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Hungarian Academy of Sciences, Hungary

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José Antonio Molina,
Complutense University of Madrid, Spain
Panayiotis G. Dimitrakopoulos,
University of the Aegean, Greece
Florian Delerue,
Institut Polytechnique de Bordeaux, France

*CORRESPONDENCE

Renaud Jaunatre

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renaud.jaunatre@inrae.fr

RECEIVED 19 January 2023

ACCEPTED 08 May 2023

PUBLISHED 26 May 2023

CITATION

Jaunatre R, Buisson E, Leborgne E and Dutoit T (2023) Soil fertility and landscape surrounding former arable fields drive the ecological resilience of Mediterranean dry grassland plant communities.
*Front. Ecol. Evol.* 11:1148226.
doi: 10.3389/fevo.2023.1148226

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© 2023 Jaunatre, Buisson, Leborgne and Dutoit. This is an open-access article distributed under the terms of the [Creative Commons Attribution License (CC BY)](https://creativecommons.org/licenses/by/4.0/). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Soil fertility and landscape surrounding former arable fields drive the ecological resilience of Mediterranean dry grassland plant communities

Renaud Jaunatre¹*, Elise Buisson², Elsa Leborgne¹,² and Thierry Dutoit²

¹Université Grenoble Alpes, INRAE, LESSEM, Saint-Martin-d'Hères, France, ²IMBE, Avignon University, CNRS, IRD, Aix Marseille University, IUT, Avignon, France

**Introduction:** Semi-natural dry grasslands are among the species-richest and most diverse habitats in Europe especially in the Mediterranean Basin. They evolved with long-term severe environmental constraints, either biotic (e.g., grazing or mowing) or abiotic (e.g., dryness or oligotrophy). Their recovery after sever anthropogenic disturbance, such as cultivation is often very slow and incomplete. A deeper understanding of the processes driving the recovery would be of great benefit to better restore or conserve these grasslands. We used the filter model as a framework to assess the relative importance of dispersion, abiotic and biotic filters as drivers of this recovery.

**Methods:** We studied the medium-term dynamics of 37 Mediterranean dry grassland plant communities after an episode of cultivation in the plain of La Crau (Southern France) by two census: after 25 years of abandonment, and revisited 10 years later (35 years of abandonment). Our aims were to characterize the temporal medium-term dynamics of these communities, to measure their taxonomic resilience and to identify the drivers. We performed structural equation modeling to measure the importance of the various drivers, with cover of remnant dry grasslands surrounding each studied community as a proxy for the dispersion filter, soil pH and phosphorus content as proxies for the abiotic filter and cover of the most dominant species as a proxy for the biotic filter.

**Results and Discussion:** Our results show that former arable field vegetation do not recover after more than 40 years. The three filters drive the recovery of dry grassland vegetation: former arable fields that have lower phosphorus content (abiotic filter) have a lower cover of dominant species (biotic filter), a higher species richness and a higher similarity with remnant dry grassland plant communities, especially if the former arable fields are surrounded by remnant dry grasslands (dispersion filter).

KEYWORDS

old field, secondary succession, resilience, plant community, dry grassland

## 1. Introduction

Impacts of land-use changes, notably abandonment of intensive agriculture, are a major topic in conservation biology (Stoate et al., 2009). The conversion from intensive cultivation to extensive grasslands often raises the issue of ecosystem ability to recover their pre-cultivation state of semi-natural species-rich grasslands (Cramer and Hobbs, 2012). This is an important
topic as ecosystems recovering after cultivation will cover more than 4 billion ha in the next decades (including both grasslands and forests; Hurtt et al., 2011). Secondary succession is an essential concern in plant ecology (Cramer et al., 2008; Cramer and Hobbs, 2012), not only for theoretically-driven studies (Connell and Slatyer, 1977; Prach and Walker, 2011; Seabloom et al., 2020), but also for conservation and restoration perspectives (Luken, 1990; Walker et al., 2007; Suding, 2011; Rapson, 2023).

Species-rich grasslands are rarely resilient to severe anthropogenic disturbances (Helm et al., 2019; Nerlekar and Veldman, 2020; Buisson et al., 2022). They are composed with species that developed strategies to survive in moderately stressed and/or disturbed environment (i.e., xericity, grazing, mowing, combined or not) but not to recover after severe disturbance (Herben et al., 2018), such as intensive arable cultivation which involve soil disturbance as well as nutrient, herbicide and pesticide additions. In order to restore these degraded ecosystems it would be useful to better understand the drivers of recovery (i.e., their relative importance, their magnitude and directions). These drivers could then be used as levers that practitioners can modify.

Processes have been linked to theoretical models in order to summarize how communities assemble (Keddy, 1992; Lortie et al., 2004; Vellend, 2016). Most of these models are structured by filters that constrain community composition starting from a regional species pool. The first filter is dispersion: species have to be able to disperse and reach the community. This ability depends both on the landscape matrix and on the dispersability of species found in adjacent areas (Gibson and Brown, 1991; Pärtel and Zobel, 1999; Lindborg and Eriksson, 2004; Herault and Thoen, 2009). The second is the abiotic filter: species have to be able to germinate, grow and reproduce under given environmental conditions, and this ability depends on each species physiological capability (Kraft et al., 2015). The last one is the biotic filter: the occurrence and nature of biotic interactions alter the first two filters either by enhancing their mesh (sensu White and Jentsch, 2004), e.g., by increasing dispersion or by facilitating growth or establishment in limiting environmental conditions; or by reducing their mesh, e.g., by competitive exclusion (Bruno et al., 2003; Lortie et al., 2004). The aim of this paper is to disentangle the effect of dispersion, abiotic and biotic filters in driving the recovery of species-rich dry grassland plant communities after decades of intensive cultivation abandonment.

In this study, we measured diversity and composition recoveries in 35 and 45 year-old former arable fields in Mediterranean semi-natural species-rich dry grasslands and we have a hypothesis for each filter. The amount of remnant ecosystem in the surrounding of the former arable fields was used as a proxy of the dispersion filter as it provides a seed source for species occurring in the dry grassland. Our hypothesis was that its importance was rather low in driving plant communities. It is indeed widely recognized that most of the species from dry grasslands do not produce a permanent seed bank or seed that disperse on large distances (Graham and Hutchings, 1988; Buisson et al., 2006; Török et al., 2018; Arruda et al., 2021). Considering the size of the former arable fields (>100,000 m²) and the fact that most of the species do not colonize more than a few meters after decades (Buisson and Dutoit, 2004, 2006), these species are not expected to disperse to the whole area of each former arable fields, whatever the amount of remnant ecosystem in the surroundings. Two soil variables that were modified during former cultivation episode were used as proxies for the abiotic filter: one is related to fertility (phosphorus content) and the other is not directly

related to soil fertility (soil pH). Phosphorus is one of the most important abiotic drivers of grassland community assembly (Yang et al., 2022). It is also known to remain in soil decades after fertilized cultivation, contrary to potassium or nitrogen (Smits et al., 2008; Jaunatre et al., 2016; Saatkamp et al., 2021). Ploughing which scraped the underlying calcareous conglomerate (Römermann et al., 2005), changed soil chemistry, increasing CaO, CaCO3 and pH. We thus selected pH as a proxy of these changes, as they may prevent the plant community to recover the composition of remnant dry grasslands [e.g., pH contributes to explain plant species distribution (Dubuis et al., 2013)]. Our hypotheses were that soil pH would influence plant community composition as soil pH is also a soil physical-parameter integrative of others changes than chemical fertility (i.e., increase of Ca and CaCO3 contents) while phosphorus content would decrease diversity through an increase in competition (Janssens et al., 1998; Palpurina et al., 2019). The biotic filter may depend on the abiotic characteristics (Belyea, 2004). During former arable field recovery, plant species have to face dense cover of ruderals or opportunistic species (Baeten et al., 2009; Öster et al., 2009). Hence, we used the dominant species cover as an indicator of potential competition in former arable fields and thus as a proxy of the biotic filter. Our hypothesis was that phosphorus content would drive the dominant species cover which itself would drive the plant community diversity. We also hypothesized that high composition recovery would only occur when species richness has recovered. The composition of the community could indeed be different from that of the remnant dry grasslands if the number of species is lower.

We monitored vegetation on 37 former arable field plots and 10 remnant dry grassland plots (in 2009 and in 2019), respectively 35 and 45 years after cultivation abandonment. We also characterized soil physico-chemical parameters on each plot and the landscape in the surroundings of each plot. In order to link the different drivers of plant community assembly we performed a structural equation modeling for each date were filter proxies were explanatory variables and vegetation diversity and composition were response variables.

# 2. Materials and methods

## 2.1. Site description

The La Crau area is a large dry grassland in southeastern France (ca. 10,000 ha; c. 43°33' N, 4°52' E; Figure 1) that has been shaped by (i) a Mediterranean climate with mean annual temperature of 15°C, a variable annual sum of precipitation between 400 and 600 mm concentrated in autumn, 4 months of summer drought, and more than 110 days with a >50 km.h-1 wind; (ii) 40 cm deep soil composed with about 50% of siliceous stones overlaying a calcareous conglomerate layer, making the alluvial water table unreachable by the roots of plants and (iii) itinerant sheep that have grazed the area over the past several 1000 years (Devaux et al., 1983; Buisson and Dutoit, 2006). This has led to a unique and species-rich plant community composed mainly by a high number of annuals yet dominated in biomass by perennials, such as *Brachypodium retusum* Pers. and *Thymus vulgaris* L. Most of the species occurring in La Crau area are species adapted to extensive grazing, xeric and oligotrophic conditions. This community characterizes the remnant ecosystem in this study (i.e., that has been grazed for millennia but that has not undergone any disturbance due to cultivation) and will henceforth be referred to as dry grasslands. The
<figure><img src="image_84.png" /><figcaption>FIGURE 1</figcaption></figure>

Map of the La Crau area in 2023. Remnant dry grasslands habitats are shown in grey. Colored large circles are remnant dry grasslands plots whereas colored small squares are former arable field plots. The different geographical areas are discriminated by different colors that are used in the ordination plot (Figure 4).

60,000 ha of original dry grasslands was characterized by relatively homogeneous vegetation composed only of different micro-patterns due to particular soil conditions and difference in sheep grazing regimes (Martin et al., 2022), before being fragmented by various severe anthropogenic disturbances, in particular, cultivation (Devaux et al., 1983; Buisson and Dutoit, 2006). Melon cultivation occurred in the La Crau area between 1971 and 1988. This cultivation implied soil disturbance, plant mortality and fertilizers application. Most of the fields

have been subsequently abandoned; they now contain various plant communities dominated by *Anisantha* species (Römermann et al., 2005).

## 2.2. Site selection

Mapping based on aerial photographs enabled us to identify more than 220 areas that (i) appear as dry grasslands on the earliest
photographs available (1947); (ii) show evidence of cultivation in
later photographs (1955–1971–1975–1978–1979–1984–1988); and
(iii) were abandoned between 1978 and 1988. After abandonment, all
these areas were grazed again with the same intensity as the remnant
dry grasslands. To avoid the introduction of an additional source of
variability, we focused only on former open-field melon cultivation
and not on large plastic tunnel fields that left less-disturbed trackways
between the tunnels (Römermann et al., 2005). Thirty-seven of these
Former Arable Fields, hereafter called FAF, were chosen and are
spatially randomly distributed. The distance between any two sampled
sites varies between 100 m and 20.9 km (Figure 1).

## 2.3. Vegetation sampling

Vegetation sampling was carried out at the center of each of the 37 FAF and of 10 dry grassland areas from all geographical zones of the La Crau area (Figure 1) in 2009 and in 2019, by the same team of botanists. On each FAF and remnant dry grassland areas, we monitored the vegetation in three 4 m² quadrats at the tips of an 11 m side triangle. A Braun-Blanquet coefficient (Braun-Blanquet et al., 1952) was given to all plant species recorded and then transformed into percentage according to Guinochet (1973; 5: 87.5%, 4: 62.5%, 3: 37.5%, 2: 17.5%, 1: 5% and +: 0.1%). To avoid biases in abundance attributions, we transformed these percentages into relative percentage to the total vegetation cover. By doing so, we keep abundance hierarchy but we lever the potential overlap between species cover by constraining the sum of abundances into the total vegetation cover. We used species richness (alpha diversity) and similarity to the remnant dry grassland communities (composition) as response variables. The similarity to the remnant dry grassland communities was calculated as 1 – the minimum Bray-Curtis distance (Bray and Curtis, 1957) with the remnant dry grassland plot communities. We used the minimum Bray-Curtis distance instead of the average value in order to reduce the effect of the heterogeneity of remnant dry grassland vegetation. This similarity index with remnant dry grassland communities was thereafter referred as composition.

## 2.4. Explanatory variables

### 2.4.1. Remnant dry grassland cover

We used remnant dry grassland percent cover within a 900 m radius circle around the centre of each field as a proxy of dispersion potential. Map of remnant dry grassland areas was provided by the national reserve environmental managers (Réserve Nationale des Coussouls de Crau, Wolff et al., 2015). The 900 m radius was chosen based on preliminary analyses showing a tradeoff between (i) a smaller radius that would have caused the landscape factors to be especially influenced by the area of each field, and (ii) a larger radius that would have made the landscape factors of two fields close together almost identical. Cover were calculated using Geographical Information System software: Quantum GIS 2.18 "Las Palmas" (QGIS Development Team, 2016).

### 2.4.2. Soil analyses

We used soil analyses as a proxy of abiotic filter. For soil analyses,
in 2009 and in 2019, a subsample of 70 g of soil was gathered in each

of the three vegetation quadrats in the topsoil layer (0-10 cm) before
being pooled to obtain one 210 g soil sample for each FAF. Samples
were then sieved with a 2 mm mesh sieve. Analyses were carried out
by the INRAE (Institut National de Recherche pour l'Agriculture
l'Alimentation et l'Environnement). Phosphorus content (P2O5; Olsen
et al., 1954) and water pH were measured according to the standard
methods described in Baize (2018).

### 2.4.3. Dominance

We used the dominance as a proxy of the biotic filter. Dominance
was calculated as the sum of abundances of the four most abundant
species in each quadrat, which represented in average 45% of the sum
of all species abundances (Appendix 1).

## 2.5. Statistical analyses

All the analyses were conducted using R 4.2.2 (R Core
Team, 2022).

The alpha diversity of remnant dry grasslands and FAF were
compared using a generalized linear mixed model performed with
LME4 package (Bates et al., 2015). The kind of plant community
(remnant dry grasslands or FAF) and monitoring years were included
as fixed factors and the monitoring years and plot interaction as
random factor to take into account the repeated nature of data.

In order to describe the vegetation of dry grasslands and FAF
together, a Non-metric Multidimensional Scaling (NMDS) ordination
was performed on the vegetation matrix to ordinate quadrats
according to their plant community characteristics (Borcard et al.,
2011) with VEGAN package (Oksanen et al., 2022). We performed a
permutational analysis of variance using Bray-Curtis distance matrices
(McArdle and Anderson, 2001), with the VEGAN::ADONIS2
function. This analysis measures the difference in the position of the
centroid by groups, but may confound location and dispersion effects
(Warton et al., 2012), we therefore also performed a test of
homogeneity of group dispersion (Anderson, 2006) with the
VEGAN::BETADISPER function.

In order to understand how the variation of landscape context,
soil parameters and cover of dominant species influence the diversity
and composition (i.e., similarity to remnant dry grassland areas) of
FAF, we built a structural equation model (SEM) with PIECEWISESEM
package (Lefcheck, 2016) for each monitoring year (2009 and 2019).
This analysis, based on pathway dependence between variables, makes
it possible to identify hypothesized relationships between selected
variables (Lefcheck, 2016). We included dominance, alpha diversity
and composition as response variables and remnant dry grassland
cover, phosphorus content, pH and dominance as predictor variables
for each plot (Figure 2). We fitted the SEMs with mixed models using
the predictor variables as fixed factors and plots as random factor. For
dominance, we used a linear model, with LMERTEST package
(Kuznetsova et al., 2017), after square root transformation in order to
fulfill parametric conditions. For alpha diversity we used a generalized
linear model, with LME4 package (Bates et al., 2015), with poisson
family and log link function. For composition, we used linear model,
with LMERTEST package (Kuznetsova et al., 2017). Once fitted,
we added missing pathways if they had been highlighted by Fisher
d-separation tests (Shipley, 2013). We chose the best SEM model as
the one with no significant missing pathways and with the lowest
<figure><img src="image_85.png" /><figcaption>FIGURE 2</figcaption></figure>

Pathway diagram representing the hypotheses tested in the structural equation modelling. Each arrow represents an expected effect, positive in green and negative in red.

<figure><img src="image_86.png" /><figcaption>FIGURE 3</figcaption></figure>

Means and standard errors of species-richness (4 m²) for 2009 and 2019 in remnant dry grasslands and former arable field plots (GLMM; z=-2.49, *p*=0.013). “NS” indicated that the effect was not significant and “*” that it was with a *p* value between 0.05 and 0.01 according to a generalized linear mixed model.

difference of dispersion (F1 = 88.98; *p* < 0.001). Some FAFs are indeed embedded within remnant dry grassland vegetation and characterized by species, such as *Brachypodium retusum* P. Beauv., *Thymus vulgaris* L., *Medicago* spp. and *Trifolium* spp. while others are still discriminated from the remnant dry grasslands and are characterized by the dominance of more ruderal type species for the Crau area, such as *Lobularia maritima* (L.) Desv., *Anisantha madritensis* (L.) Nevski or *Clinopodium nepeta* (L.) Kuntze.

According to the best fitted SEM, 2 out of 6 hypothesized relationships were confirmed for 2009 and 2019: the negative relationship between dominance and alpha diversity and the positive relationship between alpha diversity and composition (Figures 5, 6). The hypothesized negative relationship between pH and composition was confirmed in none of the models. Other relationships depended on the monitoring year. Phosphorus content was positively correlated (although non significantly) with dominance in 2009 and negatively correlated with alpha diversity in 2019. The remnant dry grassland cover had a positive relationship with composition in 2019 only. Surprisingly, a negative relationship that was not included in our a priori hypotheses was identified as missing pathways by Fisher d-separation tests (Shipley, 2013) between remnant dry grassland cover and dominance in 2009 (Figure 5).

# 4. Discussion

As reported in many studies that have focused on plant community resilience (Tomanek et al., 1955; Bonet and Pausas, 2004; Jaunatre et al., 2016; Helm et al., 2019; Isbell et al., 2019), even more than 30 years after disturbance, the species richness of the plant communities that colonized the disturbed sites are still lower than, and the composition is still considerably different from, those from the remnant dry grasslands. When we disentangle the different drivers of vegetation, we first notice that in 2009 phosphorus content and remnant dry grassland cover in the surroundings increase the abundance of the most dominant species in the FAF (e.g., *Anisantha* spp.). This dominance appears to reduce alpha diversity which itself drives the composition (i.e., a lower species richness leads to a lower similarity to the remnant dry grasslands). We then note that in 2019 while alpha diversity is reduced by dominance, it is also reduced by phosphorus content. Finally, we measured that while composition is driven by alpha diversity, it is also driven by the cover of dry grasslands in the surroundings (i.e., a higher cover of remnant dry grasslands in the surrounding leads to a higher similarity with these dry grasslands). It is therefore interesting to stress that the three filters play an

Akaike information criterion by at least 2 units (AIC; Akaike, 1974; Burnham and Anderson, 2004; Appendix 1).

# 3. Results

In total, 172 plant species were recorded: 156 in 222 FAF quadrats (37×3×2 quadrats) and 127 in 60 remnant dry grassland quadrats (10×3×2 quadrats). Despite the high number of species in common between remnant dry grasslands and FAF plots, mean species richness was significantly higher in remnant dry grassland areas than in FAFs (38.6±0.9 vs. 31.1±0.5; *z* = -2.49, *p*=0.013; *Figure 3*). There was however neither an effect of monitoring year (*z*=0.292; *p*=0.771) nor an effect of interaction between year and location (*z* = -0.011; 0.992).

There were no clear tendencies for the composition trajectories between 2009 and 2019 (Figure 4), although there is a significant year effect on the position of the centroid (F1 = 15.06; *p* < 0.001) with no difference of dispersion (F1 = 0.16; *p*=0.686). On the contrary, remnant dry grasslands and FAF have a significantly different position of centroids (F1 = 24.25; *p* < 0.001) associated with a significant
<figure><img src="image_87.png" /><figcaption>FIGURE 4</figcaption></figure>

Remnant dry grasslands (filled circles, 10 sites × 3 plots × 2 years) and FAF (filled squares, *n* = 37 FAF × 3 plots × 2 years) ordination based on NMDS of plant community composition, final stress=0.28. For clarity purpose, only the 30 most correlated species are shown (out of 172). Arrows link 2009 plots to 2019 plots. Points with the same colors are located within the same geographical zones (Figure 1).

<figure><img src="image_88.png" /><figcaption>FIGURE 5</figcaption></figure>

2009 survey pathway diagram of the best fitted SEM illustrating how dominance, alpha diversity and composition are influenced by remnant dry grasslands cover, phosphorus content, pH and dominance. Boxes represent responses and predictor variables. Only included links in this model are drawn with arrows (green if the relationship is positive and red if negative). Dotted arrows represent links that were not included in our a priori hypotheses but were added after looking for missing pathways by Fisher d-separation tests (Shipley, 2013). Numbers on arrows are the slope estimates, accompanied or not with stars (*p*<0.05; **p**<0.01; ***p**<0.001).

important role in driving the vegetation recovery, but not with the
same processes.

Surprisingly, the dispersion proxy (remnant dry grassland
cover) switched from being negatively correlated with dominance
in 2009 to being positively correlated with composition in 2019.
A possible explanation of this result, is that when remnant dry
grassland cover is low, cultivated area or former arable field
covers are high. Differential dispersal limitation can cause wide
species-specific deviations in the probability of finding a species
from the landscape species pool in the community (Grace, 2001).
Fields surrounded by cultivated areas or former arable fields may
therefore exhibit a higher abundance of anemochorous ruderal
species like Anisantha spp. that are known to exhibit higher
dispersion abilities (Gaba et al., 2017). Ten years later, the
presence of remnant dry grasslands in the vicinity promotes a
composition more similar to the dry grassland. It is well known

that presence of remnant patches in the vicinity promotes the
dispersal of remnant dry grassland species to the community
(Tansley and Adamson, 1925) and that this effect increases with
time (Cook et al., 2005). We had not hypothesized a high
importance of remnant patches as most of the species from dry
grasslands do not disperse on large distances due to the low
quantity of seeds from remnant communities in the seed bank
and the seed rain (Graham and Hutchings, 1988; Buisson and
Dutoit, 2004; Török et al., 2018; Arruda et al., 2021). Nevertheless,
on the long term (over 30 years), long distance dispersion events
(i.e., hundreds of meters) with low probability (i.e., which was
not detected by sampling design) can occur (Higgins and
Richardson, 1999) and may be favored by the presence of remnant
patches in the vicinity. This is supported by the fact that FAF tend
to differ from each other in patterns similar to those of the dry
grasslands. Such patterns are also observed on secondary
<figure><img src="image_89.png" /><figcaption>FIGURE 6</figcaption></figure>

2019 survey pathway diagram of the best fitted SEM illustrating how dominance, alpha diversity and composition are influenced by remnant dry grasslands cover, phosphorus content, pH, and dominance. Boxes represent responses and predictor variables. Only included links in this model are drawn with arrows (green if the relationship is positive and red if negative). Numbers on arrows are the slope estimates, accompanied or not with stars (*p<0.05; **p<0.01; ***p<0.001).

succession in abandoned mining sites in Central Europe (Jírová et al., 2012). However, the design of our study does not allow us to differentiate whether this pattern is a result of the dispersion or the abiotic filters. A more in depth spatial characterization of the remnant dry grasslands would be required to disentangle the effect of the species-pool it provides and the abiotic spatial organization. In order to thoroughly tackle the dispersion filter, numerous census of occurring species would be required as well as the correspondence with dispersal traits of these species (Lewis et al., 2016).

The pH does not appear to exert an effect on composition, which was not expected by our hypotheses. We expected higher changes due to the calcareous conglomerate that was broken during the cultivation phase (Buisson and Dutoit, 2004) and therefore lower composition recovery of plant community compared to remnant dry grasslands that contains some acidophilous species (Devaux et al., 1983). The range of variation of pH was however rather low: 6.9 and 7.2 being, respectively, the first and third quartiles. If relationship between pH and composition should occur, a higher range of variation would be require to detect it. Phosphorus content has first a positive relationship with dominance and then with species richness. Higher fertility leads to higher biomass of annual grasses (higher dominance) that should have increased competition (Michalet et al., 2023). Nutrient enrichment does not prevent remnant dry grassland species development in itself but enhances competition of more generalist species due to their dense cover (Huenneke et al., 1990; Buisson et al., 2015). Sowing typical remnant dry grassland species in FAF abiotic conditions or in remnant dry grassland abiotic conditions does not influence their establishment (Coiffait-Gombault et al., 2012). Our results show that species richness is limited both by biotic (dominance) and abiotic (phosphorus content) filters that interact together. This is in accordance with previous studies that show that dominance by more competitive and ruderal species on soil with higher nutrient contents can lead to a decline of remnant community species (Yurkonis and Meiners, 2004; Seabloom et al., 2020). Apart from the important feedback with the abiotic filter, the biotic filter is complicated to take into account. In our study, we only focused on the dominance of the above ground vegetation. The competition between plants may also be tackled by measurement of functional trait patterns, but inferring competition processes from these patterns may sometimes be misleading (Münkemüller et al., 2020). Besides intra guild competition, there

is also below ground interactions (Kiær et al., 2013), facilitative interactions (Brooker et al., 2007), soil microfauna (Sun et al., 2023), microbial communities (Kaisermann et al., 2017), etc. We are currently able to measure the importance of these trophic network in the driving of ecosystem functioning (Martinez-Almoyna et al., 2022), but it is still very difficult to disentangle all the direct and indirect interactions that could lead to the favoring or (dis)favoring of specific set of species.

We studied the recovery on the vegetation composition having as reference, remnant patches of remnant dry grassland vegetation (i.e., extensively grazed vegetation). Considering the vegetation dynamics that we measured after several decades of recovery, it is unlikely that these plant communities will converge toward remnant dry grassland vegetation on the medium term. As was shown in previous studies, dozens of decades after abandonment, the composition is still different from the remnant ecosystems (Jaunatre et al., 2016, for species-rich dry grassland; Nerlekar and Veldman, 2020 for various grasslands). For such non-resilient species-rich grasslands, reconstructive restoration (i.e., ending degradation, active remediation of substrate condition and reintroduction of major proportion of target biota sensu Atkinson and Bonser, 2020) would be needed to ensure a potential complete recovery of plant communities. Our result confirm the fact that the restoration of abiotic conditions prior to forcing the dispersion of target species would be a key for successful restoration.

Our results show that FAF vegetation did not recover after more than 30 years. The three filters drive the recovery of remnant dry grassland vegetation: FAF that have lower phosphorus content (abiotic filter) have a lower cover of dominant species (biotic filter), a higher species richness and a higher similarity with remnant dry grassland plant communities, especially if the FAF are surrounded by remnant dry grasslands (dispersion filter). Based on these results we therefore recommend that the restoration of species-rich semi-natural remnant dry grasslands is primarily focused on the recovery of suitable abiotic conditions [i.e., by decreasing fertility, for instance with expensive topsoil removal (Hölzel and Otte, 2003; Jaunatre et al., 2014), repeated carbon addition (Corbin and D'Antonio, 2004; Ossanna and Gornish, 2023) or long-term phosphorus mining (Schelfhout et al., 2015)] and on increasing propagule pressure of the typical remnant dry grassland species in a second phase. In order to be able to use SEM, we only used a few variables that we attributed to the three filters. These variables were chosen regarding the specific characteristics of the degradation and of the remnant ecosystem. Our study is a piece into the
comprehension of the relative importance of each filters that would benefit from both similar analyses in other ecosystems but also more in depth measure of how the propagule pressure and the abiotic characteristics drive interaction between species and therefore plant community recovery.

## Data availability statement

The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding author. Code and data are also publicly available at https://forgemia.inra.fr/renaud.jaunatre/friches_frontiers.

## Author contributions

RJ, EB, and TD conceived the design of this study. RJ performed the data analysis and wrote the first draft of the manuscript. All authors contributed to floristic surveys and approved the final version.

## Funding

This study was supported by the CDC Biodiversité, CEN PACA, the Réserve Naturelle des Coussouls de Crau, the Conseil Régional de Provence Alpes Côtes d'Azur, and CNRS RTP Ingecotech funding.

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## Acknowledgments

The authors would like to thank Jennifer Matwyschuk and Daniel Pavon (IMBE) for help with fieldwork.

## Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

## Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

## Supplementary material

The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fevo.2023.1148226/full#supplementary-material

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Lilia L. Roa-Fuentes,
Pontifical Javierian University, Colombia

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Maan Rokaya,
Institute of Botany (ASCR), Czechia
Jeremy Goodall,
ARC Centre of Excellence for Electromaterials
Science (ACES), Australia

*CORRESPONDENCE

Lucie Rivière

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lucie.rivere@uliege.be

RECEIVED 27 January 2023

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PUBLISHED 01 June 2023

CITATION

Rivière L, Sellier A, Dutoit T, Vidaller C, Buisson E and Mahy G (2023) The contribution of seedbank to the green roof plant community dynamics analogous to semi-natural grasslands. *Front. Ecol. Evol.* 11:1152319.
doi: 10.3389/fevo.2023.1152319

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© 2023 Rivière, Sellier, Dutoit, Vidaller, Buisson and Mahy. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# The contribution of seedbank to the green roof plant community dynamics analogous to semi-natural grasslands

Lucie Rivière¹*, Alizé Sellier¹, Thierry Dutoit², Christel Vidaller², Elise Buisson² and Grégory Mahy¹

¹Biodiversity and Landscape, TERRA Research Center, Gembloux Agro-Bio Tech, University of Liège, Gembloux, Belgium, ²Institut Méditerranéen de Biodiversité et d'Ecologie (IMBE), UMR Avignon University, CNRS, Aix-Marseille University, IRD, Avignon, France

Extensive green roofs have been shown to support native biodiversity and plant communities that are analogous to natural or semi-natural habitats such as grasslands. However, little is known about the role of soil seedbanks in the dynamic of extensive green roof plant communities. The purpose of this study was to analyze the seedbank that developed after 4 years of an extensive green roof analog to dry grassland plant community, seeded with 29 species. We aimed to understand the contribution of seedbank to the resilience of vegetation to harsh conditions of the roof and to colonization by surrounding spontaneous species. We monitored the plant species cover in 36 plots during 4 years in June (between 2018 and 2021), and sampled the seedbank in February 2021. Our results showed that the soil seedbank was dominated by transient spontaneous ruderals species, while the standing vegetation was still dominated by seeded grassland species. We found that seeded grassland species had poor seedbank stock, similar to their natural environments. The similarity index between the standing vegetation and the seedbank increased over time, and we measured a significant correlation between dominant species cover and their seedbank density. Spontaneous species cover was not correlated to the proportion of soil not colonized by seeded species cover, indicating that gaps in vegetation did not influence the development of spontaneous species. Our findings highlight the importance of seedbank in the dynamic of green roof vegetation and demonstrate that analogous habitat species exhibit similar behavior as in their natural environments.

KEYWORDS

plant dynamic, ruderal, spontaneous species, green infrastructure, seed persistence, urban ecology, substrate depth, sun exposure

1. Introduction

Extensive green roofs (EGR) are a type of urban ecosystem that offer a range of ecosystem services (Oberndorfer et al., 2007; Kowarik, 2011). One of the ecosystem services provided by EGRs is the support of urban native biodiversity, through the implementation of diversified native plant communities based on the analogous habitat hypothesis
(Lundholm and Richardson, 2010; Rivière et al., 2022). The analogous habitats hypothesis proposes that species which are adapted to similar abiotic conditions in natural habitats can also be successful on green roofs (Lundholm, 2006) and can serve as a starting point for selecting species. Green roofs provide solutions to develop urban grassland and herbaceous ecosystem networks in cities (Sutton et al., 2012; Williams et al., 2014; Thuring and Grant, 2015). However, harsh environmental conditions on extensive green roofs (e.g., high level of solar radiation, shallow substrate, poor water retention capacity) associated with future climate evolution can threaten the success of these native plant communities, and associated ecosystem services, when the functioning and dynamic of EGR ecosystems are not well-understood.

Green roof plant communities are dynamic. When initiated by seeding on bare substrate, the EGR plant community is marked by changes in plant composition over time as this situation relates to primary ecological succession conditions (Brown and Lundholm, 2015). The seeded plant community evolves through time in accordance to seeded species response to environmental conditions at the roof scale: roof height, slope, exposure, substrate depth and composition (Madre et al., 2014; Brown and Lundholm, 2015). EGR are also characterized by local microenvironmental variations impacting the composition and dynamic of the plant community (Heim and Lundholm, 2014; Roulston et al., 2020; Bradbury, 2021; Rivière et al., 2022). In addition to the response of seeded species to environmental conditions, a key driver of EGR plant community evolution is spontaneous plant colonization with non-seeded plant species (Thuring and Dunnett, 2019; Vanstockem et al., 2019). Weedy or unwanted species colonizing spontaneously EGR are identified as a limit to EGR acceptance and maintenance (Vanstockem et al., 2019). Spontaneous colonization by plant species is often linked to presence of bare soil or gaps in the vegetation cover due to the extreme abiotic growing conditions on EGR (Köhler, 2006; Sutton and Lambrinos, 2015).

Soil seedbank is a key component of plant community dynamics (Thompson et al., 1997; Vandvik et al., 2016). It influences the vegetation dynamic in the short and long term (Basto et al., 2018). The seedbank can provide information on species that may have formerly existed in the vegetation, species that represent the pool for the vegetation succession, and, represents the source of regenerative potential for the restoration of altered plant communities. Seedbanks on EGR [being biodiversity reservoirs (Vandvik et al., 2016)] may be crucial as an insurance for maintaining plant communities during periods of extreme conditions (e.g., extreme drought or frost) and can potentially offer a basis for restoration for vegetation gaps. Despite its importance in understanding vegetation response to extreme EGR conditions, the study of seedbank from extensive green roof (Vanstockem et al., 2018) or even urban areas (Albrecht et al., 2011) has received little attention. In an explorative study of seedbanks on 109 EGRs in temperate Europe, Vanstockem et al. (2018) confirm that EGRs do develop seedbanks, dominated by weedy plant species and exhibiting a strong discrepancy with vegetation composition.

To promote native biodiversity, EGR should be considered analogous to natural habitats (Lundholm, 2006). The harsh environmental constraints of EGR are similar to abiotic conditions encountered on species rich dry grasslands and rocky environments in temperate Western Europe (high solar radiation, low soil depth

(<20 cm) and high variability in soil water retention capacity). Western Europeans dry grasslands are composed of isolated climax herbaceous habitats on most harsher environmental conditions and, for the largest surfaces, seral semi-natural habitats originating from centuries of extensive grazing and mowing. Rivière et al. (2022), demonstrated experimentally that extensive green roofs can act as analogous habitats for native flora of European dry calcareous grasslands (71% of native seeded grassland species established on an experimental green roof). The most successful species were common dry grassland species such as grass species, succulent species (sedum sp.), Echium vulgare L. and Papaver argemone L. However, success of native grassland plant species, plant community composition and structure were demonstrated to vary through time and to depend on microenvironmental conditions at the roof scale, with large surface of bare soils left after 3 years in the most constrained conditions (low substrate depth and high solar exposition). Decrease in plant cover was also measured in years with severe drought. The contribution of spontaneous ruderal species to the plant communities increased over time. Studies of seedbank in natural situations have shown that calcareous grassland species are mainly perennial stress tolerant species presenting mostly transient seedbank that stay viable in the soil for a very limited period of time and/or are absent from the seedbank (Dutoit and Alard, 1996; Willems and Bik, 1998; Bisteau and Mahy, 2005). A lack of well-developed permanent seedbank of target seeded species can be a threat for long term maintenance of analogous dry grassland vegetation of EGR facing colonization by spontaneous ruderal species.

In this study, we analyzed the seedbank contribution to the plant community of an extensive green roof analog to dry calcareous grassland after 4 years of development in order to understand the potential contribution of seedbank to the resilience of vegetation to harsh EGR conditions and to colonization by spontaneous species. The specific questions asked are: (i) what are the main seedbank characteristics and composition of an analogous urban grassland developed on an extensive green roof, (ii) what is the level of similarity between seedbank and vegetation, (iii) how is the seedbank and abundance of spontaneous species related to vegetation gap and microenvironmental variations on EGR.

## 2. Materials and methods

### 2.1. Study site and experimental design

The studied green roof is located on top of the TERRA Research Center of Gembloux Agro-Bio-Tech, Belgium (50°33'48" N 4°41'52" E). According to Köppen (1923) climate classification, the climate of the region is oceanic temperate (Cfb) (Service Fédéral Belge, 2019).

In October 2017, the EGR was installed with a commercial substrate for EGR: lightweight substrate Zinco® based on a mix of recycled tiles, bricks and ceramic. The roof included nine parcels ranging in size from 14 to 57 m² with two substrate depths (6 and 11 cm) and an East-West orientation. The roof is bordered on three sides by walls (North, East, and South), resulting in global disparities in sun exposure across parcels. Skylights installed between parcels create an additional fine-scale shade gradient
(Figure 1). Three classes of sun exposure (maximum sun exposure on a sunny summer day) were defined: low (3–6 h), Medium (6–9 h), and High (9–12 h). The two parameters of substrate depth and sun exposure were noted: D1 (6 cm depth), D2 (11 cm depth), S1 (low sun exposure), S2 (medium sun exposure), and S3 (high sun exposure) (Rivière et al., 2022).

A mix of 29 native species from Belgian dry calcareous grasslands were selected based on their presence along the soil depth gradient in Belgian dry grasslands (Piqueray et al., 2007; Table 1). Calcareous grasslands are not present in the ecological landscape surrounding the experimental green roof but 12 seeded species have been recorded in 2 km² around it (Delescaille and Delaite, 2011). Three native Belgian Sedum species were chosen because they are succulent plants already commonly used on ExGR and adapted to harsh conditions (Butler and Orians, 2009). The species were seeded uniformly across the parcels on 10th November 2017, 830 seeds/species/m² for grass species and 110 seeds/species/m² for other species. During the research period (2017–2021), no overseeding, irrigation, fertilizers or mowing were applied. Rivière et al. (2022) studied the vegetation development according to substrate depth, sun exposure and changes over time, and showed a strong effect of time since sowing associated with the effects of these microenvironmental variation on plant community composition, plant cover and specific richness.

## 2.2. Data collection

Thirty-six permanent 1 m² plots were installed on the nine parcels reflecting the variation of substrate depth and sun exposure (Figure 1). Plant species covers were visually measured in these 36 plots in June 2018, 2019, 2020 and 2021, at the peak of vegetative development (Rivière et al., 2022).

The seedbank was sampled in February 2021, before spring germination in order to collect seeds of both transient and persistent seedbanks under Atlantic and Continental climates, following Bekker et al. (1998). In each of the 36 plots, a sample of 10 bulked soil cores (2.5 cm diameter) was collected. Cores were divided in two layers for D1 6 cm deep parcels (layer 1: 0–3 cm and layer 2: 3–6 cm) and three layers for D2 11cm deep parcels (layer 1: 0–3 cm, layer 2: 3–6 cm and layer 3: 6–11 cm). A total of 88 samples were obtained. To extract the seedbank, the dried samples were sieved at 3.66 mm in order to remove roots, vegetative parts and stones. The sieved fractions were sown in 40 cm × 28 cm × 6.5 cm seedling trays with a 3 cm Argex marble and 1 cm organic matter (La plaine Chassart©). The trays were placed in homogeneous greenhouse conditions (22°C, 76% RH) and watered regularly by capillarity. The number of seedlings per species in each tray was counted at weekly intervals and removed after identification; monitoring continued until germination ceased after 15 weeks. The seedlings of the five seeded Poaceae seedlings could not be distinguished and were classified as “grass sp.”

LEDA database (Kleyer et al., 2008) was used to determine seed mass and seed lifetime index (SLI). For seed mass, only the mean “one-seeded generative dispersule” was considered per species. A mean SLI was calculated per species based on their persistence in the seedbank: 0 (all data are transient), 1 (all data are persistent). Seedbank can be transient if the seeds are present in the seedbank

less than 1 year; short-term persistent between 1 and 5 years; or long-term persistent more than 5 years. In general, long-term seeds are found in both the deepest and shallowest levels of the substrate, whereas short-term seeds are exclusively found in the shallower layer of the substrate (Christoffoleti and Caetano, 1998).

The status of each species was determined by whether they were seeded (seeded, sd) or not (spontaneous, sp). Spontaneous species represent all unseeded species, annual or perennial, native or alien. The taxonomic reference was the “Nouvelle flore de la Belgique” (Lambinon et al., 2012). Species were classified in three ecological groups according to their natural habitat: grassland species, ruderal species and forest species (Lambinon et al., 2012; Table 1).

## 2.3. Data analyses

Similarity in species composition (presence/absence) between the initial seeding mix, species composition in vegetation in 2018–2021 and species composition in the 2021 seedbank was assessed with (1) the similarity index of Sokal and Michener (Perrier et al., 2003), and, (2) a Principal Component Analysis (PCA) (“princomp,” stats) performed on all plots. For each species, a Pearson correlation was calculated (“cor,” stats) to identify species that correlated most with the axes of the PCA.

The relationship between the most abundant species in the vegetation and the most abundant species in the seedbank (found in at least half of the plots) was determined by Pearson correlation tests (“cor,” stats) to establish if species cover was correlated to its seedbank density. To test if grassland vegetation development influenced establishment of spontaneous species by pre-empting bare soils surfaces (Vanstockem et al., 2018), we tested the regression between the spontaneous species total cover and the proportion of soil not colonized by grassland species in 2021 (100%–%total seeded species cover in 2021) with a linear regression (“lm,” cor).

To test whether microenvironmental condition (depths: D1, D2 and sun exposure: S1, S2, S3) variations on the experimental EGR influenced similarly plant community and seedbank variations, we performed independent Principal Coordinate Analysis (PCoA) on (1) cover of species in the vegetation, (2) seed density of species in seedbank, in all plots. Ordispider were used to depict the relationships between microenvironmental conditions and position of plots in the factorial space.

Data analyses were performed in R 4.0.2 (R Core Team, 2022).

# 3. Results

## 3.1. Seedbank characteristics

In 2021, 4 years after the creation of the green roof, the seedbank included 18 species (nine seeded species and nine spontaneous species, grass species considered as a single taxon due to difficulties identify seedlings) with an average of 2.5 ± 1.1 species and 120 ± 172 seeds per square meter plot. There was no seedbank observed in three plots. Seed density decreased with substrate depth with 42.8% of seed located at 0–3 cm depth, 34.7% at the 3–6 cm depth, and 22.5% at the 6–11 cm (Figure 2).
<figure><img src="image_92.png" /><figcaption>FIGURE 1
The studied extensive green roof and the permanent plots distribution.</figcaption></figure>

The standing vegetation included 29 species (16 seeded species and 13 spontaneous species, grass species considered as one taxon) with an average of 5.7 ± 1.8 species and 47.3% ± 39.3% of cover per square meter plot (Figure 2). There was no plot without vegetation cover.

The seedbank (mean SLI: 0.34 ± 0.13) and the vegetation (mean SLI: 0.25 ± 0.16) were both composed of species with a transient seedbank. All species have a SLI of less than 0.5 except *Chenopodium album* L. (0.59) and *Portulaca oleracea* L. (0.68) both spontaneous species and present in the vegetation and in the seedbank.

## 3.2. Seedbank and vegetation similarity

The seedbank was dominated by seeds from spontaneous species (84.8%), while seeds from seeded species represent only 15.23%. In contrast, seeded species represent 70.4% of plant cover in the vegetation, while spontaneous species represented only 29.6%. The seedbank was dominated by the ruderal ecological group (84.4% of all seeds) followed by grassland ecological group (15.2%). In contrast, the plant cover was dominated by species from the grassland ecological group (82.6%), followed by species from ruderal ecological groups species (17.0%). Both the seedbank and the standing vegetation had low occurrence of species from forest ecological group (0.34 and 0.40%, respectively).

The seedbank was dominated at 65.6% of *P. oleracea*. While spontaneous species dominated the seedbank (*P. oleracea*, *C.album* and *Senecio vulgaris* L. at 65.5, 9.3, and 5.9%, respectively), they were seldom developed in the vegetation with cover less than 1% per species, except for *P. oleracea* (7.56%/m²) and *Trifolium arvense* L. (5.14%/m²), a cover similar to dominant seeded species (Figure 2).

Only nine out of 24 seeded species (considering all grasses as a single taxon) were detected in the seedbank with densities ranging from 6.7 to 254.4 seeds/m², from which eight were also present in

the vegetation. Grasses (254.4 seeds/m², 9.08% cover), *Sedum acre* L. (160.7 seeds/m², 2.08% cover), *Silene vulgaris* (Moench) Garcke (87.03 seeds/m², 0.36% cover), *Sedum album* L. (53.6 seeds/m², 3.9% cover), and *Echium vulgare* L. (20.08 seeds/m², 5.03% cover) were the seeded species developing the densest seedbank. Only S. acre has a higher seedbank density than the initial seeding mix (110 seeds/m²). Eight seeded species present in the vegetation were not detected in the seedbank, including *Anthyllis vulneraria* L., one of the vegetation dominant species. Seven seeded species were not observed in the vegetation, nor in the seedbank (Figure 2).

The lowest similarity (Sokal and Michener index) in species composition was measured between the initial seeding mix and the seedbank after 4 years of vegetation development. Similarity between initial seeding mix composition and standing vegetation decreased over time from 0.75 to 0.50 while similarity between the vegetation and the seedbank increased over time from 0.5 to 0.61 (Figure 3). The vegetation differed each year a little more from the initial seeding and became more similar to the seedbank sampled in 2021.

The first and second axes of the PCA (Figure 4) based on the presence/absence of species in the initial seeding mix, the plant community in 1 m² plots in 2018, 2019, 2020, and 2021 and the seedbank in 1 m² plots in 2021 explained 61.8% of the global species composition variation (Comp1 = 54.1%-eigenvalue 1 = 2.7, Comp2 = 7.7%-eigenvalue 2 = 0.4). Axis 1 strongly differentiated the initial seeding mix from standing vegetation and seedbank. Axis 2 indicated a trend for plant community to become more similar to seedbank over years. The correlation circle indicated that the first axis was correlated with the presence of seeded species. The plant community of 2018, 2019, 2020, and 2021 and the seedbank were differentiated from the initial seeded community with the presence of spontaneous species and with the absence of seeded species that never germinated over time (*Hieracium pilosella* L., *Leucanthemum vulgare* Lam., *Hypochaeris radicata* L., *Primula veris* L., *Thymus pulegioides* L., and *Tragopogon pratensis* L.).
<table><caption>TABLE 1 Seeded or spontaneous species observed in 2018, 2019, 2020, 2021 or in the seedbank, the code used in figures and their status.</caption><thead><tr><th>Species</th><th>Code</th><th>Status</th><th>Ecological group</th></tr></thead><tbody><tr><td>Anthyllis vulneraria L.</td><td>Ant_vul</td><td>Seeded (sd)</td><td>Grassland species</td></tr><tr><td>Centaurea scabiosa L.</td><td>Cen_sca</td><td>Seeded (sd)</td><td>Grassland species</td></tr><tr><td>Daucus carota L.</td><td>Dau_car</td><td>Seeded (sd)</td><td>Grassland species</td></tr><tr><td>Dianthus carthusianorum L.</td><td>Dia_car</td><td>Seeded (sd)</td><td>Grassland species</td></tr><tr><td>Echium vulgare L.</td><td>Ech_vul</td><td>Seeded (sd)</td><td>Grassland species</td></tr><tr><td>Gram sp.</td><td>Gram_sp</td><td>Seeded (sd)</td><td>Grassland species</td></tr><tr><td>Hieracium pilosella L.</td><td>Hie_pil</td><td>Seeded (sd)</td><td>Grassland species</td></tr><tr><td>Hypochaeris radicata L.</td><td>hyp_rad</td><td>Seeded (sd)</td><td>Grassland species</td></tr><tr><td>Leucanthemum vulgare Lam.</td><td>Leu_vul</td><td>Seeded (sd)</td><td>Grassland species</td></tr><tr><td>Lotus corniculatus L.</td><td>Lot_cor</td><td>Seeded (sd)</td><td>Grassland species</td></tr><tr><td>Medicago lupulina L.</td><td>Med_lup</td><td>Seeded (sd)</td><td>Grassland species</td></tr><tr><td>Papaver argemone L.</td><td>Pap_arg</td><td>Seeded (sd)</td><td>Grassland species</td></tr><tr><td>Primula veris L.</td><td>Pri_ver</td><td>Seeded (sd)</td><td>Grassland species</td></tr><tr><td>Rhinanthus minor L.</td><td>Rhi_min</td><td>Seeded (sd)</td><td>Grassland species</td></tr><tr><td>Rumex acetosella L.</td><td>Rum_ace</td><td>Seeded (sd)</td><td>Grassland species</td></tr><tr><td>Sanguisorba minor Scop.</td><td>San_min</td><td>Seeded (sd)</td><td>Grassland species</td></tr><tr><td>Scabiosa columbaria L.</td><td>Sca_col</td><td>Seeded (sd)</td><td>Grassland species</td></tr><tr><td>Sedum acre L.</td><td>Sed_acr</td><td>Seeded (sd)</td><td>Grassland species</td></tr><tr><td>Sedum album L.</td><td>Sed_alb</td><td>Seeded (sd)</td><td>Grassland species</td></tr><tr><td>Sedum rupestre L.</td><td>Sed_rup</td><td>Seeded (sd)</td><td>Grassland species</td></tr><tr><td>Silene vulgaris (Moench) Garcke</td><td>Sil_vul</td><td>Seeded (sd)</td><td>Grassland species</td></tr><tr><td>Thymus pulegioides L.</td><td>Thy_pul</td><td>Seeded (sd)</td><td>Grassland species</td></tr><tr><td>Tragopogon pratensis L.</td><td>Tra_pra</td><td>Seeded (sd)</td><td>Grassland species</td></tr><tr><td>Verbascum sp.</td><td>Ver_sp</td><td>Seeded (sd)</td><td>Grassland species</td></tr></tbody></table>

(Continued)

<table><caption>TABLE 1 (Continued)</caption><thead><tr><th>Species</th><th>Code</th><th>Status</th><th>Ecological group</th></tr></thead><tbody><tr><td>Acer sp.</td><td>Ace_sp</td><td>Spontaneous (sp)</td><td>Forest species</td></tr><tr><td>Betula sp.</td><td>Bet_sp</td><td>Spontaneous (sp)</td><td>Forest species</td></tr><tr><td>Buddleja davidii Franch.</td><td>Bud_dav</td><td>Spontaneous (sp)</td><td>Ruderal species</td></tr><tr><td>Chenopodium album L.</td><td>Che_alb</td><td>Spontaneous (sp)</td><td>Ruderal species</td></tr><tr><td>Cirsium vulgare (Savi) Ten.</td><td>Cir_vul</td><td>Spontaneous (sp)</td><td>Ruderal species</td></tr><tr><td>Epilobium tetragonum L.</td><td>Epi_tet</td><td>Spontaneous (sp)</td><td>Ruderal species</td></tr><tr><td>Geranium sp.</td><td>Ger_sp</td><td>Spontaneous (sp)</td><td>Ruderal species</td></tr><tr><td>Lactuca serriola L.</td><td>Lac_vir</td><td>Spontaneous (sp)</td><td>Ruderal species</td></tr><tr><td>Portulaca oleracea L.</td><td>Por_ole</td><td>Spontaneous (sp)</td><td>Ruderal species</td></tr><tr><td>Senecio vulgaris L.</td><td>Sen_vul</td><td>Spontaneous (sp)</td><td>Ruderal species</td></tr><tr><td>Sonchus asper (L.) Hill</td><td>Son_asp</td><td>Spontaneous (sp)</td><td>Ruderal species</td></tr><tr><td>Sonchus oleraceus L.</td><td>Son_ole</td><td>Spontaneous (sp)</td><td>Ruderal species</td></tr><tr><td>Trifolium arvense L.</td><td>Tri_arv</td><td>Spontaneous (sp)</td><td>Grassland species</td></tr><tr><td>Trifolium pratense L.</td><td>Tri_pra</td><td>Spontaneous (sp)</td><td>Grassland species</td></tr><tr><td>Vicia sativa L.</td><td>Vic_sat</td><td>Spontaneous (sp)</td><td>Grassland species</td></tr></tbody></table>

### 3.3. Influence of plant communities on seedbank

Correlation between plant cover and seed density at 1 m² plot scale was tested for species found in the seedbank and vegetation cover in at least half of plots. Pearson correlation tests indicated a significant correlation and a high correlation for grass species (P < 0.001-cor = 0.31) and *P. oleracea* (P < 0.001-cor = 0.57). The Pearson correlation test was high with an almost significant P value for *E. vulgare* (P = 0.07-cor = 0.32), *S. acre* (P = 0.07-cor = 0.75), and *C. album* (P = 0.08-cor = 0.34). The seedbank density and the vegetation cover of *S. vulgaris* were not significantly correlated (P = 0.50-cor = -0.12).

The proportion of soil not colonized by seeded species (100%-total seeded species cover in 2021) had no significant effect on the spontaneous species cover (linear regression, P = 0.93, R² = -0.03).

### 3.4. Influence of microenvironmental variation on plant communities and seedbank

When examining the effect of microenvironmental variation on plant community composition of standing vegetation in 2021
<figure><img src="image_93.png" /><figcaption>FIGURE 2
Seedbank density (seeds/m²) and mean vegetation cover of 2021 (%) for seeded species and spontaneous species (see Table 1 for species name).</figcaption></figure>

<figure><img src="image_94.png" /><figcaption>FIGURE 3
Sokal and Michener index between 2017 seeding, 2018, 2019, 2020, and 2021 vegetation cover and 2021 seedbank.</figcaption></figure>

(taking into account plant species cover), the first and second axes of the PCoA (Figure 5A explained 25.2% of the global variation (Dim1 = 13.7%-eigenvalue = 1.9, Dim2 = 11.5%-eigenvalue = 1.6). Position of ordispider gravity centre on first axis discriminated plant communities in plots with shallow substrate depth (D1: 6 cm) (negative coordinates) from plant communities on deeper substrate (D2: 12 cm) (positive coordinates). Position of ordispider gravity centre on second axis discriminated plant communities in plots with high sun exposure S3 (negative coordinates) from plant

communities with medium to low sun exposure S1 and S2 (positive coordinates).

When examining the effect of microenvironmental variation on seedbank composition (taking into account species seed density), the first and second axes of the PCoA (Figure 5B explained 27.1% of the global variation (Dim1 = 14.4%-eigenvalue = 2.0, Dim2 = 12.7%-eigenvalue = 1.7). Position of ordispider gravity centre on first axis discriminated seedbank composition in plots with high sun exposure S3 (positive coordinates), characterized
<figure><img src="image_95.png" /><figcaption>FIGURE 4</figcaption></figure>

Principal Component Analysis (PCA) based on presence/absence species composition for initial seeding, 2018, 2019, 2020, and 2021 vegetation cover and 2021 seedbank. (A) Ordi spiders pool by year. (B) Correlation circle of species, blue species: spontaneous species, green species: seeded species.

<figure><img src="image_96.png" /><figcaption>FIGURE 5</figcaption></figure>

Principal Coordinate Analysis (PCoA) based on (A) plant composition of standing vegetation in 2021 and on (B) seedbank composition. Ordi spiders are pooled according to microenvironmental conditions. Symbols represent sun exposure modalities (circle = S1: low sun exposure, triangle = S2: medium sun exposure, and square = S3: high sun exposure) and substrate depth modalities (white = D1: shallow depth 6 cm, black = D2: deep depth 11 cm). Species are indicated with green crosses.

by the presence of *P. oleracea*, *Buddleja davidii* Franch., and *Betula* sp, from seedbank composition in plots with medium to low sun exposure S1 and S2 (negative coordinates), characterized by the presence of *C. album*, *Geranium* sp. and grass sp. Position of ordispider gravity centre on second axis discriminated seedbank composition in plots with shallow substrate depth (D1: 6 cm) (negative coordinates) from seedbank composition on deep substrate depth (D2: 12 cm) (positive coordinates).

## 4. Discussion

Increasing attention has been paid to plant community dynamics on extensive green roof in ecological literature

(Roulston et al., 2020; Bradbury, 2021; Rivière et al., 2022). Change in plant community over time is key to EGR design and their acceptability (Thuring and Dunnett, 2019; Vanstockem et al., 2019). It is also a major determinant of EGR capacity to sustain native biodiversity through development of analog habitat (Lundholm, 2006; Rivière et al., 2022). In this study, we examined the development of seedbank in relation to the installation of an analogous dry grassland on an experimental EGR. In contrast to our recent knowledge increase on above ground vegetation patterns and processes on green roofs (Madre et al., 2014; Brown and Lundholm, 2015), seedbank development and its role for green roof vegetation has been seldom explored directly (Vanstockem et al., 2018). The study of seedbank is important to understand its contribution to the resilience of vegetation to harsh EGR
conditions. Specifically, a lack of well-developed permanent seedbank of target seeded species can be a threat for long term maintenance of analogous dry grassland vegetation of EGR facing colonization by spontaneous ruderal species.

Our study of the standing plant community evolution on an experimental EGR indicates that green roofs work as an analogous habitat supporting seeded species from native dry grasslands (Rivière et al., 2022). However, the evolution over time of plant community on the EGR diverges from the initial seeding by the absence of a set of seeded grassland species and the increase in spontaneous species cover over time. The species richness of spontaneous species tends to increase to the same level as species richness of seeded species (12 vs. 16) in 4 years. Development of a high richness of spontaneous species on experimental green roofs over a short time period has been repeatedly reported. For example, Dunnett et al. (2008) in Sheffield, UK, identified 35 colonizing species for 15 planted species, Olly et al. (2011) on experimental plots in Birmingham, UK, identified 29 spontaneous species for 17 seeded species and Bates et al. (2013) identified 31 and 37 colonizing species on two brown roofs in the same city. All the spontaneous species observed on the studied EGR are anemochorous except C. album, Geranium sp, P. oleracea and Vicia sativa L. Kleyer et al. (2008). Those latter species may have been imported to the roof by birds or by people visiting the roof. This suggests that most spontaneous species reach our EGR by wind, a pattern reported by Madre et al. (2014). We cannot exclude that some species might have been introduced via seed-contaminated substrate, however, we think that this is negligible as there was very low spontaneous species development on the first year of the experience.

Using a “space for time” approach, Vanstockem et al. (2018) demonstrated the presence of seedbanks on established extensive green roofs aged of 19 years. In the current study, for the first time, we experimentally demonstrate the accumulation of a seedbank on an experimental green roof over a short period of time (4 years) from its settlement from bare substrate. A total of 18 species were found in the seedbank. Mean seedbank SLI indicated a transient global seedbank, but seed accumulation in the deepest substrate layers suggested ongoing constitution of a permanent seedbank, mainly due to spontaneous species with permanent seedbanks, such as C. album and P. oleracea.

Despite a set of common species between the seedbank and the vegetation, and a trend for plant community to become more similar to the seedbank through time, in depth comparison reveal divergence between both, with spontaneous ruderal species dominating the seedbank (mainly P. oleracea) when considering seedbank density, and seeded grassland species dominating the vegetation when considering the species cover. High dissimilarity between the composition of species of the seedbank and the vegetation has been measured in established EGR and the majority of species in EGR seedbanks are spontaneous colonizers (Vanstockem et al., 2018).

Only a small proportion (9 out of 24) of seeded grassland species are detected in the seedbank with mostly lower seed densities as compared to the original seeding, indicating that a seed reserve has not accumulated over time. Limited development of grassland seedbank is due to the non-development of some seeded

species (eight species) and to the absence of seedbank for a set of species (eight) present in the vegetation.

Comparison with the seedbank of (semi-)natural systems with comparable environmental characteristics [habitat template sensu Lundholm (2006)], such as calcareous grasslands on shallow soils, can provide useful insights on EGR seedbanks. Many authors showed that species dominating calcareous grassland vegetation were mostly absent from the seedbank, and when present were not abundant (Davies and Waite, 1998; Bisteau and Mahy, 2005; Bossuyt et al., 2006). Almost all calcareous grassland species produce short-lived (transient) seeds that stay viable in the soil for a limited time period. Grassland species invest more resources in individual survival than in seeds production (Eriksson and Eriksson, 1997).

Spontaneous species observed on the experimental EGR are mainly ruderal and for the majority (60%) have accumulated a seedbank despite low cover in the above ground vegetation, to the exception of P. oleracea, which was one of the dominant species of the vegetation. Vanstockem et al. (2018) also reported a dominance of ruderal species in seedbank of established EGR. Ruderal species allocate more resources to producing a large number of seeds rather than developing their cover (Grime, 1988; Šerá and Šerý, 2004). Ruderal spontaneous species are also present in seedbank composition of natural dry grassland but have a limited development in the vegetation (Bisteau and Mahy, 2005). In contrast to Vanstockem et al. (2018) who observed limited presence in the seedbank species pool in comparison to the vegetation, the two most abundant phanerophyte species in the seedbank of our EGR (Buddleja davidii Franch., Betula sp) were almost absent from the vegetation, indicating that seed rain may be an agent responsible for seed accumulation in the seedbank (Davies and Waite, 1998). The presence of B. davidii in the seed bank may pose a potential problem for future vegetation development on the roof as it has a very high invasive potential in disturbed, open and dry environments (Tallent-Halsell and Watt, 2009; Monty et al., 2019), even though it is currently absent from the established vegetation in permanent plots.

We demonstrate that after 4 years of development, both standing vegetation and seedbank species composition were influenced by the variation of environmental factors: substrate depth and sun exposure. The substrate depth is known to be an important factor structuring green roof vegetation both for seeded/planted species and for colonizing species (Durhman et al., 2007; Köhler and Poll, 2010; Madre et al., 2014). In more stressful environment on our EGR (low substrate depth, high sun exposure) vegetation cover was limited with large proportion of bare substrate (Rivière et al., 2022). Vegetation gaps are an important component of seeded EGR, during the vegetation development phase and along the life-cycle of EGR when extreme environmental conditions and abiotic conditions can locally affect vegetation (Vanstockem et al., 2019). Colonizing plants can develop on green roofs because there is available space due to changes in the planted populations, with a decrease in some of the planted species (Dunnett et al., 2008). Gaps in vegetation can impact seedbank characteristics and the development of vegetation by providing an opportunity for colonization by spontaneous and ruderal species (Vanstockem et al., 2018). However, in our study, we do not find a relationship between space left vacant by grassland vegetation and spontaneous species development. This can result from extreme environmental
conditions affecting in the same way the two categories of species and preventing ruderal spontaneous species to germinate and colonize bare substrate. This lack of gap colonization potential may be reinforced by the fact that seedbank density was correlated to dominant species cover in the vegetation, indicating, that seedbank accumulates in the vicinity of mature individuals growing in the most favorable environment of the green roof. Also, the role of the seedbank in gap regeneration depends on the severity of disturbance which should relocated buried seed to the surface. In undisturbed vegetation, seedlings very rarely originate from the seedbank (Milberg and Persson, 1994; Kalamees and Zobel, 2002).

In conclusion, in contrast to Vandvik et al. (2016) and Vanstockem et al. (2019) suggestion that seedbanks on EGR could serve as a diversity insurance during periods of extreme conditions (e.g., extreme drought or frost) and can therefore potentially offer a basis for restoration of vegetation gaps, we experimentally measured that the maintenance of a diversified analogous grassland on an extensive green roof cannot rely mainly on an accumulated seedbank. Grassland seeded species did not contribute significantly to the seedbank. Seeded community is therefore unstable and vulnerable to sudden and profound changes under extreme hydric stress conditions. In this case, standing vegetation will be dominated by spontaneous species and the habitat developed will no longer be analogous to dry grassland. Maintenance of target grassland vegetation, in response to disturbance by extreme events, will depend mainly on the perennial habit of grassland species and their high tolerance to stress. Punctual seeding intervention may be needed in case of extreme climatic events affecting grassland species survival.

## Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

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## Author contributions

GM and LR carried out the conception, design of the study, and wrote the first draft of the manuscript. LR and AS carried out the experiment and performed the statistical analysis. All authors contributed to the conception of the manuscript, manuscript revision, read, and approved the submitted version.

## Funding

This work was funded by ARC CityRoof (University of Liège).

## Acknowledgments

The authors acknowledge the ARC CityRoof project for funding and UCL for providing access to their greenhouses.

## Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

## Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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<figure><img src="image_97.png" /></figure>

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EDITED BY
Arnaldo Marín,
University of Murcia, Spain

REVIEWED BY
A. M. Keith,
UK Centre for Ecology and Hydrology
(UKCEH), United Kingdom
Jana Müllerová,
Jan Evangelista Purkyně University in Ústí
nad Labem, Czechia

*CORRESPONDENCE
Quentin Lambert,
qntnlmbrt@gmail.com

RECEIVED 27 January 2023

ACCEPTED 31 May 2023

PUBLISHED 22 June 2023

**CITATION**
Lambert Q, Bischoff A, Enea M and Gros R (2023). Photovoltaic power stations: an opportunity to promote European semi-natural grasslands?
*Front. Environ. Sci.* 11:1137845.
doi: 10.3389/fenvs.2023.1137845

**COPYRIGHT**
© 2023 Lambert, Bischoff, Enea and Gros. This is an open-access article distributed under the terms of the *Creative Commons Attribution License (CC BY)*. The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Photovoltaic power stations: an opportunity to promote European semi-natural grasslands?

Quentin Lambert¹*, Armin Bischoff², Morgane Enea¹,³ and
Raphael Gros¹

¹Aix Marseille Université, Université d'Avignon, IRD, CNRS, Institut Méditerranéen de Biodiversité et d'Ecologie marine et continentale (IMBE), Campus Etoile, Marseille, France, ²Université d'Avignon, Aix Marseille Université, IRD, CNRS, Institut Méditerranéen de Biodiversité et d'Ecologie marine et continentale (IMBE), IUT Avignon Avignon, France, ³Université de Sherbrooke, Département de biologie, Centre d'étude de la Forêt, Sherbrooke, QC, Canada

European semi-natural grasslands are particularly species-rich and provide many ecosystem services but depend on extensive land management. Today, these ecosystems are highly threatened by land abandonment, land use intensification and habitat destruction. The increasing construction of solar parks may contribute to habitat destruction but also provide an opportunity to restore or even create semi-natural grasslands. We studied ten solar parks along a climate gradient in Southern France to evaluate the establishment of semi-natural grasslands managed by grazing. We compared plots outside and under solar panels in terms of plant community composition, soil biodiversity and soil functioning to test whether solar panels hamper this establishment. The different microclimate under solar panels strongly affected the plant species composition and reduced the abundance of soil mesofauna and biomass of fungi and gram-negative bacteria. Outside panels, the vegetation was shaped by a climatic gradient, in particular by variations in temperature and precipitation whereas under panels variations were smaller indicating a homogenizing effect of panels on plant community composition. We found more trophic interactions between panels compared to outside and under panels suggesting a protection effect of panels between rows on the soil food web. However, plots under panels showed the lowest number of interactions demonstrating that the strong shading had a negative effect on the plant-soil food web. Solar panels therefore reduce the plant and soil biodiversity of semi-natural grasslands and disrupt ecosystem functions. In order to improve the ecological integration of solar parks, it is thus necessary to mitigate negative effects of solar panels on biodiversity or to increase the space between panels.

**KEYWORDS**

solar parks, biodiversity, plant, soil, trophic interactions, mesofauna

## 1 Introduction

Semi-natural grasslands represent 5% of the European and 20% of the French territory (U.E—SOeS, Corine Land Cover 2006). They are among the habitats of the highest species richness for many taxa at community scale (Wilson *et al.*, 2012; Habel *et al.*, 2013) and provide many ecosystem services such as pollination, carbon sequestration, soil conservation and livestock feeding (Bengtsson *et al.*, 2019). The high species richness of herbaceous plants plays a key role in the functioning of this ecosystem and drives its biodiversity by providing
resources and ecological niches resulting in a high complexity of interaction networks (Habel et al., 2013; Bengtsson et al., 2019). Semi-natural grasslands are one of the most threatened habitats in Europe (Gang et al., 2014). Land-use intensification (overgrazing, cropland), as well as construction activities (urban sprawl, industrial facilities) or, in contrast, land abandonment resulting in shrub encroachment, have led to a dramatic loss of European semi-natural grasslands. The rapid development of renewable energy in Europe such as solar park construction may further increase the pressure on semi-natural grasslands. Van de Ven et al. (2021) estimated that 3% of open habitats such as grasslands need to be transformed to solar parks in order to reach the European Union objectives for solar energy production. However, there may also be a chance for promoting semi-natural grasslands if solar parks are constructed in degraded habitats such as monospecific forests, quarries, arable land or wasteland providing an opportunity to benefit biodiversity and ecosystem functioning of new semi-natural grasslands (Hernandez et al., 2019; Lambert et al., 2022).

Semi-natural grassland biodiversity and functioning are driven by climate, soil and land use (Zarzycki and Bedla, 2017; Basile-Doelsch et al., 2020; Boonman et al., 2021). Climatic and abiotic soil conditions are also important drivers of biotic soil functioning and plant-soil interactions (Craine et al., 2012). Plant diversity affects decomposer organisms by resource diversification (beetles, mites, springtails, microorganisms) (Zak et al., 2003; Isbell et al., 2017). Steinauer et al. (2015) showed that higher plant diversity is closely related to higher microbial activity and biomass. The plant functional diversity also drives the diversity of the mesofauna decomposers (Moradi et al., 2017). These decomposers contribute to the structure of grassland plant communities by feeding on roots and translocating nutrients (Whiles and Charlton, 2006). Moreover, the mesofauna decomposer abundance directly affects mesofauna predators (such as mites, diplurans). Soil mesofauna such as plants and microorganisms are also influenced by climate (Barnett and Facey, 2016). The high floristic and faunistic biodiversity of semi-natural grasslands depends on extensive grazing or mowing avoiding shrub encroachment and a succession towards woody plant communities (Pulungan et al., 2019). The abandonment of such management leads to a strong decrease of plant diversity and related soil functions (Malcolm et al., 2006).

The construction of solar parks and subsequent use for electricity production changes environmental conditions (Hernandez et al., 2014). Clearing and vegetation management by grazing hampers shrub encroachment resulting in a dominance of herbaceous plant species (Armstrong et al., 2016). The construction of solar parks further leads to a degradation in the soil physico-chemical quality involving a reduction of aggregate stability and a compaction of the topsoil layer (Lambert et al., 2021). Choi et al. (2020) showed that total carbon and nitrogen content were two times lower in soils of solar parks than in arid grasslands. Solar panels also change microclimate resulting in a reduction of light, soil temperature and moisture (Uldrijan et al., 2021; Lambert et al., 2022; Moscatelli et al., 2022). Plant biomass and diversity were lower under solar panels while mortality and chlorophyll content were higher (Uldrijan et al., 2021; Lambert et al., 2022; Moscatelli et al., 2022). The lack of light under solar panels favors shade-tolerant species (Lambert et al., 2022) resulting in semi-natural vegetation types that can be found close to forests or in pasture woodlands.

Whereas solar radiation is always reduced under solar panels, temperature and soil moisture may also be higher, for example, during the night (temperature) or after drought periods (soil moisture) thus buffering extreme values (Lambert et al., 2021). This buffering effect on microclimate may change the relationship between vegetation and regional climate.

The lower nutrient content in solar parks and the changes in microclimate under panels also change soil microbial biomass and enzyme activities involved in N and C cycling (Lambert et al., 2021; Moscatelli et al., 2022). The effect of solar panels on vegetation and soil microorganisms leads to a reduction in soil CO₂ emission (Lambert et al., 2021) affecting biomass production (Armstrong et al., 2016). However, a better understanding of the decomposer food web and its interactions with plants is needed to evaluate consequences of solar park construction and solar panels on ecological functioning of semi-natural grasslands such as carbon cycling and sequestration.

In order to evaluate limits and chances of solar park construction and exploitation for the establishment of semi-natural grasslands, we compared plots outside and under solar panels in terms of plant community composition, soil organisms and soil functioning along a climate gradient. We expected that 1) the plant community composition outside solar panels is driven by climate and that the climate effect on plant communities is smaller under panels, 2) solar panels strongly affect plant communities and soil parameters. We finally hypothesized that 3) the changes in plant and soil communities disintegrate the soil food web network.

# 2 Materials and methods

## 2.1 Study sites and sampling design

This study was set up in ten solar parks (SPs) located in Southern France along the Rhône valley (Figure 1). The SPs are distributed along a climate gradient from the Mediterranean Sea in the south to inland sites in the north and east (Table 1). Distance to the sea was between 37 and 192 km, and sites differed in major climate characteristics from Mediterranean in the south to sub-Mediterranean at highest distances from the coastline (Table 1). Continentality increased to the north, mean annual temperature ranged from 11.8°C to 14.9°C and annual precipitation from 710 mm to 903 mm. Other environmental factors such as soil and technical characteristics are similar between SPs. The size of the SPs ranged from 1 to 7 ha, equipped with solar panel either fixed on the ground or trackers. Ground-fixed panels had a height of 1 m at the southern edge and of 2.5 m at the northern edge (inclination towards south) whereas the inclination of tracker panels changed according to the sun position. The SP were built on former wasteland, crop land or semi-natural land (Table 1). The solar panels were aligned to form rows with a gap of 2.5 m between rows. All solar parks were managed by extensive sheep grazing. Parks were additionally mown when grazing was not intensive enough or irregular.

Within each SP, five blocks of at least 50 m distance were set up in spring 2021. In each block (Supplementary Appendix S1), three
<figure><img src="image_98.png" /><figcaption>**FIGURE 1**
Geographical position of solar parks. Colors of SP position represent a mean annual temperature gradient.</figcaption></figure>

<table><caption>TABLE 1 Environmental characteristics of solar parks.</caption><thead><tr><td>ID</td><td>Sites</td><td>Solar radiation (kWh/m²/y)</td><td>Annual mean temperature (°C)</td><td>Annual precipitation (mm)</td><td>Distance to the sea (km)</td><td>Altitude (m a.s.l.)</td><td>Soil type (WRB)</td><td>Past land use (CLC nomenclature)</td></tr></thead><tbody><tr><td>1</td><td>Blauvac</td><td>1894.19</td><td>11.82</td><td>863</td><td>80</td><td>570</td><td>Calcisol</td><td>Sclerophyllous vegetation (323)</td></tr><tr><td>2</td><td>Ozon</td><td>1411.33</td><td>12.43</td><td>832</td><td>192</td><td>130</td><td>Fluvisol</td><td>Natural grasslands and pastures (321)</td></tr><tr><td>3</td><td>Saint-Georges-Bains</td><td>1522.73</td><td>12.74</td><td>876</td><td>158</td><td>107</td><td>Fluvisol</td><td>Complex cropping systems and parcels (242)</td></tr><tr><td>4</td><td>Le Pouzin</td><td>1540.99</td><td>12.97</td><td>895</td><td>145</td><td>91</td><td>Fluvisol</td><td>Complex cropping systems and parcels (242)</td></tr><tr><td>5</td><td>Les Tourettes</td><td>1578.98</td><td>12.93</td><td>903</td><td>138</td><td>85</td><td>Fluvisol</td><td>Complex cropping systems and parcels (242)</td></tr><tr><td>6</td><td>Saint-Paul-Lez-Durance</td><td>1877.75</td><td>13.07</td><td>710</td><td>57</td><td>388</td><td>Rendosol</td><td>Changing forests and shrubby vegetation (324)</td></tr><tr><td>7</td><td>Donzères</td><td>1718.5</td><td>13.64</td><td>870</td><td>108</td><td>68</td><td>Calcosol</td><td>Natural grasslands and pastures (321)</td></tr><tr><td>8</td><td>Bollene</td><td>1760.87</td><td>13.99</td><td>843</td><td>96</td><td>64</td><td>Fluvisol</td><td>Changing forests and shrubby vegetation (324)</td></tr><tr><td>9</td><td>Mollégès</td><td>1843.78</td><td>14.42</td><td>720</td><td>46</td><td>58</td><td>Calcisol</td><td>Complex cropping systems and parcels (242)</td></tr><tr><td>10</td><td>Tarascon</td><td>1775.85</td><td>14.94</td><td>743</td><td>38</td><td>18</td><td>Fluvisol</td><td>Grassland (231)</td></tr></tbody></table>

plots of 4 m² were delimited for the following treatments: outside solar panels (outside), between two rows of solar panels (between) and under solar panels (under) (N = 10 solar parks X 5 blocks X 3 treatments = 150).

## 2.2 Vegetation monitoring

The effect of solar panels on plant community composition and vegetation cover (vertical projection of above-ground plant organs) of
all vascular plant species was evaluated in each plot in late spring. To evaluate the effects of solar panels on physiological performance of the vegetation, a chlorophyll index was measured by Multiplex® 3 (FORCE-A, Orsay, France) with ten records of chlorophyll fluorescence within each plot. The Multiplex ResearchTM allows *in situ*, non-destructive and real-time measurements of chlorophyll content (Agati et al., 2011).

## 2.3 Soil sampling and physico-chemical properties

Soil was sampled in the same plots as the vegetation. In April 2021, three soil samples were randomly collected (10 cm depth) in each plot and mixed to one bulk sample. Bulk samples were sieved (mesh size: 2 mm) prior to analysis. An aliquot of samples was air-dried (1 week, 30 C) for physico-chemical analyses and another aliquot stored at 4°C for microbial analyses. Soil water content was determined after drying samples (24 h, 105°C). Total carbon (TC) and total nitrogen (TN) contents were measured by combustion in the CN FlashEA 1112 (ThermoFisher) (NF ISO 10694, NFISO 13878) elemental analyser.

## 2.4 Sampling and determination of springtails and mites

In April 2021, the soil mesofauna was sampled in stainless steel tubes (5.5 cm diameter, 6 cm depth) using two core samples per plot after vegetation removal. Mites and springtails were extracted during a period of 7 days at 25°C using the Berlese-Tullgren method (Bano and Roy, 2016) and then stored in 70% ethanol. Springtails and mites were counted under a stereomicroscope.

## 2.5 Microbial community composition and enzymatic activities

Phospholipid fatty acids (PLFA) are essential components of all living cells, but several PLFA are specific to bacterial and fungal biomass. PLFA were analyzed according to Buyer and Sasser (2012) and Aupic-Samain et al. (2021). After freeze-drying, 1 g of dry soil of each sample was extracted in 4 mL of Bligh-Dyer extractant containing 4 µL of 1,2-dinonadecanoyl-sn-glycero-3-phosphocholine (C19:0; Avanti® Polar lipids, Inc.). Lipids were separated using solid-phase extraction (SPE) on Phenomenex® (Strata SI-1 with 50 mg of silica, 55 µm, 70 Å). The resulting fatty acids methyl ester (FAME) were analysed by gas-chromatography/ mass-spectrometry (GC-MS) on an Agilent 7890 system equipped with an MSD5977. A network mass detector, an ALS7693 automatic injector and an HP5-MS apolar column (30 m × 0.25 mm × 0.25 µm; JandW Agilent Technologies). Qualitative analysis of FAMEs resulted from retention time comparison of the FAME mixture (range between C4 to C24). We analyzed 85 specific biomarkers out of 98 identified PLFAs. Each biomarker was attributed to a specific taxon such as arbuscular mycorrhiza, other fungi, actinobacteria, gram-negative bacteria, gram-positive bacteria, in accordance with Frostegård et al. (1993). Bacterial biomass was calculated as the sum

of actynobacteria, gram-negative and gram-positive bacteria biomass. Total microbial biomass was calculated as the sum of fungal and bacterial biomass. The details of the attribution of each peak to the taxa are presented in Supplementary Appendix S2.

The activity of luorescein diacetate hydrolase (FDAse) was measured according to Green et al. (2006) in order to determine the catabolic potential of microbial communities. FDAse is a complex of enzymes comprising phosphatase, cellulase, and lipase involved in the decomposition of cellulose and carbohydrates (Guénon et al., 2017).

## 2.6 *In situ* soil respiration

In May 2021, *in situ* soil CO₂ effluxes (g.CO₂.m⁻¹.h⁻¹) were recorded using a portable gas analyser (EGM-4, PP-system) after removal of aboveground vegetation. The soil CO₂ effluxes represent the respiration of plant roots, soil organisms and chemical oxidation of C compounds. The device was connected to a closed soil respiration chamber (SRC-1, PP systems Massachusetts, United States). To prevent leakage of CO₂ when placing the chamber on the soil, a PVC tube (10 cm × 11 cm) was buried 1 cm deep into the soil prior to measurements. In each plot, soil temperature was recorded *in situ* in a depth of 7 cm using the soil temperature probe (STP-1, PP-system) connected to the portable gas analyzer.

## 2.7 Statistical analyses

Climatic variables were extracted from WorldClim version 2.1 dataset using the period from 1970 to 2000 (Fick and Hijmans, 2017). The 19 climatic variables were extracted on a grid of 1 km² and represented annual means of temperature and precipitation, seasonality, and extreme values resulting from MODIS images of the same period.

All data were analysed using R software (3.6.1, R core Team, 2020). Relationships between plant community composition, bioclimatic variables, plot position (under vs. outside panels) and one-way interaction of plot position and climatic variables were analysed using Redundancy analysis (RDA) in the R package "vegan" (Oksanen et al., 2007). Prior to RDA, multicollinearity of the 19 WorldClim explanatory variables and of their interactions with panel was tested by calculating the variance inflation factor (VIF) and using a cut value of 3. VIF retained four of them as non-collinear (Precipitation of Wettest Month, Annual Temperature Range, Mean Temperature of Wettest Quarter, Temperature Seasonality). A first RDA model tested the effect of panel and these four climatic variables on plant community composition including panel-climate interactions. Starting with an initial model of explanatory variables and interactions not showing collinearity, a backward selection procedure was applied using the "adespatial" package. Significance of explanatory variables was tested using PERMANOVA. Since most climatic variables showed significant interactions with plot position, separate RDA were additionally run for plots under and outside panels to explore the influence of climatic gradients.

To analyze the effect of solar panels on plant community characteristics, Shannon index, Simpson index and evenness were
<figure><img src="image_99.png" /><figcaption>FIGURE 2</figcaption></figure>

Redundancy analyses (RDA) of environmental variables on plant communities in solar parks including panel (P) effect (A) and separately outside (B) and under solar panels (C). Climate variables were precipitation of the wettest months (PW), temperature seasonality (TS) and the interaction of solar panels with annual temperature range (P:TAR) and temperature of the wettest quarter (P:TW). Red and blue points in panel A are plots under and outside solar panels, respectively. In figure B and C, colored points represent centroids of solar park vegetation.

calculated. For each species, ecological indicator values were
obtained using Julve (1998) adapted from Ellenberg et al. (1992).
We separated species according to light indicator values in two
groups: shade tolerant (1000–5,000 lux) and heliophilous species
(>5,000 lux). We tested the habitat preference of each species in
order to evaluate whether solar panels change grassland
communities to shade-tolerant understory vegetation. The
following response variables were tested: diversity indices, total
vegetation cover, cover of shade-tolerant and heliophilous plants,
chlorophyll index, mite and springtail abundance, biomass of
microbes (AM fungi, Total Fungi, total Bacteria, Actynobacteria,
Gram –, Gram +), FDase activity, soil respiration, soil temperature
and moisture, total carbon and total nitrogen N. Linear mixed-effect
models (LMMs) were calculated including position (under vs.
between vs. outside panels) as fixed factor and SP as random

factor. A Tukey post-hoc test was run to test for differences
between the three positions when a significant effect of solar
panel was detected. When necessary, data were transformed
using the “bestNormalize” package (Peterson, 2021) to meet the
assumptions of normality and homoscedasticity of variances.

To evaluate the influence of vegetation cover and main climatic
variables on soil food web and soil CO2 effluxes (a key soil function),
a path analysis was performed using R package "lavaan" (Rosseel,
2012). The analysis was run separately for measurements outside,
between and under solar panels. Path analysis is a structural
equation modelling (SEM) technique used to show causal
relationships between several measured variables. To build a full
model (Supplementary Appendix S3), we assumed a causal
relationship between climatic variables (i.e., annual mean
temperature and precipitation) and vegetation cover on total
carbon and total nitrogen content, total bacterial and fungal biomass, abundance of springtails and mites, and soil respiration. The full models were simplified by progressively excluding insignificant variables until an appropriate minimum model was obtained. The fit of each model was evaluated by the significance of differences between the predicted and observed covariance matrices (χ²-squared tests, *p* > 0.05), by the mean square root error of the approximation index (RMSEA <0.1), by comparative fit index (CFI >0.90) and the Tucker-Lewis index (TLI >0.90) (Xia and Yang, 2019).

# 3 Results

## 3.1 Solar panel effects on plant community composition along a climatic gradient

Solar panels significantly affected plant community composition and the interactions of panels with annual temperature range (P: TAR) and temperature of the wettest month (P:TW) (Table 3; Figure 2A). Solar panels and interactions constrained the first axis of the RDA. The second RDA axis was constrained by temperature seasonality (TS) and precipitation of the wettest month (PW). Plant communities under solar panels showed less variation on the first RDA axis than those outside solar panels. Vegetation under solar panels was characterized by *Anisantha sterilis* and outside solar panels by *Lotus dorycnium*.

Outside solar panels (Figure 2B), temperature of the warmest month (TW), temperature seasonality (TS) and precipitation of the wettest month constrained the first axis of the RDA, while annual temperature range (TAR) constrained the second one (Figure 2B). The solar parks 1 and 6 were negatively correlated to precipitation of the wettest month and temperature seasonality. And they were characterized by *L. dorycnium*. Solar parks 2 to 5 were positively correlated to PW and negatively to TAR. Solar parks 8 to 10 were positively correlated to TW and TS and were characterized by *Cynodon dactylon* and *Bromus rubens*.

Under solar panels, TS and PW constrained the first axis, while TAR and TW constrained the second axis (Figure 2C). Solar panels 1–5 characterized by *A. sterilis*, grouped together at low precipitation of the wettest month and low temperature seasonality. The solar parks 7 to 10 characterized by *L. dorycnium* also grouped together but at the left part of the biplot characterized by higher precipitation of the wettest month and higher temperature seasonality. The solar park 6 was apart from the others occurring in the upper part of the biplot that was related

to lower temperatures of the warmest month and characterized by *Erigeron sumatrensis/canadensis* and *Argyrolobium zanonii*.

Plant communities of solar parks were dominated by ruderal and grassland species. Herbaceous species cover outside and under solar panels was 70 and 10 times higher, respectively, than forest type species cover (Table 2; Supplementary Appendix S5). Two non-native species (*E. sumatrensis/canadensis*, *Senecio inaequidens*) were recorded in 55 plots. Their cover represented on average 2% of total vegetation cover (Table 2; Supplementary Appendix S5).

The species richness (Figure 3A) and total vegetation cover (Figure 3B) were significantly lower under solar panels than outside panels (*p* < 0.05). The chlorophyll index (Figure 3C; Supplementary Appendix 6) increased under solar panels. The cover of shade-tolerant species (Figure 3D) was twice as high and the cover of heliophilous species (Figure 3E) 2.5 times lower (*p* < 0.001) under solar panels than outside. The cover of non-native species was four times higher (*p* < 0.001) under than outside and between solar panels (Figure 3F). Shannon index, Simpson index and evenness were significantly higher outside than under solar panels (Supplementary Appendix S6).

## 3.2 Effects of solar panels on soil physico-chemical and biological properties

Total mite abundance was higher (*p* < 0.001) outside than under or between solar panels (0.5 and 1.5 times, respectively, Figure 4A; Supplementary Appendix S6). Springtails were 1.5 times more abundant (*p* < 0.001) outside than under solar panels (Figure 4B; Supplementary Appendix S6). Soil respiration was 5.5 times lower under than between and outside solar panels (Figure 4L, *p* < 0.001). Fungi and gram-negative bacteria biomass were significantly reduced by 25% under solar panels compared with outside (Figures 4C, G), Supplementary Appendix S6). Total biomass of microorganisms was 1.25 times higher outside than under solar panels (Supplementary Appendix S6; Figure 4J). FDAse activity was 20% lower under than between and outside solar panels (Supplementary Appendix S6; Figure 4K).

Solar panels further decreased soil temperature, total carbon and total nitrogen contents but did not significantly change soil water content and C:N (ratio (Table 3; Supplementary Appendix S6). Between solar panels, the soil temperature was about 30% higher than under solar panels Table 4.

No significant differences were found between the different modalities for AM fungi, Bacterial biomass, Actinobacteria, Gram

<table><caption>TABLE 2 Effect of solar panels on plant community characteristics such as habitat preference, plant type and species origin. Mean values with standard errors in parentheses. Different letters indicate significant differences between habitat, plant type and position outside, between and under panels.</caption><thead><tr><th rowspan="2"></th><th colspan="3">Habitat</th><th colspan="2">Plant type</th><th colspan="2">Species origin</th></tr><tr><th>Ruderal habitats</th><th>Grassland</th><th>Forest</th><th>Herbaceous</th><th>Shrub</th><th>Tree</th><th>Native</th><th>Alien</th></tr></thead><tbody><tr><td>Outside</td><td>38.26 (±4.17)<sup>a</sup></td><td>31.18 (±2.72)<sup>b</sup></td><td>1.43 (±0.58)<sup>c</sup></td><td>70.66 (±4.64)<sup>a</sup></td><td>1.47 (±0.81)<sup>b</sup></td><td>0.20 (±0.11)<sup>c</sup></td><td>72.05 (±4.74)<sup>a</sup></td><td>0.57 (±0.31)<sup>b</sup></td></tr><tr><td>Between</td><td>32.85 (±3.51)<sup>a</sup></td><td>31.43 (±2.8)<sup>a</sup></td><td>0.37 (±0.16)<sup>b</sup></td><td>64.59 (±4.28)<sup>a</sup></td><td>1.34 (±0.59)<sup>b</sup></td><td>0.16 (±0.11)<sup>c</sup></td><td>65.60 (±4.28)<sup>a</sup></td><td>1.01 (±0.33)<sup>b</sup></td></tr><tr><td>Under</td><td>27.30 (±2.88)<sup>a</sup></td><td>28.36 (±3.57)<sup>a</sup></td><td>1.39 (±0.54)<sup>b</sup></td><td>56.79 (±4.04)<sup>a</sup></td><td>5.08 (±1.57)<sup>b</sup></td><td>0.31 (±0.21)<sup>c</sup></td><td>60.19 (±4.26)<sup>b</sup></td><td>3.96 (±1.00)<sup>a</sup></td></tr></tbody></table>
<figure><img src="image_100.png" /><figcaption>**FIGURE 3**
Species richness (A), total vegetation cover (B), chlorophyll index (C), total cover of shade-tolerant species (D), total cover of heliophilous species (E), total cover of non-native species (F) outside, between and under solar panels. Error bars are means ± SE; different lower-case letters indicate significant differences (p < 0.05).</figcaption></figure>

positives biomass, Fungi: Bacteria and Microbial biomass (respectively Figures 4D–F, H, I).

## 3.3 Effects of solar panels on interaction networks

Outside panels, the most parsimonious model for predicting soil bacterial biomass, springtail abundance and CO₂ effluxes included precipitation, temperature, plant cover and total C contents as significant explanatory variables. The model was characterized by ten significant relationships. Plant cover, the abundance of soil organisms and CO₂ effluxes were mainly controlled by mean annual temperature and precipitation. Annual precipitation had an indirect effect on CO₂ effluxes via the influence on bacterial biomass (–), vegetation cover (+), and soil organic carbon concentrations (+) (Figure 5A). An increase in vegetation cover and then total C content led to an increase in springtail abundance.

Between solar panels, the most parsimonious model for predicting soil bacterial biomass, abundance of mesofauna organisms and CO₂ effluxes included precipitation, plant cover and C and N contents as significant explicative variables (Figure 5B). The model is characterized by ten significant relationships. Plant cover, abundance of soil organisms and CO₂ effluxes were mainly controlled by mean annual precipitation. Collembola and mites were affected by organic carbon (+) and nitrogen content (–) and springtails additionally by vegetation cover (+). CO₂ effluxes increased with fungi biomass increase and bacterial biomass decrease.

Under panels, the most parsimonious model for predicting soil bacterial biomass and abundance of mesofauna organisms included precipitation, temperature, plant cover and C and N contents as significant explicative variables (Figure 5C). The model is characterized by seven significant relations. Annual mean temperature and precipitation controlled microbial biomass (–) only. Vegetation cover directly controlled (–)
<figure><img src="image_101.png" /></figure>

<figure><img src="image_102.png" /></figure>

<figure><img src="image_103.png" /></figure>

<figure><img src="image_104.png" /></figure>

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<figure><img src="image_107.png" /></figure>

<figure><img src="image_108.png" /></figure>

<figure><img src="image_109.png" /></figure>

<figure><img src="image_110.png" /></figure>

<figure><img src="image_111.png" /></figure>

<figure><img src="image_112.png" /></figure>

FIGURE 4

Mite abundance (A), springtail abundance (B), total fungi biomass (C), arbuscular mycorrhiza biomass (D), total bacteria biomass (E), actinobacteria biomass (F), gram-negative bacteria biomass (G), gram-positive bacteria biomass (H), fungi:bacteria ratio (I), total microbial biomass (J), fluorescein diacetate hydrolase activity (K), soil respiration (L) outside, between and under solar panels. Error bars are means ± SE; different lower-case letters indicate significant differences (*p* < 0.05).
<table><caption>TABLE 3 PERMANOVA results (RDA) of panel and climate effects on plant communities of solar parks. Degree of freedom, ANOVA F values and significance: *p < 0.1, **p < 0.05, ***p < 0.01, ****p < 0.001. P: solar panels, PW: precipitation of the wettest month, TAR: annual temperature range, TS: temperature seasonality, TW: temperature of the wettest quarter.</caption><thead><tr><th></th><th>Properties</th><th>Df</th><th>F</th><th>p-value</th></tr></thead><tbody><tr><td rowspan="7">RDA constrained by solar panel</td><td>Global RDA</td><td>5</td><td>5.20</td><td>***</td></tr><tr><td>RDA 1</td><td>1</td><td>9.83</td><td>***</td></tr><tr><td>RDA 2</td><td>1</td><td>6.10</td><td>***</td></tr><tr><td>P</td><td>1</td><td>6.35</td><td>***</td></tr><tr><td>PW</td><td>1</td><td>5.69</td><td>***</td></tr><tr><td>TS</td><td>1</td><td>5.75</td><td>***</td></tr><tr><td>P:TAR</td><td>1</td><td>2.11</td><td>***</td></tr><tr><td>P:TW</td><td>1</td><td>2.38</td><td>***</td></tr><tr><td rowspan="7">Outside solar panels RDA</td><td>Global RDA</td><td>3</td><td>4.41</td><td>***</td></tr><tr><td>RDA 1</td><td>1</td><td>6.05</td><td>***</td></tr><tr><td>RDA 2</td><td>1</td><td>5.44</td><td>***</td></tr><tr><td>PW</td><td>1</td><td>3.49</td><td>***</td></tr><tr><td>TAR</td><td>1</td><td>4.64</td><td>***</td></tr><tr><td>TS</td><td>1</td><td>4.85</td><td>***</td></tr><tr><td>TW</td><td>1</td><td>4.66</td><td>***</td></tr><tr><td rowspan="7">Under solar panels RDA</td><td>Global RDA</td><td>4</td><td>3.26</td><td>***</td></tr><tr><td>RDA 1</td><td>1</td><td>5.44</td><td>***</td></tr><tr><td>RDA 2</td><td>1</td><td>3.57</td><td>***</td></tr><tr><td>PW</td><td>1</td><td>2.99</td><td>***</td></tr><tr><td>TAR</td><td>1</td><td>3.43</td><td>***</td></tr><tr><td>TS</td><td>1</td><td>3.88</td><td>***</td></tr><tr><td>TW</td><td>1</td><td>2.75</td><td>**</td></tr></tbody></table>

mite abundance and indirectly (+) springtail abundance by an increase in organic carbon. Although bacterial biomass decreased with increasing precipitation, CO₂ effluxes under panels were explained by climatic, chemical, and biological variables. Similarly to the SEM outside and between panel, no significant interaction between bacterial biomass and mesofauna abundance was detected.

# 4 Discussion

Plant community composition outside solar panels was driven by climatic variables and reflected the climate gradient. Vegetation under solar panels varied less between the solar parks than vegetation outside panels indicating a homogenizing effect of panels on plant community composition. Solar panels affected the soil mesofauna and two microbial groups (fungi and gram-negative bacteria), and reduced the effects of annual precipitation and temperature on soil CO₂ effluxes.

## 4.1 Changes in plant community composition along a climatic gradient in solar parks

In the studied solar parks, plant communities outside panels were distributed along a climatic gradient. Through grading and clearing of vegetation, both soil quality and plant community composition change during the construction of solar park (Armstrong et al., 2016; Lambert et al., 2021). Plant establishment and spatial distribution of communities after a disturbance are driven by local climatic and soil physico-chemical parameters (Zarzycki and Bedla, 2017; Boonman et al., 2021). Chauvier et al. (2021) showed that artificial land cover (i.e., urban and agricultural area) clearly affects plant distribution along a climatic gradient in lower parts of the European Alps. Our results showed that climatic variables such as precipitation of the wettest month and temperature annual range still drive plant community composition of solar parks suggesting that solar parks do not totally disintegrate the relationship between climate and vegetation.

Outside the panels, vegetation was shaped by a climatic gradient, in particular by variations in temperature and precipitation. More precisely, we found that plant community composition was driven by the mean temperature of wettest quarter, temperature seasonality, annual temperature range and the precipitation of the wettest month. Our results suggest a progressive decrease in the sensitivity of plant species towards lower latitude to an increase in temperature variability and in the range of extreme temperature and precipitation conditions characteristic for the more continental climate of our inland sites (Maestre et al., 2009). The solar panels affected plant community composition and formed two distinct communities shaped by temperature. Under panels, mean temperature of the wettest month and annual temperature range had a smaller effect on plant communities than outside panels. Plant composition of grasslands is often driven by climate-related water

<table><caption>TABLE 4 Effect of solar panels on physico-chemical characteristics. Mean values with standard errors in parentheses. Different letters indicate significant differences (p < 0.05) between positions outside, between and under panels.</caption><thead><tr><th></th><th>Outside solar panels</th><th>Between solar panels</th><th>Under solar panels</th></tr></thead><tbody><tr><td>Soil Temperature (°C)</td><td>19.18 (±0.45)<sup>a</sup></td><td>18.02 (±0.45)<sup>b</sup></td><td>14.50 (±0.21)<sup>c</sup></td></tr><tr><td>Soil water content (%)</td><td>9.03 (±1.10)</td><td>10.20 (±1.04)</td><td>9.83 (±0.88)</td></tr><tr><td>Total Carbon content (%)</td><td>6.09 (±0.37)<sup>a</sup></td><td>5.45 (±0.38)<sup>ab</sup></td><td>5.09 (±0.32)<sup>b</sup></td></tr><tr><td>Total Nitrogen content (%)</td><td>0.28 (±0.03)<sup>a</sup></td><td>0.24 (±0.02)<sup>ab</sup></td><td>0.23 (±0.02)<sup>b</sup></td></tr><tr><td>C:N</td><td>28.64 (±2.79)</td><td>26.95 (±1.78)</td><td>25.10 (±2.73)</td></tr></tbody></table>
<figure><img src="image_113.png" /></figure>

<figure><img src="image_114.png" /><figcaption>FIGURE 5</figcaption></figure>

Path analysis models. Causal influence of mean annual precipitation (mm) and temperature (°C) on vegetation cover (%), total nitrogen content (N%), total carbon (C%), bacterial biomass, fungi biomass, springtails abundance, mites abundance and soil respiration (µmolCO₂·s⁻¹) (A) outside (d.f. = 9, χ² = 3.26, *p* = 0.095, RMSEA = 0.00, CFI = 1.00, TLI = 1.42), (B) between solar panels (d.f. = 21, χ² = 31.17, *p* = 0.07, RMSEA = 0.09, CFI = 0.94, TLI = 0.90), and (C) under solar panels (d.f. = 11, χ² = 12.02, *p* = 0.36, RMSEA = 0.04, CFI = 0.99, TLI = 0.97). Green arrows represent positive, red arrows negative relationships. Significance levels are indicated as ***p* < 0.001, **p* < 0.01, *p* < 0.05, *p* < 0.1). Non-significant paths (*p* > 0.1) were eliminated.

<figure><img src="image_115.png" /></figure>

availability (Cherwin and Knapp, 2012; Cleland et al., 2013). Armstrong et al. (2016) reported a buffer effect of panels on climatic variations leading to a reduction of ecological niche amplitude and a decrease in plant richness under a temperate-oceanic climate. We also observed a decrease in plant species richness under solar panels. In semi-natural dry grasslands, plants are often more affected by the lack of precipitation during the wettest months in autumn and winter (Dostalek and Frantik, 2011; Fischer et al., 2014). Rainfall during these periods is essential for seed germination and provides a soil water stock for the dry summer. Reduced rainfall under the panels may have limited plant establishment (Akinci, 2013; Lambert et al., 2022).

Contrary to Uldrijan et al. (2022) who showed that the cover of heliophilous plants was greater under the panels in the Czech Republic, we found that the light reduction under panels resulted

in a shift from heliophilous to shade-tolerant plant species that are more competitive under such conditions (Chen et al., 2004). This reduction of light also resulted in an increase in chlorophyll content, confirming the results of Lambert et al. (2022) who found a higher chlorophyll content in leaves of *Brachypodium retusum* under panels. Solar panels change the metabolism of the plants that need to allocate more resources to the production of chlorophyll in order to maintain a sufficient photosynthetic activity under panels (Ma et al., 2010). This higher resource allocation in chlorophyll production may have resulted in a reduction of leave, root and flower biomass. Total vegetation cover and species richness were smaller under solar panels which may be a consequence such a reduction in plant performance. These changes in plant performance and plant community composition may affect ecological functioning. Uldrijan et al. (2022) showed that plant
species under solar panels had fewer interactions with other taxa than species than outside panels. Such changes in the number of interactions may negatively affect ecosystem services provided by natural grasslands such as carbon sequestration (Uldrijan et al., 2022).

## 4.2 Effects of solar panels on soil physico-chemical and biological properties

During daytime, we measured a clear reduction of temperature by 4°C under solar panels confirming results of Moscatelli et al. (2022) and Lambert et al. (2021) under similar Mediterranean climate. However, continuous measurements showed a homogenizing effect of solar panels on temperature being lower during daytime but higher at night (Armstrong et al., 2016). Solar panels intercept rainfalls but reduce evapotranspiration (Lambert et al., 2021) resulting in non-significant panel effects on soil moisture. Accordingly, the area between panels (inter-rows) still affected by shading of panels but not protected from rainfall showed the highest soil moisture. Effects of panels on soil temperature and moisture may further depend on seasonal variations (Armstrong et al., 2016). Lower C and N contents were observed under panels resulting from a lower plant biomass production and subsequently a lower organic matter content.

We found that FDase activity, soil respiration and microbial biomass were reduced under solar panels. Climate and organic matter content are the most important drivers of soil microbial biomass and activities (Craine et al., 2012). Fluctuations in climatic parameters such as warming or drought may have important consequences for microbial activities (Brinkman and Sombroek, 1996). Microbial biomass and activities are indicators of soil functioning because they are involved in the decomposition of soil organic matter and provisioning of nutrients controlling plant growth (Abrahão et al., 2022). The lower FDase activity may be related to the lower microbial biomass under panels, especially of gram-negative bacteria. Gram-negative bacteria are particularly sensitive to drought and lower temperatures. Bacteria interact with the soil mesofauna to recycle nutrients and sequester carbon (Basile-Doelsch et al., 2020), a key ecosystem service provided by semi-natural grasslands (Bengtsson et al., 2019). In our study, the lower abundance of mites and springtails under panels may be explained by the reduction of soil temperature but also by a lower plant biomass. Mesofaunal soil communities are particularly sensitive to temperature changes. Aupic-Samain et al. (2018, 2021) showed that abundance and diversity of the mesofauna organisms were lower in systems with lower temperatures. Moreover, the lower content in soil organic carbon under panels limits the growth of such microbial and mesofaunal decomposers.

## 4.3 Effects of solar panels on trophic interactions in the soil

Path analysis revealed either positive indirect (via organic carbon content) or negative direct effects of vegetation cover on

soil mesofauna. An increase in plant cover increases the soil organic matter content, a food source for springtails that contribute to the microfragmentation of leaf litter (Berg and Laskowski, 2005). The negative direct effects of plant cover may be explained by a lower plant diversity in most productive plant communities. Zhang et al. (2022) reported in a metanalysis that soil fauna is most abundant and diverse in plant mixtures of high diversity through an increase in microhabitat complexity. Solar panels decreased plant diversity in our study and thus microhabitat diversity. In contrast, panels did not affect the positive indirect effect of vegetation cover on the abundance of mesofauna organisms. This result suggests that changes in microclimate under panels changed resource availability for such decomposers (Aupic-Samain et al., 2021).

In this study, we did not observe an effect of plant cover on microbial biomass and related interactions along the climatic along the climate gradient, neither outside nor under panels. However, plant litter is known to supply nutrients to soil microbes, and root exudates and rhizodeposition attract bacteria, fungi and nematodes (Chakraborty et al., 2012) suggesting that microbial communities are more sensitive to changes in climate than in plant cover. Changes in mean annual temperature and precipitation were the main drivers of bacterial biomass. Drenovsky et al. (2010) showed under a Mediterranean climate that land-use type and level of soil disturbance (i.e., tillage and irrigation regimes) are more important factors for the composition and biomass of the microbial community than vegetation.

We found more trophic interactions between panels compared to outside and under panels suggesting a protection effect of panels between rows on the soil food web. In the gap between panels, wind, albedo, soil temperature and solar radiation are generally lower than outside panels (Armstrong et al., 2014). Under panels, the effect of mean annual temperature and precipitation on trophic interactions diminished. The result may be explained by the homogenizing effect of panels on climate reducing maximum temperatures during daytime and summer and increasing minimum temperatures at night and during winter (Armstrong et al., 2016). Changes in precipitation and temperature are likely resulting in changes in the soil biodiversity and can alter soil biological processes and functions with potential consequences for ecosystem services (Pritchard, 2011; Nielsen and Ball, 2015). Soil CO₂ emission is an indicator of soil organic matter decomposition which integrates several biotic and abiotic components. CO₂ effluxes mainly depend on soil heterotrophic organisms (bacteria, fungi and fauna), plant roots (Bond-Lamberty et al., 2004; Moinet et al., 2019), soil temperature and moisture (Moinet et al., 2019). By changing plant communities, soil organisms and microclimate, the panels reduced direct and indirect effects of organisms in lower trophic levels, of mean annual temperature and of precipitation on CO₂ effluxes. Solar panels may thus limit ecosystem services such as carbon sequestration and nutrient cycling.

## 5 Conclusion

In solar parks, grassland communities were shaped by climatic factors such as semi-natural grasslands but solar panels partially disintegrate the relationship between climate and plant species composition. Solar panels changed plant communities towards
more shade-tolerant species, reduced plant diversity and vegetation cover, and affected plant performance. Thus, solar parks allow the establishment of semi-natural grassland communities but this establishment is limited under solar panels. Changes in soil microclimate and lower plant productivity under panels negatively affected the abundance and composition of soil organisms, trophic interactions, and CO₂ effluxes. Ecosystem services provided by soil organisms such as carbon storage, nutrient regulation, and soil conservation, are thus hampered by solar panels. The ecological integration of solar parks to favour the establishment of semi-natural grasslands needs to limit the effect of panels on plant communities and soil quality, for example, by increasing inter-row space or panel height.

## Data availability statement

The raw data supporting the conclusion of this article will be made available by the authors, without undue reservation.

## Author contributions

QL, AB, and RG contributed to conception and design of the study. QL organized the database. QL, ME, AB, and RG contributed to fields sampling. QL, AB, and RG performed the statistical analysis. QL wrote the first draft of the manuscript. All authors contributed to manuscript revision, read, and approved the submitted version.

## Funding

Funding was provided by the French Agency for Environmental Transition (ADEME) via a PhD grant to Quentin Lambert and the project PIESO (agreement N°1405C0035).

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## Acknowledgments

We thank Pierre Illac and Marine David (TotalEnergie renouvelables), Julie pastor (EDF renouvelables), Marion Henriet (Cap Vert Energie), Jerôme Wampack (Cap Vert Energie), Alexandre Cluchier (ECO-MED) and Camille Rolin (Compagnie Nationale du Rhône) for their logistic support. We further thank Morgane Enea, Amélie Augier, Cassandra Favale, Léo Rocher, Lisa Foli,, Caroline Lecareux for assistance in the lab and in the field. We are particularly grateful to Daniel Pavon for his precious help in plant identification.

## Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

## Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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EDITED BY
Zoltán Bátori,
University of Szeged, Hungary

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University of South Bohemia in České Budějovice, Czechia
László Erdős,
Hungarian Academy of Science, Hungary

*CORRESPONDENCE

Grégory Mahy

<figure><img src="image_117.png" /></figure>

g.mahy@uliege.be

RECEIVED 02 January 2023

ACCEPTED 28 April 2023

PUBLISHED 26 July 2023

CITATION

Dellicour M, Goret T, Piqueray J, Fayolle A, Bindelle J and Mahy G (2023) Success of passive and active restoration of lowland hay meadows with regard to current and historical references.

Front. Ecol. Evol. 11:1136206.
doi: 10.3389/fevo.2023.1136206

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© 2023 Dellicour, Goret, Piqueray, Fayolle, Bindelle and Mahy. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY).

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# Success of passive and active restoration of lowland hay meadows with regard to current and historical references

Maïké Dellicour¹, Thibaut Goret², Julien Piqueray³, Adeline Fayolle¹, Jérôme Bindelle¹ and Grégory Mahy¹,4*

¹Gembloux Agro-Bio Tech, University of Liège, Gembloux, Belgium, ²Natagora, Traverse des Muses, Namur, Belgium, ³Natagriwal ASBL, Gembloux, Belgium, ⁴Avignon Univ, Aix Marseille Univ, CNRS, IRD, Avignon, France

The restoration of hay meadows is one of the major challenges in temperate Europe, to overcome the drastic loss and degradation of these semi-natural habitats. Evaluation of restoration success requires the definition of a relevant reference ecosystem. Historical phytosociological relevés dating from the 1960s–1970s, before large scale agriculture intensification, may be used as the reference framework for such purposes. However, this reference framework may no longer be pertinent in our present-day ecological and socio-economic context. In this study, we assessed the success of three restoration techniques for lowland hay meadows, based on plant species richness and composition and functional traits, by comparing the restoration trajectories to historical and current references. The three restoration techniques, passive restoration mowing, active fresh hay transfer and active sowing of threshing material, were implemented depending on the initial level of degradation and the proximity to well-preserved meadows. The species composition of restored meadows clearly differed from historical references but converged toward current references, for both passive and active restoration techniques. Historical references are characterized by short, stress-tolerant dicotyledonous plant species with small specific leaf area, indicators of nutrient-poor environments. The gap observed between plant communities of historical and current meadows suggests that the historical data are an over-ambitious target for restoration purposes and can hardly be considered relevant to our present-day context. Therefore, creation of a new reference framework of lowland hay meadows is needed for the adequate implementation of restoration projects. Successful outcomes of active and passive techniques must encourage practitioners to adapt the choice of the restoration method depending on the local ecological context, i.e., level of degradation and proximity to well-preserved habitats.

KEYWORDS

grassland, restoration success, diaspore transfer, restoration reference, plant community, hay transfer, mowing
# 1. Introduction

Temperate meadows are among the habitats that host the most diverse fauna and flora in temperate Western Europe (Veen et al., 2009; Bruchmann and Hobohm, 2010; Wilson et al., 2012; Habel et al., 2013). The high conservation value of these habitats is the result of centuries, even millennia, of extensive traditional management allowing the coexistence of many species in these open environments (Isselstein et al., 2005). Since the second half of the twentieth century, changes in agricultural practices were encouraged by the implementation of the European Common Agricultural Policy (CAP). Land use changes and intensification of agricultural systems have led to the decline and degradation of species-rich meadows (Stoate et al., 2009; Veen et al., 2009), and therefore to a significant loss of animal and plant biodiversity in rural landscapes (Zechmeister et al., 2003; Marini et al., 2008; Manning et al., 2015; Emmerson et al., 2016). Specifically, lowland hay meadows are assessed as being in a poor conservation state over almost all of their range in the EU, which extends from northern Portugal to southern Sweden with a high occurrence in Belgium and Germany (European Environment Agency, 2019, 2020). Hence, ecological restoration of hay meadows is a major challenge in temperate Europe.

The definition of a clear and relevant reference ecological system is a central question for the evaluation of restoration success (Holl and Cairns, 2002; Waldén and Lindborg, 2016; Prach et al., 2019). Reference information is usually either existing intact sites or historical data (White and Walker, 1997). In Europe, a large dataset of phytosociological relevés have been collected historically (1960s–70s). Those relevés, providing information on plant communities before the major effect of agriculture intensification, are often used as references for evaluating the conservation status and restoration success of ecosystems. In southern Belgium, they have been used to build the regional version of EUNIS habitat classification (Davies et al., 2004) and are considered to be the reference framework for evaluating the outcome of EU-funded restoration programs targeting ecosystem restoration (Dufrène and Delescaille, 2005). However, this historical reference may no longer be achievable in our present-day ecological and socio-economic context. Rapid global environmental changes, mostly climate change and global eutrophication (Matson et al., 2002), may impose the reconsideration of the use of historical data as a reference for ecological restoration (Jackson and Hobbs, 2009; Higgs and Jackson, 2017). When choosing the reference data for the restoration of agriculture-dependent ecosystems, the evolution of agro-ecosystem management driven by socio-economic factors must also be taken into consideration as they greatly influence farmers' practices (McGinlay et al., 2017). In this context, former reference communities imposing very specific species assemblages may be an over-ambitious target. Despite this question of the use of historical references for evaluating the restoration of agro-ecosystems, only a small number of studies compared historical and contemporary references for evaluating large-scale, ecological restoration projects (Barak et al., 2015).

A variety of restoration techniques, including active and passive methods, are commonly used to overcome abiotic, biotic and dispersal constraints in meadow restoration projects (Sengl et al., 2017). Hay meadows have been historically managed by mowing with low fertilization. Adaptation of key management practices (fertilization, mowing regime) is seen as a passive way to restore meadows (Jones et al., 2018). Passive restoration is recommended when the habitat is

slightly degraded, has maintained a great potential for natural self-repairing and/or is integrated into a propitious landscape context, i.e., when surrounded by less impaired and traditionally managed meadows in which target species remain common (Jones et al., 2018; Prach et al., 2020). In more degraded landscape contexts and more strongly altered ecosystems, modification of management alone is insufficient for restoration. Lack of dispersal of the target species in time or space, i.e., the dispersal constraint is often identified as a major barrier (Donath et al., 2003; Hölzel et al., 2012; Pywell et al., 2012; Kiss et al., 2016). In such contexts, active restoration with assisted species dispersal combined with appropriate management practices is needed.

Plant assemblages are traditionally studied at the species taxonomic level, which allows for direct reference to a target species assemblage. Using predictions based on the taxonomic level alone may not allow the detection of functional differences, and the results obtained from a list of species in a given context cannot be extended to all contexts. Trait-based approaches are complementary to help to identify functional patterns and ecological functions independently of the studied pool of species (Lavorel and Garnier, 2002; Kahmen and Poschlod, 2004). Functional traits are useful tools to understand responses to environmental changes, such as changes in nutrient availability and disturbances (Lavorel and Garnier, 2002). As these abiotic conditions are expected to change during meadow restoration and between historical and current communities, we can also expect to see changes in functional composition (Fagan et al., 2008).

In this study, we aimed to assess the success of passive (mowing regime) and active (transfer of fresh seed-containing hay and sowing of threshing material) restoration of lowland hay meadows by confronting the restoration trajectories to historical and current references. We use an *in-situ* large scale experiment situation provided by the LIFE project in southern Belgium. We specifically addressed the following questions:

(1) Does the restoration of degraded meadows correspond to the recovery of the species and functional composition of historical and/or current references?

(2) Do passive and active restoration efforts, that were implemented depending on the initial level of meadow degradation, lead to equivalent restoration success?

# 2. Materials and methods

## 2.1. The restoration project

In southern Belgium, the ecological situation of lowland hay meadows is particularly unfavorable (Wibail et al., 2014; DEMNA, 2019). Several EU-funded LIFE projects have been launched to restore hundreds of hectares of lowland hay meadows using a variety of empirical techniques and offering great *in situ* experiments to evaluate restoration success. This study focused on the LIFE project "Prairies bocagères" (LIFE11 NAT/BE/001059; Goret et al., 2020) in which the restoration of approximately 200 hectares of lowland hay meadows was carried out in the Fagne-Famenne bioclimatic region, which is characterized by Upper Devonian shale geology and loam-rocky to clay soils. The mean annual temperature is 9.5°C and the mean annual precipitation is 976 mm (Van der Perre et al., 2015). Restoration actions were implemented in seven nature reserves with meadows
presenting different levels of degradation (Figure 1). A large number of degraded meadows were included in the restoration process each year from 2013 to 2018. This chronosequence of restoration is of significant value as it allows us to analyse changes in community composition over time (Engst et al., 2017; Sullivan et al., 2020).

The habitat targeted by restoration is the Natura 2000 habitat 6,510 protected by the EU Habitats Directive 92/43/EEC (European Community, 1992 – Annex I “Lowland hay meadows”; code EUNIS E2.22; phytosociology *Arrhenatherion elatioris* Koch 1926). *Arrhenatherion* hay meadows are permanent grasslands found on mesic soils at low altitudes (<300 m in Belgium). They are first mowed in early summer (end of June to early July). Depending on the meadow productivity and the yearly weather conditions, a second mowing or low-intensity grazing can occur at the end of summer. No or low fertilization is applied. When in good conservation status, they are characterized by oligo- to mesotrophic plant species (see lists 1 and 2, *Supplementary Table S1*).

The LIFE project implemented three restoration techniques: passive restoration mowing, active restoration with either transfer of hay containing fresh seed or direct sowing of threshing material. The restoration technique was adapted depending on the initial degradation level and the direct vicinity of the meadow following the decision-making tool of Goret et al. (2021). The techniques used depended on the initial conservation status (CS) of the meadow to be restored (Supplementary Table S2). CS was determined using indicators of plant community integrity following the evaluation grid of the Department for Nature and Agriculture Study of the Regional Administration (Supplementary Table S1; A: very good CS; B: good to medium CS; C: degraded CS; D: not a lowland hay meadow).

A total of 42 restored parcels were selected for this study. If the CS corresponded to recent degradation (B or C for less than 5 years) or a

degraded meadow (C) with an adjacent seed source (meadow with very good CS-A), the strategy was to use mowing (passive restoration), cutting and exporting biomass twice a year: once after June 15 and once after September 15. The first mowing was delayed by about a month compared to the regime applied before the restoration (May 15). During the first years of restoration, the first mowing cannot be too late, even though this is recommended to allow all species to complete their life cycle, because abiotic conditions first need to be adequate to favor forb germination and development. Mowing in mid-June helps to maximize nutrient export and to create sites where reduced competition for light allows forb seedlings to establish. Fertilization was proscribed.

If the CS was C (degraded) for more than 5 years with no adjacent grassland in CS-A or if CS was D, the restoration strategy included: stopping fertilization, soil preparation for seedling establishment (creation of bare soil strips by two harrow operations or by milling to a depth of 10 cm), assisted seed dispersal by fresh hay transfer or sowing of threshing material, and a post-restoration mowing regime similar to passive restoration. For both assisted seed dispersal methods, plant material was collected directly from a donor site in CS-A, located as close as possible (less than 10 km). Threshing material and fresh hay were always collected at the end of July, the year of restoration, in order to maximize the amount of characteristic species that had reached seed-maturity. Threshing material was collected using a small combine harvester adjusted to limit the amount of plant residues garnered. The harvest product was dried for about 48 h and then roughly sorted using an alveolar pre-sorter in order to not lose seed diversity. Through this process, less than 10% of plant debris remained in the seed mixture. In September, 25 kg per hectare was sown with a mechanical seeder in bare soil strips. For fresh hay transfer, the hay was spread on the degraded site on the same day that the donor site was mowed.

<figure><img src="image_118.png" /><figcaption>FIGURE 1
Location of the Fagne-Famenne region and the seven locations where meadow restoration occurred in the vicinity of reference nature reserves.</figcaption></figure>