The effects of seasonally variable dragonfly predation on butterfly assemblages

Ecology, 94(1), 2013, pp. 200–207� 2013 by the Ecological Society of America

The effects of seasonally variable dragonfly predationon butterfly assemblages

ANU TIITSAAR,1 ANTS KAASIK, AND TIIT TEDER

Department of Zoology, Institute of Ecology and Earth Sciences, University of Tartu, Vanemuise 46, 51014 Tartu, Estonia

Abstract. Where predation is seasonally variable, the potential impact of a predator onindividual prey species will critically depend on phenological synchrony of the predator withthe prey. Here we explored the effects of seasonally variable predation in multispeciesassemblages of short-lived prey. The study was conducted in a landscape in which we hadpreviously demonstrated generally high, but spatially and seasonally variable dragonfly-induced mortality in adult butterflies. In this system, we show that patterns of patchoccupancy in butterfly species flying during periods of peak dragonfly abundance are morestrongly associated with spatial variation in dragonfly abundance than patch occupancy ofspecies flying when dragonfly density was low. We provide evidence indicating that thisdifferential sensitivity of different butterfly species to between-habitat differences in dragonflyabundance is causally tied to seasonal variation in the intensity of dragonfly predation. Theeffect of dragonfly predation could also be measured at the level of whole local butterflyassemblages. With dragonfly density increasing, butterfly species richness decreased, andbutterfly species composition tended to show a shift toward a greater proportion of speciesflying during periods of off-peak dragonfly abundance.

Key words: butterfly conservation; dragonflies; ecological filter; Estonia; local extinction; nonselectivepredation; seasonal predation risk; window of vulnerability.

INTRODUCTION

Revealing major biotic and abiotic gradients along

which species are spatially distributed is a major goal in

community ecology. Among biotic gradients, variation

in the intensity of predation is probably one of the most

pervasive factors to affect communities at lower trophic

levels. Predators have been reported to affect prey

communities in diverse ways, ranging from facilitating

species coexistence (Shurin and Allen 2001, Eitam and

Blaustein 2010) to reducing species richness (Schoener

and Spiller 1996, Denoel et al. 2004). The potential

impact of predation on prey assemblages necessarily

depends on the phenological coupling of the predator

with individual prey species (Yang and Rudolf 2010).

Indeed, mortality risk caused by predators is rarely

uniform throughout the year, especially in seasonal

environments. For example, in boreal forests, bird

predation on larvae of folivorous insects is much higher

during the nestling period of insectivorous birds than

during the rest of the season (Remmel et al. 2009). The

influence of a predator on individual prey species, and

on the prey assemblage as a whole, would therefore

critically depend on the extent to which the vulnerable

stages of the prey species overlap with the phenology of

the predator (Both et al. 2009, Yang and Rudolf 2010).

Surprisingly, however, empirical evidence for phenolo-

gy-mediated effects of predators on composition and

species richness of prey assemblages are extremely scarce

(see Black and Hairston 1988 for a rare exception).

At least partly, the paucity of such studies relates to

the inherent difficulties associated with conducting

manipulative experiments in complex natural ecosys-

tems on appropriate spatial scales. Conveniently,

however, in multispecies prey assemblages a varying

degree of phenological overlap of the predator with

different prey species could provide a useful opportunity

to generate and test qualitative theoretical predictions

about predators’ impact on prey assemblages. Phenol-

ogy-mediated effects of predation should be especially

pronounced in prey assemblages of short-lived organ-

isms with seasonal variation in species occurrence, as is

frequently characteristic for insect assemblages.

In a recent study (Sang and Teder 2011), conducted in

seminatural calcareous grasslands in Northern Europe,

we showed that, at the local scale, dragonflies can exert a

very high predation pressure on adult butterflies. At peak

densities, they were estimated to reduce life expectancy of

an adult butterfly to less than a day (Sang and Teder

2011). However, different species in this butterfly

assemblage are unlikely to face a similar phenological

risk of dragonfly predation. In particular, dragonfly

abundance in the study region has a high seasonal

variation with peak densities in June being followed by a

gradual decline to a more than 10-fold lower density by

the beginning of August (Sang and Teder 2011). Because

Manuscript received 3 April 2012; revised 27 June 2012;accepted 16 August 2012. Corresponding Editor: J. A.Rosenheim.

1 E-mail: [email protected]

200

of a short adult period, different butterfly species flying at

different times during the season thus experience greatlyvarying levels of dragonfly predation risk.

Using this same study system, we focus here on theeffects of spatial and seasonal variation in dragonfly

predation on species composition and richness of localbutterfly assemblages. For this purpose, we analyze

patch occupancy patterns (i.e., presence/absence) ofindividual butterfly species with regard to dragonflyabundance in space and time. We predict that patterns

of patch occupancy in butterfly species flying at the timeof peak dragonfly abundance would be more affected by

the predator than in butterflies with less phenologicaloverlap with dragonflies. Consistently, we expect that in

habitat patches with high dragonfly densities butterflyspecies composition would shift toward a greater

proportion of species flying at the time when overallpredation pressure exerted by dragonflies is lower. As a

logical consequence, this loss of vulnerable species ispredicted to lead to a decrease in overall butterfly species

richness in habitat patches with high dragonfly densities.

MATERIALS AND METHODS

Study system

The impact of dragonfly predation on butterflycommunity composition and species richness was assessed

in a study area, for which we had previously demonstrat-ed (1) generally high but spatially variable levels of

dragonfly-induced mortality in butterfly populations, and(2) high seasonal variation in dragonfly predation (Sang

and Teder 2011). The study was conducted in a total of 29grassland patches along the Baltic Sea coastline in

western Estonia (58.23–58.708 N, 21.89–23.708 E; Fig.1). The area of the surveyed grasslands varied from 2.5 to

51.7 ha (mean 12.5 ha, median 9.5 ha). Patch differencesin terms of vegetation cover and abiotic environment

were minimized. To do this, only alvars, a particular typeof seminatural calcareous grassland (see Partel et al. 1999

for detailed habitat description) that were within 2 kmfrom the coastline were accepted as study sites. However,none of the sites was directly open to the sea to minimize

wind-related variation in dragonfly and butterfly behaviorbetween surveyed sites.

Sampling procedures

In the spring and summer of 2008 and 2009, repeatedsurveys were conducted to determine species composi-

tion and richness of butterfly communities and densityof dragonflies in the focal grasslands. All study sites

were surveyed nine times (five times in 2008 and fourtimes in 2009) so that the adult flight period of all focal

butterfly species was covered in both years. Thissampling effort enabled us to establish presence/absence

of a species with reasonable confidence; the presence ofmost species could have been recorded during at leastfour different transect counts.

Each of the nine sampling periods lasted for about a

week. The daily order of visiting study sites was varied in

different sampling periods. Butterflies and dragonflies

were recorded along a non-fixed transect route using

standardized transect protocol (Pollard and Yates 1993).

Transect lengths were determined by a logarithmic

function of the grassland area and ranged from 0.6 to

2.5 km per site and date. Transect walks were conducted

during the active foraging time of the insects, between

10:00 and 18:00 hours, and in case of highly favorable

weather conditions from 09:00 to 19:00 hours. Minimum

requirements for adequate weather conditions followed

the protocol of Pollard and Yates (1993). Butterflies and

dragonflies seen within 2.5 m to both sides of the

observer and 5 m ahead were counted.

All butterflies were identified to the species level in the

field, except the Leptidea species, which cannot be

unambiguously discriminated without DNA or karyo-

logical data (Dinca et al. 2011). The counts of

dragonflies were limited to true dragonflies (Odonata,

Anisoptera); damselflies were not included as they have

not been mentioned to kill butterflies (and were not

observed to attack butterflies in the study area; Sang and

Teder 2011). The majority of dragonflies (.90%)

recorded in the focal sites were Orthetrum cancellatum

Linnaeus (Libellulidae); seasonal variation in dragonfly

density thus mostly reflects the phenology of this species

(Sang and Teder 2011).

Defining the focal set of butterflies

The impact of dragonflies was examined on butterfly

species that form local populations in alvar grasslands,

and which are small enough (wingspan ,50 mm) to be

at least potentially affected by dragonfly predation. The

set of species, forming populations in alvar grasslands

FIG. 1. Map of the study sites (black dots) in Estonia,northern Europe.

January 2013 201DRAGONFLIES CHANGE BUTTERFLY ASSEMBLAGES

was determined using the opinion of experts (T.

Tammaru and E. Ounap, personal communication), the

distribution of larval host plants of butterflies, and local

butterfly literature (Viidalepp and Remm 1996). In the

analyses, butterfly species richness refers to species

richness of this focal set (altogether 30 of the total of

60 species recorded in the study sites). Habitat

generalists, forest and migrant species (see Sang et al.

2010 for classification) as well as a few large species with

wingspans .50 mm (Aporia crataegi, Argynnis adippe,

and Argynnis aglaja) were not considered. The latter

criterion was applied because butterflies larger than this

were not observed to be captured by dragonflies (Sang

and Teder 2011). The majority of the focal species (27)

are univoltine in the study region.

Quantifying spatial variation in dragonfly abundance

For each sampling period, dragonfly density at a given

site was quantified as the average number of dragonflies

observed per 100 m of transect. As relative differences in

dragonfly density between sites were fairly persistent

throughout the season within years and between the two

years (Sang and Teder 2011), we were justified to

calculate relative site-specific indices of dragonfly

density across all nine sampling dates. Principal

component analysis was used to combine density

estimates into a single variable (PCA1; referred to as

site-specific dragonfly index).

Quantifying phenological match between dragonflies

and butterflies

Dragonfly abundance in the study area strongly varies

throughout the season (Sang and Teder 2011), and thus

potentially, different butterfly species face different

levels of adult predation risk depending on their

phenological overlap with dragonfly abundance. To

quantify this phenological overlap for individual but-

terfly species, we first calculated mean dragonfly density

across study sites separately for each sampling period (N

¼ 9). Thereafter, phenological overlap for each butterfly

species was estimated as weighted mean dragonfly

density over nine sampling periods, with the weights

proportional to the number of butterfly individuals

encountered during each sampling period, as follows:

X9

i¼1

no: butterflies counted during the ith sampling period

total no: butterflies counted

3 mean dragonfly density during the ith sampling period:

ð1Þ

A higher value of phenological overlap thus means that

a higher proportion of butterfly individuals occurred at

the time of high overall dragonfly abundance.

Habitat variables

Besides variables directly related to trophic interac-

tions between dragonflies and butterflies, we also

determined major habitat variables known to affect

patch occupancy of individual species and local butterfly

species richness. The areas of focal grassland patches

were obtained from an inventory of Estonian seminat-

ural communities (as in 2004) conducted by the

Estonian Fund for Nature (Tartu, Estonia) and the

Estonian Seminatural Community Conservation Asso-

ciation (Tartu, Estonia). The data were upgraded using

high resolution orthophotos obtained from the Estonian

Land Board (Tallinn, Estonia). Habitat connectivity

was described by a simple proportional index: the area

of alvar grasslands within a 2 km radius around the

centroid of the focal grassland patch (the focal patch

included). Simple proportional index has been demon-

strated to work reasonably well in similar study systems

(e.g., Moilanen and Nieminen 2002), and has been

recommended for estimating connectivity when habitat

patches are oddly shaped and relatively close together

(Winfree et al. 2005), as was the case in the present

study. The radius of 2 km was chosen for delimiting

focal areas, as this corresponds to realistic values of

mean lifetime dispersal distances reported for butterflies

(e.g., Hanski et al. 2000, 2006).

Data analysis

First, by reanalyzing the data from our earlier study

(Table 1 in Sang and Teder 2011), we tested the

assumption (required for further analyses) that the

probability of dragonfly attack depends on phenological

overlap of butterflies with the predator. The number of

observed attacks was modeled with a Poisson regression

allowing for overdispersion in the dependent variable,

and using phenological overlap as the independent

variable. The traditional Poisson process was used to

account for the different numbers of individuals per

species.

The next step was to obtain a single metric for each

individual butterfly species that will link the data on

spatial variation in dragonfly abundance with the data

on patch occupancy of each particular butterfly. To do

this, we performed a series of logistic regressions

incorporating spatial autocorrelation. In these models,

patch occupancy (i.e., presence/absence) of a given

butterfly species was used as the dependent variable,

whereas a site-specific dragonfly index (seeMaterials and

methods: Quantifying spatial variation . . .) was included

as the explanatory variable, and habitat area (log-

transformed) and habitat connectivity (log-transformed)

as independent covariates. Inevitably, such an analysis

could not be applied to the species present/absent in

most patches. In practical terms, a logistic regression

could be performed for a species when it was present in

4–25 study sites of the total of 29 sites examined

(altogether 18 of 30 focal species).

The logistic regression coefficients obtained for

individual butterfly species were further used as esti-

mates of effect size in a meta-analytic framework to

achieve our main goal, i.e., to examine if patch

ANU TIITSAAR ET AL.202 Ecology, Vol. 94, No. 1

occupancy in butterfly species with higher phenological

overlap with dragonflies is more sensitive to the spatial

variation in dragonfly abundance. In particular, we

performed a meta-regression analysis (equivalent to a

weighted least squares regression) in which phenological

overlap was entered as the moderator variable to

determine its effect on the variation in the logistic

regression estimates. Each individual estimate was

weighted by the inverse of its variance (i.e., 1/squared

standard error; a common practice in this type of

analysis; Gurevitch and Hedges 1999, Osenberg et al.

1999) to account for different precision of parameter

estimates for different butterfly species.

We further tested if butterfly species richness associ-

ates with spatial variation in dragonfly abundance. A

spatial autoregressive model (SAR) was used for this

purpose, with site-specific dragonfly index as the

independent variable, and habitat area (log-trans-

formed) and habitat connectivity (log-transformed) as

covariates.

Finally, to quantify the effect of dragonfly predation

on species composition of local butterfly communities,

we calculated mean phenological overlap of the recorded

butterfly species for each site. We predicted that this

mean phenological overlap should be inversely related to

site-specific dragonfly index. In other words, in sites with

higher overall dragonfly abundance, butterfly species

composition should shift toward species that have a less

phenological match with dragonflies. This prediction

was tested using a spatial autoregressive model (SAR),

with mean phenological overlap of butterflies at a given

site as the dependent variable and site-specific dragonfly

index as the independent variable.

Logistic regressions were conducted, and spatial

autoregressive models fitted, using SAM 4.0 software

(Spatial Analysis in Macroecology; Rangel et al. 2010).

Meta-regression analysis was carried out using SAS

software (SAS Institute 2008).

RESULTS

A total of 11 054 individuals of 30 focal butterfly

species (see Materials and methods for criteria used to

define the set of focal species) and 9282 dragonflies were

recorded during transect counts in 29 grassland patches,

conducted during nine sampling periods in two years.

On the basis of these data, three major variables were

determined (see Materials and methods for details): (1)

patch occupancy (presence/absence) of each focal

butterfly in target sites (Table 1), (2) an index describing

site-specific dragonfly abundance (site-specific dragonfly

index), and (3) a measure describing phenological match

of each focal butterfly with dragonfly abundance

(phenological overlap; Table 1). Site-specific dragonfly

index did not correlate either with habitat area or

connectivity (Pearson correlations: r ¼ 0.001, P ¼ 0.97,

and r¼ 0.1, P¼ 0.60, respectively). There was, however,

correlation between habitat area and connectivity due to

measurement specifics; focal patch was included to

connectivity measure (Pearson correlation: r ¼ 0.4, P ¼0.03).

The Poisson regression model showed that the

probability of butterflies being attacked by dragonflies

depends on phenological overlap of butterflies with the

predator (t ¼ 2.49, P ¼ 0.019). As expected, butterflies

with higher phenological overlap with dragonflies face

higher predation risk (Fig. 2). The corresponding

parameter estimate (0.57) is equivalent to an average

of 1.8-fold increase in the attack rate when the value of

phenological overlap increases by one unit.

The effect size estimates from logistic regression

models, conducted to link the data on spatial variation

in dragonfly abundance with the patch occupancy of

individual butterfly species, were predominantly nega-

tive (14 out of the 18 species for which a test was

statistically feasible; Table 1). This indicates that the

probability of a species to be absent in a patch tended to

increase with increasing dragonfly abundance. A meta-

regression analysis, however, indicates that the magni-

tude of the effect size estimates for individual butterfly

species depends on the phenological overlap of butter-

flies with dragonflies. A significant negative effect of the

phenological overlap (F1,16 ¼ 5.2; P ¼ 0.04; Fig. 3)

suggests that patch occupancy in butterfly species flying

at the time of higher overall dragonfly abundance is

more sensitive to the increase in dragonfly abundance

than patch occupancy in species with less phenological

overlap with dragonflies. The fitted model produced an

intercept estimate of 0.05 (Fig. 3) that was not

significantly different from zero (F1,16 ¼ 0.098, P ¼0.76). This is in good agreement with the expected

impact of dragonflies on butterflies; a butterfly species

with a negligible phenological overlap with dragonflies

should show no sensitivity to spatial variation in

dragonfly abundance.

In line with the species-level findings, butterfly

species richness was lower in sites with higher dragonfly

index (SAR coefficient ¼�0.74, t ¼�3.15, P ¼ 0.004).

Also, there was a marginally nonsignificant negative

association between mean phenological overlap of

butterflies with dragonflies at a given site and site-

specific dragonfly index (spatial autoregressive model:

SAR coefficient¼�0.07, t ¼�1.83, P ¼ 0.08). In other

words, butterfly communities in dragonfly-rich sites

tended to contain more species flying during off-peak

dragonfly abundance.

DISCUSSION

We showed that patterns of patch occupancy in

butterfly species flying during periods of peak dragonfly

abundance were more sensitive to the spatial variation in

dragonfly density than patch occupancy of species flying

when dragonfly density was low. Several lines of

evidence indicate that this phenology-mediated associa-

tion plausibly has a causal basis, reflecting spatial and

seasonal variations in the intensity of dragonfly preda-

tion. The differential impact of dragonfly predation was


echoed at the level of local butterfly assemblages;

butterfly assemblages in dragonfly-rich habitat patches,

compared to dragonfly-poor patches, were generally less

species rich, and had a tendency to contain a lower

proportion of predation-sensitive species.

For predators to affect patch occupancy, not just

abundance, of their prey, their pressure on prey

populations should presumably be high and persistent

across generations. Dragonfly predation on butterflies in

the studied landscape appears to meet both of these

conditions. In particular, in an earlier paper conducted

in the same study system (Sang and Teder 2011), the life

expectancy of adult butterflies at high dragonfly

densities was estimated to be less than a day. Combined

with several daylong maturation times in adult butter-

flies (Scott 1973, 1974, Boggs and Freeman 2005), this

implies that many individuals die without leaving any

offspring. Besides these direct effects, dragonflies might

also give rise to various nonconsumptive negative effects

on local butterfly populations, e.g., through changes in

migration rates in the presence of predators (see, e.g.,

Orrock et al. 2010). Moreover, the detrimental impact of

dragonfly predation on local butterfly populations is

FIG. 2. Dragonfly attacks per butterfly as dependent onphenological overlap of butterflies with the predator. Butterflyspecies are classified according to their values of phenologicaloverlap (see Table 1 for numerical values). Attack rates perbutterfly for these groups are calculated on the basis of the datapresented in Table 1 in Sang and Teder (2011).

TABLE 1. Characteristics of the focal set of butterflies from grassland patches along the coastline of western Estonia (taxonomyafter Lafranchis [2004]).

SpeciesPhenological

overlap

Meanwingspan(mm)

No. siteswhere

recorded

No.individualscounted

Logisticregressioncoefficient SE t P

Coenonympha hero 4.72 29 6 61 �0.60 0.41 �1.45 0.15Mellicta aurelia 4.16 34 7 61 �0.92 0.53 �1.72 0.09Cyaniris semiargus 3.89 29 8 42 �0.88 0.44 �2.0 0.05Ochlodes sylvanus 3.62 30 11 23 �0.46 0.31 �1.48 0.14Polyommatus icarus 3.55 27 29 1576 na na na naCupido minimus 3.44 21.5 18 336 �0.29 0.30 �0.96 0.34Polyommatus amandus 3.16 33.5 16 91 �0.75 0.36 �2.12 0.03Aricia eumedon 2.94 25.5 1 1 na na na naCoenonympha glycerion 2.80 30 29 2101 na na na naPyrgus malvae 2.67 22 28 174 na na na naMelitaea cinxia 2.63 41.5 6 969 0.32 0.49 0.66 0.51Coenonympha pamphilus 2.62 28.5 20 147 �0.21 0.22 �0.96 0.34Erynnis tages 2.59 28.5 21 115 �0.43 0.29 �1.52 0.13Euphydryas aurinia 2.46 39.5 1 7 na na na naHamearis lucina 2.39 27 7 71 �0.34 0.45 �0.76 0.45Aricia artaxerxes 2.02 28.5 7 41 0.01 0.44 0.02 0.99Mellicta athalia 1.98 37 9 118 0.21 0.36 0.57 0.57Maculinea arion 1.80 37.5 2 7 na na na naBrenthis ino 1.60 37.5 26 615 na na na naAphantopus hyperantus 1.50 35 29 2340 na na na naLeptidea sp. 1.50 40 16 72 �0.32 0.28 �1.13 0.26Argynnis niobe 1.40 46.5 10 71 �0.41 0.42 �0.97 0.33Pyrgus alveus 1.32 27.5 4 5 0.53 0.32 1.63 0.10Maniola jurtina 1.27 42.5 29 1136 na na na naLycaena phlaeas 1.09 28 3 5 na na na naLycaena virgaureae 0.91 29 7 30 �1.22 0.71 �1.73 0.08Hipparchia semele 0.88 40.5 3 40 na na na naHyponephele lycaon 0.87 39 12 179 �0.13 0.24 �0.56 0.57Thymelicus lineolus 0.85 24.5 27 552 na na na naHesperia comma 0.56 30 14 68 �0.14 0.20 �0.72 0.47

Notes: Species are ranked according to their phenological overlap, which describes the degree of synchrony of their seasonalabundance curve with that of dragonflies (see Materials and methods for calculations). Additionally, wingspan (according toEliasson et al. [2005]), number of sites (out of 29) where the butterfly was recorded, and number of individuals counted over twoyears are given. The last four columns present the results of logistic regressions that were conducted for each species to analyze itspatch occupancy (i.e., presence/absence) as a function of site-specific dragonfly index (see Materials and methods for more details).An entry of ‘‘na’’ indicates not available.


unlikely to be short term in the study area, as relative

differences in dragonfly abundance between habitat

patches are reasonably persistent over years (which,

most likely, reflects the spatial distribution of water

bodies suitable for dragonfly breeding; Sang and Teder

2011). The populations of predation-sensitive species are

therefore unlikely to recover through immigration.

Still, due to high amplitude seasonal variation in

dragonfly abundance, not all butterfly species will

experience such high potential risk of dragonfly preda-

tion. The adult period of most temperate butterflies, i.e.,

the time when they are vulnerable to dragonfly

predation, has usually a rather distinct peak of just a

couple of weeks. Even though dragonflies were present

throughout the sampling season (more than two

months) in the study area, their density showed a strong

decrease from June to the beginning of August (Sang

and Teder 2011). Accordingly, butterflies flying during

the periods of high dragonfly abundance were more

frequently attacked than butterflies flying at the time of

low dragonfly abundance. In good correspondence with

the expectations, patch occupancy patterns of individual

butterfly species varied in the sensitivity to spatial

variation in dragonfly abundance along the gradient of

phenological overlap of the predator and the prey

species. As predicted, patch occupancy patterns in

butterflies flying during peak dragonfly abundance were

more strongly related to spatial variation in dragonfly

abundance than those in species with their adult period

having less overlap with dragonfly phenology. From the

metacommunity perspective, these results thus provide

evidence of species sorting along a predation intensity

gradient (see Garcia and Mittelbach 2008 for another

example). Indeed, there was a tendency (albeit margin-

ally nonsignificant) for butterfly species composition in

habitat patches with high dragonfly density to show a

shift toward a greater proportion of species flying during

periods when overall predation pressure exerted by

dragonflies was lower.

Nevertheless, inferring predator–prey interactions

from observational abundance data is not necessarily

straightforward. If predators track changes in prey

abundance, we can, in principle, observe both positive

and negative correlations between their abundances. In

particular, a positive correlation could be observed if

predator density increases in areas of high prey

abundance, whereas a negative correlation is expected

if the predator population is abundant enough to

suppress prey populations. Both positive and negative

correlations are expected in a strongly coupled preda-

tor–prey system, such as a specialist predator and its

prey. However, dragonflies are opportunist predators

with a very generalized diet, in which butterflies are

likely a minor food source. Furthermore, as dragonflies

spend much of their life cycle in aquatic environments,

the abundance of adult dragonflies in adjacent terrestrial

habitats should primarily be determined by the avail-

ability and suitability of larval habitats. Dragonflies are

therefore unlikely to track butterfly abundance, whereas

the ability of dragonflies to strongly reduce the number

of butterflies appears much more likely. In such a case,

we should expect a negative correlation between

predator and prey density, as we indeed documented.

Moreover, it is not easy to see an alternative

mechanism besides dragonfly predation that could have

affected patch occupancy patterns of individual butterfly

species in a consistent, phenology-based manner. The

problems related to inferring causality from observa-

tional data were mitigated by taking into account

habitat size and connectivity in the analyses, a pair of

variables that could have affected both butterfly

assemblages (e.g., Ockinger and Smith 2006, Bruckmann

et al. 2010) and dragonfly abundance. Moreover, by

limiting sampling to a single type of grassland (i.e.,

alvars) within 2 km from the coastline, we also could

control much of the variation in vegetation cover and

climatic conditions (especially temperature and windi-

ness). Inevitably, we cannot exclude the possibility that

some particular butterfly species still showed a negative

covariance with dragonfly abundance just because of a

parallel response of the prey and the predator to some

specific, unidentified environmental variable. However,

a systematic, phenology-dependent impact on the whole

butterfly assemblage would have required a more

general mechanism.

The observed reduction in butterfly species richness

with increasing dragonfly abundance could be consid-

FIG. 3. A plot illustrating that the sensitivity of patchoccupancy of individual butterfly species to between-sitedifferences in dragonfly abundance depends on the timing ofthe butterfly flight period. The negative trend (see Results forsignificance testing) suggests that patch occupancy of butterfliesflying at the time of high dragonfly abundance (higherphenological overlap) responds to site differences in dragonflyabundance more strongly than patch occupancy in species withless overlap with dragonflies. The parameter estimates for the y-axis were obtained from logistic regression analyses (seeMaterials and methods: Data analysis for details and Table 1for numerical values), each circle corresponding to one butterflyspecies. The areas of the circles are proportional to the inverseof the variance of logistic regression estimates to account fordifferent precision of parameter estimates for different butterflyspecies.


ered a straightforward consequence of the impact of the

predator on individual butterfly species. In particular,

the qualitative effect of predation on local prey species

richness has been proposed to depend on the type of

predator and the strength of competitive interactions

between prey species (Chase et al. 2002). Predators can

promote prey diversity if they selectively reduce the

density of dominant competitors (Chase et al. 2002). By

contrast, without an indirect effect on competitive

interactions between prey species, high enough general-

ist predation could result in a direct negative effect on

prey diversity (Hixon and Beets 1993, Spiller and

Schoener 1998). Our predator–prey system has much

in common with the latter. Specifically, interspecific

competition between different butterfly species is weak

or nonexistent for both larval and adult resources.

Larvae of most species do not share host plants, while

nectar-feeding adults occur at different times of the

season, and have ample supply of nectar plants available

throughout the season (A. Tiitsaar and T. Teder,

personal observation). Dragonflies, on the other hand,

show no strong preferences for any particular butterfly

species (Sang and Teder 2011). Therefore, the effect of

dragonfly predation at the level of whole butterfly

assemblage is most likely the sum of its effects on

individual butterfly species, without any further com-

munity-level processes.

Although seasonal variations in predation pressure

are perhaps common in various ecosystems (e.g., King-

solver and Srygley 2000, McCutchen 2002, Bronmark et

al. 2008, Remmel et al. 2009, Sang and Teder 2011), the

evidence for predator-driven changes in prey assemblag-

es based on phenological overlap appears to be

extremely scarce. In a rare paper similar to ours, Black

and Hairston (1988) showed that the composition and

size structure of the zooplankton community corre-

sponds to seasonal changes in the intensity and type of

predation. Also, more specifically, we are not aware of

any previous reports of predator-driven changes in

butterfly species richness. However, the results of this

study are generally in line with what has been reported

in the few other studies addressing predators’ impact on

prey species richness in terrestrial arthropod communi-

ties. Indeed, wherever predators have been shown to

affect species richness of terrestrial arthropods, their

effect has been found to be negative (rodent predation

on ground beetles [Parmenter and MacMahon 1988],

avian predation on grasshoppers [Joern 1992], lizard

predation on spiders [Schoener and Spiller 1996, Spiller

and Schoener 1998]).

The results of this study could serve as a potential

example of trophic interactions cascading across eco-

system boundaries (e.g., McCoy et al. 2009), aquatic–

terrestrial boundaries in particular. The complex,

semiaquatic life cycle of the dragonflies implies that

the interactions influencing the abundance of dragonflies

in their larval habitat may also have an indirect effect on

prey assemblages in terrestrial communities. Indeed,

Knight et al. (2005) showed that fish presence in larval

habitats had a strong effect on adult dragonfly

abundance; the density of dragonflies near fish-free

ponds was much higher than around fish-containing

ponds. The higher abundance of adult dragonflies near

fish-free ponds in turn adversely affected various groups

of insect pollinators, both because dragonflies predated

on pollinators and pollinators avoided foraging near

dragonflies.

Knowing when and how predators affect prey

community structure is also vital for conservation

biology. In particular, it has been argued that

predators may serve as useful surrogates to identify

areas of high biodiversity value (Andelman and Fagan

2000, Sergio et al. 2008). For example, Sergio et al.

(2006) showed that sites occupied by top predators

(raptors in their case) were more diverse at lower

trophic levels, such as other birds, butterflies, and

trees. Nevertheless, as our study highlights, the

usefulness of predators for indicative purposes de-

pends critically on the nature of the predator–prey

interactions. As our study indicates, strong predation

pressure, when affecting prey species proportionally to

their numbers, can lead to impoverishment of prey

communities. Dragonfly density should therefore be

treated as a component of habitat quality for

butterflies, and may thereby form a factor to be

considered in conservation management of these

insects.

ACKNOWLEDGMENTS

We thank Robert B. Davis, Toomas Esperk, Juhan Javois, LyLindman, Freerk Molleman, Toomas Tammaru, and HelenVellau for helpful comments. The Viidumae Biological Station(Environmental Board, Estonia) and Tonu Talvi provided supportduring fieldwork. The study was supported by the EstonianScience Foundation (grant number 8413), targeted financingproject SF0180122s08, and the EU through the EuropeanRegional Development Fund (Centre of Excellence FIBIR).

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The effects of seasonally variable dragonfly predation on butterfly assemblages