# Understory plant indicator values poorly perform at monitoring temporal changes in French forest soil chemical properties

Robinson Ribémont, Jean-Luc Dupouey, Benoît Richard, Jonathan Lenoir, Iris Le Roncé, Vincent Boulanger, Didier Alard, Thérèse Beaufils, Carole Begeot, Sylvaine Camaret, et al.

► To cite this version:

Robinson Ribémont, Jean-Luc Dupouey, Benoît Richard, Jonathan Lenoir, Iris Le Roncé, et al.  
Understory plant indicator values poorly perform at monitoring temporal changes in French forest soil chemical properties. Ecological Indicators, 2026, 182, pp.114490. 10.1016/j.ecolind.2025.114490.  
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# Understory plant indicator values poorly perform at monitoring temporal changes in French forest soil chemical properties

Robinson Ribémont<sup>a, *, †</sup>, Jean-Luc Dupouey<sup>b</sup>, Benoît Richard<sup>c</sup>, Jonathan Lenoir<sup>d</sup>, Iris Le Roncé<sup>e</sup>, Vincent Boulanger<sup>f</sup>, Didier Alard<sup>g</sup>, Thérèse Beaufils<sup>h</sup>, Carole Begeot<sup>h</sup>, Sylvaine Camaret<sup>i</sup>, Déborah Closset-Kopp<sup>d</sup>, Emmanuel Corcket<sup>j</sup>, Guillaume Decocq<sup>d</sup>, Richard Eynard-Machet<sup>k</sup>, François Gillet<sup>h</sup>, Arnaud Mouly<sup>h</sup>, Pascale Ruffaldi<sup>h</sup>, Fabien Spicher<sup>d</sup>, Erwin Ulrich<sup>l</sup>, Manuel Nicolas<sup>f</sup>, Estelle Langlois<sup>a</sup>, Michaël Aubert<sup>a</sup>

## ARTICLE INFO

### Keywords:

- Bioindication

- Long-term monitoring

- Understory plant community

- Edaphic condition

- Soil pH

- Soil C/N ratio

- Spatial gradients

- Temporal dynamics

## ABSTRACT

Understory plant communities are widely used to infer soil conditions through species indicator values (IVs), scores reflecting species' ecological preferences for factors such as soil acidity, moisture, or nutrient levels. While their reliability to describe soil conditions along spatial gradients is well established, their ability to capture temporal changes in soil chemistry remains largely untested at both temporal and geographical scales. We combined 26 years of vegetation monitoring with two French national soil surveys (1993–1997; 2007–2012) across 102 permanent forest plots to assess the reliability of community soil indices (CSIs) as bioindicators of soil pH, carbon-to-nitrogen ratio (C/N), and extractable phosphorus. CSIs were computed by averaging species IVs for each plot and survey. Temporal dynamics of measured soil properties and CSIs were analyzed using mixed-effects models at the national scale, while local trends were estimated separately for each plot to directly compare measured and vegetation-inferred changes. CSIs showed strong spatial correlations with measured pH and C/N, but weaker relationships for phosphorus. In contrast, no significant coupling was found between temporal changes in CSIs and measured soil parameters, with frequent mismatches in both direction and magnitude at the plot level. Measured soil dynamics were mainly driven by initial edaphic conditions and stand age, whereas CSI dynamics responded primarily to canopy openness and anthropogenic disturbances such as tree harvesting. These findings confirm the value of IVs for spatial bioindication but highlight their limited sensitivity for detecting long-term soil changes, especially where soil changes remain modest.

## 1. Introduction

In forest ecosystems, understory vegetation — comprising plant species that grow beneath the main forest canopy but above the forest

floor such as herbaceous plants, low shrubs, or woody chamaephytes (Gilliam and Roberts, 2003) — serves as a key indicator of environmental conditions (Diekmann and Dupré, 1997; Gilliam, 2007; Verstraeten et al., 2013). Because plant occurrences and local abundances

<footnote><sup>a</sup> ECODIV USC INRAE 1499, Université de Rouen Normandie, France</footnote>

<footnote><sup>b</sup> INRAE-UMR Silva, Champenoux, France</footnote>

<footnote><sup>c</sup> Office Français de la Biodiversité, DR Normandie, France</footnote>

<footnote><sup>d</sup> UMR CNRS 7058 « Ecologie et Dynamique des Systèmes Anthropisés » (EDYSAN), Université de Picardie Jules Verne, Amiens, France</footnote>

<footnote><sup>e</sup> Office National des Forêts, Unité territoriale, Grenoble, France</footnote>

<footnote><sup>f</sup> Office National des Forêts, Département RDI, Fontainebleau, France</footnote>

<footnote><sup>g</sup> Université de Bordeaux, INRAE, BIOGECO, Pessac, France</footnote>

<footnote><sup>h</sup> UMR CNRS 6249 Chrono-Environnement, Université Marie et Louis Pasteur, Besançon, France</footnote>

<footnote><sup>i</sup> UFR LLSH, Université Savoie Mont Blanc, Domaine Universitaire de Jacob-Bellecombette, Chambéry, France</footnote>

<footnote><sup>j</sup> IMBE, Aix Marseille Univ, Avignon Univ., CNRS, IRD, Marseille, France</footnote>

<footnote><sup>k</sup> e-Sciences, Cognin, France</footnote>

<footnote><sup>l</sup> Office National des Forêts, Direction Forêt et Adaptation aux Changements Climatiques, Maisons Alfort, France</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail address: robinson.ribemont1@univ-rouen.fr (R. Ribémont).</footnote>
are closely tied to environmental conditions through niche theory (Hutchinson, 1957), the species composition of plant communities is widely used to infer local environmental conditions. This approach offers a reliable and cost-effective alternative to direct measurements (Diekmann and Dupré, 1997; Diekmann, 2003). In his seminal work, Ellenberg (1974) introduced the concept of plant species “Indicator Values” (IVs), which assign an ordinal score — based on expert knowledge from field observations and experimental measurements — to each species according to its ecological preferences for factors such as soil acidity, moisture, or nutrient levels. Initially developed from vegetation surveys in Central Europe, these IVs were later refined through niche modelling — such as the IVs from Dengler et al. (2023) — and regionally calibrated to better fit other contexts across Europe, leading to databases such as CATMINAT (Julve, 2009) or the EcoPlant IVs (Gégout et al., 2005), which are specifically adapted for vegetation in French metropolitan managed forest. IVs, available for many plant species, allow indirect estimation of local environmental conditions by averaging the values of species co-occurring within the same plant community (Diekmann, 2003).

For instance, the IVs of forest understory plant species have been used to model trophic status and map soil pH across French forests (de Tillesse and Devillez, 1995; Coudun and Gégout, 2005), as well as to assess light availability and soil conditions in Carpathian woodlands (Dzwonko, 2001). Understory vegetation was also widely used to monitor soil acidification and nutrient imbalances, especially as nitrogen (N) and sulfur (S) dispositions, both having significantly influenced temperate forest dynamics since the late 1980s (de Vries et al., 2014; Wang et al., 2022). As bioindicators of temporal environmental change, understory dynamics are assumed to reflect shifts in forest ecosystem health (Wittig, 1992; Thimonier et al., 1994; Gilliam, 2007; Boulanger et al., 2015). For example, Riofrío-Dillon et al. (2012) used EcoPlant IVs to reconstruct long-term changes in soil acidity across French forests, while Sebesta et al. (2011) applied Ellenberg's IVs to track soil acidification in Ukrainian forests over six decades.

While the reliability of plant IVs as bioindicators along spatial gradients is well established through correlative-based models relating community weighted means of plant IVs to actual field measurements (Wamelink et al., 2002; Gégout et al., 2003, 2007, 2008; Carpenter and Goodenough, 2014), their reliability over time remains largely untested. Few studies have compared temporal changes in environmental variables obtained from field measurements with those inferred by vegetation resurveys relying on plant IVs, particularly in forest ecosystems (Van Calster et al., 2007; Baeten et al., 2009; Dölle and Schmidt, 2009; McGovern et al., 2011). When such comparisons have been made, they typically focused on overall trends — that is, testing whether there was a good match in the direction of change (a decrease vs. an increase) — rather than assessing whether the magnitude of change estimated from bioindication was coupled with that estimated from empirical field measurements. Some studies have shown a consistent decrease in soil acidity or increase in nutrient availability, as bioindicated by vegetation trends, such as in the Meerdal Forest in Belgium (Baeten et al., 2009), or across British grasslands (McGovern et al., 2011). However, others reported a complete disconnect between measured temporal changes in soil parameters and trends obtained through bioindication, as observed for soil acidity in the Tournibus Forest in Belgium (Van Calster et al., 2007), or across German deciduous forests (Dölle and Schmidt, 2009). Moreover, these studies often have limited scope, focusing on small areas or semi-permanent plots with widely spaced sampling dates — for example, Van Calster et al. (2007) only compared data from 1967 and 2005, while Dölle and Schmidt (2009) worked on 10 plots within the Göttingen Experimental Garden. They also tended to overlook potential confounding factors, beyond soil conditions, that might influence understory composition — such as stand characteristics, management practices, and disruptive natural events (e.g., storms, fires) (Bricca et al., 2023). Canopy disturbances induced by such events may alter light availability and soil moisture in the forest understory (Gálhidy et al.,

2006), potentially favouring early-successional and light-demanding species (Ares et al., 2009), thereby affecting the coupling between measured changes in soil characteristics and changes inferred from community-mean IVs.

To our knowledge, only Richard et al. (2021) conducted a pioneering large-scale (Metropolitan France), long-term (1995–2017) comparison between changes in measured annual mean temperature and thermophilization rates inferred from multiple resurveys (i.e., more than three resurveys) of understory plant species composition. Their study revealed a lag between the temporal dynamics of measured temperature and those inferred from bioindication, with the rate of change in community-inferred temperature being three times slower than that estimated from weather stations — a lag partly influenced by stand characteristics and anthropogenic disturbances. However, a similar assessment is still lacking for soil conditions: to date, no study has explicitly tested whether temporal changes in soil properties inferred from changes in plant community composition lag behind those observed through direct field measurements. This gap is all the more critical as recent results by Saenger et al. (2024) have shown a significant increase in the C/N ratio in the soils of French forests. Given the slow and complex dynamics of temperate forest ecosystems (Gilliam, 2007) and the large-scale nature of environmental changes (Lindenmayer et al., 2022), standardized, large-scale, and long-term monitoring of both vegetation and soil is therefore crucial to evaluate the degree of coupling between observed environmental changes and those inferred from plant community shifts (Bakker et al., 1996).

In this context, the present study, backed by 26 years monitoring of both edaphic characterisation and vegetation inventories on permanent forest plots, aims to: (i) evaluate the capacity of forest understory plant species to infer variation in soil chemistry across the broad spatial extent of metropolitan France; (ii) assess the reliability of permanent vegetation plots to monitor changes in soil chemistry over a 26-year period; and (iii) identify the determinants influencing the degree of coupling between observed (i.e., from field measurements) and inferred (i.e., from community-mean IVs) changes in soil chemistry. We hypothesize that community-inferred soil chemistry provides a reliable proxy for both the spatial pattern and temporal dynamics of measured soil conditions across France, with the strength of coupling varying by soil parameter and sampling depth. As Indicator Values (IVs) for soil pH and soil C/N ratio exhibit strong spatial concordance with these soil parameters (Riofrío-Dillon, 2013), we expect the understory vegetation to track long-term changes in soil C/N — a major trend recently documented in French forests (Saenger et al., 2024). Because plant-available phosphorus is difficult to quantify and showed little variation across the network, a decoupling between soil phosphorus availability and plant indicator values is hypothesized. We anticipate that these spatial and temporal couplings will be stronger in topsoil layers, where vegetation-based indicators are typically calibrated. Finally, we hypothesize that temporal coupling may further depend on stand characteristics, disturbance regimes, and canopy openness.

To address these objectives, we used floristic inventories and soil data collected between 1993 and 2021 on permanent forest plots from the RENECOFOR monitoring program, the French component of the European International Co-operative Programme on the Assessment and Monitoring of Air Pollution Effects on Forests (ICP Forests). Established in 1985, ICP Forests monitors key indicators of forest ecosystem health — including soil chemistry, understory plant species composition, meteorological conditions, stand structure, and disturbance history — through standardized protocols applied at varying intervals, from continuous to decadal monitoring. This long-term and multi-scalar dataset provides a robust basis for assessing the correspondence between trends in community-mean IVs and directly measured environmental changes.
## 2. Materials and methods

### 2.1. Long-term monitoring of forest ecosystems across a national network

#### 2.1.1. Renecofor

Established in 1992 by the French National Forest Office (ONF), the RENECOFOR network is a national long-term monitoring program consisting of 102 permanent plots distributed across metropolitan France (Fig. S1, supplementary material). These plots represent diverse ecological contexts and include forests dominated by tree species such as *Fagus sylvatica*, *Quercus petraea*, *Quercus robur*, *Pinus sylvestris*, *Pinus nigra* var. *corsicana*, *Pinus pinaster*, *Pseudotsuga menziesii*, *Larix decidua*, *Picea abies*, and *Abies alba* (Fig. 1 & Table S1; Ulrich, 1995; Nicolas et al., 2014). Each plot covers two hectares and is located within a mature, even-aged stand, with a 0.5-hectare fenced central zone designed to exclude large herbivores (Fig. 1; Boulanger et al., 2018). Between 1993 and 2021, vegetation monitoring and soil sampling were carried out on these plots to track long-term changes in soil conditions and understory plant communities across France (Fig. S2).

#### 2.1.2. Vegetation monitoring

The vegetation monitoring program follows a standardized inventory protocol initiated in 1995 across 102 plots, with surveys repeated every five years until 2021, conducted twice annually (spring and summer) (Fig. 1 & Fig. S2). To minimize observer bias and ensure data consistency, all RENECOFOR botanical experts participated in calibration exercises held one year prior to each resurvey campaign (Archaux et al., 2006, 2009; Camaret and Brèthes, 1994).

Vascular plant inventories were conducted in eight 2 m × 50 m subplots, equally distributed inside and outside the 0.5-hectare fenced area. Only the four subplots inside the fenced zone were used in this study, as soil measurements were restricted to this area. Analyses focused on the understory vegetation layers, defined as the herbaceous layer (herbaceous and woody plants up to 0.3 m in height) and the lower shrub layer (woody species from 0.3 to 2 m in height). Following Richard et al. (2021), only taxa identified to species or subspecies level were included.

Due to storm damage, sample sizes varied across the six survey campaigns after the 1995 baseline: 87 plots in 2000 (post-Lothar and Martin storms), 48 plots in 2010 (post-Xynthia storm), and 100 plots in 2005, 2015, and 2021 (Fig. 1 & Fig. S2). The final dataset comprises floristic inventories from these six campaigns (1995, 2000, 2005, 2010, 2015, and 2021), covering 2141 subplots and 835 species in total.

#### 2.1.3. Soil sampling

For each of the 102 permanent plots, soil samples were collected during two distinct periods: between 1993 and 1997, and again between 2007 and 2012, resulting in an average interval of about 15 years per plot. Sampling was conducted during the same season for each plot to minimize seasonal variability (Fig. S2).

Within the 0.5-hectare fenced central zone, three soil layers (0–10 cm, 10–20 cm, and 20–40 cm) were sampled at five 13.5 m × 13.5 m subplots located at the plot center and the four corners. For each layer and subplot, one composite sample was formed by pooling soil collected from five points arranged in a systematic 4.5 m × 4.5 m grid nested within each subplot, carefully avoiding disturbed areas (e.g., logging residues, skidder tracks) and proximity to living trees (Fig. 1 & Fig. S2; Saenger et al., 2024).

Soil sampling involved excavating a 50 × 50 × 40 cm pit. An undisturbed soil core was taken using a Kopecky cylinder (250 cm³) to measure bulk density, while the remaining soil was collected for chemical analyses (Saenger et al., 2024). In total, 3,060 soil samples were analyzed across the two sampling periods.

This study focused on soil chemical parameters that can be inferred from plant Indicator Values (IVs) as provided by the CATMINAT and EcoPlant databases (e.g., pH and nutrient availability). Exchangeable

soil acidity was measured with a glass electrode in a 0.01 mol·L⁻¹ CaCl₂ solution (pH CaCl₂), while active acidity was assessed as pH in water using a 1:5 (v/v) soil-to-deionized water suspension (pH H₂O).

Extractable phosphorus (P, in g·kg⁻¹) was determined using the Dyer method for samples with pH CaCl₂ below 6.5, and the Joret-Hébert method for samples with pH CaCl₂ above 6.5. The carbon-to-nitrogen ratio (C/N) was calculated from soil organic carbon (C) and total nitrogen (N) contents. In the 0–10 cm layer, both C and N were measured by dry combustion after decarbonation. For the 10–20 cm and 20–40 cm layers, C was quantified by sulfochromic oxidation at 30 °C after decarbonation (Anne method), while N was determined by the Kjeldahl method (Fig. 1, Saenger et al., 2024).

### 2.2. Understory community soil index computation

#### 2.2.1. Computing CSIs

We inferred edaphic conditions using the community soil index (CSI, Fig. 1). CSI values were calculated by averaging the indicator values related to soil chemistry across all understory species present in a given subplot survey (with at least two species). Because abundance-weighted calculations yielded similar results but can introduce bias (Diekmann, 2003, Iseli et al., 2025), we based our CSI calculations on presence/absence data.

#### 2.2.2. Transferring species IVs from CATMINAT to EcoPlant for extensive and quantitative indicators

Understory plant species IVs for soil chemical parameters were extracted from the CATMINAT (Julve, 2009) and EcoPlant (Gégout et al., 2005) databases and combined to leverage their respective strengths.

The CATMINAT database provides plant species IVs for soil parameters, but on ordinal scales. The following IVs were used: nutrient (N), representing soil nutrient availability from hyper-oligotrophic (1) to polytrophic (9), and reaction (R), indicating soil acidity from hyper-acidiphilic (1) to hyper-basiphilic (9). Although CATMINAT covers the majority of species in our dataset, its unitless IVs prevent direct comparison with quantitative soil parameters.

The EcoPlant database is a national compilation of phytosociological surveys from French forests, linked to detailed environmental data, including soil analyses (physical and chemical properties from the A horizon). Species IVs for soil pH, C/N ratio, and extractable P were derived from 2,000 to 5,600 plots containing both vegetation and soil data. These IVs are expressed in units that are directly comparable to the measured soil parameters (Gégout et al., 2005).

While EcoPlant provides IVs for pH measured in water (pH H₂O), this study also examines its potential to reflect pH CaCl₂. The soil analysis methods used in EcoPlant (described in Gégout and Jabiol, 2001) are consistent with those of the RENECOFOR network for pH and C/N. However, for extractable phosphorus, EcoPlant applies the Duchaufour method, which is preferred over the Dyer method for non-calcareous soils. Finally, EcoPlant covers fewer species than CATMINAT.

To address this limitation, three transfer functions were developed to calculate CSI values for pH H₂O, C/N ratio, and extractable phosphorus. A generalized additive model (GAM) was used to convert the unitless CATMINAT CSI values, based on R and N indices, into EcoPlant CSI values compatible with soil chemical parameters. The transfer functions were calibrated using an independent dataset of 45,377 floristic surveys from the National Forest Inventory (NFI). The explained deviance, which reflects the model fit, was 81.7% for pH CSI, 74.8% for soil C/N ratio CSI, and 53.4% for soil extractable phosphorus.

This approach combines the extensive species coverage of CATMINAT with the quantitative precision of EcoPlant (Fig. 1; Fig. S3-S6; see also Richard et al., 2021, and Lenoir et al., 2013 for similar methods).
<figure><img src="image_3.png" /><figcaption>**Fig. 1.** The workflow diagram shows a map of the RENECOFOR network, highlighting the 102 plots where data were collected over two decades. It includes two datasets: soil surveys (in brown) and botanical surveys (in green). The Community Soil Index (CSI) and Community Light Index (CLI; in yellow) were calculated using the EcoPlant and CATMINAT Indicator Values databases. CSIs and measured soil parameter means were compared across the network, and correlations between them were tested. The same analyses were performed with temporal trends of CSIs and soil parameters at national and local scale. The study also examined how plot disturbances and stand characteristics influenced these dynamics and the matching between them. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>
## 2.3. Stand characteristics and disturbances

### 2.3.1. Collecting factors potentially influencing the correspondence between temporal changes of measured soil parameters and temporal changes of CSI values

The RENECOFOR program also provided stand and disturbance data for all 102 plots.

Stand age at the start of the botanical surveys in 1995 was considered a potential driver of mismatch, as stand aging may influence understory dynamics (Jules et al., 2008). We also considered the baseline soil parameter values (pH CaCl₂, pH H₂O, C/N ratio, and P) for each site, i.e., the first measurement taken between 1993 and 1997 during the initial soil inventory campaign.

A distinction was also made between coniferous and deciduous stands due to their contrasting understory plant species compositions (Barbier et al., 2008). Disturbance history included the cumulative number of both natural and anthropogenic events from 1995 to 2021. Natural disturbances, such as windthrow, were distinguished from anthropogenic disturbances related to forest management practices (thinning, cleaning, tree extraction). Each event was counted once regardless of intensity, resulting in two variables per plot: cumulative natural disturbances and cumulative anthropogenic disturbances. Variation in canopy openness, which affects understory composition and dynamics, was assessed using light preference indicators of understory species. The community light index (CLI) was calculated from CATMINET values representing species shade tolerance (L), ranging from hyper-sciophilic (1) to hyper-heliophilic (9), following the same protocol as for the CSI, but without transformation, keeping the original ordinal and unitless scale. Changes in CLI over time were used as a proxy for canopy openness variation.

## 2.4. Statistical analysis

### 2.4.1. Testing the reliability of CSI values across the entire RENECOFOR network

To test the spatial reliability of CSI values in inferring soil chemical parameters across the entire RENECOFOR network, we compared, in a paired manner: (1) CSI values from the 1995 floristic inventory against empirical values of the studied soil parameters as measured in each soil layer during the first soil sampling campaign (1993–1997); (2) CSI values from the 2015 floristic inventory, which is more complete than the 2010 inventory, against empirical values of the studied soil parameters as measured in each soil layer during the second soil sampling campaign (2007–2012). The choice of the most appropriate floristic inventory campaigns to be validated against actual soil measurements was guided by the need to balance temporal proximity with the soil sampling campaigns and to maximize the number of available sites for analysis at those periods.

For each soil parameter and each period, we used paired Wilcoxon signed-rank tests to compare mean values between CSI and measured data, and Spearman's rank correlations to assess the strength of their association. These non-parametric tests were chosen due to the non-normality of the data and because vegetation and soil data were collected on the same plots.

### 2.4.2. Assessing the temporal changes in soil chemical parameters and CSI values

Soil was sampled twice over a 15-year interval, whereas vegetation was inventoried six times at 5-year intervals. To make the datasets comparable, we modelled both as continuous functions of time, allowing estimation of annual rates of change and direct comparison of soil and vegetation temporal dynamics despite the asynchrony of their characterization.

To investigate the temporal dynamics of the empirically-measured soil parameters (pH H₂O, pH CaCl₂, C/N ratio, and extractable phosphorus) across soil layers (0–10 cm, 10–20 cm, and 20–40 cm), linear

mixed-effects models (LMMs) were applied, using the same methodology as presented in Saenger et al. (2024). For each soil parameter (n = 4), soil layer (n = 3), and sampling period (n = 2; 1993–1997 and 2007–2012), measurements from the five soil subplots were averaged to obtain a single composite value per plot (n = 99–101), these values were used as the response variable. The variable “Year” was included as a continuous fixed effect variable, and not as a factor to enable direct comparisons between soil and vegetation surveys, with 1995 representing the initial campaign and 2010 the follow-up soil sampling campaign. Plot identity (ID; n = 98–100) was included as a random effect on both the intercept and the slope to account for the paired structure of the longitudinal data.

A similar modelling approach was used to analyse the dynamics of CSI values (pH, C/N ratio, and extractable phosphorus). For each of the four vegetation subplots, within each permanent plot, and for each of the six survey periods, one CSI value was available and used as the response variable. The continuous variable “Year” was included as a fixed effect. Given the higher number of observations in the vegetation dataset (i.e., we did not pool the four vegetation subplots together), a more detailed random effect structure was implemented, incorporating subplot ID nested within plot ID. The main variable of interest, “Year”, represented the six vegetation monitoring campaigns (1995, 2000, 2005, 2010, 2015, and 2021), with plot ID encompassing 48, 87, or 100 levels, depending on the number of permanent plots that were available during a given vegetation monitoring campaign, and subplot ID encompassing four levels for the four inner vegetation subplots.

Although soil and vegetation were not sampled at identical intervals, using “Year” as a continuous predictor allowed both datasets to be analysed within the same temporal framework, thereby enabling consistent comparison of annual trends in soil properties and vegetation-based indicator values.

To allow for between-site variation in the temporal dynamics of both soil parameters and CSI values, we included random slope terms in our LMMs, allowing the slope of the “Year” variable to vary by level of the random effect variable (e.g., plot ID). The estimated coefficient of the “Year” variable in each model was used as a proxy for the temporal trends in soil parameters and CSI values denoted $\gamma_{Measured}^t$ and $\gamma_{Bio-Indicated}^t$, respectively.

Normality and homoscedasticity in model residuals were visually assessed. LMMs were fitted using Restricted Maximum Likelihood (REML), and the significance of predictors was assessed using Wald's test after Satterthwaite's method was applied to approximate the degrees of freedom (Christensen, 2018).

### 2.4.3. Testing the degree of coupling between temporal changes in soil parameters and temporal changes in CSI values

To compare the dynamics of measured and bioindicated soil parameters at the plot level, separate linear models were fitted for each plot and each mineral soil layer, with both measured values and CSIs regressed against the “Year” variable. The resulting temporal trends were extracted and denoted as $\alpha_{Measured}^t$ for soil parameters and as $\alpha_{Bioindicated}^t$ for CSIs.

The average $\alpha_{Measured}^t$ and average $\alpha_{Bioindicated}^t$ across all plots were then compared using paired Wilcoxon tests, applied separately for each soil parameter and soil layer. This non-parametric test was chosen due to the non-normal distribution and the paired structure of the data.

A Spearman rank correlation test was then applied to assess the relationship between $\alpha_{Measured}^t$ and $\alpha_{Bioindicated}^t$, accounting for the non-normality of the data. In contrast to the first approach based on a single linear mixed-effects model across all plots, which estimates an overall trend while accounting for the hierarchical structure of the data, this method focuses on exploring the local dynamics specific to each plot. It allows for the direct evaluation of the concordance between vegetation and soil dynamics at a fine scale, without these being attenuated or masked by the global average produced by the mixed-effects model at large scale.
To estimate canopy openness variation at the plot scale, the dynamics of CLI values were modelled using the same approach as for CSI, resulting in the calculation of α^CLI, which represents the temporal trend of CLI values for each plot individually.

### 2.4.4. Investigating factors influencing the degree of coupling between changes in soil chemical parameters and changes in CSI values

To assess correspondence (similarity or discrepancies) between the temporal dynamics of soil parameters and CSI values within each plot, the following offset variable was computed:

$$ \Delta \text{dynamics} = \alpha \text{Measured} - \alpha \text{Bioindicated} $$

Δ dynamics represents the difference between soil parameter changes over time (α^Measured) and the associated temporal dynamics in CSI values (α^Bioindicated). A positive value of Δ dynamics indicates that α^Measured is greater than α^Bioindicated while a negative value indicates the opposite. The mean and standard deviation of Δ dynamics were then computed for each soil parameter and soil layer across all RENECOFOR plots.

To investigate how stand characteristics and disturbance history influence the correspondence between measured and bioindicated soil dynamics, we fitted linear mixed-effects models (LMMs) using Δ dynamics as the response variable, separately for each soil parameter and soil layer. The following covariates were included as fixed effects: stand age in 1995, dominant tree species type (coniferous or deciduous), cumulative number of anthropogenic disturbances, cumulative number of natural disturbances, the canopy openness variation (α^CLI), baseline soil parameter values (pH CaCl₂, C/N ratio, and P; pH H₂O was excluded due to its high correlation with pH CaCl₂) were included as fixed effects to assess their influence on the correspondence between measured and bioindicated soil dynamics. This modeling choice also aimed to account for potential “regression to the mean” effects (Mazalla and Diekmann, 2022), by explicitly considering whether the initial soil conditions could influence the magnitude or direction of subsequent temporal trends.

Plot ID was included as a random effect on the intercept to account for variability between plots across the network.

To disentangle whether these factors influenced the measured soil dynamics or the vegetation-inferred dynamics—or both—we fitted the same models using α^Measured and α^Bioindicated separately as response variables. This allowed us to assess whether stand characteristics and disturbance regimes differentially affected observed soil changes and the vegetation bioindicated response.

For each model, all possible covariate combinations were evaluated and ranked using the corrected Akaike Information Criterion (AICc). The difference in AICc values (ΔAICc) between each model and the best candidate model (i.e., the model with the lowest AICc value) was calculated. For models with ΔAICc < 3, model averaging was applied to estimate the conditional mean of each coefficient and its 95% confidence interval. All continuous predictors, except the cumulative number of anthropogenic and natural disturbances, were standardized (z-scores) to facilitate comparability and interpretation of effect sizes (Schielzeth, 2010). Predictor significance was assessed using Type III ANOVA, applied to models fitted with Maximum Likelihood (ML) estimation.

### 2.4.5. Statistical precision and used packages

The variance explained by each LMM was assessed using the r. squared.GLMM function from the “MuMln” package v1.47.1 (Barton, 2013). Both marginal (R², for fixed effects) and conditional (R², for both fixed and random effects) R² values (Nakagawa and Schielzeth, 2013) were calculated. Results were considered statistically significant if the p-value was less than 0.05.

All statistical analyses were performed in R version 4.2.2, with models built using the “lme4” package v1.1.31 (Bates et al., 2014) and the “nlme” package v3.6.160 (Pinheiro et al., 2013). Data visualisation and management utilised additional packages including “ggplot2” v3.4.2 (Wickham, 2016), “dplyr” v1.1.2 (Wickham, 2014), “cowplot” v1.1.1 (Wilke, 2024), and “purrr” v0.3.5 (Wickham and Henry, 2023).

## 3. Results

### 3.1. Evaluating the reliability of understory IVs as bioindicators of soil chemistry across the broad spatial scale of the RENECOFOR network

At the national scale, community soil indexes (CSIs) were positively correlated with measured soil parameters across all soil layers and for both sampling periods (First sampling period: 1995 vegetation survey vs. 1993–1997 soil sampling; Second sampling period: 2015 vegetation survey vs. 2007–2012 soil sampling), except for extractable phosphorus concentrations (Fig. 2A, C, E, G & Table 1).

CSI values for soil pH and C/N ratio showed strong positive correlations with measured values of pH H₂O and C/N ratio, respectively, across all soil layers and for both soil sampling periods (rho > 0.60, p < 10⁻³; Fig. 2C, E & Table 1). For both parameters, correlation strength decreased with depth (Fig. 2A, C, E, G & Table 1). Similarly, strong correlations were found between CSI values for soil pH and measured pH CaCl₂ during both sampling periods in the 0–10 cm layer (rho > 0.80, p < 10⁻³; Fig. 2A) and in the 10–20 cm layer (rho > 0.60, p < 10⁻³; Table 1), while it was weaker but still significant in the 20–40 cm layer (rho < 0.50, 10⁻³ < p < 0.01; Table 1).

CSI values for extractable phosphorus concentrations were weakly but significantly correlated with measured values in the 0–10 cm layer (rho = 0.421, p = 0.001; Fig. 2G), and in the 10–20 cm layer during the first sampling period (rho = 0.458, p < 0.001; Table 1). No significant correlation was observed in the deeper layer during the first period, nor in any layer during the second period.

Community soil indexes (CSIs) consistently overestimated measured soil chemical parameters across all soil layers and both sampling periods, with significantly higher average values than measurements, according to paired Wilcoxon tests (p < 0.05; Fig. 3 & Table S2).

For soil pH CaCl₂, average measured values across the 102 plots ranged from 4.04 ± 1.00 to 4.54 ± 1.09 depending on the sampling period and soil layer, while pH CSI values ranged from 5.27 ± 0.66 to 5.28 ± 0.67. Measured pH (H₂O) values were closer to pH CSI values, ranging from 4.76 ± 0.98 to 5.18 ± 1.13 (Fig. 3A and B & Table S2).

For soil C/N ratios, measured values ranged from 16.21 ± 5.87 to 20.83 ± 8.49, whereas CSI values ranged from 21.00 ± 5.11 to 21.23 ± 5.36 depending on the vegetation survey year (Fig. 3C & Table S2).

For soil extractable phosphorus concentrations, measured values ranged from 0.03 ± 0.08 g/kg to 0.04 ± 0.07 g/kg across all layers and both periods, whereas CSI values were substantially higher, ranging from 0.11 ± 0.05 g/kg to 0.12 ± 0.06 g/kg. This overestimation, by a factor of two to three, is consistent across layers and periods and was expected given the different measurement methods used (Fig. 3D & Table S2).

### 3.2. Assessing how changes in CSI values matches with soil chemistry changes over the long temporal extent of the RENECOFOR network

#### 3.2.1. Soil national trends

Although these results have already been published (Saenger et al., 2024), we briefly report here the main trends in measured soil chemical parameters at the national scale, as they provide essential context for comparison with the vegetation-based soil trajectories presented in the following sections.

According to LMMs, C/N ratios in all soil layers increased significantly over the study period (p < 10⁻³), ranging from 0.964 ± 0.090 per decade in the 0–10 cm layer to 1.982 ± 0.130 per decade in the 20–40 cm layer (Fig. 3C & Table 2). Measured pH CaCl₂ significantly decreased in the 0–10 cm layer (–0.045 ± 0.010 per decade, p < 10⁻³, Fig. 3A & Table 2), while pH H₂O significantly increased in the 20–40 cm layer (0.049 ± 0.012 per decade, p < 10⁻³, Table 2).

In contrast, pH CaCl₂ tended to decrease in the 10–20 cm layer (–0.022 ± 0.011 per decade, p = 0.051, Table 2), and pH H₂O also tended to decrease in the 0–10 cm layer (–0.017 ± 0.010 per decade, p
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(caption on next page)
**Fig. 2.** The figure is divided into eight panels. Panels A and B depict respectively the spatial and temporal relationships between bioindicated and measured pH CaCl₂ values. Panels C and D show respectively the spatial and temporal relationships for bioindicated and measured pH H₂O. Panels E and F illustrate respectively the spatial and temporal relationships between bioindicated and measured C/N ratios. Finally, panels G and H present respectively the spatial and temporal relationships for bioindicated and measured extractable phosphorus (g/kg), all in the 0–10 cm soil layer. For the spatial relationships shown in panels A, C, E, and G, two comparisons are tested: vegetation surveys from 1995 versus soil sampling from 1993 to 1997 (displayed in green), and vegetation surveys from 2015 versus soil sampling from 2007 to 2012 (shown in yellow). For the temporal relationships in panels B, D, F, and H, the dynamics of bioindicated values are compared with those of measured values. Points are colored blue when both dynamics change in the same direction (matchings), and red when they move in opposite directions (mis-matching). Spearman correlation coefficients (Rho) are reported along with their significance levels, bioindicated as follows: “ns” for not significant, “.” for p < 0.1, “” for p < 0.05, “*” for p < 0.01, and “**” for p < 0.001. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

<caption>**Table 1**</caption>

Results of spatial and temporal spearman's correlations between soil measured and bioindicated parameters, including significance level. The table reports Spearman's rho correlation coefficients and significance levels, with the following symbols: p > 0.1 = "n.s."; 0.1 > p > 0.05 = "."; 0.05 > p > 0.01 = "*"; 0.01 > p > 0.001 = "**"; p < 0.001 = "***".

<table><caption>N = 99</caption><thead><tr><th colspan="8">Spearman's correlation</th></tr><tr><th colspan="4">Vegetation survey 1995 vs. Soil sampling<br/>1993-1997</th><th colspan="4">Vegetation survey 2015 vs. Soil sampling<br/>2007-2012</th></tr><tr><th>Layer</th><th>Parameter</th><th colspan="2">Rho</th><th colspan="2">Sign.</th><th colspan="2">α<sup>Measured</sup> vs.<br/>α<sup>Bioindicated</sup></th><th>Average<br/>Δ dynamics per decade</th></tr></thead><tbody><tr><td rowspan="4">10-20 cm</td><td>pH CaCl<sub>2</sub></td><td>0.631</td><td>***</td><td>0.667</td><td>***</td><td>0.069</td><td>ns</td><td>-0.045 ± 0.144</td></tr><tr><td>pH H<sub>2</sub>O</td><td>0.722</td><td>***</td><td>0.786</td><td>***</td><td>0.111</td><td>ns</td><td>-0.013 ± 0.158</td></tr><tr><td>C/N</td><td>0.695</td><td>***</td><td>0.727</td><td>***</td><td>-0.028</td><td>ns</td><td>2.17 ± 2.97</td></tr><tr><td>P (g/kg)</td><td>0.458</td><td>***</td><td>0.309</td><td>ns</td><td>-0.040</td><td>ns</td><td>-0.003 ± 0.035</td></tr><tr><td rowspan="4">20-40 cm</td><td>pH CaCl<sub>2</sub></td><td>0.384</td><td>**</td><td>0.415</td><td>**</td><td>0.101</td><td>ns</td><td>-0.019 ± 0.211</td></tr><tr><td>pH H<sub>2</sub>O</td><td>0.673</td><td>***</td><td>0.739</td><td>***</td><td>0.148</td><td>ns</td><td>0.027 ± 0.221</td></tr><tr><td>C/N</td><td>0.623</td><td>***</td><td>0.622</td><td>***</td><td>-0.065</td><td>ns</td><td>2.10 ± 2.85</td></tr><tr><td>P (g/kg)</td><td>0.162</td><td>ns</td><td>0.115</td><td>ns</td><td>-0.071</td><td>ns</td><td>-0.001 ± 0.036</td></tr></tbody></table>

= 0.08, Fig. 3B & Table 2). Extractable phosphorus concentrations in the 20–40 cm layer showed a non-significant increasing trend (0.002 ± 0.001 g/kg per decade, p = 0.05, Table 2).

### 3.2.2. CSIs national trends

Among the vegetation-based indicators, only the CSI values for soil C/N exhibited a significant temporal trend over the past 26 years, with a modest national-scale decrease of approximately 0.2 units (–0.0076 ± 0.0038 unit/year, p = 0.05, Fig. 3C & Table 2). However, this change remained weak relative to the spatial variability observed across the network in a given year e.g. over 27 times smaller than the national standard deviation in 1995 (Table S2).

Across all models assessing CSI and measured soil parameter dynamics, the fixed effect of time explained only a negligible portion of the variance (R²m ranging from < 10⁻³ to 0.04), while the random effects structure captured the vast majority (R²c ranging from 0.83 to 0.94, Table 2 & S3), emphasizing the predominance of spatial heterogeneity over temporal shifts in both measured soil parameters and their vegetation-based indicators.

### 3.2.3. Comparison between soil and CSIs trends

When comparing the local temporal trends of measured soil parameters (α<sup>Measured</sup>) and bioindicated CSIs (α<sup>Bioindicated</sup>) across plots, paired Wilcoxon tests revealed significant differences between the two for all soil parameters and layers (p < 0.05). The average local dynamics observed in soil measurements and vegetation-based indicators thus differ consistently. Furthermore, Spearman correlation tests showed no significant positive correlations between α<sup>Measured</sup> and α<sup>Bioindicated</sup> across parameters and layers (all p-values >0.05; Fig. 2B, D, F, H & Table 1), highlighting frequent mismatches in the direction and magnitude of local temporal changes between observed soil conditions and their vegetation-based signals. In the 0–10 cm soil layer, these mismatches were particularly frequent, with opposite trends observed in 57% of the plots for soil pH CaCl₂, 55% for soil pH H₂O, 60% for the C/N ratio, and 52% for extractable phosphorus (red dots in Fig. 2B, D, F, H).

The differences in local dynamics between CSIs values (α<sup>Bioindicated</sup>) and directly measured soil parameters (α<sup>Measured</sup>) referred to as Δ

dynamics, were consistently high across all plots and soil layers in the RENECOFOR network. These Δ dynamics values indicate substantial mismatches between vegetation-inferred trends and actual soil changes at the plot scale.

Depending on the soil layer considered, average Δ dynamics ranged from –0.069 ± 0.134 to 0.027 ± 0.221 units per decade for soil pH (CaCl₂ or H₂O), from 0.974 ± 1.67 to 2.17 ± 2.97 units per decade for the C/N ratio, and from –0.001 ± 0.036 to 0.005 ± 0.036 g/kg per decade for extractable phosphorus (Table 2).

### 3.3. Identifying factors that influence the degree of coupling between measured changes in edaphic conditions and inferred changes from plant IVs

Given the stronger correlations between measured and bioindicated values in surface soils, the analysis of Δ dynamics and its potential drivers focused on the 0–10 cm layer (Fig. 4). Results for deeper layers (10–20 cm and 20–40 cm) are available in the supplementary material (Figs. S7a and b) but will not be detailed further here.

For soil pH CaCl₂, measured temporal changes increased significantly with stand age (0.324 ± 0.099 SE), as did Δ dynamics (0.294 ± 0.101 SE, Fig. 4). For pH H₂O, measured dynamics increased with both stand age (0.245 ± 0.100 SE), baseline pH CaCl₂ (0.210 ± 0.100 SE) and decreased with baseline C/N (–0.210 ± 0.103 SE, Fig. 4). These same relationships were observed for Δ pH H₂O dynamics, which rose with stand age (0.224 ± 0.100 SE) and decreased with baseline C/N (–0.233 ± 0.101 SE, Fig. 4).

For the C/N ratio, measured dynamics decreased with stand age (–0.219 ± 0.101 SE), and increased with baseline pH CaCl₂ (0.369 ± 0.099 SE) and baseline C/N (0.206 ± 0.105 SE, Fig. 4). Consequently, Δ C/N dynamics decreased with stand age (–0.287 ± 0.100 SE) and increased with baseline pH CaCl₂ (0.301 ± 0.097 SE, Fig. 4). Finally, vegetation-inferred dynamics increased with changes in the community light index (0.357 ± 0.103 SE, Fig. 4).

For extractable phosphorus, measured dynamics declined with higher baseline P levels (–0.263 ± 0.102 SE). Vegetation-inferred dynamics also decreased with baseline P (–0.336 ± 0.153 SE), cumulative
<figure><img src="image_12.png" /><figcaption>**Fig. 3.** The figure is divided into four panels. Panel A shows measured and bioindicated pH (CaCl₂), Panel B presents measured and bioindicated pH (H₂O), Panel C displays measured and bioindicated C/N ratio, and Panel D depicts measured and bioindicated extractable phosphorus (g/kg). Green lines with green circles represent the national trends bioindicated by understory vegetation (bioindicated values), while brown lines with diamonds show the national trends based on soil measurements. Faded points in the background correspond to average CSI or measured soil parameter values from all RENECOFOR plots. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

anthropogenic disturbances (–0.087 ± 0.041 SE), and community light index dynamics (–0.418 ± 0.102 SE, Fig. 4). In contrast, Δ phosphorus dynamics were positively related to community light index dynamics (0.362 ± 0.097 SE, Fig. 4).

In the models assessing measured soil parameter dynamics, vegetation-inferred CSI dynamics, and the difference between them (Δdynamics) in the 0–10 cm soil layer, fixed effects explained between 8% and 27% of the variance (R²m = 0.05–0.23, Table S3), while random effects accounted for 89% to 95% of the variance (R²c = 0.88–0.91, Table S3).

## 4. Discussion

This study assessed the potential of forest understory plant species IVs to reflect the observed variation in soil chemical parameters across large spatial and temporal extents, while also investigating factors influencing the degree of coupling between empirically measured and floristically inferred changes in edaphic conditions. Our main results confirm the usefulness of plant IVs in capturing spatial variation in soil chemical parameters. However, significant discrepancies between measured and inferred soil dynamics raise question about their reliability for monitoring long-term changes in soil chemistry. These discrepancies varied according to stand age and baseline soil parameters but appeared mostly independent of disturbance history and canopy openness.

### 4.1. Correlations at the RENECOFOR spatial scale support the ecological relevance of indicator values, despite consistent overestimations

This study confirms the reliability of understory vegetation IVs in reflecting spatial variations in soil chemical parameters, particularly soil pH H₂O, pH CaCl₂ and C/N ratio. Strong positive correlations between CSIs and measured soil parameters across the RENECOFOR network support the first hypothesis: understory IVs reliably reflect soil chemistry along the large spatial extent covered by the RENECOFOR network in France which encompasses a diverse range of macroclimatic contexts. These results are consistent with former studies using Ellenberg values (Schaffers and Sýkora, 2000; Ewald, 2003; Carpenter and Goodenough, 2014) or EcoPlant IVs (Gégout et al., 2005, 2008; Riofrío-Dillon, 2012, 2013).

These previous assessments, which focused on topsoil parameters (0–10 cm to 0–15 cm depth), suggest that understory vegetation is a reliable indicator of topsoil properties, as root systems are concentrated in the upper 0–15 cm of soil (Hendrick and Pregitzer, 1996). The observed decline in correlation with depth in this study is consistent with this premise. A key finding, however, is that correlations for pH H₂O and C/N ratios remained strong even in deeper soil layers. This likely reflects the fact that these two parameters vary less with depth within a given site than they do between sites, so that IVs still capture meaningful spatial variation beyond the topsoil. Despite these significant correlations, mean comparison tests revealed that CSI values were
<caption>**Table 2**</caption>

Linear mixed model coefficients assessing the dynamics of understory CSIs, CLI, and measured soil parameters. The table reports significance levels, as well as marginal and conditional R² values, which represent the proportion of variance explained by the fixed effect alone, and by both fixed and random effects, respectively. For models assessing the temporal dynamics of indicator values, the fixed effect is time, represented by the “Year” variable, while the random effect includes subplot ID nested within plot ID to account for both intra- and inter-plot variability. In models assessing the dynamics of measured soil parameters, the fixed effect remains the same, whereas the random effect includes only plot ID.

<table><thead><tr><th rowspan="2">Library</th><th rowspan="2">Indicator Value</th><th colspan="2">N</th><th rowspan="2">Dynamic per decade ± SD γ<sup>Measured</sup></th><th rowspan="2">Sign.</th><th rowspan="2">R<sup>2</sup>m</th><th rowspan="2">R<sup>2</sup>c</th></tr><tr><th>Plot</th><th>Sub-plot</th></tr></thead><tbody><tr><td>EcoPlant</td><td>C/N<sub>indic</sub></td><td>536</td><td>2241</td><td>-0.076 ± 0.038</td><td>*</td><td>&lt;10<sup>-3</sup></td><td>0.90</td></tr><tr><td rowspan="3">Layer 0–10 cm</td><td><strong>Parameter</strong></td><td></td><td></td><td>γ<sup>Measured</sup></td><td></td><td></td><td></td></tr><tr><td>pH CaCl<sub>2</sub></td><td>200</td><td>996</td><td>-0.045 ± 0.010</td><td>***</td><td>&lt;10<sup>-3</sup></td><td>0.94</td></tr><tr><td>pH H<sub>2</sub>O</td><td>200</td><td>998</td><td>-0.017 ± 0.010</td><td>.</td><td>&lt;10<sup>-3</sup></td><td>0.94</td></tr><tr><td rowspan="3">10–20 cm</td><td>C/N</td><td>200</td><td>998</td><td>0.964 ± 0.090</td><td>***</td><td>0.01</td><td>0.88</td></tr><tr><td>pH CaCl<sub>2</sub></td><td>200</td><td>996</td><td>-0.022 ± 0.011</td><td>.</td><td>&lt;10<sup>-3</sup></td><td>0.92</td></tr><tr><td>C/N</td><td>200</td><td>998</td><td>1.941 ± 0.124</td><td>***</td><td>0.04</td><td>0.85</td></tr><tr><td rowspan="3">20–40 cm</td><td>pH H<sub>2</sub>O</td><td>198</td><td>982</td><td>0.049 ± 0.012</td><td>***</td><td>0.001</td><td>0.93</td></tr><tr><td>C/N</td><td>198</td><td>986</td><td>1.982 ± 0.130</td><td>***</td><td>0.04</td><td>0.83</td></tr><tr><td>P (g/kg)</td><td>196</td><td>948</td><td>0.002 ± 0.001</td><td>.</td><td>&lt;10<sup>-3</sup></td><td>0.92</td></tr></tbody></table>

significantly higher than measured soil values. These discrepancies are consistent with previous findings by Gégout (2007), who assessed the predictive reliability of IVs from the EcoPlant database estimating soil pH and C/N ratio at a national scale. His study reported average deviations of 0.8 units for soil pH and 5 units for soil C/N ratio between empirically-measured and floristically-inferred values. Thus, similar to Ellenberg IVs (Wamelink et al., 2002), EcoPlant IVs cannot entirely replace direct soil measurements. Nonetheless, at the spatial extent of the RENECOFOR permanent monitoring network, which encompasses a wide range of climatic conditions, the understory vegetation reliably reflects variations in soil pH (both H₂O and CaCl₂) as well as in the C/N ratio.

Beyond these observations, our study provides the first assessment of the reliability of CSI for extractable phosphorus. The EcoPlant CSI for extractable phosphorus showed moderate reliability in the upper soil horizon during the first sampling period, but declined during the second, with values significantly higher than measured extractable phosphorus concentrations in both periods. This reduced reliability may partly result from the inherent difficulties in measuring extractable phosphorus.

Phosphorus occurs in soil as PO₄, PO₄²⁻ or PO₄³⁻ ions, adsorbed onto soil colloids due to their attraction to positively charged iron and aluminium hydroxides, calcium carbonate particles, or indirectly to calcium, iron, or aluminium ions bound to negatively charged clay surfaces. Assimilable phosphorus moves between these compartments via the soil solution (Bonneau et al., 2003). The Duchaufour extraction method, used in the EcoPlant database for non-calcareous soils, involves two successive extractions—one in acidic and one in alkaline conditions—allowing the recovery of phosphorus bound to calcium (on clays and carbonates), phosphorus adsorbed onto aluminium hydroxides, and to a lesser extent, phosphorus retained by iron hydroxides (Bonneau et al., 2003). In contrast, the Dyer method, used in the RENECOFOR network for non-calcareous soils to ensure consistency with ICP-Forests protocols, relies on a single extraction using a 2% citric

acid solution (Dyer and Wrenshall, 1941). This method may underestimate available phosphorus in nutrient-poor soils, as it may not solubilize all forms of phosphorus bound to different soil constituents (Bonneau et al., 2003). Moreover, the Duchaufour method appears to better reflect the ability of plants to access assimilable phosphorus in forest soils (Bonneau et al., 2003). Since the vast majority of RENECOFOR plots are located on non-calcareous soils (pH CaCl₂ < 6.5), the observed discrepancy between extractable phosphorus values from EcoPlant and those measured on RENECOFOR plots likely originates from methodological differences. The EcoPlant IVs remains ecologically relevant, as it is based on a Duchaufour method that better reflects plant functioning in forests and that is strongly positively correlated with isotopic dilution techniques, which are considered the most accurate for measuring assimilable phosphorus (Achat et al., 2016). Because IVs are calibrated using Duchaufour method, which better reflects plant-available phosphorus than the Dyer one used on RENECOFOR plots, some mismatch at the spatial scale is understandable. It is therefore essential, when quantifying a key nutrient such as phosphorus, to adopt soil extraction methods that best reflect its biological availability to forest vegetation. Without this methodological consistency, comparisons between soil data and vegetation indicators risk being biased and misleading.

## 4.2. Mismatches between measured and inferred changes in soil conditions challenge the use of forest understory plant IVs for monitoring soil changes

The literature remains divided on the reliability of plant IVs for tracking soil chemical changes over time. While some studies have reported a strong similarity between floristically-inferred and empirically-measured changes in soil acidity or nutrient availability (Baeten et al., 2009; Riofrio-Dillon, 2013), others have highlighted substantial decoupling over time, particularly regarding trends in soil pH (Van Calster et al., 2007; Dölle and Schmidt, 2009; Sebesta et al., 2011). The results of our study align with the latter, as significant discrepancies were observed between temporal CSI values and the corresponding temporal dynamics in soil chemical measurements, both through the lack of alignment between measured and bioindicated national trends (i. e., between γ<sup>Measured</sup> and γ<sup>Bioindicated</sup>, respectively) and the absence of correlation between individual plot-level measured and bioindicated dynamics (i.e., between α<sup>Measured</sup> and α<sup>Bioindicated</sup>, respectively). This decoupling between measured and inferred changes thereby directly contradicts the hypothesis of this study, which assumed a good match between both measured and inferred temporal dynamics.

Authors using vegetation indicator values to reflect environmental variations rely on the assumption that species respond immediately and without delay or lagging dynamics (i.e., dispersal and establishment lags) to environmental changes, thereby maintaining their ecological niche (ter Braak and van Dame, 1989). However, several studies have shown that this response can be delayed. For instance, Bertrand et al. (2011, 2016) and Richard et al. (2021) reported a lagged response of understory species to climate warming, particularly to increases in annual mean temperature. This results in a “climatic debt”, where the thermophilization of understory community occurs more slowly than the rise in macroclimatic temperatures. In light of such findings, the lack of similarity between the dynamics of measured soil parameters and those bioindicated by the understory vegetation (both at national and local scales, as described in the present study) may stem from differing lagged responses between aboveground and belowground compartments. In other words, plant communities might respond with a temporal delay to edaphic changes, but conversely, soil conditions could also evolve more slowly than vegetation composition, for example under the influence of long-term nitrogen deposition. This difference in lagging dynamics could explain the observed mismatch between vegetation-based indicators and measured soil changes over time.

However, it is necessary to put the magnitude of environmental changes and their ecological effects into perspective. Across the
<figure><img src="image_13.png" /><figcaption>**Fig. 4.** Coefficient estimates and 95% confidence intervals extracted from linear mixed-effects models assessing the influence of various explanatory variables on soil parameter dynamics, both measured and bioindicated, as well as their differences. Shapes of the points correspond to the response variables in the models: diamonds represent measured soil parameter dynamics, circles indicate soil parameter dynamics derived from plant indicators (indicated dynamics), and triangles show the differences between measured and bioindicated dynamics. Explanatory variables are listed vertically and include: stand age in 1995; measured values in 1995 for pH CaCl₂, pH H₂O, C/N ratio, and soil phosphorus concentration; cumulative number of anthropogenic and natural disturbances from 1995 to 2021; and the dynamics of the CLI. Points are colored based on the significance and direction of their effect: blue for significant positive effects, red for significant negative effects, and gray for non-significant effects. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>
RENECOFOR network, mean annual temperature increased by 0.22 to 0.28 °C per decade between 1995 and 2015—an increase substantial enough to drive ecological shifts such as the thermophilization of plant communities (Richard et al., 2021). In contrast, changes in edaphic conditions, while statistically significant, appear far less pronounced (Saenger et al., 2024). For example, the pH CaCl₂ measured in the 0–10 cm soil layer decreased only slightly, from 4.11 to 4.04 in 15 years. Similarly, pH H₂O dropped marginally from 4.78 to 4.76 in the same layer (Saenger et al., 2024). These shifts are considerably smaller than those reported by Baeten et al. (2009), who documented a 0.7-unit decrease in pH H₂O over 47 years in the top 0–15 cm soil layer, and a slight but significant decline in the understory community Ellenberg IV for soil acidity in the Meerdal forest, Belgium. In comparison, soil acidification trends observed within the RENECOFOR network appear too limited in magnitude to trigger a significant response from the understory vegetation. Furthermore, although the increase in the C/N ratio from 19.07 to 20.51 in the 0–10 cm soil layer represents the most pronounced edaphic change observed across RENECOFOR in this layer (Saenger et al., 2024), this shift does not necessarily imply a significant alteration of soil trophic conditions. Indeed, the C/N ratio remains within the 15–25 range, which is characteristic of moder humus forms (Jabiol et al., 2007), and thus does not indicate a marked trophic transition likely to trigger a strong floristic response. MacDonald et al. (2002) suggest that the C/N ratio must exceed 25 units in forest soils for nitrogen availability to decline, significantly reducing the abundance of nitrophilous species. Comparable with those depicted across the RENECOFOR, increases in the C/N ratio have been observed during stand rotations in beech forests (Hedde et al., 2008) and oak-hornbeam forests (Trap et al., 2009), with changes of 1.5 and 1.9 units, respectively. In these silvicultural contexts, understory communities typically do not undergo significant species turnover but rather adjustments in the relative abundance of existing species (Aubert et al., 2003, 2004; Archer et al., 2007).

To provide a more quantitative perspective on the sensitivity of understory vegetation to soil changes, we estimated the minimum detectable effect (MDE)—the smallest soil change that can be reliably detected by variation in community IVs—using simple linear regressions between soil parameters measured in the first sampling campaign and CSI derived from the 1995 floristic inventory (methodology following Vittinghoff et al., 2012; see Table S4 caption). This analysis indicates that only relatively large changes in soil properties are likely to be detected by CSI: 0.226 units for pH CaCl₂, 0.232 units for pH H₂O, and 1.524 units for the C/N ratio (Table S4). These minimum detectable variations exceed the actual soil variations observed between the first and second RENECOFOR surveys, explaining why community IVs have not reflected these changes.

In light of these findings, the absence of a floristic response to the C/N ratio increase or the soil pH decrease at RENECOFOR sites appears consistent, and suggests that the sensitivity of IVs to environmental change may be relatively low, meaning that the magnitude of variation in an environmental parameter required to produce a significant shift in IVs is higher than the soil changes observed so far in this network.

### 4.3. Factors influencing measured and bioindicated soil dynamics differ, explaining the mismatches

The difference between the weak pH shift measured in the RENECOFOR network and the significant decrease reported by Baeten et al. (2009) can be explained by two main factors. First, the temporal window: our study covers approximately 15 years of soil chemical analysis and 26 years of vegetation-inferred, much shorter than the 47 years analyzed by Baeten et al. (2009), while forest soils acidify gradually over decades, notably due to litter accumulation (Knoep and Swank, 1994). Extending the survey over time could reveal plots that cross edaphic thresholds, prompting a shift toward acidophilous or more oligotrophic species. Second, the spatial scale: Baeten et al. (2009) studied a small set

of plots in a relatively homogeneous forest, while RENECOFOR encompasses diverse edaphic conditions nationally, complicating the detection of a uniform trend. This heterogeneity justifies partitioning the network to better capture soil chemistry dynamics, as proposed by Saenger et al. (2024), who classified plots by trophic soil levels and showed that the most acidic sites experience the strongest acidification and greatest increases in C/N ratio.

Our results, although we did not partition the network by trophic soil levels but analyzed each site individually at the plot scale, are consistent with this pattern: the higher the baseline pH, the smaller the pH decrease; likewise, the higher the baseline C/N ratio, the greater the tendency to increase. Thus, edaphic dynamics measured in the 0–10 cm soil layer strongly depend on initial pH, C/N ratio, phosphorus concentration values, and stand age at the beginning of the monitoring. Marginal R² values (considering fixed effects only) explain about 20% of soil dynamics variation, which is substantial. However, these initial site characteristics have no significant effect on CSI dynamics, i.e., vegetation response. Therefore, baseline site properties increase the mismatch intensity by influencing soil dynamics without affecting vegetation.

Understory CSI dynamics are mainly influenced by cumulative anthropogenic disturbances (thinning, cleaning, tree extraction) and canopy openness, bioindicated by increases in CLI values. These disturbances correlate with decreased bioindicated phosphorus dynamics and increased bioindicated C/N dynamics—contrary to literature suggesting canopy openings reduce soil carbon content and increase nutrient availability (Tong et al., 2024), which would favor species with low soil C/N index and high extractable phosphorus index. Specifically, our results showed that increased canopy openness led to decreases in bioindicated phosphorus and increases in bioindicated C/N values over time. Generalist and opportunistic species benefit from higher light availability, often outcompeting forest specialists and promoting biotic homogenization (De Pauw et al., 2022). The recruitment of these species may shift community-averaged soil-related IV values independently of actual soil chemistry. We hypothesize that this effect is partly due to the broader ecological amplitudes of euryecious species: by establishing in communities previously dominated by nutrient-specialist species, they dilute or blur the bioindicative signal of stenoecious species, resulting in either increases or decreases in community-averaged bioindicated nutrient values, even though soil properties remain largely unchanged. This highlights that understory vegetation and soil compartments respond to different drivers: vegetation reacts directly to environmental disturbances, whereas soil dynamics depend more on initial site conditions.

Ultimately, only baseline extractable phosphorus concentration similarly affects both measured and bioindicated dynamics, thus not contributing to the mismatch. However, given challenges in accurately measuring soil phosphorus, the weak spatial correlation between measured and bioindicated values, and the lack of correlation in their temporal dynamics, this finding remains difficult to interpret.

## 5. Conclusion

This study provides a comprehensive assessment of the reliability of understory vegetation as a bioindicator of soil chemistry at both spatial and temporal scales in French forests. While synthetic indices (CSI) derived from vegetation exhibit strong correlations with soil chemical parameters at the spatial scale, they fail to accurately track temporal changes. At the national scale, vegetation-based dynamics do not reflect the long-term trends observed in directly measured soil properties. At the plot level, these discrepancies are even more pronounced, with no significant correlation between bioindicated and measured changes over time. These inconsistencies arise from distinct processes driving vegetation and soil dynamics, as well as the limited sensitivity of vegetation indices to the relatively small variations in soil parameters observed within the network. Understory vegetation is strongly influenced by other environmental factors, particularly forest management and
changes in the canopy structure, which modify light availability. Therefore, although understory indicator values remain valuable tools for estimating spatial variation in soil properties, their use for monitoring temporal changes in edaphic conditions should be approached with caution.

To improve the sensitivity of bioindication, future approaches should move beyond mean ecological optima and instead consider species ecological thresholds — the environmental limits beyond which species appear or disappear from communities. Such threshold-based metrics could better capture early or subtle ecosystem changes. Long-term forest monitoring would also benefit from consistent soil analysis methods and the development of indicators tailored to temporal responsiveness, while accounting for forest structural attributes that influence vegetation-soil coupling. These improvements will strengthen our ability to detect, understand, and anticipate ecological change in temperate forests.

### CRediT authorship contribution statement

**Robinson Ribémont:** Writing – review & editing, Writing – original draft, Visualization, Validation, Resources, Methodology, Investigation, Formal analysis, Conceptualization. **Jean-Luc Dupouey:** Writing – review & editing, Validation, Resources, Methodology, Investigation, Conceptualization. **Benoît Richard:** Writing – review & editing, Visualization, Validation, Methodology, Data curation, Conceptualization. **Jonathan Lenoir:** Writing – review & editing, Validation, Resources, Methodology, Conceptualization. **Iris Le Roncé:** Writing – original draft, Validation, Resources, Methodology, Investigation, Conceptualization. **Vincent Boulanger:** Writing – review & editing, Validation, Resources. **Didier Alard:** Writing – review & editing, Resources. **Thérèse Beaufils:** Writing – original draft, Resources. **Carole Begeot:** Writing – review & editing, Resources. **Sylvaine Camaret:** Writing – review & editing, Resources. **Déborah Closset-Kopp:** Resources. **Emmanuel Corcket:** Resources. **Guillaume Decocq:** Writing – review & editing, Resources. **Richard Eynard-Machet:** Resources. **François Gillet:** Writing – review & editing, Resources. **Arnaud Mouly:** Writing – original draft, Resources. **Pascale Ruffaldi:** Resources. **Fabien Spicher:** Resources. **Erwin Ulrich:** Resources. **Manuel Nicolas:** Writing – original draft, Validation, Supervision, Resources, Project administration, Methodology, Funding acquisition, Conceptualization. **Michaël Aubert:** Writing – review & editing, Writing – original draft, Validation, Supervision, Resources, Project administration, Methodology, Investigation, Funding acquisition, Conceptualization.

### 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

The authors wish to acknowledge all the people who participated in the RENECOFOR network since the onset of the monitoring programme, especially those who contributed to setting up and maintaining the permanent plots or to data collection. Many thanks to: A. Aidoud, F. Archaux, V. Badeau, S. Barbier, P. Behr, A. Bernard, A. Blanc, J. Bodin, J-M Boissier, F. Bonne, L. Bourjot, A. Brêthes, S. Chauchard, A. Chevalier, R. Chevalier, P. Coquillard, G. Corriol, S. Daviller, J-F. Dobremez, Y. Dumas, G. Dumé, F. Elie, F. Elie, M. Forêt, F. Forgeard, M. Gallet, C. Gauberville, F. Gérémia, J. Gueugnot, P. Heuzé, S. Huet, C. Kieffer, X. Lassudrie-Duchène, M. Legay, M. Lembke, L. Malzieu, S. Malécot, V. Malécot, P. Margerie, H. Martin, L. Malzieu, N. Metral, W. Mirlyaz, P.

Montpied, E. Pares, T. Paul, J-F. Picard, E. Quibel, P. Raimbault, F. Richard, D. Rittié, V. Rousselet, P. Saliou, J-M. Savoie, R. Schipfer, A. Schmitt, L. Seytre, J. Timbal, J. Touffet, M. Trémolières, E. Thirion and A. Weitner for their invaluable contribution to the floristic surveys of the RENECOFOR network since 1995. We would like to thank the University of Rouen Normandy for Robinson Ribémont's PhD grant and the French National Forest Office (ONF) for financial support for this study and for the management of the RENECOFOR network.

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.ecolind.2025.114490.

### Data availability

Data will be made available on request.

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