<table><caption>TABLE 1 Number of meadow parcels per restoration technique and years since restoration.</caption><thead><tr><th></th><th>1-2 years (2017-18)</th><th>3-4 years (2015-16)</th><th>5-6 years (2013-14)</th></tr></thead><tbody><tr><td>Restoration mowing</td><td>4</td><td>6</td><td>4</td></tr><tr><td>Transfer of fresh seed-containing hay</td><td>/</td><td>6</td><td>6</td></tr><tr><td>Threshing material sowing</td><td>5</td><td>6</td><td>5</td></tr></tbody></table>

<table><caption>TABLE 2 Hypotheses defining expected changes in plant community traits of restored meadows.</caption><thead><tr><th>Attributes</th><th>Hypotheses</th></tr></thead><tbody><tr><td>Leaf dry matter content (LDMC)</td><td>Increases with soil nutrient depletion (Lavorel et al., 2007). A high LDMC reflects a resource conservation strategy that should prevail in reference and restored ecosystems (Lavorel and Garnier, 2002)</td></tr><tr><td>Specific leaf area (SLA)</td><td>Decreases with soil nutrient depletion (Lavorel et al., 2007). A low SLA reflects a resource conservation strategy that should prevail in reference and restored ecosystems (Lavorel and Garnier, 2002)</td></tr><tr><td>Height</td><td>Decreases with soil nutrient depletion and thus with the reduction of competition for light that should prevail in reference and restored ecosystems (Hautier et al., 2009)</td></tr><tr><td>Raunkiaer's biological types distribution</td><td>Proportion of therophytes increases with restoration due to adaptation of the mowing regime</td></tr><tr><td>Life form distribution</td><td>Proportion of grasses decreases as proportion of dicotyledonous and legume plants increases with restoration</td></tr><tr><td>Ellenberg's N index</td><td>Decreases with soil nutrient depletion which should prevail in reference and restored ecosystems</td></tr><tr><td>CSR strategy distribution</td><td>Proportion of competitive and ruderal plants decreases while proportion of stress-tolerant plants increases during restoration (Walker et al., 2004)</td></tr></tbody></table>

## 2.2. Vegetation sampling

A space-for-time substitution approach was used to characterize the restoration trajectory based on plant communities. In 2019, we chose meadows that have been restored for one to 6 years (restoration age) with an equal repartition among three age classes since restoration: 1-2 years, 3-4 years and 5-6 years (Table 1). Altogether, the restored meadows covered a total of 58.6 ha.

A complete botanical survey of each meadow was undertaken before the restoration (initial state) and once at the end of the project in 2019 (restored state). These surveys were conducted from mid-May to the end of June and were carried out by walking the entire meadow, avoiding edges. The cover of all herbaceous species encountered at the parcel scale was visually estimated using the Braun-Blanquet scale (r = one individual, +=<1%, 1 = 1-5%, 2 = 5-25%, 3 = 25-50%, 4 = 50-75%, 5 = >75%). Braun-Blanquet scale was chosen to use the same method and precision of abundance estimation as historical references. The list was considered exhaustive if no other species was found after a walk of approximately 10 min in the meadow. The nomenclature used in this study follows the flora of Lambinon and Verloove (2015).

## 2.3. Reference data

Community recovery trajectories were evaluated against two types of reference data representing Arrhenatherion meadows in favorable CS: (1) six meadows in nature reserves of Fagne-Famenne region that were meadows in the best conservation status in the region (as defined in Supplementary Table S1; hereafter current references). Floristic composition of the six current references was surveyed following the same protocol as described above for restored parcels and (2) 36 historical surveys recorded from 1955 to 1963, and one recorded in 1996, in the Fagne-Famenne region (hereafter historical

references). Historical surveys were taken from the data used for the phytosociological description of the EUNIS typology in southern Belgium which served to define the very good conservation state of meadows (CS-A). For the historical references, one relevé per meadow was realized with sampling area varying from 50 to 200 m².

## 2.4. Functional traits

Seven functional traits were retained to characterize the functional differences between reference, restored and degraded plant communities. Each trait was selected because it is expected to change with restoration according to predetermined hypotheses (Table 2).

Information on leaf attributes (leaf dry matter content LDMC and specific leaf area SLA) and canopy height of species was extracted from the LEBA database (Kleyer et al., 2008). For traits where several values per species were given, the average of the values from countries close to Belgium (Germany, Netherlands, United Kingdom, France) was calculated. If no height data was provided for a species, the maximum height given in the flora of Lambinon and Verloove (2015) was used. Raunkiaer's biological types were taken from Lambinon and Verloove (2015). When two biological types were mentioned, the first cited was retained. Life forms (grass, legume and other dicotyledonous plant) were also included in the analysis. The N-Ellenberg's index classifying species according to their nutrient requirements was taken from the Baseflor database (Julve, 1998). Grime's CSR values of each survey were calculated using the electronic spreadsheet-based tool of Hunt et al. (2004) which relies on the CSR types of 1,000 European species identified by Hodgson et al. (1999). The calculation of CSR positions in this spreadsheet is based on the relative abundance of species in the survey. If a species was not present in the spreadsheet, the CSR type in the BioFlor database (Kühn and Klotz, 2002) was used.

For the analysis of changes in plant community functional traits between the historical reference and the current system (current
reference and restored meadows), we proceeded to the following calculations. (1) Nominal traits (Raunkiaer's biological types and life forms) were transformed to as many binary traits as there were values for the trait. We then computed trait relative abundance as the proportion of total cover occupied by species sharing the trait. Only therophyte and hemicryptophyte were retained as binary traits for Raunkiaer's biological types as other types were too sparse in this habitat and (2) For numeric attributes (LDMC, SLA, canopy height, Ellenberg index), we calculated the community weighted mean (CWM; Violle et al., 2007; Lavorel et al., 2008) taking species cover as weight. Cover values were derived from the van der Maarel coefficients using the median cover (Supplementary Table S3). CWM of LDMC was only computed for grass species (hereafter LDMCgrass) in order to avoid effects due to changes in the proportion of life forms; the different life forms have contrasting ranges of LDMC (Cruz et al., 2002; Al Haj Khaled et al., 2005). Before any further calculations, the square root transformation was applied to variables when necessary in order to improve normality. This was done for the relative abundances of legumes, hemicryptophytes and therophytes, and S and R values.

## 2.5. Statistical analysis

Prior to data analysis, the Braun-Blanquet scale was transformed into van der Maarel (1979; Supplementary Table S3). All analyses were conducted using R statistical software (R Core Team, 2021).

To determine whether the restoration of degraded meadows corresponds to the recovery of the species composition of historical and/or current references, we used a principal coordinate analysis (PCoA) based on Bray-Curtis dissimilarity (Legendre and Legendre, 2012). In order to visualize the global trajectory and to explore the variation in species composition between historical and current references, the PCoA was first run using the entire dataset (historical references included). Because we observed differences between historical references and other situations, we used the multipatt() function of the “indicspecies” package with the IndVal index to identify species characteristics of the historical reference, current references and restored meadows (func = “IndVal.g”; De Cáceres et al., 2010; De Cáceres, 2020). This function was used because it allows the identification of indicator species of combinations of groups.

Because historical references were found to be different from current references in their species and functional composition, we focused the analysis of restoration trajectories on current references. A PCoA was performed on a restricted dataset excluding the historical references but with current references, in order to better examine variation in species composition between the initial and final states of restored parcels. The latter allowed the restoration trajectory of each meadow and technique-dependent trajectories to be visualized. PCoA were performed using the “vegan” package (Oksanen et al., 2020) and the cmdscale() command in the “stats” package.

Species richness of each survey was calculated as the number of observed species. In order to evaluate whether active and passive restoration led to changes in species composition, similarity with current references was calculated for all initial states and restored states at different periods. We first calculated the average current reference by computing the mean of species abundance across the six current references, and then we computed the Bray-Curtis distance between all restored sites and this average reference. Similarity was

calculated as 1 Bray-Curtis distance. To determine whether species richness and similarity significantly increase with the age of restoration, linear regressions were computed separately for the three restoration techniques. To test for significant differences in slopes and intercepts, analysis of covariance (ANCOVA) was performed using the anova_test() command in the “rstatix” package (Kassambara, 2021) with species richness or similarity as the dependent variable, restoration technique as the grouping variable and restoration age as the covariate. Post-hoc tests were carried out using the emmeans_test() command of the “rstatix” package with a Bonferroni correction for multiple comparisons. To examine whether species richness of 5–6 year old restored parcels was similar to that of current references, analysis of variance (ANOVA) followed by a Dunnett test was performed. The glht() function of the “multcomp” package (Hothorn et al., 2021) was used for the Dunnett test. To evaluate whether the species composition of restored parcels was similar to that of current references, we compared similarities between restorations and current references with similarity found within current targets. Plots were visualized using the “ggplot2” package (Wickham et al., 2020).

## 3. Results

### 3.1. Restoration success against historical targets

The first two dimensions of the PCoA performed on the entire dataset (historical references included) explained 18.22% of the existing floristic variation (12.92 and 5.30%, respectively for the first two PCoA axes; Figure 2). The third dimension explained 4.69% of the variation but did not show any meaningful pattern. The species composition of the historical references (HR) clearly differed from all other floristic surveys (the six current references (CR), the initial state of restored meadows, the final states of restored meadows whatever the age of restoration), with the historical references exclusively positioned on the negative values of the first axis and others mainly situated on the positive values. The ordination on the second axis revealed a global trajectory where the plant communities of restored meadows become more distant from their initial degraded states over time. Correlations between plant attributes and PCoA scores showed that plant communities of historical references were more stress-tolerant (S) and had a higher proportion of dicotyledonous and legume species compared to current reference and restored parcels (Figure 2). In contrast, current plant communities (reference and restoration parcels) had a higher proportion of grasses and had a more competitive (C) and eutrophic (Ellenberg N) character. They were also characterized by a higher SLA and height. Plant communities of initial degraded states in restored meadows presented a higher ruderal strategy (R) and were more tolerant of frequent disturbances, showing the effect of the intensive mowing regime on degraded meadows. Initial states of restored meadows had a higher proportion of hemicryptophytes compared to final states of restoration and current references. Restored plant communities had a higher proportion of therophytes, dicotyledonous and legume species and a higher LDMCgrass.

We found seven significant exclusive indicator species of historical references that were not found in current references nor in restored meadows, and we identified eight other significant exclusive indicator species of historical references that were found at low frequency in current references and in restored meadows (Figure 3). Moreover, four
A

<figure><img src="image_119.png" /></figure>

B

<figure><img src="image_120.png" /><figcaption>FIGURE 2</figcaption></figure>

(A) Principal coordinate analysis (PCoA) performed on the 127 floristic surveys: x IS: initial states of restored parcels (n=42), □ final states for 1–2-year-old restoration (n=9), ○ final states for 3–4-year-old restoration (n=18), △ final states for 5–6-year-old restoration (n=15), ■ CR: current references (n=6), ● HR: historical references (n=37). The first two axes explained 18.22% of the floristic variation (PCoA1: 12.92%, PCoA2: 5.30%).

(B) Correlation circle showing relations between PCoA scores and community plant functional traits.

species from the list of rare, threatened and protected spermatophyte species of the Walloon Region (Saintenoy-Simon et al., 2006) were only found in a very small number of historical references (*Alchemilla filicaulis*, *Alchemilla glaucescens*, *Festuca ovina*, *Ophioglossum vulgatum*). Inversely, nine significant exclusive indicators of current references were never found in historical references (e.g., *Myosotis arvensis*, *Elymus repens*). The complete list of all recorded species is provided in the Supplementary Table S4 as well as the value of *p* associated with their indicator status and their frequency in each group.

## 3.2. Recovery of species richness and species composition against current references

Species richness significantly increased with time for the three restoration techniques (mowing: *F*=13.94, *p*<0.001, threshing material sowing: *F*=10.49, *p*=0.003, and hay transfer: *F*=19.1, *p*<0.001; Figure 4). The slopes of the three regression lines were not significantly different according to the ANCOVA (*p*=0.972) and each restoration trajectory gained on average 2.4 species per year. However, intercepts were significantly different (*p*=0.003), in line with the fact that initial states differed among the meadows restored with the different techniques. *Post-hoc* tests showed that initial species richness of parcels restored with fresh hay transfer was significantly lower than that of parcels restored only by mowing (p-ajd=0.002) but was not significantly different from that of parcels restored by threshing material sowing (p-adj=0.190). The initial species richness was not significantly different for the latter two (p-ajd=0.207). ANOVA and Dunnett tests revealed a significantly lower final species richness of parcels restored with fresh hay transfer (5–6 years after restoration) compared to species richness of current references. Other restoration

methods led to similar species richness to current references (46.7 species) with for mowing: 45.5 species, *t* = −0.264, *p*=0.988, threshing material sowing: 36.4 species, *t* = −2.479, *p*=0.062, and fresh hay transfer: 33.5 species, *t* = −3.335, *p*=0.011.

Similarity of restored plant communities to the average species composition of current references significantly increased with time from restoration for the three restoration techniques (mowing: *F*=12.18, *p*=0.002, threshing material sowing: *F*=25.86, *p*<0.001, and hay transfer: *F*=10.17, *p*=0.004; Figure 5). The slopes and intercepts of the three regression lines were not significantly different from one another (slopes: *p*=0.605, intercepts: *p*=0.803). Each restoration trajectory gained on average 2.37% similarity to current references per year. Similarity between final states of restored plant communities and current references was close to the mean similarity observed among current references (mean similarity among current references: 0.52, mean similarity between final states and current references: 0.51 for mowing, 0.50 for fresh hay transfer, 0.52 for sowing; Figure 5). This result indicates restoration success since the recovery of species composition attained the level of similarity found within references.

## 3.3. Restoration trajectories for restoration methods

The first two axes of the ordination performed on the restricted dataset excluding the historical references explained 16.55% of the floristic variation (PCoA1: 9.93%, PCoA2: 6.62%; Figure 6). In order to visualize the parcels' individual trajectories, each initial state was linked to its final state. Most of the individual trajectories converged towards current references, even when species composition was initially far from that of current references. In some cases, final states reached current references (Figure 6A). The initial states of the restored meadows covered a wide range of floristic variation, which is in accordance with
<figure><img src="image_121.png" /><figcaption>**FIGURE 3**
Frequency of significant exclusive indicator species (identified by the multipatt function) observed in historical references, current references, initial and final states of restored meadows. Species abbreviations: Pla.med, *Plantago media*; Bri.med, *Briza media*; Car. car, *Carex caryophyllea*; Vio.hir, *Viola hirta*; Pot.ste, *Potentilla sterilis*; Hie.pil, *Hieracium pilosella*; Ran.bul, *Ranunculus bulbosus*; Gle.hed, *Glechoma hederacea*; Leo.his, *Leotodon hispidus*; Alc.xan, *Alchemilla xanthochlora*; Ave.pub, *Avenula pubescens*; Gal.ver, *Galium verum*; Col.aut, *Colchicum autumnale*; Lys.num, *Lysimachia nummularia*.</figcaption></figure>

<figure><img src="image_122.png" /><figcaption>**FIGURE 4**
Box-plots of species richness variation for initial state of restored meadows (year 0), species richness of restored meadows at different restoration ages and current references (CR). Lines represent linear regression models between restored meadow species richness and time since restoration began for the three restoration methods. Hay: fresh hay transfer, Sowing: sowing of threshing material, Mowing: passive restoration by mowing regime adaptation (Mowing: *F*=13.94, *p*<0.001, Sowing: *F*=10.49, *p*=0.003, Hay: *F*=19.1, *p*<0.001).</figcaption></figure>

the different initial conservation status for meadows restored with passive (mowing) and active (hay transfer and sowing) restoration techniques. However, most of them differed from the other surveys in their position on the positive values of the first axis (Figure 6A).

In order to examine technique-dependent ecological trajectories, this PCoA was then presented with a dissociation of the initial and final states of the three restoration techniques (Figure 6B). This representation allowed clear differences to be observed between the initial states (IS) of communities restored by the different restoration techniques. Initial states of meadows restored by transfer of fresh seed-containing hay (Ha) and by threshing material sowing (So) were clearly more distant from current references than those of meadows restored by restoration mowing (Mo). This representation also showed that, regardless of the restoration technique, the final states (FS) of plant communities converged in the direction of current references.

<figure><img src="image_123.png" /><figcaption>**FIGURE 5**
Linear regression models showing significant relationships between species composition similarity (1 Bray-Curtis distance) with the average species composition of current references (CR) and time since restoration began for the three restoration methods. Hay: fresh hay transfer, Sowing: sowing of threshing material, Mowing: passive restoration by mowing regime adaptation (Mowing: *F*=12.18, *p*=0.002, Sowing: *F*=25.86, *p*<0.001, Hay: *F*=10.17, *p*=0.004). Within CR=average similarity within current references. Slopes and intercepts of the three regression lines are not significantly different (slopes: *p*=0.605, intercepts: *p*=0.803).</figcaption></figure>

# 4. Discussion

Our results highlight a strong floristic difference between historical references, based on historical phytosociological data (1960s–70s) of lowland meadows, and current references based on existing meadows with the best conservation status in the studied region, toward which the final states of restoration converged. Historical meadows differed from current references and restored meadows by being mostly short, stress-tolerant dicotyledonous plant communities with small SLA. Such
<figure><img src="image_124.png" /></figure>

FIGURE 6
Principal coordinate analysis (PCoA) performed on species composition for restored meadows and current references. The first two axes explained 16.55% of the floristic variation (PCoA1: 9.93%, PCoA2: 6.62%). (A) Arrows link the initial state and final state of each restored meadow to show individual ecological trajectories. Initial states (n=42), final states (n=42), current target references (n=6). (B) PCoA with dissociation of the initial and final states of the three restoration techniques to display technique-dependent ecological trajectories. Mo.IS: initial states of mowing (n=14), Mo.FS: final states of mowing (n=14), Ha. IS: initial state of fresh hay transfer (n=12), Ha.FS: final state of fresh hay transfer (n=12), So.IS: initial state of sowing (n=16), So.FS: final state of sowing (n=16), current reference targets (n=6).

a functional composition indicates a prevailing resource conservation strategy in historical references compared to restored meadows and current references. This was confirmed by the detection of indicator species of historical references that are known to grow in low nutrient environments. In contrast, restored meadows and current references were characterized by a more important contribution of tall, long-lived and competitive graminoids with high SLA, and a set of indicator species that preferentially develop in more nutrient-rich environments (e.g., *Elymus repens*, *Geranium pyrenaicum*, *Lapsana communis*). Those results provide relevant information about community changes between the historical and contemporary states of ecosystems (Barak et al., 2015; Higgs and Jackson, 2017) and show that significant changes occurred over a time span of *ca.* 65 years (between 1955 and 2020). This pattern is in accordance with overall plant community changes induced by anthropogenic drivers (Timmermann et al., 2015), specifically nitrogen deposition induced by anthropogenic activities in the 20th century, which is recognized as one of the major threats to plant biodiversity in European herbaceous ecosystems (Stevens et al., 2004; Bobbink et al., 2010; Stevens et al., 2018).

The gap observed between historical and current meadows with very good conservation status suggests that the historical data based on phytosociological surveys dating from ~60 years ago, and being the basis for the European habitat typology in Belgium, may be an over-ambitious target for restoration purposes in the current global environmental and socio-economic context. As often mentioned in the ecological restoration literature, the desire to reach, at all costs, a static reference state of the past in the current context of rapid and unprecedented global changes is most likely to be in vain (Harris et al., 2006; Choi et al., 2008; Allison, 2017). Achievable and sustainable outcomes cannot be reached with a target that may satisfy a nostalgic need but is inconsistent with the future (Harris et al., 2006). The expected future reduction in nitrogen deposition is unlikely to induce significant species recovery (Payne et al., 2017). In addition to historical global eutrophication which has already impacted grassland ecosystems in the temperate regions, climate change

and increasingly frequent climate extremes are expected to cause long-term shifts in plant community composition (Gornish and Tylianakis, 2013; Shi et al., 2015). Also, societal expectations may prevent the return to the historical reference because it no longer matches a socially and economically acceptable goal for agriculture-dependent meadows (Choi, 2007; McNellie et al., 2020). For these reasons, history should not serve as a target but rather as a guide for restoration (Harris et al., 2006; Higgs et al., 2014). Achieving a specific taxon-based assemblage is no longer absolutely sought, but rather a set of functions, ecological processes and ecosystem services that restore ecosystem health (SER, 2004; Choi et al., 2008) and allow biodiversity restoration on a larger scale than that of the phytosociological survey, i.e., at the landscape or ecosystem scale (Choi, 2007). In the same line, the historical targets included plant community composition described by surveys following the Zurich-Montpellier method, i.e., a non-random and subjective sampling method where the most typical and representative sampling points of the studied phytosociological association were used (Becking, 1957). They are therefore the most biologically valuable representation of the *Arrhenatherion* association at the time, which has probably accentuated the contrast observed with current floristic communities. In addition, the number of current references was limited in this study because of the restricted availability of such meadows in current European temperate agricultural landscapes. Thus, there is an important need to create a well-defined reference framework of lowland hay meadows for the different essential steps of a restoration project; identification of objectives, monitoring and assessment. Future restoration evaluations of lowland hay meadows in temperate Europe will need adapted reference frameworks that would be more consistent with the current regional ecological and socio-economic context. To build a better reference framework, future research should improve the representativeness of *Arrhenatherion* meadows under conservation management by increasing their number and extending sampling to meadows outside nature reserves, i.e., meadows under an agri-environment scheme and Natura 2000 sites. It will also be a great
opportunity to include baseline values of multiple ecosystem services (Wortley et al., 2013).

Both passive and active restoration efforts led to convergence in species richness, species composition and functional traits towards current targets, despite different pre-restoration states. Species richness increased at the same rate through time (2.4 species per year) in restored meadows whatever the restoration technique, and meadows restored with passive and sowing methods reached species richness similar to current references. The final species richness of meadows restored by hay transfer did not reach that of current references because of lower richness in initial states. Thus, having started with a lower number of species, meadows restored by fresh hay transfer may need more time to reach current reference species richness. All techniques showed successful restoration of species composition. Also, despite current and restored meadow flora indicating a richer nutrient context than historical meadows, restoration led to changes in functional composition. Observed patterns of changes are complex and may suggest that functional composition of restored meadow is not yet stabilized after 6 years. In contrast to pre-restoration communities dominated by mow-tolerant, long-lived grass with high SLA, restored communities showed higher contributions of short-lived dicotyledonous species, indicating fast resource-acquisition strategy, and grasses with higher LDMC, indicating a more conservative resource-acquisition strategy. These results demonstrate that adapting the restoration technique depending on the initial degradation state and the direct vicinity of the meadow is relevant. Our study highlights the efficiency of soil preparation and transfer of seed-containing plant material for restoring more impaired meadow sites. This is consistent with the outcomes of several studies that have tested the effectiveness of species introduction to restore lowland hay meadows or alluvial meadows (Edwards et al., 2007; Schmiede et al., 2012; Baasch et al., 2016; Harvolk-Schöning et al., 2020). Success of passive recovery also attests to the efficiency of management extensification as a means of restoring slightly degraded meadows with target species possibly remaining in the seed bank. This is congruent with the results of previous studies that reported a positive effect on species richness after cessation of fertilization and implementation of extensive management through mowing or grazing (Pallett et al., 2016; Van Vooren et al., 2018). In slightly altered landscapes that provide seed sources, stopping disturbance alone is a good tool to proceed successfully towards valuable meadow habitats and achieve restoration goals (Ruprecht, 2006). Postponing mowing from spring to summer was shown to be effective in promoting plant and invertebrate diversity in European meadows (Humbert et al., 2012). Similarly, a twice-a-year defoliation frequency was shown to be efficient in enhancing plant and insect richness and increasing export of potassium in meadow ecosystems (Uchida and Ushimaru, 2014; Piqueray et al., 2019). Before any restoration intervention, restoration practitioners should explore the local ecological conditions in order to decide which type of restoration action is most likely to succeed (Prach et al., 2020; Goret et al., 2021). Passive restoration should be favoured in mildly impaired sites, with low environmental stress and intermediate productivity, located in more natural and well-preserved landscapes (Prach et al., 2020). Financial and practical factors must also be considered. Passive recovery naturally requires lower costs than seed transfer, while fresh hay spreading additionally imposes organizational constraints. Fresh hay has to be transferred immediately to the receptor site after cutting, as storage would compromise seed viability due to rapid fermentation,

and the large volume of fresh hay to be transferred requires proximity between donor and receptor sites, but compared to threshing, green haymaking is less time-consuming, requires only standard machinery (Blakesley and Buckley, 2016) and produces a more efficient seed harvest yield (Scotton and Ševčíková, 2017). The hay layer on the receptor site can also favor seedling establishment (Loydi et al., 2013).

## Data availability statement

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

## Author contributions

MD, TG, JP, and GM contributed to the conception and design of the study. MD and TG carried out the field work. MD, AF, JP, and GM designed and performed the statistical analysis. MD and GM wrote the first draft of the manuscript. All authors contributed to the article and approved the submitted version.

## Funding

The study was supported by the Fund for Research Training in Industry and Agriculture (PhD Grant - FRIA, F.R.S. - FNRS) of the Walloon Region.

## Acknowledgments

The authors would like to thank the team of the LIFE project "Prairies bocagères" and Natagora for making this work possible and making the data available.

## 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.2023.1136206/full#supplementary-material
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Julien Pétillon,
University of Rennes 1, France

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Reza Erfanzadeh,
Tarbiat Modares University, Iran
Csaba Tölgyesi,
University of Szeged, Hungary

*CORRESPONDENCE

Aure Durbecq

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aure.durbecq@gmail.com

RECEIVED 27 January 2023

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PUBLISHED 09 August 2023

CITATION

Durbecq A, Jaunatre R, Buisson E,  
Favale C, Maudieu N and Bischoff A (2023)  
Persisting effects of seed bed preparation  
and early grazing on plant communities in  
grassland restoration.  
*Front. Ecol. Evol.* 11:1152549.  
doi: 10.3389/fevo.2023.1152549

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© 2023 Durbecq, Jaunatre, Buisson, Favale, Maudieu and Bischoff. 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.

Persisting effects of seed
bed preparation and early
grazing on plant communities
in grassland restoration

Aure Durbecq<sup>1,2*</sup>, Renaud Jaunatre<sup>3</sup>, Elise Buisson<sup>4</sup>,
Cassandra Favale<sup>4</sup>, Nolwenn Maudieu<sup>4</sup> and Armin Bischoff<sup>4</sup>

¹Aix Marseille University, Avignon University, CNRS, IRD, IMBE, Marseille, France, ²Ecological Consultancy ECO-MED, Marseille, France, ³University Grenoble Alpes, Institut National de recherche pour l'agriculture, l'alimentation et l'environnement (INRAE), Laboratoire des écosystémes et sociétés en montagne (LESSEM), St-Martin-d'Hères, France, ⁴Avignon University, Aix Marseille University, CNRS, IRD, IMBE, Avignon, France

Semi-natural mountain grasslands are species-rich and provide a wide range of ecosystem services, such as biomass production for livestock grazing and carbon sequestration. Land abandonment but also habitat destruction are threatening these ecosystems highlighting the need for restoration when degraded. The transfer of plant material containing seed collected at non-degraded reference sites is increasingly used for restoration but the appropriate methods to prepare the transfer and to manage the grassland afterwards are still under debates. This study aims at testing the effects of seed bed preparation by harrowing and livestock grazing on the establishment of transferred targets and community trajectories towards the reference system. In a previous study, first year seedling counts showed a positive effect of seed bed preparation and a marginally significant negative effect of grazing on the seedling recruitment of the transferred species. We asked whether the results on first year seedling densities translate into significant effects on plant cover and whether these effects on plant cover change in the following three years. Both grazing and harrowing effects on species establishment were tested using a full factorial split-plot experimental design on five replicate sites. Data on species richness and vegetation cover for both the whole plant community and the transferred species were analysed during four years after seed transfer. The cover of transferred species increased whereas their species richness remained unchanged. We found a weak positive effect of harrowing on total cover whereas grazing had no effect. Seed bed preparation by harrowing had a clear positive effect on transferred target species cover. The negative effect of grazing on first-year seedling recruitment of transferred target species turned into a positive effect in the fourth year. However, this effect was only apparent in the harrowed plots resulting in a significant grazing-by-harrowing interaction. The similarity of the plant communities to the reference increased from the first to the fourth year and this increase was stronger in grazed and harrowed plots. In conclusion, the results confirmed the positive effect of seed bed preparation on restoration by seed transfer and demonstrated that initial grazing exclusion is not needed in mountain grasslands of our study area.

KEYWORDS

community ecology, exclosures, livestock grazing, near-natural restoration methods,
semi-natural grassland, soil disturbance
Introduction

While grasslands occupy an important place in the maintenance and functioning of terrestrial ecosystems, their degradation is still ongoing (Bardgett et al., 2021). Since 2021-2030 was announced the decade of ecosystem restoration by the United Nations, it is becoming crucial to consider grasslands as essential ecosystems to conserve and restore terrestrial biodiversity and ecosystem services, as well as to mitigate climate change (Temperton et al., 2019; Zhao et al., 2020; Programme UNE and Nations, 2022). In their recent review, Bengtsson et al. (2019) addressed readers directly in “Grasslands are more important for ecosystem services than you might think” and detailed numerous grassland ecosystem services. Furthermore, many authors have highlighted the importance of conserving grasslands, integrating them into long-term restoration (and monitoring) projects as well as implementing initiatives to enhance their conservation (Bengtsson et al., 2019; Temperton et al., 2019; Veldman et al., 2019; Buisson et al., 2022).

To restore species-rich grasslands or, at least, to accelerate their recovery, it is widely accepted that the lack of seed dispersal must be overcome (Török et al., 2011; Halassy et al., 2016; Török et al., 2018). The seed bank often contains annual plant species or disturbance-tolerant perennials (Erfanzadeh et al., 2016; Buisson et al., 2018), but it often lacks grassland specialists (Kiss et al., 2016), in particular if the seed bank was diminished by topsoil disturbance (compaction, removal). This may be achieved by active supply of seeds, preferably locally harvested using “near-natural restoration methods” or “nature-based solutions” (Tischew and Kirmer, 2007; Auestad et al., 2016; Valkó et al., 2022). These methods include transferring mown hay (Auestad et al., 2016; Kövendi-Jakó et al., 2019) or seed containing material collected by a brush harvester (Albert et al., 2019; Durbecq et al., 2022) from nearby non-degraded grasslands (Kiehl et al., 2010; Török et al., 2011; Scotton et al., 2012; Scotton, 2019). Furthermore, seed addition experiments showed that enhancing restoration by sowing solely is not always successful. Plant recruitment and establishment are further limiting steps of grassland restoration (Turnbull et al., 2000; Pywell et al., 2002; Bissels et al., 2006; Clark et al., 2007; Buisson et al., 2021). Seed bed preparation by topsoil disturbance before hay or seed transfer is therefore recommended by several authors (Kiehl et al., 2010; Muller et al., 2014; Klaus et al., 2017; Bischoff et al., 2018). Soil disturbance by ploughing, tilling or harrowing at shallow depth (<20 cm) is suggested to enhance the recruitment of the sown species (Hölzel et al., 2003; Schmiede et al., 2012; Long et al., 2014; Czerwiński et al., 2018) by (i) opening space and limiting competition with pre-existing vegetation (Poschlod and Biewer, 2005; Pywell et al., 2006; Klaus et al., 2017; Bischoff et al., 2018), and (ii) improving environmental conditions for germination and seedling recruitment, such as the creation of micro-reliefs increasing seed-soil adhesion (Chambers, 2000; Isselin-Nondedeu et al., 2006; Kiehl et al., 2010). However, soil disturbance may reduce the protecting effect of existing vegetation limiting high solar radiation and desiccation of young seedlings (Gibson, 2009).

The creation of recruitment gaps can also be achieved via livestock trampling (Kladivová and Münzbergová, 2016). European semi-natural grasslands are shaped by extensive grazing

or mowing management (Gibson, 2009; Kuneš et al., 2015). Traditional grazing by livestock is needed to prevent natural succession towards woody vegetation and favours the recruitment of typical grassland species (Hejcman et al., 2013). Moreover, as long-term post-restoration management measure, extensive grazing may increase the establishment of semi-natural grassland target species by creating regeneration gaps (Török et al., 2011; Freitag et al., 2021; Tölgyesi et al., 2022). However, at the beginning of ecological restoration including seed addition, the restoration site may need fencing to protect the seedlings of sown species from livestock trampling and feeding during the first years after sowing (Bakker, 2003; Rother et al., 2013; Buisson et al., 2015; Vidaller et al., 2019).

Our objective was to assess the effects of seed bed preparation by harrowing and the effect of initial grazing. The grazing exclusion treatments were fenced only the first two years after sowing while traditional grazing management was applied thereafter. First year seedling counts showed a positive effect of seed bed preparation on seedling recruitment and that grazing had a marginally significant negative effect on seedling recruitment of transferred species (Durbecq et al., 2021). In this study, we asked whether the results on first year seedling densities translate in significant effects on plant cover and whether these effects on plant cover change in the following years. More specifically, we addressed the following questions: (i) does the weak negative effect on seedling recruitment change to a positive effect in the following three years? (ii) does the positive effect of harrowing observed in the beginning persist over the following years and may thus affect the final outcome of restoration? (iii) does the mid-term grazing effect depend on seed bed preparation resulting in a significant interaction of both treatments?

We first hypothesize that an initial negative effect of grazing on transferred species changes into a positive effect after establishment since typical grassland species are favoured by grazing in the long run (Saatkamp et al., 2018; Martin et al., 2022), and that both annual weeds and ruderal plants dominating in early succession later disappear (Labadessa et al., 2020). We further hypothesize that seed preparation has a long-term positive effect on mountain grassland restoration in limiting competition by undesired perennial species and facilitating seedling recruitment of transferred species. (Freitag et al., 2021; Martin et al., 2022).

Methods

Study sites location and description

The study was conducted at five degraded mountain grassland sites in the Southern Alps, in the French Hautes-Alpes department (Figure 1). The degradation was caused by the renovation and extension of an electricity line including the construction of transitory access tracks and platforms, leading to the removal of vegetation and soil compaction-decompaction in spring 2018. The rapid return of species-rich semi-natural grasslands was identified as major objective to compensate for grassland degradation. We selected sites representative of the study zone impacted by
<figure><img src="image_127.png" /><figcaption>FIGURE 1
Location of the study area with the three restoration sites (in green) along the construction of a new electricity line (in orange).</figcaption></figure>

construction work. Selection criteria were elevation (between 1100 and 1300 meters above sea level (m asl)), traditional management by grazing not including undergazed sites with shrub encroachment and time since the end of the construction work (< 6 months). We avoided driest and most humid sites, extreme slopes, strong soil modification (gravel addition) and particularly stony sites. Three sites were located in the municipality of L'Argentière-la-Bessée (between 44°78'77"N, 6°56'32"E and 44°78'31"N, 6°56'45"E;

1,058 to 1,132 m asl). The fourth site was in Embrun (44° 58'47"N; 6°48'31"E; 1,312 m asl) and the fifth site was in La- Bâtie-Neuve (44°58'54"N, 6°20'75"E; 1,264 m asl).

The sites were all located in the upper Durance valley at the upper montane altitudinal belt where the climate is temperate to sub-Mediterranean. The Durance valley is characterized by 740 mm of annual precipitation and average July and January temperatures of 20°C and 2°C, respectively, with an annual mean of 10.7°C
(Embrun meteorological station, 863 m asl). Due to the higher elevation compared with the meteorological station, temperatures of the study sites are 1.5 to 3°C lower. At the study sites, snow and frost occur from October to April with a usually continuous snow cover in January and February.

The grasslands of the upper Durance valley are traditionally managed and maintained open by cattle or sheep grazing, and to a lesser degree by mowing. The mountain grasslands of the area are part of a transhumance system with winter/spring grazing in the valleys and summer grazing in the alpine belt. Thus, our sites were grazed twice a year in late spring and autumn with low grazing intensity (<0.5 animal units/ha). The prevailing grazing management is transhumance with winter grazing in the bottom of the valley and summer grazing in the alpine part. The montane altitudinal belt of our study site is thus grazed twice a year for a short period in late spring and autumn. At this montane level, grasslands are characterised by Mesobromion plant communities. Accordingly, they belong to the Natura 2000 priority habitat type 'Semi-natural dry grasslands of Festuco-Brometalia and scrubland facies on calcareous substrates' (N6210, EU directive habitat 92/43/EEC; Calaciura and Spinelli, 2008). Grasslands of this habitat type are often nutrient-poor and particularly species-rich (WallisDeVries et al., 2002). The main environmental drivers of plant communities are elevation, slope, aspect and bedrock (Durbecq et al., 2020).

## Seed material and field experiment

Local seeds were harvested in a traditionally managed and non-degraded semi-natural grassland of the Durance valley, in Freissinières (44°73'61" N; 6°56'72" E; 1,100 m asl), where the climatic and soil parameters are similar to the five study sites. Plant material was collected in July 2018 (seed production period for more than 80% of species in this region) with an integrated system combining beater brush harvester and vacuum system, all mounted on a quad to allow access to narrow and steep mountain sites (Appendix 1). We obtained 2.6 kg of dry plant material harvested in 4,000 m² source grassland. Seeds represented 60% of the harvested material and vegetative parts 40%. The average density of collected seeds was 887 seeds/g of plant material (counted in ten samples of 0.5 g of harvested plant material).

We established a full-factorial split-plot experiment at the five replicate study sites with seed bed preparation and seeding as split-plot factors, and grazing as whole-plot factor (see Durbecq et al., 2021). This resulted in eight plots of 4 m × 4 m treatments arranged in rectangles with 1 m distance between treatments (except for one site where they were separated by 50 cm distance due to lack of space; total = 40 plots). After mowing the already existing vegetation with a brush cutter, seed bed preparation and seeding were randomly assigned to eight plots within half-blocks: four plots exposed to livestock grazing comprising i) no seeding, no seed bed preparation (including removing existing vegetation), ii) seeding only, iii) seeding and seed bed preparation, iv) seed bed preparation only. The remaining four other plots, comprising the same treatments were fenced to exclude grazing. The non-sown control

plots were only used to distinguish transferred and spontaneously emerging species but not included in the statistical analysis.

In October 2018, we implemented the seed bed preparation treatment by harrowing the soil (8 cm depth) using a rotary cultivator (Appendix 1). For the seeding treatment, we adjusted sown seed density by weight using 3.75 g/m² of seeds which corresponds to 104 g of harvested plant material per plot and approximately 5,700 seeds/m². This density is at the upper range of seed densities recommended for alpine grassland restoration projects in the Southern French Alps (2-4 g/m², Koch et al., 2015). The first half-blocks were fenced before the grazing periods and the other half-blocks were exposed to the local extensive grazing twice a year, in October and June. The exclosures were removed after the second-year vegetation survey, in September 2020. The three sites located in l'Argentière-la-Bessée were grazed by sheep and the two other sites by cattle corresponding to the sheep and cattle grazing ratio of the study region. The sites were neither irrigated nor fertilised.

## Measurements and data analysis

Vegetation surveys were conducted at the restoration sites and at the source site of plant material transfer. The cover of all vascular plant species was visually estimated as the vertical projection of all above-ground plant organs.

For each plant species of the source site, percentage cover was estimated in five quadrats of 3 m × 3 m in early June 2018. The total plant cover for each quadrat may exceed 100% due to overlapping vegetation (Londo, 1976). Additionally, the proportion of individuals with mature seeds was estimated for all species occurring at the source site just before the harvest in July 2018. We found altogether 58 species with seeds (Appendix 2).

At restored sites, the 3 m × 3 m quadrats were located in the centre of the 4 m × 4 m quadrats to avoid edge effects, in June 2019, 2020, 2021 and 2022. For each site, species occurring in each sown treatment were compared to control plots, and those occurring in the control were identified as spontaneously emerging species. Species with ripe seeds occurring at the source site of plant material transfer were considered as "harvested species" (58 species, Appendix 2). Those harvested species that did not occur in the four control plots of the same restoration site (with largely higher cover in the seed transfer plots, ratio > 5:1) were identified as "transferred species" (27 species, Table 1). We can, however, not exclude that some species with seeds were finally not harvested because seeds or fruits were too low in the canopy or still strongly attached to the plants. Furthermore, the method may underestimate the density of transferred species since the occurrence of a species in unsown control plots does not necessarily mean that the species was not transferred.

## Analysis

We used cover and species richness of the whole plant community (including both transferred species and species
<table><caption>TABLE 1 List of transferred species.</caption><thead><tr><th></th><th></th><th></th></tr></thead><tbody><tr><td>Alyssum alyssoides</td><td>Festuca rubra</td><td>Onobrychis viciifolia</td></tr><tr><td>Arrhenatherum elatius</td><td>Festuca valesiaca</td><td>Pimpinella saxifraga</td></tr><tr><td>Astragalus danicus</td><td>Galium glaucum</td><td>Plantago media</td></tr><tr><td>Bromopsis erecta</td><td>Galium verum</td><td>Picris hieracioides</td></tr><tr><td>Bunium bulbocastanum</td><td>Heracleum sphondylium</td><td>Ranunculus bulbosus</td></tr><tr><td>Camelina microcarpa</td><td>Hypericum perforatum</td><td>Rhinanthus alectorolophus</td></tr><tr><td>Campanula glomerata</td><td>Knautia arvensis</td><td>Salvia pratensis</td></tr><tr><td>Centaurea jacea</td><td>Lathyrus pratensis</td><td>Scorzonera hispanica</td></tr><tr><td>Dactylis glomerata</td><td>Leucanthenum vulgare</td><td>Silene nutans</td></tr></tbody></table>

emerging from the soil seed bank or seed rain) and of transferred species only for each of the four years after sowing (2019, 2020, 2021 and 2022) to analyse the effect of early grazing and of seed bed preparation on grassland restoration. To focus our analyses on the effects of seed bed preparation and early grazing on both whole community and transferred species, analyses were performed with the two treatments “Grazing” and “Harrowing” and their interactions. Plant cover complied with the assumption of linear models (normality, homoscedasticity). Thus, linear mixed-effects models (LMM) were fitted using lmer function (R package lme4, Bates et al., 2015). Species richness was analysed using generalised linear mixed-effects models (GLMM) with Poisson error distribution and log-link function. No overdispersion occurred in Poisson models. In LMM and GLMM, the significance of treatments and interactions was tested using the Anova function (R package car, Fox & Weisberg 2019). The two treatments “Grazing” and “Harrowing” were fitted as fixed effects, while “site” was fitted as random effect. To consider the split plot experimental design (“Harrowing” and “Seeding” as split plot factors and “Grazing” as whole plot factor), grazing was tested against the site × grazing interaction whereas harrowing and the harrowing × grazing interactions were tested against the model error. As we were interested in the specific effect of treatments on vegetation at different years, data analyses were performed separately for each year. *Post-hoc* Tukey tests were calculated in the case of significant treatment effects or interactions to analyse which differences between treatment combinations were significant (emmeans package, Lenth, 2020).

To compare the composition of the plant communities in the different treatments in 2019 and in 2022, we applied a Non-Metric Multidimensional Scaling (NMDS) based on Bray-Curtis distance (Borcard et al., 2011), and we used a permutation multivariate analysis of variance (PERMANOVA, Anderson, 2001) with 9,999 permutations to analyse whether the plant composition was significantly different between treatments (R package vegan, Oksanen et al., 2022). Similarity to reference communities was calculated as 1 - the minimum Bray-Curtis distance (Bray and Curtis, 1957) between restoration sites and the reference site. We used the minimum Bray-Curtis distance instead of the average value in order to reduce the effect of the heterogeneity of references

(Durbecq et al., 2020). We analysed grazing, harrowing effects and their interaction using the same model design as in the univariate analysis. We performed all analyses using R 4.2.0 (R Core Team, 2022).

# Results

## Whole community

The plant cover of the whole community (considering all species) in the four sown treatments increased over years with an average total cover of 28% (all treatments combined) in 2019, 54% in 2020, 61% in 2021, and 67% in 2022 (Figure 2). The grazing treatment had no effect on total cover. In the first year after sowing (2019, Figure 2A), the harrowing × grazing interaction was significant, with a greater cover in the non-grazed and non-harrowed treatment (Table 2A). One year later (2020, Figure 2B), this interaction was marginally significant but for different reasons than in 2019: the total plant cover was lower in the grazed and non-harrowed plots leading also to a significant harrowing main effect with plant cover being lower in non-harrowed plots (Table 2A, Figure 2B). In 2021 (Table 2A, Figure 2C), the third year after sowing, there was no effect of the treatments, whereas in 2022 the total plant cover was again lower in the non-harrowed plots (marginally significant, Table 2A, Figure 2D).

The average species richness of the plant communities was 27 species in 2019 compared to an average of 32 species for the following three years (Figure 3). No difference in total species richness was observed between the treatments and the interaction was never significant (Table 2B).

The NMDS ordination revealed a clear separation of plant community composition between restoration treatments in 2019 and in 2022 (Figure 4). 2022 polygons were smaller than 2019 polygons and approached the source site in terms of species composition, indicating higher species similarity with the source site after three years. However, the source site was still clearly separated from the restoration sites in 2022. The PERMANOVA showed differences in plant species composition of the plant communities between treatments, both in 2019 and 2022. In 2019, a marginally significant effect of the grazing treatment was found ($F_{1,19} = 1.50^{(*)}$), with typical species of the grazed plots occurring more on the bottom of NMDS axis 2, such as *Alyssum alyssoides* (L.) L., *Reseda phyteuma* L., *Eryngium campestre* L., *Centaurea paniculata* L., *Stachys recta* L., *Saponaria ocymoides* L. and *Ononis pusilla* L. Neither the harrowing effect nor harrowing × grazing interactions were significant. In 2022, the effect of grazing and the harrowing × grazing interaction on species composition were marginally significant (respectively $F_{1,19} = 1.61^{(*)}$ and $F_{1,19} = 1.71^{(*)}$), distinguishing the two non-grazed plots from the two grazed plots. The similarity to references was not significantly affected by grazing or by harrowing treatments in 2019 ($\chi^2_G = 0.30$ and $\chi^2_H = 1.95$). However, in 2022, grazing, harrowing and their interactions had a significant effect on similarity to the reference site ($\chi^2_G = 16.3^{***}$, $\chi^2_H = 6.40^*$ and $\chi^2_{G×H} = 7.41^{**}$).
<figure><img src="image_128.png" /></figure>

<figure><img src="image_129.png" /></figure>

<figure><img src="image_130.png" /></figure>

<figure><img src="image_131.png" /><figcaption>FIGURE 2</figcaption></figure>

Total plant cover of the four sown treatments in (A) 2019, (B) 2020, (C) 2021 and (D) 2022. The direction of significant effects is specified using ">": p < 0.05 and ">*": p < 0.1 (marginally significant). Significant harrowing × grazing interactions are indicated using "*" (p < 0.05). Error bars represent ±SE and different letters indicate significant differences between treatments (p < 0.05).

The grazed and harrowed plots had the highest (0.45+-0.04 SE) while the non-grazed plots had the lowest similarity to the reference site (0.31+-0.02 SE and 0.36+-0.03 SE for harrowed and not harrowed respectively, Appendix 3).

## Transferred species

Altogether 27 species of the source grassland were found in the sown restoration treatments (Table 1). After correction for species

<table><caption>TABLE 2 Effect of grazing, harrowing and their interactions on a) cover and b) richness of the whole plant community in 2019, 2020, 2021 and 2022.</caption><thead><tr><th rowspan="2">a)</th><th colspan="3">Transferred sp. cover 2019</th><th colspan="3">Transferred sp. cover 2020</th><th colspan="3">Transferred sp. cover 2021</th><th colspan="3">Transferred sp. cover 2022</th></tr><tr><th>df</th><th>χ<sup>2</sup></th><th>p</th><th>df</th><th>χ<sup>2</sup></th><th>p</th><th>df</th><th>χ<sup>2</sup></th><th>p</th><th>df</th><th>χ<sup>2</sup></th><th>p</th></tr></thead><tbody><tr><td>Grazing (G)</td><td>1</td><td>0.843</td><td>0.359</td><td>1</td><td>0.318</td><td>0.573</td><td>1</td><td>2.20</td><td>0.138</td><td>1</td><td>8.68</td><td>0.003 **</td></tr><tr><td>Harrowing (H)</td><td>1</td><td>0.843</td><td>0.359</td><td>1</td><td>5.21</td><td>0.022 *</td><td>1</td><td>2.57</td><td>1.109</td><td>1</td><td>10.44</td><td>0.001 **</td></tr><tr><td>G × H</td><td>1</td><td>0.388</td><td>0.533</td><td>1</td><td>1.93</td><td>0.16</td><td>1</td><td>1.06</td><td>0.303</td><td>1</td><td>9.67</td><td>0.002 **</td></tr></tbody></table><table><thead><tr><th rowspan="2">b)</th><th colspan="3">Transferred sp. richness 2019</th><th colspan="3">Transferred sp. richness 2020</th><th colspan="3">Transferred sp. richness 2021</th><th colspan="3">Transferred sp. richness 2022</th></tr><tr><th>df</th><th>χ<sup>2</sup></th><th>p</th><th>df</th><th>χ<sup>2</sup></th><th>p</th><th>df</th><th>χ<sup>2</sup></th><th>p</th><th>df</th><th>χ<sup>2</sup></th><th>p</th></tr></thead><tbody><tr><td>Grazing (G)</td><td>1</td><td>1.15</td><td>0.284</td><td>1</td><td>0.013</td><td>0.908</td><td>1</td><td>0.937</td><td>0.333</td><td>1</td><td>1.67</td><td>0.196</td></tr><tr><td>Harrowing (H)</td><td>1</td><td>0.202</td><td>0.653</td><td>1</td><td>0.530</td><td>0.467</td><td>1</td><td>1.50</td><td>0.220</td><td>1</td><td>0.245</td><td>0.620</td></tr><tr><td>G × H</td><td>1</td><td>0.119</td><td>0.730</td><td>1</td><td>0.073</td><td>0.787</td><td>1</td><td>1.49</td><td>0.223</td><td>1</td><td>1.23</td><td>0.267</td></tr></tbody></table>

Results of LM with χ² and significance levels: *: p < 0.1; **: p < 0.05.
<figure><img src="image_132.png" /><figcaption>FIGURE 3
Species richness of the whole plant community in the four sown treatments in (A) 2019, (B) 2020, (C) 2021 and (D) 2022. Error bars represent ±SE.</figcaption></figure>

also occurring in control treatments, the number of considered transferred species was between four and eight per plot depending on year and treatment. The cover sum of these transferred species (pooled for treatments) clearly increased over the years with, on average, 1% in 2019, 9% in 2020, 18% in 2021, and 22% in 2022 (Figure 5). In the first and third years after sowing (2019 and 2021), the treatments had no effect on the transferred species cover (Table 3A, Figures 5A, C). However, both in 2020 and 2022, transferred species cover was significantly higher in the harrowed

plots, particularly when the latter where grazed (Table 3A, Figures 5B, D). The year 2022 was characterised by significant effects of both treatments and their interaction (Table 3A). Transferred species cover was the highest in the grazed and harrowed plots, averaging at 34% (Figure 5D) and representing almost half of the whole community cover (76%, Figure 2D). In grazed and non-harrowed plots, however, the cover was only 14%, and it was 24% in the non-grazed and non-harrowed plots (Figure 5D). The significant harrowing × grazing interaction

<figure><img src="image_133.png" /><figcaption>FIGURE 4
NMDS of plant species composition of the whole community in the four sown treatments in 2019 (dotted lines) and 2022 (full lines) compared to the plant species composition at the donor site. The two intermediate years 2020 and 2021 are not shown for clarity. Polygons indicate the position of the outmost plots in each treatment (stress = 0.24). Transferred species are shown in bold.</figcaption></figure>
<figure><img src="image_134.png" /><figcaption>FIGURE 5</figcaption></figure>

Sum of cover of transferred species in the four sown treatments in (A) 2019, (B) 2020, (C) 2021 and (D) 2022. The direction of significant effects is specified using ">": p < 0.05. Significant harrowing × (p < 0.05).

showed that the grazing effect depended on the seed bed preparation by harrowing and vice versa.

The transferred species richness was similar between treatments in the first three years after sowing (Table 3B, Figures 6A–C). We found a marginally significant effect of the grazing treatment in 2022 with slightly more transferred species in the grazed than in non-grazed plots (Table 3B, Figure 6D).

Discussion

Four years after the transfer of seed-containing plant material to degraded mountain grassland sites in the southern French Alps,

transferred species reached cover values of 15 to 35% depending on the restoration treatments (Figure 5D). Seed bed preparation favoured the establishment of these species (35% in the grazed treatment). This positive effect of such soil disturbance had already been found in the first-year seedling counts (Durbecq et al., 2021) but not in first and second-year plant covers. Similarly, initial grazing increased the establishment of the transferred species by the fourth year. The small negative effect of initial grazing on first-year seedling recruitment (Durbecq et al., 2021) was not significant in the longer term and turned into a small positive effect at the end of the study period after the removal of fences. The effects of grazing on total cover and total species richness were not or marginally

<table><caption>TABLE 3 Effect of grazing, harrowing and their interaction on transferred species a) cover, and b) richness, in 2019, 2020, 2021 and 2022.</caption><thead><tr><th rowspan="2">a)</th><th colspan="3">Total cover 2019</th><th colspan="3">Total cover 2020</th><th colspan="3">Total cover 2021</th><th colspan="3">Total cover 2022</th></tr><tr><th>df</th><th>χ<sup>2</sup></th><th>p</th><th>df</th><th>χ<sup>2</sup></th><th>p</th><th>df</th><th>χ<sup>2</sup></th><th>p</th><th>df</th><th>χ<sup>2</sup></th><th>p</th></tr></thead><tbody><tr><td>Grazing (G)</td><td>1</td><td>0.024</td><td>0.877</td><td>1</td><td>0.000</td><td>1.00</td><td>1</td><td>0.004</td><td>0.950</td><td>1</td><td>0.223</td><td>0.636</td></tr><tr><td>Harrowing (H)</td><td>1</td><td>0.076</td><td>0.782</td><td>1</td><td>5.27</td><td>0.021 *</td><td>1</td><td>1.20</td><td>0.273</td><td>1</td><td>3.636</td><td>0.056 °</td></tr><tr><td>G × H</td><td>1</td><td>4.12</td><td>0.042 *</td><td>1</td><td>3.56</td><td>0.059 °</td><td>1</td><td>0.720</td><td>0.396</td><td>1</td><td>1.82</td><td>0.177</td></tr></tbody></table><table><thead><tr><th rowspan="2">b)</th><th colspan="3">Total richness 2019</th><th colspan="3">Total richness 2020</th><th colspan="3">Total richness 2021</th><th colspan="3">Total richness 2022</th></tr><tr><th>df</th><th>χ<sup>2</sup></th><th>p</th><th>df</th><th>χ<sup>2</sup></th><th>p</th><th>df</th><th>χ<sup>2</sup></th><th>p</th><th>df</th><th>χ<sup>2</sup></th><th>p</th></tr></thead><tbody><tr><td>Grazing (G)</td><td>1</td><td>2.36</td><td>0.124</td><td>1</td><td>0.012</td><td>0.914</td><td>1</td><td>0.000</td><td>1.00</td><td>1</td><td>0.906</td><td>0.341</td></tr><tr><td>Harrowing (H)</td><td>1</td><td>1.45</td><td>0.229</td><td>1</td><td>1.65</td><td>0.200</td><td>1</td><td>0.616</td><td>0.432</td><td>1</td><td>0.460</td><td>0.498</td></tr><tr><td>G × H</td><td>1</td><td>3.07</td><td>0.08</td><td>1</td><td>0.145</td><td>0.703</td><td>1</td><td>0.273</td><td>0.601</td><td>1</td><td>0.091</td><td>0.763</td></tr></tbody></table>

Results of LM with χ² and significance levels: °, p < 0.1; *, p < 0.05.
<figure><img src="image_135.png" /><figcaption>FIGURE 6
Richness of transferred species in the four sown treatments in (A) 2019, (B) 2020, (C) 2021 and (D) 2022. Error bars represent ±SE.</figcaption></figure>

significant in the end showing that effects of treatments on transferred species were not strong enough to change these whole community parameters. Plant community composition, however, showed a clear development towards the reference community used as a source for seed material. This development was favoured by initial grazing whereas the effect of seed bed preparation was not significant.

The positive effects of harrowing that were observed on transferred species establishment in our restored sites indicated that the potential negative effects of seed bed preparation, such as the elimination of the protective effect of pre-existing vegetation on seedlings (e.g. against UV and drought, Gibson, 2009) were compensated by positive effects. Such positive effects include the destruction of the dense and deep root systems of competitive perennial species not targeted for restoration, reducing interspecific competition (Pywell et al., 2006; Klaus et al., 2017; Bischoff et al., 2018). Recruitment success may also be improved by a better seed adhesion to the soil after seed bed preparation (Chambers, 2000; Isselin-Nondedeu et al., 2006). Moreover, harrowing allows soil aeration increasing nitrogen mineralization (Davies et al., 2001; Steinmann, 2002). Thus, preparing the soil before seed transfer releases nutrients that increase biomass production as a short-term effect of soil tillage that disappears later on (Kristensen et al., 2003; Seabloom et al., 2003). Kiss et al. (2021) also found a beneficial effect of soil disturbance on the cover of transferred species during dry grasslands restoration after five years of revegetation monitoring. Similarly, Schmiede et al. (2012) showed improved establishment of plant species by harrowing. In a previous study on first-year seedling recruitment in our restoration sites, the number of seedlings of spontaneously emerging species was lower in the harrowed plots and the number of seedlings of transferred species and their richness were higher (Durbecq et al., 2021). Although not

apparent in first-year cover, the current study confirms the positive effect of harrowing on seedling recruitment on transferred species cover in the long run. Four years after sowing the cover values of transferred species were also higher in harrowed than in non-harrowed plots. Several studies already suggested to prepare the soil before sowing but there are few studies analysing mid- to long-term effects on restoration success. In a recent meta-analysis, Slodowicz et al. (2023) found 187 publications that studied active grassland restoration, but only 13 lasted more than three years. Seed bed preparation has often positive effects on the recruitment of sown species (Edwards et al., 2007; Schmiede et al., 2012; Bischoff et al., 2018) but contrary to our mid-term results, long-term restoration studies found that such initial soil disturbance effects are less visible in the long run (Harvolk-Schöning et al., 2020; Sommer et al., 2023).

In our study, seed bed preparation had no significant effect on species richness. Similarly, Edwards et al. (2007) observed no effect of soil tillage on the restoration of calcareous grasslands monitored over four years. After grassland degradation, a rise of species richness is typically observed in the first years of restoration due to the germination of short-lived species abundant in the seed bank (Freitag et al., 2021, Valkó et al., 2022). However, Freitag et al. (2021) found a decrease in ruderal species richness over years in favour of transferred species. This effect was not observed in our study, since the transferred species richness did not change during the four years monitoring. We thus conclude that most transferred species were already recruited in the first year and just increased in cover.

Total plant cover was not affected by grazing, but our results showed a strong interaction between grazing and seed bed preparation in the beginning. The initially higher total cover in the non-grazed, non-harrowed treatment suggested that both grazing and seed bed preparation contributed to a reduction in
competition the first year. This significant interaction disappeared
afterwards. In several studies, grazing exclusion failed to increase
grassland plant cover in early successional stages but long-term
grazing maintained relatively higher diversity or stability (Milotić
et al., 2010; Song et al., 2020). In our study, the plant species
composition of the whole community developed towards the
reference community demonstrating the success of seed transfer
four years after sowing. Interestingly, initial grazing had a
marginally significant positive effect on this development towards
the reference community, whereas the effect of grazing on seedling
recruitment of transferred species was slightly negative in a previous
study on seedling recruitment (Durbecq et al., 2021).

Although the magnitude of the effect on seedling recruitment
was small, our results indicated that mountain grassland
communities are finally favoured by grazing even in the
beginning and that an initial damage of young seedlings does not
translate into a worse establishment of transferred species. Grazing
is an important driver in grassland ecosystems. It influences the
composition of grassland communities by creating spatial
heterogeneity and constantly producing new micro-patterns by
plant removal and trampling favourable for the establishment of
grassland species (Kladivová and Münzbergová, 2016, Sommer
et al., 2023, Martin et al., 2022). Similarly, grazing promotes
environmental heterogeneity and biodiversity by redistributing
and selectively concentrating nutrients (Austrheim and Eriksson,
2001). Grazing is therefore crucial to maintain the high species
richness of grasslands (Dupré and Diekmann, 2001; Pykälä, 2003;
Saatkamp et al., 2018). However, it remains unclear whether its
intensity, frequency and/or timing drive this effect (Adler
et al., 2001).

In our study, the grazing effect was still apparent after four years
although grazing was only excluded for four years. Transferred
species establishment was higher in the initially grazed treatments,
thus explaining the development of the plant community towards
the reference. Initial negative effects of grazing due to seedling
damage later turned into a positive effect because grazing damages
other ruderal species more than typical mountain grassland species
of the reference community, thus leading to a selective effect of
grazing on species establishment over the years. However, the
positive grazing effect was particularly strong in the harrowed
plots resulting in a significant grazing-by-harrowing interaction
effect on transferred species cover in the fourth year. Freitag et al.
(2021) noted that it is still not well known whether grazing interacts
with soil disturbance to shape grassland species establishment, but
grazing exclusion in the first years may favour transferred species
that need protection against solar radiation provided by ruderal
species particularly occurring in non-grazed and/or non-harrowed
plots. Conversely, the non-harrowed plots may be more attractive
for grazing animals than the harrowed plots due to both a higher
plant biomass and palatability. In agreement with Freitag et al.
(2021), our results suggest that a better understanding of
interactions between grazing and seed bed preparation in driving
the long-term establishment of transferred species is needed to
improve management recommendations in grassland restoration
involving seed-addition.

Conclusion

Sowing seeds harvested in a species-rich reference community allowed the restoration of the plant species composition of mountain grasslands towards this targeted reference state. In agreement with Labadessa et al. (2020), we found that even early extensive grazing had a small positive effect on transferred species suggesting that initial grazing exclusion is not necessary in this grassland type of our study region. Seed bed preparation had a positive effect not only on seedling recruitment but also in later stages of plant succession and can thus be strongly recommended for restoration in montane grasslands although negative effects on soil organisms and functioning need to be considered. The strong grazing-by-harrowing interaction showed that a combination of both is the best strategy to restore this grassland type. However, research is still needed to better understand how soil disturbance interacts with grazing management in the long run.

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/s.

Author contributions

The project was conceived by AB, RJ and EB. Experimental
design was set up by AB, AD, RJ and EB. All the authors contribute
to floristic surveys. AD, CF, NM and RJ performed data analysis
with assistance of the other authors. AD wrote the first draft of the
manuscript. All authors contributed and approved the final version.

Funding

Financial supports were provided by ANRT (National Agency
of Research and Technology) via a grant CIFRE N°2017/0478, the
environmental consultancy ECO-MED, RTE (Electricity
Transmission Network) and the project "France Relance" funded
by the French government and the European Union.

Acknowledgments

We thank Alexis Versino, Alice Dupré-la-Tour, Charlotte
Rohmer, Emile Melloul, Léo Rocher, Lola Gogniat, Margaux
d'Ambly and Moritz Hammerl for field assistance. Many thanks
to Daniel Pavon for his botanist expertise and Alexandre Cluchier
from ECO-MED for supporting the project. We also thank the
reviewers for helpful suggestions on the manuscript. The authors
declare that they have no conflict of interests.

Conflict of interest

Author AD was employed by the company Ecological
consultancy ECO-MED.
The remaining 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

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## Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fevo.2023.1152549/full#supplementary-material

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EDITED BY

Thierry Dutoit,
UMR7263 Institut Méditerranéen de
Biodiversité et D'écologie marine et
continentale (IMBE), France

REVIEWED BY

Hong Hanh Nguyen,
Senckenberg Research Institute and
Natural History Museum Frankfurt,
Germany
Irene Petrosillo,
University of Salento, Italy

*CORRESPONDENCE

Clément Gros

<figure><img src="image_137.png" /></figure>

clement.gros@institut-agro.fr

Hervé Daniel

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hervé.daniel@institut-agro.fr

RECEIVED 26 January 2023

ACCEPTED 04 October 2023

PUBLISHED 26 October 2023

CITATION

Gros C, Bulot A, Aviron S, Beaujouan V and Daniel H (2023) Both management practices and landscape influence plant communities in urban grasslands. *Front. Ecol. Evol.* 11:1151913.
doi: 10.3389/fevo.2023.1151913

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© 2023 Gros, Bulot, Aviron, Beaujouan and Daniel. 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.

# Both management practices and landscape influence plant communities in urban grasslands

Clément Gros¹*, Adeline Bulot¹, Stéphanie Aviron²,³, Véronique Beaujouan¹ and Hervé Daniel¹*

¹Institut Agro, Ecole Supérieure des Agricultures, INRAE, BAGAP, Angers, France, ²Institut Agro, Ecole Supérieure des Agricultures, INRAE, BAGAP, Rennes, France, ³LTSER Zone Atelier Armorique, Rennes, France

The development of urban areas now requires the integration of biodiversity issues, and this leads to better consideration of their seminatural habitats. Among these habitats, urban grasslands subjected to mowing management practices are commonly promoted over lawns to enhance biodiversity in cities. Despite their ecological value, relatively little attention has been paid to the effects of urban grassland management regimes or the landscape contexts of these habitats in terms of biodiversity. This study aims to investigate the effects of mowing practices and the landscape context of urban grasslands on species diversity and composition and the ecological strategies of plant communities. In this study, 66 sites (mown grasslands) were selected in the Angers and Rennes conurbations of western France according to their management practices (regarding mowing) and landscape gradient (more or less urbanized). The results show that mowing practices and landscape composition did not affect the richness or diversity of plant species but significantly influenced the composition of communities. Partitioning analysis showed that landscape composition explained twice as much of the variance in plant species composition as mowing practices did. Landscape composition favors plant species according to their strategies, preferential habitats, and life spans. Furthermore, diversification of management practices limits the establishment of nonnative species and induces a wider range of functional strategies, as late mowing favors competitors and disfavors stress-tolerant species. Nevertheless, management practices need to be put into perspective in the context of urban grasslands. Thus, this research brings new perspectives to recommendations for the management of urban green spaces.

KEYWORDS

herbaceous green spaces, low intensity mowing, urbanization, plant traits, biodiversity conservation, meadows
# 1 Introduction

Among seminatural habitats in Europe, grasslands present important challenges for biodiversity conservation (Lindborg et al., 2008; Napoleone et al., 2021), especially due to their high species richness (Habel et al., 2013). In contrast to agricultural grasslands, urban grasslands remain understudied (Cochard et al., 2019) in spite of their contribution to urban biodiversity (McKinney, 2002; Fischer et al., 2013; Vega and Küffer, 2021; Mugnai et al., 2023). Urban grasslands also provide valuable ecosystem services such as improvement of human well-being (Wu, 2014), climate regulation, or recreational use (Zhao et al., 2020). The practical application of these services takes the form of nature-based solutions, such as the implantation of grasslands to reduce urban heat islands (Zhao et al., 2020). Urban grasslands are associated with different uses because their establishment results from various urban planning choices (e.g., parks, residential areas, land reserves, derelict areas) or constraints (e.g., flood-prone areas, roadsides) (Klaus, 2013). Urban grasslands can be defined as seminatural herbaceous green spaces located in moderately to strongly urbanized areas and managed by city stakeholders. Unlike other herbaceous green spaces in cities, such as lawns, urban grasslands are characterized by relatively low-intensity management practices that aim to limit the establishment of bushes and woody plants (Politi Bertoncini et al., 2012), maintain open herbaceous habitats for biodiversity, and promote seminatural aesthetic elements in cities. The most common type of urban grasslands are urban meadows, managed by mowing (Humbert et al., 2012; Klaus, 2013) and neither fertilized nor sprayed with pesticides. Because of the absence of productive goals, urban grasslands tend to be mowed late in the season, but management choices by stakeholders regarding the timing and frequency of mowing may vary widely, depending on their objectives.

Recent changes in urban management practices, especially the generalization of lower-intensity management, have been developed ahead of scientific knowledge and bring new questions about the effects of low-intensity management of urban herbaceous spaces on plant biodiversity (Aronson et al., 2017). Regarding mowing, beneficial effects can be expected from moderate disturbance frequency (e.g., a maximum of two cuts per year), as this is known to maintain relatively high plant species richness (Parr and Way, 1988; Bakker et al., 2002). However, most studies still compare the biodiversity of contrasting types of urban green spaces rather than focusing on urban grasslands per se (Sehrt et al., 2020). In other types of herbaceous habitats, the effect of mowing frequency has mainly been studied by comparing lawns, which do not harbor much biodiversity due to their intensive management (Klaus, 2013), to urban grasslands (Norton et al., 2019; Sehrt et al., 2020). Urban grasslands with reduced mowing frequency were found to harbor higher plant species richness and the occurrence of typical meadow species when compared with intensively managed lawns (Sehrt et al., 2020). To our knowledge, the impact of mowing timing on urban grasslands also remains underinvestigated. In agricultural grasslands, late mowing is known to allow plant species to produce more seeds (Smith et al., 2002; Leng et al., 2011; Chaudron et al., 2016) and to complete their

life cycle (Jantunen et al., 2007), which could favor competitive strategies (Johansen et al., 2019). However, existing literature has found contradictory effects of late mowing on biodiversity. It can affect species richness either negatively if delayed from spring to fall or positively if delayed from spring to summer (Grime, 2001; Humbert et al., 2012; Chaudron et al., 2016). There is thus a gap in the body of knowledge regarding the effects of the diverse mowing regimes applied by city stakeholders, in terms of both frequency and timing, on the diversity of plant communities in urban grasslands.

Beyond the effects of local management practices, the flora of urban grasslands is also likely to be influenced by their landscape context. Urbanization (defined as the process of growth and densification of urban areas) is now considered one of the major threats to biodiversity (Foley et al., 2005; Grimm et al., 2008). Urbanization induces several mechanisms, including habitat destruction, that cause the fragmentation and spatial isolation of remnant habitat patches (Williams et al., 2009; Aronson et al., 2017). Such changes have been found to decrease the frequency and abundance of native plant species (Hooftman et al., 2021) in favor of exotic species (Thompson and Jones, 1999; Smart et al., 2005; Hahs et al., 2009). For urban grasslands, although the degree of spatial isolation of grasslands did not appear to induce changes in the composition of plant communities (Cochard et al., 2017), the high occurrence of buildings and other impervious surfaces in the urban matrix was found to increase the risk of extinction for native species (Williams et al., 2006). High degrees of urbanization in the landscape context of urban grasslands are likely to affect their flora through the deleterious impacts of pollution (chemical and light) and human preferences (e.g., the introduction of exotic species) (McDonnell and Pickett, 1990; Williams et al., 2009). This leads to the selection of perennial plant species and species adapted to human disturbances (Hill et al., 2002; Lososová et al., 2006; Godefroid and Koedam, 2007). Moreover, studies have shown that plants in urban contexts tend to be more tolerant of, or favored by, high soil fertility and alkalinity (Knapp et al., 2009; Williams et al., 2015; Tautenhahn et al., 2008; Hill et al., 2002; Pyšek, 1995; Roy et al., 1999). Thus, identification of the drivers of plant species diversity in urban grasslands requires not only consideration of the local management regimes applied by stakeholders but also of the degree of urbanization in the landscape context of grasslands.

Therefore, the goal of the present paper is to investigate the effects of management regimes (mowing) and landscape composition along a rural-urban gradient on plant communities in urban grasslands. More specifically, our objectives are (1) to investigate the effects of management regimes (mowing date and frequency), the urban matrix composition in urban grasslands' landscape context, and the relative influence of these two drivers on plant species composition and diversity in urban grasslands, and (2) to examine the effects of management regimes and landscape context characteristics on plant species richness and plant strategies.

Based on the literature, it is expected that urban grassland plant communities may vary in richness, diversity, and composition according to both management practices and landscape composition. However, management practices are expected to be
more likely to affect communities by favoring species according to their ecological strategies, while landscape composition should favor species according to their life span and their preferential habitat. Furthermore, we expect to find few nonnative species (Albrecht and Haider, 2013; Cochard et al., 2019).

# 2 Methods

## 2.1 Studied areas

The study was carried out in two urban areas of western France: Angers (47°28′N, 0°33′W) which covers 667 km² and contains 302,000 inhabitants, and Rennes (48°06′N, 1°40′W), which covers 705 km² and contains 460,000 inhabitants (INSEE 2022 https://www.insee.fr) in the "Zone Atelier Armorique". Both urban areas belong to the Armorican massif and are characterized by a temperate oceanic climate (average annual rainfall in Rennes: 677 mm, Angers: 668 mm; average annual temperature in Rennes: 11.7°C, Angers: 11.9°C). In the two cities, the bedrock is acidic (mainly schist and altered granite). The two cities consist of a high-built town center surrounded by low-built suburban areas, in which seminatural areas (woodlands, rivers and lakes, grasslands, and green spaces) are present. The cities are embedded within a landscape of hedgerow networks connecting mosaics of annual crops, grasslands, woodlands, and hedges.

## 2.2 Selection of sampling sites

Urban grasslands were selected in the two urban areas with the aim of sampling grasslands distributed along a landscape gradient more or less urbanized and characterized by contrasting management regimes with regard to mowing practices. The procedure used to select the sampled grasslands is described in the following sections.

### 2.2.1 Characterization of the rural-urban gradient

Land-cover maps of the two conurbations were produced using QGIS software (different versions from 2020 to 2022, http://qgis.org), using French public spatial databases and satellite or aerial images to obtain similar information for both cities. First, we collected vector data of buildings, roads, forests, woods, hedgerows, and water land cover from the high-resolution BD TOPO 3-0® database (IGN, 2018) and vector data for agricultural land uses (crops, temporary and permanent grasslands) from the Graphical Parcel Register database (RPG, 2018; www.data.gouv.fr). To classify the unassigned surfaces, we calculated the Normalized Difference Vegetation Index (NDVI) from SPOT 6-7 satellite images and infrared aerial images: higher values were considered to indicate herbaceous surfaces and lower values to indicate bare soils or mineral surfaces.

These different maps were merged to produce the final continuous land-cover maps of the two conurbations. The proportion of buildings and road surfaces is a common proxy for

urbanization (Hill et al., 2002; Géron et al., 2021). Thus, all buildings, road surfaces, and other nonvegetated areas were grouped and considered "impervious surfaces". The map of impervious surfaces was then rasterized at 5 m × 5 m resolution. Moving window analysis was then used to characterize pixels according to the proportion of impervious surfaces in their landscape context (across a 500-m buffer area) in the two urban areas using CHLOE software (Boussard and Baudry, 2017). This led to the production of a final map (at 5 m × 5 m resolution) where the value of each pixel corresponded to the proportion of impervious surfaces in its surroundings. This map was used to characterize the landscape gradients in the two conurbations (Figure 1).

We also calculated the proportions of three other land-cover classes, i.e., crops, woody, and herbaceous surfaces, to describe the two urban areas. To characterize artificial habitats other than impervious surfaces, annual crops and temporary agricultural grasslands were grouped into "crops". Permanent grasslands and other nonagricultural surfaces (i.e., wastelands, herbaceous green spaces, and roadsides) were grouped as "herbaceous surfaces" (Cochard et al., 2019; Pithon et al., 2021). Forests, woods, and hedgerows were grouped as "woody surfaces".

### 2.2.2 Survey of management regimes on urban grasslands

Interviews with city green space managers of the two urban areas were performed in order to identify the management practices they use for mowed urban grasslands (grazed grasslands were excluded) and to identify potential sites for sampling. Three management practices that are commonly applied on urban grasslands were retained in this study: one late mowing a year (1LM) performed after July to achieve biodiversity goals, one early mowing a year (1EM) between May and July, and mowing twice or three times a year (23M) with the first mowing between May and July. Subsequently, 149 potential sites were identified where one of these management practices was applied.

### 2.2.3 Site selection

By crossing data on grassland management regimes and the map of landscape gradient in the two conurbations, 66 mowed grasslands (Figure 2) managed by cities were selected: 36 in the Angers area and 30 in the Rennes area. Selected grasslands were distributed along a gradient of impervious surfaces ranging from 2% to 83% at 500 m. In total, 24 of the selected grasslands were under a 1EM management regime, and 21 grasslands were under 1LM or 23M management regimes. Only grasslands larger than 0.5 ha with fixed management for at least 5 years were selected to avoid the effects of recent perturbations. We also excluded wet habitats to restrict the study to grasslands under mesophilic conditions.

In order to ensure independence between management practices and landscape context characteristics during site selection, a Tukey's test was performed to test for differences in the proportions of impervious, woody, herbaceous, and cropped surfaces according to mowing management regimes. No significant differences were found (Figure 3).
<figure><img src="image_139.png" /><figcaption>**FIGURE 1**
Geographical location of the two study areas and the studied grasslands with indications of management practices (mown two or three times a year (23M), mown early once a year (1EM), and mown late once a year (1LM)). Proportion of impervious surfaces was calculated in a 500-m radius around 5*5 m grids.</figcaption></figure>

## 2.3 Vegetation survey and landscape characterization

### 2.3.1 Vegetation survey

In each selected grassland, five plots (330 plots in total) of 8 m² (2 m by 4 m) were defined with at least 5 m between each plot and from the grassland edges. In each plot, all plant species were recorded using

the nomenclature defined by Tison and de Foucault (2014). An adjusted Braun-Blanquet scale method (Braun-Blanquet, 1932; Van der Maarel, 2005) was used to estimate species abundance in each plot. The fieldwork was performed in May 2021 in Angers and May 2022 in Rennes, before the first mowing of the grasslands.

Beyond the effects of mowing practices and landscape context on species richness and diversity, studying functional composition

<figure><img src="image_140.png" /><figcaption>**FIGURE 2**
Photo illustrating examples of the grasslands studied.</figcaption></figure>
<figure><img src="image_141.png" /><figcaption>FIGURE 3</figcaption></figure>

Comparison of four land-cover proportions (impervious surfaces, crops, woody surfaces, and herbaceous surfaces) within a 500-m radius of grasslands according to their management practices (mown two or three times a year (23M), mown early once a year (1EM), and mown late once a year (1LM)). Tukey's test did not show any significant differences in the proportion of each land-cover type between management practices (*p*-value > 0.05).

helps to facilitate an understanding of how plant communities respond to these variables. Thus, we collected information on plant traits and adaptive strategies. First, we collected competitor/stress-tolerator/ruderal (CSR) strategies for each species according to the database provided by Grime et al. (1988). We transformed CSR strategies into three variables that corresponded to the coordinates of each plant species in Grime's triangle: competitors (C), stress tolerators (S), and ruderals (R). A habitat preference, adapted from Julve's (2015) categories, was assigned to each species. Species characterizing meadows and grasslands (including swards or lawns) were considered grassland species, and those related to wastelands and crops were considered anthropogenic habitat species (Klotz et al., 2002). Finally, for each species, Ellenberg scores for reaction, nitrogen, and light were extracted from Julve's (2015) database.

### 2.3.2 Landscape characterization

Using land-cover maps of impervious surfaces and other land covers, the landscape context of each plot was described across a circular buffer with a 500-m radius according to the proportions of impervious surfaces (mean = 41%, SD = 17%, range from 2% to 83%), woody surfaces (mean = 16%, SD = 8%, range from 4% to 37%), crops (mean = 8%, SD = 11%, range from 0% to 55%), and herbaceous surfaces (mean = 31%, SD = 10%, range from 10% to 69%).

In addition, these four landscape metrics were calculated across circular buffers with radii of 250 m and 1,000 m. However, landscape metrics were highly correlated among the three radius sizes (Spearman correlation rs > 0.7; Supplementary Figure S1), suggesting high redundancy between the three spatial scales. For this reason, only the scale of 500 m was used in analyses, as it was

found to optimally reflect the responses of plant communities to landscape structure (Jackson and Fahrig, 2012; Jackson and Fahrig, 2015).

## 2.4 Statistical analysis

### 2.4.1 Analyses of the composition of plant species assemblages

Canonical correspondence analysis (CCA) was performed on plant assemblages of the sampled plots to scale to test the effects of management type (one late mowing, one early mowing, and two to three mowings) and the four calculated landscape characteristics (proportions of impervious, cropped, woody, and herbaceous surfaces across a 500-m buffer area). This multivariate method is appropriate for measuring variation in plant species assemblages according to their environment (Ter Braak, 1986). To limit the strong effect of rare species, only species detected in more than 5% of plots were retained in the analyses. First, a separate CCA was undertaken to test each management and landscape variable individually. Correlations between significant variables (*p*-value < 0.05) were checked using the Pearson method to avoid multicollinearity. No variables were highly correlated (*r* < 0.7). Subsequently, variance partitioning was performed using partial CCA in order to evaluate the relative contribution of mowing practices and landscape metrics. The forward selection procedure was used to select significant mowing and landscape variables (*p*-value < 0.05) (Lepš and Šmilauer, 2003). To avoid site effects, sites were computed as block factors (a block of five replicates per grassland). All variables were significant in the global CCA. All CCAs were performed in CANOCO 5 (Ter Braak and Šmilauer, 2012).
## 2.4.2 Analyses of species diversity, plant traits, and strategies

Generalized linear mixed models (GLMM) were used to test the effect of management type (one late mowing 1LM; one early mowing 1EM; two or three mowing 23M) and the same four landscape variables (proportions of land cover types) as in the CCA on plant species diversity (species richness and Shannon diversity index) as well as plant functional traits and strategies (CSR mean score, mean trophic level and species richness for habitat preferences and life span). The CSR score and Ellenberg index were weighed by abundance. The GLMMs were run using the lmer function of the lme4 package (Bates et al., 2015). Simple effects were tested in all models at the plot scale. Sites and urban areas were used as random effects in the models. Landscape metrics were standardized (mean = 0, SD = 1) in order to compare the effect strength (or relative importance) of the coefficients across variables. All independent variables respected the Gaussian distribution except for the proportion of crops that were log-transformed. For each plant variable, an averaged model was produced using the MuMin package (Barton, 2009); this included all models with delta AICc values lower than 15. We checked for multicollinearity in the models using the variance inflation factor (VIF) and the performance package (version 0.10.0, Lüdecke et al., 2021). The proportion of wood was excluded in the models, as this variable led to a high VIF (> 5). Visual inspection of residual plots did not reveal any obvious deviations from homoscedasticity or normality.

# 3 Results

## 3.1 Overall characteristics of plant communities in urban grasslands

A total of 153 species were observed in the 330 plots. The most common species were *Holcus lanatus* L. (85%), *Dactylis glomerata*

L. (67%), *Arrhenatherum elatius* L. (65%), and *Rumex acetosa* L. (60%). Only three exotic species were found: *Oenothera* sp. (1%), *Briza maxima* L. (0.6%), and *Hordeum vulgare* L. (0.3%). The mean species richness found in 8 m² plots was 15.4 (SD = 4.13, max = 34, min = 6). The cumulative mean species richness in each grassland plot was 29 (SD = 7.05, max = 52, min = 11). The most abundant species were *Holcus lanatus* L. (frequency of 51% with a plot abundance higher than 25%), *Arrhenatherum elatius* L. (36%), *Anthoxanthum odoratum* L. (18%), and *Dactylis glomerata* L. (18%).

## 3.2 Effects of management regimes and landscape variables on the composition of species assemblages

The arrangement of the 59 most frequent species along the two first axes of the CCA is shown in Figure 4. The forward selection procedure included the four land-cover proportions in the landscape group of the variance, partitioning CCA and mowing practices into a second group. The first two axes explained 39.5% of the relationships between plant species assemblages and environmental variables. All selected environmental variables explained 19.2% of the total variation. The first axis opposed plots surrounded by a high proportion of impervious surfaces within 500 m to plots mowed early and surrounded by high proportions of woods and herbaceous surfaces within 500 m. Plots surrounded by a high proportion of impervious surfaces were characterized by several grasses (i.e., *Avena barbata* Pott., *Trisetum flavescens* L., and *Vulpia bromoides* L.) and typical grassland dicotyledons such as *Vicia sativa* L. and *Myosotis discolor* Pers. *Daucus carota* L., while plots surrounded by a high proportion of woods and herbaceous surfaces were characterized by a wood species, *Stellaria graminea* L., and many frequently occurring grasses (*Holcus lanatus* L., *Anthoxanthum odoratum* L., *Poa pratensis* L. and *Bromus hordeaceus* L.).

<figure><img src="image_142.png" /><figcaption>FIGURE 4</figcaption></figure>

Biplot of canonical correspondence analysis representing environmental variables selected by the forward selection procedure and plot position (left) and a scatterplot of the 20 most influential species (right). For full names, see Supplementary Table S1. Arrows represent continuous landscape variables ("Proportion of Imper" for impervious surfaces, "Crops" for crop surfaces, "Wood" for woody surfaces, and "Herb" for herbaceous surfaces), and the red triangle represents mown variables (mown two or three times a year (23M), mown early once a year (1EM), and mown late once a year (1LM)) resulting from the forward selection procedure as shown. The color of the plots corresponds to mowing practices (23M plots in dark blue, 1EM plots in green, and 1LM plots in light blue).
The second axis was related to management practices and opposed plots under late mowing regimes to plots that were mowed two or three times a year. It also distinguished agricultural landscape contexts from landscapes with high proportions of herbaceous and woody surfaces, independently of the proportion of impervious surfaces (Figure 4). No grasses were associated with late mowing, but some species occurring in wastelands were found (*Cirsium arvense* (L.) Scop., *Rumex crispus* L.). Grasslands mowed only once early in the year were associated with the typical grassland species *Poaceae* (*Holcus lanatus* L., *Anthoxanthum odoratum* L., *Poa pratensis* L., and *Bromus hordeaceus* L.), while grasslands mowed two or three times a year were associated with lawn species (*Hypochaeris radicata* L. and *Plantago lanceolata* L.).

Variance partitioning showed that landscape context explained 13.2% (*p*-value < 0.01) of the total variance, while management practices explained 5.7% (*p*-value < 0.01) of the total variance (Figure 5). This represents, respectively, 68.6% and 29.7% of the explained variance. The interaction between landscape and management variables explained less than 1% of the total variance.

## 3.3 Effect of management type and landscape characteristics on species diversity indices and on plant strategies

GLMM did not show any significant effect of management practices or landscape composition on species richness, Shannon index, Ellenberg index, or life form species richness (Supplementary Table S2).

The GLMM performed for plant strategies showed a significant effect of management type on plant CSR scores (Table 1). Scores for competitors in late-mown grasslands (1LM) were 8% higher than in early-mown grasslands (1EM) and 11% higher than in grasslands that were mowed two or three times a year (23M) (Figure 6). Late-

mown grasslands also contained fewer stress-tolerant species than early-mown grasslands and grasslands which were mowed two or three times a year (Figure 6). No significant difference was found between early mown grasslands and grasslands mown twice or three times a year for both stress-tolerant and competitor plant species (Table 1). Management type did not have significant effects on ruderal score, the species richness of grassland and anthropogenic habitats, or the species richness of annual species.

GLMM also showed a significant response of plant CSR scores to two landscape variables (Table 1). A higher proportion of impervious surfaces within a 500-m radius resulted in more ruderal plant species (Figure 7), while the proportion of crops within 500 m had a positive effect on competitor scores in plant communities and a negative effect on stress-tolerant species. The proportion of impervious surfaces also had a significant and positive effect on the species richness of annual species and of species associated with anthropogenic habitats (Table 1; Figure 8).

# 4 Discussion

## 4.1 Effect of management regimes and landscape characteristics on the diversity and composition of plant species assemblages

The total species richness (153 species) found in the 66 urban grasslands appeared to be relatively important in the studied region, especially in comparison to herbaceous green spaces with higher management intensity (i.e., mowed up to 20 times a year) in the same urban area (Chollet et al., 2018). Compared with other urban habitats, this species richness represents three times the species richness found in Paris lawns (Bertoncini et al., 2012) and is similar to species richness in the woodlands of Angers and Rennes (Vallet et al., 2008). This highlights the ecological importance of ordinary grassland habitats in urbanized areas, which previous studies have also found (Muratet et al., 2007; Threlfall et al., 2016). Furthermore, only three exotic species were observed in the present study. This result contradicts other studies that have found high occurrences of these species in urban habitats (Pyšek, 1998; Kowarik, 2008). Unlike unmanaged habitats such as wasteland, which harbors many nonnative species (Albrecht and Haider, 2013), grassland under low-intensity management practices such as mowing could maintain relatively competitive flora that may prevent the installation of nonnative species. Our study confirms the value of urban meadows for the conservation of native species in cities (English et al., 2022).

No effect of mowing practices or landscape composition was found on plant species richness and diversity in urban grasslands. We expected that lower management frequency (i.e., one cut a year compared to two or three cuts a year), as well as late mowing, would lead to higher species richness and diversity, as other studies have highlighted in the context of agricultural grasslands (Smith et al., 2000; Gaujour et al., 2012). However, our focus on a narrow range of mowing intensities (i.e., one early cut, one late cut, or two to three cuts a year) may explain the lack of response of plant richness and

FIGURE 5
Venn diagram representation of CCA variance partitioning due to land-cover proportion and mowing practices. Both landscape and management variance explanations were significant (**: *p*-value < 0.01).
<table><caption>TABLE 1 Final mean models for the effect of mown practices and land-cover proportion in a 500-m radius on CSR strategy score, optimal habitat, and annual species richness. Significant results, i.e. estimates whose 95% confidence interval do not include zero, are in bold.</caption><thead><tr><th>Response variable</th><th>Independent variable</th><th>Estimate</th><th>Adj SE</th><th>z</th><th>Lower 95% CI</th><th>Upper 95% CI</th></tr></thead><tbody><tr><td rowspan="7">Competitor score</td><td>Intercept</td><td>0.336</td><td>0.014</td><td>24.294</td><td><strong>0.309</strong></td><td><strong>0.363</strong></td></tr><tr><td>Impervious surfaces</td><td>0.000</td><td>0.001</td><td>0.102</td><td>-0.003</td><td>0.002</td></tr><tr><td>Herbaceous surfaces</td><td>0.000</td><td>0.000</td><td>0.002</td><td>0.000</td><td>0.000</td></tr><tr><td>Crops</td><td>0.013</td><td>0.005</td><td>2.586</td><td><strong>0.003</strong></td><td><strong>0.023</strong></td></tr><tr><td>1EM vs. 1LM</td><td>-0.027</td><td>0.013</td><td>2.146</td><td><strong>-0.052</strong></td><td><strong>-0.002</strong></td></tr><tr><td>23EM vs. 1LM</td><td>-0.038</td><td>0.012</td><td>3.138</td><td><strong>-0.062</strong></td><td><strong>-0.014</strong></td></tr><tr><td>23EM vs. 1EM</td><td>-0.011</td><td>0.012</td><td>0.926</td><td>-0.034</td><td>0.012</td></tr><tr><td rowspan="7">Stress tolerator score</td><td>Intercept</td><td>0.256</td><td>0.007</td><td>37.665</td><td><strong>0.243</strong></td><td><strong>0.269</strong></td></tr><tr><td>Impervious surfaces</td><td>-0.004</td><td>0.005</td><td>0.751</td><td>-0.014</td><td>0.006</td></tr><tr><td>Herbaceous surfaces</td><td>0.004</td><td>0.004</td><td>0.937</td><td>-0.004</td><td>0.012</td></tr><tr><td>Crops</td><td>-0.009</td><td>0.004</td><td>2.126</td><td><strong>-0.017</strong></td><td><strong>-0.001</strong></td></tr><tr><td>1EM vs. 1LM</td><td>0.021</td><td>0.009</td><td>2.244</td><td><strong>0.003</strong></td><td><strong>0.039</strong></td></tr><tr><td>23EM vs. 1LM</td><td>0.036</td><td>0.009</td><td>3.96</td><td><strong>0.018</strong></td><td><strong>0.054</strong></td></tr><tr><td>23EM vs. 1EM</td><td>0.015</td><td>0.009</td><td>1.644</td><td>-0.003</td><td>0.033</td></tr><tr><td rowspan="7">Ruderal score</td><td>Intercept</td><td>0.407</td><td>0.012</td><td>35.288</td><td><strong>0.384</strong></td><td><strong>0.430</strong></td></tr><tr><td>Impervious surfaces</td><td>0.012</td><td>0.005</td><td>2.276</td><td><strong>0.002</strong></td><td><strong>0.022</strong></td></tr><tr><td>Herbaceous surfaces</td><td>-0.004</td><td>0.006</td><td>0.698</td><td>-0.015</td><td>0.007</td></tr><tr><td>Crops</td><td>-0.005</td><td>0.005</td><td>0.917</td><td>-0.016</td><td>0.006</td></tr><tr><td>1EM vs. 1LM</td><td>0.005</td><td>0.012</td><td>0.424</td><td>-0.018</td><td>0.028</td></tr><tr><td>23EM vs. 1LM</td><td>0.002</td><td>0.016</td><td>0.122</td><td>-0.030</td><td>0.034</td></tr><tr><td>23EM vs. 1EM</td><td>-0.004</td><td>0.013</td><td>0.316</td><td>-0.029</td><td>0.021</td></tr><tr><td rowspan="7">Grassland habitat species richness</td><td>Intercept</td><td>9.885</td><td>0.649</td><td>15.238</td><td><strong>8.614</strong></td><td><strong>11.156</strong></td></tr><tr><td>Impervious surfaces</td><td>-0.122</td><td>0.409</td><td>0.298</td><td>-0.924</td><td>0.680</td></tr><tr><td>Herbaceous surfaces</td><td>-0.098</td><td>0.333</td><td>0.294</td><td>-0.751</td><td>0.555</td></tr><tr><td>Crops</td><td>-0.394</td><td>0.283</td><td>1.392</td><td>-0.949</td><td>0.161</td></tr><tr><td>1EM vs. 1LM</td><td>0.527</td><td>0.611</td><td>0.863</td><td>-0.670</td><td>1.724</td></tr><tr><td>23EM vs. 1LM</td><td>0.472</td><td>0.594</td><td>0.795</td><td>-0.692</td><td>1.636</td></tr><tr><td>23EM vs. 1EM</td><td>-0.055</td><td>0.591</td><td>0.093</td><td>-1.214</td><td>1.104</td></tr><tr><td rowspan="7">Anthropic habitat species richness</td><td>Intercept</td><td>4.573</td><td>0.293</td><td>15.617</td><td><strong>3.999</strong></td><td><strong>5.147</strong></td></tr><tr><td>Impervious surfaces</td><td>0.898</td><td>0.289</td><td>3.104</td><td><strong>0.331</strong></td><td><strong>1.465</strong></td></tr><tr><td>Herbaceous surfaces</td><td>0.089</td><td>0.368</td><td>0.242</td><td>-0.632</td><td>0.810</td></tr><tr><td>Crops</td><td>0.309</td><td>0.293</td><td>1.056</td><td>-0.265</td><td>0.883</td></tr><tr><td>1EM vs. 1LM</td><td>-0.395</td><td>0.536</td><td>0.737</td><td>-1.445</td><td>0.655</td></tr><tr><td>23EM vs. 1LM</td><td>-0.511</td><td>0.524</td><td>0.975</td><td>-1.538</td><td>0.516</td></tr><tr><td>23EM vs. 1EM</td><td>-0.116</td><td>0.520</td><td>0.223</td><td>-1.136</td><td>0.904</td></tr><tr><td rowspan="3">Annual species richness</td><td>Intercept</td><td>3.939</td><td>0.503</td><td>7.825</td><td><strong>2.952</strong></td><td><strong>4.926</strong></td></tr><tr><td>Impervious surfaces</td><td>0.789</td><td>0.288</td><td>2.739</td><td><strong>0.224</strong></td><td><strong>1.354</strong></td></tr><tr><td>Herbaceous surfaces</td><td>-0.206</td><td>0.378</td><td>0.545</td><td>-0.947</td><td>0.535</td></tr></tbody></table>

(Continued)
<table><caption>TABLE 1 Continued</caption><thead><tr><th>Response variable</th><th>Independent variable</th><th>Estimate</th><th>Adj SE</th><th>z</th><th>Lower 95% CI</th><th>Upper 95% CI</th></tr></thead><tbody><tr><td></td><td>Crops</td><td>0.13</td><td>0.339</td><td>0.383</td><td>-0.535</td><td>0.795</td></tr><tr><td></td><td>1EM vs.1LM</td><td>0.249</td><td>0.596</td><td>0.418</td><td>-0.919</td><td>1.417</td></tr><tr><td></td><td>23EM vs.1LM</td><td>0.566</td><td>0.582</td><td>0.973</td><td>-0.574</td><td>1.706</td></tr><tr><td></td><td>23EM vs.1EM</td><td>0.317</td><td>0.576</td><td>0.55</td><td>-0.813</td><td>1.447</td></tr></tbody></table>

diversity to grassland management. In contrast, previous studies showing the effects of mowing regimes on plant species richness have compared more contrasting intensities of management practice in urban herbaceous spaces from lawns to grasslands (Socher et al., 2012; Rudolph et al., 2017; Norton et al., 2019; Sehrt et al., 2020). Another hypothesis could be that the effects of management practices on species richness found in the literature might be related to the presence of high proportions of nonnative plants. Indeed, such species have been found by other studies to represent up to 20% of plant species richness in unmanaged habitats (Muratet et al., 2007). In the present study, the lack of species gain related to the installation of nonnative species might then explain the absence of an effect of mowing regimes on species richness and diversity.

Moreover, the lack of a significant effect of landscape composition on plant richness or diversity may be explained by the length of the landscape gradient considered in the present study. Indeed, the studied urban grasslands were distributed along a gradient ranging from moderately urbanized (2%) to highly urbanized landscapes (83%), because these extensive habitats are less frequent in rural landscapes (Fischer et al., 2013; Horák et al., 2022). Although many studies have found significant increases in plant richness in highly to moderately urbanized landscapes (McKinney, 2008), the impact of urbanization on species richness is nevertheless likely to strongly vary according to the geographical location of cities, historical and economic factors, and the spatial

scale considered (McKinney, 2008). In the specific context of the present study, the landscape gradient considered may be too narrow to detect any variability in plant richness or diversity. In contrast to species richness and diversity, we found that the species composition of plant communities in urban grasslands was significantly influenced by local mowing practices and landscape composition. Analysis of plant species assemblages showed that intensively managed grasslands (i.e., mowed two to three times a year), as opposed to grasslands mowed once late in the year, were especially characterized by plant species that are typical of lawns. The strength of the influence of mowing practices on floristic composition found in the present study is comparable with that found in another study carried out in an agricultural context (Barbaro et al., 2004). This highlights the potential for the implementation of late mowing practices to add value by enhancing the composition of plant communities in urban grasslands. However, the difference in species composition between extensively and frequently mown grasslands was not associated with an increase in species richness or diversity. This suggests that there is no optimum in our range of management practices and that, on the contrary, the diversification of urban grassland management practices can lead to a wider range of floristic composition.

Plant species assemblages in urban grasslands were, more importantly, impacted by the composition of the surrounding landscapes. Grasslands with a high proportion of herbaceous and

A

<figure><img src="image_143.png" /></figure>

B

<figure><img src="image_144.png" /><figcaption>FIGURE 6</figcaption></figure>

Graphical representation of the significant effect of mowing practices (mown two or three times a year (23M), mown early once a year (1EM), and mown late once a year (1LM)) on competitor score (A) and stress tolerator score (B) (n = 330) according to mean model results (*p < 0.05; **p < 0.01; ***p < 0.001).
<figure><img src="image_145.png" /><figcaption>FIGURE 7
Graphical representation of significant effects of crop proportion on competitor score (A) and stress tolerator score (B) and of impervious surface proportion on ruderal score (C) according to mean model results (*p* < 0.05; **p* < 0.01; ***p* < 0.001).</figcaption></figure>

woody habitats in their surroundings were characterized by contrasting species assemblages in comparison with grasslands embedded in landscapes with many impervious surfaces. Along an urban-rural gradient, Cochard et al. (2019) have also observed an effect of landscape composition on the plant species assemblages of extensively managed road verges, and many grassland species seemed to be negatively affected by the increasing proportion of built-up areas in the landscape. In rural contexts, many studies have found an effect of landscape patterns on plant diversity and composition (Schmucki et al., 2012; Olsen et al., 2018; Kimberley et al., 2021). In Schmucki et al. (2012), the presence of seminatural elements in the surroundings seemed to affect the composition of

plant communities and increase local plant species richness (Schmucki et al., 2012), thus contrasting with agricultural landscape contexts (Loos et al., 2021).

In urban contexts, plant communities are the result of different specific filters applied to the global species pool (Williams et al., 2009; Aronson et al., 2016), on which factors acting at both landscape and local scales induce selective pressures (Williams et al., 2015). In this study, we found an influence of both landscape composition and local management practices on species assemblages, with a greater influence of variations in landscape composition than of local mowing practices. The differences in plant community composition observed between

<figure><img src="image_146.png" /><figcaption>FIGURE 8
Graphical representation of impervious surface proportion on species richness of anthropogenic habitat plants (A) and annuals (B) according to mean model results (*p* < 0.05; **p* < 0.01; ***p* < 0.001).</figcaption></figure>
urban and rural grasslands can be linked to the increased effect of
environmental change in the city center compared to suburban
areas (i.e., water stress, pollution, and the urban heat island effect)
(Williams et al., 2009). It could also be explained by the effects of
landscape configuration that were not specifically considered in the
present study. Habitat fragmentation (e.g., inscribed via the mean
proximity size index) and landscape complexity (e.g., edge density
and largest patch index) can strongly affect plant composition in
urban contexts (Peng et al., 2019). Habitat fragmentation was found
to increase the biotic homogenization of plant communities
(Zeeman et al., 2017), while landscape complexity has been
shown to be positively related to plant species diversity (Peng
et al., 2019). Although we did not specifically address the
potential effects of landscape configuration, it has been shown in
landscape ecology that landscape composition and configuration
are highly correlated (Fahrig, 2003; Fahrig et al., 2011). In an urban
context, higher levels of urbanization can also be associated with
higher habitat fragmentation and landscape complexity (Yeh and
Huang, 2009; Buyantuyev and Wu, 2010; Peng et al., 2019). Thus,
the changes in plant communities found along our landscape
gradients may be partially explained by variations in landscape
configuration underlying landscape composition. However,
distinguishing the respective effects of these two landscape
components is difficult (Fahrig, 2003; Fahrig et al., 2011). Other
landscape factors can act as filters for plant communities. Habitat
transformation and fragmentation might also filter species,
especially in relation to the dispersal strategies that determine
their capacity to reach a suitable environment. Although such
processes remain unclear (Williams et al., 2015), previous studies
have suggested that wind-dispersed species might be more likely to
disappear in urban environments (Williams et al., 2005; Sodhi et al.,
2008; Knapp et al., 2010), and isolated urban grasslands may limit
seed arrival (Fischer et al., 2013). Thus, further study of seed rain
composition in urban contexts is needed to better understand how
the landscape changes induced by urbanization drive the selection
of species plant strategies in urban habitats.

## 4.2 Influence of mowing and landscape composition on plant strategies in urban grasslands

Although mowing practices and landscape composition did not
influence plant species richness in our sample, they had a significant
effect on species composition by favoring certain plant strategies. In
agreement with our hypothesis, an effect of mowing practices on
plant CSR strategy was found. Indeed, there were more competitive
species (such as *Dactylis glomerata* L. and *Arrhenatherum elatius* L.)
in late-mown than in early-mown grasslands, however frequently
they were mowed (1EM or 23M). One cut before flowering could
prevent plant reproduction (Gaujour et al., 2012), and a cut before
seed exports could select a competitive strategy (Johansen et al.,
2019). In addition, vegetation observed under late mowing
conditions was denser and could prevent the establishment of
new plants (Smith and Haukos, 2002), thus favoring the
development of competitor species. Stress-tolerant species (such

as *Lotus corniculatus* L.) were less observed in late-mown grasslands
compared to early-mown grasslands (1EM and 23M). This result
indicates that early management of grasslands may cause more
disturbance (Kahmen et al., 2002).

Beyond the effects of local management practices on plant
strategies, our results show that urban grasslands harbored less
stress-tolerant and more competitive plant species when
surrounded by high crop coverage in their landscape context,
which contradicts the results of a previous study showing
enhanced competitor strategy in more urbanized contexts
(Chocholoušková and Pyšek, 2003). However, considering the
complexity of urban systems and the various methods used to
assess CSR strategies, the response of CSR plant traits to
urbanization remained unclear (Williams et al., 2015). Actually,
both types of plant strategy (i.e., stress tolerators and competitors)
might reflect not only plant responses to landscape composition but
also the degree of grassland disturbance (Herben et al., 2018).
Indeed, a high score for ruderal species was observed in
grasslands in more urbanized landscapes, as shown in Knapp
et al. (2009). Since this strategy is associated with disturbed
habitats, urban grasslands seem to undergo more disturbance in
highly urbanized landscape contexts. In addition, the richness of
annual species and of species associated with anthropogenic
habitats was also higher in grasslands in more urban contexts,
confirming the hypothesis that grasslands located in the most
urbanized landscapes tend to be more disturbed.

## 5 Conclusion

Our study illustrates the importance of considering factors that
act at both local and landscape scales when identifying the drivers of
plant communities in urban grasslands. This study also provides
evidence that assessing effects in terms of species richness is not
sufficient and that community composition must also be
considered. Accordingly, although mowing practices and
landscape composition did not influence plant species richness or
diversity, we found that they were both important drivers of the
composition of plant species assemblages. Nevertheless, they
affected plant communities in different ways and to different
extents. Landscape composition had the strongest influence; this
was mostly characterized by effects on CSR strategies, preferential
habitats, and the life span of plant species. This highlights the
important role of landscape composition in filtering plant species
communities in urban grasslands in comparison with local drivers.
Our study therefore provides evidence that favors rethinking urban
grassland planning not only by changing local management
practices but also by promoting the maintenance of seminatural
green spaces in the landscape context. Our findings also support
changes in management practices for herbaceous vegetation in
cities, favoring extensive management practices such as late
mowing. Such practices, which are becoming increasingly
common in cities (Watson et al., 2020; Marshall et al., 2023),
should be promoted. More generally, the importance of urban
grasslands in contributing to biodiversity conservation and
ecosystem services has been neglected (Klaus, 2013). Given
the plant diversity observed in urban grasslands, their restoration or establishment should be promoted as a nature-based solution to provide high-quality habitat for animal species. This is particularly important for pollinating arthropods such as Lepidoptera and Hymenoptera, which are also highly affected by urban environments (Kurylo et al., 2020; Horák et al., 2022). In this context, this study contributes to addressing conservation challenges in the urban context by highlighting the importance of not separating grassland management from urban planning considerations.

## Data availability statement

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

## Author contributions

CG, AB, SA, VB, and HD conceived and designed the study. CG gathered and analyzed the data with the help of SA, AB, VB, and HD. CG, AB, SA, VB, and HD wrote the manuscript. All authors contributed to the article and approved the submitted version.

## Funding

This study was financed by "l'Institut Agro Rennes Angers" and the "Conseil Régional des Pays de la Loire".

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## Acknowledgments

We thank M. Durieu, P. Collignon, and F. Glodt for their help in collecting field data and T. Rodier for spatial data analysis. We also thank "Angers Loire Métropole", "Rennes Métropole", "Zone Atelier Armorique", and all stakeholders involved for their help in sampling the sites.

## 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.2023.1151913/full#supplementary-material

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