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\begin{document}

\title{Climate exerts stronger control on topsoil carbon persistence than plant
input in alpine grasslands}



\author[1]{Donghai Wu}%
\author[1]{Xiangtao Xu}%
\author[2]{Haicheng Zhang}%
\affil[1]{Cornell University}%
\affil[2]{Université Libre de Bruxelles}%


\vspace{-1em}



  
  \date{May 26, 2024}


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\selectlanguage{english}
\begin{abstract}
Chen et al. (2021) concluded that plant input governs topsoil carbon
persistence in alpine grasslands. We demonstrated that the excluded
direct effect of precipitation on topsoil \selectlanguage{greek}Δ\selectlanguage{english}14C in their analysis was in
fact significant and strong. Our results provide an alternative
viewpoint on the drivers of soil carbon turnover.%
\end{abstract}%



\sloppy


\textbf{Climate exerts stronger control on topsoil carbon persistence
than plant input in alpine grasslands}

Donghai Wu\textsuperscript{1,*}, Xiangtao Xu\textsuperscript{1},
Haicheng Zhang\textsuperscript{2}

\textsuperscript{1}Department of Ecology and Evolutionary Biology,
Cornell University, Ithaca, NY 14850, USA

\textsuperscript{2}Department Geoscience, Environment \&
Society-BGEOSYS, Universit\selectlanguage{ngerman}é libre de Bruxelles, 1050 Bruxelles, Belgium

\textbf{E-mail addresses:} Donghai Wu (dw623@cornell.edu), Xiangtao Xu
(xx286@cornell.edu), and Haicheng Zhang (haicheng.zhang@ulb.be)

\textbf{Keywords:} climate, plant input, priming effect, radiocarbon,
soil carbon turnover, Tibetan Plateau

\textbf{Type of article:} Technical Note; \textbf{Number of words in the
abstract:} 49; \textbf{Number of words in the main text:}
799;\textbf{Number of references:} 11; \textbf{Number of figures:} 2

\textbf{Author for correspondence:}

Donghai Wu; \textbf{Telephone:} +1 607 262 0847;
\textbf{E-mail:}dw623@cornell.edu

\textbf{Statement of authorship}

D.H.W. conceived of the comment and performed statistical analyses.
D.H.W., X.T.X. and H.C.Z. drafted the manuscript.

\textbf{Data accessibility statement}

The data supporting the results of this study is downloaded from the
supplementary material of Chen \emph{et al.} (2021).

\textbf{Abstract}

Chen \emph{et al.} (2021) concluded that plant input governs topsoil
carbon persistence in alpine grasslands. We demonstrated that the
excluded direct effect of precipitation on topsoil
\selectlanguage{greek}Δ\selectlanguage{english}\textsuperscript{14}C in their analysis was in fact significant and
strong. Our results provide an alternative viewpoint on the drivers of
soil carbon turnover.

\textbf{Main text}

Understanding the driving mechanisms of soil organic carbon (SOC)
persistence is crucial to project future carbon-climate feedback
(Schmidt \emph{et al.} 2011). Chen \emph{et al.} (2021)
(\emph{C21}hereafter) hypothesized that plant carbon input, as a proxy
of priming effect, governs the topsoil carbon turnover across alpine
grasslands in Tibetan Plateau. A structure equation model was built to
assess the relationships between environmental variables and
\selectlanguage{greek}Δ\selectlanguage{english}\textsuperscript{14}C, an indicator of soil carbon turnover
(Shi\emph{et al.} 2020; Wu \emph{et al.} 2021). However, only plant
carbon input was considered as a direct effect on \selectlanguage{greek}Δ\selectlanguage{english}\textsuperscript{14}C
in their final optimized model while the direct climate effect was
reported to be non-significant (\textbf{Fig. 3} in \emph{C21} ). From
the model, plant carbon input exerts a stronger direct effect on topsoil
carbon turnover, while precipitation only has a weaker indirect effect.
The resultant lack of direct climate (mainly precipitation) effect on
soil carbon turnover is surprising and contradictive to previous reports
that soil carbon turnover is directly regulated by water availability
especially in arid and semi-arid regions (Carvalhais \emph{et al.} 2014;
Wu \emph{et al.} 2018).

We built and compared three possible structure equation models
(\textbf{Fig. 1} ) using the datasets provided in \emph{C21} . Model-A
only considered the direct effect of plant input on topsoil
\selectlanguage{greek}Δ\selectlanguage{english}\textsuperscript{14}C. By contrast, model-B included direct effects on
topsoil \selectlanguage{greek}Δ\selectlanguage{english}\textsuperscript{14}C from both plant input and precipitation.
Model-C further included an additional direct effect from mineral
protection. Model-A generated similar statistical results with\emph{C21}
, showing stronger total effect from plant input than from
precipitation. In model-B, the two path coefficients related to
\selectlanguage{greek}Δ\selectlanguage{english}\textsuperscript{14}C from both plant input and precipitation are still
significant (\emph{P} \textless{} 0.05), and overall model performance
(AIC = 19.945) is better than model-A (AIC = 21.989). Moreover, the
strength of precipitation direct effect is comparable to that of plant
input. As a result, the total standardized effect of precipitation is
much larger than plant carbon input. In model-C, all three path
coefficients related with \selectlanguage{greek}Δ\selectlanguage{english}\textsuperscript{14}C fail to reject the null
hypothesis based on significance level of 0.05 because of severe
multicollinearity between the variables. Overall, the dataset
in\emph{C21} best supported model-B that includes direct climate effect.
We could not include the chemical composition of soil organic matter,
which nevertheless had no significant effects for topsoil in \emph{C21}
, in all the three models because the data was not made available.

We also conducted partial correlation analysis to detect the relative
importance of direct effects from climate, plant input, and soil mineral
composition on \selectlanguage{greek}Δ\selectlanguage{english}\textsuperscript{14}C. Partial correlation analysis
assesses the correlation between \selectlanguage{greek}Δ\selectlanguage{english}\textsuperscript{14}C and one specific
environmental factor after removing the effects of other environmental
factors. The results showed that precipitation is still the most
importance factor in regulating the topsoil carbon turnover than other
factors (\textbf{Fig. 2} ). Furthermore, we find precipitation governs
the topsoil carbon turnover in alpine steppe, while temperature and
mineral protection play major roles across alpine meadow. The difference
likely implies that SOC decomposition is primarily limited by water in
the drier alpine steppe, which is partially relieved in the wetter
alpine meadow. In all cases, our results indicate that plant input does
not play the dominant role in regulating the topsoil carbon turnover.

The overlook of direct climatic effects on SOC turnover can also bias
the interpretation of the significant relationship between vegetation
indices and topsoil \selectlanguage{greek}Δ\selectlanguage{english}\textsuperscript{14}C on a global scale
(\textbf{Fig. 4} in \emph{C21} ). \emph{C21} interpreted these
relationships as a universal law of the plant input effects on topsoil
carbon turnover. However, the correlation can arise because preferable
climatic conditions (e.g., warm and moist) increase both plant input and
SOC decomposition rate (Davidson \& Janssens 2006). Direct climatic
effects on SOC turnover should be carefully removed before interpreting
the plant input effects from these global data.

Aside from overestimating the direct effects of plant input on SOC
turnover, \emph{C21} treated the amount of plant input as a proxy of
priming effects, which is however questionable. According to incubation
experiments using soils from the same ecosystems by Chen \emph{et
al.}(2019), the amount of plant input does not show significant direct
effect on soil priming intensity. Furthermore, components of plant input
(shoot, root and mycorrhizal) usually have quite different effects in
regulating priming effects (Huang \emph{et al.} 2021). Therefore, it is
unreasonable to simply use the amount of plant input as the indicator of
priming effect.

Despite \emph{C21} likely overestimated the contribution of plant input
on SOC turnover, the study raised an important scientific question on
the relation between SOC turnover and priming effect. Observed earth
greening increased the fresh carbon input in soil system, which may
promote the priming effects and accelerate the SOC turnover (Terrer
\emph{et al.} 2021). This crucial process is still missing in most
existing ecosystem models (Wu \emph{et al.} 2020), which may lead to an
overestimation of the terrestrial soil carbon storage potential.

\textbf{Acknowledgement}

We thank funding from Cornell University CALS to X. X., and the
`Lateral-CNP' project (No. 34823748) supported by the Fonds de la
Recherche Scientifique-FNRS to H. Z.

\textbf{Conflict of interest}

The authors declare no conflict of interest.



\textbf{References}

Carvalhais, N., Forkel, M., Khomik, M., Bellarby, J., Jung, M.,
Migliavacca, M. \emph{et al.} (2014). Global covariation of carbon
turnover times with climate in terrestrial ecosystems. \emph{Nature} ,
514, 213-217.

Chen, L., Fang, K., Wei, B., Qin, S., Feng, X., Hu, T. \emph{et
al.}(2021). Soil carbon persistence governed by plant input and mineral
protection at regional and global scales. \emph{Ecology Letters} , 24,
1018-1028.

Chen, L., Liu, L., Qin, S., Yang, G., Fang, K., Zhu, B. \emph{et
al.}(2019). Regulation of priming effect by soil organic matter
stability over a broad geographic scale. \emph{Nature Communications} ,
10, 5112.

Davidson, E.A. \& Janssens, I.A. (2006). Temperature sensitivity of soil
carbon decomposition and feedbacks to climate change. \emph{Nature} ,
440, 165-173.

Huang, J., Liu, W., Yang, S., Yang, L., Peng, Z., Deng, M. \emph{et
al.}(2021). Plant carbon inputs through shoot, root, and mycorrhizal
pathways affect soil organic carbon turnover differently. \emph{Soil
Biology and Biochemistry} . 160, 108322.

Schmidt, M.W.I., Torn, M.S., Abiven, S., Dittmar, T., Guggenberger, G.,
Janssens, I.A. \emph{et al.} (2011). Persistence of soil organic matter
as an ecosystem property. \emph{Nature} , 478, 49-56.

Shi, Z., Allison, S.D., He, Y., Levine, P.A., Hoyt, A.M., Beem-Miller,
J. \emph{et al.} (2020). The age distribution of global soil carbon
inferred from radiocarbon measurements. \emph{Nature Geoscience} , 13,
555--559.

Terrer, C., Phillips, R.P., Hungate, B.A., Rosende, J., Pett-Ridge, J.,
Craig, M.E. \emph{et al.} (2021). A trade-off between plant and soil
carbon storage under elevated CO2. \emph{Nature} , 591, 599-603.

Wu, D., Liu, D., Wang, T., Ding, J., He, Y., Ciais, P. \emph{et
al.}(2021). Carbon turnover times shape topsoil carbon difference
between tibetan plateau and arctic tundra. \emph{Science Bulletin} . In
press.

Wu, D., Piao, S., Liu, Y., Ciais, P. \& Yao, Y. (2018). Evaluation of
CMIP5 Earth System Models for the spatial patterns of biomass and soil
carbon turnover times and their linkage with climate. \emph{Journal of
Climate} , 31, 5947-5960.

Wu, D., Piao, S., Zhu, D., Wang, X., Ciais, P., Bastos, A. \emph{et
al.}(2020). Accelerated terrestrial ecosystem carbon turnover and its
drivers. \emph{Global Change Biology} , 26, 5052-5062.

\textbf{Figure legends}

\textbf{Figure 1} Three structure equation models (SEM) with different
assumptions on direct and indirect relationships between environmental
factors and topsoil \selectlanguage{greek}Δ\selectlanguage{english}\textsuperscript{14}C are compared based on the
available datasets for alpine grasslands provided by Chen \emph{et al.}
(2021). (a) Model-A includes direct effect of plant carbon input and
indirect effect of precipitation on \selectlanguage{greek}Δ\selectlanguage{english}\textsuperscript{14}C. (b) Model-B
adds direct effect of precipitation on \selectlanguage{greek}Δ\selectlanguage{english}\textsuperscript{14}C. (c)
Model-C adds direct effect of precipitation and mineral protection and
indirect effect of plant carbon input on \selectlanguage{greek}Δ\selectlanguage{english}\textsuperscript{14}C. Fit
indices, including degree of freedom (\emph{DF} ), Chi-squareis
(\selectlanguage{greek}\emph{χ\textsuperscript{2}}\selectlanguage{english} ), probability level (\emph{P} ), Akaike
information criterion (\emph{AIC} ), comparative fit index (\emph{CFI}
), goodness of fit (\emph{GFI} ), root mean square residual (\emph{RMR}
) and root mean square error of approximation (\emph{RMSEA} ), are
listed on the left panel of each model. Similar to Chen \emph{et al.}
(2021), precipitation is the first principal component (PC1) of mean
annual precipitation (MAP), precipitation of the wettest month (PWM),
and precipitation of the wettest quarter (PWQ); Plant C input is the PC1
of plant carbon input in topsoil, normalized difference vegetation index
(NDVI), enhanced vegetation index (EVI) and leaf area index (LAI);
mineral protection is the PC1 of molar ratios of dithionite- and
oxalate-extractable Fe/Al oxides to SOC (Fe\textsubscript{d} +
Al\textsubscript{d} and Fe\textsubscript{o} + Al\textsubscript{o}), and
the molar ratios of exchangeable
Ca\textsuperscript{2+}(Ca\textsubscript{exe}) and
Mg\textsuperscript{2+}(Mg\textsubscript{exe}) to SOC. Logarithm
transformation is performed for the four variables of mineral protection
before principal component analysis (PCA).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

\textbf{Figure 2} Partial correlations between topsoil
\selectlanguage{greek}Δ\selectlanguage{english}\textsuperscript{14}C and four environmental factors in alpine
grasslands based the available datasets provided by Chen \emph{et
al.}(2021). The partial correlation between \selectlanguage{greek}Δ\selectlanguage{english}\textsuperscript{14}C and
each environmental factor is calculated by using the other three factors
as the control variables. The partial correlation is also performed in
alpine steppe and alpine meadow separately. Similar to Chen \emph{et
al.} (2021), precipitation is the first principal component (PC1) of
mean annual precipitation (MAP), precipitation of the wettest month
(PWM), and precipitation of the wettest quarter (PWQ); temperature is
the PC1 of mean annual temperature (MAT), minimum temperature of the
coldest month (MTCM) and mean temperature of the coldest quarter (TCQ);
Plant C input is the PC1 of plant carbon input in topsoil, normalized
difference vegetation index (NDVI), enhanced vegetation index (EVI) and
leaf area index (LAI); mineral protection is the PC1 of molar ratios of
dithionite- and oxalate-extractable Fe/Al oxides to SOC
(Fe\textsubscript{d} + Al\textsubscript{d} and Fe\textsubscript{o} +
Al\textsubscript{o}), and the molar ratios of exchangeable
Ca\textsuperscript{2+} (Ca\textsubscript{exe}) and
Mg\textsuperscript{2+} (Mg\textsubscript{exe}) to SOC. Logarithm
transformation is performed for the four variables of mineral protection
before principal component analysis (PCA).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

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