Metapopulation dynamics and conservation of the marsh fritillary butterfly: Population viability...

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Biological Conservation 126 (2005) 569–581

BIOLOGICAL

CONSERVATION

Metapopulation dynamics and conservation of the marsh fritillarybutterfly: Population viability analysis and management

options for a critically endangered species in Western Europe

Nicolas Schtickzelle a,*, Julie Choutt a,*, Philippe Goffart b,Violaine Fichefet a,b, Michel Baguette a

a Biodiversity Research Centre, Catholic University Louvain (UCL), 4-5 Place Croix du Sud, B-1348 Louvain-la-Neuve, Belgiumb Centre de Recherche de la Nature, des Forets et du Bois (CRNFB), 23 Avenue Marechal Juin, B-5030 Gembloux, Belgium

Received 21 August 2004; received in revised form 15 June 2005; accepted 22 June 2005Available online 19 August 2005

Abstract

This study investigates the dynamics and viability of a marsh fritillary butterfly Euphydryas aurinia metapopulation in a Belgiansuccessional landscape. Based on capture–mark–recapture and winter nest census data, we first estimated demography (survival andrecruitment rates, population size, density dependence) and dispersal parameters (emigration rate, effect of patch connectivity ondispersal, mortality during dispersal). Then using RAMAS/GIS platform, we parameterised a population viability analysis(PVA) model with these parameters to simulate the future of this metapopulation under different scenarios.

The metapopulation does not seem viable even if natural reforestation is controlled by adequate management. In its present state,the patch system is not able to sustain enough individuals: due to the large temporal fluctuations in demographic parameters, a car-rying capacity far higher than currently would be necessary to limit extinction risk to 1%, suggesting the existence of an extinctiondebt for the species in Belgium. The situation of E. aurinia appears much worse compared to two other fritillary species threatened inBelgium, for which similar PVA are available. It is therefore urgent to increase the carrying capacity of the patch system. How andwhere it is achieved are of secondary importance for the gain in viability: improvement of habitat quality through restoration, orincrease of habitat quantity via enlargement of existing patches and/or creation of new habitat in the matrix. A regime of manage-ment based on regular re-opening and maintenance of habitat patches may be the only guarantee of long-term persistence for thiscritically endangered species in Belgium.� 2005 Elsevier Ltd. All rights reserved.

Keywords: Butterflies; Capture–mark–recapture; Euphydryas aurinia; Extinction debt; RAMAS–GIS, virtual migration model

1. Introduction

Destruction and fragmentation of natural habitatsare the main causes of extinction (Saunders et al.,1991; Fahrig and Merriam, 1994; Fahrig, 1997; Sihet al., 2000). These processes affect the persistence ofpopulations (Ceballos and Ehrlich, 2002), which become

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* Corresponding authors. Tel.: +32 10 47 20 52; fax: +32 10 47 34 90.E-mail address: [email protected] (N. Schtickzelle).

smaller and smaller and therefore more susceptible toextinction from stochastic perturbations (Shaffer, 1981;Gilpin and Soule, 1986; Caughley, 1994). Populationviability analysis (PVA) has emerged as a tool to providepractical conservation guidelines for threatened species(Shaffer, 1990; Boyce, 1992; Beissinger and McCullough,2002; Morris and Doak, 2002). PVA projects(meta)population abundance to predict the extinctionrisk or viability of (meta)populations in a particularlandscape under specific conditions; then it allows the

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570 N. Schtickzelle et al. / Biological Conservation 126 (2005) 569–581

identification of the most important factors contributingto the persistence of (meta)population and the rankingof various habitat management options (Akcakayaand Sjogren-Gulve, 2000; Beissinger and McCullough,2002). Viability of a (meta)population expresses theprobability that it will persist for a given time period(Akcakaya and Sjogren-Gulve, 2000). Spatially struc-tured (meta)population models are particularly suitablefor PVA of metapopulations with relatively few habitatpatches, they are parameterised with both demographicand habitat data and explicitly model local populationdynamics (Akcakaya, 2000).

Butterflies suffer from environmental changes thathave occurred everywhere in Europe in the 20th cen-tury (Thomas, 1984, 1991; Van Swaay and Warren,1999). Many species show an important decline in dis-tribution and abundance and are now protected underspecific laws (Warren, 1992; Van Swaay and Warren,1999). The marsh fritillary butterfly, Euphydryas auri-

nia ROTTEMBOURG 1775, has declined in most Euro-pean countries over recent decades (Thomas andLewington, 1991; Van Swaay and Warren, 1999) andis listed as ‘‘threatened’’ in Annexe II of the 1992European Community Habitats and Species Directive.In Belgium, the species has disappeared from Flanders(northern Belgium) since 1959 (Maes and Van Dyck,2001) and subsists only in a handful of localities inWallonia (southern Belgium) (Goffart et al., 1996;Goffart and De Bast, 2000; Goffart et al., 2001).Remaining populations are on the verge of extinction.Like other butterfly species (Thomas, 1984; Warren,1992), the persistence of E. aurinia now depends lar-gely on human management of its habitat, which hasto be based on sound ecological analyses (Thomas,1991).

In our paper, unlike other studies concerned with thisthreatened species (Warren, 1994; Bulman, 2001; Goff-art et al., 2001; Joyce and Pullin, 2003; Konvickaet al., 2003; but see Wahlberg et al., 2002a), we explorequantitatively the viability of E. aurinia in a successionallandscape from south-eastern Belgium, and assess theefficiency of various management measures to ensureits persistence. First, we estimated demographic and dis-persal parameters. Second, using these parameters andthe existing software RAMAS/GIS (Akcakaya, 2002),we designed a PVA model to investigate managementoptions that might realistically be applied in the frameof a specific action plan such as restoration and mainte-nance of existing patches through shrub cutting, crea-tion of new habitat patches, creation of corridors topromote dispersal. We then estimated minimum viablemetapopulation size (number of butterflies required torestrict extinction risk to 1% in the next 100 years: Shaf-fer, 1981), and compared it with two other vulnerablefritillary species in Belgium, for which this kind ofPVA is available.

2. Species and study system

E. aurinia is a univoltine species, with adults flyingfrom May to July in Belgium (Goffart et al., 1996).Adults appear relatively polyphagous and opportunistic.They have been observed feeding on many forbs, ofwhich Ranunculus ssp., Cirsium ssp., Leucantherum vulg-

are, Myosotis ssp., Rubus ssp. are the most frequentlyvisited (Goffart et al., 2001) (plant nomenclature fromLambinon et al., 2004). In Belgium, larvae feed on Succ-

isa pratensis in wet meadows, but on chalk grasslandsother food plants are used: Knautia arvensis and Scabi-

osa columbaria (Warren, 1986; Goffart et al., 1996). Dur-ing the flight period, males adopt a perching behaviour(Scott, 1974), they sit mostly along hedges and investi-gate passing objects in search of females (Goffartet al., 1996). Females mate only once and lay large eggbatches (60–600 eggs/batch), the first one typically inthe natal patch. After hatching, larvae spin a silken nestaround the food plant. Their entire larval developmentlasts six instar stages (Porter, 1981). Larvae feed andbask gregariously during sunny days until September,when the fourth instar larvae enter an obligatory dia-pause in a collective conspicuous winter nest in the her-baceous vegetation. The diapause ends in February andthe larvae resume feeding and basking collectively. Theybecome solitary at the end of the fifth instar, owing tothe increase in their food needs (Porter, 1984).

We studied a metapopulation of south-easternBelgium on a site known as the Baquet (50�08 0N,4�46 0E). The site consists of semi-natural humid grass-lands and heaths established on clay soils, now aban-doned and partly invaded by shrubs and trees(Crataegus monogyna, Prunus spinosa, Frangula alnus,Betula pendula, Populus tremula). In 1995, E. aurinia

occupied 11 habitat patches (clearings) covering a totalarea of 0.92 ha in the Baquet (Fig. 1). This metapopu-lation is the system whose viability is analysed in ourPVA model.

3. Estimation of metapopulation dynamics parameters

3.1. Intrageneration demography

The Baquet metapopulation was studied by Capture–mark–recapture (CMR) in 1995. Habitat patches werevisited daily, weather permitting, during the flight sea-son of the species (from 23 May until 2 July). All de-tected butterflies were netted and marked with anindividual number on the underside of the left hind wingusing a thin point permanent pen and immediately re-leased at the capture site. Sex, date and spatial coordi-nates were recorded at each capture. We used theseCMR data to study demographic parameters in E. auri-

nia populations for the 1995 generation.

Fange deBihain

Baquet

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Fig. 1. Location of the two study sites of the marsh fritillary inBelgium and network of habitat patches for the Baquet site for which aPVA was effected. Black areas: habitat patches occupied by the speciesin 1995; grey areas: habitat patches created in scenarios B1 to B4; linesrepresent walking paths.

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Fig. 2. Daily changes in demographic parameters during the 1995flight period in the Baquet metapopulation: (a) changes in dailysurvival (closed circles, the same for males and females) andcatchability for males (closed triangles) and females (open triangles)and (b) development of daily population size for males (closedtriangles) and females (open triangles). Bars indicate 95% confidenceintervals.

N. Schtickzelle et al. / Biological Conservation 126 (2005) 569–581 571

Demographic analyses of CMR data have two mainaspects: (1) the estimation of demographic parameters(daily survival ui, catchability pi, recruitment rate bi,daily Ni and total Ntot population sizes during the flightperiod); and (2) the determination of factors responsiblefor variation in these demographic parameters. Theseobjectives can be achieved with the constrained linearmodels (CLM), a multiple regression method specificallydeveloped for CMR data analysis (for details and refer-ences, see Schtickzelle et al., 2002). To do this, we ap-plied the same methodology as successfully used ontwo other fritillary butterflies, the bog fritillary Proclos-

siana eunomia ESPER (Schtickzelle et al., 2002) and thecranberry fritillary Boloria aquilonaris STICHEL (Baguetteand Schtickzelle, 2003).

One hundred and five individuals were captured (76males and 29 females) and recaptured 202 times in total

(170 times for males and 32 times for females). Themodel selected as best describing variations in demo-graphic parameters was u � psþt Bsþtlinþtlin2 . Daily survivalwas constant (0.937 ± 0.008) during the flight periodwith no difference between males and females(Fig. 2(a)); catchability varied between days with a con-stant sex difference, male catchability being higher thanfemale catchability (Fig. 2(a)); and daily recruitment ratefollowed a parabola with no difference between sexes(not shown). This structure (set of factors responsiblefor variations) is similar to that of two other fritillaryspecies (B. aquilonaris: Baguette and Schtickzelle, 2003;P. eunomia: Schtickzelle et al., 2002). Development ofthe daily population size during the flight period is givenin Fig. 2(b). Estimated total population size was 57 ± 20

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Fig. 3. Density dependence function (Eq. (1)) for the Bihain popula-tion growth rate Rt according to population density expressed in thenumber of caterpillar winter nests (Wt�1). Rmax: maximum growth rateand K: carrying capacity.


females and 99 ± 13 males, which gives a sex-ratio of 1.7males for 1 female. Total population size might be under-estimated for males because we missed the beginning ofthe flight period (already high abundance of males onthe first sampling day: Fig. 2(b)). However, this shouldhave only a limited impact as the survival rate of E. auri-nia in this population was very high. Indeed, becausesuch a high survival rate implied a relatively long resi-dence time of adults in the population, it was highlyprobable that adults already present in the populationbefore starting CMR sampling would still be there atthat time, captured and counted in the total.

3.2. Intergeneration demography

Yearly autumnal nest censuses (Lewis and Hurford,1997) were carried out on the Grande Fange de Bihainpopulation (Table 1), another population of the speciesin Belgium (Fig. 1). Using the time series of these nestcounts, we investigated density-dependent populationregulation. The use of another population to study inter-generation demography is justified by the fact that thearea of available habitat changed dramatically in the Ba-quet during the last decade due to natural vegetationsuccession and by a management operation in 1997 thatstrongly affected population trends and natural popula-tion growth. The chosen density dependence function,which corresponds to the logistic or Ricker equation(Akcakaya, 2002), gives the population growth rate(Rt) as a negative exponential function of the numberof nests in the previous winter (Wt�1):

Rt ¼ Rmax � e� lnRmax �W t�1

K ð1Þwith Rmax the maximum growth rate and K the carryingcapacity.

This function was fitted (p = 0.007) to data and itsparameters estimated with the SAS Proc NLIN proce-dure (SAS Institute Inc., 2003) (Fig. 3). It provided esti-mates of the maximum growth rate: Rmax = 3.11, and ofthe carrying capacity: K = 224 winter nests for the5.75 ha of suitable habitat in Bihain, which correspondsto 39 winter nests/ha (Fig. 3). We considered that den-

Table 1Winter nest numbers (Wt) and associated population growth rates (Rt)of E. aurinia for the Grande Fange de Bihain site

Wt Rt

1994 61 1.3931995 85 2.3291996 198 1.3781997 273 0.3261998 89 0.3371999 30 3.8332000 115 2.2082001 254 1.8662002 474

sity dependence parameters (K and Rmax) estimated onthe Bihain population could be used for the Baquetmetapopulation in the PVA model. In many PVAs,the generalisation of parameter estimates between land-scapes and/or related species is necessary because dataare frequently lacking for threatened species, this pointwill be discussed below (Discussion). In order to expressthe carrying capacity K in butterflies/ha, which weneeded because populations are modelled at the adultstage in the PVA, we converted the number of caterpil-lar winter nests into the number of adults, taking advan-tage of the simultaneous availability for the 1995generation, in the Baquet metapopulation, of estimatesof the total adult population and the number of winternests. Twenty-four winter nests were observed in the fall1995. These were the progeny of 57 females; a K of 39winter nests/ha corresponds therefore to 93 females/ha. The sex-ratio in the adult population was estimatedat 1.7 males for 1 female; therefore, 160 males had to becounted in addition to the 93 females, which gaveK = 253 adults/ha.

Given these results, the population dynamics ofE. aurinia seem to be controlled by density-dependentprocesses. The insight that density-dependent processesmatter in butterfly population dynamics is recent(Hanski, 1999). Two main factors are probably in-volved: the shortage of larval feeding resources andnatural enemy attacks (mainly parasitoids, Hanski,1999). Both are likely to occur in E. aurinia. Ford andFord (1930) reported, for example, extreme food plantdefoliation due to larval pullulation and ensuing foodplant shift, whereas the impact of parasitoids on popu-lation dynamics was demonstrated by Porter (1981). InBelgium, both larval food plant shift and parasitoid


cocoons were detected (Ph. Goffart, personalobservation).

3.3. Between-patch dispersal

We applied the virtual migration (VM) model (Han-ski et al., 2000) to the same CMR data set as for intra-generation demography (year 1995 generation in theBaquet metapopulation), as done previously for otherbutterfly species (Petit et al., 2001; Wahlberg et al.,2002b; Schtickzelle and Baguette, 2003). The model in-cludes parameters describing dispersal and survival ratesand how they depend on the area and connectivity ofhabitat patches. Six parameters (numbered below) weredirectly estimated, others were derived from these: (1)the probability of survival in patch (/p), equal for allpatches; the probability of emigration from patch i

(ei), which is a power function of patch area Ai

ei ¼ g � Afemi ð2Þ

with (2) g describing dispersal propensity (emigrationprobability for a patch of unit size, here 1 ha) and (3)fem scaling emigration to patch area; the connectivityof patch i, measured by

Si ¼X

i6¼j

1

ð1þ DijÞaAfimj ; ð3Þ

where (4) a determines the effect of distance Dij betweenpatch i and any other patch j on dispersal and (5) fimscales immigration to receiver patch area Aj; the survivalduring dispersal from patch i (umi) is assumed to increaseas a sigmoid function of the connectivity of patch i

umi ¼ S2i =kþ S2

i ð4Þwith (6) k expressing the mortality during dispersal(square of the connectivity for which survival during dis-persal equals 0.5).

The 1995 CMR data of E. aurinia for the Baquet sitecomprised 83 between-patch movements (between twoconsecutive recaptures), 12 for females and 71 for males,with a maximum distance of 523 m for females, 409 mfor males. The mean distance of individual movementswas 179 m for females, 94 m for males. Twenty-five per-cent (3/12) of female movements exceeded 200 m, only

Table 2Dispersal parameter estimates (with 95% confidence interval) for E. aurinia odata for the 1995 generation

Parameter Estimate (95% co

Females

/p, within patch daily survival 0.912 (0.863–1.000g, emigration propensity 0.001 (0.000–0.238fem, scaling of emigration with patch area 0.396 (0.000–0.875a, distance dependence of dispersal probability 5.402 (0.000–11.61k, dispersal mortality index 0.000 (0.000–6.696fim, scaling of immigration with patch area 0.029 (0.000–0.712

3% (2/71) for male movements. Table 2 presents theVM parameter estimates and their 95% confidence inter-vals for males, females and pooled sexes. On comparingmale and female parameter estimates, differences werenot found to be significant (confidence intervals over-lap), but males tended to move shorter distances thanfemales (a males > a females). Emigration propensityfrom a patch of unit size (g) was low for the two sexes.Daily survival within a patch (up) was higher than 0.9for both sexes, which was in full agreement with the va-lue obtained with CLM. Male and female mortalitiesduring dispersal remained close to zero. The parametersscaling immigration (fim) and emigration (fem) to patcharea were quite similar.

The comparison with two other studies (Wahlberget al., 2002b; Wang et al., 2004), investigating dispersalin the same species with the same model, indicated thatparameter estimates were globally similar. Raw move-ment statistics were also in the same order of magnitudeas those obtained by Fischer (1997) in Germany andMunguira et al. (1997) in Spain.

4. PVA model parameterisation

Due to the small number of habitat patches, struc-tured population models taking local dynamics into ac-count were particularly suitable for our PVA (Akcakayaand Sjogren-Gulve, 2000). We chose RAMAS/GIS ver-sion 4.0 software package (Akcakaya, 2002) to designthe PVA model. We applied the methodology as success-fully performed for two other PVAs on fritillary butter-flies in Belgium (P. eunomia: Schtickzelle and Baguette,2004; B. aquilonaris: Schtickzelle et al., 2005).

Our model incorporated the internal dynamics ofeach local population (vital rates and immigration/emigration) and is based on (1) a density dependencefunction, (2) the dispersal between sites and (3) thecorrelation of local dynamics. Only the adult stage wasmodelled, with no differentiation between males andfemales.

The density dependence function (Eq. (1)) was thecore of the demographic part of the PVA model. Weassumed that each population had the same density

n the Baquet site as obtained using virtual migration model on CMR

nfidence interval)

Males Pooled sexes

) 0.936 (0.913–1.000) 0.941 (0.915–1.000)) 0.017 (0.001–0.274) 0.012 (0.001–0.262)) 0.212 (0.000–0.475) 0.232 (0.000–0.479)0) 14.741 (10.585–20.592) 12.018 (9.019–15.742)) 0.040 (0.000–0.592) 0.068 (0.000–0.822)) 0.130 (0.000–0.521) 0.128 (0.000–0.486)


dependence function and a carrying capacity function ofits area; patch area was used as a surrogate for resourceavailability.

The dispersal part of our PVA model consisted of anestimate of the dispersal probability pij between all pairsof patches. The matrix expressed the proportion of indi-viduals in each patch i that successfully disperse to eachpatch j. Dispersal probability reflected the combinationof two processes: emigration from patch i and successfuldispersal to patch j after emigration. The dispersal prob-ability pij used in RAMAS/GIS therefore correspondedto the product of emigration probability from i (ei), sur-vival probability from i (umi) and probability of endingin j (Sij/Si, i.e. contribution of patch j to connectivity ofpatch i)

pij ¼ ei � umi �Sij

Si. ð5Þ

As sexes were not modelled separately in the PVA, sex-pooled parameter estimates were used to compute pijdispersal matrix to input in the PVA model.

Persistence of populations can be influenced by thesynchrony of their local dynamics, driven by environ-mental conditions. The estimate of such a correlation re-quires time series of local population sizes for severallocal populations. Unfortunately, such data were notavailable for E. aurinia. Therefore we used the correla-tion function estimated on the bog fritillary, a closely re-lated and well-studied species, for which a PVA wasconducted in a habitat network localised in a similarlandscape nearby (Schtickzelle and Baguette, 2004).The correlation qij of local dynamics between patches iand j was estimated for this species as a negative expo-nential function of between-patch distance (Dij ex-pressed in m here)

qij ¼ 0.906 � e�Dij=603.7. ð6Þ

This function was used in the model to compute all thecorrelation rates between pairs of patches according tothe distance between them.

Environmental stochasticity was applied to the popu-lation growth rate (Rt): for each time step the model sam-pled one Rt from a lognormal distribution with the meangiven by the density dependence function and a standarddeviation equalling 0.74. This standard deviation wasestimated from the magnitude of environmental effectson population growth of P. eunomia (Schtickzelle andBaguette, 2004) because they were not directly availablefor E. aurinia. The use of surrogate data will be discussedlater (Section 6). It was therefore supposed that the meangrowth rate was determined only by the density depen-dence function, while its yearly variation was due toclimatic conditions (Schtickzelle and Baguette, 2004).Demographic stochasticity was also integrated duringthe simulation, the numbers of individuals being sampledfrom binomial distributions (Akcakaya, 2002). The

stochasticity in the dispersal rate was incorporated intoRAMAS/GIS through a common coefficient of variation(cv = std/mean) for all the dispersal probabilities. It wascomputed as the mean cv value on the whole dispersalmatrix, i.e. for all pairs of patches.

The time horizon for prediction was fixed at 100 years(one year corresponds to one generation and one simula-tion time step) and the number of replications used was1000. The Allee effect, while possibly important in smallpopulations, was not considered due to the lack of dataand the difficulty of assessing it (Lande et al., 2002).

5. PVA model analysis

The result of a PVAmay be expressed inmany differentforms (Akcakaya and Sjogren-Gulve, 2000). Here weused four parameters as viability indices: (a) themetapop-ulation trajectory, which corresponds to the number ofbutterflies present in themetapopulation at each time step(i.e. generation) during the 100 simulated years; (b) themetapopulation occupancy, which shows the change inthe number of occupied patches through time; (c) theinterval percent decline, which indicates the probabilitythat the metapopulation will decline by a given propor-tion from its initial size, at least once during the simulatedtime period and (d) finally the interval quasi-extinctionrisk, i.e. the probability that the population will fall undera definite abundance threshold during at least one of the100 simulated generations (Akcakaya, 2002).

5.1. Natural succession of habitat and status quo situation

In the Baquet study site, the marsh fritillary butterflyoccupies clearings which are a dynamic habitat throughtime: wet meadows and heaths grow old following aban-donment, and clearings are progressively recolonised byshrubs and forest, decreasing the habitat suitability forE. aurinia, and therefore the carrying capacity of thehabitat patches. Our first step was to analyse the Baquetmetapopulation viability, in case its current landscapefollowed this natural vegetation succession, and meta-population parameters (demography, dispersal, correla-tion of local dynamics) remained stable through time.Obviously, the real succession due to the progressiveclosing of clearings by shrubs will cause the carryingcapacity to drop to zero after a few years (here we used10 years), ending in deterministic population extinction.Consequently, such a situation was not a suitable refer-ence for further model analysis by sensitivity analysisand ranking of scenarios, and we therefore considereda status quo situation, where the landscape structurewould be maintained by long-term management (e.g.light grazing or mowing) in its 1995 state.

Viability results for the natural succession and statusquo situations are presented in Fig. 4. It is obvious that

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Fig. 4. Metapopulation viability expressed in four viability variablesfor natural succession (thick line) and status quo (thin line) situations(see text for scenario details). Line represents mean value of the 1000replications; bars show standard deviation (for a and b), 95%confidence interval (for c and d). The natural succession of habitatwould lead to deterministic extinction at year 10 (extinction risk = 1,percent decline = 100%).


the metapopulation will be driven deterministically toextinction if the natural succession of the vegetation(invasion by shrubs and finally forest) is not controlledby some management. Even if the 1995 situation interms of habitat quality and quantity is maintained inthe long term (status quo situation), the metapopulationviability cannot be guaranteed: (1) there is a 50% risk ofan 85% decline in abundance; (2) the risk of falling be-low 20 individuals is 39%; (3) the expected minimummetapopulation size would be only 25 individuals forthe whole patch network.

5.2. Sensitivity analysis

Afterwards, to assess the degree to which eachparameter affects metapopulation viability, we per-formed a sensitivity analysis of six model parameters,using the status quo situation as the reference situation:(1) the maximum growth rate (Rmax), (2) the standarddeviation of the growth rate (stdRt) reflecting thestrength of environmental stochasticity, (3) the carryingcapacity (K), (4) the dispersal rates pij, (5) the cv of dis-persal rate reflecting the strength of the stochasticity ondispersal rates and (6) the slope of correlation-distancefunction. We first imposed an increase and a decreaseof 25% and 50% on the value of the parameter. If no dif-ference in viability occurred in this first sensitivity anal-ysis, we imposed an increase and a decrease of 95% onthe parameter value. To assess more completely the ef-fect of dispersal, and therefore the possibility of improv-ing viability by promoting dispersal using corridors, weenlarged the magnitude of the changes applied, by test-ing the effect of total absence of dispersal between sites(�100% in dispersal rates) as well as the effect of con-necting all habitat patches by a very large number of dis-persal events (adding 0.05 to each pij, i.e. 20 times theaverage dispersal value in the system; this correspondsto half of the individuals being successful dispersers,i.e. 5% of individuals in each population dispersing toeach of the 10 patches).

Sensitivity analysis results are presented in Fig. 5. Thestatus quo simulation served as a comparative basis. Interms of quasi-extinction risk, metapopulation viabilityis improved when the curve is more to the right. Allthe effects, except the maximum population growth rateRmax, influenced viability in the expected direction: via-bility was improved by a higher carrying capacity, lowertemporal fluctuations in demography (environmentalstochasticity), lower correlation of local populationdynamics, higher dispersal rates. In other words, severalconclusions can be drawn from these observations: (a)increasing the maximum population growth rate wouldnot automatically improve metapopulation viability asmight have been expected, i.e. more offspring at low den-sity is not the best solution for metapopulation viability.This was due to the interaction of two effects: (1)increasing Rmax increased Rt for population densityW < K but decreased it for W > K, and (2) environmen-tal stochasticity (i.e. standard deviation around the ex-pected Rt curve) transformed the density dependencecurve into a band which broadened the possible rangeof Rt (including very large but mainly very low valueswhen the curve approached the zero value) for a givendensity. Therefore we recorded a value of Rmax (i.e. adensity-dependence curve) for which the distributionof Rt values was optimal for population growth, ascan be seen in Fig. 6, where the optimal curve wasaround Rmax = 2.33 (i.e. �25% of original value).

Maximum growth rate Rmax

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xtin

ctio

n ri

sk

Environ. stochasticity stdRt cv of dispersal rates

Metapopulation size

–100%+0.05

– 95% – 50% – 25%Status quo

+ 25%+ 50%+ 95%

Fig. 5. Sensitivity analysis of six parameters for the Baquet population. Results are expressed in terms of interval quasi-extinction risk (see text fordetails). The vertical dashed line indicates the threshold of quasi-extinction (20 individuals).


A higher Rmax greatly increased the frequency of Rt nearzero, while a lower value decreased frequency of large Rt

(>2). (b) For the carrying capacity (K) and the magni-tude of environmental stochasticity (sdtRt), we observeda clear and significant effect on population viability. In-deed, decreasing K, which could correspond to habitatpatches being overgrown, or increasing sdtRt (i.e.increasing the environmental stochasticity, which affectsdemographic parameters and creates temporal fluctua-tions in growth rate and abundance) resulted in animportant decrease in metapopulation viability, and viceversa. (c) Changing the distance range of correlation inlocal dynamics had a limited effect. (d) Changing the dis-persal parameters had a relatively small effect on the re-sponse of the population structure when they vary from25% and 50%; however, with a drop of 95% and 100%the risk of quasi extinction was much greater, illustrat-ing that there was a buffer effect due to metapopulationdynamics (rescue effect and recolonisations) but that adispersal level lower than currently observed would still

be sufficient to play this role. A significant increase inviability was only obtained by adding 0.05 dispersalprobability to all pairs of patches, which was equivalentto connecting all populations with a dispersal rate corre-sponding to 20 times the average observed at the meta-population level, i.e. transforming the metapopulationinto a patchy population (Harrison, 1991). (e) The sto-chasticity on dispersal rate had virtually no effect.

5.3. Scenarios

Sensitivity analysis revealed the high effect of carryingcapacity on metapopulation viability, contrary to be-tween-patch dispersal and correlation, which did affectmetapopulation viability but only when changing thevalues of these parameters drastically, rendering thembiologically unrealistic. Therefore, we assessed metapop-ulation viability in scenarios representing realistic man-agement options increasing the overall carrying capacityof the system by (1) improving habitat quality via resto-

0

1

1

2

2

3

3

4

4 50R t

Fre

quen

cy (

%)

1.56 (-50%)2.33 (-25%)3.113.89 (+25%)4.67 (+50%)

0

1

2

3

4

5

0 500 1000Density

Rt

Fig. 6. Distribution of population growth rate Rt as simulated underdensity-dependent conditions (Eq. (1)) with the five Rmax values used insensitivity analysis and a standard deviation around the expected Rt

(density dependence curve) of 0.74. Smaller graph in insert gives thecorresponding density dependence functions. The optimal curve forpopulation growth was obtained around Rmax = 2.33 (i.e. �25% oforiginal value), as indicated by lowest quasi-extinction risk of Fig. 5.This is where the balance between the frequencies of small and largegrowth rates was optimal; there are not too many very low Rt and fewRt > 1 (as in 0%, +25% and +50% curves), but also not too few largeRt (>2, as in �50% curve).

0

0.2

0.4

0.6

0.8

1

0 50 100Metapopulation size

Inte

rval

qua

si-e

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150

Status quoA) Restoration & managementB1) Merging 3-4-5 into 3000 m² patchB2) Creatingo ne new 3000m² patchB3) Creating three new1 000m² patchesB4) Creating three new3 000m² patches

Fig. 7. Metapopulation viability under specific conservation scenarios:restoration and management of existing habitat (A) or creation of newhabitat (B1–B4) (see text for scenario details). Results are expressed interms of the interval quasi-extinction risk (see text for details). Thevertical dashed line indicates the threshold of quasi-extinction (20individuals).


ration and maintenance of existing habitat patches (sce-nario A) or (2) increasing habitat quantity by enlargingexisting patches or creating new habitat patches in thematrix (scenarios B1–B4).

In scenario A, we postulated that habitat restorationwould occur in order to double the carrying capacity K

of each habitat patch the year after restoration. Despitethe absence of more specific information, this doublingappeared to be realistically attainable in every patchsince more than 3/4 of their area is already covered byold Calluna and/or recolonised by shrubs (the first stagein the succession to forest), which has been shown toconsiderably reduce habitat carrying capacity, judgingfrom winter nest relative densities (Goffart, personalobservations). These restored patches would be main-tained in their new state (K twice the initial value) byan annual management programme applying rotationalrestoration and/or maintenance to prevent encroach-ment by shrubs (scenario A). A second way of managingthis metapopulation would be to create new habitatpatches in the matrix by clearing dense shrubby areas,dominated by Crataegus monogyna, where Succisa prat-

ensis plants still thrive in the understorey (the secondstage in the succession to forest, which is, as such,unsuitable for E. aurinia due to lack of light). We inves-tigated three such scenarios (Fig. 1): (B1) the creation ofone large habitat patch (3000 m2) merging and enlargingpatches 3, 4 and 5; (B2) the creation of a new large hab-itat patch (3000 m2) in the matrix (near the centre of thepatch system); (B3) the creation of the same habitat areain the matrix but separated in three new habitat patchesof 1000 m2 each, and (B4) same as B3 but three patchesof 3000 m2 each. Scenarios A and B4 simulated the dou-bling of the area of suitable habitat, hence of the carry-

ing capacity of the system, either by improving habitatquality (A) or increasing habitat quantity (B4).

Results of these scenarios are presented in Fig. 7 andTable 3. Compared to the status quo situation, it ap-pears that restoration and maintenance of habitat qual-ity (A) as well as new habitat creation (B1–B4) wouldboth increase overall metapopulation abundance andviability; indeed they both increased the overall carryingcapacity of the patch system. Viability increased propor-tionally to the area of habitat created (B4 > B1–B2–B3 > status quo), but was not much affected by the placewhere suitable habitat was created, whether by enlargingexisting populations (B1), or creating one big (B2) orseveral small patches (B3). There was only a small buffereffect resulting from more distinct habitat patches due tometapopulation dynamics, as may be seen by comparingB3 to B2 (3000 m2 added but 3 vs. 1 new patches); this isless visible when comparing A to B4 (doubling total car-rying capacity in the patch system by improving habitatquality of existing patches in A vs. increasing habitatarea with new patches in B4).

5.4. Minimum viable population size (MVP) estimations

From these results, it is clear that the prime elementfor the conservation of E. aurinia in Belgium is to in-crease the metapopulation carrying capacity. The ques-tion of how many individuals are necessary to ensurepersistence of the Baquet metapopulation arises, i.e.the minimum viable population (MVP) (Shaffer, 1981;Reed et al., 2003), that is the smallest number of individ-uals required for a population or species to have a pre-determined probability of persistence over a specifiedperiod of time. Traditionally, a population is consideredto be viable if over 100 generations it has a 1% probabil-ity of extinction. Due to the uncertainties of the modelparameter, the absence of Allee and other negative ef-fects occurring in very small populations, the behaviourof the PVA model was not expected to be reliable at very

Table 3Evaluation of conservation scenarios through some variables expressing metapopulation viability

Scenario Quasi-extinction risk offalling below 20 individuals

Expected minimummetapopulation size

Average final metapopulationsize after 100 years

Status quo 0.388 25 160(A) Restoration and management 0.088 59 356(B1) Merging patches 3, 4 and 5 0.190 38 211(B2) Creation of a new 3000 m2 patch 0.169 38 225(B3) Creation of three new 1000 m2 patches 0.112 42 215(B4) Creation of three new 3000 m2 patches 0.053 58 339


low population sizes. Therefore it was not realistic toconsider extinction only if abundance fell to zero; in-stead, we chose 20 individuals as the extinction thresh-old, below which the metapopulation was supposed tobecome extinct. Practically, this MVP was determinedby changing the value for the total carrying capacityof the patch system (number of individuals the patchsystem is able to sustain), and searching for the one witha 1% risk of dropping below 20 individuals over the 100years simulation time frame, while keeping all otherparameters unchanged. MVP for E. aurinia proved tobe extremely high: 1740 individuals, i.e. +650% com-pared to current situation (232 individuals).

By way of comparison, we computed MVP for twoother closely related species, i.e. P. eunomia (Schtickzelleand Baguette, 2004) and B. aquilonaris (Schtickzelleet al., 2005), using the same procedure as defined previ-ously. Nevertheless, to maintain the same MVP defini-tion for the three species, the extinction threshold hadto be adjusted to compensate for differences in total hab-itat area in the patch systems analysed for these threespecies. The extinction threshold of 20 individuals on0.92 ha for E. aurinia therefore corresponded to 137individuals on 6.28 ha for P. eunomia and 570 individu-als on 26.23 ha for B. aquilonaris. For P. eunomia MVPwas estimated at 784 individuals, i.e. 18% below the cur-rent capacity of the patch system. The patch system isable to host enough individuals (and even 18% more)to ensure metapopulation persistence. For B. aquilon-

aris, MVP was estimated at 22946 individuals, i.e. 21%above the current capacity of the patch system. Thecapacity of the patch system to host individuals shouldtherefore be increased by 21% to ensure metapopulationpersistence.

6. Discussion

Population viability analyses often deal with highlythreatened species and systems, for which data arescarce. This has two main consequences: (1) no data ex-ist to formally validate the model; (2) the use of surro-gate information from other landscapes and/or relatedspecies is necessary to replace missing data (Bart,1995; White, 2000; Boyce, 2002; Frankham, 2002; Sch-

tickzelle et al., 2005). In the case of E. aurinia in Bel-gium, we may confidently use the model to designconservation guidelines for the following reasons: (1)the structure of the model is similar to that of P. euno-mia (Schtickzelle and Baguette, 2004), which has beensuccessfully validated using high quality datasets, withtime series long enough to determine reliable variationsin demography and dispersal, and concerns a speciessharing many common features with E. aurinia in termsof demographic and dispersal parameters. (2) Most ofthe information used to parameterise the model was ob-tained directly from E. aurinia in Belgium. Furthermore,surrogate information was taken from closely relatedspecies with similar ecology and patch systems locatedin the same region (southern Belgium). Finally, sensitiv-ity analyses indicated that parameters generalised fromother species/systems (local dynamics correlation, tem-poral variation in dispersal) have only a limited effecton model predictions, except environmental stochastic-ity, one of the most difficult parameters to estimate inany PVA and always associated with considerableuncertainty.

Predictions of the model indicate that the future ofone of the largest marsh fritillary metapopulations inBelgium is not guaranteed: (1) under natural vegetationsuccession (the habitat of the species is highly transientand is rapidly overgrown by shrubs and trees), the meta-population will deterministically go extinct with the dis-appearance of its habitat, expected to occur in a fewyears; (2) maintaining the habitat stable over time wouldnot be sufficient. The status quo scenario predicts a 0.39probability that the metapopulation will fall below 20individuals, a level where extinction seems inevitable.The only practical solution to achieve efficient conserva-tion appears to be an increase in the overall carryingcapacity of the patch system (number of individualsthe patch system is able to sustain). Indeed, we are un-able to see how it would be possible to multiply dispersalby 20 even by creating corridors, to reduce correlationof local dynamics by 95%, or to limit the effects of envi-ronmental stochasticity on population demography. Theminimum viable population (MVP) necessary to limitextinction risk to 1% over 100 years is large, consideringthat the habitat must be able to sustain 1740 individuals,i.e. 7.5 times (+650%) more than the present-day situa-


tion. Even if the absolute results of PVA models, such asthis one, should be used with caution and taken as anorder of magnitude instead of exact values, +650% isclearly far higher than the current situation. ComparingMVP with values obtained in the same region for twoother closely related species (P. eunomia: Schtickzelleand Baguette, 2004; B. aquilonaris: Schtickzelle et al.,2005) makes this point even more obvious. As far aswe can see from the available data, the capacity of thepatch system to sustain individuals is sufficient (even18% higher) for P. eunomia and a limited increase(21%) would be necessary for B. aquilonaris to obtaina viable metapopulation. This seems to indicate the exis-tence of an extinction debt (Tilman et al., 1994; Hanskiand Ovaskainen, 2002) for E. aurinia in Belgium: there isnot enough habitat left for the species to survive, butthere is a time lag before the species effectively disap-pears following habitat lost. A substantial effort of hab-itat restoration (increase habitat quantity/quality) istherefore urgent for E. aurinia in Belgium to counteractthis predicted extinction.

In practical terms, the restoration of existing habitatpatches appears initially to be the most obvious way ofimproving the carrying capacity of the patch system.Then, creation of new patches and/or enlargement ofsome existing ones could considerably improve meta-population viability, the more patches the better, loca-tion being relatively secondary provided new patchesare in the colonization range of the species. Restorationof habitat quality implies the rejuvenation of grasslandsand heaths, and the clearing of shrubs to enhance light-ing of the herbaceous vegetation including the foodplant S. pratensis and its accessibility to butterflies.The regular management of habitat patches by theappropriate (light) regime of grazing or mowing, in or-der to maintain their quality and carrying capacity forE. aurinia, appears vital for the species. Such regularmanagement in the long term could ensure the persis-tence of a short sward, open vegetation structure favour-able for the basking of larvae. Moreover, under this typeof management Succisa pratensis could develop largerleaves, on which females prefer to deposit their eggs,thus decreasing the risk of larvae starvation (Porter,1981). This confirms conclusions of previous studieson E. aurinia (Warren, 1994; Bulman, 2001; de Boissieuand Chevrier, 2001; Goffart et al., 2001; Wahlberg et al.,2002a; Anthes et al., 2003; Konvicka et al., 2003). Nev-ertheless, in contrast to these studies, we believe we haveshown here, for the first time in the literature, that an ex-tremely large habitat area is required to host a viablemetapopulation. This high MVP value for E. aurinia

seems to be a consequence of the large value of its max-imum population growth rate (Rmax): this creates largetemporal fluctuations in abundance, increasing extinc-tion risk for a given population size (see Fig. 6 and thevery considerable effect of environmental stochasticity

stdRt on Fig. 5). From a biological point of view, thesefluctuations may be due to two different features: (1)the incidence of parasitism, which is likely to vary widelyfrom year to year, and (2) the egg-laying strategy. SinceE. aurinia females lay principally a single or a few largeegg batches, the impact of any mortality on this species(from predation, parasitism, local habitat destruction,bad weather, etc.) is high compared to the impact on aspecies laying eggs at many different places (like B. aqui-lonaris), considering that all or nearly all the eggs arelaid in the same basket (den Boer, 1968; Baguette andSchtickzelle submitted).

In more general terms, as shown by recent PVAs onanother butterfly species (Lopinga achine SCOPOLI: Berg-man and Kindvall, 2004) and on a grouse species (Tym-

panuchus phasianellus L.: Akcakaya et al., 2004), ourPVA illustrates the importance of taking into accountlandscape dynamics to provide sound guidelines forthe long-term conservation of species associated withsuccessional habitats. This appears clearly in the differ-ence of viability induced by natural succession of thevegetation compared to the status quo situation.

To conclude, the launching of a conservation planseems urgent to avoid the extinction of the largest meta-population of E. aurinia forecast in Belgium. It must becoupled with the maintenance, and if possible, the rein-forcement of the current monitoring of populationdynamics to refine estimates of carrying capacityaccording to habitat quality and, if possible, other meta-population parameters, to assess more precisely the im-pact of natural vegetation succession and managementmeasures on habitat quality, and to adapt management,according to the way species respond to the proposedconservation programme.

Acknowledgements

We thank Anne Weiserbs for her field work and twoanonymous reviewers for their valuable comments. Eve-lyne Merle and Sofie Vandewoestijne corrected the Eng-lish. Capture licences and financial support were grantedby the ‘‘Ministere de la Region Wallonne’’. N. Schtick-zelle is a Postdoctoral Researcher of the Belgian Na-tional Fund for Scientific Research (Charge de

Recherches FNRS). This is contribution BRC068 ofthe Biodiversity Research Centre at UCL.

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