# Complementarity between the CSR and plant economic space frameworks reveals selection of marsh plant community strategies in the Seine estuary

Markus Neupert, Michaël Aubert, Estelle Langlois

► To cite this version:

Markus Neupert, Michaël Aubert, Estelle Langlois. Complementarity between the CSR and plant economic space frameworks reveals selection of marsh plant community strategies in the Seine estuary. Estuarine, Coastal and Shelf Science, 2025, 315, pp.109158. 10.1016/j.ecss.2025.109158. hal-04924881

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Submitted on 1 Feb 2025

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# Complementarity between the CSR and plant economic space frameworks reveals selection of marsh plant community strategies in the Seine estuary

Markus Neupert* and Michaël Aubert, Estelle Langlois

Ecodiv USC INRAE 1499, University of Rouen Normandy, France

## ARTICLE INFO

### Keywords:

- Disturbance gradients

- Estuary

- Grassland management

- Grazing

- Mowing

- Oligohaline marshes

- Plant economic space

- Tidal restriction

## ABSTRACT

Estuarine marsh vegetation communities are subjected to steep abiotic gradients, shaping landscape and community composition through erosion and accretion dynamics, hydroperiod and salinity. In the context of climate change, these gradients are prone to change, with potential consequences on vegetation community structure.

We investigated plant traits and strategies in the oligohaline marshes of the highly anthropized Seine estuary. Infrastructures such as dykes and roads modify the local hydrodynamics through tidal restriction, thus changing the abiotic gradients. Across three sites with contrasting hydro-physico-chemical properties, we surveyed the vegetation communities along three topographical levels. Functional trait data from the TRY and GRoOT databases were applied to the vegetation surveys using community-weighted means.

Functional traits were linked to strategies of oligohaline marsh-communities, and have confirmed the elevation gradient to be the most contributory variable to community structure. Across the study sites, the community composition of the high marshes converged, while the low marsh communities of one restricted site diverged due to freshwater retention.

Community-level functional traits merged into two major axes of the plant economic space (PES), corresponding to resource conservation and resource acquisition. The deduced life strategies of plant communities within Grime's CSR framework allowed us to quantify stress and disturbance of each site: elevation was a good indicator of stress tolerance, while higher tolerance to disturbance was mostly associated with grazing. Tidal restriction was responsible for stretched stress- and disturbance-gradients, effectively expanding the area occupied by these ecosystems while the maximum stress- and disturbance levels were similar with or without tidal restriction.

## 1. Introduction

Plant functional traits are frequently used as indicators of organismal performance (Violle et al., 2007) and markers for ecological strategies selected by environmental constraints (Reich, 2014). These strategies reflect converging biological trait sets, and multiple frameworks have been proposed to link these traits to environmental conditions and ecological strategies. Assessing the relationship between environment and strategies in constrained ecosystems is particularly valuable in the current context of climate change, to predict possible changes in vegetation assemblages and the associated ecosystem processes (Pierce et al., 2017).

One of the most prominent strategy frameworks is the CSR theory proposed by Grime (1974, 1977), where competitor (C), stress tolerator (S) and ruderal (R) strategies reflect appropriate trait combinations,

deriving from the fundamental theory of r-selection and K-selection in disturbed environments (MacArthur and Wilson, 1967; Southwood, 1988). Developed on herbaceous species in Great Britain, many applications for this system have since been described outside of its initial geographical range (Cerabolini et al., 2010), and extended to more growth forms (Pierce et al., 2013). It has been successfully used to shed light on ecological processes such as dominance and coexistence (Rosado and de Mattos, 2017), environmental stress (Silva et al., 2018) and disturbance (Li and Shipley, 2017), cattle grazing (Wang et al., 2018), biological invasion (Guo et al., 2018), with spatial scales ranging from a few centimetres (Pierce et al., 2014), to landscapes (Kattenborn et al., 2017) and to world-wide biomes (Pierce et al., 2017). It might even be applicable outside of the plant kingdom (Pierce and Cerabolini, 2018).

A more recent framework in the functional classification systems is

<footnote>* Corresponding author.</footnote>

<footnote>E-mail address: markus.neupert1@univ-rouen.fr (M. Neupert).</footnote>
the plant economics spectrum (PES), arising from the trade-off between fast acquisitive, and slow conservative resource allocation (Díaz et al., 2016; Wright et al., 2004). The two extremities of this spectrum are formed by co-occurring functional traits of leaves (Wright et al., 2004), litter (Freschet et al., 2012), roots (de la Riva et al., 2018), wood (Chave et al., 2009) and whole plant traits (Blonder et al., 2015). Recent studies have combined the above- and belowground aspects, merging the leaf economics spectrum (LES) and the root economics spectrum (RES) into a three-dimensional integrated framework of plant form and function, where the three orthogonal axes of this system represent the fast-slow resource allocation gradient, the fungal collaboration gradient, and the plant size gradient (Klimešová and Herben, 2021; Weigelt et al., 2021).

Grime's CSR system and the plant economics spectrum (PES) arise from the same premise of resource economics and trade-offs: stress tolerant (S) species display a slow return on resource investment, and ruderal (R) species, the opposite, fast return on resource investment. Competitor (C) species form the orthogonal component to the slow-fast gradient, and are related to greater plant size. Both the CSR system and the PES summarize a multidimensional space of traits and functions into a reduced space of strategies. At global scale, Díaz et al. (2016) demonstrated that 74% of trait variation fits into the axes of plant size and fast-slow economics. Similarly, Pierce et al. (2017) obtained 60% of concordance between a 14 traits space and a reduced space of three traits representative of the CSR-triangle extremes (respectively leaf area LA, leaf dry matter content LDMC, and specific leaf area SLA). Few studies have investigated possible origins of these trait combinations, with mixed results: they might be the coincidental result of covarying environmental drivers (Lloyd et al., 2013), mathematical correlations (Osnas et al., 2013), physiological dependency (Pérez-Harguindeguy et al., 2013), genetic constraints (W. Chen et al., 2021; Donovan et al., 2011), or they could actually reflect plant strategies, resulting from environmental constraints selecting against unfit trait combinations (Laughlin and Messier, 2015; Zeballos et al., 2017). In fact, a recent study by Joswig et al. (2022) has linked 77% of global plant trait variation to climate and soil factors, along with further evidence for the existence of the global spectrum of plant form and function. However, Bruelheide et al. (2018) highlight the importance of local scale factors for functional trait composition.

Ecosystems with environmental gradients are thus ideal for the study of trait combinations. Indeed, plant species turnover reflects environmental filtering of strategies and trait covariation is expected to increase with increasing environmental harshness (Westoby and Wright, 2006). Some research efforts have been able to confirm this (Di Biase et al., 2021; Kurze et al., 2021; Zeballos et al., 2017), while others observed opposite tendencies with divergences between root and leaf traits (Weemstra et al., 2022), or an overall weak trait coordination (Silva et al., 2018). In addition to contradictory results, these functional frameworks have shown boundaries in some conditions: a decoupling between analogous root and leaf traits was observed along elevational gradients (Weemstra et al., 2022), nutrient gradients (Delpiano et al., 2020), and aridity gradients (Carvajal et al., 2019), which suggests that there are limits to the application of the global plant economics spectrum. Rosado and de Mattos (2017) found that CSR strategies can be insufficient to explain species dominance in settings with extreme conditions, and that the concept of a second filter, operating beyond the initial requirements for survival, might be needed to accurately explain species assemblages (Keddy, 1992). Additional complexity arises when comparing functional responses within or between species (Zhou et al., 2022), emphasizing the need for further investigation into the relationships between environmental factors, plant traits and plant strategies.

In this study, we have used the CSR and PES frameworks to investigate the functional assemblages of oligohaline salt marsh vegetation communities in northern France, along gradients of inundation frequency and salinity altered by human intervention. Oligohaline marshes

mark the boundary between marshes under the direct influence of saline water, and freshwater marshes still subjected to tidal forces, with an average annual salinity between 0.5 PSU and 5 PSU (Odum et al., 1984). The intertidal gradients of flooding and salinity in these systems are driving the zonation patterns observed in vegetation communities, and are strongly related to the elevational gradient (Pennings et al., 2005; Wasson et al., 2019). Under changing climatic conditions, these gradients are expected to shift, as are the numerous ecosystem services associated with oligohaline marshes (Millennium Ecosystem Assessment, 2005). Current infrastructure (e.g. dykes) widely used in these ecosystems has already tampered with flooding gradients, and provides the opportunity to compare marshes with restricted tidal influence, and marshes connected to the tidal regime.

In a context of oligohaline marshes either totally connected to the estuary's tidal regime or with tidal restriction brought about by infrastructure, a previous study of ours has revealed a strong alteration of soil parameters through tidal restriction, with significant effects on plant species turnover and community composition (Neupert et al., 2024). This present study builds upon these findings, and attempts to test if and how tidal restriction may select different trait combinations/strategies at the community-level. Within this overarching objective, we hypothesize that 1) higher environmental constraint, i.e. the effect of the flooding and salinity gradients, will select traits associated with a slow return on resource investment across study sites, and 2) that observed trait combinations can be integrated as strategies within the CSR and the PES framework. To test these hypotheses, we applied community-weighted mean (CWM, Díaz et al., 2007) trait values to oligohaline marsh plant communities, which help detect shifts in environmental filters along gradients (Ackerly and Cornwell, 2007), and take into account the disproportionate influence of more abundant species on ecosystem processes, as described by the mass-ratio hypothesis (Grime, 1998).

## 2. Material & methods

### 2.1. Study sites

The Seine estuary in northern France is a highly anthropized, macrotidal estuary in the temperate climate zone with a mean temperature of 11.5 °C (Météo France, 2023). With an annual average flow rate of 1400 m³ s⁻¹, the riverbed has been heavily channelized to accommodate the harbour activities of the cities Le Havre and Rouen (Lemoine and Verney, 2015). These alterations have spread to the neighbouring wetlands, with infrastructures such as dykes and culverts giving control over the water budget of these areas to permit agricultural activities, mainly mowing and livestock grazing. Despite these modifications, several thousand hectares of the Seine estuary and its wetlands have been granted the status of national nature reserve in 1997 (Réserves Naturelles de France, 2023).

In this context, we selected three sites within the nature reserve, belonging to the estuary's oligohaline marshes (Fig. 1). Within each site, three topographical levels were chosen for data acquisition, spanning an elevational gradient of 40 cm perpendicular to the riverbed which was determined using a digital elevation model of the Seine estuary (GIP Seine-Aval, 2012). Under most circumstances, the elevational gradient reflects the flooding duration of the study sites. However, two of the chosen study sites are embanked, and cut off from the tidal regime by a dyke equipped with watergates, allowing for a managed waterflow. The third site, on the opposite southern shore, serves as a control for tidal restriction, as the waterflow is unrestricted between the river and the wetlands. Due to constraints stemming from the national nature reserve status, farming activities and safety concerns in hunting areas, the land-uses of the final site selection were mostly out of our control.

Control site A, situated on the south shore, is grazed and inserted along the water salinity gradient of the estuary's riverbed at a level of approx. 15 PSU. Inserted at the same level of salinity, but on the north
<figure><img src="image_3.png" /><figcaption>Fig. 1. The study area within the Seine estuary in northern France (49°26'N, 0°13'E). The northern shore is disconnected from the Seine's tidal regime through the dyke (illustration from Neupert et al., 2024).</figcaption></figure>

shore behind the dyke, is study site B, which is subjected to tidal restriction and mowing with late season grazing. Closer to the estuary's mouth is study site C, that is restricted, mowed, with a water salinity of approx. 20 PSU. Note that the average annual salinity of the marshes is lower than that of the water in the riverbed. This difference is due to precipitation, inland freshwater runoff, and, depending on the study site, the distance from the river. Despite the higher water salinity, the salinity of our study sites falls within the bracket of oligohaline marshes. For instance, according to a previous cartography of the Seine estuary's soils, the three study sites have soil salinity values ranging from 0.48 PSU to 3.6 PSU (Maison de l'Estuaire, 2018a, p.67). We have carried out further soil analyses to describe the study sites' abiotic conditions (please see Neupert et al., 2024), which unexpectedly revealed study site C to have lower soil conductivity than study site B, due to a very localised freshwater upwelling anomaly (Soueid-Ahmed et al., 2017). Within the boundaries of the national nature reserve, grazing is allowed from April 1st to December 31st, and late season grazing starts in August; mowing is permitted to start from July 8th (Maison de l'Estuaire, 2018a, 2018b).

Heterogeneous weather data from the years prior to our study suggest increasingly variable temperatures and precipitation, between and within years. To account for this temporal variation, we surveyed the vegetation of the selected study sites for three years, from 2020 to 2022. Given the relative stability of these communities (the dyked communities have not been majorly disturbed since the dyke's construction in 1905), each year's inventory was considered a replicate in the following analyses.

## 2.2. Vegetation surveys

Vegetation surveys were carried out each year before the start of the authorized mowing period (starting July 8th). Using the Braun-Blanquet scale on each of the 9 study zones, we assessed species abundance in a 4 × 4 m square in a physiognomically homogeneous vegetation zone representative of each study zone, divided into 16 subplots of 1 m²; this square was placed approximately in the same location each year, resulting in three temporal replicates of the inventoried vegetation communities. The square's surface was chosen in accordance with the recommendations of Chytrý & Otýpková (2003), as it enables the detection of the least frequent species, and allows for the calculation of meaningful community metrics. Vegetation height was measured at ten random points within the sampling area. Biomass production was sampled at the peak of plant production in July, by gathering all

above-ground biomass within 40 × 40 cm square, replicated three times close to the survey area. The collected biomass was dried at 40 °C during 48h and weighed.

## 2.3. Functional traits

We requested functional trait data for the species we found in the vegetation inventories from the TRY-Database (Kattge et al., 2020) and the Global Root Traits (GRooT) database (Guerrero-Ramírez et al., 2021). The requested traits were related to growth performance and life strategies that would enable us to detect environmental filtering along the abiotic gradients of each study site. As there is a significant species turnover along the elevation gradient of the three sites (Neupert et al., 2024), we expect stronger between-species trait variation than within-species trait variation along the studied environmental gradients; while the actual trait values of the inventoried species might differ from those in the databases and change along the environmental gradients, the use of mean trait values should not obfuscate variation patterns at the community scale. After discarding traits with values for less than half of all the species encountered during our surveys, the following traits were used in the analyses presented in this paper: leaf C content, leaf N content, leaf dry matter content (LDMC), specific leaf area (SLA), mean root diameter, root tissue density, root C content, root N content, root mycorrhizal colonisation, specific root area and specific root length. These trait-data were completed with Ellenberg's Indicator Values (EIV; Salinity, Nitrogen, Light, Moisture, Temperature, pH) and Grime's CSR-strategies for each species. The qualitative CSR categories were transformed into numerical values for C, S and R by using their coordinates within the ternary diagram (e.g. C = 1C; CS = 1/2C + 1/2S; CSR = 1/3C + 1/3S + 1/3R).

The collected abundance and trait data were used to then compute the community weighted mean values for each trait across the 16 plots per survey (k) following the formula:

$$CWM_k = \sum a_{ik}t_i$$

with $a_{ik}$ being the relative abundance of species i in plot k and $t_i$ the trait value of species i.

## 2.4. Statistical analysis

Given the local constraints of the national nature reserve that led to a low-replication sampling design of nine sampling areas within three sites, we have chosen a multivariate data analysis approach mostly
based on visual inspection. To reveal the selected plant traits by the abiotic constraints of each of the nine study sites, Principal Component Analysis (PCA) (Pearson, 1901) was performed on the correlation matrix of the CWM-values of aboveground- and belowground-traits from the databases TRY and GRooT as well as our samples of biomass and plant height. The high heterogeneity in species richness between zones tended to skew the data in favor of low-richness zones, where the CWMs would be inflated due to few species presenting high class-mean percentages; to compensate this distortion, Braun-Blanquet's 5 classes were used as a semi-quantitative abundance variable rather than percentages, and the calculation of the CWMs was adjusted with the regional richness γ (number of unique species recorded across all study sites, equal to 66 species) using the following formula:

$$CWM_k = \sum \frac{a_{ik}}{\gamma} t_i$$

with $a_{ik}$ being the relative abundance of species i in plot k and $t_i$ the trait value of species i. The ratio $a_{ik}/\gamma$ represents the contribution of species i to the regional species richness γ and standardises the obtained CWM-values across zones, regardless of their overall species richness. To facilitate the interpretation and projection of points onto the two axes formed by the 12 trait-variables, the PCA was rotated using a Varimax transformation (Kaiser, 1958) to constrain the variable-conglomerates onto the principal component axes (corresponding to the x, y and z axes of the graphical output). To help with the interpretation of this analysis' output, various variables were included as supplementary variables in the final plot, such as Grime's CSR strategies, Ellenberg's Indicator Values (EIV) and soil parameters from our soil surveys (authors' personal data).

The inclusion of Grime's CSR values revealed the CSR triangle in the first two axes of the rotated PCA (x, y), in which the vector $\overrightarrow{CS}$ indicates increasing stress levels, and $\overrightarrow{CR}$ increasing disturbance levels (Keddy, 2001). The (x,y) coordinates of each study zone were orthogonally projected onto the x and y axes, as well as onto the vectors of the CSR-triangle representing stress ($\overrightarrow{CS}$) and disturbance ($\overrightarrow{CR}$). This projection yielded arbitrary values, expressed as percentages, quantifying the expression of each of the strategies in the observed communities, and the type of constraint they are subjected to (stress and/or disturbance). To explain these values, we fitted linear models with the strategy value as response variable and elevation, distance from the river and management type as explanatory variables.

All statistical analyses were carried out using R 4.2.2 (R-Core-Team, 2022). in RStudio (RStudio Team, 2020), with the *tidyverse* (Wickham et al., 2019), *magrittr* (Milton Bache and Wickham, 2022), *vegan* (Oksanen et al., 2022), *ade4* (Dray and Dufour, 2007) and *PCAmixdata* (Chavent et al., 2017) packages.

# 3. Results

## 3.1. Varimax transformed principal components analysis

The Varimax-transformed PCA of the adjusted CWMs of plant traits yielded the following results: across the three study sites, the high levels converged on one side of the plot, and the low levels diverged on the opposite side Fig. 2B). A grouping of the communities according to their land use type also reveals a clear separation of the mowed and the grazed sites, with the sites being mowed and then grazed later in the season bridging the gap between the two previous land use type (see Fig. 2D).

Out of the 12 trait-variables, 11 formed two strong and perpendicular axes (Fig. 2A): on the x axis (34.9% relative inertia), root N concentration, specific root area, leaf N, leaf C and mean root diameter are associated with an acquisition strategy in the leaf economic spectrum, and a collaboration strategy with fungal partners in the root economic

spectrum. High root tissue density, root C concentration, specific root length, LDMC, SLA and low plant height converged on the y-axis (32.7% relative inertia), are associated with a conservation strategy in the leaf economic spectrum, and a “do it yourself” strategy regarding fungal partners in the root economic spectrum. The z-axis (9.4% relative inertia) is upheld by biomass alone.

The CWMs of the CSR strategies were projected as supplementary variables, and formed a triangle in the x,y-plane (Fig. 2C). The plant communities of the high zones display a mostly competitive lifestrategy, while the lower zones are either stress-tolerant or ruderal communities. The acquisition axis (x) presents a strong negative correlation with the stress gradient sensu Grime ($\overrightarrow{CS}$), while the conservation axis (y) is strongly correlated with the disturbance gradient ($\overrightarrow{CR}$).

We further projected the CWMs of Ellenberg's Indicator Values as well as soil parameters we measured on our study sites (namely pH, conductivity, NO₃ content and total cation exchange capacity). There is a strong association between the R-strategy, the nitrogen EIV and NO₃ content, and a somewhat higher tolerance to salinity. The C-strategy is associated with increased plant height, higher cation exchange capacity and higher continentality and temperature sensu Ellenberg. Finally, the S-strategy is only associated with a higher Ellenberg's moisture value.

## 3.2. Regression after projection

After projection the scores of our study sites onto the conservation- and acquisition-axes, as well as onto the vectors representing stress ($\overrightarrow{CS}$) and disturbance ($\overrightarrow{CR}$) of the CSR-triangle (Fig. 2D), we performed a linear regression on the arbitrary percentages that resulted from the projection (Table 1). Higher acquisition within plant communities is mostly explained by management type: at identical elevation and distance from the river, acquisition increases significantly by 1957.34 ± 742.78 % in mowed sites compared to grazed ones. Elevation is another significant factor: for every 10 cm increase in elevation, acquisition increases by 19.60 ± 5.11 % (p = 6.79e-04). The huge increase due to mowing is however interacting with elevation and distance, since the positive effect of mowing on acquisition is reduced by -71.79 ± 26.40 % (p = 1.13e-02) for every 10 cm increase in elevation, and increased by 26.11 ± 9.23 % (p = 8.73e-03) for every 100 m increase in distance away from the river.

The conservation strategy of plant communities can also mostly be explained by land management type, with a -204.36 ± 41.96 % (p = 3.64e-05) decrease in mowed communities compared to grazed communities. The decrease of conservation is less important in the mixed management type, with a -166.31 ± 20.95 % (p = 5.48e-06) loss. Distance from the river does however increase the prevalence of conservation within grazed communities, by 49.92 ± 17.48 % (p = 7.86e-03) for every 100 m. The interaction between elevation and distance shows that for every 10 cm in elevation gain, the positive effect of distance on the conservation strategy is reduced by -1.18 ± 0.43 % (p = 1.09e-02).

Because of their high correlation, the results for the regression of stress-quantity are analogous (but of the opposite sign) to those of the acquisition strategy, and the results for the amount of disturbance are similar to those of the conservation strategy.

# 4. Discussion

## 4.1. Link between abiotic parameters and potential strategies of the plant communities

### 4.1.1. Plant traits reveal the plant economic spectrum at the community-level

Root and leaf traits converge, in a singular/joint community trait spectrum.

We hypothesized that estuarine abiotic parameters would select
<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Principal components analysis (PCA) performed on community-weighted means of traits related to resource management, transformed using a Varimax rotation. The Varimax rotation maximises the sum of variances of the squared loadings (correlations between variables and factors), which results in the most influential variables being matched to the x and y axes of the projected space. Loadings are provided in the supplemental material. A) CWM traits projected on the first two rotated axes of the PCA. The leaf traits correlated with the x-axis are related to resource acquisition strategies, and the root traits are related to collaboration with fungal symbionts. The leaf traits correlated with the y axis are related to resource conservation, and the root traits to a "do it yourself"-strategy, not relying on fungal partners. Both axes are major factors in the leaf economic space and the root economic space, into which the vegetation communities of the study sites can be projected. B) Coordinates of the CWMs of the vegetation communities in the Varimax-transformed PCA. The blue colour guide gives the topographical level of each community, and the bright coloured ellipses and centroids give the management type of the sites. C) Supplementary variables projected in the Varimax transformed PCA: Grime's CSR strategies (blue), Ellenberg's indicator values (red) and soil parameters measured on the study sites (green). Joining the extremities of the CSR-variables results in a triangle shape, akin to the Grime-triangle where the vector **CS** indicates increasing stress levels, and **CR** increasing disturbance levels. D) Orthogonal projection of the CWM-values onto the **CS** and **CR** vectors, to quantify stress- and disturbance-levels of the study sites. The projected points are represented in the raincloud plots in the margins, with stress-levels grouped by topographical level, and disturbance-levels grouped by management type.
Abbreviations: LDMC = leaf dry matter content; Leaf_N = leaf nitrogen content; Leaf_C = leaf carbon content; RC = root carbon content; RDiam = mean root diameter; RN = root nitrogen content; RTD = root tissue density; SLA = specific leaf area; SRA = specific root area; SRL = specific root length. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)</figcaption></figure>
<table><caption>**Table 1**
Regression coefficients of four linear models explaining the relationship that the response variables “stress vector CS”, “perturbation vector CR”, “acquisition” and “conservation” hold with the explaining variables “elevation”, “distance”, “management type” and their interactions. Insignificant coefficients have been intentionally left blank. The response variables' values have been obtained through orthogonal projection of the previously performed PCA's CWMs on each of the corresponding axes.</caption><thead><tr><th rowspan="2">Term (Baseline: Elevation 0, Distance 0, Grazed)</th><th colspan="3">Stress (CS)</th><th colspan="3">Perturbation (CR)</th><th colspan="3">Acquisition</th><th colspan="3">Conservation</th></tr><tr><th>estimate</th><th>std.error</th><th>stat</th><th>p.value</th><th>sig.</th><th>estimate</th><th>std.error</th><th>stat</th><th>p.value</th><th>sig.</th><th>estimate</th><th>std.error</th><th>stat</th><th>p.value</th><th>sig.</th></tr></thead><tbody><tr><td>(Intercept)</td><td>835.37</td><td>139.13</td><td>6.00</td><td>2.09E-06</td><td>***</td><td>-79.48</td><td>186.71</td><td>-0.43</td><td>6.73E-01</td><td>-687.33</td><td>171.86</td><td>-4.00</td><td>4.43E-04</td><td>***</td><td>157.90</td><td>159.69</td><td>0.99</td><td>3.31E-01</td></tr><tr><td>Elevation (10 cm steps)</td><td>-23.62</td><td>4.13</td><td>-5.71</td><td>4.53E-06</td><td>***</td><td>2.08</td><td>5.20</td><td>0.40</td><td>6.92E-01</td><td>19.60</td><td>5.11</td><td>3.84</td><td>6.79E-04</td><td>***</td><td>-4.47</td><td>4.44</td><td>-1.01</td><td>3.22E-01</td></tr><tr><td>Distance (100 m steps)</td><td>5.07</td><td>2.66</td><td>1.91</td><td>6.72E-02</td><td>.</td><td>51.01</td><td>20.44</td><td>2.50</td><td>1.85E-02</td><td>-1.30</td><td>3.28</td><td>-0.40</td><td>6.95E-01</td><td></td><td>49.92</td><td>17.48</td><td>2.86</td><td>7.86E-03</td><td>**</td></tr><tr><td>Management Mowed</td><td>-1716.48</td><td>601.30</td><td>-2.85</td><td>8.18E-03</td><td>**</td><td>-204.69</td><td>49.06</td><td>-4.17</td><td>2.50E-04</td><td>1957.34</td><td>742.78</td><td>2.64</td><td>1.38E-02</td><td>*</td><td>-204.36</td><td>41.96</td><td>-4.87</td><td>3.64E-05</td><td>***</td></tr><tr><td>Management Mowed + Grazed</td><td>1010.92</td><td>637.78</td><td>1.59</td><td>1.25E-01</td><td></td><td>-116.51</td><td>24.49</td><td>-4.76</td><td>4.98E-05</td><td>-281.98</td><td>787.85</td><td>-0.36</td><td>7.23E-01</td><td></td><td>-116.31</td><td>20.95</td><td>-5.55</td><td>5.48E-06</td><td>***</td></tr><tr><td>Elevation:Management Mowed</td><td>61.73</td><td>21.37</td><td>2.89</td><td>7.55E-03</td><td>**</td><td></td><td></td><td></td><td></td><td>-71.79</td><td>26.40</td><td>-2.72</td><td>1.13E-02</td><td>*</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Elevation:Management Mowed + Grazed</td><td>-28.89</td><td>17.69</td><td>-1.63</td><td>1.14E-01</td><td></td><td></td><td></td><td></td><td></td><td>7.75</td><td>21.86</td><td>0.35</td><td>7.26E-01</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Distance:Management Mowed</td><td>-25.07</td><td>7.47</td><td>-3.35</td><td>2.37E-03</td><td>**</td><td></td><td></td><td></td><td></td><td>26.11</td><td>9.23</td><td>2.83</td><td>8.73E-03</td><td>**</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Distance:Management Mowed + Grazed</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Elevation:Distance</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Adjusted R<sup>2</sup>: 0.7745</td><td></td><td></td><td></td><td></td><td></td><td>-1.21</td><td>0.51</td><td>-2.38</td><td>2.42E-02</td><td></td><td></td><td></td><td></td><td></td><td>-1.18</td><td>0.43</td><td>-2.72</td><td>1.09E-02</td><td>*</td></tr><tr><td>F-statistic: 17.68 on 7 and 27 DF, p-value:</td><td></td><td></td><td></td><td></td><td></td><td>Adjusted R<sup>2</sup>: 0.6152</td><td></td><td></td><td></td><td>Adjusted R<sup>2</sup>: 0.6590</td><td></td><td></td><td></td><td></td><td>Adjusted R<sup>2</sup>: 0.7165</td><td></td><td></td><td></td><td></td></tr><tr><td>1.428e-08</td><td></td><td></td><td></td><td></td><td></td><td>F-statistic: 11.87 on 5 and 29 DF, p-value:</td><td></td><td></td><td></td><td>F-statistic: 10.39 on 7 and 27 DF, p-value:</td><td></td><td></td><td></td><td></td><td>F-statistic: 18.19 on 5 and 29 DF, p-value:</td><td></td><td></td><td></td><td></td></tr></tbody></table>

different plant trait combinations illustrating fitting strategies within plant communities. With that objective in mind, plant trait databases have been used i) to identify potential life-strategies in the estuarine vegetation communities, and ii) to try to link them to the abiotic parameters of estuarine marshes. Our analysis has resulted in two major axes regrouping most plant traits (Fig. 2A): the first one is comprised of traits associated with increased resource acquisition and collaboration between plants and fungal partners, representing a fast return on resource investment, while the second one is comprised of traits linked to better resource conservation and a “do-it-yourself” approach, representing a slow return on resource investment (Bergmann et al., 2020; Reich, 2014; Wright et al., 2004). This sub-hypothesis is thus validated.

Most studies distinguish the conservation gradient of the leaf economics spectrum (LES) from the collaboration gradient of the root economics space (RES). Interestingly, our results show overlaying traits from both economic spaces, implying a correlation between root and leaf traits, which would be in line with findings affirming a convergence among traits to satisfy the metabolic supply chain (Reich, 2014). Also, rather than producing one single axis, the trait-variables form a two-dimensional plane; we assume that this is due to the use of CWM, suggesting that at the community-level, our estuarine vegetation associations present characteristics belonging to the fast and the slow investment strategies.

In this scope, we found the community of the high marshes to be mostly acquisitive/collaborative in nature, while the low marshes tend towards the conservative/do-it-yourself approach. Like previous results linking abiotic properties and vegetation community composition on these same study sites (Ref et al., 2024), the high marshes converge to present comparable characteristics, while the low marshes exhibit a much larger range, which is mostly driven by site C and its unique freshwater dynamics: as it turned out, the upwelling fresh water in provenance from the cray cliffs heavily dilutes the salinity constraint brought by the tides (Soueid-Ahmed et al., 2017). In addition to heavy rainfall and an undersized culvert (Maison de l'Estuaire, 2018a), this can result in long periods of freshwater retention on the low elevation levels of this site.

### 4.1.2. Plant strategies align with the stress gradient hypothesis

We linked these observations to the abiotic characteristics of our study sites by projecting Grime's competitor-stress tolerator-ruderal strategy scores for the studied communities as supplementary variables in this analysis, as well as Ellenberg's Indicator Values and soil characteristics measured at the study sites. The extremities of the C, S and R scores projected the CSR-triangle into the two dimensions of the transformed PCA, which allows for an arbitrary quantification of the stress- and disturbance-values along the topographical gradient of our study sites, confirm the increasing stress-tolerance of the vegetation communities towards the low marshes.

Our most stress tolerant communities, as suggested by the S-score, are the low marshes of site C; the Ellenberg moisture value confirms the freshwater supply and peculiar ability of this study site to retain said freshwater, and to therefore host appropriate, water-logging tolerant plant communities. As expected, the overall S-score declines towards the higher marshes, indicating milder conditions, and providing further evidence for the elevation gradient as a proxy of estuarine constraints. The communities presenting the most competitive species are defined by a high C-score, and are essentially the high marshes of the restricted sites B and C. The competition gradient is also opposed to the conservation/ do-it-yourself strategy; in the sense where conservation is a survival- strategy in harsh conditions, this result is in line with the stress- gradient hypothesis (Bertness and Callaway, 1994) which predicts more frequent competitive interactions between plant species in benign abiotic conditions. This aligns with a substantial body of literature linking marsh plant distribution to abiotic constraints in lower marshes, where physical tolerances prevent the persistence of competitive species in favor of stress tolerant ones (Adams, 1963; Angiolini et al., 2013; Hou
et al., 2020). In contrast, the more benign conditions of high marshes enable the establishment of these species, delineating the boundary for the distribution of slow-growing, stress-tolerant species (Bertness and Ellison, 1987; Engels and Jensen, 2010; Pennings et al., 2005; Snow and Vince, 1984). The C-score is further correlated to average plant height, an indicator of competition for light, and total cation exchange capacity, indicating high nutrient availability. This aligns with findings of Craine et al. (2001) and Daou et al. (2021), who found an overall decrease in LDMC with increasing soil fertility. Finally, the R-score mostly separates the grazed sites from the mowed sites, which agrees with the definition of “disturbance” being damaging to the vegetation (Grime, 1974). The R-score is also somewhat correlated to Ellenberg’s salinity tolerance, which suggests that saltwater intrusions might be a source of disturbance.

This allows us to validate the second sub-hypothesis: the observed trait combinations can be integrated as strategies into the CSR and the PES framework.

### 4.1.3. Management type selects different life strategies of the plant communities

It has previously been shown that abiotic stress and interactions within the plant communities are insufficient to explain marsh zonation (Silvestri et al., 2005), and that herbivores affect individual growth performance and community composition of salt marsh plant communities (He et al., 2015; Levin et al., 2002). In the case of grazing pressure from livestock, plant community composition may be altered through trampling, compacting the soil and roughening the topsoil, thus increasing the frequency and duration of waterlogging (Dausse et al., 2012), as well as habitat heterogeneity, which is known to raise plant species richness (Davidson et al., 2017; Gillet et al., 2010). The effect of grazing might however interact with abiotic properties, salinity for instance, and decrease plant diversity (Bakker, 1989), while also possibly promoting halophytes through the joint effect of trampling and high soil salinity (van Klink et al., 2015). A recent long-term study by Q. Chen et al. (2021) provided evidence for different successional trajectories of plant communities under different management regimes, including grazing and mowing, and found that land abandonment resulted in significantly decreased plant diversity.

Due to the surprising freshwater dynamics of our mowed study site C, we are not able to directly compare the abiotic characteristics between grazed and mowed sites. However, the potential life strategies of the studied plant communities revealed that there was a significant increase in ruderality in the grazed communities compared to the mowed ones (205 %), which is consistent with findings from Olff and Ritchie (1998) who found that long-term grazing may promote the dominance of grazing-tolerant and grazing-resistant species. Compared to the effect of mowing, cattle grazing causes further disturbances through trampling and soil compaction, which is exacerbated by high soil humidity (Elschot et al., 2023; van Klink et al., 2015), leading in turn to increased heterogeneity and the formation of persistent water pools (Q. Chen et al., 2021), thus promoting ruderal species. The communities of study site B, with mowing and late season grazing, also presented a reduction of ruderality compared to grazing, albeit not as strong as the exclusively mowed site. Since we found resource conservation to be highly correlated to disturbance, grazing therefore promoted plants with conservative plant traits.

Mowing significantly reduced stress-tolerance in plant communities. We also found that mowing attenuated the decrease in stress-tolerance with increasing distance from the river; in other words, higher stress levels were maintained over longer distances on the mowed restricted site. However, this could also be explained by the tidal restriction of this site, as well as its unique capacity of freshwater retention.

## 4.2. Tidal restriction lengthens gradients: implications and perspective

### 4.2.1. Functioning of restricted wetlands, and ecological conservation

Compared to control site A, the tidal restriction of sites B and C seems to have resulted in stress gradients (and the associated acquisition strategy of plant communities) covering longer distances; where the extremities of the stress-gradient of control site A are separated by approximately 300 m, equivalent stress-values are separated by ~1000 m on both sites B and C. Since disturbance (and the correlated conservation strategy of plant communities) is almost exclusively linked to grazing, we did not observe longer disturbance gradients with tidal restriction.

The lengthening of the stress gradient as a consequence of tidal restriction might be explained by the man-made drainage systems behind the dyke responsible for the tidal restriction. Indeed, the ditches responsible for drainage also carry the water from the river further inland, and might therefore propagate the constraints linked to water salinity and the nutrients it carries. This joins the study of Hartmann and Stock (2019), who have observed four times shorter drainage systems in naturally grown marshes, opposed to the dense drainage structures in man-made marshes.

In other cases, like our study site C, tidal restriction can be responsible for freshwater retention and lower salinity (Emery and Fulweiler, 2017; Roman and Burdick, 2012), therefore shifting the plant communities there towards freshwater or brackish wetland communities (Warren et al., 2002). Our results indicate indeed very different species on site C, with an overall higher tolerance to stress and an affinity for high moisture, as indicated by Ellenberg's Indicator Values. While we expected C to present higher constraints of salinity due to its closer positioning towards the river mouth, the difference between sites B and C might not only be explained by the unexpected upwelling of freshwater on site C, but also by different culvert-size - area-to-drain ratios, which may be responsible for the poor evacuation of excess water on site C (Maison de l'Estuaire, 2018a). In a way, "tidal restriction" transpires as an umbrella concept, comprising localised complexities reaching beyond the altered tidal forces, which should be considered in future research about tidal marshes and their response to modified abiotic constraints.

## 4.3. Limitations

This study has used mean trait values from public databases. At the small spatial scale presented here, this might lead to inaccuracies in interpretation (Albert et al., 2012), and underestimate actual community trait response to environmental gradients (Cordlandwehr et al., 2013). The results from this study operate under the assumption that between-species trait variation is higher than intraspecific trait variation, which has been proven true in many cases (Garnier et al., 2001; Shipley et al., 2006), but may also obfuscate biologically meaningful processes (Westerband et al., 2021).

In addition, the sampling design of this study is the result of many constraints: land protection status, small spatial gradients, highly diverse and sometimes misreported land use, as well as unforeseen local heterogeneity in environmental parameters (Neupert et al., 2024) have led to few replications of the studied communities, with data analysis relying heavily on visual interpretation.

Despite these limitations, this study validates the trait convergence into axes of the plant economic space in estuarine marsh plant communities and highlights a potential complementarity with Grime's CSR framework. In ecosystems subjected to stress and disturbance, this provides interesting perspectives for quantifying said stress and disturbance gradients.
## CRediT authorship contribution statement

**Marksupert:** Writing – review & editing, Writing – original draft, Validation, Supervision, Methodology, Conceptualization.
**Michaël Aubert:** Writing – review & editing, Writing – original draft, Visualization, Methodology, Investigation, Formal analysis, Data curation, Conceptualization.
**Estelle Langlois:** Writing – review & editing, Writing – original draft, Validation, Supervision, Resources, Project administration, Methodology, Investigation, Funding acquisition, Conceptualization.

## Funding information

This study was financially supported by the GIP SA (Groupement d'Intérêt Public – Seine-Aval) [E.L.] and a Ph.D. grant from the University of Rouen Normandy [M.N.].

## Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Acknowledgements

For their expert field-knowledge and substantial help with site selection and data acquisition, we thank the Maison de l'Estuaire, in particular Thomas Lecarpentier and Marion Girondel. We thank our colleagues from the Ecodiv lab for their input at various stages of this study, and especially Philippe Delporte, Sylvaine Buquet and Evelyn Blanchard for their essential assistance with the soil analyses.

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.ecss.2025.109158.

## Data availability

Data will be made available on request.

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