# An extreme drought and heatwave event led to collapse of facilitation by metallophyte species in metalliferous ecosystems

Florian Delerue, Hugo Randé, David Nemer, Valérie Laperche, Valérie

Sappin-Didier, Lucie Cormont, Johanne Gresse, Richard Michalet

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Florian Delerue, Hugo Randé, David Nemer, Valérie Laperche, Valérie Sappin-Didier, et al. An extreme drought and heatwave event led to collapse of facilitation by metallophyte species in metalliferous ecosystems. Plant and Soil, 2025, 10.1007/s11104-025-07611-3. hal-05119086

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1 **Title:**

2 An extreme drought and heatwave event led to collapse of facilitation by metallophyte species

3 in metalliferous ecosystems.

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5 **Authors**

6 Florian Delerue*¹, Hugo Randé¹, David Nemer¹, Valérie Laperche², Valérie Sappin-Didier³,

7 Lucie Cormont¹, Johanne Gresse⁴, Richard Michalet¹

8 ¹Univ. Bordeaux, CNRS, Bordeaux INP, EPOC, UMR 5805, F-33600 Pessac, France

9 ²Bureau de Recherches Géologiques et Minières (BRGM), 45060 Orleans, France

10 ³ISPA, Bordeaux Sciences Agro, INRAE, Villenave d'Ornon, France

11 ⁴ Plant Ecology Group, Institute of Evolution and Ecology, University of Tübingen,

12 Tübingen, Germany

13 **\*Corresponding author:**

14 fdelerue@bordeaux-inp.fr; tel. (33) 5 56 84 69 18;

15 ORICD: 0000-0002-9809-5321

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17 Hugo Randé: 0000-0003-1707-1107

18 David Nemer: 0000-0002-2140-0180

19 Johanne Gresse: 0009-0004-7600-315X

20 Richard Michalet: 0000-0002-6617-4789

21

22 **Acknowledgments**

23 We thank the French National Forest Office (ONF) and the city of Sentein for providing access

24 to the experimental sites. This work was funded by the French National Research Agency

25 (ANR-19-CE02-0013).
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**Abstract:**

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* Aims: Recent studies have highlighted the importance of plant facilitation in metal-rich environments. Among the positive effects of metallophyte nurse plants, the improvement of microclimatic conditions for neighboring plants has been reported. Here, we aim to assess if plant facilitation persists with the additional stress caused by the extreme drought and heatwave that occurred in 2022 in the northern hemisphere.

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* Methods: We conducted four transplant experiments with grass species placing them under nurse plants or in areas where nurse plants have been removed. These experiments were carried out along soil pollution gradients at various sites and elevation in a former mining valley in the French Pyrenees. The first three experiments were conducted in 2020 and 2021, while the fourth was done in 2022. Each year, transplant survival was monitored at the end of the growing season.

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* Results: The 2020 and 2021 experiments confirmed the increase of positive interactions as metal-pollution increased, aligning with the Stress Gradient Hypothesis. However, during the extreme year, facilitation disappeared, likely because dominant nurse plants were weakened and no longer influenced neighboring plants.

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* Conclusions: Metallophyte species in temperate regions are not necessarily drought-tolerant and are certainly affected by severe drought and heatwaves. This additional intense stress, in habitats already challenged by metal presence in soils, likely compromised their ability to improve micro-climatic conditions for neighboring plants. This collapse of facilitative effects was observed at both low- and high-elevation sites, highlighting the strong impact of such meteorological event in metalliferous ecosystems.

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**Keywords:**

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Climate change; stress gradient hypothesis; collapse of facilitation; drought; heatwave; metalliferous grassland; metallophyte species

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**Introduction**

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The Stress Gradient Hypothesis (SGH, Bertness and Callaway 1994) proposes that plant-plant interactions switch from competition to facilitation as environmental stress increases. Since its initial formulation, numerous studies have confirmed the pivotal role of facilitation for plant community assemblages in harsh environments like alpine or arid ecosystems (e.g. Callaway et al. 2002, Bertness et al. 2024). By alleviating environmental stress, nurse plants expand the realized niche of less stress-tolerant beneficiary species (Bruno et al. 2003). In so doing, facilitation has been shown to enhance species richness, evenness, and functional and phylogenetic diversity under conditions unfavorable for plant growth (Vega-Álvarez et al. 2019). Ultimately, facilitation contributes to mitigating environmental severity and improving ecosystem functioning (Wright et al. 2017, Michalet et al. 2021). Overall, facilitation is a key process with positive impacts on biodiversity in harsh environments, with effects even more pronounced for rare species (Soliveres et al. 2015).

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Plant communities of metalliferous environments develop naturally on metal- or metalloid- (hereafter simply referred to as “metals”) rich outcrops and soils, such as Ni-Cr-Co-rich serpentines (Harrison and Rajakaruna 2011), intrusion-related hydrothermal Zn-Cd-Pb-rich calamine outcrops, or spontaneous copper-rich metalliferous soils in central Africa (Baker 1981, Reeves et al. 2018). Numerous metalliferous ecosystems have also been created by anthropogenic activities such as mining and smelting (Hou et al. 2020). Because metals become toxic to most living organisms beyond a certain available concentration, metalliferous soils drive local adaptation, and their plant communities exhibit a high level of endemism. These communities consist of species or ecotypes that have developed specific adaptations to cope with metal-related stress (Faucon et al. 2010, Harrison and Rajakaruna 2011). This unique biodiversity also represents an important resource for phytoremediation technologies. As a result, metalliferous vegetation is legally protected under European law (European Habitats Directive 92/43/EEC, Appendix I, habitat type 6130). However, metalliferous flora has declined in several European regions, highlighting the need for deeper ecological understanding of metalliferous ecosystem functioning to support their conservation or restoration (Baumbach 2012).

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As in other harsh ecosystems, facilitation has been shown to be the dominant plant-plant interaction in metalliferous plant communities (e.g. Zvereva and Kozlov 2007, Domínguez et al. 2015, Yang et al. 2015). This facilitation more specifically benefits species or ecotypes less tolerant to metal-induced stress (Nemer et al. 2022, 2023), likely leading to more diverse
metalliferous plant communities. At first glance, given the significant metal-stress in these habitats, it seems logical to expect that metal-tolerant species reduce soil toxicity, thereby enabling less metal-tolerant species to develop. It has been proposed that certain metallophytes species, known as metal hyperaccumulators, can uptake large amounts of metals and deplete the metal content in the soil solution, potentially benefiting neighboring plants (Whiting et al. 2001). However, this effect has never been demonstrated in realistic field studies, as the metals absorbed are later released through litter decomposition, potentially leading to metal enrichment near hyperaccumulating plants, which can be detrimental to their neighbors (Boyd and Martens 1998). A more likely alternative is that the primary positive effects of metal tolerant species on soil properties stem from the stimulation of microbial and enzymatic community (Yang et al. 2015), the enrichment of soil organic matter, which adsorbs metal ions and reduces their bioavailability, and the increase availability of nutrients (Frérot et al. 2006, Navarro-Cano et al. 2019).

However, studies demonstrating the improvement of soil conditions by metallophytes species are scarce in the literature, whereas many other studies have shown that the improvement of microclimatic conditions also plays a role in facilitative interactions in metalliferous systems. In fact, metalliferous systems are often subjected to multiple stresses, as they can be located in regions with arid (e.g. Domínguez et al. 2015) or cold (e.g. Zvereva and Kozlov 2007) climates and in mountain ranges (e.g. Nemer et al. 2023). In the study by Zvereva and Kozlov (2007), protection from cold winds by benefactor plants drove facilitative effects. In Domínguez et al (2015), the most likely facilitative effect was a mitigation of drought stress during the arid Mediterranean summer. Randé et al. (2023a) showed that a decrease in Vapor Pressure Deficit (VPD) by metallophytes species was probably beneficial for plants less tolerant to metals during hot and dry days in summer, even in a region with a temperate climate. The mitigation of VPD has been identified as a crucial mechanism explaining facilitation by nurse plants in various ecosystems (Muhamed et al. 2013, Guignabert et al. 2020, Aguirre et al. 2021). In summary, when climatic stressors are involved, canopies of nurse plants provide shelter that improves climatic conditions, enabling beneficiary plants to better cope with metal-induced stress.

Ongoing climate change imposes additional constraints on ecosystems and has strong impact on those already under strong environmental stress such as deserts or xeric shrublands (Li et al. 2018), as well as those being affected by anthropogenic activities (Staudt et al. 2013). Forest ecosystems show some resilience to rising temperature and summer drought, thanks to the buffering effect of tree canopies on the understorey microclimate (De Frenne et al. 2021,
Michalet et al. 2023). However, many metalliferous ecosystems consist of open grasslands (Baumbach 2012) and could therefore be particularly vulnerable to climate change. In the summer of 2022, an extraordinary heatwave affected the Northern Hemisphere, impacting North America, Europe and Asia (Lu et al. 2023). In western Europe average temperatures were between 4 and 5 °C above the mean of the past 40 years (Ibebuchi and Abu 2023). Such heatwaves, occurring alongside anticyclones, are accompanied by intense drought (Faranda et al. 2023). This raises the question of how plant-plant interactions in metalliferous systems may be altered, particularly in terms of facilitation during such climatic events. This question is especially relevant given that the frequency of such compound climatic events is expected to increase in the future (Zscheischler et al. 2020).

To address this question, we studied several metalliferous ecosystems in a temperate region (the French Pyrenees). In such ecosystems, moderate climatic stress can occur during dry and sunny summer days, triggering facilitation (Randé et al. 2023a), as observed in other open habitats in temperate regions (Delerue et al. 2015). However, intense drought and heat waves likely impose much stronger additional stress on plant communities and species already experiencing significant metal-related stress and not specifically drought-tolerant. There is abundant literature on the outcome of plant-plant interactions under increasing aridity in arid or semi-arid ecosystems (e.g. Filazzola et al. 2020, Zhang et al. 2022, Lucero et al. 2022, Verdú et al. 2024). However, the situation in these metalliferous systems is different and corresponds more to the occurrence of a second stressor, requiring distinct functional and physiological adaptations. In such a situation a cancelling out the effect of the initial stress and collapse of facilitation are probable (He and Bertness 2014). While some studies have examined the modification of plant-plant interactions under simultaneous drought and disturbance (e.g. grazing, Smit et al. 2009, Filazzola et al. 2018), few have addressed this question in the context of a second, unrelated stress. More specifically, regarding the impact of drought and heatwaves, a study in a nutrient poor system in a temperate region reported a collapse of facilitation during a prolonged drought (Guignabert et al. 2020). Similarly, an intense drought episode in peatlands led to the disappearance of positive effects from nurse plants (Koyama and Tsuyuzaki 2013). In forest ecosystems, Saccone et al. (2009) found a collapse of facilitation for a species sensitive to high VPD during the 2003 European heatwave. Based on these findings, we expect a similar decline in plant facilitation in non-arid metalliferous environments during a heatwave event.

Additionally, in the study region, metalliferous ecosystems are found at different elevation, in both mountainous (ca. 1000 m a.s.l.) and subalpine (ca 2000 m a.s.l.) locations. This raises the
questions of neither drought and heatwave have a differential impact on plant-plant interactions depending on altitude. On the one hand, in addition to metal-related stress, metallophyte plant species at high elevations must cope with a shorter vegetative season, with higher photo-inhibition and greater temperature difference between night and day (Choler 2001). Therefore, in line with the rationale presented in the previous paragraph, the occurrence of an additional distinct stressor (such as a drought and heatwave) could have more severe consequences at high elevation and accelerate the collapse of facilitation. On the other hand, given that temperature decreases while rainfall and air humidity increase with altitude, we may expect lower drought and heatwave intensity, and thus a reduced impact on plant-plant interactions. In fact, rising summer temperatures have been shown to alleviate altitude-related stress and enhance ecosystem productivity in alpine or subalpine grasslands in many regions worldwide (e.g. Choler et al. 2021, Liu et al. 2024).

Here, our main objective was to assess potential changes in plant-plant interactions along gradients of soil metal pollution following the drought and heatwave of summer 2022 in Europe. First, we analyzed how these interactions varied during normal climatic years (in 2020 and 2021) by synthesizing results from several experimental studies conducted at multiple sites in a former mining valley in the French Pyrenees, spanning different elevations (mountainous and subalpine vegetation belts). Then, we reported measurements of plant-plant interactions taken in 2022 at the same experimental sites, using the same methodology. We made three different hypotheses: i) facilitation is important in environments with high metal-induced stress even at higher altitude in the studied valley (consistent with the literature on plant-plant interactions along metal pollution gradients); ii) facilitation decreases during stochastic extreme droughts and heatwaves (in line with the limited studies on the impact of such climatic events on plant-plant interactions in non-arid ecosystems); iii) the modification of plant-plant interactions will differ between high and low elevations. This modification could be either more pronounced at higher elevation due to greater cumulative stress accelerating the collapse of facilitative interactions or less significant due to a lower intensity of the drought and heatwave.

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## 181 Materials and Methods

### 182 *Studied sites*

This study was conducted in a French Pyrenean valley (42°49'18.5"N, 0°54'56.4"E) with several remarkable sites with significant metal pollution (Geoderis 2015) resulting from former
mining activities. The primary toxic metals are Zn, Pb and Cd (Supplementary Figure S1).
Some metallophyte species commonly found at these sites (*Armeria muelleri*, *Minuartia verna*,
*Arenaria multicaulis*, *Hutchinsia alpina*) were likely already present in the area on natural
metal-rich outcrops before mining activities began in the 19th century. When mining activities
ceased 60 years ago, these metallophyte species colonized mining wastes near the mining
galleries at high elevation (from 1800 to 2100 m a.s.l) and several former ore-washing sites at
lower elevation (from 900 to 1100 m a.s.l). In several locations near the pollution sources, soils
exhibit extremely high contamination levels, with total Zn and Pb concentrations reaching up
to 10 %, and high bioavailability of toxic Cd, Zn and Pb as estimated by acetic acid extraction
(Table S1). Among the various notable sites (Figure S1), we selected site S1 at low elevation
(1100 m a.s.l) and sites S2, S3 and S4 at higher elevations (1900, 2000 and 2050 m a.s.l
respectively) for our experiments. Large soil-metal pollution gradients were identified during
preliminary soil analyses conducted in 2019. The climate in the region is temperate oceanic,
with an average temperature of 9.5°C and annual rainfall of 1280 mm at the low-elevation site
for the period 1991-2020. The high-elevation sites, approximately 150 m apart, are colder and
wetter, with an average temperature of 3.2°C and annual rainfall of 1597 mm over the same
period (https://www.ibbr.cnr.it/climate-dt/, (Marchi et al. 2020)).

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Characterisation of the Metal Pollution Gradients and the MPG metric in the different sites

A soil characterisation campaign was conducted in summer 2020 across the different sites, with 40 soil samples (0.5 kg each, collected at a depth of 0-15 cm) harvested in each site. Each site was first divided into a regular grid of 40 cells covering the whole studied surface. Within each cell, the location of the soil sample was randomly assigned, ensuring a semi-systematic sampling approach. The precise location of each soil sample was recorded using a differential GPS (© Leica GS10). From these soil samples and additional field surveys, several variables related to soil metal content were determined at each location, including total Zn, Pb (ZnTot, PbTot) and bioavailable Zn, Pb, Cd estimated by acetic acid extraction (Znaa, Pbaa, Cdaa). Additionally, other environmental variables such as soil texture, organic matter content, C/N ratio, pH, soil moisture, and vegetation volume in summer were measured (see Supplementary Table S1). Kriging maps were then generated using the GSTools Python library (Müller et al. 2022) for each of these variables to estimate their value continuously across the study sites (see Figure 1a and b for Znaa in sites S1 and S4). During the various experiments (see later paragraph), the precise position of each experimental plot was also determined using the same
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GPS device. The predicted values from the kriging maps for all environmental variables were then used to estimate their values at each experimental plot location (Figure 1a,b).

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The choice of the most accurate variable to characterize the position of each experimental plot along metal pollution gradients can be somewhat subjective. For instance, Cd is a highly toxic element, but Zn and Pb total concentrations and bioavailability can be also very high, potentially having strong impacts on living organisms. Additionally, many variables with the potential to influence plant-plant interactions were highly correlated with soil metal content. To summarize the complex metal pollution gradients and the environmental variables associated with them, we performed Principal Component Analyses (PCA) considering all variables. Variables varying over several orders of magnitude (metal concentrations) were log-transformed before performing PCA. The first principal component of the PCA (Supplementary Figure S2) was used as a quantitative metric of the Metal Pollution Gradients (MPG), as all variables related to soil metal content were correlated with this axis. Another advantage of this metric for further statistical analyses is its normal distribution (verified by Shapiro-Wilk tests). PCA were performed using the variables measured for the 40 soil samples from each site, along with estimated values for each experimental plot derived from kriging maps. This allowed us to position the experimental plots along the MPG gradients, i.e., along the corresponding principal component axis. We conducted two separate PCA: one for the low-elevation site and another for the high-elevation sites, as mining residues and wastes slightly differed in origin and chemical composition between the two elevations (Geoderis 2015). Additionally, the different elevations correspond to different bioclimatic contexts, potentially leading to different patterns of variation in plant-plant interactions in these two environments.

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## *Variation of plant communities along metal pollution gradients*

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Across the different sites, a gradual shift in plant community composition can be observed (Figure 1c) from the most polluted soils near former mining galleries (or ore-washing factories) dominated by a few metallophyte species, to common mountainous or subalpine grasslands (Nemer et al. 2022, 2023, Randé et al. 2023b). The grass species *Festuca rubra* and *Agrostis capillaris* are abundant in non-polluted soils but are also present in polluted areas, although their relative abundance decreases along the gradient, with only a few individuals remaining in the most polluted zones. Plant communities in moderately polluted soils exhibit a mixed composition, with metallophyte species predominating closer to the pollution source and grass species becoming more dominant farther from it. This results in the establishment of four distinct habitats (Habitat ‘H1’ to Habitat ‘H4’, from most to least polluted), each characterized

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by a specific species composition (Figure 1c), and showing a strong correspondence with the variables used to define the metal-pollution gradients (Supplementary Figure S2, Table S1).

## Monitoring of meteorological conditions during the study

A first weather station (WS-GP1 Delta-T Devices) was installed in late March 2021, less than 1 km from site S1, at the same elevation and with similar exposure. A second weather station was installed in late May 2021 at the top of site S2. The different loggers were positioned 2m above the soil, recording one value per hour. Data from these stations provided information on temperature (T, °C), rainfall (mm) and air relative humidity (RH, %) in 2021 and during the 2022 heatwave at low and high elevations. Additionally, we obtained reference climatic data for the period 1990-2019 for the same location (site S1 and S2) using the ClimateDT web tool available at https://ibbr.cnr.it/climate-dt/. ClimateDT downscales historical monthly, seasonal, and annual climate variables for any location worldwide from a 1-km grid (Marchi et al. 2020). Data used from ClimateDT included monthly minimum and maximum temperatures (monthly means of the daily minimum and maximum temperatures, but not the absolute minimum or maximum recorded each month (Harris et al. 2020)) and monthly cumulative rainfall. Since no weather stations were installed in 2020, ClimateDT data were also used to provide a general characterization of meteorological conditions for that year.

Data obtained from our weather station were used to compute daily minimum and maximum temperatures as well as cumulative rainfall. These values were then aggregated to compute monthly minimum and maximum temperatures and total rainfall, enabling comparisons with ClimateDT data for the reference period. We also calculated the anomalies (i.e., the difference between a given year and the mean value for the 1990-2019 reference period) for the specific climatic conditions of 2022. Additionally, temperature and relative humidity recorded hourly by the weather station were used to calculate the Vapor Pressure Deficit (VPD) as follows:

$$VPD = \frac{100-RH}{100} \times 610.7 \times 10^{\frac{7.5T}{237.3+T}} \quad (1)$$

## Experimental design: the transplantation experiments

2020 and 2021 experiments.

A first experiment was conducted in 2020 (at high-elevation site S2), followed by two others in 2021 (one at low-elevation site S1, one at high-elevation sites S2-S3-S4), all following the same procedure (Table 1). First, in the sites used for the different experiments, we positioned several
plots of approximately 1m² along the gradient of metal pollution (see red circles in Figure 1a,b).

We then collected individuals of easily transplantable perennial grass species (*Festuca rubra* or *Agrostis capillaris*) within each site to serve as phytometer targets. Their root systems were freed from soil and immediately transplanted into each plot, either within the existing vegetation in the most abundant nurse species (Control neighboring modality), or in areas where the canopy of the same nurse species had been removed by cutting it at the ground level before transplantation (Removal neighboring modality). Transplanted individuals were tagged with plastic cable ties to ease relocation. The removal areas represented surfaces of 0.15 to 0.20 m in diameter, a size is appropriate for the small vegetation in studied communities. Transplantation always took place at the beginning of the growing season, and survival was monitored until early autumn at the corresponding elevation. Full details on the methods used in these experiments can be found in Nemer et al. (2022, 2023) and in Randé et al. (2023b). Table 1 also provides an overview of these experiments and their objective. These experiments were designed to address specific questions, such as the facilitative responses of different ecotypes of the same transplanted species or the distinction of the various effects of nurse plants (e.g., canopy effects vs. soil modifications). Here, we reanalysed the corresponding results using a more synthetic approach to facilitate the comparison with the experiment conducted in 2022 (see below). For each plot in each experiment, we calculated the average survival rate of all transplanted individuals, considering the different ecotypes or species and/or different nurse species. This average survival rate was then compared to the position of the different plots along the metal pollution gradients, as defined by the MPG metric (see statistical analyses paragraph).

Experiment of 2022.

In 2022, following the same transplantation protocol, we conducted a new experiment across all four sites. In each site, we transplanted *Festuca rubra* individuals within the most dominant species near each of the 40 locations used during the soil characterization campaign (Control neighboring modality), and in cleared areas created near the same 40 locations by cutting the canopy of other individuals of the same dominant species (Removal neighboring modality). We chose *Festuca rubra* as target species due to its ubiquity across all studied sites. In site S1, four individuals were transplanted per location and modality. In the three high-elevation sites, only two individuals were transplanted per location because of the high number of locations, resulting in a total of 800 transplants for the entire experiment. We followed the same transplantation and monitoring dates as in the previous experiments of 2020 and 2021. Note that in each location and treatment, half of the transplanted individuals were harvested from a
polluted area of the site, while the other half came from an unpolluted area. Since the origin of the transplants did not significantly influence their response to plant interactions, the corresponding results are not presented here.

### Transplant survival performance and plant-plant interaction indices

The primary response variable measured for the transplants was their survival at the end of the experiment. The response calculated for each plot was the mean survival rate [0,1] of the transplants within the same neighboring modalities. Based on these survival rates, we used the importance index ($I_{imp}$) to assess the variation in plant interactions along metal pollution gradients in each experiment. For each plot, the $I_{imp}$ index was calculated as follows (Seifan et al. 2010):

$$I_{imp} = \frac{P_C - P_R}{|P_C - P_R| + |P_R - P_{max}|} \quad (2)$$

where $P_C$ is the survival rate with neighbors (control modality), $P_R$ is the survival rate without neighbors (removal modality), and $P_{max}$ is the maximum survival rate in the experiment. In our case, this index has two advantages compared to the commonly used Relative Intensity Index (RII, Armas et al. 2004). First, it allows for the evaluation of the ecological importance of plant interactions along metal-pollution gradients in comparison to changes in transplant response caused by other biotic or abiotic forces and constraints. This is particularly relevant here, as other climatic constraints were certainly at play, especially in 2022. Second, the calculation of the importance index is always possible, even when all transplants have died in both neighboring modalities, whereas using the RII in such situations results in a null denominator.

### Statistical analyses

All statistical analyses were conducted using R software (R Core Team 2021). First, regarding meteorological data, for a given hour (e.g., 10 am), the same weather station provided VPD values for the desired period (e.g., all days of June) in both 2021 and in 2022. These two paired series of VPD values were then compared using a paired t-test.

Second, for the transplant experiments, we analysed the variation in plant survival rates and $I_{imp}$, calculated for each plot along the metal-pollution gradients. The results for different elevations (low or high elevation) and different years (2020, 2021 or 2022) were analyzed separately. For the experiments conducted in 2020 (high-elevation site, S2) and 2021 (low-elevation site, S1), we used mixed modelling with a random effect to account for block effects. For the experiments
conducted in 2021 and 2022 (3 high-elevation sites, S2-S3-S4) we used mixed modelling with a random effect to account for site effects (see table 1). For the experiment conducted at the low-elevation site in 2022, we used simple linear modelling without random effects, as it was not organized in blocks. Instead, measurements were taken at 40 locations across the site. For all experiments, we analyzed the relationship between survival rates (response variable, arcsine-transformed) with the MPG metric at the corresponding plot, the neighboring modality and their interactions (See also Supplementary Table S2). Similar models were performed to test the effect of the MPG metric on plant survival rate independently for each neighboring modality. To examine plant-plant interaction responses, we performed linear mixed models for each experiment to test the relationship between I<sub>imp</sub> (calculated for each plot) and the corresponding MPG metric (See also Supplementary Table S3). Finally, the normality and homoscedacity of all linear models residuals were systematically checked to ensure that the assumptions of linear modeling were met.

## 362 Results

### 363 *The exceptional heatwave and drought of 2022*

At the low-elevation site, the heatwave began in May 2022 and lasted until August with daytime temperature anomalies (maximum temperature) reaching approximately +3°C. For four consecutive months, these temperatures were among the highest recorded since 1990 (Figure 2a). Nighttime temperature anomalies (minimum temperature) were also observed, though they were less pronounced (Figure 2c). At high elevation, maximum temperatures were also among the highest recorded since 1990, but the anomalies were smaller compared to the low-elevation site (Figure 2b). In contrast, nighttime anomalies were much more pronounced, with temperatures exceeding the 1990-2019 mean by 5 to 6°C. In comparison, 2021 fell within the range of the average temperatures relative to the reference period, while 2020 was warmer than average, though with much lower anomalies than those recorded in 2022 (Figure 2).

Rainfall anomalies indicated that 2022 was exceptionally dry, and a prolonged drought lasting several months and a particularly severe peak in July (Figure 2e,f). There were 18 consecutive days without precipitation, and only 16mm of rainfall recorded between July 1st and 28th at high elevation. In contrast, 2020 was a wet year (except for July), while 2021 was drier than average but much less than 2022.
The combination of high temperatures and low humidity in 2022 led to very high atmospheric drought (VPD), particularly during summer (June and July, Fig. 3). The difference compared to 2021 was substantial at high elevation, with an average daytime VPD increase of +0.42 KPa for both months. The overall average increase, considering both nighttime and daytime, was +0.34 in June and +0.38kPa in July. At low elevation, the difference was also significant but less pronounced in June. The exceptionally high nighttime temperatures recorded at high elevation likely contributed to high VPD early in the day, already noticeable by 10 a.m. (Figure 3). In August, VPD remained higher in 2022 compared to 2021, though the difference was less pronounced, likely due to the return of normal rainfall levels, which increased air relative humidity (not shown).

*Plant-plant interactions along metal-pollution gradients in climatic normal years (2020 and 2021)*

In 2021, at the low-elevation site, a significant shift from negative to positive interactions along the metal pollution gradient was observed (Figure 4c). At high elevation, in 2020 at site S2, transplant survival with neighbors significantly increased along the metal-pollution gradient, while survival in the removal modalities decreased. In 2021, at high-elevation sites S2, S3 and S4, survival in the removal modalities also declined along the gradient (Figure 4b). These survival differences along the gradients resulted in a significant shift from negative to positive plant-plant interactions as soil metal pollution increased in both 2020 and 2021 (Figure 4d).

*Plant-plant interactions along metal-pollution gradients under the drought and heatwave in 2022*

In 2022, observations of the vegetation in July showed the severe impact of the drought and heatwave at both elevations, and in both polluted and non-polluted areas (Figure 5). Immediately after snowmelt, metalliferous plant communities at high elevation appeared normal, with abundant flowering (Figure 5c). However, by July, these communities showed marked drying of aboveground biomass. The impact remained visible even a year later, in 2023, with some metallophyte species in high-elevation metalliferous communities still displaying symptoms of the past drought event (Figure 5f). Regarding our transplants, average survival rates were much lower than in previous years (22% at low elevation compared to 73% in 2021, and only 3% at high elevation compared to 61% and 58% in 2020 and 2021 respectively). Some differences in survival along the metal pollution gradients were still observed. At low elevation, survival increased in the control modality (Figure 4a) and marginally (P=0.08) in the removal
411 modality, resulting in no clear variation in plant-plant interactions (Figure 4c). At high
412 elevation, despite a slight increase in survival in the removal modality (Figure 4b), the overall
413 low survival rates across all modalities led to the absence of significant interactions along the
414 gradient.

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416 Discussion.

417 Consistent with our first hypothesis, our results showed that in climatically normal years, plant-
418 plant interactions shifted from competition to facilitation along metal stress gradients in
419 metalliferous ecosystems, supporting the predictions of the Stress Gradients Hypothesis. In
420 2022, the study area was severely affected by the compound drought and heatwave that
421 impacted the entire Northern Hemisphere. Under such extreme climatic conditions, and in line
422 with our second hypothesis, both facilitation in polluted areas and competition in areas with
423 low pollution levels disappeared. This was likely due to the dominant plants, which usually
424 exert facilitative or competitive effects, being themselves affected by the extreme conditions.
425 Contrary to our third hypothesis, these results did not differ between low- and high-elevation
426 sites, highlighting the overwhelming impact of the intense drought and heatwave at both
427 elevations.

428 *Variation in plant-plant interactions along metal-pollution gradients follows the Stress*
429 *Gradient Hypothesis during normal years*

430 Our results from the experiments conducted in 2020 and 2021 revealed a clear shift from
431 competition to facilitation as pollution increased at both elevations, as predicted by the Stress
432 Gradient Hypothesis. These findings support previous studies on metalliferous ecosystems
433 (Zvereva and Kozlov 2007, Domínguez et al. 2015, Yang et al. 2015, Navarro-Cano et al. 2019)
434 and clearly establish metalliferous ecosystems as model ecosystems for studying plant
435 interactions and community assembly under environmental constraints. Beyond specific
436 physiological impacts and adaptations related to metal toxicity at the cellular level, high metal
437 exposure leads to ecosystems with low plant biomass (Baumbach 2012), similar to alpine or
438 arid ecosystems. These similar effects of different stressors (metal toxicity, cold or high
439 temperatures) are expected to result in similar consequences at high levels of biological
440 organization, such as plant communities (Simmons et al. 2021). Indeed, low competitive
441 interactions in low-biomass plant communities have been recognized since Grime's pioneering
442 work (Grime 1973), and facilitation emerges as the dominant form of plant-plant interaction in
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various stressed ecosystems, including the overlooked metalliferous systems, regardless of
elevation and climatic region (Zvereva and Kozlov 2007, Domínguez et al. 2015, Nemer et al.
2023, Randé et al. 2023b).

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In the various experiments conducted here, the difference in transplant performance between areas under the nurse canopies and those where these canopies were removed suggests that the positive effects observed were primarily short-term effects, occurring during the active metabolic activity and lifespan of the nurse plants (e.g., nutrient uptake, water evapotranspiration or shading (Michalet et al. 2015, Noumi et al. 2016)). Long-term effects related to changes in soil properties are only found in the most polluted habitats (Figure 1c) and were therefore not assessed in this study. Among the potential short-term effects, the alleviation of climatic stress through shading, reducing photo-inhibition, buffering extreme temperatures and lowering air dryness (i.e., VPD) was likely involved in the observed responses. These climatic stressors, whose mitigation is particularly important for plant facilitation in mountainous and alpine grasslands (Choler et al. 2001), may be further intensified by the limited vegetation and low plant cover in severely polluted habitats.

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*The exceptional drought and heatwave in 2022 across different elevations*

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The different years considered in this study exhibited distinct climatic characteristics. While 2020 was a hot and wet year, 2021 included some dry months but with normal temperatures. In contrast, 2022 was marked by both a severe heatwave and drought, in line with the compound climatic event that affected the entire northern hemisphere. This event was exceptional in both intensity and in duration, lasting several months. At first glance, one might assume that such an extreme climatic event would be less pronounced at higher elevation due to the natural decrease in temperature and increase in air humidity with elevation, which leads to a lower VPD. However, beyond absolute temperature or VPD, it is the deviation from average conditions (i.e., the anomaly) at a given elevation that may determine the impact on organisms adapted to their local environment. In 2022, this deviation was not lower at higher elevation but was, in fact, greater for minimum temperature and VPD in June, corresponding to early spring at this elevation. This observation aligns with studies on climate change in the Alps, which suggest that relative temperature increases in summer are expected to be more pronounced at higher elevations than at lower ones (Gobiet et al. 2014). Additionally, the recorded increase of VPD at high elevation in June and July 2022 compared to 2021 (+0.34 and +0.38kPa, considering both nighttime and daytime) was even greater than the increase observed during the intense 2003 heatwave in the Alps at similar elevations (subalpine belt), which was +0.22 kPa above
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average years (Jolly et al. 2005). These findings indicate that, in case of severe drought and
heatwaves, climatic anomalies at high elevation are not necessarily less pronounced than at
lower elevations.

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*Additional stress due to climate change in metalliferous grasslands and consequences for plant-plant interactions*

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Climate change manifests in various ways. In southern Europe, there is a clear progressive trend
towards rising average temperatures together with drier summer seasons (Jacob et al. 2014). In
other words, some regions with temperate or sub-Mediterranean climates are gradually shifting
toward a more Mediterranean-like climate. While the impact of such gradual changes on plant-
plant interactions in metalliferous ecosystems is beyond the scope of this study, we can infer
that: i) Since most metalliferous ecosystems in Europe are composed of herbaceous
communities (Ernst 1990, Baumbach 2012), they will be particularly vulnerable to this changes;
ii) studies on plant-plant interactions in metalliferous semi-arid ecosystems, which have
demontrating positive interactions (Domínguez et al. 2015, Navarro-Cano et al. 2019), may
provide insights into future dynamics; iii) the possible shift in dominant species toward more
drought-tolerant ones raises concerns about the conservation of the unique biodiversity of
metalliferous grasslands in temperate regions (Baumbach 2012).

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Climate change also leads to a higher frequency of extreme events, such as stochastic compound drought and heatwaves. Southern and Western Europe are particularly vulnerable to intense heatwaves compared to other mid-latitudes regions (Jacob et al. 2014, Rousi et al. 2022). When such events affect plant communities in otherwise temperate regions, species that are not specifically drought-tolerant may be particularly impacted. Rising air temperatures have little effect on plant physiology as long as plants can continue evaporating, as evaporation cools plant tissues (De Boeck et al. 2016). However, during drought conditions, stomatal closure prevents transpiration cooling, leading to heat stress that can severely damage or even kill plant tissues. One of the main drivers of this strong impact on plant communities is the combined effect of soil drought (caused in this case by a prolonged absence of rainfall) and air drought (VPD). Previous studies in the same region strongly suggest that the ability of metallophyte nurse plants to mitigate high VPD plays a key role in facilitative mechanisms (Randé et al. 2023a). However, in the extreme drought and heatwave conditions observed here, all plants, including nurse species, were affected (Figure 4). This likely compromised their ability to lower VPD to levels

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more favorable for or neighboring plants. Even in low-pollution areas, competitive species suffered similar impacts, leading to the disappearance of their competitive effects.

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In the ecological literature, the absence of facilitation in stressed environments is most often explained by beneficiary species stress-tolerance and their absence of response to the improvement of the local environment by benefactors (Graff and Aguiar 2017, Liancourt et al. 2017, Filazzola et al. 2020). Here, our observations suggest another reason for the collapse of facilitation in case of drought and heatwaves: an impact on benefactor herbaceous species themselves because there are drought-sensitive. Actually, metalliferous plant species in the region often have low Leaf Dry Matter Content (LDMC) and low Leaf: Mass Area ratio (LMA) (Randé et al. 2023a, 2024). Such functional attributes are correlated with important photosynthetic activity and soil-resource acquisition (Wright et al. 2004, Reich 2014). Reversely, drought-tolerant species are resource-conservative and have opposite characteristics (Poorter et al. 2009, Blumenthal et al. 2020, Kramp et al. 2022). Therefore, the functional attributes of metallophytes species in the region of the study are consistent with their high sensitivity to drought. Noticeably, during the beginning of the drought and heatwave in spring 2022, Randé et al (2024) showed that facilitative effects of the most resource-acquisitive metallophyte species stopped while the facilitative effects of the least resource-acquisitive metallophyte species were still detectable.

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*Different consequences of drought and heatwaves at different altitudes?*

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Contrary to our third hypothesis, our results did not reveal either a lower or higher consequences of the drought and heatwave on plant-plant interactions at high versus low elevation, as facilitation collapsed similarly across all studied sites. Regarding a potential lower impact at higher elevations, we found that the minimum temperature and VPD anomalies were not reduced at high altitude, likely explaining why plant-plant interactions in the subalpine sites were just as affected. Regarding a potentially greater impact due to the combined stresses of elevation, metal pollution and the drought and heatwave, no differences were detected in the short term during study period. However, some elements of our results, along with existing literature on subalpine and alpine vegetation, suggest more significant long-term consequences. At high elevation, the shorter growing season has two key implications. First, strong phenological constraints prevent vegetation from regreening after an extreme climatic episode, whereas low-elevation grasslands are more resilient due to their longer growing season, which
allows for recovery (Cremonese et al. 2017, De Boeck et al. 2018). Additionally, faster recovery at low elevation is facilitated by higher soil fertility and more favorable conditions for plant growth (De Boeck et al. 2018). Second, combined drought and heatwaves are more likely to occur in summer, as seen in June and July in this study. However, at high elevation, this period corresponds to the beginning of the growing season, exacerbating the impacts on plant communities compared to those at lower elevation, where plants are further along in their phenological cycle (Cremonese et al. 2017). The pronounced increase in VPD observed in June at high elevation directly affected the studied plant communities at the start of their growing season, coinciding with our transplantations. This may explain the lower transplant survival rate compared to the low-elevation site. Another explanation is that the VPD anomaly in June was more pronounced at high elevation, likely intensifying its impact on both nurse plants and transplants.

For these reasons, the long-term legacy of such climatic events is expected to be more significant at higher elevations. Previous studies (Cremonese et al. 2017, De Boeck et al. 2018) have documented more persistent shifts in alpine plant communities following summer drought and heatwaves, with an increase in drought-tolerant grasses at the expense of forbs. In metalliferous grasslands, hyper-tolerant metallophytes that dominate polluted habitats and serve as nurse plants are forbs (Nemer et al. 2022, Randé et al. 2023b). If their long-term resilience to such climatic events is lower (as suggested, for instance, by Figure 4f for *Armeria muelleri*), this could have profound consequences for plant community composition and the positive plant-plant interactions typically observed in normal years.

*Conclusion: the role of plant facilitation in buffering climate change impact on metalliferous biodiversity in mountainous environments*

The ability of plant facilitation to buffer heatwaves and drought has been demonstrated for certain beneficiary species in forest systems, provided that water availability is not completely interrupted (Saccone et al. 2009). In temperate regions, deciduous broad-leaved trees effectively reduce VPD in the understory (Michalet et al. 2023), offering a strong example of how plant facilitation can mitigate summer drought stress. However, in more open grasslands, such strong buffering effects are less likely, as dominant herbaceous plants are sensitive to climatic extremes, which can lead to the collapse of facilitation, as observed in this study. Metalliferous grasslands are unique in both their floristic composition and their adaptation to metal-induced stress, and positive plant-plant interactions likely play a role in shaping these rare plant
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community. A decline in facilitation in harsh environments has been proposed as an early
warning signal of an impending critical transition toward degraded vegetation cover
(Verwijmeren et al. 2013). The results presented here raise concerns about the long-term
dynamics of these rare plant communities. At low elevation, the long-term impact of the 2022
drought and heatwave will depend on the ability of metallophyte species to recover after such
extreme events. At high elevation, potentially greater negative effects on metallophyte nurse
plants could disrupt plant facilitation and biodiversity in these ecosystems over the long term.
However, these long-term consequences remain to be fully evaluated.

582
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808 **Statements & Declarations**

809 **Fundings:**

810 This work has been supported by the SIXP project and the French National Research Agency

811 (ANR-19-CE02-0013-01).

812 **Competing Interests**

813 The authors declare no competing interest.

814 **Author Contributions**

815 Florian Delerue conceived the study with help from Richard Michalet. All authors contributed

816 significantly to field work. Valérie Sappin-Didier and Valérie Laperche performed soil

817 analyses. Florian Delerue, Hugo Randé, David Nemer and Lucie Cormont analysed the data.

818 All authors contributed significantly to the writing of the manuscript and gave their approval.

819 **Data Availability**

820 The datasets generated during the current study are available from the corresponding author on

821 reasonable request.

822
<figure><img src="image_2.png" /><figcaption>b)</figcaption></figure>

<figure><img src="image_3.png" /><figcaption>c)</figcaption></figure>

<figure><img src="image_4.png" /></figure>

H1

H2

H3

H4

<figure><img src="image_5.png" /></figure>

<figure><img src="image_6.png" /></figure>

<figure><img src="image_7.png" /></figure>
**Figure 1.** Description of the studied sites and the metal-pollution gradients.

a,b): Maps of the pollution gradients (represented here by Zn acetic acid-extractable concentrations - Znaa) for the low elevation site S1 (a) and the highest-elevation site S4 (b). In all sites, the locations of soil samples collected for pollution level quantification are indicated by black-line circles. These positions were recorded using a differential GPS (© Leica GS10, precision lower than 3 cm). Znaa concentrations at each point are represented using a color scale. Based on soil variable characterization at these points and their geographical coordinates, we interpolluted polluted values across the entire site using ordinary kriging. The positions of experimental plots for the different experiments conducted in 2020 and 2021 were determined using the same differential GPS and are shown by red-line circles. Pollution values for each experimental plot correspond to the kriged predictions at their precise location. Raster orthophoto images were obtained via UAV acquisition in June 2020.

c) Change in plant communities along the pollution gradients. In all sites, plant communities varied across four distinct habitats (from left to right). In the most polluted habitat (H1, left) only a few metallophytes species can grow. The dominant species visible in H1 and H2 is *Armeria muelleri*, other dominant metallophytes species are *Arenaria multicaulis*, *Hutchinsia alpina* and *Minuartia verna*. In the least polluted areas (H4, right), normal plant communities develop, with dominant species including *Festuca rubra* and *Festuca Gautieri*. In intermediate areas, metallophytes species dominate near the highly polluted zones (H2) and become less abundant as pollution levels decrease toward the non-impacted area (H3). The images shown are from site S2.
<figure><img src="image_8.png" /></figure>

<figure><img src="image_9.png" /></figure>

<figure><img src="image_10.png" /></figure>

<figure><img src="image_11.png" /></figure>

<figure><img src="image_12.png" /></figure>

<figure><img src="image_13.png" /></figure>
**Figure 2.** Comparisons of temperatures and rainfall between the studied years and the 1990-2019 period.

In all panels, boxplots represent the distribution of the corresponding variable for the period 1990-2019, obtained from the ClimateDT web tool (https://ibbr.cnr.it/climate-dt/). Data for 2020 were also retrieved from this source, as weather stations had not yet been installed at this time. For the other studied years (2021 and 2022), data were collected from our own meteorological stations (see methods). The color code for the different years is shown in the legend of panel a). Panels a), c) and e) correspond to the low-elevation site S1, where records were available earlier in the spring (April). Panels b), d) and f) correspond to the high-elevation site S2. In all panels, for each month, temperature and rainfall anomalies for the year 2022 (difference from the 1990-2019 mean) are indicated above the x-axis.
<figure><img src="image_14.png" /><figcaption>**Figure 3.** Variation in Vapor Pressure Deficit in June and July during the day in 2021 compared to 2022. In all panels, the mean (+/- SE) is represented for all days of the month at the corresponding hour. For each hour, the results of the paired t-test are shown at the bottom above the x-axis (***, P<0.001; **, P<0.01; *, P<0.05). See the legend in panel a) for the two different years. Panels a) and c) show the variation of VPD for the low-elevation site in June and July, respectively. Panels b) and d) show the variation of VPD for the high-elevation site S2 in June and July, respectively. The results of the paired t-tests considering all values from 10 to 19h are shown on the top of the panels, along with the mean increase of VPD in 2022 compared to 2021.</figcaption></figure>
<figure><img src="image_15.png" /><figcaption>**Figure 4.** Survival rates and *I*<sub>imp</sub> indices variation along the gradients of metal pollution during the different years of the study.</figcaption></figure>

The color code is the same as in Figures 2 and 3 (green for 2020; blue for 2021, orange for 2022). Panels a) and c) correspond to experiments at low elevation (site S1), while panels b and d) correspond to experiments at high elevation (sites S2, S3 and S4). In all panels, for each experiment, the variation of the response variable (either survival rates or *I*<sub>imp</sub>) with the MPG was tested. Fitted lines from statistical models are shown only in cases of significant variation along the MPG gradient (***, P<0.001; **, P<0.01, *P*<0.05).
In a) and b), solid lines are for survival variation with neighboring vegetation in place (“control modality”), while dotted lines are for survival variation without neighbors (“removal modality”). Squares are for response without neighbors, triangles are for control response with neighbors. In c) and d), Pearson correlation coefficients are also shown at the top-left corner. Detailed statistical results are provided in Supplementary Tables S2 (for survival performances) and S3 (for $I_{imp}$).
<figure><img src="image_16.png" /><figcaption>**Figure 5.** Visible impact of the drought and heatwave on plant communities.</figcaption></figure>

Panels a) and b) show pictures of the low-elevation site S1 (the site perimeter is indicated by the red line). In June or July, grasslands at this altitude remain green in average climatic years, as seen in panel a). However, in 2022, all grasses and forbs dried prematurely (panel b).

Panels c) and f) show pictures of the high-elevation site S2. In early spring, right after snowmelt, a bloom of *Armeria muelleri* flowers was observed in highly contaminated areas as in previous years (panel c). However, by summer, vegetation in the contaminated area had completely dried out (panel d), as in non-contaminated
areas of this same site (panel e). In early spring 2023 (panel f), the impacts of the previous year heatwave were
still visible, with only a few *Armeria muelleri* flowers present. Note that pictures c,d and e were taken from
the same location within site S2.
<table><caption>Table 1. Overview of the experimental designs conducted in 2020 and 2021 at the different sites in the valley.</caption><thead><tr><th>Year of experi-ment</th><th>Site (elevation m a.s.l)</th><th>Grass species transplanted</th><th>Dominant “nurse species” (Polluted habitats H1-H2 / Low polluted habitats (H3-H4)</th><th>Total number of plots</th><th>Number of spatial blocks</th><th>Number of corresponding transplants</th><th>Transplantation and end of monitoring</th><th>Rationale</th><th>Reference</th></tr></thead><tbody><tr><td>2020</td><td>S2 (1900)</td><td>Two ecotypes of Agrostis capillaris (one from polluted and one from un-polluted habitats)</td><td>Armeria muelleri / Festuca rubra</td><td>16</td><td>4</td><td>192</td><td>Early June – End of September</td><td>Test of the SGH and of the difference in facilitative responses between ecotypes with different levels of metal-tolerance.</td><td>(Nemer et al. 2022)</td></tr><tr><td>2021</td><td>S2 (1900); S3 (2000); S4 (2050)</td><td>Two ecotypes of Festuca rubra (one from polluted and one from un-polluted habitats)</td><td>Armeria muelleri, Arenaria multicaulis, Minuartia verna, Hutchinsia alpina / Festuca rubra, Festuca eskia, Agrostis capillaris</td><td>24</td><td>3 (each site was considered as a block)</td><td>576</td><td>Early June – End of September</td><td>Generalization of the results of 2020 in different sites with different bedrocks, and with diverse nurse species</td><td>(Nemer et al. 2023)</td></tr><tr><td>2021</td><td>S1 (1100)</td><td>Festuca rubra and Agrostis capillaris from low-polluted habitat</td><td>Hutchinsia alpina, Arenaria multicaulis / Festuca rubra, Deschampsia flexuosa</td><td>16</td><td>4</td><td>288</td><td>Mid-April – Mid-October</td><td>Distinction between soil and canopy effects of different nurse species along a metal-pollution gradient</td><td>(Randé et al. 2023b)</td></tr></tbody></table>
<figure><img src="image_17.png" /><figcaption>**Figure S1.** Location of the experimental sites.</figcaption></figure>
Site S1 is at low elevation (approximately 1100 m a.s.l.). Sites S2, S3 and S4 are located in the subalpine belt (ranging from 1900 to 2050 m a.s.l.). All sites are situated in a valley in the French Pyrenees, where important mining activities ceased 60 years ago.
<figure><img src="image_18.png" /></figure>

Plot Diagram

a)

<figure><img src="image_19.png" /></figure>

Variables Diagram

b)

<figure><img src="image_20.png" /></figure>

Plot Diagram

c)

<figure><img src="image_21.png" /></figure>

Variables Diagram

d)

**Figure S2.** Principal component analyses displaying the variability of soil metal variables and other environmental variables across the four habitats along the studied gradients.

Panels a) and b) correspond to the low-elevation site S1, while panels c) and d) correspond to the high-elevation sites (S2, S3, S4). The data used for these PCA were obtained from the soil contamination survey and interpolated data for experimental plots (see Figure 1a,b).

Cd_aa: cadmium extracted with acetic acid; Pb_aa: lead extracted with acetic acid; Zn_aa: zinc extracted with acetic acid; Cd_tot: total cadmium concentration; Pb_tot: total lead concentration;
<caption>**Table S1.** Variation of metal concentrations and environmental variables across the four habitats along the metal-pollution gradients in the experimental sites.</caption>

a) Low-elevation site (S1)

<table><thead><tr><th>Variables</th><th>unit</th><th>H1</th><th>H2</th><th>H3</th><th>H4</th><th>P-value</th></tr></thead><tbody><tr><td>Pb<sub>TOT</sub></td><td>mg.kg<sup>-1</sup></td><td>4487 ± 485</td><td>c</td><td>4833 ± 673</td><td>c</td><td>2860 ± 398</td><td>b</td><td>1169 ± 169</td><td>a</td><td><strong>&lt;0.001</strong></td></tr><tr><td>Zn<sub>TOT</sub></td><td>mg.kg<sup>-1</sup></td><td>17168 ± 1865</td><td>b</td><td>15019 ± 1397.5</td><td>b</td><td>6698 ± 1280.8</td><td>a</td><td>3762 ± 993.6</td><td>a</td><td><strong>&lt;0.001</strong></td></tr><tr><td>Cd<sub>AA</sub></td><td>mg.kg<sup>-1</sup></td><td>18 ± 3.0</td><td>c</td><td>16 ± 2.8</td><td>bc</td><td>10 ± 1.1</td><td>ab</td><td>5 ± 0.7</td><td>a</td><td><strong>&lt;0.001</strong></td></tr><tr><td>Pb<sub>AA</sub></td><td>mg.kg<sup>-1</sup></td><td>120 ± 22.4</td><td>c</td><td>105 ± 20.9</td><td>bc</td><td>49 ± 11.4</td><td>ab</td><td>22 ± 6.4</td><td>a</td><td><strong>&lt;0.001</strong></td></tr><tr><td>Zn<sub>AA</sub></td><td>mg.kg<sup>-1</sup></td><td>1087 ± 181</td><td>b</td><td>931 ± 163</td><td>b</td><td>364 ± 65</td><td>a</td><td>114 ± 24</td><td>a</td><td><strong>&lt;0.001</strong></td></tr><tr><td>Clay &amp; fine silt</td><td>g.kg<sup>-1</sup></td><td>300 ± 14.0</td><td>a</td><td>344 ± 20.6</td><td>ab</td><td>401 ± 15.6</td><td>bc</td><td>439 ± 10.4</td><td>c</td><td><strong>&lt;0.001</strong></td></tr><tr><td>Fine &amp; coarse sand</td><td>g.kg<sup>-1</sup></td><td>534 ± 18.0</td><td>b</td><td>459 ± 21.9</td><td>a</td><td>432 ± 17.1</td><td>a</td><td>420 ± 8.2</td><td>a</td><td><strong>&lt;0.001</strong></td></tr><tr><td>Organic matter</td><td>g.kg<sup>-1</sup></td><td>51 ± 5.2</td><td>a</td><td>73 ± 7.3</td><td>ab</td><td>78 ± 6.8</td><td>b</td><td>116 ± 7.4</td><td>c</td><td><strong>&lt;0.001</strong></td></tr><tr><td>Relative humidity</td><td>%</td><td>13 ± 1.5</td><td></td><td>10 ± 2.1</td><td></td><td>9 ± 1.2</td><td></td><td>11 ± 1.2</td><td></td><td><strong>&gt;0.1</strong></td></tr><tr><td>Vegetation volume</td><td>dm<sup>3</sup></td><td>76 ± 27</td><td>a</td><td>164 ± 44</td><td>a</td><td>488 ± 193</td><td>a</td><td>496 ± 124</td><td>a</td><td><strong>&lt;0.001</strong></td></tr><tr><td>pH<sub>H<sub>2</sub>O</sub></td><td></td><td>6.2 ± 0.2</td><td>b</td><td>6.1 ± 0.1</td><td>b</td><td>5.6 ± 0.1</td><td>ab</td><td>5.1 ± 0.1</td><td>a</td><td><strong>&lt;0.001</strong></td></tr><tr><td>C/N</td><td></td><td>11.7 ± 0.1</td><td></td><td>11.9 ± 0.1</td><td></td><td>12.1 ± 0.4</td><td></td><td>11.2 ± 0.1</td><td></td><td><strong>&lt;0.1</strong></td></tr></tbody></table>
b) High-elevation sites (S2, S3, S4)

<table><thead><tr><th>Variables</th><th>unit</th><th>H1</th><th>H2</th><th>H3</th><th>H4</th><th>P-value</th></tr></thead><tbody><tr><td rowspan="5">Metals</td><td>Pb<sub>TOT</sub></td><td>mg.kg<sup>-1</sup></td><td>36276 ± 2689</td><td>c</td><td>28475 ± 2102</td><td>c</td><td>20117 ± 2417</td><td>ab</td><td>5390 ± 1068</td><td>a</td><td><strong>&lt;0.001</strong></td></tr><tr><td>Zn<sub>TOT</sub></td><td>mg.kg<sup>-1</sup></td><td>75108 ± 9539</td><td>c</td><td>24875 ± 2892</td><td>b</td><td>8507 ± 1225</td><td>a</td><td>2787 ± 280</td><td>a</td><td><strong>&lt;0.001</strong></td></tr><tr><td>Cd<sub>AA</sub></td><td>mg.kg<sup>-1</sup></td><td>43 ± 3.6</td><td>c</td><td>19 ± 1.6</td><td>b</td><td>8 ± 0.9</td><td>a</td><td>3 ± 0.3</td><td>a</td><td><strong>&lt;0.001</strong></td></tr><tr><td>Pb<sub>AA</sub></td><td>mg.kg<sup>-1</sup></td><td>667 ± 89.6</td><td>c</td><td>629 ± 65.2</td><td>c</td><td>380 ± 57.4</td><td>b</td><td>66 ± 25.2</td><td>a</td><td><strong>&lt;0.001</strong></td></tr><tr><td>Zn<sub>AA</sub></td><td>mg.kg<sup>-1</sup></td><td>3143 ± 305</td><td>c</td><td>1225 ± 166</td><td>b</td><td>330 ± 82</td><td>a</td><td>71 ± 9</td><td>a</td><td><strong>&lt;0.001</strong></td></tr><tr><td rowspan="7">Environmental</td><td>Clay &amp; fine silt</td><td>g.kg<sup>-1</sup></td><td>191 ± 12.0</td><td>a</td><td>186 ± 6.1</td><td>ab</td><td>225 ± 9.6</td><td>bc</td><td>234 ± 7.8</td><td>c</td><td><strong>&lt;0.001</strong></td></tr><tr><td>Fine &amp; coarse sand</td><td>g.kg<sup>-1</sup></td><td>695 ± 17.9</td><td>d</td><td>647 ± 11.4</td><td>c</td><td>562 ± 12.4</td><td>b</td><td>511 ± 6.7</td><td>a</td><td><strong>&lt;0.001</strong></td></tr><tr><td>Organic matter</td><td>g.kg<sup>-1</sup></td><td>47 ± 3.8</td><td>a</td><td>73 ± 3.8</td><td>b</td><td>101 ± 5.7</td><td>c</td><td>124 ± 6.6</td><td>d</td><td><strong>&lt;0.001</strong></td></tr><tr><td>Relative humidity</td><td>%</td><td>14 ± 0.7</td><td>a</td><td>19 ± 0.5</td><td>b</td><td>21 ± 1.1</td><td>bc</td><td>22 ± 0.6</td><td>c</td><td><strong>&lt;0.001</strong></td></tr><tr><td>Vegetation volume</td><td>dm<sup>3</sup></td><td>41 ± 11</td><td>a</td><td>49 ± 13</td><td>a</td><td>115 ± 21</td><td>ab</td><td>139 ± 37</td><td>b</td><td><strong>&lt;0.05</strong></td></tr><tr><td>pH<sub>H<sub>2</sub>O</sub></td><td></td><td>7.6 ± 0.1</td><td>c</td><td>7.2 ± 0.1</td><td>b</td><td>6.5 ± 0.1</td><td>a</td><td>6.4 ± 0.1</td><td>a</td><td><strong>&lt;0.001</strong></td></tr><tr><td>C/N</td><td></td><td>14.6 ± 0.3</td><td>c</td><td>13.1 ± 0.2</td><td>b</td><td>12.6 ± 0.2</td><td>b</td><td>11.0 ± 0.2</td><td>a</td><td><strong>&lt;0.001</strong></td></tr></tbody></table>

These results correspond to the soils sampled during the contamination survey. The different habitats correspond to those presented in Figure 1c and were initially classified based on the composition of plant communities. Different lowercase letters indicate significant differences in the considered variable between habitats (ANOVA followed by post hoc Tukey pairwise comparisons). Total metals concentrations in the soil (Pb<sub>TOT</sub>, Zn<sub>TOT</sub>), were determined after acid digestion using a mixture of hydrofluoric and perchloric acids (NF X 31–147, 1996), followed by analysis of the solutions using inductively coupled plasma mass spectrometry (ICP-MS). To measure bioavailable metals (Cd<sub>AA</sub>, Pb<sub>AA</sub>, Zn<sub>AA</sub>),
extractions with 0.11M acetic acid were performed (Rauret et al., 1999), followed by analysis of the solutions using inductively coupled plasma atomic emission spectroscopy (ICP-AES). Soil texture (Clay, Silt and Sand content) was estimated using the Robinson pipette method (NF X 31-107, 1983). Dry combustion was used to determine the carbon content (CTOT) and total nitrogen content (NTOT) after carbonate correction (CTOT: NF ISO 10694:1995, NTOT: NF ISO 13878). Organic matter (OM) content was calculated by multiplying soil organic carbon content (NF ISO 14235: 1998) by 1.72 (Pribyl, 2010). Soil pH was measured in water with a 1:5 soil: water ratio (NF ISO 10390:2005). Soil moisture (volume %) was measured in the field seven days after the last rainfall, on a warm, sunny day on June 22, using a portable moisture probe (https://delta-t.co.uk/product/ml3/). The probe was inserted at four locations in each plot, and the four values were averaged to estimate soil moisture. Vegetation cover (%) and mean vegetation height were also recorded in each plot, allowing for the calculation of vegetation volume (m³) (volume m³ = vegetation cover % on 1m² multiplied by mean vegetation height (m). PCA<sub>MPG</sub> is the synthetic variable representing the metal pollution gradient obtained from the PCA of all other variables (see Figure. S2).
<table><thead><tr><th rowspan="2">a)</th><th colspan="3">Survival (2021)</th></tr><tr><th>dF</th><th>F</th><th>P</th></tr></thead><tbody><tr><th>Factors</th><td></td><td></td><td></td></tr><tr><th>MPG</th><td>1,28</td><td>1.23</td><td>0.276</td></tr><tr><th>Neighbouring modality</th><td>1,28</td><td>1.14</td><td>0.294</td></tr><tr><th>MPG X Neighbouring modality</th><td>1,28</td><td>2.08</td><td>0.159</td></tr></tbody></table>

<table><thead><tr><th rowspan="2">b)</th><th colspan="3">Survival (2022)</th></tr><tr><th>dF</th><th>F</th><th>P</th></tr></thead><tbody><tr><th>Factors</th><td></td><td></td><td></td></tr><tr><th>MPG</th><td>1,74</td><td>12.80</td><td><strong>&lt;0.001***</strong></td></tr><tr><th>Neighbouring modality</th><td>1,74</td><td>4.29</td><td><strong>0.041*</strong></td></tr><tr><th>MPG X Neighbouring modality</th><td>1,74</td><td>0.36</td><td>0.547</td></tr></tbody></table>

<table><thead><tr><th rowspan="2">c)</th><th colspan="3">Survival (2020)</th></tr><tr><th>dF</th><th>F</th><th>P</th></tr></thead><tbody><tr><th>Factors</th><td></td><td></td><td></td></tr><tr><th>MPG</th><td>1,28</td><td>0.01</td><td>0.943</td></tr><tr><th>Neighbouring modality</th><td>1,28</td><td>5.54</td><td><strong>0.025*</strong></td></tr><tr><th>MPG X Neighbouring modality</th><td>1,28</td><td>25.23</td><td><strong>&lt;0.001***</strong></td></tr></tbody></table>

<table><thead><tr><th rowspan="2">d)</th><th colspan="3">Survival (2021)</th></tr><tr><th>dF</th><th>F</th><th>P</th></tr></thead><tbody><tr><th>Factors</th><td></td><td></td><td></td></tr><tr><th>MPG</th><td>1,44</td><td>0.99</td><td>0.324</td></tr><tr><th>Neighbouring modality</th><td>1,44</td><td>2.67</td><td>0.109</td></tr><tr><th>MPG X Neighbouring modality</th><td>1,44</td><td>8.15</td><td><strong>0.006**</strong></td></tr></tbody></table>

<table><thead><tr><th rowspan="2">e)</th><th colspan="3">Survival (2022)</th></tr><tr><th>dF</th><th>F</th><th>P</th></tr></thead><tbody><tr><th>Factors</th><td></td><td></td><td></td></tr><tr><th>MPG</th><td>1,234</td><td>7.04</td><td><strong>0.008**</strong></td></tr><tr><th>Neighbouring modality</th><td>1,234</td><td>2.60</td><td>0.107</td></tr><tr><th>MPG X Neighbouring modality</th><td>1,234</td><td>1.79</td><td>0.181</td></tr></tbody></table>
**Table S2:** Results of the linear (mixed) models analysing the effect of the Metal Pollution Gradient (MPG), Neighbouring modality, and their interactions on plant survival (with arcsine transformation) in the different experiments.

a) Results for survival rates in 2021 at the low-elevation site S1 (corresponding to Fig 5a, blue dots).

b) Results for survival rates in 2022 at the low-elevation site S1 (corresponding to Fig 5a, orange dots).

c) Results for survival rates in 2020 at the high-elevation site S2 (corresponding to Fig 5b, green dots).

d) Results for survival rates in 2021 at the high-elevation sites S2-S3-S4 (corresponding to Fig 5b, blue dots).

e) Results for survival rates in 2022 at the high-elevation sites S2-S3-S4 (corresponding to Fig 5b, orange dots).
<table><thead><tr><th rowspan="2">a)</th><th rowspan="2">MPG</th><th colspan="3">I<sub>imp</sub></th></tr><tr><th>dF</th><th>F</th><th>P</th></tr></thead><tbody><tr><td>2021</td><td></td><td>1,14</td><td>9.89</td><td><strong>0.008**</strong></td></tr><tr><td>2022</td><td></td><td>1,37</td><td>0.05</td><td>0.944</td></tr></tbody></table>

<table><thead><tr><th rowspan="2">b)</th><th rowspan="2">MPG</th><th colspan="3">I<sub>imp</sub></th></tr><tr><th>dF</th><th>F</th><th>P</th></tr></thead><tbody><tr><td>2020</td><td></td><td>1,14</td><td>34.05</td><td><strong>&lt;0.001***</strong></td></tr><tr><td>2021</td><td></td><td>1,22</td><td>15.57</td><td><strong>&lt;0.001***</strong></td></tr><tr><td>2022</td><td></td><td>1,117</td><td>2.57</td><td>0.111</td></tr></tbody></table>

**Table S3:** Results of the linear (mixed) model analysing the effect of the Metal Pollution Gradient (MPG) on I<sub>imp</sub> indices.

a) Results for 2021 and 2022 at the low-elevation site S1 (corresponding to Fig 5c)

b) Results for 2020, 2021 and 2022 at the high-elevation sites S2-S3-S4 (corresponding to Fig 5d). Results for 2020 where obtained on site S2 only (see methods and Table S1 c)