# Bat Activity in South Mediterranean Forests: A Seasonal Acoustic Survey

Afaf Boukhari, Moncef Ait-Abdesselam, Fatima Zohra Bissaad, Farid Bounaceur, Inès Khazar, Mouna Bouhani, Stéphane Aulagnier

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Afaf Boukhari, Moncef Ait-Abdesselam, Fatima Zohra Bissaad, Farid Bounaceur, Inès Khazar, et al. Bat Activity in South Mediterranean Forests: A Seasonal Acoustic Survey. Diversity, 2025, 17 (11), pp.750. 10.3390/d17110750. hal-05511190

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Article

# Bat Activity in South Mediterranean Forests: A Seasonal Acoustic Survey

Afaf Boukhari <fnref n="1" />, Moncef Ait-Abdesselam <fnref n="2" />, Fatima Zohra Bissaad <fnref n="3" />✉, Farid Bounaceur <fnref n="4" />✉, Inès Khazar <fnref n="5" />, Mouna Bouhani <fnref n="6" /> and Stéphane Aulagnier <fnref n="5" />,*✉

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

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Academic Editor: Sergei V. Kruskop

Received: 29 September 2025

Revised: 20 October 2025

Accepted: 23 October 2025

Published: 28 October 2025

**Citation:** Boukhari, A.; Ait-Abdesselam, M.; Bissaad, F.Z.; Bounaceur, F.; Khazar, I.; Bouhani, M.; Aulagnier, S. Bat Activity in South Mediterranean Forests: A Seasonal Acoustic Survey. *Diversity 2025*, *17*, 750. https://doi.org/10.3390/d17110750

**Copyright:** © 2025 by the authors.
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## Abstract

South Mediterranean forests are known for their spatial and temporal variability in fauna and flora species abundance. Using acoustic surveys and generalized linear models, we investigated the nocturnal activity of bats in five forest sites in northeast Algeria from March to November. A total of 12 species were detected: *Rhinolophus blasii*, *R. hipposideros*, *Tadarida teniotis*, *Miniopterus schreibersii*, *Eptesicus isabellinus*, *Hypsugo savii*, *Pipistrellus kuhlii*, *P. pipistrellus*, *Plecotus gaisleri*, *Myotis cappaccinii*, *M. emarginatus*, and *M. punicus*. Activity levels varied significantly among months and sites. Overall bat activity and *P. kuhlii* activity increased from spring to summer, peaked in August, and slightly decreased in early autumn. Activity levels also differed markedly among forest sites, with low activity levels in the urban forest site, and *E. isabellinus* was mostly active in only two forest sites. These results suggest that both environmental conditions and location-specific factors shape the activity patterns.

**Keywords:** chiroptera; Kuhl's pipistrelle; isabelline serotine; forest; echolocation calls; phenology; temperature; Algeria

## 1. Introduction

The Mediterranean area is recognized as one of the 36 global biodiversity hotspots [1], and also as one of the most threatened [2]. With an area of 8779 million km<fnref n="2" />, the Mediterranean region is shared by 26 countries, five of which are countries on the southern side. Within this region, forests cover 85 million ha, about 9.7% of the total area, amounting to 2% of the world's forest area [3]. South Mediterranean forests are known for their high diversity of fauna and flora species, and for their distinctive climate characterized by cold and wet winters, as well as hot and dry summers [4]. In northern Algeria, with approximately 800,000 ha [5], the Aleppo pine (*Pinus halepensis*) covers 35% of the wooded areas [6]. With

<footnote n="1">¹ Laboratoire Technologies Douces, Valorisation, Physico-Chimie des Matériaux Biologiques et Biodiversité, Université M'Hamed Bougara, Boumerdes 35000, Algeria; a.boukhari@univ-boumerdes.dz</footnote>

<footnote n="2">² Laboratoire d'Hygiène et Pathologie Animales, Faculté des Sciences de la Nature et de la Vie, Université Ibn Khaldoun, Tiaret 14000, Algeria; moncef.agrovet@gmail.com</footnote>

<footnote n="3">³ Laboratoire de Recherche de Bioinformatique, Microbiologie Appliquée et Biomolécules (LBMAB), Faculté des Sciences, Université M'Hamed Bougara, Boumerdes 35000, Algeria; f.bissaad@univ-boumerdes.dz</footnote>

<footnote n="4">⁴ Équipe de Recherche Biologie de la Conservation en Zones Arides et Semi Arides, Laboratoire Agronomie Environnement, Institut des Sciences, Université de Tissemsilt, Tissemsilt 38000, Algeria; fbounaceur@yahoo.fr</footnote>

<footnote n="5">5 Comportement et Écologie de la Faune Sauvage, INRAE, Université de Toulouse, CS 52627, 31326 Castanet-Tolosan CEDEX, France; ines-khazar@inrae.fr</footnote>

<footnote n="6">6 Laboratoire de Biosurveillance de l'Environnement, Faculté des Sciences de Bizerte, Université de Carthage, Zarzouna 7021, Tunisia; bouhanimouna4@gmail.com</footnote>

<footnote>* Correspondence: stephane.aulagnier@inrae.fr</footnote>
a potential area estimated to be 1,807,000 ha [7], the holm oak (*Quercus ilex*) is another significant component of Algeria's forest ecosystems. The Algerian climax forests of this species are located in the semi-arid and lower sub-humid bioclimatic stages [8,9].

Bats represent a species-rich mammalian order comprising more than 1400 species worldwide [10]. Forests are a key habitat for bats throughout the world [11–13], and almost half of known bat species worldwide use trees as roosts for at least part of the year [14]. Forests provide both foraging and roosting resources for bats, and in return, bats provide significant ecosystem services to forests [15], including control of phytophagous insects [16,17]. Thus, bat conservation in forests is crucial not only for maintaining biodiversity, but also for a sustainable forest management.

In the Mediterranean area, bats are moderately diversified, with 63 species belonging to eight families, and only 22 species belonging to six families in Northwestern Africa [18]. While significant interest in bats of this region recently arose (e.g., [19–21]), studies on bats in Mediterranean forests were mainly concentrated in European areas. In Italy, high bat activity was reported in wooded areas, particularly in broad-leaved woodlands (oaks *Quercus* spp., beech *Fagus sylvatica*, and chestnut *Castanea sativa*) [22], rather than in conifer plantations. In southern Portugal, riparian habitats surrounded by autochthonous broad-leaved forests provided optimal foraging areas for bats [23]. In southern Spain, assemblages of forest bats were driven primarily by forest composition and structure rather than by climate [24].

There is currently limited information available about spatial and temporal patterns of bat activity in south Mediterranean forests. In central Tunisia, several pine forests of the Jebel Mghilla National Park were surveyed only once [25]. In the Near East, bats were studied in planted pine forests [26]. In Morocco, different forest types, including several argane (*Argania spinosa*) woodlands, were sampled in the coastal area of the Safi-Essaouira provinces [27]. In northern Algeria, the winter activity of two bat species was investigated in five different habitats, near and inside the forests of Bejaia region [28]. Comparing bat richness and activity in urban and rural habitats in the Constantine area, only two forest sites were sampled [29]. However, to date, no research on bat activity has occurred in Northwestern African forests.

Acoustic monitoring is a highly effective method to survey bat assemblages and understand behavior and habitat use of different bat species [30]. This technique enables annual surveys and provides insights into spatial and temporal aspects of bat behavior [31]. Herein, we investigated the seasonal variation in bat activity in Mediterranean forests using acoustic detection in five sites in northeast Algeria and generalized linear models. (1) We expected to find seasonal variations in measured bat activity, with significant interactions among sites, months, temperature and hygrometry, for either the overall bat community or for the most active species. (2) We predicted that activity would increase during spring because of increased energy requirements due to gestation and lactation, and that a secondary peak of activity in autumn would be observed, due to resource acquisition before the wintering period.

## 2. Material and Methods

### 2.1. *Study Areas*

Field work was conducted in two areas in northeast Algeria, approximately 160 km apart: Bordj Bou Arreridj (36°04′03.8″ N 4°46′02.7″ E), and Chettaba, in the vicinity of Constantine (36°21′16.3″ N 6°38′43.4″ E) (Figure 1, Table 1).
<figure><img src="image_5.png" /><figcaption>**Figure 1.** Location of the five sites surveyed for bat activity in forests in northeast Algeria.</figcaption></figure>

<table><caption>**Table 1.** Main characteristics of the five sites surveyed for bat activity in forests in northeast Algeria.</caption><thead><tr><td>Site</td><td>Coordinates</td><td>Characteristics</td></tr></thead><tbody><tr><td>Bordj Zemoura</td><td>36°17′15.8″ N 4°49′29.5″ E</td><td>Open forest, with a density of 0.08 tree/m² at an altitude of 930 m, including villages, and agricultural lands. Tree height ranges from 2 to 6 m. The forest covers an area of 658.99 km² composed in majority of Aleppo pines, and less substantially of holm oaks.</td></tr><tr><td>Boumergad</td><td>36°3′32.46″ N 4°48′41.51″ E</td><td>Moderately dense forest (0.14 tree/m²) at an altitude of 1009 m, in the midst of the city, surrounded by urban habitats and agricultural lands. Tree height ranges from 2 to 7 m. A small forest of 7 km², composed of Aleppo pines.</td></tr><tr><td>Djaafra</td><td>36°16′49.75″ N 4°38′18.51″ E</td><td>Dense forest (0.2 tree/m²) at an altitude of 1200 m, including some villages, roads and agricultural lands. Tree height ranges from 3 and 13 m. The forest covers an area of 1132.77 km² composed in majority of Aleppo pines and less substantially Atlas cedars and holm oaks.</td></tr><tr><td>Chettaba Aleppo Pine Forest</td><td>36°17′57.2″ N 6°27′22.1″ E</td><td>Dense forest (0.18 tree per m²) at an altitude of 866 m, including agricultural lands. Tree height ranges from 1 to 3 m. Composed of Aleppo pine trees.</td></tr><tr><td>Chettaba Holm Oak Forest</td><td>36°19′16.9″ N 6°27′34.7″ E</td><td>Dense maquis of holm oak trees (0.22 tree per m²) at an altitude of 991 m. Tree height ranges from 5 to 13 m.</td></tr></tbody></table>

Forests in Bordj Bou Arreridj cover an area of 80,799 ha and are mainly composed of Aleppo pine (76%), with some holm oak (19%) and Atlas cedar *Cedrus atlanticus* (1%). The bioclimate is semi-arid, characterized by an average annual thermal amplitude of 21.3 °C and an average annual rainfall less than 400 mm [8].
Forests of the Chettaba massif cover an area of 2398 ha [32], and are characterized by a semi-arid bioclimate, with an average annual rainfall between 670 and 800 mm [33]. These forests are composed either of holm oak (1127 ha) or Aleppo pine [34,35].

Three sites were selected from the first region, namely Bordj Zemoura, Boumergad, and Djaafra, and two from the second, an Aleppo pine stand and a holm oak stand.

## 2.2. Acoustic Sampling

We conducted acoustic sampling at each site from March to November 2023 when climatic conditions were favorable (without rain or strong winds) each month for two hours, starting at sunset. Bat calls were recorded along a transect walked at a slow and constant speed using a Batlogger M (Elekon AG, Lucerne, Switzerland) equipped with firmware version 2.6.2 in Bordj Bou Arreridj forests. The acoustic data were recorded at a frequency of 312.5 kHz, with a recording duration of 5 s, the default setting of the Batlogger. In Chettaba forests, we used a time-expansion D240X ultrasonic detector (Petterson Electronik Uppsala, AB, Sweden) with a frequency range from 10 to 128 kHz, connected by cable to a digital recorder (Edirol R-09HR, Roland Corporation, Shanghai, China), fitted with a memory card. For both devices, only 5 s sequences containing bat calls were manually analyzed using BatSound, v.3.10 (Petterson Elektronik AB) for spectrogram analyses. We used a simple frequency of 44.100 samples/s, 16 bits/sample and selected 512 pt FFT with a Hamming window for analysis [36].

Recorded calls were identified at the species level, based on signal shape and call parameters [37], and on published data from Northwest Africa [21,38–40]. *Rhinolophus blasii* and *R. hipposideros* are easily identifiable as they produce specific FM/CF/FM-type signals, featuring a long constant frequency (CF) portion and differing in the maximum energy frequency, ranging from 95.4 to 98.4 kHz for *R. blasii*, and 115.9 to 121.0 kHz for *R. hipposideros* [21,40]. *Tadarida teniotis*, *Miniopterus schreibersii*, *Pipistrellus pipistrellus*, *P. kuhlii*, *Hypsugo savii* and *Eptesicus isabellinus* were identified by their FM/QCF-type signals with maximum energy frequencies at 12.83 ± 1.14 kHz, 56.51 ± 0.6 kHz, 47.72 ± 3.71 kHz, 38.71 ± 1.03 kHz, 32.40 ± 1.71 kHz and 25.97 ± 1.50 kHz, respectively [39]. *Plecotus gaisleri* produce FM calls, which can be identified by their shape and their starting frequency, which is 31.5–58.9 kHz [39]. *Myotis capaccinii*, *M. emarginatus* and *M. punicus* emit abrupt FM-type signals with terminal frequencies ranging from 30 to 35 kHz, 34 to 48 kHz, and 21.4 to 28.5 kHz, respectively [38,40].

Additionally, during the two-hour survey, air temperature and hygrometry were measured every 30 min, using an IHM6150SI pocket thermo-hygrometer type K digital (precision ± 1 °C and ±5% RH). We also recorded the cloud cover and the wind speed.

## 2.3. Data Analysis

To estimate bat activity levels, we summed up bat passes for periods of 30 min over the 2 h surveys. Then, we used generalized linear models to investigate bat activity variation and to identify the key factors influencing either the activity of all bat species pulled together, or solely *P. kuhlii* activity and *E. isabellinus* activity separately. All analyses were conducted using the R software version 4.4.0 [41]. The factors that might influence bat activity include study site, month, air temperature, hygrometry and the 30 min period of sampling. The latter we included either as a random effect to control for variability in the recording across the four periods of 30 min, or as fixed effect in some models to evaluate its impact on mean bat activity. We used a negative binomial distribution with the glmmTMB R package (version 1.1.11) to model overdispersion [42] in all models (i.e., for the overall bat activity, as well as for *P. kuhlii* and *E. isabellinus* separate activities). We constructed candidate models with two-way interactions to reflect our hypotheses, and we included
the null model in our comparisons to evaluate the plausibility of all candidate models. We selected the best models using the Akaike's information criterion for small sample size (AICc) [43,44] with the aictab function from the AICcmodavg package in R (version 2.3.3) [45]. During the model selection process of *E. isabellinus*, the inclusion of hygrometry and periods led to convergence errors, which forced their exclusion from the final selection.

To check whether the residuals of the models were uniformly distributed and for under- or over-dispersion [46], we used the DHARMA R package (version 0.4.7). This package uses a simulation-based approach to generate readily interpretable residuals for generalized linear (mixed) models. These residuals are standardized for values between 0 and 1, allowing for a clear interpretation when comparing to residuals from a linear model (see Supplementary Materials for more details).

Furthermore, multicollinearity among predictor variables was investigated using the performance R package (version 0.13.0) [47], by calculating the variance inflation factor (VIF). All selected best models were checked and variables included exhibited acceptable VIFs between 1.34 and 2.37 (see Supplementary Materials for more details).

### 3. Results

A total of 1738 bat passes were recorded across the five sites: 527 in Bordj Zemoura, 152 in Boumergad, 536 in Djaafra, 287 in Chettaba forest of Aleppo pine, 236 in Chettaba forest of holm oak, for a total of 12 species: *Rhinolophus blasii*, *R. hipposideros*, *Tadarida teniotis*, *Miniopterus schreibersii*, *Eptesicus isabellinus*, *Hypsugo savii*, *Pipistrellus kuhlii*, *P. pipistrellus*, *Plecotus gaisleri*, *Myotis capaccini*, *M. emarginatus*, and *M. punicus* (Table 2). The most active species in four sites were *P. kuhlii* and *E. isabellinus*; in Djaafra, a third species, *H. savii*, was very active. Interestingly, *T. teniotis* was particularly active in the Chettaba forests.

<table><caption>**Table 2.** Percentages of bat species activity recorded in five forests in northeast Algeria between March and November 2023.</caption><thead><tr><th>Species</th><th>Bordj Zemoura</th><th>Boumergad</th><th>Djaafra</th><th>Chettaba<br>Aleppo Pine</th><th>Chettaba Holm<br>Oak</th></tr></thead><tbody><tr><td>Rhinolophus blasii</td><td>1.14</td><td>0.00</td><td>0.00</td><td>0.00</td><td>0.42</td></tr><tr><td>Rhinolophus hipposideros</td><td>1.14</td><td>0.00</td><td>0.00</td><td>0.70</td><td>1.27</td></tr><tr><td>Tadarida teniotis</td><td>0.38</td><td>4.61</td><td>6.16</td><td>19.16</td><td>15.25</td></tr><tr><td>Miniopterus schreibersii</td><td>0.38</td><td>0.00</td><td>1.31</td><td>5.92</td><td>2.12</td></tr><tr><td>Eptesicus isabellinus</td><td>37.57</td><td>2.63</td><td>34.89</td><td>6.97</td><td>4.24</td></tr><tr><td>Hypsugo savii</td><td>5.12</td><td>0.66</td><td>25.00</td><td>4.18</td><td>5.93</td></tr><tr><td>Pipistrellus kuhlii</td><td>49.53</td><td>90.13</td><td>30.04</td><td>50.17</td><td>63.14</td></tr><tr><td>Pipistrellus pipistrellus</td><td>2.85</td><td>1.32</td><td>0.37</td><td>6.97</td><td>1.27</td></tr><tr><td>Plecotus gaisleri</td><td>1.71</td><td>0.00</td><td>0.37</td><td>0.00</td><td>0.00</td></tr><tr><td>Myotis capaccinii</td><td>0.19</td><td>0.66</td><td>1.87</td><td>0.00</td><td>0.42</td></tr><tr><td>Myotis emarginatus</td><td>0.00</td><td>0.00</td><td>0.00</td><td>0.57</td><td>0.00</td></tr><tr><td>Myotis punicus</td><td>0.00</td><td>0.00</td><td>0.00</td><td>2.66</td><td>2.66</td></tr></tbody></table>

The model including the site and the month as fixed effects with no interaction and the sampling period as a random effect (AICcWt = 0.85, Table 3), provided the best explanation for the overall bat activity than the model with temperature. Bats were most active at Bordj Zemoura (*p* < 0.001), followed by Djaafra (*p* = 0.001), where bats were significantly more active than at the reference site Boumergad (*p* = 0.04) (Figure 2). On the other hand, bat activity in the forests of Chettaba (Aleppo pine and holm oak forests) was not significantly different from bat activity in Boumergad. In terms of temporal variations, most of the activity was recorded between July and October. The peak of activity occurred in August (*p* < 0.001).
<table><caption>**Table 3.** Candidate generalized linear models to investigate predictors of bat activity (month, region, site, temperature and period of the night) in five forests in northeast Algeria from March to November 2023. Model classification is based on the corrected Akaike's Information Criterion (ΔAICc) and Akaike weights (AICcWt). K refers to the number of parameters. The retained model is in bold.</caption><thead><tr><th>Models (Overall Bat Activity)</th><th>K</th><th>AICc</th><th>ΔAICc</th><th>AICcWt</th></tr></thead><tbody><tr><td><strong>Site + Month</strong></td><td>15</td><td><strong>1114.33</strong></td><td><strong>0.00</strong></td><td><strong>0.85</strong></td></tr><tr><td>Site × Temperature</td><td>12</td><td>1117.82</td><td>3.50</td><td>0.15</td></tr><tr><td>Region × Month + Period</td><td>23</td><td>1123.57</td><td>9.24</td><td>0.01</td></tr><tr><td>Region × Month</td><td>20</td><td>1127.21</td><td>12.88</td><td>0.00</td></tr><tr><td>Region + Month</td><td>12</td><td>1129.59</td><td>15.26</td><td>0.00</td></tr><tr><td>Site + Temperature</td><td>8</td><td>1131.26</td><td>16.93</td><td>0.00</td></tr><tr><td>Site × Month + Period</td><td>50</td><td>1134.91</td><td>20.58</td><td>0.00</td></tr><tr><td>Site × Month</td><td>47</td><td>1135.58</td><td>21.25</td><td>0.00</td></tr><tr><td>Region × Temperature</td><td>6</td><td>1136.14</td><td>21.82</td><td>0.00</td></tr><tr><td>Region + Temperature</td><td>5</td><td>1140.67</td><td>26.34</td><td>0.00</td></tr><tr><td>Site × Hygrometry</td><td>12</td><td>1151.02</td><td>36.70</td><td>0.00</td></tr><tr><td>Site × Hygrometry</td><td>8</td><td>1152.10</td><td>37.77</td><td>0.00</td></tr><tr><td>Site</td><td>7</td><td>1155.73</td><td>41.40</td><td>0.00</td></tr><tr><td>Region + Hygrometry</td><td>5</td><td>1159.21</td><td>44.88</td><td>0.00</td></tr><tr><td>Region × Hygrometry</td><td>6</td><td>1160.26</td><td>45.94</td><td>0.00</td></tr><tr><td>Region</td><td>4</td><td>1164.63</td><td>50.31</td><td>0.00</td></tr><tr><td>Null</td><td>2</td><td>1170.57</td><td>56.24</td><td>0.00</td></tr></tbody></table>

<figure><img src="image_6.png" /><figcaption>**Figure 2.** Monthly variations in overall bat activity across five forests in northeast Algeria according to the best models (Tables 3 and 4) using the number of passes as a measure of activity level. Z: Bordj Zemoura, B: Boumergad, D: Djaafra, CH1: Chettaba Aleppo pine forest, CH2: Chettaba holm oak forest. The period was included as a random effect in the predictions.</figcaption></figure>
<table><caption>**Table 4.** Candidate generalized linear models to explain *Pipistrellus kuhlii* activity (month, region, site, temperature and period of the night) in five forests in northeast Algeria from March to November 2023. Model classification is based on the corrected Akaike's Information Criterion (ΔAICc) and Akaike weights (AICcWt). K refers to the number of parameters. The retained model, according to the principle of parsimony and an AICc < 2, is in bold.</caption><thead><tr><th>Models (P. kuhlii)</th><th>K</th><th>AICc</th><th>ΔAICc</th><th>AICcWt</th></tr></thead><tbody><tr><td>Region × Month + Period</td><td>23</td><td>865.37</td><td>0.00</td><td>0.55</td></tr><tr><td><strong>Site + Month</strong></td><td><strong>15</strong></td><td><strong>866.18</strong></td><td><strong>0.82</strong></td><td><strong>0.37</strong></td></tr><tr><td>Region × Month</td><td>20</td><td>869.86</td><td>4.49</td><td>0.06</td></tr><tr><td>Region + Month</td><td>12</td><td>872.09</td><td>6.72</td><td>0.02</td></tr><tr><td>Site × Temperature</td><td>12</td><td>885.06</td><td>19.69</td><td>0.00</td></tr><tr><td>Region × Temperature</td><td>6</td><td>887.77</td><td>22.40</td><td>0.00</td></tr><tr><td>Site × Month + Period</td><td>50</td><td>889.72</td><td>24.36</td><td>0.00</td></tr><tr><td>Site × Month</td><td>47</td><td>891.28</td><td>25.92</td><td>0.00</td></tr><tr><td>Region + Temperature</td><td>5</td><td>891.88</td><td>26.52</td><td>0.00</td></tr><tr><td>Site + Temperature</td><td>8</td><td>893.99</td><td>28.63</td><td>0.00</td></tr><tr><td>Region + Hygrometry</td><td>5</td><td>913.59</td><td>48.23</td><td>0.00</td></tr><tr><td>Site × Hygrometry</td><td>12</td><td>915.44</td><td>50.07</td><td>0.00</td></tr><tr><td>Region × Hygrometry</td><td>6</td><td>915.68</td><td>50.32</td><td>0.00</td></tr><tr><td>Region</td><td>4</td><td>916.60</td><td>51.23</td><td>0.00</td></tr><tr><td>Hygrometry + Site</td><td>8</td><td>916.72</td><td>50.35</td><td>0.00</td></tr><tr><td>Site</td><td>7</td><td>918.32</td><td>52.96</td><td>0.00</td></tr><tr><td>Null</td><td>2</td><td>918.99</td><td>53.63</td><td>0.00</td></tr></tbody></table>

For *P. kuhlii* activity, similarly to the overall bat activity, we selected the model including the month and the site with no interaction (AICcWt = 0.37, Table 4) as the most appropriate as it included only two variables versus the two-way interaction between site and month and period for the model with the lower AICc, according to the principle of parsimony [48]. Moreover, the AICc difference between these models was small (ΔAICc = 0.82), and the model including the region is possibly biassed by the use of different detector types (Batlogger/Petterson). Month was a better predictor then temperature. Among the sites, only the holm oak forest of Chettaba was significantly different (p = 0.03) from the reference site Boumergad (Figure 3). Bat activity was significantly higher from June to October, with a peak in August (p < 0.001), and a secondary peak in October.

For *E. isabellinus*, the second most active bat species in our study, the site and the month (with no interaction) are also key predictors of bat activity (AICcWt = 0.82, Table 5). Flight activity varied significantly across sites (Figure 4). Bordj Zemoura (p < 0.001) and Djaafra (p < 0.001) exhibited higher levels of bat activity compared to Boumergad. In contrast, the activity in Chettaba holm oak forest (p = 0.189) and Aleppo pine forest (p = 0.128) were not significantly different from the reference site. Bat activity peaked significantly in August (p = 0.009), while a significant decline can be observed in November (p = 0.005). Activity in October tended to decrease, though this result was marginally significant (p = 0.071). For the months of April (p = 0.747), May (p = 0.543), June (p = 0.635), July (p = 0.264), and September (p = 0.213), bat activity was not significantly different from activity measured in March.
<figure><img src="image_7.png" /></figure>

**Figure 3.** Monthly variations in *Pipistrellus kuhlii* activity across five forests in northeast Algeria according to the best models (Table 4) using the number of passes as a measure of activity level. The period was included as a random effect in the predictions.

<figure><img src="image_8.png" /></figure>

**Figure 4.** Monthly variations in *Eptesicus isabellinus* activity across five forests in northeast Algeria according to the best models (Table 5) using the number of passes as a measure of activity level. The period was included as a random effect in the predictions.
<table><caption>**Table 5.** Candidate generalized linear models to explain *Eptesicus isabellinus* activity (month, region, site and temperature) in five forests in northeast Algeria from March to November 2023. Model classification is based on the corrected Akaike's Information Criterion (ΔAICc) and Akaike weights (AICcWt). K refers to the number of parameters. The retained model is in bold.</caption><thead><tr><th>Models</th><th>K</th><th>AICc</th><th>ΔAICc</th><th>AICcWt</th></tr></thead><tbody><tr><td><strong>Site + Month</strong></td><td>15</td><td><strong>479.14</strong></td><td><strong>0.00</strong></td><td><strong>0.92</strong></td></tr><tr><td>Site + Temperature</td><td>8</td><td>484.04</td><td>4.91</td><td>0.08</td></tr><tr><td>Site</td><td>7</td><td>498.75</td><td>19.61</td><td>0.00</td></tr><tr><td>Region × Temperature</td><td>6</td><td>507.98</td><td>28.84</td><td>0.00</td></tr><tr><td>Region × Month</td><td>20</td><td>514.33</td><td>35.19</td><td>0.00</td></tr><tr><td>Region + Month</td><td>12</td><td>514.36</td><td>35.23</td><td>0.00</td></tr><tr><td>Region + Temperature</td><td>5</td><td>516.66</td><td>37.52</td><td>0.00</td></tr><tr><td>Site × Month</td><td>47</td><td>520.78</td><td>41.64</td><td>0.00</td></tr><tr><td>Region</td><td>4</td><td>524.28</td><td>45.15</td><td>0.00</td></tr><tr><td>Null</td><td>2</td><td>542.85</td><td>63.72</td><td>0.00</td></tr></tbody></table>

# 4. Discussion

The aim of our study was to explore the seasonal variations in bat activity in South Mediterranean forests. This was quite challenging as the authors in [29] reported that forests were the least visited sites by bats among the habitat types they sampled in a nearby area. A similar result was published for Spain [49]. The lack of potential prey, an explanation for low bat activity that was suggested for cereal fields [50], is very unlikely to apply to forests. These may be less attractive to bats that forage in open space and may mainly be visited by species exhibiting high maneuverability, which typically emit very weak echolocation calls [37], and are therefore difficult to detect: rhinolophids, *Plecotus*, and most *Myotis* species [51,52].

Our results show that bat activity is mainly shaped by seasonality, including month and site. There was a critical challenge in interpreting our findings because of the limited baseline data in the area. Furthermore, the monitoring design (active recording by one observer) and the use of two different detectors were methodological limitations. Active acoustic monitoring induces short recording duration, which significantly lowers the probability of detection for some species [53,54]. However, mobile acoustic transects could detect more bat activity than fixed-point surveys as it was reported for open landscapes [54,55]. The use of one device per site and night is another limitation as it can often miss some bat species, mainly in woody vegetation [56–58]. An optimal field monitoring would include several passive detectors set for several nights on the ground, in the canopy, and in forest clearings [59]. Lastly, the site effect must be carefully considered due to the pitfalls of comparing datasets collected with two different bat detector brands. However, both detectors were time expansion detectors, and were used in the same field design.

## 4.1. *Bat Assemblage Composition*

Despite those possible pitfalls, bat assemblages were quite similar among the five forests, except the noticeable lower species richness in the suburban forest of Boumergad. Such result was reported in central Spain where species richness was lowered by urbanization despite the presence of synanthropic species [60]. This sensitivity of bats to urbanization was reviewed in the last decade [61]. In our dataset, except one *Myotis* species, the assemblage of this suburban forest is composed of the most widely distributed species in Northwestern Africa [62–65], particularly in the cities [29].

These species were also the most acoustically detected, with *P. kuhlii* being the main one, particularly in Boumergad, due to its synurbic adaptations [66], but it is also often the most active species in the whole Mediterranean area [22,25,67–72], even in arid
zones [39,73]. The second most active species was *E. isabellinus* at Bordj Zemoura and Djaafra, and *T. teniotis* in Boumergad and Chettaba forests. As the calls of the latter are easy to detect and identify, the use of two different bat detectors was probably not a bias. Similarly, *T. teniotis* was also the second most active species in the city of Constantine, nearby Chettaba forests [29], whereas *E. isabellinus* was the second most active species in Tunisian studies [25,39,50,74,75], in coastal forests of Morocco [27], and over small ponds in dry Mediterranean forests of Spain [76]. *H. savii* was the third most acoustically detected in Djafra, probably because of its mountainous nature [77]. It was also particularly active in two previously sampled western Algerian regions [21] and outside the city of Constantine, but not in the forests [29]. *P. pipistrellus* and *M. schreibersii* were mainly recorded in the Chettaba Aleppo pine forest. Both species were previously recorded in the Constantine area, being less active than the previous ones [29]. On the contrary, *P. pipistrellus* was the most active bat species in coastal Morocco [27] and in various surveys in Spain [76,78]. *M. schreibersii* can be considered “locally frequent” as in Jbel Mghilla National Park (Tunisia) [23]. The activity of the other species, which are more difficult to detect (see above), cannot be compared among sampled sites and with other bat studies in Northwestern Africa.

## 4.2. Spatial and Temporal Variation in Bat Activity

The highest bat activity was recorded in Bordj Zemoura forest, which is an open forest suitable for a large number of species, including short-range, mid-range, and long-range echolocators foraging from the ground to over the canopy [79]. In a nearby area, Djaafra forest, which is higher and denser, with taller trees, exhibited the second highest bat activity. Chettaba forests, 300 km apart, are similarly dense, and mainly differ from each other by the tree composition (Aleppo pine vs. holm oak). In conifer-dominated landscapes, bat activity was more closely related to forest structural complexity and prey availability than to tree species composition [80]. Similar findings were reported in several different forest types [24,81,82]. On the contrary, an increasing richness with the abundance of conifers and no noted effects of structure were found in a French pine forest [83]. At last, in these four Algerian forests, no relation could be inferred between tree height and bat activity, as it was previously reported [26]. Only the tree density seemed to slightly influence bat activity. A similar result was recorded in southeast Spain [49]. In temperate pine forests, higher bat activity was recorded in young stands and open areas of pine forests [84,85], due to higher prey availability [86,87]. However, some studies have shown higher insect availability inside or at the edges of stands [87–90]. Other stand parameters may be more important than the availability of insects [87,91,92].

Located in an urban environment, Boumergad forest is exposed to multiple anthropogenic pressures that significantly influence bat activity. This finding aligns with broader research about the negative impact of urbanization on bats [61,93]. Artificial light modifies nocturnal insect behavior and abundance [94], while noise pollution deteriorates the echolocation efficiency of bats [95].

Bat activity was significantly higher during summer, particularly in August, and early autumn among all sites, while in arid areas of Tunisia and Algeria, a lower bat activity was reported in August than in the previous and next months [39,73]. This low summer activity was related to shallow torpor during the hottest days, whereas nightly temperature is never so high in the surveyed Mediterranean forests, which grow to heights between 900 and 1200 m a.s.l. In southeast Spain, Lison and Calvo [76] reported an increasing bat activity in forest from June to September, and a parallel decreasing activity at ponds and emergence of insect prey [96,97], suggesting a displacement or widening of the hunting areas [76]. Contrary to our hypothesis, the energy requirements of pregnancy do not imply higher spring activity of bats which give birth from March to June in North Africa [98,99].
Lactation in July could increase the energy demand but it is more likely that forests offer more insect prey as summer drought increases in open areas [100–102].

### 4.3. *Spatial and Temporal Activity of P. kuhlii and E. isabellinus*

Most of the overall bat activity in the five studied forests is allotted to *P. kuhlii*, with a lower value in Djaafra forest, where *H. savii* and *E. isabellinus* were particularly active, and almost in Boumergad forest. Kuhl's pipistrelle is a generalist open air aerial insectivore that may utilize cluttered environments as well, as long as clutter density is not very high [103]. The high level of *P. kuhlii* activity in this suburban forest highlights the synanthropic behavior of this species [61,66,104]. The lower activity in Djaafra forest could be related to interspecific resource competition as the three species (*P. kuhlii*, *H. savii* and *E. isabellinus*) share the same guild [105,106], and forage on flying insects, mainly Lepidoptera [107].

The observed seasonal activity pattern of *P. kuhlii* among sites aligns with its reproductive biology and energy requirements in Mediterranean climates [108], with low activity in early spring, followed by an increase as the season progresses. The peak of activity in August, also recorded in Tiaret [21], can be related to young gaining independent flight [108] after parturition, usually occurring from mid-May to late June [100,109,110] or sometimes earlier in Lebanon, Syria, Iran, Libya, and Saudi Arabia [111–114]. August is also the molting season for this species, an energetically stressful period requiring more active foraging [115]. The second lower peak of activity in October is probably related to the mating season, and to the storing of reserves [100].

*Eptesicus isabellinus* was mainly active in Bordj Zemoura and Djaafra forests, and rarely detected in Chettaba forests, and mostly in the suburban Boumergad forest. Even if this species can roost in buildings [116], its foraging areas are located far from human settlements [117]. The low levels of activity in Chettaba forests are more difficult to explain. Neither altitude nor tree density or composition can be related to the difference from Bordj Zemoura and Djaafra forests. In fact, *E. isabellinus* was poorly recorded in Constantine area [29] and in Tiaret region (western Algeria) [21], contrary to most areas surveyed in Tunisia [20,25,50,74]. Interestingly, Djaafra forest is located at an altitude of 1200 m, over the 300–600 m optimal range for the species in Spain [117].

The temporal distribution of *E. isabellinus* activity is marked by a single, very sharp peak in August, mostly in Bordj Zemoura and Djaafra forests. A similar pattern was reported from Andalucía, where it was correlated with temperature and insect abundance [118]. Moreover, a different diet between the pregnancy and the lactation—post-lactation period was recorded [119], supporting a displacement of foraging areas from agricultural zones to an Aleppo pine forest. In southeast Spain, the activity of *E. isabellinus* in forest increased from June to September, when it decreased at ponds [76]. So, even if *E. isabellinus* occurs in Mediterranean areas that consist mainly of forests and scrublands, it prefers to forage close to watercourses and ponds [117] or also in pastures with trees [118] and agricultural fields [49]. In the arid Bou Hedma National Park (Tunisia), a different seasonal activity pattern was found with two peaks in late spring—early summer and early autumn, which exclude the hottest summer months, and is more related to the biological cycle of the bat [39,120]. In this park, lactation occurs in May and June, as it was observed in other parts of Tunisia [121], and in Libya [111].

## 5. Conclusions

Despite limitations, this study is the first to explore the spatial and seasonal activity of bats in South Mediterranean forests, a poorly documented topic, highlighting the importance of forests as habitats for several species, at least during some periods of the year. Our results suggest that forests should deserve special attention in bat conservation
efforts throughout the region. Nevertheless, more studies are needed to improve our knowledge on the relation between forests and roosting/foraging bats, including the impact of management [10] and fires [122,123], and develop conservation strategies in order to limit the consequences of the recent environmental changes.

**Supplementary Materials:** The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/d17110750/s1. Table S1: Summary of multicollinearity checking for key predictor variables in models for overall bat activity, *Pipistrellus kuhlii* and *Eptesicus isabellinus* activity. Figure S1: DHARMa residual diagnostics—QQ plot and residuals vs. predicted values for the best model of overall bat activity. No significant deviations were detected. Figure S2: DHARMa residual diagnostics—QQ plot and residuals vs. predicted values for the best model of *Pipistrellus kuhlii* activity. No significant deviations were detected. Figure S3: DHARMa residual diagnostics—QQ plot and residuals vs. predicted values for the best model of *Eptesicus isabellinus* activity. No significant deviations were detected.

**Author Contributions:** Conceptualization and methodology, A.B., M.A.-A., F.Z.B., F.B. and S.A.; investigation, A.B. and M.A.-A.; data curation, A.B., M.A.-A., I.K. and M.B.; writing—review and editing, A.B., M.A.-A., F.Z.B., F.B., I.K. and S.A. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

**Institutional Review Board Statement:** Not applicable.

**Data Availability Statement:** Raw data can be obtained on request to the first author.

**Acknowledgments:** The authors are grateful to Ferhat Boukhari and Abdelnour Tabi for their help during field trips, as well as Ridha Dalhoumi. The authors would also like to acknowledge all the members of the Forest Conservation of Bordj Bou Arreridj for their invaluable assistance and protection. Smail and Hamza Abbaz hosted the second author in the Chettaba forest.

**Conflicts of Interest:** The authors declare no conflicts of interest.

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