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Bioscore 2: Potential distribution of butterflies and assessment of environmental pressures in Europe

Bioscore 2: Potential distribution of butterflies and assessment of environmental pressures in Europe

Text

Chris van Swaay, Oliver Schweiger, Josef Settele, Elisabeth Kühn, Alexander Harpke,

Martin Wiemers, Martin Musche, Guy Pe’er, Constanti Stefanescu, Benoit Fontaine,

Romain Julliard, David Roy, Tom Brereton, Lars B. Pettersson, Mikko Kuussaari, Janne

Heliola, Reto Schmucki

Reportnumber

VS2014.003

Production

De Vlinderstichting

Mennonietenweg 10

Postbus 506

6700 AM Wageningen

T 0317 46 73 46

E [email protected]

www.vlinderstichting.nl

Butterfly Conservation Europe

P.O. Box 506

NL-6700 AM Wageningen

E [email protected]

www.bc-europe.eu

Commisioned by

Planbureau voor de Leefomgeving

Jaap Wiertz, Arjen Hinsberg, Onno Knol, Marjon Hendrikx

Preferred citation

Van Swaay, C.A.M., Schweiger, O., Settele, J., Kühn, E., Harpke, A., Wiemers, M.,

Musche, M., Pe’er, G., Stefanescu, C., Fontaine, B., Julliard, R., Roy, D., Brereton, T.,

Pettersson, L.B., Kuussaari, M., Heliola, J., Schmucki, R. (2014) Bioscore 2: Potential

distribution of butterflies and assessment of environmental pressures in Europe.

Report VS2014.003, De Vlinderstichting, Wageningen & Butterfly Conservation

Europe

December 2014

Revised version September 2015

Niets uit deze uitgave mag worden verveelvoudigden/of openbaar gemaakt d.m.v. druk, fotokopie, microfilm of op welke andere wijze dan ook zonder voorafgaande toestemming van De Vlinderstichting, noch mag het zonder een dergelijke toestemming gebruikt worden voor enig ander werk dan waarvoor het is vervaardigd.

De Vlinderstichting & BCE 2014 / Bioscore 2: butterflies

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Content

1. Introduction ..................................................................................................4

2. Material and method .....................................................................................5

Approach .................................................................................................................... 5

Species selection ........................................................................................................ 6

Distribution data ........................................................................................................ 7

Butterfly Monitoring Data .......................................................................................... 9

Climate and soil models ........................................................................................... 10

Habitat preference ................................................................................................... 12

Dose-response functions ......................................................................................... 12

3. Results ......................................................................................................... 15

Species selection ...................................................................................................... 15




4. Discussion .................................................................................................... 24




Conclusions .............................................................................................................. 28

Literature ............................................................................................................. 29

Annex I: Example of the analysis .......................................................................... 31

Annex II: R-scripts ................................................................................................ 35

TRIMmaps ................................................................................................................ 35

Cut-off ...................................................................................................................... 36


Univariate analysis ................................................................................................... 42

Annex III: Habitat preference ............................................................................... 51

Annex IV: species list ........................................................................................... 54


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1. Introduction

This report investigates the factors that explain the distribution and occurrence of

96 European butterflies and tries to establish the relationships between the

occurrence of these species and stress factors influencing the quality of their

habitat.

Policy makers need tools to evaluate the effects of policy measures on the

environment. Tools for evaluating effects of environmental policies on Europe’s

biodiversity are scares. In 2005 the Netherlands Environmental Assessment Agency

(PBL) developed, together with several institutes, a first set of such tools in BioScore

1 (www.bioscore.eu). Since then PBL has used these tools in scenario studies.

However, BioScore 1.0 wasn’t fit for all policy related questions and needed

extension towards additions pressures and drivers. Furthermore the dose-response

functions between environmental pressures and biodiversity in BioScore 1.0 were

primarily based on expert judgment.

BioScore 2 extends the models of BioScore 1 with field data and connects them

closer to the needs of policy makers in Europe. The model, developed in close

cooperation with Alterra, should make it possible to study the effects of future

spatial environmental scenarios based on anticipated land-use changes, policies and

strategies with environmental impacts, such as green infrastructure strategy, Natura

2000, restoration and rewilding projects , the Common Agricultural Policy, Nitrate

Directive (affecting Nitrogen deposition) and Water Framework Directive (affecting

water quality and sources of soil pollution).

The main target of the model is on European biodiversity. However, the Netherlands

Environmental Assessment Agency wants also to use the tool the examine the

situation in the Netherlands as part of the Atlantic region in NW Europe. The

methods and their implementation are relevant throughout Europe.

This report focuses on the information needed in BioScore 2.0 with respect to

butterflies. Other reports deal with the other groups: plants, birds and mammals.


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2. Material and method

Main objective of this project is to develop a European model to study the effects

of current and future spatial environmental scenarios regarding land-use and

policy, with especial focus on the Netherlands as part of the Atlantic region in NW

Europe.

Approach

In BioScore 2 models are not only developed for butterflies, but also for birds,

mammals and plants (these assessments can be found in other reports). To ensure

coherence among all taxonomic groups, the same methods and approaches are

applied for all groups. Here, we employ and illustrate the methodology based on

butterfly data.

The basics for the approach are illustrated in figure 1:

Make a species selection.

Collect distribution data for these species.

Use the distribution data together with climatic, soil and elevation variables

to produce niche models. This results in maps of potential distribution of the

species (figure 1) with information on the probability of occurrence of the

species in each square of 5x5km.

Make a selection of the locations which fulfill the needs of the species with

respect to climate, elevation and soil conditions to derive a map of potential

distribution map. This is done by establishing a cutoff value for the

probability of occurrence. All squares with a higher probability are

considered to be part of the range of the species.

Derive dose-effect relationships between the selected butterfly species

within their range and human threats on habitat quality and species

occurrence/abundance. This was done based on the data of the European

Butterfly Monitoring Schemes ((quantative relation in figure 1).

In a later stage, these will be used to produce final models for scenario- and

policy-studies in Bioscore 2.

Figure 1: Structure of Bioscore 2.


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Species selection

In Bioscore 1.0, 77 butterfly species were assessed. For Bioscore 2 this list has been

reviewed and expanded according to the following criteria (species do not have to

fulfill all criteria):

The species is assessed in Bioscore 1 (see www.bioscore.eu).

The species is listed in the annexes II and IV of the Habitats Directive.

The species is a ‘typical species’ for at least one of the habitats mentioned in

Annex I of the Habitats Directive.

The species occurs on the European Red List as either (Critically)

Endangered (CR+ EN), Vulnerable (VU) or Near Threatened (NT) (Van Swaay

et al., 2010).

The species is used for the identification of High Nature Value Farmland

(HNV Farmland, see Paracchini et al., 2008).

The species has a high ‘Area under the curve’ - AUC (>0.75) in the climate

models of Settele et al. (2008), indicating good models can be built.

Species should occur in several biogeographic regions throughout Europe.

But special attentions is needed for the Atlantic region in Europe (figure 2)

or several species should at least have their main distribution in NW Europe.

The species is characteristic for habitat types for which the Netherlands has

an international responsibility, especially wetlands, dunes and heathland.

Monitoring data should be available from at least 50 transects.

Note that some of these criteria are – obviously - opposing each other. For instance,

species which occur in the Atlantic region and have monitoring data available are

typically widespread and common in Europe, almost always resulting in a low AUC in

Settele et al. (2008). On the other hand rare species, listed on the Habitats Directive,

often have a limited distribution and a high AUC in Settele et al. (2008), but also tend

to be rare in the Atlantic region and often only occur at a few monitoring sites, thus

having to few sites for establishing butterfly-environment relationships.

The list of selected species is summarized in Annex IV.

Figure 2: biogeographical regions in Europe. The Atlantic region is indicated in light-blue (European Environment Agency (EEA)).


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Distribution data

Distribution data at a scale of 50x50 km are available for Europe in the LepiDiv

database (UFZ, Leipzig-Halle), partly based on the ‘Distribution Atlas of Butterflies in

Europe’ (Kudrna et al., 2011). The advantage of this data is that has a very good

coverage of Western and Central Europe, which also means that there are only few

false-negatives (meaning that where the species is not recorded, it is most probably

really not present). These maps are representative for the period between 1980 and

2010. The quality of the maps in Eastern Europe is far worse and Russian data is not

available (figure 3). For the Bioscore 2 project these data were removed.

Part of the criticism on the climate models of Settele et al. (2008) were based on the

fact that only European distribution data were used for the models, resulting in poor

performance of all species at the southern edged of the investigated area. To

overcome this, we added distribution data from Northern Africa to the distribution

maps. This was done based on the distribution maps from the IUCN Red List for

Mediterranean Butterflies, which have been digitized to the same grid level as the

European distribution data (figure 3).

Altogether, our evaluation included 100 species, listed in table 1 along with the

number of squares where each species is recorded.

Figure 3: Location of the grid squares that were used for the climate and soil models.


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Table 1: Number of squares where each species is recorded.

Species Number of 50x50km squares

Aglais io 1605

Aglais urticae 1780

Anthocharis cardamines 1694

Anthocharis euphenoides 137

Apatura ilia 696

Apatura iris 758

Aphantopus hyperantus 1317

Aporia crataegi 1254

Araschnia levana 833

Arethusana arethusa 307

Argynnis adippe 1063

Argynnis aglaja 1422

Argynnis niobe 730

Argynnis paphia 1399

Aricia agestis 938

Aricia artaxerxes 518

Aricia eumedon 560

Boloria aquilonaris 524

Boloria dia 782

Boloria euphrosyne 1163

Boloria selene 1264

Brenthis daphne 455

Brenthis ino 903

Brintesia circe 574

Callophrys rubi 1922

Carcharodus alceae 910

Carterocephalus palaemon 712

Carterocephalus silvicolus 318

Celastrina argiolus 1901

Charaxes jasius 369

Coenonympha arcania 971

Coenonympha glycerion 736

Coenonympha pamphilus 2112

Coenonympha tullia 652

Colias alfacariensis 649

Cupido argiades 589

Cupido minimus 1021

Cyaniris semiargus 1196

Erebia ligea 653

Erynnis tages 1174

Euphydryas aurinia 731

Euphydryas maturna 223

Favonius quercus 1278

Glaucopsyche alexis 935

Gonepteryx cleopatra 696

Gonepteryx rhamni 1809

Hamearis lucina 595

Hesperia comma 1152

Heteropterus morpheus 367

Hipparchia semele 1031

Hipparchia statilinus 788

Iphiclides podalirius 1224

Issoria lathonia 1641

Lampides boeticus 857


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Species Number of 50x50km squares

Lasiommata maera 1266

Lasiommata megera 1716

Libythea celtis 317

Limenitis camilla 687

Limenitis reducta 520

Lycaena dispar 587

Lycaena hippothoe 794

Lycaena phlaeas 2119

Lycaena tityrus 899

Lycaena virgaureae 939

Maniola jurtina 1986

Melanargia galathea 1035

Melitaea athalia 1260

Melitaea cinxia 937

Melitaea diamina 549

Melitaea didyma 1060

Melitaea phoebe 716

Nymphalis antiopa 1168

Ochlodes sylvanus 1363

Papilio machaon 1950

Pararge aegeria 1851

Phengaris alcon 422

Phengaris arion 615

Phengaris nausithous 259

Phengaris teleius 302

Plebejus argus 1298

Plebejus idas 964

Plebejus optilete 550

Polygonia calbum 1514

Polyommatus amandus 806

Polyommatus bellargus 830

Polyommatus coridon 760

Polyommatus escheri 182

Polyommatus icarus 1967

Pyronia tithonus 708

Satyrium ilicis 739

Satyrium pruni 542

Satyrium spini 651

Satyrium w-album 752

Spialia sertorius 789

Thecla betulae 835

Thymelicus acteon 986

Thymelicus lineola 1273

Thymelicus sylvestris 1372

Butterfly Monitoring Data

At present over 20 countries in Europe engage in Butterfly Monitoring Schemes

(BMS), that are based on repeated visits to fixed transects. All together regular

counts are made on 4000 globally and more than 3000 transects in Europe (Van

Swaay et al., 2012). The method is described and discussed in Van Swaay et al. (2008)

(including quality control) and ultimately are based on Pollard (1977).

For this report, data were used from the database created within the FRB funded

project LOLA (How LOcal-scale processes build up the Large-scale response of

Butterflies to global changes: Integrative analysis across Monitoring Schemes, see


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http://www.cesab.org/index.php?option=com_content&view=article&id=53:lola-

bms&catid=23&Itemid=112&lang=en). The LOLA scheme covers data from six

countries in Europe (Finland, Germany, Netherlands, United Kingdom, France and

Catalonia). To these we added data from the Swedish BMS

(http://www.dagfjarilar.lu.se/). We note that BMSs run in additional countries, but in

Bioscore 2 we focused on long running schemes in the Western half of Europe.

Data for the period used in this report (2010-2012) were available for more than

3000 transects (figure 4), resulting in 95,000 records for the selected species. Clearly

the transects are not distributed at random or at a grid scale across Europe. Most

transects are located in the Netherlands and the United Kingdom. Van Swaay et al.

(2012) give an overview of the characteristics of the Butterfly Monitoring Schemes

on the following points:

Starting year

Area represented (w=whole country, r=region)

Average transect length

Number of transects per year 2009-2011

Number of counts on a transect per year

Counts by volunteers or professionals

Method to choose sites (free, by co-ordinator, grid or random)

Representativeness for agricultural grassland

Are nature reserves overrepresented

Climate and soil models

Using the distribution data – including North Africa – models can be built to produce

first maps of potential distribution. These models are produced using the same

Figure 4: Location of the BMS transects that were used for the dose-response functions.

http://www.cesab.org/index.php?option=com_content&view=article&id=53:lola-bms&catid=23&Itemid=112&lang=en

http://www.cesab.org/index.php?option=com_content&view=article&id=53:lola-bms&catid=23&Itemid=112&lang=en

http://www.dagfjarilar.lu.se/


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method as for birds, mammals and plants, the other three species groups in Bioscore

2. Table 2 shows an overview of the parameters.

Table 2: Parameters used in the selection of the potential distribution.

Parameter Explanation

Bio 3 isothermality (bio2/bio7)(*100) (=mean diurnal range/ temperature annual range)

Bio 4 temperature seasonality (standard deviation * 100)

Bio 6 Minumum temperature of coldest month

Bio 9 Mean temperature of driest quarter

Bio 14 Precipitation during driest month

Bio 15 Precipitation seasonality (coefficient of variation)

Bio 18 precipitation of warmest quarter

Bio 28 Annual mean soil moisture index

Apet Actual divided by potential evapotranspiration

Tsum Temperature sum of growing season

Alt Altitude

Soil_clay Clay content in the top soil

Soil_oc Organic carbon content in the top soil

Soil_silt Silt content in the top soil

pH pH-H20 in the top soil

Salt Presence of brackish or salty soils. 0 = no salt in soil, 1= brackish soil, 2 = salty soil or sea

For the regression modelling we considered both Maxent and Boosted Regression

Trees (BRT), a version of Generalized Boosting Models (GBMs). Both Maxent and BRT

are machine-learning techniques, able to handle nonlinear relationships and to take

into account synergistic effects between the different factors affecting a species’

distribution (Couce et al. 2013). Maxent (Phillips et al. 2006) is widely used in

ecological studies, including the prediction of climate change impacts on a species or

ecosystem’s potential distribution. To date, BRT is used less widely, despite having

comparable predictive capabilities (Elith et al. 2006,2008). Although Maxent has

some possibilities to include absence data, BRTs are better equipped to deal with

presence-absence data sets. We tested this also for the dataset of a plant species,

where the predictions resulting from the BRT showed a wider range in predictions,

and performed better in areas where the species was expected to be absent. Next to

the better inclusion of known absences in the modelling, BRTs also have the

advantage that the model description can be saved for later projections used in

BioScore. For the modelling we used a suite of R-scripts, called TRIMmaps and also

available as R-package (Hallmann et al. 2014). TRIMmaps can be used for both the

spatial modelling of both presence-only, presence-absence and count data and

features a wide range of regression techniques amongst which GLM, GAM, MARS,

BRT and Random Forest. Within TRIMmaps, Maxent can be used to generate pseudo-

absences on locations with a low habitat suitability (Van Hinsberg et al., 2014).

Models were built using TRIMmaps 1.10.2 (Hallmann et al., 2014) under R version

3.0.3. The used script is presented in Annex II.

In order to be used within BioScore the predicted probabilities of occurrence had to

be transformed to predicted presences and absences. A cutoff was chosen, so that

the proportion of correctly predicted occurrences (sensitivity) is comparable to the

proportion of correctly predicted absences (specificity) (Van Hinsberg et al., 2014).

The script is presented in Annex II. For butterflies the factor in this script was set to

1.2 based on expert judgment by the first author of the results of trial-and-error with

the factor ranging between 1.0 and 1.5.


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Habitat preference

In order to determine the suitable land cover classes in de Corine Land Cover map,

for each species an overlay was made with the observations and map. From these

the proportion of observations in each land cover-class was determined. Classes with

more than three per cent of the observations were considered to be (major) habitats

for the species (Annex III).

This habitat-classification was then edited by a butterfly-expert to include known

habitats, even if only a few observations were made in them.

Dose-response functions

The modelling of the relationships between the selected butterfly species and

environmental parameters is the next step in producing the quantitative relations

(figure…). The dose response functions were derived using univariate regressions

within the distribution range following Oostermeijer & Van Swaay (1998).

The Netherlands Environmental Assessment Agency collected data for each of the

BMS sites for the following parameters (for more details see Van Hinsberg et al.,

2014):

a. Sulphur deposition: Total deposition of oxidized Sulphur per square meter

(mg S/m2) in 2008.

b. Nitrogen deposition in rural area: Total reduced and oxidized nitrogen (NHx

and NOx) deposition

c. Nitrogen application in grasslands and agricultural area as a proxy for

agricultural intensity (kg N per hectare of utilized agricultural area) in 2002.

d. Forest management types (FMA’s). Five FMA’s are distinguished:

1. Nature reserve,

2. close-to-nature,

3. Combined objective forestry,

4. even-aged forestry and

5. short rotation forestry.

e. Desiccation: annual total water abstraction as a fraction of available long-

term freshwater resources in 2006. It is a proxy for water scarcity. Severe

water stress is indicated by a WEI>0.4.

f. Fragmentation: Spatial Cohesion of different ecosystems (level 2 Corine

Land Cover; CLC) in Europe for four spatial scales (10 km, 20 km, 50 km and

100 km). This results in maps containing values between 0 and 1. Zero

meaning no habitat present in a circle of almost two times the dispersal

distance and 1 meaning surroundings completely covered with the

ecosystem. Based on the thresholds given by Rybicki and Hanski (2013)

metapopulation processes start to occur when the ecosystem is less than

20% present in the landscape. This corresponds with values of 0.2 in the

LARCH-SCAN output maps. Table 3 shows the ecosystems taken into

account.

g. Impact of roads (in hectares) in a radius of 500 meter around all middle and

large roads.

h. Impact of urbanisation (in hectares) in a radius of 500 meter around all

buildings. In the urbanisation map sparse urbanisation (a lot fo small villages

over a large area) have a heavier weight than concentrated urbanisation

(one large city on a small area).


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Table 3: Ecosystems taken into account for fragmentation.

code ecosystem

3.1 Forest

3.2 shrub and/or herbaceous vegetation associations

3.3 open spaces with little or no vegetation

4.1 inland wetlands

4.2 coastal wetlands

5.1 inland waters

The relationships between the presence of butterfly species and these

environmental parameters were entered into a logistic regression analyses (Jongman

et al., 1987). The basic hypothesis of the statistical analyses is that the butterfly-

environment relationships take the shape of a Gaussian or unimodal response curve

(as depicted in figure 5).

In this model, the probability of observing a butterfly species is related to the

parameters via Eq. (1). In the cases where species occur mainly at one of the

extremes of the scale, this Gaussian curve attains the shape of a sigmoidal, often

nearly linear, response. If the b2 term of the unimodal regression model was zero or

significantly positive, this suggests a linear relationship (a bimodal response (b2 > 0) is

considered ecologically unlikely). In such cases, the sigmoidal model given in Eq. (2)

was tested as an alternative hypothesis:

Figure 5: Response curve of Lasiommata megera for Nitrogen application in grasslands and agricultural areas.


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Stepwise model selection used to select significant variables explaining butterfly

occurrence. This model selection uses the Akaike information Criterion (AIC), where

the best models have the lowest AIC-values.

In order to assess model performance, we calculated the AUC-values for ten cross-

validations, with 10% independent observations. Models with AUC values for the full

model above 0.6 were considered to be more meaningful, whereas models with AUC

values below 0.55 were less meaningful. Furthermore we only present the significant

relationships.

Although AUC-values provide information in the global performance of the

regression model, they do not give insight in the local model performance. Especially

in ranges of the x-variable with low numbers of observations, the apparent

relationship may be biased due to the low number of observations. In order to test

local model quality or robustness, a bootstrap-procedure was developed. In this

procedure a random subset of 50% of the observations is selected and a model is

made with this subset. This is done 20 times, resulting in 20 different relationships

(figure 6) between the observations and the covariate (Van Hinsberg et al., 2014).

Figure 6: Twenty different relationship between de observations and the covariate Nitrogen application in grasslands and agricultural areas of Lasiommata megera.


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3. Results

Annex I gives an example of the results for one species to illustrate the results

presented in this chapter.

Species selection

From the 100 species listed in Annex IV, 96 could be used for all steps. Leptidea

sinapis was discarded as recent research has shown that this is a species complex

consisting of at least three species (L. sinapis s.s., L. juvernica and L. reali) (Dinca et

al., 2011). For an unknown reason Pyrgus malvae never produced a result for the

first step based on climate and soil variables. The number of transects in the

Butterfly Monitoring Schemes for Phengaris teleius and P. nausithous proved to be

too low to produce all univariate responses in the dos-response functions.


For selected 96 species models were produced based on climate and soil parameters

(table 4).

The lowest mean ‘receiver operating characteristic’ (roc) was derived for Callophrys

rubi (0.78), the highest for Gonepteryx cleopatra (0.97). C. rubi also had one of the

lowest AUC values in Settele et al. (2008) (AUC=0.63), and G. cleopatra among the

highest (AUC=0.92). In Settele et al. (2008), who treat many more species (n=294),

there are also species with a much higher AUC (up to 0.99), but such values occur

only for species with a very limited distribution and very clear ecological borders, like

high alpine species.

In the Bioscore analysis these species were not included, primarily due to the low

number of criteria they could meet, and scarcity of such local and rare species in

BMS transects.

Table 4: Quality of the models per species for climate and soil parameters as a result from TRIMmaps. MAE = mean absolute error; MFE = mean forecast error; RMSE = root-mean-square error; corr = correlation between observed and modelled values; expl.dev = explained deviance; cv.corr.mean = mean correlation cross-validation; roc.mean, roc.mim, roc.max = ROC (receiver operating characteristic).

Species MAE MFE RMSE corr expldev cv_corr mean

Roc mean

Roc min

Roc max

Aglais io 0.16 -0.00019 0.26 0.85 65.75 0.75 0.92 0.91 0.94

Aglais urticae 0.19 0.00001 0.29 0.79 55.09 0.69 0.90 0.88 0.93

Anthocharis cardamines 0.23 -0.00015 0.31 0.76 50.16 0.64 0.87 0.86 0.88

Anthocharis euphenoides 0.05 0.00030 0.14 0.83 66.79 0.49 0.93 0.91 0.95

Apatura ilia 0.16 0.00014 0.25 0.84 63.64 0.71 0.92 0.91 0.93

Apatura iris 0.17 -0.00004 0.26 0.83 61.84 0.72 0.92 0.92 0.94

Aphantopus hyperantus 0.13 0.00031 0.23 0.90 73.86 0.80 0.95 0.94 0.96

Aporia crataegi 0.28 0.00004 0.33 0.77 47.44 0.55 0.81 0.80 0.82

Araschnia levana 0.13 -0.00027 0.21 0.90 73.79 0.78 0.95 0.93 0.96

Arethusana arethusa 0.11 0.00029 0.21 0.80 59.90 0.54 0.90 0.89 0.92

Argynnis adippe 0.23 -0.00017 0.31 0.79 53.84 0.65 0.88 0.86 0.89

Argynnis aglaja 0.26 0.00024 0.34 0.74 45.79 0.63 0.86 0.83 0.88

Argynnis niobe 0.23 -0.00001 0.32 0.73 46.53 0.56 0.85 0.84 0.87


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Roc mean

Roc min

Roc max

Argynnis paphia 0.23 0.00019 0.30 0.80 55.18 0.66 0.87 0.86 0.90

Aricia agestis 0.21 -0.00033 0.29 0.80 56.96 0.66 0.89 0.88 0.91

Aricia artaxerxes 0.17 0.00040 0.27 0.76 50.74 0.60 0.87 0.84 0.90

Aricia eumedon 0.18 -0.00001 0.27 0.77 53.11 0.59 0.87 0.83 0.90

Boloria aquilonaris 0.13 0.00011 0.24 0.82 62.84 0.72 0.94 0.92 0.95

Boloria dia 0.19 0.00013 0.28 0.81 59.22 0.66 0.90 0.88 0.91

Boloria euphrosyne 0.24 -0.00002 0.32 0.78 52.63 0.64 0.87 0.85 0.89

Boloria selene 0.20 0.00019 0.29 0.82 60.23 0.73 0.91 0.91 0.92

Brenthis daphne 0.16 0.00026 0.25 0.77 54.03 0.56 0.88 0.86 0.90

Brenthis ino 0.15 -0.00003 0.24 0.87 68.89 0.75 0.93 0.92 0.94

Brintesia circe 0.14 0.00011 0.23 0.84 65.36 0.69 0.93 0.91 0.94

Callophrys rubi 0.27 -0.00043 0.34 0.64 32.89 0.47 0.78 0.75 0.81

Carcharodus alceae 0.24 0.00008 0.31 0.76 50.15 0.58 0.85 0.83 0.89

Carterocephalus palaemon 0.20 0.00005 0.29 0.78 53.98 0.66 0.90 0.86 0.93

Carterocephalus silvicolus 0.07 0.00016 0.17 0.85 72.86 0.76 0.97 0.96 0.98

Celastrina argiolus 0.25 0.00022 0.33 0.69 39.55 0.52 0.80 0.77 0.83

Charaxes jasius 0.07 0.00006 0.16 0.89 75.86 0.74 0.96 0.95 0.98

Coenonympha arcania 0.18 -0.00024 0.26 0.86 64.94 0.71 0.91 0.89 0.93

Coenonympha glycerion 0.13 -0.00012 0.22 0.89 71.00 0.74 0.93 0.91 0.95

Coenonympha pamphilus 0.18 0.00021 0.28 0.71 44.56 0.56 0.84 0.82 0.90

Coenonympha tullia 0.16 0.00003 0.26 0.81 61.32 0.70 0.93 0.91 0.94

Colias alfacariensis 0.18 0.00046 0.28 0.79 56.34 0.66 0.90 0.90 0.91

Cupido argiades 0.19 0.00031 0.28 0.78 54.19 0.57 0.87 0.86 0.89

Cupido minimus 0.28 0.00065 0.35 0.71 42.29 0.58 0.84 0.81 0.87

Cyaniris semiargus 0.25 -0.00005 0.33 0.76 49.70 0.65 0.87 0.86 0.89

Erebia ligea 0.13 0.00013 0.23 0.86 69.02 0.75 0.94 0.92 0.96

Erynnis tages 0.26 -0.00056 0.34 0.75 47.22 0.62 0.86 0.84 0.87

Euphydryas aurinia 0.24 -0.00049 0.32 0.71 42.94 0.55 0.84 0.82 0.86

Euphydryas maturna 0.09 0.00019 0.20 0.75 55.23 0.55 0.90 0.88 0.91

Favonius quercus 0.27 -0.00038 0.33 0.76 48.30 0.59 0.84 0.82 0.85

Glaucopsyche alexis 0.27 0.00019 0.33 0.75 45.50 0.53 0.82 0.81 0.83

Gonepteryx cleopatra 0.08 0.00020 0.17 0.92 80.61 0.83 0.97 0.96 0.99

Gonepteryx rhamni 0.21 0.00033 0.29 0.78 51.95 0.62 0.86 0.84 0.87

Hamearis lucina 0.19 -0.00002 0.29 0.75 51.20 0.61 0.89 0.88 0.90

Hesperia comma 0.26 0.00033 0.33 0.77 49.01 0.61 0.85 0.83 0.88

Heteropterus morpheus 0.13 -0.00013 0.23 0.79 57.17 0.55 0.89 0.87 0.92

Hipparchia semele 0.23 0.00023 0.31 0.78 53.07 0.62 0.87 0.84 0.89

Hipparchia statilinus 0.15 0.00035 0.25 0.85 65.48 0.73 0.93 0.91 0.94

Iphiclides podalirius 0.22 -0.00011 0.31 0.80 55.86 0.68 0.88 0.87 0.89

Issoria lathonia 0.18 -0.00017 0.27 0.83 61.09 0.70 0.89 0.87 0.90

Lampides boeticus 0.11 0.00003 0.21 0.89 75.51 0.83 0.97 0.96 0.98

Lasiommata maera 0.26 0.00006 0.33 0.77 49.23 0.60 0.84 0.83 0.86

Lasiommata megera 0.19 0.00034 0.28 0.81 58.27 0.71 0.89 0.87 0.92

Libythea celtis 0.11 0.00014 0.20 0.81 61.12 0.54 0.91 0.88 0.93

Limenitis camilla 0.17 0.00002 0.26 0.81 60.35 0.68 0.91 0.90 0.93

Limenitis reducta 0.13 -0.00017 0.23 0.82 63.21 0.65 0.92 0.91 0.94

Lycaena dispar 0.19 -0.00015 0.28 0.76 53.12 0.64 0.90 0.89 0.91

Lycaena hippothoe 0.18 0.00031 0.27 0.82 60.49 0.68 0.90 0.89 0.93

Lycaena phlaeas 0.19 0.00022 0.29 0.73 46.37 0.59 0.85 0.82 0.88

Lycaena tityrus 0.17 -0.00008 0.25 0.86 65.58 0.73 0.92 0.91 0.93

Lycaena virgaureae 0.17 0.00020 0.27 0.84 62.77 0.74 0.92 0.90 0.93

Maniola jurtina 0.14 0.00005 0.23 0.85 65.89 0.75 0.91 0.89 0.92

Melanargia galathea 0.14 -0.00023 0.23 0.89 72.12 0.78 0.94 0.93 0.95

Melitaea athalia 0.22 -0.00022 0.30 0.80 55.36 0.69 0.89 0.89 0.91

Melitaea cinxia 0.27 0.00027 0.34 0.73 44.44 0.55 0.83 0.82 0.84


17


Roc mean

Roc min

Roc max

Melitaea diamina 0.17 -0.00004 0.26 0.79 56.58 0.63 0.90 0.88 0.92

Melitaea didyma 0.24 0.00010 0.32 0.77 52.02 0.64 0.87 0.86 0.88

Melitaea phoebe 0.20 0.00018 0.28 0.80 55.80 0.62 0.89 0.86 0.90

Nymphalis antiopa 0.22 0.00017 0.30 0.81 56.50 0.69 0.90 0.88 0.91

Ochlodes sylvanus 0.22 0.00011 0.31 0.79 55.12 0.71 0.91 0.90 0.91

Papilio machaon 0.18 0.00025 0.28 0.78 53.41 0.63 0.86 0.84 0.88

Pararge aegeria 0.21 -0.00060 0.30 0.77 51.43 0.61 0.85 0.83 0.87

Phengaris alcon 0.17 0.00032 0.27 0.72 50.41 0.55 0.89 0.87 0.91

Phengaris arion 0.19 -0.00002 0.28 0.78 53.07 0.58 0.87 0.85 0.90

Phengaris nausithous 0.06 0.00002 0.15 0.89 75.89 0.69 0.95 0.94 0.97

Phengaris teleius 0.09 0.00001 0.19 0.84 66.42 0.63 0.92 0.91 0.94

Plebejus argus 0.28 -0.00015 0.34 0.74 45.01 0.59 0.83 0.81 0.88

Plebejus idas 0.24 0.00041 0.32 0.76 49.66 0.62 0.86 0.85 0.88

Plebejus optilete 0.12 -0.00013 0.22 0.85 67.73 0.76 0.95 0.94 0.96

Polygonia calbum 0.21 0.00015 0.29 0.81 57.17 0.70 0.89 0.88 0.91

Polyommatus amandus 0.19 -0.00005 0.27 0.82 59.20 0.67 0.89 0.88 0.93

Polyommatus bellargus 0.21 -0.00043 0.29 0.79 55.58 0.64 0.88 0.85 0.91

Polyommatus coridon 0.18 0.00014 0.27 0.82 60.99 0.70 0.91 0.90 0.93

Polyommatus escheri 0.07 -0.00006 0.17 0.77 59.66 0.53 0.93 0.91 0.95

Polyommatus icarus 0.19 -0.00004 0.28 0.79 54.67 0.67 0.88 0.85 0.90

Pyronia tithonus 0.15 -0.00005 0.25 0.84 64.23 0.71 0.93 0.91 0.94

Satyrium ilicis 0.24 0.00013 0.32 0.74 46.89 0.56 0.85 0.84 0.87

Satyrium pruni 0.18 0.00018 0.27 0.77 54.26 0.60 0.89 0.87 0.91

Satyrium spini 0.21 0.00048 0.30 0.75 49.90 0.54 0.85 0.83 0.87

Spialia sertorius 0.14 -0.00009 0.23 0.88 71.13 0.74 0.93 0.93 0.94

Thecla betulae 0.22 0.00035 0.31 0.76 51.36 0.63 0.88 0.86 0.90

Thymelicus acteon 0.20 0.00018 0.29 0.80 58.28 0.69 0.90 0.86 0.93

Thymelicus lineola 0.20 0.00002 0.28 0.83 60.18 0.70 0.90 0.89 0.91

Thymelicus sylvestris 0.22 -0.00002 0.30 0.81 56.59 0.68 0.88 0.86 0.90

Habitat preference

The final habitat selection is presented in Annex III.


For the dose-response curves only BMS transects were used within the range of the

species. The range is determined as all 5x5km squares where the probability of the

species (result from the climate and soil models) is over the cut-off value. Table 5

shows the number of transects within the range as well as the number of transects

within the range where the species was reported. This percentage ranges from over

80% for common and widespread species within their range (as Gonepteryx

cleopatra and Maniola jurtina) down to as low as 1% for rare and localized species

such as Phengaris alcon and P. teleius.

All univariate models are given in a separate .csv table.

Table 5: An overview of the number of transects within the range for each species, as well as the number of occupied transects in the range of the species.

species short Number of transects within the range

Number of transects with the species within the range

Percentage

Aglais io 2151 1794 83

Aglais urticae 2150 1780 83

Anthocharis cardamines 2178 1583 73

Anthocharis euphenoides 42 15 36


18



Percentage

Apatura ilia 511 58 11

Apatura iris 820 63 8

Aphantopus hyperantus 2017 1386 69

Aporia crataegi 1186 214 18

Araschnia levana 1010 539 53

Arethusana arethusa 152 18 12

Argynnis adippe 963 259 27

Argynnis aglaja 1489 419 28

Argynnis niobe 502 22 4

Argynnis paphia 1685 753 45

Aricia agestis 1540 508 33

Aricia artaxerxes 401 96 24

Aricia eumedon 274 26 9

Boloria aquilonaris 449 28 6

Boloria dia 453 72 16

Boloria euphrosyne 1019 212 21

Boloria selene 1441 258 18

Brenthis daphne 147 41 28

Brenthis ino 822 289 35

Brintesia circe 173 81 47

Callophrys rubi 1576 563 36

Carcharodus alceae 352 87 25

Carterocephalus palaemon 795 121 15

Carterocephalus silvicolus 228 75 33

Celastrina argiolus 2008 1226 61

Charaxes jasius 36 11 31

Coenonympha arcania 645 202 31

Coenonympha glycerion 294 70 24

Coenonympha pamphilus 2011 1199 60

Coenonympha tullia 784 27 3

Colias alfacariensis 460 94 20

Cupido argiades 132 29 22

Cupido minimus 1342 194 14

Cyaniris semiargus 888 170 19

Erebia ligea 421 167 40

Erynnis tages 1385 348 25

Euphydryas aurinia 1116 55 5

Euphydryas maturna 90 27 30

Favonius quercus 2016 366 18

Glaucopsyche alexis 425 74 17

Gonepteryx cleopatra 43 37 86

Gonepteryx rhamni 1922 1530 80

Hamearis lucina 658 44 7

Hesperia comma 1201 92 8

Heteropterus morpheus 406 21 5

Hipparchia semele 1938 262 14

Hipparchia statilinus 65 25 38

Iphiclides podalirius 201 95 47

Issoria lathonia 1184 449 38

Lampides boeticus 44 22 50

Lasiommata maera 666 192 29

Lasiommata megera 1497 401 27


19



Percentage

Libythea celtis 37 18 49

Limenitis camilla 1432 245 17

Limenitis reducta 184 78 42

Lycaena dispar 177 24 14

Lycaena hippothoe 620 108 17

Lycaena phlaeas 1979 1397 71

Lycaena tityrus 917 180 20

Lycaena virgaureae 725 235 32

Maniola jurtina 1939 1645 85

Melanargia galathea 1203 688 57

Melitaea athalia 868 266 31

Melitaea cinxia 850 122 14

Melitaea diamina 612 33 5

Melitaea didyma 159 43 27

Melitaea phoebe 173 47 27

Nymphalis antiopa 1351 264 20

Ochlodes sylvanus 2124 1411 66

Papilio machaon 749 243 32

Pararge aegeria 1688 1156 68

Phengaris alcon 798 6 1

Phengaris arion 421 15 4

Phengaris nausithous 229 14 6

Phengaris teleius 210 3 1

Plebejus argus 1575 245 16

Plebejus idas 671 133 20

Plebejus optilete 432 99 23

Polygonia calbum 2014 1258 62

Polyommatus amandus 655 229 35

Polyommatus bellargus 531 142 27

Polyommatus coridon 757 146 19

Polyommatus escheri 57 21 37

Polyommatus icarus 2170 1670 77

Pyronia tithonus 1436 878 61

Satyrium ilicis 947 49 5

Satyrium pruni 604 61 10

Satyrium spini 232 31 13

Spialia sertorius 263 44 17

Thecla betulae 1654 110 7

Thymelicus acteon 317 57 18

Thymelicus lineola 1846 917 50

Thymelicus sylvestris 1481 630 43

For relationships with a significant and negative value for b2, an optimum could be

calculated (figure 5; table 6). For species with a significant value for b1, but no

significant value for b2, we delineate only the direction of the relationship by a signal

of + or -. For species with a positive value for b2, no relationship is given in table 6 as

this is biologically hard to interpret. However the values are given in the .csv file.

Table 6 summarizes the results for the relationships between species occurrence and

desiccation, Nitrogen application, Nitrogen deposition and Sulphur deposition.


20

Table 6: Summary of the dose-relationships for desiccation (Desi), Nitrogen application (Napp), Nitrogen deposition (Ndep) and Sulphur deposition (Sdep). In case of a Gaussian relationship the optimum value is given, in case of a sigmoidal relationship the direction of the trend is given. Only significant results are presented. Species Desi Napp Ndep Sdep

Aglais io 1364 664

Aglais urticae - 321 -

Anthocharis cardamines 0.32 313 1015 -

Anthocharis euphenoides 1411 666

Apatura ilia -

Aphantopus hyperantus 184 -

Aporia crataegi 38 -

Araschnia levana -

Arethusana arethusa -

Argynnis adippe - - 37

Argynnis niobe +

Argynnis paphia 7 -

Aricia agestis - 114

Aricia artaxerxes - - 776 288

Aricia eumedon -

Boloria aquilonaris -

Boloria dia 0.26 - -

Boloria euphrosyne - -

Callophrys rubi - -

Carcharodus alceae - -

Carterocephalus palaemon - 1346 +

Carterocephalus silvicolus 207

Celastrina argiolus 0.53 +

Coenonympha arcania -

Coenonympha glycerion -

Coenonympha pamphilus 0.54 226 1354 +

Coenonympha tullia 811

Colias alfacariensis 0.30 -

Cupido argiades - 1085 493

Cupido minimus 0.24 - -

Cyaniris semiargus 0.42

Erynnis tages 91 427

Euphydryas aurinia - -

Euphydryas maturna - -

Favonius quercus 0.33 +

Glaucopsyche alexis 0.34

Gonepteryx rhamni -

Hamearis lucina - -

Hesperia comma - -

Heteropterus morpheus -

Hipparchia semele -

Hipparchia statilinus 0.21 1520

Iphiclides podalirius 0.68

Issoria lathonia + 134 839

Lampides boeticus 0.21

Lasiommata megera 145

Libythea celtis 1613

Limenitis camilla 0.17 -

Lycaena dispar +

Lycaena hippothoe - -

Lycaena phlaeas 0.46 + +


21

Species Desi Napp Ndep Sdep

Lycaena tityrus 61 -

Lycaena virgaureae 89

Maniola jurtina 0.53 254 1522 669

Melanargia galathea 148 598

Melitaea cinxia 462

Melitaea diamina - -

Melitaea didyma 0.27 +

Melitaea phoebe 0.22

Nymphalis antiopa -

Ochlodes sylvanus -

Papilio machaon 0.46 + +

Pararge aegeria 0.52 2756 813

Phengaris alcon - -

Phengaris arion - - 426

Plebejus argus -

Plebejus idas - - - -

Plebejus optilete -

Polygonia calbum 0.56 + 1785 735

Polyommatus coridon - 126

Polyommatus escheri 0.19

Polyommatus icarus 0.45 255 1381 +

Pyronia tithonus 0.21 +

Satyrium ilicis - - -

Satyrium spini -

Thecla betulae 108

Thymelicus lineola -

Thymelicus sylvestris 162

Table 7 summarizes the fragmentation of the 10km scale value for:

3.1 Forest

3.2 shrub and/or herbaceous vegetation associations

3.3 open spaces with little or no vegetation

10 km is chosen as this is considered most relevant for butterflies. The other values

are available in the .csv.

Table 7: Summary of the dose-relationships between butterfly species and the fragmentation of forest, shrub and open spaces. In case of a Gaussian relationship the optimum value is given, in case of a sigmoidal relationship the direction of the trend is given. Only significant results are presented. 3.1 Forest 3.2 shrub and/or herbaceous vegetation associations 3.3 open spaces with little or no vegetation Zero meaning no habitat is present in a circle of almost two times the dispersal distance and 1 meaning surroundings completely covered with the ecosystem. Species 3.1 Forest 3.2 Shrub 3.3 Open

spaces

Anthocharis cardamines 0.34

Anthocharis euphenoides 0.48

Apatura ilia 0.16 +

Aphantopus hyperantus + 0.03

Aporia crataegi + 0.35 +

Araschnia levana + 0.09 -

Arethusana arethusa + 0.27

Argynnis adippe 0.65 0.26 +


22

Species 3.1 Forest 3.2 Shrub 3.3 Open spaces

Argynnis aglaja + 0.29 +

Argynnis niobe 0.20 0.11

Argynnis paphia +

Aricia agestis 0.21 0.26

Aricia artaxerxes - 0.10

Aricia eumedon -

Boloria aquilonaris 0.19

Boloria dia + 0.29 0.17

Boloria euphrosyne 0.75 0.31 +

Boloria selene + 0.55

Brenthis daphne 0.57 0.22

Brenthis ino 0.76 0.13

Brintesia circe 0.29

Callophrys rubi 0.38 +

Carcharodus alceae + 0.29

Carterocephalus palaemon 0.37

Carterocephalus silvicolus 0.54 +

Celastrina argiolus -

Charaxes jasius +

Coenonympha arcania 0.78 0.25 +

Coenonympha glycerion 0.59 0.19

Coenonympha pamphilus 0.28 0.44 0.27

Coenonympha tullia 0.50

Colias alfacariensis 0.23

Cupido argiades 0.46

Cupido minimus 0.34 +

Cyaniris semiargus 0.41

Erebia ligea 0.73 0.28 +

Erynnis tages - 0.33 0.29

Euphydryas aurinia 0.35

Euphydryas maturna + -

Favonius quercus 0.27

Glaucopsyche alexis + 0.33 +

Gonepteryx cleopatra + +

Gonepteryx rhamni 0.62 -

Hamearis lucina 0.23

Hesperia comma 0.52 0.32 0.32

Heteropterus morpheus +

Hipparchia semele + 0.32 0.16

Hipparchia statilinus 0.41 0.31

Iphiclides podalirius 0.38

Issoria lathonia 0.33 0.27 0.30

Lampides boeticus 0.26 -

Lasiommata maera 0.24 +

Lasiommata megera 0.42

Limenitis camilla 0.52 - +

Limenitis reducta 0.31 0.05

Lycaena dispar +

Lycaena hippothoe 0.53 0.09

Lycaena phlaeas 0.23 0.29 0.23

Lycaena virgaureae 0.74 0.21 +

Maniola jurtina - + +

Melanargia galathea + -

Melitaea athalia + 0.15 -

Melitaea cinxia 0.55 0.33 0.30


23

Species 3.1 Forest 3.2 Shrub 3.3 Open spaces

Melitaea diamina 0.44 0.36 0.26

Melitaea didyma + 0.32 +

Melitaea phoebe 0.33

Nymphalis antiopa 0.82 0.24 +

Ochlodes sylvanus + 0.16

Papilio machaon 0.27

Pararge aegeria - 0.22

Phengaris alcon -

Phengaris arion +

Plebejus argus +

Plebejus idas 0.61 0.18

Plebejus optilete 0.56 0.16

Polygonia calbum - +

Polyommatus amandus 0.61 0.23 +

Polyommatus bellargus 0.27 +

Polyommatus coridon 0.29

Polyommatus escheri 0.28

Polyommatus icarus 0.07 0.27

Pyronia tithonus - -

Satyrium ilicis 0.19 +

Satyrium pruni 0.44 +

Satyrium spini +

Spialia sertorius 0.30 0.22

Thecla betulae 0.48 -

Thymelicus acteon 0.29

Thymelicus lineola + 0.19 0.26

Thymelicus sylvestris +


24

4. Discussion

In order to evaluate the potential effect of future land-use scenarios, environmental strategies and policies on biodiversity, Bioscore 2 adds solid models to perform such studies. This short report focuses only on the results for butterflies, and their relationship with several important anthropogenic stressors. A more complete overview is given by Van Hinsberg et al. (2014).


The parameters (climatic and soil variables and altitude) are relatively easily available

and cover most of the specific needs of butterflies. However the use of altitude can

be debated, as in the end this is a climatic factor as temperature drops with altitude.

Although the ROC value of the models produced by TRIMmaps cannot be compared

directly to the AUC value of the maps from Settele et al. (2008), they are generally

good enough to indicate that the quality of the maps is good (average ROC value in

this project is 0.89, the AUC in Settele et al. 2008 is 0.84 – but over a much larger

species set). In general the maps produced by TRIMmaps proved to be very useful

and close to the real distribution in the last decades of the 20th century, thus

providing a good basis for the dose-response curves.

Expert judgement comparisons of the prediction maps with the real present

distribution also showed that the maps for most species performed very well.

Habitat preference

The final habitat selection is presented in Annex III. As this analysis was not

weighted, the results (certainly for very common and widespread species) not only

reflect the habitat preference of each butterfly, but also the preference of volunteers

in terms of where they like to walk their transects. In some cases, habitats where

only few transects were available (e.g. peat bogs) were added manually to a number

of species where this was relevant.


The dose-response functions are hard to interpret. For this reason, two result-tables

were produced (tables 6 and 7), one with a few of the main environmental pressures

and one with the results for fragmentation. These pressures are discussed in more

detail:

Desiccation. It seems reasonable to expect that species which prefer wet or

moist conditions have an optimum preference below 0.2 or a declining

sigmoidal curve. However almost all optima (of the 43 species with a

significant relationship) found have a value over 0.2, indicating some form

of water scarcity. The results seem to indicate that the more widespread

European butterflies, like Maniola jurtina, Celastrina argiolus, Coenonympha

pamphilus and Polygonia c-album prefer situations with water stress (value

over 0.4). Of the only two species with an increasing relationship (Issoria

lathonia and Lycaena dispar), the latter one is even considered to be a

typical species of wetlands and marshlands, though in some parts of Europe

is also occurs in dryer conditions. We assume that the map used as input for

this pressure was too course, that way missing the relevant small moist and

wet habitats where the butterflies actually occur. We advise not to use this

factor, at least in this scale, as the results do not seem plausible.

Nitrogen application. This is a proxy for agricultural intensity. Levels over

200 kg N per hectare indicate intensive agriculture, levels under 100 kg N


25

per hectare indicate more semi-natural conditions in 2002. Of the 38 species

with a significant relationship, the only species with an optimum value over

200 kg N per hectare are Coenonympha pamphilus, Maniola jurtina,

Polyommatus icarus, Anthocharis cardamines and Aglais urticae, all of which

are widespread species in Europe who have shown to be able to survive in a

range of environments including urban, and even under intensive

agriculture as in the Netherlands. Three species have a rising linear

relationship (Pyronia tithonus, Lycaena phlaeas and Polygonia c-album), and

these are also common and widespread species able to survive on small

patches in an intensive agricultural landscape. On the other hand some

species which are also widely distributed in this intensive agricultural land,

like Aglais io, don’t have a significant relationship. The species with an

optimum below 100 kg N per hectare or a declining relationship are all

species with a clear preference for situations with little intensive agricultural

use. However, also here some of the ‘usual suspects’, species with a clear

preference for poor N conditions, don’t show a significant relationship.

However, all together the results for N application seem plausible.

Nitrogen deposition. The effect of nitrogen deposition depends strongly on

the soil and vegetation. For this reason, the exceedance of Nitrogen

deposition over a critical load is a better measure. Only 35 species showed a

significant relationship. In our data we expected most butterflies to prefer

low Nitrogen deposition, meaning a low optimum or a declining trend. This

was also the case for most species.

Additionally, some species with known affiliation to high-nitrogen

deposition (e.g. species associated with nettle - Aglais io, Aglais urticae) or

ruderal vegetation (e.g. Polyommatus icarus) indeed have shown such an

affiliation. Thus, most relationships seemed plausible.

However there are a few unexpected outliers showing a high optimum for

Nitrogen deposition, like Hipparchia statilinus, a species related to

extremely poor soils in most of its range. There is no good explanation for

this, except that most of the transects were on remnants of very poor soils

in an area with high nitrogen deposition (e.g. In the Netherlands and Eastern

Germany). Pararge aegeria also has shown a very high optimum for

Nitrogen deposition, which does not meet the known habitat preference of

the species (i.e., forests and forest-edges). These results may therefore

likely relate to the resolution of the information. Furthermore, one must

consider that the analyses used data from across several countries, thus

potentially introducing also larger-scale heterogeneity. It is interesting to

see this species has expanded in many parts of Europe, both in Finland

(maybe as a reaction to global warming) and in the Netherlands. The latter

might have contributed to this very high preference for Nitrogen deposition.

Sulphur deposition. Only 34 species show a significant relationship with

sulphur deposition. More than 400 mg S/m2 is considered a high value for

sulphur deposition. The results show that most of the calculated optima are

high values. These are mostly abundant and common species, like Maniola

jurtina, Polygonia c-album and Pararge aegeria, probably indicating that

these species can survive well in areas with high to very high sulfur

deposition values. Notably, also, for seven species where both a Ndep and

Sdep “optimum values” could be extracted, there was a strong positive

correlation between the two: a logarithmic regression between Ndep and

Sdep yielded an R2 of 0.889. While these results give some assurance that

species affiliated with anthropogenic pressures respond similarly to both N

and S, there were also results that could not be confirmed. For instance a

species like Anthocharis euphenoides, who is typical for forest edges on


26

calcareous soils in the Mediterranean, had a high optimum for sulphur

deposition. Also the linear relationships seem hard to interpret. Therefore,

for the time being, we advise not to use the results for sulphur deposition,

as long as they cannot be confirmed to offer a plausible, ecologically sound,

interpretable relation.

Analyses of fragmentation effects, for the time being are based on analysing the

landscape characteristics within a 10 km radius in terms of forest, shrubs and open

spaces, as these are the three most relevant habitat types for butterflies:

Forest. 61 of the butterfly species investigated show a significant

relationship with the fragmentation of the forest. The effect of forest

fragmentation on butterflies depends on the habitat preference for the

butterfly as well as its mobility. Species with a high optimum or an

increasing relationship, prefer large and more or less continuous forest. This

is also found for typical forest species as Boloria euphrosyne and Erebia

ligea. However there are also high optimal values for Plebejus idas and

Polyommatus amandus, which seem hard to explain given their preference

for grasslands or heathlands. Of course this can mean they prefer small

patches of their habitat situated within a large patch of forest. On the other

hand, species with a declining relationship mostly refer to species avoiding

large patches of woodland. One of the major exceptions is Pararge aegeria,

in most of Europe considered a typical woodland species (figure…). One of

the explanations could be that this species prefers small fragmented

patches of woodland over large forests.

Although some of the results of forest fragmentation on the occurrence of

butterflies can be debated, in general the results seem plausible enough to

be used.

Figure …: Response curve of Pararge aegeria for the fragmentation of woodland on a 10 km scale..


27

Shrub and/or herbaceous vegetation associations fragmentation. 71 of the

investigated species have shown a significant relationship with shrub

fragmentation. Many butterflies prefer a semi-open landscape, offering

both sunny and shady conditions. Shrubs and herbaceous vegetations are

the favorite habitat of many species. As an intermediate feature between

true woodland and true open areas, it is difficult to interpret the results.

Open spaces with little or no vegetation. 43 butterflies have shown a

significant relationship with open spaces. Optimum values were relatively

low, with the highest ones recorded for Hesperia comma and Issoria

lathonia, species which are known to prefer open ground. Rising values are

reported for a high number of species, with a few strange ones as well (e.g.

the woodland species Limenitis camilla). However, in general the results

seem plausible.


28

Conclusions

Although not the first approach to investigate the relationship between the

distribution of butterflies and climate (see also Settele et al., 2008), this is

the first attempt to add soil parameters and also investigate dose-response

relationships with environmental parameters, including Nitrogen application

and deposition and fragmentation, beyond the national level (Oostermeijer

& Van Swaay, 1998).

The TRIMmaps approach with the climate and soil variables and the

distribution data from the Kudrna et al. (2011) atlas produced useful and

detailed range maps via General Boosted Models (GBM).

Butterfly Monitoring Data from seven countries over a gradient from

Finland to Catalunya provided more than 3000 transects to study habitat

preferences and the dose-relationships between the occurrence of butterfly

species and the parameters. It would be interesting and important to

explore how the results may look when extending to all European countries

where data are available, and potentially differentiating between

bioclimatic regions given that species habitat preferences, and

environmental impacts, may differ across species’ geographic distribution.

Habitat preferences proved too difficult to analyse and interpret. This is

likely related to several independent problems of scale, resolution, and

source of information. First, CLC maps are too coarse for such an approach

(e.g. small patches of calcareous grassland can be ‘hidden’ in large patches

of coniferous woodland, resulting in the species seeming to prefer

coniferous woodland). The results had to be corrected by hand by the first

author, based on his own expert judgement. Second, CLC maps do not

incorporate information on habitat quality or heterogeneity: e.g. grasslands

are classified as such regardless of whether their management is extensive

or highly intensive. And third, the Butterfly Monitoring Schemes’ data were

provided for entire transects and associated with the coordinates of the

centroid. De facto, transect lengths may vary from 300 to 1000 m and

beyond, and hence, the exact information on butterfly locality may be easily

lost. It is likely that the impacts of such spatial inaccuracies are particularly

large for butterflies or habitats that are patchy, or for butterflies that prefer

heterogeneous environments (e.g. Pararge aegeria). A scale-specificity

analysis may thus prove highly useful.

Dose-response curves were produced for six variables (of which forest

management was separated into five types and fragmentation was actually

calculated for six sub categories on four different scales). Most of them

seemed to generate plausible results, with the main exceptions being

desiccation and sulphur deposition, the first one probably because the input

data were too coarse. It is however hard to compare with other literature,

as this is the first time such relationships are investigated in this detail on

this scale.

This reports indicates high usability of the statistical modelling approach to

assess the potential impacts, especially of nitrogen depositions and

fragmentation, on butterfly species. It therefore shows the sensitivity, and

potential usefulness, of butterflies as bio-indicator for environmental quality

(e.g. soil quality) etc. Considering current processes of agricultural

intensification especially in new Member States; and at the same time a lack

of biodiversity monitoring in those same Member States – it is highly

imperative to establish monitoring in all Member States.


29

Literature

Dinca, V.; Lukhtanov, V.A.; Talavera, G. & Vila, R. (2011): Unexpected layers of cryptic

diversity in wood white Leptidea butterflies. - Nature Communications 2, DOI:

10.1038/ncomms1329

Hallmann, C., Kampichler, C. & Sierdsema, H. (2014). TRIMmaps: an R package for the

analysis of species abundance and distribution data. SOVON, Nijmegen.

Jongman, R.H.G.; Braak, C.J.F. ter; Tongeren, O.F.R. van (1987): Data analysis in

community and landscape ecology. Pudoc Wageningen.

Kudrna, O.; Harpke, A.; Lux, K.; Pennerstorfer, J.; Schweiger, O; Settele, J.; Wiemers,

M. (2011): Distribution atlas of butterflies in Europe. - Gesellschaft für

Schmetterlingschutz, Halle, Germany

Oostermeijer, J.G.B. & Swaay, C.A.M. van (1998): The relationship between

butterflies and environmental indicator values : a tool for conservation in a changing

landscape. - Biological Conservation 86 (3), 271-280

Paracchini, M.L.; Petersen, J.-E.; Hoogeveen, Y.; Bamps, C.; Burfield, I.; Swaay, C. van ;

JRC Joint Rearch Institute; IES Institute of Environment and Sustainability (2008):

High nature value farmland in Europe : an estimate of the distribution patterns on

the basis of land cover and biodiversity data. ((EUR - Scientific and Technical

Research series) (JRC Scientific and Technical Reports)) - Office for Official

Publications of the European Communities, Luxembourg

Pollard, E. (1977): A method for assessing changes in the abundance of butterflies.

Biological Conservation 12 (2), 115-134

Rybicki, J. & Hanski, I. (2013): Species–area relationships and extinctions caused by

habitat loss and fragmentation. - Ecology Letters DOI: 10.1111/ele.12065

Settele, J.; Kudrna, O.; Harpke, A.; Kühn, I.; Swaay, C. van; Verovnik, R.; Warren, M.;

Wiemers, M.; Hanspach, J.; Hickler, T.; Kühn, E.; Halder, I. van; Veling, K.;

Vliegenthart, A.; Wynhoff, I.; Schweiger, O. (2008): Climatic risk atlas of European

butterflies. - Pensoft, Sofia.

Van Hinsberg, A., Hendriks, M., Hennekens, S., Sierdsema, H., Van Swaay, C.,

Rondinini, C., Santini, L., Delbaere, B., Knol, O. & Wiertz, J. (2014): BioScore 2.0. A

tool to assess the impacts of European Community policies on Europe’s biodiversity

First Draft. Final version planned for December 2014. Draft used for review.

Van Swaay CAM, Nowicki P, Settele J, Van Strien AJ, 2008. Butterfly monitoring in

Europe: methods, applications and perspectives. Biodiversity and Conservation

17(14): 3455-3469

Van Swaay, C.A.M., Van Strien, A.J., Harpke, A., Fontaine, B., Stefanescu, C., Roy, D.,

Maes, D., Kühn, E., Õunap, E., Regan, E., Švitra, G., Prokofev , I. Heliölä, J., Settele, J.,

Pettersson, L.B., Botham, M., Musche, M., Titeux, N., Cornish, N., Leopold, P.,

Julliard, R., Verovnik, R., Öberg, S., Popov, S., Collins, S., Goloshchapova, S., Roth, T.,


30

Brereton, T. & Warren, M.S. (2012). The European Butterfly Indicator for Grassland

species 1990-2011. Report VS2012.019, De Vlinderstichting, Wageningen

Van Swaay CAM, Van Strien AJ, Harpke A, Fontaine B, Stefanescu C, Roy D, Maes D,

Kühn E, Õunap E, Regan E, Švitra G, Prokofev I, Heliölä J, Settele J, Pettersson LB,

Botham M, Musche M, Titeux N, Cornish N, Leopold P, Julliard R, Verovnik R, Öberg S,

Popov S, Collins S, Goloshchapova S, Roth T, Brereton T, Warren MS (2013).The

European Butterfly Indicator for Grassland species 1990-2011. European

Environmental Agency, No 11/2013; ISBN: 978-92-9213-402-0


31

Annex I: Example of the analysis

To show how the analysis has been done, one species analysis is presented here in

more detail to show the workflow and results. Plebejus optilete is a species of bogs

and open coniferous forests with Vaccinium in the undergrowth, mainly occurring in

Northern Europe and the Alps.

Plebejus optilete

Distribution maps / biogeographical ranges

In this first step we want to regress the species occurrence data to a limited set of

ecological relevant climatic and soil variables. In this step we want to use the

regression techniques of TRIMMaps. For this step we have selected four soil

variables. In addition we have selected a set of climate variables. This first step will

result in species specific distribution maps or biogeographical ranges.

Partial dependence plots

Only the dependences plots which attribute with more than 10% are presented here.

Main parameters for this species are Tsum (temperature sum of growing season),

bio_9 (mean temperature of driest quarter) and bio_3 (isothermality), making this a

species with a preference for cool conditions (also in winter).


32

GBM-statistics

speccode Plebejus_optilete

MAE 0,118207

MFE -0,00013

RMSE 0,220656

corr 0,848541

expldev 67,72647

cv_corr,mean 0,755443

roc_mean 0,94928

roc_min 0,9414

roc_max 0,9597

Predicted presence map

The result of the TRIMmaps analysis is given, to the right the result for this species in

Settele et al. (2008).

For this species the cutoff value with factor 1.2 was calculated as 0.51.


33

Habitat preference

The CLC preference for this species was calculated as: Scientific Name Clc3 Clc3_name Freq Perc Habitat

Plebejus_optilete 24 coniferous forest 66 33,7 1

Plebejus_optilete 25 mixed forest 52 26,5 1

Plebejus_optilete 21 land principally occupied by agriculture with significant natural vegetation

25 12,8 1

Plebejus_optilete 12 non-irrigated arable land 24 12,2 1

Plebejus_optilete 29 transitional woodland-scrub 11 5,6 1

Plebejus_optilete 2 discontinuous urban fabric 7 3,6 1

Plebejus_optilete 41 water bodies 3 1,5 0

Plebejus_optilete 11 port and leisure facilities 2 1,0 0

Plebejus_optilete 36 peat bogs 2 1,0 1

Plebejus_optilete 44 sea and ocean 2 1,0 0

Plebejus_optilete 23 broad-leaved forest 1 0,5 0

Plebejus_optilete 27 moors and heath lands 1 0,5 0

The column Habitat (=1) represents the selected habitats. Peat bogs (CLC3=36) were

added manually.

Dose-response curves

For this step BMS data was used. First a selection was made of all transects within

the range of the species. The following map shows the resulting distribution from the

cutoff (green) as well as the transects within and outside the range, and the ones

having the species present.


34

The following dose-response curves showed a significant relationship:

Factor Dose-response function Interpretation

Fragmentation of forest (3.1) on a

10, 20 and 50 km scale

Significant optimum curve The species prefers half open

forest.

Fragmentation of shrub and/or

herbaceous vegetation (3.2) on a

10, 20, 50 and 100 km scale

Significant optimum curve The species prefers a low

amount of shrubs.

Fragmentation of inland waters

(5.1) at a 10, 20 and 50 km scale

Significant optimum curve Often lakes are present in the

surroundings

Forest management 3: combined

objective forestry

Significant optimum curve


35

Annex II: R-scripts

For future use all R-script are presented in this annex.

TRIMmaps

setwd("C:/trimmaps/")

source("TRIMmaps.r")

source("brt.functions.cv.r")

library(TRIMmaps)

setwd("E:/Bioscore2Butterflies/Step1/")

outdir <- "E:/Bioscore2Butterflies/Step1/Output/"

dir.create(outdir)

crs <- "+proj=laea +lat_0=52 +lon_0=10 +x_0=4321000 +y_0=3210000 +ellps=GRS80

+units=m +no_defs"

trimdata5x5 <- data2TRIMmaps(

plot.data="E:/Bioscore2Butterflies/Step1/Butterfly_Atlas_ETRS.csv",

crs=crs,

named="trimdata5x5",

outdir=outdir,

add.zeroes=TRUE,

generate.zeroes=FALSE,

user.dir="E:/ Bioscore2Butterflies/Step1/ClimateSoil_5km_asc",

user.crs=crs,

user.all.question = FALSE

)

save(trimdata5x5, file = "E:/Bioscore2Butterflies/Step1/trimdata5x5.RData")

load("E:/ Bioscore2Butterflies/Step1/trimdata5x5.RData")

trim.gbm <- TRIMmaps(

TRIMdata = trimdata5x5,

model.type = 'gbm',

gbm.control = gbmTRIMOptions(tree.complexity = 2),

data.type = 'presence',

resid.int.method = NULL,

driver = c("asc"),

out.dir = "E:/ Bioscore2Butterflies/Step1/Output/AtlasGBM5x5",

vars.subs = "-YEAR",

spec.subs = c("Cupido_minimus")

)

TRIMmapsSummary(trim.gbm)


36

Cut-off

#source("Cutoff_Optimised.r")

library(raster)

library(sp)

library(maptools)

gpclibPermit()

FACTOR = 1.2

Cutoff.Optimised <- function (Obs, Fit)

{

SumObs <- sum(Obs)

LengObs <- length(Obs)

tt <- c(100)

Cut <- c(0, 0, 0)

if (length(unique(Fit)) == 1) {

Cut[1] <- unique(Fit)

Cut[2] <- 100 * sum((Fit >= Cut[1])[Obs == 1])/SumObs

Cut[3] <- 100 * sum((Fit < Cut[1])[Obs == 0])/(LengObs -

SumObs)

Cut <- t(Cut)

}

else {

if (min(Fit) < 0)

Fit[Fit < 0] <- 0

Quant <- quantile(Fit)

i <- Quant[1]

a <- 2

while (i <= Quant[5]) {

se <- sum((Fit >= i)[Obs == 1])/SumObs

sp <- sum((Fit < i)[Obs == 0])/(LengObs - SumObs)

tt[a] <- abs(FACTOR*se - sp) ## specifity is twice as important as sensitivity

if (tt[a] > tt[a - 1])

break

i <- i + ((Quant[5] - Quant[1])/1000)

a <- a + 1

}

b <- (i - ((Quant[5] - Quant[1])/1000))

Cut[1] <- b

Cut[2] <- 100 * sum((Fit >= b)[Obs == 1])/SumObs

Cut[3] <- 100 * sum((Fit < b)[Obs == 0])/(LengObs - SumObs)

Cut <- t(Cut)

dimnames(Cut) = list(NULL, c("CutOff", "se", "sp"))

}

return(Cut)

}

## choose directory

setwd("E:/Chris/Bioscore/Bioscore2Butterflies/Bioscore2Butterflies/Step1/Output/At

lasGBM5x5/RESULTS6")


37

filenames <- list.files(getwd(), pattern="RData$", recursive=TRUE, full=TRUE)

filenames <- sub(".RData", "", filenames)

filenames <- filenames[grep("preds", filenames, invert = TRUE)]

filenames <- filenames[grep("cutoff", filenames, invert = TRUE)]

filenames

#speciesnames <-

read.table('F:/Project/Oz/S2013.079_Kwaliteitsbepaling_SNL/BMP/Speciesnames.tx

t', sep=";", header=T, as.is = T) ## adjust table according to your own names

#str(speciesnames)

### BRT models

#i <- "gbm.TRIM_presence_10"

progbar <- winProgressBar(title = "Progress", min = 0, max = length(filenames))

counter <- 1

for (i in filenames[1:length(filenames)]) {

## load dataframe with results from BRT-analysis

gbm.model <- get(load(paste(i,".RData", sep="")))

rm(list = ls(pattern= "gbm.TRIM"))

gc()

memory.size()

Obs <- gbm.model$data$y

Fit <- gbm.model$fitted

par(mfrow=c(1,2))

hist(Obs)

hist(Fit)

par(mfrow=c(1,1))

cutoffs <- Cutoff.Optimised(Obs,Fit)

cutoffs <- data.frame(cutoffs)

save(cutoffs, file = paste(i, "_cutoff.RData", sep = ""))

cutoffs$Species <- i

if(!file.exists("all.cutoffs.csv")) {

write.table(cutoffs, file = "all.cutoffs.csv", row.names = FALSE)

} else {

write.table(cutoffs, file = "all.cutoffs.csv", append = TRUE,

row.names = FALSE, col.names = FALSE)

}

info <- sprintf("% of %i species done", counter, length(filenames))

setWinProgressBar(progbar, counter, label = info)

counter <- counter + 1

}

setwd("E:/Chris/Bioscore/Bioscore2Butterflies/Bioscore2Butterflies/Step1/Output/At

lasGBM5x5/RESULTS6/")


38

filenames <- list.files(getwd(), recursive=TRUE, full=TRUE)

filenames <-filenames[grep(".asc",tolower(filenames),fixed=T)]

filenames <-filenames[grep("predictedr_",tolower(filenames),fixed=T)]

filenames <-filenames[grep("presabs",tolower(filenames),fixed=T, invert = TRUE)]

filenames

progbar <- winProgressBar(title = "Progress", min = 0, max = length(filenames))

counter <- 1

for(i in filenames)

{

speciescode <- sub(".asc$", "", i)

speciescode <- sub("predictedr_presence_", "", speciescode)

#load(paste("cutoff_gbm.TRIM_presence_",speciescode,".RData", sep="") )

SPLIT <- unlist(strsplit(speciescode, "/"))

PATH <- paste(SPLIT[1:(length(SPLIT)-1)], collapse = "/")

load(paste(PATH, "/", "gbm.TRIM_presence_", SPLIT[length(SPLIT)],

"_cutoff.RData", sep = ""))

grd <- read.asciigrid(fname=i)

grd[[1]] <- ifelse(grd[[1]] < cutoffs$CutOff,0,1)

filename <- sub(".asc$", "", i)

write.asciigrid(grd,paste(filename,"_presabs_raw.asc",sep=""))

info <- sprintf("% of %i species done", counter, length(filenames))

setWinProgressBar(progbar, counter, label = info)

counter <- counter + 1

}


39

Habitat preference

###################################################

#

# Spatial overlays of observations and clc-map

#

# Henk Sierdsema

# July 2014

#

###################################################

library(maptools)

library(sp)

library(rgdal)

library(foreign)

#library(TRIMmaps)

library(beepr)

#setwd("b:/Europa/Data/Birds/maps3")

setwd("d:/Sovon/Project/OZ/S2013.148_EU verspreiding en

drukfactoren/Data/Butterflies/")

wd <- getwd()

contour <- readShapeLines("contour.shp")

filenames <- list.files(getwd(), recursive=FALSE, full=FALSE)

filenames <-filenames[grep(".csv",tolower(filenames),fixed=T)]

filenames

obs <- read.table("Butterfly_BMS_ETRS.csv",sep=",", header=T, as.is=T)

str(obs)

## Loop over species

for (i in unique(obs$Species)) {

## retrieve observations

observations <- obs[obs$Species==i,]

gc()

str(observations)

## make subset of relevant observations

gc()

## project observations

coordinates(observations) <- ~x+y

proj4string(observations) <- "+proj=laea +lat_0=52 +lon_0=10 +x_0=4321000

+y_0=3210000 +ellps=GRS80 +units=m +no_defs" ## ETRS / ETRS89/LAEA

epsg:3035

speciesname <- i

gc()

# Make plot

#plot(contour)

#plot(observations[observations$Number >0,"Scientific" ], pch=1, cex=0.5,

col="blue",add=T)

#title(speciesname)


40

## Write to shape

writeOGR(observations,paste("C:/eu/butterflies/Shapes/",speciesname,".shp",sep="

" ), "obs", "ESRI Shapefile",overwrite_layer=TRUE )

gc()

} ## end for

beep("fanfare")

## make python script for overlays

setwd("C:/eu/butterflies/Shapes/")

python <- data.frame(list.files(getwd(), pattern=".shp", recursive=FALSE,

full=FALSE))

str(python)

names(python) <- "shape"

head(python)

# Process: Extract Values to Points

path1 <- "C:\\EU\\Butterflies\\Shapes\\"

path2 <- "C:\\Basis\\Gis\\Europa\\Corine\\Corine_combi\\"

path3 <- "C:\\EU\\Butterflies\\Shapes\\CLC\\"

python$script <- paste("arcpy.gp.ExtractValuesToPoints_sa(",

"$",path1,python$shape,"$,",

"$",path2, "lc2k100mt2.tif$, ",

"$",path3, gsub(".shp","",python$shape),"_clc_shp$, $NONE$,

$VALUE_ONLY$)",sep="")

head(python)

## write to text-file; REPLACE $ by " and \ by \\ before execution !!!!!

write.table(python$script,"python_clc.py",row.names=F, col.names=F, quote=F)

## !!! THEN REPLACE REPLACE $ by " and \ by \\ before execution !!!!!

## start ArcGIS, open the Python window and first run the command: import arcpy

## Then run the commands in the created python-script

## Calculate totals per CLC-class

setwd(paste("C:/EU/Butterflies/Shapes/CLC/",sep=""))

## read tavle with clc-classes

clc_codes <- read.table(paste(wd,"/","corine_legend_full_edt.csv",sep=""),sep=";",

header=T, as.is=T)

str(clc_codes)

clc_codes <- subset(clc_codes,select=c(Clc1,Clc2,Clc3,Clc3_name))

## retrieve filenames


filenames <-filenames[grep(".dbf",tolower(filenames),fixed=T)]

#filenames <-filenames[grep("occurrence",tolower(filenames),fixed=T)]

filenames

## Append all dbf-file into one dataframe


41

dat1 <- read.dbf(filenames[1], as.is=T)


obs <- read.dbf(i, as.is=T)

dat1 <- rbind(dat1,obs)

} ## endfor dat1

str(dat1)

unique(dat1$Scientific)

gc()

save(dat1,file="clcdat.RData")

gc()

#load("dat1.RData")

## total number of positive observations within range

species_tot_nobs1 <- data.frame(table(dat1$Species ))

str(species_tot_nobs1)

names(species_tot_nobs1) <- c("Scientific","Total")

# per Source

species_nobs1_clc <- with(dat1, table(Species, RASTERVALU))

clc_tab1 <- aggregate(observed ~ Species + RASTERVALU, data= dat1, length)

str(clc_tab1)

names(clc_tab1) <- c("Scientific","Clc3","Freq")

totals <- aggregate(Freq ~ Scientific, data=clc_tab1[clc_tab1$Clc3>0,], sum)

totals

names(totals) <- c("Scientific","Total")

clc_tab2 <- merge(clc_tab1,totals, by="Scientific")

head(clc_tab2)

## CLC level 3 percentages

clc_tab2$Perc <- clc_tab2$Freq/clc_tab2$Total*100

clc_tab_clc3 <- merge(clc_tab2,clc_codes,by="Clc3")

str(clc_tab_clc3)

clc_tab_clc3$Habitat <- ifelse(clc_tab_clc3$Perc>=3,1,0)

clc_tab_clc3 <- subset(clc_tab_clc3,

select=c(Scientific,Clc3,Clc3_name,Freq,Perc,Habitat,Total,Clc1,Clc2))

clc_tab_clc3 <- with(clc_tab_clc3,clc_tab_clc3[

order(Scientific,Clc3,Clc3_name,Freq,Perc,Habitat,Total,Clc1,Clc2), ])

write.table(clc_tab_clc3,"CLC_perc_clc3_cutoff3.csv",sep=";",row.names=F)

## CLC level 2 percentages

clc_tab_clc2 <- aggregate(Perc ~ Scientific+Clc2+Clc1, data=clc_tab_clc3, sum)

clc_tab_clc2$Habitat <- ifelse(clc_tab_clc2$Perc>=5,1,0)

clc_tab_clc2 <- subset(clc_tab_clc2, select=c(Scientific,Clc2,Perc,Habitat,Clc1))

clc_tab_clc2 <- with(clc_tab_clc2,clc_tab_clc2[

order(Scientific,Clc2,Perc,Habitat,Clc1), ])

head(clc_tab_clc2)

write.table(clc_tab_clc2,"CLC_perc_clc2.csv",sep=";",row.names=F)


42

Univariate analysis

#####################################################

#

# Univariate models

#

# Authors: Henk Sierdsema, Stephan Hennekens, Christian Kampichler

#

# Last update 1-10-2014

#

#####################################################

#

#

# First run the script up to Beep(Fanfare)

# After that the rest after the .csv files have been copied to a directory.

#

# v3.1_3: stepwise regression switched off

library(tcltk)

library(cvAUC)

library(beepr)

## Define directory with csv-files containing observations and covariate date

setwd("E:/Chris/Bioscore/Bioscore2Butterflies/Bioscore2Butterflies/Step4_v3_2")


filenames <- filenames[grep(".csv",tolower(filenames),fixed=T)]

filenames

wd <- getwd() ## capture working directory

## Select to limit data or not

## TRUE: Equal number of 0 and 1

LIMITDATA <- FALSE

FILEEXT <- ifelse(LIMITDATA,"_datlim", "_nodatlim")

## Create directory to hold results

modeldir <- "Models"

dir.create(paste(getwd(),modeldir,sep="/"))

bootdir <- "Models_boot"

dir.create(paste(getwd(),bootdir,sep="/"))


Dataset <- read.table(i,sep=";", na.strings="-9999", header=T, as.is=T)

str(Dataset)

## change name of field with observed numbers into 'observed'

names(Dataset)[names(Dataset)=="PresAbs"] <- "observed"

## All presences are supposed to be within the range


43

Dataset[Dataset$observed==1,]$range <- 1

## Select only observations within the range

Dataset <- Dataset[Dataset$range==1,]

## Change observed numbers in presence and absence (or better: detection and

non-detection)

Dataset$observed <- ifelse(Dataset$observed==0,0,1)

## Retrieve species name from file name

SPECIES <- gsub("_covars.csv","",i)

SPECIES

## Set paths for output

PATH1 <- paste(getwd(),"/",modeldir,"/",SPECIES,"/",sep="")

dir.create(PATH1)

PATH2 <- paste(getwd(),"/",bootdir,"/",SPECIES,"/",sep="")

dir.create(PATH2)

## Make text-file for output

SUMMnames <-

c("species","variable","rownames","Estimate","Std..Error","z.value","Pr...z..","Expld

ev","AIC","AUC","AUCmin","AUCmax","AUCmean","NAbs","Npres","Ntot")

write(SUMMnames, file=paste(PATH1,SPECIES, "_glm_summary",FILEEXT,".csv",

sep = ""),ncolumns=length(SUMMnames), sep=";")

### !! Choose one of the two lines below !! ###

# FACTORS <- names(Dataset)[4:length(names(Dataset))] ## Adjust according to

your file; this retrieves all covariate names

FACTORS <- names(Dataset)[c(4:36)] ## Adjust according to your file; this

retrieves only pressures

FACTORS

FACTOR <- FACTORS[1] # for testing

for(FACTOR in FACTORS) {

## MAKE SUBSET FOR CHOSEN VARIABLE

#hist(get(eval(FACTOR)), data = Dataset)

TEMP <- Dataset[,c("observed", FACTOR)]

TEMP <- na.omit(TEMP) ## removes all lines with missing values

TEMP0 <- TEMP[TEMP$observed == 0,]

TEMP1 <- TEMP[TEMP$observed >= 1,]

#dim(TEMP0)

#dim(TEMP1)

#SELECT <- sample(1:nrow(TEMP1), nrow(TEMP0), replace = F)

#SubData <- rbind(TEMP0, TEMP1[SELECT,])

## assumption: there are more lines with 1 than with 0

if(LIMITDATA) {

if(nrow(TEMP1) > nrow(TEMP0)) {

SELECT <- sample(1:nrow(TEMP1),

nrow(TEMP0), replace = F)

SubData <- rbind(TEMP0, TEMP1[SELECT,])

}


44

## but when there are more lines with 0 than with 1 do

this:

if(nrow(TEMP1) < nrow(TEMP0)) {

# tk_messageBox(type = c("ok"),

# "yes", caption = "", default = "")

SELECT <- sample(1:nrow(TEMP0), nrow(TEMP1),

replace = F)

SubData <- rbind(TEMP0[SELECT,], TEMP1)

}

if(nrow(TEMP1) == nrow(TEMP0)) {

SubData <- TEMP

}

} else {

SubData <- TEMP

}

if(nrow(SubData)>= 10) {

SubData$squared <- SubData[,2]^2

##old ## stepwise variable selection

##old glm.model <- (glm(observed ~ get(eval(FACTOR))

+ squared, family = binomial(logit), data = SubData))

##old SUMM <- summary(glm.model)

## stepwise variable selection

# glm.model <- step(glm(observed ~ get(eval(FACTOR)) + squared, family =

binomial(logit), data = SubData),direction="both") ##

glm.model1 <- glm(observed ~ get(eval(FACTOR)) , family

= binomial(logit), data = SubData)

glm.model2 <- glm(observed ~ get(eval(FACTOR)) +

squared, family = binomial(logit), data = SubData)

AIC1 <- AIC(glm.model1)

AIC2 <- AIC(glm.model2)

AICs <- data.frame(rbind(AIC1, AIC2))

names(AICs) <- "AIC"

# str(AICs)

bestmodel <- which(AICs$AIC == min(AICs$AIC))

if (bestmodel == 1)

{glm.model <- glm.model1}

if (bestmodel == 2)

{glm.model <- glm.model2}

SUMM <- summary(glm.model)

SUMM

## Tenfold cross validation on 10% independent data

AUC <- as.numeric()

for (j in 1:10) {

data <- glm.model$data

rnd <- runif(dim(data)[1],0,1)

# add random number

data$rnd <- runif(dim(data)[1],0,1)

# select 90% of data for modelling


45

data.model <- data[data$rnd > 0.1, 1:(dim(data)[2]-1)]

# select model to run

ifelse (dim(data.model)[2]==2,

glm.model.subset <- glm(observed ~ get(eval(FACTOR)), family =

binomial(logit), data = data.model),

glm.model.subset <- glm(observed ~ get(eval(FACTOR)) + squared, family =

binomial(logit), data = data.model)

)

# select 10% indepent data and make predictions

dat.independent <- data[data$rnd <= 0.1, 1:(dim(data)[2]-1)]

if (length(table(dat.independent$observed))==2) {

dat.independent$pred <-

predict(glm.model.subset,dat.independent[,2:dim(dat.independent)[2]],

type="response")

# calculate AUC

AUC[j] <- cvAUC(dat.independent$pred,dat.independent[,1])$cvAUC

} # end if

} ## end cv AUC

AUC

c

glm_summary <- data.frame(SUMM$coefficients)

glm_summary$rownames <- row.names(glm_summary)

glm_summary$species <- SPECIES

glm_summary$variable <- FACTOR

glm_summary$AIC <- AIC(glm.model)

glm_summary$Expldev <- (glm.model$null.deviance -

glm.model$deviance)/glm.model$null.deviance *100

glm_summary$AUC <-

cvAUC(glm.model$fitted.values,glm.model$y)$cvAUC

glm_summary$AUCmin <- min(AUC)

glm_summary$AUCmax <- max(AUC)

glm_summary$AUCmean <- mean(AUC)

glm_summary$NAbs <- length(subset(glm.model$y,glm.model$y==0))

glm_summary$NPres <- length(subset(glm.model$y,glm.model$y==1))

glm_summary$Ntot <- length(glm.model$y)

glm_summary

glm_summary <- subset(glm_summary, select =

c(species,variable,rownames,Estimate,Std..Error,z.value,

Pr...z..,Expldev,AIC,AUC,AUCmin,AUCmax,AUCmean,NAbs,NPres,Ntot))

write.table(glm_summary, file=paste(PATH1,SPECIES,

"_glm_summary",FILEEXT,".csv", sep =

""),sep=";",row.names=F,col.names=F,append=T)

## PREDICTIE

newdata <- SubData[,2:3]

newdata <- newdata[order(newdata[,1]),]

preds <- predict(glm.model, newdata = newdata, type =

"response")

plot(preds ~ newdata[,1], type = "l",col="blue",

xlab = FACTOR, ylab = "Presence", ylim = c(0,1)

)

points(x = SubData[,2], y = SubData$observed, cex = 0.25)


46

## MAKE PREDICTION PLOTS

png(paste(PATH1, SPECIES, "_", FACTOR, FILEEXT, ".png", sep =

""), width=1000, height=800)

plot(preds ~ newdata[,1], type = "l", col="blue",

xlab = FACTOR, ylab = "Presence", ylim = c(0,1),

main = paste(SPECIES, FACTOR, FILEEXT))

points(x = SubData[,2], y = SubData$observed, cex = 0.25)

dev.off()

#}

#####################################################

#

# model bootstrapping

#

#####################################################

nboots <- 20

j <- 1 # for testing

for (j in 1:nboots) {

data <- glm.model$data

rnd <- runif(dim(data)[1],0,1)

# add random number

data$rnd <- runif(dim(data)[1],0,1)

# select 90% of data for modelling

data.model <- data[data$rnd >= 0.5, 1:(dim(data)[2]-1)]

# select model to run

try(assign(paste("glm.model.boot",j,sep=""),

(glm(observed ~ get(eval(FACTOR)) + squared, family = binomial(logit), data =

data.model)))

,silent=T)

# calculate model predictions



assign(paste("preds",j,sep=""),

predict(get(paste("glm.model.boot",j,sep="")), newdata = newdata, type =

"response"))

assign(paste("preds",j,sep=""),

cbind(newdata[,1],get(paste("preds",j,sep=""))))

} # end for 1:nboots

## make plot

png(paste(PATH2, SPECIES, "_", FACTOR, FILEEXT, "_boot.png",

sep = ""), width=1000, height=800)



preds <- predict(glm.model, newdata = newdata, type =

"response")

plot(preds ~ newdata[,1], type = "l",col="blue",

xlab = FACTOR, ylab = "Predictions", ylim = c(0,1),

main=FACTOR


47

)


points(get(paste("preds",j,sep=""))[,2] ~ newdata[,1], type = "l",col="grey")

}

points(preds ~ newdata[,1], type = "l",col="red",lwd=2)

dev.off()

} # end for if(nrow(SubData)>= 10)

## combine bootstraps

try(assign(paste(FACTOR,".preds.boot",sep=""),data.frame(preds1)), silent=T)

if (length(get(paste(FACTOR,".preds.boot",sep="")))>0 ) {


assign(paste(FACTOR,".preds.boot",sep=""),

rbind(get(paste(FACTOR,".preds.boot",sep="")),data.frame(get(paste("preds",j,sep

=""))) ) )

} # end for boots

tmp <- get(paste(FACTOR,".preds.boot",sep=""))

tmp$sample.id <- row.names(tmp)

tmp$species <- SPECIES

names(tmp) <- c(FACTOR,"pred","sample.id","species")

assign(paste(FACTOR,".preds.boot",sep=""), tmp)

} # end for if

# tail(tmp)

## save bootstrap predictions to table

write.table(get(paste(FACTOR,".preds.boot",sep="")),paste(PATH2,SPECIES,"_",FA

CTOR,"_bootstraps.csv",sep=""),sep=";",row.names=F)

## bootstrap summaries

boot.summaries <- aggregate(tmp[,2] ~ tmp[,1], data= tmp, mean )

names(boot.summaries) <- c("value", "mean")

boot.summaries.sd <- aggregate(tmp[,2] ~ tmp[,1], data= tmp, sd)

names(boot.summaries.sd) <- c("value", "sd")

boot.summaries <- merge(boot.summaries, boot.summaries.sd, by="value")

head(boot.summaries)

boot.summaries$min <- boot.summaries$mean - boot.summaries$sd

boot.summaries$max <- boot.summaries$mean + boot.summaries$sd

boot.summaries$range <- boot.summaries$max - boot.summaries$min

boot.summaries$ratio <- boot.summaries$sd/boot.summaries$mean

boot.summaries$species <- SPECIES

png(paste(PATH2, SPECIES, "_", FACTOR, FILEEXT,

"_bootstrapsummaries.png", sep = ""), width=1000, height=800)

plot(boot.summaries$value,boot.summaries$max, col="grey",

ylim=c( min(boot.summaries$min)-0.05,max(boot.summaries$max)+0.05),

ylab="bootstrap predictions", xlab=FACTOR, main=FACTOR )

points(boot.summaries$value,boot.summaries$mean, col="red")

points(boot.summaries$value,boot.summaries$min, col="grey")


48

#lines(boot.summaries$value,boot.summaries$ratio, col="blue",

xlab=FACTOR, main=FACTOR )

legend("topright",col=c("red","grey"),lwd=2,legend= c("bootstrap

mean","bootstrap sd" ))

abline(h = 0, col = "gray60", lty="dashed")

dev.off()

## local regressions

# declare groups by quantiles

boot.summaries$groups <- cut(boot.summaries[,1],

quantile(boot.summaries[,1],probs = seq(0, 1, 0.05), na.rm = T))

boot.summaries$groupnr <- as.numeric(boot.summaries$groups)

k <- 1

coeff.max <- coeff.min <- NA

for (k in 1:length(na.omit(unique(boot.summaries$groupnr)))) {

dat.sel <- boot.summaries[boot.summaries$groupnr==k,]

ff1 <- try(coeff.max[k] <- coefficients(lm(max~value,data=dat.sel

))[2],silent=T)

ff2 <- try(coeff.min[k] <- coefficients(lm(min~value,data=dat.sel ))[2],silent=T)

} # end for 1:length(na.omit(unique(boot.summaries$groupnr)))

if (class(ff1) != "try-error" | class(ff2) != "try-error") {

quants <- quantile(boot.summaries[,1],probs = seq(0, 1, 0.05), na.rm = T)

coeff.max

coeff.min

local.coefficients <- data.frame(cbind(quants[1:20],coeff.max,coeff.min))

names(local.coefficients) <- c("break","coeff.max","coeff.min")

local.coefficients$quantile <- row.names(local.coefficients)

local.coefficients$variable <- FACTOR

bootstraps.cutoffs <- try(local.coefficients[(local.coefficients$coeff.max <= 0

& local.coefficients$coeff.min >= 0) |

(local.coefficients$coeff.max >= 0 & local.coefficients$coeff.min <=

0),], silent=T)

if (dim(bootstraps.cutoffs)[1]>0) {

local.coefficients <- merge(local.coefficients,bootstraps.cutoffs,

by="break",all.x=T)

write.table(local.coefficients,paste(PATH2,SPECIES,"_",FACTOR,"_boot_cutoffs.cs

v",sep=""),sep=";",row.names=F)

} # end if (dim(bootstraps.cutoffs)[1]>0)

} ## end if (class(ff1) != "try-error" & class(ff2) != "try-error")

} ## end for FACTORS per species

} ## end for i in filenames[1:length(filenames)] (all species files)

beep("fanfare")


49

## Step 5: Combine the csv-files into one file

#####################################################

###########

## At the end of the script all model resutls from the simple models (in ‘Models’)

## are merged in two files with all species.

## To do this all csv files from the directories per species have to be copied

## to one directory. This can be done easily in the windows explorer

## by searching for ‘*.csv’.

## The crosstable 'Univariate_models_all_xtab.csv' is the output for PBL.

setwd("E:/Chris/Bioscore/Bioscore2Butterflies/Bioscore2Butterflies/Step5")


filenames <- filenames[grep(".csv",tolower(filenames),fixed=T)]

filenames

wd <- getwd() ## capture working directory

outfile="Univariate_models_all.csv"

dat <- read.table(filenames[1], sep=";", header=T, as.is=T)

str(dat)

write.table(dat,outfile,sep=";",row.names=F)


dat <- read.table(i, sep=";", header=T, as.is=T)

write.table(dat, outfile, sep=";", col.names = FALSE,

row.names = F, append = TRUE)

}

## Crostabulate estimates per variable

#####################################################

##

models <- read.table("Univariate_models_all.csv", sep=";", header=T, as.is=T)

str(models)

## part1

part1 <- subset(models, select=c(species,variable,rownames,

Estimate,Std..Error,z.value,Pr...z..))

str(part1)

part1$id <- paste(part1$species,"_",part1$variable,sep="")

part1.1 <- part1[part1$rownames=="(Intercept)",]

head(part1.1)

names(part1.1) <-

c("Species","Variable","rowname","Intercept_estimate","Intercept_SE","Intercept_z

val","Intercept_Pval","id")

part1.1 <- part1.1[,-3]

part1.2 <- part1[part1$rownames=="get(eval(FACTOR))",]

head(part1.2)

names(part1.2) <-

c("Species","Variable","rowname","Variable_estimate","Variable_SE","Variable_zval

","Variable_Pval","id")


50

part1.2 <- part1.2[,-3]

part1.3 <- part1[part1$rownames=="squared",]

head(part1.3)

names(part1.3) <-

c("Species","Variable","rowname","Varsquared_estimate","Varsquared_SE","Varsqu

ared_zval","Varsquared_Pval","id")

part1.3 <- part1.3[,-3]

part1.tot <- merge(part1.1,part1.2[,3:7], by="id",all.x=T)

part1.tot <- merge(part1.tot,part1.3[,3:7], by="id",all.x=T)

head(part1.tot)

## part 2

part2 <- unique(subset(models,

select=c(species,variable,Expldev,AIC,AUC,AUCmin,AUCmax,AUCmean,NAbs,Npre

s,Ntot)))

part2$id <- paste(part2$species,"_",part2$variable,sep="")

str(part2)

#3 combine two parts

models.xtab <- merge(part1.tot,part2[3:12], by="id",all.x=T)

str(models.xtab)

models.xtab <- models.xtab[,2:24]

## write to csv-file

write.table(models.xtab,"Univariate_models_all_xtab.csv",sep=";",row.names=F)


51

Annex III: Habitat preference

For each species the preference per CLC3 habitat type is given as the percentage of

the total number of transect which can be appointed to that CLC3 type. A cutoff

value of 3% was used, unless expert judgment of the first author made him to decide

to add extra CLC3 types. This is the case where the percentage in the table is under 3.

Explanation of the CLC3 codes:

Clc3 Clc3_name

1 continuous urban fabric

2 discontinuous urban fabric

3 industrial and commercial units

7 mineral extraction sites

10 green urban areas

11 port and leisure facilities

12 non-irrigated arable land

13 permanently irrigated land

15 vineyards

16 fruit trees and berry plantation

18 pastures

20 complex cultivation patterns

21 land principally occupied by agriculture with significant natural vegetation

23 broad-leaved forest

24 coniferous forest

25 mixed forest

26 natural grasslands

27 moors and heath lands

28 sclerophyllous vegetation

29 transitional woodland-scrub

30 beaches, sand, dunes

32 sparsely vegetated areas

35 inland marshes

36 peat bogs

41 water bodies

Species 1 2 3 7 10 11 12 13 15 16 18 20 21 23 24 25 26 27 28 29 30 32 35 36 41

Aglais io

9

2

18

14 6 6 15 10 5 3

1 Aglais urticae

9

2

19

15 6 6 12 10 5 3

Anthocharis cardamines

8

18

14 7 7 17 10 5 2 Anthocharis euphenoides

9 8 13 13 4

20 19

8

Apatura ilia

9

19

10 6 16 13 10 4

5 Apatura iris

18

11 6 5 27 11 7

5

Aphantopus hyperantus

7

20

13 6 8 16 13 5 3

1 Aporia crataegi

10

6 5 10 6 29 14 5

5

Araschnia levana

8

3

19

13 10 7 12 10 6 2

1 Arethusana arethusa

7

22

13

18

11

13

13

Argynnis adippe

5

11

4 2 11 13 28 15 2

4 Argynnis aglaja

4

13

12

9 14 20 9 5 3

3

Argynnis niobe

5

9

2 5 8 5 58 1

5 Argynnis paphia

7

14

9 4 7 23 20 7 1

1 2

Aricia agestis

5 1

2 3 23

17 4 5 14 6

8

1 Aricia artaxerxes

7

13

6

7 18 25 14 3

Aricia eumedon

4

4

7

22

15 35

6 Boloria aquilonaris

5

10 5 54 12

2

7

2


52

Species 1 2 3 7 10 11 12 13 15 16 18 20 21 23 24 25 26 27 28 29 30 32 35 36 41

Boloria dia 4 5

15

7 9 15 9 8 8 5

4 10 Boloria euphrosyne

4

6

5

8 19 35 15 1

4

Boloria selene

7

8

8 15 24 16 4

3

4 Brenthis daphne 4 7

16

6 11 4 11 6 8 5

4 11

4

Brenthis ino

6

14

7

13 5 32 14

2 Brintesia circe

8 4 5

8 5 11 16 7 5

14 8

4

Callophrys rubi

5

13

12 3 7 12 18 10

5 3

1 Carcharodus alceae

6

21 4

6 11 5 9 8 5

10 4

Carterocephalus palaemon

6

9 15 11 11 14 13

4

2 Carterocephalus silvicolus

5

12

16

29 34

2

Celastrina argiolus

9

3

14

14 7 7 16 10 5

3 1 1 Charaxes jasius

7

5 4 22 20 10

22 8

Coenonympha arcania

6

17

3 5 7 8 31 9 2

1 4 Coenonympha glycerion

7

25

5 10

14 20 4

4

Coenonympha pamphilus

8 1 1 1 2 20

14 7 6 12 8 4 6 4

1 Coenonympha tullia

4

20

11

13 21 16

7

5

Colias alfacariensis

5

26

6 8 5 11 6 4

9 8 Cupido argiades

11

22

3 11 12 5 7 5 8 2

3

Cupido minimus

8

2

21

19 5 6 9 13

2

4 Cyaniris semiargus

7

13

7 9 13 7 10 18 4

Erebia ligea

5

13

12 3 33 23

3

2 Erynnis tages

5

17

19 5 6 20 8 4 3

3

Euphydryas aurinia

10

10 6 8 16 17 6 2

10 5 Euphydryas maturna

8

3

20 5 7 43

5

8

Favonius quercus

5

14

12 10 6 24 10 4

3 Glaucopsyche alexis 3

13

7 9

21 9 2

9 7

5

Gonepteryx cleopatra

7 3 6

6 3 15 16 6 4

21 9 Gonepteryx rhamni

8

2

17

12 6 6 16 14 5 2 2

1

Hamearis lucina 4

23

8 8

30 4 4 7

4 Hesperia comma

4

12

15 3 7 10 16

12 5

6

Heteropterus morpheus

7

15

9 4 11 11 26 4 7

4 Hipparchia semele

5

5

8

4 13 13 4 15 12 3 4 3

Hipparchia statilinus

7 11 13 11 6 4

23 13 3 3 Iphiclides podalirius

5

13

4

9 4 12 14 4 4

12 8

3

Issoria lathonia

7

22

7 7 5 11 11 4 16

1 Lampides boeticus

9 5

9 4 11 21 6 4

13 10

Lasiommata maera

7

10

10

35 18

4 Lasiommata megera

5

13

19 6 5 12 9

7

3

Leptidea sinapis

13

6 9 13 21 13

6 4 Libythea celtis

11

7 4 20 21 4 4

12 12

Limenitis camilla

12

13 7 7 35 7 8

2 Limenitis reducta

8

4

7 3 15 17 4 3

17 9

5

Lycaena dispar

5

10

9 11 3

5

48 Lycaena hippothoe

5

19

4

14 6 21 18 1

4

4

Lycaena phlaeas

7

15

16 6 6 14 10 4 5 3 1

1 Lycaena tityrus

7

19

13 9 6 5 12 5 3 9

6

Lycaena virgaureae

6

15

4

11 7 32 14 2

4 Maniola jurtina

8

2 2 17

15 7 6 16 8 3 4 2 1 1

Melanargia galathea

8

25

17 6 7 17 4

2 Melitaea athalia

5

14

5

8 7 37 12 1 4

3

Melitaea cinxia

4

14

15 8 8 10 17

6

3 6 Melitaea diamina

3

23

5 8 6 17 14 18

5

Melitaea didyma

11

3 10 9 15

8

14 16 Melitaea phoebe

8 8

9 12 12 15

6

11 15

Nymphalis antiopa

8

16

10 7 33 14

3 Ochlodes sylvanus

6

17

13 6 7 16 12 6 3 3

2

Papilio machaon

7

2

14

7 11 6 8 13 8 2

4 4 Pararge aegeria

7

3

14

16 7 5 18 8 4 4

1


53

Species 1 2 3 7 10 11 12 13 15 16 18 20 21 23 24 25 26 27 28 29 30 32 35 36 41

Phengaris alcon

9 3 5

10 5

62 Phengaris arion 4 5

14

19 4 6 10 31 4 2

Phengaris nausithous

10

10

19

5 14 20 12 8 Phengaris teleius

9

14 59

14

5

Plebejus argus

4

6

5 4 9 4 25 13 1 11 2 3

5 Plebejus idas

6

8

13 4 36 21

1

4

Plebejus optilete

4

12

13

34 27

6

1 Polygonia c-album

8

3

16

15 8 6 18 6 5

1

Polyommatus amandus

5

17

3

12 3 32 16 2

3 Polyommatus bellargus

5

21

22 7 6 6 6 4 5

6

Polyommatus coridon

5

4 26

19 5 4 12 5

4

5 Polyommatus escheri

5

5 10

25 7 9

15 12

10

Polyommatus icarus

8

2

18

14 7 6 14 8 4 4 Pyrgus malvae

4

16

14 4 6 16 11 8 4 3

Pyronia tithonus

6

17

18 9 6 21 4 3 2 3

1 Satyrium ilicis

12

3

3 3 17 28 6 10

5

Satyrium pruni

6

24

10 6 18 7 5 17

2 Satyrium spini

7

8

4 11 7 8 10 8

13 10

4

Satyrium w-album

13

6 3 21

8 8 6 14 9 6

3 Spialia sertorius 4 3

6

3 11 6 9 14

8

14 10

6

Thecla betulae

14

2

24

2 4 5 12 6 15 7

2 Thymelicus acteon

14 4

9 10 7 13 11 3 5

11 6

Thymelicus lineola

8

2

19

10 7 8 9 15 7 5 Thymelicus sylvestris

6

21

15 6 8 17 6

3


54

Annex IV: species list

This appendix gives an overview of all the species used in this analysis. Species

names follow the Fauna Europaea (http://www.faunaeur.org), version of January

2014.

Aglais io

Aglais urticae

Anthocharis cardamines

Anthocharis euphenoides

Apatura ilia

Apatura iris

Aphantopus hyperantus

Aporia crataegi

Araschnia levana

Arethusana arethusa

Argynnis adippe

Argynnis aglaja

Argynnis niobe

Argynnis paphia

Aricia agestis

Aricia artaxerxes

Aricia eumedon

Boloria aquilonaris

Boloria dia

Boloria euphrosyne

Boloria selene

Brenthis daphne

Brenthis ino

Brintesia circe

Callophrys rubi

Carcharodus alceae

Carterocephalus

palaemon

Carterocephalus silvicolus

Celastrina argiolus

Charaxes jasius

Coenonympha arcania

Coenonympha glycerion

Coenonympha pamphilus

Coenonympha tullia

Colias alfacariensis

Cupido argiades

Cupido minimus

Cyaniris semiargus

Erebia ligea

Erynnis tages

Euphydryas aurinia

Euphydryas maturna

Favonius quercus

Glaucopsyche alexis

Gonepteryx cleopatra

Gonepteryx rhamni

Hamearis lucina

Hesperia comma

Heteropterus morpheus

Hipparchia semele

Hipparchia statilinus

Iphiclides podalirius

Issoria lathonia

Lampides boeticus

Lasiommata maera

Lasiommata megera

Leptidea sinapis

Libythea celtis

Limenitis camilla

Limenitis reducta

Lycaena dispar

Lycaena hippothoe

Lycaena phlaeas

Lycaena tityrus

Lycaena virgaureae

Maniola jurtina

Melanargia galathea

Melitaea athalia

Melitaea cinxia

Melitaea diamina

Melitaea didyma

Melitaea phoebe

Nymphalis antiopa

Ochlodes sylvanus

Papilio machaon

Pararge aegeria

Phengaris alcon

Phengaris arion

Phengaris nausithous

Phengaris teleius

Plebejus argus

Plebejus idas

Plebejus optilete

Polygonia c-album

Polyommatus amandus

Polyommatus bellargus

Polyommatus coridon

Polyommatus escheri

Polyommatus icarus

Pyrgus malvae

Pyronia tithonus

Satyrium ilicis

Satyrium pruni

Satyrium spini

Satyrium w-album

Spialia sertorius

Thecla betulae

Thymelicus acteon

Thymelicus lineola

Thymelicus sylvestris

http://www.faunaeur.org/

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