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Public participation in scientific research, now commonly referred to as citizen science, is increasingly promoted as a possibility to overcome the large-scale data limitations related to biodiversity and conservation research. Furthermore, public data-collection projects can stimulate public engagement and provide transformative learning situations. However, biodiversity monitoring depends on sound data collection and warranted data quality. Therefore, we investigated if and how trained and supervised pupils are able to systematically collect data about the occurrence of diurnal butterflies, and how this data could contribute to a permanent butterfly monitoring system. We developed a specific assessment scheme suitable for laypeople and applied it at 35 sampling sites in Tyrol, Austria. Data quality and its explanatory power to predict butterfly habitat quality was investigated comparing data collected by pupils with independent assessments of professional butterfly experts. Despite substantial identification uncertainties for some species or species groups, the data collected by pupils was successfully used to predict the general habitat quality for butterflies using a linear regression model (r² = 0.73, p <0.001). Applying the proposed method in a citizen science context with laypeople could support both the long term monitoring of butterfly habitat quality, as well as the efficient selection of sites for professional in-depth assessments.
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Vol.:(0123456789)
1 3
J Insect Conserv (2017) 21:677–688
DOI 10.1007/s10841-017-0010-3
ORIGINAL PAPER
Simplified andstill meaningful: assessing butterfly habitat quality
ingrasslands withdata collected bypupils
JohannesRüdisser1 · ErichTasser2· JanetteWalde3· PeterHuemer4·
KurtLechner1· AloisOrtner1· UlrikeTappeiner1,2
Received: 28 November 2016 / Accepted: 28 June 2017 / Published online: 10 July 2017
© The Author(s) 2017. This article is an open access publication
method in a citizen science context with laypeople could
support both the long term monitoring of butterfly habitat
quality, as well as the efficient selection of sites for profes-
sional in-depth assessments.
Keywords Lepidoptera· Citizen science· Monitoring·
Indicator· Data quality· Volunteer
Introduction
Biodiversity assessments and continuous monitoring
schemes are important and generally recognized prerequi-
sites to efficiently address the ongoing biodiversity crises
(Schmeller etal. 2015). Nevertheless, resources for collect-
ing data on biodiversity are—and will always be—limited
(Baan et al. 2013; Rüdisser 2015). Public participation
seems to be an interesting option to support the collection
and processing of biodiversity data (Domroese and Johnson
2017; Amano etal. 2016), and, at the same time, to generate
authentic opportunities for environmental education (Chen
and Cowie 2013). Such projects can lead to public engage-
ment and can have transformative learning potential (Bela
etal. 2016; Dickinson etal. 2012; Hobbs and White 2012;
Lewandowski and Oberhauser 2017). The involvement and
contribution of volunteers to academic research by collect-
ing and submitting observation data has a long tradition in
ecology and especially ornithology. For decades—the long-
est-running bird counting project in the US has collected
data for over 110 years so far—thousands of amateur and
professional ornithologists worldwide have been contrib-
uting to an otherwise unfeasible database (Tulloch etal.
2013). Public participation in scientific research, now often
called ‘citizen science’ (CS) (cf. Irwin 1995), was for many
years rarely reported in scientific publications. However,
Abstract Public participation in scientific research,
now commonly referred to as citizen science, is increas-
ingly promoted as a possibility to overcome the large-scale
data limitations related to biodiversity and conservation
research. Furthermore, public data-collection projects
can stimulate public engagement and provide transforma-
tive learning situations. However, biodiversity monitor-
ing depends on sound data collection and warranted data
quality. Therefore, we investigated if and how trained and
supervised pupils are able to systematically collect data
about the occurrence of diurnal butterflies, and how this
data could contribute to a permanent butterfly monitoring
system. We developed a specific assessment scheme suit-
able for laypeople and applied it at 35 sampling sites in
Tyrol, Austria. Data quality and its explanatory power to
predict butterfly habitat quality was investigated comparing
data collected by pupils with independent assessments of
professional butterfly experts. Despite substantial identifi-
cation uncertainties for some species or species groups, the
data collected by pupils was successfully used to predict the
general habitat quality for butterflies using a linear regres-
sion model (r² = 0.73, p <0.001). Applying the proposed
* Johannes Rüdisser
Johannes.Ruedisser@uibk.ac.at
1 Institute ofEcology, University ofInnsbruck,
Sternwartestraße 15, 6020Innsbruck, Austria
2 Institute forAlpine Environment, EURAC, Viale Druso 1,
39100Bolzano, Italy
3 Department ofStatistics, University ofInnsbruck,
Universitätsstraße 15, 6020Innsbruck, Austria
4 Tiroler Landesmuseen Betriebsges.m.b.H.,
Naturwissenschaftliche Sammlung, Feldstraße 11a,
6020Innsbruck, Austria
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678 J Insect Conserv (2017) 21:677–688
1 3
this changed dramatically around the beginning of the cur-
rent decade (Follett and Strezov 2015). Out of 1935 CS-
related publications published before 2015-12-15 retrieved
from the Web of Science by Kullenberg and Kasperowski
(2016), 98% were published after 2000 and 75% after 2010.
The observed boom in CS publications might still underes-
timate the potential for citizen science, because only a small
portion of CS projects contribute data to peer-reviewed
scientific articles (Theobald etal. 2015). The fast develop-
ment of the internet, digital databases, and mobile devices
has stimulated the development of innumerable CS pro-
jects with almost limitless thematic applications (Kobori
etal. 2016; Tulloch etal. 2013), but the majority of stud-
ies applying CS can still be found in biology, ecology and
conservation research (Kullenberg and Kasperowski 2016).
CS appears to be a powerful possibility to gather data
about biodiversity over large temporal and spatial scales,
and consequently make valuable contributions to monitor-
ing activities. Today birds are the species most commonly
observed in CS projects followed by terrestrial inverte-
brates, with the vast majority of projects focusing on but-
terflies (Follett and Strezov 2015). Both species groups
are already used for the calculation of global and Euro-
pean biodiversity indicators covering species population
and community composition trends (Butchart etal. 2010;
van Swaay et al. 2008; van Swaay et al. 2015). Butterfly
monitoring schemes have been established in many Euro-
pean countries with the help of highly dedicated volunteers
(EEA—European Environment Agency 2013).
Biodiversity monitoring depends on sound data collec-
tion, appropriate sampling designs and data quality (Loos
etal. 2015). While data quality issues are not distinctive to
CS projects, sampling schemes that incorporate data from
many observers —with different levels of knowledge and
experience—can lead to varying levels of error and specific
bias that must be addressed (Dickinson etal. 2010; McDon-
ough MacKenzie etal. 2017; Ward 2014). When dealing
with quality issues of biodiversity data collected by laypeo-
ple two main topics must be distinguished: (1) error and
bias related to observation and identification accuracy and
(2) sampling effort in respect to time, space, and taxonomic
identification level (Lewandowski and Specht 2015). While
the second aspect is strongly determined by the applied
sampling scheme, both aspects are influenced by the skills
and motivation of the involved observers. Many tradi-
tional CS projects were built upon the gratuitous contri-
bution of experienced and skilled volunteers (Lukyanenko
etal. 2016), now more and more projects involve laypeo-
ple. While the terms laypeople, layperson, volunteer, and
citizen scientist are often used synonymously, one should
be aware that the competence of volunteers contributing
in citizen science projects can range from very experi-
enced (often highly qualified and with specialised formal
education) to totally unexperienced. In this article we focus
on unexperienced volunteers hereinafter being referred to
as ‘laypeople’. To involve laypeople in biodiversity moni-
toring in an inclusive way—as proposed by Lukyanenko
et al. (2016)—simplified assessment protocols with flex-
ible identification levels need to be developed and must be
evaluated in regards to explanatory power and achievable
data quality.
The citizen science project Viel-Falter (http://www.
viel-falter.at), which was launched in 2013 aims to investi-
gate if and how trained and supervised pupils can system-
atically collect data about the occurrence of butterflies, and
how the data could be used to support a permanent biodi-
versity monitoring program as requested by international
conventions (Convention on Biological Diversity (CBD)
2010; Han etal. 2014). While international experience has
shown that involving qualified volunteers can substantially
contribute to the implementation of such monitoring tasks
(Amano etal. 2016; Burgess et al. 2016), only few stud-
ies have focused on laypeople or pupils (cf. Abadie etal.
2008; Ballard etal. 2017; Olivier et al. 2015). Therefore,
we developed and applied a specific assessment scheme
suitable for pupils or any other layperson, to systemati-
cally collect data on butterflies. By comparing with inde-
pendent assessments conducted by professional butterfly
experts, we investigated if the achieved data quality is suf-
ficient to support a permanent butterfly monitoring system.
Additionally, we investigated how the pupil’s motivation to
engage in butterfly observation activities develops during
the course of the project and what project factors might be
crucial to support a continuous engagement. In this article,
we focus on the question of whether the proposed simpli-
fied butterfly assessment scheme, which can be executed
by any trained layperson, could be used to determine but-
terfly habitat quality of different sites. We investigated if
we can predict a butterfly habitat quality index, based on
comprehensive expert assessments on species level, with
data derived from a simplified assessment scheme. Using
data from both experts and laypeople we tried to separate
the effects of the information reduction caused by the sim-
plified assessment scheme from the effects related to inac-
curate data collection.
Methods
Participating schools andstudy sites
From 2013 to 2015 548 pupils, of ages ranging from 6 to
20years and from 14 different schools, collected data at 35
sampling sites in Tyrol, Austria. The sites which are distrib-
uted all over Tyrol were chosen to represent characteristic
butterfly grassland habitats in and around settlement areas.
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679J Insect Conserv (2017) 21:677–688
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Thus, the sites, ranging in altitude from 505 to 1485 m
above sea level, included hay meadows (n = 27), pastures
(n = 4), and fallow land or ruderal sites (n = 4). Land use
intensity ranged from none or extensive (one cut per year)
to very intensive (three or more cuts per year).
Simplified assessment scheme
Butterfly surveys were performed by supervised school
classes using a predefined and visualised list of 13 charac-
teristic species and ten species groups (Fig. 1). To facili-
tate the assessments morphologically similar species were
grouped. The species groups consisted of species from
the same genus (Coenonympha, Erebia), from a group of
genera (Apatura & Limenitis), from the same subfamily or
tribe (Coliadinae, Heliconiinae, Melitaeini, Pyrginae), and
from the same family (Lycaenidae, Hesperiidae, Pieridae).
The selection of species and species groups was based on
habitat preferences (Spiss 2014). The species and species
groups used in Viel-Falter were similar to those used by
Olivier etal. (2015) for butterfly monitoring in private gar-
dens in France.
In order to ensure survey quality, pupils and their teach-
ers were intensively trained during two half-day workshops
in butterfly identification and on how to perform the sur-
vey. Additionally they received butterfly identification keys
and learning materials. During the surveys, pupils scattered
themselves across the study site, remaining at least 10m
apart. All pupils used the graphic butterfly list (Fig. 1) to
support identification and counted all the butterflies within
a semi-circle of five meters and for a period of 5min. If
the pupils observed a species that was not on the list or
they were unsure about, they were asked to record it in
the aggregated groups indicated with rectangles in Fig.1.
Schools were asked to conduct at least three surveys in
May, June, and July and—following the recommendations
of Pollard & Yates (1993)—between 10:00 and 17:00, dur-
ing sunny or warm days with no or only low wind speed.
Fig. 1 The simplified butterfly assessment scheme used by pupils for
identification and systematic counting of butterflies. The original ver-
sion used by the schools was printed with German names and in A3
format so that the size of the illustrated butterflies corresponded to
their natural size. Rectangles indicate the aggregated species groups
used to predict butterfly habitat quality
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680 J Insect Conserv (2017) 21:677–688
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After conducting the surveys the pupils transferred the data
into the project database using an online application devel-
oped for this purpose (http://www.viel-falter.at). All analy-
ses presented in this article are based on data collected in
the years 2013–2015.
Expert butterfly surveys
At all study sites (n = 35) a butterfly expert conducted four
comprehensive butterfly assessments on species level.
These expert assessments were executed in the same season
as by the schools (May–July) for at least 2years between
2013 and 2015. Consecutive assessments at the same site
were separated by at least three weeks. Assessments were
conducted, as recommended by Pollard & Yates (1993),
between 10:00 and 17:00, during sunny or warm days, and
with no or only low wind speed. To guarantee a similar
sampling effort at each study site, diurnal butterflies were
counted for 30min from an area of approximately 2500m².
Estimating butterfly habitat quality
Although butterflies are widely accepted as a meaningful
and applicable indicator to investigate and monitor biodi-
versity changes (Pearman and Weber 2007; van Swaay
et al. 2008, EEA—European Environment Agency 2012;
Feest etal. 2011), there is no commonly applied indicator
that measures butterfly habitat quality. The two most com-
monly applied measures for habitat quality are species rich-
ness and abundance. While species richness—if measured
properly—is a natural measure of biodiversity (Gotelli and
Colwell 2001), abundance incorporates aspects of ecosys-
tem functions and services (Schwartz etal. 2000). There-
fore, we combined both species richness and abundance to a
butterfly habitat quality index (BHQ). BHQ was calculated
for each site by multiplying the rarefied species richness
(Sest) with butterfly abundance (A) estimated on the basis
of the pooled data from four expert surveys at each site.
Gotelli and Colwell (2001) clearly illustrated that the num-
ber of observed species at different sites using a standard-
ized sampling effort depends not only on species richness,
but also on the number of individuals, or the mean density
of individuals. Thus the observed number of species per
sampling represents species density—the number of spe-
cies per sampling area—and not species richness, which is
the number of observed species in relation to the number of
potential species. In order to account for the effect of abun-
dance in the estimation of species richness, we used indi-
vidual-based rarefied species richness for 50 individuals for
the calculation of the BHQ (Chao etal. 2014). Rarefaction
is a technique computing the expected number of species
(Sest) from a so-called individual-based rarefaction curve
that provides the estimated dependence of the number of
species on the accumulated number of individuals (Gotelli
and Colwell 2001). This curve is constructed by drawing
repeatedlly random subsamples from the sampled at a site
and plotting the mean number of obtained species against
the subsample size (i.e., the number of sampled individu-
als). Sest was computed using EstimateS (Version 9, R. K.
Colwell, http://purl.oclc.org/estimates). Both Sest and A
were normalized with the min–max method. Hence BHQ
was calculated as:
where Sest, i is the rarefied species richness at site i and Ai
the abundance at the corresponding site.
BHQ, which theoretically ranges from 0 to 1, was used
as dependent variable in all of the following analyses.
However, before using BHQ, we validated its relevance for
biodiversity and conservation related aspects by comparing
BHQ values at all sites with the number of butterfly species
on the national Red List and with the number of individu-
als from Red List species (cf. Höttinger and Pennerstorfer
2005). For this we estimated Spearman’s rank correlation
coefficient, because this relationship might not be linear.
Analysis oftheexplanatory power ofthesimplified
assessment scheme
To investigate the validity of the simplified assessment
scheme, we explored if the BHQ which was obtained on
the basis of detailed expert surveys could be assessed with
data from a simplified assessment scheme. To separate the
effects of the information reduction caused by the aggre-
gation into species groups, from effects related to possible
biases or errors resulting from wrong butterfly identifica-
tion by laypeople, the analysis was split in two different
steps. First, the explanatory power of the simplified assess-
ment scheme to predict butterfly habitat quality was ana-
lysed using only the data collected by experts, and second
the same analysis was conducted with the data collected by
schools (Fig.2): to investigate the explanatory power of the
reduced assessment scheme to predict BHQ we aggregated
the individual species abundance data from the expert sur-
veys to the following nine species groups: Pieridae (excl.
Coliadinae), Coliadinae, Nymphalinae (excl. Melitaeini),
Lycaenidae, Satyrinae, Apatura & Limenitis, Heliconii-
nae plus Melitaeini, Hesperiidae (without Pyrginae), and
Pyrginae (Species groups are indicated with rectangles
in Fig.1). These nine species groups complemented with
the three very characteristic and large species (Parnas-
sius apollo, Papilio machaon, and Nymphalis antiopa)
were then used as independent variables to predict BHQ
with a linear regression model. Iphiclides podalirius was
𝙱𝙷𝚀
i=
(
Sest,iSest,Min
)
Sest
,
Max Sest
,
Min
(
AiAMin
)
AMax AMin
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681J Insect Conserv (2017) 21:677–688
1 3
excluded from analysis, because it occurs in only small
parts of the study region. We used linear regression model
because BHQ is based equally on Sest and A and therefore a
direct recalculation on the basis of aggregated species data
does not make sense, because the aggregation to species
groups impairs the calculation of Sest. Because the dataset
comprised only 35 sites but 12 independent variables, the
degrees of freedom were small, consequently the power of
the statistical tests weak, and the risk for overfitting is high
(Babyak 2004). Therefore, we opted to reduce the number
of variables using principal component analysis (PCA)
with Varimax as rotation method. A PCA extracts orthogo-
nal components, each component comprises all variables
highly correlated with each other but separating these vari-
ables form the ones not highly correlated with them. Com-
ponents with an eigenvalue larger than one were employed
to identify groups of variables containing almost the same
information. In our case the variables used in PCA were
the pooled abundance data from the four expert surveys
aggregated to the nine species groups plus the three char-
acteristic species listed above. Based on the PCA results,
we selected a species or species group as representative for
each component. Hence, the selection of these variables
was done completely independent from the BHQ which
was used (as dependent variable) in the regression step:
estimating the explanatory power of the simplified assess-
ment scheme to predict BHQ, just using the selected repre-
sentatives as explanatory variables in the linear regression
model (Fig.2).
Results
Data collection andcommitment ofschools
Between 2013 and 2015, the 14 schools involved conducted
159 site visits and 2616 individual butterfly assessments.
Although most schools participated with a very high level
of commitment, two schools, due to internal organizational
and motivational reasons, did not manage to conduct at
least three independent visits per site following the out-
lined assessment protocol. After exclusion of the data from
those schools, the final dataset used for the validation of the
simplified assessment scheme consisted of 2456 individual
assessments during 151 site visits at 30 different sites. The
Experts Pupils
Observed
species
abundance
at each site
(n = 35)
1
.
.
.
.
.
.
73
Aggregated
to 12 species
groups used
by pupils
1
.
.
.
.
12
A & Sest
BHQ
PCA
Representa-
tives
1
.
.
.
5
Representa-
tives
1
.
.
.
5
(n = 30)(n = 35)
(n = 35)
Fig. 2 Chart of the applied approach to estimate the explanatory
power of the simplified butterfly assessment scheme in order to pre-
dict the Butterfly Habitat Quality Index (BHQ). BHQ was calculated
at each investigated site on the basis of the rarefied species rich-
ness (Sest) and butterfly abundance (A). 1−adj. r²1 = estimation of
the information loss if the nine species groups and three character-
istic species instead of all individual species are employed. adj. r²2
= explanatory power if just five representatives are used. adj. r²3 =
explanatory power of the pupils data using only the five representa-
tives
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682 J Insect Conserv (2017) 21:677–688
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number of school visits per site ranged thereby from 3 to 9
resulting in 30–163 individual assessments per site.
Despite some organizational problems with a few
schools, 83% of the pupils stated in an anonymous online
survey that they would like to participate in a similar pro-
ject again. 87% of the pupils reported a high degree of
interest and enjoyment in participating.
During 140 site visits the butterfly experts identi-
fied 73 different butterfly species at 35 sites. The number
of recorded species per site ranged from 5 to 28 and the
number of individuals ranged from 17 to 191. Aglais urti-
cae, Coenonympha pamphilus and Polyommatus icarus
were the species with the highest frequency, occurring
at 91, 80, and 77% of the investigated sites respectively.
The most abundant species was Maniola jurtina with 308
observed individuals, followed by Polyommatus icarus
with 182, Coenonympha pamphilus with 178, Aglais urti-
cae with 154, and Pieris rapae with 149 observed individu-
als. Fourteen species occurred only at two sites and 13 spe-
cies only at one site.
Data accuracy andquality
Comparing the data collected by pupils with the data from
expert surveys revealed that the degree of accordance
varied substantially between different species or species
groups (Table1). Pearson’s correlation coefficient between
abundance data from experts and schools ranged from not
significant for Nymphalis antiopa, Apatura & Limenitis,
Coliadinae & Pyrginae to above 0.9 for Parnassius apollo
and Hesperiidae (Table1).
Butterfly habitat quality (BHQ) andendangered species
An important aspect of butterfly habitat conservation is
the protection of endangered species. To investigate the
indicator capacity of BHQ for rare or endangered species,
we compared it with the occurrence of Red List species
(Fig.3). Both Red List species richness and Red List spe-
cies abundance were significantly correlated with BHQ
(Spearman R = 0.73, p < 0.001, and R = 0.71, p < 0.001,
respectively). Red List species richness and Red List spe-
cies abundance were also correlated (but with lower cor-
relation coefficients) with the two components of BHQ
Table 1 Range of individuals counted at four expert visits summa-
rized per site (Min, Median, Max), the portion of the corresponding
species or species group of the total butterfly abundance, and Pear-
son’s correlation coefficient between abundance data from experts
and schools (significance level p < 0.05)
Species/species groups Min Median Max Portion of
abundance
Pearson r
Parnassius apollo 0 0 27 1.5 0.94
Hesperiidae (ex. Pyr-
ginae)
0 2 44 8.7 0.92
Papilio machaon 0 0 9 1.4 0.64
Nymphalinae (ex.
Melitaeini)
0 5 22 11.5 0.60
Heliconiinae & Meli-
taeini
0 1 37 4.8 0.56
Pieridae (ex. Coliaedi-
nae)
1 8 27 19.1 0.51
Satyrinae 0 18 96 34.5 0.47
Lycaenidae 0 5 36 13.1 0.37
Nymphalis antiopa 0 0 1 0.1 Not sig
Apatura & Limenitis 0 0 1 0.1 Not sig
Coliadinae 0 3 10 4.7 Not sig
Pyrginae 0 0 7 0.5 Not sig
(a) (b)
Fig. 3 Relation between Butterfly Habitat Quality Index (BHQ) and (a) number of Red List species (Spearman R = 0.74) and (b) abundance of
Red List species (Spearman R = 0.72) at 35 investigated sites
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683J Insect Conserv (2017) 21:677–688
1 3
namely rarefied species richness (Spearman R = 0.53,
p < 0.05, and R = 0.37, p < 0.05, respectively) and abun-
dance (Spearman R = 0.60, p < 0.001, and R = 0.65, p < 0
0.001, respectively). This indicates that BHQ is a sensitive
proxy for the occurrence of rare species and has more pre-
dictive power than each of its components.
Explanatory power ofthesimplified assessment scheme
The linear regression model to predict BHQ using all 12
variables of the aggregated species groups based on expert
data provided a very good fit (r² = 0.92, adjusted r² = 0.87,
p < 0.001, n = 35). Although all variance inflation fac-
tors (VIFs) were smaller than four we opted to reduce the
number of independent variables to increase the power
of the statistical tests and to reduce collinearities in order
to obtain appropriate standard errors and therefore p val-
ues. This was appropriate due to the relatively low num-
ber of sampling sites (n = 35). Thus, we applied PCA with
Varimax rotation using all 12 variables of the aggregated
species groups from the expert’s data and selected a rep-
resentative variable for each component. Based on the
PCA results we selected Parnassius apollo, Nymphalinae,
Lycaenidae, Heliconiinae & Melitaeini and Hesperiidae
as representative variables. All selected variables—with
the exception of Hesperiidae—were those with the highest
component loadings. Hesperiidae were selected instead of
Apatura & Limenitis, because they occur more frequently.
Nymphalis antiopa, a typical species in woody habitat,
would have been representative for a 6th component, but
we excluded the species from further analysis due to its
rarity in the study area and our focus on grasslands. The
linear regression model to predict BHQ using only the
five selected variables still provided a very appropriate
fit (r² = 0.86, adjusted r² = 0.84, p < 0.001, n = 35) with all
variables being significant (p < 0.05) and all VIFs being
smaller then 1.3. In the next step, we assessed the explana-
tory power of the data collected by schools. We used the
same five selected variables and estimated the model again
via linear regression analysis. The model based on the data
collected by schools still provided a good fit (r² = 0.73,
adjusted r² = 0.67, p < 0.001, n = 30), alt hough the variables
Lycaenidae and Nymphalinae were not significant (Fig.4).
Discussion
Estimating butterfly habitat quality
While inconsistent or low data quality can be a severe
obstacle to the scientific use of citizen science data (Bur-
gess etal. 2016), a systematic review of peer-reviewed arti-
cles dealing with the quality of data collected by volunteers
revealed that many citizen science projects already proved
its potential to collect high-quality data (Lewandowski and
Specht 2015). Although well-designed and executed citi-
zen science projects with adequate volunteer training can
provide biodiversity data comparable to data collected by
professionals (Holt etal. 2013; Kremen etal. 2011; Lewan-
dowski and Specht 2015; Lovell et al. 2009) and support
the conservation of endangered species (Jue and Daniels
2015), new survey schemes involving laypeople should be
rigorously evaluated regarding its adequacy and applicabil-
ity for the involved volunteers and its final data quality. The
characteristics of survey protocols including preliminary
training, survey duration, or number of surveyors can have
important implication on detection probability and identi-
fication quality (Albergoni etal. 2016). Furthermore, the
desirable inclusion of a broad array of citizens in the col-
lection of biodiversity data (Lukyanenko etal. 2016) may
make it necessary to use higher level classification instead
of species identification, and the resulting loss of informa-
tion must be evaluated regarding its remaining informative
value for the desired purpose (cf. Le Féon etal. 2016).
To compare butterfly diversity of different sites (or for
analysing trends in time) one needs applicable and mean-
ingful measures (MacDonald etal. 2017). There is a wide
consensus that diversity of an ecosystem consists of two
components: richness (the number of species or traits) and
evenness (the proportional abundance of species or traits
among a community) (Hillebrand and Matthiessen 2009;
Tuomisto 2012). While species richness is a commonly
applied and straightforward measure of diversity there
Fig. 4 Correlation of expert based Butterfly Habitat Quality Index
(BHQ) values with predicted (modelled) BHQ values using aggre-
gated abundance data of Parnassius apollo, Nymphalinae, Lycaeni-
dae, Heliconiinae & Melitaeini, and Hesperiidae collected by schools
as independent variables (r² = 0.73, adjusted r² = 0.67, p < 0.001,
n = 30)
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
684 J Insect Conserv (2017) 21:677–688
1 3
is much debate about adequate measures for community
abundance composition (Tuomisto 2012). The increasing
emphasis on the question how biodiversity affects ecosys-
tem functions and related services, by both research and
politics, has further boosted this debate. Although some
authors reported effects of community abundance composi-
tion on ecosystem functions (Hillebrand etal. 2008; Tilman
et al. 2014), the effects of changing evenness on ecosys-
tem properties cannot be generalised and might be more
influenced by specific arrangements of dominant species
than by evenness per se (Winfree etal. 2015; Wohlgemuth
etal. 2016). MacDonald etal. (2017) analysed data from
a long-term butterfly monitoring program and revealed
that species richness and evenness of the observed butter-
fly communities were negatively related. They conclude
that evenness might be an important and helpful addi-
tional measure to investigate differences in butterfly com-
munities but should not be used in a compound index (e.g.
Shannon-Wiener index or Simpson’s index). Therefore we
deliberately avoided using evenness indicators as a measure
for habitat quality in our study but opted to consider over-
all butterfly abundance as an additional measure related
to ecosystem functions and services. It is well known that
population sizes of most butterfly species fluctuate from
year to year due to changing weather conditions for exam-
ple (Pollard and Yates 1993; Thomas 2005). Although we
aggregated data from four expert surveys in two consecu-
tive years for the calculation of BHQ to reduce the effect
of short term population fluctuations, the relatively short
time period of our survey weakens its informative value
regarding absolute habitat quality. However, population
fluctuations of butterfly species are generally similar over
large areas and therefore relative values of population index
comparing different sites remain fairly stable (Pollard and
Yates 1993; Roy et al. 2015). Thus we are confident that
BHQ is a suitable metric to compare different sites and to
estimate the explanatory power of the simplified butterfly
assessment conducted by laypeople.
Butterfly data collected by laypeople should be used and
analysed thoughtfully. Our systematic and planned com-
parison of expert butterfly assessments with data collected
by pupils using a simplified assessment scheme illustrates
both potentials and limitations of such data. Comparison
of abundance data from species and species groups col-
lected by pupils with the data from experts revealed sub-
stantial differences. While some of these differences might
be caused by time lags between the surveys conducted
by pupils and experts, the varying degree of accordance
(Table1) indicates substantial identification uncertainties.
This is in line with Vantieghem etal. (2016) who encoun-
tered substantial species identification errors among mor-
phologically similar skipper butterflies examining photo-
graphs uploaded with observations from citizen scientists.
Data collected by laypeople using aggregated species
groups should definitely not be seen as a substitute to but-
terfly assessment on species level. Such detailed and high
quality data are irreplaceable for species distribution and
population estimations in the context of nature conserva-
tion and environmental impact assessments. However, data
from simplified butterfly assessment schemes applicable
for laypeople could complement the professional collection
and processing of biodiversity data in the context of long
term biodiversity monitoring (Rüdisser 2015; Theobald
etal. 2015).
The abundance data of Parnassius apollo and the four
species groups Nymphalinae, Lycaenidae, Heliconiinae
& Melitaeini, and Hesperiidae collected by pupils could
explain 73% of the variance of the butterfly habitat quality
measured by the BHQ index. The BHQ index was obtained
on the basis of completely independent and detailed
expert surveys at species level. Considering the heteroge-
neous character of the involved school classes as well as
the observed and reported identification uncertainties, the
predictive power of the data collected by pupils is surpris-
ingly high. As BHQ is calculated on the basis of species
richness and total butterfly abundance assessed by experts
one could speculate that the high explanatory power of the
data collected by pupils could be driven mainly by butterfly
abundance differences and therefore just counting butter-
flies without any further identification would lead to simi-
lar results. However Pearson correlation between BSQ and
the overall butterfly abundance assessed by pupils was low
(r = 0.23) and not significant, indicating that species iden-
tification—at least at the coarse level proposed—in fact is
important for adequately predicting the BHQ. As we had
only 30 sites with parallel butterfly assessments of pupils
and experts we opted to reduce the number of independent
variables (observed species groups by pupils) to increase
statistical power. The selection of these variables was based
on a PCA including all independent variables and not on
the explanatory power in the subsequent regression model.
It is important to notice that the decision to use only five
explanatory variables to predict BSQ instead of the whole
dataset has only methodological reasons. Therefore fur-
ther studies leading to a larger sample size would, per-
mit the inclusion of the data of additional species groups
as independent variables, and likely lead to an even better
predictability of the BHQ. Although our results should be
evaluated with further investigations (including additional
sites), the results are already very good news for citizen sci-
ence initiatives that aim to foster biodiversity assessments
with the help of laypeople. The proposed observation and
reporting method for diurnal butterflies can be relatively
easily applied by any layperson after elementary training.
Although these assessments might not serve to draw con-
clusions about the occurrence of individual species, they
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
685J Insect Conserv (2017) 21:677–688
1 3
could permit meaningful predictions about the general
habitat quality for butterflies on the investigated sites and
its surroundings. As adult (nectarivorous) butterflies—on
which we are focusing—might use different habitats than
their larval stages, results should be interpreted from a
landscape perspective rather than focusing on individual
site characteristic.
Another option to use simplified butterfly surveys is in
the context of result-oriented measures for biodiversity
protection as done by Stolze etal. (2015). Especially, if
considering ecosystem services such as pollination or the
potential for resilience in ecosystems, rough but affordable
estimates based on volunteer surveys could complement
biodiversity monitoring programs as proposed by Obrist
and Duelli (2010). However, simplified butterfly surveys
conducted by laypeople should not be seen as a substitute
for expert surveys. The use of species groups impedes con-
clusions about distinct species and it is impossible to calcu-
late estimates on beta or gamma diversity at the landscape
scale (Obrist and Duelli 2010). For inventories, biogeo-
graphical studies, or prioritization of sites for conservation,
expert surveys on species level are needed (Krell 2004).
Citizen science andbiodiversity monitoring
The principal aim of the presented study was to investigate
whether trained and supervised pupils together with their
teachers are able to systematically collect data about the
occurrence of diurnal butterflies. While international expe-
riences showed that involving adult (and often experienced)
volunteers can substantially contribute to the successful
implementation of biodiversity monitoring programmes
(Miller-Rushing etal. 2012; Schmeller etal. 2009; Pocock
etal. 2015), until now only few projects have focused on
juvenile or unexperienced volunteers. Our findings sup-
port the assumption that even data from laypeople (such
as pupils) may contribute to biodiversity monitoring tasks.
While relaying on volunteers might seem a cost-effective
approach to collect large scale biodiversity data (Gardiner
etal. 2012; Theobald etal. 2015), one should be aware that
CS projects are not cheap or even free of charge (Chan-
dler et al. 2016). The development and maintenance of
a well-functioning framework for effective data collec-
tion, storage and analysis, as well as the training and sup-
port of volunteers to achieve the desired data quality are
often demanding, time consuming and hence costly tasks
(Tulloch et al. 2013). However cost analysis of CS pro-
jects should consider that CS often provides many benefits
beyond its merely scientific aim (McKinley et al. 2017).
Involving volunteers in the collection of biodiversity data
can increase both scientific and environmental literacy
as well as engagement. In our case the cooperation with
schools enabled us to reach many and different pupils, but
also resulted in additional challenges. Communication and
organisational aspects were often demanding and fluctua-
tion of responsible teachers due to school change or leave
was surprisingly high in some schools. While very young
pupils clearly needed more support from their teachers,
organisational aspects and the motivation of the involved
pupils were better in primary schools than in middle and
high schools. However, the high degree of interest and
enjoyment in participating in the project, as well as sev-
eral grassroots projects autonomously initiated by the par-
ticipating schools including peer teaching, environmental
protection, and awareness rising activities confirmed the
potential to promote education and empowerment with
well-designed CS projects.
Conclusions
Summarizing, data collected by laypeople using the pro-
posed survey method could support both the long-term
monitoring of butterfly habitat quality, as well as the effi-
cient selection of sites for further investigation conducted
by experts. In practice, monitoring-data from laypeople
could serve for ‘surveillance’ purposes as proposed by
Forrester etal. (2015) to facilitate early protection of envi-
ronmental changes. Furthermore spatially comprehensive
observation by volunteer laypeople could be the basis for a
cost efficient site selection for focused and complementary
expert assessments. However, for an effective biodiversity
monitoring a long term perspective including the necessary
funding for a continuous collection of data by both experts
and laypeople is needed. Promoting an easy way of butterfly
observation could broaden the addressed target groups and
enthuse more individuals for nature observation and biodi-
versity issues. In successful CS projects (some) laypeople
gradually gain experience, resulting in positive effects on
data quality and meaningfulness. They sometimes even turn
into experts. Butterfly observation conducted by laypeople
should not substitute expert assessments, but rather com-
plement them. More data—collected by both professionals
and laypeople—are needed to develop a robust forecasting
tool for butterfly habitat quality based on the simplified sur-
vey scheme, to establish benchmarks, and to strengthen the
results of this study.
Acknowledgements Open access funding provided by Univer-
sity of Innsbruck and Medical University of Innsbruck. Our special
thanks go to 24 teachers and 548 pupils of the participating schools
(BRG in der Au, HLW Kufstein, NMS Fließ, NMS Längenfeld, NMS
Umhausen, NMS Weer, PHT/PMS der Pädagogischen Hochschule
Tirol, Reithmanngymnasium, VS Brandberg, VS Innervillgraten, VS
Obsteig, VS Schwendt, VS Steinach and, VS Tux). The project Viel-
Falter was financially supported by the Federal Ministry of Science,
Research, and Economy (Sparkling Science Project Viel-Falter, SPA
04/018 and Top Citizen Science Initiative; TCS 12 Viel-Falter TCS).
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
686 J Insect Conserv (2017) 21:677–688
1 3
Ulrike Tappeiner and Johannes Rüdisser are a member of the research
area ‘Alpine Space— Man and Environment’ at the University of
Innsbruck. Finally, we wish to thank Hans-Peter Wymann for provid-
ing butterfly pictures and Jacob Dein and Julliane de Oliveira Rüdis-
ser for proof reading.
Open Access This article is distributed under the terms of the
Creative Commons Attribution 4.0 International License (http://
creativecommons.org/licenses/by/4.0/), which permits unrestricted
use, distribution, and reproduction in any medium, provided you give
appropriate credit to the original author(s) and the source, provide a
link to the Creative Commons license, and indicate if changes were
made.
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... Butterflies are excellent indicators for biodiversity assessments in the cultural landscape (Thomas, 2005, Hilpold et al., 2018 and serve as flagship species and help to communicate conservation goals to the general public (Feest et al., 2011). In order to survey the butterflies in this study, we applied a methodology for non-experts based on the work of Rüdisser et al. (2017). For surveys a predefined and visualised list of 13 characteristic butterfly species and 10 species groups was used. ...
... Usually, all surveys on a farm could be completed in 1 day. Due to logistical constraints, butterfly surveys were conduct only once per patch and not repeated during the season, as recommended by Rüdisser et al. (2017). Therefore, we did not analyse butterfly surveys at patch level, but pooled them by phytosociological patch type and region. ...
... More than half of the farmers interviewed were certain that they were capable to conduct the proposed assessment on their farms if supported by regional advisory services (Wezel et al., 2018). This is furthermore reinforced by the outcomes of other monitoring and citizen science projects involving laypeople (Follett and Strezov, 2015;Kullenberg and Kasperowski, 2016;Rüdisser et al., 2017). ...
... In biodiversity conservation studies, morphospecies have been used as a first step in sorting and identifying survey samples, to find patterns in taxonomic groups, and to describe gross species richness of single sites [28]. To teach identification skills, researchers have developed simple morphospecies identification guides that focus on few distinguishing morphological features, easily visible to the unaided eye [26,[32][33][34][35][36]. Several citizen-science studies have demonstrated that experts can successfully teach "para-taxonomists" to identify morphospecies of large flying insects with simplified graphic ID-guides [26,32,[34][35][36][37][38]. ...
... To teach identification skills, researchers have developed simple morphospecies identification guides that focus on few distinguishing morphological features, easily visible to the unaided eye [26,[32][33][34][35][36]. Several citizen-science studies have demonstrated that experts can successfully teach "para-taxonomists" to identify morphospecies of large flying insects with simplified graphic ID-guides [26,32,[34][35][36][37][38]. Indeed, experts around the globe have used para-taxonomy to train volunteers to identify various organisms, and species diversity has been documented for plants, birds, and large insects with well-established citizen science protocols, forming the backbone of community and citizen science [32,34,35,[37][38][39][40][41]. ...
... Several citizen-science studies have demonstrated that experts can successfully teach "para-taxonomists" to identify morphospecies of large flying insects with simplified graphic ID-guides [26,32,[34][35][36][37][38]. Indeed, experts around the globe have used para-taxonomy to train volunteers to identify various organisms, and species diversity has been documented for plants, birds, and large insects with well-established citizen science protocols, forming the backbone of community and citizen science [32,34,35,[37][38][39][40][41]. In expert-assisted community science programs, morphospecies ID-guides can be used in a stepwise progression from para-taxonomic/genera identifications performed by citizen scientists, to taxonomic/species identifications performed by experts [41]. ...
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Declines in native bee communities due to forces of global change have become an increasing public concern. Despite this heightened interest, there are few publicly available courses on native bees, and little understanding of how participants might benefit from such courses. In October of 2018 and 2019, we taught the ‘Native Bees of Texas’ course to the public at The University of Texas at Austin Lady Bird Johnson Wildflower Center botanical gardens in an active learning environment with slide-based presentations, printed photo-illustrated resources, and direct insect observations. In this study, we evaluated course efficacy and learning outcomes with a pre/post-course test, a survey, and open-ended feedback, focused on quality improvement findings. Overall, participants’ test scores increased significantly, from 60% to 87% correct answers in 2018 and from 64% to 87% in 2019, with greater post-course differences in ecological knowledge than in identification skills. Post-course, the mean of participants’ bee knowledge self-ratings was 4.56 on a five-point scale. The mean of participants’ ratings of the degree to which they attained the course learning objectives was 4.43 on a five-point scale. Assessment results provided evidence that the course enriched participants’ knowledge of native bee ecology and conservation and gave participants a basic foundation in bee identification. This highlights the utility of systematic course evaluations in public engagement efforts related to biodiversity conservation.
... Butterflies are excellent indicators for biodiversity assessments in the cultural landscape (Thomas, 2005, Hilpold et al., 2018 and serve as flagship species and help to communicate conservation goals to the general public (Feest et al., 2011). In order to survey the butterflies in this study, we applied a methodology for non-experts based on the work of Rüdisser et al. (2017). For surveys a predefined and visualised list of 13 characteristic butterfly species and 10 species groups was used. ...
... Usually, all surveys on a farm could be completed in 1 day. Due to logistical constraints, butterfly surveys were conduct only once per patch and not repeated during the season, as recommended by Rüdisser et al. (2017). Therefore, we did not analyse butterfly surveys at patch level, but pooled them by phytosociological patch type and region. ...
... More than half of the farmers interviewed were certain that they were capable to conduct the proposed assessment on their farms if supported by regional advisory services (Wezel et al., 2018). This is furthermore reinforced by the outcomes of other monitoring and citizen science projects involving laypeople (Follett and Strezov, 2015;Kullenberg and Kasperowski, 2016;Rüdisser et al., 2017). ...
Article
Farmers are important actors for regional development and biodiversity protection. Agri-environment-climate measures (AECM) are therefore a central tool of the European Union to support its biodiversity conservation policy. AECM generally reward farmers for fulfilling predefined management actions or avoiding specific practices. In contrast, result oriented AECM are intended to reward farmers for the outcome of nature friendly management practices. This approach gives more flexibility in management and hence promotes farmers engagement and autonomy. Besides educational activities and agricultural advisory services farmers need user friendly tools to assess biodiversity in order to meet result oriented AECM. Thus, we present a biodiversity assessment scheme for farmland using a set of indicators, which covers different aspects of biodiversity (flower colour index, butterfly abundance, landscape structuring degree, patch diversity index, aggregated biodiversity index) and can be applied at different spatial scales. The assessment scheme is applied on 44 farms in five countries (France, Switzerland, Germany, Italy, and Austria). To evaluate its appropriateness the relationship between the indicators and land-use intensity and plant species richness is investigated. Grasslands with low land-use intensity are more colourful grasslands, have significantly more butterflies and a higher aggregated biodiversity index than moderately and intensively used grasslands. The influence of management intensity on the landscape structuring degree is not significant. All indicators correlate with plant species richness at all spatial scales. The proposed assessment scheme serves as a tool for the detection of differences in biodiversity resulting from land-use practices, and can assist the monitoring of ROMs.
... Butterflies are excellent indicators for biodiversity assessments in the cultural landscape (Thomas, 2005, Hilpold et al., 2018 and serve as flagship species and help to communicate conservation goals to the general public (Feest et al., 2011). In order to survey the butterflies in this study, we applied a methodology for non-experts based on the work of Rüdisser et al. (2017). For surveys a predefined and visualised list of 13 characteristic butterfly species and 10 species groups was used. ...
... Usually, all surveys on a farm could be completed in 1 day. Due to logistical constraints, butterfly surveys were conduct only once per patch and not repeated during the season, as recommended by Rüdisser et al. (2017). Therefore, we did not analyse butterfly surveys at patch level, but pooled them by phytosociological patch type and region. ...
... More than half of the farmers interviewed were certain that they were capable to conduct the proposed assessment on their farms if supported by regional advisory services (Wezel et al., 2018). This is furthermore reinforced by the outcomes of other monitoring and citizen science projects involving laypeople (Follett and Strezov, 2015;Kullenberg and Kasperowski, 2016;Rüdisser et al., 2017). ...
Article
Farmers are important actors for regional development and biodiversity protection. Agri-environment-climate measures (AECM) are therefore a central tool of the European Union to support its biodiversity conservation policy. AECM generally reward farmers for fulfilling predefined management actions or avoiding specific practices. In contrast, result oriented AECM are intended to reward farmers for the outcome of nature friendly management practices. This approach gives more flexibility in management and hence promotes farmers engagement and autonomy. Besides educational activities and agricultural advisory services farmers need user friendly tools to assess biodiversity in order to meet result oriented AECM. Thus, we present a biodiversity assessment scheme for farmland using a set of indicators, which covers different aspects of biodiversity (flower colour index, butterfly abundance, landscape structuring degree, patch diversity index, aggregated biodiversity index) and can be applied at different spatial scales. The assessment scheme is applied on 44 farms in five countries (France, Switzerland, Germany, Italy, and Austria). To evaluate its appropriateness the relationship between the indicators and land-use intensity and plant species richness is investigated. Grasslands with low land-use intensity are more colourful grasslands, have significantly more butterflies and a higher aggregated biodiversity index than moderately and intensively used grasslands. The influence of management intensity on the landscape structuring degree is not significant. All indicators correlate with plant species richness at all spatial scales. The proposed assessment scheme serves as a tool for the detection of differences in biodiversity resulting from land-use practices, and can assist the monitoring of ROMs.
... More tangibly, our study assessed how accurate diversity estimates are for these two pollinator groups for a sampling design that is already in use. For instance, the Austrian Butterfly Monitoring System integrates 50-m transects sampled by volunteers with more intensive samples from experts (Rüdisser et al., 2017), and a similar design was used in an Italian monitoring programme within agroecosystems (D'Antoni et al., 2020). These simple designs can provide general information on the habitat quality for pollinators at a site (Rüdisser et al., 2017). ...
... For instance, the Austrian Butterfly Monitoring System integrates 50-m transects sampled by volunteers with more intensive samples from experts (Rüdisser et al., 2017), and a similar design was used in an Italian monitoring programme within agroecosystems (D'Antoni et al., 2020). These simple designs can provide general information on the habitat quality for pollinators at a site (Rüdisser et al., 2017). To the best our knowledge, this is a first attempt to evaluate in more detail how accurate similar approaches are, which is important given that uncertainty in baseline conditions hinder efforts to quantify the effectiveness of conservation actions (Buckland & Johnston, 2017). ...
Article
Full-text available
Widespread declines in insects will threaten ecosystem functioning and services. Nevertheless, a lack of data hinders assessments of population and biodiversity trends for many insect groups and thus effective conservation actions. Implementing cost‐effective, unbiased, and accurate monitoring programmes targeting different groups across a larger geographical range has therefore become a key conservation priority. We evaluated a sampling protocol designed for community science initiatives targeting butterflies and bees. Specifically, we tested how well a short (200‐m long) version of traditional Pollard walk transects, designed to be accessible for large numbers of community scientists, captures changes in alpha and beta diversity of these two pollinator groups. We used resampling methods to simulate and assess scenarios varying in sampling intensity and frequency. We found that alpha and beta diversity of butterflies and bees were estimated at similar accuracies across different scenarios, which suggests that even short transects can provide useful information on diversity patterns for both taxa. However, common sampling frequencies resulted in low accuracies (e.g. one sample every 10 days finds on average ~50% of the species present at a site). We discuss our results in the context of developing large scale, structured monitoring systems for multiple insect taxa, and how information on biodiversity patterns can inform the expansion of monitoring schemes. We explain why, moving forward, even rapid sampling designs similar to the approach tested here will be useful given a higher potential to involve community scientists, data integration techniques, and the opportunities to sample under‐represented habitat types Implementing effective monitoring programmes targeting different taxa across large geographical ranges is a priority to understand changes in pollinator biodiversity A short version of Pollard walks can provide useful information on diversity patterns for butterflies and bees Quick sampling protocols have the potential to reduce spatial biases in monitoring, complement ongoing schemes via data integration, and facilitate community science initiatives
... In managed grasslands, successful citizen science projects already engage farmers in monitoring of vegetation [14], insects [15], ecosystem services [16] and yield [10], but not livestock behaviour. In the past, tracking technology has been quite expensive, so that telemetry studies are usually conducted on few individuals. ...
Article
Full-text available
Engaging farmers as citizen scientists may be a cost-efficient way to answering applied research questions aimed at more sustainable land use. We used a citizen science approach with German horse farmers with a dual goal. Firstly, we tested the practicability of this approach for answering ‘real-life’ questions in variable agricultural land-use systems. Secondly, we were interested in the knowledge it can provide about locomotion of horses on pasture and the management factors influencing this behaviour. Out of 165 volunteers, we selected 40 participants to record locomotion of two horses on pasture and provide information on their horse husbandry and pasture management. We obtained complete records for three recording days per horse from 28 participants, resulting in a dataset on more individual horses than any other Global Positioning System study published in the last 30 years. Time spent walking was greatest for horses kept in box-stall stables, and walking distance decreased with increasing grazing time. This suggests that restrictions in pasture access may increase stress on grass swards through running and trampling, severely challenging sustainable pasture management. Our study, involving simple technology, clear instructions and rigorous quality assessment, demonstrates the potential of citizen science actively involving land managers in agricultural research.
... The confidence of the farmers and agronomists to record species and the accuracy of our non-experts could be improved through training (Kremen et al., 2011;Sharma et al., 2019) if species-level data from transects were considered an essential outcome. However when assessing crop pollination, abundance recorded to broad taxonomic groups is often sufficient to predict levels of pollination service (Rader et al., 2016) but a level of training is still required to ensure this is recorded accurately, even to morphospecies (Rüdisser et al., 2017;Mason and Arathi, 2019). The significant interaction between recorder type and crop for some taxa, namely honeybees and solitary bees, indicates that the crop in which the survey is carried out affects the likelihood of a record being made by our different recorders. ...
Article
Full-text available
Insects pollinate many globally important crops and therefore rapid and effective means to measure crop pollinators and pollination are required to support national monitoring schemes and allow localised measurements of pollinator supply and demand to crops. We tested user-friendly protocols for assessing pollinators and pollination in crops to better understand the capacity and willingness of a group of farmers and citizen scientists to implement such techniques in the field. We asked the different recorder groups including farmers and agronomists, non-expert volunteers and experienced researchers to complete three pollinator and pollination service assessment echniques: transect walks, pan trapping and pollinator exclusion and supplementary pollination. Recorders provided feedback on each method through a questionnaire and the data collected using different methods were compared. Our volunteer members of the public, and farmers and agronomists were able to implement all assessment echniques in apple, bean and oilseed rape fields. The experienced researchers and volunteer members of the public were more willing to record bumblebees to species level on transects than the farmers and agronomists. There was also a significant interaction between recorder and crop type for certain insect taxa demonstrating that in certain crops some taxa may be easier to record than others. All our recorder groups found transects and pan traps straightforward and enjoyable to implement. Our non-expert volunteers were willing to use pollinator exclusion and supplementary pollination techniques as part of a wider scheme, the farmers and agronomists who implemented the technique were less positive about applying this method more widely. We have demonstrated that volunteer recorders, including farmers and agronomists, can be engaged and are able to implement methods to assess pollinators and pollination, although additional training is necessary to ensure accurate species data collection. For the more direct and time consuming measures of pollination service, both training and additional support may be needed, particularly for farmers. The tools developed and tested here will be valuable for wider pollinator monitoring schemes and for integration into standard agronomic practices.
Technical Report
Full-text available
Insekten sind die artenreichste Tiergruppe auf der Erde. Es ist derzeit rund eine Million Arten bekannt, das entspricht mehr als 60 % aller Tierarten. Sie sind formen- und farbenreich, haben eine Vielzahl an Ernährungs- und Verhaltensweisen hervorgebracht und sind für den Menschen von großer Bedeutung. Das sogenannte „Insektensterben“, ein dramatischer Rückgang der Insektenbiomasse (und der Individuenzahlen) hat in den letzten Jahren für große Aufmerksamkeit gesorgt. Die Zahl der in Österreich vorkommenden Insektenarten wird auf rund 40.000 geschätzt. Während die Zahlen für besser bekannte Insektengruppen (z. B. Schmetterlinge, Heuschrecken, Libellen) sehr zuverlässig sind, sind die Angaben für einige artenreiche Insektengruppen (z. B. Fliegen, Hautflügler) nur Schätzwerte. Rund 345 Insektenarten gelten als in Österreich endemisch, d. h. sie kommen weltweit nur hier vor. Für jene endemischen Insektenarten, die über der Baumgrenze leben, gilt der Klimawandel als wesentlicher Gefährdungsfaktor. Umgekehrt führen Klimawandel und Globalisierung zu einem Anstieg der Artenzahlen in Österreich, wobei beide Faktoren vor allem relativ anspruchslose „Allerweltsarten“ begünstigten. Folgende Ökosystemleistungen von Insekten sind von besonderer Bedeutung: die Bestäubung, die Schädlingskontrolle, ihre Rolle als Nahrungsgrundlage für terrestrische Wirbeltiere und die Honigproduktion. Insekten spielen eine negative Rolle als Krankheitsüberträger oder Schädlinge in der Land- und Forstwirtschaft. Der Kenntnisstand von Verbreitungsdaten zu den Insektengruppen in Österreich ist heterogen. Der Schwerpunkt von GBIF-Austria1 liegt auf Schmetterlingsdaten, Verbreitungsatlanten oder vergleichbare Publikationen liegen nur für einige (populäre) Gruppen vor, und fehlen für die meisten Insektengruppen. Die Biodiversitätsarchive Österreichs (wissenschaftliche Belegsammlungen) sind unterdotiert. Um aktuellen Ansprüchen gerecht zu werden, sind Investitionen erforderlich, insbesondere hinsichtlich der Digitalisierung der Daten. Bestehende Monitoringprogramme (z. B. FFH-Richtlinie, Wasserrahmen-Richtlinie, BINATS2, ÖBM3-Kulturlandschaft) decken einen kleinen Teil der Insektenvielfalt ab, standardisierte, langfristig gesicherte Freiland-Erhebungen fehlen jedoch. Im schulischen und universitären Bereich sind Anstrengungen erforderlich, die organismische Biologie zu stärken und damit auch eine Grundlage für das Verständnis und die Akzeptanz von Schutzbemühungen zu schaffen. Bürgerwissenschaften (Citizen Science) können hier einen Ansatzpunkt bilden, eine wissenschaftliche Grundlage aber nicht ersetzen. 1 GBIF-Austria ist eine vom Bundesministerium für Klimaschutz, Umwelt, Energie, Mobilität, Innovation und Technologie geförderte Initiative österreichischer naturwissenschaftlicher Institutionen und Vereine. Ziel der Initiative ist es, Daten zur heimischen Artenvielfalt in großem Umfang über das Internet zugänglich zu machen und somit die internationale GBIF-Initiative auf nationaler Ebene umzusetzen (www.gbif.at). 2 Biodiversity – Nature – Safety 3 Österreichisches Biodiversitäts-Monitoring Insektenarten in Österreich Rolle der Insekten im Ökosystem relativ geringer Kenntnisstand Insekten in Österreich – Zusammenfassung 6 Umweltbundesamt  REP-0739, Wien 2020 Rückgänge von Insektenpopulationen wurden schon ab den 1990er-Jahren (bzw. viel früher) festgestellt. Mit der „Krefeld-Studie“ in Deutschland im Jahr 2017 rückte das Thema in den Fokus sowohl der Wissenschaft als auch einer breiteren Öffentlichkeit. Die Ursachenforschung gestaltet sich jedoch aufgrund der komplexen Zusammenhänge und der wenigen belastbaren Langzeituntersuchungen schwierig. Regionale Studien und Erklärungen für den lokalen Rückgang von Insektenpopulationen sind nicht in der Lage, ein offenbar globales Phänomen ausreichend zu erklären. Zu den übergeordneten Faktoren, die zum Insektensterben beitragen, zählen  Verlust an Lebensraum,  Verschlechterung der Lebensraumqualität, insbesondere durch Verlust von Lebensraumstruktur,  Klimawandel,  Insektizide,  Schadstoffeinträge, insbesondere flächendeckende Stickstoffeinträge,  Lichtverschmutzung,  gebietsfremde Arten,  Fragmentation der Landschaft und  Metapopulationsdynamik. Insektensterben ist ein komplexes und multifaktorielles Phänomen. Es ist nicht zu erwarten, dass es nur eine einzige Hauptursache für den Biodiversitätsverlust auf allen räumlichen Skalen und funktionellen Ebenen gibt. Für Österreich liegen keine quantitativen Daten vor, die einen Insektenrückgang belegen oder widerlegen könnten. Indizien, insbesondere lokale Studien und Gefährdungsanalysen (Rote Listen) lassen aber keinen Zweifel, dass die Rückgänge in Österreich stattgefunden haben und stattfinden. Auch wenn für viele Insektengruppen keine aktuellen Gefährdungsanalysen vorliegen, zeigen die vorhandenen Daten übergeordnete Bedrohungsbilder: Besonders gefährdet sind Insektenarten in ostösterreichischen Offenlandstandorten sowie Arten von natürlichen Fließgewässer-Uferstandorten, Feuchtwiesen, Quellen und Mooren. Es besteht hoher Forschungsbedarf, insbesondere die komplexen Ursachen für das Insektensterben betreffend. Es liegen jedoch genügend Informationen vor, um bereits jetzt Maßnahmen einzuleiten und umzusetzen, die die Gefährdung der Insekten in all ihrer Vielfalt und in ihrer ökologischen Leistungsfähigkeit reduzieren. Die gesellschaftliche Aufgabe besteht darin, Handlungsoptionen auszuloten, konkrete Maßnahmen für diese Erfordernisse auszuarbeiten und diese gemeinsam mit allen administrativen Organen und Interessengruppen sowie einer interessierten Öffentlichkeit umzusetzen.
Article
Full-text available
The fate of humans and insects intertwine, especially through the medium of plants. Global environmental change, including land transformation and contamination, is causing concerning insect diversity loss, articulated in the companion review Scientists' warning to humanity on insect extinctions. Yet, despite a sound philosophical foundation, recognized ethical values, and scientific evidence, globally we are performing poorly at instigating effective insect conservation. As insects are a major component of the tapestry of life, insect conservation would do well to integrate better with overall biodiversity conservation and climate change mitigation. This also involves popularizing insects, especially through use of iconic species, through more media coverage, and more inclusive education. Insect conservationists need to liaise better with decision makers, stakeholders, and land managers, especially at the conceptually familiar scale of the landscape. Enough evidence is now available, and synthesized here, which illustrates that multiple strategies work at local levels towards saving insects. We now need to expand these locally-crafted strategies globally. Tangible actions include ensuring maintenance of biotic complexity, especially through improving temporal and spatial heterogeneity, functional connectivity, and metapopulation dynamics, while maintaining unique habitats, across landscape mosaics, as well as instigating better communication. Key is to have more expansive sustainable agriculture and forestry, improved regulation and prevention of environmental risks, and greater recognition of protected areas alongside agro-ecology in novel landscapes. Future-proofing insect diversity is now critical, with the benefits far reaching, including continued provision of valuable ecosystem services and the conservation of a rich and impressive component of Earth's biodiversity.
Article
Full-text available
The ecological consequences of species loss are widely studied, but represent an end point of environmental forcing that is not always realised. Changes in species evenness and the rank order of dominant species are more widespread responses to directional forcing. However, despite the repercussions for ecosystem functioning such changes have received little attention. Here, we experimentally assess how the rearrangement of species dominance structure within specific levels of evenness, rather than changes in species richness and composition, affect invertebrate particle reworking and burrow ventilation behaviour - important moderators of microbial-mediated remineralisation processes in benthic environments - and associated levels of sediment nutrient release. We find that the most dominant species exert a disproportionate influence on functioning at low levels of evenness, but that changes in biomass distribution and a change in emphasis in species-environmental interactions become more important in governing system functionality as evenness increases. Our study highlights the need to consider the functional significance of alterations to community attributes, rather than to solely focus on the attainment of particular levels of diversity when safeguarding biodiversity and ecosystems that provide essential services to society.
Article
Full-text available
Species richness and evenness, the two principle components of species diversity, are frequently used to describe variation in species assemblages in space and time. Compound indices, including variations of both the Shannon–Wiener index and Simpson’s index, are assumed to intelligibly integrate species richness and evenness into all-encompassing measures. However, the efficacy of compound indices is disputed by the possibility of inverse relationships between species richness and evenness. Past studies have assessed relationships between various diversity measures across survey locations for a variety of taxa, often finding species richness and evenness to be inversely related. Butterflies are one of the most intensively monitored taxa worldwide, but have been largely neglected in such studies. Long-term butterfly monitoring programs provide a unique opportunity for analyzing how trends in species diversity relate to habitat and environmental conditions. However, analyzing trends in butterfly diversity first requires an assessment of the applicability of common diversity measures to butterfly assemblages. To accomplish this, we quantified relationships between butterfly diversity measures estimated from 10 years of butterfly population data collected in the North Saskatchewan River Valley in Edmonton, Alberta, Canada. Species richness and evenness were inversely related within the butterfly assemblage. We conclude that species evenness may be used in conjunction with richness to deepen our understandings of assemblage organization, but combining these two components within compound indices does not produce measures that consistently align with our intuitive sense of species diversity.
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Ecology studies often require large datasets. The benefits of citizen science for collecting such datasets include the extension of spatial and temporal scales, and cost reduction. In classical citizen science, citizens collect data and send them directly to scientists. This may not be possible for the many biological groups for which specimen identification is difficult and requires high-level expertise. Here we report the results of an expert-assisted citizen science program where teachers from 20 French agricultural high schools collected bees, which were identified to species level by a panel of expert bee taxonomists. Overall the dataset included 70 collections (year × sampling site combinations) that resulted in 4574 specimens belonging to 195 species. We analysed this dataset using data freely available at a national scale on agriculture intensity and landscape composition. We found that species richness increased with increasing proportion of herbaceous semi-natural elements; species dominance decreased with increasing crop diversity; the proportion of above ground nesting species and specimens increased as the intensity of agricultural practices decreased. Comparing the results obtained with identification to species level and those obtained with higher taxa or parataxonomic approaches, we found that the loss of taxonomic resolution resulted in the non-significance of some results on the effects of environmental variables on bee assemblage-level attributes. Our study suggests that identification to species level is of great importance to detect the effects of global change on bees and that an expert-assisted citizen science paradigm could provide relevant results to guide conservation measures at a national scale.
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The number of collaborative initiatives between scientists and volunteers (i.e., citizen science) is increasing across many research fields. The promise of societal transformation together with scientific breakthroughs contributes to the current popularity of citizen science (CS) in the policy domain. We examined the transformative capacity of citizen science in particular learning through environmental CS as conservation tool. We reviewed the CS and social-learning literature and examined 14 conservation projects across Europe that involved collaborative CS. We also developed a template that can be used to explore learning arrangements (i.e., learning events and materials) in CS projects and to explain how the desired outcomes can be achieved through CS learning. We found that recent studies aiming to define CS for analytical purposes often fail to improve the conceptual clarity of CS; CS programs may have transformative potential, especially for the development of individual skills, but such transformation is not necessarily occurring at the organizational and institutional levels; empirical evidence on simple learning outcomes, but the assertion of transformative effects of CS learning is often based on assumptions rather than empirical observation; and it is unanimous that learning in CS is considered important, but in practice it often goes unreported or unevaluated. In conclusion, we point to the need for reliable and transparent measurement of transformative effects for democratization of knowledge production. This article is protected by copyright. All rights reserved.
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