrane filters or precombusted glass fiber filters (48, 55).
Samples were then stored, refrigerated, or frozen before freeze-drying in
the laboratory. For selected samples (mostly floodplain materials), the sed-
iment was wet sieved to <63 μm to remove large fragments that may have
been directly introduced (e.g., via riverbank erosion). The <63-μm fraction is
also considered to more closely resemble the riverine suspended load owing
to its overall smaller grain size compared to deposited sediments (45, 56, 57).
Sample aliquots were processed for the content, stable carbon isotopic
composition, and radiocarbon content of bulk OC. Carbonates were removed
using the acid-fumigation method (58) prior to bulk OC analysis. OC content
and stable isotope composition were measured via elemental analyzer
isotope ratio mass spectrometry (IRMS). Bulk OC radiocarbon analysis was
performed either at the National Ocean Science Accelerated Mass Spec-
trometry (NOSAMS) facility (Woods Hole Oceanographic Inst. [WHOI]) or at
the Laboratory of Ion Beam Physics (LIP) using established procedures
Plant-wax lipids were extracted from freeze-dried sample aliquots using a
mixture of dichloromethane and methanol. The lipid extract was subse-
quently treated to obtain a fatty-acid fraction, and the latter was derivat-
ized to obtain fatty-acid methyl esters (FAMEs). The FAMEs were further
purified via column chromatography and ultimately isolated by preparative
capillary gas chromatography (62). Isolated compounds were subsequently
analyzed by accelerator mass spectrometry (AMS) (
C contents) and gas
chromatography (GC)-IRMS (δ
C). Resulting
C data are corrected for any
derivative carbon as well as methodological and instrumental blanks (see
below) and reported using the fraction modern (Fm) notation and as ra-
diocarbon ages (63). We report abundance-weighted average
C composi-
tions of long-chain FA homologs in the 24 to 32 carbon number range. In
some cases, sample availability and/or technical issues led to averaging over
a subset of homologs within this carbon number range. For coastal sedi-
ments that may have additional sources of midchain FA homologs (e.g., ref.
35), we used corresponding δ
C values from GC-IRMS to select homologs
that are exclusively derived from terrestrial vascular plants.
Lignin-derived phenols were recovered by alkaline oxidative hydrolysis
(CuO oxidation) of freeze-dried or solvent-extracted sediments (64). Result-
ing hydrolysis products were then purified via high-performance liquid
chromatography (50, 64) and subsequently measured for
C content
by AMS.
C data corrections. Compound-specific radiocarbon data were
corrected using mass-balance equations for procedural blanks and, in the
case of fatty acids, for the addition of derivative carbon during methylation
with methanol with a known
C composition. Procedural blanks varied
depending on the instrumental set up used (e.g., at WHOI versus at ETH) and
the measurement date, as these methods have been continuously refined
over the course of this study. Overall, procedural blanks were in the range of
1to2μg C, with compositions intermediate between modern and dead.
Details of blank assessments and corrections can be found in Santos et al.
(65), French et al. (20), Haghipour et al. (66), and Feng et al. (64). The
PNAS Eglinton et al. Climate control on terrestrial biospheric carbon turnover
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uncertainty related to blank corrections varies from sample to sample, pri-
marily according to sample size and
C age. Overall, these uncertainties
remain below 0.05 Fm units and are therefore small compared to the vari-
ability observed across our entire dataset.
Geospatial Analysis. All geospatial analyses were performed in ArcGIS version
10 (ESRI Corporation). River basin outlines were calculated either upstream of
each sampling point (for suspended-sediment and bank-sediment samples)
or at the river mouth (for shelf-deposit samples), and environmental control
variables were calculated using the following data sources: 1) Elevation,
slope, and floodplain fractional area (here defined as area with slope less
than 1% rise) were calculated using the United States Geological Survey
(USGS) GTOPO30 digital elevation model with 30-arc-second resolution
[1 km at the equator (67)]. 2) Catchment-averaged MAT, temperature sea-
sonal coefficient of variability (TCV), MAP, and precipitation seasonal coef-
ficient of variability (PCV) were calculated from the raster data set of New
et al. (68) with 10resolution. 3) τ
and NPP were calculated from the raster
data set of Bloom et al. (5) with 1° resolution. 4) Total ecosystem carbon
turnover time (ecosystem τ) and soil carbon stocks were calculated from the
raster data set of Carvalhais et al. (4) with 0.5° resolution. 5) Anthropogenic
land-use fractional area was calculated as the sum of croplands,”“urban
and built-up,and cropland/natural vegetation mosaicpixels (Interna-
tional Geosphere Biosphere Program classification) extracted from the USGS
Global Land Cover Characterization raster data set version 2.0 with
30-arc-second resolution [1 km at the equator (69)]. 6) Soil carbon
C ages
(0 to 30 cm, 30 to 100 cm, and 0 to 100 cm depth integrated) were calculated
from the raster data set of Shi et al. (34) with 0.5° resolution.
All other environmental control variables were compiled from the liter-
ature (15, 17, 70). When comparing biomarker
C ages and soil turnover
times, we implicitly assume that the soil turnover times determined using
estimates of soil C stocks and NPP are representative of the average turnover
time over the timescales reflected in the biomarker
C ages (centuries to
millennia). While short-term small variations in NPP have likely occurred over
these timescales and regional-scale climate variability during the late Holo-
cene locally impacted soil carbon stocks (71), this relatively quiescent time
interval is unlikely to have seen large, globally coherent variations in soil
carbon stocks. As such, temporal variations in τ
are unlikely to contribute
significantly to the systematic difference observed between τ
and bio-
C ages (i.e., since plant-wax fatty-acid and lignin-phenol
on average 43.6- and 16.6-fold greater, respectively, than corresponding τ
For spatially resolved control variables (i.e., elevation, slope, floodplain
fraction, MAT, TCV, MAP, PCV, τ
, NPP, ecosystem τ, and soil C stocks),
weighting factors were calculated using hydrologically conditioned versions
of the GTOPO30 digital elevation model [i.e., Hydro1k and HydroSHEDS (72,
73)]. Hydrologic distance upstream of each sampling location was calculated
using the Flow Lengthfeature in ArcGIS and was weighted as an
exponential decay following
where wis the weighting factor (ranging from 0 to 1), lis the upstream
distance, and kis the reciprocal of the prescribed e-folding distance. All
spatially resolved control variables were taken as the weighted-catchment
Statistical Analyses.
Multiple linear regression. Regression analyses were performed using the
Numpy and Scipy packages in Python version 3.5; all analysis code is provided
in Dataset S1. To determine the strength of relationships between envi-
ronmental control variables and biomarker and POC response variables, OLS
MLR was performed following standard practices (74). In summary, all data
were first whitenedby subtracting the mean and dividing by the SD for
each variable in order to facilitate comparisons between variables with
differing units and ranges. The regression parameter matrix, B, was then
calculated as
where Xis the whitened matrix of environmental control variables, and Yis
the whitened matrix of biomarker and POC response variables. The matrix of
MLR-predicted response variable best estimates,
Y, was then calculated as
and the matrix of residuals was calculated as
Y. [4]
The matrix of correlation strengths between Xand Y,termedS
then calculated following
where nis the number of samples in the data set, and correlation Pvalues
were calculated individually for each control and response variable pair us-
ing standard OLS methods (74). Reported MLR r
values are the square of the
diagonal elements of S
. The optimal e-folding kvalue was calculated using
an inverse approach by repeating Eqs. 15and choosing the value that
maximizes the average resulting r
value (SI Appendix, Fig. S3). Because
scatterplot variables presented in this study generally contain uncertainty in
both xand yvariables (e.g., Figs. 3 and 4), predictive relationships were
calculated using reduced major-axis regression (75). Note that some envi-
ronmental control variables, and particularly biomarker response variables,
contained mis sing data entries; missing data were ignored when per-
forming statistical calculations by utilizing Numpy maskedarrays (ref-
erence Dataset S1 for details).
Finally, we test if and how averaging all samples of a given sample type
(i.e., suspended sediment, bedload sediment, or shelf/slope sediment) within
a given river basin impacts the results of our statistical analysis. We repeated
Eqs. 15using either catchment averagedor all reported samplesas the
response variable dataset (i.e., SI Appendix, Tables S1 and S2, respectively).
Resulting correlation coefficients exhibit only small differences and are
largely independent of our choice of response variable dataset (SI Appendix,
Fig. S8). We therefore use the catchment averaged dataset for all subse-
quent calculations as this avoids potential biases that could arise because of
uneven sampling density between river basins.
Redundancy analysis. To extract the canonical axes and to determine the
loadings of each sample, environmental control variable, and biomarker and
POC response variable onto each canonical axis, RDA was performed fol-
lowing Legendre and Legendre (76) using the Numpy and Scipy packages in
Python version 3.5. All analysis code is provided in Dataset S1. RDA is a ca-
nonical extension of MLR that transforms all control variables into orthog-
onal (linearly uncorrelated) axes and determines the fraction of sample
variance explained by each orthogonal axis; it is ideally suited for situations
with highly correlated control variables, as is the case here (76). Heuristically,
RDA extracts the principal components of response variables as predicted by
control variables; that is, it calculates the amount of variance within the
response variables that can be explained by the set of control variables and
maps all variables onto a set of underlying orthogonal axes. Briefly, RDA
involves independently performing principal component analyses on
Y, the
matrix of MLR-predicted response variables, and on Y
, the matrix of resid-
uals. Analogous to Eq. 5, the correlation matrix between MLR-predicted
response variables is first calculated as
Y. [6]
Then, the following eigenvalue problem is solved:
uj=0, [7]
where λ
is the jth canonical eigenvalue, u
is the jth canonical eigenvector,
and Iis the identity matrix; the response variable loadings onto the jth ca-
nonical axis (termed species scores) are thus the entries of u
. Eqs. 6and 7
are then repeated using Y
instead of
Y to calculate the noncanonical ei-
genvalues and eigenvectors. The percentage of response variable variance
explained by each axis is calculated as λ
divided by the sum of all (canonical
plus noncanonical) eigenvalues. Finally, constrained sample loadings (con-
strained site scores) onto each canonical axis, termed Z, are calculated as
and control variable loadings (constraining variable scores) onto each
canonical axis, termed C, are calculated as
where Uis the column-wise matrix of u
eigenvectors. Species scores, con-
strained site scores, and constraining variable scores for any two canonical
axes can then be plotted as in Fig. 2B; the angles between species scores and
constraining variable scores thus represent the strength of their linear cor-
relation. Reference Dataset S1 for further details and analyses.
Eglinton et al. PNAS
Climate control on terrestrial biospheric carbon turnover
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Data Availability. All study data are included in the article and/or supporting
ACKNOWLEDGMENTS. We thank R. Spencer and R.M. Holmes (Colville);
F. Filip (Danube); A. Winter, E. Tinacba, and F. Siringan (Cagayan); P. Cai and
H. Zhang (Pearl); J. Zhang (Yangtze); S.-L. Wang and L.-H. Chung (Gaoping);
K. Hughen (Unare); N. Blair (Waiapu); S. Marsh and S. Gillies (Fraser);
D. Eisma (Congo); and T. Kenna (Ob) for sample collection and fieldwork
assistance or for provision of samples. We are very grateful to A. McNichol
and other members of NOSAMS (WHOI) and to H.-A. Synal of LIP for expert
advice and technical assistance. We thank C. Johnson (WHOI) and M. Jäggi
(ETH) for measurements of bulk elemental and stable isotopic data. We
thank Z. Shi (University of California, Irvine) for soil mean age data. This
work was supported by grants from the US NSF (OCE-0928582 to T.I.E. and
V.V.G.; OCE-0851015 to B.P.-E., T.I.E., and V.V.G.; and EAR-1226818 to B.P.-E.),
Swiss National Science Foundation (200021_140850, 200020_163162, and
200020_184865 to T.I.E.), and National Natural Science Foundation of China
(41520104009 to M.Z.).
1. P. Friedlingstein et al., Global carbon budget 2019. Earth Syst. Sci. Data 11, 17831838
2. B. N. Sulman et al., Multiple models and experiments underscore large uncertainty in
soil carbon dynamics. Biogeochem. 141, 109123 (2018).
3. C. Jones et al., Twenty-first-century compatible CO
emissions and airborne fraction
simulated by CMIP5 earth system models under four representative concentration
pathways. J. Clim. 26, 43984413 (2013).
4. N. Carvalhais et al., Global covariation of carbon turnover times with climate in ter-
restrial ecosystems. Nature 514, 213217 (2014).
5. A. A. Bloom, J. F. Exbrayat, I. R. van der Velde, L. Feng, M. Williams, The decadal state
of the terrestrial carbon cycle: Global retrievals of terrestrial carbon allocation, pools,
and residence times. Proc. Natl. Acad. Sci. U.S.A. 113, 12851290 (2016).
6. M. S. Torn, S. E. Trumbore, O. A. Chadwick, P. M. Vitousek, D. M. Hendricks, Mineral
control of soil organic carbon storage and turnover. Nature 389, 170173 (1997).
7. M. W. Schmidt et al., Persistence of soil organic matter as an ecosystem property.
Nature 478,4956 (2011).
8. S. E. Trumbore, C. I. Czimczik, Geology. An uncertain future for soil carbon. Science
321, 14551456 (2008).
9. R. Z. Abramoff, M. S. Torn, K. Georgiou, J. Tang, W. J. Riley, Soil organic matter
temperature sensitivity cannot be directly inferred from spatial gradients. Global
Biogeochem. Cycles 33, 761776 (2019).
10. A. A. Berhe, R. T. Barnes, J. Six, E. Marin-Spiotta, Role of soil erosion in biogeo-
chemical cycling of essential elements: Carbon, nitrogen, and phosphorus. Annu. Rev.
Earth Planet. Sci. 46, 521548 (2018).
11. T. J. Battin et al., The boundless carbon cycle. Nat. Geosci. 2, 598600 (2009).
12. B. A. Fisher, A. K. Aufdenkampe, K. Yoo, R. E. Aalto, J. Marquard, Soil carbon redis-
tribution and organo-mineral associations after lateral soil movement and mixing in a
first-order forest watershed. Geoderma 319, 142155 (2018).
13. S. Doetterl et al., Erosion, deposition and soil carbon: A review of process-level con-
trols, experimental tools and models to address C cycling in dynamic landscapes. Earth
Sci. Rev. 154, 102122 (2016).
14. R. G. Hilton, Climate regulates the erosional carbon export from the terrestrial bio-
sphere. Geomorphology 277, 118132 (2017).
15. X. Feng et al., Differential mobilization of terrestrial carbon pools in Eurasian Arctic
river basins. Proc. Natl. Acad. Sci. U.S.A. 110, 1416814173 (2013).
16. J. I. Hedges, R. G. Keil, R. Benner, What happens to terrestrial organic matter in the
ocean? Org. Geochem. 27, 195212 (1997).
17. V. Galy, B. Peucker-Ehrenbrink, T. Eglinton, Global carbon export from the terrestrial
biosphere controlled by erosion. Nature 521, 204207 (2015).
18. R. A. Berner, Burial of organic carbon and pyrite sulfur in the modern ocean: Its
geochemical and environmental significance. Am. J. Sci. 282, 451473 (1982).
19. S. Tao, T. I. Eglinton, D. B. Montluçon, C. McIntyre, M. Zhao, Pre-aged soil organic
carbon as a major component of the Yellow river suspended load: Regional signifi-
cance and global relevance. Earth Planet. Sci. Lett. 414,7786 (2015).
20. K. L. French et al., Millennial soil retention of terrestrial organic matter deposited in
the Bengal Fan. Sci. Rep. 8, 11997 (2018).
21. N. E. Blair, R. C. Aller, The fate of terrestrial organic carbon in the marine environ-
ment. Annu. Rev. Mar. Sci. 4, 401423 (2012).
22. T. R. Marwick et al., The age of river-transported carbon: A global perspective. Global
Biogeochem. Cycles 29, 122137 (2015).
23. D. R. Griffith, R. T. Barnes, P. A. Raymond, Inputs of fossil carbon from wastewater
treatment plants to U.S. rivers and oceans. Environ. Sci. Technol. 43, 56475651
24. T. I. Eglinton et al., Variability in radiocarbon ages of individual organic compounds
from marine sediments. Science 277, 796799 (1997).
25. T. I. Eglinton, G. Eglinton, Molecular proxies for paleoclimatology. Earth Planet. Sci.
Lett. 275,116 (2008).
26. C. N. Jex et al., Lignin biogeochemistry: From modern processes to Quaternary ar-
chives. Quat. Sci. Rev. 87,4659 (2014).
27. T. van der Voort et al., Diverse soil carbon dynamics expressed at the molecular level.
Geophys. Res. Lett. 44, 1184011850 (2017).
28. J. D. Hemingway et al., Mineral protection regulates long-term global preservation of
natural organic carbon. Nature 570, 228231 (2019).
29. J. D. Hemingway, E. Schefuß, B. J. Dinga, H. Pryer, V. Galy, Multiple plant-wax com-
pounds record differential sources and ecosystem structure in large river catchments.
Geochim. Cosmochim. Acta 184,2040 (2016).
30. V. Galy, T. Eglinton, C. France-Lanord, S. Sylva, The provenance of vegetation and
environmental signatures encoded in vascular plant biomarkers carried by the
GangesBrahmaputra rivers. Earth Planet. Sci. Lett. 304,112 (2011).
31. S. Doetterl et al., Soil carbon storage controlled by interactions between geochem-
istry and climate. Nat. Geosci. 8, 780783 (2015).
32. Z. Luo, G. Wang, E. Wang, Global subsoil organic carbon turnover times dominantly
controlled by soil properties rather than climate. Nat. Commun. 10, 3688 (2019).
33. N. Dubois et al., Indonesian vegetation response to changes in rainfall seasonality
over the past 25,000 years. Nat. Geosci. 7, 513517 (2014).
34. Z. Shi et al., The age distribution of global soil carbon inferred from radiocarbon
measurements. Nat. Geosci. 13, 555559 (2020).
35. J. E. Vonk et al., Temporal deconvolution of vascular plant-derived fatty acids ex-
ported from terrestrial watersheds. Geochim. Cosmochim. Acta 244, 502521 (2019).
36. S. Arndt et al., Quantifying the degradation of organic matter in marine sediments: A
review and synthesis. Earth Sci. Rev. 123,5386 (2013).
37. B. P. Boudreau, B. R. Ruddick, On a reactive continuum representation of organic
matter diagenesis. Am. J. Sci. 291, 507538 (1991).
38. D. C. Forney, D. H. Rothman, Common structure in the heterogeneity of plant-matter
decay. J. R. Soc. Interface 9, 22552267 (2012).
39. D. H. Rothman, Earths carbon cycle: A mathematical perspective. Bull. Am. Math. Soc.
52,4764 (2015).
40. W. Knorr, I. C. Prentice, J. I. House, E. A. Holland, Long-term sensitivity of soil carbon
turnover to warming. Nature 433, 298301 (2005).
41. C. J. Hein, M. Usman, T. I. Eglinton, N. Haghipour, V. V. Galy, Millennial-scale hy-
droclimate control of tropical soil carbon storage. Nature 581,6366 (2020).
42. J. J. Cole et al., Plumbing the global carbon cycle: Integrating inland waters into the
terrestrial carbon budget. Ecosystems (N. Y.) 10, 171184 (2007).
43. Q. Hua, M. Barbetti, A. Z. Rakowski, Atmospheric radiocarbon for the period
19502010. Radiocarbon 55, 20592072 (2013).
44. M. Yu et al., Molecular isotopic insights into hydrodynamic controls on fluvial sus-
pended particulate organic matter transport. Geochim. Cosmochim. Acta 262,7891
45. C. V. Freymond et al., I: Constraining instantaneous fluxes and integrated composi-
tions of fluvially discharged organic matter. Geochem. Geophys. Geosyst. 19,
24532462 (2018).
46. E. Garzanti et al., Mineralogical and chemical variability of fluvial sediments 2.
Suspended-load silt (Ganga-Brahmaputra, Bangladesh). Earth Planet. Sci. Lett. 302,
107120 (2011).
47. J. Bouchez, M. Lupker, J. Gaillardet, C. France-Lanord, L. Maurice, How important is it
to integrate riverine suspended sediment chemical composition with depth? Clues
from Amazon river depth-profiles. Geochim. Cosmochim. Acta 75, 69556970 (2011).
48. B. M. Voss et al., Seasonal hydrology drives rapid shifts in the flux and composition of
dissolved and particulate organic carbon and major and trace ions in the Fraser River,
Canada. Biogeosci. 12, 55975618 (2015).
49. M. Yu et al., Impacts of natural and human-induced hydrological variability on par-
ticulate organic carbon dynamics in the Yellow River. Environ. Sci. Technol. 53,
11191129 (2019).
50. E. E. Martin et al., Age of riverine carbon suggests rapid export of terrestrial primary
production in tropics. Geophys. Res. Lett. 40, 56875691 (2013).
51. Y. Wu, T. I. Eglinton, J. Zhang, D. B. Montluçon, Spatiotemporal variation of the
quality, origin, and age of particulate organic matter transported by the Yangtze
river (Changjiang). J. Geophys. Res. Biogeosci. 123, 29082921 (2018).
52. J. D. Hemingway et al., Hydrologic controls on seasonal and inter-annual variability of
Congo River particulate organic matter source and reservoir age. Chem. Geol. 466,
454465 (2017).
53. S. Trumbore, Radiocarbon and soil carbon dynamics. Annu. Rev. Earth Planet. Sci. 37,
4766 (2009).
54. L. Giosan et al., Massive erosion in monsoonal central India linked to late Holocene
land cover degradation. Earth Surf. Dyn. 5, 781789 (2017).
55. V. Galy, T. I. Eglinton, Protracted storage of biospheric carbon in the Ganges-
Brahmaputra basin. Nat. Geosci. 4, 843847 (2011).
56. J. E. Vonk et al., Arctic deltaic lake sediments as recorders of fluvial organic matter
deposition. Front. Earth Sci. 4, 77 (2016).
57. C. V. Freymond et al., Evolution of biomolecular loadings along a major river system.
Geochim. Cosmochim. Acta 223, 389404 (2018).
58. J. H. Whiteside et al., Pangean great lake paleoecology on the cusp of the end-Triassic
extinction. Palaeogeogr. Palaeoclimatol. Palaeoecol. 301,117 (2011).
59. A. P. McNichol, E. A. Osborne, A. R. Gagnon, B. Fry, G. A. Jones, TIC, TOC, DIC, DOC,
PIC, POCunique aspects in the preparation of oceanographic samples for
Nucl. Instrum. Methods Phys. Res. B 92, 162165 (1994).
60. M. Christl et al., The ETH Zurich AMS facilities: Performance parameters and reference
materials. Nucl. Instr. Meth. Phys. Res. Sec. B 294,2938 (2013).
61. C. P. McIntyre et al., Online
C and
C gas measurements by EA-IRMS-AMS at ETH
Zürich. Radiocarbon 59, 893903 (2017).
62. T. I. Eglinton, L. I. Aluwihare, J. E. Bauer, E. R. M. Druffel, A. P. McNichol, Gas chro-
matographic isolation of individual compounds from complex matrices for radiocar-
bon dating. Anal. Chem. 68, 904912 (1996).
63. M. Stuiver, H. A. Polach, Discussion: Reporting of
C data. Radiocarbon 19, 355363
PNAS Eglinton et al. Climate control on terrestrial biospheric carbon turnover
Downloaded by guest on February 20, 2021
64. X. Feng et al.,
C and
C characteristics of higher plant biomarkers in Washington
margin surface sediments. Geochim. Cosmochim. Acta 105,1430 (2013).
65. G. M. Santos et al., Blank assessment for ultra-small samples: Chemical extraction and
separation vs. AMS. Radiocarbon 52, 13221335 (2010).
66. N. Haghipour et al., Compound-specific radiocarbon analysis by elemental analyzer-
accelerator mass spectrometry. Precision and limitations. Anal. Chem. 91, 20422049
67. Earth Resources Observation and Science Center/U.S. Geological Survey/U.S. Depart-
ment of the Interior, USGS 30 ARC-second Global Elevation Data, GTOPO30 (1997).
Research Data Archive at the National Center for Atmospheric Research, Computa-
tional and Information Systems Laboratory. Ac-
cessed 13 March 2020.
68. M. New, D. Lister, M. Hulme, I. Makin, A high-resolution data set of surface climate
over global land areas. Clim. Res. 21,125 (2002).
69. A. S. Belward, ed., The IGBP-DIS global 1 km land cover data set (DISCover)-proposal
and implementation plans: IGBP-DIS Working Paper No. 13, Toulouse, France, pp. 61
70. B. Peucker-Ehrenbrink, Land2Sea database of river drainage basin sizes, annual water
discharges, and suspended sediment fluxes. Geochem. Geophys. Geosyst. 10, 6 (2009).
71. R. H. Smittenberg, T. I. Eglinton, S. Schouten, J. S. Damsté, Ongoing buildup of re-
fractory organic carbon in boreal soils during the Holocene. Science 314, 12831286
72. K. L. Verdin, S. K. Greenlee, Development of continental scale digital elevation
models and extraction of hydrographic featuresin Proc. 3rd Int. Conf./Workshop on
Integrating GIS and Environmental Modeling, Santa Fe, New Mexico, January 21-26,
1996 (National Center for Geographic Information and Analysis, Santa Barbara, Cal-
ifornia, 1996).
73. B. Lehner, K. Verdin, A. Jarvis, New global hydrography derived from spaceborne
elevation data. Eos (Wash. D.C.) 89,9394 (2008).
74. N. R. Draper, H. Smith, Applied Statistical Analysis (John Wiley & Sons, Inc., New York
City, New York, ed. 3, 1998), pp. 217234.
75. J. M. Rayner, Linear relations in biomechanics: The statistics of scaling functions.
J. Zool. 206, 415439 (1985).
76. P. Legendre, L. Legendre, Numerical Ecology: Developments in Environmental Mod-
elling (Elsevier B.V., Amsterdam, The Netherlands, ed. 3, 2012), pp. 424520.
Eglinton et al. PNAS
Climate control on terrestrial biospheric carbon turnover
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... Previous studies in inland water systems have a long history focusing on the European, Arctic, North American, and a handful of Himalayan mountain river systems. These studies have reported the organic carbon (OC) flux and its dynamics, source characterization, and the fate of OC derived from catchment soils in major river systems worldwide (Bianchi et al., 2018;Butman et al., 2015;Drake et al., 2018;Eglinton et al., 2021;Galy et al., 2007;Goldsmith et al., 2015;Märki et al., 2021;Raymond et al., 2013;Resplandy et al., 2018;Wang et al., 2021). Notably, the large rivers have a significant role in transporting approximately one-third of the total organic matter (OM) buried in marine sediments of terrestrial origin (Burdige, 2005;Li et al., 2017a,b). ...
... By exporting and burying the terrestrial carbon pool in marine sediments, rivers evade ~1.8 Pg of carbon to the atmosphere annually (Raymond et al., 2013;Argerich et al., 2016) out of 5.1 Pg of carbon that they receive from the catchment (Drake et al., 2018). Hence, accurate estimation of biologically derived OC and tracing their fate (degradation vs. preservation) in rivers are crucial to validate these estimates (Bianchi et al., 2018;Eglinton et al., 2021). For example, Hilton (2017) indicated that the release of POC in mountainous terrains is affected by hydrologically driven erosional processes, climate, and the steepness of mountain slopes. ...
... However, modification of C:N ratios could occur during early diagenesis accompanied by microbial immobilization of the nitrogenous component and remineralization of carbon (Meyers, 1994;Venkatesh and Anshumali, 2020). Thus, post-depositional alteration in OM confounds the interpretation of OM sources in fluvial systems based on bulk parameters (C:N and TOC), and alternative proxies are sought, e.g., diagnostic biomarkers and compound-specific isotope analyses of marker compounds (Bianchi et al., 2002;Eglinton et al., 2021;Ertel and Hedges, 1984;Hirave et al., 2021;Nasir et al., 2016;Onstad et al., 2000). For example, lignin phenols are indicators of vascular plants that could help differentiate the dominant input of OM from the catchment and its fate in soils (Ertel and Hedges, 1984;Hedges and Ertel, 1982;Jex et al., 2014;Spencer et al., 2009). ...
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The Himalayan rivers yield the most significant flux of continental sediments into the ocean. Organic matter (OM) transported by these rivers provides a peek at the influence of diverse geological terrains, soil types, vegetation, and climate on carbon cycling within a narrow boundary. We analyzed suspended and bedload sediments from four Himalayan rivers to trace their sources, elucidate their fate during fluvial transport, and estimate the organic carbon (OC) flux. Hence, total OC (TOC), dissolved organic carbon (DOC), C:N ratios, and lignin phenols were measured. Consistent with the erosional intensity in the rivers, suspended sediment load input followed the order: Kaligandaki > Myagdikhola > Aadhikhola > Tinahukhola. C:N values in rivers from the Lesser Himalayas and Siwalik indicate sediments from mixed biogenic sources. In contrast, high TOC and C/N values in the trans-Himalaya rivers flowing through barren landscapes reflect the erosion of catchment sediments yielding petrogenic carbon. The suspended matter in rivers from the Lesser Himalayas and Siwalik has higher lignin phenol concentrations than the trans-Himalaya and Higher Himalaya rivers. The lignin phenol ratios indicate higher degradation in rivers from the trans and Higher Himalaya sections. This implies that only a small fraction of the terrestrial OM transported by these rivers deposits in the ocean sink. In contrast, rivers from the Lesser Himalayas and Siwaliks sequester a significant amount of OM bound to their bedload. As a result, these rivers transferred lower particulate OC (POC) but higher DOC than similar rivers worldwide. Rivers from Lesser Himalayas and Siwaliks transfer > 90 % of annual POC flux during monsoons. Finally, although Himalayan rivers transport less OC than other global rivers traversing densely vegetated landscapes, the sheer number of these rivers has significant implications on the fate and transport of total OC from catchments sediments.
... Anthropogenic activity is the most important driver of landscape transformation (Butzer, 1964;Ellis, 2015;Ellis et al., 2013;Goldewijk et al., 2017;Kaplan et al., 2011). Human disturbance affects surface reservoirs and exacerbates potential changes in the global climate system (Eglinton et al., 2021;Friedlingstein et al., 2018). In this sense, landscapes with a predominantly urban matrix show alterations in their biogeochemical cycles that potentially affect regional and global atmospheric climates (Lorenz & Lal, 2009). ...
... On the other hand, the carbon residing in vegetation and soils is three times that of atmospheric carbon (Eglinton et al., 2021) and is a key player in mitigating or increasing the accumulation of greenhouse gases. However, the terrestrial carbon cycle constitutes one of the major uncertainties affecting global change modeling (Carvalhais et al., 2014). ...
... Urban expansion in the tropics would contribute about 5% of atmospheric emissions due to deforestation and land use change (Baccini et al., 2012;Bonilla-Bedoya et al., 2020). This scenario intensifies the conversion of the global biogeochemical cycle which latently affects climate and surface carbon pools (Butzer, 1964;Lorenz & Lal, 2009;Kaplan et al., 2011;Ellis, 2015;Ellis et al., 2013;Goldewijk et al., 2017;Friedlingstein et al., 2018;Eglinton et al., 2021). ...
The unique characteristics of a city amplify the impacts of climate change; therefore, urban planning in the 21st century is challenged to apply mitigation and adaptation strategies that ensure the collective well-being. Despite advances in monitoring urban environmental change, research on the application of adaptation-oriented criteria remains a challenge in urban planning in the Global South. This study proposes to include urban land management as a criterion and timely strategy for climate change adaptation in the cities of the Tropical Andes. Here, we estimate the distribution of the soil organic carbon stock (OCS) of the city of Quito (2,815 m.a.s.l.; population 2,011,388; 197.09 km²) in the following three methodological moments: i) field/laboratory: city-wide sampling design established to collect 300 soil samples (0–15 cm) and obtain data on organic carbon (OC) concentrations in addition to 30 samples for bulk density (BD); ii) predictors: geographic, spectral and anthropogenic dimensions established from 17 co-variables; and iii) spatial modeling: simple multiple regression (SMRM) and random forest (RFM) models of organic carbon concentrations and density as well as OCS stock estimation. We found that the spatial modeling techniques were complementary; however, SMRM showed a relatively higher fit both (OC: r² = 20%, BD: r² = 16%) when compared to RFM (OC: r² = 8% and BD: r² = 5%). Thus, soil carbon stock (0–0.15 m) was estimated with a spatial variation that fluctuated between 9.89 and 21.48 kg/m²; whereas, RFM showed fluctuations between 10.38 and 17.67 kg/m². We found that spatial predictors (topography, relative humidity, precipitation, temperature) and anthropogenic predictors (population density, roads, vehicle traffic, land cover) positively influence the model, while spatial predictors have little influence and show multicollinearity with relative humidity. Our research suggests that urban land management in the 21st century provides key information for adaptation and mitigation strategies aimed at coping with global and local climate variations in the cities of the Tropical Andes.
... Previous studies have indicated an important role of mineral protection, e.g. by occlusion within inaccessible pore space or formation of bonds to solid-phase minerals, in the preservation of terrestrial biomolecules (35,41). This mineral protection, which is important in soils and during fluvial transport and deposition to sediments, can effectively preserve terrestrial plant long-chain n-alkanes and fatty acids, which primarily derive from leaf waxes, for thousands of years (18,70). It is possible that most long-chain n-alkanes and fatty acids in the sediments studied were transported and subsequently protected in sediments via associations with soil minerals. ...
Even though lake sediments are globally important organic carbon (OC) sinks, the controls on long-term OC storage in these sediments are unclear. Using a multi-proxy approach, we investigate changes in diatom, green algae, and vascular plant biomolecules in sedimentary records from the past centuries across five temperate lakes with different trophic histories. Despite past increases in the input and burial of organic carbon in sediments of eutrophic lakes, biomolecule quantities in sediments of all lakes are primarily controlled by post-burial microbial degradation over the time scales studied. We, moreover, observe major differences in biomolecule degradation patterns across diatoms, green algae, and vascular plants. Degradation rates of labile diatom DNA exceed those of chemically more resistant diatom lipids, suggesting that chemical reactivity mainly controls diatom biomolecule degradation rates in the lakes studied. By contrast, degradation rates of green algal and vascular plant DNA are significantly lower than those of diatom DNA, and in a similar range as corresponding, much less reactive lipid biomarkers and structural macromolecules, including lignin. We propose that physical shielding by degradation-resistant cell wall components, such as algaenan in green algae and lignin in vascular plants, contributes to the long-term preservation of labile biomolecules in both groups and significantly influences the long-term burial of OC in lake sediments.
... . While there is clear evidence from molecular biomarkers that some old soil carbon is exported to freshwater sediments (Douglas et al., 2014;Eglinton et al., 2021;Freimuth et al., 2021), the extent to which these millennial carbon reservoirs contribute to bulk sediment reservoirs remains unclear. ...
Freshwater sediments are important carbon reservoirs, but the extent to which different components of soil or aquatic organic matter (OM) are deposited in these sediments is not well defined. Bulk sediment carbon (δ¹³C) and nitrogen (δ¹⁵N) stable isotope ratios, as well as radiocarbon, are valuable tracers for sediment OM sources, but there are few studies comparing the isotopic composition of soil and sediment OM at the catchment scale. We analyzed spatial variation in δ¹³C, δ¹⁵N, ¹⁴C, and C:N ratios in OM from soils, stream and lake sediments, and aquatic plants and algae, in a temperate forest lake catchment in southern Quebec, and used a Bayesian model to estimate source mixtures for sediment OM. Sediments at the stream mouths entering the lake were characterized by high C:N ratios, high fraction modern carbon (Fm), and low δ¹³C, indicating preferential deposition of plant-derived OM. In contrast, sediments sampled further upstream during a period of low streamflow indicated a larger proportion of microbial OM based on low C:N ratios and high δ¹⁵N. In lake sediments we observed zonation of OM isotopic composition by water depth. Shallow sediments (0–1 m water depth) were characterized by high amounts of plant-derived OM, while intermediate-depth sediments (1–3 m) were characterized by high δ¹³C, indicating an increased input of OM from aquatic plants. Deep lake sediments (> 4 m) were characterized by low δ¹³C and Fm values, which likely reflect greater input of phytoplankton OM. Stream sediments downstream of the lake exhibited high δ¹⁵N and low Fm values, implying a greater input of aged microbial biomass from soils. Our results indicate catchment-scale spatial differentiation in the source of OM in sediments, with zones of preferential deposition of terrestrial plant, aquatic plant, phytoplankton, and soil microbial biomass.
... Bulk measurements yield average carbon isotopic signals while offering limited information on the heterogeneous nature of various OC origins and reactivities. Compoundspecific radiocarbon analysis (CSRA) of highly diagnostic biomarkers is a powerful approach to constrain OC sources and transport processes (e.g., Feng et al., 2015) because they retain the isotopic signals of their corresponding OC sources and avoid interferences from other carbon pools (e.g., Eglinton et al., 1997Eglinton et al., , 2021. However, the application of CSRA is often limited due to low concentrations of target compounds, because biomarkers typically constitute ≤ 1% of total OC and can be subject to production, transport, and preservation biases (Zhao et al., 2006) leaving the majority of fluvially exported OC largely uncharacterized. ...
The Yellow River is one of the largest suppliers of sediments and organic carbon (OC) to the ocean. Previous studies have revealed that OC transported by the Yellow River largely derives from the erosion of the Chinese Loess Plateau, which is dominated by pre-aged soil carbon and could be efficiently preserved in marine sediments. Here, we used ramped oxidation radiocarbon analysis (RPO-¹⁴C) to characterize the age and reactivity distribution of OC in two Yellow River suspended sediment samples and six Bohai Sea and Yellow Sea (BS–YS) surface sediments from a transect along the sediment transport pathway. RPO-¹⁴C independently characterizes the full spectrum of OC thermal stability and isotope compositions to reveal the source, age and reactivity structure of OC transported by the Yellow River and preserved in Chinese marginal sea sediments. We calculated the activation energy (E) distribution—a proxy for bonding environment and by extension reactivity—which, combined with ¹⁴C and stable carbon isotope (δ ¹³C) compositions, reveals OC origin and stability. Our data suggest that 96% of OC in Yellow River suspended sediments is biospheric and weathered petrogenic, while unweathered petrogenic OC only accounts for 4% which is almost an order of magnitude lower than the fossil OC estimates (32%) based on compound specific ¹⁴C analysis. RPO data reveal the prevalence of aged biospheric loess OC in the Yellow River. We use δ ¹³C, ¹⁴C and RPO-derived activation energy data to quantify the contribution of terrestrial OC to surface sediments in the BS–YS. The resulting estimates of terrestrial OC proto-burial efficiencies yield an average value of 89 ± 30%, revealing overall very high terrestrial OC preservation in the BS–YS. Additionally, and somewhat counter intuitively, we find that the preservation of terrestrial OC decreases with increasing E. This pattern may arise from an enhanced preservation of a pre-aged C4 plants derived fraction of the loess-derived OC associated with secondary clays characterized by smaller grain size and higher surface area. Alternatively, the high E component of the Yellow River OC might comprise partially weathered petrogenic carbon, undergoing further mineralization during transport from rivers to marginal sea sediments via marine organic matter priming.
... The IPCC Special Report on Climate Change and Land (SRCCL) estimated that agriculture, forestry and other land use activities accounted for around 23% of the total net anthropogenic emissions of GHG (IPCC, 2021). LUCC affected GHG emissions mainly through land-use categories conversions (Eglinton et al., 2021;Liu and Zhao, 2018). For instance, the conversion from forests or grasslands to croplands may reduce SOC storage (Alidoust et al., 2018;Thangavel et al., 2019). ...
Soil organic carbon (SOC) storage in arid inland regions is significantly affected by land use and land cover change (LUCC) associated with climate change and agricultural activities. A systematic evaluation to the LUCC effects on SOC storage could enable us to better manage soil carbon pools in arid inland regions. Here, we evaluated the effects of LUCC on SOC storage in the Hexi Regions based on high-resolution SOC and LUCC maps derived from Landsat imagery and digital soil mapping using machine learning algorithm and environmental covariates. The results showed that SOC generally increased from northwest to southeast over the Hexi Regions with an average stock of 7.15 kg C m⁻² at a soil depth of 100 cm and a total storage of 2783.05 Tg C. The SOC stock and storage in the Qilian Mountains (mountains) was about 3.90 and 4.55 times higher than that in the Hexi Corridor (plains), respectively. It was estimated that LUCC over the past four decades caused a net increase of 23.41 and 18.19 Tg C in total SOC storage for the Qilian Mountains and Hexi Corridor, respectively. Specifically, the development in grasslands quality as well as the land-use category conversion from the bare land to grassland mainly contributed to the increase in SOC storage of the Qilian Mountains, where the LUCC was mainly driven by climate change. By contrast, the SOC storage change in the Hexi Corridor was mainly associated with the conversion from sandy land and low-cover grassland to cropland as well as sandy land to grassland, being mainly affected by intense cropland expansion and desertification control. Our results highlighted the importance of climate change and cropland expansion in enhancing SOC storage of the Qilian Mountains and Hexi corridor, respectively.
... Similarly, effects of hydrological dynamics (i.e., low/high flow conditions) and hydrodynamic sorting are unknown. As organic carbon and lipid biomarkers (e.g., fatty acids, n-alkanes, brGDGTs) are generally enriched in the fine grained sediment fraction due to associations with mineral surfaces, this may result in specific distributions with river depth (e.g., Galy et al., 2008;Freymond et al., 2018a;Kirkels et al., 2020a) as well as in differential transport, export, and burial efficiency of certain compounds in (marine) sedimentary records (e.g., Keil et al., 1997;Goñi et al., 2000;Bianchi et al., 2018;Freymond et al., 2018b;Yu et al., 2019;Hou et al., 2020;Kirkels et al., 2020a;Li et al., 2020;Eglinton et al., 2021). ...
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Branched glycerol monoalkyl glycerol tetraethers (brGMGTs) are a group of membrane-spanning lipids produced by (yet) unidentified bacteria. They are characterized by a C-C bond connecting the two alkyl chains, which is thought to enhance membrane stability at higher temperatures. So far, they have been found in peats, lakes, and marine sediments, where their abundance relative to that of branched glycerol dialkyl glycerol tetraethers (brGDGTs) increases with temperature. However, the preferred niche(s) for production and the origin of brGMGTs in the terrestrial and marine realm remain unknown. Here we explore the occurrence of brGMGTs in soils, suspended particulate matter (SPM) and riverbed sediments in the Godavari River basin, and compare these with brGMGTs in a Holocene sediment core from the Bay of Bengal close to the Godavari River mouth. BrGMGTs are mostly detected in agricultural and/or regularly inundated soils, and in the river at sites with standing water and/or agricultural/wastewater effluents, as well as in the delta, where low oxygen conditions and/or high nutrient levels prevail. In contrast, brGMGTs are continuously present in the marine sediment core, but with a different isomeric composition than in the terrestrial realm, indicating a primarily marine source. The stable brGMGT distribution downcore and brGMGT-inferred temperature estimates which resemble the bottom water temperature, may suggest marine brGMGT production in the deep water column and/or sediments. However, to establish the proxy potential of brGMGTs for paleoreconstructions in the terrestrial and marine realm, brGMGT sources and environmental controls require further study.
Terrestrial biomes in the U.S. can be managed for SOC sequestration. Sequestration for climate change adaptation and mitigation occurs when the soil C inputs are derived from atmospheric carbon dioxide (CO2) fixed by photosynthesis within a biome, and the synthesized SOC is protected and stabilized for long periods of time. Aside soil and land-use management practices, elevated CO2, nitrogen (N) additions, warming, irrigation and increases in biomass but also natural disturbances affect SOC stocks. The SOC sequestration in managed land of forest biomes in the U.S. can be managed by practices including: (i) harvesting, (ii) thinning, (iii) fertilization, (iv) liming, (v) drainage, (vi) irrigation, (vii) tree species selection and (viii) control of understory vegetation, and by managing natural disturbances. Management of stand-replacing disturbances (i.e., fire, insect outbreaks) is particularly promising to enhance SOC sequestration. However, forest management is focused on producing timber by silviculture and, until recently, not on soil management including SOC stocks resulting in limited understanding on how to enhance forest SOC sequestration. Fire strongly affects SOC sequestration in the boreal forest/taiga biome, but it is unclear how recent changes in fire size, severity and intensity together with changes in insect and pathogen outbreaks alter SOC stocks. Importantly, it is not possible to fully control and manage SOC sequestration in boreal U.S. forests because of its scale and remoteness. In contrast, forest management interventions in the temperate coniferous forest biome during harvesting, thinning, reforestation and prescribed burning can potentially enhance SOC sequestration. Reducing the extent of harvested area on a landscape level, N-fertilization, and introduction/favoring faster-growing trees species and those more tolerant of heat or drought are among the management options. The SOC sequestration in both temperate coniferous, and broadleaf and mixed U.S. forest biomes share the same key SOC vulnerabilities associated with harvest and fire. Specifically, recent changes in fire regimes in western U.S. forests are a major concern for the fate of SOC. In the tropical forest biome, hurricanes, typhoons and cyclones may increasingly affect SOC sequestration. Otherwise, SOC sequestration in tropical forests may be enhanced by: (i) fire management, (ii) prevention of grass invasions, (iii) selection of high-SOC species for plantations, (iv) mixed-species plantations, (v) reforestation of burned areas, (vi) grazer density control, (vii) reforestation, (viii) facilitation of N-fixer establishment, (ix) control of soil erosion, (x) selection of high-SOC species or genetic families on degraded soils and for plantations, and (xi) retaining logging residues. The SOC sequestration in the temperate grassland, savanna, and shrubland biome in the U.S. may be enhanced by: (i) improved grazing management, (ii) fertilization, (iii) irrigation, (iv) increasing species diversity, and (v) sowing legumes and improved grass species. In contrast, management activities to increase SOC sequestration in the tundra biome are limited. Terrestrial wetlands in the U.S. are not managed for SOC sequestration. However, restoration of drained peatlands to wetlands, wetland agriculture (‘paludiculture’) and reduction in peat mining may contribute to SOC sequestration. The potential for management of SOC sequestration in deserts and xeric shrublands is limited as plants are often near their physiological limits for temperature and water stress. Among the opportunities to enhance SOC sequestration are restoration of degraded lands and improved grazing management. Management practices to enhance cropland SOC sequestration in the U.S. include: (i) maintaining permanent cropland cover with vegetation (i.e., elimination of summer fallow, use of perennials and cover crops), (ii) protecting the soil from erosion (i.e., reduced tillage or no-till (NT), maintaining residue cover), and (iii) improved nutrient and water management. Irrigation, and applying organic fertilizers and biochar can also contribute to SOC sequestration in U.S. croplands. Human activities, i.e., land clearing, removal of vegetation, and disturbance of soils including adding impervious cover associated with construction activities affect SOC sequestration in settlements and urban areas. However, these soils are not managed for SOC sequestration, and any recommendations on SOC-enhancing soil and land use management practices are premature. This chapter will summarize potential alterations in SOC sequestration by soil and land-use management practices, and the effects of climate and global changes on sequestration processes. The chapter will also present approaches for carbon monitoring and accounting in terrestrial ecosystems in the U.S., and how SOC sequestration in terrestrial biomes is affected by natural disturbances and how sequestration can potentially be enhanced by management interventions.
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Much attention has been focused on fine‐grained sediments carried as suspended load in rivers due to their potential to transport, disperse, and preserve organic carbon (OC), while the transfer and fate of OC associated with coarser‐grained sediments in fluvial systems have been less extensively studied. Here, sedimentological, geochemical, and biomolecular characteristics of sediments from river depth profiles reveal distinct hydrodynamic behavior for different pools of OC within the Mackenzie River system. Higher radiocarbon (¹⁴C) contents, low N/OC ratios, and elevated plant‐derived biomarker loadings suggest a systematic transport of submerged vascular plant debris above the active riverbed in large channels both upstream of and within the delta. Subzero temperatures hinder OC degradation promoting the accumulation and waterlogging of plant detritus within the watershed. Once entrained into a channel, sustained flow strength and buoyancy prevent plant debris from settling and keep it suspended in the water column above the riverbed. Helical flow motions within meandering river segments concentrate lithogenic and organic debris near the inner river bends forming a sediment‐laden plume. Moving offshore, we observe a lack of discrete, particulate OC in continental shelf sediments, suggesting preferential trapping of coarse debris within deltaic and neritic environments. The delivery of waterlogged plant detritus transport and high sediment loads during the spring flood may reduce oxygen exposure times and microbial decomposition, leading to enhanced sequestration of biospheric OC. Undercurrents enriched in coarse, relatively fresh plant fragments appear to be reoccurring features, highlighting a poorly understood yet significant mechanism operating within the terrestrial carbon cycle.
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Aged organic carbon (OC) is widely observed in surficial sediments deposited on continental margins, resulting from several factors including old OC input and sedimentological processes. While the supply of old OC to marine sediments has been examined and quantified in a range of continent margin settings, there have been few attempts to constrain the extent to which hydrodynamic processes, specifically lateral transport, contribute to the old age of sedimentary OC. Here, we propose a mathematical index, bAgedOC, to differentiate the causes of sedimentary aged OC, and apply this approach to two typical sediment transport pathways that follow the supply and dispersal of terrestrial OC in the marine environment, and further extrapolate it to submarine canyon, Arctic and deep‐sea environments. The index, representing a modification of a widely used quantitative method, provides a novel approach to distinguish the contribution of lateral transport to the pre‐depositional aging of OC in the oceanic region.
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Soils contain more carbon than the atmosphere and vegetation combined. An increased flow of carbon from the atmosphere into soil pools could help mitigate anthropogenic emissions of carbon dioxide and climate change. Yet we do not know how quickly soils might respond because the age distribution of soil carbon is uncertain. Here we used 789 radiocarbon (∆14C) profiles, along with other geospatial information, to create globally gridded datasets of mineral soil ∆14C and mean age. We found that soil depth is a primary driver of ∆14C, whereas climate (for example, mean annual temperature) is a major control on the spatial pattern of ∆14C in surface soil. Integrated to a depth of 1 m, global soil carbon has a mean age of 4,830 ± 1,730 yr, with older carbon in deeper layers and permafrost regions. In contrast, vertically resolved land models simulate ∆14C values that imply younger carbon ages and a more rapid carbon turnover. Our data-derived estimates of older mean soil carbon age suggest that soils will accumulate less carbon than predicted by current Earth system models over the twenty-first century. Reconciling these models with the global distribution of soil radiocarbon will require a better representation of the mechanisms that control carbon persistence in soils. Soils may accumulate less carbon and with a slower turnover than Earth system models predict, according to analysis of the age distribution of global soil carbon, which finds that the mean age of soil carbon is older than that in simulated in models.
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The storage of organic carbon in the terrestrial biosphere directly affects atmospheric concentrations of carbon dioxide over a wide range of timescales. Within the terrestrial biosphere, the magnitude of carbon storage can vary in response to environmental perturbations such as changing temperature or hydroclimate¹, potentially generating feedback on the atmospheric inventory of carbon dioxide. Although temperature controls the storage of soil organic carbon at mid and high latitudes2,3, hydroclimate may be the dominant driver of soil carbon persistence in the tropics4,5; however, the sensitivity of tropical soil carbon turnover to large-scale hydroclimate variability remains poorly understood. Here we show that changes in Indian Summer Monsoon rainfall have controlled the residence time of soil carbon in the Ganges–Brahmaputra basin over the past 18,000 years. Comparison of radiocarbon ages of bulk organic carbon and terrestrial higher-plant biomarkers with co-located palaeohydrological records⁶ reveals a negative relationship between monsoon rainfall and soil organic carbon stocks on a millennial timescale. Across the deglaciation period, a depletion of basin-wide soil carbon stocks was triggered by increasing rainfall and associated enhanced soil respiration rates. Our results suggest that future hydroclimate changes in tropical regions are likely to accelerate soil carbon destabilization, further increasing atmospheric carbon dioxide concentrations.
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Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere – the “global carbon budget” – is important to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe data sets and methodology to quantify the five major components of the global carbon budget and their uncertainties. Fossil CO2 emissions (EFF) are based on energy statistics and cement production data, while emissions from land use change (ELUC), mainly deforestation, are based on land use and land use change data and bookkeeping models. Atmospheric CO2 concentration is measured directly and its growth rate (GATM) is computed from the annual changes in concentration. The ocean CO2 sink (SOCEAN) and terrestrial CO2 sink (SLAND) are estimated with global process models constrained by observations. The resulting carbon budget imbalance (BIM), the difference between the estimated total emissions and the estimated changes in the atmosphere, ocean, and terrestrial biosphere, is a measure of imperfect data and understanding of the contemporary carbon cycle. All uncertainties are reported as ±1σ. For the last decade available (2009–2018), EFF was 9.5±0.5 GtC yr−1, ELUC 1.5±0.7 GtC yr−1, GATM 4.9±0.02 GtC yr−1 (2.3±0.01 ppm yr−1), SOCEAN 2.5±0.6 GtC yr−1, and SLAND 3.2±0.6 GtC yr−1, with a budget imbalance BIM of 0.4 GtC yr−1 indicating overestimated emissions and/or underestimated sinks. For the year 2018 alone, the growth in EFF was about 2.1 % and fossil emissions increased to 10.0±0.5 GtC yr−1, reaching 10 GtC yr−1 for the first time in history, ELUC was 1.5±0.7 GtC yr−1, for total anthropogenic CO2 emissions of 11.5±0.9 GtC yr−1 (42.5±3.3 GtCO2). Also for 2018, GATM was 5.1±0.2 GtC yr−1 (2.4±0.1 ppm yr−1), SOCEAN was 2.6±0.6 GtC yr−1, and SLAND was 3.5±0.7 GtC yr−1, with a BIM of 0.3 GtC. The global atmospheric CO2 concentration reached 407.38±0.1 ppm averaged over 2018. For 2019, preliminary data for the first 6–10 months indicate a reduced growth in EFF of +0.6 % (range of −0.2 % to 1.5 %) based on national emissions projections for China, the USA, the EU, and India and projections of gross domestic product corrected for recent changes in the carbon intensity of the economy for the rest of the world. Overall, the mean and trend in the five components of the global carbon budget are consistently estimated over the period 1959–2018, but discrepancies of up to 1 GtC yr−1 persist for the representation of semi-decadal variability in CO2 fluxes. A detailed comparison among individual estimates and the introduction of a broad range of observations shows (1) no consensus in the mean and trend in land use change emissions over the last decade, (2) a persistent low agreement between the different methods on the magnitude of the land CO2 flux in the northern extra-tropics, and (3) an apparent underestimation of the CO2 variability by ocean models outside the tropics. This living data update documents changes in the methods and data sets used in this new global carbon budget and the progress in understanding of the global carbon cycle compared with previous publications of this data set (Le Quéré et al., 2018a, b, 2016, 2015a, b, 2014, 2013). The data generated by this work are available at (Friedlingstein et al., 2019).
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Soil organic carbon (SOC) in the subsoil below 0.3 m accounts for the majority of total SOC and may be as sensitive to climate change as topsoil SOC. Here we map global SOC turnover times (τ) in the subsoil layer at 1 km resolution using observational databases. Global mean τ is estimated to be [Formula: see text] yr (mean with 95% confidence interval), and deserts and tundra show the shortest ([Formula: see text] yr) and longest ([Formula: see text] yr) τ respectively. Across the globe, mean τ ranges from 9 (the 5% quantile) to 6332 years (the 95% quantile). Temperature is the most important factor negatively affecting τ, but the overall effect of climate (including temperature and precipitation) is secondary compared with the overall effect of assessed soil properties (e.g., soil texture and pH). The high-resolution mapping of τ and the quantification of its controls provide a benchmark for diagnosing subsoil SOC dynamics under climate change.
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The balance between photosynthetic organic carbon production and respiration controls atmospheric composition and climate1,2. The majority of organic carbon is respired back to carbon dioxide in the biosphere, but a small fraction escapes remineralization and is preserved over geological timescales³. By removing reduced carbon from Earth’s surface, this sequestration process promotes atmospheric oxygen accumulation² and carbon dioxide removal¹. Two major mechanisms have been proposed to explain organic carbon preservation: selective preservation of biochemically unreactive compounds4,5 and protection resulting from interactions with a mineral matrix6,7. Although both mechanisms can operate across a range of environments and timescales, their global relative importance on 1,000-year to 100,000-year timescales remains uncertain⁴. Here we present a global dataset of the distributions of organic carbon activation energy and corresponding radiocarbon ages in soils, sediments and dissolved organic carbon. We find that activation energy distributions broaden over time in all mineral-containing samples. This result requires increasing bond-strength diversity, consistent with the formation of organo-mineral bonds⁸ but inconsistent with selective preservation. Radiocarbon ages further reveal that high-energy, mineral-bound organic carbon persists for millennia relative to low-energy, unbound organic carbon. Our results provide globally coherent evidence for the proposed⁷ importance of mineral protection in promoting organic carbon preservation. We suggest that similar studies of bond-strength diversity in ancient sediments may reveal how and why organic carbon preservation—and thus atmospheric composition and climate—has varied over geological time.
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Developing and testing decadal‐scale predictions of soil response to climate change is difficult because there are few long‐term warming experiments or other direct observations of temperature response. As a result, spatial variation in temperature is often used to characterize the influence of temperature on soil organic carbon (SOC) stocks under current and warmer temperatures. This approach assumes that the decadal‐scale response of SOC to warming is similar to the relationship between temperature and SOC stocks across sites that are at quasi steady state; however, this assumption is poorly tested. We developed four variants of a Reaction‐network‐based model of soil organic matter and microbes using measured SOC stocks from a 4,000‐km latitudinal transect. Each variant reflects different assumptions about the temperature sensitivities of microbial activity and mineral sorption. All four model variants predicted the same response of SOC to temperature at steady state, but different projections of transient warming responses. The relative importance of Qmax, mean annual temperature, and net primary production, assessed using a machine‐learning algorithm, changed depending on warming duration. When mineral sorption was temperature sensitive, the predicted average change in SOC after 100 years of 5 °C warming was −18% if warming decreased sorption or +9% if warming increased sorption. When microbial activity was temperature sensitive but mineral sorption was not, average site‐level SOC loss was 5%. We conclude that spatial climate gradients of SOC stocks are insufficient to constrain the transient response; measurements that distinguish process controls and/or observations from long‐term warming experiments, especially mineral fractions, are needed.
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Soils contain more carbon than plants or the atmosphere, and sensitivities of soil organic carbon (SOC) stocks to changing climate and plant productivity are a major uncertainty in global carbon cycle projections. Despite a consensus that microbial degradation and mineral stabilization processes control SOC cycling, no systematic synthesis of long-term warming and litter addition experiments has been used to test process-based microbe-mineral SOC models. We explored SOC responses to warming and increased carbon inputs using a synthesis of 147 field manipulation experiments and five SOC models with different representations of microbial and mineral processes. Model projections diverged but encompassed a similar range of variability as the experimental results. Experimental measurements were insufficient to eliminate or validate individual model outcomes. While all models projected that CO2 efflux would increase and SOC stocks would decline under warming, nearly one-third of experiments observed decreases in CO2 flux and nearly half of experiments observed increases in SOC stocks under warming. Long-term measurements of C inputs to soil and their changes under warming are needed to reconcile modeled and observed patterns. Measurements separating the responses of mineral-protected and unprotected SOC fractions in manipulation experiments are needed to address key uncertainties in microbial degradation and mineral stabilization mechanisms. Integrating models with experimental design will allow targeting of these uncertainties and help to reconcile divergence among models to produce more confident projections of SOC responses to global changes.
Hydrodynamic sorting has been shown to strongly influence the composition and age of organic carbon (OC) during sediment transport and burial in the marine environment, yet sorting effects on terrestrial OC (OCterr) in fluvial systems remain poorly understood. We conducted size fractionation of suspended particle samples from the lower Yellow River, China, and examined variations in mass distribution and carbon isotopic (δ¹³C and Δ¹⁴C) composition of bulk OC and specific biomarkers among grain size fractions in order to investigate the influence of hydrodynamic sorting and selective transport on organic matter export. In general, the 16–32 μm and 32–63 μm fractions contributed the most of sediment mass while the majority of the OC resided in the 16–32 μm fraction. Over 80% of OC and n-fatty acids (FAs) were concentrated in <32 μm fractions. Significant differences in OC%, surface area (SA), Δ¹⁴COC, n-FAs contents, and compound-specific ¹³C and ¹⁴C compositions were found among size fractions. Of particular note was a progressive decrease of Δ¹⁴C values (i.e., increase in ¹⁴C age) of long-chain (C26+28+30) FAs with decreasing grain size. Taken together, the bulk and molecular characteristics imply two distinct types of selective OCterr transport in the Yellow River. Coarser particles (>32 μm), characterized by relatively low SA, OC%, and Δ¹⁴COC values, but higher Δ¹⁴C values of C26+28+30 FAs, are inferred to reflect a combination of bedrock-derived detrital sediment and fresh vascular-plant material (e.g., plant fragments). In contrast, finer particles (<32 μm), exhibiting higher SA, OC%, and lower Δ¹⁴C26+28+30FAs values, reflect preferential transport of pre-aged, mineral soil-derived OC that is susceptible to repeated mobilization, as well as widespread dispersal in marginal seas. The latter, once buried in marine sediments, could account for the high burial efficiency of OCterr in the adjacent Bohai Sea and Yellow Sea. Thus, hydrodynamic sorting processes induce heterogeneity of composition and selective transport of OC. Bulk and molecular ¹⁴C measurements of size-fractionated particles facilitate both elucidation of these processes and assessment of their impact on OC cycling in (and export from) rivers.
Natural and human-induced hydrological changes can influence OC composition in fluvial systems, with biogeochemical consequences in both terrestrial and marine environments. Here, we use bulk and molecular carbon isotopes (¹³C and ¹⁴C) to examine spatiotemporal variations in particulate OC (POC) composition and age from two locations along the course of the Yellow River during 2015–2016. Dual carbon isotopes enable deconvolution of modern, pre-aged (millennial age) soil and fossil inputs, revealing heterogeneous OC sources at both sites. Pre-aged OC predominated at the upstream site (Huayuankou) throughout the study period, mostly reflecting the upper riverine OC. Strong downstream (Kenli) intra-annual variations in modern and pre-aged OC were caused by increased contributions from modern aquatic OC production under the drier and less turbid conditions during this El Niño year. The month of July, which included the human-induced water and sediment regulation (WSR) event at Kenli, accounted for 82% of annual POC flux, with lower modern OC contribution compared with periods of natural seasonal variability. Both natural and human-induced hydrological events clearly exert strong influence on both fluxes and composition of Yellow River POC which, in turn, affect the balance between OC remineralization and burial for this major fluvial system.
We examine instrumental and methodological capabilities for microscale (10-50 µgC) radiocarbon analysis of individual compounds in the context of paleoclimate and paleoceanography applications for which relatively high precision measurements are required. An extensive suite of data for 14C-free and modern reference materials processed using different methods and acquired using an elemental analyzer-accelerator mass spectrometry (EA-AMS) instrumental setup at ETH-Zurich was compiled to assess the reproducibility of specific isolation procedures. In order to determine the precision, accuracy and reproducibility of measurements on processed compounds, we explore results of both reference materials and of three classes of compounds (fatty acids, alkenones and amino acids) extracted from sediment samples. We utilize a MATLAB code developed to systematically evaluate constant contamination model parameters, which in turn can be applied to measurements of unknown process samples. This approach is computationally reliable and can be used for any blank assessment of small-size radiocarbon samples. Our results show that a conservative lower estimate of the sample sizes required to produce relatively high-precision 14C data (i.e., with acceptable errors of <5% on final 14C ages) and high reproducibility in “old” samples (i.e., F14C ≈ 0.1) using current isolation methods are 50 µgC and 30 µgC for alkenones and fatty acids, respectively. Moreover, when the F14C is > 0.5, a precision of 2% can be achieved for alkenone and fatty acid samples containing ≥15 µgC and10 µgC, respectively.