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Ecology and Evolution. 2020;10:928–939.www.ecolevol.org
1 | INTRODUCTION
Phenology, the timing of seasonal events in the life cycles of
fauna and flora, has been identified as an important metric to
track ecological responses to climate change (Altermatt, 2012;
Cohen, L ajeunesse, & Rohr, 2018; Diamond, Frame, Martin, &
Buckley, 2011; Forister & Shapiro, 2003; Parmesan & Yohe,
2003; Stefanescu, Penuelas, & Filella, 2003; Wilson et al., 2005;
Received: 15 August 2019
|
Revised: 29 November 2019
|
Accepted: 3 December 2019
DOI: 10.100 2/ece3.5951
ORIGINAL RESEARCH
Butterfly phenology in Mediterranean mountains using space-
for-time substitution
Konstantina Zografou1,2 | Andrea Grill1 | Robert J. Wilson3 | John M. Halley2 |
George C. Adamidis1 | Vassiliki Kati2
This is an op en access article under t he terms of the Creat ive Commons Attributio n License, which permits use, dist ribution and reproduc tion in any medium,
provide d the orig inal work is proper ly cited .
© 2019 The Auth ors. Ecology and Evol ution pub lished by J ohn Wiley & Sons Ltd.
1Institute of Ecology and Evolution,
University of Bern, Bern, Switzerland
2Department of Biological Applications
and Technology, Univer sity of Ioannina ,
Ioannina, Greece
3Museo Nacional de Ciencias Naturales
(MNCN-CSIC), Madrid, Spain
Correspondence
Konstantina Zogr afou, Institute of
Ecology and Evolution, Universit y of
Bern, Baltzerstrasse 6, CH-3012, Bern,
Switzerland.
Email: konstantina.zografou@iee.unibe.ch
Funding information
European Union; Greek National Funds
Abstract
Inferring species' responses to climate change in the absence of long-term time series
data is a challenge, but can be achieved by substituting space for time. For exam-
ple, thermal elevational gradients represent suitable proxies to study phenological
responses to warming. We used butterfly data from two Mediterranean mountain
areas to test whether mean dates of appearance of communities and individual spe-
cies show a delay with increasing altitude, and an accompanying shortening in the du-
ration of flight periods. We found a 14-day delay in the mean date of appearance per
kilometer increase in altitude for butterfly communities overall, and an average 23-
day shift for 26 selected species, alongside average summer temperature lapse rates
of 3°C per km. At higher elevations, there was a shortening of the flight period for
the community of 3 days/km, with an 8.8-day average decline per km for individual
species. Rates of phenological delay differed significantly between the two mountain
ranges, although this did not seem to result from the respective temperature lapse
rates. These results suggest that climate warming could lead to advanced and length-
ened flight periods for Mediterranean mountain butterfly communities. However,
although multivoltine species showed the expected response of delayed and short-
ened flight periods at higher elevations, univoltine species showed more pronounced
delays in terms of species appearance. Hence, while projections of overall community
responses to climate change may benefit from space-for-time substitutions, under-
standing species-specific responses to local features of habitat and climate may be
needed to accurately predict the effects of climate change on phenology.
KEYWORDS
changing climate, developmental delay, elevational gradient, emergence time, flight period
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ZOGRA FOU et Al.
Zografou et al., 2015). There is evidence for both communities
and individual species of butterfly that adults emerge earlier as
the climate warms (Dell, Sparks, & Dennis, 2005; Lopez-Villalta,
2010; Stefanescu et al., 2003; Wilson et al., 2005) and that flight
periods have become longer (Menzel et al., 2006). These pheno-
logical changes as the climate warms could result from adaptations
(Schilthuizen & Kellermann, 2014) including increased numbers of
generations in multivoltine species (Altermatt, 2010), or the fa-
cilitation of another generation in principally univoltine species
(Fischer & Fiedler, 2002).
The most robust forecasts of phenological responses to climate
change combine high-quality monitoring data collected over an
extended time period (de Arce Crespo & Gutiérrez, 2011; Banet &
Trexler, 2013) with an identified dependence of a phenological trait
on reliable environmental cues (Reed, Waples, Schindler, Hard, &
Kinnison, 2010). Detailed phenological time series have been an-
alyzed for butter flies in northern and central Europe (Altermatt,
2012; Roy & Sparks, 2000; Van Strien, Plantenga, Soldaat, Swaay, &
WallisDeVries, 2008) and elsewhere (Brooks et al., 2017; Diamond
et al., 2011), but many other regions lack long enough time series of
monitoring data. Populations of species respond differently to cli-
matic conditions in dif ferent parts of their geographic ranges (Mills
Simon et al., 2017; Scranton & Amarasekare, 2017), so evidence of
how phenology varies with climate in a variety of different locations
(including relatively unexplored areas) can increase understanding
both of individual species responses and of the likely effects of cli-
mate change on the phenology of a more representative range of
ecological communities.
When no long-term data are available, an alternative approach to
studying phenology is to substitute space for time, by assuming that
the spatial relationship between an environmental factor (e.g., ele-
vation) and a phenological response (e.g., time of appearance) can be
used as a proxy for the temporal relationship (Banet & Trexler, 2013).
In this way, studies investigating how phenotypic traits change along
latitudinal or elevational gradients can contribute to the prediction
of species responses to climate change (de Arce Crespo & Gutiérrez,
2011; Gutiérrez & Menéndez, 1998; Hodkinson, 2005; Leingärtner,
Krauss, & Steffan-Dewenter, 2014; Merrill et al., 2008). However,
space-for-time substitution can become less valid at certain spa-
tiotemporal scales (Blois, Williams, Fitzpatrick, Jackson, & Ferrier,
2013) or lead to underestimations of changes in diversity (França
et al., 2016) especially under the pressure of a changing environ-
ment (Damgaard, 2019). Therefore, the implicit use of space-for-time
substitution should be treated with caution in modeling community
responses to climate change.
Information on species' ecological and life-history traits has
also been used to fur ther understanding of interspecific varia-
tion in phenological responses to climate change (Diamond et al.,
2011; Kharouba, Paquette, Kerr, & Vellend, 2014; Leingärtner et
al., 2014). For example, species whose flight periods occur ear-
lier in the year and less mobile species appear more sensitive to
temperature variation than late flying or more mobile species
(Kharouba et al., 2014), and multivoltine species may be more able
to respond to warming by increasing the frequency of their annual
generations (Altermatt, 2009, 2010). Although earlier emergence
dates and increased numbers of generations have been widely
documented, it has also been shown that some insects could be
negatively affec ted by warmer climates. If juvenile stages com-
plete development in late summer instead of entering the over-
wintering stage, a lack of sufficient time and suitable conditions
to breed could lead to population declines (“the lost generation
hypothesis”) (Glazaczow, Orwin, & Bogdziewicz, 2016; Van Dyck,
Bonte, Puls, Gotthard, & Maes, 2015; van der Kolk, WallisDeVries,
& Vliet, 2016). Also for butterflies performing a photoperiodically
induced summer dormancy, like Mediterranean Maniola butterflies
(Van Dyck et al., 2015), climate warming might have negative ef-
fects on populations. If the summer drought became extended and
the butterfly deposited her eggs before the onset of vegetation
regrowth, triggered by a shor tened photoperiod at the beginning
of autumn, the young larvae would have no suitable fresh grasses
to feed on and starve to death. Other negative consequences of
climate change could include phenological mismatches between
trophically interacting species such as butterflies and their host
plants (Bale et al., 2002; Parmesan & Yohe, 2003; Visser & Both,
2005). Overall, climate change is responsible for well-studied
phenological shifts, but their magnitude and direction can largely
vary even between species inhabiting the same latitude (Diez et
al., 2012). For these reasons, it is impor tant to assess the effect
of climate change on phenology from a comprehensive range of
environments and ecological communities.
Elevational gradients are potentially useful space-for-time
proxies because they combine significant variation in temperature
over short geographic distances (Körner, 2007) with minimal vari-
ability in photoperiod (Fielding, Whittaker, Butterfield, & Coulson,
1999; Hodkinson, 2005). In addition, microclimate and habitat
conditions (including vegetation structure and canopy cover) vary
over elevational gradients (Suggitt et al., 2011) and can buffer
ecological communities against coarse-scale trends and patterns
in climate change (Gillingham, Huntley, Kunin, & Thomas, 2012;
Sug gitt et al., 2012). Therefore, phenolog y can vary markedly over
elevational gradients but also within an altitudinal belt depending
on habitat type, and Altermatt (2012) showed that the seasonal
appearance of butterflies is influenced by both of these variables.
Testing local effects of elevation and habitat on phenology using
space-for-time assumption could be a valid approach to under-
standing and predicting ecological responses to climate change
(Banet & Trexler, 2013; Hodgson et al., 2011; Leingärtner et al.,
2014). Although this method has some caveats (e.g., it cannot
track year-to-year changes in species phenology), it can, however,
serve as a short-term “tracking device” that mimics the longer sea-
sons and milder winters that are expected as the climate warms
(EEA, 2017; van der Wiel, Kapnick, & Vecchi, 2017).
In general, increasing elevation is expected to influence spe-
cies' phenology by shortening the annual activity window, forcing
stages in the life cycle to appear later while maintaining synchrony
with resources and suitable environmental conditions (Brown &
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ZOGR AFOU et Al .
Lomolino, 1998; Despland, Humire, & Martín, 2012; Hodkinson,
2005). There is much evidence that temperate butterflies be-
come active later annually at higher elevations (de Arce Crespo &
Gutiérrez, 2011; Illán, Gutiérrez, Díez, & Wilson, 2012; Merrill et
al., 2008; Sha piro, 1975). In ad di tion, there is eviden ce that climate
warming has led to both earlier appearance and extension of the
flight period at high elevations (Konvička, Beneš, Čížek, Kuras, &
Klečková, 2016). In this paper, using space-for-time inferences we
frame phenological responses to climate change in a biota which
lacks long-term data. By examining the phenology of butterfly
communities in two mountainous areas in Greece, across differ-
ent elevations and habitat types, we investigate the following re-
search questions:
1. Is there a delay in the appearance of species and a progres-
sive shortening of the flight period at higher elevations? We
predict that phenological windows of activit y will be shorter,
better synchronized and delayed with elevation (Despland et
al., 2012; Illán et al., 2012).
2. Are altitudinal patterns of butterfly phenology consistent with
the temperature lapse rates recorded for each mountain system?
We expect to find steeper changes in emergence patterns when
temperature lapse rate is steep and therefore climatic differences
between elevations are more pronounced.
3. Do phenological patterns differ among different habitat types
(agricultural areas, gr assland, and forest)? We expect that phenol-
ogy may var y with elevation at a different rate in different habitat
types (Zografou et al., 2015). For example, microclimates in for-
ests that have a denser canopy compared to open habitats are
less influenced by direct radiation (Scherrer & Körner, 2011), po-
tentially leading to longer delays in emergence compared to open
habitats (e.g., grasslands).
4. Do the responses of individual species follow consistent patterns
with elevation in terms of time of the appearance and duration of
the flight period? We expect that univoltine species will show less
pronounced altitudinal variation in phenology as a result of lesser
adaptability compared with multivoltine species.
2 | MATERIALS AND METHODS
2.1 | Study system
Our study area consisted of two mountain regions that differ in
geographic position, areal extent, biome, climate type, and topog-
raphy. The Rodopi mountain chain (Rodopi hereafter: long. 24° 23′,
lat. 41°23′; maximum elevation 2,323 m) is located in NE Greece,
whereas Grammos (long. 20°50′, lat. 40°21′; maximum elevation
2,520 m) is located in NW Greece (Figure S1, Table S1, but see
also Zografou, Wilson, Halley, Tzirkalli and Kati (2017) for detailed
descriptions). Both systems share a low human population densit y
and associated low-intensity human activities, as well as high cov-
erage by protected are as of the Natura 20 00 networ k. The climate
in Rodopi is at the transition between Mediterranean and a con-
tinental climate (Mavromatis, 1980) with a mean annual tempera-
ture of 11.4°C and mean annual precipitation of 1, 200 mm, while
the climate in Grammos is humid continental (Korakis, 2002) with
a mean annual temperature of 8–12°C and mean annual rainfall of
1,500 mm.
2.2 | Butterfly sampling
Butter flies were recorded at 41 sites in Rodopi and 26 in Grammos.
The minimum distance between nearest neighboring sites was ap-
proximately 2 km (SD ± 0.5) so that each site effectively represents
an independent sampling unit. The lack of spatial autocorrelation
between nearby sites was verified in terms of alpha and beta com-
ponents of diversity in a previous study where we investigated
diversit y patterns of butterflies and Orthoptera across different
spatial scales (Zografou et al., 2017). Each mountain was partitioned
into four elevation zones (0–500 m, 501–1,000 m, 1,001–1,500 m,
and 1,501–2,000 m) and each zone contained sites representing
the three dominant habitats found in the study system (agricultural
fields, grasslands, and forests), with the exception that agricul-
tural areas were not present above 1,500 m (Figure S1, Table S1).
Permanent transect routes were established at two to six sites rep-
resentative of each habitat type per altitudinal zone in each moun-
tain range, recording geographic location (UTM) and elevation (m)
using a hand-held GPS unit. On each site visit, the transect was
walked at a steady pace under weather conditions that were suitable
for butterfly activity (Pollard & Yates, 1993) recording all butter-
flies observed along a standardized length and width of 300 × 5 m.
Butter flies were captured with the help of hand net, identified in
situ, and when necessary photographic material was also collected
for confirming identification in the laborator y. We visited each site
five times from April until August 2012 (Rodopi) and four times
from May until August 2013 (Grammos—no sampling conducted in
April due to unsuitable weather). Each transect was walked with a
maximum sampling interval of 20 days between visit s: This was the
minimum interval which was feasible for a single field observer to
achieve, given unpredictable weather and occasionally inaccessible
sites particularly at higher elevations.
2.3 | Phenological descriptors
The timing and duration of flight periods were calculated to describe
species' phenology along the altitudinal gradient. For each species,
the timing of flight period was summarized per site as the weighted
mean date (hereafter mean date) by summing counts per visit, ac-
cording to the formula:
Mean date
=∑
Visits
(Individuals per visit)
×
(Date of visit)
Total number of individuals
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ZOGRA FOU et Al.
Date was estimated in Julian dates, and data were summed for
each species across all visits (1 January = 1, 1 February = 32, etc.).
Mean date is a commonly used descriptor in phenological stud-
ies for but terflies and considered to be more reliable than other
phenological measures such as the first day of adult appearance
(Van Strien et al., 2008). In addition, as the occurrence of but te rfly
individuals in temperate species follows an approximately normal
frequency distribution (Arce Crespo & Gutierrez, 2011), the use
of mean date considers to be a safe approach (Moussus, Julliard,
& Jiguet, 2010). We acknowledge the lack of multiple visits per
month (e.g., weekly) but we emphasize that the main purpose is to
examine relative differences in the degree of phenological shift,
rather than to get unbiased estimates of the extent to which phe-
nology change. We also calculated the duration of flight period as
the standard deviation about the mean date (Brakefield, 1987). At
the community level, we used all species for which the estimation
of the two phenological descriptors could be generated (87) and
when comparing the mean flight dates of butterfly communities
between the two mountains and across different habitat t ypes,
only species present in both mountains (87) or all habitat types
(33) were considered.
At species level, we analyzed data for 26 species that (a) were
recorded in more than three sites with at leas t two records per site,
(b) do not overwinter as adults (e.g., Inachis io, Gonepteryx rhamni)
or fly in early spring (e.g., Anthocharis cardamines, Callophrys rubi)
as numbers of these species could be underestimated due to the
dates when we began sampling, and (c) were not long-distance
migrants (Colias crocea, Issoria lathonia, Pieris brassicae) as ap-
pearance in the mountains will be biased by population situations
elsewhere. Information on overwintering stage and voltinism was
extracted from published sources (Pamperis & Stavridis, 2009;
Tolman & Lewington, 1997). To investigate interspecific relation-
ships of species phenology with sample size and with elevational
range, we also calculated the following measures for each species:
the number of sites where the species was present, the minimum
elevation, the maximum elevation, and the elevational range
(maximum–minimum).
2.4 | Data analysis
For our first hypothesis, we inves tigated variation with elevation in
the timing and duration of the flight period. We carried out linear-
mixed models where the mean date and standard deviation about
the mean date were modeled as a function of altitude, mountain,
and habitat. In addition, species were included as a random ef-
fect. Models were validated by checking for homoscedasticity and
normality of the residuals (Zuur, Ieno, Walker, Saveliev, & Smith,
2009), and in all cases, diagnostic graphs showed that model as-
sumptions were met (Figure S2). For these models, altitude slope
represented the delay (in days/km).
To investigate our second research question, we evaluated
whether butterfly assemblages occurring in Rodopi have greater
elevational delays in emergence compared with their counter-
parts in Grammos (considering species common to both mountain
ranges), as a result of the different rates of climatic variation with
elevation between the two mountains. We did this using stan-
dardized major axis (SMA) analysis. SMA is especially suitable
when the prime interest is to inspect the slopes to see how each
pair of variables is related to each other, rather than predicting Y
(phenological descriptor) from X (elevation). In addition, SMA is a
slope-fitting technique that shows how one variable scales against
another, and slopes are fitted via a permutation test by minimizing
the residual variance in X and Y dimensions simultaneously rather
than Y alone (Domínguez et al., 2012; Falster & Westoby, 2005)
resulting thus in a less biased outcome compared to traditional ap-
proaches such as ANCOVA (Warton, Wright, Falster, & Westoby,
2006). As a result, the sampling error which in our case is derived
by the high topographic variability of mountain ranges can be
minimized and biased slopes avoided (Legendre, 2001). Although
SMA has been recommended particularly for allometric studies
(Warton, Wright, & Wang, 2012), it can also be applied to ecologi-
cal responses to environmental variables in the context of climate
change (Zografou, 2015). Mountain was used as the grouping fac-
tor, and we discarded the first sampling in Rodopi (April 2012) in
order to ensure that data (of four visits between May and August)
were comparable between the two mountain ranges.
For the third research question, we used the same approach and
tested whether butterfly assemblages that occur in forests have lon-
ger phenological delays (steeper slopes) with elevation compared to
their counterparts in grasslands or agriculture areas.
To investigate our last research question regarding variation
in the phenology of individual species with elevation, we ran
general linear models for the 26 selected species, using the re-
gression slope to estimate the delay in days/m. To account for be-
tween mountain and habitat variation, both terms were included
in the models and p values were corrected using Benjamini and
Hochberg (1995) adjustment method. We also tested whether the
elevational delay was related to the number of sites where a spe-
cies was present and the species' elevational range. Species that
are present in more sites or species with wider elevational ranges
may be expected to have longer delays compared to those whose
distributions are limited to fewer sites or high elevations only, be-
cause the latter species may exhibit flight periods synchronized
within a narrow phenological window, for example, avoiding the
risk of unfavorable weather conditions in late summer (Illán et al.,
2012).
The analyses were conducted in R (version 3.3.1; R Core Team,
2014), specifically using lm function (Chambers, 1992; Wilkinson
& Rogers, 1973) for general linear models and lme4 package for
mixed-effects models (Bates, Mächler, Bolker, & Walker, 2015), and
the SMATR 3 package (Warton, Duursma, Falster, & Taskinen, 2012)
for SMA analysis.
To visualize our general linear- and mixed-effects models with
partial residual plots, we extracted adjusted data using the “vis-
reg” function in the “VISREG” package (Breheny & Burchett, 2017).
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ZOGR AFOU et Al .
Finally, the “ggplot” function of the “ggplot 2” package (Wickham,
2016) was used and ggplot2 library for the graphical representation
of our results.
2.5 | Temperature lapse rate
We collected temperature data at each site using a Hobo data logger,
to determine the gradient in seasonal temperature over elevation
(lapse rate) overall and for each mountain. The logger was placed
in full shade at the beginning of each transect walk and recorded
temperature (°C) each minute until the end of the sampling event
(approximately 90 min). We used the same logger to record tempera-
ture during each site visit. A mean value for temperature at each site
was calculated using all temperature measurement s from each visit
between May and August. Lapse rate was calculated by regressing
the mean temperature per site against elevation.
3 | RESULTS
3.1 | Phenological patterns at the community level
We found a positive and significant relationship between the
variables “mean date” and “elevation” for the whole species pool
(i.e., butterflies that occur in both mountains) (p < .001; mean
date = 169 + 14.11 × elevation), a delay of 14 days for every kilo-
meter increase in elevation. There was considerable variation in
mean flight date around this main pattern, with a 112-day interval
between mean flight date at the earliest low-elevation site and the
latest high-elevation site (Figure 1a). The relationship between the
duration of flight period and elevation was also significant (p = .01;
duration = 23.20–2.71 × elevation). The negative slope indicates a
shortening of the duration of the butterfly flight period with increas-
ing elevation of approximately 3 days per kilometer (Figure 1b).
3.2 | Phenological patterns between mountains and
across habitats
The relationship between mean date and elevation differed between
mountains (LR test: 6.95, p = .007, n = 67) indicating a different rate
at which but terfly assemblages delayed their appearance date with
elevation (Figure 2). Both ecoregions had positive slopes, but Rodopi
showed a steeper slope, indicating a bigger delay of but terfly ap-
pearances in days for every kilometer increase in elevation (30 days
for Rodopi and 16 days for Grammos). On the other hand, regres-
sions for flight period duration in both regions had a negative slope
(Grammos: −9.02, Rodopi: −10.06) and no significant differentiation
emerged between mountains (LR test: 0.19, p = .67, n = 67), signify-
ing a similar rate at which the duration of flight period changed with
elevation. No significant differentiation in the rate of delay for but-
terfly assemblages across the three habitat types (Grammos LR test:
0.22, p = .91, n = 26; Rodopi LR test: 0.69, p = .74, n = 41) suggests
that habit at type has little or no impact.
3.3 | Phenological patterns at species level
We analyzed elevational patterns for 26 species: 20 had a positive
slope when testing the relationship bet ween the mean date and
elevation, and six had a negative slope (Table 1). Significant slopes
were positive for 11 species indicating a delay in the flight date with
increase in elevation, and negative for one species indicating an op-
posite trend (Table 1). Of the species showing significant delays,
Pontia edusa had the biggest delay (53.28 days/km) and Polyommatus
icarus the smallest delay (10.57 days/km). The opposite pattern was
seen for Plebejus idas (−20.21 days/km). Relationships between the
duration of the flight period and elevation showed 20 negative and
six positive slopes, out of which four were significant with negative
relationship (Table 1). Erynnis tages had the steepest (57.37 days/
km) negative slope or decrease of its flight period with elevation and
Melanargia galathea had the smal les t dec rease (10 days/km ) (Tab le 1).
We found no interspecific evidence of effects on the species' eleva-
tional delay of the number of sites where species occurred (p = .98,
n = 26) or the width of the species' elevational range (p = .40, n = 26).
3.4 | Temperature lapse rate
Considering both mountains and years, we found a significant de-
cline of temperature with elevation of 3°C for every kilometer
(R2 = .42, p < .001, n = 67; mean temperature during sampling
events = 24.8–3.2 × elevation). For Rodopi, mean temperature de-
creased by 3°C per kilometer in 2012 (R2 = .34, p < .0 01, n = 41; mean
temperature = 24.1–2.7 × elevation), and for Grammos, temperature
decreased by 5°C per km in 2013 (R2 = .63, p < .001, n = 26; mean
temperature = 26.8–4.58 × elevation).
4 | DISCUSSION
4.1 | Date of appearance
In the two Mediterranean mountains studied, flight dates occurred
later for butterfly communities at higher elevations, in agreement
with the few previous studies of Mediterranean mountain butter-
fly communities (de Arce Crespo & Gutiérrez, 2011; Gutiérrez &
Menéndez, 1998; Illán et al., 2012). The flight dates of individual
species also generally occurred later at higher elevations (see also
Forister & Shapiro, 2003). On the basis of a temperature lapse rate
of approximately 3°C per every kilometer in elevation increase, our
findings suggest that a 1°C decrease in mean seasonal temperature
could be associated with a 4.66-day phenological delay at the com-
munity level and an 7.71-day (average) phenological delay at the
species level. A similar trend of a 3.7-day phenological delay for the
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ZOGRA FOU et Al.
entire butterfly community has been repor ted from Spain (de Arce
Crespo & Gutiérrez, 2011; Illán et al., 2012).
The majority of univoltine species (70%) delayed the day of
appearance with increasing elevation, whereas only 31.25% of
the multivoltine species showed both delay and advance (Table 1).
Overall, shifts in phenology for less flexible species, as the ones with
a single annual reproductive cycle, are less pronounced than showed
here (Macgregor et al., 2019). It is not, however, the first time where
univoltine butterflies seem to be more prone to develop adaptations
against the dry and hot summer of Mediterranean region (Garcia-
Barros, 1988). A potential butterfly strategy for increasing caterpil-
lars' sur vival rate is to avoid the dry summer period and becoming
more active on the cooler and wet months of early spring (Lopez-
Villalta, 2010).
On the other hand, previous work suggest s that species with
multiple generations may take advantage of warming conditions by
increasing the number of generations (Altermatt, 2010) and thus
showing thermal plasticity in life cycle regulation (Van Dyck et al.,
2015). Greater synchrony in time of emergence across temperature
gradients for multivoltine species has also been interpreted as a
possible sign of adaptation to local climatic conditions (Roy et al.,
2015). The altitudinal delays we observe are thus likely to be sub-
ject to plastic variation depending on annual climatic conditions.
For example, Suggitt et al. (2012) found that butterflies occurring in
both Brit ain and Catalonia can shift their use of different habitats or
different local microclimates in response to year-to-year variation in
climate. They concluded that species preferred the cooler conditions
provided by closed habitats such as forests in hot years but were
associated with warmer, more open habitats such as grasslands in
cold years. Hence, although the altitudinal delay we observed for a
species such as Aporia crataegi (24.89 days/km) was relatively close
to the delay recorded in Spain with a similar approach (33 days/km,
Illán et al., 2012), these rates are unlikely to represent fixed attri-
butes of the species.
Because of caveats imposed by the space-for-time method such
as the incapacity of tracking species responses in the long term or
for detecting the ef fect of extreme weather conditions in succes-
sive summer periods, it is safer to follow the general trend implied
by the slope and to interpret the observed patterns in the light of
traits that make species susceptible to climate change. For example,
P. idas (−20.21) was recorded earlier at higher elevation and cooler
conditions. A possible explanation could be earlier availability of
food resources at higher elevations: Similarly to butterflies, plants
also have shortened their life cycles and advance their flowering and
seed production as the climate has warmed (Steltzer & Post, 2009).
Alternatively, negative species patterns might be regulated by an
evolutionary adaptability to warmer climate, through an increased
voltinism, despite the cooler local conditions at high elevation. An
earlier appearance and prolonged flight period within areas above
the timberline has also been reported for an alpine butterfly species
FIGURE 1 Partial residuals and prediction lines showing effects of elevation on (a) mean date (days since 1 January, 1 January = 1) and (b)
duration of the flight period (standard deviation about the mean date). Dots correspond to the mean date of a species per sampling site
934
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ZOGR AFOU et Al .
(Erebia epiphron) in Czech Republic over the last decades (Konvička
et al., 2016).
4.2 | Duration of the flight period
Almost a 3-day decline in the duration of the flight period for the
community and an 8.86-day average decline considering responses
by individual species over elevation are in agreement with previous
findings in the Mediterranean area (de Arce Crespo & Gutiérrez,
2011; Illán et al., 2012). We argue, however, that the lack of signifi-
cant individual responses for most of the species tested is not simply
due to a lack of statistical power, given that interspecific variability
in elevational delays did not appear to be associated with sample size
or elevational range.
Difference in species' diet spectrum might drive phenological
changes at the level of individual species. We noticed that out of the
four species that shifted their flight periods along elevation, three
were woody feeders and only one was herbaceous plant feeder.
According to Altermatt (2010), species feeding on herbaceous plants
have smaller shifts in flight periods than the woody feeders because
the second group has a narrow window to match the phenolog y to
the flushing leaves. While herbs can produce leaves throughout the
growing season, woody plants usually flush their leaves simultane-
ously (Feeny, 1976), and only for a short time of the year can accom-
modate the needs of herbivores for fresh and palatable resources.
For the four species that exhibited a negative pattern in their dura-
tion of the flight period, three were multivoltine species and one was
a univoltine species. In this context, the more pronounced phenolog-
ical patterns for multivoltine life cycles are indeed a function of the
species' voltinism.
4.3 | Phenological patterns between mountains
The most striking feature is the inconsistency between elevational
delays of but terfly assemblages between the t wo mountains with
respect to temperature lapse rate. In particular, Rodopi showed a
delay of 30 days for every kilometer increase in elevation compared
to only 16 days in Grammos. However, the temperature lapse rate
for Grammos in 2013 was −4.58°C/km, almost double that recorded
in Rodopi in 2012 (−2.7°C/km). Indeed, Rodopi is located at higher
latitude but in close vicinity to the sea, creating thus a mixture of
Mediterranean and continental climate and a shallower temperature
lapse rate, as opposed to Grammos which is located at the northwest
edge of Pindos mountain range, where a mountainous continental
climate prevails (Korakis, 2002; Xirouchakis, 2005).
A tempting explanation would be that the most abundant species
that emerge later in the season drive the observed patterns, influenc-
ing altitudinal delays disproportionally (de Arce Crespo & Gutiérrez,
2011). However, this explanation is not valid in our case. The three
most abundant species in Rodopi, P. icarus (407), Coenonympha pam-
philus (351), and C. crocea (303) counting for 21% of the total records
were present at both sites of low and high elevation (127–1,745 m;
127–1,458 m and 127–1,745 m, respectively) and had more than one
broods covering the whole sampling period (from May to August),
suggesting no such effect.
We argue that the steeper temperature lapse rate (5°C) along
the elevation in Grammos may have driven species to better syn-
chronize their activity resulting in a smaller delay overall. Empirical
evidence suggests that populations from more variable environ-
ments have higher levels of plasticity which could preadapt them
to extremes (Chevin & Hoffmann, 2017). When such extremes
are lacking, it is logical to assume that species responses are not
masked by phenotypic plasticity and therefore are steeper and
more pronounced. Another explanation could be that species in
Rodopi are closer to their upper thermal limits, and it is unlikely to
evolve physiological tolerances to increased temperature (Araújo
Miguel et al., 2013; Mills Simon et al., 2017). As a result, their per-
formances are steeper and declines more pronounced compared
to Grammos. Similarly, another study confirmed that for species
adapted to high mean temperatures, it is more likely to experi-
ence detrimental phenological shifts to warmer climate (Scranton
& Amarasekare, 2017). Further research to test the consistency
of the patterns on each mountain and the establishment of
FIGURE 2 Variation in relationships between mean date and
elevation for the two mountains. Gray line and dots correspond to
Grammos and black to Rodopi. Only butterfly species present in
both mountains were considered for the calculation of the mean
date. Each dot corresponds to a sampling site (total number of sites,
n = 67), and dotted lines refer to nonsignificant regression lines
|
935
ZOGRA FOU et Al.
TABLE 1 Results of the linear regressions for the mean date (days since 1 Januar y, 1 January = 1) against elevation for the 26 selected speciesa
Species Intmd SEmd Slopemd days/km Pmd IntdSEdSloped days/km PdNo. of sites Min alt. Max alt. Range (max–min)
Aporia crataegiu143.48 6 .14 24.89 *** 25.48 3.42 −4.66 ns 17 128 1,516 1,388
Argynnis paphiau182.97 5.42 13.90 ** 11.9 2 4.41 −0.10 ns 26 128 1 ,410 1,282
Aricia agestis 155.93 21.00 38.55 *41 .3 4 14.20 −13 .94 ns 12 406 1,453 1,047
Brenthis daphneu144. 25 12.27 2 9.67 *23.91 3.52 1.73 ns 8128 1,205 1,077
Brintesia circeu170.05 4.89 22.40 *** 12.96 1.77 −1.4 9 ns 12 420 1,638 1,218
Coenonympha arcaniau165.29 14 .8 0 12.60 ns 2 7. 39 27. 8 4 −9.61 ns 7860 1,410 550
Coenonympha pamphilus 184.13 9.8 4 −5.05 ns 22.71 6.88 10.47 ns 32 128 1,532 1,404
Erynnis tages 1 51.99 13.95 −5.00 ns 102. 31 3.89 −57.3 8 * 5 406 1,247 842
Iphiclides podalirius 161. 85 18 .14 11. 38 ns 22.33 11.0 3 5.06 ns 20 128 1, 516 1,388
Lasiommata megera 185.33 9. 90 −7.8 4 ns 36. 55 9.14 −0.07 ns 8128 1,74 6 1,618
Leptidea duponcheli 210.50 2 9. 64 −4.55 ns 18.34 24.63 1.21 ns 8252 1 ,745 1,493
Leptidea sinapis 174.4 8 13.18 20.80 ns 44.40 14 .0 9 −18.18 ns 18 128 1,3 41 1, 213
Lycaena phlaeas 159.47 12.93 2 7. 59 *42.16 13.38 −6.98 ns 17 128 1,458 1,330
Lysandra bellargus 172.67 48.23 4 4 .51 ns 43.85 1.71 −23 .33 ** 6128 1,188 1,060
Maniola jurtinau154.38 5.76 37.72 *** 22.64 3.39 −5.69 ns 20 128 1,532 1,404
Melanargia galatheau167.25 2.91 23.62 *** 24.16 4.17 −1 0.23 *19 422 1,532 1,110
Melitaea didyma 160.88 10.28 13.78 ns 28.17 6.08 −8.13 ns 12 128 1,247 1,119
Parnassius mnemosyneu121.09 13.88 29.75 ns 26. 89 11. 33 −5.07 ns 41, 516 1 ,912 396
Pieris mannii 191.18 18.80 16.72 ns 28.9 0 3.21 −1 5.4 2 * 6 433 1,3 41 908
Pieris rapae 194.76 10.64 14.87 ns 23. 35 8.49 −6.10 ns 17 128 1,458 1,330
Plebejus argus 191.68 −7. 0 8 8.78 ns 16.8 8 7.10 5.83 ns 19 128 1, 516 1,388
Plebejus idas 210.76 8.22 −20. 21 *13.85 6 .76 10.81 ns 7128 1,035 907
Polyommatus icarus 180.00 5.62 10.57 *26.79 4.94 −9. 5 3 ns 42 128 1,746 1,618
Pontia edusa 146.16 8.77 53.28 ** 22 .96 8.45 −6.10 ns 8420 1, 341 921
Pyronia tithonusu221.25 41.27 −8.06 ns 37.07 3. 28 −27.04 ns 4546 1,035 489
Thymelicus sylvestrisu163.50 0.42 17. 23 ** 18.53 1.31 −8.37 ns 4128 1,410 1,282
Note: Int: intercept, SE: standard error, subscript md corresponds to mean date and d to the duration of the flight period. Univoltine species are indicated by the subscript letter u, while the rest have more
than one generation. Significance codes: 0 “***” 0.001 “**” 0.01 “*” 0.05 “ns” nonsignificant.
The species are in alphabetical order. The number of sites occupied, the minimum and maximum elevations (m), and the elevational range for each species are included too.
aSelected species are spe cies recorded in more than three sites with at least two records per site and species overwintering as egg, pupae, or larvae, excluding thus early spring flyers and species
overwintering as adults for which phenolog y may not be recorded comprehensively.
936
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ZOGR AFOU et Al .
permanent meteorological stations within each region might help
to inform our findings.
4.4 | Predictions and conclusions
On the basis of the different climatic scenarios proposed for the
Eastern Mediterranean and Middle East, the mean temperature rise
will be about 1–3°C in the near future (2010–2039), 3–5°C by mid-
centur y (2040–2069), and 3.5–7°C by the end of the century (2070–
2099) (Lelieveld et al., 2013). Under the first scenario, based on the
space-for-time substitution butterfly phenology would advance by
4.66–14 days in terms of the community and by 7.71–23.11 days for
individual species; under the second scenario, the advances would
be 14–23.33 days and 23.11–38.53 days, respectively, and under the
third scenario, 16.33–32.66 days and 26.97–53.94 days. However,
given the complexit y and dynamism of the natural system, such
radical changes of temperature are likely also to change many other
contributing factors too, such as weather conditions at different
times of year, as well as ecological community structure, where we
are likely to see warm-adapted species expanding at the expense of
cold-adapted ones (Zografou et al., 2014). A further aspect of system
complexity is the ongoing forest encroachment that tends to coun-
teract climate change, benefiting woodland species at the expense
of others (Slancarova et al., 2016).
While it is difficult to foresee how organisms are going to cope
under the ongoing changes in climate, it is possible that advanced
emergences could threaten serious trophic disruption between in-
teracting groups. Our findings both confirm an earlier and prolonged
activity at lower elevations overall. At the same time, confound ex-
pectations for ectotherms such as signs of earlier appearance in high
elevations for multivoltine organisms and more pronounced shifts
in flight periods for the woody feeders challenge the idea that these
species assemblages have special thermal traits that confer adaptive
advantage under new conditions.
ACKNOWLEDGMENTS
This research has been cofinanced by the European Union (European
Social Fund—ESF) and Greek National Funds through the Operational
Program “Education and Lifelong Learning” of the National Strategic
Reference Framework (NSRF)—Research Funding Program:
Heracleitus II. Investing in knowledge society through the European
Social Fund. We are grateful to “Proodeutiki Enosi Pyrsogiannis” and
the personnel of the Management Body of Rodopi Mountain Range
National Park for research support.
CONFLICT OF INTEREST
None declared.
AUTHOR CONTRIBUTIONS
VK, JMH, RJW, AG, and KZ conceived and designed the experi-
ments. KZ performed the experiments, and KZ and GCA analyzed
the data. KZ wrote the first draft of the manuscript, and AG, RJW,
JMH, VK, and GCA contributed to subsequent versions of the article
and agreed on the final version to be published.
OPEN RESE ARCH BADGE
This article has earned an Open Data Badge for making publicly
available the digitally-shareable data necessar y to reproduce the re-
ported results. The data is available at https ://datad ryad.org/stash/
s h a r e / 0 w y A a Q C l p d v i K i C 3 L 8 U f g _ U 0 p O c H f H V X 8 P w K 9 t U f K I 0 .
DATA AVAIL ABI LIT Y S TATEM ENT
Data connected to the manuscript with the title “Butterfly phenol-
ogy in Mediterranean mountains using space-for-time substitu-
tion” have been deposited in a publicly accessible repository Dr yad:
https ://datad ryad.org/stash/ share/ 0wyAa QClpd viKiC 3L8Ufg_
U 0 p O c H f H V X 8 P w K 9 t U f K I 0 .
ORCID
Konstantina Zografou https://orcid.org/0000-0003-4305-0238
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SUPPORTING INFORMATION
Additional suppor ting information may be found online in the
Supporting Information section.
How to cite this article: Zografou K, Grill A, Wilson RJ, Halley
JM, Adamidis GC, Kati V. Butterfly phenology in
Mediterranean mountains using space-for-time substitution.
Ecol Evol. 2020;10:928–939. https ://doi.org/10.1002/
ec e3 . 5951
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