# Managing hedgerows for biodiversity: Disentangling the effects of trimming, structure and connectivity on the use of linear features by bats

Noé Tarcy, Jérémy S P Froidevaux, Christian Kerbiriou, Camille Leroux, Florence Matutini

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## To cite this version:

Noé Tarcy, Jérémy S P Froidevaux, Christian Kerbiriou, Camille Leroux, Florence Matutini. Managing hedgerows for biodiversity: Disentangling the effects of trimming, structure and connectivity on the use of linear features by bats. Journal of Applied Ecology, In press, 10.1111/1365-2664.70205. hal-05384899

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# Managing hedgerows for biodiversity: Disentangling the effects of trimming, structure and connectivity on the use of linear features by bats

Noé Tarcy<fnref n="1" />,<fnref n="2" /> ID

Jérémy S. P. Froidevaux<fnref n="3" />,<fnref n="4" /> ID

Christian Kerbiriou<fnref n="3" /> ID

Camille Leroux<fnref n="3" />,<fnref n="5" /> ID

Florence Matutini<fnref n="1" />,<fnref n="3" /> ID

**Correspondence**

Noé Tarcy

Email: noetarcy.pro@gmail.com

**Funding information**

Leverhulme Trust, Grant/Award Number: ECF-2020-571; Association Nationale de la Recherche et de la Technologie, Grant/Award Number: 2019/1566

**Handling Editor:** Elisa Fuentes-Montemayor

**Abstract**

1. Hedgerows are key semi-natural elements in European agricultural landscapes supporting diverse wildlife, including bats, that depend on these linear elements for foraging and commuting. Despite replanting initiatives, many hedgerows remain degraded due to intensive management practices such as overtrimming. To develop effective and generalisable conservation guidelines, more research is needed to understand how both management practices and intrinsic hedgerow characteristics influence bat species with different foraging strategies.

2. We conducted passive acoustic monitoring across 448 hedgerows to assess the influence of six characteristics on bat activity: trimming method, height, width, foliage density, connectivity and woody plant diversity. We examined responses for two species associated with hedgerow landscapes (*Barbastella barbastellus*, *Rhinolophus ferrumequinum*) and three echolocation guilds: short-, mid- and long-range echolocators (SRE, MRE, LRE), corresponding to clutter, edge and open-space foragers, respectively.

3. Pollarding consistently increased bat activity, especially for tree-dwelling species, likely by maintaining mature trees with larger crowns and diversified microhabitats. Coppicing generally reduced activity for SRE, *B. barbastellus* and *R. ferrumequinum*, while increasing LRE activity. Taller, tree-filled hedgerows generally supported higher bat activity, whereas width and foliage density had more limited effects. Finally, uninterrupted hedgerow segments enhanced SRE and MRE activity, highlighting the importance of local habitat continuity in creating effective corridors for bat movement. Woody plant diversity was also positively associated with SRE activity. These results were mostly consistent across simplified open croplands and structurally complex *bocage* landscapes.

4. **Synthesis and applications.** Our findings underscore the value of extensive hedgerow management in promoting on-farm bat activity and support the integration of

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<footnote n="1">¹Direction de la Recherche et Appui Scientifique, Office Français de la Biodiversite Auffargis-Saint-Benoist, Le Perray-en-Yvelines, France</footnote>

<footnote n="2">²Ecole Supérieure des Agricultures, Angers, France</footnote>

<footnote n="3">³Centre d'Ecologie et des Sciences de la Conservation (CESCO), Muséum National d'Histoire Naturelle, Centre National de la Recherche Scientifique, Sorbonne Université, Station Marine, Concarneau, Paris, France</footnote>

<footnote n="4">⁴Université Marie & Louis Pasteur, CNRS, Chrono-Environnement, Besançon, France</footnote>

<footnote n="5">⁵Auddicé Biodiversité, ZAC du Chevalement, Roost-Warendin, France</footnote>
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simple hedgerow quality assessment protocols into agri-environment schemes. Consistent with previous research, we confirm the importance of tall, tree-filled hedgerows and emphasise the need to preserve mature trees. We also provide novel insights: hedgerow continuity and low-intensity trimming methods can further improve habitat suitability, particularly for tree-dwelling bats. Our management recommendations align with evidence from other taxonomic groups, including birds and invertebrates, and can contribute to broader biodiversity conservation strategies.

### KEYWORDS

acoustic monitoring, agri-environment schemes, automated species identification, biodiversity conservation, Chiroptera, field margin, pollarding

# 1 | INTRODUCTION

Over the past several decades, agricultural intensification has resulted in simplified and homogenised landscapes through cropland expansion, habitat fragmentation and widespread removal of natural and semi-natural elements (Fischer & Lindenmayer, 2007). Notably, linear woody elements such as hedgerows have experienced widespread declines across Europe (e.g. Baltensperger, 1987; Sklenicka et al., 2009). Combined with the increased use of chemical inputs, these changes contribute to the ongoing biodiversity crisis threatening the resilience of agroecosystems (Dainese et al., 2019). In response, ecological intensification of farming systems has been proposed to mitigate these impacts by reintegrating and supporting ecosystem functions in agricultural practices (Bommarco et al., 2013).

Originally used for livestock containment, hedgerows deliver essential services in agroecosystems, such as flood prevention, crop sheltering, soil protection and carbon sequestration (Montgomery et al., 2020). They also support diverse wildlife, including a range of natural predators contributing to natural pest regulation (Albrecht et al., 2020) and are recognised as key components of ecological networks for maintaining landscape heterogeneity and connectivity (Staley et al., 2023; Tscharntke et al., 2021). Recently, numerous studies have highlighted the benefits of hedgerow presence on bat activity in cultivated landscapes (e.g. Chávez et al., 2025; Heim et al., 2018; Lacoeuilhe et al., 2018). Many bat species forage more intensively along hedgerows, where insect prey abundance is typically higher than in adjacent crop fields (Pollard & Holland, 2006) and depend heavily on these linear elements for commuting between roosting and foraging sites (Frey-Ehrenbold et al., 2013; Froidevaux et al., 2017). Insectivorous bats also contribute to biodiversity-based ecosystem services, serving as potential bioindicators (Russo et al., 2021) and natural pest control agents by consuming a wide range of nocturnal arthropods (Ramírez-Fráncel et al., 2022). Yet, bat populations have suffered global declines since the 1950s, likely due to agricultural intensification and habitat loss (Frick et al., 2020). Consequently, all bat species in Europe are protected under Annex IV

of the Habitat Directive (92/433/EEC), emphasising the need to preserve hedgerows to maintain their populations (Heim et al., 2018).

Hedgerow conservation and restoration efforts have progressed in Europe through the implementation of agri-environment schemes (AESs) and offer a promising strategy for supporting farmland biodiversity and ecosystem services (Alignier et al., 2020; Montgomery et al., 2020). Yet, management prescriptions have had mixed outcomes (e.g. Fuentes-Montemayor et al., 2011), and many hedgerows remain degraded due to overtrimming, excessive gaps and continued removal driven by agricultural intensification, high management costs and loss of traditional practices (Arnaiz-Schmitz et al., 2018; Staley et al., 2015). Consequently, structurally complex and mature hedgerows are becoming increasingly rare, despite offering a broader range of resources for diverse wildlife (Kratschmer et al., 2024). Replanting alone is unlikely to reverse this trend, as newly established hedgerows require decades and appropriate management to reach the ecological value of mature ones (Biffi et al., 2024). Therefore, a clearer understanding of how hedgerow management affects biodiversity is needed to enhance the effectiveness of AESs.

At the local scale—where most AESs operate—hedgerows exhibit high variability in their structure, ranging from lines of mature trees to dense shrub rows or multi-strata formations (Baudry et al., 2000). While this diversity may provide a range of roosting sites, foraging opportunities and navigational aids (e.g. Boughey et al., 2011; Foxley et al., 2023; Toffoli, 2016), its effects on bats remain under-researched in comparison to other taxonomic groups (Kratschmer et al., 2024). To date, only a few hedgerow-level characteristics (e.g. height, tree presence and maturity) have been studied. For instance, little is known about the effects of commonly used trimming methods (e.g. vertical trimming, coppicing), though reduced trimming frequency can benefit bats by increasing prey availability (Froidevaux et al., 2019). Additionally, the structural continuity of hedgerows likely affects their use as corridors (Biffi et al., 2024; Pinaud et al., 2018), yet few studies have tested this effect across bat species with different foraging behaviours, limiting its generalisation as a restoration guideline. This underscores the need to identify intrinsic characteristics enhancing hedgerow quality for bats.
Using passive acoustic monitoring, we aimed to disentangle the relative effects of six hedgerow characteristics (i.e. trimming method, height, width, foliage density, connectivity and woody plant diversity) on the activity of three bat guilds: short-, mid- and long-range echolocators (SRE, MRE, LRE), corresponding to clutter, edge and open-space foragers, respectively (Denzinger & Schnitzler, 2013). We focused primarily on guild activity as we expected different responses to hedgerow characteristics based on foraging preferences. As SRE forage more intensively in clut- tered environments, they may prefer wide, overgrown hedgerows (Toffoli, 2016). MRE commute and forage along hedgerows, often favouring tree-filled ones, though their preferences for specific characteristics remain unclear (Boughey et al., 2011). Greater hedgerow connectivity, width and woody plant diversity are therefore expected to have positive effects, particularly for these two guilds. LRE are highly mobile generalists that forage above the vegetation and may primarily respond to landscape-scale features (Fuentes-Montemayor et al., 2017). We therefore expected LRE to be less influenced by hedgerow characteristics than SRE (i.e. gleaning bats or species with high-frequency echolocation calls). To assess the applicability of our results in diverse agricultural set- tings, we sampled hedgerows across a gradient of landscape con- texts, from open croplands to more structurally complex wooded and *bocage* landscapes.

## 2 | MATERIALS AND METHODS

### 2.1 | Study sites and sampling design

Our sampling included hedgerows from three French regions (see Figure 1a) with different landscape configurations (i.e. hedgerow density) and compositions (i.e. cropland, grassland and forest cover; see Figure S1 in Supporting Information). We sampled 448 hedgerows within 1×1km windows including 4 to 8 hedgerows with contrasting characteristics. We defined hedgerows as a linear feature at least 0.5m high, primarily composed of woody plants and forming part of a management unit (Baudry et al., 2000). To ensure consistent experimental conditions, hedgerows were primarily adjacent to crop fields in mixed crop-livestock farming systems, isolated from woodlands and urban centres (no artificial lighting) and positioned at least 100m away from a wind turbine, a phone mast and a very-high-voltage power line.

### 2.2 | Bat acoustic monitoring

We recorded bat echolocation calls along hedgerows, from 30 min before sunset to 30 min after sunrise, over 1227 nights between 2020 and 2023 (66% in 2020, 2% in 2021, 6% in 2022 and 25% in 2023; mean night length = 9.18 h). Each hedgerow was sampled within a single year during two periods of high bat activity, in May–July and August–September. For each period, monitoring consisted

of either one or multiple nights per hedgerow (mean = 5 ± 3 SD nights). We used passive acoustic devices—primarily Song Meters (Wildlife Acoustics, USA), SM4BAT® (1031 nights) and SM2BAT® (11 nights), alongside a smaller number of AudioMoths v1.2® (Open Acoustic Devices, USA; 185 nights)—which were placed 1–2 m above the ground, oriented upward and parallel to the hedgerow. Despite using different devices, we applied standardised trigger settings to limit detection heterogeneity, and recordings were systematically conducted under favourable and homogenous weather conditions (i.e. no rain, low wind speed, temperature at sunset >10°C), following the French National Bat Monitoring Programme guidelines (https://www.vigienature.fr/fr/page/proto cole-point-fixe; see Appendix S1). No confounding effects were found between device types and hedgerow characteristics (see Figure S2). This study did not require approval from an animal ethics committee as it did not involve procedures that fall under animal experimentation regulations.

### 2.3 | Bat species identification

As acoustic sampling cannot differentiate individual bats, we used bat activity—the number of bat passes per site per night—as a proxy for abundance (e.g. Barré et al., 2019). A bat pass was defined as a 5-second sequence containing at least one echolocation call (Kerbiriou et al., 2019). As manual classification would have been prohibitively time-consuming, we used automated species identification with the *Tadarida* software (Bas et al., 2017), which assigned each bat call to the most accurate taxonomic level along with a confidence index (for more details, see Appendix S1). Following Barré et al. (2019), we excluded data with a maximum confidence index below 0.5, reducing false positives while retaining a high number of passes (see Table S1). Then, we validated our statistical results using a more conservative threshold of 0.9, which further limited false positives but excluded more true positives (see Figure S3).

To account for differences in echo-morphological traits, we grouped taxa according to their echolocation call structures into three foraging guilds: short- (SRE: *Barbastella* spp., *Plecotus* spp., *Rhinolophus* spp., *Myotis* spp.), mid- (MRE: *Pipistrellus* spp.) and long-range echolocators (LRE: *Eptesicus* spp., *Nyctalus* spp.), cor- responding to clutter, edge and open-space foragers, respectively (Frey-Ehrenbold et al., 2013). We restricted species-level bat ac- tivity analyses to taxa with a low probability of misidentification in acoustic surveys, focusing on two species associated with hedge- row landscapes: *Barbastella barbastellus* (Barbar; Zeale et al., 2012) and *Rhinolophus ferrumequinum* (Rhifer; Froidevaux et al., 2017). As *Pipistrellus pipistrellus* (*Pippip*) represented 76.9% of MRE passes, we analysed it separately from the remaining species in the guild which we grouped into one taxon, *Pipistrellus kuhlii/nathusii* (*Pipnatkuh*). Because AudioMoths may be less sensitive to high-frequency calls than Song Meters (Kunberger & Long, 2023), we used a re- duced dataset for *Rhifer* and *Pipnatkuh* excluding recordings from
## (a) STUDY REGIONS & SAMPLING DESIGN

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Source: BD Haie® (DSB)

## (b) VARIABLE SELECTION

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## (c) MODEL SELECTION

<figure><img src="image_8.png" /><figcaption>**FIGURE 1** Summary of (a) the study design, (b) the statistical workflow of the procedure used to select covariates for each response variable and (c) the model selection procedure. The map shows the distribution of sampled hedgerows across three study areas in France: West (n=268), North (n=138) and East (n=42). Points located within 10 km of each other are clustered in concentric circles for clarity.</figcaption></figure>
AudioMoths (n=304 hedgerows). This decision was taken to ensure the reliability of our results, after checking differences in activity levels between the two devices (see Appendix S2).

## 2.4 | Hedgerow characteristics

Hedgerow data were collected concurrently with the deployment of acoustic devices, using a standardised protocol (Table 1) applied to a 50 m segment spanning 25 m on each side of the device (i.e. mean acoustic detection range of most bat species encountered; Kerbiriou et al., 2019; Figure 1a). The protocol was originally designed for a citizen science monitoring programme (Vigie-Chiro, MNHN, France), aiming to collect a broad set of characteristics per site by non-specialist observers. Thus, it was intentionally kept simple and categorical variables were used instead of continuous ones to minimise observer bias and ensure comparability between sites. All fieldwork was conducted with the prior consent of landowners and farmers, who granted access to private lands.

Different tree-rejuvenation practices can increase or decrease the vertical complexity of hedgerows by shaping vegetation openness, branching structure and crown development (Staley et al., 2015). Such changes might influence habitat suitability for bats, especially for species seeking cluttered environments for foraging or shelter (Froidevaux et al., 2021; Murphy et al., 2012). We therefore tested the effect of three distinct trimming methods on bat activity, identified visually based on the predominant practice used for each hedgerow: vertical trimming (i.e. lateral branches are cut in a straight vertical line from base to crown); coppicing

(i.e. trees are cut at ground level); and pollarding (i.e. trees are cut higher to preserve the crown). Height was classified into three categories to differentiate shrubs (<5 m), hedgerows with sparse tree canopy covering under 50% of length (5–7 m), and tall hedgerows dominated by tree canopy over 50% of length (>7 m). Width was used to distinguish single-row (≤2 m) from double-row hedgerows (>2 m) and woody plant diversity allowed us to differentiate mono- or duo-generic hedgerows (≤2 genera) from more taxonomically diversified ones (>2 genera). Foliage density was visually assessed based on the presence of vegetation gaps and suitability for bat movements and was categorised as low (improved manoeuvrability) or high (reduced manoeuvrability; for reference photos, see Figure S4). Finally, hedgerow connectivity was determined by counting the number of nodes to assess structural continuity. Description of hedgerow variables, their reason for inclusion and expected effects on bat activity are presented in Table 1 (for variable distribution, see Figure S2).

## 2.5 | Landscape variables

To control for residual variations related to landscape composition and configuration, we selected seven landscape variables that may influence bat activity either positively (i.e. forest and grassland cover, hedgerow and river density; Frey-Ehrenbold et al., 2013; Froidevaux et al., 2021, Froidevaux et al., 2019; Heim et al., 2017) or negatively (i.e. cropland cover; Frey-Ehrenbold et al., 2013), depending on the guild (see Table S2). These variables were computed within three buffer sizes previously used in European studies (1000, 1500 and

<table><caption>TABLE 1 Description of hedgerow variables included in the study and expected bat responses from scientific literature and/or expert opinion (+ positive effect; – negative effect).</caption><thead><tr><td>Variable</td><td>Description</td><td>Reason for inclusion and expected bat responses</td></tr></thead><tbody><tr><td>Trimming method</td><td>Predominant practice (i.e. applied to >40% of length): vertical trimming, coppicing, pollarding.</td><td>Vertical structural complexity, trimming intensity (Froidevaux et al., 2019, 2021; Murphy et al., 2012; Staley et al., 2015, 2023): SRE, MRE (+ Pollarding; – Vertical trimming).</td></tr><tr><td>Height</td><td>Mean hedgerow height assessed by geometry: <5 m, 5–7 m, >7 m.</td><td>Hedgerow structure, tree presence (Boughey et al., 2011; Lacoeuilhe et al., 2018; Toffoli, 2016): All guilds (+).</td></tr><tr><td>Width</td><td>Width of the shrub component: ≤2 m, >2 m.</td><td>Hedgerow structure: LRE (+); Forest-like core conditions: SRE (+) (Boughey et al., 2011; Foxley et al., 2023).</td></tr><tr><td>Foliage density</td><td>Visual assessment across all strata based on manoeuvrability for bats: low, high.</td><td>Habitat navigability and structure, indirect measure of "clutter" space (Boughey et al., 2011; Denzinger & Schnitzler, 2013): MRE (–), SRE (+).</td></tr><tr><td>Connectivity</td><td>Number of nodes (i.e. extremities connected to adjacent hedgerows): isolated (0), dead-end (1), connected (2).</td><td>Landscape corridors, foraging and commuting movements (Biffi et al., 2024; Frey-Ehrenbold et al., 2013; Pinaud et al., 2018): SRE, MRE (+).</td></tr><tr><td>Woody plant diversity</td><td>Genus-level woody plant diversity: ≤2 genera, >2 genera.</td><td>Resource provision (Biffi et al., 2024; Kratschmer et al., 2024): All guilds (+).</td></tr></tbody></table>
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3000m radii; Laforge et al., 2021) to use the most relevant scale of each variable in our models (see Statistical analysis section). We also calculated the distance to the nearest forest and water point, assuming a logarithmic relationship with bat activity (Froidevaux et al., 2021).

## 2.6 | Statistical analysis

The statistical workflow is presented hereafter and summarised in Figure 1b,c. All statistical analyses were conducted in R v.4.4.2 (R Core Team, 2024), with package references provided in Appendix S3.

### 2.6.1 | Model building

To assess the effects of hedgerow characteristics on bat activity, we first built full models in which we included bat activity as the response variable and the six hedgerow characteristics (i.e. target variables) as fixed effects, using generalised linear mixed models (GLMMs; R package *glmmTMB*) with a negative binomial error distribution to handle overdispersion. The same random structure was used across all models (Figure 1b), comprising: (i) Julian day as a random effect to account for inter-night variation; (ii) night length (log-transformed) as an offset to correct for differences in sampling effort, as recordings spanned from May to September; and (iii) the hedgerow identifier accounting for repeated recordings across nights, with a “cluster” effect nested within the hedgerow identifier to group hedgerows located within 3, 5 or 8 km of one another. These distances reflect the average nightly movement range of each bat guild/species and were defined as follows: 3 km for *Pippip*

and *Pipnatkuh*, 5 km for *Rhifer* and 8 km for all others (Froidevaux et al., 2023). To avoid model overparameterisation, we included a maximum of three landscape covariates at their best scale of effect using the procedure summarised in Figure 1b (for more details, see Appendix S4). All continuous variables were checked for strong correlation using the Kendall rank correlation coefficient ($r < |0.5|$), Freckleton, 2002; see Figure S5) and standardised (i.e. centred and scaled) for regression coefficient comparability.

For each response variable, we used the *dredge* function (R package *MuMln*) to rank all possible model combinations by Akaike Information Criterion (AIC) and retained the model containing at least all target variables with the lowest AIC, hereafter referred to as the “selected model” (Figure 1c). Model intercepts were based on mean values of continuous predictors (Table S2) and fixed reference levels for categorical predictors (see Figure 2 caption). To obtain partial effect plots shown in Figure 3, we used the *ggeffects* package. Post hoc pairwise comparisons excluding the model intercept were conducted using Tukey's method for *p* value adjustment, using the *lsmeans* package. The effect of a predictor was considered significant when its *p* value <0.05. Finally, we calculated the proportion of variance explained by each hedgerow characteristic to compare their individual contributions to bat activity (Nakagawa & Schielzeth, 2013). This was calculated as the difference in marginal pseudo-*R*² (i.e. variance explained by the fixed effects) between each selected model and a reduced model excluding the tested variable, using the *glmm.hp* package.

### 2.6.2 | Model diagnostic and validation

In both full and selected models, we checked for overdispersion and assessed model assumptions by visual inspection of residuals

<figure><img src="image_10.png" /><figcaption>**FIGURE 2** Estimates, 95% confidence intervals (CI) and significance levels for hedgerow characteristics in selected models. Reference group used as model intercept: trimming method = vertical trimming; height = <5 m; width = ≤2 m; foliage density (FD) = low; connectivity = dead-end; woody plant diversity (WPD) = ≤2 genera.</figcaption></figure>
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Hedgerow characteristics

<figure><img src="image_12.png" /><figcaption>FIGURE 3 Predicted bat activity and 95% confidence intervals for hedgerow characteristics in selected models. Significance level: \*p*<0.05, \*\*p*<0.01, \*\*\*p*<0.001. C, coppicing; CO, connected; DE, dead-end; IS, isolated; P, pollarding; V, vertical trimming.</figcaption></figure>

using the *simulateResiduals* and *testResiduals* functions (R package *DHARMa*). We also found no evidence of multicollinearity using the *check_collinearity* function (R package *performance*), as the variance inflation factor (VIF) was below 3 for all predictors (Zuur et al., 2010). Finally, we checked for spatial autocorrelation of residuals by visual inspection and applied Moran's I method with the *Moran.I* function (R package *ape*; see Figure S6).

3 | RESULTS

Over 10,040 recording hours, we detected 78,803 SRE passes (5.3%; of which 23,154 from *Barbar* and 2004 from *Rhifer*),

1,358,125 MRE passes (91.9%; of which 1,054,453 from *Pippip* and 303,672 from *Pipnatkuh*) and 40,448 LRE passes (2.7%). Bat passes were detected at every hedgerow (mean = 1204.1 ± 1369.6 SD), with SRE occurring in 89.3%, MRE in 100% and LRE in 83.5% of hedgerows. Selected model estimates for hedgerow characteristics are summarised in Figure 2 (see Table S3 for complete model output and Table S4 for results of post hoc tests). As we detected significant effects of landscape covariates on bat activity, we conducted a supplementary analysis to check our results in three landscape contexts (i.e. open croplands, *bocage* croplands and wooded farmlands). In each context, we found that the effects of hedgerow characteristics on bat activity were mostly consistent with the results presented hereafter, with only small variations in
wooded farmlands (see Appendix S5). For results and interpretation of landscape covariates, please refer to Appendix S6.

## 3.1 | Relative importance of hedgerow characteristics on bat activity

Among a set of six hedgerow characteristics, trimming method was the strongest predictor of bat activity across most models, followed by connectivity and height. For *Pippip*, height was the most influential characteristic, accounting for over half of the explained variance. In contrast, width, foliage density and woody plant diversity had a relatively minor influence on bat activity across models (Figure 4; for individual pseudo-R² values, see Table S5).

## 3.2 | Effect of trimming method

Pollarding resulted in the highest predicted activity for SRE, *Barbar* and *Pipnatkuh*, with activity levels increasing 1.7- and 2.1- and 1.7-fold compared to vertical trimming (*p* < 0.001; *p* < 0.001; *p* < 0.01), and 2.1-, 2.6- and 2.5-fold compared to coppicing, respectively (*p* < 0.001; *p* < 0.01; *p* < 0.001; Figure 3). Pollarding also significantly increased *Pippip* activity by a factor of 1.3 compared to vertical trimming (*p* < 0.05). *Rhifer* activity was lowest in coppiced hedgerows compared to all other trimming methods. In contrast, LRE activity was highest in coppiced hedgerows, with a 2-fold increase relative to vertically trimmed hedgerows (*p* < 0.001), and

a 1.4-fold increase compared to pollarded hedgerows (*p* < 0.05; Figure 3).

## 3.3 | Effect of height

A similar pattern was found for SRE, *Barbar* and *Pipnatkuh*, as their activity was consistently lower in shrubs (<5 m) compared to hedgerows containing trees (>5 m). Relative to hedgerows under 5 m, activity was 1.6-, 2.4- and 1.7-fold higher in 5–7 m hedgerows (*p* < 0.01; *p* < 0.01; *p* < 0.05) and increased by 1.6-, 2.6- and 1.9-fold in those over 7 m (*p* < 0.01; *p* < 0.001; *p* < 0.01), respectively (Figure 3). Notably, *Pippip* activity increased consistently with hedgerow height, and more than doubled in tallest relative to shortest hedgerows (*p* < 0.001; Figure 3). LRE and *Rhifer* activity remained unaffected by height.

## 3.4 | Effect of width

Only *Pipnatkuh* showed a significantly negative response to width (*p* < 0.05), although predicted activity generally decreased in wider hedgerows (Figure 2).

## 3.5 | Effect of foliage density

While predicted activity for LRE decreased with foliage density by a factor of 0.8 (*p* < 0.05), this variable had no significant effect across other models (Figure 2).

<figure><img src="image_13.png" /></figure>

FIGURE 4 Proportion of bat activity variance explained by each fixed predictor (i.e. partial marginal pseudo-R²), related to hedgerow characteristics (in colour) and landscape covariates (in grey), in each selected model.
## 3.6 | Effect of connectivity

We detected a consistent positive effect of hedgerow connectivity on SRE, *Barbar* and *Pippip* activity, with significant 1.7-, 3.1- and 1.5-fold increases in connected versus dead-end hedgerows, respectively (*p* < 0.001 for all; see Figure 3). We also found that *Pipnatkuh* activity was lowest in dead-end hedgerows compared to both connected (*p* < 0.05) and isolated ones (*p* < 0.05). No significant differences were found between isolated and connected hedgerows in other models, however.

## 3.7 | Effect of woody plant diversity

SRE activity was 1.3-fold greater in hedgerows with higher woody plant diversity compared to those with lower diversity (*p* < 0.05), although this variable had no significant influence on other taxa (Figure 2).

# 4 | DISCUSSION

Across 448 hedgerows, we found that the hedgerow trimming method, height and connectivity were primary drivers of bat activity, with prominent and consistent effects for SRE, *Barbar*, *Pippip* and *Pipnatkuh*, while other characteristics had relatively minor effects. We reported more significant effects for MRE and SRE than for LRE, which was expected given that LRE is specialised for high-altitude, open-space foraging (Fuentes-Montemayor et al., 2017) and is less likely to interact with hedgerows locally. Additionally, their calls can be detected within a broader radius (~80 m) which might include multiple hedgerows, limiting the detection of local effects (Denzinger & Schnitzler, 2013).

The SRE guild showed contrasting responses: *Barbar* activity increased with hedgerow height, pollarding and connectivity, whereas *Rhifer* activity remained unaffected by these variables. While the former result was expected as *Barbar* typically uses linear elements for roosting and foraging (Zeale et al., 2012), the latter is surprising. Indeed, *Rhifer* uses hedgerows for foraging (Froidevaux et al., 2017) and previous studies have reported higher *Rhifer* activity near linear semi-natural elements with selective use of specific features, such as tall and overgrown trees (Finch et al., 2020; Foxley et al., 2023; Froidevaux et al., 2019). Furthermore, height and pollarding can be linked to increased insect availability and therefore were expected to positively influence the activity of both species (e.g. Froidevaux et al., 2019; Merckx et al., 2012). The non-significant effect of connectivity on *Rhifer* activity at the hedgerow level was also unexpected (Pinaud et al., 2018), although the positive effect of hedgerow density at the 1000 m scale (Appendix S6) was consistent with *Rhifer*'s known dependence on connected landscape elements for commuting (Froidevaux et al., 2017). One possible explanation is that *Rhifer* was predominantly detected in hedgerows within landscapes

characterised by high hedgerow densities (i.e. "West" area) with an abundance of alternative linear or edge habitats. This likely facilitated a broader distribution of activity across the landscape, reducing the selectivity of particular hedgerow characteristics.

## 4.1 | Pollarding: Benefits of a low-intensity trimming method

We compared four characteristics describing hedgerow structure—trimming method, height, width and foliage density—and found that trimming method was the strongest predictor of bat activity along with height in short- to mid-range echolocators, while width and foliage density had relatively minor effects. Particularly, pollarding was the most beneficial method for SRE, a tree-dwelling species (*Barbar*) and a habitat generalist group (*Pipnatkuh*). Pollards typically feature a large trunk and a structurally complex crown with diversified tree microhabitats (e.g. deadwood, cracks, loose bark), which likely constitute favourable roosting sites for bats, while also benefiting bird communities (Froidevaux et al., 2022; Regnery et al., 2013). As the practice of pollarding has become increasingly rare, such trees are generally more mature, which is a factor known to enhance both bat and bird activity in hedgerows (Biffi et al., 2024; Froidevaux et al., 2022). Additionally, pollarding is typically carried out on longer cutting cycles than coppicing, maintaining higher structural complexity, which may also have benefited SRE by increasing prey abundance (e.g. *Lepidoptera*; Froidevaux et al., 2019; Staley et al., 2016).

Coppicing hedgerows may reduce habitat quality for wildlife by decreasing berry provision, woody biomass and structural complexity for several years post-intervention (Staley et al., 2015). Since we reported a negative effect of coppicing on SRE activity, it is possible that recently coppiced hedgerows temporarily lack the dense vegetation which some clutter-adapted species depend on for shelter, prey capture and spatial orientation (Denzinger & Schnitzler, 2013; Froidevaux et al., 2021). While a previous study has documented a similar negative response to coppicing in a woodland species (*Plecotus auritus*; Murphy et al., 2012), our results suggest that this effect may extend to other short- and mid-range echolocators—such as *Barbar*, *Rhifer* and *Pipnatkuh*—that commonly inhabit European hedgerow landscapes. In wooded farmlands, however, SRE and *Pippip* activity was higher in coppiced than in vertically trimmed hedgerows (Appendix S5), with a particularly strong effect for *Barbar*, a woodland specialist, suggesting that the effects of coppicing may be context-dependent and especially relevant in crop-forest mosaics. In contrast, LRE activity benefited more from coppicing than any other method. The reduced woody biomass in recently coppiced hedgerows may explain this effect by allowing their low-frequency echolocation calls to travel with minimal obstruction, potentially facilitating prey detection and movement. Indeed, while LRE typically forages in open spaces, species like *Eptesicus serotinus* can be more active near hedgerows and shrubs (Lacoeuilhe et al., 2018).
<figure><img src="image_14.png" /></figure>

## 4.2 | Height and tree presence are key drivers of bat activity

Bat activity generally increased with hedgerow height, aligning with previous findings for bats (e.g. Foxley et al., 2023; Toffoli, 2016), birds and small mammals (Kratschmer et al., 2024). This effect appears to be driven by the presence of trees which are known hotspots of insect diversity, including key prey groups for many European bats (e.g. *Diptera, Lepidoptera*; Merckx et al., 2012) and likely provided roosting opportunities for tree-dwelling species, such as *Barbar* (Lacoeuilhe et al., 2018; Toffoli, 2016). As edge space foragers, pipistrelles have a strong affinity for linear elements like hedgerows (Verboom & Huitema, 1997), and in our study, *Pippip* activity increased with both tree presence and height. This positive relationship likely reflects two mechanisms: first, taller hedgerows may act as windbreaks, reducing energy expenditure during flight (Verboom & Spoelstra, 1999); second, they may enhance foraging conditions by drawing airborne insects in the recirculating air behind such structures (Lewis & Dibley, 1970). Additionally, height was positively associated with tree maturity and crown width (see Appendix S7), both known to benefit bat and bird communities (Biffi et al., 2024): tall trees with large canopies may act as better acoustic landmarks for species commuting along linear elements (Frey-Ehrenbold et al., 2013; Verboom & Spoelstra, 1999).

Although we observed lower *Pipnatkuh* activity with increasing hedgerow width, and lower LRE activity with increasing foliage density, these patterns may result from lower detection probability due to call attenuation in wide or dense hedgerows. Thus, our initial hypotheses could not be supported; namely, that SRE would benefit from wider hedgerows due to their forest-like conditions and that MRE and SRE discriminate between hedgerows of different foliage densities. We also acknowledge that our foliage density and width variables may have been too coarse to detect fine variations in bat activity, as we could not distinguish between vegetation strata (e.g. a dense shrub layer is likely less obstructive to bat movement than a dense tree canopy). As existing literature remains scarce, further investigation using finer metrics is needed to assess the importance of this characteristic (e.g. see Boughey et al., 2011; Foxley et al., 2023).

## 4.3 | Hedgerow structural continuity matters

Hedgerows and other linear landscape elements serve as navigational aids for bats with structure-bound ecologies, and even small gaps may disrupt their use (Pinaud et al., 2018). Indeed, we found that the structural continuity of hedgerows significantly enhanced bat activity, as short- to mid-range echolocators were more active in well-connected hedgerows than in those with dead ends. The avoidance of dead-end hedgerows may reflect reduced corridor quality due to elevated predation risk when crossing discontinuities, which certain clutter-adapted species may avoid by making detours along continuous linear elements (Limpens & Kapteyn, 1991). In contrast, *Pipnatkuh* appeared to use both well-connected and

isolated hedgerows. As *Pipnatkuh* tend to use open spaces more extensively than edge specialists (e.g. *Pippip*) and clutter-adapted species (Denzinger & Schnitzler, 2013; Heim et al., 2017), this group may venture more readily into isolated hedgerows, which could act as stepping stones between forested areas or other hedgerows (Froidevaux et al., 2022).

## 4.4 | Benefits of plant diversity

Hedgerows provide important food resources for bats, including insect prey (Boughey et al., 2011; Verboom & Spoelstra, 1999). Increasing plant diversity within and around hedgerows has been shown to benefit both bats and birds (Biffi et al., 2024; Froidevaux et al., 2019) by supporting greater invertebrate diversity and abundance through the provision of diverse microhabitats and continuous resources over time (Kratschmer et al., 2024). Consistently, our findings reveal a positive effect of taxonomically diverse hedgerows on SRE activity. Since we could only include a binary plant diversity metric, future studies should assess whether these benefits extend to other bat guilds by using finer data, including different vegetation types (woody vs. non-woody) and adjacent flower strips (Albrecht et al., 2020).

## 4.5 | Management implications

Consistent with a growing body of evidence, this study highlights the importance of hedgerow management for promoting on-farm bat activity (e.g. Boughey et al., 2011; Froidevaux et al., 2019). In line with previous findings, we confirm the importance of tall, tree-filled hedgerows for bats and emphasise the need to preserve mature trees (e.g. Biffi et al., 2024; Lacoeuilhe et al., 2018). We also provide novel insights, showing that structural hedgerow continuity and pollarding can further increase habitat suitability, particularly for tree-dwelling or clutter-adapted species. Moreover, our results remained mostly consistent across different landscape contexts, suggesting that these drivers of bat activity may apply both in simplified agricultural settings and in highly structured *bocage* landscapes (see Appendix S5). Finally, our findings support the integration of simple hedgerow quality assessments in agri-environment schemes, as such protocols can yield useful ecological information.

Based on our results, we recommend: (i) Prioritising the preservation of hedgerows containing tall and mature trees with potential roosts; (ii) Increasing the density of well-connected hedgerows by prioritising the restoration of partially disconnected segments; (iii) Maintaining plant diversity in existing and newly planted hedgerows; and (iv) Opting for low-intensity trimming methods (e.g. pollarding) to preserve the crown width and vertical structural complexity, in addition to implementing low-frequency trimming regimes (see Froidevaux et al., 2019). While we found that coppicing may temporarily reduce habitat suitability for some clutter-adapted species (e.g. *Rhifer, Barbar*), implementing it on short sections over
<figure><img src="image_15.png" /></figure>

a long rotation could create a favourable structural mosaic while also minimising short-term impacts on small mammals and birds (see Staley et al., 2015). As part of the European energy transition, there is growing interest in using by-products from hedgerow maintenance for timber, which may incentivise intensive trimming practices. Instead, we suggest exploring opportunities in sectors that can make effective use of cuttings from low-intensity trimming regimes (e.g. biomass energy, wood mulching). Our findings align with those for other taxonomic groups, including certain birds (e.g. Heath et al., 2017; Rösch et al., 2024) and invertebrates (particularly *Lepidoptera*; Kratschmer et al., 2024) and could support recommendations for wider biodiversity conservation strategies.

## AUTHOR CONTRIBUTIONS

Jérémy S. P. Froidevaux, Camille Leroux, Christian Kerbiriou and Florence Matutini conceived the ideas and designed the methodology; Jérémy S. P. Froidevaux, Camille Leroux and Florence Matutini collected most of the data; Noé Tarcy analysed the data and led the writing of the manuscript. All authors contributed critically to the drafts and interpretation of the results and gave final approval for publication.

## ACKNOWLEDGEMENTS

We are grateful to all landowners and farmers for allowing us to work on their property. We thank Kévin Barré and Isabelle Le Viol for their contribution in designing the hedgerow survey protocol, as well as Kirsty Park, Sophie Morin, Théo Dokhelar and Clément Calenge for their valuable advice on methodology and sampling design. We deeply thank all the participants involved in data collection, especially Pauline Lefebvre, Guillaume Nard, Olivier Crouzet, Sylvain Haie, Gilles Holé, Quentin Fournet, Bernard Mauvy and all Vigie Nature volunteers. We also thank the IN2P3 Computing Centre and Didier Bas for their support in data processing and long-term archiving. Finally, we are grateful to the associate editor and the two anonymous reviewers for their insightful comments, which helped to improve the quality of this manuscript. Jérémy S. P. Froidevaux was funded by the Leverhulme Trust through an early-career fellowship (award ref. ECF-2020-571). Camille Leroux was funded by the Association Nationale de la Recherche et de la Technologie (grant No. 2019/1566) and Auddicé biodiversité.

## CONFLICT OF INTEREST STATEMENT

The authors declare no conflict of interest. Jérémy S. P. Froidevaux is an Associate Editor of Journal of Applied Ecology, but took no part in the peer review and decision-making processes for this paper.

## DATA AVAILABILITY STATEMENT

Data available from the Dryad Digital Repository: https://doi.org/10.5061/dryad.9s4mw6mvz (Tarcy et al., 2025).

## STATEMENT ON INCLUSION

All authors were based in France, where the study was conducted. The research was grounded in local primary data collected by

researchers, technicians, student interns and volunteers from the Vigie-Chiro citizen science monitoring programme. Vigie-Chiro is co-coordinated by Christian Kerbiriou (French National Museum of Natural History), and for each scientific output involving data from this monitoring scheme, feedback is shared with volunteers (500+) via dedicated summaries posted on the programme's forum. Florence Matutini is a researcher at the French Biodiversity Agency (OFB), and whenever possible, insights regarding the research questions and methodologies employed are communicated to relevant stakeholders through dedicated media, such as the OFB technical journal "Biodiversité" or training courses. This work also contributes to improving the identification of conservation priorities for the regulatory protection of hedgerows and protected species, as part of a national hedgerow monitoring scheme (DSB). Relevant literature published by scientists from the region was cited where appropriate.

## ORCID

Noé Tarcy https://orcid.org/0009-0001-0473-2319

Jérémy S. P. Froidevaux https://orcid.org/0000-0001-6850-4879

Christian Kerbiriou https://orcid.org/0000-0001-6080-4762

Camille Leroux https://orcid.org/0000-0002-4984-3485

Florence Matutini https://orcid.org/0000-0002-7013-929X

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SUPPORTING INFORMATION

Additional supporting information can be found online in the
Supporting Information section at the end of this article.

Table S1. Summary table of bat activity used in this paper.

Table S2. Description of raw (not scaled) landscape variables used in our modelling procedure at all buffer radius sizes.

Table S3. Estimates, standard errors and significance levels from selected models for fixed predictors (hedgerow characteristics and landscape covariates) on bat activity.

Table S4. Results from post-hoc pairwise comparisons of hedgerow variables with more than 2 levels (i.e. trimming method, height, connectivity) excluding the model intercept, using Tukey's method for *p*-value adjustment.

Table S5. Relative contribution of hedgerow characteristics to predicting bat activity (marginal pseudo-R²).

Figure S1. Mean proportion of cropland, forest and grassland cover in each study region within a 1500 m buffer radius.

Figure S2. Distribution of hedgerow variables across the full dataset (n=448) and two sub-datasets isolating recordings obtained with one type of acoustic detector.

Figure S3. Verification of results from selected models for each bat species/group using two datasets, each one considering an identification of bat passes with a maximum error risk tolerance of 0.5 or 0.1.

Figure S4. Reference photos for the hedgerow foliage density metric.

Figure S5. Correlation coefficients of all raw (not scaled) landscape variables in all buffer sizes from Kendal rank correlation tests.

Figure S6. Moran scatterplots of residuals from full and selected generalized linear mixed models assessing spatial autocorrelation in bat activity data across hedgerows.

Appendix S1. Details on bat acoustic monitoring, device settings and automated species identification procedure.

Figure S7. Distribution of meteorological variables across all 1227 recorded nights: daily averaged wind speed, daily minimum
<figure><img src="image_17.png" /></figure>

temperature, and daily rainfall.

**Appendix S2.** Effect of the type of acoustic monitoring device on bat activity.

**Figure S8.** Predicted bat activity levels and 95% confidence intervals between the two main types of acoustic devices used in this study (AudioMoth v1.2® vs. SM device family, i.e. SM4BAT® and SM2BAT®) from univariate GLMMs.

**Appendix S3.** References for R Packages used.

**Appendix S4.** Landscape variable selection and composition of full models.

**Table S6.** AIC from univariate GLMMs including the hedgerow identifier nested within the cluster identifier, Julian day and an offset (log-transformed nightly recording duration) as random effects to select (1) the best buffer size for proportion and density landscape variables and (2) the best three landscape variables to include in full models.

**Table S7.** Comparison of full, null and selected models with associated AIC and pseudo-R².

**Appendix S5.** Testing bat response variation to hedgerow characteristics in three landscape contexts.

**Figure S9.** Principal component analysis plot of landscape characteristics (r=3km) around each sampled hedgerows in the global dataset (n=448).

**Figure S10.** Comparison of estimates and 95% confidence intervals for hedgerow characteristics between the reference model (i.e. all landscape types included), and the three landscape-specific models (i.e. open croplands, bocage croplands, wooded farmlands).

**Appendix S6.** Landscape covariate results and interpretation.

**Figure S11.** Model predictions for bat activity against retained landscape covariates in each selected model.

**Appendix S7.** Assessing crown shape and tree maturity in relation to hedgerow height in a sub-dataset.

**Figure S12.** Hedgerow crown shape illustrated with examples captured on field.

**Figure S13.** Relationships between (i) hedgerow height and maximum trunk width, and (ii) hedgerow height and crown shape.

**How to cite this article:** Tarcy, N., Froidevaux, J. S. P., Kerbiriou, C., Leroux, C., & Matutini, F. (2025). Managing hedgerows for biodiversity: Disentangling the effects of trimming, structure and connectivity on the use of linear features by bats. *Journal of Applied Ecology*, *00*, 1-14. https://doi.org/10.1111/1365-2664.70205