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\DOI{10.5802/crgeos.228}
\datereceived{2022-10-19}
\daterevised{2023-04-12}
\datererevised{2023-06-15}
\dateaccepted{2023-06-27}
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\dateposted{2023-07-19}
\begin{document}


\begin{noXML}

\RubricEF{Biogeochemistry}{Biog\'eochimie}

\TopicEF{Research article}{Article de recherche}

\title{How the origin of sedimentary organic matter impacts the benthic
nutrient fluxes in shallow coastal mudflats}

\alttitle{Quel est l'impact de l'origine de la mati\`ere organique
s\'edimentaire sur les flux benthiques de nutriments dans des
vasi\`eres c\^oti\`eres peu profondes~?}

\author{\firstname{Laurent} \lastname{Jeanneau}\CDRorcid{0000-0002-7588-0490}\IsCorresp}
\address{Univ Rennes, CNRS, G\'{e}osciences Rennes, UMR 6118, F-35000
Rennes, France}
\email[L. Jeanneau]{laurent.jeanneau@univ-rennes1.fr}

\author{\firstname{Emilie} \lastname{Jard\'e}\CDRorcid{0000-0001-8108-4055}}
\addressSameAs{1}{Univ Rennes, CNRS, G\'{e}osciences Rennes, UMR 6118,
F-35000 Rennes, France}
\email[E. Jard\'e]{emilie.jarde@univ-rennes1.fr}

\author{\firstname{Justine} \lastname{Louis}\CDRorcid{0000-0002-8589-5768}}
\address{Univ Rennes, CNRS, ECOBIO, UMR 6553, F-35000 Rennes, France}
\address{Ifremer, DYNECO, F-29280 Plouzan\'{e}, France}
\email[J. Louis]{justine.louis@ceva.fr}

\author{\firstname{Alexandrine} \lastname{Pannard}\CDRorcid{0000-0003-2997-5744}}
\addressSameAs{2}{Univ Rennes, CNRS, ECOBIO, UMR 6553, F-35000 Rennes,
France}
\email[A. Pannard]{alexandrine.pannard@univ-rennes1.fr}

\author{\firstname{Marine} \lastname{Liotaud}}
\addressSameAs{1}{Univ Rennes, CNRS, G\'{e}osciences Rennes, UMR 6118,
F-35000 Rennes, France}
\email[M. Liotaud]{marine.liotaud@univ-rennes1.fr}

\author{\firstname{Fran\c{c}oise} \lastname{Andrieux-Loyer}\CDRorcid{0000-0001-6433-012X}}
\addressSameAs{3}{Ifremer, DYNECO, F-29280 Plouzan\'{e}, France}
\email[F. Andrieux-Loyer]{Francoise.Andrieux@ifremer.fr}

\author{\firstname{G\'erard} \lastname{Gruau}\CDRorcid{0000-0003-3455-2738}}
\addressSameAs{1}{Univ Rennes, CNRS, G\'{e}osciences Rennes, UMR 6118,
F-35000 Rennes, France}
\email[G. Gruau]{gerard.gruau@univ-rennes1.fr}

\author{\firstname{Florian} \lastname{Caradec}}
\addressSameAs{3}{Ifremer, DYNECO, F-29280 Plouzan\'{e}, France}
\email[F. Caradec]{Florian.Caradec@ifremer.fr}

\author{\firstname{Emilie} \lastname{Rabiller}}
\addressSameAs{3}{Ifremer, DYNECO, F-29280 Plouzan\'{e}, France}
\email[E. Rabiller]{Emilie.Rabiller@ifremer.fr}

\author{\firstname{Nathalie} \lastname{Lebris}}
\addressSameAs{2}{Univ Rennes, CNRS, ECOBIO, UMR 6553, F-35000 Rennes,
France}
\email[N. Lebris]{nathalie.lebris@univ-rennes1.fr}

\author{\firstname{Anniet} \lastname{Laverman}\CDRorcid{0000-0003-1232-1189}}
\addressSameAs{2}{Univ Rennes, CNRS, ECOBIO, UMR 6553, F-35000 Rennes,
France}
\email[A. Laverman]{anniet.laverman@univ-rennes1.fr}

\shortrunauthors

\keywords{\kwd{Sedimentary organic matter}\kwd{Organic matter
origin}\kwd{C and N Stable isotope}\kwd{Lipid composition}\kwd{Benthic
N and P fluxes}\kwd{Intertidal mudflat}}

\altkeywords{\kwd{Mati\`ere organique s\'edimentaire}\kwd{Origine de la
mati\`ere organique}\kwd{Isotopes stables de C et N}\kwd{Composition
lipidique}\kwd{Flux benthiques de N et P}\kwd{Vasi\`ere intertidale}}

\begin{abstract}
The origin of sedimentary organic matter (SOM)
has often been mentioned as a driver of
SOM reactivity. This was quantified by statistically
relating the isotopic and lipid composition of SOM to benthic nutrient
fluxes in 200 intertidal mudflats sampled along the Brittany coast
(France). The origin of SOM explained 24\% and 31\% of the variance of
$\text{NH}_4^+$ and $\text{PO}_4^{3-}$ fluxes, respectively. The
$\text{NH}_4^+$ fluxes were driven by the uptake by phytoplankton of
dissolved anthropogenic N exported from agricultural catchments. Their
sedimentation is favoured by low hydrodynamic conditions, enriching the
sediments with labile OM. The $\text{PO}_4^{3-}$ fluxes were driven by
the sedimentation of particulate P exported through agricultural soil
erosion.{\vspace*{-2pt}}
\end{abstract}

\begin{altabstract}
L'origine de la mati\`ere organique s\'edimentaire (MOS) a souvent
\'et\'e mentionn\'ee comme un facteur contr\^olant sa r\'eactivit\'e.
Ce lien a \'et\'e quantifi\'e via une relation statistique entre la
composition isotopique et lipidique de la MOS et les flux de nutriments
benthiques dans 200 vasi\`eres intertidales \'echantillonn\'ees le long
de la c\^ote bretonne (France). L'origine de la MOSexplique 24\% et
31\% de la variance des flux de $\text{NH}_4^+$ et de
$\text{PO}_4^{3-}$, respectivement. Les flux de $\text{NH}_4^+$ sont
contr\^ol\'es par l'absorption par le phytoplancton de l'azote
anthropique dissous export\'e par les bassins versants agricoles. Leur
s\'edimentation est favoris\'ee par de faibles conditions
hydrodynamiques, enrichissant les s\'ediments en MO labile. Les flux de
$\text{PO}_4^{3-}$ sont dus \`a la s\'edimentation de particules de P
export\'ees par l'\'erosion des sols agricoles.
\end{altabstract}

\maketitle
\vspace*{-.7pc}

\twocolumngrid

\end{noXML}

\section{Introduction}\label{sec1}

Benthic nutrient fluxes in coastal ecosystems, especially in shallow
environments, are impacted by{\break} both biotic and abiotic processes
in the sediment \citep{Boyntonetal2018, Santschietal1990,
Stahlbergetal2006}. One of the major processes of the early diagenesis
of sediment is the microbial degradation of sedimentary organic matter
(SOM), producing ammonium ($\text{NH}_4^+$) and phosphate
($\text{PO}_4^{3-}$) and the consequent fluxes of N and P at the
sediment--water {interface} \citep{FlintKamykowski1984,
SigmonCahoon1997, Andersonetal2003}.

The degradation of SOM, and consequently N and P fluxes, depends on its
quantity and composition but also on external factors such as
physico-chemical characteristics of the sediments (porosity, grain-size
and mineralogy), temperature and availability of electron acceptors as
well as microbial and macrobenthic activities and diversities
\citep{Arndtetal2013, Freitasetal2021, LaRoweetal2020}. However, the
relative significance of these factors remains poorly quantified
however \citep{Freitasetal2021}, preventing the prediction of the
effect of climate and environmental changes as well as the development
of mitigation strategies.

Although the amount of SOM has been reported as an important driver of
mineralization activity in coastal areas \citep{Slothetal1995,
Mesnageetal2007}, the composition of SOM has also been shown to play a
key role in benthic nutrient cycling \citep{AndrieuxLoyeretal2014,
Albertetal2021}. The composition of SOM in coastal areas is tightly
linked to its origin and is the result of a large diversity of
allochthonous and autochthonous primary producers (such as
phytoplankton, benthic macroalgae, microphytobenthos, seagrasses
andwoody plants), as well as anthropogenic OM \citep[][and references
therein]{BianchiBauer2011}. Among natural sources of OM, the pathways
from sources to sediments and the probability of early biodegradation
differ greatly between autochthonous and allochthonous OM. Allochtonous
OM has undergone transport from sources to sediments and along its
transfer has possibly been subject to biodegradation and can be
protected by interactions with mineral surfaces, in contrast to
autochthonous OM which only experiences vertical transport and
settling. Consequently, the latter would be richer in labile molecules,
which could explain the higher reactivity in sediments following
phytoplanktonic blooms \citep{Franzoetal2019, Grenzetal2000}. Moreover,
anthropogenic sources of OM from sewage and soil fertilization by
agricultural amendments may also drive sedimentary P cycling
\citep{AitBallaghetal2020, Khaliletal2018}. However, the respective
role of allochtonous {versus} autochthonous and anthropogenic
{versus} natural OM on the biodegradability of SOM and the
diffusive and advective benthic nutrient fluxes remains unclear. 
\looseness=-1

In order to investigate the relationships between the origin of SOM and
benthic nutrient fluxes, 200 intertidal mudflat sediments from 12
mudflats along the coastline of Brittany, France were sampled. In this
region, marine intertidal mudflats are influenced by river discharges
and large tidal fluctuations. Agricultural intensification and the
urbanization of watersheds have led to coastal eutrophication, where
macroalgal ``green tides'' occur regularly in spring
\citep{Menesguenetal2019, Perrotetal2014, Schreyersetal2021}. Designing
the investigation at the scale of the region allows the investigation
of variations in the proportions of natural (marine {versus}
terrestrial) and anthropogenic sources of SOM, while terrestrial plant
precursors, land use and sources of anthropogenic pressure remain
unchanged. The composition of SOM was studied by a combination of
isotopic ($\rmdelta^{13}$C and $\rmdelta^{15}$N values) and lipid
(aliphatic hydrocarbons (\textit{n}-alkanes and hopanes), sterols and
stanols, fatty acids, fatty alcohols, and aromatic hydrocarbons
including polycyclic aromatic hydrocarbons-PAH) analyses classically
used to investigate the sources of SOM \citep{Freeseetal2008,
Volkmanetal2000}. The ability of sediments to release $\text{NH}_4^+$
and $\text{PO}_4^{3-}$ fluxes through diffusion were determined using
incubations with intact sediment cores under the same ambient
experimental conditions (dark, controlled temperature, artificial
overlying water) \citep{Louisetal2021}. Finally such a dataset allows
the use of canonical redundancy analysis (RDA) and variance
partitioning in order to explain the spatial variability of the
$\text{NH}_4^+$ and $\text{PO}_4^{3-}$ fluxes as independent parameters
related to (i)~the variation of the SOM origin (elemental and isotopic
ratios, lipid markers) and (ii)~the variation of the elemental
composition (N, P, C) and physical properties (grain size, porosity) of
the sediment \citep[from][]{Louisetal2021}.

The different analytical techniques will shed light on the composition
of the SOM, which will allow the determination of its spatial
variability and origin. In combination with statistical analysis this
will allow relating quantitatively the impact of the origin of SOM on
benthic nutrient fluxes in shallow coastal \mbox{environments}.

\section{Materials and methods}\label{sec2}
\subsection{Study sites and sampling}\label{sec2.1}

All of the study sites were macrotidal mudflats, located in Brittany,
north-western France, and have been described in detail previously
\citep{Louisetal2021}. In short, 200 sediment samples were collected at
low tide at 45 sites and were classified into 12 mudflats
(Figure~\ref{fig1}) during spring 2019. Sampling sites were divided
into three classes: bay, lower estuary and middle estuary according to
their distance to the mouth; from 0 to 5~km and from 5 to 15~km,
respectively. Sediment cores (10~cm depth) were sampled with PVC tubes
(diameter $=$ 6~cm, $h = 20$~cm) to measure the nutrient benthic
fluxes, and another tube (diameter $=$ 9~cm, $h = 5$~cm) was sampled to
characterize the SOM (elemental, isotopic and lipid composition) and
physico-chemical parameters (grain size distribution and porosity) in
the upper 5~cm layer. Even though the flux measurements were carried
out with 10~cm depth sediments, the nutrient fluxes are the result of
gradients over the first 2 to 5~cm \citep{LeeKim1990,
Akbarzadehetal2018}. In order to capture this reactive zone, the first
five~cm of the sediment were analyzed for physico-chemical parameters.
Elemental composition (total organic~C, total~N, total, organic and
iron oxide bound~P), grain-size distribution, porosity and benthic
nutrients fluxes were described in a previous paper
\citep{Louisetal2021}, while isotopic and lipid compositions are the
new data discussed in this paper.

\begin{figure*}
\includegraphics{fig01}
\caption{\label{fig1}Location of the mudflats and sampling sites ($n =
45$) on the Brittany coast. Goulven Bay and Aber Wrac'h are two
different mudflats, as are Vannes Estuary and Morbihan Gulf. The
sampling sites were divided into three groups according to their
geographic locations into ``Bay'' (pink), ``Lower estuary'' (green) and
``Middle estuary'' (blue).}
{\vspace*{-.42pc}}
\end{figure*}

\subsection{Isotopic analysis}\label{sec2.2}

\looseness=-1
The carbon and nitrogen isotopic compositions ($\rmdelta^{13}$C and
$\rmdelta^{15}$N values) were determined using an element analyzer
(FLASH{\texttrademark} EA 2000 IRMS) coupled with an isotopic ratio
mass spectrometer (DELTA~V{\texttrademark} plus). An aliquot of the
5~cm pooled sample was freeze-dried, crushed and acid-treated with 2N
HCl to remove the carbonate and was subsequently rinsed with deionized
water. After centrifugation, the carbonate-free sample was dried at
60~{\textdegree}C, and ground before being placed into a tin capsule
for stable C isotopic analysis. A second aliquot without an
acidification treatment was used for the total nitrogen in bulk OM (TN)
and stable N isotopic analysis. All isotopic analyses were performed
once and data were expressed in the conventional delta notation
relative to the Vienna Pee Dee Belemnite for $\rmdelta^{13}$C values and
to atmospheric $\text{N}_2$ for $\rmdelta^{15}$N values. Data calibration
and determination of the accuracy and reproducibility was carried out
using two international standards IAEA-N2 and IAEA-CH-6 and a certified
standard IVA33802151 organic-rich sediment. Repeated analysis indicated
analytical uncertainty better than ${\pm}$0.2\textperthousand.

\subsection{Lipid marker analysis}\label{sec2.3}

An aliquot of the 5~cm pooled sample was freeze-dried and crushed.
Approximately 30~g of this sample was extracted with approximately
100~mL of dichloromethane using an accelerated solvent extractor
(Dionex{\texttrademark}ASE{\texttrademark} 200) as per the following
conditions, modified from \citet{Derrienetal2011}: 33~mL cells, 5~min
heating at 100~{\textdegree}C and 60~bars, 10~min static phase,
completed with 80\% flush and 20-s purge with nitrogen. Elemental
sulfur was removed from the total lipid fraction by reduction on
metallic copper. After evaporation of DCM under a gentle stream of
nitrogen, the total lipid extract was fractionated into aliphatic
hydrocarbons, aromatic hydrocarbons and polar compounds on a silica
column by successive elution with cyclohexane,
cyclohexane/dichloromethane (2/1, v/v) and
methanol/{\ubreak}dichloromethane (1/1,~v/v). Polar fractions were
analyzed by capillary gas chromatograph-mass spectrometer (QP2010SE
\mbox{GC-MS}, Shimadzu) after derivatization using a mixture of
N,O-bis(trimethylsilyl) trifuoroacetamide (BSTFA) and
trimethylchlorosilane (TMSC) (99/1,~v/v), whereas the aliphatic and
aromatic fractions were analyzed without further treatment. The
injector used was in splitless mode and maintained at a temperature of
310~{\textdegree}C. The chromatographic separation of the three lipid
fractions was performed on a \mbox{SLB-5MS} capillary column (length
$=$ 60~m, diameter $=$ 0.25~mm, film thickness $=$ 0.25~$\rmmu$m) under
the following temperature conditions: 70~{\textdegree}C (held for
1~min) to 130~{\textdegree}C at 15~{\textdegree}C/min, the
130~{\textdegree}C to 300~{\textdegree}C (held for 15~min) at
3~{\textdegree}C/min. The helium flow was maintained at 1~ml/min. The
chromatograph was coupled to the mass spectrometer by a transfer line
heated at 280~{\textdegree}C. The ionization was performed by
electronic impact and analyses were performed in full scan mode. The
organic compounds were quantified by using an external calibration
adding internal standards in the solutions containing the aliphatic
hydrocarbons (internal standards: $n\text{C}_{20}\text{D}_{42}$;
$n\text{C}_{24}\text{D}_{50}$; $n\text{C}_{30}\text{D}_{62}$;
$\rmalpha$-cholestane), aromatic hydrocarbons (internal standards:
Naphthalene-D8; Acenaphtene-D10; Phenanthrene-D10; Chrysene-D12;
Perylene-D12) and polar compounds (internal standards:
$n\text{C}_{20}\text{D}_{42}$; $n\text{C}_{24}\text{D}_{50}$;
$n\text{C}_{30}\text{D}_{62}$; $\rmalpha$-cholestane) as per
\citet{Jeanneauetal2008}.

These analyses provided the total concentration of lipid markers in the
dry mass sediment. The \mbox{results} were also expressed as the
proportion of each lipid marker as a function of the sum of all lipid
compounds quantified in the sediment. Unless otherwise stated, results
are presented as the average ${\pm}$ the standard deviation.

\subsection{Benthic nutrient fluxes} \label{sec2.4}

The $\text{NH}_4^+$ and $\text{PO}_4^{3-}$ fluxes are previous results
described in detail in \citet{Louisetal2021}. Briefly, intact sediment
cores were incubated in the dark for 4~h directly on site in a mobile
laboratory under controlled temperature within one hour of sampling.
The \mbox{overlying} water was replaced by 150 mL nutrient-free
artificial seawater and gently aerated and stirred by bubbling in order
to preserve the oxic conditions in the overlying water and to prevent
the build-up of concentration gradients at the sediment--water column
interface. The core incubations were conducted under controlled
temperature in the dark and by using nutrient-free artificial seawater
as the overlying water in order to exclude these environmental
variables (temperature and light) related to benthic $\text{NH}_4^+$
and $\text{PO}_4^{3-}$ fluxes and focusing on the relation with the
sedimentary characteristics. These standardized core incubations
allowed to assess benthic fluxes mimicking the mudflat submerged during
high tide by the oxygenated coastal water and are thus potential
\mbox{benthic} fluxes, throughout the manuscript indicated as ``benthic
fluxes''. Water samples were collected in the overlying water after 2~h
and 4~h of incubation, filtered (0.22~$\rmmu$m) and stored at
4~{\textdegree}C for less than 3~days until nutrient analysis. The
$\text{NH}_4^+$ and $\text{PO}_4^{3-}$ fluxes
($\rmmu\text{mol}{\cdot}\text{m}^{-2}{\cdot}\text{h}^{-1}$) across the
sediment--water interface were estimated by using the change in the
molar concentration of the solute in the known volume of overlying
water as a function of incubation time and the surface area of the
sediment core. If the rate of nutrient release from the sediment did
not follow a linear trend over the incubation period, only the sample
at 2~h was considered in the flux estimation. Fluxes have already been
described in \citet{Louisetal2021}. The data are given in Supplementary
Table~S1. 

\subsection{Data analysis}\label{sec2.5}
\subsubsection{Classification of lipid markers for the investigation of
SOM sources} \label{sec2.5.1}

A total of 180 compounds from five chemical classes (sterols and
stanols, fatty acids, fatty alcohols, aliphatic hydrocarbons and
aromatic hydrocarbons) were identified in the sediment samples and were
classified according to the literature (Table~\ref{tab1}) and
diagnostic ratios (Table~\ref{tab2}) into eleven categories: ten
sources (microbial matter, terrestrial plants, phytoplankton, green
macroalgae, pelagic and benthic microalgae, algal matter, macrophytes,
fecal matter, crude oil and petroleum by-products, combustion products)
and one for ubiquist compounds that can derive from multiple sources.
For the categories ``Microbial matter'', ``Terrestrial plants'',
``Crude oil or petroleum by-products'' and ``Combustion products'', the
compounds were classified according to their functional groups as well
as the correlations among them established by a Principal Component
Analysis (PCA) finally resulting in 16 lipid marker groups
(\mbox{Table}~\ref{tab1}). The PCA analysis was performed using the
\mbox{R-studio} software with the ``FactoMineR'' package.

\begin{table*}[t!] %tab1
\tabcolsep=3.5pt\fontsize{8.5}{10.2}\selectfont
\caption{\label{tab1}Classification of identified lipid markers in term
of chemical classes, sources and groups\vspace*{-2pt}}
\begin{tabular}{cccc}
\thead
Chemical classes & Compounds & Sources & Groups\\
\endthead
\morerows{3}{\raisebox{-2pc}{Fatty acids}} & \textit{iso} and \textit{anteiso}
${n\text{C}_{17:0}}^{1,2}$ & Microbial matter & Bacteria 1\\
& \textit{iso} and \textit{anteiso} ${n\text{C}_{15:0}}^{1,2}$ &
Microbial matter & Bacteria 2\\
& \parbox[t]{6.5cm}{\raggedright\hangindent1.5pc $n\text{C}_{20:0}$ to $n\text{C}_{30:0}$;
$\rmomega$-hydroxy acids $n\text{C}_{16}$ to $n\text{C}_{24}$;
$\rmalpha$,~$\rmomega$~diacids $n\text{C}_{16}$ to
${n\text{C}_{26}}^{1,2}$\vspace*{2pt}} & Terrestrial plants & Plants 4\\
& \parbox[t]{6.5cm}{\raggedright\hangindent1.5pc $n\text{C}_{10:0}$ to $n\text{C}_{19:0}$;
${br}\text{C}_{12:0}$; ${br}\text{C}_{13:0}$; ${br}\text{C}_{14:0}$;
${br}\text{C}_{16:0}$; $n\text{C}_{16:1}$; $n\text{C}_{18:1}$;
$n\text{C}_{20:1}$; $n\text{C}_{22:1}$;
${n\text{C}_{18:2}}^{1,2}${\vspace*{9pt}}} & Ubiquist &\\

\morerows{1}{Fatty alcohols} & $n\text{C}_{20:0}$ to
${n\text{C}_{30:0}}^{3}$ & Terrestrial plants & Plants 3\\
& $n\text{C}_{11:0}$ to ${n\text{C}_{19:0}}^{3}$ & Ubiquist
&{\vspace*{9pt}}\\

\morerows{4}{\raisebox{-2pc}{\parbox[t]{2cm}{\centering Sterols and stanols}}} &
\parbox[t]{6.5cm}{\raggedright\hangindent1.5pc  Coprostanol; Epicoprostanol;
24-Ethylcoprostanol; 24-Ethylepicoprostanol$^{4}$\vspace*{2pt}} & Fecal
matter & Fecal\\
& Cholesta-5,22(E)dien-3$\rmbeta$-ol; Brassicasterol$^{5,6}$ &
Phytoplankton & Phytoplankton\\
& Campesterol; Stigmasterol$^{7}$ & Terrestrial plants & Plants 2\\
& Fucosterol; Isofucosterol$^{8}$ & Green macroalgae & Green
macroalgae\\
& \parbox[t]{7.1cm}{\raggedright\hangindent1.5pc Cholesterol; Sitosterol;
5$\rmalpha$-Cholestan-3-one; Cholestanol; Epicholestanol; Campestanol;
Sitostanol$^{6}$} & Ubiquist &{\vspace*{12pt}}\\

\morerows{5}{\raisebox{-5.5pc}{\parbox[t]{2cm}{\centering Aliphatic hydrocarbons}}} &
$n\text{C}_{11}$ to $n\text{C}_{14}$ \textit{n}-alkanes;
Bicyclohexane$^{9,10}$ & \parbox[t]{2.85cm}{\centering Crude oil or
petroleum by-products}{\vspace*{2pt}} & \parbox[t]{2.5cm}{\centering
Petroleum by-product 1}\\
& $n\text{C}_{15}$ to $n\text{C}_{19}$ \textit{n}-alkanes$^{7,11}$ &
\parbox[t]{2.6cm}{\centering Pelagic and benthic
microalgae}{\vspace*{2pt}} & Microalgae\\
& \parbox[t]{6.5cm}{\raggedright\hangindent1.5pc $n\text{C}_{20}$ to $n\text{C}_{26}$
\textit{n}-alkanes with an odd over even predominance (CPI
{\textgreater} 2)$^{11,12,13}$}{\vspace*{2pt}} & Macrophytes &
Macrophytes\\
& \parbox[t]{7.1cm}{\raggedright\hangindent1.5pc $n\text{C}_{20}$ to $n\text{C}_{26}$
\textit{n}-alkanes without an odd over even predominance (CPI $\sim$
1); $n\text{C}_{26}$ to $n\text{C}_{35}$ \textit{n}-alkanes without an
odd over even predominance (CPI $\sim$ 1); Tricyclic and pentacyclic
triterpanes (hopanes)$^{10}$}{\vspace*{2pt}} &
\parbox[t]{2.6cm}{\centering Crude oil or petroleum by-products} &
\parbox[t]{2cm}{\centering Petroleum by-product 2}\\
& \parbox[t]{6.5cm}{\raggedright\hangindent1.5pc $n\text{C}_{26}$ to $n\text{C}_{35}$
\textit{n}-alkanes with an odd over even predominance (CPI
{\textgreater} 5)$^{3,14}$}{\vspace*{2pt}} & Terrestrial plants &
Plants 1\\
& Neophytadiene (3 isomers)$^{15,16}$ & Algal matter &
Algae{\vspace*{12pt}}\\

\morerows{1}{\raisebox{-3pc}{\parbox[t]{2cm}{\centering Aromatic hydrocarbons}}} &
\parbox[t]{7.5cm}{\raggedright\hangindent1pc Phenanthrene; Anthracene; methyl-, dimethyl-
and trimethyl-phenanthrene and -anthracene; Fluoranthene; Pyrene
methyl-and dimethyl-fluoranthene and -pyrene$^{17}$}{\vspace*{2pt}} &
Combustion products & Combustion 1\\
& \parbox[t]{7.5cm}{\raggedright\hangindent1pc Benzonaphthothiophene; Benzo(a)anthracene;
Chrysene; methyl-and dimethyl-benzo(a)anthracene and -chrysene;
Benzofluoranthene; Benzopyrene; methyl-benzofluoranthene and
-benzopyrene; Perylene; Dibenzo(a,h)anthracene; Indeno[1,2,3-cd]pyrene;
Benzo(g,h,i)perylene$^{17}$}{\vspace*{2pt}} & Combustion products & Combustion 2
\botline
\end{tabular}
\tabnote{(1)~\citet{Derrienetal2017};
(2)~\citet{MezianeTsuchiya2000}; (3)~\citet{EglintonHamilton1967};
(4)~\citet{Leemingetal1996}; (5)~\citet{Cooketal2004};
(6)~\citet{Volkman1986}; (7)~\citet{Brasseletal1978};
(8)~\citet{Iatridesetal1983}; (9)~\citet{Woolfendenetal2011};
(10)~\citet{PetersMoldovan1993}; (11)~\citet{Jaffeetal2001};
(12)~\citet{Chevalieretal2015}; (13)~\citet{Fickenetal2000};
(14)~\citet{BrayEvans1961}; (15)~\citet{LopezRosalesetal2019};
(16)~\citet{Santosetal2015}; (17)~\citet{Yunkeretal2002}.}
{\vspace*{-.37pc}}
\end{table*}

\begin{table*}[t!] %tab2
\tabcolsep6.5pt
\caption{\label{tab2}Diagnostic ratios calculated on the distributions
of \textit{n}-alkanes and PAHs\vspace*{-2pt}}
\begin{tabular}{cccc}
\thead
Ratio & Formula & Values & Sources\\
\endthead
\multicolumn{4}{l}{{\textit{n}-Alkanes}{\vspace*{4pt}}}\\
\morerows{1}{CPI$^{1}$} & \morerows{1}{$\displaystyle\frac{1}{2}
\left(\displaystyle\frac{\sum n\mathrm{C}_{25,27,29,31,33,35}}
{\sum n\mathrm{C}_{24,26,28,30,32}}+\displaystyle\frac{\sum n
\mathrm{C}_{25,27,29,31,33,35}}{\sum n\mathrm{C}_{24,26,28,30,32,34}}
\right)$} & ${\sim}$1 & Petrogenic OM\\
&& ${>}$5 & Terrestrial plants{\vspace*{10pt}}\\

\morerows{1}{TAR$^{2}$} & \morerows{1}{$\displaystyle\frac{\sum n
\mathrm{C}_{25,27,29,31}}{\sum n\mathrm{C}_{15,17,19}}$} & ${<}$1 &
Aquatic OM\\
&& ${>}$5 & Terrestrial OM{\vspace*{10pt}}\\

\morerows{2}{Paq$^{3}$} & \morerows{2}{$\displaystyle\frac{\sum n
\mathrm{C}_{23,25}}{\sum n\mathrm{C}_{23,25,29,31}}$} & ${<}$0.25 &
Terrestrial plants\\
&& 0.4--0.6 & Emerged aquatic plants\\
&& ${>}$0.6 & Submerged aquatic plants{\vspace*{10pt}}\\

\multicolumn{4}{l}{{PAHs}{\vspace*{4pt}}}\\

\morerows{2}{R1$^{4}$} & \morerows{2}{$\displaystyle\frac{\mathit{Benzo}
(a)\mathit{anthracene}}{\mathit{Benzo}(a)\mathit{anthracene}+
\mathit{Chrysene}}$} & ${<}$0.2 & Petrogenic OM\\
&& 0.2--0.35 & Mixed sources\\
&& ${>}$0.35 & Combustion{\vspace*{9pt}}\\

\morerows{1}{R2$^{5}$} & \morerows{1}{$\displaystyle\frac{\mathit{Benzo}
(a)\mathit{pyrene}}{\mathit{Benzo}(a)\mathit{pyrene}+\mathit{Benzo}(e)
\mathit{pyrene}}$} & ${\sim}$0.5 & Freshly emitted road particles\\
&& ${<}$0.5 & Aged road particles{\vspace*{9pt}}\\

R3$^{6}$ & $\displaystyle\frac{\mathit{Retene}}{\mathit{Retene}+
\mathit{Chrysene}}$ & ${\sim}$1 & Wood combustion{\vspace*{9pt}}\\

\morerows{2}{R4$^{4}$} & \morerows{2}{$\displaystyle\frac{\mathit{Indeno}
[1,2,3-cd]\mathit{pyrene}}{\mathit{Indeno}[1,2,3-cd]\mathit{pyrene}+
\mathit{Benzo}(ghi)\mathit{perylene}}$} & ${<}$0.2 & Petrogenic OM\\
&& 0.2--0.5 & Combustion of petrogenic OM\\
&& ${>}$0.5 & Combustion of recent OM and coal{\vspace*{9pt}}\\

\morerows{1}{R5$^{7}$} & \morerows{1}{$\displaystyle\frac{\mathit{Benzo}
(a)\mathit{pyrene}}{\mathit{Benzo}(ghi)\mathit{perylene}}$} &
${<}$0.6 & Non traffic related sources\\
&& ${>}$0.6 & Traffic related sources
\botline
\end{tabular}
\tabnote{(1)~\citet{BrayEvans1961}; (2)~\citet{Ortizetal2013};
(3)~\citet{Fickenetal2000}; (4)~\citet{Yunkeretal2002}; 
(5)~\citet{Oliveiraetal2011}; (6)~\citet{Yanetal2005}; 
(7)~\citet{Katsoyiannisetal2007}.}{\vspace*{-.35pc}}
\end{table*}

\subsubsection{Statistical analyses} \label{sec2.5.2}
{\vspace*{-.1pc}}

The differences were investigated by student \mbox{$t$-test}, unless
otherwise specified, and are mentioned in the text for $p$-values $<$
0.05.

\subsubsection{Canonical redundancy analysis (RDA) and variance
partitioning} \label{sec2.5.3} 

The RDA allows determining linear relationships between responses
(matrix~$Y$) and explanatory (\mbox{matrix}~$X$) variables. In the
present study, the authors aimed to explain the benthic nutrient fluxes
(response variables) by the sedimentary parameters (explanatory
variables). To explain the spatial variability in the $\text{NH}_4^+$
and $\text{PO}_4^{3-}$ fluxes (response variables), two constrained
analyses were carried out in parallel. For the first analysis,
explanatory variables were qualitative parameters related to SOM
sources (elemental ratios, isotopic compositions and the proportions of
lipid marker groups), hereafter called ``SOM origin''. Explanatory
variables for the second analysis were previously published in
\citet{Louisetal2021}. They were quantitative parameters related to the
sediment composition (the contents of TN, TOC, iron oxide-bound P
(Fe-P) and organic P (Org-P)) and the microbial access to SOM
(percentage of mud and porosity), hereafter called the
``physico-chemical composition''. A Canonical Redundancy Analysis (RDA)
was chosen in both cases, assuming a linear relationship between the
response and explanatory variables. This dissymmetrical analysis
combines the concepts of ordination and regression, with explanatory
variables used to recalculate the response variables. The new canonical
axes are thus a linear combination of the initial explanatory
variables. The best explanatory variables in each matrix were selected
by the ``ordistep'' function of the ``vegan'' package
\citep[R~software;][]{Borcardetal2018, Oksanenetal2013} based on the
Akaike Information Criterion (AIC) through automatic permutation tests
of the ordination model and forward model selection. The
non-collinearity between the selected variables was then checked with
the Variance Inflation Factor (VIF) (a threshold value was set at~10).\looseness=1

In order to determine the linear relationships between all of the
previously selected variables and the benthic nutrient fluxes, an RDA
was performed and tested by the permutation test.

With the selected explanatory variables, variance partitioning was then
used to quantify the variance proportion of the $\text{NH}_4^+$ and
$\text{PO}_4^{3-}$ fluxes \mbox{independently} explained by both the
``SOM origin'' and ``physico-chemical composition'' parameters
\citep{Borcardetal1992}. Therefore, a comparison between the effects of
the SOM origin on benthic nutrient fluxes with those of other known
sedimentary characteristics was established. The significance of each
fraction of interest was tested by the permutation test.

The ``rda'', ``vif.cca'', ``anoca.cca'' and ``varpart'' functions of
the ``vegan'' package (R~software) were used to perform the RDA
analysis, VIF calculation, permutation test and variance partitioning,
respectively. The ``dist.dudi'' function of the ``ade4'' package and
the ``hclust'' function (R~software) were used to perform the
hierarchical cluster analysis. The data were previously standardized
with the ``scale'' function prior to the multivariate analysis.

\section{Results}\label{sec3}
\subsection{Benthic nutrient fluxes}\label{sec3.1}

These data are described in details in \citet{Louisetal2021}. This
section summarized the main results that are presented in
Figure~\ref{fig2}. The average of the $\text{NH}_4^+$ fluxes was $101
\pm 117~\rmmu\text{mol}{\cdot}\text{m}^{-2}{\cdot}\text{h}^{-1}$. A
large spatial variability of $\text{NH}_4^+$ fluxes was observed
at both the regional and local (mudflat) scale. As an example, large
differences were observed within the Vannes Estuary with $410 \pm
58~\rmmu\text{mol}{\cdot}\text{m}^{-2}{\cdot}\text{h}^{-1}$ for the
sampling site \#41 and only $12 \pm
12~\rmmu\text{mol}{\cdot}\text{m}^{-2}{\cdot}\text{h}^{-1}$ for the
site \#42. The $\text{PO}_4^{3-}$ fluxes were generally 20-fold lower
than the $\text{NH}_4^+$ fluxes,{\vspace*{1.5pt}} with an average flux
of $17 \pm 20~\rmmu\text{mol}{\cdot}\text{m}^{-2}{\cdot}\text{h}^{-1}$
(Figure~\ref{fig2}). High $\text{PO}_4^{3-}$ fluxes were observed for
the sites located in the Lorient Bay (e.g. site \#13, $\text{mean} = 51
{\pm} 7~\rmmu\text{mol}{\cdot}\text{m}^{-2}{\cdot}\text{h}^{-1}$), Pont
L'Abb\'{e} (e.g. site \#28, $\text{mean} = 66 +
71~\rmmu\text{mol}{\cdot}\text{m}^{-2}{\cdot}\text{h}^{-1}$) and along
the Rance Estuary (e.g. site \#33, $\text{mean} = 54 +
42~\rmmu\text{mol}{\cdot}\text{m}^{-2}{\cdot}\text{h}^{-1}$).

\begin{figure}
\includegraphics{fig02}
\caption{\label{fig2}Boxplot of the benthic (a)~$\text{NH}_4^+$ and
(b)~$\text{PO}_4^{3-}$ fluxes ($\rmmu\text{mol}{\cdot}\text{m}^{-2}
{\cdot}\text{h}^{-1}$) at the sediment--water interface displayed by
mudflat. The color of the dots differentiates the ecosystems between
middle estuaries (blue), lower estuaries (green) and bays (pink).}
\end{figure}

\subsection{SOM characterization {via} isotopic and lipid
analysis} \label{sec3.2}
\subsubsection{Isotopic compositions} \label{sec3.2.1}

The $\rmdelta^{13}$C values varied from ${-}24.3$ to
${-}16.2${\textperthousand}, with the highest values measured in the Gulf
of Morbihan ($\text{mean value} = {-}17.0 \pm 0.6${\textperthousand},
sites~\#43, \#44 and~\#45). Lower values of $\rmdelta^{13}$C
(${{<}{-}}22${\textperthousand}) corresponded to sediments
collected in the upstream sites of Lorient Bay (sites \#13 and \#14,
$\text{mean values} = {-}23.0 \pm 0.4${\textperthousand} and ${-}23.4 \pm
1.7${\textperthousand}), the Trieux Estuary (sites~\#1 and~\#2,
$\text{mean values} = {-}22.9 \pm 0.3${\textperthousand} and ${-}23.6 \pm
0.5${\textperthousand}), and Port la For\^{e}t (site~\#31, $\text{mean
value} = 22.6 \pm 0.5${\textperthousand}) (Figure~\ref{fig3}). The
$\rmdelta^{15}$N values varied from 5.8 to 10.5{\textperthousand}.
Highest values of $\rmdelta^{15}$N were measured in the Vannes Estuary
(site~\#41, $\text{mean value} = 9.5 \pm 0.7${\textperthousand}), as
well as in the Rance Estuary ($\text{mean value} = 8.4 \pm
0.4${\textperthousand}). The lowest values of $\rmdelta^{15}$N were
measured in the bay of Aber Wrac'h (site~\#22, $\text{mean value} = 6.1
\pm 0.3${\textperthousand}). In general, the sediments from middle
estuaries were characterized by the highest values of $\rmdelta^{15}$N
and the lowest values of $\rmdelta^{13}$C and the opposite was observed
for the sediments sampled in the bays (Figure~\ref{fig3}).

\begin{figure*}
\vspace*{-1pt}
\includegraphics{fig03}
\vspace*{-1pt}
\caption{\label{fig3}Ranges of variation of the isotopic ratios
$\rmdelta^{13}$C and $\rmdelta^{15}$N as a function of the type of
\mbox{environment}.}
\end{figure*}

\subsubsection{Distribution and sources of lipids} \label{sec3.2.2}

Considering all the samples, the relative proportion of the five
investigated chemical classes is dominated by sterols and stanols
(Figure~\ref{fig4}). They represent $30 \pm 12\%$ (mean ${\pm}$ sd) of
the investigated compounds. Fatty acids (FA) and aliphatic hydrocarbons
(especially \textit{n}-alkanes) represented similar proportions of the
investigated compounds followed by fatty alcohols. Aromatic
hydrocarbons occurred in low proportions in these sediments,
representing \mbox{$2 \pm 4\%$} of the investigated compounds.

\begin{figure*}
\includegraphics{fig04}
\vspace*{-1pt}
\caption{\label{fig4}Ranges of variation of the proportion of the
investigated chemical classes among identified compounds.}
\vspace*{-2pt}
\end{figure*}

The distribution of the 17 sterol and stanol compounds were dominated
by cholesterol ($31 \pm 9\%$ of sterol and stanol proportions) and
sitosterol ($20 \pm 6\%$). Source specific sterols and stanols
represented $12 \pm 2\%$, $9 \pm 3\%$, $6 \pm 2\%$ and $4 \pm 3\%$ for
terrestrial plants, phytoplankton \citep{Volkman1986, Cooketal2004},
fecal matter \citep{Leemingetal1996} and green macroalgae
\citep{Iatridesetal1983}, respectively. This chemical class was
composed of $70 \pm 5\%$ of ubiquist compounds. The spatial variability
of the distribution of the 4 fecal stanols (coprostanol,
epicoprostanol, 24-ethylcoprostanol and 24-ethylepicoprostanol) was
investigated by a statistical treatment modified from
\citet{Derrienetal2011}, \citet{Derrienetal2012} and
\citet{Jardeetal2018}. This statistical treatment allows the
determination of the proportion of the three main fecal matter sources
in Brittany (pig slurry, cow manure and waste water treatment plant
effluents) into the fecal stanol distribution \citep{Jardeetal2018}
(Figure~S1). The distribution of fecal stanols in the samples resulted
mainly from cow manure ($68 \pm 14\%$) and pig slurry ($11 \pm 15\%$)
exports and inputs from waste water treatment plant (WWTP) effluent or
non-connected installations ($21 \pm 8\%$). 

The second most important chemical class in terms of proportion are
fatty acid compounds. Fatty acids (FAc) represented $25 \pm 11\%$ among
the investigated compounds (Figure~\ref{fig4}). Their distribution was
dominated by $n\text{C}_{16:0}$ that represented $24 \pm 4\%$ of FAc.
Due to their occurrence in both bacteria and algae
\citep{Cranwelletal1987, MezianeTsuchiya2000}, low molecular weight
(LMW) FAc from $n\text{C}_{10:0}$ to $n\text{C}_{19:0}$ were classified
as ubiquist, with the exception of \textit{iso} and \textit{anteiso}
$\text{C}_{15:0}$ and $\text{C}_{17:0}$ that are specific of bacteria.
Ubiquist and bacterial FAc represented $66 \pm 12\%$ and $5 \pm 2\%$ of
FAc, respectively. The apparent low proportion of bacterial FAc is due
to the fact that a large portion of the LMW FAc was classified as
ubiquist. High molecular weight (HMW) FAc from $n\text{C}_{20:0}$ to
$n\text{C}_{30:0}$, $\rmomega$-hydroxy FAc and $\rmalpha, \rmomega$ diacids
originated from terrestrial plants\break \citep{EglintonHamilton1967},
representing $29 \pm 8\%$ of the FAc.

Fatty alcohols (FAl) represented $15 \pm 7\%$ of the investigated
compounds (Figure~\ref{fig4}). Their distribution was dominated by
$n\text{C}_{24}$, $n\text{C}_{26}$ and $n\text{C}_{28}$ that
represented $15 \pm 5\%$, $26 \pm 6\%$ and $16 \pm 5\%$ of FAl,
respectively. HMW FAl from $n\text{C}_{20}$ to $n\text{C}_{30}$ with a
predominance of molecules with an even number of carbon are
characteristic of terrestrial plants \citep{Cranwelletal1987},
representing $86 \pm 9\%$ of FAl. The remaining (FAl from
$n\text{C}_{11}$ to $n\text{C}_{19}$) were classified as
ubiquist{\break} due to their occurrence in both bacteria and algae
\citep{Cranwell1974}.

The chemical class of aliphatic hydrocarbons represented $26 \pm 18\%$
of the investigated compounds and was dominated by \textit{n}-alkanes
($94 \pm 5\%$) (Figure~\ref{fig4}). This chemical class exhibits the
most variable distribution as highlighted by the mean relative standard
deviation that is significantly higher for \textit{n}-alkanes than for
the other chemical classes (Wilcoxon test, $p < 0.001$). The main
compounds of this distribution were $n\text{C}_{13}$ ($16 \pm 14\%$)
and $n\text{C}_{29}$ ($12 \pm 6\%$). The carbon preference index (CPI)
ranged from 1.7 to 8.6 with a mean value of $5.0 \pm 1.4$
(Table~\ref{tab2}; Figure~\ref{fig5}a). This range highlights that the
proportion of crude oil and/or petroleum by-products in the aliphatic
fingerprint changed from a low proportion for CPI $>$ 5 to a
high proportion for CPI close to~1 \citep{BrayEvans1961}. The
terrestrial to aquatic ratio (TAR) ranged from 0.9 to 39 with a mean
value at $7.2 \pm 5.4$ (Figure~\ref{fig5}b). Only two \mbox{samples} (\#44 and
\#45 in the Gulf of Morbihan) exhibit \mbox{TAR $<$ 1}
characteristic of \textit{n}-alkanes coming mainly from \mbox{marine}
sources \citep{BourbonniereMeyers1996}. Out of the 200 samples, 114
have \mbox{TAR $>$ 5} characteristic of
\mbox{\textit{n}-alkanes} originating mainly from terrestrial sources.
TAR values from 1 to 5 were deduced for 83 samples, indicating a
combination of marine and terrestrial sources. Such variabilities
indicate a combination of several sources of OM for this chemical class
as highlighted by the different types of chromatograms
(Figure~\ref{fig6}). The first source (\mbox{Figure}~\ref{fig6}a) was
characterized by low CPI and the occurrence of a large hump (unresolved
complex mixture, UCM) on the chromatogram. This source was also
characterized by higher proportion of tricyclic and pentacyclic
triterpanes with values of the 22S/(22S $+$ 22R) homohopane that ranged
from 0.41 and 0.62 with an average value at $0.53 \pm 0.03$. This
highlights the input of crude oil and/or heavy petroleum by-products
such as road asphalt or lubricant oils \citep{PetersMoldovan1993}. The
second source (\mbox{Figure}~\ref{fig6}b) was characterized by a
monomodal distribution of LMW \textit{n}-alkanes from $n\text{C}_{11}$
to $n\text{C}_{15}$ with $n\text{C}_{13}$ as the maximum. This
distribution is very similar to the fingerprint of the diesel described
by \citet{Woolfendenetal2011}. The third source (Figure~\ref{fig6}c)
was characterized by the predominance of HMW \textit{n}-alkanes
(${>}n\text{C}_{20}$) with a large odd-over-even predominance resulting
in CPI $>$ 5 characteristic of the input of terrestrial
plants for \textit{n}-alkanes from $n\text{C}_{25}$ to $n\text{C}_{35}$
and of emergent aquatic plants from $n\text{C}_{21}$ to
$n\text{C}_{25}$. The Paq ratio (Figure~\ref{fig5}c) can be used as a
balance between these two natural sources \citep{Fickenetal2000}. For
109 samples, the proportion of HMW \textit{n}-alkanes was higher than
50\% of \textit{n}-alkanes and the CPI was higher than~3, indicating
that the distribution of \mbox{\textit{n}-alkanes} was dominated by
natural inputs. For these samples, Paq ranged from 0.13 to 0.39
(average value of $0.26 \pm 0.06$). For Paq $<$ 0.25 (43
samples), the HMW \textit{n}-alkanes were inherited from only
terrestrial plants. The remaining samples (66 samples) are
characterized by a combination of terrestrial and emergent aquatic
plants (0.25 $<$ Paq $<$ 0.4), which proportions have
been determined using an end member mixing approach with 0.25 for
terrestrial plants and 0.4 for emergent aquatic plants. The proportion
of HMW \textit{n}-alkanes coming from terrestrial plants in these 66
samples was $65 \pm 23\%$ (mean ${\pm}$ SD).

\begin{figure*}[t!]
\includegraphics{fig05}
{\vspace*{-.15pc}}
\caption{\label{fig5}Ranges of variation of ratios calculated on the
distribution of \textit{n}-alkanes. The formulas and diagnostic values
are given in Table~\ref{tab2}.}
\end{figure*}

\begin{figure*}[t!]
\includegraphics{fig06}
{\vspace*{-.25pc}}
\caption{\label{fig6}Typical chromatograms of aliphatic fractions from
mudflats of Brittany: (a)~crude oil dominated chromatogram from Morlaix
Bay, site~\#18; (b)~diesel dominated chromatogram from Vannes Estuary,
site~\#40; (c)~terrestrial plants dominated chromatogram from Trieux
Estuary site~\#4. Pie charts represent the proportion of molecular
markers from terrestrial plants (dark green), algae (light green),
microalgae and bacteria (gold), diesel (light grey) and crude oil or
heavy refined petroleum by-product (dark grey) in the aliphatic
fraction.}
{\vspace*{-.8pc}}
\end{figure*}

Aromatic hydrocarbons including polycyclic aromatic hydrocarbons (PAHs)
represented $2 \pm 4\%$ (mean ${\pm}$ sd) of the investigated
compounds. This proportion ranged from 0.1 to 47\% but is below 5\% for
the majority (90\%) of the samples. Two sites are characterized by high
proportion of aromatic hydrocarbons, one in Ria d'Etel ($22 \pm 18\%$,
site~\#5) and the other in Auray River ($10 \pm 5\%$, site~\#37). Among
this chemical class, the 16~PAHs listed by the US Environmental
Protection Agency (EPA) are often quantified in sediments as a measure
of the anthropogenic pressure (Figure~S2). Their sum was $425 \pm
655$~ng/g and ranged from 21 to 7489~ng/g of dry sediment. The
distribution of PAHs depends on the sources of burned OM and can be
used for their identification. The predominance of non methylated PAHs
that represented $78 \pm 4\%$ (mean ${\pm}$ sd) of PAHs highlighted
that they were mainly pyrogenic \citep{Yunkeretal2002}. Isomeric ratios
calculated on PAH allow determining the main pyrogenic source
(Table~\ref{tab2}; Figure~S3). The R4 ratio indicates a combination of
pyrogenic OM coming from the combustion of petroleum and modern OM
\citep{Yunkeretal2002}. The R3 ratio indicates a low contribution of
wood burning \citep{Yanetal2005},
while the R1 and R5 ratios indicate emissions from road traffic as the
main contributors of{\break} this pyrogenic OM \citep{Katsoyiannisetal2007,
Yunkeretal2002}. Moreover the R2 ratio highlights that
these particles have been aged by photolysis \citep{Oliveiraetal2011}.

\subsection{Variance partitioning and canonical redundancy analysis}
\label{sec3.3}

Significant variables from the various datasets were selected to
determine the relationship between biodegradability and OM sources
while taking the sediment properties into account. The grain-size
distribution, the porosity, the elemental composition of bulk OM and
benthic nutrient fluxes were taken from \citet{Louisetal2021}. Benthic
$\text{NH}_4^+$ and $\text{PO}_4^{3-}$ fluxes are shown in
Figure~\ref{fig6}. In the ``SOM origin'' data matrix, the selected
variables were $\rmdelta^{13}$C values, $\rmdelta^{15}$N values, and six
lipid marker groups: ``Fecal'', ``Petroleum products'', ``Microalgae'',
``Plants~3'', ``Plants~4'' and ``Bacteria~1'' (see Table~\ref{tab1} for
the composition of these groups). Regarding the ``physico-chemical
composition'' data matrix, the selected variables were the Fe-P, Org-P
and the porosity. With these selected \mbox{variables} in both data matrices,
the variance partitioning of the $\text{NH}_4^+$ and $\text{PO}_4^{3-}$
fluxes was carried out and the results are presented in
Figure~\ref{fig7}. The selected ``SOM origin'' and ``physico-chemical
\mbox{composition}'' parameters explained 30 and 34\% of the variance
of the $\text{NH}_4^+$ and $\text{PO}_4^{3-}$ fluxes respectively. The
variables related to ``SOM origin'' significantly explained 16\% of the
variance of the $\text{NH}_4^+$ fluxes (\mbox{F $=$ 5.3}, $p = 0.001$)
and 16\% of the variance of $\text{PO}_4^{3-}$ fluxes (F $=$ 5.8, $p =
0.001$), compared to 6 and 3\% with the ``physico-chemical
composition'' variables. The ``SOM origin'' and ``physico-chemical
composition'' variables shared between 8 and 15\% of the variance
partitioning of the $\text{NH}_4^+$ and $\text{PO}_4^{3-}$ fluxes,
respectively.

\begin{figure*}
\includegraphics{fig07}
\caption{\label{fig7}Variation partitioning of the $\text{NH}_4^+$ and
$\text{PO}_4^{3-}$ fluxes. ``SOM origin'' corresponds to $\rmdelta^{13}$C
values, $\rmdelta^{15}$N values and the proportions of the lipid marker
groups. ``Physico-chemical composition'' corresponds to the selected
parameters related to the composition (Fe-P, Org-P) and physical
properties (porosity) of the sediment.}
\end{figure*}

In order to relate the benthic nutrient fluxes with the selected
variables from the SOM origin, the isotopic composition and the
physical and chemical properties, a canonical redundancy analysis (RDA)
was performed (Figure~\ref{fig8}). A total of 32.1\% of the variance in
combined benthic fluxes of $\text{NH}_4^+$ and $\text{PO}_4^{3-}$ was
explained by the eleven selected parameters (permutation test: F $=$
7.99; $p = 0.001$). The first canonical axis (F $=$ 59.55; $p = 0.001$)
represents 64.6\% of the constrained variance (20.7\% of the total
variance) of benthic fluxes data, and is explained by \mbox{Fe-P} ($r =
0.67$; $p = 0.043$), porosity ($r = 0.65$; $p = 0.014$), P-Org ($r =
0.62$; $p = 0.021$), Fecal markers ($r = 0.61$; $p = 0.001$),
$\rmdelta^{15}N$ values ($r = 0.42$; $p = 0.02$) and Microalgal markers
($r = 0.38$; $p= 0.001$). The second canonical axis (F $=$ 32.63; $p =
0.001$) represents 35.4\% of the constrained variance (11.4\% of total)
of benthic fluxes data, and is mainly explained by the $\rmdelta^{13}$C
values ($r = -0.76$; $p = 0.002$), followed by Petroleum products
markers ($r = -0.40$; $p = 0.001$). The $\text{NH}_4^+$ fluxes were
positively correlated with the first axis ($r = 0.64$) and negatively
with the second axis ($r = - 0.77$). The $\text{PO}_4^{3-}$ fluxes were
positively correlated with the first axis ($r = 0.77$) and the second
axis ($r = 0.64$). Finally, the RDA showed a positive association
between the $\text{PO}_4^{3-}$ fluxes and the Org-P and Fe-P contents
as well as the proportions of fecal markers. For{\vspace*{1.5pt}} the
$\text{NH}_4^+$ fluxes, a positive association was observed with
$\rmdelta^{13}$C and $\rmdelta^{15}$N values, the porosity and the
proportions of microalgal and petroleum product \mbox{markers}.

\begin{figure*}
\includegraphics{fig08}
{\vspace*{-.1pc}}
\caption{\label{fig8}Correlation circles of the Canonical Redundancy
Analysis (RDA) showing the correlations between (a)~the explanatory
variables, related to the SOM origin (in red), isotopic composition (in
blue) and physico-chemical properties (in black) of the sediment, and
(b)~the response variables ($\text{NH}_4^+$ and $\text{PO}_4^{3-}$
fluxes).}
{\vspace*{-.5pc}}
\end{figure*}

\section{Discussion} \label{sec4}
\subsection{What is the spatial variability in the origin of SOM?}
\label{sec4.1}

In the investigated mudflats, the SOM originated from several natural
and anthropogenic sources. 

The natural OM sources included terrestrial plants, bacteria,
freshwater algae, marine phytoplankton, microphytobenthos, as well as
\textit{Ulva} sp., a~green macroalgae highly present along the coast of
Brittany. From 2008 to 2020, the average surface of stranded
\textit{Ulva} on mudflats was 1284 ha (Environmental Observatory of
Brittany). The lipids present in the mudflats of Brittany were mainly
of natural origin. Their proportion, including ubiquist compounds from
natural sources such as cholesterol and LMW FAc, ranged from 24\%
(Auray River, site~\#39) to 97\% (Ria Etel, site~\#6) of the analyzed
compounds with a mean value of $81 \pm 15\%$ (mean ${\pm}$ SD) 
(\%Nat---Figure~\ref{fig9}). The isotopic data and the distribution of
\textit{n}-alkanes indicated spatial variations of the contribution of
these primary producers to the SOM. From middle estuaries to bays,
$\rmdelta^{15}$N values and TAR decreased while $\rmdelta^{13}$C values and
Paq increased with significant differences between bays, lower
estuaries and middle estuaries, with the exception of TAR for which the
only significant difference was between bays and middle estuaries. 

\begin{figure}
\includegraphics{fig09}
\caption{\label{fig9}Ranges of variation of ratios calculated on the
distribution of lipids. \%Nat is the proportion of natural markers
among all identified compounds, including ubiquist compounds. \%Terr is
the proportion of terrigenous markers among all identified compounds,
including ubiquist compounds. \%Diesel is the proportion of diesel
markers among petrogenic markers.}
\end{figure}

Based on the isotopic shift between C3 terrestrial plants
\citep[$\rmdelta^{13}\text{C} = -28.7 \pm 0.5${\textperthousand}][]
{Davoultetal2017, Meyers1994} and marine organisms [phytoplankton
$\rmdelta^{13}\text{C} = -21.3 \pm 1.2${\textperthousand};
\citealp{Lienartetal2017} and \textit{Ulva} sp. $\rmdelta^{13}\text{C} = -15.8 \pm
4.4${\textperthousand}; \citealp{Bertoetal2013, Duboisetal2012,
Rieraetal1996}], the increase in $\rmdelta^{13}$C values along the
estuaries was associated to an increasing proportion of marine OM. This
was reinforced by the correlations between $\rmdelta^{13}$C values and
the proportion of terrestrial markers among identified compounds
(\%Terr---Figure~\ref{fig9}) ($r = {-}0.33$; $p < 0.0001$), the Paq ($r =
0.53$; $p < 0.0001$) and TAR ($r = {-}0.39$; $p < 0.0001$) ratios
quantifying the terrestrial {versus} aquatic or marine origin of
\textit{n}-alkanes. From middle estuaries to bays the proportion of
aquatic, calculated using the Paq ratio, and marine, calculated using
the TAR ratio, \textit{n}-alkanes increased, indicating an increase in
the proportion of SOM inherited from marine organisms, which was
recorded by the rise in $\rmdelta^{13}$C values. Such a trend was in
accordance with observations in other estuaries around the world
\citep{Ankitetal2017, Chevalieretal2015, Gireeshkumaretal2015,
LopesdosSantosVane2020}.

Microbial transformations of nitrogen induce changes in $\rmdelta^{15}$N
values due to the enzymatic preference for the lighter
${}^{14}\text{N}$. Therefore, the anthropogenic inputs from WWTP
and agriculture, \mbox{subject} to \mbox{microbial} activity, generally show an
increase in adjacent sedimentary $\rmdelta^{15}$N values
\citep{FinlayKendall2007, Rumoloetal2011, Savage2005}. The~occurrence
of WWTP, individual septic systems and the elevated agricultural
density (80\% of the area) are both potential sources in the current
study. When considering all the samples, $\rmdelta^{15}$N values
negatively correlated with the Paq \textit{n}-alkane ratio ($r =
-0.34$, $p<0.0001$). High $\rmdelta^{15}$N values were associated with
high proportion of terrigenous \textit{n}-alkanes. Since Brittany is a
highly agricultural area, we assume that terrigenous \textit{n}-alkanes
were indicators of soil erosion and thus of agricultural inputs.
$\rmdelta^{15}$N values were also positively correlated with the
proportion of human fecal stanols among analyzed compounds ($r = 0.15$,
$p = 0.035$). Consequently the $\rmdelta^{15}$N values indicated that
mudflats of Brittany would be influenced by both waste water management
and agricultural practices.

Overall, the correlations between isotopic and lipid data were
relatively weak, which could be due to the fact that isotopic data
result from the combination of all the sources of OM (natural and
anthropogenic ones). Lipid analysis highlighted four sources of
anthropogenic OM. Two are characteristic of petroleum products: diesel
and potentially degraded crude oil. Both of these contaminations
occurred in all mudflat sediments with variable proportions as
indicated by the ratio diesel/petrogenic
(\%Diesel---Figure~\ref{fig9}). The petrogenic contamination was mainly
due to crude oil in Northern mudflats, while diesel dominated in
Southern mudflats, with the exception of the Rance Estuary in the
north, the Lorient Bay and the Ria d'Etel in the south. Compared to
Northern mudflats, the Rance Estuary \mbox{sediments} were enriched
with diesel ($p < 0.01$), which could be due to the low hydrodynamics
induced by the tidal power plant that closes this estuary since the
60's \citep{Rtimietal2021}.
Compared to Southern mudflats, the concentration of diesel markers were
lower in the Lorient Bay ($p < 0.001$) and the Ria d'Etel ($p<0.001$).
Since the land use is very similar in upstream areas along the southern
coast, these differences could be due to a combination of
geomorphological and hydrological conditions that could impact the
sedimentation of diesel droplets \citep{Gearingetal1980}.

The third source of anthropogenic OM was pyrogenic and was
characterized by the occurrence and the distribution of PAHs
\citep{Yunkeretal2002}. Its occurrence in mudflat sediments, quantified
by the sum of the concentration of the 16 PAHs listed by the
US-Environmental Protection Agency (US-EPA) was in the same range but
slightly higher than in the European Northern Sea and the Baltic Sea
\citep{Wangetal2020}. The background contamination in sediments of the
eastern North Atlantic has been determined by the OSPAR commission, a
cooperation between 15 governments and the European Union to protect
the marine environment of the North-East Atlantic (OSPAR, 2017). Among
the present samples, 24\% of the mudflat sediments presented higher
contamination levels than this background. Most of these were from the
Ria d'Etel (25\% of the contaminated sediments), Auray River (23\%) and
Lorient Bay (21\%). Isomeric ratios calculated on PAH allowed
identifying particles coming from the emissions from road traffic and
aged by photolysis as the main pyrogenic source (Figure~S3). This was
in agreement with one of the main source of PAHs identified in Northern
and Baltic seas \citep{Wangetal2020}.

Finally, the anthropogenic OM from fecal contaminations was highlighted
by the occurrence and the distribution of fecal stanols. The sum of
their concentrations ranged from 4 to 1934~ng/g of dry sediment ($276
\pm 261$~ng/g; mean ${\pm}$ sd). This was 10 times lower than in the
Tay Estuary (Scotland) before the building of a WWTP
\citep{ReevesPatton2005} and 5 times lower than the mean concentrations
in Jinhae Bay (South Korea) \citep{Choietal2002}. Only 4 samples of the
study exhibited a coprostanol concentration higher than 500~ng/g, which
has been suggested as the threshold to indicate a significant fecal
contamination \citep{LeemingNichols1996}. Based on this, fecal
contamination was small in mudflat sediments along the Brittany
coastline. This fecal contamination resulted mainly from cow manure
($68 \pm 14\%$) and pig slurry ($11 \pm 15\%$) exports and inputs from
WWTP effluent or non-connected installations \mbox{($21 \pm 8\%$)},
which was in accordance to the \mbox{combination} of sources of fecal
contamination that has been determined in rivers of Brittany
\citep{Jardeetal2018}.

\subsection{How the origin of SOM impacts benthic nutrient fluxes?}
\label{sec4.2}

The origin of SOM has often been suggested as a driver of SOM
biodegradation and benthic nutrient fluxes \citep{LaRoweetal2020}. This
large dataset allowed for the first time to significantly quantify this
relationship for $\text{NH}_4^+$ and $\text{PO}_4^{3-}$ fluxes produced
by mudflats in tidal environments. The benthic $\text{NH}_4^+$ and
$\text{PO}_4^{3-}$ fluxes, measured from the sediment core incubations,
were significantly correlated ($p < 0.005$) to the SOM origin. The
percentage of variance for the $\text{NH}_4^+$ and $\text{PO}_4^{3-}$
fluxes explained only by the ``SOM origin'' variables was 16\%,
compared to 6\% (for $\text{NH}_4^+$ fluxes) and 3\% (for
$\text{PO}_4^{3-}$ fluxes) for the ``physico-chemical composition''
variables. By combining all variables, the explained percentage of
variance for the $\text{NH}_4^+$ and $\text{PO}_4^{3-}$ fluxes reached
30 and 34\% \mbox{respectively.} The origin of SOM was thus a significant
driver of benthic nutrient fluxes. The RDA highlighted that the
$\text{NH}_4^+$ and $\text{PO}_4^{3-}$ fluxes were not correlated to
the same variables.

The $\text{NH}_4^+$ flux was positively related to the isotopic
signature of the bulk sediment OM ($\rmdelta^{15}$N and $\rmdelta^{13}$C
values), the porosity and the proportions of microalgal and diesel
markers. In geographically closed areas, low hydrodynamic conditions
improve the sedimentation of (i)~fine particles, increasing the
porosity of sediments \citep{Meade1966} and (ii)~diesel droplets
lighter than seawater through flocculation. In such low hydrodynamic
conditions, the development of microalgae promoted by anthropogenic N
coming from watersheds and their sedimentation must have brought to the
sediment ${}^{13}\text{C}$-enriched \citep{Cooketal2004, Lietal2016,
Ogrincetal2005} and ${}^{15}\text{N}$-enriched \citep{Savage2005,
FinlayKendall2007, Rumoloetal2011} marine OM, increasing the proportion
of microalgal markers in the lipidic fraction of SOM. Phytoplanktonic
blooms are known to enrich sediments in nitrogen and labile SOM
\citep{Franzoetal2019}. These inputs have promoted a quick response of
the bacterial reactivity in the sediment \citep{Hardisonetal2013,
Pruskietal2019}, which has induced seasonal variability in benthic
nitrogen fluxes \citep{Grenzetal2000}. The present results showed that
phytoplanktonic blooms could also be responsible for spatial
variability in benthic nitrogen fluxes from mudflat sediments. The
$\text{NH}_4^+$ flux was thus partially controlled by the origin of SOM
that might have been driven by the geomorphology. Under low
hydrodynamic conditions, an algal bloom might have been stimulated,
especially if the coastal system was impacted by nitrogen loading from
the watershed, and thus become an OM source for the coastal sediment.

The geographical configuration of the Gulf of Morbihan which opens onto
the Atlantic Ocean through a narrow passage 850 m wide and covering an
area of $115~\text{km}^2$ illustrated perfectly these relationships
between geomorphology, origin of SOM and benthic $\text{NH}_4^+$ flux.
This area combined the samples from the Auray River, Vannes and the
southern internal coast of the Gulf (Figure~\ref{fig1}). In this area,
the porosity was significantly higher ($p < 0.0001$) than in other
mudflats, which indicated an area with low hydrodynamic conditions.
These conditions have improved the assimilation of dissolved
anthropogenic nitrogen by microalgae and their sedimentation. Higher
sedimentation rates were emphasized by significantly higher proportions
of diesel markers in this area ($p < 0.0001$). As a consequence, SOM
was enriched in OM from microalgae as indicated by significantly higher
$\rmdelta^{15}$N ($p < 0.01$), $\rmdelta^{13}$C ($p < 0.0001$) and
microalgal markers ($p < 0.001$), which induced a mean $\text{NH}_4^+$
flux that was two times higher than in the other mudflats of Brittany
($p = 0.001$).

This link between topography, algal bloom, sedimentation and enrichment
in sedimentary heavy nitrogen seems in accordance with the main mode of
export of nitrogen from agricultural watersheds that is mainly under
dissolved form \citep{Mulhollandetal2008}. Before storage in mudflat
sediments, N must be fixed by aquatic primary producers. In 2021, the
mean Q90 (the value below which at least 90\% of the data fall) nitrate
concentration at the regional scale was $41 \pm 25$~mg/L (data from the
Environment Observatory in Brittany). Consequently, reducing benthic
N~fluxes from mudflat sediments will require a decrease in the
dissolved N~fluxes from agricultural catchments and then a decrease in
the amount of N fertilizers used by the chemical conventional
agriculture to avoid N-surplus \citep{Leipetal2011}.

The $\text{PO}_4^{3-}$ flux was positively related to the proportion of
fecal markers, as well as the Org-P and \mbox{Fe-P}. Fecal markers
occurring in the investigated mudflats originated mainly from the
erosion of agricultural soils amended with bovine manure and pig slurry
($79 \pm 29\%$) and from WWTP and individual septic systems ($21 \pm
8\%$). This finding seems to be in accordance with the increase in
$\text{PO}_4^{3-}$ fluxes {via} a larger dissolution rate of
Fe-P due to reductive conditions in sediments impacted by urban and
agricultural activities in the Bay of Brest in Brittany
\citep{Khaliletal2018}. Biogeochemical modelling used to investigate the
mechanisms behind these relationships has highlighted the role of the
input of labile OM from urban and agricultural activities. This would
have led to high mineralization rates and in the increase in
$\text{PO}_4^{3-}$ fluxes through both mineralization and subsequent
reductive Fe-P dissolution \citep{AitBallaghetal2020}.

Org-P and Fe-P in sediments may come from direct sedimentation of
particulate Org-P and Fe-P exported by watersheds or from sedimentation
of marine primary producers that would have assimilated dissolved P
\citep{Watsonetal2018}. In rivers of Brittany, over the period 2007 to
2011, the mean fluxes of particulate P has been 1.7 times higher than
the mean fluxes of dissolved P \citep{Boletal2018}. The correlations
between sedimentary P stocks and $\rmdelta^{13}$C (\mbox{Org-P}: $r =
{-}0.21$, $p<0.01$; Fe-P: $r = {-}0.32$, $p < 0.001$), Paq (Org-P: $r =
{-}0.27$, $p<0.001$; Fe-P: $r = {-}0.30$, \mbox{$p < 0.001$}) and the
concentration of fecal markers (Org-P: $r = 0.40$, $p<0.001$; Fe-P: $r
= 0.37$, $p < 0.001$) seems to indicate that sedimentary P stocks were
mainly associated with terrestrial OM. This could highlight particulate
P as the main source of sedimentary P through soil erosion. Moreover
the median benthic $\text{PO}_4^{3-}$ fluxes were positively correlated
to the watershed area ($r = 0.8$, $p<0.001$). Intensive agricultural
activities cover 60\% of the area in Brittany and result in one of the
world's largest P surpluses \citep{MacDonaldetal2011}, which may affect
the sediment composition through agricultural soil erosion, which can
be considered as a transport vector of fecal matter and particulate
phosphorus. Therefore, benthic $\text{PO}_4^{3-}$ fluxes seem to be
driven by the surface area of the watersheds and the land use in the
catchment. Consequently, agricultural practices allowing a decrease in
P surplus and soil erosion should induce a decrease in sedimentary P
stocks and then a decrease in benthic $\text{PO}_4^{3-}$ fluxes.

\subsection{A large proportion of the variance of benthic nutrient
fluxes remained unexplained} \label{sec4.3}

In the present study, we focused on the link between the benthic
nutrient fluxes and the sediment composition, while comparing the
significant effect of SOM origin {versus} quantity.
Nevertheless, a large part of the variance in the benthic
$\text{NH}_4^+$ and $\text{PO}_4^{3-}$ fluxes on the regional scale
remained unexplained (70 and 66\%, respectively) (Figure~\ref{fig7}).
Microbial abundance and diversity were not assessed in the current
study, and abundance and/or diversity may play an additional role in
nutrient cycling. As shown by \citet{Abelletal2013}, the composition of
the bacterial community has been related to the nature of the OM in
estuarine systems, and their combination may lead to a shift in the
benthic nutrient fluxes. In addition, bioturbation, mediated by
macrofauna activities, was likely present in the sediment core
incubations and possibly involved in the spatial variability of the
$\text{NH}_4^+$ and $\text{PO}_4^{3-}$ fluxes. Bioturbation includes
particle reworking and burrow ventilation, promoting exchanges of
solute between the porewater and the overlying water and stimulating
microbial activities \citep{GrafRosenberg1997}. The review and
experimental studies of \citet{Karlsonetal2007} and
\citet{RenzForster2014} have highlighted the stimulation of the
$\text{NH}_4^+$ and $\text{PO}_4^{3-}$ \mbox{effluxes} by benthic
macrofauna, with a magnitude that varied according to the density,
burrowing depth and ventilation of polychaetes and bivalves.
Bioturbation promotes $\text{NH}_4^+$ effluxes through the
mineralization of SOM {via} an additional supply of electron
acceptors (e.g. oxygen, nitrate), the remobilization of burial OM, and
$\text{NH}_4^+$ excretion from macrofauna \citep[][and references
therein]{Welsh2003}). The release of $\text{PO}_4^{3-}$ in the presence
of macrofauna also depends on the sediment redox status and components
(e.g iron oxides). In fact, bioturbation increases \mbox{P-Org}
mineralization and then regenerated $\text{PO}_4^{3-}$ can be either
released by porewater flushing or adsorbed into iron oxides under oxic
conditions. Therefore, \citet{Nizzolietal2007} observed that
the bioturbation by polychaete \textit{Nereis} spp.\ had site specific
effects on the flux, with sediment acting either as a sink or a source
of $\text{PO}_4^{3-}$.

\section{Conclusion} \label{sec5}

To the best of our knowledge, this is the first study that describes
the variability in the SOM origin through a broad sampling campaign of
marine mudflats at the regional scale (Brittany), relating SOM origin
to benthic nutrient fluxes. The combination of isotopic and lipid data
emphasized the SOM in Brittany intertidal mudflats as a mixture of
natural (marine and continental) and anthropogenic OM. Its composition
varied according to the anthropogenic pressures of watersheds as well
as the hydrodynamic conditions. Benthic nutrient fluxes were
significantly driven by the origin of SOM that explained 24\% and 31\%
of the variance of $\text{NH}_4^+$ and $\text{PO}_4^{3-}$ fluxes,
respectively. Benthic nutrient fluxes were linked to terrestrial export
of N and P but were not correlated to the same parameters. The
$\text{NH}_4^+$ fluxes were driven by the uptake by phytoplankton of
dissolved anthropogenic N exported from agricultural catchments. Their
sedimentation was favoured by low hydrodynamic conditions, enriching
the sediments with labile OM. The $\text{PO}_4^{3-}$ fluxes were driven
by the sedimentation of particulate P exported through agricultural
soil erosion. \looseness=-1

A significant fraction of the spatial variability in the benthic
nutrient fluxes remains unexplained by the investigated sedimentary
characteristics. The spatial variability of benthic macrofauna and
\mbox{microbial} biomass might play a role in this unexplained variance
and should be considered in future \mbox{investigations}.

\section*{Conflicts of interest}

The authors declare that they have no conflict of \mbox{interest}.

\section*{Acknowledgments}

This work was funded by Loire-Bretagne Water Agency and the Regional
Council of Brittany (France). It was carried out as a part of the IMPRO
research project. The authors would like to thank Josette Launay
(CRESEB) for the project coordination. The authors would also like to
thank P.~Petitjean, C.~Petton, G.~Bouger, O.~Jambon and
C.~Roose-Amsaleg for their assistance in the field. The CEVA, the
Syndicat Mixte EPTB Rance-Fremur, the Pays de Guingamp, the Syndicat
Mixte des Bassins du Haut-L\'{e}on, the Syndicat des Eaux du
Bas-L\'{e}on, Lorient-Agglom\'{e}ration, the Syndicat Mixte du SAGE
Ouest-Cornouaille (OUESCO), Concarneau-Cornouaille-Agglom\'{e}ration,
the Syndicat Mixte de la Ria d'Etel (SMRE) and the Syndicat Mixte du
Loc'h et du Sal (SMLS) are acknowledged for their help preparing the
field campaign. The authors would also like to thank O.~Lebeau
(Plateforme Isotopes Stables, IUEM) for the elemental and isotopic
analyses, and Marie-Claire Perello (EPOC, University of Bordeaux) for
the particle-size analyses. S.~Mullin is acknowledged for carefully
checking the English content. Two anonymous reviewers are acknowledged
for improving the quality of this manuscript.

\section*{Supplementary data}

Supporting information for this article is available on the journal's
website under \printDOI\ or from the author.

\CDRsupplementaryTwotypes{supplementary-material}{\cdrattach{crgeos-228-suppl.pdf}}

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