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Greving, H., T. Bruckermann, A. Schumann, T. M. Straka, D. Lewanzik, S. L. Voigt-Heucke, L. Marggraf, J. Lorenz, M. Brandt, C.
C. Voigt, U. Harms and J. Kimmerle. 2022. Improving attitudes and knowledge in a citizen science project about urban bat ecology.
Ecology and Society 27(2):24. https://doi.org/10.5751/ES-13272-270224
Research
Improving attitudes and knowledge in a citizen science project about urban
bat ecology
Hannah Greving 1 , Till Bruckermann 2,3 , Anke Schumann 4, Tanja M. Straka 4,5 , Daniel Lewanzik 4 , Silke L. Voigt-
Heucke 4,6 , Lara Marggraf 4 , Julia Lorenz 4,6, Miriam Brandt 4 , Christian C. Voigt 4 , Ute Harms 3 and Joachim
Kimmerle 1,7
ABSTRACT. In order to deal with the current, dramatic decline in biodiversity, the public at large needs to be aware of and participate
in biodiversity research activities. One way to do this is citizen science projects, in which researchers collaborate with volunteering
citizens in scientific research. However, it remains unclear whether engaging in such projects has an impact on the learning outcomes
of volunteers. Previous research has so far presented mixed results on the improvement of citizens’ attitudes and knowledge, mostly
because such research has focused only on single aspects of citizen science projects in case studies. To address these limitations, we
investigated the impact of an urban bat ecology project on citizens’ attitudes and knowledge about bats, and on their engagement with
citizen science. We also examined whether the degree of citizen participation (i.e., collecting data vs. collecting and analyzing data) had
an influence on the outcomes. We conducted four field studies and used a survey-based, experimental, pre-/post-measurement design.
To vary the degree of participation, we assessed the post measurement in one group directly after data collection, whereas, in a second
group, we assessed it after data collection and analysis, at the end of the project. Across all studies, the results demonstrated that citizens’
content knowledge of urban bat ecology increased, and their attitudes toward bats and toward their engagement in citizen science
improved during their participation. Citizens’ degrees of participation did not influence these outcomes. Thus, our research illustrates
that citizen science can increase awareness of urban bat conservation, independently of citizens’ degree of participation. We discuss
the implications of our findings for the citizen science community.
Key Words: attitudes; bats; citizen science; content knowledge; ecology
INTRODUCTION
Projects for public participation in scientific research have the
potential to address scientific and societal issues (Shirk et al.
2012), such as the conservation of biodiversity. One way citizens
can participate in such research is to volunteer for citizen science
projects, in which professional scientists collaborate with
volunteers in scientific research (Heigl et al. 2019). For examples
of citizen science see https://www.birds.cornell.edu/citizenscience
(Bonney et al.2009), https://www.ispotnature.org (Silvertown et
al. 2015), and https://www.zooniverse.org (Cox et al. 2015).
Previous research has highlighted the potential benefits of citizen
science projects for citizens’ individual learning outcomes, among
other outcome categories (Shirk et al. 2012, Phillips et al. 2018).
In particular, it has been suggested that citizens might gain
knowledge and skills, or change their attitudes or behavior (Bela
et al. 2016). It has also been assumed that such projects increase
citizens’ feelings of psychological ownership for the citizen science
project (Pierce et al. 2001, 2003) and feelings of pride in their
participation (Rotman et al. 2014, Jordan et al. 2015, Haywood
et al. 2016, Lewis 2016).
However, the potential of citizen science to increase such learning
outcomes is not well understood, because robust scientific
evidence is lacking (Toomey and Domroese 2013, Jordan et al.
2015, Phillips et al. 2018). Even though most citizen science
researchers agree that such projects should increase citizens’
content knowledge of the project topic and improve their attitudes
toward the topic and toward citizen science and science in general
(see also Bruckermann et al. 2021a), research results are mixed.
Although some research has demonstrated increases in content
knowledge (Brossard et al. 2005, Trumbull et al. 2005, Jordan et
al. 2011), other research has found little or no improvement in
scientific understanding or attitudes (Trumbull et al. 2000, Crall
et al. 2013), or has not systematically investigated outcomes such
as ownership and pride (for an exception see Greving et al. 2020).
There may be two reasons for these mixed findings. First, they
may be caused by a lack of clearly conceptualized measures of
learning outcomes (Becker-Klein et al. 2016, Phillips et al. 2018,
Peter et al. 2019). For example, instruments consisting of several
questions were used that were based on participants’ self-reports,
but these instruments had low internal consistencies (Brossard et
al. 2005, Crall et al. 2013). Other research used only indicator
variables, e.g., interest for motivation (Rotman et al. 2014), and
relied on subjective assessments of knowledge and attitude
changes (Toomey and Domroese 2013). Second, the mixed
findings may have been caused by a lack of rigorous study designs,
e.g., experimental studies (Phillips et al. 2018, Dickinson and
Crain 2019, Aristeidou and Herodotou 2020, Kloetzer et al.
2021). Indeed, many previous studies only described citizen
science projects without using any statistical tests (Fernandez-
Gimenez et al. 2008, Toomey and Domroese 2013). If using
statistics, these studies were mostly pre-/post-test studies
(Druschke and Seltzer 2012, Sickler et al. 2014, Peter et al. 2019).
Such methods may have prevented previous researchers from
1Leibniz-Institut für Wissensmedien, Tübingen, 2Leibniz University Hannover, 3IPN - Leibniz Institute for Science and Mathematics Education,
Kiel, 4IZW - Leibniz Institute for Zoo and Wildlife Research, Berlin, 5Technische Universität Berlin, 6MfN - Natural History Museum Berlin,
Leibniz Institute for Evolution and Biodiversity Research, 7Eberhard Karls University of Tübingen
Ecology and Society 27(2): 24
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Fig. 1. Schematic overview of the pre-/post-measurement design with additional
experimental manipulation of degree of participation (data collection only group vs. data
collection and analysis group).
drawing more general conclusions concerning the extent to which
citizens’ participation can improve their individual learning
outcomes (Masters et al. 2016).
According to models of public participation in scientific research
(PPSR; Shirk et al. 2012), the degree of citizens’ participation is
the extent to which they are involved in different steps of the
scientific research process. These models of PPSR represent
different project models that vary in the possible degree of
citizens’ participation. In contributory project models, citizens
only collect and contribute data to scientific research, whereas in
collaborative projects, they additionally engage in data analysis
to interpret the research findings (Shirk et al. 2012). There is,
however, a lack of systematic investigation of whether the degree
of participation influences the outcomes of citizen science
projects. One experimental study focused on the effects of projects
on individual learning outcomes, and used a rigorous
experimental design (Dickinson and Crain 2019), comparable to
before-after control-impact designs (Christie et al. 2019).
Although this study found no difference between a participant
and a control group, it also did not consider the different degrees
of participation.
In the research presented here, we used an experimental design
and rigorous measures in order to analyze data from four field
studies of a citizen science project about urban bat ecology. We
investigated whether participating in the project increased
citizens’ content knowledge of urban bat ecology, and improved
their attitudes toward bats and engagement in citizen science, and
their feelings of psychological ownership and pride. To answer
these research questions, we used an experimental pre-/post-
measurement design, and varied the point in time of the post
measurement between two groups to which participants were
randomly assigned (Fig. 1). In the data collection only group,
participants engaged in data collection and, directly afterwards,
completed the post measure. In the data collection and analysis
group, we assessed the post measure after both data collection
and data analysis were completed. Although we expected, overall,
that participation in the project would have a positive effect on
citizens’ attitudes and knowledge, we also assumed that a higher
degree of participation (i.e., participating in both data collection
and analysis) should be even more beneficial for improving
citizens’ learning outcomes (Lawrence 2006, Bonney et al. 2009).
Therefore, we stated the following hypotheses:
. Attitudes toward bats improve during participation
(hypothesis 1a); this improvement is stronger for the data
collection and analysis group than for the data collection
only group (hypothesis 1b).
. Content knowledge for bat ecology increases during
participation (hypothesis 2a); this increase is stronger for the
data collection and analysis group than for the data
collection only group (hypothesis 2b).
. Attitudes toward engagement in citizen science improve
during participation (hypothesis 3a); this improvement is
stronger for the data collection and analysis group than for
the data collection only group (hypothesis 3b).
We exploratively tested the effects of participation and degree of
participation on psychological ownership and pride.
METHODS
In order to test our hypotheses, we conducted four field studies
using identical procedures (Table 1). These studies were part of a
citizen science project about urban bat ecology called “Bat
Researchers” that took place in a German metropolitan city. The
biological aim of the project was to investigate the presence of
bats in the urban ecosystem. The citizens’ task was to walk along
a pre-defined route on two evenings during a two-week period
and record the echolocation calls of flying bats with a bat detector
capable of detecting and recording ultrasonic frequencies. After
the data collection only group had completed their walks, they
returned the bat detectors to the project scientists. Then, the data
collection and analysis group did their evening walks and after
completion handed over the bat detectors to the project scientists.
Based on the ultrasonic recordings on the bat detectors, the
scientists identified the bat species and provided the data to both
groups for further analysis and discussion of the results.
We used an online platform for all the other activities that
participants could perform in the project besides data collection
with the bat detector. In particular, the platform provided tutorials
for the identification of bat species and information about urban
bat ecology to support participants in data collection and analysis.
On this platform, participants uploaded their collected data and
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Table 1. Descriptives for each field study and across all field studies including conduction period, N, gender, age, education, and mother
tongue
Field study Field study 1 Field study 2 Field study 3 Field study 4 All field studies
Conduction period April/May 2019 Sept./Oct. 2019 May/June 2020 Sept./Oct. 2020 April 2019-Oct. 2020
N37 38 34 30 139
Gender 24 female
13 male
20 female
18 male
15 female
19 male
20 female
9 male
1 diverse
79 male
59 female
1 diverse
Age: M (SD),
range
46.65 (12.90), 18-66 43.53 (11.74), 24-78 41.00 (11.56), 19-62 44.87 (12.70), 20-70 44.03 (12.27), 18-78
Education: Top 3 56.8% university
degree
16.2% general
qualification for
university entrance
8.1% doctoral/
postdoctoral degree
68.4% university
degree
7.9% doctoral/
postdoctoral degree
7.9% qualification
for advanced
technical college
entrance
64.7% university
degree
14.7% general
qualification for
university entrance
8.8% doctoral/
postdoctoral degree
63.3% university
degree
10.0% doctoral/
postdoctoral degree
10.0% general
qualification for
university entrance
63.3% university
degree
11.5% general
qualification for
university entrance
8.6% doctoral/
postdoctoral degree
Mother tongue 37 German 36 German
2 other
34 German 26 German
4 other
133 German
6 other
Note: We tested for differences between the field studies concerning the dependent variables and the explorative variables. But the
field studies mostly did not differ from each other on each of these variables: attitude toward bats: T1: F(3, 135) < 1, ns, T2: F(3,
135) = 2.28, p = 0.082; content knowledge: T1: F(3, 135) < 1, ns, T2: F(3, 135) < 1, ns; attitudes toward engagement in CS:
attitudes: T1: F(3, 135) = 1.45, p = 0.233, T2: F(3, 135) < 1, ns; intentions: T1: F(3, 135) < 1, ns, T2: F(3, 135) < 1, ns; behavioral
beliefs: T1: F(3, 135) < 1, ns, T2: F(3, 135) < 1, ns; control beliefs: T1: F(3, 135) < 1, ns, T2: F(3, 135) = 1.05, p = 0.372; normative
beliefs: T1: F(3, 135) < 1, ns, T2: F(3, 135) = 1.28, p = 0.283; psychological ownership: T1: F(3, 135) < 1, ns, T2: F(3, 135) < 1, ns;
pride: T1: F(3, 135) < 1, ns, T2: F(3, 135) = 2.94, p = 0.035. At T2, participants of the second field study were prouder of their
participation (M = 3.97, SD = 0.99) than participants of the fourth field study (M = 3.31, SD = 0.78), Mdiff = 0.66, SE = 0.23, p =
0.005.
downloaded the species identifications provided by the scientists.
They had the opportunity to analyze their own data as well as the
complete dataset of all routes on which participants collected
data. They could, for example, examine the correlations between
bat activity and environmental features, such as proximity to
water or tree cover. To analyze the data, participants followed a
structured analysis process comparable to the usual scientific
analysis process, i.e., formulate the research question, formulate
hypotheses, specify the independent and dependent variables as
well as their relationship, run tests for differences or for
associations, and inspect, visualize, and interpret the findings.
Citizens could discuss their findings and questions concerning the
project and the topic with other citizens and with the project
scientists in a forum.
Via this platform, participants also filled out questionnaires. After
filling out the pre-measure questionnaire (T1), participants in the
data collection only group (N = 64) filled out the post-measure
questionnaire (T2) after data collection was completed.
Participants in the data collection and analysis group (N = 75)
filled out the post-measure questionnaire (T2) after both data
collection and data analysis were completed (Fig. 1). All of the
measures (Table 2) at T1 and T2 were identical in all four field
studies. Other measures not reported here were emotions toward
bats, attitudes toward science, epistemological beliefs, and
motivation. Demographic data were only assessed at T1. An
institutional ethics committee approved both questionnaires
(ethics approval number: LEK 2018/062).
Dropout analysis and participants
We recruited participants via public outreach campaigns targeted
at the general public. These participants themselves chose to
participate in the project, were very likely quite interested in bats,
and were willing to invest their leisure time in participating in the
project. Each recruited participant could only participate once in
one of the field studies, and each participant recorded the bats’
echolocation calls with the bat detector. Across all four field
studies, 224 participants filled out the pre-measure questionnaire,
and 139 participants also completed the post-measure
questionnaire. This was a dropout rate of 37.9% of those filling
out the questionnaire. However, these participants did not drop
out of the project. Participants who dropped out did not differ
from those participants who completed both questionnaires in
their gender, χ²(2) = 0.78, p = 0.678. Participants who completed
both questionnaires were older, t(222) = -2.88, p = 0.004, and had
a higher level of education, t(222) = -0.29, p = 0.023, than those
participants who only filled out the pre-measure questionnaire.
Thus, we included 139 participants in our analyses; see Table 1
for demographics.
Measures
The details of each measure are presented in Table 2. We assessed
participants’ attitudes toward bats with 12 rating-scale items
based on general attitude approaches (Bohner and Dickel 2011,
Albarracin and Shavitt 2018). To measure citizens’ content
knowledge of urban bat ecology, we pre-identified the most
relevant topics from the perspective of citizens and scientists
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Table 2. Measures used in the field studies with their number of items, example items, Cronbach’s alphas, means, standard deviations,
and references
Variable N items Example αT1 αT2 MT1 (SDT1)MT2 (SDT2) References
Attitudes toward
bats
12 (RS) “Bats are fascinating animals.” 0.67 0.61 4.68 (0.28) 4.73 (0.24) Albarracin and
Shavitt 2018
Content knowledge 29 (SC/
MC)
“Which statement about bat
reproduction is correct?”
0.47 0.47 55.38%
(9.10%)
59.32%
(8.43%)
Bruckermann et al.
2022
Attitudes toward engagement in citizen science: 0.88 0.87 3.68 (0.58) 3.81 (0.55) Summers and Abd‐
El‐Khalick 2018
Attitude 3 (RS) “Citizen science projects make
sense.”
0.82 0.80 4.39 (0.59) 4.49 (0.57) Summers and Abd‐
El‐Khalick 2018
Intentions 3 (RS) “I will engage in citizen science
projects in the future.”
0.94 0.92 4.16 (0.82) 4.30 (0.75) Summers and Abd‐
El‐Khalick 2018
Behavioral beliefs 3 (RS) “Citizen science projects help me
understand the world around
me.”
0.76 0.77 3.70 (0.80) 3.70 (0.80) Summers and Abd‐
El‐Khalick 2018
Control beliefs 3 (RS) “Participating in citizen science
projects is easy for me.”
0.76 0.64 3.69 (0.70) 3.98 (0.65) Summers and Abd‐
El‐Khalick 2018
Normative beliefs 3 (RS) “Some of my peers engage in
citizen science projects.”
0.83 0.81 2.45 (1.06) 2.61 (1.08) Summers and Abd‐
El‐Khalick 2018
Psychological
ownership
3 (RS) “The ‘Bat Researchers’ project
feels like it is mine.”
0.82 0.85 1.96 (0.86) 2.02 (0.94) Peck and Shu 2009,
Pierce et al. 2001
Pride 3 (RS) “When I think about my
participation in the ‘Bat
Researchers’ project, I am proud
of myself.”
0.79 0.82 3.72 (0.98) 3.61 (0.97) Lewis 2016,
Lewis and Sullivan
2005
Note: RS = rating scale on a 5-point scale ranging from 1 (does not apply at all) to 5 (completely applies), SC = single-choice
questions, MC = multiple-choice questions.
(Bruckermann et al. 2022) by means of a Delphi approach (e.g.,
Blanco-López et al. 2015). Using these topics as a basis, we then
constructed 29 single- and multiple-choice questions. Finally, we
divided participants’ correct answers by the total number of
questions and assessed their content knowledge as the percentage
of correct answers.
We assessed participants’ attitudes toward engagement in citizen
science with five underlying dimensions (Summers and Abd‐El‐
Khalick 2018) following the theory of planned behavior (Ajzen
1991, Fishbein and Ajzen 2010). With three rating-scale items
each, we measured attitudes toward citizen science, intentions to
engage in citizen science projects, behavioral beliefs, control
beliefs, and normative beliefs. Similarly, we measured
psychological ownership (Pierce et al. 2001, 2003, Peck and Shu
2009) as well as pride (Lewis and Sullivan 2005, Lewis 2016) with
three rating-scale items each.
Statistical analysis
To test our hypotheses, we conducted mixed analyses of variance
(ANOVAs) with degree of participation (data collection only
group vs. data collection and analysis group) as between-group
factor and participation (between the measurement points T1 vs.
T2) as within-group factor in all analyses. We used SPSS Version
22.0 for this purpose (IBM Corporation 2013). We set the level
of significance < 0.05 and used two-tailed tests throughout all
analyses.
RESULTS
All test statistics are presented in Table 3. Compared with T1, all
participants had a more positive attitude toward bats and more
content knowledge of urban bat ecology at T2 (Table 2, Fig. 2),
which supported hypotheses 1a and 2a. There were no further
effects, which did not support hypotheses 1b and 2b.
Fig. 2. Means and standard errors for the dependent variables
attitudes toward bats and content knowledge about bats, and for
the explorative variables ownership and pride for the data
collection only group (N = 64) and the data collection and
analysis group (N = 75) for the first (T1) and second
measurement point (T2).
For the five underlying dimensions of attitudes toward engagement
in citizen science, there were similar results. Compared with T1,
participants in both groups had a more positive attitude, higher
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Table 3. Test statistics for the main effect of participation, the main effect of degree of participation, and the interaction effect between
participation and degree of participation for the dependent variables attitude toward bats, content knowledge, attitude toward engagement
in citizen science (CS) with its attitudinal domains attitudes, intentions, behavioral beliefs, control beliefs, and normative beliefs, and the
explorative variables psychological ownership and pride
Dependent variable Participation Degree of participation Participation × degree of
participation
F(1, 137) pηp²F(1, 137) pηp²F(1, 137) pηp²
Attitude toward bats 8.82 0.004 0.061 < 1 ns - < 1 ns -
Content knowledge 30.65 < 0.001 0.183 < 1 ns - 1.79 0.184 -
Attitude toward engagement in
CS
12.17 0.001 0.082 < 1 ns - < 1 ns -
Attitudes 5.58 0.020 0.039 < 1 ns - 1.11 0.293 -
Intentions 5.40 0.022 0.038 1.11 0.294 - 2.27 0.134 -
Behavioral beliefs < 1 ns - < 1 ns - < 1 ns -
Control beliefs 24.65 < 0.001 0.152 < 1 ns - < 1 ns -
Normative beliefs 4.38 0.038 0.031 < 1 ns - 5.03 0.027 0.035
Psychological ownership < 1 ns - 3.02 0.084 - 1.07 0.303 -
Pride 1.14 0.287 - 7.41 0.007 0.051 < 1 ns -
Note: Test statistics for the difference between the two degree of participation groups at the two measurement points for the
participation × degree of participation interaction for the dependent variable normative beliefs: T1: F(1, 137) = 2.07, p = 0.152; T2:
F(1, 137) < 1, ns.
intentions, stronger control beliefs, and stronger normative beliefs
at T2 (Table 2, Fig. 3), which supported hypothesis 3a. None of the
other effects was significant, with the exception of the interaction
effect between participation and degree of participation for
normative beliefs (Table 3). However, the degree of participation
groups did not differ at each of the measurement points. Thus,
overall, there was no support for hypothesis 3b. Finally, when we
included the five underlying dimensions of attitudes as an
additional within-group factor into the mixed ANOVA, this
analysis also found that all participants had a more positive attitude
toward engagement in citizen science in general at T2 than at T1
(Table 2).
Fig. 3. Means and standard errors for the dependent variables
attitudes toward citizen science (CS), intentions to engage in CS,
control beliefs, and normative beliefs (all belonging to attitudes
toward engagement in CS) for the data collection only group (N
= 64) and the data collection and analysis group (N = 75) for the
first (T1) and second measurement point (T2).
With respect to psychological ownership, we did not find any
significant differences. The data collection only group experienced
more pride than the data collection and analysis group, but the
other effects were not significant (Fig. 2).
DISCUSSION
The research presented here investigated the impact of a citizen
science project about urban bat ecology on citizens’ content
knowledge about bats, and attitudes toward bats and toward
engagement in citizen science. Our findings demonstrated that
knowledge increased and attitudes improved during citizens’
participation in the research process. In particular, the increase in
citizens’ content knowledge about urban bat ecology was more
pronounced than the improvement in their attitudes, which is in
line with previous research (Peter et al. 2019). Most previous
studies agree that citizen science projects enhance citizens’ content
knowledge (Druschke and Seltzer 2012, Bela et al. 2016, Haywood
et al. 2016). Findings on citizens’ attitudes have been less
conclusive and revealed small to negative changes in attitudes
(Brossard et al. 2005, Druschke and Seltzer 2012). Our study adds
to the picture by showing significant and medium-sized changes
for both attitudes toward bats and toward engagement in citizen
science. Moreover, our findings extend previous studies by not
only distinguishing between attitudes toward bats and science-
related attitudes (e.g., Peter et al. 2019), but also differentiating
among various attitudinal domains, which we captured using
multi-items measures. Based on the theory of planned behavior
(Ajzen 1991, Fishbein and Ajzen 2010), our findings showed
different changes in the attitudinal domains. They revealed
stronger changes in citizens’ beliefs about their ability to
participate in citizen science, along with no changes in their beliefs
about its usefulness for their personal lives. Thus, our research
has demonstrated that “Bat Researchers” project had the
potential to improve citizens’ learning outcomes.
Furthermore, this research set out to investigate whether the
degree of citizens’ participation in the research process has an
impact on their learning outcomes. Inquiry-based learning
opportunities combine citizens’ participation in the different steps
of the research process and in scaffolding structures that support
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their understanding (Aristeidou et al. 2020). If citizens participate
in the data collection and are supported with a tutorial to record
bats’ echolocation calls, they could increase their knowledge of
distinguishing among bat species. For instance, Prather et al. 2013
demonstrated the influence of identifying galaxies on citizens’
knowledge about galaxy morphology. If citizens participate in
data analyses and are provided with the data and a tool to test
their assumptions on the influence of environmental features on
bat species, they may develop their knowledge in a different way
and increase their understanding of urban bat ecology. Our
findings demonstrated that these degrees of citizens’ participation
did not seem to have an influence on the outcomes. This means
that citizens’ additional engagement in data analysis did not affect
any improvements in attitudes toward bats or toward engagement
in citizen science, or any increase in knowledge acquisition about
bats. Our findings extend previous research on the relationship
between degree of participation and learning outcomes by using
the exact same citizen science project and research context (i.e.,
bat ecology) in both conditions and by directly comparing
learning outcomes of citizens who could participate on a
contributory level (i.e., providing data) with those learning
outcomes of citizens who could participate on a collaborative
level (i.e., analyzing data and discussing findings with citizens and
scientists).
Our findings may also contradict previous assumptions that were
derived from the so-called “Arnstein’s ladder” (Arnstein 1969; see
Haklay 2013 for an overview). These assumptions postulated that
the higher the degree of participation, the better for citizen science
outcomes. On the one hand, participating in the offered activities
of the project was in our study enough to increase learning
outcomes, independently of the degree of participation. This
finding may be good news for the citizen science community,
because learning from participation in projects does not seem to
be limited to higher degrees of participation but may depend on
the offered activities. On the other hand, it could also be that
citizens did not engage in the data analysis sufficiently enough (T.
Bruckermann, H. Greving, M. Stillfried, A. Schumann, M.
Brandt, and U. Harms, unpublished manuscript) to have any
additional effect on the outcome measures. In line with previous
research, learning outcomes may indeed be more closely related
to the prerequisites in projects, such as citizens’ goals and abilities
for participation, e.g., motivation (Phillips et al. 2019) and
scientific reasoning skills (Stylinski et al. 2020), than to the degree
of participation (Shirk et al. 2012). Behavioral data from future
research on how citizens actually participate in different scientific
activities may help explain why engaging in the data analysis had
no additional effects. Future research may also need to specify
either the prerequisites of the participants, e.g., scientific
reasoning skills (Bruckermann et al. 2021b), or the prerequisites
of the project, e.g., training on data analyses (Gray et al. 2017),
under which citizens’ opportunities to analyze data have beneficial
effects for outcome measures in similar projects.
Finally, the findings of the explorative measures were informative.
We found that the data collection only group experienced more
pride in their participation. Here, the assumption that a higher
degree of participation would benefit outcomes also did not hold
(Shirk et al. 2012). In contrast, asking citizens directly after their
evening walks and data collection could have activated their
feelings of pride more readily. These feelings might have already
faded away for those citizens in the data analysis group who
answered the post measurement at the end of the project. Apart
from this, citizens could have seen their contribution as just
collecting data, not analyzing it (Phillips et al. 2019). This
suggestion is also in line with recent research that analyzed activity
patterns of citizens who used an online platform during a citizen
science project (T. Bruckermann, H. Greving, M. Stillfried, A.
Schumann, M. Brandt, and U. Harms, unpublished manuscript).
These data showed that citizens were mainly active during data
collection and more passive during data analysis. But more
research needs to be conducted about the conditions under which
the engagement in data analysis has beneficial effects on citizen
science outcomes.
The strengths of the studies were their standardized and rigorous
approaches. We conducted externally valid studies and used
samples of participants that were representative of typical citizen
science volunteers. The sample size was also large enough to
generate sufficient statistical power. We used established and
objective measures that, overall, had sufficient internal
consistencies. Moreover, by employing both a data collection only
group and a data collection and analysis group, the long-debated
construct of degree of participation (Shirk et al. 2012) was
successfully implemented and experimentally tested, a relevant
step forward for the citizen science community.
There were also some limitations. First, the results revealed that
citizens’ attitudes toward bats and toward engagement in citizen
science improved. On the one hand, these are merely attitudes and
it is unclear whether citizens would also act in accordance with
their attitudes. Thus, there may be a gap between attitude and
behavior in the areas of bat conservation and engagement in
citizen science. On the other hand, there is a solid body of research
and several frameworks that clearly indicate that attitudes are
highly relevant predictors of behavioral intentions and actual
behavior, e.g., theory of planned behavior (Ajzen 1991, Fishbein
and Ajzen 2010; for other models see Sheeran et al. 1999, Webb
and Sheeran 2006, Albarracin and Shavitt 2018). This means that,
although we did not assess actual behavior, the changes that we
found in attitudes have the potential to initiate behavioral changes
in citizens. Second, we developed our questionnaire on content
knowledge based on questions that citizens frequently ask about
bats living in the city. The changes in knowledge might have been
different if we had asked citizens for their formal scientific
knowledge of bat ecology instead of their specific local knowledge
(Stocklmayer and Bryant 2012).
Third, our project about urban bat ecology was open to and
directed at the public, and citizens could apply for it if they were
interested in participating. Thus, we did not analyze a sample that
was representative of the general population, but rather a self-
selected sample of citizens who showed a general interest in bats,
meaning the findings of our studies may be limited to people who
are already enthusiastic about bats. We also had a high dropout
rate of citizens who did not fill out the post measurement,
although they continued participating in the project itself. This
dropout may have been caused by the fact that participation in
the whole project and the questionnaires was completely
voluntary; we did not give citizens any incentives for their
participation. Researchers could possibly pay monetary
incentives to their participants for completing questionnaires in
Ecology and Society 27(2): 24
https://www.ecologyandsociety.org/vol27/iss2/art24/
future studies. This could help address both concerns, i.e., create
a more diverse and representative sample, and decrease dropout
across the points of measurement.
Finally, the sample size of the participating citizens across the
field studies created enough statistical power to conduct mixed
ANOVAs. But with an even larger sample size, we could have
calculated larger path models with latent variables to test our
hypotheses (Bruckermann et al. 2021a). Because of the expected
sample size, we also implemented the variation in degree of
participation with two groups. With a larger sample size, we
could have also measured citizens’ actual level of participation
in the different scientific tasks and could have used those
measurements as predictors in the models. On the other hand,
such an approach could have produced subsamples with totally
different sample sizes, because citizens might have rather engaged
in data collection than data analysis (T. Bruckermann, H.
Greving, M. Stillfried, A. Schumann, M. Brandt, and U. Harms,
unpublished manuscript).
CONCLUSION
In summary, our research investigated the impact of a citizen
science project about urban bat ecology on citizens’ knowledge
acquisition about urban bat ecology, and their attitudes toward
bats and toward engagement in citizen science. Our findings
present evidence that attitudes and knowledge improved during
citizens’ participation, largely independently of their degree of
participation (i.e., whether they only engaged in data collection,
or in data collection and analysis). Thus, if citizen science
practitioners wish to conduct a project in order to increase
citizens’ attitudes and knowledge, it may be enough to engage
them in data collection along with the other offered activities (e.
g., tutorials), because additional data analysis did not alter the
effect. However, we acknowledge that, if citizens understand and
learn in the future that they can also be valuable data analysts,
additional engagement in data analysis may have the potential
to further improve attitudes and increase knowledge.
Responses to this article can be read online at:
https://www.ecologyandsociety.org/issues/responses.
php/13272
Author Contributions:
Hannah Greving and Till Bruckermann equally contributed to this
article and share first authorship.
Acknowledgments:
This work was supported by the German Federal Ministry of
Education and Research (BMBF) under Grants [01|O1725, 01|
O1727, 01|O1728]. The funding source was neither involved in the
conducting of the research nor the preparation of the article.
Data Availability:
The data/code that support the findings of this study are openly
available in psycharchives at https://doi.org/10.23668/psycharchives.5363.
Ethical approval for this research study was granted by the Leibniz-
Institut für Wissensmedien, Tübingen, Germany, ethics approval
number LEK 2018/062.
LITERATURE CITED
Ajzen, I. 1991. The theory of planned behavior. Organizational
Behavior and Human Decision Processes 50(2):179-211. https://
doi.org/10.4135/9781446249215.n22
Albarracin, D., and S. Shavitt. 2018. Attitudes and attitude
change. Annual Review of Psychology 69:299-327. https://doi.
org/10.1146/annurev-psych-122216-011911
Aristeidou, M., and C. Herodotou. 2020. Online citizen science:
a systematic review of effects on learning and scientific literacy.
Citizen Science: Theory and Practice 5(1):69. https://doi.
org/10.5334/cstp.224
Aristeidou, M., E. Scanlon, and M. Sharples. 2020. Learning
outcomes in online citizen science communities designed for
inquiry. International Journal of Science Education, Part B 10
(4):277-294. https://doi.org/10.1080/21548455.2020.1836689
Arnstein, S. R. 1969. A ladder of citizen participation. Journal
of the American Institute of Planners 35(4):216-224. https://doi.
org/10.4324/9780429261732-36
Becker-Klein, R., K. Peterman, and C. Stylinski. 2016. Embedded
assessment as an essential method for understanding public
engagement in citizen science. Citizen Science: Theory and
Practice 1(1):305. https://doi.org/10.5334/cstp.15
Bela, G., T. Peltola, J. C. Young, B. Balázs, I. Arpin, G. Pataki, J.
Hauck, E. Kelemen, L. Kopperoinen, A. van Herzele, et al. 2016.
Learning and the transformative potential of citizen science.
Conservation Biology 30(5):990-999. https://doi.org/10.1111/
cobi.12762
Blanco-López, Á., E. España-Ramos, F. J. González-García, and
A. J. Franco-Mariscal. 2015. Key aspects of scientific competence
for citizenship: a Delphi study of the expert community in Spain.
Journal of Research in Science Teaching 52(2):164-198. https://
doi.org/10.1002/tea.21188
Bohner, G., and N. Dickel. 2011. Attitudes and attitude change.
Annual Review of Psychology 62:391-417. https://doi.
org/10.1146/annurev.psych.121208.131609
Bonney, R. E., H. L. Ballard, R. C. Jordan, E. McCallie, T. B.
Phillips, J. L. Shirk, and C. C. Wilderman. 2009. Public
participation in scientific research: defining the field and assessing
its potential for informal science education. A CAISE Inquiry
Group Report, Center for Advancement of Informal Science
Education (CAISE), Washington, D.C., USA.
Brossard, D., B. V. Lewenstein, and R. E. Bonney. 2005. Scientific
knowledge and attitude change: the impact of a citizen science
project. International Journal of Science Education 27
(9):1099-1121. https://doi.org/10.1080/09500690500069483
Bruckermann, T., H. Greving, A. Schumann, M. Stillfried, K.
Börner, S. E. Kimmig, R. Hagen, M. Brandt, and U. Harms.
2021a. To know about science is to love it? Unraveling cause-effect
relationships between knowledge and attitudes toward science in
citizen science on urban wildlife ecology. Journal of Research in
Science Teaching 58(8):1179-1202. https://doi.org/10.1002/
tea.21697
Ecology and Society 27(2): 24
https://www.ecologyandsociety.org/vol27/iss2/art24/
Bruckermann, T., T. M. Straka, M. Stillfried, and M. Krell. 2021b.
Context matters: Accounting for item features in the assessment
of citizen scientists’ scientific reasoning skills. Citizen Science:
Theory and Practice 6(1). https://doi.org/10.5334/cstp.309
Bruckermann, T., M. Stillfried, T. M. Straka, and U. Harms. 2022.
Citizen science projects require agreement: a Delphi study to
identify which knowledge on urban ecology is considered relevant
from scientists’ and citizens’ perspectives. International Journal
of Science Education, Part B: Communication and Public
Engagement 12(1):75-92. https://doi.org/10.1080/21548455.2022.2028925
Christie, A. P., T. Amano, P. A. Martin, G. E. Shackelford, B. I.
Simmons, and W. J. Sutherland. 2019. Simple study designs in
ecology produce inaccurate estimates of biodiversity responses.
Journal of Applied Ecology 56(12):2742-2754. https://doi.
org/10.1111/1365-2664.13499
Cox, J., E. Y. Oh, B. Simmons, C. Lintott, K. Masters, A.
Greenhill, G. Graham, and K. Holmes. 2015. Defining and
measuring success in online citizen science: a case study of
Zooniverse projects. Computing in Science & Engineering 17
(4):28-41. https://doi.org/10.1109/MCSE.2015.65
Crall, A. W., R. C. Jordan, K. Holfelder, G. J. Newman, J.
Graham, and D. M. Waller. 2013. The impacts of an invasive
species citizen science training program on participant attitudes,
behavior, and science literacy. Public Understanding of Science
22(6):745-764. https://doi.org/10.1177/0963662511434894
Dickinson, J. L., and R. Crain. 2019. An experimental study of
learning in an online citizen science project: insights into study
design and waitlist controls. Citizen Science: Theory and Practice
4(1):26. https://doi.org/10.5334/cstp.218
Druschke, C. G., and C. E. Seltzer. 2012. Failures of engagement:
lessons learned from a citizen science pilot study. Applied
Environmental Education & Communication 11(3-4):178-188.
https://doi.org/10.1080/1533015X.2012.777224
Fernandez-Gimenez, M. E., H. L. Ballard, and V. E. Sturtevant.
2008. Adaptive management and social learning in collaborative
and community-based monitoring: a study of five community-
based forestry organizations in the western USA. Ecology and
Society 13(2):4. https://doi.org/10.5751/ES-02400-130204
Fishbein, M., and I. Ajzen. 2010. Predicting and changing
behavior: the reasoned action approach. Psychology, New York,
New York, USA. https://doi.org/10.4324/9780203838020
Gray, S., R. C. Jordan, A. Crall, G. Newman, C. Hmelo-Silver, J.
Huang, W. Novak, D. Mellor, T. Frensley, M. Prysby, et al. 2017.
Combining participatory modelling and citizen science to support
volunteer conservation action. Biological Conservation
208:76-86. https://doi.org/10.1016/j.biocon.2016.07.037
Greving, H., T. Bruckermann, and J. Kimmerle. 2020. This is my
project! The influence of involvement on psychological ownership
and wildlife conservation. Current Research in Ecological and
Social Psychology 1:100001. https://doi.org/10.1016/j.cresp.2020.100001
Haklay, M. 2013. Citizen science and volunteered geographic
information: overview and typology of participation. Pages
105-122 in D. Sui, S. Elwood, and M. Goodchild, editors.
Crowdsourcing geographic knowledge. Springer, Dordrecht, The
Netherlands. https://doi.org/10.1007/978-94-007-4587-2_7
Haywood, B. K., J. K. Parrish, and J. Dolliver. 2016. Place-based
and data-rich citizen science as a precursor for conservation
action. Conservation Biology 30(3):476-486. https://doi.
org/10.1111/cobi.12702
Heigl, F., B. Kieslinger, K. T. Paul, J. Uhlik, and D. Dörler. 2019.
Opinion: toward an international definition of citizen science.
Proceedings of the National Academy of Sciences of the United
States of America 116(17):8089-8092. https://doi.org/10.1073/
pnas.1903393116
IBM Corporation. 2013. IBM SPSS Statistics for Windows,
Version 22.0. IBM Corporation, Armonk, New York, USA.
Jordan, R. C., A. Crall, S. Gray, T. B. Phillips, and D. Mellor.
2015. Citizen science as a distinct field of inquiry. BioScience 65
(2):208-211. https://doi.org/10.1093/biosci/biu217
Jordan, R. C., S. A. Gray, D. V. Howe, W. R. Brooks, and J. G.
Ehrenfeld. 2011. Knowledge gain and behavioral change in
citizen-science programs. Conservation Biology 25(6):1148-1154.
https://doi.org/10.1111/j.1523-1739.2011.01745.x
Kloetzer, L., J. Lorke, J. Roche, Y. Golumbic, S. Winter, and A.
Jõgeva. 2021. Learning in citizen science. Pages 283-308 in K.
Vohland, A. Land-Zandstra, L. Ceccaroni, R. Lemmens, J.
Perelló, M. Ponti, R. Samson, and K. Wagenknecht, editors. The
science of citizen science. Springer, Cham, Switzerland. https://
doi.org/10.1007/978-3-030-58278-4_15
Lawrence, A. 2006. ‘No personal motive?’ Volunteers,
biodiversity, and the false dichotomies of participation. Ethics,
Place & Environment 9(3):279-298. https://doi.org/10.1080/136
68790600893319
Lewis, M. 2016. Self-concious emotions: embarrassment, pride,
shame, guilt, and hubris. Pages 792-814 in L. F. Barrett, M. Lewis,
and J. M. Haviland-Jones, editors. Handbook of emotions.
Fourth edition. Guilford, New York, New York, USA.
Lewis, M., and M. Sullivan. 2005. The development of self-
conscious emotions. Pages 185-201 in A. Elliot and C. Dweck,
editors. Handbook of competence and motivation. Guilford,
New York, New York, USA.
Masters, K., E. Y. Oh, J. Cox, B. Simmons, C. Lintott, G. Graham,
A. Greenhill, and K. Holmes. 2016. Science learning via
participation in online citizen science. Journal of Science
Communication 15(03). https://doi.org/10.22323/2.15030207
Peck, J., and S. B. Shu. 2009. The effect of mere touch on perceived
ownership. Journal of Consumer Research 36(3):434-447. https://
doi.org/10.1086/598614
Peter, M., T. Diekötter, and K. Kremer. 2019. Participant
outcomes of biodiversity citizen science projects: a systematic
literature review. Sustainability 11(10):2780. https://doi.
org/10.3390/su11102780
Phillips, T. B., H. L. Ballard, B. V. Lewenstein, and R. Bonney.
2019. Engagement in science through citizen science: moving
beyond data collection. Science Education 103(3):665-690.
https://doi.org/10.1002/sce.21501
Ecology and Society 27(2): 24
https://www.ecologyandsociety.org/vol27/iss2/art24/
Phillips, T. B., N. Porticella, M. Constas, and R. E. Bonney. 2018.
A framework for articulating and measuring individual learning
outcomes from participation in citizen science. Citizen Science:
Theory and Practice 3(2):3. https://doi.org/10.5334/cstp.126
Pierce, J. L., T. Kostova, and K. T. Dirks. 2001. Toward a theory
of psychological ownership in organizations. Academy of
Management Review 26(2):298-310. https://doi.org/10.2307/259124
Pierce, J. L., T. Kostova, and K. T. Dirks. 2003. The state of
psychological ownership: integrating and extending a century of
research. Review of General Psychology 7(1):84-107. https://doi.
org/10.1037/1089-2680.7.1.84
Prather, E. E., S. Cormier, C. S. Wallace, C. Lintott, M. Jordan
Raddick, and A. Smith. 2013. Measuring the conceptual
understandings of citizen scientists participating in Zooniverse
projects: a first approach. Astronomy Education Review 12(1).
https://doi.org/10.3847/AER2013002
Rotman, D., J. Hammock, J. Preece, D. Hansen, C. Boston, A.
Bowser, and Y. He. 2014. Motivations affecting initial and long-
term participation in citizen science projects in three countries.
Pages 110-124 in M. Kindling and E. Greifeneder, editors.
iConference 2014 Proceedings (Berlin, Germany). iSchools,
Champaign, Illinois, USA.
Sheeran, P., S. Orbell, and D. Trafimow. 1999. Does the temporal
stability of behavioral intentions moderate intention-behavior
and past behavior-future behavior relations? Personality and
Social Psychology Bulletin 25(6):724-734. https://doi.
org/10.1177/0146167299025006007
Shirk, J. L., H. L. Ballard, C. C. Wilderman, T. Phillips, A.
Wiggins, R. Jordan, E. McCallie, M. Minarchek, B. V.
Lewenstein, M. E. Krasny, et al. 2012. Public participation in
scientific research: a framework for deliberate design. Ecology
and Society 17(2):29. https://doi.org/10.5751/ES-04705-170229
Sickler, J., T. M. Cherry, L. Allee, R. R. Smyth, and J. Losey. 2014.
Scientific value and educational goals: balancing priorities and
increasing adult engagement in a citizen science project. Applied
Environmental Education & Communication 13(2):109-119.
https://doi.org/10.1080/1533015X.2014.947051
Silvertown, J., M. Harvey, R. Greenwood, M. Dodd, J. Rosewell,
T. Rebelo, J. Ansine, and K. McConway. 2015. Crowdsourcing
the identification of organisms: a case-study of iSpot. ZooKeys
480:125-146. https://doi.org/10.3897/zookeys.480.8803
Stocklmayer, S. M., and C. Bryant. 2012. Science and the public
—what should people know? International Journal of Science
Education, Part B 2(1):81-101. https://doi.org/10.1080/0950069
3.2010.543186
Stylinski, C. D., K. Peterman, T. B. Phillips, J. Linhart, and R.
Becker-Klein. 2020. Assessing science inquiry skills of citizen
science volunteers: a snapshot of the field. International Journal
of Science Education, Part B 10(1):77-92. https://doi.
org/10.1080/21548455.2020.1719288
Summers, R., and F. Abd-El-Khalick. 2018. Development and
validation of an instrument to assess student attitudes toward
science across grades 5 through 10. Journal of Research in Science
Teaching 55(2):172-205. https://doi.org/10.1002/tea.21416
Toomey, A. H., and M. C. Domroese. 2013. Can citizen science
lead to positive conservation attitudes and behaviors? Human
Ecology Review 20(1):50-62.
Trumbull, D. J., R. E. Bonney, D. Bascom, and A. Cabral. 2000.
Thinking scientifically during participation in a citizen-science
project. Science Education 84(2):265-275. {a href="https://doi.
org/10.1002/(SICI)1098-237X(200003)84:2
Appendix 1. Items of all reported measures.
Pride
When I think about my participation in the “Bat Researchers” project, …
1. … I am proud of myself.
2. … I am very satisfied with myself.
3. … I feel confident.
Attitudes toward bats
1. Bats are impressive animals.
2. Bats need to be protected.
3. I get excited about having bats near my house/flat (e.g., below the roof top).
4. Bats are intelligent animals.
5. We need to promote the protection of bats.
6. Bats are dangerous animals.
7. Habitats of bats near my house/flat (e.g., old houses, dead trees) should be kept.
8. Bats carry severe germs.
9. Bats are fascinating animals.
10. Bats are threatening animals.
11. Bats do not belong to people’s close surroundings.
12. It is important to better protect bats.
Attitudes toward engagement in CS
1. I think that citizen science projects make sense.
2. I want to participate in further citizen science projects.
3. Participating in citizen science projects is easy for me.
4. I want to engage in future citizen science projects.
5. Citizen science projects help me understand the world around me.
6. I consider citizen science projects a good cause.
7. I want to continue to learn something in further citizen science projects.
8. I can manage even difficult situations in citizen science projects.
9. Citizen science projects help me protect the environment.
10. People in my direct surroundings engage in citizen science projects.
11. Citizen science projects help me make better choices about my health.
12. I think that citizen science projects get us somewhere.
13. It is normal for people in my direct surroundings to talk about citizen science projects.
14. It is easy for me to try to understand new topics of citizen science projects.
15. Other people in my direct surroundings are also enthusiastic about citizen science projects.
Attitudes: 1., 6., 12.; Intentions: 2., 4., 7.; Behavioral beliefs: 5., 9., 11.; Control beliefs: 3., 8.,
14.; Normative beliefs: 10., 13., 15.
Psychological ownership
1. The “Bat Researchers” project feels like it is mine.
2. I feel like I personally own the “Bat Researchers” project.
3. I feel like I possess the “Bat Researchers” project.
Topic-specific knowledge about bats
1. Which statement about reproduction in bats is correct? (one answer is correct)
A. Fertilization occurs immediately after mating in bats.
B. The female bat moves into roosts alone after mating.
C. Bats have 1-2 young per year.
D. Bats mate in spring.
2. Which statement about raising young in bats is correct? (one answer is correct)
A. Bats build nests for their young.
B. Bats feed insects to their young.
C. Bats lay eggs.
D. Bats lactate their young.
3. What is the risk of being bitten by a bat? (one answer is correct)
A. Bats can bite, but their small teeth cannot hurt human skin.
B. Bats can bite, but thick gloves protect you.
C. Bats will bite if you enter an attic or basement where bats are present.
D. Bats will bite if you enter the territory of a bat roost in the woods.
E. Bats will bite when they mistake a human finger for prey.
4. What possible danger could bats pose to/in your building? (one answer is correct)
A. The acid in bat droppings could damage the masonry.
B. A bat roost could expand largely in a building.
C. Bats can bite if you touch them.
D. Dropped young bats may behave aggressively.
E. Young bat may accidentally get lost into living rooms in the spring.
5. Assign the habitats of bats to their respective functions (one assignment per habitat).
(1) Tree holes and cracks
Hunting and drinking
Foraging and orientation
Foraging and migration
Day roosts
(2) Open area
Hunting and drinking
Foraging and orientation
Foraging and migration
Day roosts
(3) Waterbodies
Hunting and drinking
Foraging and orientation
Foraging and migration
Day roosts
(4) Caves and rock cracks
Hunting and drinking
Foraging and orientation
Foraging and migration
Day roosts
(5) Vegetation edges
Hunting and drinking
Foraging and orientation
Foraging and migration
Day roosts
6. Which statements about the impact of urban growth on bats are CORRECT or FALSE?
A. Building development in cities has an impact on bats because bats find roosts in and at
buildings at all times of the year.
B. Tall buildings have an impact on bats because bats hunt in open areas.
C. Artificial light in cities does not affect bats because it does not affect their
echolocation.
D. Roads have no effect on bats because bats can fly over them.
7. In what types of roosts can bats live in the city?
A. Tree cracks
B. Burrows
C. Buildings
D. Nesting boxes
E. Home-made nests
8. Which statements about the respective habitat of the four bat groups can be derived from
the diagram? Complete the sentences (one bat group per statement).
[Figure available upon request by the authors.]
(1) Bats of group a
have advantages from man-made structures (e.g., light sources), but also use
natural habitats (peri-urban specialists).
cope only in rural areas and not in urban areas (urban sensitive/avoidant bats).
cope in rural as well as urban and peri-urban areas (urban-tolerant bat species).
benefit more from urban than from rural habitats (urban specialists).
(2) Bats of group b
have advantages from man-made structures (e.g., light sources), but also use
natural habitats (peri-urban specialists).
cope only in rural areas and not in urban areas (urban sensitive/avoidant bats).
cope in rural as well as urban and peri-urban areas (urban-tolerant bat species).
benefit more from urban than from rural habitats (urban specialists).
(3) Bats of group c
have advantages from man-made structures (e.g., light sources), but also use
natural habitats (peri-urban specialists).
cope only in rural areas and not in urban areas (urban sensitive/avoidant bats).
cope in rural as well as urban and peri-urban areas (urban-tolerant bat species).
benefit more from urban than from rural habitats (urban specialists).
(4) Bats of group d
have advantages from man-made structures (e.g., light sources), but also use
natural habitats (peri-urban specialists).
cope only in rural areas and not in urban areas (urban sensitive/avoidant bats).
cope in rural as well as urban and peri-urban areas (urban-tolerant bat species).
benefit more from urban than from rural habitats (urban specialists).
9. Which TWO characteristics do winter roosts for bats in the city definitely require? (TWO
answers are correct)
A. Roosts must be rather dry, like rooms with heating systems.
B. Roosts must be frost-free, like basements.
C. Roosts must provide enclosed hanging places, such as narrow crevices.
D. Roosts must be quiet.
E. Roosts must be able to warm up easily, such as attics.
10. Bats inhabit different roosts, which differ in their function. Which statement about roosts
of bats is true? (one answer is correct)
A. After mating, summer roosts are used by both female and male bats.
B. Roosts where females raise young are called nurseries.
C. Summer roosts are often also used as winter roosts.
D. Winter roosts are used by female and male bats.
11. Which of the following does NOT have a direct impact on bats using roosts? (one answer
is correct).
A. Protect tree cavities
B. Put up feeding places
C. Create diversity in the garden
D. Avoid pesticides
12. What is the most LIKELY consequence bats can have in a building? (one answer is
correct)
A. Bats bring nesting material into their roost.
B. Bats leave droppings in their roost.
C. Bats nibble on house insulation.
D. Bats spread parasites such as lice and ticks.
E. Bats enlarge existing cracks in house facades.
13. Why do bats benefit from a near-natural garden with many different plant species? (one
answer is correct)
A. A semi-natural garden with many different plant species provides more hiding places.
B. A semi-natural garden leaves more fruit from many different types of plants.
C. In a semi-natural garden, the flowering times of the different plant species attract more
insects.
D. In a near-natural garden with many different plant species, there is less chemical
exposure to pesticides.
14. Why can putting up bat boxes be helpful for bats? (one answer is correct)
A. Bat boxes provide opportunities to bats to explore new hunting areas.
B. Bats settle in new areas because of bat boxes.
C. Bat boxes create additional roosts.
D. Bat boxes facilitate nest building for raising young.
15. Which TWO factors do you need to consider when putting up a bat box? (two answers are
correct)
Putting up a bat box is done ...
A. ... protected behind trees.
B. ... in larger numbers.
C. ... in northern orientation.
D. ... with different types of boxes.
E. ... like a bird box.
F. ... for cleaning purposes at chest level.
16. Bats are endangered throughout Germany and need our help and protection in cities as
well. Which of the following does NOT help protect bats? (one answer is correct)
A. Increasing plant diversity in allotments enhances the foraging habitat of bats.
B. Avoiding the use of wood preservatives reduces the risk of bats becoming ill in their
roosts.
C. Avoiding insecticides maintains the food base of bats.
D. Supplementary feeding with mealworms bridges the winter period for bats.
17. Which of these factors influences whether or not a bat will accept a bat box? (one answer
is correct)
A. Bats will only accept bat boxes if they are placed at least 10 m above the ground.
B. Bats prefer different boxes depending on the species.
C. Depending on their origin, bats prefer different boxes.
D. Depending on their age, bats prefer different boxes.
E. Female and male bats prefer different bat boxes.
18. Why do wind turbines pose a threat to bats? (one answer is correct)
A. Wind turbines and the pressure of the rotor blades push bats to the ground.
B. Wind turbines are especially dangerous to young bats.
C. Wind turbines injure the internal organs of bats.
D. Wind turbines injure male bats during hunting.
E. Only bat species that migrate between summer and winter roosts are injured by wind
turbines.
19. Which statement about the protection of bat roosts in buildings is true? (one answer is
correct)
A. If a bat roost is in and at the façade of a building, the entire building is protected.
B. Protection applies to buildings with consistently occupied bat roosts.
C. Bat roost protection is based on population size.
D. Bats in and on the building are subject to a year-round disturbance ban.
E. Bat roosts shall be protected in buildings only for the duration of hibernation.
20. Which of these statements is a CORRECT or FALSE justification for the need to protect
bat roosts?
A. Bats have few offspring, so disturbance is particularly severe.
B. Summer roosts are visited only once by the same bat, but regularly by different bats.
C. If bats are disturbed in their winter roosts, they will not mate in the spring.
D. When bats are disturbed in the maternity roost, there is a risk that young will be left
behind.
21. Which legal basis must you consider if you encounter bats on your property? (one answer
is correct)
A. Dead animals are excluded from the law.
B. Abandoned roosts may be sealed.
C. Injured, helpless, or sick animals must be reported.
D. Violations of shelter regulations are punishable only as misdemeanors.
22. On average, what percentage of their own body weight do bats ingest in food each night?
(one answer is correct)
A. 5%
B. 15%
C. 20%
D. 30%
E. 50%
23. How many species of bats are found in the city of [blinded for review]? (one answer is
correct)
A. 3
B. 9
C. 12
D. 18
E. 20
24. Which bat species are native to Germany and which are not?
[Figure available upon request by the authors.]
A. Fringed-lipped bat
B. Grey long-eared bat
C. Common noctule
D. Short-tailed leaf-nosed bat
E. Barbastelle bat
F. Vampire bat
G. White bat
H. Pipistrelle bat
25. Name the body parts of the bat by matching the numbers with the appropriate label from
the list. [Figure available upon request by the authors.]
(1) 1
2nd finger
Thumb claw
Flight skin
Hind foot
Ear
Tragus
Forearm
(2) 2
2nd finger
Thumb claw
Flight skin
Hind foot
Ear
Tragus
Forearm
(3) 3
2nd finger
Thumb claw
Flight skin
Hind foot
Ear
Tragus
Forearm
(4) 4
2nd finger
Thumb claw
Flight skin
Hind foot
Ear
Tragus
Forearm
(5) 5
2nd finger
Thumb claw
Flight skin
Hind foot
Ear
Tragus
Forearm
(6) 6
2nd finger
Thumb claw
Flight skin
Hind foot
Ear
Tragus
Forearm
(7) 7
2nd finger
Thumb claw
Flight skin
Hind foot
Ear
Tragus
Forearm
26. Which behavior is the CORRECT thing to do when you want to protect bats in your
home? (one answer is correct)
A. Feed bats insects.
B. Do not disturb bats.
C. Pay no further attention to bats.
D. Check bat roosts regularly.
E. Keep bat roosts warm and dry.
27. How can bats be supported during hibernation? (one answer is correct)
A. Bats build nests for hibernation and therefore should be supported by bat boxes.
B. Bats need protected winter roosts and should therefore be translocated to such roosts.
C. Bats find little food in winter and therefore should be given supplementary feeding.
D. Bats require energy when waking from hibernation and therefore should not be
disturbed.
28. What measures for living harmoniously together with bats in buildings are
ACCEPTABLE or NOT ACCEPTABLE for all citizens?
A. Seal entrance and exit openings
B. Install droppings boards
C. Contact conservation authority
D. Relocate non-protected species
E. Scaring away by, for example, aluminum strips
29. Which of these statements about bats are CORRECT or FALSE?
A. During their fast flight maneuvers, bats can get caught in people's hair.
B. The saliva of some bat species prevents blood clotting.
C. Bats always fly out of their roosts to the left.
D. Bats can locate obstacles no thicker than a human hair.
E. Bats have similar vision to other mammals.
F. Bats are rodents.
G. Bats rely only on their echolocation when flying.
H. Some bat species feed on human blood.
I. Vampire legends derive from vampire bats.
