Journal of Biogeography. 2021;00:1–16.
Received: 29 March 2021
Revised: 23 July 2021
Accepted: 4 August 2021
Reduced host- plant specialization is associated with the rapid
range expansion of a Mediterranean butterfly
Anika Neu1 | Stefan Lötters2 | Linda Nörenberg1 | Martin Wiemers3,4 | Klaus Fischer1
This is an open access article under the terms of the Creat ive Commo ns Attri bution-NonCo mmercial License, which permits use, distribution and reproduction
in any medium, provided the original work is properly cited and is not used for commercial purposes.
© 2021 The Authors. Journal of Biogeography published by John Wiley & Sons Ltd.
1Zoological Institute and Museum,
University of Greifswald, Greifswald,
2Biogeography Department, Trier
University, Trier, Germany
3Senckenberg Deutsches Entomologisches
Institut, Müncheberg, Germany
4Department of Community Ecology, UFZ
– Helmholt z Centre for Environment al
Research, Halle, Germany
Anika Neu, Zoological Institute and
Museum, University of Greifswald, 17489
Klaus Fischer, Biology Department,
Institute of Integrated Natural Sciences,
University of Koblenz- Landau, Koblenz,
Handling Editor: Evan Economo
Aim: Species ranges are highly dynamic, shifting in space and time as a result of com-
plex ecological and evolutionary processes. Disentangling the relative contribution
of both processes is challenging but of primary importance for forecasting species
distributions under climate change. Here, we use the spectacular range expansion (ca.
1000 km poleward shift within 10 years) of the butterfly Pieris mannii to unravel the
factors underlying range dynamics, specifically the role of (i) niche evolution (changes
in host- plant preference and acceptance) and (ii) ecological processes (climate change).
Location: Provence- Alpes- Côte d’Azur, France; North Rhine- Westphalia, Rhineland-
Palatinate and Hesse, Germany.
Tax o n: Insect and angiosperms.
Methods: We employed a combination of (i) common garden experiments, based on
replicated populations from the species’ historical and newly established range and
host- plant species representative for each distribution range, co- occurrence analyses
and (ii) grid- based correlative species distribution modelling (SDM) using Maxent.
Results: We observed changes in oviposition preference, with females from the newly
established populations showing reduced host- plant specialization and also an overall
increased fecundity. These changes in behaviour and life history may have enabled
using a broader range of habitats and thus facilitated the recent range expansion. In
contrast, our results indicate that the range expansion is unlikely to be directly caused
by anthropogenic climate change, as the range was not constrained by climate in the
Main conclusions: We conclude that evolution of a broader dietary niche rather than
climate change is associated with the rapid range expansion, and discuss potential in-
direct consequences of climate change as trigger for the genetic differences found.
Our study thus illustrates the importance of species interactions in shaping species
distributions and range shifts, and draws attention to indirect effects of climate change.
Embracing this complexity is likely the key to a better understanding of range dynamics.
biogeography, host- plant preference, insect- plant interaction, niche evolution, niche following,
Pieris mannii, range dynamics, species distribution modelling
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1 | INTRODUC TION
Understanding the factors underlying species range dynamics is
a key concern of ecology, evolutionary and conservation biology
(Gillson et al., 2013; Hallatschek & Nelson, 2010; Rumpf et al., 2018).
Typically, ecological processes have been implied as the principal
drivers promoting shifts in species’ distribution and abundance
(Caughley et al., 1988; Gallardo et al., 2020). Hence, range shifts
are thought to mainly result from “niche following”, that is, species
following their abiotic or biotic niche in response to changes in the
environment (Guisan et al., 2017; Holt, 2009).
As all species are confined to a specific combination of climatic
conditions, abundance and distribution patterns are strongly sus-
ceptible to climatic changes (Bennie et al., 2013; Hastings et al.,
2020). Consequently, understanding the factors underlying range
shifts has gained renewed and increasing interest in the current era
of anthropogenic climate change (IPCC, 2014; Pinsky et al., 2020).
Indeed, a multitude of studies from various taxa have attributed
range contractions, shifts and expansions to current changes in cli-
mate (Mason et al., 2015; Parmesan & Yohe, 2003). Additionally, the
crucially important role of biotic factors, including for instance food
availability, for species range dynamics is becoming increasingly
clear (Flores- Tolentino et al., 2020; Godsoe et al., 2017; Matthysen,
2012). The relevance of such factors is especially obvious in phy-
tophagous insects such as butterflies, with their ranges depending
strongly on the presence and abundance of specific host plants for
larval feeding (Hill et al., 2009; Warren et al., 2001).
De spit e wid esprea d n iche conse r vati sm (L iu et al. , 202 0 ; Pet erso n,
2011; Wiens et al., 2010) range dynamics do not always result from
niche following but alternatively from niche evolution (Pearman et al.,
2008; Pfenninger et al., 2007; Stroud, 2021). Rapid genetic changes
in range limiting traits such as host- plant use, dispersal ability or prop-
agule pressure may promote range dynamics even in the absence
of environmental change (Holt, 2003; Kirkpatrick & Barton, 1997).
Evolutionary novelty may, amongst others, arise from hybridization
and admixture events (Krehenwinkel et al., 2015; Lewontin & Birch,
1966; Springer & Gompert, 2020). Thus, range dynamics are often
characterized by a complex interplay of evolutionary and ecological
processes (Bush et al., 2016; Kubisch et al., 2014). While ecological
processes have often been found to underlie range shifts, evidence
for a crucial role of evolutionary processes is still relatively scarce
(Benito Garzón et al., 2019; Hill et al., 2011). Most examples for rapid
evolution associated with range shifts relate to dispersal- related
morphology (Simmons & Thomas, 2004; Taylor- Cox et al., 2020), but
also physiology (Leonard & Lancaster, 2020; Liebl & Martin, 2013),
life histor y (Phillips et al., 2010) and behaviour including resource use
(Lancaster, 2020; Singer & Parmesan, 2020; Thomas et al., 2001).
Disentangling the relative contribution of ecological and evo-
lutionary factors though is difficult, especially as feedbacks be-
tween both occur frequently (Kubisch et al., 2014; Odling- Smee
et al., 2013). Although empirical studies targeting this complexity,
by investigating the causes underlying range dynamics at different
levels, will greatly improve our abilities to better predict species
responses to environmental change, there are still few such exam-
ples (Sexton et al., 2009; but e.g. Pfenninger et al., 2007; Turlure
et al., 2016). Here, we use the Southern Small White butterfly, Pieris
mannii (Mayer, 1851), as a unique study system to explore ecological
and evolutionary factors associated with range dynamics. This spe-
cies has shown a spectacular range expansion in recent years, hav-
ing moved poleward by approximately 1000 km within a period of
10 years (Wiemers, 2016). This range expansion is roughly 60 times
faster than the global average range expansion rate of 16.9 km per
decade (Chen et al., 2011), and thus unparalleled even among but-
terflies (Parmesan et al., 1999; Pöyry et al., 2009).
The range expansion of P. mannii may have been caused by
changes in climatic variables (niche following), niche evolution, for
example, changes in host- plant specialization or life history, or a com-
bination of both ecological and evolutionary factors (Forister et al.,
2010; Thomas et al., 2001). Indeed, recent studies on Lepidoptera
indicate that reduced host specialization and concomitantly broader
dietary niches may play a crucially important role in range expansions
(Lancaster, 2020; Singer & Parmesan, 2020) as they may relax spe-
cies habitat associations (Pateman et al., 2012). Thus, niche evolution
may have been an important factor for the rapid range expansion of
P. mannii. Note that concomitant evolutionary change in expanding
populations may occur very rapidly, for example, based on (i) hybrid-
ization and admixture events (Krehenwinkel et al., 2015; Lewontin
& Birch, 1966), (ii) non- random membership of edge demes through
spatial sorting (Canestrelli et al., 2016; Chuang & Peterson, 2016) or
(iii) strong genetic bases for traits like host- plant preference (Nylin
et al., 2005; Thomps on, 1988). We thus hypothesize that niche evolu-
tion is associated with the rapid range expansion of P. mannii and pre-
dict that edge and core populations differ in dietary niche breadth,
including host- plant preference and the degree of specialization (rep-
resented by a higher number of host plants accepted and a greater
eve nness of egg dist ribut ion across plant species; Scho onhoven et al.,
(2014)). Alternatively, changes in climatic variables may have enabled
the colonization of new areas at higher latitudes, as even very small
changes in climatic variables can erase thresholds and thus enable
rapid range expansions (Crozier, 2003; Paradis et al., 2008; Rochlin
et al., 2013). However, the speed of the northward expansion of
P. mannii suggests that climatic change is not solely responsible.
We use a combination of approaches including species distribution
modelling to test for the impact of climatic variables, species co-
occurrence analyses and common garden experiments based on rep-
licated populations from historical and newly established sites to test
for reduced host- plant specialization in the latter.
2 | MATERIALS AND METHODS
2.1 | Study organism
Pieris mannii (Lepidoptera: Pieridae) is a widespread Mediterranean
butterfly species, with a distribution ranging from Morocco across
Mediterranean Europe to Turkey and Syria (Kudrna et al., 2011). The
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species uses several host plants for larval development, all belong-
ing to the family Brassicaceae (Lafranchis et al., 2015). Adults are
nectar feeders, accepting a wide variety of species including the
genera Lavendula and Syringa (Settele et al., 2015). Pieris mannii is
polyvoltine, having up to five generations a year which tend to over-
lap (Wiemers, 2016). Diapause takes place in the pupal stage (Settele
et al., 2015). Pieris mannii entails several subspecies (Ziegler &
Eitschberger, 1999), including P. mannii alpigena (Verity, 1911) which
is currently expanding its distribution range northwards (Hensle &
Seizmair, 2015). Originating from south- east France, the species has
successfully colonized large parts of Germany within a short period
of time since 2008 (Figure 1).
FIGURE 1 Range expansion of P. mannii ssp. alpigena since 2008 into Germany. Ranges at different time points are presented as alpha
hull polygons (Burgman & Fox, 2003), based on point occurrences derived from GBIF.org (01.12.2020; https://doi.org/10.15468/ dl.h7huwn),
Reinhardt et al. (2020), Wiemers et al. (2020) and data sources outlined in Section 2.2. Black dots represent sampling sites of replicated Pieris
mannii populations in Germany (G1- 3) and southern France (F1- 3)
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2.2 | Population sampling and offspring rearing
In June and July 2018, we caught fresh, mated females in three
replicated populations each in Southeast- France (historical range;
F1: Valbonne 43.63°N/7.02°E, F2: Saorge 43.98°N/7.55°E, F3:
Roquefort- la- Bédoule 43.26°N/5.65°E) and Germany (new range;
G1: Verl 51.87°N/8.52°E, G2: Bad Kreuznach 49.84°N/7.87°E, G3:
Habitzheim 49.85°N/8.88°E; Figure 1). The minimum straight dis-
tance between populations was 80 km. Females were transferred
to climate chambers at Greifswald University for egg laying at 60%
relative humidity, 25°C, and a photoperiod of L18:D6. These condi-
tions were used throughout all experiments. Females were placed
individually into breeding boxes (30 × 20 × 21 cm), being fed with a
20 vol% sugar solution and fresh flowers, which were replaced every
other day. Boxes were further equipped with a leaf of greenhouse-
grown rape (Brassica napus) as oviposition substrate. Resulting eggs
were collected and kept separated by female. F1 offspring was
reared individually in translucent plastic boxes (10.5 × 8 × 4.5 cm)
lined with moistened filter paper and ad libitum access to cuttings of
rape for feeding. These caterpillars were used to perform larval host
preference tests (see below). Afterwards, they were reared under
the conditions outlined above until adult eclosion. Adult butterflies
were mated randomly within populations, but excluding sib matings.
To eliminate the potential environmental effects on behaviour, we
reared a F2 generation as outlined above, which was used to explore
female oviposition preference and host acceptance.
2.3 | Selection and growth of host plants
To compare host- plant preferences between populations from the
historical and new range, we chose plant species assumed to be im-
portant in each type of range. Although the species uses multiple
plant species of the Brassicaceae family across its historic distribu-
tion range, the distinct geographic subspecies differ in their host-
plant use (Ziegler & Eitschberger, 1999). In Southern France, where
the subspecies P. mannii alpigena is located, females are known to
mainly use plant species of the genera Diplotaxis and Iberis as host
plants (Lafranchis et al., 2015; Still, 2003). On this account, we se-
lected Iberis sempervirens and Diplotaxis tenuifolia as host plants
representative for the historical range. For the new range, faunis-
tic observations suggest that the species still uses its historic host
plants but additionally integrates new host plants such as Alliaria
petiolata, Brassica oleracea and Sinapis arvensis into its diet (Geier,
2016; Hensle & Seizmair, 2020; Pähler, 2016). Based on these ob-
servations we here chose the aforementioned plant species, which
are also commonly used by the closely related species Pieris rapae
and Pieris napi (Tolman & Lewington, 1998), as potentially impor-
tant hosts for the newly established populations. Note that, to date,
even more plant species of the Brassicaceae family have been re-
ported to be used as host plants by P. mannii within the new range
(e.g. Lunaria annua and Alyssum murale; Köhler, 2021). Except for A.
petiolata, which we collected in the wild (because of its obligatory
seed dormancy; Lhotská, 1975), all plants used here were grown
from seeds in a greenhouse. Phylogenetically, Diplotaxis tenuifolia,
Brassica oleracea and Sinapis arvensis (Brassiceae tribe) are close re-
lated to Alliaria petiolata (Thlaspideae tribe) than to Iberis sempervi-
rens (Iberideae tribe) (Al- Shehbaz et al., 2006).
2.4 | Larval host preference
In most Lepidopteran species the adult life stage is considered as
the most important for host- plant choice, due to its high mobility
(García- Barros & Fartmann, 2009). However, recent studies suggest
that the larval life stage may still play an important role, as larvae
can adjust or modify the females’ host choice. For instance, larvae of
the comma butterfly (Polygonia c- album) have been found to partici-
pate in host- plant choice by rejecting least favourable host species
(Gamberale- Stille et al., 2014) and in experiments with Pieris bras-
sicae, another member of the Pieridae family, larvae were able to
adjust host preference in accordance with plant infestation status
(with or without aphids), while adult females were not (Soler et al.,
2012). The need for larval participation may become especially im-
portant when the mothers’ risk to make suboptimal choices is high,
for example, through increased levels of stress or a generalist ovi-
position strategy (García- Barros & Fartmann, 2009 and references
therein). To account for the share of the larval life stage in host- plant
choice, we here performed a host- plant preference assay using 3rd
and 4th instar larvae of P. mannii. This way we ensured high mobil-
ity, reduced the risk of pupation before the end of the experiment,
and minimized biases arising from larvae age (Chew, 1980). For test-
ing, larvae were released individually in the centre of a feeding box
(translucent plastic box, 15 × 10 × 6 cm), containing similar- sized
leaves of plant species B. oleracea, D. tenuifolia, I. sempervirens and S.
arvensis (A. petiolata was not used here), each provided at the same
distance from the centre. After one hour, the choice of each larva
for one of the plant species was recorded. If a lar va was not found
on a leaf (32% of the cases) but resting beneath or next to a leaf,
typically after having fed on the respective leaf, this was also scored
as preference. For every individual, we repeated this procedure on
five consecutive days. Leaf cuttings were randomly arranged within
boxes. We tested 123 larvae from France (F1: 53, F2: 45, F3: 25) and
118 from Germany (G1: 29, G2: 50, G3: 39).
2.5 | Oviposition preference and host acceptance
After mating, females were transferred individually to translucent
boxes (30 × 20 × 21 cm) equipped with sugar solution and fresh
flowers for feeding. Each box contained simultaneously one leaf of
each of the five host- plant species given ab ove, being pla ce d individ-
ually in water- filled glass jars. Eggs were collected and counted daily
for the following 10 days. Flowers and leaves were replaced each
day, and the positions of leaves within boxes and boxes within the
climate chamber were randomized daily to avoid position effects.
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We used 75 females from France (F1: 26, F2: 22, F3: 27) and 65 from
Germany (G1: 24, G2: 16, G3: 25).
2.6 | Co- occurrence analysis
To test whether possible changes in host use may have occurred
prior to or during the range expansion, we ran a probabilistic co-
occurrence analysis (Veech, 2013) of P. mannii and its most im-
portant host plants in the new range (A. petiolata, D. tenuifolia and
I. sempervirens, bas ed on the result s of th e ovi pos iti on pr efe ren ce ex-
periment), using the R package “cooccur” (Griffith et al., 2016). This
analysis allows to identify patterns of species interactions at a spatial
scale (Veech, 2013). We here used it to explore changes in the co-
occurrence between P. mannii and the aforementioned plant species.
If the changes in female host- plant preference have occurred during
the range expansion, we would expect the co- occurrence probability
for A. petiolata and D. tenuifolia to increase and for I. sempervirens to
decrease over time. In turn, if the preference changed beforehand,
we would expect the co- occurrence probability to remain constant.
We therefore compared the probability of P. mannii to co- occur with
the aforementioned plant species between the early (2008– 2013)
and late phase (2014– 2019) of the range expansion into Germany.
We created two species- by- site presence– absence matrices (27 km2
resolution) from species point occurrences in Germany, using the
R command “lets.presab.points” from the ‘letsR’ package (Vilela &
Villalobos, 2015). The matrices contained all available plant data
and presence data of P. mannii from one of two time windows. The
geographic area was restricted to the occurrence of P. mannii within
the respective time window. Point occurrence data for plant species
were obtained from GBIF (www.gbif.org; using the R command “occ”
of the ‘spocc’ package; Chamberlain et al. (2021)).
2.7 | Distribution modelling
Grid- based correlative species distribution models, SDMs (Franklin,
2010), were used to predict the potential distribution of P. mannii
prior to its recent range expansion (i.e. before 1991). For modelling,
we compiled two data sets, namely (i) 148 geo- referenced occur-
rence data for P. mannii from its entire range prior to 1991 (www.
gbif.org, license doi.org/10.15468/dl.oc2aoa; www.ufz.de/lepidiv),
and (ii) a subset of the above data set including all 55 records for the
subspecies alpigena. The latter was used to investigate the potential
distribution of the specific subspecies which is currently showing
the massive range expansion. We acknowledge that P. mannii alpi-
gena may not be a valid subspecies in a taxonomic sense (cf. Braby
et al., 2012), which is not relevant in the given context as we sim-
ply wanted to generate a data set based on putative source popu-
lations. As ecological predictors we used high resolution climate
data from the WorldClim 1.4 database (www.world clim.org) for the
period 1960– 1990, with a spatial resolution of 30 arc sec (Hijmans
et al., 2005). We used 6 of the 19 bioclimatic variables provided by
WorldClim, selected via pairwise Pearson correlation analyses to
avoid effects of multicollinearity (|r| > 0.7), which is important when
projecting SDMs into new space (Dormann et al., 2013). The predic-
tor set included Bio4 (tem perature seasonality), Bio6 (minimum tem-
perature of coldest month), Bio7 (temperature annual range), Bio11
(mean temperature of coldest quarter), Bio12 (annual precipitation)
and Bio17 (precipitation of driest quarter).
‘Maxent 3.4.1’ (Elith et al., 2006; Phillips et al., 2006, 2017;
Phillips & Dudík, 2008) was used for modelling (https://biodi versi
tyinf ormat ics.amnh.org/open_sourc e/maxen t/). It makes predic-
tions on the suitability of a geographic area for a taxon by taking
environmental data from geo- referenced species records and ran-
dom background data (Phillips et al., 2006; Yackulic et al., 2013).
Various settings for SDM building allow fine- tuning (Phillips et al.,
2006; Phillips & Dudík, 2008), which requires some caution (Elith
et al., 2006, 2011; Yackulic et al., 2013). In brief, we ran Maxent
in two ways employing different feature types (linear + quadratic;
hinge) to calculate response curves of the ecological predictors, here
bioclimatic variables. We allowed Maxent to extrapolate response
curves beyond the minimum and maximum values determined by
the predictor values. Given the high number of species records, we
used the subsample approach (100 replicates) with 25% of the re-
cords randomly set aside as test data. The background was chosen
as a window enclosing all species records and the number of ran-
dom background points was set to 100,000. All other settings were
default (Merow et al., 2013; Phillips et al., 2017; Phillips & Dudík,
2008; Shcheglovitova & Anderson, 2013). As recommended (Elith
et al., 2010), we performed a multivariate environmental similarity
surface (MESS) analysis. Thereby, we explored if climatic conditions
in the projection area (i.e. Germany) markedly varied from those in
the area used for model training (i.e. at species presence in southern
Europe). If so, the increased variation may cause misleading results
(Elith et al., 2010), which was not the case here (Figure S1).
Maxent calculates the Area Under Curve (AUC), referring to the
receiver operating characteristic curve, as a measure of predictive
accuracy (Phillips et al., 2006). AUC values may range 0 to 1, that
is, from no to high predictive ability; values ≥0.91 describe ‘high’,
≥0.71 ‘moderate’, ≥0.5 ‘low’ model accuracy (Swets, 1988). Although
criticized (Yackulic et al., 2013), the AUC is a standard measure in
ecological applications and considered informative as it mirrors the
model's ability to distinguish between species records and back-
ground points (Merow et al., 2013). To account for AUC critics, we
also calculated True Skill Statistics (TSS; Allouche et al., 2006).
The Maxent default (complementary log- log) Cloglog output
format was used for mapping SDMs in ‘DIVA- GIS 7.5’ (http://www.
divag is.org; Hijmans et al., 2001). Cloglog values may range 0 to 1,
and we explored four methods to identify limits (thresholds) for dis-
criminating areas that are suitable versus unsuitable to the study
(sub)species (Liu et al., 2005): minimum training presence, 10 per-
centile training presence, maximum training sensitivity plus spec-
ificity, and maximum test sensitivity plus specificity (Table 2). For
evaluating SDM results, P. mannii distribution records in Germany
since 1991 were gathered from various online sources (www.ufz.
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de/lepid iv/, www.ufz.de/tagfa lter- monit oring/, artenfinder.rlp.de,
www.wande rfalt er.org, www.falte rfunde.de).
2.8 | Statistical analyses
We tested for differences in the six bioclimatic predictor variables
used among historical (before 1991) and new (after 1991) sites with
Mann– Whitney- U test s as data were not no rmally distr ibuted. Larval
feeding (number of choices per plant; Poisson distribution with log-
link function) and oviposition (egg numbers per plant; negative bi-
nomial distribution with log- link function, due to overdispersion of
data) preferences were analysed with repeated measures general-
ized linear mixed models (GLMMs). We included country, host- plant
species (repeated measure) and the country by host- plant interac-
tion as fixed factors. Furthermore, we added population nested
within country, and family (i.e. the offspring of an individual female)
nested within country and population (for larval preference only as
number s per family were very low fo r ov iposition preferen ce) as ran-
dom effects. All families with <3 offspring were excluded from the
analyses. To control for variation in testing days (e.g. due to mor-
tality, pupation), we included the number of testing days per indi-
vidual as covariate. For the oviposition experiment, we additionally
calculated the total number of accepted plant species per individual
female and the “evenness” (counting the number of eggs per plant
as “individuals per species”) of egg distribution across plant species.
Both measures were analysed using GLMMs with the same random
effect structure as outlined above, country as fixed effect (plant
species accepted: gamma distribution; evenness: negative binomial
distribution with log- link function). Differences among groups were
located with the Tukey posthoc test. Best distribution fit of GLMMs
was evaluated based on visual inspection. Test statistics for fixed
effects and interactions were obtained by ANOVA “type- III” analy-
ses and for random effects by pairwise model comparison via like-
lihood ratio tests (Bolker et al., 2009). To test for the consistency
of female oviposition choice over the testing period, we calculated
the repeatability R as the proportion of the total variation that is
reproducible among repeated measurements of the same individual
(Nakagawa & Schielzeth, 2010). We used data on egg- laying choice
on the different plant species (yes or no) from the second to the
fifth day of egg- laying, as during this period females laid most eggs
(69%). For the repeatability analysis, we used the ‘rpt’ command of
the ‘rptR’ package with binary data and plant species as fixed factor,
set to non- adjusted in order to calculate “enhanced agreement re-
peatability” (Stoffel et al., 2017). We ran the analyses separately for
French and German females. All data were analysed using R. 3.6.2
with the packages ‘lme4’ to compute generalized mixed models, “car”
to perform ANOVA analyses, ‘multcomp’ to perform posthoc tests,
and ‘fitdistrplus’ to evaluate model distribution fit. Throughout,
all means are given ±1 SE (Bates et al., 2018; Delignette- Muller &
Dutang, 2015; Fox et al., 2007; Hothorn et al., 2021; R Core Team,
3 | RESULTS
3.1 | Female oviposition and larval host preference
Daily egg numbers differed significantly among origins (Germany:
7.5 ± 0.5 > France: 6.7 ± 0.4) and host plant s (Table 1). Overall, high-
est daily egg numbers were deposited on I. sempervirens (3.8 ± 0.3)
followed by A. petiolata (1.6 ± 0.2), D. tenuifolia (1.1 ± 0.1), B. ol-
eracea (0.4 ± 0.01), and finally S. arvensis (0.2 ± 0.02; Figure 2a).
However, German and French females showed strikingly different
responses to specific host plants, indicated by the significant host
plant by country interaction. French females laid significantly more
eggs than German ones on host- plant I. sempervirens, while it was
the other way round on A. petiolata and D. tenuifolia. The pattern of
reduced host specialization in German females was confirmed when
testing for the total number of host- plant species accepted per fe-
1 = 5 6.07, p < 0.001) and the evenness of egg distribution
across plant species (χ2
1 = 65.32, p < 0.001; Figure 3). Repeatability
Larval preference Oviposition preference
df χ2pdf χ2p
Host plant 3 69.85 <0.0 01 4566 .19 <0.001
Country 10.99 0.318 154.32 <0.001
Host plant * Country 3 23.12 <0.0 01 4199.0 1 <0.001
Days of observation 166.07 <0.001 114.66 <0.001
Population [Country] 22.36 0.308 20. 24 0.885
Family [Country*Population] 32.35 0.501 - - -
Residual deviance 659.53, df = 842 693.26, df = 685
Marginal R2 (marginal coefficient of determination for generalized mixed- effect models,
representing the variance explained by fixed effect components; Nakagawa et al. (2017)) for lar val
preference (host plant: R2 = 0.18; country R2 = 0.06; host plant* country R2 = 0.21) and oviposition
preference (host plant: R2 = 0.27; countr y R2 = 0.08; host plant* country R2 = 0.40). Significant
p- values are shown in bold.
TAB LE 1 Results of repeated measures
generalized mixed models for the effects
of host plant (repeated measure), country,
host plant by country interaction (all
fixed), days of observation (covariate),
population (random, nested within
country), and family (random, nested
within population and country; for larval
preference only) on larval feeding and
female oviposition preference in Pieris
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analyses revealed moderate but significant repeatability within
German females (R = 0.376, CI = 0.201– 0.603, p < 0.001), and low
repeatability within French females (R = 0.093, CI = 0.052– 0.141,
p < 0.001). Larval feeding preferences also differed significantly
among host plants but not by country of origin (Table 1). The most
frequently chosen host plant was I. sempervirens (44 ± 2%) followed
by B. oleracea (25 ± 2%), S. arvensis (16 ± 1%) and finally D. tenuifo-
lia (14 ± 1%). These patterns prevailed in larvae from both origins,
except that D. tenuifolia was more frequently chosen by French
than German larvae (significant host plant by country interaction;
3.2 | Co- occurrence analysis
In the area occupied by P. mannii during the early phase of the range
expansion, the highest co- occurrence probability was found with A.
petiolata (40.5%), followed by D. tenuifolia (30%) and I. sempervirens
(12%). A similar pattern was found for the late phase of the range
expansion: A. petiolata (32.9%), D. tenuifolia (23.6%), I. sempervirens
FIGURE 2 Oviposition preferences of Pieris mannii females (a)
and feeding preferences of P. mannii larvae (b) from Germany (G)
and France (F) across host plants (A = Alliaria petiolata, B = Brassica
oleracea, S = Sinapis arvensis, D = Diplotaxis tenuifolia, I = Iberis
sempervirens). Significant differences between German and French
individuals in the use of specific host plants are indicated by
asterisks. Photo courtesy of Martin Wiemers
FIGURE 3 Number of host- plant species accepted for
oviposition (a) and evenness of egg- distribution across plant
species (b) in Pieris mannii females from Germany (G) and France (F).
Significant differences between German and French individuals are
indicated by asterisks
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(8.5%). The probability for P. mannii to co- occur with all plant spe-
cies declined from the early to the late phase of the range expansion
(Figure 4), likely caused by the larger area (more “sites”) covered in
the late time window.
3.3 | Distribution modelling
Accuracy of final SDMs was moderate to high (AUC 0.730– 0.856;
TSS 0.714– 0.856). Under all four thresholds (Table 2), the modelled
potential distributions, based exclusively on records from before
1991, extended widely into Germany, largely explaining the new re-
cords since 1991. This was true at both the species (Figure 5a) as
well as subspecies levels (Figure 5b). Potential distribution analyses
thus suggest that before 1991 the climate in Germany was already
suitable for P. mannii. Accordingly, Bio4 (temperature seasonality),
Bio6 (minimum temperature of coldest month) and Bio17 (pre-
cipitation of driest quarter) did not differ between historical and
new range (Mann– Whitney- U tests; Bio4: U = 155610, p= 0.2 51;
Bio6: U = 154062, p = 0.165; Bio17: U = 16 4278, p = 0.856).
However, significant differences were found for variables Bio7
(U = 129280, p < 0.001), Bio11 (U = 148034, p = 0.021) and Bio12
(U = 133216, p < 0.001). Annual temperature range (Bio7: 27.3 ± 0.2
vs. 26.5 ± 0.1℃), mean temperature of the coldest month (Bio11:
2.13 ± 0.25 vs. 1.23 ± 0.08) and annual precipitation (Bio12:
858.4 ± 16.2 vs. 80 4.3 ± 7.0) we re hi gher at his tor ical tha n new site s.
4 | DISCUSSION
4.1 | Female oviposition preference
Females oviposited on all plant species offered in our choice tests,
which all belong to the Brassicaceae family to which pierids are
strongly linked (Munguira et al., 2009). Oligophagous species such
as P. mannii often accept hosts closely related to their primary hosts
(Scriber & Ording, 2005). Nevertheless, strong preference hierar-
chies often exist, ensuring that optimal hosts will be preferentially
used without completely neglecting alternative ones (Wiklund,
1981). Considering the preference rank order found here, B. olera-
cea and S. ar vensis were of low attractiveness for both German and
French females, indicating a negligible role of those plant species
for the range expansion of P. mannii. Interestingly, French females
showed very little flexibility, evidenced by a strong preference for
one of their native host plants, I. sempervirens. In contrast, and in line
with our hypothesis on niche evolution, German females from the
new range were much more flexible, that is, they showed reduced
host- plant specialization, readily using A. petiolata, D. tenuifolia and
I. sempervirens, generally accepting more plant species and distribut-
ing their eggs more evenly across them. These findings challenge
the notion that the range expansion of P. mannii is related to the oc-
currence of I. sempervirens, commonly planted as garden ornament
in central Europe for decades already (Hensle & Seizmair, 2015;
Kratochwill, 2011). The reduced specialization of German females
likely enables the use of a wider range of habitats in the new range.
While I. sempervirens is typically found in artificial rock garden sites
within settlements, comprising similar habitats to ones originally oc-
cupied by P. mannii, A. petiolata commonly grows at the edges of
deciduous forests as well as at disturbed places, while D. tenuifo-
lia is part of weedy plant communities (Dennis, 2010; Gupta, 2009;
Our findings thus suggest that reduced host specialization may
have enabled the use of a broader range of habitat types, which may
in turn have facilitated the recent range expansion (Platts et al., 2019;
Wilson et al., 2010). Similarly, the Short- tailed Blue (Cupido argiades)
seems to have lost its association with specific habitats and host
plants over the course of its range expansion (Landeck et al., 2012).
Likewise, the range expansion of the butterfly Polygonia c- album was
associated with a broadening of its dietary range (Braschler & Hill,
2007; Pratt, 1986). Thus, successful range expansions in Lepidoptera
seem to be often associated with increased generalism, in particular
with regard to dietar y niche breadth (Boyes et al., 2019; Lancaster,
2020; Singer & Parmesan, 2020; Warren et al., 2001; but e.g. Bridle
et al., 2014).
Interestingly, German females showed a significantly higher
fecundity than French ones. As data are based on F2 generation
FIGURE 4 Probability for P. mannii to co- occur with the
plant species Alliaria petiolata (A), Diplotaxis tenuifolia (D) and
Iberis sempervirens (I) for the early (2008– 2013) and late (2014–
2019) phase of the range expansion into Germany. Mapped
point occurrences of Pieris mannii with either Alliaria petiolata
(A), Diplotaxis tenuifolia (D) or Iberis sempervirens in Germany are
provided in Figure S3
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offspring, this difference as well as the above outlined difference
in oviposition preference must have a genetic basis. The fecundity
data suggest a genetically increased reproductive effort in the newly
established populations, which may contribute to establishing suc-
cessfully in the new range (cf. Burton et al., 2010; Wolz et al., 2020).
Propagule pressure has been frequently found to be a strong predic-
tor of invasion success (Alzate et al., 2020; Lockwood et al., 2005).
However, the increased reproductive effort is counter- intuitive, as
range expansions and invasions typically entail an ‘r- selected’ phase
(Burton et al., 2010). In any case, the current range expansion of
P. mannii is associated with genetically based behavioural and life
history differences among populations from the historic and new
4.2 | Larval feeding preference
In contrast with above, German and French larvae strongly pre-
ferred I. sempervirens, while differences among origins were only
marginal. Interestingly, results on oviposition and larval feeding
preferences were in broad agreement for French individuals, but
less so for German ones. Such potential mismatches between adult
FIGURE 5 Minimum and maximum potential distributions of Pieris mannii (derived from entire range occurrence data); (a) and P. mannii
ssp. alpigena (derived from subspecies occurrence data); (b) before 1991 in Germany, based on highest and lowest thresholds discriminating
suitability (cf. Table 2). Known records of the species in Germany since 1991 are indicated by blue dots. Distribution models shown here are
cut- offs of European- wide models provided in Figure S2
TAB LE 2 Overview of the four calculated Cloglog thresholds used to discriminate suitable from unsuitable areas for the species Pieris
mannii and the subspecies Pieris mannii alpigena in Maxent, using different modelling feature types (‘linear +quadratic’ and ‘hinge’; see
Material and methods)
Taxon and modelling feature
10 percentile training
Maximum training sensitivity
Maximum test sensitivity
P. mannii linear +quadratic 0.0317 0.4387 0.5 651 0.5391
P. mannii hinge 0.1052 0.3798 0.4953 0.4709
P. mannii alpigena linear
0.2119 0.3 876 0.3756 0.3804
P. mannii alpigena hinge 0.2557 0.4342 0.4 042 0.3954
Cloglog values range from 0 to 1. To assess the value (threshold) within this range, which divides suitable from unsuitable areas for the study (sub)
species, various methods are available of which we used four (Liu et al., 2005; see Figure 5).
NE U Et al.
and immature preferences were frequently observed in butterflies
(Chew & Robbins, 1984; Gratton & Welter, 1998), often entailing
a broader host range in larvae than in adult females (Singer, 1984;
Wiklund, 1975). This probably indicates that caterpillars have only
a secondary role in host- plant selection, owing to their low mobility
(Munguira et al., 2009; but e.g. Gamberale- Stille et al., 2014). Thus,
selection on host- plant preferences is expected to be stronger in the
mobile females, which are in the first place responsible for finding
suitable host plants for offspring development and thereby govern
range expansions. However, note that A. petiolata, being most pre-
ferred by German females for oviposition, was not tested in larval
feeding trials. Furthermore, these results may be affected by ma-
ternal effects, such that they need to be interpreted with caution.
Apart from preferences, offspring performance on different host
plants has previously been shown to be important. Thus, while the
ability to use novel hosts is clearly beneficial for range expansions,
it may be detrimental to offspring performance (Scriber & Slansky,
1981; Tabashnik, 1983). Although female preference usually mirrors
offspring performance (García- Barros & Fartmann, 2009), as females
should be selected for choosing plants that maximize larval fitness
(‘mother- knows- best’ paradigm; Thompson, 1988), this is not always
true (Jaenike & Holt, 1991; König et al., 2016; Kuczyk et al., 2021;
Mayhew, 2001). While the successful range expansion of P. mannii
does not suggest that this is the case here, offspring performance on
different host plants should be evidently included in future studies.
4.3 | Climate change as driver of the
Species distribution modelling indicated that the climatic conditions
in central Europe, including Germany, were already suitable for P.
mannii before 1991, as almost all new records outside the historical
range are located inside the predicted distribution range based on
climatic data before 1991 (Figure 5a). Consequently, climate does
not seem to limit the species’ range in the first place. These find-
ings are in sharp contrast with those of Settele et al. (2008), which
did not even predict P. mannii to expand into Germany under future
climate change scenarios. We suggest that such differences to the
aforementioned study relate to larger grid size (i.e. 50 × 50 km UTM
vs. 30 arc sec) and a more basic modelling approach (i.e. GLM versus
Maxent) strongly affecting model performance (Franklin, 2010).
We obtained very similar results when only using the subspecies
alpigena for model building, from which the expanding populations
most likely originate (Hensle & Seizmair, 2015; Ziegler & Eitschberger,
1999). The fact that the highest suitability values for alpigena were
found for mountain ranges within Germany might be due to a high
proportion of records within the Alps and Pyrenees, where it only
occurs at lower altitudes though (Ziegler & Eitschberger, 1999).
Thus, these high values might be an artefact caused by a low resolu-
tion of species records. However, the recent range expansion, having
started in 2008, is unlikely to be caused in the first place by climatic
factors, even though populations in the new range encounter partly
different climatic conditions, as indicated by significant differences
in some bioclimatic variables. The fact that other P. mannii popula-
tions, for example, in the Rhone Valley, northern France or Austria,
are currently not expanding (Friedrich, 2013), provides further evi-
dence against climate change as main driver.
4.4 | Processes underlying the current range
expansion of P. mannii
Our results suggest that the spectacular range expansion of P. mannii
was not in the first place triggered by niche following in response to
ongoing anthropogenic climate change. Instead, it seems to be as-
sociated with genetic adaptation in host use, namely the broadening
of the host range, and in reproductive effort. In its historic range
the species is restricted to xerothermic habitats and associated host
plants (Lafranchis et al., 2015; Ziegler & Eitschberger, 1999), while
German populations accept a much broader range of host plants,
enabling the use of novel habitats such as moist meadows (Hensle,
2016; Hensle & Seizmair, 2015, 2017, 2020; Köhler, 2021; Pähler,
2016). Note that P. mannii did not show any ob vio us range ex pans ion s
before 2008 (Friedrich, 2013; Schurian & Siegel, 2016). Changes in
habitat and dietary niche breadth have been shown to play an im-
portant role for the range expansions in phytophagous insects also
in other studies (Braschler & Hill, 2007; Bridle et al., 2014; Lancaster,
2020; Singer & Parmesan, 2020). In our case, substantial popula-
tion differences must have evolved within a short period of time.
However, it remains unclear whether the genetic changes in host-
plant use and life history triggered the range expansion or occurred
during the course of it (as it has been shown in the Mountain pine
beetle; Cullingham et al., 2011). Indeed, recent, large- scale studies
on Lepidopteran niche breadth showed that, for species undergoing
poleward range expansions, young populations close to the north-
ern edge of their range tend to have a broader niche breadth than
“older” ones (Lancaster, 2020; Singer & Parmesan, 2020). Note that
the broadening of niche breadth may also emerge as a non- adaptive
consequence of range shifts (Lancaster, 2020; Singer & Parmesan,
2020), possibly because of a higher likelihood to include novel hosts
with increasing geographic range (Forister & Jenkins, 2017). If this
would similarly apply to the range expansion of P. mannii, we would
expect temporal and spatial changes in host- plant use during the
course of expansion. However, co- occurrence analyses showed no
increase in the spatial co- occurrence of P. mannii with A. petiolata
and D. tenuifolia over time. While the observed declines across plant
species may comprise methodological artefacts, co- occurrence pat-
terns seemed to be rather stable. Note, moreover, that the broader
range of hosts used by females from expanding populations was
consistent both at the population (indicated by the non- significant
effect of population) and the individual level (indicated by the sig-
nificant repeatability of female host choice).
We therefore speculate that some populations of the subspe-
cies alpigena broadened their host- plant use within the original dis-
tribution area prior to the onset of range expansion. This may have
NEU Et al.
resulted from genetic modifications, for example, novel mutations,
chromosome inversions or admixture between individuals from dif-
ferent source populations. Already Lewontin and Birch (1966) could
show for tephritid flies that hybridization caused the emergence
of range expanding phenotypes, which may also apply to intraspe-
cific hybridization of genetically differentiated populations. In our
case, admixture of the subspecies alpigena and rossii seems plausi-
ble, with the latter occurring throughout much of Italy (Ziegler &
Eitschberger, 1999). Perhaps, both subspecies came into contact
through dispersal events (e.g. in response to extreme heat in the
summer of 2007), which has subsequently boosted the range expan-
sion. To confirm this assumption, large- scaled specimen collections
of P. mannii across its historic and new distribution range and corre-
sponding genetic analysis (using mitochondrial sequences, nuclear
microsatellite and SNP markers; Krehenwinkel & Tautz, 2013) will be
needed. An alternative explanation is that moister habitats became
thermally suitable and / or xerothermic habitats are too hot and dry
for successful larval development in the course of anthropogenic
climate change (cf. Davies et al., 2006; Pateman et al., 2012, 2016).
Indeed, evolution of host- plant preferences can occur rapidly in but-
terflies (Buckley & Bridle, 2014; Singer et al., 1993). Thus, potential
host shifts within the historic range may have facilitated the subse-
quent range expansion.
Apart from reduced host specialization, evolutionary changes in
other phenotypic traits, such as dispersal- related morphology (Hill
et al., 1999) or thermal tolerance (Krehenwinkel & Tautz, 2013) may
underlie the current range expansion of P. mannii. Changes in flight
morphology though were not apparent among the populations sam-
pled (though a detailed analysis is missing), and changes in thermal
tolerance appear to be an unlikely explanation, because climate does
not seem to be the limiting factor for the range of P. mannii in the
first place. Similarly, unintentional introductions through commer-
cial plant trade appear unlikely, considering the continuous expan-
sion over time rather than a sudden occurrence at specific patches.
Evidence suggests that the species expanded its range through the
Swiss midlands (Hensle, 2016; Schanowski, 2013). Also note that
I. sempervirens and D. tenuifolia were already available in Germany
long before the range expansion started. Furthermore, changes in
agricultural practices, as have been shown to underlie the rapid host
range evolution of Edith's checkerspot butterfly (Euphydryas editha)
populations (Singer et al., 1993), cannot be expected as the driver
of P. mannii's shift in host- plant preference as the newly integrated
plant species are rather representatives of uncultivated areas. Thus,
although alternative explanations exist, they seem to be rather un-
likely as drivers of the current range expansions.
4.5 | Conclusions
We explored the role of climate change as well as changes in host-
plant preferences for the range expansion of the butterfly P. mannii.
While the recent range expansion is unlikely to be directly caused by
anthropogenic changes in climate, we document a broader host- plant
use and increased fecundity in expanding populations. We conclude
that reduced host specialization was likely the primary trigger of
the fast range expansion. Thus, our results provide evidence for a
central role of evolutionary shifts in insect– plant interactions dur-
ing range expansions, and concurrently raise awareness for the high
complexity of the causes underlying such phenomena. Though we
can largely rule out a direct role of climate change, we assume that
it has been involved in the documented host- plant shifts by facilitat-
ing hybridization, pointing towards indirect effects of climate change
(Godsoe, Holland, et al., 2017). While our study provided some in-
teresting first insights into the complexity of a spectacular range ex-
pansion, several issues warrant further investigation. These include
(1) population genetic approaches to determine the origin of the
range expansion, the degree of genetic differentiation and to test
for hybridization among subspecies, (2) investigations of populations
from the former distribution edge, and (3) preference- performance
experiments scoring offspring fitness. Only if we are able to com-
prehend the factors promoting or preventing a species’ ability to es-
tablish new populations beyond the current range, we will have the
fundamental knowledge needed for appropriate actions to protect-
ing biodiversity, especially in the current era of anthropogenic global
change (Hoffmann & Sgró, 2011).
We thank Norbert Hirneisen, Elisabeth Kühn and Annalena
Schotthöfer for providing distribution data, and Heinrich Biermann,
Michael Ochse, Ute Zengerling- Salge, Thomas Geier, Rudolf Pähler
and Philipp Deichmann for help with butterfly determination
and fieldwork. This study adheres to the ethical guidelines of the
University of Greifswald and the German Animal Welfare Act. No
permits were needed to conduct this study.
CONFLICT OF INTEREST
The authors declare no competing interests.
DATA AVAIL AB ILI T Y STAT E MEN T
Data from this study have been deposited in Dryad Digital
Repository: Oviposition, and larval choice data, data points used
for species distribution modelling and the supporting information
material are stored within the Dryad Data Repository at https://doi.
Allouche, O., Tsoar, A., & Kadmon, R. (2006). Assessing the accuracy of
species distribution models: Prevalence, kappa and the true skill
statistic (TSS). Journal of Animal Ecology, 43 (6), 1223– 1232. https://
Al- Shehbaz, I. A ., Beilstein, M. A., & Kellogg, E. A. (2006). Systematics
and phylogeny of the Brassicaceae (Cruciferae): An overview.
Plant Systematics and Evolution, 259(2– 4), 89– 120. https://doi.
o r g / 1 0 . 1 0 0 7 / s 0 0 6 0 6 - 0 0 6 - 0 4 1 5 - z
Alzate, A., Onstein, R. E., Etienne, R. S., & Bonte, D. (2020). The role
of preadaptation, propagule pressure and competition in the col-
onization of new habitats. Oikos, 129(6), 820– 829. https://doi.
NE U Et al.
Bates, D., Maechler, M., Bolker, B., Walker, S., Bojesen, R. H., Singmann,
H., Dai, B., Scheipl, F., Grothendieck, G., & Green, P. (2018). lme4:
Linear mixed- effects models using Eigen and S4. R package ver-
sion 1.1- 18- 1. Retrieved from: http://CRAN.R- Proje ct.Org/Packa
Benito Garzón, M., Robson, T. M., & Hampe, A. (2019). ΔTraitSDMs:
Species distribution models that account for local adaptation and
phenotypic plasticity. New Phytologist, 222(4), 1757– 1765. https://
Bennie, J., Hodgson, J. A ., Lawson, C. R., Holloway, C. T. R., Roy, D. B.,
Brereton, T., Thomas, C. D., & Wilson, R. J. (2013). Range expansion
through fragmented landscapes under a variable climate. Ecolog y
Letter s, 16(7), 921– 929. https://doi.org/10.1111/ele.12129
Bolker, B. M., Brooks, M. E., Clark, C. J., Geange, S. W., Poulsen, J. R.,
Stevens, M. H. H., & White, J. S. S. (2009). Generalized linear
mixed models: A practical guide for ecology and evolution. Tre nd s
in Ecology and Evolution, 24 (3), 127– 135. https://doi.org/10.1016/j.
Boyes, D. H., Fox, R., Shortall, C. R., & Whittaker, R . J. (2019). Bucking
the trend: The diversity of anthropocene ‘winners’ among British
moths. Frontiers of Biogeography, 11(3), e43862. https://doi.
o r g / 1 0 . 2 1 4 2 5 / f 5 f b g 4 3 8 6 2
Braby, M. F., Eastwood, R., & Murray, N. (2012). The subspe-
cies concept in butterflies: Has its application in taxonomy
and conservation biology outlived its usefulness? Biological
Journal of the Linnean Society, 106(4), 699– 716. https://doi.
org /10.1111/j.1095- 8312.2012.019 09.x
Braschler, B., & Hill, J. K. (2007). Role of larval host plants in the
climate- driven range expansion of the butterfly Polygonia c-
album. Journal of Animal Ecology, 76(3), 415– 423. https://doi.
org /10.1111/j.1365- 2656.2007.01217.x
Bridle, J. R., Buckley, J., Bodsworth, E. J., & Thomas, C. D. (2014).
Evolution on the move: Specialization on widespread resources as-
sociated with rapid range expansion in response to climate change.
Proceedings of the Royal Society B: Biological Sciences, 281(1776),
Buckley, J., & Bridle, J. R. (2014). Loss of adaptive variation during evolu-
tionary responses to climate change. Ecology Letters, 17(10), 1316–
1325. ht tps://doi.org /10.1111/ele.1234 0
Burgman, M. A., & Fox, J. C. (2003). Bias in species range estimates from
minimum convex polygons: Implications for conservation and op-
tions for improved planning. Animal Conservation, 6, 19– 28. https://
doi.org/10.1017/S1367 94300 3003044
Burton, O. J., Phillips, B. L., & Travis, J. M. J. (2010). Trade- offs and
the evolution of life- histories during range expansion. Ecology
Letter s, 13, 1210– 1220. https://doi.org/10.1111/j.1461- 0248.
Bush, A ., Mokany, K., Catullo, R., Hoffmann, A., Kellermann, V., Sgrò, C.,
McEvey, S., & Ferrier, S. (2016). Incorporating evolutionary adapta-
tion in species distribution modelling reduces projected vulnerabil-
ity to climate change. Ecology Letters, 19 (12), 1468– 1478. https://
Canestrelli, D., Porretta, D., Lowe, W. H., Bisconti, R., Carere, C., &
Nascetti, G. (2016). The tangled evolutionary legacies of range
expansion and hybridization. Trends in Ecology and Evolution, 31(9),
677– 688. https://doi.org/10.1016/j.tree.2016.06.010
Caughley, G., Grice, D., Barker, R., & Brown, B. (1988). The edge of
the range. Journal of Animal Ecology, 57(3), 771– 785. https://doi.
Chamberlain, S., Ram, K., & Hart, T. (2021). Package ‘spocc’. Interface to
Species Occurrence Data Sources. https://github.com/ropen sci/
Chen, I. C., Hill, J. K., Ohlemüller, R., Roy, D. B., & Thomas, C. D. (2011).
Rapid range shifts of species associated with high levels of climate
warming. Science, 333(60 45), 1024– 1026. https://doi.org/10.1126/
Chew, F. S. (1980). Foodplant preferences of Pieris caterpillars
(Lepidoptera). Oecologia, 46 (3), 347– 353. https://doi.org/10.1007/
B F 0 0 3 4 6 2 6 3
Chew, F. S., & Robbins, R. K. (1984). Egg- laying in butterflies. In R. I.
Vane- Wright, & P. R. Ackery (Eds.), The biolog y of butterflies (pp. 65–
79). Academic Press.
Chuang, A., & Peterson, C. R. (2016). Expanding population edges:
Theories, traits, and trade- offs. Global Change Biology, 22(2), 494–
512. htt ps://doi.org/10.1111/gcb.13107
Crozier, L. (2003). Winter warming facilitates range expansion: Cold tol-
erance of the butterfly Atalopedes campestris. Oecologia, 135(4),
6 4 8 – 6 5 6 . h t t p s : / / d o i . o r g / 1 0 . 1 0 0 7 / s 0 0 4 4 2 - 0 0 3 - 1 2 1 9 - 2
Cullingham, C. I., Cooke, J. E. K., Dang, S., Davis, C. S., Cooke, B. J., &
Coltman, D. W. (2011). Mountain pine beetle host- range expansion
threatens the boreal forest. Molecular Ecology, 20(10), 2157– 2171.
https://doi.or g/10.1111/ j.1365- 294X. 2011.050 86 .x
Davies, Z. G., Wilson, R. J., Coles, S., & Thomas, C. D. (2006). Changing
habitat associations of a thermally constrained species, the
silver- spotted skipper butterfly, in response to climate warm-
ing. Journal of Animal Ecology, 75 (1), 247– 256. https://doi.
org /10.1111/j.1365- 2656.2006.010 44.x
Delignette- Muller, M.- L., & Dutang, C. (2015). Package ‘fitdistr-
plus’. Journal of Statistical Software, 64(4), 1– 34. https://doi.
Dennis, R. L. H. (2010). A resource- based habitat view for conservation:
Butterflies in the British landscape. Wiley- Blackwell.
Dormann, C. F., Elith, J., Bacher, S., Buchmann, C., Carl, G., Carré,
G., Marquéz, J. R. G., Gruber, B., Lafourcade, B., Leitão, P. J.,
Münkemüller, T., Mcclean, C., Osborne, P. E., Reineking, B.,
Schröder, B., Skidmore, A. K., Zurell, D., & Lautenbach, S. (2013).
Collinearity: A review of methods to deal with it and a simula-
tion study evaluating their performance. Ecography, 36 (1), 27– 46.
https://doi.or g/10.1111/ j.1600- 0587.2012. 073 48.x
Elith, J., H. Graham, C., P. Anderson, R., Dudík, M., Ferrier, S., Guisan, A.,
J. Hijmans, R., Huettmann, F., R. Leathwick, J., Lehmann, A., Li, J., G.
Lohmann, L., A. Loiselle, B., Manion, G., Moritz, C., Nakamura, M.,
Nakazawa, Y., McC. M. Overton, J., Townsend Peterson, A., … E.
Zimmermann, N. (20 06). Novel meth od s im pr ove prediction of spe-
cies’ distributions from occurrence data. Ecography, 29(2), 129– 151.
Elith, J., Kearney, M., & Phillips, S. J. (2010). The art of modelling range-
shifting species. Methods in Ecolog y and Evolution, 1(4), 330– 342.
Elith, J., Phillips, S. J., Hastie, T., Dudík, M., Chee, Y. E., & Yates,
C. J. (2011). A statistical explanation of MaxEnt for ecolo-
gists. Diversity and Distributions, 17(1), 43– 57. https://doi.
org /10.1111/j.1472- 4642.2010 .0 0725.x
Flores- Tolentino, M., García- Valdés, R., Saénz- Romero, C., Ávila- Díaz, I.,
Paz, H., & Lopez- Toledo, L. (2020). Distribution and conservation of
species is misestimated if biotic interactions are ignored: The case
of the orchid Laelia speciosa. Scientific Reports, 10(1), 9542. https://
d o i . o r g / 1 0 . 1 0 3 8 / s 4 1 5 9 8 - 0 2 0 - 6 3 6 3 8 - 9
Forister, M. L., & Jenkins, S. H. (2017). A neutral model for the evolution
of diet breadth. American Naturalist, 190 (2), E40– E54. https://doi.
Forister, M. L., McCall, A. C., Sanders, N. J., Fordyce, J. A., Thorne, J. H.,
O’Brien, J., Waetjen, D. P., & Shapiro, A. M. (2010). Compounded
effects of climate change and habitat alteration shift patterns of
butterfly diversity. Proceedings of the National Academy of Sciences
of the United States of America, 107(5), 2088– 2092. https://doi.
Fox, J., Graves, S., Heiberger, R., Monette, G., Nilsson, H., Ripley, B.,
Weisberg, S., & Fox, M. J. (2007). Companion to applied regression.
The car Package.
Franklin, J. (2010). Mapping species distributions: Spatial inference and pre-
diction. Cambridge University Press.
NEU Et al.
Friedrich, E. (2013). Der Karstweißling Pieris mannii ( May er, 1851) er rei cht
Nordwürttemberg (Lepidoptera: Pieridae). Beobachtungen, Zuchten,
Reflexionen im Jahre 2012. Mitteilungen Des Entomologischen
Vereins Stuttgart , 43, 64– 69. https://www.zobod at.at/pdf/Mitt- Ent-
V e r - S t u t t g a r t _ 4 8 _ 2 0 1 3 _ 0 0 6 4 - 0 0 6 9. p d f
Gallardo, B., Castro- Díez, P., Saldaña- López, A., & Alonso, Á. (2020).
Integrating climate, water chemistry and propagule pressure indica-
tors into aquatic species distribution models. Ecological Indications,
112, 106060. https://doi.org/10.1016/j.ecoli nd.2019.106060
Gamberale- Stille, G., Söderlind, L., Janz, N., & Nylin, S. (2014). Host plant
choice in the Comma Butterfly - larval choosiness may ameliorate
effects of indiscriminate oviposition. Insect Science, 21(4), 499– 506.
htt ps://doi.org /10.1111/1744- 7917.12059
García- Barros, E., & Fartmann, T. (2009). Butterfly oviposition: Sites, be-
haviour and modes. In J. Settele, T. Shreeve, M. Konvička, & H. Van
Dyck (Eds.), Ecology of Butterflies in Europe (pp. 29– 42). Cambridge
Geier, T. (2016). Beobachtungen zum Auftreten des Arealerweiterers
Pieris mannii (Mayer, 1851) im Gebiet der unteren Nahe (Rheinland-
Pfalz) mit Nachweisen dreier Raupennahrungspflanzen im Freiland
(Lepidoptera: Pieridae). Nachrichten Des Entomologischen Vereins
Apollo, 37, 27– 40.
Gillson, L., Dawson, T. P., Jack, S., & McGeoch, M. A. (2013).
Accommodating climate change contingencies in conservation
strategy. Trends in Ecology and Evolution, 28(3), 135– 142. https://
Godsoe, W., Holland, N. J., Cosner, C., Kendall, B. E., Brett, A., Jankowski,
J., & Holt, R. D. (2017). Interspecific interactions and range limits:
Contrasts among interaction types. Theoretical Ecology, 10(2), 167–
1 7 9 . h t t p s : / / d o i . o r g / 1 0 . 1 0 0 7 / s 1 2 0 8 0 - 0 1 6 - 0 3 1 9 - 7
Godsoe, W., Jankowski, J., Holt, R. D., & Gravel, D. (2017). Integrating
biogeography with contemporary niche theory. Trends in
Ecology and Evolution, 32(7), 488– 499. https://doi.org/10.1016/j.
Gratton, C ., & Welter, S. C. (1998). Oviposit ion preference an d larval per-
formance of Liriomyza helianthi (Diptera: Agromyzidae) on normal
and novel host plants. Environmental Entomology, 27(4), 926– 935.
Griffith, D. M., Veech, J. A., & Marsh, C. J. (2016). Cooccur: Probabilistic
species co- occurrence analysis in R. Journal of Statistical Software,
Guisan, A ., Thuiller, W., & Zimmermann, N. E. (2017). Habitat suitability
and distribution models: W ith applications i n R. Cambridge University
Gupta, S. K. (2009). Biology and Breeding of Crucifers. CRC Press.
Hallatschek, O., & Nelson, D. R. (2010). Life at the front of an ex-
panding population. Evolution, 64(1), 193– 206. https://doi.
org /10.1111/j.1558- 5646.200 9.008 09.x
Hastings, R. A., Rutterford, L. A., Freer, J. J., Collins, R. A., Simpson, S. D.,
& Genner, M. J. (2020). Climate change drives poleward increases
and equatorward declines in marine species. Current Biology, 30(8),
1572– 1577. https://doi.org/10.1016/j.cub.2020.02.043
Hensle, J. (2016). Die Ausbreitung von Pieris mannii (MAYER, 1851) im
Tessin (Schweiz). Atalanta, 47, 95– 97.
Hensle, J., & Seizmair, M. (2015). Papilionidae, Pieridae, Nymphalidae,
Lycaenidae und Hesperiidae. Atalanta, 47, 1– 69.
Hensle, J., & Seizmair, M. (2017). Papilionidae, Pieridae, Nymphalidae,
Lycaenidae und Hesperiidae. Atalanta, 48, 7– 79.
Hensle, J., & Seizmair, M. (2020). Papilionidae, Pieridae, Nymphalidae,
Lycaenidae und Hesperiidae 2019. Atalanta, 51(3), 219– 223.
Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G., & Jarvis, A. (2005).
Very high resolution interpolated climate surfaces for global land
areas. International Journal of Climatology, 25(15), 1965– 1978.
Hijmans, R. J., Guarino, L., Cruz, M., & Rojas, E. (2001). Computer tools
for spatial analysis of plant genetic resources data: 1. DIVA- GIS.
Plant Genetic Resources Newsletter, 127, 15– 19. https://diva- gis.org/
Hill, J. K., Griffiths, H. M., & Thomas, C. D. (2011). Climate change and
evolutionary adaptations at species’ range margins. Annual Review
of Entomology, 56(1), 143– 159. https://doi.org/10.1146/annur ev-
e n t o - 1 2 0 7 0 9 - 1 4 4 7 4 6
Hill, J. K., Ohlemüller, R., Fox, R., & Thomas, C. D. (2009). Climate warm-
ing and distribution changes in butterflies. In J. Settele, T. Shreeve,
M. Konvička, & H. Van Dyck (Eds.), Ecology of Butterflies in Europe
(pp. 315– 321). Cambridge University Press.
Hill, J. K., Thomas, C. D., & Blakeley, D. S. (1999). Evolution of flight mor-
phology in a butterfly that has recently expanded its geographic
range. Oecologia, 121(2), 165– 170. https://doi.org/10.1007/s0044
Hoffmann, A . A., & Sgró, C. M. (2011). Climate change and evolutionary
adaptation. Nature, 470(7335), 479– 485. https://doi.org/10.1038/
n a t u r e 0 9 6 7 0
Holt, R . D. (2003). On the evolutionary ecology of species’ ranges.
Evolutionary Ecolog y Research, 5, 159– 178. https://people.clas.ufl.
edu/rdhol t/files/ 126.pdf
Holt, R. D. (2009). Bringing the Hutchinsonian niche into the 21st cen-
tury: Ecological and evolutionary perspectives. Proceedings of the
National Academy of Sciences, 106(Suppl. 2), 19659– 19665. https://
Hothorn, T., Bretz, F., & Westfall, P. (2008). Simultaneous infer-
ence in general parametric models. Biometrical Journal, 50(3),
IPCC. (2014). Summary for policymakers. In C. B. Field, V. R. Barros, D. J.
Dokken, K. J. Mach, M. D. Mastrandrea, T. E. Bilir, M. Chatterjee, K.
L. Ebi, Y. O. Estrada, R. C. Genova, B. Girma, E. S. Kissel, A. N. Levy,
S. MacCracken, P. R. Mastrandrea, & L. L. White (Eds.), Climate
change 2014: Impacts, adaptation, and vulnerability. Part A: Global
and sectoral aspect s. Contribution of Working Group II to the Fifth
Assessment Report of the Intergovernmental Panel on Climate Change
(pp. 1– 34). Cambridge University Press.
Jaenike, J., & Holt, R. D. (1991). Genetic variation for habitat preference:
Evidence and explanations. A merican Naturalist, 137, 67– 90 . http s://
Kirkpatrick, M., & Barton, N. H. (1997). Evolution of a species’ range. The
American Naturalist, 150(1), 1– 23. https://doi.org/10.1086/286054
Köhler, J. (2021). Bemerkenswerte Beobachtungen zu Biologie und
Verhalten des Karstweißllings Pieris mannii (MAYER, 1851) an einer
Population im Wendland im Nordosten Niedersachsens. Atalanta,
52(1), 6– 9.
König, M. A. E., Wiklund, C., & Ehrlén, J. (2016). Butterfly oviposition
preference is not related to lar val per formance on a polyploid herb.
Ecology and Evolution, 6(9), 2781– 2789. https://doi.org/10.1002/
Kratochwill, M. (2011). Der Karstweißling Pieris mannii (MAY ER,
1851), neu in Bayern und Vorarlberg. Beiträge Zur Bayrischen
Entomofaunistik, 11, 9 – 1 1 . h t t p : / / w w w . a b e - e n t o m o f a u n i s t i k . o r g /
sites/ abe/files/ pub/bbe_11__009_014.pdf
Krehenwinkel, H., Rödder, D., & Tautz, D. (2015). Eco- genomic analy-
sis of the poleward range expansion of the wasp spider Argiope
bruennichi shows rapid adaptation and genomic admixture. Global
Change Biology, 21(12), 4 320– 4332 . https://doi.org/10.1111/
Krehenwinkel, H., & Tautz, D. (2013). Northern range expansion of
European populations of the wasp spider Argiope bruennichi is as-
sociated with global warming- correlated genetic admixture and
population- specific temperature adaptations. Molecular Ecology,
22(8), 2232– 2248. https://doi.org/10.1111/mec.12223
Kubisch, A., Holt, R. D., Poethke, H. J., & Fronhofer, E. A. (2014).
Where am I and why? Synthesizing range biology and the eco-
evolutionary dynamics of dispersal. Oikos, 123(1), 5– 22. https://doi.
org /10.1111/j.1600 - 0706.2013.00706.x
NE U Et al.
Kuczyk, J., Müller, C., & Fischer, K. (2021). Plant- mediated indirect ef-
fects of climate change on an insect herbivore. Basic and Applied
Ecology, 53, 100– 113. https://doi.org/10.1016/j.baae.2021.03.009
Kudrna, O., Harpke, A., Lux, K., Pennerstorfer, J., Schweiger, O., Settele,
J., & Wiemers, M. (2011). Distribution Atlas of Butterf lies in Europe.
Gesellschaft Für Schmetterlingsschutz.
Lafranchis, T., Jutzeler, D., Guillosson, J.- Y., Kan, P., & Kan, B. (2015). La
vie des papillons. Ecologie, biologie et comportement des Rhopalocères
de France. Diatheo.
Lancaster, L. T. (2020). Host use diversification during range shifts
shapes global variation in Lepidopteran dietary breadth. Nature
Ecology and Evolution, 4, 963– 969. https://doi.org/10.1038/s4155
9 - 0 2 0 - 1 1 9 9 - 1
Landeck, I., Donner, D., Reinhardt, R., Renner, W., Renner, J., & Gelbrecht, J.
(2012). Häufigkeitszunahme von Cupido argiades (PALLAS, 1771) in
Brandenburg mit einem Überblick zu aktuellen Ausbreitungstendenzen
in benachbarten Regionen (Lepidoptera, Lycaenidae). Märkische
Entomologische Nachrichten, 14(1), 1– 12. https://www.zobod at.at/pdf/
M a e r k i s c h e - E n t - N a c h r_ 2 0 1 2 _1 _ 0 0 0 1 - 0 0 1 2 . p d f
Leonard, A. M., & Lancaster, L. T. (2020). Maladaptive plasticity facilitates
evolution of thermal tolerance during an experimental range shift.
BMC Evolutionary Biology, 20(1), 1– 11. https://doi.org/10.118 6/
s 1 2 8 6 2 - 0 2 0 - 1 5 8 9 - 7
Lewontin, R. C ., & Birch, L. C. (1966). Hybridization as a source of vari-
ation for adaptation to new environments. Evolution, 20(3), 315–
336. ht tps://doi.org/10.1111/j.1558- 5646.196 6.tb033 69.x
Lhotská, M. (1975). Notes on the ecology of germination of Alliaria petio-
lata. Folia Geobotanica et Phytotaxonomica, 10(2), 179– 183. https://
doi.org/10.10 07/bf028 52858
Liebl, A. L., & Martin, L. B. (2013). Stress hormone receptors change as
range expansion progresses in house sparrows. Biology Letters, 9(3),
Liu, C., Berr y, P. M., Dawson, T. P., & Pearson, R. G. (2005). Selecting
thresholds of occurrence in the prediction of species distributions.
Ecography, 28(3), 385– 393. https://doi.org/10.1111/j.0906- 7590.
Liu, C., Wolter, C., Xian, W., & Jeschke, J. M. (2020). Most invasive spe-
cies largely conserve their climatic niche. Proceedings of the National
Academy of Sciences, 117(38), 1– 9. https://doi.org/10.1073/
p n a s . 2 0 0 4 2 8 9 1 1 7/ - / D C S u p p l e m e n t a l
Lockwood, J. L., Cassey, P., & Blackburn, T. (2005). The role of propa-
gule pressure in explaining species invasions. Trends in Ecology
and Evolution, 20(5), 223– 228. https://doi.org/10.1016/j.
Mason, S. C., Palmer, G., Fox, R., Gillings, S., Hill, J. K., Thomas, C. D., &
Oliver, T. H. (2015). Geographical range margins of many taxonomic
groups continue to shift polewards. Biological Journal of the Linnean
Society, 115(3), 586– 597. https://doi.org/10.1111/bij.12574
Matthysen, E. (2012). Multicausality of dispersal. In J. Clobert, M.
Baguette, T. G. Bentonand, & J. M. Bullock (Eds.), Dispersal ecology
and evolution (pp. 2– 18). Oxford University Press.
Mayhew, P. J. (2001). Herbivore host choice and optimal bad mother-
hood. Trends in Ecology and Evolution, 16(4), 165– 167. https://doi.
o r g / 1 0 . 1 0 1 6 / S 0 1 6 9 - 5 3 4 7 ( 0 0 ) 0 2 0 9 9 - 1
Merow, C., Smith, M. J., & Silander, J. A. (2013). A practical guide to
MaxEnt for modeling species’ distributions: What it does, and why
inputs and settings matter. Ecography, 36(10), 1058– 1069. ht tps://
doi.org/10.1111/j.160 0- 0587.2013.07872.x
Munguira, M. L., García- Barros, E., & Martín Cano, J. (2009). Butterfly
herbivory and larval ecology. In J. Settele, T. Shreeve, M. Konvička,
& H. Van Dyck (Eds.), Ecolog y of Butterflies in Europe (pp. 43– 54).
Cambridge University Press.
Nakagawa, S., Johnson, P. C. D., & Schielzeth, H. (2017). The coef-
ficient of determination R2 and intra- class correlation coeffi-
cient from generalized linear mixed- effects models revisited and
expanded. Journal of the Royal Societ y Interface, 14(13 4), ht tp s://d oi.
Nakagawa, S., & Schielzeth, H. (2010). Repeatability for Gaussian and
non- Gaussian data: A practical guide for biologists. Biological
Reviews, 85(4), 935– 956. https://doi.org/10.1111/j.1469- 185X.
Nylin, S., Nygren, G. H., Windig, J. J., Janz, N., & Bergström, A. (2005).
Genetics of host- plant preference in the Comma Butterfly
Polygonia c- album (Nymphalidae), and evolutionary implications.
Biological Journal of the Linnean Society, 84, 755– 765. https://doi.
org /10.1111/j.1095- 8312.200 4.00433.x
Odling- Smee, J., Erwin, D. H., Palkovacs, E. P., Feldman, M. W., & Laland,
K. N. (2013). Niche construction theory: A practical guide for
ecologists. Quarterly Review of Biology, 88(1), 3– 28. https://doi.
Pähler, R . (2016). Ein Blick auf die aktuelle Arealexpansion und
Einbürgerung des Karstweißlings Pieris mannii (MAYER, 1851) in
Deutschland sowie Anmerkungen zu den Flugzeiten (Lep., Pieridae).
Melanargia, 28, 117– 135.
Paradis, A., Elkinton, J., Hayhoe, K., & Buonaccorsi, J. (2008). Role of
winter temperature and climate change on the survival and future
range expansion of the Hemlock Woolly Adelgid (Adelges tsugae)
in eastern North America. Mitigation and Adaptation Strategies for
Global Change, 13(5– 6), 541– 554. https://doi.org/10.1007/s1102
7 - 0 0 7 - 9 1 2 7 - 0
Parmesan, C., Ryrholm, N., Stefanescu, C., Hill, J. K., Thomas, C. D.,
Descimon, H., Huntley, B., Kaila, L., Kullberg, J., Tammaru, T.,
Tennent, W. J., Thomas, J. A., & Warren, M. (1999). Poleward shifts
in geographical ranges of butterfly species associated with re-
gional warming. Let ters to Nature, 399(6736), 579– 583. https://doi.
Parmesan, C., & Yohe, G. (2003). A globally coherent fingerprint of cli-
mate change impacts across natural systems. Nature, 421(6918),
37– 42. https://doi.org/10.1038/natur e01286
Pateman, R. M., Hill, J. K., Roy, D. B., Fox, R., & Thomas, C. D. (2012).
Temperature- dependent alterations in host use drive rapid range
expansion in a butterfly. Science, 336(6084), 1028– 1030. https://
Pateman, R. M., Thomas, C. D., Hayward, S. A . L., & Hill, J. K. (2016).
Macro- and microclimatic interactions can drive variation in spe-
cies’ habitat associations. Global Change Biology, 22(2), 556– 566.
Pearman, P. B., Guisan, A., Broennimann, O., & Randin, C. F. (2008). Niche
dynamics in space and time. Trends in Ecology and Evolution, 23 (3),
149– 158. https://doi.org/10.1016/j.tree.2007.11.005
Peterson, A. T. (2011). Ecological niche conservatism: A time- structured
review of evidence. Journal of Biogeo graphy, 38(5), 817– 827. https://
Pfenninger, M., Nowak, C., & Magnin, F. (2007). Intraspecific range
dynamics and niche evolution in Candidula land snail species.
Biological Journal of the Linnean Society, 90 (2), 303– 317. https://doi.
org /10.1111/j.1095- 8312.2007.0 0724.x
Phillips, B. L ., Brown, G. P., & Shine, R. (2010). Life- history evolution in
range- shifting populations. Ecology, 91(6), 1617– 1627. https://doi.
Phillips, S. J., Anderson, R. P., Dudík, M., Schapire, R. E., & Blair, M. E.
(2017). Opening the black box: An open- source release of Maxent.
Ecography, 40(7), 887– 893. https://doi.org/10.1111/ecog.03049
Phillips, S. J., Anderson, R. P., & Schapire, R. E. (2006). Maximum en-
tropy modeling of species geographic distributions. Ecological
Modelling, 190 (3– 4), 231– 259. https://doi.org/10.1016/j.ecolm
Phillips, S. J., & Dudík, M. (2008). Modeling of species distributions with
Maxent: New extensions and a comprehensive evaluation. Ecography,
31(2), 161– 175. https://doi.org/10.1111/j.0906- 7590.2008.5203.x
NEU Et al.
Pinsky, M. L., Selden, R. L., & Kitchel, Z. J. (2020). Climate- driven shifts
in marine species ranges: Scaling from organisms to communi-
ties. Annual Review of Marine Science, 12(1), 153– 179. https://doi.
o r g / 1 0 . 1 1 4 6 / a n n u r e v - m a r i n e - 0 1 0 4 1 9 - 0 1 0 9 1 6
Platts, P. J., Mason, S. C., Palmer, G., Hill, J. K., Oliver, T. H., Powney,
G. D., Fox, R., & Thomas, C. D. (2019). Habitat availability explains
variation in climate- driven range shifts across multiple taxonomic
groups. Scientific Reports, 9(1), 15039. https://doi.org/10.1038/
s 4 1 5 9 8 - 0 1 9 - 5 1 5 8 2 - 2
Pöyry, J., Luoto, M., Heikkinen, R. K., Kuussaari, M., & Saarinen, K.
(2009). Species traits explain recent range shifts of Finnish
butterflies. Global Change Biology, 15(3), 732– 743. https://doi.
org /10.1111/j.1365- 2486.2008 .01789.x
Pratt, C. (1986). A history and investigation into the fluctuations of
Polygonia c- album L. the Comma bu tterfl y. Entomo logist’s Record and
Journal of Variation, 98 ( 1 9 7 – 2 0 3 ) , 2 4 4 – 2 5 0 .
R Core Team. (2020). R: A language and environment for statistical comput-
ing. R Foundation for Statistical Computing. https://www.r- proje
ct . o r g /.
Reinhardt, R., Harpke, A., Caspari, S., Dolek, M., Kühn, E., Musche, M.,
Trusch, R., Wiemers, M., & Settele, J. (2020). Verbreitungsatlas der
Tagfalter und Widderchen Deutschlands. Ulmer.
Rochlin, I., Ninivaggi, D. V., Hutchinson, M. L., & Farajollahi, A. (2013).
Climate change and range expansion of the Asian Tiger Mosquito
(Aedes albopictus) in Northeastern USA: Implications for pub-
lic health practitioners. PLoS O ne, 8(4), e60874. https://doi.
Rumpf, S. B., Hülber, K ., Klonner, G., Moser, D., Schütz, M., Wessely, J.,
Willner, W., Zimmermann, N. E., & Dullinger, S. (2018). Range dy-
namics of mountain plants decrease with elevation. Proceedings
of the National Academy of Sciences of the United States of America,
115(8), 1848– 1853. https://doi.org/10.1073/pnas.17139 36115
Schanowski, A. (2013). Auswirkungen des Klimawandels auf die
Insektenfauna - Forschungsbericht KLIMOPASS. LUBW Landesanstalt
Für Umwelt, Me ssungen Und Naturschutz Baden- Württemberg, 1 9 – 2 0 .
https://fachd okume nte.lubw.baden - wuert tembe rg.de/servl et/
is/10971 4/U51- W03- N11.pdf?comma nd=downl oadCo ntent &filen
ame=U 5 1- W 0 3 - N 1 1 . p d f & F I S =91063
Schoonhoven, L. M., Van Loon, J. J. A ., & Dicke, M. (2014). Insect- plant
biology, 2nd ed. Oxford University Press.
Schurian, K., & Siegel, A. (2016). Beitrag zur Biologie und Ökologie des
Karstweißlings Pieris mannii (Mayer, 1851) in Hessen (Lepidoptera:
Pieridae). Nachrichten Des Entomologischen Vereins Apollo, N.F, 37,
1 5 – 2 1 .
Scriber, J. M., & Ording, G. J. (2005). Ecological speciation without
host plant specialization; possible origins of a recently described
cryptic Papilio species. Applicata, 115 , 247– 263. https://doi.
Scriber, J. M., & Slansk y, F. (1981). The nutritional ecology of immature
insects. Annual Review of Entomology, 26 (1), 183– 211. https://doi.
Settele, J., Kudrna, O., Harpke, A., Kühn, I., van Swaay, C., Verovnik, R.,
Warren, M., Wiemers, M., Hanspach, J., Hickler, T., Kühn, E., van
Halder, I., Veling, K., Vliegenthart, A., Wynhoff, I., & Schweiger, O.
(2008). Climatic Risk Atlas of European Butterflies. Biodiversity and
Ecosystem Risk Assessment, 1, 1– 712. https://doi.org/10.3897/biori
Settele, J., Steiner, R., Reinhardt, R., Feldmann, R., & Hermann, G. (2015).
Schmetterlinge. Die Tagfalter Deutschlands, 3rd ed. Ulmer.
Sexton, J. P., McInt yre, P. J., Angert, A . L., & Rice, K. J. (20 09). Evolution
and ecology of species range limits. A nnual Review of Ecology,
Evolution, a nd Systematics, 40(1 ), 41 5– 43 6. ht t ps: //do i.org/1 0 .1146/
annur ev.ecols ys.110308.120317
Shcheglovitova, M., & Anderson, R. P. (2013). Estimating optimal com-
plexity for ecological niche models: A jackknife approach for
species with small sample sizes. Ecological Modelling, 269, 9– 17.
Sim mo ns , A. D., & Thomas, C. D. (200 4). Changes in dispersal during spe-
cies’ range expansions. The American Naturalist, 164(3), 378– 395.
Singer, M. C. (1984). Butter fly- hostplant relationships: Host quality, adult
choice and larval success. In R . Vane- Weight, & P. R. Ackery (Eds.),
The biology of butterflies (pp. 82– 88). Academic Press.
Singer, M. C., & Parmesan, C. (2020). Colonizations drive host shifts,
diversification of preferences and expansion of herbivore diet
breadth. BioRxiv, 017830 Pre. https://doi.org/10.1101/2020.03.
Singer, M. C., Thomas, C. D., & Parmesan, C. (1993). Rapid human-
induced evolution of insect– host associations. Nature, 366(6 456),
681– 683. https://doi.org/10.1038/366681a0
Soler, R., Pineda, A., Li, Y., Ponzio, C., van Loon, J. J. A., Weldegergis, B.
T., & Dicke, M. (2012). Neonates know better than their mothers
when selecting a host plant. Oikos, 121(12), 1923– 1934. https://doi.
org /10.1111/j.1600 - 0706.2012.20415.x
Springer, A., & Gompert, Z. (2020). Species collisions, admixture, and the
genesis of biodiversity in poison frogs. Molecular Ecology, 29(11),
1937– 194 0. https://doi .org/10.1111/mec.15402
Still, J. (2003). Schmetterlinge und Raupen Europas. Mosaik.
Stoffel, M. A ., Nakagawa, S., & Schielzeth, H. (2017). rptR: Repeatability
estimation and variance decomposition by generalized linear mixed-
effects models. Methods i n Ecology and Evolution , 8(11), 1639– 1644.
https://doi.or g/10.1111/2041- 210X.12797
Stroud, J. T. (2021). Island species experience higher niche expansion
and lower niche conservatism during invasion. Proceedings of the
National Academy of Sciences of the United States of America, 118 (1),
e2018949118. https://doi.org/10.1073/pnas.20189 49118
Swets, J. A. (1988). Measuring the accuracy of diagnostic systems.
Science, 240 (4857), 1285– 1293. https://doi.org/10.1126/scien
Tabashnik, B. E. (1983). Host range evolution: The shift from na-
tive Legume host to Alfalfa by the butterfly. Colias Philodice
Eriphyle. Evolution, 37 (1), 150– 162. https://doi.or g/10.1111/
j . 1 5 5 8 - 5 6 4 6 . 1 9 8 3 . t b 0 5 5 2 3 . x
Taylor- Cox, E. D., Macgregor, C. J., Corthine, A., Hill, J. K., Hodgson, J. A.,
& Saccheri, I. J. (2020). Wing morphological responses to latitude
and colonisation in a range expanding butterfly. Pee rJ, 8, e10352.
Thomas, C. D., Bodsworth, E. J., Wilson, R. J., Simmons, A. D., Davies, Z.
G., Musche, M., & Conradt, L. (2001). Ecological and evolutionary
processes at expanding range margins. Nature, 411(6837), 577– 581.
Thompson, J. N. (1988). Evolutionary ecology of the relationship be-
tween oviposition preference and per formance of offspring in phy-
tophagous insects. Entomologia Experimentalis et Applicata, 47(1),
3 – 1 4 . h t t p s : // d o i . o r g / 1 0 . 1 1 1 1 / j . 1 5 7 0 - 7 4 5 8 . 1 9 8 8 . t b 0 2 2 7 5 . x
Tolman, T., & Lewington, R. (1998). Die Tagfalter Europas und
Nordwestafrikas. Franckh- Kosmos.
Turlure, C., Schtickzelle, N., Van Dyck, H., Seymoure, B., & Rutowski, R.
(2016). Flight morphology, compound eye structure and disper-
sal in the Bog and the Cranberr y Fritillary Butterflies: An inter-
and intraspecific comparison. PLoS One, 11(6), 1– 17. https://doi.
Veech, J. A. (2013). A probabilistic model for analysing species co-
occurrence. Global Ecolog y and Biogeography, 22(2), 252– 260.
Vilela, B., & Villalobos, F. (2015). letsR: a new R package for data handling
and analysis inmacroecology. Methods in Ecology and Evolution, 6,
1129– 1234. https://doi.org/10.1111/2041- 210X.12401
Warren, M. S., Hill, J. K., Thomas, J. A., Asher, J., Fox, R ., Huntley, B.,
Roy, D. B., Telfer, M. G., Jeffcoate, S., Harding, P., Jeffcoate, G.,
NE U Et al.
Willis, S. G., Greatorex- Davies, J. N., Moss, D., & Thomas, C. D.
(2001). Rapid responses of British butterflies to opposing forces of
climate and habitat change. Nature, 414(6859), 65– 69. https://doi.
Wiemers, M. (2016). Augen auf für neue Arten – zur Bestimmung und
weiteren Ausbreitung des Karstweißlings Pieris mannii (Maye r,
1851) in Deutschland. Oedippus, 32, 34– 36. https://www.ufz.de/
e x p o r t / d a t a / 6 / 1 2 6 8 7 6 _ T M D J a h r e s b e r i c h t 2 0 1 5 . p d f
Wiemers, M., Schmitz, O., Caspari, A., & Berner, D. (2020). Augen auf für
neue Arten - Neues zum Karstweißling (Pieris mannii) mit der Bitte
um Mitarbeit. Oedippus, 38, 45– 47. https://www.ufz.de/expor t/
Wiens, J. J., Ackerly, D. D., Allen, A. P., Anacker, B. L., Buckley, L . B.,
Cornell, H. V., Damschen, E. I., Jonathan Davies, T., Grytnes, J.
A., Harrison, S. P., Hawkins, B. A., Holt, R. D., McCain, C. M.,
& Stephens, P. R. (2010). Niche conservatism as an emerging
principle in ecology and conser vation biology. Ecology Letters,
13(10), 1310– 1324. https://doi.org/10.1111/j.1461- 0248.2010.
Wiklund, C. (1975). The evolutionary relationship between adult ovi-
position preferences and larval host plant range in Papilio mach-
aon L. Oecologia, 18(3), 185– 197. https://doi.org/10.1007/BF003
Wiklund, C. (1981). Generalist vs. specialist oviposition behaviour in
Papilio machaon (Lepidoptera) and functional aspects on the hier-
archy of oviposition preferences. Oikos, 36(2), 163– 170. https://doi.
Wilson, R. J., Davies, Z. G., & Thomas, C. D. (2010). Linking habi-
tat use to range expansion rates in fragmented landscapes: A
metapopulation approach. Ecography, 33(1), 73– 82. https://doi.
org /10.1111/j.1600 - 0587.2009.06 03 8.x
Wolz, M., Klockmann, M., Schmitz, T., Pekár, S., Bonte, D., & Uhl, G.
(2020). Dispersal and life- history traits in a spider with rapid range
expansion. Movement Ecology, 8(2), 1– 11. https://doi.org/10.1186/
s 4 0 4 6 2 - 0 1 9 - 0 1 8 2 - 4
Wonning, P. R. (2016). Gardener’s guide to Perennial Candytuft: Perennial
Candytuft – Iberis sempervirens. Mossy Feet Books.
Yackulic, C. B., Chandler, R., Zipkin, E. F., Royle, J. A., Nichols, J. D.,
Campbell Grant, E. H., & Veran, S. (2013). Presence- only model-
ling using MA XENT: When can we trust the inferences? Methods in
Ecology and Evolution, 4(3), 236– 243. https://doi.org/10.1111/2041-
Ziegler, H., & Eitschberger, U. (1999). Der Karstweißling Pieris mannii
(MAYER, 1851) Systematik, Verbreitung, Biologie (Lepidoptera,
Pieridae). Neue Entomologische Nachrichten Aus Dem Entomologischen
Museum Dr. Ulf Eitschberger, 45, 1– 219.
Anika Neu is broadly interested in the ecological and evolution-
ary processes shaping species distribution patterns. This work
represents a component of her PhD project that aims at uncover-
ing the role of evolutionary and ecological factors for the recent
and rapid range expansion of the Southern Small White butterfly
(Pieris mannii), focusing on different behavioural and life- history
traits. She and the other authors here collaborated to apply a
multi- methodological approach, bringing together the expertise
for fieldwork and laboratory experiments with Lepidoptera as
well as species distribution modelling.
Author contributions: A.N. and K.F. conceived of the study. A.N.
conducted the field work, analysed the data and performed, to-
gether with L.N., the laboratory experiments. S.L. performed the
SDM. M.W. contributed to species records. A.N., K.F. and S.L.
wrote the manuscript. All authors contributed critically to the
drafts and approved the manuscript for publication.
Additional supporting information may be found online in the
Supporting Information section.
How to cite this article: Neu, A., Lötters, S., Nörenberg, L.,
Wiemers, M., & Fischer, K. (2021). Reduced host- plant
specialization is associated with the rapid range expansion of
a Mediterranean butterfly. Journal of Biogeography, 00, 1– 16.