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UNIVERSITE DE PERPIGNAN VIA DOMITIA
MASTER "BIOLOGIE INTEGREE : MOLECULES, POPULATIONS ET DEVELOPPEMENT DURABLE"
MENTION PROFESSIONNELLE "BIODIVERSITE ET DEVELOPPEMENT DURABLE"
EFFECTS OF FOREST
COMPOSITION ON
ACTIVITY AND
STRUCTURE OF BAT
COMMUNITIES. LANDES DE GASCOGNE FOREST, SOUTHWESTERN FRANCE
GAÜZERE PIERRE
ANNEE UNIVERSITAIRE 2011 -2012
SOUS LA DIRECTION DE LUC BARBARO ET YOHAN CHARBONNIER
BIOGECO – UMR 1202 INRA BORDEAUX 1
REMERCIEMENTS
« Une âme délicate est gênée de savoir qu'on lui doit des remerciements, une âme grossière, de
savoir qu'elle en doit. »
Friedrich Nietzsche
Au risque de passer pour un grossier personnage, je tiens à remercier tous ceux qui m’ont fait
confiance et m’ont accompagné pour me permettre de réaliser cette étude dans les meilleures
conditions qui soient.
Ainsi je tiens à remercier particulièrement Luc Barbaro et Yohan Charbonnier. Premièrement pour
m’avoir offert l’opportunité de réaliser mon stage de fin d’étude sur un sujet aussi passionnant que
celui ci. D’autre part pour avoir bénéficié d’un encadrement et d’un appui pédagogique très
enrichissant qui m’ont permis de progresser dans de nombreux domaine, et d’appréhender mon
travail de la meilleure des façons. Enfin, pour m’avoir laissé une marge de manœuvre et d’initiative
conséquente, nécessaire à l’épanouissement de tout stagiaire. Merci surtout pour votre bonne
humeur, votre disponibilité, votre gentillesse, et vos conseils !
D’une manière plus générale, merci à l’ensemble de l’équipe Ecologie des Communautés pour son
accueil et son soutien. En particulier un grand merci à Jean Yves Barnagaud pour son aide
précieuse, sa disponibilité et sa pédagogie à toute épreuve, ainsi qu’à Fabrice Vétillard pour sa
réactivité et son efficacité, qui ont grandement participé au confort dans lequel ce travail a pu être
réalisé.
Pour finir, merci aux nombreuses chauves-souris qui se sont prêtées au jeu et n’ont eu de cesse de
crier sous nos détecteurs pour révéler quelques uns de leurs secrets intimes. Ce modèle d’étude tout
à fait nouveau pour moi s’est révélé particulièrement grisant, frustrant parfois, mais toujours
passionnant.
CONTENTS
1 Introduction ........................................................................................................................ 1
2 Materials & Methods ......................................................................................................... 4
2.1 Study area and design ............................................................................................................ 4
2.1.1 The Landes de Gascogne forest ..................................................................................... 4
2.1.2 Design ............................................................................................................................ 5
2.2 Data collection ....................................................................................................................... 5
2.2.1 Bat sampling .................................................................................................................. 5
2.2.2 Echolocation call analysis .............................................................................................. 6
2.2.3 Habitat characterization ................................................................................................. 6
2.3 Data analysis.......................................................................................................................... 8
2.3.1 Species activity patterns during night ............................................................................ 8
2.3.2 Effects of abiotic factors on bat activity ........................................................................ 8
2.3.3 Effects of habitat variables effects on bat activity ......................................................... 8
2.3.4 Community assemblages among plots ........................................................................... 9
3 Results .............................................................................................................................. 10
3.1 Bat community sampling..................................................................................................... 10
3.2 Species activity patterns during the night............................................................................ 11
3.3 Effects of abiotic factors on bat activity .............................................................................. 12
3.4 Habitat variables effects on bat activity .............................................................................. 12
3.5 Community assemblage among plots .................................................................................. 14
4 Discussion ........................................................................................................................ 16
4.1 Bat community sampling..................................................................................................... 16
4.2 Species activity patterns during the night............................................................................ 17
4.3 Effects of abiotic factors on bat activity .............................................................................. 17
4.4 Influence of forest composition ........................................................................................... 18
4.5 Influence of habitat structure ............................................................................................... 20
4.6 Management ........................................................................................................................ 21
4.7 Conclusion ........................................................................................................................... 22
5 Bibliography..................................................................................................................... 23
1
1 INTRODUCTION
The contribution of bats to ecosystem services has been widely overlooked until recently. Their role
on the control of pests is significant, as they provide essential top-down control of herbivorous
insects (Williams-Guillén et al., 2008; Böhm et al., 2011; Kunz et al., 2011). For instance, herbivore
populations are more intensely affected by bats than by birds in forests (Kalka et al., 2008).
The 92/43/CEE Directive of 21 May 1992 on the conservation of natural habitats and wild fauna
and flora requires the strict protection of all European bat species (listed in Annex IV), and the
designation of special conservation areas for the 12 species listed in Annex II. More than half of the
34 species of western Europe are forest bats (Arthur & Lemaire, 2009). Chiropterans are
particularly vulnerable to human disturbance and environmental changes. Loss or modifications to
nursery sites, roosts and foraging grounds can lead to dramatic decrease in bat populations (Boyd &
Stebbings, 1989). Thus, human - induced modifications in land use is known as one of the several
causal factors responsible for drastic population declines for some European bat species (Stebbings
& Griffith, 1986; Arthur & Lemaire, 2009).
In particular, intensive logging simplifies vegetation structure and composition, and removes dead
trees. Such treatments reduce roost and food availability, thus affecting distribution, diversity and
density of bats (Mccomb et al., 1986; Graves, 1999; Russo et al., 2010). Hence, deciphering finely
the factors that influence bat occurrence and predation pressure is therefore essential to evaluate
their conservation status as well as their contribution as providers of services such as biological
control to silviculture.
As for many taxa, the broad-scale spatial distribution of bat species are primarily controlled by
climatic factors, while landscape and habitat features influence fine-scale abundance patterns
(Ethier & Fahrig, 2011; Razgour et al., 2011). At landscape and local scales, habitat features are
known to influence fine-grained patterns in animal populations in general (Fahrig, 2003) and
specifically for bat distribution (Ethier & Fahrig, 2011). Landscape composition is an important
factor influencing bat abundance within a region (Walsh & Harris, 1996a), but connectivity
between suitable habitats are also preponderant (Walsh & Harris, 1996b). At very local scales,
structure and composition of forest stands strongly influence habitat quality (Kalcounis et al., 1999;
Kanuch et al., 2008; Adams et al., 2009; Plank et al., 2011; Jung et al., 2012) by affecting the
availability and accessibility of food, roosts, protection from predators (Baxter et al., 2006) and
2
micro-climatic conditions (Chen et al., 1999). First, food availability (Walsh & Harris, 1996b;
Müller et al., 2012; Dodd et al., 2012) and roost density (Walsh & Harris, 1996a; Brigham et al.,
1997; Menzel et al., 2002; Elmore et al., 2004; Russo et al., 2005, 2010) play a major role in
structuring forest-dwelling bat communities. It is thus essential to address the effects of forest
management on bats through its effects on food availability. Forest architecture also determine
wildlife movements, which is particularly decisive for the distribution of flying vertebrates such as
birds or bats (Jung et al., 2012). Morphology highly influences bat habitat selection and foraging
grounds. Wide wingspan species have fast and straight flight, while smaller species have a slow and
sinuous flight (Norberg & Rayner, 1987). Therefore, larger species are restricted to open habitats
while more maneuverable bats use natural resources from more closed habitats (Aldridge &
Rautenbach, 1987; Crome, 1988; Krusic et al., 1996).
Managers therefore need to be informed about bat responses to forest structure and composition. In
the French forest of Landes de Gascogne (see figure 1.), stands are dominated by monospecific
maritime pine (Pinus pinaster) plantations (Maizeret, 2005), resulting in a low stand complexity
(Lindenmayer et al., 2006). Presence of deciduous trees, generally as shrub in the understorey layer,
represents the most common structural complexification and is generally related to low logging
intensity and old stands. Multi-taxa studies conducted in the Landes de Gascogne forest (Barbaro et
al., 2005, 2007; van Halder et al., 2008) highlighted the benefits of deciduous and mixed stands on
abundance and diversity of birds, carabids, spiders and butterflies.
Potential benefits of mixed-species production forests over monocultures are largely documented by
an increasing body of scientific literature. These benefits involve (i) higher habitats diversity for
biodiversity, (ii) improved growth rates, (iii) better recreational value, (iv) improved soil conditions,
(v) reduced damage to focal tree species by grazers pest insects and fungal pathogens and (vi)
reduced risk of wind and fire damage (Hartley, 2002; Lindenmayer et al., 2006; Nichols et al.,
2006; Felton et al., 2010; Richards et al., 2010)
While the effects of mixed species forest are well documented in European birds (Adams &
Edington, 1973; Felton et al., 2010), we are not aware of any published study highlighting the
effects of polycultural logging on bats in temperate forest. However, Kalcounis et al., (1999) report
a higher activity in mixed wood compared with monocultures in nearctic boreal forest.
Moreover, our understanding of European forest-dwelling bats ecology and behavior is still poor.
Some studies (Krusic et al., 1996; Menzel et al., 2002; Jung et al., 2012) highlight the use of habitat
and roosts needs, or foraging patterns and response to prey abundance (Müller et al. 2012) in
3
European forests. Given their ecological importance, bats have to be included in future forests
conservation and management plans and should also be considered in agricultural management
strategies based on natural pest control. Even so, they are still an overlooked faunal assemblage, and
habitat management is limited by the poor understanding of their ecology.
We investigated the effects of forest composition and structure on bat species richness and activity.
We specifically tested the following predictions:
i. Activity and taxonomic diversity increase with deciduous tree proportion at stand scale
ii. This increase is driven by food and/or roost site availability and/or vegetation structure
iii. Vegetation structure, roost site and food availability increase with deciduous tree proportion.
We used automatic bat call sampling in 21 stands of a French forest, along a pine-oak mixture
gradient. We also monitored vegetation structure, availability and diversity of prey and roost site
availability. Besides the value of exploring the stand-scale responses of a sensitive and little known
taxa to environmental variables in a responsible forest management perspective; the issue is
particularly sensitive in the Landes de Gascogne forest where bats could be crucial regulators of
herbivorous insect pests.
4
2 MATERIALS & METHODS
2.1 STUDY AREA AND DESIGN
2.1.1 THE LANDES DE GASCOGNE FOREST
Study took place in the north of the Landes de Gascogne forest. This area consists of an intensively
managed forest spanning c.10 000 km² (44°40’N to 44°44’N, 0°57’W to 0°46’W, see figure1.).
Coniferous Mixt Deciduous
Sampling plots
Figure 1.Sampling plots in the Landes de Gascogne Forest. A=Situation of the Landes de Gascogne forest in
Southwestern France. B=Maritime pine plantation of the Landes de Gascogne forest bringed out in purple.
C = position of the 21 sampling plots in study area..
A B
C
5
Landscape is largely dominated by stands of native maritime pine (Pinus pinaster) with a rotation
cycle of 40-50 years, creating mosaic landscapes composed of different even-aged stands from
clearcuts to mature pine stands. Remnants of deciduous woodlands dominated by Quercus robur,
Quercus pyrenaica and Betula pendula represent the only smalls (<5ha) and isolated patches of
semi-natural habitats within this plantation forest (Barbaro et al., 2007). The area is under influence
of a thermo-atlantic climate (mean annual temperature 12°C, mean annual rainfall 700 mm) and
the elevation is low (c. 50 m a.s.l.). Podzols established on a sandy substrate account for most soils
(Maizeret, 2005).
2.1.2 DESIGN
Experimental units consist of 50 m ray circular forest plot with homogenous forest composition and
structure. In order to avoid any bias in the monitoring of bat activity, each plot respected the
absence of crossing paths, water bodies, buildings and a minimal distance of 1.6km from a major
road (>30000 vehicles per day, Berthinussen & Altringham, 2012) . A number of 21 plots were
distributed in forest stands with variable proportions of pedunculate oaks (Quercus robur) and
maritime pines (Pinus pinaster) to achieve the most possible continuous gradient of oak-pine
mixture (see section 2.2.3.1). The geographical distribution of the plots was carried out randomly
and according to availability in aimed stands.
2.2 DATA COLLECTION
2.2.1 BAT SAMPLING
Automatic ultrasound bat detector systems (Sound Meter SM2Bat + platform, Wildlife acoustics)
fitted with multidirectional microphones (SMX-US weatherproof ultrasonic microphone, Wildlife
acoustics) were used for bat activity sampling. Devices were calibrated so as to detect all bat calls
while avoiding noise recording (sampling rate: 38.4 kHz, channel = MonoL, compression=WAC0,
Dig HPF left = fs/48, effective parameters remained constant throughout the study). Timers were
set to record the full night (1 h before sunset until 1 h after sunrise). Bat detector systems were
deployed in the center of each circular plot. Devices range detection is extremely variable,
depending on vegetation, obstacles, humidity, temperature, air pressure, wind, and frequency ,
amplitude, loudness and directionality of the bat call itself. According to Wildlife acoustics (see
http://www.wildlifeacoustics.com/support-resources/frequently-asked-questions), most bat species
can be detected well over 30m, with a likely maximum of about 100m. As all sampling plots are
6
within forest, a guestimate is that maximal range detection of the SM2 Bat+ detector should be
around 50 m. This distance corresponds to the ray of experimental unit (see 2.1.2.) Each plot was
investigated for two consecutive nights during May and June 2012. We did not sample on nights
with rain, when wind was >30 km/h or when the ambient temperature fell below 10.0°C (Hayes,
1997; Bat Conservation Trust, 2007).
2.2.2 ECHOLOCATION CALL ANALYSIS
Recorded sequences were stored, listened and measured with the software BatSound 4.1 (Pettersson
Elektronik AB, Uppsala, Sweden). This application dedicated to acoustic analysis allows to
transform acoustics data into sonograms. Following the Barataud method (Barataud, 2012), all
sequences and sonograms were analyzed by a trained operator to spot bat calls and identify the
species or the lowest possible Operational Taxonomic Unit (OTU) behind the recorded pass.
Number of bat passes and number of minutes sampled were recorded. A pass was defined as a
series of two or more consecutive calls with a maximum duration of 5 seconds, separated from
other calls by one second or more (e.g. Thomas, 1988; Hayes, 1997; Wilson & Barclay, 2006).
Walsh et al., (2004) suggested that bat activity (number of bat passes per species at a site) may be
used to quantify bat relative abundance provided that:
(i) there is no change in equipment sensitivity over time;
(ii) there is no trend in species detectability across time or sites;
(iii) passes can be reliably and consistently identified to species
(iv) survey points are consistent from site-to-site.
Since our sampling protocol meets these assumptions, we used bat activity as a measure of relative
abundance. Moreover, characteristics of echolocation calls permit to assess foraging activity.
During predation, bats emit short echolocation pulses at high repetition rate just before contact with
the prey. These pulses are known as feeding buzzes (Griffin et al., 1960). For each recorded pass,
we noted presence or absence of a feeding buzz.
2.2.3 HABITAT CHARACTERIZATION
2.2.3.1 Mixture gradient assessment
Mixture gradient was represented by the percent of basal area occupied by deciduous species trees
in each plot. Measures of basal area of the stems in a stand were performed following the angle
count method. (Bitterlich, 1984). Scores from deciduous or coniferous species were separately
noted, thus permitting calculation of deciduous species basal area, coniferous species basal area and
total basal area. Three tallying were performed from randomly distributed points within each plot.
7
From this gradient, 3 modalities were also defined to segregate plots: “Pine” (<25% deciduous
basal area), “Mixt” (25% - 75% deciduous basal area) and “Deciduous” (>75% deciduous basal
area).
2.2.3.2 Vegetation structure
To define vegetation structure within forest plots, we used a method inspired from Prodon &
Lebreton (1981), as the difference that only 4 strata were distinguished (see Table 1). Within each
layer, the relative degree of cover (Cv) was estimated by comparison with a reference figure (see
Appendix I). Cover of a layer is defined as the percentage of ground covered by the vertical
projection of the aerial portion of plants from the layer in a 0.2ha buffer. Small openings in the
canopy and intraspecific overlap are excluded (Prodon & Lebreton, 1981; Anderson, 1986). Cover
variables of each strata were estimated visually by the same two observers within each experimental
units during may 2012.
Mean height of canopy was also measured using a rangefinder. Three measures were recorded
within the plot and averaged to obtain mean canopy height value.
Cover of Herbaceous plants (<0.5m) Cv0.5%
Shrub Cover (50cm-2m) Cv2%
Tree cover (2m-16m) Cv16%
High tree cover (16m-32m) Cv32%
Vegetation height Vh (m)
Number of layer with Cv% > 25% Nlayer
Index of stratification diversity (Shannon and Weaver)
Mean canopy height Height
Table 1. Vegetation structure variables
2.2.3.3 Roost site availability
To roughly assess the susceptibility of each plot to contain suitable roosts, we noted each tree
corresponding to the 1 to 7 decay stages, based on the British Columbia wildlife-tree classification
system (see Appendix II). This system is based on characteristics such as the percentage of bark
remaining, number of limbs present, condition of the top, heartwood and sapwood (Vonhof &
Barclay, 1996; Brigham et al., 1997). We also noted all trees presenting cavities and/or peeled bark.
8
2.3 DATA ANALYSIS
2.3.1 SPECIES ACTIVITY PATTERNS DURING NIGHT
For each hour, percent of total activity recorded on all nights was calculated to bring up activity
patterns of bats species throughout the night. Patterns were calculated only with species with more
than 30 contacts.
2.3.2 EFFECTS OF ABIOTIC FACTORS ON BAT ACTIVITY
We analyzed the effects of abiotic variables on bat activity with Generalized Linear Mixed Models
(GLMM) (Bolker et al., 2009) in R 2.14.1 (R Development Core Team, 2010) and package lme4
(Bates, 2005). The response variable was the number of call sequences in each sampling night
(Actn). The explanatory variables were Proportion of visible moon (moon), Minimal night
temperature (temp.min in °C) and Session of recording (session, May or June). We accounted for
the hierarchical structure of the data (repeated sampling period) by adding simple random “plot”
effect to the model intercept.
EQUATION (1)
Αctn = αp + β*moonn + γ*temp.minn + δ*sessionn + (1|plot)
Model validation was assessed by “residuals repartition” and “deviation from normality”(normal Q-
Q) interpretation plots (see Appendix III). The model was heteroscedastic and unbiased. We fitted
all possible fixed effect structures nested within maximum model (1) and ranked them on the basis
of their Akaike’s Information Criterion adjusted for small samples (AICc; Burnham & Anderson,
2002,). We considered that the average of all models differing by less than 2 AICc units was the
best possible fixed effects model structure given our data. We considered only relative measures of
model performance as provided by the AICc, as no accurate measure of fit exists for mixed models
(Orelien & Edwards, 2008).
2.3.3 EFFECTS OF HABITAT VARIABLES EFFECTS ON BAT ACTIVITY
2.3.3.1 Community level analysis
We analyzed the effects of local habitat in the same way as 2.3.2. The response variable was the
number of call sequences in each hour/sampling night/plot (Acti,j,h). The explanatory variables were
Cover of Herbaceous plants (cv0.5), Low shrubs Cover (cv2), Tree cover (cv16), High tree cover
(cv32), Vegetation height (vh, in m), Indice of structure diversity (isw) and Number of dead tree
(dt). We accounted for the hierarchical structure of the data by adding nested random “session” (s),
“plot” (p) and “hour” (h) effects to the model intercept.
9
EQUATION (2)
Αcti,j,h=αs,p,h + β*cv0.5p + γ*cv2p + δ*cv16p + ε*cv32p +ζ*Vhp + η*Iswp +
θDtp + (1| session/plot/hour)
We fitted all possible fixed effect structures nested within maximum model (2) and ranked them on
the basis of their Akaike’s Information Criterion adjusted for small samples (AICc; Burnham &
Anderson, 2002,) as for precedent model (1).
2.3.3.2 Species level analysis
The same model (2) was also fitted on species or Operational Taxonomic Unity (OTU, see 3.1) with
enough contact to ensure receivable model structure (total number of contact > 190).
2.3.4 COMMUNITY ASSEMBLAGES AMONG PLOTS
A sampling plots x species matrix was created, where each cell encode the presence (1) or absence
(0) of the bat species. Two plots with less than two occurrences of species were excluded from the
analysis. Considering this matrix, Non-Metric Multi Dimensional Scaling (NMDS) using Bray-
Curtis dissimilarities was performed with R 2.14.1 (R Development Core Team, 2010) by
metaMDS function from vegan R package, (Holland, 2008; Oksanen, 2011). In order to fit
ecological variables onto species ordination among plots, environmental fitting from vegan R
package was calculated with all environmental variables (see Table 1). Non-metric
multidimensional scaling is an ordination technique that differs in several ways from nearly all
other ordination methods.
(i) Contrary to other ordination methods, a small number of axes are explicitly chosen prior to
the analysis and the data are fitted to those dimensions; there are no hidden axes of variation.
(ii) In contrast to other ordination methods are analytical and therefore result in a single unique
solution to a set of data, NMDS is a numerical technique that iteratively seeks a solution and stops
computation when an acceptable solution has been found.
(iii) MDS is not an eigenvalue-eigenvector technique like principal components analysis or
correspondence analysis that ordinates the data such that axis 1 explains the greatest amount of
variance. As a result, an MDS ordination can be rotated, inverted, or centered to any desired
configuration.
10
3 RESULTS
3.1 BAT COMMUNITY SAMPLING
We recorded 14738 bats calls in 926 hours of recording divided into 84 nights. More than 79% of
all bat calls were identified to species level. Otherwise, calls were assigned to an OTU combining
two or more species:
- Nyctaloid potentially including N. leisleri and Eptesicus serotinus
- Myotis sp. potentially including all Myotis species
- Plecotus sp. potentially including all Plecotus austriacus and Plecotus auritus
- P.kuhnat potentially including P.kuhlii and P.nathusius
P. nathusius is considered as a rare species in Aquitaine, and P. kuhlii as a species widely
distributed. There is a good chance that contacts attributed to the OTU P.natkuh reflect P. kuhlii
activity. (see Appendix VI.).
Altogether, we detected at least twelve bat species. Activity was almost six times higher in May
than in June. Four species or OTU represented 97.5 % of total activity. Pipistrellus pipistrellus was
by far the most contacted species with 58.6 % of activity. Generally, the most contacted species
were present in a large part of plots. P.pispistrellus was totally ubiquitous. P.kuhnat, E.serotinus
and P.kuhlii was also widely distributed through sampling sites.
OTU May June Overall % Activity % Presence
Pipistrellus pipistrellus 7397 1236 8633 58.6 100.0
P.kuhnat 2561 329 2890 19.6 85.7
Eptesicus serotinus 1257 231 1488 10.1 90.5
Pipistrellus kuhlii 1206 149 1355 9.2 85.7
Nyctaloid 61 73 134 0.9 76.2
Myotis sp. 48 44 92 0.6 76.2
Chiroptera sp indet. 7 25 32 0.2 47.6
Barbastellus barbastellus 7 19 26 0.2 23.8
Plecotus sp. 17 9 26 0.2 28.6
Nyctalus leislerii 6 11 17 0.1 19.0
Pipistrellus pygmaeus 9 8 17 0.1 14.3
Nyctalus noctula 1 9 10 0.1 14.3
Pipistrellus nathusius 6 4 10 0.1 19.0
Rhinolophus hipposideros 5 1 6 < 0.1 14.3
Rhinolophus ferruquimenum 1 1 2 < 0.1 9.5
Sum 12589 2149 14738 100 100
Table 2.Number of bat calls recorded in May, June and overall study for each species or OTU. Proportion of
representation (% activity) and proportion of sampling plots were species or OTU is present (% presence).
11
3.2 SPECIES ACTIVITY PATTERNS DURING THE NIGHT
0
5
10
15
20
25
30
20 21 22 23 00 01 02 03 04 05 06
% t
ota
l bat
act
ivit
y
Myostis sp
20 21 22 23 00 01 02 03 04 05 06
Pipistrellus kuhlii
0
5
10
15
20
25
20 21 22 23 00 01 02 03 04 05 06
% t
ota
l bat
act
ivit
y
Pipistrellus pipistrellus
20 21 22 23 00 01 02 03 04 05 06
Pipistrellus pygmaeus
20 21 22 23 00 01 02 03 04 05 06
Plecotus sp
P.Kuhlii & P.pipistrellus
Pipistrellus kuhlii
20 21 22 23 00 01 02 03 04 05 06
Pipistrellus pipistrellus
20 21 22 23 00 01 02 03 04 05
Night time (hour)
Barbastellus barbastellus
0
10
20
30
40
50
60
70
20 21 22 23 00 01 02 03 04 05 06
% t
ota
l bat
act
ivit
y
Night time (hour)
Eptesicus serotinus
0
5
10
15
20
25
20 21 22 23 00 01 02 03 04 05 06
Night time (hour)
ALL SPECIES
A
D
CB
E F
G H I
Specific activity patterns along nightFigure 2. Species night activity pattern, representing by proportion of total activity for each hour of night. Only species
with more than 30 contacts are presented.
Bimodal pattern was observed on total activity. The first peak was during the first hour after sunset
and the second around two hours before sunrise. The activity dropped in the middle of night.
Substantial variability was observed among species, with “early night bats” like Eptesicus serotinus
(G) or Barbastella barbastellus (H), “middle night bats” like Myotis sp.(D), Pipistrellus pipistrellus
(A), Plecotus sp. (E) and “late night bats” like Pipistrellus kuhlii (B) and Pipistrellus pygmaeus (F).
Activity patterns of P.pipistrellus and P.kuhlii seems to be particularly opposed, as P.kuhlii peak tie
in a drop of P.pipistrellus activity. Special attention should be focused on OTU with low rates of
contact (B.barbastellus, Myotis sp., Plecotus sp, P. pygmaeus.), as activity pattern could be biased
by the lack of data.
12
3.3 EFFECTS OF ABIOTIC FACTORS ON BAT ACTIVITY
ABIOTIC VARIABLES ESTIMATES
(Intercept) moon Session (May) Temp.min
Chiroptera 1.451 0.091 0.299 0.306
Table 3. Fixed effects structure of best fitted GLMM model explaining effects of abiotic variables on total bat activity.
Abiotic effects seem to explain a part of variability observed among sampling nights. Activity in
May was significantly higher than activity in June. Activity increased substantially with Minimal
night temperature. Although selected in model structure, percentage of visible moon seems to have
a negligible influence on activity.
3.4 HABITAT VARIABLES EFFECTS ON BAT ACTIVITY
3.4.1 COMMUNITY LEVEL ANALYSIS
HABITATS VARIABLES ESTIMATES
(Intercept) % deciduous cv2 cv16 cv32 isw height dt
Chiroptera 1.146 0.333 0.099 -0.0850 0.148 0.073 0.051 -0.235
Table 4. Fixed effects structure of best fitted GLMM model explaining effects of environmental variables on total bat
activity.
The magnitude of the model coefficient related to forest composition (%deciduous) was the highest
one, indicating a strong effect of forest composition on total bat activity (Table 4.). Activity
decreased as Number of dead tree per plot (dt) increased. Vegetation structure variables seem to
have lesser influence, but were retained in model structure. Activity decreased as a function of tree
cover (cv16), while structural diversity indice (isw), shrub cover (cv2) and canopy tree cover (cv32)
influenced activity positively.
13
3.4.2 SPECIES LEVEL ANALYSIS
HABITATS VARIABLES ESTIMATES
OTU (Intercept) % deciduous cv2 cv16 cv32 isw height dt
E.serotinus -3.968 0.212 -0.190 -0.101 -0.097 -0.248 0.413 -0.469
P.kuhnat -1.469 -0.605 -0.113 -0.262 -0.942 -0.135 -- 0.161
P.pipistrellus 0.276 0.637 0.385 -0.409 0.347 0.247 -0.255 -0.254
Table 5.Fixed effects structure of best fitted GLMM model explaining effects of environmental variables on activity of
species or OTU modelised
Species level responses to habitat factors were variable depending on species. Responses patterns of
P.kuhnat and E.serotinus were similar. For these species, activity decreased with increase of
deciduous proportion and vegetation structure complexification all strata confounded. P.pipistrellus
activity increased with proportion of deciduous tree, shrub cover (cv2), high tree cover (cv32) and
structural diversity indice (isw). Tree cover (cv16) had negative effect on P.pipistrellus activity.
E.serotinus activity increased as a function of tree height, P.pipistrellus activity decreased when
tree height increased. No effect of tree height was observed for P.kuhnat.
Figure 3. Barplots presenting distribution of activity among stand modalities. All species with more than 30 contacts
were plotted; underlined names indicate tested in GLMM, p<0.05
14
As seen in 3.4.1, responses of bat activity to forest composition were pronounced and variable from
one species to another. For the entire community, activity clearly increased with proportion of
deciduous along the mixture gradient. Only Plecotus sp. seems to show higher activity in “Pine”
plots.
P.nat/kuh shows clear preference for “Mixt” plots. P.pipistrellus was more active in “Deciduous”
than “Mixt” and “Pine”. Activity of Myotis sp and Nyctaloid OTUs did not show strong
differences between modalities. These results could be explained by (i) the low number of contact
and (ii) that these OTUs represent several bat species with different ecological requirements.
3.5 COMMUNITY ASSEMBLAGE AMONG PLOTS
Figure 4. Biplot based on non-metric multidimensional scaling (NMDS) of bat assemblages among sites. Results of
environmental fitting for variables with significant association (see Table 6.) are presented in blue vectors. The angle and length
of vector loadings indicate the direction and strength of associations, respectively. A = Distances between sampling sites (colored
points) on the ordination reflect dissimilarity. Modalities of plots are presented by ellipses and colors of points ( black =
“Deciduous”, red=”Mixt”, green=”Pine”). B = Distances between bat species reflect (orange text) dissimilarity in composition.
A B
15
VECTORS
FACTORS
NMDS1 NMDS2 r2 Pr(>r)
r2 Pr(>r)
deciduous 0.279 0.960 0.435 0.007 **
modalities 0.245 0.046 *
cv0.5 0.384 -0.923 0.139 0.301 cv2 -0.064 0.998 0.091 0.472 cv16 0.557 0.830 0.178 0.219
NMDS1 NMDS2 cv32 -0.887 -0.463 0.146 0.289
Deciduous 0.142 0.230
tree_height -0.999 -0.046 0.066 0.595
Mixt -0.017 -0.043 dt -0.994 0.109 0.202 0.149
Pine -0.062 -0.082
isw -0.132 0.991 0.023 0.852 basal_area 0.057 -0.998 0.320 0.050 *
Table 6. Results of environmental fitting calculated on NMDS
Plotting vectors of habitat metrics illustrated that deciduous gradient (p <0.01, Table 6.) structured
the ordination of species with Rhinolophus hipposideros (rhihip), Nyctalus noctula (Nycnoc),
Nyctalus leisleri(Nyclei) associated to high proportion of deciduous tree. And the ordination of
plots, with a substantial segregation of “Deciduous” plots (black ellipse) from “Mixt” and “Pine”
(respectively green and red ellipse) (p < 0.05, Table 6.).
P.kuhnat (pipnatkuh), P.pipistrellus (pippip), E.serotinus (eptser), Myotis sp. (myospp) and
Nyctaloid (nyctaloid) were the most ubiquitous species as they present in more than 75% of plots
(see 3.1)
Long-eared bats Plecotus sp. (plespp) seems associated to “Pine” plots, as other species only
appears in “Mixt” or “Deciduous” plots.
Impact of Basal area in the ordination was also retained as significant (p<0.05, Table 6.), but its
effects on bat distribution seem less obvious. Plotting of value surface on sites space ordination
(see Appendix IV .) reveals that there was no linear relationship between basal area and ordination.
16
4 DISCUSSION
4.1 BAT COMMUNITY SAMPLING
Our results must be viewed in the light of biases inherent to the use of ultrasonic detectors to assess
bat activity and habitat selection. It is actually impossible to distinguish between recordings of the
same individual flying in front of the bat detector several times and several individuals flying in
front of the bat detector only once (O’Farrell & Gannon, 1999). Nevertheless, this method has been
used convincingly by other researchers studying relative habitat use by bats (Barclay, 1999).
As described by Hayes (1997) activity fluctuated significantly among sampling nights (mean
number of bat pass per night = 181, sd=556) and among species (mean number of bat pass per
species =1215, sd=2627). Total bat activity was strongly influenced by overwhelming presence of
P.pipistrellus in recorded calls. Three nights with extremely high number of bat passes were
excluded from data analysis. Very high levels of bat flight activity were recorded in plot 060A (May
16, 2012 – 3493 passes) and plot 095A (May 11, 2012 – 3344 passes and May 12, 2012 – 1937
passes). As described in other studies (Ceľuch & Kropil, 2008), these high activities are probably
due to exceptional insect swarming.
At least 12 species were contacted out of 25 bat species known in Aquitaine. According to the
number of forest-dwelling species potentially represented by OTU Myotis spp (6 species),
taxonomic diversity was surprisingly high for the Landes de Gascogne plantation forests. The high
number of collected data shed new light on the occurrence and distribution of bats in the regions
that still remains poorly informed (see Appendix VI.)
P. pipistrellus was by far the most often contacted bat species, which is consistent with its
widespread distribution, being the most common bat in the study area (see Appendix VI.). Its high
flexibility in habitat requirements; with a preference for forests explain its spatial pervasiveness.
High activity recorded for P.kuhlii and Eptesicus serotinus were surprising since this species are
considered as scarce in closed forest habitats (Arthur & Lemaire, 2009). However, mature pine
plantation forests have relatively low tree density, allowing some open habitat species to penetrate
into forest stands as demonstrated for other taxa in the same study area (Barbaro et al., 2005; van
Halder et al., 2008).
17
4.2 SPECIES ACTIVITY PATTERNS DURING THE NIGHT
The bimodal pattern of activity observed is typical for the majority of insectivorous bats, well
known and largely described in literature (e.g Krusic et al., 1996; Rydell et al., 1996; Hayes, 1997).
However, activity patterns show variability depending on the species (see figure 3.). Overnight
activity pattern of P.kuhlii and P.pipistrellus were clearly different and opposed (see figure 1.C.)
This segregation was surprising as this species are quite similar on habitat and diet requirements
(Arthur & Lemaire, 2009). This could therefore represent a temporal segregation in ecological niche
and/or interspecific competition for foraging.
4.3 EFFECTS OF ABIOTIC FACTORS ON BAT ACTIVITY
4.3.1 MOON
Our results confirm findings of many studies, that is moonlight has no overall effect on activity by
forest-dwelling insectivorous bats (Negraeff & Brigham, 1995; Hecker & Brigham, 1999; Kalka &
Kalko, 2006). Furthermore, other studies anecdotally report that lunar illumination suppresses
activity (Fenton et al., 1977) or could be responsible of a shift in habitats. These behaviors could be
linked either by increasing risk of predation or changing distribution of insects (Williams, 1936).
Both mechanisms has been actually proved relevant for other nocturnal feeding vertebrates such as
storm petrels (Mougeot & Bretagnolle, 2000).In the present study, as light penetration is limited in
forest stands, effects of moonlight appear to be more limited here than in open fields.
4.3.2 SESSION
Foraging activity changes over the course of the reproductive season. Jong & Ahl, (1991) observed
changes in the Common pipistrelle (P.pipistrellus) habitat use between early and late summer. As
noticed in our study, forest areas were preferentially used in spring, and then bats gradually shifted
to open environments during the summer. Early summer is presumably one of the critical periods
for bats, just after hibernation. In particular, lactating females have high energy demands and must
increase their prey intake accordingly. This can be accomplished by an increase in foraging time
(Barclay, 1989), but may also be facilitated by increased foraging efficiency following parturition
(Kunz, 1974), and/or increased insect abundance in the environment (Anthony & Kunz, 1977; Swift
et al., 1985). An abundance of food at this time implies early reproduction which may be important
for subsequent survival of young (Swift et al., 1985). This fact shows the importance of forest
habitat in early summer as critical feeding sites for the local bat fauna. Later in the summer, an
overall increase in bat activity is expected when juvenile bats begin flying and foraging on their
own (Wilson, 2004).
18
4.3.3 TEMPERATURE
Temperature effect on bat activity was clear, but must be seen through its indirect influence on
insect prey abundance. As endothermic animals, bats are not limited by night temperature for
foraging activities. Furthermore, cold temperatures inhibit the flight activity of insects (Taylor,
1963), and are generally associated with low levels of bat activity (Hickey & Fenton, 1996;
Wilkinson & Barclay, 1997).
4.4 INFLUENCE OF FOREST COMPOSITION
Results from global linear mixed models support the prediction that total bat activity increase with
deciduous tree proportion at stand scale. A number of small-scale studies have already identified
semi-natural broadleaved woodland sites as preferred foraging areas for vespertilionids bats (Swift
et al., 1985; Furlonger et al., 1987) and more particularly for the Common pipistrelle (Pipistrellus
pipistrellus) and the Northern bat Eptesicus nilssonii (Jong & Ahl, 1991). Otherwise, fewer bats are
recorded in coniferous plantations than deciduous in Walsh & Harris (1996b), indicating that it
represents a less optimal woodland type. Some studies also report that old growth and natural
conifer forest can be prime habitat for some forest dwelling bats (Jung et al., 1999), whereas other
studies indicate that conifer and mixed stands has less dense understories allowing bats to forage
more easily (Kalcounis et al., 1999). Our results tend to be in favor of the first theory, indicating
that broad leaved forest stands represent more optimal habitat for bats in general. In a landscape of
intensely managed pine forest, deciduous patches are the only remnants of semi-natural habitats.
P. pipistrellus, P.kuhnat and Eptesicus serotinus covered near than 90% of total bat calls. As these
species almost exclusively use buildings as day roosts, their use of forest is restricted to foraging
activity. Hence higher activity recorded in deciduous habitats should be widely driven by higher
food disponibility and foraging suitability. Different forest types also provide different food and
habitat for insects (Molles Jr., 1982). Although we have no data on insect abundance, we suspect
that abundance and diversity of insects were lower in pure maritime pine stands, less structurally
complex than mixed pine-oak or pure deciduous stands. Previous research has suggested that
coniferous forests provide relatively few foraging opportunities for bats (Thomas, 1988; Jong &
Ahl, 1991) and that bat activity recorded in coniferous forests primarily reflects roosting and
commuting bats (Thomas, 1988; Patriquin & Barclay, 2003). Forest types may also differ in insect
diversity and abundance because of the production of resins synthesized by conifers as a defensive
secretion against insect attack (Funk & Croteau, 1994). Finally, previous studies conducted in the
Landes de Gascogne Forest has shown that conifer plantations contain lower numbers of insect
19
species (Barbaro et al., 2005) than deciduous woodlands patches acting as refuges and source
habitats for arthropods (Barbaro & van Halder, 2009).
As the activity of dominant P.pipistrellus widely influenced results extrapolated from the all-
species-confounded activity, we also need to examine other species responses. Results from GLMM
report that activity of P.kuhnat - that could be attributed to the Kuhl's Pipistrelle (Pipistrellus
kuhlii) (see 4.1) - was generally reduced along composition gradient. Results from modalities
plotting (see figure 2.) indicate that mixed stands hosted four times more activity than pure
deciduous or pine stands. Beyond the effect of deciduous proportion tree along the mixture gradient,
Kalcounis et al. (1999) also showed that deciduous-conifer mixedwood forest stands harbored more
activity than pure pine or pure deciduous stands. Furthermore, mixed woodlands could have also
higher insect diversity and abundance than monospecific pine stands (Kalcounis et al., 1999;
Barbaro et al., 2005). We could therefore make the assumption that mixed woodlands provide more
foraging opportunity than pure stands for this species. This support the hypothesis of improved
habitat quality in mixed than monospecific stands. Moreover, it also shows that responses can be
strongly various among closely related species, as pointed out by Jung et al., (1999).
Results from the NMDS highlight the significant role played by the high proportion of deciduous
tree on bat community composition. Pure deciduous woodlands were characterized by the presence
of species not or rarely found in pine or mixed stands (Nyctalus noctula, Nyctalus leisleri,
Rhinolophus hipposideros). Common Noctule (N.noctula) and Leisler's Bat N.leisleri generally
have clear preference for deciduous stands, but also use conifer forests for foraging. However, they
only use deciduous trees for summer roosts (Arthur & Lemaire, 2009). Trees have several potential
roost sites, including foliage and bark, crevices beneath loose bark, abandoned hollows of
woodpeckers, and naturally formed cavities (Brigham et al., 1997). In pure maritime pine stands,
trees presenting these characteristics are scarcer than in mixedwood and deciduous stands. As roost
density play a major role in the organization of forest-dwelling bat communities (Walsh & Harris,
1996a; Brigham et al., 1997; Menzel et al., 2002; Elmore et al., 2004; Russo et al., 2005, 2010), the
presence of Nyctalus species in our plots appears logically driven by deciduous tree proportion
through suitable roost availability.
This suggests that both roost sites and foraging opportunities increase with deciduous tree
proportion at the stand scale, but probably also at the landscape scale (Barbaro et al., 2007)
20
4.5 INFLUENCE OF HABITAT STRUCTURE
As deciduous tree proportion, several variables describing vegetation structure were retained in the
models, but had weaker effects. Vertical stratification is known to play a substantial role for bat
activity (Kalcounis et al., 1999; Plank et al., 2011). Especially, canopy tree cover seems to
significantly influence total activity in our study area.
Vegetation structure can have effect on foraging through (i) its impact on foraging accessibility and
(ii) its impact on insect prey availability.
(i) Morphology determines the selection of foraging habitats in bats, and differences
in morphology will result in the partitioning of spatial resources (Aldridge &
Rautenbach, 1987). Larger-winged bats are not adapted to closed habitat, and the
most cluttered forests can only host forest specific bats (e.g. Myotis sp.).
Reduction of the amount of structural volume in the understory to mid-canopy
provide more suitable habitat for foraging bats (Titchenell et al., 2011).
(ii) As indicated in Luque et al. (2007), forest architecture (i.e. the relative coverage
by the different vegetation levels) is important for the composition of moth
communities. Forest structure and especially differences at the shrub level can
play a role on the densities of some moth species. Generally, higher structural
heterogeneity within the stand is related to higher diversity and abundance in
moths.
Our results shows that total bat activity increased with shrub (0.5m-2m) and high tree (+16m) cover,
but decreased with tree (2m-16m) cover. In the light of theories outlined above, we can presume that
more dense stands seem to improve moth prey abundance, but opened tree layer (2m-16m) are
important for bat movement during hunting. This is remarkably consistent with previous studies on
compared prey availability and accessibility for another nocturnal aerial feeding vertebrate, the
European nightjar Caprimulgus europeus (Sierro et al., 2001).
P.kuh/nat and the the serotine bat (E.serotinus) activities were clearly driven by low vegetation
cover values in all strata. These results are not surprising in the sight of their ecologic requirements.
These species are known to be scarce in closed habitats (see 4.), but occurred frequently in a wide
part of forest stands. However, their activity seems to be more important in low-structured stands.
E. serotinus is a wide wingspan bat (c.350mm), so its morphology and echolocation call design fit it
in the “open land” or “woodland edge foragers” guild. This bat is also generalist and opportunist,
so its abundance and ubiquity conceal its large ecological requirements. We presume that general
high activity recorded for this species in forest stands is due to increased food availability, but low
21
cluttered stands are more suitable in regard of their morphology. This results are consistent with
Titchenell et al., (2011), demonstrating that the reduction of the amount of structural volume in the
understory to mid-canopy provide suitable habitat for foraging bats.
Number of dead trees was used to roughly assess the susceptibility of each plot to contain suitable
roosts. Unlike Brigham et al. (1997), our presumption that activity should increase with the number
of dead tree was not verified. Our results show weak and negative effect of number of dead tree on
activity. Several factors may be responsible for this :
(i) The dominant the species contacted do not use roosts in tree but in buildings.
(ii) Most species contacted use pine stands only for foraging and do not roost in pines, so
that dead tree availability do not affect bat occurrence within pine stands, mainly
driven by prey availability
(iii) All dead trees listed were maritime pine (Pinus pinaster). As this tree presents a thin
and straight trunk, it should not be a potential roost source once dead.
Our study demonstrated that other habitat variables than deciduous tree proportion were also
important for bat communities, since they influenced bat distribution patterns in forests. This
influence is based on the effects of vegetation structure through its effects on prey availability and
flying suitability. Several other habitat variables not tested in this study could explain bat
community composition in plantation forest, notably suitable roost availability. Above all,
management of forest stands stay to be the most important features in forest-dwelling bat
distribution and conservation.
4.6 MANAGEMENT
Due to current and potential future declines of bat populations, it has become increasingly important
for natural resource managers to understand how forest management practices influence bat habitat
relationships. Our results show the sensitivity of bats to forest composition and structure. In
industrial plantations these features are greatly influenced by scale stand management
(Lindenmayer et al., 2006). As many other studies (Jung et al., 1999; Kalcounis et al., 1999; Jaberg
et al., 2006), we conclude that deciduous stands are of fundamental ecological importance for many
chiropteran species, especially in monospecific plantation forests. The differences observed
between deciduous woodlands and pine stands are due to their distinct tree composition but also to
differential management. Plantation stands are typically characterized by a uniform and intensive
management, whereas management of deciduous woodlands is more variable in time and space
allowing a greater structural diversity. Maintenance of mature deciduous wood lands would
22
increase the availability of roosts (Brigham et al., 1997) used by bats depending on old deciduous
trees.
Consequently, these measures could improve diversity and abundance of bats communities. A
comparative approach on the quantification of leaf damage highlighted the importance of local
attributes such as tree age, forest composition and species richness of vertebrate predators for
control of arthropod herbivory in temperate forests (Böhm et al., 2011). As bat predation both
directly reduces arthropod abundance on plants and indirectly reduce herbivory, they are likely key
regulators of insect pests affecting productivity in the Landes de Gascogne forest. Given their
ecological importance, bats should be included in future forest management strategies based on
biological pest control.
4.7 CONCLUSION
The present study assesses the importance of mixedwood and deciduous forest stands for bats in a
monospecific and intensely managed pine plantation landscape. Both activity and diversity of bat
communities were positively influenced by deciduous tree cover along a pine-oak composition
gradient. Indeed, the integration of deciduous trees in pine plantations appear to create high value
habitats for foraging, and could represent critical feeding sites when lactating females have to
increase their prey intake in early summer. Others explanatory variables measured at the local scale
(i.e vegetation structure) explained a significant part of data variability, and are more or less linked
to the mixture gradient. Logging should take into account stand management impact on bats, as they
could play a key role in insect pest regulation.
As other animal forest vertebrate communities such as birds, multi-scale factors are required to
explain distribution and organization of bat communities (Miles et al., 2006; Kanuch et al., 2008).
Our study, as well as those of (Krusic et al., 1996) and (Grindal & Brigham, 1998), suggested that
forest-dwelling bats may require a matrix of forest types on a landscape, which includes older
forests. Some old-growth deciduous stands should be maintained across a landscape in forest
management planning, and others planned for the future via lengthening of rotation cycles to enable
old-growth stand characteristics to develop. Harvesting that creates a mosaic of patches with
different tree densities is likely to satisfy the requirements of more species than a system with less
diverse harvesting styles. Further studies should focus on effects of landscape structure and
composition, and assess importance of semi-natural habitat remnants like deciduous tree islands
within plantation (Lindenmayer et al., 2006). As foreground providers of essential top-down control
of herbivorous insects (Kalka et al., 2008), the conservation of vulnerable and endangered taxa like
chiropteran has to be at the forefront of biodiversity consideration in forest management.
23
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Appendix I. Reference pattern used for cover estimation from Prodon & Lebreton, (1981)
Appendix II. British Columbia wildlife-tree- classification system
2
0 10 20 30 40 50 60
-4-2
02
4
GLMM residuals repartition
Fitted Values
Re
sid
ua
ls
-3 -2 -1 0 1 2 3
-20
24
Normal Q-Q Plot
Theoretical Quantiles
Sa
mp
le Q
ua
ntile
s
Appendix II. GLMM validation plots
Appendix III.Surface fitting of basal area variable to ordination Appendix IV. Surface fitting of deciduous tree proportion
variable to ordination
Appendix V. Bat distribution maps in Aquitaine , from http://www.faune-aquitaine.org
4
ABSTRACT
Bats are key insectivorous vertebrates in forest ecosystems, both because of their high
conservation value and major functional role of insect predation. Here, we examined
the effects of forest stand composition and structure on bat communities in the
context of an intensively managed maritime pine forest. We sampled bat activity by
echolocation call monitoring along a pine-oak composition gradient ranging from 0 to
100% oak cover in the tree canopy (n = 21 forest stands). Data were collected in
May-June 2012 using bat-detectors (14738 bats calls recorded in 84 nights) in the
Landes de Gascogne forest (Aquitaine, France). Bat forest habitat was quantified by
10 environmental variables characterizing tree species composition, vertical
vegetation structure and roost site availability (dead trees). General Linear Mixed
Models and Non-Metric Multidimensional Scaling reveal that both activity and
diversity of bat communities were positively influenced by deciduous tree cover
along the composition gradient. This suggests that both roost site availability and
foraging opportunities increase with deciduous tree proportion at the stand scale.
Vegetation structure also explained a significant part of data variability in bat
activity. This highlighted the influence of vegetation structure on bat distribution
patterns in plantation forests through its effects on prey availability and flying
suitability. Forest-dwelling bats are known to provide essential top-down control of
herbivorous insects that could be critical in commercial forests and are also
particularly threatened due to a high sensitivity to habitat perturbations. As logging
practices strongly influence stand scale characteristics, forest management should
take into account bat conservation to benefit from ecosystem services provided by
bats.
KEY WORDS
Bat activity, Mixed-wood forest, Forest structure, Habitat-use, Foraging, Forest
management, Bat conservation.