Climate control on terrestrial biospheric carbon turnover

Abstract

Significance
Terrestrial organic-carbon reservoirs (vegetation, soils) currently consume more than a third of anthropogenic carbon emitted to the atmosphere, but the response of this “terrestrial sink” to 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.

Full text

Climate control on terrestrial biospheric
carbon turnover
Timothy I. Eglinton
a,b,1,2
, Valier V. Galy
b,1,2
, Jordon D. Hemingway
b,c
, Xiaojuan Feng
a,b,d
, Hongyan Bao
e,3
,
Thomas M. Blattmann
a,4
, Angela F. Dickens
b,5
, Hannah Gies
a
, Liviu Giosan
f
, Negar Haghipour
a,g
, Pengfei Hou
h
,
Maarten Lupker
a
, Cameron P. McIntyre
a,g,6
, Daniel B. Montluçon
a
, Bernhard Peucker-Ehrenbrink
b
, Camilo Ponton
f,7
,
Enno Schefuß
b,i
, Melissa S. Schwab
a
, Britta M. Voss
b,8
, Lukas Wacker
g
, Ying Wu
e
, and Meixun Zhao
h
a
Department of Earth Sciences, ETH Zurich, 8092, Switzerland;
b
Department of Marine Chemistry and Geochemistry, Woods Hole Oceanographic
Institution, Woods Hole, MA 02543;
c
Department of Earth and Planetary Sciences, Harvard University, Cambridge, MA 02138;
d
State Key Laboratory of
Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China;
e
State Key Laboratory of Estuarine and
Coastal Research, East China Normal University, Shanghai 200062, China;
f
Department of Geology and Geophysics, Woods Hole Oceanographic Institution,
Woods Hole, MA 02543;
g
Laboratory for Ion Beam Physics, Department of Physics, ETH Zurich, 8093 Zurich, Switzerland;
h
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
i
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 (
14
C) ages on two groups of molec-
ular tracers of plant-derived carbon—leaf-wax lipids and lignin
phenols—from a globally distributed suite of rivers. We find sig-
nificant negative relationships between the
14
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
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 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-change–induced perturbations of soil OC
turnover and stocks.
radiocarbon
|
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 magnitude—and
even the sign—of 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
soil
) (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
Significance
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
sink”to 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) .
1
T.I.E. and V.V.G. contributed equally to this work.
2
To whom correspondence may be addressed. Email: timothy.eglinton@erdw.ethz.ch or
vgaly@whoi.edu.
3
Present address : State Key Laborator y of Marine Environmen tal Science, Colle ge of
Ocean and Earth Sciences, Xiamen University, Xiamen 361102, China.
4
Present address: Biogeochemistry Research Center, Japan Agency for Marine-Earth Sci-
ence and Technology, Yokosuka 237-0061, Japan.
5
Present address: Wisconsin Department of Natural Resources, Bureau of Air
Management, Madison, WI 53707.
6
Present address: Accelerator Mass Spectrometry Laboratory, Scottish Universities Envi-
ronmental Research Centre, East Kilbride G75 0QF, United Kingdom.
7
Present address : Geology Departm ent, Western Washi ngton University, Bellingham,
WA 98225.
8
Present address: Environmental Assessment Program, Washington State Department of
Ecology, Lacey, WA 98503.
This article contains supporting information online at https://www.pnas.org/lookup/suppl/
doi:10.1073/pnas.2011585118/-/DCSupplemental.
Published February 15, 2021.
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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
14
C activity (repor-
ted as age in
14
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
14
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
2
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
14
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
14
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)
14
C age measurements of
source-specific “biomarker”tracer 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 biomarkers—plant-
wax lipids (25) and lignin-derived phenols (26). Because of their
hydrophobic nature, plant waxes reside in the particulate
phase—particularly via association with mineral surfaces (27)—
and persist in soils and downstream environments (24). The
abundances, distributions, and stable isotopic compositions (
13
C/
12
C,
reported as δ
13
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 (“fatty”acids [FAs]) since
n-alkanes can be influenced by contributions from bedrock- or
fossil-fuel–derived sources that impact corresponding
14
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
14
C ages of these two biomarker classes
0º
60º N
60º S
0º
60º N
60º S
0º 180º E180º W
latitude
biomarker 14C age
latitude
longitude
8,000
4,000
00255075
absolute latitude at river mouth (º)
fatty acids
lignin phenols
14C age (yr)
0 5,000 10,000
A
B
C
Fig. 1. Riverine biomarker
14
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-
phenol
14
C ages (SI Appendix, Table S1). Rivers with catchment areas smaller
than 30,000 km
2
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).
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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
14
C and, in se-
lected cases, δ
13
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
14
C ages and δ
13
C
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
14
C y in our sample set (n=43), whereas bio-
marker ages range from above modern (reflecting incorporation
of nuclear-bomb–derived
14
C) to more than 10,000
14
C y for fatty
acids (n=44) and from above modern to almost 5,000
14
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
14
C ages and δ
13
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
14
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
factor—the e-folding distance—that 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
2
=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
2
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
14
C ages are
significantly correlated with climate variables—chiefly, catchment-
weighted mean annual temperature (MAT) and precipitation
(MAP) (Fig. 2Aand SI Appendix, Table S4). Globally, biomarker
14
C ages decrease (become younger) with increasing MAT and
MAP; this phenomenon manifests as a relationship between
biomarker
14
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
14
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-use–change impacts
on biomarker
14
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
14
C
ages display a significant negative correlation with runoff (Fig. 2).
While other variables—for 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
14
Cagesdis-
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
0
4
-4
-2
2
-4 -2 0 42
RDA1 (35 % variance)
RDA2 (24 % variance)
1
4
5
6
7
8
9
10
11
12 13
14
i
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)
iii
ii
iv
v
3
2
15 16
correlation coefficient (r)
-1
0
1
AB
Fig. 2. Multivariate statistical analysis. (A) Matrix of Pearson correlation coefficients (rvalues) between environmental control variables (x-axis) and POC and
biomarker
14
C and δ
13
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 type”refers 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
14
C and δ
13
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.
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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
14
C metrics, particularly fatty-acid
14
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
13
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
13
C values correspond
to lower seasonal variation in precipitation, as observed in other
regional studies (e.g., 33).
Relationships with τ
soil
and Mean Age. Plant-wax fatty-acid and, to a
lesser extent, lignin-phenol
14
C ages display positive relationships
with catchment-weighted τ
soil
estimated from remote-sensing–derived
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
14
C ages
are on average 43.6- and 16.6-fold greater than the corresponding
τ
soil
. To further probe these relationships, we additionally con-
sidered recent spatially resolved estimates of soil
14
C ages (34).
Biomarker
14
C ages display similar positive relationships with
catchment-weighted soil
14
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
soil
,
however, fatty-acid and lignin-phenol
14
C ages are on average
2.5- and 5.0-fold lower than corresponding soil
14
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
14
C ages and catchment-weighted soil
14
C ages integrated from 0 to 30 cm depth, in which biomarkers
display similar or slightly older
14
C ages than corresponding soils,
and integrated from 30 to 100 cm depth, in which soils display
significantly older
14
C ages than corresponding biomarkers (SI
Appendix, Fig. S6).
Nonetheless, the observed discrepancy between biomarker
14
C
ages and τ
soil
contrasts with the much closer agreement between
biomarker
14
C ages and soil mean carbon age, regardless of soil
integration depth (Fig. 3 and SI Appendix, Fig. S6). This offset
between both biomarker
14
C ages and catchment-weighted soil
14
C ages on one hand and τ
soil
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
soil
to
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
14
C ages (Materials and
6,000
8,000
10,000
4,000
2,000
0
0 10050
r
2
= 0.65
slope = 40.1 ± 3.9
r
2
= 0.47
slope = 24.4 ± 5.2
150 200
τ
soil
(yr)
0 10,0005,000
r
2
= 0.61
slope = 0.62 ± 0.07
r
2
= 0.58
slope = 0.34 ± 0.06
15,000
soil mean carbon age (yr)
fatty acid
14
C age (yr)
6,000
8,000
10,000
4,000
2,000
0
lignin
14
C age (yr)
AB
CD
Fig. 3. Relationships between τ
soil
, soil mean carbon age, and biomarker
14
C age. (Aand B) Plant-wax fatty-acid and (Cand D) lignin-phenol
14
C ages as a
function of weighted-catchment τ
soil
(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
2
values (Materials and Methods). Uncertainty (±1σ)is
always smaller than marker points.
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Methods), the biomarker
14
C age versus τ
soil
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
14
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
precipitation—a positive climate feedback.
Interpreting Exported Carbon Ages. Lignin-phenol
14
C ages, fatty-
acid
14
C ages, and τ
soil
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
soil
and
both biomarker classes despite differences in their absolute
14
C
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
soil
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
soil
. This implies
that basin-scale information on the latter can be gleaned from
14
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 Earth’s
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 “conveyors”or
“reactors”(42).
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
2
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
14
C
signature, and hence the
14
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
3
4
2
1
-20 -10 0 10 3020 1.2 1.6 2.0 2.4
MAT (ºC) log
10
MAP (cm yr
-1
)
log
10
14
C age or τ
soil
(yr)
AB
Fig. 4. Environmental controls on τ
soil
and biomarker
14
C ages. Logarithmic
plant-wax fatty-acid
14
C ages (black circles), lignin-phenol
14
C ages (white
squares), and catchment τ
soil
[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
soil
-reduced major-
axis regression lines. Relationship slopes and r
2
values are as follows: (A)
fatty-acid
14
C ages: slope =−0.036 ±0.004, r
2
=0.62; lignin-phenol
14
C ages:
slope =−0.036 ±0.006, r
2
=0.67; and τ
soil
: slope =−0.030 ±0.002, r
2
=0.83.
(B) Fatty-acid
14
C ages: slope =−1.46 ±0.17, r
2
=0.55; lignin-phenol
14
C ages:
slope =−1.44 ±0.30, r
2
=0.56; and τ
soil
: slope =−1.13 ±0.11, r
2
=0.72.
Uncertainty (±1σ) is always smaller than marker points.
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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
14
C charac-
teristics. In the Yellow River, for example, plant-wax
14
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
14
C characteristics reveals
no systematic offsets between sample type. In the Danube River, vertical
water-column profiles of plant-wax δ
13
C and
14
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
14
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
14
C age of biomarkers. The
limited number of investigations of temporal variability in rivers does not
reveal marked seasonal variations in biomarker
14
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
14
C ages. This
suggest that interannual variations are not a significant driver of the ob-
served trends in biomarker
14
C ages.
Sample collection date. Samples have been collected between 1976 and 2016, a
period of time over which atmospheric
14
C, and hence freshly produced
terrestrial plant organic matter, has exhibited a marked decline in response
to the redistribution of “bomb
14
C”(from above-ground thermonuclear
weapons testing in the late 1950s and early 1960s) in the earth’s surface
carbon reservoirs (43, 53). Recently synthesized biospheric carbon then
transmits this bomb
14
C signal through river basins, inducing time-variable
deviations from natural
14
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
14
C spike clearly manifests itself in plant-wax fatty acids, the induced vari-
ability is small relative to the overall
14
C variability observed within the
global dataset presented in this study. These muted changes in plant-wax
fatty-acid
14
C ages require the presence of at least two different aged
populations—one 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
14
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
14
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
biomarker
14
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
14
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
(59–61).
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) (
14
C contents) and gas
chromatography (GC)-IRMS (δ
13
C). Resulting
14
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
14
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
13
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
14
C content
by AMS.
Biomarker
14
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
14
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
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uncertainty related to blank corrections varies from sample to sample, pri-
marily according to sample size and
14
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 10’resolution. 3) τ
soil
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 mosaic”pixels (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
14
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
14
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
14
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
soil
are unlikely to contribute
significantly to the systematic difference observed between τ
soil
and bio-
marker
14
C ages (i.e., since plant-wax fatty-acid and lignin-phenol
14
Cagesare
on average 43.6- and 16.6-fold greater, respectively, than corresponding τ
soil
).
For spatially resolved control variables (i.e., elevation, slope, floodplain
fraction, MAT, TCV, MAP, PCV, τ
soil
, 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 Length”feature in ArcGIS and was weighted as an
exponential decay following
w = e−kl,[1]
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
averages.
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 “whitened”by 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
B=(XTX)−1XTY,[2]
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
^
Y=XB,[3]
and the matrix of residuals was calculated as
Yr=Y−
^
Y. [4]
The matrix of correlation strengths between Xand Y,termedS
XY
,was
then calculated following
SXY =
1
nXTY,[5]
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
2
values are the square of the
diagonal elements of S
XY
. The optimal e-folding kvalue was calculated using
an inverse approach by repeating Eqs. 1–5and choosing the value that
maximizes the average resulting r
2
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 “masked”arrays (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. 1–5using either “catchment averaged”or “all reported samples”as 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
r
, the matrix of resid-
uals. Analogous to Eq. 5, the correlation matrix between MLR-predicted
response variables is first calculated as
S^
Y
^
Y=
1
n
^
YT^
Y. [6]
Then, the following eigenvalue problem is solved:
S^
Y
^
Y−λjI
()
uj=0, [7]
where λ
j
is the jth canonical eigenvalue, u
j
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
j
. Eqs. 6and 7
are then repeated using Y
r
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
j
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
Z=
^
YU,[8]
and control variable loadings (“constraining variable scores”) onto each
canonical axis, termed C, are calculated as
C=BU,[9]
where Uis the column-wise matrix of u
j
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
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Data Availability. All study data are included in the article and/or supporting
information.
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.).
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