European Grassland Butterfly
Indicator 1990-2020
Technical report

Butterfly Conservation Europe 2022 | EUROPEAN GRASSLAND BUTTERFLY INDICATOR
1990-20201

European Grassland Butterfly
Indicator 1990-2020
Technical report

Butterfly Conservation Europe 2022 | EUROPEAN GRASSLAND BUTTERFLY INDICATOR
1990-20202

European Grassland Butterfly Indicator 1990-2020
Technical report
Van Swaay, C.A.M.1, 2, Dennis, E.B.3, Schmucki, R.4, Sevilleja, C.G.1,2, Arnberg, H.33, Åström, S.6,
Balalaikins, M.7, Barea-Azcón, J.M. 5, Bonelli, S.8, Botham, M.4, Cancela, J.P.51, Collins, S.1, De Flores,
M.18, Dapporto, L.44, Dopagne, C.12, Dziekanska, I.13, Escobés, R.14, Faltynek Fric, Z.30, FernándezGarcía, J.M.16, Fontaine, B.17, Glogovčan, P.46, Gracianteparaluceta, A.16, Harpke, A.15, 50, Harrower, C.4,
Heliölä, J.19, Houard, X., 18, Judge, M.25, Kolev, Z.20, Komac, B.21, Kühn, E.15, Kuussaari, M.19, Lang, A.23,
Lysaght, L.25, Maes, D.26, McGowan,D.11, Mestdagh, X.27, Middlebrook, I.3, Monasterio, Y.14, Monteiro,
E.28, Munguira, M.L.9,1, Musche, M.15, Olivares, F.J.24, Õunap, E.29, Ozden, O.30, Pavlíčko, A.32, Pendl,
M.22, Pettersson, L.B.33, Rákosy, L.36, Roth, T.37, Rüdisser, J.38, Šašić, M.39, Scalercio, S.47, Settele, J.15, 48,
49, Sielezniew, M.40, Sobczyk-Moran, G.18, Stefanescu, C.31, Švitra, G.42, Szabadfalvi, A.43, Tiitsaar, A.29,
Titeux, N.27, Tzirkalli, E.45, Ubach, A.31, Verovnik, R.10, Vray, S.27, Warren, M.S.3, Wynhoff, I.1,2 & Roy,D.B.4 (2022). European Grassland Butterfly Indicator 1990-2020 Technical report. Butterfly
Conservation Europe & SPRING/eBMS (www.butterfly-monitoring.net) & Vlinderstichting report
VS2022.039.
1

Butterfly Conservation Europe2
De Vlinderstichting/Dutch Butterfly Conservation,
Wageningen, Netherlands
3
Butterfly Conservation, East Lulworth, Dorset, UK4
UK Centre for Ecology & Hydrology, Wallingford, UK
5
Environment and Water Agency (Ministry of
Sustainability, Environment and Blue Economy of
the Andalusian Government), Provincial Office ofGranada, Granada, Spain.
6
Norwegian Institute for Nature Research (NINA),
Trondheim, Norway
7
Institute of Life Sciences and Technology, Daugavpils
University, Daugavpils, Latvia
8
Zoolab Department of Life Sciences and Systems
Biology University of Turin
9
Universidad Autónoma de Madrid, Spain
10
University of Ljubljana, Biotechnical Faculty,
Ljubljana, Slovenia
11
Jersey, Channel Islands
12
Natagriwal asbl, Gembloux, Belgium
13
University of Bialystok, Poland
14
ZERYNTHIA Association, Spain
15
Helmholtz Centre for Environmental Research,
Department of Conservation Biology and SocialEcological Systems, Halle, Germany
16
Hazi Foundation, Spain
17
Muséum National d'Histoire Naturelle, Paris
18
Office for insects and their environment – Opie,
Guyancourt, France
19
Finnish Environment Institute, Natural Environment
Centre, Helsinki, Finland
20
Natural Museum of Natural History, Sofia
21
Centre d'Estudis de la Neu i de la Muntanyad'Andorra (CENMA), Andorra
22
Universität Wien, Austria
23
Büro Lang, Germany

24

Sierra Nevada Butterfly Monitoring Network. Sierra
Nevada National Park and Natural Park, Pinos Genil(Granada), Spain.
25
National Biodiversity Data Centre, Carriganore, Co.
Waterford, Ireland
26
Research Institute for Nature and Forest (INBO),
Brussels, Belgium
27
Luxembourg Institute of Science and Technology,
Environmental Research and Innovation (ERIN)
Department, Observatory for Cimate, Environment
and Biodiversity (OCEB), Belvaux, Luxembourg
28
TAGIS - Centro de Conservacao des Borboletas dePortugal
29
University of Tartu, Estonia
30
Cyprus Herbarium and Natural History Museum,
Near East University, Nicosia, Cyprus
31
Butterfly Monitoring Scheme, Museu de Ciències
Naturals de Granollers, Spain
32
Czech Butterfly Conservation Society
33
Swedish Butterfly Monitoring Scheme, University ofLund, Lund, Sweden
34
Natagriwal asbl, Gembloux, Belgium
36
Department Taxonomy and Ecology, Babes-Bolyai
University, Cluj, Romania
37
Hintermann & Weber AG, Reinach, Switzerland andUniversity of Basel, Zoological Institute, Basel,
Switzerland
38
University of Innsbruck, Department of Ecology
39
Croatian Natural History Museum
40
Butterfly Monitoring Scheme, Museu de Ciències
Naturals de Granollers, Spain
41
University of Bialystok, Poland
42
Ukmerge, Lithuania
43
Hungarian Lepidoptera Monitoring Network, Jozsef
Szalkay Hungarian Lepidopterists' Society
44
ZEN Lab, Department of Biology University of
Florence, Italy

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45

Cyprus Butterfly Study Group, Nicosia, Cyprus
Society for the Conservation and Study of
Lepidoptera in Slovenia, Slovenia
47
Council for Agricultural Research and Economics,
Research Centre for Forestry and Wood, Rende (CS),
Italy
48German Centre for Integrative Biodiversity Research(iDiv), Halle-Jena-Leipzig, Leipzig, Germany
49Martin-Luther-University Halle-Wittenberg, Institutefor Biology, 06120 Halle, Germany
50Helmholtz Centre for Environmental Research,
Department of Community Ecology, Halle, Germany
51
University of Azores.
46

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Citation
Van Swaay, C.A.M., Dennis, E.B., Schmucki, R., Sevilleja, C.G., Arnberg, H., Åström, S., Balalaikins, M.,
Barea-Azcón, J.M. , Bonelli, S., Botham, M., Cancela, J.P., Collins, S., De Flores, M., Dapporto, L.,
Dopagne, C., Dziekanska, I., Escobés, R., Faltynek Fric, Z., Fernández-García, J.M., Fontaine, B.,
Glogovčan, P., Gracianteparaluceta, A., Harpke, A., Harrower, C., Heliölä, J., Houard, X., Judge, M.,
Kolev, Z., Komac, B., Kühn, E., Kuussaari, M., Lang, A., Lysaght, L., Maes, D., McGowan,D., Mestdagh,X., Middlebrook, I., Monasterio, Y., Monteiro, E., Munguira, M.L., Musche, M., Olivares, F.J., Õunap,
E., Ozden, O., Pavlíčko, A., Pendl, M., Pettersson, L.B., Rákosy, L., Roth, T., Rüdisser, J., Šašić, M.,
Scalercio, S., Settele, J., , Sielezniew, M., Sobczyk-Moran, G., Stefanescu, C., Švitra, G., Szabadfalvi, A.,
Tiitsaar, A., Titeux, N., Tzirkalli, E., Ubach, A., Verovnik, R., Vray, S., Warren, M.S., Wynhoff, I., & Roy,
D.B.
(2022). European Grassland Butterfly Indicator 1990-2020 Technical report. Butterfly Conservation
Europe & SPRING/eBMS (www.butterfly-monitoring.net) & Vlinderstichting report VS2022.039.

Keywords
Butterfly, Grassland, Monitoring, Trend, Index, Europe, European Union, Indicator, Biodiversity
December 2022

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Acknowledgements:
We thank the European Union for funding the SPRING (Strengthening Pollinator Recovery throughINdicators and monitorinG)-project (Contract no. 09.02001/2021/847887/SER/EVN.D.2 Preparatory
Action for EU Pollinator Monitoring Scheme and Indicators).
The European Butterfly Monitoring Scheme (eBMS) is indebted to the constituent National Butterfly
Monitoring Schemes, their funders and all volunteers who contribute data. At the time of producing
this report, the eBMS is a partnership of the following organisations: the Natural Environment
Research Council (acting through the Centre for Ecology & Hydrology), Butterfly Conservation UK,
Helmholtz-Zentrum für Umweltforschung GmbH – UFZ, De Vlinderstichting, Catalonia BMS, Finnish
Environment Institute (SYKE), Butterfly Conservation Europe (BCE), Research Institute Nature andForest (INBO), Muséum National d'Histoire Naturelle (MNHN) CNRS-UPMC, Lund University, National
Biodiversity Data Centre (NBDC) Ireland, Luxembourg Institute of Science and Technology (LIST),
Asociación Española para la Protección de las Mariposas y su Medio -ZERYNTHIA, Universidad
Autónoma de Madrid, University of Ljubljana, Hungarian Lepidoptera Monitoring Network as part of
the Szalkay Hungarian Lepidopterist’s Society, Zoolab Department of Life Sciences and Systematics,
TAGIS – Centro de Conservacao des Borboletas de Portugal, Biodiversity Monitoring Switzerland,
Czech Butterfly Conservation Society, University of Innsbruck - Butterfly Monitoring Viel-Falter, andCroatian Natural History Museum.
The UK Butterfly Monitoring Scheme is organized and funded by Butterfly Conservation, the Centre
for Ecology and Hydrology, British Trust for Ornithology, and the Joint Nature Conservation
Committee. The UKBMS is indebted to all volunteers who contribute data to the scheme.
Jacqueline Loos, Mikael Molander and Lászlo Rákosy were important contributors to the Romanianpart of the AMIGA project.
The Butterfly Monitoring Program in Spain managed by ZERYNTHIA has the support of the BasqueCountry Government, the Hazi Foundation, Valle de Aranguren Council (Navarre) and many otherregional and local governments of the autonomous communities of the Iberian Peninsula and the
Canary Islands.
The Dutch BMS is a co-operation between Dutch Butterfly Conservation and Statistics Netherlands(CBS), part of the Network Ecological Monitoring (NEM) and financed by the Ministry of Agriculture,
Nature and Food Quality (LNV).
The Spanish BMS is coordinated by the SOCEME (Sociedad para la Conservación y Estudio deMariposas Españolas) and the Universidad Autónoma de Madrid (UAM), with the collaboration ofOrganismo Autónomo de Parques Nacionales (OAPN), Estación Biológica de Doñana, Parque Nacionalde Doñana, Parque Nacional de Sierra Nevada, Observatorio del Cambio Global de Sierra Nevada,
Consorcio Parque Nacional Picos de Europa, Parque Nacional Sierra de Guadarrama, Parque Nacionalde las Islas Atlánticas de Galicia, Asociación de Naturalistas Palentinos, Sociedade Galega de Historia
Natural, Sociedad Entomológica Ambiental de Castilla-La Mancha (SEACAM), Fundación Internacionalpara la Restauración de Ecosistemas (FIRE), Sociedad Hispano-Luso-Americana de Lepidopterologia,
Vida Silvestre Ibérica, ALAS, el bosque animado, AVAN, Museus de la Universitat de Valéncia Historia
Natural (MUVHN) and the Universities of Granada, Castilla-La Mancha, da Coruña, and Rey Juan
Carlos. Other National Parks collaborating with the network are: Cabañeros, Cabrera, Caldera deTaburiente, Garajonay, Monfragüe, Ordesa y Monte Perdido, Tablas de Daimiel, Teide, andTimanfaya.
The Nature Conservation Agency of Latvia was kind to supply the Latvian data.
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The Irish Butterfly Monitoring Scheme is funded by the Department of Housing, Local Governmentand Heritage.
The German BMS is a cooperation between the Helmholtz Centre for Environmental Research - UFZ,
German Butterfly Conservation (GfS) and science4you.
The Catalan BMS is funded by the Departament de Territori i Sostenibilitat de la Generalitat deCatalunya.
The Italian Butterfly Monitoring Scheme is a cooperation among University of Turin, University of
Florence and the Council for Agricultural Research and Agricultural of Cosenza.
The Swedish Butterfly Monitoring Scheme is coordinated by Lund University and is funded by the
Swedish Environmental Protection Agency.
The French BMS (« Suivi Temporel des Rhopalocères de France, STERF ») is a cooperation between
the MNHN (Muséum national d’Histoire naturelle) and Opie (Office pour les insectes et leurenvironnement), with the support of French biodiversity Office (OFB), as part of the national plan for
butterflies.
the Society for the Conservation and Study of Lepidoptera in Slovenia was kind to supply the
Slovenian data.
Data was provided by ICTS-RBD Biodiversity Monitoring Program (Doñana Biological Station, CSIC).
The BMS in Luxembourg is coordinated by the Luxembourg Institute of Science and Technology and isco-funded by the Ministry of the Environment, Climate and Sustainable Development.

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Contents

Chapter 1 / Introduction......................................................................................................................... 9
Chapter 2 / Butterfly Monitoring in Europe ........................................................................................ 10
Chapter 3 / From butterfly counts to indicators ................................................................................. 13
Introduction....................................................................................................................................... 13
Fieldwork ........................................................................................................................................... 13
European Butterfly Monitoring Scheme database............................................................................ 13
Transect selection ............................................................................................................................. 14
Calculating population trends ........................................................................................................... 15
Producing European and EU27 Grassland Butterfly Indicators ......................................................... 16
Chapter 3 / Grassland Butterfly Trends ............................................................................................... 17
Introduction....................................................................................................................................... 17
Species trends in EU27 ...................................................................................................................... 17
Species trends in Europe ................................................................................................................... 18
Chapter 4 / Grassland Butterfly Indicator............................................................................................ 20
Introduction....................................................................................................................................... 20
Grassland Butterfly Indicator ............................................................................................................ 20
Chapter 5 / Conclusions ........................................................................................................................ 21
References............................................................................................................................................. 22
Annexe I / Statistical method ............................................................................................................... 24
Annexe II / Glossary .............................................................................................................................. 27

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Chapter 1 / Introduction
There is mounting evidence of widespread declines in the diversity and abundance of insects acrossthe globe (Sánchez-Bayo and Wyckhuys 2019, Seibold et al. 2019, van Klink et al. 2020, Wagner2020). This gives a stark warning of the precarious state of biodiversity, and demonstrates that
addressing the gap in knowledge of the status of insects is vital (Cardoso et al. 2020, Samways et al.
2020). Insects are estimated to comprise more than half of all described species and are a dominantcomponent of biodiversity in most ecosystems (Bar-On et al. 2018). Insects also provide a crucial rolein the functioning of ecosystems. They are not only related to the supply of many ecosystem services
such as pollination, biological control, soil fertility regulation and diverse cultural ecosystem services
but also to disservices such as damage to crops and spread of diseases to livestock andhumans (Gutierrez-Arellano and Mulligan 2018, Noriega et al. 2018). There is a pressing need toassess the status of insects to set and evaluate conservation targets.
At the Convention on Biological Diversity (CBD) meeting in Nagoya (Japan), the Strategic Plan for
Biodiversity 2011–2020 was adopted. It proposed five goals and 20 “Aichi” biodiversity targets. Inline with this plan, a new EU biodiversity strategy was adopted by the European Commission in May
2011. This strategy provided a framework for the EU to meet its biodiversity targets and global
commitments as a party to the CBD. The Headline Target in the existing EU Biodiversity Strategy 2020is to halt the loss of biodiversity and the degradation of ecosystem services in the EU by 2020, andrestore them, in so far as feasible, while stepping up the EU contribution to averting global
biodiversity loss. Under Target 3A the EU is committed to increasing the contribution of agriculture
to biodiversity recovery. Further, the EU Biodiversity Strategy 2030 includes the development of acoherent framework for monitoring, assessing and reporting on progress in implementing actions.
Such a framework is needed to link existing biodiversity data and knowledge systems with thestrategy, to help assess achievement of the goals and to streamline EU and global monitoring,
reporting and review obligations.
Some of the EU biodiversity indicators provide specific measurements and trends on genetic, species
and ecosystem/landscape diversity, but many have a more indirect link to biodiversity. Very few havebeen explicitly established to assess biodiversity. The status indicators on species only cover birds,
bats and butterflies, since these are the only taxa/species groups for which reasonably harmonizedEuropean monitoring data are available (EEA, 2012). This technical report builds upon previous
technical reports for the EU Grassland Butterfly Indicator (e.g., van Swaay et al. 2019).
Butterflies are ideal biological indicators: they are well-documented, measurable, sensitive toenvironmental change, occur in a wide range of habitat types, represent many other insects, and are
popular with the public because of their beauty. Field monitoring is essential to assess changes intheir abundance. Indicators based on butterfly monitoring data are valuable to understand the stateof the environment and help evaluate policy and implementation. Trained volunteers are a costeffective way of gathering robust data on butterflies, more so when supported by informative
materials and efficient online recording.

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Chapter 2 / Butterfly Monitoring in Europe
Butterfly monitoring enjoys a growing popularity in Europe, mainly supported by Butterfly
Conservation Europe (BCE) and its partners. While Butterfly Monitoring Schemes are present in a
growing number of countries and new ones are being initiated in many places, long time-series are
currently only available for a limited number of countries. For the indicators in this report, we useddata from 22 countries (Figure 1): Andorra, Austria, Belgium, Czech Republic, Estonia, Finland,
France, Germany, Hungary, Ireland, Jersey, Latvia, Lithuania, Luxembourg, Norway, Romania,
Slovenia, Spain, Sweden, Switzerland, The Netherlands and the United Kingdom.
The indicators use field data up to and including the 2020 field season. The method for calculating
indicators has been greatly improved and enhanced. During 2020, more than 2,500 standardised
butterfly transects distributed across 22 monitoring schemes were used to inform the EU27
Grassland Butterfly Indicator and almost 5,000 from 25 schemes for the European Grassland
Butterfly Indicator (Figure 2). Since 1990 over 6,350 and 11,500 separate transects have contributed
to the EU27 and Europe indicators, respectively.

Europe’s semi-natural
grasslands contain a wealth ofbutterfly species and numbers.

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Figure 1: The density of
Butterfly Monitoringtransects visited per 50 km
grid across all schemesthat have contributed tothe eBMS database.
Densities are calculated
from sites that have been
visited at least once since
1990.

Schemes contributing to the European Indicators up
to 2020 (schemes in the EU27 are marked with EU27)
Andorra: since 2004
Austria (Tirol) EU27: since 2018
Belgium (Flanders) EU27: since 1991
Belgium (Wallonie) EU27: since 2010
Czech Republic EU27: since 2010
Estonia EU27: since 2004
Finland EU27: since 1999
France EU27: since 2005
Germany EU27: since 2005
Hungary EU27: since 2016
Ireland EU27: since 2007
Italy EU27: since 2016
Jersey: since 2004
Latvia EU27: since 2015
Lithuania EU27: since 2009
Luxembourg EU27: since 2010
Netherlands EU27: since 1990
Norway: since 2009
Romania EU27: since 2013
Spain (Catalonia) EU27: since 1994,
Spain (Basque Country) EU27: since 2010
Spain (other regions) EU27: since 2014
Slovenia EU27: since 2007
Sweden EU27: since 2009
Switzerland: since 2003
United Kingdom: since 1976

Other active schemes (data not yet included within
indicators)
Armenia: since 2003
Austria (other regions) EU27: since 2020
Cyprus Island EU27: since 2019
Israel: since 2009
Portugal EU27: since 2019
Russia (Bryansk region): since 2013
Ukraine (Transcarpathia): since 1974
Malta EU27: since 2020
Bulgaria EU27: since 2020
Poland EU27: since 2020
Croatia EU27: since 2020

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Figure 2: a) Number of transects that contributed to the Grassland Butterfly Indicator in Europe (pale blue) and EU27 (darkblue); b) number of Butterfly Monitoring Schemes (BMS) that contributed to the Grassland Butterfly Indicator in Europe(pale yellow) and EU27 (dark yellow). Only transects that have at least one record for at least one of the 17 selected species
are included in the Grassland Butterfly Indicator.

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Chapter 3 / From butterfly counts to indicators
Introduction
Butterflies can be found all over Europe and are one of the best-known groups of insects. Although
popular, until recently little was known about their density and trends. In this chapter we will
illustrate how counts are made and how they can be used to detect trends and to build indicators.

Fieldwork
The butterfly indicators are based on the fieldwork of
thousands of trained professional and volunteer
recorders, counting butterflies on more than 10,000
transects scattered widely across Europe, with almost
5,000 visited in 2020. These counts are made understandardised conditions, providing high-quality datathat are suitable to assess species status and trends.
National co-ordinators collect the data and performthe first quality control.

Recording butterflies on a transect.

All schemes apply the method initially developed for
the UK Butterfly Monitoring Scheme (Pollard & Yates,
1993). The counts are conducted along fixed transects
of 0.5 to 3 kilometres in length, divided into smaller
sections for recording. The fieldworkers record all
butterflies that are observed 2.5 metres to their right,
2.5 metres to their left, 5 metres ahead of them and 5
metres above them (Van Swaay et al. 2008). Butterfly
counts are conducted between March-April to
September-October, depending on the region. In someplaces (e.g., Andalucia, Canary Islands) there areplaces where monitoring takes place all year round,sometimes stopping in July-August during the hot anddry summer. Visits are only conducted when weather conditions meet specific criteria. The
recommended number of visits varies from every week, e.g., in the UK, Catalonia and theNetherlands, to 3-5 visits annually in France. Austria and Switzerland BMS use a stratified sampling
design with a subset of transects monitored every year. This protocol enables to maintain highfrequency monitoring in areas where access present specific challenges (alpine sites). In thisprotocol, all sites are monitored at least once every four years.

European Butterfly Monitoring Scheme database
The European Butterfly Monitoring Scheme (eBMS) database collates standardised butterfly counts
recorded along Pollard walks repeated in time (since 1976 in the UK). Since 1990, volunteers andprofessionals have recorded more than 5.5 million butterfly count events documented at the species
level in the eBMS database. These counts have been recorded over more than 991,000 monitoring
events (e.g., BMS transect visits). In terms of sampling effort, these visits took place in over 12,000transects (Figure 1), distributed across 25 monitoring schemes and 23 countries (Figure 2b), withnearly 7,000 monitoring sites located in EU27 Member States.

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The eBMS database is updated on an annual cycle, each update being released as a major version(e.g., v4.0), with subsequent corrections, additions and bug fix being identified and released as minor
version updates (e.g., v4.2). Source data files are sent by national Butterfly Monitoring Schemes andprocessed programmatically to ensure adequate standardisation and formatting of the data before
being integrated into the eBMS database.

Transect selection
To be able to draw proper inferences on the temporal population trends at the national or regional
level, transects should ideally best be selected in a grid, random or stratified random manner(Sutherland, 2006). Several recent schemes, e.g., in Switzerland, France and in parts of Austria, havebeen designed in this manner (Henry et al. 2008). If a scheme aims to monitor rare species, scheme
co-ordinators preferentially locate transects in areas where rare species occur, leading to an overrepresentation of special and protected areas. In most schemes transects were selected by freechoice of observers, which in some cases has led to the overrepresentation of protected sites innatural areas and the under-sampling of the wider countryside and urban areas (Pollard & Yates,
1993). However, this is not the case in all countries (e.g., Germany, Kühn et al. 2008).

Lysandra bellargus is one of the ‘Blues’
listed on the species list of theEuropean Grassland Butterfly
Indicator.

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Calculating population trends
Population trends can be calculated at different levels by combining observations at the site level,
the monitoring scheme level or across schemes in each region. For the European Grassland Butterfly
Indicator and the EU27 Member States Grassland Butterfly Indicator, we combined the abundanceindices calculated at the site level to produce scheme-level indices, which we then combined to
produce European and EU27 population trends for each species.
In a first step, we calculated annual abundance indices for each species at the site level. For eachspecies and year, we estimated flight periods (Dennis et al. 2016) based on counts recorded, daily
accumulated growing degree days (GDD) and latitude of each monitoring site. We estimated species
annual flight curves using generalised additive models (GAM) fitted independently for seven major
geographical units (i.e., United Kingdom and Republic of Ireland, Mediterranean, Continental West,
Continental East, Alps, Northern Baltic, and Eastern Baltic). Using the local annual flight curves, we
imputed the expected values for each missing weekly count to calculate standardised annual
abundance indices at the site level. These abundance indices are species-specific and estimate thetotal annual adult butterfly density expected along a 1 km transect at a given site. Estimates oftransect density were only calculated on transects with at least 3 years of monitoring history.
For each species and monitoring scheme (BMS), local densities were then integrated into an annual
summary index that represents an estimate of the total number of adult butterflies expected along a
1 km transect in each BMS. Such annual collated species abundance indices were calculated for eachspecies and year where at least three transect estimates were available. We calculated these collated
indices at the BMS level by fitting a generalised linear Poisson model (GLM) with site and year effects
and using the proportion of the flight period covered as a weighting factor. The inclusion of suchweighting allows us to reduce the influence of site indices derived from fewer visits which arepotentially more biased. To exclude unreliable estimates derived from counts for only a minimal
number of years and sites, multiple filters (see details in Annexe I) were applied. For each BMS, thetime series of species collated indices were then transformed to the log10 scale and standardised toa value of 2 for the first year (Figure 1).
To combine species trends across multiple schemes (BMSs), we combined the annual collated indicesby calculating the weighted geometric mean of the exponentiated index, where the first year is set to
100 (i.e., 102 = 100, where 2 is the standardised pooled index for the reference year). For each year,
the geometric mean was weighted by the area of the species' range sampled in each BMS andincluded in the mean for a given year. Starting from a standardised value of 100 in the first year, theindices of BMS included in the dataset after that year were set to the value of the weighted
geometric mean of the year in which they entered the dataset. This approach allows new schemes tocontribute to the compiled index without affecting the index of their entry year and the trend of
previous years. If a BMS has some missing years after it has started contributing to the collated index,
the missing values are replaced and kept constant with the last non-missing value. In this way,
missing values are informed by their own scheme and only for the years following their firstcontribution. For more details on the methods used to calculate population trends, see Annexe I.

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Producing European and EU27 Grassland Butterfly Indicators
The Grassland Butterfly Indicator is the combined population trend of 17 selected grassland species
monitored across Europe (Figure 3). The indicator can be calculated from population trends
estimated for the whole European region or restricted to the 27 EU Member States. Species' trends
are combined by calculating the geometric mean of the species' collated annual indices describedabove. Following the same approach that we used to combine species-specific population indicesacross monitoring schemes (BMS) and accounting for species that start late in the time series, we set
the first year (1990) as the base year and gave each species the same weighting. By averaging therelative rather than absolute abundance indices of species and giving each species equal weight in
the resulting indicators, this indicator provides a consistent measure of biodiversity like the bird
indicators described by Gregory et al. (2005). If positive and negative changes in the indices balanceeach other out, we assume that their mean value remains stable. On the other hand, if more species
decrease than increase, the mean should decrease and vice versa. The trend in the geometric meanis therefore considered a measure of biodiversity change. For more details on the indicator method
used, see Annexe I.

Widespread species: Ochlodes sylvanus, Anthocharis cardamines, Lycaena phlaeas, Polyommatus icarus, Lasiommata
megera, Coenonympha pamphilus and Maniola jurtina

Specialist species: Erynnis tages, Thymelicus acteon, Spialia sertorius, Cupido minimus, Phengaris arion, Phengaris
nausithous, Polyommatus bellargus, Cyaniris semiargus, Polyommatus coridon and Euphydryas aurinia
Figure 3: Seventeen butterflies were used to build the European Grassland Butterfly Indicator, comprising seven widespreadand ten specialist species.

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Chapter 3 / Grassland Butterfly Trends
Introduction
The European Grassland Butterfly Indicator is built from European species trends. In this chapter, wegive an overview of the trends of grassland butterflies in the EU27 and Europe. These trends arecalculated for each of the 17 species by a weighted combination of all trends by Butterfly Monitoring
Scheme (BMS).

Species trends in EU27
From the 17 indicator species, one shows a significant
moderate increase (the Orange Tip, Anthocharis
cardamines), three are stable, five show a significant
moderate decline and for six species no significant trend
could be established in the participating EU27 countries(Figure 4). For two species (Phengaris arion and P.
nausithous) there was not enough data to calculate atrend.
Numbers of the Orange Tip (Anthocharis cardamines)
are increasing in the EU27.

Figure 4: Total percentage change of the 15 species of the Grassland Butterfly Indicator in the EU27. Notethat the time period for the trend varies among species due to variation in data quantity.

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Species trends in Europe
From the 17 indicator species, three are stable, six show a significant moderate decline, one asignificant strong decline (Large Blue, Phengaris arion) and for six species no significant trend could
be established in the participating European countries (Figure 5). For one species (Phengaris
nausithous) there was not enough data to calculate a trend.

Figure 5: Total percentage change of the 16 species of the Grassland Butterfly Indicator in Europe. For
Euphydryas aurinia there is a non-significant (uncertain) positive change of 2395%. Note that the time
period for the trend varies among species due to variation in data quantity.

Numbers of the Common Blue
(Polyommatus icarus) show adecline in Europe.

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Extensively grazed meadows can
support large populations of
threatened grassland butterflies.

When interpreting the species trends, it is important to realise that:• The coverage of the species’ populations and thus the representativeness of the data is lower atthe beginning of the time series (see also figure 1). As more countries join in later, the indicesimprove in accuracy each year.
• Large year-to-year fluctuations or a low number of transects, can cause large confidence
intervals, leading to uncertain trends.
• Because of the filters we had to apply (see Annexe I) there was not enough data for somespecies, notably in the EU27, as important countries with a strong butterfly monitoring scheme,
such as Switzerland and the United Kingdom had to be excluded.
• Not all EU27 member states have a Butterfly Monitoring Scheme. The trends shown only
represent the countries in map 1, which means they are based on a wide range of countries,
including the larger ones such as France, Germany and Spain. However, extra data from thecountries in the eastern part of the EU27 would make the results more representative. Although
this expansion is foreseen in the SPRING project, it will take some years before enough data
become available and contribute to the indicators.
• Apart from the schemes included in the EU27 indicator, the European trend is determined by thedata collected in the United Kingdom, Norway, Andorra and Switzerland. For many species, these
non-EU27 countries in the analysis represent only a minor part (sometimes less than 10%) of the
distribution as compared to the EU27 countries. This means that the European trends in thisreport are dominated by the trend in the EU27. However for some species (e.g. the Large Blue
Phengaris arion) the butterfly monitoring data from Switzerland makes it possible to generate asignificant trend, where the EU27 countries did not have enough transects to calculate a reliable
trend.
• As new countries and schemes join in and new data become available, trends can change anddiffer from previous versions of the indicator. For some species, this can even result in a change
in the direction of the trend.

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Chapter 4 / Grassland Butterfly Indicator
Introduction
The European Grassland Butterfly Indicator has been updated for the EU27 countries and Europe as awhole. In this chapter both indicators are presented.

Grassland Butterfly Indicator
For both indicators the 2020 value is significantly lower than the start value of the indicator. In thelast ten years the indicator shows a linear decline of 32% in the EU27 and 36% in Europe. Due to thefiltering by the minimum number of Butterfly Monitoring Schemes (BMS), the Grassland Butterfly
Indicator for the EU27 starts one year later than the European one (Figure 6). For the EU27 there is agreater uncertainly in the yearly estimates for this indicator due to less data included. Compared toprevious Grassland Butterfly Indicators the United Kingdom is now not represented in the EU27
indicator anymore, resulting in large confidence intervals.
The main reasons for the decline are intensification of agricultural grasslands, and nitrogen
deposition in nature reserves (WallisDeVries & Van Swaay, 2017), especially in NW Europe.
Furthermore, substantial decreases probably already happened before the start of the indicator
calculation in 1990 (Van Strien et al. 2019). In some parts of Europe climate warming led to anincrease in the numbers of some of the widespread generalist butterflies. However the recent
accumulation of extremely hot and dry summers has reversed this trend leading to new declines.
As new BMSs are starting up, inclusion of their recent data means the rate of decline of the overall
European and EU27 indicators changes as compared with previous calculations.

Figure 6: Grassland butterfly indicators for EU27 and Europe. Shaded areas represent 95% confidence intervals.

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Chapter 5 / Conclusions
•
•
•
•
•

•

•
•

This report gives an update of an indicator for Grassland Butterflies, which gives the trend of
a selection of butterflies characteristic of European grasslands.
The indicator is based on national Butterfly Monitoring Schemes from across Europe, most ofthem members of the European Union (see Figure 1).
This report shows the Grassland Butterfly Indicator has especially declined in the last ten
years by 36% since 1990 across Europe and by 32% across the EU Member States with
schemes in the EU27 (Figure 6).
In North-western Europe, intensification of farming is the most important threat to grassland
butterflies. Protecting remaining semi natural-grasslands in these areas and reversing
fragmentation is essential to halt further losses.
In many parts of the rest of Europe, abandonment is the key factor in the decline ofgrassland butterflies. Only if young farmers see a future for their families, while at the sametime respecting long established farming traditions, grassland butterflies can be saved.
Redirection of CAP funding to support sustainable farming of HNV areas is vital.
The increase in the duration, frequency and intensity of heatwaves and droughts as aconsequence of climate change has also contributed to the declines of grassland butterfliesin the last ten years. More research would be needed to establish the exact size of the
impact. However stopping further climate warming would certainly help most grassland
butterflies.
The completion of the Natura 2000 network across Europe is an important way to help thesebutterflies. In addition, restoration or creation of mosaics of habitats at a landscape scale,both inside and outside Natura 2000 areas, are needed.
This Grassland Butterfly Indicator will become one of the components to monitor for
Member States in the upcoming Nature Restoration Law.

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Annexe I / Statistical method
Data collection
All data was first collected at a regional or national level, and after validation added to the eBMS
database (version 4.2). This is a standardised database containing the following tables:
1. Butterfly count data table
2. Monitoring visit table
3. Site geographical information table
4. Habitat type table
5. Habitat type description table
6. Species name table
Grassland butterfly indicator
Step 1 – produce species site indices
For each species and year, flight periods were estimated based on the combined effect of latitude
and local climate condition (Schmucki et al., 2016). In our model, we used the local records of daily
accumulated growing degree days (GDD) and the site latitude as covariates and the spline
formulation of the generalised abundance index approach (GAI, Dennis et al., 2016). The GDD andthe latitude variable were modelled as interactions, smoothing on the main and a tensor productinteraction. For all smoothed terms, we used penalised spline (P-spline) as basis. All flight curves
were computed with the R package rbms (Schmucki et al., 2022) that fits GAMs using the gamfunction implemented in the R package mgcv v1.8-4 (Wood, S.N. 2017).
The daily accumulation of growing degree day was calculated with the R package climateExtract(Schmucki, R. 2022), using the daily grided temperature data available from the E-OBS climate
dataset v.25. This dataset, developed by the ECA&D project and available from Copernicus Climate,provides European climate data on a 0.11o grid (i.e., ca. 10 km resolution). For better model fitting,
we further stratify our dataset into seven large geographic regions (i.e., United Kingdom andRepublic of Ireland, Mediterranean, Continental West, Continental East, Alps, Northern Baltic, andEastern Baltic). This stratification was sufficiently broad to include a large number of sites whileallowing for wide geographical variation in butterfly phenology. By including the daily accumulated
GDD and site-specific latitude in our model, we were able to derive reliable estimates of local flightperiod curves. These flight curves were then used to produce local estimates for the weekly butterfly
counts that we used to input the missing counts.
Estimates of weekly counts were derived from a negative binomial GLM fitted on the observedcounts, including site parameters and the shape of flight curve as offset. When combined with theobserved counts, these estimates allow us to generate complete time-series of weekly butterfly
counts for each site and species. These time-series were then used to calculate annual site-level
abundance indices for each species. We use these inputted time-series to compute local annual site
indices. The site-level species abundance index was computed by summing the weekly counts ofadult butterflies over the entire monitoring season. This standardised abundance index represents
the total number of adult butterflies that are expected to be counted over a monitoring season at agiven site. This abundance index was standardised to 1 km transect to provide a comparable proxyof butterfly density. To avoid including highly unreliable site index estimates, we excluded all site
indices that had weekly predictions larger than 10 times the largest observed count – this threshold
was set to avoid extreme values, most likely due to unreliable flight curves or GLM model. We also
exclude all sites with less than 3 years of monitoring history.
To be able to produce suitable estimates of precision of the subsequent indices, indicators andtrends, a bootstrapping approach was taken, as is typical for these modelling approaches, to accountfor sources of uncertainty from multiple model stages (Dennis et al. 2013). Hence the site indiceswere randomly resampled 1000 times while keeping the number of transects sampled per year the
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same as in the original data. The subsequent stages of analysis (steps 2-4) were then applied to eachbootstrap, which could then be combined to produce confidence intervals.
Step 2 - species collated indices per BMS
Species site-level indices were combined per monitoring scheme (BMS) to produce a collated species
index (density estimates) for each species and BMS. Annual collated abundance indices were
calculated for each species where estimate of local abundance index were available for at least threesites (transects) in the given year. We derived the scheme-level species indices by fitting a Poisson
generalised linear model (GLM) with site and year effects on site-level indices. In this GLM, we also
included the proportion of the flight period surveyed as a weighting (Brereton et al., 2018) on all thesite indices. This model allows us to derive annual scheme-level estimates of butterfly densities (total
number of butterflies expected) per 1 km transect for each species recorded in each BMS. Using abootstrap resampling approach, we generated the empirical distribution of the collated indices fromwhich we can derive confidence intervals and account for uncertainty. For each species and BMS, we
computed the collated index from 1000 bootstrap samples (with replacement) of the sites monitored
in the original dataset. The collated indices and magnitude of confidence intervals were then checked
for reliability, in consultation with National BMS coordinators. Species scheme-level collated indiceswere filtered to the first year beyond which the species was observed on at least 3 sites per year, andvery short time series (less than 3 years) were excluded.
Step 3 - species collated indices for EU27 and Europe
For each BMS, the time series of species collated indices were then transformed into the log10 scaleand standardised to a value of 2 for the first year. This standardisation enables us to calculate,compare and integrate the relative change over time of a given species across monitoring schemes(BMS). For each of the 17 selected species, we combined time series of the standardised collated
indices across the BMS located in the region of interest (i.e., Europe or EU27 Member States). For
each species, collated indices were filtered to the first year beyond which at least two BMS weremonitored each year. Annual collated indices were combined by calculating the weighted geometricmean of the exponentiated index, where the first year is 100 (i.e., 102 = 100, where 2 is thestandardised collated index). For each year, the geometric mean was weighted by the area of thespecies' range sampled in each BMS. Species’ range (distribution area in hectares) was estimated per
BMS as the overlap between the species distribution map (from www.iucnredlist.org) and the convex
hull of the monitoring sites in the BMS. The weighted geometric mean was then used as the species
collated index for Europe or EU27.
Starting from a standardised value of 100 in the first year, the first year of every time series ofscheme-level collated indices that entered the dataset after the first year was set to the value of theweighted geometric mean of the year they entered the dataset. This approach aligns the trend of
new schemes with the older schemes and contribute to the compiled index without affecting thetrend of previous years. If a time series of a BMS has some missing values in years after it has started
contributing to the collated index, the missing values are replaced and kept constant with the last
non-missing value. Thereby, missing values are informed by their own scheme and only for the years
following their first contribution. This follows the approach already used for combining species
indices to produce multi-species indicators (step 4).
Step 4 - producing EU27/European Grassland Butterfly Indicator
The European or EU27 indices were combined by taking the geometric mean of the indices. We
applied the same approach as the one implemented in the BRCindicators R package (August et al.,2017), with equal weight being given to each species. This approach accounts for missing values andintegrates species with late entry in the dataset. A smoothed indicator was produced using a loesssmooth with span=0.75 and degree=2 (as in Soldaat et al., 2017). The same approach was applied to
produce multi-species indices and smoothed indicators for each of 1000 bootstraps, from which

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quantiles were taken to produce 95% confidence intervals around the indicators. All values were
rescaled such that the smoothed indicator started at 100.
Trends were estimated by applying linear regression to the smoothed indicator (and similarly tounsmoothed species-level European/EU27 indices). Trends were estimated for each bootstrap, fromwhich 95% confidence intervals around the actual trend were produced and used to assess
significance. Trends were classified based on the multiplicative slope estimate, as in TRIM
(Pannekoek & van Strien, 2005).

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Annexe II / Glossary
•
•
•
•
•
•

ABLE: Assessing ButterfLies in Europe: an EU project aiming at capacity building for butterfly
monitoring, collecting butterfly monitoring data into the eBMS, producing tools for analysisof the data and produce trends and indicators.
BGR: Biogeographical Region
BMS: Butterfly Monitoring Scheme
CBD: Convention on Biological Diversity
eBMS: European Butterfly Monitoring Scheme, the database that holds all butterfly
monitoring data.
SPRING: Strengthening Pollinator Recovery through INdicators and monitorinG

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