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\begin{document}

\title{Drying and fragmentation drive the dynamics of resources, consumers and
ecosystem functions across aquatic-terrestrial habitats in a river
network.}



\author[1]{Romain Sarremejane}%
\author[1]{Teresa Silverthorn}%
\author[1]{Angélique Arbaretaz}%
\author[2]{Amélie Truchy}%
\author[1]{Nans Barthélémy}%
\author[3]{Naiara Lopez-Rojo}%
\author[4]{Arnaud Foulquier}%
\author[5]{Laurent Simon}%
\author[1]{Hervé Pella}%
\author[6]{Gabriel Singer}%
\author[7]{Thibault Datry}%
\affil[1]{INRAE Centre Lyon-Grenoble Auvergne-Rhône-Alpes}%
\affil[2]{Swedish University of Agricultural Sciences}%
\affil[3]{Universite Grenoble Alpes}%
\affil[4]{Laboratoire d'Ecologie Alpine}%
\affil[5]{University Claude Bernard Lyon 1}%
\affil[6]{University of Innsbruck}%
\affil[7]{INRAE}%


\vspace{-1em}



  
  \date{May 18, 2024}


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\selectlanguage{english}
\begin{abstract}
Disturbance and connectivity control biodiversity, ecosystem functioning
and their interactions across connected aquatic and terrestrial
ecosystems, that form a meta-ecosystem. In rivers, detrital organic
matter (OM) is transported across terrestrial-aquatic boundaries and
along the river network and decomposed on the way by diverse communities
of organisms, including microorganisms and invertebrates. Drying
naturally fragments most river networks and thereby modify organism
dispersal and OM transfers across ecosystems. This may prevent organisms
from reaching and consuming OM, generating mismatches between community
composition and decomposition. However, little evidence of the effects
of drying on river network-scale OM cycling exists. Here, we aim to
examine the effects of fragmentation by drying on the structure of
consumer communities and ecosystem functioning within interacting
aquatic-terrestrial river ecosystems. We monitored leaf resource stocks,
invertebrate communities and decomposition rates in the instream and
riparian habitats of 20 sites in a river network naturally fragmented by
drying. Although instream resource quantity and quality increased with
drying severity, decomposition decreased due to changes in invertebrate
communities and particularly leaf-decomposer abundance.
Invertebrate-driven decomposition peaked at intermediate levels of
upstream connectivity, suggesting that intermediate levels of
fragmentation can promote the functioning of downstream ecosystems. We
found that the variability in community composition was unrelated to
variability in decomposition at sites with low connectivity and high
drying severity, suggesting that such conditions can promote mismatches
between community composition and decomposition. Decomposition instream
was correlated to decomposition in the riparian area, revealing one of
the first network-scale evidence of the links between ecosystem
functions across terrestrial-aquatic boundaries. Our river network-scale
study thus demonstrates the paramount effect of drying on the dynamics
of resources, communities and ecosystem functioning in river networks,
with crucial implications for the adaptive management of river networks
and preservation of their functional integrity.%
\end{abstract}%



\sloppy


\textbf{Drying and fragmentation drive the dynamics of resources,
consumers and ecosystem functions across aquatic-terrestrial habitats in
a river network.}

Abstract

Disturbance and connectivity control biodiversity, ecosystem functioning
and their interactions across connected aquatic and terrestrial
ecosystems, forming a meta-ecosystem. In rivers, detrital organic matter
(OM) is transported across terrestrial-aquatic boundaries and along the
river network and decomposed on the way by diverse communities of
organisms, including microorganisms and invertebrates. Drying naturally
fragments most river networks and thereby modify organism dispersal and
OM transfers across ecosystems. This may prevent organisms from reaching
and consuming OM, generating mismatches between community composition
and decomposition. However, little evidence of the effects of drying on
river network-scale OM cycling exists. Here, we aim to examine the
effects of fragmentation by drying on the structure of consumer
communities and ecosystem functioning within interacting
aquatic-terrestrial river ecosystems. We monitored leaf resource stocks,
invertebrate communities and decomposition rates in the instream and
riparian habitats of 20 sites in a river network naturally fragmented by
drying. Although instream resource quantity and quality increased with
drying severity, decomposition decreased due to changes in invertebrate
communities and particularly leaf-decomposer abundance.
Invertebrate-driven decomposition peaked at intermediate levels of
upstream connectivity, suggesting that intermediate levels of
fragmentation can promote the functioning of downstream ecosystems. We
found that the variability in community composition was unrelated to
variability in decomposition at sites with low connectivity and high
drying severity, suggesting that such conditions can promote mismatches
between community composition and decomposition. Decomposition instream
was correlated to decomposition in the riparian area, revealing one of
the first network-scale evidence of the links between ecosystem
functions across terrestrial-aquatic boundaries. Our river network-scale
study thus demonstrates the paramount effect of drying on the dynamics
of resources, communities and ecosystem functioning in river networks,
with crucial implications for the adaptive management of river networks
and preservation of their functional integrity.

\textbf{Key words} : subsidies, meta-ecosystems, metacommunities,
biodiversity-ecosystem functioning relationships (BEF),

\textbf{Introduction}

Aquatic and terrestrial ecosystems are connected through fluxes of
resources (e.g. carbon and nutrients) and organisms, mediated by
physical forces (water, wind) and dispersal, as part of a meta-ecosystem
(Loreau et al. 2003). River networks are typical meta-ecosystems, in
which resources are exchanged across longitudinal (i.e. from headwaters
to downstream sections) and lateral (i.e. between riparian and instream
habitats) dimensions (Gounand et al. 2018, Harvey et al. 2020, Cid et
al. 2022) and transformed by organisms through ecosystem functions such
as primary production or decomposition (Hotchkiss et al. 2015, Tiegs et
al. 2019). Ecosystem functioning and community composition naturally
change along the river network as the basal trophic resource switches
from brown -- i.e. based on coarse detrital organic matter riparian
input -- to green -- i.e. based on instream algal production -- from
upstream to downstream (Vannote et al. 1980, Finlay 2001). Although the
structuring of resources, communities and their functioning along the
river network have been theorized \textgreater{} 40 years ago (Vannote
et al. 1980, Gounand et al. 2018), limited empirical evidence of the
mechanisms driving such structuring exist, apart from simulations
(Jacquet et al. 2022a).

Leaf-litter is a key source of organic carbon and nutrients in rivers,
exchanged in both lateral (through leaf fall and runoff) and
longitudinal (up to downstream flow) dimensions (Catal\selectlanguage{ngerman}àn et al. 2022,
Scherer-lorenzen et al. 2022). The decomposition rate of leaf litter in
both aquatic and terrestrial habitats depends on several factors
including, leaf chemical quality and palatability (Pastor et al. 2014,
del Campo et al. 2021a), environmental factors such as humidity and
temperature (Tiegs et al. 2019, Annala et al. 2022) and decomposer
community (i.e. microorganisms and invertebrates) composition and
activity (Abelho and Descals 2019, Boyero et al. 2021). Although
leaf-decomposer (shredder) invertebrate communities typically differ
between instream and riparian habitats, terrestrial and aquatic
decomposers may colonize riverbed and riparian habitats during streambed
drying (Corti and Datry 2016) and overbank flooding events (Hutchens and
Wallace 2002, Steward et al. 2022), respectively. Thus, lateral movement
of consumers could link aquatic and terrestrial exchanges of organic
matter (Abelho and Descals 2019, Barthélémy et al. 2022,
Scherer-lorenzen et al. 2022), but little evidence of such links exists
at the river network scale (Harvey et al. 2020). As a result of
decreasing leaf input from headwaters to mainstem, decomposer abundances
and their resource use also typically decrease from up to downstream
(Collins et al. 2016, Jacquet et al. 2022a). However, dispersal
limitation mechanism preventing species from reaching their favoured
habitat or resource because of low connectivity, could lead to
sub-optimal functioning in isolated headwaters, despite abundant
resources (Leibold et al. 2017, Thompson et al. 2017).

Drying, i.e. the loss of surface water, is one of the most fundamental
types of disturbance in river ecosystems, triggering drastic changes in
environmental conditions that induce shifts in community composition
(Boulton 2003, Datry et al. 2014) and carbon-related biogeochemical
processes (Datry et al. 2018). By fragmenting the river continuum,
drying can also alter the transport and dispersal of resources and
organisms, limiting exchanges between populations (i.e.
metapopulations), communities (metacommunities) and ecosystems across
the network (Gauthier et al. 2020, Cid et al. 2022). Typically, the
diversity and abundance of aquatic organisms such as invertebrates
decreases as drying frequency and duration increase (Datry et al. 2014,
Soria et al. 2017, Sarremejane et al. 2020) but the colonization of dry
riverbeds by terrestrial organisms can counteract those diversity losses
(Corti and Datry 2016, Steward et al. 2022). As a result of the negative
effect of drying on shredder invertebrates, decomposition rates are
usually lower in rivers that experience drying, particularly during
their dry phase (Corti et al. 2011) but also during their flowing phase
due to legacy effects (Datry et al. 2011, del Campo et al. 2021b).
Fungal and bacterial decomposition may however be little affected by
drying, if sufficient moisture preserves microbial activity (Riedl et
al. 2013, Foulquier et al. 2015). Drying may also affect leaf-litter
quality, altering labile carbon contents and nutrient immobilization by
microbial communities, which often reduce palatability for invertebrates
(Pastor et al. 2014, del Campo et al. 2021a). At least 50\% of the
world's river networks dry, on average, for one day per year due to
geological and/or climatic features (Messager et al. 2021), and this
proportion is likely to increase due to climate change and other
anthropogenic alterations (e.g. water abstraction, irrigation; Datry et
al. 2023). We urgently need to understand how fragmentation by drying
modifies spatial and temporal interactions between organisms and
resources at the river meta-ecosystem scale, including across
aquatic-terrestrial boundaries (Scherer-lorenzen et al. 2022) to be able
to predict when and where adaptive management strategies may be needed
to preserve functional river ecosystems under global changes.

Drying and fragmentation may create mismatches between resource and
consumer availability if drying and low connectivity prevent efficient
aquatic consumers from being present and use resources (Thompson et al.
2017). Such mismatches could result in changes in the biodiversity --
ecosystem functioning (BEF) relationship as communities among highly
disturbed or isolated environments may not be composed of the most
efficient consumers; hence translating into a weak or null BEF
relationship across networks fragmented by drying (Brose and Hillebrand
2016, Leibold et al. 2017). However, intermediate levels of disturbances
and connectivity may strengthen the BEF by 1) allowing selection of the
species best adapted to the environment and resource use and 2)
preventing the dominance of functionally inefficient, highly competitive
species occurring under low levels of disturbance and high connectivity
(Cardinale and Palmer 2002, Leibold et al. 2017).

Here, we aim to examine how resource quality and quantity, the structure
of consumer communities and ecosystem functioning respond to drying
across lateral and longitudinal dimensions. To do so, we monitored
across three seasons leaf resource stocks, invertebrate communities and
decomposition rates in the instream and riparian habitats of 20 sites in
a river network fragmented by drying. We hypothesize (H1, Fig. 1) that
increasing flow intermittence promotes accumulation of leaves of low
chemical quality (low conditioning by microorganisms), decreases in
invertebrate abundance and diversity and thus lower decomposition rates
instream. Instream responses to drying may also change with connectivity
and effects may be stronger in less connected headwaters that typically
have lower diversity and post-drying recolonization capacity, likely
reducing functioning rates there (H2, Fig. 1). Drying may strengthen
relationships between instream and riparian resource community and
functioning due to homogenization of environmental conditions among
these two habitats as the riverbed dries (H3, Fig. 1). Relationship
between riparian and instream communities and functioning may also be
stronger in isolated headwaters where riparian and instream habitats are
more environmentally similar (e.g. shading, moisture) and edge effects
more dominant than in more connected downstream sections (H4; e.g.
Harvey et al. 2019). We also hypothesised that the relationship between
communities and functioning may weaken with increasing flow
intermittence and isolation, potentially driven by mismatches (H5).

\textbf{Material and Methods}

\textbf{Study sites and environmental conditions}

In 2021, we monitored, across three seasons (Spring: March-April;
Summer: June-July and Autumn: November-December), 20 sites located in
the Albarine river catchment in France (Datry 2012, Gauthier et al.
2020). Although under an oceanic climate, the Albarine is subject to
natural flow intermittence due to a permeable karstic bedrock and porous
alluvial sediments that induce drying events (lasting from weeks to
months) in headwaters and in the main-channel (Fig. S1). The Albarine
has a total network length of 150 kilometers of which at least 37\% are
known to be intermittent (Fig. S1). The riparian zone is dominated by
deciduous trees such as ash (\emph{Fraxinus excelsior} ) and hazel
(\emph{Corylus avellana} ), although coniferous trees (e.g. \emph{Picea
abies} ) can dominate in the upper parts of the river. The 20 sampling
sites were selected based on a-priori knowledge of their flow regime in
order to represent a gradient of flow intermittence and equal
distribution of perennial and intermittent reaches among headwaters and
the mainstem (Fig. S1).

\textbf{Environmental conditions}

We estimated the number of dry/ponded and flowing days at each
intermittent site, using photo trap cameras, taking twice-daily picture
of the river channel. From these pictures we determined the flow
permanence (FP; proportion of days flowing during the study period) and
the total duration of flowing days during each sampling campaign at each
site. We also calculated spatial variables representing site
connectivity using geographic information system analyses and digitized
network maps that included information on the permanence regime of each
reach (i.e. intermittent vs. perennial), based on long-term observations
(Datry 2012, Gauthier et al. 2020). To estimate the site location within
the network we calculate the distance to the source (DS). We measured
distances to the nearest perennial site for intermittent reaches. To
estimate upstream connectivity; i.e. the amount of perennial reaches
potentially serving as colonization source, we calculated the length,
and the percentage of all perennial reaches upstream (\%UC) from each
site (see e.g. Sarremejane et al. 2017). For graphical representation
and analyses, sites were sometimes divided into flow regime categories:
perennial (FP = 1; n=10) and intermittent (FP \textless{} 1; n=10) and
into network location categories: headwaters (DS \textless{} 10km, n=14)
and mainstem (DS \textgreater{} 10km, n=6).

We also measured a set of environmental variables at each site and in
each season. When water was present, pH, conductivity and oxygen
concentrations instream were measured using a multiprobe (Hach) and
total dissolved nitrogen and soluble reactive phosphorus (SRP) were
measured from filtered water samples in the laboratory. Dissolved
nitrogen was determined in a TOC-Analyzer with Nitrogen-Analyzer
(Shimadzu) and soluble reactive phosphorous (SRP) was determined with
the molybdate blue reaction (spectrophotometer U-3900H, Hitachi). Flow
velocity was determined using a flowmeter at 10 to 30 (depending on
channel width) random locations at each reach. The wetted width was
averaged across 10 transects. Instream bryophyte and substrate (bedrock,
boulders, cobbles, gravel and sand) cover was determined visually.
Riparian soil and instream sediment samples were dried (70\selectlanguage{english}0C) and burnt
(5500\selectlanguage{english}C) to ashes in the laboratory to determine moisture and organic
matter content, respectively. In summer, we estimated canopy cover in
the instream and in the riparian area at each site, using a vertical
tube (15 cm diameter) at 10 random locations. We also identified all
ligneous plants located in the riparian area (\textless{}5m from the
stream) and estimated their relative density and the total tree richness
at each site. Temperature was recorded hourly for the duration of the
study instream and in the riparian area using iButton dataloggers (Maxim
Integrated Products, USA). Site altitude above sea level was also
determined from a digitized elevation model.

\textbf{Leaf litter quantity}

To estimate leaf-litter standing stocks, we sampled leaf material at 10
random locations using a 0.11 m\textsuperscript{2} quadrat, on the
riparian ground (5 on each bank, \textless{}1m from the bank top) and on
the streambed at each site and in each season. In riparian habitat and
on the streambed when dry, all visible detrital leaf material was
hand-picked within the quadrat. In flowing instream habitats, organic
matter was collected in a driftnet (mesh size = 500 \selectlanguage{greek}µ\selectlanguage{english}m) attached to
downstream side of the quadrat, after hand-disturbing the substrate to
suspend organic matter in the water column. For each habitat, the 10
samples were combined in the field and taken to the lab where they were
rinsed and cleaned from sediment, invertebrates, twigs and rocks.
Remaining leaf material was then oven-dried at 70\selectlanguage{ngerman}°C for 78h, and weighed
to estimate dry mass. A subsample was then burnt at 550°C to estimate
ash-free dry mass.

\textbf{Leaf litter quality}

In each campaign, additional leaf material was randomly hand-picked next
to each quadrat locations in the riparian and instream area, separately,
to estimate overall carbon and nitrogen content of each site's
habitat-specific leaf stocks. Samples were preserved in a freezer and
random sub-samples (approx., 10\% of original sample) were freeze dried
for one day, grinded into powder and encapsulated (0.005g ± 10\%) in tin
capsules. Nitrogen (\%N) and carbon (\%C) concentrations were determined
with an elemental analyser (FlashEA 1112 HT, Thermo Electron
Corporation), using aspartic acid (C: 36.09\%; N: 10.52\%) as a
calibration standard, and sorghum floor as quality control (C: 41.26 \%
± 0.20 ; N: 1.47\% ± 0.25).

\textbf{Leaf litter decomposition}

To measure leaf litter decomposition rates, we enclosed 4 ± 0.05 g and 2
± 0.05 g of dried Alder (\emph{Alnus glutinosa)} leaves in 1 cm mesh
(coarse-mesh) and 250 \selectlanguage{greek}μ\selectlanguage{english}m mesh (fine-mesh) bags, respectively. Where
coarse-mesh bags allowed invertebrate to enter and consume leaves,
fine-mesh bags only permitted microorganism-driven (i.e. by fungi and
bacteria) decomposition (e.g. Datry et al. 2011, Foulquier et al. 2015).
Alder leaves were collected in autumn 2020 from a single site, near
Chambery, France (approx. 80 km from the Albarine), air-dried inside the
laboratory and then oven-dried for 48h at 70\selectlanguage{ngerman}°C prior to being weighed
and enclosed into the bags. Six coarse- and six fine- mesh bags were
incubated for 29 \(\pm\) 4 days (mean\(\ \pm\) SD)
in the riparian (n=3 of each mesh-type) and instream (n=3) habitats at
each site and campaign.

At the end of the incubation period, bags were taken to the laboratory
where remaining leaves were cleaned from sediments and other organic
matter (e.g. invertebrates, twigs, other leaves). Remaining leaves were
then oven-dried and ashed as described in the ``\emph{Leaf litter
quantity''} section. Decomposition rates were calculated for each litter
bags based on the negative exponential decay model (K) corrected for the
duration of incubation and cumulative temperatures (i.e. degree days;
Swan et al. 2021). The decomposition rates were then averaged across the
three bags exposed in each habitat (instream vs. riparian) at each site
and in each season. Of the total 720 mesh bags incubated in the field
nine and four fine-mesh and six and two coarse-mesh bags were lost in
the instream and riparian habitats, respectively, due to floods mainly.

\textbf{Invertebrate community}

Aquatic and terrestrial invertebrates (size \textgreater{} 500 \selectlanguage{greek}μ\selectlanguage{english}m)
present in each coarse-mesh bag were picked, identified under the
microscope and counted in the lab. More than 15,000 individuals were
found, of which 86\% and 14\% were dominantly aquatic and terrestrial,
respectively. Taxa were mostly identified at genus and family taxonomic
levels (Table S1). We calculated taxa richness in each site and campaign
as the sum of taxa identified.

We used a fuzzy-coding approach to determine aquatic and terrestrial
taxa feeding habits from different bibliographic sources (Table S1).
Values of 1 to 3 were given to indicate weak to strong affinity with
each feeding category (e.g. leaf-shredder or grazer, Tachet et al. 2010)
to account for variability in feeding preferences within taxa (e.g.
feeding habits may change during taxon life cycles) and among species
within the same taxon. We then transformed this information into
percentage affinity for each feeding category. To calculate the
abundance of leaf-shredders in each sample we summed the products of
each taxon's percentage affinity to the ``leaf-shredder'' category with
its abundance. Trait information was missing for 39 taxa representing
0.2\% and 9.1\% of the total aquatic and terrestrial individuals,
respectively, which were thus not taken into account when calculating
leaf-shredder abundances. Most of those individuals (55.7\%) were ants,
which have very diverse feeding habits and little impact on leaf litter
decomposition. Total taxa richness and leaf-shredder abundances were
divided by the number of coarse-mesh bags retrieved in each habitat and
campaign to account for bag losses. One site, affected by a scouring
flood, was not sampled in summer.

\textbf{Statistical analyses}

Prior to running models and analyses, we used variance inflation factor
(VIF) analysis to assess multicollinearity among predictor variables,
and sequentially removed those with the highest VIF until all had a VIF
\textless{} 3 (Zuur et al. 2010). Total number of flowing day, distance
to the nearest perennial site, and the length of upstream perennial
reaches were collinear with other variables and were thus removed from
analyses. We also used Pearson's correlation coefficients to assess
correlations between FP and remaining connectivity variables (i.e. DS
and \%UC). To characterize site environmental conditions we performed a
principal component analysis (PCA), including all environmental
variables (except FP, DS and \%UC) averaged across season for each site.
We further used a permutational analyses of variance (PERMANOVA based on
Euclidean distance) to assess if environmental characteristics differed
among intermittent and perennial sites.

To address H1 and H2, we designed a set of linear mixed effect models
(Table 1) to estimate the effects of environmental, hydrological and
connectivity variables on the responses of the instream leaf stock
quantity (dry mass), leaf litter quality (\%N, \%C and C/N),
invertebrate community structure (taxa richness, and leaf-shredder
abundance) and decomposition rates driven by invertebrates (coarse-mesh
bags; CK) and microorganisms (fine-mesh bags: FK; Table 1 \& S2). To
test H3 and H4, in each model, we also included the riparian response
(equivalent to instream response; e.g. riparian CK for instream CK) and
its interaction with FP and DS as continuous predictor allowing us to
assess if relationship between instream and riparian processes changed
with flow intermittence and along the river network (Table 1). To test
H5, we included either richness or leaf-shredder abundance as predictor
and their interaction with FP and DS in CK models only, allowing us to
assess if the link between communities and functioning changed with
hydrological and spatial variables (Table 1 \& S2). Polynomial
regressions were in some case attempted, when data exploration revealed
clear non-linear relationships.

We used a model-averaging approach to select the most influential set of
predictors and to calculate parameter estimates and significance for
each response variables. This way, all potential combinations of
predictors were assessed but only parameters of models with a \selectlanguage{greek}Δ \selectlanguage{english}Akaike's
information criteria corrected for small-sample sizes (AICc) \textless{}
2 from the model with the lowest AICc were selected and their estimates
averaged (Anderson 2008). Pseudo Nagelkerke's R\textsuperscript{2} were
also calculated to estimate model goodness-of-fit (Nagelkerke 1991) and
averaged when more than one model was selected. To account for the
non-independence of samples from individual sites, we included site as a
random factors in each model. All predictors was centred around their
mean and scaled (i.e. divided by their standard deviation). In a few
cases, response variables were transformed, and outliers removed (Table
2) to ensure model assumptions of heteroscedasticity and normal
distribution of residuals. To reduce the disproportionate effect of a
few large DS values, DS was log-transformed in all models.

To address H3 and H4 in greater detail, we used another set of LMM to
determine if leaf stock characteristics, community composition and
decomposition differed between habitats (riparian vs. instream) and if
differences depended on site location (headwater vs. mainstem) and flow
regimes (intermittent vs. perennial). In these models, instream and
riparian responses were treated together as response variable and the
categorical variables habitat, location and flow regime and their
interactions were used as predictor. We also calculated pair-wise
Euclidean distances between 1) leaf resource characteristics (stock, C/N
and \%N) and 2) ecosystem functions (CK and FK), and 3) Bray-Curtis
dissimilarity between invertebrate communities to assess and visualize
differences in resource, functioning and communities among habitats,
locations and flow regimes. We used non-metric multidimensional scaling
(NMDS) to visualize community variability and PCA ordinations to
visualize resource and ecosystem function variability. We used PERMANOVA
to further assess if leaf stock characteristics, community composition
and decomposition among sites differed depending on their location
(headwater vs. mainstem), habitats (riparian vs. instream) and flow
regime (intermittent vs. perennial). We used ``season'' as a strata
argument to constrain permutations among seasons. We used post-hoc tests
when interactions between habitat and location or habitat and flow
regime were significant to identify which group combinations differed
from each other.

To further explore H5 and determine how the relationship between
instream invertebrate-driven decomposition (CK) and invertebrate
community changes with flow regime and locations we used a Procrustes
analysis, comparing the similarity between two matrices in
multidimensional space (Peres-Neto and Jackson 2001). For this, we
compared the pair-wise Euclidean distance between CK values and the
Bray-Curtis community dissimilarity matrix among perennial,
intermittent, headwater and mainstem groups, separately.

We used R version 3.5.0 (R Core Team~2018) for all analyses, including
the packages vegan (Oksanen et al. 2019) for PERMANOVA and Procrustes
analyses, lme4 (Bates et al. 2015) and MuMIn (Barto\selectlanguage{polish}ń \selectlanguage{english}2019) for model
building and averaging, usdm (Naimi et al. 2014) for VIF analyses.

\textbf{Results}

Site location within the network, i.e. distance to source (DS), was
uncorrelated to flow permanence (FP) and upstream connectivity (i.e. the
percentage of perennial upstream reaches (\%UC)), confirming that
perennial and intermittent sites were evenly distributed throughout the
river network (Fig S1 \& S2). Upstream connectivity was moderately
correlated with FP (r = 0.58, Fig. S1). Environmental characteristics
were similar among intermittent and perennial sites (PERMANOVA: F =
1.243, p = 0.259; Fig S3) but differed among network locations due to
differences in mean wetted width, instream canopy cover, pH, and soil
moisture and organic matter contents between headwaters and the mainstem
(PERMANOVA: F = 4.192, p = 0.001; Fig. S4).

\textbf{Effects of drying and connectivity on instream processes (H1 \&
H2)}

Instream leaf stocks decreased with FP and increased with \%UC. The
negative effect of FP on instream leaf stock was stronger in autumn than
in other campaigns (Table 2, Fig. 2a). Leaf C/N content increased (i.e.
quality decreased) with FP (Fig 2b), but mainly so during spring and
autumn, while it decreased with DS in spring and summer (Table 2, Fig
2c). \%N showed opposite patterns to C/N and likely drove changes in
C/N, whereas \%C increased with FP and \%UC (Table S3). Leaf C/N was
lower in summer and autumn than in spring, while leaf stock was higher
in autumn than in other seasons (Table 2).

Instream invertebrate richness and leaf-shredder abundance increased
with FP (Fig. 2d) and this effect was more pronounced as DS increased
particularly for richness (Fig2e) but less so for shredder abundances
(Table 2). Shredder abundance also increased with \%UC in spring and
autumn (Fig 2f), whereas richness decreased with increasing C/N, i.e. as
leaf quality decreased (Table 2).

CK increased with FP (Fig. S5) and this effect was mediated through
changes in shredder abundances, when those were included in the models
(Table 3). CK also increased with UC\% and data exploration revealed
that an orthogonal quadratic (Table S4) (i.e. bell shaped) regression
(AICc = -510.9) was a better fit than linear (AICc = -503.5),
particularly at low DS (i.e. among headwaters; Table S4, Fig 2g). An
even better set of models (AICc = - 518.0), including network location
(categorical) as interacting factor with DS (continuous), revealed that
CK increased with DS among headwaters only (Fig 2h; Table S4).
Decomposition rates in fine mesh bags (FK) increased with DS in summer
(Table 3, Fig. 2i) and slightly decreased with leaf-stock quantity.

\textbf{Relationship between instream and riparian processes H3 \& H4.}

Leaf stocks were higher, and C/N, shredder abundances, CK and FK lower,
in the riparian than in the instream habitat, respectively (Table S5).
Invertebrate richness did not differ between the instream and riparian
habitats, except at mainstem, where instream richness was higher than
riparian richness (Table S5).

The relationships between riparian and instream responses were
relatively weak but selected for most of the response variables, apart
from C/N (Table 2 \& 3). Although invertebrate community responses were
weak, instream invertebrate taxa richness tended to decrease and
increase with riparian richness at low and high DS, respectively (Table
2; Fig S6). Contrastingly, instream leaf-shredder abundances increased
with increasing riparian abundance, and more so as FP increased (Table
2; Fig S6). Instream CK increased with riparian CK but only among sites
with higher DS, i.e. the mainstem (Table 3, Fig. 3a). Instream FK was
positively related to riparian FK, particularly in spring and this
relationship increased as flow permanence decreased, i.e. among
intermittent sites (Table 3, Fig. 3b).

Leaf stock characteristics differed between network locations and
habitats (Table 4) and instream vs. riparian leaf stock characteristics
tended to be more similar in the mainstem (pairwise PERMANOVA: F =
3.096; p = 0.034) than in headwaters (F= 10.788, p = 0.001; Fig. 4a).
Invertebrate communities differed among habitats, locations and flow
regimes (Table 4); significant interactions indicated that communities
differed more among locations and flow regimes instream than in the
riparian area (Fig 4b, c). Decomposition differed among flow regimes and
habitats (Table 4) with greater differences among flow regimes instream
(F = 12.127; p \textless{} 0.001) than in the riparian zone (F = 0.078;
p = 0.760; Fig 4d).

\textbf{Relationships between instream communities and decomposition
(H5)}

Shredder abundance had a strong positive effect on instream CK and this
effect decreased with increasing DS and UC\% (Table 3, Fig. 5). Richness
was however not selected among the best predictors when included in
similar models. The multivariate variation in community was related with
the variability in CK among perennial sites (Procrustes; r = 0.431, p=
0.002) and headwaters (r = 0.438, p= 0.001), marginally so for mainstem
sites (r = 0.450, p = 0.06) but not among intermittent sites (r = 0.286,
p = 0.121).

\textbf{Discussion}

Recent microcosm experiments and model simulations have unravelled how
lateral (Harvey et al. 2020) and longitudinal (Jacquet et al. 2022a)
exchanges of resources can shape network-scale biodiversity and how
drying patterns may influence the dynamic of resource processing
(Catal\selectlanguage{ngerman}àn et al. 2022) and communities (Jacquet et al. 2022b) within
river networks. However, comprehensive field-based evidence is missing.
Our network-scale field study confirms the paramount effect of drying in
governing resource stocks, community composition, ecosystem functioning
and their relationships in river meta-ecosystems. As expected in H1,
flow intermittence had a positive effect on instream leaf quantity, but
a negative effect on shredder abundance and hence on organic matter
decomposition rates. Partially agreeing with H2, invertebrate
communities and decomposition rates changed throughout the river network
in response to upstream connectivity, but not always linearly.
Interestingly, invertebrate-driven decomposition peaked at intermediate
levels of upstream fragmentation, revealing the potential positive
effects of upstream drying on the functioning of downstream ecosystems.
Instream and riparian responses were generally weakly related but the
relationship between riparian and instream decomposition rates changed
with flow permanence (FK; H3) and network location (CK; H4), suggesting
that the links between riparian and instream ecosystem functioning can
change depending on the disturbance regime and network-scale
connectivity. Although invertebrate richness did not directly relate to
decomposition, community composition and shredder abundance did, and the
strength of these relationships was lower among intermittent and
headwater sites (H5), suggesting that flow intermittence and low
connectivity may induce mismatches between community composition and
ecosystem functioning.

Although the negative effects of flow intermittence on decomposition can
be linked to changes in leaf resource nutritional quality (del Campo et
al. 2021a), most field studies attribute these negative effects to
losses of primary consumers such as shredders (Datry et al. 2011,
Schlief and Mutz 2011, Abril et al. 2015, Northington and Webster 2017).
We found that increasing flow intermittence had a seasonal positive
effect on instream resource quality and quantity, but the negative
effects on invertebrate richness and shredder abundances drove the
associated decreases in decomposition. Our results echo those from
Catalàn et al. (2022), who demonstrated through simulations that coarse
particulate organic matter (e.g. leaves) tend to accumulate and remain
unprocessed -- thus conserving a higher reactivity -- on dry riverbeds.
Such accumulation can result from decreases in transport caused by low
or null water discharges and the absence or low abundance of efficient
decomposers such as aquatic shredders (Northington and Webster 2017). In
our study, decomposition was associated to invertebrate community
variability among perennial but not intermittent sites, suggesting that
flow intermittence may create mismatches between community structure and
ecosystem functions, here decomposition. Such mismatches may result from
the variable levels and paths of recovery communities are on in these
frequently disturbed environments, preventing communities from reaching
equilibrium and thus use available resources optimally (Fukami et al.
2010, Brose and Hillebrand 2016, Datry et al. 2016). Conversely, in
hydrologically more stable, perennial sites, communities are more likely
to be at equilibrium with their environment and their composition may
better reflect resource availability and uses. As commonly found in the
literature, microbially driven decomposition was little affected by flow
intermittence, likely as a result of the microorganisms' capacities to
(1) sustain activity during dry phases, especially if moisture is
preserved and (2) to recover their activity within days to weeks of flow
resumption (Foulquier et al. 2015, Arroita et al. 2018, Truchy et al.
2020).

River dendritic structure, i.e. multiple small streams branching into
larger rivers, promotes gradual increases in biodiversity (Finn et al.
2011, Altermatt 2013) and metabolic activity (Diamond et al. 2021) as
influx of organisms and resources (e.g. nutrients and dissolved organic
matter) increase with water discharge from up to downstream. Because of
this structure, communities in headwaters typically respond more
strongly to disturbance than in the mainstem where mass dispersal can
override the negative effect of disturbance (Tornwall et al. 2017).
However, responses to natural drying can contrast with this pattern due
to the fragmenting effect of drying, reducing connectivity and dispersal
even in the mainstem (Crabot et al. 2020, Gauthier et al. 2020). We
found that invertebrate richness responded more strongly to drying in
the mainstem than in headwaters. This bigger loss of species with
increasing intermittence in the mainstem sites may thus result from
reduced mass effects in this fragmented network and from the higher
richness typical of low-order perennial rivers, where communities have
more to lose when facing a disturbance than already species-poor
headwaters. We also found that invertebrate-driven decomposition
increased with the distance to the source and was related to shredder
abundance in headwaters only, agreeing with the theory that ecosystem
functioning and BEF relationships should peak at intermediate levels of
network connectivity (Leibold et al. 2017). Stronger relationship
between decomposition and community multidimensional variability in
headwaters than in mainstems may also reflect the high dependence of
smaller, shaded streams on leaf resources and the stronger community
specialization towards the use of leaf resource in the former.
Contrastingly, microorganism-driven decomposition and leaf litter
quality increased linearly with distance to the source, suggesting that
microorganisms activity may increase from upstream to downstream, at
least during the summer season, when microbial activity is likely
boosted by higher temperature (Friberg et al. 2009). This may in turn
increase leaf litter quality through microbial nitrogen immobilization
from the water column.

The effects of drying on communities and ecosystem functions are
relatively well documented at local reach scales (Datry et al. 2011,
Foulquier et al. 2015), whereas network-scale responses owing to
fragmentation remain overlooked, although evidence is emerging for fish
(Jaeger et al. 2014) and invertebrate communities (Gauthier et al. 2020,
Sarremejane et al. 2021). The proportion of perennial reaches upstream
had a positive effect on leaf stocks, shredder abundance and
invertebrate-driven decomposition, suggesting that connectivity to
upstream perennial habitats is key in determining downstream leaf
transfer (Catalàn et al. 2022), communities (Pineda-morante et al. 2022,
Fournier et al. 2023) and ecosystem functions (Harvey et al. 2017).
Interestingly, decomposition was higher when upstream connectivity was
intermediate, suggesting that intermediate extents of flow intermittence
upstream may promote higher functional rate in downstream habitats. In
rivers, where the flux of resources and organisms is directional (i.e.
dictated by the unidirectional flow of water), downstream ecosystem
functions may thus depend on the disturbance regime in -- and
connectivity to -- upstream habitats. An intermediate amount of upstream
intermittence could therefore promote downstream decomposition rates by
providing 1) pulses of high quality leaf resources and 2) influx of
diverse species best adapted to resource use, from a variety of upstream
habitats, following rewetting events (Northington and Webster 2017, del
Campo et al. 2021a, Catalàn et al. 2022).

Decomposition is usually faster in aquatic than in riparian habitats due
to moisture limitation hindering microbial activity and the presence of
less efficient and abundant detritivore invertebrate communities in the
latter (Hutchens and Wallace 2002, Tiegs et al. 2019, Lohse et al. 2020,
Simões et al. 2021). Accordingly, we found lower microorganism- and
invertebrate-driven decomposition, shredder abundance and invertebrate
richness in network-wide riparian habitats. We found weak evidence of
relationships between instream and riparian invertebrate communities and
changes along flow permanence gradients, suggesting that community
overlap between these habitats was limited. Although long dry phases
(i.e. \textgreater{} year) may lead to invertebrate community overlap
between riparian and instream habitats due to convergences in
environmental characteristics (Steward et al. 2022), the dry phases the
Albarine experienced in 2021 were likely too short (\textless{} 2
months) to promote such community homogenization. However, the
relationship between instream and riparian invertebrate-driven
decomposition, and more weakly so instream and riparian invertebrate
richness, changed with the distance to the source. The negative
relationships observed in headwaters may suggest a compensation
phenomenon where high invertebrate community richness and decomposing
activity in riparian habitat may promote low richness and decomposition
in adjacent instream habitats, and vice versa (Abelho and Descals 2019).
This may occur if riparian decomposers use high quality compounds,
leaving instream communities with lower quality resource entering
through runoff, though we did not observe a negative relationship
between leaf litter quality and quantity across both habitats.
Conversely, microbial-led decomposition instream and in riparian
habitats were positively related and this relationship was stronger
among intermittent sites, suggesting that the microbial decomposer
communities in both habitats may be related and more strongly so as flow
intermittence increases. Instream and riparian microbial communities are
likely to interact as suggested by compositional overlap between
riparian soil and instream bacterial communities (Ruiz-González et al.
2015) and between senescent leaf and instream leaf-litter fungal
communities (Koivusaari et al. 2019). Site-specific homogeneity in
environmental characteristics such as humidity may also explain
co-variability between riparian and instream microbial decomposition,
particularly in headwaters where tree canopy may help preserving
humidity in both the riparian and instream area.

\textbf{Conclusion}

Our results highlight the importance of flow intermittence on river
meta-ecosystem dynamics and particularly on network-scale coarse organic
matter cycling. Drying, by promoting the accumulation of unprocessed
leaf litter, can affect the river network carbon processing and budget,
modifying the timing and quantity of organic matter transport to
recipient ecosystems (e.g. lakes, flood plains and oceans; Datry et al.
2018, Catalàn et al. 2022). However, intermediate drying extent could
potentially stimulate ecosystem functions of downstream ecosystems. Our
results are also some of the first to unravel the links between aquatic
and terrestrial functioning in river networks from a large-scale field
study (Scherer-lorenzen et al. 2022). By identifying where, when and
under which conditions organic matter may accumulate and hotspots of
decomposition occur, our research will help better predict impacts of
climate change on biogeochemical cycles. As climate change exacerbate
river drying in space and time, accounting for flow intermittence and
its effects on both biodiversity and ecosystem functioning is also key
to inform management actions that support healthy, functional ecosystems
which deliver benefits to our societies, such as pollution attenuation
(via microbial nutrient processing) and climate regulation (via carbon
cycling; Palmer and Ruhi 2019).

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\begin{longtable}[]{@{}llllllll@{}}
\toprule
\begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
Instream response variables\strut
\end{minipage} & \begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
Instream response variables\strut
\end{minipage} & \begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
Instream response variables\strut
\end{minipage} & \begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
Instream response variables\strut
\end{minipage} & \begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
Instream response variables\strut
\end{minipage} & \begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
Instream response variables\strut
\end{minipage} & \begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
Hypothesis tested\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Resource\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Resource\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Community structure\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Community structure\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Functioning\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Functioning\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Predictor\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Leaf stock\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
C/N\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Taxa richness\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Shredder abundance\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Coarse-mesh bag K\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Fine-mesh bag K\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Campaign\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
x\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
x\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
x\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
x\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
x\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
x\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Flow permanence (FP)\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
x\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
x\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
x\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
x\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
x\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
x\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
H1\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Distance to the source (DS)\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
x\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
x\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
x\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
x\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
x\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
x\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
H2\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Upstream connectivity (UC\%)\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
x\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
x\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
x\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
x\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
x\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
x\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
H1, H2\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Riparian response (RR)\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
x\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
x\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
x\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
x\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
x\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
x\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
H3,H4\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Tree diversity\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
x\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
x\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Tree canopy cover\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
x\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Instream leaf stock\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
x\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
x\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
x\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
x\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Instream C/N\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
x\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
x\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
x\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
x\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Richness/ Shredder abundance (R/A)\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
x\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
H5\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Interactions\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Interactions\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Interactions\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Interactions\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Interactions\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Interactions\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Campaign * DS\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
x\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
x\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
x\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
x\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
x\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
x\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
H2\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Campaign * FP\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
x\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
x\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
x\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
x\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
x\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
x\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
H1\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Campaign * UC\%\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
x\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
x\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
x\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
x\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
x\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
x\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
H1, H2\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Campaign * RR\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
x\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
x\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
x\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
x\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
x\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
x\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
H3, H4\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
FP * DS\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
x\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
x\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
x\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
x\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
x\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
x\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
H1, H2\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
FP * UC\%\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
x\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
x\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
x\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
x\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
x\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
x\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
H1, H2\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
RR * FP\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
x\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
x\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
x\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
x\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
x\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
x\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
H3\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
RR * DS\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
x\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
x\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
x\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
x\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
x\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
x\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
H4\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
UC * DS\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
x\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
x\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
x\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
x\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
x\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
x\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
H3, H4\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
R/A * UC\%\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
x\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
H5\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
R/A * FP\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
x\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
H5\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
R/A * DS\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
x\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
H5\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table 1} : Set of predictors used in each global model,
subjected to model selection and averaging. Richness and shredder
abundance were each used as predictors for coarse-mesh bag decomposition
rates (K) in separate models.\selectlanguage{english}
\begin{longtable}[]{@{}llllllllll@{}}
\toprule
& & \textbf{Leaf stock} & \textbf{Leaf stock} & \textbf{C/N} &
\textbf{C/N} & \textbf{Taxa richness} & \textbf{Taxa richness} &
\textbf{Shredder abundance} & \textbf{Shredder abundance}\tabularnewline
\midrule
\endhead
& Transformation & log & log & & & & & log & log\tabularnewline
& Outlier removed & 2 & 2 & 3 & 3 & 3 & 3 & 3 & 3\tabularnewline
& No. models averaged & 7 & 7 & 2 & 2 & 5 & 5 & 8 & 8\tabularnewline
& Average adj. R\textsuperscript{2} & 0.823 & 0.823 & 0.788 & 0.788 &
0.688 & 0.688 & 0.756 & 0.756\tabularnewline
& Contrast & Est. & P & Est. & P & Est. & P & Est. & P\tabularnewline
Season & Spring - Summer & -0.064 & 0.633 & \textbf{-26.645} &
\textbf{\textless{}0.001} & \textbf{-1.901} & \textbf{0.008} &
\textbf{-0.500} & \textbf{0.025}\tabularnewline
& Spring - Autumn & \textbf{1.544} & \textbf{\textless{}0.001} &
\textbf{-25.465} & \textbf{\textless{}0.001} & \textbf{-2.609} &
\textbf{\textless{}0.001} & -0.331 & 0.126\tabularnewline
Flow permanence (FP) & & \textbf{-0.244} & \textbf{0.027} &
\textbf{8.071} & \textbf{0.002} & \textbf{2.038} &
\textbf{\textless{}0.001} & \textbf{1.064} &
\textbf{\textless{}0.001}\tabularnewline
Distance to source (DS) & & 0.069 & 0.342* & \textbf{-14.332} &
\textbf{\textless{}0.001} & 0.426 & 0.269 & -0.006 &
0.966*\tabularnewline
Upstream connectivity (UC\%) & & \textbf{0.224} & \textbf{0.030} & & & &
& 0.295 & 0.161\tabularnewline
Riparian response (RR) & & 0.100 & 0.105* & & & 0.016 & 0.933* & 0.140 &
0.213*\tabularnewline
Tree diversity & & 0.093 & 0.159* & & & & & &\tabularnewline
Instream C/N & & & & & & \textbf{-0.706} & \textbf{0.028} & 0.143 &
0.143*\tabularnewline
\textbf{Interactions} & \textbf{Interactions} & \textbf{Interactions} &
\textbf{Interactions} & \textbf{Interactions} & \textbf{Interactions} &
\textbf{Interactions} & \textbf{Interactions} & \textbf{Interactions} &
\textbf{Interactions}\tabularnewline
Season * DS & Spring - Summer & & & \emph{6.487} & \emph{0.059} &
\textbf{-1.094} & \textbf{0.005*} & &\tabularnewline
& Spring - Autumn & & & \textbf{11.028} & \textbf{0.001} & -0.607 &
0.131* & &\tabularnewline
Season * FP & Spring - Summer & 0.059 & 0.655 & \textbf{-7.978} &
\textbf{0.005} & -0.281 & 0.553 & &\tabularnewline
& Spring - Autumn & \textbf{-0.346} & \textbf{0.014} & \emph{-5.173} &
\emph{0.064} & 0.772 & 0.119 & &\tabularnewline
Season * UC\% & Spring - Summer & -0.059 & 0.668* & & & & &
\textbf{-0.565} & \textbf{0.012}\tabularnewline
& Spring - Autumn & \textbf{-0.374} & \textbf{0.005*} & & & & & 0.088 &
0.673\tabularnewline
FP * DS & & & & \emph{3.594} & \emph{0.074*} & \textbf{0.883} &
\textbf{0.007} & \textbf{0.341} & \textbf{0.041*}\tabularnewline
FP * UC\% & & & & & & & & 0.145 & 0.330*\tabularnewline
RR * FP & & & & & & 0.196 & 0.204* & \textbf{0.170} &
\textbf{0.028*}\tabularnewline
RR * DS & & & & & & \textbf{0.538} & \textbf{0.020*} & &\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table 2} : Results from LMM selection and averaging for resource
and community variables. Adjusted R\textsuperscript{2} (adj.
R\textsuperscript{2}), P values (P) and estimates (Est.) were averaged
among the models with a delta AICc \textless{} 2 from the best model.
The number of averaged models, the transformation used on response
variable, the number of outliers removed are also indicated. Bold and
italic values indicate P \textless{} 0.05 and P \textless{} 0.10,
respectively. * indicate variables that were selected in \textless{}
50\% of the averaged best model, which we considered as secondary and
less important.

\textbf{Table 3} : Results from LMM selection and averaging for
ecosystem functions, i.e. coarse- (CK) and fine- (FK) mesh bag
decomposition. Adjusted R\textsuperscript{2} (adj.
R\textsuperscript{2}), P values (P) and estimates (Est.) were averaged
among the models with a delta AICc \textless{} 2 from the best model.
The number of averaged models, the transformation used on response
variable, the number of outliers removed are also indicated. Bold values
indicate P \textless{} 0.05. * indicate variables that were selected in
\textless{} 50\% of the averaged best model, which we considered as
secondary and less important.\selectlanguage{english}
\begin{longtable}[]{@{}llllll@{}}
\toprule
& & \textbf{CK} & \textbf{CK} & \textbf{FK} & \textbf{FK}\tabularnewline
\midrule
\endhead
& Transformation & & & &\tabularnewline
& Outlier removed & 3 & 3 & 2 & 2\tabularnewline
& No. models averaged & 2 & 2 & 6 & 6\tabularnewline
& Average adj. R\textsuperscript{2} & 0.652 & 0.652 & 0.737 &
0.737\tabularnewline
& Contrast & Est. \selectlanguage{ngerman}× 10\textsuperscript{-3} & P & Est. \selectlanguage{ngerman}×
10\textsuperscript{-4} & P\tabularnewline
Season & Spring - Summer & & & \textbf{5.502} &
\textbf{\textless{}0.001}\tabularnewline
& Spring - Autumn & & & 0.693 & 0.672\tabularnewline
Flow permanence (FP) & & 0.793 & 0.275* & -0.490 & 0.526\tabularnewline
Distance to source (DS) & & 0.859 & 0.389 & -0.359 &
0.672\tabularnewline
Upstream connectivity (UC\%) & & \textbf{4.409} & \textbf{0.039} & 1.496
& 0.308\tabularnewline
Riparian response (RR) & & -0.149 & 0.625 & \textbf{3.246} &
\textbf{\textless{}0.001}\tabularnewline
Instream leaf stock & & & & \textbf{-1.024} &
\textbf{0.047}\tabularnewline
Shredder abundance (Sab) & & \textbf{1.098} & \textbf{0.007} &
&\tabularnewline
Season * DS & Spring - Summer & & & \textbf{8.223} &
\textbf{\textless{}0.001}\tabularnewline
& Spring - Autumn & & & 0.965 & 0.367\tabularnewline
Season * FP & Spring - Summer & & & 0.960 & 0.241*\tabularnewline
& Spring - Autumn & & & \textbf{2.456} & \textbf{0.009*}\tabularnewline
Season * RR & Spring - Summer & & & 2.101 & 0.255\tabularnewline
& Spring - Autumn & & & \textbf{-2.531} & \textbf{0.011}\tabularnewline
RR * FP & & & & \textbf{-1.413} & \textbf{0.008}\tabularnewline
RR * DS & & \textbf{1.087} & \textbf{0.002} & &\tabularnewline
UC\% * DS & & \textbf{5.430} & \textbf{0.015} & 2.543 &
0.103*\tabularnewline
Sab * FP & & & & &\tabularnewline
Sab * DS & & \textbf{-1.383} & \textbf{0.004} & &\tabularnewline
Sab * UC\% & & \textbf{-1.726} & \textbf{0.004} & &\tabularnewline
\bottomrule
\end{longtable}\selectlanguage{english}
\begin{longtable}[]{@{}lllllll@{}}
\toprule
& \textbf{Quality} & \textbf{Quality} & \textbf{Community} &
\textbf{Community} & \textbf{Functioning} &
\textbf{Functioning}\tabularnewline
\midrule
\endhead
& F & P & F & P & F & P\tabularnewline
Network location & \textbf{18.108} & \textbf{0.001} & \textbf{3.092} &
\textbf{0.002} & 2.427 & 0.088\tabularnewline
Habitat & \textbf{19.661} & \textbf{0.001} & \textbf{27.624} &
\textbf{0.001} & \textbf{71.860} & \textbf{0.001}\tabularnewline
Flow regime & 1.034 & 0.326 & \textbf{3.173} & \textbf{0.001} &
\textbf{7.314} & \textbf{0.002}\tabularnewline
Habitat * Network location & 1.209 & 0.249 & \textbf{2.818} &
\textbf{0.003} & 2.7403 & 0.103\tabularnewline
Habitat * Flow regime & 0.302 & 0.738 & \textbf{2.691} & \textbf{0.003}
& \textbf{10.149} & \textbf{0.003}\tabularnewline
Network location * Flow regime & \textbf{4.17} & \textbf{0.005} & 1.01 &
0.324 & 0.039 & 0.958\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table 4:} Results from permutational analyses of variance
(PERMANOVA)\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

\textbf{Fig. 1:} Conceptual figure illustrating the links between
resources, communities and functioning at the network scale (a); and the
effects of increasing flow intermittence (drying) and network
connectivity on instream resource quantity and quality, consumer
community diversity or abundance and functioning rates (b).
Intermittence and connectivity may also affect the corelation between
instream and riparian resource, community and functioning
characteristics (c) and the corelation between consumers and ecosystem
functioning (i.e. the biodiversity -- ecosystem functioning {[}BEF{]}
relationship, d)\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

\textbf{Fig. 2} : Responses of leaf resource characteristics (a-c),
invertebrate community structure (d-f) and ecosystem functions (g-i) to
flow permanence (FP), upstream proportion of perennial reaches (upstream
connectivity: UC\%) and upstream distance to the source (DS). Response
variables include: leaf stock mass (a), carbon to nitrogen leaf contents
(C/N; b-c), shredder abundances (d,f), invertebrate richness (e) and
coarse and fine mesh leaf bag decomposition rates (Coarse K and Fine K,
g-i). Full and dotted lines indicate significant and non-significant
relationships, respectively. Responses were grouped by seasons, flow
regime and locations to illustrate interactions. Regressions in (g) were
fitted with a quadratic polynomial function.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

\textbf{Fig. 3} : Relationships between instream and riparian
decomposition driven by invertebrates (CK; a) and microorganisms (FK; b)
across network locations and sites with different flow regimes.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

\textbf{Fig. 4:} Bidimensional ordinations showing variability in leaf
characteristics (a), invertebrate communities (b,c) and decomposition
rates (d) between instream and riparian habitats at sites with different
flow regimes (intermittent vs. perennial) and network locations
(intermittent and perennial). Only combinations showing significant
(PERMANOVA; see Table 4) differences in leaf characteristics, community
composition or decomposition between habitats are shown in this figure.
Principal component analyses (PCA; a, d) and non-metric multidimensional
scaling (NMDS; b, c) were used depending on data characteristics (see
Material and Methods section).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image5/image5}

\end{center}
\end{figure}

\textbf{Fig. 5:} Relationships between leaf-shredder abundance and
invertebrate-driven decomposition (Coarse K) among sites with different
levels of upstream connectivity (a) and network locations (b; headwater
vs. mainstem)

\selectlanguage{english}
\FloatBarrier
\end{document}

