Advertisement

Journal of Insect Conservation

, Volume 20, Issue 6, pp 1021–1032 | Cite as

Adult longevity and its relationship with conservation status in European butterflies

  • Terezie Bubová
  • Martin Kulma
  • Vladimír Vrabec
  • Piotr NowickiEmail author
Open Access
ORIGINAL PAPER

Abstract

Many European butterfly species are currently experiencing serious declines, and may be threatened with extinction. Nevertheless, due to limited knowledge on the species biology and ecology, detailed assessments of endangerment level are not possible, and instead identifying species of conservation concern has to rely on proxies. Earlier studies suggested several characteristics, including host plant specificity, overwintering stage, patch size requirements or mobility, as potentially useful indicators of butterfly species vulnerability, but the usefulness of adult longevity in this respect has not been considered so far. Based on the information gathered through an extensive literature search we investigated the relationship between adult life span, flight period length or the temporal fragmentation index calculated as the ratio of the two parameters, and conservation status of European butterflies. We found that the species classified in one of the IUCN conservation concern categories (i.e. Endangered, Near Threatened, or Vulnerable) lived shorter as adults and were characterised by higher values of the temporal fragmentation index, while there was no particular pattern concerning flight period length. We believe that the apparent effects detected reflect the fact that shorter adult life span, and thus increased temporal fragmentation, in combination with protandry, i.e. earlier emergence of males, decrease individual chances of finding mating partners. Such a situation leads to lower effective population size and reduced viability, especially in the case of small populations. All concerned, the investigated parameters reflecting adult longevity may serve as ‘early warning’ indicators, helping to flag-up butterfly species possibly at risk.

Keywords

Extinction risk Flight period Life span Species vulnerability Temporal fragmentation Threat level 

Introduction

Butterfly populations in Europe have declined drastically in recent decades (Thomas et al. 2004; EEA 2011). These negative trends have prompted the launching of numerous programmes for butterfly conservation (Warren and Bourn 2011). However, for effective conservation, it is necessary to properly identify species threatened with extinction, and Red Lists are compiled for this purpose. Assessments of species positions on such lists should ideally be based on thorough knowledge of their biology and ecology and how these affect the species vulnerability to threats (Margules and Pressey 2000; Mattila et al. 2006). Despite the fact that butterflies comprise one of the most studied invertebrate groups, such knowledge is nevertheless available for only a very limited number of butterfly species (van Swaay 2002; Wenzel et al. 2006; Müller et al. 2010). Therefore, for their successful conservation, it is important to identify traits that predispose butterfly species to extinction risk (Mattila et al. 2006). Life history traits and/or ecological characteristics could be used as indicators of potential vulnerability to threats as many of these characteristics are common among species of conservation concern (Statzner et al. 2001; Mattila et al. 2006; Nylin and Bergström 2009).

Studies conducted to date have identified a wide range of butterfly characteristics that can potentially act as proxies for extinction risk. One of the most commonly addressed aspects in this respect is the division into generalists and specialists (Nylin and Bergström 2009; Ali and Agrawal 2012; Bartoňová et al. 2014). The latter group includes a disproportionally high number of threatened species due to their stricter habitat and host plant requirements (Hodgson 1993; Purvis et al. 2000; Fontaine et al. 2007). Another well-established pattern relates to life history and voltinism, where univoltine species and/or those overwintering in the egg or larval stage are more susceptible to climate change, which has recently become one of the most serious drivers of butterfly declines (Hodgson 1993; Conrad et al. 2004; Mattila et al. 2006; Nylin and Bergström 2009). In addition, low mobility was typically reported for threatened butterflies (Kotiaho et al. 2005; Mattila et al. 2006; Nylin and Bergström 2009; Habel et al. 2015). This is not surprising, because less mobile species with low colonization success rates are more vulnerable to the effects of habitat fragmentation, which is nowadays a crucial threat for butterflies (Thomas 1995; Novacek and Cleland 2001; Baguette and Schtickzelle 2006; Franzen and; Johannesson 2007). Also as a consequence of habitat fragmentation, butterflies with greater patch size requirements are highly represented among species of conservation concern (Cowley et al. 1999; Kotiaho et al. 2005; Baguette and Stevens 2013).

It has recently been pointed out that the extinction risk of butterfly populations is likely to depend not only upon classic spatial fragmentation of their habitats, but also upon their fragmentation over time (Nowicki et al. 2005b). The latter derives from the fact that the individual life span of the adult butterflies is usually much shorter than is the length of adult occurrence season dubbed as the flight period. Consequently, groups of individuals from different parts of a season do not have the chance to mate with one another. This problem is further exacerbated by protandry, i.e. earlier emergence of males in the season as compared with females, which is typical for butterflies (Wiklund and Fagerström 1977). The extent of and a temporal fragmentation (sensu Nowicki et al. 2005b) depends upon adult life span and flight period length and so it is highly variable among butterflies. Surprisingly, the effects of temporal fragmentation on butterfly species extinction risk have not been investigated so far.

In the present study we evaluated the relationships between adult life span, flight period length, and a temporal fragmentation index (defined as the ratio of flight period length to adult life span) on one hand, and the species conservation status, as reported in the European Red List (van Swaay et al. 2010), on the other hand. Butterflies that have short life spans and long flight season length will have fewer opportunities for males and females representing different daily cohorts to meet together (Nowicki et al. 2005b). Consequently, we hypothesised that a higher level of threat should be associated with: (a) shorter adult life span; (b) longer flight period length; and (c) higher values of the temporal fragmentation index. We tested the above hypotheses using the data gathered through an extensive literature review.

Methods

Literature search

In order to gather information on adult life span and flight period length in butterflies, we searched for mark-recapture studies (which typically assess both these parameters) within the following databases: ISI Web of Science (http://apps.webofknowledge.com/), Scopus (http://www.scopus.com/home.url), and Google Scholar (http://scholar.google.com/). We used “(re)capture” and “butterfly/butterflies” or “Lepidoptera” as the searched keyword combinations. We restricted our search results to only European species, because relatively little information was available for all others, with the slight exception of a small number of North American species. We also utilised relevant grey literature on the subject, in particular academic theses or reports known to us.

We considered only adult life span data originating from field studies, and thus we excluded literature on adult life span measured in controlled conditions, i.e. physiological longevity. This is because when butterflies are raised in enclosures or laboratories, their recorded longevity is known to overestimate the life span actually achieved in nature (Karlsson and Wiklund 2005; Nowicki et al. 2005b). We likewise excluded mark-recapture estimates of butterfly residence time assessed in clearly open populations, which are subject to substantial emigration and thus considerably underestimate butterfly longevity (Nowicki et al. 2005a). An exception was made for the cases in which the emigration rate could be estimated and accounted for, e.g. using the Virtual Migration model (Hanski et al. 2000). Consequently, we believe that the life span estimates we used in our study represent true adult longevities and not just adult residence times within the surveyed populations.

Quite often the literature sources did not explicitly report adult life span but instead they gave adult survival rate estimates. In such cases we converted survival rate (\(\phi\)) into adult life span (e) using the following formula: \(e={{(1-\phi )}^{-1}}-0.5\) (Nowicki et al. 2005b). Whenever the studies reported adult life span or survival estimates for males and females separately, we used the mean of the two values because they typically differed rather little, i.e. by less than 10%. It should be noted that adult life span values provided by mark-recapture studies are restricted to a single season, and thus they do not account for the fact that certain individuals flying during the season might also be on the wing in the previous or in the following year in species overwintering as adults. Nevertheless, due to the high mortality of overwintering adults such individuals are relatively few. Furthermore, their existence does not undermine the usefulness of single-season data for our study, in which adult life span is understood as the time during which an adult flies and breeds within a single generation, and not as its total life expectancy, including the period of inactive overwintering.

Regarding flight period length, we calculated it as the total number of days of adult occurrence inclusive of the first and last day. Thus, for example, a flight period from 1 to 30 July corresponds to a length of 30 days (and not 29 days). We excluded the studies that did not cover the entire flight period, which was either explicitly mentioned by the authors or was clear from the reported results, with relatively high daily numbers of individuals recorded at the beginning or the end of the study period. In a few cases, we also used information on flight period length from studies other than mark-recapture surveys, e.g. behavioural observations conducted from the very start to the very end of the season. On the other hand, we decided not to use the information on flight period provided by several general books on butterflies (e.g. Settele et al. 1999; Beneš et al. 2002). Even though such information was easily available for most European species, it turned out to be too superficial to be applicable in our analyses. Specifically, the information was given in the form of general statements, mentioning that e.g. species X flies from early July to mid-August, thus allowing only very coarse assessments of flight period length with margins of error of as much as 10–15 days.

Data handling and analysis

The extent of temporal fragmentation was calculated as the ratio between flight period length and average adult life span, both measured in days. In the case of studies spanning several years (e.g. Schtickzelle et al. 2002; Nowicki et al. 2009), we treated the data from each year separately. In making the calculations, we endeavoured to use data on life span and flight period length from the same population and year. This usually was possible because most mark-recapture studies reported both. Otherwise, we paired together data on adult life span and flight period from the closest-lying locations, but in rare cases these locations were quite distant from one another and represented different biogeographic regions. For multivoltine species, we relied on information provided by studies conducted on the first generation whenever possible. This is because the population size of the first generation is generally much smaller than those of later generations and it is thus critical for species persistence, and furthermore the occurrence of later generations is sometimes facultative (Franzen and Johannesson 2007; Fric et al. 2010; Nabielec and Nowicki 2015).

For species with more than one record available, we calculated median values of adult life span, flight period length, and temporal fragmentation index and used them in the subsequent analyses, since the distributions of records were often non-normal (right-skewed). In turn, it is worth noting that the distribution of median values among species was normal for all of the three parameters considered.

For the purpose of the analyses, we adopted the Red List status of European butterflies in accordance with van Swaay et al. (2010). Despite our original expectations, the sample sizes of species for which we were able to gather data were small in the three conservation concern categories, i.e. Endangered (EN), Vulnerable (VU), and Near Threatened (NT). We therefore decided to pool these into a single ‘conservation concern’ category (CC) and analyse it against the species categorized as Least Concern (LC).

We analysed the relationship between adult life span, flight period length or temporal fragmentation index, and conservation status (CC vs. LC, dichotomous dependent variable) using logistic regression analysis. For each predictor, we conducted two separate analyses, using the full data set gathered as well as what we term a ‘core data set’. The core data set excluded species for which data quality was problematic for various reasons. These included cases of (a) species for which data on adult life span and flight period length came from distant populations, representing different biogeographic regions; (b) species for which only the data for the second generation was available; and (c) Maculinea alcon, the conservation status of which is questionable, apparently due to its uncertain systematic status, with two distinct forms existing, namely M. alconalcon’ and M. alconrebeli’ (Als et al. 2004; Steiner et al. 2005; Pecsenye et al. 2007; Sielezniew et al. 2012). It is classified as LC by van Swaay et al. (2010), however many authors regard both forms to be under threat in Europe (WallisDeVries 2004; Tartally et al. 2008; Czekes et al. 2014).

Obviously, a common problem with cross-species analyses is that records for related species may not be fully independent from one another. The standard solution in such cases is controlling for phylogenetic autocorrelations (Martins and Hansen 1996). However, this was not possible in our study, because a full phylogenetic tree with scalable inter-specific distances is not yet available for European butterflies (cf. Cowley et al. 2001; Bartoňova et al. 2014). Therefore, in order to verify the risk of phylogenetic autocorrelation biases, we instead applied the intraclass correlation coefficients (Stanish and Taylor 1983; Lessells and Boag 1987) to test for potential repeatability of our records within species as well as higher taxa, namely tribes, subfamilies, and families (but not at the genus level, because we rarely had data for more than one species per genus). The testing yielded significant results for species (life span: r I  = 0.7379, P < 0.0001; flight period length: r I  = 0.5513, P < 0.0001), but not for any higher taxa (life span: r I  = 0.0539, P = 0.6691 for tribes; r I  = 0.1166, P = 0.4962 for subfamilies; r I  = 0.0408, P = 0.6386 for families; flight period length: r I  = 0.1922, P = 0.2835 for tribes; r I  = 0.1401, P = 0.4468 for subfamilies; r I  = 0.1312, P = 0.4213 for families). Such an outcome indicates that our data records were highly repeatable within species, but fairly independent among them. This, in combination with the fact that despite a relatively small sample size we managed to gather data for a wide range of European butterfly species, makes us believe that the results of our analyses are not biased by potential phylogenetic autocorrelations.

Results

We successfully gathered relevant information for 50 species of European butterflies, including 4 classified as EN, 5 as VU, and 6 as NT, as well as 35 species classified as LC (Table 1). The average adult life span of these species ranged from ca. 2.5 to 15 days. The flight period length was between 20 and 50 days in most cases, although some clear outliers could also be noticed. The shortest adult occurrence season was reported for Pseudophilotes bavius (median value of 16.5 days), whereas in satyrid butterflies it sometimes approached or exceeded 70 days (in Maniola jurtina and Coenonympha pamphilus respectively). There was no apparent correlation between flight period length and adult life span (Pearson’s correlation: r = 0.1794, P = 0.2125), and consequently the ratio of the two parameters, which we defined as the temporal fragmentation index, varied greatly from ca. 2 to more than 12 (Table 1).

Table 1

Summary information on adult life span, flight period length, and the temporal fragmentation index (i.e. ratio of flight period length to adult life span) gathered for European butterflies

Species

Status

Life span (days)

Flight period (days)

Temporal fragmentation

Sources

Colias myrmidone a

EN

3.54 (3.32–3.75)

26 (21–31)

7.47 (5.60–9.34)

Szentirmai et al. (2014)

Lycaena helle

EN

7.19 (5.45–7.83)

39 (29–64)

5.87 (3.87–8.90)

Fischer et al. (1999), Bauerfeind et al. (2009), Reymond (2014), Turlure et al. (2014), Nabielec and Nowicki (2015)

Phengaris (=Maculinea) arion

EN

3.53 (3.07–4.26)

37 (32–39)

10.28 (9.15–10.92)

Bonelli et al. (2013)

Coenonympha oedippus

EN

3.00 (2.50–4.20)

27.5 (18–28)

7.98 (6.67–10.00)

Örvössy et al. (2013)

Phengaris (=Maculinea) teleius

VU

3.01 (1.61–4.16)

38 (28–56)

12.01 (9.12–40.00)

Nowicki et al. (2005a, b, 2009, 2014), Vodă et al. (2010)

Euphydryas maturna

VU

6.53 (4.75–8.30)

42.5 (36–49)

7.33 (4.34–10.32)

Wahlberg et al. (2002), Konvička et al. (2005)

Lopinga achine

VU

6.30 (5.95–6.64)

28 (25–31)

4.43 (4.20–4.67)

Bergman and Landin (2002), Streitberger et al. (2012)

Coenonympha tullia

VU

3.05 (2.80–3.30)

24.5 (21–28)

7.99 (7.50–8.48)

Turner (1963), Warren (1992), Komonen et al. (2004)

Erebia sudetica

VU

4.00

29

7.25

Nowicki et al. (2005a)

Thymelicus acteon b

NT

7.00

41

5.86

Thomas (1983), Buszko and Masłowski (2008)

Parnassius mnemosyne

NT

9.06 (5.05–11.15)

51 (31–51)

5.66 (4.57–10.10)

Schmidt (1989), Seufert (1990), Konvička and Kuras (1999)

Parnassius apollo

NT

3.73 (3.20–4.26)

29 (25–33)

8.08 (5.87–10.30)

Brommer and Fred (1999), Komonen et al. (2004), Fred et al. (2006)

Iolana iolas

NT

5.88 (3.63–8.12)

49.5 (49–50)

9.83 (6.16–13.50)

Rabasa et al. (2005, 2007), Heer et al. (2013)

Phengaris (=Maculinea) nausithous

NT

2.84 (2.02–5.74)

40 (23–56)

12.64 (4.36–23.76)

Pfeifer et al. (2000, 2007), Nowicki et al. (2005a, b, 2014), Vodă et al. (2010)

Euphydryas desfontainii

NT

5.55

36

6.49

Pennekamp et al. (2014)

Pyrgus sidae

LC

9.20

25

2.72

Hernándes-Roldán et al. (2009)

Hesperia comma

LC

4.40 (3.00–10.30)

35 (23–50)

7.95 (2.23–15.63)

Thomas (1983), Komonen et al. (2004), Soulsby and Thomas (2012)

Zerynthia polyxena

LC

5.28 (4.40–6.17)

29 (20–38)

5.94 (3.24–8.64)

Örvössy et al. (2005), Batáry et al. (2008), Celik (2012)

Leptidea sinapis a

LC

8.35 (6.50–10.20)

38.5 (33–44)

5.00 (3.24–6.77)

Warren et al. (1986), Komonen et al. (2004), Friberg et al. (2008)

Leptidea reali a

LC

7.60

44

5.79

Friberg et al. (2008)

Anthocharis cardamines

LC

6.95 (5.60–8.30)

28.5 (21–34)

4.30 (2.53–6.07)

Courtney and Duggan (1983), Dempster (1997)

Lycaena virgaureae

LC

6.43 (6.20–6.65)

31 (29–33)

4.84 (4.36–5.32)

Fjellstad (1998), Komonen et al. (2004), Haaland (2015)

Lycaena hippothoe

LC

9.60 (7.00–10.00)

28 (28–32)

2.92 (2.80–4.57)

Fischer (1998), Fischer and Fiedler (2001), Komonen et al. (2004)

Satyrium w-album b

LC

6.90

28

4.06

Warren (1992), Komonen et al. (2004)

Cupido minimus b

LC

15.00

31

2.07

Morton (1985), Komonen et al. (2004)

Pseudophilotes bavius

LC

2.80 (2.40–5.40)

16.5 (12–28)

5.47 (2.50–11.67)

Crişan et al. (2014)

Phengaris (=Maculinea) alcon c

LC

2.44 (1.62–5.98)

29 (18–60)

11.88 (5.59–29.27)

Seufert (1993), Nowicki et al. (2005a, 2009), Timuș et al. (2013)

Plebejus argus

LC

3.35 (3.20–3.50)

30 (20–40)

9.11 (5.71–12.50)

Warren (1992), Lewis et al. (1997), Cormont et al. (2011)

Aricia eumedon b

LC

3.59

19

5.30

Seufert (1993), Komonen et al. (2004)

Polyommatus icarus a

LC

4.40 (3.40–5.40)

26 (18–34)

5.80 (5.29–6.30)

Dowdeswell et al. (1940), Scott (1973), Komonen et al. (2004)

Polyommatus bellargus

LC

9.10 (8.10–10.10)

26.5 (24–29)

2.98 (2.38–3.58)

Davis et al. (1958)

Polyommatus coridon

LC

5.52 (3.70–8.70)

56.5 (24–63)

10.79 (4.21–14.86)

Davis et al. (1958), Nowicki et al. (2005a), Schmitt et al. (2006)

Argynnis paphia b

LC

11.50

38

3.30

Magnus (1954), Komonen et al. (2004)

Argynnis aglaja

LC

8.20

58

7.07

Zimmermann et al. (2009)

Brenthis ino

LC

9.74 (5.70–13.79)

40.5 (35–46)

5.30 (2.54–8.07)

Zimmermann et al. (2005), Fric et al. (2010)

Boloria eunomia

LC

8.23 (3.00–11.55)

35.5 (26–45)

4.32 (2.68–11.00)

Schtickzelle et al. (2002), Turlure et al. (2010)

Boloria euphrosyne

LC

9.00 (5.97–11.10)

29 (24–32)

3.39 (2.61–4.02)

Baguette and Neve (1994), Komonen et al. (2004), Al Dhaheri (2009)

Boloria aquilonaris

LC

4.26

21

4.93

Turlure et al. (2010)

Euphydryas aurinia

LC

6.40 (2.24–15.37)

31 (15–42)

3.16 (2.60–12.95)

Munguira et al. (1997), Wahlberg et al. (2002), Anthes et al. (2003), Komonen et al. (2004), Schtickzelle et al. (2005), Fric et al. (2010), Zimmermann et al. (2011), Casacci et al. (2015)

Melitaea cinxia

LC

5.80

33

5.69

Wahlberg et al. (2002)

Melitaea Didyma

LC

7.00 (5.50–8.00)

46 (30–51)

5.75 (5.45–7.29)

Vogel and Johannesen (1996)

Melitaea diamina

LC

8.59 (6.35–10.61)

29 (29–51)

4.57 (3.38–4.81)

Hanski et al. (2000), Wahlberg et al. (2002), Fric et al. (2010)

Melitaea athalia

LC

10.00 (5.45–11.26)

35 (30–55)

4.88 (3.50–5.50)

Warren (1987), Wahlberg et al. (2002), Fric et al. (2010), Cormont et al. (2011)

Pararge aegeria a

LC

9.50

23

2.42

Warren (1992), Komonen et al. (2004)

Lasiommata megera a

LC

4.20

40

9.52

Parr et al. (1968), Harker and Shreeve (2008)

Coenonympha pamphilus a

LC

7.30

76

10.41

Wickman (1985)

Aphantopus hyperantus

LC

3.95 (3.60–4.29)

36.5 (35–38)

9.29 (8.86–9.72)

Sutcliffe et al. (1997), Soulsby and Thomas (2012)

Maniola jurtina

LC

6.55 (6.51–10.83)

67 (51–78)

7.84 (6.19–11.91)

Tudor and Parkin (1979), Brakefield (1982), Lörtscher et al. (1997), Cormont et al. (2011)

Erebia aethiops

LC

6.50

33

5.08

Slámova et al. (2013)

Minois dryas

LC

4.47 (3.50–6.74)

25.5 (19–32)

5.17 (4.75–6.75)

Pellet and Gander (2009), Bilnicki (2015)

Whenever more than one record was available for a species, we present the median with the range (min–max) in parentheses. Conservation status follows the European Red List of Butterflies (van Swaay et al. 2010): EN endangered, VU vulnerable, NT near threatened, LC least concern. Superscripts indicate species which were excluded from the core data set used in the analyses for various reasons

aData available only for the second or third generation

bData on adult life span and flight period length came from different regions

cQuestionable conservation status—see Methods for the rationale

As indicated by Red List categories, extinction risk generally increased with increasing adult life span (Fig. 1a). Similarly, butterflies in the three categories collectively characterized as CC had higher temporal fragmentation index values (Fig. 1c). On the other hand, there was no clear pattern concerning flight period length, which turned out to be slightly elevated among NT species. The latter result was partly due to strong variation within this particular group (Fig. 1b).

Fig. 1

Adult life span (a), flight period length (b), and temporal fragmentation index (c) in relation to the conservation status of European butterflies (EN endangered, VU vulnerable, NT near threatened, LC least concern). The values shown represent means (with their SEs) across all the investigated species in each category. Different conservation concern categories (EN, VU, NT) are treated separately only for the graphic presentation, but they were pooled together in the analyses (see Table 2)

Table 2

Results of multiple logistic regression analyses of factors affecting conservation status (conservation concern vs. least concern) of European butterflies

Predictor

Data set (with sample size)

Parameter value (± SE)

Model fit

Intercept

Estimate

χ2

P

R 2

Life span

Full (n = 50)

1.29 ± 0.91

−0.37 ± 0.16

6.83

0.0089

0.27

Core (n = 37)

1.82 ± 1.10

−0.42 ± 0.19

6.05

0.0138

0.33

Flight period length

Full (n = 50)

−1.12 ± 0.98

0.01 ± 0.03

0.09

0.7685

0.01

Core (n = 37)

−1.18 ± 1.20

0.02 ± 0.03

0.25

0.6192

0.01

Temporal fragmentation

Full (n = 50)

−3.18 ± 0.99

0.35 ± 0.13

7.85

0.0051

0.29

Core (n = 37)

−3.85 ± 1.31

0.48 ± 0.18

9.44

0.0021

0.41

The logistic regression analyses confirmed the above patterns, revealing significant relationships with species conservation status (CC vs. LC) in the case of adult life span and temporal fragmentation index, but no effect whatsoever for flight period length (Table 2). It is noteworthy that the effect of adult longevity and temporal fragmentation increased (despite considerably smaller sample sizes) when the analyses were restricted to the core data set, thus excluding species for which the quality of the data gathered was problematic. The threshold value for adult life span at which a species had 50% probability of being listed in one of the CC categories was 3.53 days for the full data set and 4.31 days for the core data set. In the case of temporal fragmentation index, the respective thresholds were 9.19 and 8.01.

Discussion

The selection of species on which we based our investigation may not be fully representative for the whole spectrum of European butterflies. In particular, an underrepresented group are the species from Mediterranean region as well as from northern Europe, the conservation status of which is potentially less related to adult longevity and more to the life history parameters beyond the scope of the present study, such as wintering stage and voltinism (Mattila et al. 2006; Nylin and Bergström 2009). Another limitation of our database is the fact that it lacked very common species, mostly of Nymphalidae and Pieridae families, which are neglected in mark-recapture studies, apparently because of the lack of scientific and conservation interest in them. Nevertheless, based on anecdotal information such species are reported to have long-living adults (Settele et al. 1999; Beneš et al. 2002), and thus we believe that their inclusion would have actually strengthened the outcome of our analyses.

Our results indicated the existence of clear relationships between adult life span and temporal fragmentation index on one hand and conservation status of European butterflies on the other. Moreover, the relationships proved to be significant, regardless of whether the full data set or the core data set was used, which increases our confidence in these findings. In contrast, butterfly conservation status was not linked in any way to flight period length as defined in our study.

Butterfly adult life span is a part of an adaptive life history which involves mating and egg laying strategy (Carey 2001; Beck and Fiedler 2009). The average life span reported in our study typically reached only a few days. Such a short life span implies that the mating must take place shortly after eclosion to minimise delay and allow most of the females to oviposit their eggs before they die (Scott 1973; Beck and Fiedler 2009). Hence, in species with short life spans adult butterflies have a very narrow time window to copulate. Adult life span also affects realised fecundity, i.e. the number of eggs laid, which in turn can have a critical impact on population viability (Fischer et al. 2006; Pijpe 2007; Haeler et al. 2014). Low quantities of oviposited eggs over the long term are bound to result in population decline. It is thus not surprising that the results of our study confirmed that short life span corresponds to higher threat level in European butterfly species.

Alternative hypotheses explaining the relationship between adult life span or temporal fragmentation index and conservation status involve mobility and predation. Since both emigration probability and movement distance are typically time-dependent (Hanski et al. 2000), short-lived butterflies may be expected to emigrate in lower numbers and move shorter distance throughout their adult lifetime. Species with such characteristics are more likely to experience the negative effects of habitat fragmentation, and hence be more prone to being threatened (Kotiaho et al. 2005; Franzen and; Johannesson 2007; Habel et al. 2015), especially in the highly fragmented landscapes of Europe. However, the plausibility of the above explanation is undermined by empirical studies indicating that dispersal capabilities and butterfly life span may be negatively correlated, since investing in mobility and longevity is subject to a developmental trade-off (Hanski et al. 2006; Niitepõld and Hanski 2013).

Additionally, the better conservation status of species with long-living adults might possibly be attributed to their lower mortality due to predation. It has been reported that butterflies with anti-predator defence features (such as aposematism, eye-spots, etc.) have longer life spans, but it must be stressed that the effect of these anti-predator defences on longevity was rather weak and mostly restricted to tropical butterflies (Beck and Fiedler 2009). Furthermore, although predation on adult butterflies may sometimes be considerable, it is nonetheless of relatively little importance for population dynamics as compared with predation experienced at the egg, larval or pupal stages (Dempster 1984; Warren 1992).

An increased level of temporal fragmentation was also found to correspond to higher species vulnerability in our study. A possible explanation is that if butterflies live for only a limited part of the flight period then they may have lower chances of finding mates. These low mating opportunities are further decreased by the fact that males emerge earlier in the season than females, usually by several days, due to protandry (Pfeifer et al. 2000; Petit et al. 2001; Nowicki et al. 2005b). This phenomenon is common in insects, particularly in species for which the flight season is very long and the majority of the population occurs at the beginning of the flight season, such as is the case of the mayfly (Gibbs and Siebenmann 1996; Takemon 2000). Protandry prevents inbreeding and ensures that only strong males survive long enough to mate. Moreover, it also supports immediate female fertilisation, which minimises the risk of females dying before mating (Fagerström and Wiklund 1982; Zonneveld and Metz 1991; Zonneveld 1992; Morbey and Ydenberg 2001).

Although all the aforementioned effects are considered positive, some negative consequences of protandry are also known. Since a fraction of males may not survive long enough to mate with the later-emerging females, some females will consequently go unmated, and will thus be unable to lay eggs. This lost reproductive potential can significantly decrease population size and could also lead to population extinctions. The protandry effect might therefore be evolutionarily advantageous at higher population densities but, if the population density decreases, it can be harmful (Calabrese and Fagan 2004). Additionally, weather conditions could affect the proper timing of male and female emergence by up to several days (Schtickzelle et al. 2002; Robinet and Roques 2010). For instance, rainy days at the beginning of the flight period in 1983 were found to influence individual development in Euphydryas editha bayensis, resulting in the first females emerging 14 days after males, which had a clear negative effect on the population dynamics of the species (Dobkin et al. 1987; Baughman 1991). Furthermore, a high level of temporal fragmentation, namely short individual life span in relation to long flight period, in combination with protandry seriously reduced the effective population size (sensu Lande and Barrowclough 1987; Hill 1972), thus accelerating the loss of genetic variability in small populations.

In contrast to several earlier studies, which suggested that extinction risk in butterflies decreases with lengthening flight period (Komonen et al. 2004; Kotiaho et al. 2005; Franzen and; Johannesson 2007), our analyses did not reveal any link between flight period length alone and species conservation status. The most straightforward explanation for such a result could be that flight period length per se is unimportant for species extinction risk, and it only matters in combination with adult longevity. Nevertheless, we hypothesise that the situation is more complex, namely a longer time of adult occurrence has both positive and negative consequences for species viability. On the one hand, as discussed above, it increases the temporal fragmentation of butterfly populations and may reduce mating opportunities for both sexes. On the other hand, a longer flight period allows for the compensation of the negative effects of stochastic changes during the flight period such as unfavourable weather conditions, and inappropriate management interventions, e.g. inappropriate timing of meadow mowing (Cormont et al. 2011). An extended adult occurrence season improves the resilience of butterfly populations to catastrophic events such as floods or fires (cf. Konvička et al. 2002; Kajzer-Bonk et al. 2013; Nowicki et al. 2015), because only a small fraction of individuals is affected if a short-term disturbance happens during the flight period.

Our findings demonstrate that both adult life span and temporal fragmentation index may serve as useful ‘early warning’ indicators, helping to flag-up butterfly species possibly at risk from among those for which detailed information essential for evaluating threat level is lacking. Regretfully, as our literature search implies, the estimates of adult life span and flight period length (needed for calculating temporal fragmentation index) are not readily available for most butterflies either. However, they are relatively easy to get through mark-recapture studies. These are simple to plan and conduct, and they may be carried out with the help of amateur naturalists. This gives the longevity-related parameters analysed in the present study a substantial advantage over the proxies for species vulnerability previously suggested by other authors (Kotiaho et al. 2005; Mattila et al. 2006; Nylin and Bergström 2009), such as host plant specificity, overwintering stage, patch size requirements or mobility (see Introduction for their rationale), because assessing the latter characteristics typically requires specialist expertise.

Obviously, the main drawback of mark-recapture studies is their labour-intensity. Therefore, it would be highly desirable if the information on adult longevity and flight period length could be extracted from the well-established butterfly monitoring schemes based on transect counts. Evaluating flight period length with transect counts requires increased frequency of the transect surveys, because biweekly counts, as currently adopted in most monitoring schemes (van Swaay et al. 2008), are not enough for this purpose. In turn, assessing adult longevity with transects counts appears more difficult to accomplish. Zonneveld (1991) developed a theoretical model for the estimation of life span from transect count data, but its applicability has so far been hampered by rigorous assumptions, which are difficult to meet in real world situations (Nowicki et al. 2008). Nevertheless, more recent developments based on this model, such as the Insect Count Analyzer (INCA) software (Longcore et al. 2003), are promising and give some hope that transect counts can be reliably used to derive butterfly life span estimates in the near future.

Notes

Acknowledgements

The study was funded by the Polish National Science Centre Grant DEC-2013/11/B/NZ8/00912 and by the Internal Grant Agency of the Czech University of Life Sciences Prague (CIGA) through its Project No. 20152004. We thank Ian Fiala for improving the English of the manuscript.

References

  1. Al Dhaheri SSO (2009) The ecology and conservation of the Pearl bordered Fritillary Butterfly (Boloria euphrosyne) in Scotland. Dissertation, University of AberdeenGoogle Scholar
  2. Ali JG, Agrawal AA (2012) Specialist versus generalist insect herbivores and plant defense. Trends Plant Sci 17:293–302PubMedCrossRefGoogle Scholar
  3. Als TD et al (2004) The evolution of alternative parasitic life histories in large blue butterflies. Nature 432:386–390PubMedCrossRefGoogle Scholar
  4. Anthes N, Fartmann T, Hermann G (2003) Wie lässt sich der Rückgang des Goldenen Scheckenfalters (Euphydryas aurinia) in Mitteleuropa stoppen? Erkenntnisse aus populationsökologischen Studien in voralpinen Niedermoorgebieten und der Arealentwicklung in Deutschland. Naturschutz Landschaftsplanung 35:279–287Google Scholar
  5. Baguette M, Nève G (1994) Adult movements between populations in the specialist butterfly Proclossiana eunomia (Lepidoptera, Nymphalidae). Ecol Entomol 19:1–5CrossRefGoogle Scholar
  6. Baguette M, Schtickzelle N (2006) Negative relationship between dispersal distance and demography in butterfly metapopulations. Ecology 87:648–654PubMedCrossRefGoogle Scholar
  7. Baguette M, Stevens VM (2013) Predicting minimum area requirements of butterflies using life-history traits. J Insect Conserv 17:645–652CrossRefGoogle Scholar
  8. Bartoňová A, Beneš J, Konvička M (2014) Generalist-specialist continuum and life history traits of Central European butterflies (Lepidoptera)—are we missing a part of the picture? Eur J Entomol 111:543Google Scholar
  9. Batáry P, Örvössy N, Kőrösi Á, Peregovits L (2008) Egg distribution of the southern festoon (Zerynthia polyxena) (Lepidoptera, Papilionidae). Acta Zool Acad Sci H 54:401–410Google Scholar
  10. Bauerfeind SS, Theisen A, Fischer K (2009) Patch occupancy in the endangered butterfly Lycaena helle in a fragmented landscape: effects of habitat quality, patch size and isolation. J Insect Conserv 13:271–277CrossRefGoogle Scholar
  11. Baughman JF (1991) Do protandrous males have increased mating success? The case of Euphydryas editha. Am Nat 138:536–542CrossRefGoogle Scholar
  12. Beck J, Fiedler K (2009) Adult life spans of butterflies (Lepidoptera: Papilionoidea + Hesperioidea): broadscale contingencies with adult and larval traits in multi-species comparisons. Biol J Linn Soc 96:166–184CrossRefGoogle Scholar
  13. Beneš J et al (2002) Motýli České republiky: Rozšíření a ochrana I, II. SOM, PrahaGoogle Scholar
  14. Bergman KO, Landin J (2002) Population structure and movements of a threatened butterfly (Lopinga achine) in a fragmented landscape in Sweden. Biol Conserv 108:361–369CrossRefGoogle Scholar
  15. Bilnicki K (2015) Within-season population dynamics of the dryad butterfly (Minois dryas) in relation to habitat type. Bachelor Thesis, University of KrakówGoogle Scholar
  16. Bonelli S, Vrabec V, Witek M, Barbero F, Patricelli D, Nowicki P (2013) Selection on dispersal in isolated butterfly metapopulations. Popul Ecol 55:469–478CrossRefGoogle Scholar
  17. Brakefield PM (1982) Ecological studies on the butterfly Maniola jurtina in Britain. II. Population dynamics: the present position. J Anim Ecol 51:727–738CrossRefGoogle Scholar
  18. Brommer J, Fred MS (1999) Movement of the Apollo butterfly Parnassius apollo related to host plant and nectar plant patches. Ecol Entomol 24:125–131CrossRefGoogle Scholar
  19. Buszko J, Masłowski J (2008) Motyle dzienne Polski. Wydawnictwo Koliber, Nowy SączGoogle Scholar
  20. Calabrese JM, Fagan WF (2004) Lost in time, lonely, and single: reproductive asynchrony and the Allee effect. Am Nat 164:25–37PubMedCrossRefGoogle Scholar
  21. Carey JR (2001) Insect biodemography. Annu Rev Ecol 46:79–110Google Scholar
  22. Casacci L et al (2015) Dispersal and connectivity effects at different altitudes in the Euphydryas aurinia complex. J Insect Conserv 19:265–277CrossRefGoogle Scholar
  23. Celik T (2012) Adult demography, spatial distribution and movements of Zerynthia polyxena (Lepidoptera: Papilionidae) in a dense network of permanent habitats. Eur J Entomol 109:217–227CrossRefGoogle Scholar
  24. Conrad KF, Woiwod IP, Parsons M, Fox R, Warren MS (2004) Long-term population trends in widespread British moths. J Insect Conserv 8:119–136CrossRefGoogle Scholar
  25. Cormont A, Malinowska AH, Kostenko O, Radchuk V, Hemerik L, WallisDeVries MF, Verboom J (2011) Effect of local weather on butterfly flight behaviour, movement, and colonization: significance for dispersal under climate change. Biodivers Conserv 20:483–503CrossRefGoogle Scholar
  26. Courtney S, Duggan A (1983) The population biology of the orange tip butterfly Anthocharis cardamines in Britain. Ecol Entomol 8:271–281CrossRefGoogle Scholar
  27. Cowley MJR, Thomas CD, Thomas JA, Warren M (1999) Flight areas of British butterflies: assessing species status and decline. P Roy Soc Lond B Bio 266:1587–1592CrossRefGoogle Scholar
  28. Cowley MJR, Thomas CD, Roy DB, Wilson RJ, Leon-Cortes JL, Gutierrez D, Bulman CR, Quinn RM, Moss D, Gaston KJ (2001) Density–distribution relationships in British butterflies. I. The effect of mobility and spatial scale. J Anim Ecol 70:410–425CrossRefGoogle Scholar
  29. Crişan A, Sitar C, Craioveanu MC, Vizauer TC, Rakosy L (2014) Multiannual population size estimates and mobility of the endemic Pseudophilotes bavius hungarica (Lepidoptera: Lycaenidae) from Transylvania (Romania). North-West. J Zool 10:S115–S124Google Scholar
  30. Czekes Z, Markó B, Nash DR, Ferencz M, Lázár B, Rákosy L (2014) Differences in oviposition strategies between two ecotypes of the endangered myrmecophilous butterfly Maculinea alcon (Lepidoptera: Lycaenidae) under unique syntopic conditions. Insect Conserv Divers 7:122–131CrossRefGoogle Scholar
  31. Davis G, Frazer J, Tynan A (1958) Population numbers in a colony of Lysandra bellargus Rott. (Lepidoptera: Lycaenidae) during 1956. In: Proceedings of the Royal Entomological Society of London. Gen Entomol 1-3:31–36Google Scholar
  32. Dempster JP (1984) The natural enemies of butterflies. In: Vane-Wright RI, Ackery PR (eds) The biology of butterflies. Academic Press, London, pp 97–104Google Scholar
  33. Dempster JP (1997) The role of larval food resources and adult movement in the population dynamics of the orange-tip butterfly (Anthocharis cardamines). Oecologia 111:549–556CrossRefGoogle Scholar
  34. Dobkin D, Olivieri I, Ehrlich P (1987) Rainfall and the interaction of microclimate with larval resources in the population dynamics of checkerspot butterflies (Euphydryas editha) inhabiting serpentine grassland. Oecologia 71:161–166CrossRefGoogle Scholar
  35. Dowdeswell WH, Fisher R, Ford EB (1940) The quantitative study of populations in the lepidoptera I. Polyommatus icarus rott. Ann Eugenic 10:123–136CrossRefGoogle Scholar
  36. EEA (2011) Are Lepidoptera an effective ‘umbrella group’ for biodiversity conservation? Publications Office of the European Union, BelgiumGoogle Scholar
  37. Fagerström T, Wiklund C (1982) Why do males emerge before females? Protandry as a mating strategy in male and female butterflies. Oecologia 52:164–166CrossRefGoogle Scholar
  38. Fischer K (1998) Population structure, mobility and habitat selection of the butterfly Lycaena hippothoe (Lycaenidae: Lycaenini) in western Germany. Nota Lepidopterologica 21:14–30Google Scholar
  39. Fischer K, Fiedler K (2001) Resource-based territoriality in the butterfly Lycaena hippothoe and environmentally induced behavioural shifts. Anim Behav 61:723–732CrossRefGoogle Scholar
  40. Fischer K, Beinlich B, Plachter H (1999) Population structure, mobility and habitat preferences of the violet copper Lycaena helle (Lepidoptera: Lyceanidae) in Western Germany: implications for conservation. J Insect Conserv 3:43–52CrossRefGoogle Scholar
  41. Fischer K, Bot A, Brakefield P, Zwaan BJ (2006) Do mothers producing large offspring have to sacrifice fecundity? J Evol Biol 19:380–391PubMedCrossRefGoogle Scholar
  42. Fjellstad WJ (1998) The landscape ecology of butterflies in traditionally managed Norwegian farmland. Dissertation, University of DurhamGoogle Scholar
  43. Fontaine B et al (2007) The European union’s 2010 target: putting rare species in focus. Biol Conserv 139:167–185CrossRefGoogle Scholar
  44. Franzén M, Johannesson M (2007) Predicting extinction risk of butterflies and moths (Macrolepidoptera) from distribution patterns and species characteristics. J Insect Conserv 11:367–390CrossRefGoogle Scholar
  45. Fred MS, O’Hara RB, Brommer JE (2006) Consequences of the spatial configuration of resources for the distribution and dynamics of the endangered Parnassius apollo butterfly. Biol Conserv 130:183–192CrossRefGoogle Scholar
  46. Friberg M, Bergman M, Kullberg J, Wahlberg N, Wiklund C (2008) Niche separation in space and time between two sympatric sister species—a case of ecological pleiotropy. Evol Ecol 22:1–18CrossRefGoogle Scholar
  47. Fric Z, Hula V, Klimová M, Zimmermann K, Konvička M (2010) Dispersal of four fritillary butterflies within identical landscape. Ecol Res 25:543–552CrossRefGoogle Scholar
  48. Gibbs KE, Siebenmann M (1996) Life history attributes of the rare mayfly Siphlonisca aerodromia Needham (Ephemeroptera: Siphlonuridae). J N Am Benthol Soc 15:95–105CrossRefGoogle Scholar
  49. Haaland C (2015) Abundances and movement of the Scarce Copper butterfly (Lycaena virgaureae) on future building sites at a settlement fringe in southern Sweden. J Insect Conserv 19:255–264CrossRefGoogle Scholar
  50. Habel JC, Segerer A, Ulrich W, Torchyk O, Weisser WW, Schmitt T (2015) Butterfly community shifts over 2 centuries. Conserv Biol 135:648–656Google Scholar
  51. Haeler E, Fiedler K, Grill A (2014) What prolongs a butterfly’s life?: trade-offs between dormancy, fecundity and body size. PloS One 9:e111955PubMedPubMedCentralCrossRefGoogle Scholar
  52. Hanski I, Alho J, Moilanen A (2000) Estimating the parameters of survival and migration of individuals in metapopulations. Ecology 81:239–251CrossRefGoogle Scholar
  53. Hanski I, Saastamoinen M, Ovaskainen O (2006) Dispersal-related life-history trade-offs in a butterfly metapopulation. J Anim Ecol 75:91–100PubMedCrossRefGoogle Scholar
  54. Harker RJ, Shreeve TG (2008) How accurate are single site transect data for monitoring butterfly trends? Spatial and temporal issues identified in monitoring Lasiommata megera. J Insect Conserv 12:125–133CrossRefGoogle Scholar
  55. Heer P, Pellet J, Sierro A, Arlettaz R (2013) Evidence-based assessment of butterfly habitat restoration to enhance management practices. Biodivers Conserv 22:239–252CrossRefGoogle Scholar
  56. Hernandez-Roldan JL, Munguira ML, Martin J (2009) Ecology of a relict population of the vulnerable butterfly Pyrgus sidae on the Iberian Peninsula (Lepidoptera: Hesperiidae). Eur J Entomol 106:611–618CrossRefGoogle Scholar
  57. Hill WG (1972) Effective size of populations with overlapping generations. Theor Popul Biol 3:278–289PubMedCrossRefGoogle Scholar
  58. Hodgson JG (1993) Commonness and rarity in British butterflies. J Appl Ecol 30:407–427CrossRefGoogle Scholar
  59. Kajzer-Bonk J, Nowicki P, Bonk M, Skórka P, Witek M, Woyciechowski M (2013) Local populations of endangered Maculinea (Phengaris) butterflies are flood resistant. J Insect Conserv 17:1105–1112CrossRefGoogle Scholar
  60. Karlsson B, Wiklund C (2005) Butterfly life history and temperature adaptations; dry open habitats select for increased fecundity and longevity. J Anim Ecol 74:99–104CrossRefGoogle Scholar
  61. Komonen A, Grapputo A, Kaitala V, Kotiaho JS, Päivinen J (2004) The role of niche breadth, resource availability and range position on the life history of butterflies. Oikos 105:41–54CrossRefGoogle Scholar
  62. Konvička M, Kuras T (1999) Population structure, behaviour and selection of oviposition sites of an endangered butterfly, Parnassius Mnemosyne, in Litovelské Pomoraví. Czech Republic. J Insect Conserv 3:211–223CrossRefGoogle Scholar
  63. Konvička M, Nedvěd O, Fric Z (2002) Early-spring floods decrease the survival of hibernating larvae of a wetland-inhabiting population of Neptis rivularis (Lepidoptera: Nymphalidae). Acta Zool Hung 48:79–88Google Scholar
  64. Konvička M et al (2005) For whom the bells toll: Demography of the last population of the butterfly Euphydryas maturna in the Czech Republic. Biologia 60:551–557Google Scholar
  65. Kotiaho JS, Kaitala V, Komonen A, Päivinen J (2005) Predicting the risk of extinction from shared ecological characteristics. P Natl Acad Sci USA 102:1963–1967CrossRefGoogle Scholar
  66. Lande R, Barrowclough GF (1987) Effective population size, genetic variation, and their use in population management. Viable Populations for Conservation:87–123Google Scholar
  67. Lessells CM, Boag PT (1987) Unrepeatable repeatabilities: a common mistake. Auk 104:116–121CrossRefGoogle Scholar
  68. Lewis OT et al. (1997) Three ways of assessing metapopulation structure in the butterfly Plebejus argus. Ecol Entomol 22:283–293CrossRefGoogle Scholar
  69. Longcore T, Mattoni R, Zonneveld C, Bruggeman J (2003) INsect Count Analyzer: A tool to assess responses of butterflies to habitat restoration. Ecol Restor 21:60–61Google Scholar
  70. Lörtscher M, Erhardt A, Zettel J (1997) Local movement patterns of three common grassland butterflies in a traditionally managed landscape. M Sch Entomol Gesell 70:43–56Google Scholar
  71. Magnus D (1954) Methodik und Ergebnisse einer Populations markierung des Kaisermantels. Dtsch Ent Tag Hamburg 1953:187–197Google Scholar
  72. Margules CR, Pressey RL (2000) Systematic conservation planning. Nature 405:243–253PubMedCrossRefGoogle Scholar
  73. Martins EP, Hansen TF (1996) The statistical analysis of interspecific data: a review and evaluation of phylogenetic comparative methods. In: Martins E (ed) Phylogenies and the comparative method in animal behaviour. Oxford University Press, New York, Oxford, pp 22–75Google Scholar
  74. Mattila N, Kaitala V, Komonen A, Kotiaho JS, Paeivinen J (2006) Ecological determinants of distribution decline and risk of extinction in moths. Conserv Biol 20:1161–1168PubMedCrossRefGoogle Scholar
  75. Morbey YE, Ydenberg RC (2001) Protandrous arrival timing to breeding areas: a review. Ecol Lett 4:663–673CrossRefGoogle Scholar
  76. Morton A (1985) The population biology of an insect with a restricted distribution: Cupido minimus Fuessly (Lepidoptera: Lycaenidae). Dissertation, University of SouthamptonGoogle Scholar
  77. Müller F, Baessler C, Schubert H, Klotz S (2010) Long-term ecological research. Springer, BerlinCrossRefGoogle Scholar
  78. Munguira ML, Martín J, García-Barros E, Viejo JL (1997) Use of space and resources in a Mediterranean population of the butterfly Euphydryas aurinia. Acta Oecol 18:597–612CrossRefGoogle Scholar
  79. Nabielec J, Nowicki P (2015) Drivers of local densities of endangered Lycaena helle butterflies in a fragmented landscape. Popul Ecol 57:649–656CrossRefGoogle Scholar
  80. Niitepõld K, Hanski I (2013) A long life in the fast lane: positive association between peak metabolic rate and lifespan in a butterfly. J Exp Biol 216:1388–1397PubMedCrossRefGoogle Scholar
  81. Novacek MJ, Cleland EE (2001) The current biodiversity extinction event: scenarios for mitigation and recovery. P Natl A Sci 98:5466–5470CrossRefGoogle Scholar
  82. Nowicki P et al (2005a) Less input same output: simplified approach for population size assessment in Lepidoptera. Popul Ecol 47:203–212CrossRefGoogle Scholar
  83. Nowicki P, Witek M, Skorka P, Settele J, Woyciechowski M (2005b) Population ecology of the endangered butterflies Maculinea teleius and M. nausithous and the implications for conservation. Popul Ecol 47:193–202CrossRefGoogle Scholar
  84. Nowicki P, Settele J, Henry P-Y, Woyciechowski M (2008) Butterfly monitoring methods: the ideal and the real world. Isr J Ecol Evol 54:69–88CrossRefGoogle Scholar
  85. Nowicki P, Bonelli S, Barbero F, Balletto E (2009) Relative importance of density-dependent regulation and environmental stochasticity for butterfly population dynamics. Oecologia 161:227–239PubMedCrossRefGoogle Scholar
  86. Nowicki P, Vrabec V, Binzenhöfer B, Feil J, Zakšek B, Hovestadt T, Settele J (2014) Butterfly dispersal in inhospitable matrix: rare, risky, but long-distance. Landscape Ecol 29:401–412CrossRefGoogle Scholar
  87. Nowicki P, Marczyk J, Kajzer-Bonk J (2015) Metapopulations of endangered Maculinea butterflies are resilient to large-scale fire. Ecohydrology 8:398–405CrossRefGoogle Scholar
  88. Nylin S, Bergström A (2009) Threat status in butterflies and its ecological correlates: how far can we generalize? Biodivers Conserv 18:3243–3267CrossRefGoogle Scholar
  89. Örvössy N, Kőrösi Á, Vozár Á, Batáry P, Peregovits L (2005) Microhabitat preference of the Southern Festoon (Zerynthia polyxena). In: Kühn E, Feldmann R, Thomas J, Settele J (eds) Studies on the ecology and conservation of butterflies in Europe. Leipzig – Halle. Pensoft, Sofia – Moscow, pp 24–25Google Scholar
  90. Örvössy N, Kőrösi Á, Batáry P, Vozár A, Peregovits L (2013) Potential metapopulation structure and the effects of habitat quality on population size of the endangered False Ringlet butterfly. J Insect Conserv 17:537–547CrossRefGoogle Scholar
  91. Parr M, Gaskell T, George B (1968) Capture-recapture methods of estimating animal numbers. J Biol Educ 2:95–117CrossRefGoogle Scholar
  92. Pecsenye K, Bereczki J, Tihanyi B, Toth A, Peregovits L, Varga Z (2007) Genetic differentiation among the Maculinea species (Lepidoptera: Lycaenidae) in eastern Central Europe. Biol J Linn Soc 91:11–21CrossRefGoogle Scholar
  93. Pellet J, Gander A (2009) Comparaison de méthodes pour l’estimation de l’abondance des populations de papillons de jour: établissement d’un protocole de suivi du Grand Negre des bois Minois dryas sur la rive sud du lac de Neuchâtel. Entomo Helvetica 2:201–216Google Scholar
  94. Pennekamp F, Garcia-Pereira P, Schmitt T (2014) Habitat requirements and dispersal ability of the Spanish Fritillary (Euphydryas desfontainii) in southern Portugal: evidence-based conservation suggestions for an endangered taxon. J Insect Conserv 18:497–508CrossRefGoogle Scholar
  95. Petit S, Moilanen A, Hanski I, Baguette M (2001) Metapopulation dynamics of the bog fritillary butterfly: movements between habitat patches. Oikos 92:491–500CrossRefGoogle Scholar
  96. Pfeifer M, Andrick U, Frey W, Settele J (2000) On the ethology and ecology of a small and isolated population of the Dusky Large Blue Butterfly Glaucopsyche (Maculinea) nausithous (Lycaenidae). Nota Lepidopterologica 23:147–172Google Scholar
  97. Pfeifer MA, Henle K, Settele J (2007) Populations with explicit borders in space and time: concept, terminology, and estimation of characteristic parameters. Acta Biotheor 55:305–316PubMedCrossRefGoogle Scholar
  98. Pijpe J (2007) The evolution of lifespan in the butterfly Bicycus anynana. Dissertation, University of LeidenGoogle Scholar
  99. Purvis A, Gittleman JL, Cowlishaw G, Mace GM (2000) Predicting extinction risk in declining species. P Roy Soc Lond B Bio 267:1947–1952CrossRefGoogle Scholar
  100. Rabasa SG, David Gutierrez D, Escudero A (2005) Egg laying by a butterfly on a fragmented host plant: a multi-level approach. Ecography 28:629–639CrossRefGoogle Scholar
  101. Rabasa SG, David Gutierrez D, Escudero A (2007) Metapopulation structure and habitat quality in modelling dispersal in the butterfly Iolana iolas. Oikos 116:793–806CrossRefGoogle Scholar
  102. Reymond A (2014) Ecology and conservation of a Violet Copper (Lycaena helle) metapopulation. Master Thesis, University of LausanneGoogle Scholar
  103. Robinet C, Roques A (2010) Direct impacts of recent climate warming on insect populations. Integ Zool 5:132–142CrossRefGoogle Scholar
  104. Schmidt A (1989) Die Großschmetterlinge des Vogelsberges. Untersuchungen zur Ökologie und Faunistik der Großschmetterlinge (Makrolepidoptera) des Vogelsberges unter besonderer. Das Künanzhaus Suppl 3:1–210Google Scholar
  105. Schmitt T et al (2006) The Chalk-hill Blue Polyommatus coridon (Lycaenidae, Lepidoptera) in a highly fragmented landscape: How sedentary is a sedentary butterfly? J Insect Conserv 10:311–316CrossRefGoogle Scholar
  106. Schtickzelle N, Le Boulengé E, Baguette M (2002) Metapopulation dynamics of the bog fritillary butterfly: demographic processes in a patchy population. Oikos 97:349–360CrossRefGoogle Scholar
  107. Schtickzelle N, Choutt J, Goffart P, Fichefet V, Baguette M (2005) Metapopulation dynamics and conservation of the marsh fritillary butterfly: population viability analysis and management options for a critically endangered species in Western Europe. Biol Conserv 126:569–581CrossRefGoogle Scholar
  108. Scott JA (1973) Lifespan of butterflies. J Res Lepid 12:225–230Google Scholar
  109. Settele J, Feldmann R, Reinhardt R (1999) Die Tagfalter Deutschlands. Ulmer StuttgartGoogle Scholar
  110. Seufert W (1990) Untersuchungen zur Ökologie des Schwarzen Apollo (Parnassius mnemosyne L.; Lepidoptera: Papilionidae) in der Rhön. Dissertation, University of WürzburgGoogle Scholar
  111. Seufert P (1993) Grundlagen zum Schutz der Tagfalter (Lepidoptera: Papilionoidea, Hesperioidea) in Naturschutzgebied ‘‘Mäusberg’’ (Landkr. Main-Spessart). Abh Naturwiss Ver Würzburg 34:75–104Google Scholar
  112. Sielezniew M, Rutkowski R, Ponikwicka-Tyszko D, Ratkiewicz M, Dziekańska I, Švitra G (2012) Differences in genetic variability between two ecotypes of the endangered myrmecophilous butterfly Phengaris (=Maculinea) alcon – the setting of conservation priorities. Insect Conserv Diver 5:223–236CrossRefGoogle Scholar
  113. Slámova I, Klečka J, Konvička M (2013) Woodland and grassland mosaic from a butterfly perspective: habitat use by Erebia aethiops (Lepidoptera: Satyridae). Insect Conserv Diver 6:243–254CrossRefGoogle Scholar
  114. Soulsby RL, Thomas JA (2012) Insect population curves: modelling and application to butterfly transect data. Meth Ecol Evol 3:832–841CrossRefGoogle Scholar
  115. Stanish W, Taylor N (1983) Estimation of the intraclass correlation coefficient for the analysis of covariance model. Am Stat 37:221–224Google Scholar
  116. Statzner B, Hildrew AG, Resh VH (2001) Species traits and environmental constraints: entomological research and the history of ecological theory. Annu Rev Ecol 46:291–316Google Scholar
  117. Steiner F, Schlick-Steiner B, Höttinger H, Nikiforov A, Moder K, Christian E (2005) Maculinea alcon and M. rebeli (Insecta: Lepidoptera: Lycaenidae)–one or two alcon blues? Larval cuticular compounds and egg morphology of East Austrian populations. Annalen des Naturhistorischen Museums in Wien. Serie B für Botanik und Zoologie:165–180Google Scholar
  118. Streitberger M, Hermann G, Kraus W, Fartmann T (2012) Modern forest management and the decline of the Woodland Brown (Lopinga achine) in Central Europe. Forest Ecol Manag 269:239–248CrossRefGoogle Scholar
  119. Sutcliffe OL, Thomas CD, Peggie D (1997) Area-dependent migration by ringlet butterflies generates a mixture of patchy population and metapopulation attributes. Oecologia 109:229–234CrossRefGoogle Scholar
  120. Szentirmai I, Mesterházy A, Varga I, Schubert Z, Sándor LC, Ábrahám L, Kőrösi Á (2014) Habitat use and population biology of the Danube Clouded Yellow butterfly Colias myrmidone (Lepidoptera: Pieridae) in Romania. J Insect Conserv 18:417–425CrossRefGoogle Scholar
  121. Takemon Y (2000) Reproductive behavior and morphology of Paraleptophlebia spinosa (Ephemeroptera: Leptophlebiidae): implications of variation in copula duration. Limnology 1:47–56CrossRefGoogle Scholar
  122. Tartally A, Nash DR, Lengyel S, Varga Z (2008) Patterns of host ant use by sympatric populations of Maculinea alcon and M. “rebeli” in the Carpathian Basin. Insect Soc 55:370–381CrossRefGoogle Scholar
  123. Thomas J (1983) The ecology and status of Thymelicus acteon (Lepidoptera: Hesperiidae) in Britain. Ecol Entomol 8:427–435CrossRefGoogle Scholar
  124. Thomas J (1995) The conservation of declining butterfly populations in Britain and Europe: priorities, problems and successes. Biol J Linn Soc 56:55–72CrossRefGoogle Scholar
  125. Thomas J et al (2004) Comparative losses of British butterflies, birds, and plants and the global extinction crisis. Science 303:1879–1881PubMedCrossRefGoogle Scholar
  126. Timuș N, Craioveanu C, Sitaru C, Rus A, Rákosy L (2013) Differences in adult phenology, demography, mobility and distribution in two syntopic ecotypes of Maculinea alcon (cruciata vs. pneumonanthe) (Lepidoptera: Lycaenidae) from Transilvania (Romania). Entomologica romanica 18:21–30Google Scholar
  127. Tudor O, Parkin DT (1979) Studies on phenotypic variation in Maniola jurtina (Lepidoptera: Satyri-dae) in the Wyre forest, England. Heredity 42:91–104CrossRefGoogle Scholar
  128. Turlure C, Choutt J, Van Dyck H, Baguette M, Schtickzelle N (2010) Functional habitat area as a reliable proxy for population size: case study using two butterfly species of conservation concern. J Insect Conserv 14:379–388CrossRefGoogle Scholar
  129. Turlure C, Van Dyck H, Goffart P, Schtickzelle N (2014) Resource-based habitat use in Lycaena helle: Significance of a functional, ecological niche-oriented approach. In: Habel JCh (ed) Jewels In The Mist: a synopsis on the highly endangered butterfly species the Violet Copper, Lycaena helle, 1st edn. Pensoft Publishers, Bulgaria, pp 67–86Google Scholar
  130. Turner J (1963) A quantitative study of a Welsh colony of the large heath butterfly, Coenonympha tullia Müller (Lepidoptera). In: Proceedings of the Royal Entomological Society of London. Series A, General Entomology, vol 7-9. Wiley Online Library, pp 101–112Google Scholar
  131. van Swaay C (2002) The importance of calcareous grasslands for butterflies in Europe. Biol Conserv 104:315–318CrossRefGoogle Scholar
  132. van Swaay C, Nowicki P, Settele J, van Strien AJ (2008) Butterfly monitoring in Europe: methods, applications and perspectives. Biodivers Conserv 17:3455–3469CrossRefGoogle Scholar
  133. van Swaay C et al. (2010) European red list of butterflies. Publications Office of the European Union, LuxembourgGoogle Scholar
  134. Vodă R, Timuş N, Paulini I, Popa R, Mihali C, Crişan A, Rákosy L (2010) Demographic parameters of two sympatric Maculinea species in a Romanian site (Lepidoptera: Lycaenidae). Entomologica romanica 15:25–32Google Scholar
  135. Vogel K, Johannesen J (1996) Research on population viability of Melitaea didyma (Esper, 1779)(Lepidoptera, Nymphalidae). Species Survival Fragmented Landscapes 35:262–267CrossRefGoogle Scholar
  136. Wahlberg N, Klemetti T, Selonen V, Hanski I (2002) Metapopulation structure and movements in five species of checkerspot butterflies. Oecologia 130:33–43CrossRefGoogle Scholar
  137. WallisDeVries MF (2004) A quantitative conservation approach for the endangered butterfly Maculinea alcon. Conserv Biol 18:489–499Google Scholar
  138. Warren M (1987) The ecology and conservation of the heath fritillary butterfly, Mellicta athalia. II. Adult population structure and mobility. J Appl Ecol 24:483–498CrossRefGoogle Scholar
  139. Warren MS (1992) Butterfly populations. In: Dennis RLH. The ecology of butterflies in Britain. 1st (ed). Oxford University Press, Oxford, pp 73–92Google Scholar
  140. Warren MS, Bourn NA (2011) Ten challenges for 2010 and beyond to conserve Lepidoptera in Europe. J Insect Conserv 15:321–326CrossRefGoogle Scholar
  141. Warren M, Pollard E, Bibby T (1986) Annual and long-term changes in a population of the wood white butterfly Leptidea sinapis. J Anim Ecol 55:707–719CrossRefGoogle Scholar
  142. Wenzel M, Schmitt T, Weitzel M, Seitz A (2006) The severe decline of butterflies on western German calcareous grasslands during the last 30 years: a conservation problem. Biol Conserv 128:542–552CrossRefGoogle Scholar
  143. Wickman PC (1985) The influence of temperature on the territorial and mate locating behaviour of the small heath butterfly, Coenonympha pamphilus (L.) (Lepidoptera: Satyridae). Behav Ecol Sociobiol 16:233–238CrossRefGoogle Scholar
  144. Wiklund C, Fagerström T (1977) Why do males emerge before females? Oecologia 31:153–158CrossRefGoogle Scholar
  145. Zimmermann K, Fric Z, Filipová L, Konvička M (2005) Adult demography, dispersal and behaviour of Brenthis ino (Lepidoptera: Nymphalidae): how to be a successful wetland butterfly. Eur J Entomol 102:699–706CrossRefGoogle Scholar
  146. Zimmermann K, Konvička M, Fric Z, Čihaková V (2009) Demography of a common butterfly on humid grasslands: Argynnis aglaja (Lepidoptera: Nymphalidae) studied by mark-recapture. Pol J Ecol 57:715–727Google Scholar
  147. Zimmermann K et al (2011) Demography of adults of the Marsh fritillary butterfly, Euphydryas aurinia (Lepidoptera: Nymphalidae) in the Czech Republic: Patterns across sites and seasons. Eur J Entomol 108:243–254CrossRefGoogle Scholar
  148. Zonneveld C (1991) Estimating death rates from transect counts. Ecol Entomol 16:115–121CrossRefGoogle Scholar
  149. Zonneveld C (1992) Polyandry and protandry in butterflies. B Math Biol 54:957–976CrossRefGoogle Scholar
  150. Zonneveld C, Metz J (1991) Models on butterfly protandry: virgin females are at risk to die. Theor Popul Biol 40:308–321PubMedCrossRefGoogle Scholar

Copyright information

© The Author(s) 2016

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  1. 1.Department of Zoology and FisheriesCzech University of Life SciencesPrague 6Czech Republic
  2. 2.Institute of Environmental SciencesJagiellonian UniversityKrakówPoland

Personalised recommendations