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Article

Insectivorous Bats in Eastern Mediterranean Planted Pine Forests—Effects of Forest Structure on Foraging Activity, Diversity, and Implications for Management Practices

by
Claudia Allegrini
*,
Carmi Korine
and
Boris R. Krasnov
Mitrani Department of Desert Ecology, Swiss Institute of Dryland, Environmental and Energy Research, Jacob Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, Midreshet Ben-Gurion 8499000, Israel
*
Author to whom correspondence should be addressed.
Forests 2022, 13(9), 1411; https://doi.org/10.3390/f13091411
Submission received: 7 July 2022 / Revised: 24 August 2022 / Accepted: 30 August 2022 / Published: 2 September 2022
(This article belongs to the Special Issue Effects of Forest Management Practices on Bat Habitat and Community Structure)
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Abstract

:
Bats are primarily forest mammals and forest structure may affect their communities through the level of vegetation clutter. Pine plantations are typically even-aged managed forests that lack structural complexity. However, an understory layer can enhance the heterogeneity of these forests, making them suitable for several animal taxa. We hypothesized that species composition, richness, and foraging activity of insectivorous bats in pine plantations vary according to forest structure, specifically with the density of the understory. We measured pine density, Diameter at Breast Height (DBH), canopy closure, and vegetation cover of 29 pine (Pinus halepensis) plantations of the Judean Lowlands, Israel, and collected acoustic data on resident bats. We found that bat species richness and total activity increased in forests with large tree DBH and dense shrubs. Cluttered-habitat species foraged preferentially in forests with large tree DBH and high pine density, while open-habitat species preferred forests with well-developed canopies and dense shrubs. Pipistrellus pipistrellus and Eptesicus serotinus foraged in mature forests with well-developed bushes and these species are endangered in Israel. We conclude that mature planted pine forests with a well-developed under-canopy are suitable foraging grounds for insectivorous bats. Management plans for planted pine forests should consider our findings to support bat populations, including rare and endangered species.

1. Introduction

Forests are one of the most important habitats for insectivorous bat species: they offer protection from predators and vital resources, such as roosting and feeding grounds [1,2]. Many bat species roost in trees, which offer different cavity types, foliage, and suitable microclimatic conditions [1,3]. Bats use trees as shelters during adverse weather, as feeding perches, and for orientation cues. Forest edges and tree lines, for example, are excellent navigational aids while commuting between feeding and roosting sites [4]. Forests are also stopover sites for migratory bats and are used for reproduction and hibernation [5]. Furthermore, depending on the composition and abundance of vegetation, forests host a wide range of insect species and other arthropods important to insectivorous bats. As such, forest loss is a major threat associated with bat population declines, including endangered species [6].
Forest structure and complexity are pivotal for the diversity of wildlife communities in both managed and unmanaged forests [7,8,9]. Forest structure usually consists of the main canopy cover of uneven-aged trees, an understory layer of young trees, and a lower layer of bushes, shrubs, and grass, which together form the under-canopy layer. This vertical structure determines the complexity of the forest interior, including its level of clutter [10]. Clutter, i.e., the density and complexity of the surrounding vegetation, is an important selective factor for many species, including birds and bats, which are particularly affected when flying at different heights in the forest interior [11,12]. Forestry management practices have been receiving growing attention in conservation studies since they can impact the forest structure and complexity in different ways: for instance, thinning can positively affect bat activity [13,14]. Another common practice is the removal of snags and dead trees, which are fundamental roosting and nesting sites for protected species of bats and birds [15,16,17,18,19]. Bats are particularly affected by the canopy density, tree density, and vegetation composition [18,20].
As a consequence of the level of vegetation clutter, different species of bats exploit different habitats within a forest. Some species prey in the forest interior, flying in dense vegetation and gleaning insects from surfaces, while others prey on the wing or through perch-hunting, along forest edges and above the canopy [1]. Bats’ selection of a foraging site reflects their sensory and motor adaptations, and vegetation clutter has been a significant constraint during their evolution [21]. Wing morphology has been theorized to reflect bat foraging strategies [1,22]. Therefore, differences in wing morphological features may be predictors of habitat use. Bats characterized by fast flight and low maneuverability (high aspect ratio and wing loading) may have limited access to cluttered forest interior and forage mainly in open-space habitats [23,24]. Slow and more maneuverable species (low aspect ratio and wing loading) may forage in the forest interior, while edge foragers have intermediate characteristics; thus, they are more maneuverable than open-space foragers but still quite fast in flight [24]. Echolocation characteristics of bats also correlate with the foraging strategy and the position of the prey item, e.g., in open areas or dense vegetation [22,25,26]. Therefore, to some extent, echolocation behavior and call structure may predict habitat use by bats and their foraging style. The increasing level of clutter has also worked as a selective factor, shaping different forms of echolocation. Only highly specialized bats can fly and forage in highly cluttered environments, while less specialized bat species fly mainly in open-space environments [1,21]. For instance, as clutter distribution varies with canopy height, foraging intensity varies as well, and bats may display vertical stratification [27,28]. Therefore, structural clutter plays a significant role in determining the composition of bat assemblages and activity in forests [29,30,31].
Several studies have found that tree stand age affects the species composition and activity of bats through its different structural and ecological characteristics, including levels of vegetation clutter, tree size and density, and abundance and composition of insect fauna [18,32,33]. For example, when considering tree size, forest-dwelling bats roost preferentially in cavities of tall trees with large trunk diameters (diameter at breast height, DBH) or snags [34,35,36]. In addition, bats use forest gaps and linear elements of the forest for orientation when foraging and commuting [37]. As such, studying the association between bat species composition and forest structure may provide valuable insights for bat species protection and forest management practices focused on enhancing biodiversity. Most studies on the influence of forest structure on species composition and activity of insectivorous bats have been conducted in fragmented tropical forests [38,39,40], or managed stands of temperate forests. These studies focused on the consequences of habitat loss or the effect of different types of management in native forests [4,24,41]. Planted forests are still a concern from an ecological point of view since their plant diversity is usually low, and the stand structure lacks complexity [42]. Nevertheless, they are largely used to control erosion, as multifunctional forests for recreational purposes, carbon sequestration, or wood production for commercial purposes [43,44,45]. To date, an increasing number of studies dedicated to the impact of plantations on biodiversity have shown a positive response of many taxa, mainly when their management is focused on increasing the complexity of the understory layer [43,44,45,46,47]. However, Rodriguez-San Pedro and Simonetti [48] compared bat activity in pine plantations of Monterey pine (Pinus radiata) with and without native understory and did not find significant differences in their activity.
The primary purpose of our research was to gain insights into forest management practices that prioritize bat conservation in eastern Mediterranean planted pine forests. Pine plantations in Israel are the result of more than 80 years of afforestation projects by the Keren Kayemeth LeIsrael–Jewish National Fund (KKL-JNF) in the Mediterranean zone. They were initially planted as monocultures of two pine species highly suitable for degraded arid and semiarid environments: the native Aleppo pine (Pinus halepensis) and the exotic Calabrian pine (Pinus brutia Ten.) [49]. The current management policy of KKL-JNF is a minimal intervention to promote local species and natural processes, using the most recent developments in forestry and ecology [49]. As a result, these plantations consist of coniferous forest stands, whose structure and vegetation composition varies depending on the age and spatial distribution throughout the country. Young forests (less than 30 years old) are composed of dense, short pine trees of small diameter (~19 cm) and are mainly without an understory layer. Mature forests (30–80 years old) are less dense, and the pine trees are more than 15 m in height, with a large diameter (over 100 cm), and developed canopies, with or without an understory layer [50]. Furthermore, these pine plantations are subject to various disturbances, such as fire, resulting in the decline of shrubland and woodland areas [51].
Most of the species of bats from the Judean Lowlands, Israel, are locally endangered and occur at the southern edge of their range of distribution, e.g., forest-dwelling species of European bats from the Vespertilionidae and Rhinolophidae families. Therefore, as range-edge populations, they might show local adaptations to the unique local environmental conditions [52]. We hypothesized that species composition and foraging activity of the bats varied according to forest structure and, specifically, with the density of the understory, and predicted that small, maneuverable species would show a preference for forest sites with a dense understory as these bats will be less affected by its density. In contrast, larger and less maneuverable species will select less dense forest sites. In addition, we predicted that species richness and total activity would be greater in forests with large trees (high DBH) than in forests with small trees [53].

2. Materials and Methods

2.1. Research Area

The study area consists of 13 planted pine forests in the Judean Lowlands, Israel, characterized by stands of different ages and forest structures. The native understory vegetation includes species of the typical Mediterranean maquis: (1) woody vegetation of dwarf shrubs (e.g., Cistus creticus L., Sarcopoterium spinosum L.); shrubs (e.g., Rhamnus lycioides L., Pistacia lentiscus L.); vines (e.g., Ephedra foeminea Forssk., Smilax aspera L.); trees (e.g., Quercus calliprinos Webb., Quercus ithaburensis Decne.), Pistacia palaestina Boiss., Pistacia lentiscus L., and Styrax officinalis L. [54]. All studied sites are situated in the warm Mediterranean region, characterized by 19 °C of mean annual temperature and a yearly average rainfall from 400 mm in the center to 1000 mm in the north (Israel Meteorological Service data) [55].

2.2. Acoustic Monitoring and Sound Analysis

We used passive acoustic monitoring simultaneously for four consecutive nights in 29 plots located at the 13 planted pine forests during summer (July–August) 2019. We avoided recording during the period of full moon ± 5 days. We placed an ANABAT II detector (Titley Electronics, Ballina, NSW, Australia) at 1.5 m height on the trunk of a pine in the center of each of the 29 plots, with microphones pointing upwards, oriented at a 45° angle towards a gap in the canopy. The sensitivity of each detector was calibrated to a value of 7. We selected and analyzed manually good quality calls, performing a visual check of all recordings with Analook software (version 4.2n, 2017). A call was considered of good quality if the resulting spectrogram clearly showed both the shape and frequency of the emission. We recorded bat species composition at each forest site based on the species identified, while bat species richness was calculated as the total number of species detected in each forest site. We manually identified bat calls and species according to known species-specific acoustic characteristics [56,57,58]. Due to the high call overlap between the species of the genus Myotis, we could not separate them, and the Myotis species were grouped.

2.3. Measurement of Forest Structure

In each forest, we selected one to four plots according to their tree size, age, and the characteristics of their understory layer (height, density, and species composition). The 29 plots consist of the following four categories: (1) seven mature forest plots with well-developed understory (small trees and bushes higher than 1.5 m, between 30% and 100% cover), (2) eight mature forest plots with less developed understory (small trees and bushes higher than 1.5 m, <30% cover), (3) seven mature forest plots without understory (shrubs less than 40 cm in height, without small trees or bushes), and (4) seven young forest plots (Figure 1). Measurements were taken during summer 2019.
The distance between plots was at least 500 m. In each forest plot, we selected a central pine tree as a data point where we recorded bat echolocation calls and measured the vegetation structure on four transects placed on the cardinal points (N, S, E, W) perpendicular to each other. The description of forest structure variables is presented in Table 1.

2.4. Statistical Analysis

We analyzed the data using Generalized Linear Models [60]. Prior to analysis, we excluded all the nights with detector failure and carried out data exploration following the protocol described in Zuur et al. [61]. Dependent variables included species richness, total activity (passes per night), and the activity of the cluttered-habitat species (Pipistrellus pipistrellus, Pipistrellus kuhlii, Hypusgo savii, Eptesicus serotinus), and the open-habitat species (Rhinopoma microphyllum, Rhinopoma cystops, Tadarida teniotis, Taphozous nudiventris, and Miniopterus schreibersii). We excluded the highly cluttered-habitat species (genus Myotis and Rhinolophus) from the analysis, as the number of echolocation calls recorded was lower than 30. All the models were fitted using R (version 4.0; [62]), function “glm” of the package “stats” [63]), using a Poisson distribution with log-link function due to count data, except for the model for species richness. For the latter, we used a Gaussian distribution with an identity link function after testing for normality through a Shapiro–Wilk test. Whenever the Poisson model was overdispersed, we used a negative binomial distribution with a log-link function, function “glm.nb” of the package MASS [64]. As explanatory variables, we used the density of the understory and DBH as a proxy for the age of forests. After excluding highly correlated variables (r > 0.8, p < 0.05), other covariates included in the model were pine density, accessibility index, canopy closure index, and percentage of vegetation cover, including bushes, shrubs, and clearings (“no cover”). The percentage of clearings (“no cover”) was not included in the final models because of collinearity. For each dependent variable, we considered models with the same explanatory variables and covariates but different error distributions. We chose the model with the lowest Akaike’s Information Criterion (AIC) value, corrected for small sample size [65]. To test the significance and main effect of the explanatory variables, we used a likelihood ratio test to compare the model’s fit with that of a null model, which included only secondary covariates [66]. We z-transformed the explanatory variables to enhance the interpretability of the resulting coefficients. We checked for model stability by comparing the whole model to the one corresponding while removing a variable at a time. Thereafter, we checked for the presence of collinearity, determining the Variance Inflation factors [67]) for the linear version of the model. We derived the confidence intervals using the function “confint” for the goodness of the estimates.

3. Results

3.1. Species Occurrences

We recorded 4876 bat passes and identified 12 species: 7 cluttered-habitat species (Pipistrellus pipistrellus, P. kuhlii, Hypsugo savii, Eptesicus serotinus, Rhinolophus ferrumequinum, R. hipposideros, and R. euryale), including Myotis as a group, and five open-habitat foragers (Rhinopoma microphyllum, Rhinopoma cystops, Tadarida teniotis, Taphozous nudiventris, and Miniopterus schreibersii) (Table S2 in Supplementary Materials). The most common species was P. kuhlii, found in all the forest plots, followed by T. nudiventris, E. serotinus, T. teniotis, and P. pipistrellus. Rhinopoma spp and Rhinolophus spp were among the rarest species in the recordings (Table S2).

3.2. Species Richness and Forest Structure

DBH influenced bat species richness (full-null model comparison: χ2 = 14.32, df = 21, p = 0.002). Specifically, species richness increased with increasing DBH and shrub cover (Table 2, Figure 2).

3.3. Total Activity and Forest Structure

Total activity positively increased with DBH, pine density, and shrubs cover (full-null model comparison: χ2 = 8.87, df = 20, p = 0.011, Table 2, Figure 3), while all other variables had no significant effect on total activity (Table 2).

3.4. Species Composition and Forest Structure

Cluttered-habitat species activity (χ2 = 10.14, df = 21, p = 0.001) increased with increasing DBH and pine density (full-null model comparison: χ2 = 10.14, df = 21, p = 0.001, Table 2, Figure 4), while all other variables had no significant effect on cluttered-habitat species. Activity of open-habitat species positively increased with canopy closure and shrub cover (full-null model comparison: χ2 = 4.25, df =21, p = 0.004, Table 2, Figure 5).

3.5. Species-Level Effects of Forest Structure Variables

Foraging activity of P. kuhlii and P. pipistrellus increased with increasing DBH (full-null model comparison: χ2 = 7.38, df = 21, p = 0.006, Table 3). In addition, the foraging activity of P. pipistrellus also increased with pine density, accessibility, and bush cover (full-null model comparison: χ2 = 5.09, df = 21, p = 0.02, Table 3). The foraging activity of E. serotinus was positively affected by increased DBH, followed by pine density, accessibility, and percentage of bush cover (full-null model comparison: χ2 = 6.37, df = 21, p = 0.01, Table 3). We do not present results for T. teniotis in Table 3, because none of the variables reached significance.

4. Discussion and Conclusions

4.1. Species Richness and Forest Structure

We tested the effect of several forest structure measures on species composition, species richness, and activity of bats. We predicted that species richness would be the greatest in forest plots with large trees (greater DBH), and our results confirmed this prediction. As previously found in other forest systems, mature pine plantations with wide trunks (31.85–44.60 cm DBH) and well-developed shrubs may favor high species richness of bats [7,18,19,32,68]. To our knowledge, the importance of shrub cover for increasing species richness of bats may be unprecedented. Similar findings are well known for other taxa foraging in pine plantations, such as birds, in Israel and other countries [69,70]. Presumably, this layer offers feeding grounds for arthropods without affecting bat flight in the forest interiors, as it consists primarily of dwarf shrubs (Cistus creticus and Sarcopoterium spinosum) and grass, both hardly exceeding 70 cm in height [49].

4.2. Total Activity and Forest Structure

Diameter at Breast Height significantly enhanced the activity of bats in pine plantations, particularly in forests with a relatively high pine density. Previous studies in plantations of red pines (Pinus resinosa Ait.) [71] in Michigan, USA, found no significant effect of pine density on the activity of bats, presumably due to the low abundance of insects in both thinned and unthinned forest stands [71]. However, in Ponderosa pine (Pinus ponderosa) forests in the southwestern United States, different tree densities had different effects, depending on the species of bats [72]. As suggested by Gonsalves, et al. [73], changes in tree density may affect insect abundance and consequently affect insect prey preferred by bats. Densities of pine trees in our study area varied from 2 to 43 trees in a 60 × 60 m Square (~5 to 119 trees/hm2), which is far below the threshold (3000 trees/ hm2) identified in previous studies that causes a drop in the activity of bats and that may be dependent on the landscape context [74,75].

4.3. Species Composition and Forest Structure

Species composition changed according to the forest structure: cluttered-habitat species foraged preferentially in forests with large DBH and relatively high pine density, which may be explained by their high maneuverability. Large trunk diameters characterize mature forests, which are generally preferred by maneuverable species for the complexity of their under-canopy space. Open-habitat species mainly were active in forests with greater canopy closure and dense shrub cover. Shrub cover may increase insect abundance and diversity, and its density may not affect either guild since it hardly reaches 70 cm above the forest floor.
Analyses of the foraging activity of individual species revealed outcomes that could not be detected by the analysis of total activity and species richness. For example, the foraging activity of Pipistrellus pipistrellus increased with increased pine density, accessibility, and bush cover, confirming that, partly due to its ability to fly in the forest interior, pine plantations with under-canopy native vegetation should be part of conservation plans for this species [75]. The foraging activity of Eptesicus serotinus was mainly affected by greater DBH, confirming that cluttered-habitat species forage preferentially in mature forest stands, with well-developed under-canopy vegetation, as shown by the positive effect of accessibility index and bush cover on the activity of this species. Therefore, the level of clutter in the under-canopy positively affected individual species among cluttered-habitat species (i.e., P. pipistrellus and E. serotinus), contrary to what was observed in managed pine forests of temperate areas of Europe, Chile, and North America (South Carolina) [15,48,76,77].
Despite the availability of studies on the effect of the under-canopy vegetation on bat activity and species richness, the results of previous studies are hardly comparable to the findings of the present study for the following reasons: (1) most of the studies on bats have been conducted in tropical areas, where climatic conditions, bat species, and plant species are different to the local situation [78,79,80]; and (2) studies conducted in similar climatic zones focus on forests of native vegetation, characterized by different forest structure, either of broadleaf tree and plant species or coniferous species of larger sizes with both vertical and in some cases horizontal development that is not comparable to the Aleppo pine plantations [8,9,14,19,81,82]. Studies addressing a similar topic with comparable environmental conditions were carried out in the Mediterranean area of central Chile, in commercial plantations of Pinus radiata, a pine species of similar size to the Aleppo pine [48]. In this study, they compared pine plantations with and without native understory and did not find a significant difference in bats’ activity in different plantations. Furthermore, they did not find a significant difference in activity between plantations and native temperate forests, as long as the level of clutter in the forest interior was kept at an intermediate level of development, either through controlled fire or cutting [48]. The under-canopy vegetation may enhance the quality of insects. However, other studies have found no significant difference in the abundance of insects between forests managed for timber production with different under-canopy development, and, overall, the abundance of insects did not affect total bat activity [23,41].
The importance of the understory layer to compensate for the potential negative effects of plantations on species richness has already been demonstrated in other mammals of medium size, such as kodkods (Leopardus guigna), culpeo foxes (Pseudalopex culpaeus), lesser grisons (Conepatus chinga), and Southern pudu deer (Pudu puda) [83]. We demonstrated that, although the understory layer did not have a significant effect, other forest structure features, namely, tree density, bushes, shrubs, and for certain species the accessibility to the forest interior, are essential to enhance bat activity. However, management actions to control the level of clutter to keep it at an intermediate level are recommended, as vegetation clutter has adverse effects on species richness and activity of all the bat species [11,12,14,18].

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/f13091411/s1, Table S1: Bat passes per species and percentage of sites in which they occurred in pine plantations of the Judean Lowlands, Israel (total number of sites = 29); Table S2: Parameters used for the species identification: frequencies (middle, start, end) and call structure for each bat species; Table S3: Forest structure parameters of each type of forest (mean value and standard deviation), measured as % of cover in pine (Pinus halepensis) plantations of Judean Lowlands, Israel; Table S4 Full list of models with respective Akaike Information Criterion values modified for small sample size (AICc). Variable names and description are provided in the Materials and Methods section.

Author Contributions

C.A., B.R.K. and C.K. conceived the ideas; C.A. and C.K. designed the methodology; C.A. and C.K. collected the data; C.A. analyzed the data and led the writing of the manuscript. All authors contributed critically to the drafts and gave final approval for publication. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by grants from the Keren Kayemeth LeIsrael–Jewish National Fund to CK (600200117).

Data Availability Statement

Data will be available from the Mendeley Digital Repository.

Acknowledgments

We would like to acknowledge Uzi Shamir for collecting acoustic data in the field, and Keren Kayemeth LeIsrael–Jewish National Fund for providing the data on the age of forests. This is paper number 1117 of the Mitrani Department of Desert Ecology.

Conflicts of Interest

The authors declare no conflict of interest.

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Forests 13 01411 g001 550
Figure 1. Forest structure measurements and acoustic sampling were carried out in 29 forest plots in the Judean Lowland, Israel, in summer 2019. Study area is shown on the right panel while studied plots are shown in the left panel.
Figure 1. Forest structure measurements and acoustic sampling were carried out in 29 forest plots in the Judean Lowland, Israel, in summer 2019. Study area is shown on the right panel while studied plots are shown in the left panel.
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Forests 13 01411 g002 550
Figure 2. Effect of (a) Diameter at breast height (DBH) and (b) shrub cover on species richness of insectivorous bats in pine plantations in the Judean Lowlands, Israel (total number of sites = 29). The dashed line depicts the fitted model, all other predictors being at their average. The points depict the original data. The dotted lines depict CIs.
Figure 2. Effect of (a) Diameter at breast height (DBH) and (b) shrub cover on species richness of insectivorous bats in pine plantations in the Judean Lowlands, Israel (total number of sites = 29). The dashed line depicts the fitted model, all other predictors being at their average. The points depict the original data. The dotted lines depict CIs.
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Figure 3. Effect of (a) Diameter at breast height (DBH) and (b) pine density on total activity of insectivorous bats in pine plantations in the Judean Lowlands, Israel (total number of sites = 29). The dashed line depicts the fitted model, all other predictors being at their average. The points depict the original data. The dotted lines depict CIs.
Figure 3. Effect of (a) Diameter at breast height (DBH) and (b) pine density on total activity of insectivorous bats in pine plantations in the Judean Lowlands, Israel (total number of sites = 29). The dashed line depicts the fitted model, all other predictors being at their average. The points depict the original data. The dotted lines depict CIs.
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Figure 4. Effect of (a) Diameter at breast height (DBH) and (b) pine density on foraging activity of cluttered-habitat species of insectivorous bats in pine plantations in the Judean Lowlands, Israel (total number of sites = 29). The dashed line depicts the fitted model, all other predictors being at their average. The points depict the original data. The dotted lines depict CIs.
Figure 4. Effect of (a) Diameter at breast height (DBH) and (b) pine density on foraging activity of cluttered-habitat species of insectivorous bats in pine plantations in the Judean Lowlands, Israel (total number of sites = 29). The dashed line depicts the fitted model, all other predictors being at their average. The points depict the original data. The dotted lines depict CIs.
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Figure 5. Effect of (a) canopy closure and (b) shrub cover on foraging activity of open-habitat species of insectivorous bats in pine plantations in the Judean Lowlands, Israel, during summer 2019 (total number of sites = 29). The dashed line depicts the fitted model with all the other predictors being at their average. The dotted lines are confidence intervals. The points depict the original data.
Figure 5. Effect of (a) canopy closure and (b) shrub cover on foraging activity of open-habitat species of insectivorous bats in pine plantations in the Judean Lowlands, Israel, during summer 2019 (total number of sites = 29). The dashed line depicts the fitted model with all the other predictors being at their average. The dotted lines are confidence intervals. The points depict the original data.
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Table
Table 1. Description of forest structure variables and method used to measure them in pine plantations of the Judean Lowlands, Israel (total number of sites = 29).
Table 1. Description of forest structure variables and method used to measure them in pine plantations of the Judean Lowlands, Israel (total number of sites = 29).
Forest VariablesDescription
Pine densityNumber of pine trees (Pinus brutia or Pinus halepensis) in 60 × 60 m plots around the acoustic sampling point
Canopy closure indexCategories: (a) open (without any trees), (b) semi-closed (trees with few branches, the light can penetrate), closed (dense canopy, the light does not penetrate); measured along 40 m transects with five measurements every 8 m.
Vegetation coverFour categories of vegetation cover that included bushes, trees, understory, and clearings (“no cover”). Additionally, we categorized the shrub cover as follows: 1: 0%–0%, 2: 40%–70%, and 3: 70%–100% and calculated the average value for each plot. We took the measurements along cross-transects (20 m) following the line interception method: with a tape measure recording where each plant begins and ends. We calculated the difference between the beginning and end to obtain the intercept [59]. When these intercepts are added and the sum divided by the total line length, the result is a percent cover estimate for a transect.
Accessibility indexMeasurements of vegetation clutter taken every 8 m along a transect starting from the acoustic sampling point (total: 40 m). We recorded a categorical index at four heights (0.5 m, 2 m, 4 m, and more than 4 m) based on the following five categories: (a) no trees, or trees with 0 branches; (b) 1–2 branches; (c) 3–5 branches; (d) 6–8 branches, and (e) more than eight branches and we calculated the average value for each plot.
Tree diameterMeasured at 1 m and 1.5 m in height (DBH), on 2 pine trees for each direction on the cross-transect (N, S, W, E).
Table
Table 2. The effect of forest structure explanatory variables on species richness, total activity, and foraging guilds (cluttered and open-habitat species) of insectivorous bat species in pine plantations in the Judean Lowlands, Israel (total number of sites = 29). Shown are model exponentiated variable estimates (Est.), lower (2.5%), and upper (97.5%) confidence intervals on parameter estimates, z-values, and p-values of the models.
Table 2. The effect of forest structure explanatory variables on species richness, total activity, and foraging guilds (cluttered and open-habitat species) of insectivorous bat species in pine plantations in the Judean Lowlands, Israel (total number of sites = 29). Shown are model exponentiated variable estimates (Est.), lower (2.5%), and upper (97.5%) confidence intervals on parameter estimates, z-values, and p-values of the models.
ModelExplanatory VariableEst.SE2.5%97.5%z-Valuep-Value
Species richnessIntercept5.100.234.635.5822.480.00
Understory−0.040.32−0.710.63−0.130.90
Pine density0.300.42−0.571.170.710.49
DBH0.900.400.071.742.250.04
Canopy closure0.660.34−0.051.371.920.07
Accessibility−0.730.37−1.490.04−1.970.06
Shrub cover0.630.260.081.182.400.03
Bushes−0.210.24−0.710.29−0.880.39
Total activityIntercept138.770.14105.67182.2435.480.00
Understory1.060.200.721.560.290.77
Pine density2.140.261.293.552.960.00
DBH2.260.251.393.673.300.00
Canopy closure1.180.210.781.780.790.43
Accessibility1.020.230.651.590.080.94
Shrub cover1.430.161.041.972.220.03
Bushes1.090.150.821.460.610.54
Cluttered-habitat speciesIntercept79.060.1559.04105.8829.330.00
Understory1.240.210.821.871.020.31
Pine density1.950.281.133.352.420.02
DBH2.950.271.754.964.080.00
Canopy closure0.900.230.581.40−0.470.64
Accessibility0.850.240.531.37−0.660.51
Shrub cover1.010.170.721.420.070.95
Bushes0.940.160.691.28−0.400.69
Open-habitat speciesIntercept23.470.2014.9336.8818.210.00
Understory0.740.280.391.39−0.420.67
Pine density1.910.370.824.451.650.10
DBH1.790.350.804.011.110.27
Canopy closure2.390.301.214.723.240.00
Accessibility0.910.320.441.90−0.350.72
Shrub cover2.120.231.253.582.640.01
Bushes1.320.210.822.121.740.08
Table
Table 3. The effect of forest structure explanatory variables on activity of five insectivorous bat species in pine plantations in the Judean Lowlands, Israel (total number of sites = 29). Shown are model exponentiated variable estimates (Est.), lower (2.5%), and upper (97.5%) confidence intervals on parameter estimates, z-values, and p-values of the model.
Table 3. The effect of forest structure explanatory variables on activity of five insectivorous bat species in pine plantations in the Judean Lowlands, Israel (total number of sites = 29). Shown are model exponentiated variable estimates (Est.), lower (2.5%), and upper (97.5%) confidence intervals on parameter estimates, z-values, and p-values of the model.
ModelExplanatory VariableEst.SE2.5%97.5%z-Valuep-Value
Pipistrellus kuhliiIntercept49.650.1735.4369.5622.690.00
Understory0.780.260.471.31−0.930.35
Pine density1.640.310.883.061.550.12
DBH2.900.311.585.333.430.00
Canopy closure0.820.240.511.32−0.830.41
Accessibility0.800.270.471.36−0.830.40
Shrub cover0.970.190.661.43−0.150.88
Bushes0.980.180.691.40−0.100.92
Eptesicus
Serotinus		Intercept9.200.235.7814.659.360.00
Understory1.360.340.692.670.890.38
Pine density2.520.461.026.242.010.04
DBH3.530.461.438.692.740.01
Canopy closure0.620.320.331.18−1.460.15
Accessibility2.390.361.174.882.390.02
Shrub cover0.640.280.371.11−1.590.11
Bushes1.550.240.972.481.820.07
Pipistrellus
Pipistrellus		Intercept2.640.331.395.042.950.00
Understory0.920.440.392.18−0.190.85
Pine density6.120.591.9319.373.080.00
DBH7.070.622.1223.613.180.00
Canopy closure2.070.390.964.491.850.06
Accessibility0.340.520.120.95−2.060.04
Shrub cover1.050.350.532.070.140.89
Bushes0.350.370.170.73−2.810.00
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Allegrini, C.; Korine, C.; Krasnov, B.R. Insectivorous Bats in Eastern Mediterranean Planted Pine Forests—Effects of Forest Structure on Foraging Activity, Diversity, and Implications for Management Practices. Forests 2022, 13, 1411. https://doi.org/10.3390/f13091411

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Allegrini C, Korine C, Krasnov BR. Insectivorous Bats in Eastern Mediterranean Planted Pine Forests—Effects of Forest Structure on Foraging Activity, Diversity, and Implications for Management Practices. Forests. 2022; 13(9):1411. https://doi.org/10.3390/f13091411

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Allegrini, Claudia, Carmi Korine, and Boris R. Krasnov. 2022. "Insectivorous Bats in Eastern Mediterranean Planted Pine Forests—Effects of Forest Structure on Foraging Activity, Diversity, and Implications for Management Practices" Forests 13, no. 9: 1411. https://doi.org/10.3390/f13091411

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