Mono-specific forest plantations are valuable bat habitats: implications for wind energy development [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

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