Document [original]

ORIGINAL ARTICLE

Mono-specific forest plantations are valuable bat habitats:

implications for wind energy development

Sascha Buchholz

1,2

&Volker Kelm

&Simon J. Ghanem

Received: 6 March 2020 /Revised: 6 November 2020 /Accepted: 17 November 2020

#The Author(s) 2020

Abstract

Near-natural or semi-natural forests such as relatively undisturbed and old deciduous or mixed woodland are considered worth

protecting and ecologically valuable habitats for bat conservation. In contrast, mono-specific forest plantations are considered

ecologically less valuable; thus, decision-makers recommend these plantations as suitable locations for wind power stations and

therefore want to further expand wind turbines in these habitats. This is expected to have a strong negative impact on the

landscape because forests would be cleared for wind turbine pads and access roads and wind turbines rise above the trees with

adverse impacts for bats. Therefore, we argue that, in light of bat conservation, the suitability of forest plantations for wind energy

development is not, per se, warranted and that implications of wind power stations, even in mono-specific forest plantations,

should be assessed and evaluated. We conducted long-term bat activity monitoring and recorded bat echolocation calls above the

canopies of different forest sites (coniferous monoculture plantations and semi-natural mixed deciduous forests) in Germany and

compared different forest types in terms of species richness, total bat activity, activity of the three bat species groups and species

composition. Generalised linear models revealed that forest type and the amount of forest biotopes did not enhance bat activity.

Ordination showed that species composition was not affected by forest type, location and connectivity. Mono-specific forest

plantations can harbour a diverse bat fauna with high species activity and are, therefore, valuable bat habitats just as near-natural

or semi-natural woodlands are. Environmental impact assessment and mitigation measures are vital in all forest types before and

after planning for wind energy turbines. In particular, future planning and approval processes must consider the importance of

mono-specific forest plantations for bat species protection.

Keywords Chiroptera .Coniferous plantation .Environmental impact assessment .Renewable energy .Wind farm .Wind

power .Woodland

Introduction

Woodlands are vital for many bat species by providing habi-

tats, roosting sites and hunting grounds (e.g. Jantzen and

Fenton 2013; Kalda et al. 2015; Charbonnier et al. 2016a).

In general, the functional quality of woodlands is determined

by a number of landscape and habitat parameters such as

landscape connectivity and complexity (Boughey et al.

2011; Frey-Ehrenbold et al. 2013; Fuentes-Montemayor

et al. 2013). It has been widely shown that anthropogenic

impacts negatively influence the distribution of forest bat spe-

cies by affecting roosting and foraging habitats (frag-

mentation, Lesińskietal.2007; habitat loss, Crampton

and Barclay 1998; logging, Fukui et al. 2011). Furthermore,

negative changes in bat density and diversity are often

related to wind farms or wind turbines, respectively

(Kunz et al. 2007a,2007b;Hornetal.2008;Cryan

and Barclay 2009;Rydelletal.2010), which is of par-

ticular importance because many bat species are endangered at

global, national and regional levels (Mickleburgh et al. 2002;

Jones et al. 2009).

In Germany, renewable energies are forecasted to rise to

about 40–45% of the gross electricity consumption by 2025 to

*Sascha Buchholz

sascha.buchholz@tu-berlin.de

Department of Ecology, Technische Universität Berlin,

Rothenburgstr. 12, D-12165 Berlin, Germany

Berlin-Brandenburg Institute of Advanced Biodiversity Research

(BBIB), D-14195 Berlin, Germany

K&S Environmental Consult, Sanderstr. 28,

D-12047 Berlin, Germany

https://doi.org/10.1007/s10344-020-01440-8

/ Published online: 3 December 2020

European Journal of Wildlife Research (2021) 67: 1

fulfil climate protection goals and up to 80% by 2050, with

wind energy being the most important energy source

(Informationsportal Erneuerbare Energien 2020). Aside from

open landscapes, woodlands are considered potential loca-

tions for wind power stations. The German Federal Agency

of Nature Conservation (BFN 2011) estimates that about 14%

of existing German woodlands are suitable sites. In contrast to

near-natural or semi-natural woodlands, mono-specific forest

plantations are lower quality habitats for bats, and decision-

makers are recommending them for wind power stations (e.g.

MUGV Brandenburg 2011). Decision-makers argue that

mono-specific forest plantations—characterised by one tree

species (pine in NE-Germany) (Klimo et al. 2000)—are eco-

logically less valuable, as they emaciate the soil, are particu-

larly sensitive to windfall and pests and are less diverse in

plant and animal species (Klimo et al. 2000; MUGV

Brandenburg 2011). Therefore, wind energy development in

mono-specific forest plantations is seen as ecologically sound

and, thus, has become the focus of attention of wind power

operators (BFN 2011). The general consensus is there-

fore that the expansion of wind turbines in these plan-

tations should be promoted although landscape changes

are associated with this (for instance cleared forests for

wind turbine pads and access roads as, wind turbines

above the remaining forest patches).

In contrast, near-natural or semi-natural woodlands, such

as deciduous and mixed deciduous forests, are considered

worth protecting and ecologically valuable habitats, and sev-

eral studies prove their importance for bat conservation

(Kaňuch and Krištín 2005;Kaňuch and Celuch 2007; Zahn

et al. 2008; Kalda et al. 2015). This is especially true in terms

of wind energy development and its negative effects on bats

and bat habitats. It is known that higher collision rates may

occur due to higher bat activities above the canopy level

(Grunwald and Schäfer 2007; Brinkmann et al. 2011; Zahn

et al. 2014). Furthermore, indirect impacts, such as habitat

loss, can be expected through logging, as many bat species

strongly depend on tree holes as roosting sites (Meschede and

Heller 2000; Dietz et al. 2009).

To ensure environmentally sustainable energy production,

the implications of wind power stations in mono-specific for-

est plantations versus near-natural or semi-natural woodlands

should be assessed and evaluated. A number of studies

highlighted the adverse effects of wind power stations for bats

and bat habitats in woodlands (e.g. Brinkmann et al. 2011;

Hötker et al. 2006; Aschoff et al. 2006,Kunzetal.2007b),

but most studies focused on near-natural or semi-natural

woodlands while forest plantations have been nearly

neglected in Europe (but see Kirkpatrick et al. 2017a). This

is a drawback because the adverse effects of wind turbines

may even be stronger through the attraction effects of wind

turbines for bats (Cryan et al. 2014). We hypothesise that

mono-specific forest plantations can be valuable bat habitats

and that stand type is not necessarily a predictor of bat activity

as long as appropriate features such as tree holes, food avail-

ability and beneficial habitat structure or connectivity, respec-

tively, are provided at the landscape and habitat level

(Charbonnier et al. 2016b).

Methods

Field methods

We recorded bat echolocation calls with the batcorder system

(Batcorder—ecoObs, Nuremberg, Germany) above the cano-

pies of 24 different forest sites in Germany (Appendices Fig. 3

and Table 3). All sites were in proposed wind farms in a

landscape dominated by different forest types. Bat activity

can be highly variable with the daily variation of activity pat-

terns being as high as the variation between different sites

(Mathews et al. 2016, pers. observation). There are several

guidelines recommending minimum buffer zones of 200 m

to habitats of importance to bats (Rodrigues et al. 2015;

Kelm et al. 2014). Consequently, we defined a 200-m radius

for each location around the recording device to assess the

environmental variables using QGIS 3.4 (Quantum GIS

Development Team 2018 - © GeoBasis-DE/LGB, dl-de/by-

2-0). In this radius, we calculated the amount of the forest

biotopes (in percent) within each site and evaluated the avail-

able forest type and assigned it as a coniferous plantation

(consisted of a single pine species (Pinus sylvestris)) or mixed

deciduous forest (if at least 50% of the forest cover belonged

to this category) (Table 1). All forest patches showed a similar

age structure for around 50 years (± 20 years). To assess the

connectivity, we measured all linear landscape elements, e.g.

forest edges, hedges, roads or streams. We measured the dis-

tance (in m) from the recording device to the nearest forest

edge that marked a change in habitat type (roads or streams

were not considered) or water body. We defined water bodies

as those with standing water and a minimum size of 8 × 8 m.

At each site, we recorded calls for 57–183 entire nights

(sunset to sunrise) between April and November from 2011

to 2018. To ensure comparability, all surveys were conducted

on mild nights (> 10 °C ambient temperature), without rain

and low wind speeds (visual assessment with ≤small branches

moving, which according to the Beaufort scale is a maximum

speed of 3.4–5.4 m/s). We detected the bat passes above the

canopy with a batcorder fixed in a weatherproof box that was

attached on a pole protruding the treetops about 1.5 m. The

data transmission was facilitated via a GSM module, and the

power supply was provided by a solar panel. All batcorders

recorded the full frequency spectrum between 16 and

150 kHz in real time with a sampling rate of 500 kHz. We

used a threshold of 27 dB and a pre-trigger of 400 ms

for the recordings.

1 Page 2 of 12 Eur J Wildl Res (2021) 67: 1

We analysed the ultrasound recordings with the batIdent

and bcAnalyze software (ecoObs, Nuremberg, Germany) to

organise and identify species-specific echolocation calls. This

software automatically measures the primary sound parame-

ters of the recorded echolocation calls and calculates a proba-

bility value that a call is from a specific bat species. The

software identifies bat echolocation calls from simultaneous

recordings of up to three individuals per sequence (Runkel

and Marckmann 2009). After the automatic analysis, we man-

ually validated all echolocation calls for misidentifications

and, when possible, identified additional calls following the

criteria of species identification described in Hammer and

Zahn (2009)andSkiba(2003). Where the recording quality

allowed, we identified the following bats to species level:

Barbastella barbastellus,Eptesicus serotinus,Myotis

daubentonii,Myotis myotis,Myotis nattereri,Nyctalus

leisleri,Nyctalus noctula,Pipistrellus nathusii,Pipistrellus

pipistrellus,Pipistrellus pygmaeus. As some bat species are

acoustically nearly indistinguishable, we combined the re-

cordings of Myotis brandtii and Myotis mystacinus to the spe-

cies group Mbra and the species Plecotus auritus and Plecotus

austriacus to the species group Plecotus. Furthermore, we

included all Myotis-like, Nyctalus-like, Pipistrellus-like and

Plecotus-like calls in the categories Myotis sp., Nyctaloid,

Pipistrelloid and Plecotus sp. For testing environmental ef-

fects on bat species composition and activity, we first classi-

fied all bat species following their collision risk at wind tur-

bines as well as their foraging and echolocation behaviour to

one of the three groups, Nyctaloid group (high collision risk—

open space forager), Pipistrelloid group (medium collision

risk—edge space forager) and Myotis/Plecotus group (low

collision risk—narrow space forager). For detailed informa-

tion about classifying bats in groups and guilds, see Denzinger

and Schnitzler (2013). To describe bat activity, we counted the

number of bat passes for each of the different species or

groups. A ‘bat pass’was defined as a sequence of at

least two echolocation pulses of a passing bat (Thomas

and West 1989). Therefore, we assume that the number

of bat passes is highly correlated with bat activity. In

addition to the already high reliability and precision of

the batcorder system, we professionally calibrated all

used microphones prior to their field use to warrant the same

detectability under similar conditions. Consequently, all zero

observations were regarded statistically as true absences of

bats in a certain range.

Data analysis

The data analysis was done using the free software package

R-3.4.4 (R Core Team 2019). To test, if environmental vari-

ables(explanatoryvariables:amountofforestpatches,con-

nectivity, distance to forest edge, distance to next water body,

forest type) had a significant effect (alpha = P< 0.05) on total

bat activity, the species group activity and species numbers as

response variables, we performed generalised linear models

(GLM). Furthermore, we used the number of recording days

as an offset variable to correct for different recording intensi-

ties. To avoid multicollinearity, we only included environ-

mental parameters with correlations of |r| < 0.7. To compen-

sate for the detected overdispersion, the standard errors were

corrected using a quasi-Poisson GLM (Crawley 2007;Zuur

et al. 2009). The most appropriate models were stepwise

(backward) determined by analysis of deviance (Zuur et al.

2009). The residual deviance was used as a goodness-of-fit

measure by calculating the pseudo-R

(Dobson 2002). Direct

comparisons of categorical variable ‘forest type’were con-

ducted with post hoc Tukey’s pairwise comparisons using R

package LSMEANS.

In order to detect groupings of sample sites and the impact

of environmental variables on species composition, bat fre-

quencies were subjected to non-metric multidimensional scal-

ing (NMDS) using VEGAN and MASS packages in R. For

ordination, the frequencies of each species were square root

transformed. NMDS is an iterative ordination method that

places samples in a k-dimensional space using the ranked

distances between them (McCune and Grace 2002). The ordi-

nation was based on the Bray-Curtis dissimilarity matrix. In

search of a stable solution, a maximum of 100 random starts

was used. We applied Kruskal’s stress formula multiplied by

100, as given in McCune and Grace (2002). After two trials,

two convergent solutions were found for a three-dimensional

model. Afterwards, environmental data was fitted onto the

ordination. The significance of the explanatory power of each

variable was evaluated with a Monte-Carlo randomisation test

(10,000 permutations) (Manly 1997) using R function

‘envfit’.

Table 1 Description of environmental variables

Variable name Scale Explanation

Amount of forest patches Ratio % in a 200-m radius around the study plot

Connectivity Metric Linear landscape elements in a 200-m radius, e.g. forest edges, hedges, streams, roads

Distance to the forest edge Metric Is the distance in metres from the study plot to the nearest forest edge

Distance to the next water body Metric Is the distance in metres from the study plot to the nearest water body (minimum pond size)

Forest type Categorical Coniferous plantation, mixed deciduous

Page 3 of 12 1Eur J Wildl Res (2021) 67: 1

Results

In all, we detected 10 bat species (Appendix Table 3). Most

frequent were Nyctalus noctula,Pipistrellus pipistrellus,

Pipistrellus pygmaeus and Pipistrellus nathusii that occurred

at all study sites. Generalised linear models revealed that total

bat activity (total bat passes) (Chi

= 1.56, P= 0.20, GLM)

and number of bat species (Chi

=0.65, P= 0.42, GLM) in

coniferous plantations and mixed deciduous forests did not

differ significantly (Fig. 1). Moreover, forest type did not sig-

nificantly affect the activity level of the three species groups

(Table 2), Myotis/Plecotus group (Chi

=0.48, P=0.49,

GLM), Nyctaloid group (Chi

= 0.83, P= 0.36, GLM) and

Pipistrelloid group (Chi

=2.82,P= 0.09, GLM). Bat activity

level in general (Chi

=3.87,P= 0.05, GLM) and the activity

level of the Nyctaloid (Chi

=3.72, P=0.05, GLM) and

Pipistrelloid groups (Chi

=3.07, P= 0.08, GLM) increased

with closer distance to the next water body as a statistical

trend. Also, as a statistical trend, the amount of forest

patches negatively affected total bat activity (Chi

3.40, P=0.07, GLM) and Nyctaloid group activity

(Chi

=3.34, P= 0.07, GLM) while Myotis/Plecotus spe-

cies responded positively (Chi

=3.08, P=0.08, GLM).

All other environmental variables had no impact on the

activity levels of the groups or on the number of en-

countered species.

The NMDS based on the bat species activity level revealed

no significant effects of the environmental variables (Fig. 2).

None of the variables (connectivity, distance to forest edge,

distance to next water body, forest or forest type) af-

fected species groups (amount of forest patches: F=0.1,

=0.01, P= 0.96; connectivity: F=0.4, R

=0.02, P=

0.73; distance to forest edge: F=0.4, R

= 0.02, P=

0.75; distance to next water body: F=1.4, R

=0.06,

P= 0.25; forest type: F=2.1, R

=0.09, P=0.10; per-

mutational multivariate analysis of variance with 9999

permutations).

Discussion

Impact of forest type

It is well known that near-natural or semi-natural woodlands,

such as deciduous and mixed deciduous forests, are ecologi-

cally valuable bat habitats; thus, they are considered worth

protecting (e.g. Jantzen and Fenton 2013; Kalda et al. 2015;

Charbonnier et al. 2016a,2016b). However, we found no

significant differences in bat densities in mixed deciduous

forest and mono-specific forest plantations. Given that none

of the bat groups responded to forest type, we encourage a

change of thinking regarding the ecological value of mono-

specific forest plantations for bats. Even if forest plantations

are ecologically critical, they still provide a habitat with equal-

ly high bat activity as near-natural or semi-natural woodlands

(Pereira et al. 2016; da Silva et al. 2019). Moderate silvicul-

tural methods can also promote bat diversity (Kirkpatrick et al.

2017a), such as harvesting, which creates a patch mosaic with

different tree densities (Perry et al. 2007;Blakeyetal.2016;

Kirkpatrick et al. 2017b).

Kalda et al. (2015) found only a minor impact of forest type

on bat diversity. They concluded that natural and anthropo-

genic woodlands could be seen as a single land-cover type

regardless of the dominating tree species. In addition,

Glendell and Vaughan (2002) found forest plantations to be

suitable habitats for bats, as they showed that bats similarly

forage in semi-natural and plantation woodlands. Bats depend

on a variety of landscape and habitat properties, for example

forest edge structures for commuting and foraging (Hein et al.

2009;Kelmetal.2014;Morrisetal.2010; Jantzen and Fenton

2013), open patches (Loeb and O’Keefe 2006; Perry et al.

2007; Kirkpatrick et al. 2017b), diverse surrounding land-

scape compositions (Boughey et al. 2011; Frey-Ehrenbold

et al. 2013; Heim et al. 2018), water bodies (Morris et al.

2009; Heim et al. 2018) and roosting sites (Kühnert et al.

2016;Coronadoetal.2017; Dietz et al. 2018). However, all

these features potentially exist in all diverse, near-natural and

mono-specific forest plantations (Cruz et al. 2016;Kirkpatrick

et al. 2017a). According to Ruczyński et al. (2010) and Toth

et al. (2015), woodland bats may change their roost site

preferences and could, thus, occupy habitats previously

considered less suitable. Runkel (2009) observed that bats

generally use a wide range of forest habitats, but only a few

specialists succeed in niche partitioning in forests with a low

spacing between trees. Our results show that many open and

edge space species, such as Pipistrellus pipistrellus,

Pipistrellus nathusii,Eptesicus serotinus and Nyctalus

noctula, also had high activity in coniferous forestry zones

which adds to findings of Kirkpatrick et al. (2017b) for Sitka

spruce (Picea sitchensis) plantations.All these species are

negatively affected by wind energy development

(Brinkmann et al. 2011) and at risk from wind turbines

(Müller et al. 2013).

Differences in species composition naturally occur across

different regions; however, other drivers—such as landscape

composition, habitat structure and stand origin—are compa-

rable (Kalda et al. 2015). In our study, species composition

was not influenced by forest type, even though Pipistrellus

species usually show a preference for open patches in forests,

such as thinnings and windbreaks (Kirkpatrick et al. 2017b),

which are habitat conditions that rather occur in mixed decid-

uous forests. However, Pipistrellus pipistrellus is a generalist

species that can reach high densities in manifold landscapes

(Davidson-Watts et al. 2006). Moreover, Myotis daubentonii

can use open space landscapes (Kalda et al. 2015) but strongly

depends on ponds and lakes as primary foraging habitats

1 Page 4 of 12 Eur J Wildl Res (2021) 67: 1

(Glendell and Vaughan 2002; Dietz et al. 2009). In general,

Myotis species are known to select habitat structure over forest

type. Thus, bats of the Myotis genus are able to negotiate

cluttered woodland interiors (Glendell and Vaughan 2002).

Table 2 Regression results (quasi-Poisson GLM). Positive effects on dependent variables are expressed as ↑(for increasing) and negative ones are

expressed as ↓(for decreasing)

Response Amount of forest patches Connectivity Distance to the

forest edge

Distance to the

next water body

Forest type

Total bat passes all groups Chi

= 3.40, P=0.07(↓)Chi

= 0.40, P=0.53 Chi

=1.33,P=0.25 Chi

=3.87,P=0.05(↓)Chi

= 1.56, P=0.21

Species number Chi

= 1.53, P=0.22 Chi

= 0.26, P=0.61 Chi

=2.24,P=0.26 Chi

=1.26,P=0.26 Chi

= 0.65, P=0.42

Myotis/Plecotus group Chi

= 3.08, P=0.08(↑)Chi

= 0.20, P=0.65 Chi

=1.92,P=0.17 Chi

=1.32,P=0.25 Chi

= 0.48, P=0.49

Nyctaloid group Chi

= 3.34, P=0.07(↓)Chi

= 0.36, P=0.54 Chi

=1.29,P=0.26 Chi

=3.72,P=0.05(↓)Chi

= 0.83, P=0.36

Pipistrelloid group Chi

= 2.62, P=0.11 Chi

= 0.32, P=0.57 Chi

=1.05,P=0.31 Chi

=3.07,P=0.08(↓)Chi

= 2.82, P=0.09

Fig. 1 Bat activity and number of

bat species (mean and standard

error of the mean) in coniferous

plantations and mixed deciduous

forest did not differ significantly

between both forest types (bat

activity: Chi

=1.56,P=0.20,

GLM; number of bat species:

Chi

=0.65,P= 0.42, see

Table 2). For bat activity, addi-

tional post hoc Tukey’s pairwise

comparisons of categorical vari-

able ‘forest type’have been con-

ducted:

z.ratio = 1.23, P=0.22

Page 5 of 12 1Eur J Wildl Res (2021) 67: 1

Impact of spatial variables

There were no significant effects of forest type, but we found

that distance to the next water body had a positive impact on

the activity level of the Nyctaloid group, as well as on the

overall bat activity. Water bodies play an important role as a

bat habitat, as they are visited for drinking, offer an abundance

of prey (Fukui et al. 2006) and are landmarks for bat orienta-

tion and navigation (Serra-Cobo et al. 2000).

Habitat connectivity is a very important driver for bat di-

versity and density, while fragmentation and isolation, in con-

trast, negatively affect bats (Gorresen and Willig 2004;Yates

and Muzika 2006;Lesiński et al. 2007) especially in the pres-

ence of wind turbines (Roscioni et al. 2014). The amount of

forest area did not affect our three functional bat groups.

However, generally, the amount of forest enhances bat activ-

ity, which corroborates the great importance of woodlands

(e.g. Jantzen and Fenton 2013; Kalda et al. 2015; Neece

et al. 2018). In contrast, higher stocking levels and lesser

distances between tree trunks can decrease the activity of cer-

tain species (Runkel 2009). This is especially true for

Pipistrellus species and barbastelle bats (Barbastella

barbastellus) that are active in semi-open airspace and are

dependent on thinnings (Kaňuch and Krištín 2005; Dietz

et al. 2009).

Impact of wind development in plantations

Harvesting operations in forests do not differ crucially from

interventions in monoculture plantations. They are usually

excluded from any BACI (before-after-control-impact design,

Smith 2002) examination (Mortimer 2006) and nevertheless

lead to fragmentation, depreciation or loss of habitats (Farneda

et al. 2015). The impact of wind turbine construction in forests

is no exception at first, as habitat alteration through linear

clearings for the installation and maintenance may result in

habitat alteration or roost loss as well (Meschede and Heller

2000). Still, the extent of interference in plantations varies

greatly depending on the tree species planted. While the loss

of roosting opportunities due to felling in Picea sitchensis

plantations is limited (Kirkpatrick et al. 2017a), several bat

species are affected by possible roost loss in Pinus nigra and

Pinus sylvestris plantations (Mortimer 2006,pers.

observation). The main difference between the impact of har-

vesting and wind turbine construction is the possible attraction

effects for bats at wind turbines (Cryan et al. 2014). Habitat

alteration, attracting lights or accumulated insect prey at wind

turbines, may also foster an increased bat activity in forest

plantations due to increased foraging opportunities especially

for Nyctalus or Pipistrellus species (Ober et al. 2020;

Kirkpatrick et al. 2017a). Installed wind turbines could act

as an ecological trap in the newly emerged foraging areas

(Tscharntke et al. 2012). Yet, a comparative study with a

broad dataset of 193 nacelle height monitoring surveys

showed that the overall activity at nacelle height is not neces-

sarily higher at wind turbines in forest habitats compared to

open habitats (Reers et al. 2017). Our results show a high bat

activity over the canopy of monocultural plantations.

Moreover, Reers et al. (2017) argue that bat activity also needs

to be measured at the lowest rotor outreach. Thus, longer-term

monitoring of possible impact zones should be considered for

wind turbines with larger rotor diameter. Currently, the eval-

uation of negative effects of wind energy development in for-

ests is limited because bat fatality searches are incredibly dif-

ficult; the heterogeneous environment of forest grounds hin-

ders finding possible carcasses or carcasses may get caught in

the surrounding trees (an umbrella effect). Snapshot acoustic

monitoring or the limits of carcass searches may underesti-

mate the collision risk for bats in forested areas.

Fig. 2 Ordination plot (NMDS, 2 dimensions, stress = 0.18) based on bat

species activity (number of bat passes). Forest types are displayed as

triangles (= coniferous plantations) and circles (= mixed deciduous

forests). Environmental variable did not affect species distribution (amount

of forest patches: F=0.1,R

=0.01,P= 0.96; connectivity: F=0.4,R

0.02, P= 0.73; distance to forest edge: F=0.4,R

=0.02,P= 0.75; distance

to next water body: F=1.4,R

= 0.06, P= 0.25; forest type: F=2.1,R

0.09, P= 0.10; Permutational multivariate analysis of variance with 9999

permutations). Species abbreviations: B.bar = Barbastella barbastellus,

E.ser = Eptesicus serotinus,M.bra=Myotis brandtii/mystacinus,M.dau=

Myotis daubentonii, M.myo = Myotis myotis,M.nat=Myotis nattereri,

N.lei = Nyctalus leisleri, N.noc = Nyctalus noctula, P.nat = Pipistrellus

nathusii,P.pip=Pipistrellus pipistrellus, P.pyg = Pipistrellus pygmaeus,

Pl.au = Plecotus auritus/austriacus

1 Page 6 of 12 Eur J Wildl Res (2021) 67: 1

Conclusions

Mono-specific forest plantations can be considered just as

valuable bat habitats as near-natural or semi-natural wood-

lands. This is especially true when water bodies are existent

in the immediate vicinity because these habitats have an over-

all positive effect on bat activity. While many studies use

acoustic snapshots to compare bat activity or even conduct

risk assessment during planning processes, our study strongly

demonstrates the importance of long-term monitoring above

the canopy to evaluate bat activity. All forest types potentially

harbour a diverse bat fauna with high species activity.

Therefore, wind turbine development in mono-specific forest

plantations is far from ecologically sound. Our results suggest

that environmental impact assessments and mitigation mea-

sures are vital in all forest types before and after planning

for wind energy turbines. In this context, it is mandatory to

include the entire bat fauna by also focussing on species hunt-

ing above the canopy level, because migration behaviour and

species composition may differ among different forest strata.

We are aware that industrial wind energy production is needed

to meet the growing demand for energy but, if bat activity is

high, we need to include mitigation measures for all habitat

types to assure sustainable development.

Acknowledgements We would like to thank Fred Albespy, Bela

Catherin Bruhn, Charlotte Foisl, Michael Schlockermann, Jan Skroblin

and Lena Strauss for providing assistance with fieldwork. Many thanks to

Tanja Straka for giving feedback on the manuscript and Monika Egerer

for linguistic revision.

Funding Open Access funding enabled and organized by Projekt DEAL.

Appendix 1

Fig. 3 Location of the study

region in Germany (a)and

location of study plots

(dots = coniferous plantations and

triangles = deciduous mixed

forests) (b). Inlet cshows an

example of a study site together

with the landscape configuration.

The coordinates can be taken

from Appendix Table 3

Page 7 of 12 1Eur J Wildl Res (2021) 67: 1

Table 3 Raw data

Species

Site Coordinates Forest type Distance Connect Forest Dist. water Sample days Total calls Species richness Bbar Eser Mdau Mmyo Mnat

S1 52.209301 12.994693 Deciduous mixed 20 526 78.8 2288.48 120 7552 7 2 63 1 0 0

S2 53.131853 12.281886 Deciduous mixed 9 371 67.6 1785.97 132 5377 9 2 164 0 5 44

S3 52.327387 12.816236 Coniferous 67 1300 85.2 1892.01 118 3414 9 0 12 5 2 1

S4 52.321702 12.833637 Deciduous mixed 438 892 100 2306.7 106 29,832 7 0 922 0 0 2

S5 52.437083 14.407141 Deciduous mixed 20 1126 12.3 557.95 94 3859 6 77 4 0 0 0

S6 52.711300 13.851796 Coniferous 905 0 100 1968.43 133 2775 7 8 0 7 0 0

S7 52.709137 13.847546 Deciduous mixed 183 402 95.7 1354.22 149 3046 10 23 16 14 2 2

S8 52.705288 13.864434 Deciduous mixed 69 132 89.8 759.3 149 4587 10 20 70 13 5 1

S9 52.069321 14.313989 Coniferous 66 1502 62.5 1449.37 171 2594 8 2 3 3 2 0

S10 52.064800 14.331467 Coniferous 325 1725 100 1726.15 92 1085 5 0 7 0 0 0

S11 52.073611 12.738917 Coniferous 235 846 100 1896.64 175 6461 8 7 41 1 0 0

S12 51.907136 13.360436 Coniferous 655 1753 99 370.63 168 2026 10 1 2 3 2 3

S13 51.512111 13.857972 Deciduous mixed 45 771 80.2 2483.12 57 899 5 0 6 0 1 0

S14 51.537869 13.868861 Coniferous 35 2443 93.6 510.9 63 3513 7 2 0 3 0 2

S15 52.09886 12.800883 Coniferous 6 0 62.1 1984.15 121 3513 9 4 40 2 0 2

S16 52.254468 14.233894 Coniferous 164 491 97.5 2022.65 136 4565 9 7 13 3 0 1

S17 52.250753 14.227750 Deciduous mixed 193 556 100 2890.41 140 6013 7 2 19 2 0 0

S18 52.210028 13.377722 Coniferous 30 990 36.8 1131 183 19,198 9 1 542 1 0 2

S19 52.689167 13.640000 Deciduous mixed 3 402 36.3 1797.74 145 18,243 8 7 3 0 2 0

S20 52.046885 14.312772 Coniferous 318 2008 99.2 336.9 164 5078 8 2 10 0 3 1

S21 52.348281 13.700361 Coniferous 290 2644 90.5 1642.63 134 2520 6 0 16 1 0 0

S22 51.516972 14.436722 Deciduous mixed 62 1043 66.9 1258.4 88 829 7 1 2 0 1 0

S23 51.528111 14.414944 Deciduous mixed 140 682 96 1098.81 147 2671 10 2 5 8 6 5

S24 51.543556 14.443361 Deciduous mixed 508 1184 93.1 525.35 153 3523 10 12 69 5 1 1

Species Species groups Functional group

Site Nlei Nnoc Pnat Ppip Ppyg Mbra Myotis sp. Nyctaloid Pipistrelloid Plecotus sp. Group total Nyctaloid group Pipistrelloid group Myotis group Myotis/Plecotus group

S1 0 4309 269 2034 31 0 3 667 0 9 7541 5197 2334 10 19

S2 11 1266 220 1004 13 9 57 1809 0 11 5364 3961 1237 166 177

S3 1 1683 224 875 209 9 31 239 0 7 3407 2023 1308 76 83

S4 11 2660 171 4404 4876 3 38 16,253 95 20 29,812 20,210 9546 56 76

S5 0 2007 494 867 6 1 3 314 17 4 3778 2387 1384 7 11

S6 7 1424 110 756 29 1 35 256 24 1 3017 1832 1114 71 72

Appendix 2

1 Page 8 of 12 Eur J Wildl Res (2021) 67: 1

Table 3 (continued)

S7 6 1306 114 986 14 4 24 365 0 6 2766 1785 919 62 68

S8 9 1667 263 1510 37 4 41 732 0 8 4559 2634 1810 115 123

S9 0 1592 241 116 284 0 30 188 0 14 2578 1882 641 55 69

S10 0 746 128 24 21 0 5 117 9 2 1083 895 182 6 8

S11 1 3589 354 398 6 1 4 1410 1 5 6447 5677 759 11 16

S12 1 1376 133 66 5 5 17 295 3 11 2014 1763 207 44 55

S13 0 157 91 595 0 0 8 16 0 5 974 522 332 120 125

S14 0 1259 311 1609 6 5 70 142 8 8 3503 1452 1934 117 125

S15 10 1722 98 506 12 1 12 809 0 16 3493 2852 616 25 41

S16 3 3248 193 398 181 4 27 326 39 11 4547 3687 811 49 60

S17 4005 227 672 450 2 28 348 114 9 6002 4474 1463 65 74

S18 5 6372 775 1981 24 2 18 8073 494 12 19,185 15,879 3274 32 44

S19 6 8350 1068 6401 50 1 20 2020 38 8 18,228 10,630 7557 41 49

S20 2898 282 213 512 4 159 613 0 27 5049 3773 1007 269 296

S21 0 1982 167 26 2 0 4 251 0 0 2520 2317 195 8 8

S22 0 415 78 46 4 6 29 159 0 7 821 636 128 57 64

S23 2 1343 138 101 9 6 52 413 362 17 2650 1941 610 99 116

S24 1 1127 186 167 1 5 26 1478 5 62 3447 3030 359 58 120

Page 9 of 12 1Eur J Wildl Res (2021) 67: 1

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