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PRIMARY RESEARCH ARTICLE
Metapopulation dynamics in a changing climate: Increasing
spatial synchrony in weather conditions drives
metapopulation synchrony of a butterfly inhabiting a
fragmented landscape
Aapo Kahilainen
1
|
Saskya van Nouhuys
1,2
|
Torsti Schulz
1
|
Marjo Saastamoinen
1
1
Metapopulation Research Centre,
Organismal and Evolutionary Biology
Research Programme, Faculty of Biological
and Environmental Science, University of
Helsinki, Helsinki, Finland
2
Department of Entomology, Cornell
University, Ithaca, New York
Correspondence
Aapo Kahilainen, Metapopulation Research
Centre, Organismal and Evolutionary Biology
Research Programme, Faculty of Biological
and Environmental Science, University of
Helsinki, Finland
Email: aapo.kahilainen@helsinki.fi
Funding information
Biotieteiden ja Ymp€
arist€
on Tutkimuksen
Toimikunta, Grant/Award Number: 213457,
265461, 273098; H2020 European Research
Council, Grant/Award Number: 637412
Abstract
Habitat fragmentation and climate change are both prominent manifestations of global
change, but there is little knowledge on the specific mechanisms of how climate
change may modify the effects of habitat fragmentation, for example, by altering
dynamics of spatially structured populations. The long-term viability of metapopula-
tions is dependent on independent dynamics of local populations, because it mitigates
fluctuations in the size of the metapopulation as a whole. Metapopulation viability will
be compromised if climate change increases spatial synchrony in weather conditions
associated with population growth rates. We studied a recently reported increase in
metapopulation synchrony of the Glanville fritillary butterfly (Melitaea cinxia) in the
Finnish archipelago, to see if it could be explained by an increase in synchrony of
weather conditions. For this, we used 23 years of butterfly survey data together with
monthly weather records for the same period. We first examined the associations
between population growth rates within different regions of the metapopulation and
weather conditions during different life-history stages of the butterfly. We then exam-
ined the association between the trends in the synchrony of the weather conditions
and the synchrony of the butterfly metapopulation dynamics. We found that precipita-
tion from spring to late summer are associated with the M. cinxia per capita growth
rate, with early summer conditions being most important. We further found that the
increase in metapopulation synchrony is paralleled by an increase in the synchrony of
weather conditions. Alternative explanations for spatial synchrony, such as increased
dispersal or trophic interactions with a specialist parasitoid, did not show paralleled
trends and are not supported. The climate driven increase in M. cinxia metapopulation
synchrony suggests that climate change can increase extinction risk of spatially struc-
tured populations living in fragmented landscapes by altering their dynamics.
KEYWORDS
climate change, dispersal, Lepidoptera, life history, Melitaea cinxia, metapopulation dynamics,
population synchrony, precipitation, temperature, trophic interactions
----------------------------------------------------------------------------------------------------------------------------------------------------------------------
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium,
provided the original work is properly cited.
©2018 The Authors. Global Change Biology Published by John Wiley & Sons Ltd
Received: 10 November 2017
|
Accepted: 1 April 2018
DOI: 10.1111/gcb.14280
Glob Change Biol. 2018;1–14. wileyonlinelibrary.com/journal/gcb
|
1
1
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INTRODUCTION
The two most prominent manifestations of human-induced global
change are habitat loss with habitat fragmentation and climate
change. Although the former continues to be the main causative
agent in driving species extinctions (Millennium Ecosystem Asses-
ment, 2005; Newbold et al., 2015; Pimm et al., 2014; Tittensor
et al., 2014), the effects of climate change are expected to increase
to matching levels during the coming decades (Leadley et al., 2010).
With the rapid advance of both, one of the most central questions
in the contemporary research on biodiversity, conservation and ecol-
ogy is how these two facets of global change jointly influence natu-
ral populations (Eigenbrod, Gonzalez, Dash, & Steyl, 2015; Holyoak
& Heath, 2016; Mantyka-Pringle, Martin, & Rhodes, 2012; McGill,
Dornelas, Gotelli, & Magurran, 2015; Oliver & Morecroft, 2014).
However, as detailed long-term data on the dynamics of spatially
structured metapopulations in fragmented landscapes are available
for only a few systems, the exact mechanisms by which climate
change modifies the effects of habitat fragmentation are not well
understood (Holyoak & Heath, 2016; Oliver & Morecroft, 2014).
Moreover, studies on the joint effects of habitat fragmentation and
climate change primarily focus on changes in the mean climatic con-
ditions. Changes in variability of weather conditions can also influ-
ence populations in fragmented landscapes, and variability is likely to
change as climate change advances. Information on how changes in
climatic variability can influence populations inhabiting fragmented
landscapes is of utmost importance (Alexander & Perkins, 2013;
Easterling, 2000; Huntingford, Jones, Livina, Lenton, & Cox, 2013;
IPCC, 2013; Lawson, Vindenes, Bailey, & van de Pol, 2015).
Metapopulations inhabiting fragmented landscapes are likely very
vulnerable to changes in variability of weather conditions. The long-
term stability of a metapopulation relies on independent population
dynamics in different parts of the landscape, such that decreases in
abundance at one region are balanced out by increases in another,
and colonization of unoccupied habitat continuously makes up for
local extinctions (Hanski, 1999; Hanski & Woiwod, 1993; Hastings &
Harrison, 1994; Heino, Kaitala, Ranta, & Lindstr€
om, 1997). The inde-
pendence of population dynamics over the landscape can be chal-
lenged by climate change if climatic conditions are related to
population growth rates. Below, we will briefly outline the mecha-
nisms that can disrupt independence and introduce synchrony in
population dynamics and how they might be influenced by climate
change.
Independence of population dynamics in different parts of a
metapopulation can be disrupted by three primary mechanisms: (1)
increasing spatial extent of synchrony in environmental conditions
influencing population growth rate (i.e., Moran effect; Moran, 1953),
(2) increased dispersal of individuals between local populations, and
(3) a change in the spatial extent of trophic interactions (e.g., a
predator with a geographic range different from that of the prey)
(Liebhold, Koenig, & Bjørnstad, 2004). Climate change can drive
metapopulation synchrony via any of the above mentioned
mechanisms. First, climate change can create a Moran effect via a
decrease in spatial environmental variability (Allstadt, Liebhold, John-
son, Davis, & Haynes, 2015; Koenig & Liebhold, 2016; Liebhold
et al., 2004; Post & Forchhammer, 2004; Ranta, Kaitala, & Lindstrom,
1999). Second, as temperature and wind conditions influence the
dispersal propensity of many taxa (Cormont et al., 2011; Kuussaari,
Rytteri, Heikkinen, Heli€
ol€
a, & von Bagh, 2016), increasing tempera-
ture has the potential to drive increasing dispersal resulting in phase
locking (Fox, Vasseur, Hausch, & Roberts, 2011; Gyllenberg, S€
oder-
backa, & Ericsson, 1993). Third, climate change can alter the spatial
extent of trophic interactions influencing the dynamics of the sys-
tem, for example, by enabling colonization of new species preying
on the focal population or changing the dynamics of existing preda-
tor populations. Although we are unaware of studies explicitly docu-
menting the latter case, there are several examples of distribution
changes in top predators and changes in the interactions within food
chains due to climate change (Gilman, Urban, Tewksbury, Gilchrist, &
Holt, 2010; Harley, 2011; Hazen et al., 2012; Romo & Tylianakis,
2013), all of which can disrupt metapopulation dynamics by changing
the density-dependence structure of the prey populations in a given
system. Lastly, all the above-described mechanisms may respond to
climate change simultaneously or interact with each other. If all the
mechanisms are not considered, the driver of population dynamics
synchrony can be misidentified. Therefore, to understand the role of
climate change, it is crucial that all of the potential mechanisms are
considered.
The Glanville fritillary butterfly (Melitaea cinxia) metapopulation
in the Finnish archipelago and its associated parasitoids have been
extensively studied for over two decades with the aim of under-
standing habitat fragmentation and metapopulation dynamics (Hanski
& Ovaskainen, 2000; Nieminen, Siljander, & Hanski, 2004; Ojanen,
Nieminen, Meyke, P€
oyry, & Hanski, 2013). A temporal increase in
the coherence of the M. cinxia metapopulation dynamics was
reported by Hanski and Meyke (2005) and Tack, Mononen, and Han-
ski (2015). Increasing frequency of late summer drought events was
suggested as the potential driver of the change, but no change in cli-
matic variability was detected (Tack et al., 2015). Since climate
change is expected to have greater influence on spring than on sum-
mer conditions in the northern hemisphere, the previous study may
have missed important aspects of climate change by focusing solely
on late summer precipitation conditions (Bonsal, Zhang, Vincent, &
Hogg, 2001; Huntingford et al., 2013; Robeson, 2004). Finally, other
potential explanations, such as increasing dispersal, or changes in
predation, have not been addressed.
Here, using data on the metapopulation dynamics of M. cinxia
from 1993 to 2015 together with monthly weather data, we exam-
ine the association of synchrony of the metapopulation dynamics of
M. cinxia with climate. More specifically, we focus on answering the
following questions: (i) How has the spatial extent of synchrony of
population growth rate changed across time? (ii) How are different
weather conditions over the entire life cycle associated with M. cinx-
ia population growth rate across different regions of the
2
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KAHILAINEN ET AL.
metapopulation? (iii) Has spatial synchrony of the influential weather
conditions changed with time, and is the synchrony of weather asso-
ciated with metapopulation synchrony? (iv) Can changes in the syn-
chrony of the M. cinxia metapopulation be attributed to changes in
dispersal propensity or trophic interactions? Our results shed light
on the potential mechanisms by which climate change can alter
metapopulation dynamics in a fragmented landscape, and thus con-
tributes to the understanding of the potential interaction between
habitat fragmentation and climate change.
2
|
MATERIALS AND METHODS
2.1
|
The Finnish Glanville fritillary butterfly
metapopulation
The Glanville fritillary butterfly, M. cinxia, inhabits a large network of
ca. 4400 dry meadows containing at least one of its host plants, rib-
wort plantain (Plantago lanceolata) or spiked speedwell (Veronica spi-
cata: Plantaginaceae) in the
Aland islands on the southwestern coast
of Finland (Nieminen et al., 2004). The Finnish M. cinxia metapopula-
tion is univoltine. Its development can be divided to egg, predia-
pause larval (instars from 1st to 4th/5th), postdiapause larval (instars
from 4th/5th to 7th), chrysalis, and adult stages (Kuussaari, van Nou-
huys, Hellemann, & Singer, 2004; Murphy, Wahlberg, Hanski, & Ehr-
lich, 2004; Wahlberg, 2000). Based on our observations, the first
adults normally emerge in late May or early June. The flight season
lasts approximately 30 days (mean 30 days, median 32 days;
Table S1), ending by early July. The first prediapause larval nests
start appearing in mid-July, first overwintering silk nests (4th or 5th
instar) can be found in mid-August, and most nests have entered
diapause by the beginning of September. The larvae diapause until
late March, after which they go through from two to three additional
larval stages before pupation in mid-May (Kuussaari et al., 2004;
Saastamoinen, Ikonen, Wong, Lehtonen, & Hanski, 2013; Wahlberg,
2000).
Since 1993, the suitable meadows in
Aland have been censused
for M. cinxia occupancy and population size by counting the number
of overwintering larval nests every autumn, followed up by a check
of overwintering mortality of the nests the following spring (Niemi-
nen et al., 2004; Ojanen et al., 2013). The initial number of surveyed
habitat patches was ca. 1200. Then, between 1998 and 1999 an
extensive remapping of potential habitats was conducted, after
which the number of surveyed patches increased threefold. Cur-
rently, ca. 4400 habitat patches are surveyed (Hanski et al., 2017;
Ojanen et al., 2013). Estimates of the detection probability of each
overwintering nest varies between 0.5 and 0.6, but the probability
of incorrectly inferring a habitat patch as unoccupied is only 0.1
(Ojanen et al., 2013). Typically, the undetected populations are small,
consisting of one or very few larval nests, so their contribution to
the metapopulation dynamics is negligible. For the few cases in
which more nests were observed in the spring than in the previous
autumn, the autumn nest count has been corrected to match that of
the spring nest count. Other than changes in the number of habitat
patches surveyed, the changes to the systematic survey protocol and
sampling effort have been minor throughout the years, which makes
observations across years comparable (Ojanen et al., 2013).
Due to aggregation of habitat patches in the landscape, the
Aland islands can be subdivided into semi-independent habitat patch
networks (SINs), with habitat connectivity that is high enough to
allow for frequent exchange of dispersing individuals between
patches within the same SIN (Hanski, Moilanen, Pakkala, & Kuus-
saari, 1996). Using hierarchical clustering implemented in the soft-
ware SPOMSIM (Moilanen, 2004), a recent study clustered the
entire metapopulation to 125 SINs that differ in patch number, size
and connectivity (Hanski et al., 2017). Of these, 33 SINs can be con-
sidered viable according to spatially explicit metapopulation theory
(i.e., metapopulation capacities above a species specific extinction
threshold; Hanski et al., 2017). Each of the viable SINs contain on
average 82 habitat patches (median =69, SD =39) with an average
area of a patch of 2260 m
2
(median =687 m
2
,SD =5467 m
2
). The
viable SINs are distributed throughout the
Aland islands (Hanski
et al., 2017).
In the present study, we chose to focus on the dynamics of SINs
rather than on individual habitat patches. We do so because local
populations in individual habitat patches frequently go extinct, so
time series of local population growth rate dynamics would be very
heterogeneous. Furthermore, the spatial scale of our weather data
better matches the spatial scale of the SINs rather than the individ-
ual habitat patches. We exclude the nonviable SINs from the
analyses because many of them are unoccupied for all or most of
the 23-year study period.
2.2
|
Natural enemies of M. cinxia
Melitaea cinxia has been observed to be host to a generalist pupal
parasitoid species Pteromalus apum, and prey to lady beetles (Coc-
cinellidae), lacewings (Chrysopidae), pentatomid bugs (Pentatomidae),
red ants (Myrmica rubra), spiders, and dragonflies (Odonata) (van
Nouhuys & Hanski, 2004; van Nouhuys & Kraft, 2012). The rate of
predation by these generalists has not been systematically recorded,
however, we do not expect them to have greatly impacted the syn-
chrony of population dynamics of the host because we have
observed no evidence of large changes in predator community over
time, and mobile individuals of these taxa would not be likely to
track M. cinxia density in the landscape. Furthermore, M. cinxia are
chemically defended by sequestered plant defensive chemicals (Reu-
dler & van Nouhuys, 2018; Suomi, Sir
en, Jussila, Wiedmer, & Riek-
kola, 2003) and are thus not likely to be prey to many invertebrate
and vertebrate predators (Kuussaari et al., 2004).
While we do not expect a large role for generalist predators, M.
cinxia larvae are frequently parasitized by two specialist parasitoid
wasp species, Cotesia melitaearum (Braconidae: Microgastrinae) and
Hyposoter horticola (Ichneumonidae: Campoplaginae) (van Nouhuys &
Hanski, 2004, 2005). Of the two species, only C. melitaearum has
the potential to influence synchrony of the M. cinxia metapopulation
dynamics. This is because the highly mobile H. horticola invariably
KAHILAINEN ET AL.
|
3
parasitizes one third of the caterpillars in almost every nest every
year across the metapopulation (Montovan, Couchoux, Jones, Reeve,
& van Nouhuys, 2015; van Nouhuys & Ehrnsten, 2004; van Nouhuys
& Hanski, 2002). The sedentary C. melitaearum, on the other hand, is
restricted to the northwestern side of the archipelago in most years
and inhabits only well-connected M. cinxia SINs (van Nouhuys &
Hanski, 2002). Where present, C. melitaearum can be locally abun-
dant, potentially driving local populations of M. cinxia to extinction
(Lei & Hanski, 1997). Furthermore, rate of parasitism and subsequent
parasitoid population size is related to spring temperature (van Nou-
huys & Lei, 2004). The M. cinxia populations are surveyed for
C. melitaearum every spring when the mortality of the overwintering
nests is surveyed (Ojanen et al., 2013). At this time, the overwinter-
ing generation of the C. melitaearum larvae leave the host and spin
white silken cocoons that are visible in the M. cinxia nests (van Nou-
huys & Lei, 2004). Although otherwise spanning the entire study
period, the data for the occurrence of C. melitaearum in 2010 was
unfortunately lost due to a hard drive break down.
2.3
|
Weather data
The weather data used in the analyses is a part of ClimGrid, which is
a gridded climatology dataset with a cell size of 10 km *10 km
(Aalto, Pirinen, & Jylh€
a, 2016), provided by the Finnish Meteorologi-
cal Institute. We obtained monthly average temperature and precipi-
tation sum estimates for months that approximately match the
different life-history stages of M. cinxia (see above), for the area cov-
ering the
Aland islands (from 59.9°to ca. 60.5°Lat. and from ca.
19.5°to ca. 20.9°Lon.). Weather conditions from September to the
following February were averaged to reflect average conditions dur-
ing diapause and, for this period, we also extracted average snow
cover depth and incorporated it into analyses of population growth
rate (see below). These analyses can be found in the supplementary
material, but for simplicity, we will restrict our focus to precipitation
and temperature in the main text. For the rest of the weather condi-
tions, we considered each month separately: March, April, and May
were considered to reflect postdiapause larval conditions, June con-
ditions to reflect the adult stage conditions (Table S1), and July and
August conditions to reflect prediapause larval conditions (Kuussaari
et al., 2004; Murphy et al., 2004). It is more difficult to associate
egg and pupal stages with any particular month as they last a shorter
period of time and overlap with the timing of larval and adult stages.
The egg stage mostly coincides with the adult stage in June, but can
partly coincide with prediapause larval stages in early July, and the
pupal stage occurs mostly in May coinciding with late instars of
postdiapause larval development (Murphy et al., 2004).
As nonstationarity due to temporal trends in time series data
may bias analyses (Bjørnstad, Ims, & Lambin, 1999; Liebhold et al.,
2004; Legendre & Legendre, 2012; but see Chevalier, Laffaille,
Ferdy, & Grenouillet, 2015), we tested for temporal trends in each
of the weather variables and detrended the variables whenever
trends were detected. We estimated the number of smooth tempo-
ral basis functions using the R package SpatioTemporal (Lindstr€
om,
Szpiro, Sampson, Bergen, & Oron, 2013), by fitting up to five smooth
orthogonal basis functions in addition to an intercept model. The
appropriate number of functions was selected based on BIC values
obtained via cross-validation, and the selected functions were then
used as covariates in linear regressions to withdraw residuals that
were then used as detrended weather variables.
2.4
|
Associations between growth rate and
weather conditions
We calculated the log-transformed population growth rates for each
viable SIN for each year as r
i,t
=log [(N
i,t
+1)/(N
i,t-1
+1)], where N
i,t
is the number of overwintering larval nests in SIN iat time t. For
simplicity, we refer to the log-transformed population growth rate
simply as population growth rate throughout the manuscript.
To examine during which life-history stages are weather condi-
tions most influential for the population growth rates, we built a set
of Bayesian linear mixed models, each corresponding to different
biological hypotheses for the influence of weather conditions on
population growth rate (Table 1). Each model, excluding the null
model, included a set of detrended weather covariates corresponding
to different life-history stages or a combination of them. Since the
habitat patches in a single SIN can fall into several different weather
data grid cells, weather covariates for each SIN were calculated as
the weighted average of the weather conditions of the individual
habitat patches. The weights for each patch were obtained from the
spatially explicit metapopulation model, by estimating the contribu-
tion of the patch to the metapopulation capacity of the SIN based
on the spatial location, size, and quality of the habitat patch (for
details, see Hanski et al., 2017). Also, as the different weather vari-
ables vary at different scales, we standardized them to a mean of
zero and unit variance for the analyses. SIN identity was added as a
random intercept and, to account for density dependence in the
growth rate (Nieminen et al., 2004), we included a first-order auto-
correlation term for population growth rate in all of the models. In
addition to the models reported in Table 1, we analyzed a version of
model 1 that also included average snow cover depth (Table S2).
We then conducted model selection based on the “leave-one-
out”information criterion (LOOIC; Vehtari, Gelman, & Gabry, 2016)
to choose the most informative model(s) for further inspection and
to be used in downstream analyses of the synchrony of weather
conditions.
2.5
|
Temporal trends in the metapopulation and
weather synchrony
For estimating how the spatial extent of synchrony in population
growth rates has changed with time across the entire metapopula-
tion, we divided the data into seven 5-year time periods, each over-
lapping the previous one by 2 years. The only exception is the last
time window from 2012 to 2015, which covers only 4 years. Shifting
the frame of the time windows such that the first one contains
4 years (i.e., 1994–1997) instead of the last one does not change
4
|
KAHILAINEN ET AL.
the results (results not shown). Within each of these time windows,
we counted pairwise cross-correlations in population growth rate
between all pairs of SINs, transformed them to Fisher’szto account
for the truncated distribution, and divided them into distance bins
with 10 km increments to match the resolution of the weather data.
We then withdrew average z-transformed cross-correlations and
estimated the standard error of the average pairwise correlation
from 1000 bootstrapped datasets. In each dataset, each SIN was
represented only once to avoid pseudoreplication (Koenig & Knops,
1998).
For the detrended weather variables, we calculated syn-
chronies and their confidence intervals in different distance
classes in each time window, similar to that done for the SIN
growth rate (see above). When conducting the Fisher’sztrans-
formation to the weather variables, cross-correlations with a
value of one were removed from the data. Such cases appeared
only in the temperature variables and represent only 0.2% of all
pairwise cross-correlations. We then combined the individual syn-
chrony estimates to obtain an estimate of overall synchrony in
weather conditions that are central for M. cinxia SIN growth
rates. For this, we calculated the weighted median across the
synchronies in different weather variables, using the absolute val-
ues of the coefficients obtained from the selected linear model
of the relationship between SIN growth rate and weather vari-
ables (see above). We chose to use median to avoid overestima-
tion of the correlation due to some extreme values. Combining
z-transformed correlation values describing synchrony in different
weather variables comes with the difficulty that they differ in
their overall levels and ranges. Hence, variables that have wider
ranges of variability would dominate after combining, which
would complicate the biological interpretation of the combined
correlation. Therefore, we standardized the z-transformed pair-
wise correlations to a zero mean and unit variance for each
weather variable prior combining them.
To verify the temporal and distance trends in growth rate
andweathersynchrony,weranBayesian linear models with
time window, distance class and their interaction as covariates
(see details in “2.8 Implementation of statistical analyses”). As
the synchrony estimates in each distance class in each time win-
dow are averages or medians of pairwise Fisher’sz-transformed
Pearson correlations between SINs or raster cells, we incorpo-
rated the standard error in the response to the linear models
(for estimating standard error, see above). To account for tem-
poral autocorrelation between consecutive time windows in the
above-described analyses, the models were run with a first-order
autocorrelation term.
In order to study the association between population growth
rate and weather synchronies, we derived estimated residual syn-
chronies and their standard errors from the above-described mod-
els and used them in a Bayesian linear model accounting for error
in both the response (residual population growth rate synchrony)
and the predictor (residual weather synchrony). We focused on
residuals instead of raw variables in order to obtain a robust esti-
mate of the association avoiding including any spatial and/or tem-
poral trends in the estimates of the association between the two
synchronies. As the association between the two synchronies can
TABLE 1 Models for hypotheses regarding the relationship between SIN growth rate and weather. The table includes the covariates, the
LOOIC value, and the standard error of the LOOIC for each model
No. Hypothesis Covariates LOOIC SE
1 Full T
D
+T
Mar
+T
Apr
+T
May
+T
Jun
+T
Jul
+T
Aug
+P
D
+P
Mar
+P
Apr
+P
May
+P
Jun
+P
Jul
+P
Aug
1977.26 44.00
2 Diap., postdiap. & adult T
D
+T
Mar
+T
Apr
+T
May
+T
Jun
+P
D
+P
Mar
+P
Apr
+P
May
+P
Jun
2002.33 43.27
3 Diap., adult & prediap. T
D
+T
Jun
+T
Jul
+T
Aug
+P
D
+P
Jun
+P
Jul
+P
Aug
2016.91 44.04
4 Postdiap., adult, prediap. T
Mar
+T
Apr
+T
May
+T
Jun
+T
Jul
+T
Aug
+P
Mar
+P
Apr
+P
May
+P
Jun
+P
Jul
+P
Aug
1976.47 43.17
5 Diap. & postdiap. T
D
+T
Mar
+T
Apr
+T
May
+P
D
+P
Mar
+P
Apr
+P
May
2019.64 42.64
6 Diap. & adult T
D
+T
Jun
+P
D
+P
Jun
2106.96 43.55
7 Diap. & prediap. T
D
+T
Jul
+T
Aug
+P
D
+P
Jul
+P
Aug
2025.19 44.43
8 Postdiap. & adult T
Mar
+T
Apr
+T
May
+T
Jun
+P
Mar
+P
Apr
+P
May
+P
Jun
2002.93 43.81
9*Post- & prediap. T
Mar
+T
Apr
+T
May
+T
Jul
+T
Aug
+P
Mar
+P
Apr
+P
May
+P
Jul
+P
Aug
1988.70 42.69
10 Adult & prediap. T
Jun
+T
Jul
+T
Aug
+P
Jun
+P
Jul
+P
Aug
2027.23 42.18
11 Diap. T
D
+P
D
2110.65 43.69
12 Postdiap. T
Mar
+T
Apr
+T
May
+P
Mar
+P
Apr
+P
May
2030.82 42.87
13 Adult T
Jun
+P
Jun
2105.49 42.57
14 Prediap. T
Jul
+T
Aug
+P
Jul
+P
Aug
2035.86 42.73
15 Temperature T
D
+T
Mar
+T
Apr
+T
May
+T
Jun
+T
Jul
+T
Aug
2026.02 43.85
16 Precipitation P
D
+P
Mar
+P
Apr
+P
May
+P
Jun
+P
Jul
+P
Aug
1999.48 44.69
17 Null Random intercept and autocorrelation only 2112.83 42.44
P
D
: average diapause period precipitation; P
Mon:
monthly precipitation; T
D
: average diapause period temperature; T
Mon
: monthly average temperature.
KAHILAINEN ET AL.
|
5
differ in different distance classes, we included distance class and
its interaction with weather synchrony in the analysis.
2.6
|
Temporal trends in connectivity and
colonization dynamics
As we do not have annual mark–recapture data spanning the entire
metapopulation, our data do not contain direct estimates of dispersal
between habitat patches. However, we do have annual data on habi-
tat patch occupancies, local extinctions, and (re)colonizations. From
these, we can derive proxies for dispersal. First, we calculated patch
connectivity (S
i,t
), which is associated with dispersal potential
between occupied habitat patches and thus reflects dispersal that
cannot be observed from the data directly. Patch connectivity
approximates the expected number of immigrants to patch iin year
tas the sum of the individual immigrant contributions from all other
occupied habitat patches in that particular year (Hanski, 1994). We
calculated S
i,t
similarly to Hanski et al. (2017), with the modification
that the exponential dispersal kernel was corrected to account for
two-dimensional dispersal:
Si;t¼X
j6¼i
Aim
ia2
2p
eadi;jNj;t1
Aim
iis the area of patch iin hectares scaled by the exponent im,
which represents the effects of patch size on immigration probability
(Hanski, Alho, & Moilanen, 2000). ða2=2pÞeadi;jis the two-dimen-
sional exponential dispersal kernel (Clark, Silman, Kern, Macklin, &
HilleRisLambers, 1999), in which ais the parameter describing the
scale of dispersal and d
i,j
is the distance between patches iand jin
kilometers. N
j,t1
is the number of observed winter nests in patch j
in the fall of the previous year. Parameter values used in the calcula-
tion of connectivity were a=2 and im =0.44 as estimated by
Hanski et al. (2017). The value of acorresponds to a mean dispersal
distance of 1 km (Nathan, Klein, Robledo-Arnuncio, & Revilla, 2012),
a value derived from M. cinxia mark–recapture data, population
dynamics models and landscape genetic studies (Fountain et al.,
2017; Hanski, Kuussaari, & Nieminen, 1994; Hanski et al., 2017).
The population level connectivities were then averaged to SIN level
by using weights for each patch obtained from the spatially explicit
metapopulation model (see above) and log-transformed.
Second, in addition to patch connectivity, we examined the pro-
portion of the population within each SIN resulting from local colo-
nization events for each study year. This describes the relative
importance of colonization events for the whole SIN and estimates
the part of the dispersal events that can be observed in the data
(i.e., the ones that have resulted in colonization). The proportion of
the population resulting from observed colonization events within
each SIN was estimated as the proportion of overwintering nests
found in habitat patches unoccupied in the previous spring.
Temporal trends in both connectivity and colonization were esti-
mated with a Bayesian generalized linear mixed effects model (bino-
mial in the former, Gaussian in the latter), with both the intercept
and slope allowed to vary between SINs. For the model examining
the proportion of new colonizations, we included the proportion of
patches occupied in the previous year as a predictor to account for
a saturation effect (i.e., if the proportion of occupied patches within
a SIN is very high then there are few patches that can become
newly colonized).
As the number of surveyed patches increased manifold after
1999 (see above), and as such large differences in the numbers of
patches could bias analyses conducted on ratios, we decided to con-
sider only the 2000–2015 data for our analyses of colonization and
connectivity. Although the number of surveyed patches is very dif-
ferent pre- and post-1999, the majority of the habitat patches dis-
covered in the remapping are small and/or of low quality and
therefore they contribute very little to the total number of nests,
and hence to the dynamics of the metapopulation. Therefore, they
are not expected to bias other analyses (Hanski & Meyke, 2005;
Hanski et al., 2017).
2.7
|
Temporal trends in the parasitoid C.
melitaearum
We examined the temporal trends in both the proportion of viable
M. cinxia SINs that are occupied by the specialist parasitoid C. meli-
taearum, and in the proportion of C. melitaearum occupied patches
within SINs. For the latter, to avoid zero-inflation in the data, we
used a subset of SINs that have been occupied by the parasitoid in
at least eight of the study years (i.e., over a third of the study per-
iod). The former captures temporal trends in the metapopulation
wide distribution of the parasitoid and the latter describes changes
in the distributions within SINs. Both were analyzed using Bayesian
generalized linear mixed effects models with a binomial distribution
and a first-order autocorrelation term. For the latter, intercepts and
slopes were allowed to vary between SINs.
2.8
|
Implementation of statistical analyses
All statistical analyses were implemented in R (version 3.3.2; R
Core Team, 2016). The linear and generalized linear mixed models
were implemented using the packages brms (version 1.7.0;
B€
urkner, 2017) and RStan (version 2.14.1; Stan Development
Team, 2016) as interfaces for the Stan statistical modeling plat-
form (Carpenter et al., 2017). Prior to running the models, correla-
tions between covariates and variance inflation factors were
examined. In all the analyses, the variance inflation factors
remained below 5, and hence, we did not consider there to be
any serious multicollinearity issues. For all models, we ensured
that each estimate had a minimum of 10,000 effective samples
and that b
Rvalues were below 1.05. In practice, this meant that
for each model we ran four chains for 35,000 iterations with a
warm-up period of 5000 iterations and a thinning rate of 10 iter-
ations.
As a weakly informative prior for the coefficients of covariates,
we used a normal distribution with a mean of zero and a standard
deviation of 10 in all models. For the residual standard deviation
6
|
KAHILAINEN ET AL.
(r
res
), we set a prior following half Student’stdistribution with 3
degrees of freedom and a scale of 10. For the models with random
effects, we set priors following a half Cauchy distribution with a
scale of five for the standard deviations of the random intercepts
and slopes.
Convergence and mixing of chains was inspected visually using
the bayesplot R package (Gabry, 2017).
3
|
RESULTS
3.1
|
Detrending weather variables
There were temporal trends only in May temperature, March pre-
cipitation, and August precipitation, and for each of them, a single
temporal basis function was selected (Figure S1, Table S3). For
May temperature, the temporal basis function suggests an increas-
ing linear trend, with temperature increasing each year by ca. 0.03
°C. For March precipitation, the smooth basis function suggests a
cyclically fluctuating trend with peaks at ca. 8–9 year intervals,
and for August precipitation, there is a unimodal trend, with
increasing precipitation until 2008 after which precipitation has
started to decline. An intercept model was selected for the rest
of the weather variables.
3.2
|
Associations between population growth rate
and weather conditions
Melitaea cinxia population growth rate is positively associated with
most of the examined precipitation variables (Table 2), and nega-
tively associated with diapause and July temperatures. No additional
weather variables have coefficients differing from zero, if a credible
interval (Cr.I.) of 90% is considered instead of the reported 95%
interval. The strongest association is with May precipitation and the
only precipitation variables not exhibiting associations with growth
rate are diapause period (September to February) and August precip-
itations (Table 2). Our additional analyses with snow cover as a
covariate suggest increasing snow cover reduces population growth
rate (Table S2).
The above-described coefficients derive from the full model,
which best describes the relationship between population growth
rate and weather conditions. However, the model for postdiapause,
adult, and prediapause conditions (model number 4 in Table 1) was
very similar with respect to the LOOIC value and the difference
between the two models cannot be distinguished from zero
(Table S4). Since the full model contains variables that have coeffi-
cients that differ from zero, but which are not in model 4, choosing
the full model minimizes the risk of omitting potentially important
variables from further downstream analyses.
3.3
|
Synchrony in population growth rate and
weather
There has been an increase in synchrony of population growth rate
over time and this has occurred across different distance classes
(Figure 1a). In distance classes up to 30 km, synchrony seems to be
increasing until the 2003–2007 time window, after which the corre-
lations plateau. The cross-correlations seem to exhibit an interaction
with both the time window and the distance class: the temporal
trend in synchrony is stronger in shorter distance classes, whereas
the temporal trend is less clear in the longer distance classes
(Table 3). That being said, also the longest distance classes exhibit a
clear increase in the last time window (Figure 1a). Note that the
intercept refers to the first time window (1994–1998) and first dis-
tance class (0–10 km; Table 3).
The synchrony of weighted average weather conditions increases
with time and decreases with increasing distance class (Figure 1b,
Table 3). The increase in the synchrony of weather conditions with
time is similar across distances as the interaction term between time
window and distance did not differ from zero (neither 95% nor 90%
Cr.I; Table S5). With few exceptions, the general trend of increasing
synchrony with time, especially in the two latter time windows, also
holds when observing each of the weather variables separately (Fig-
ures S2 and S3).
Finally, there is a tendency for the residual population growth
rate synchrony to be positively associated with residual weather syn-
chrony (Figure 1c, Table 3). However, due to large standard errors in
the estimates of the detrended residuals, the 95% Cr.I. does not
TABLE 2 Estimated coefficients, their estimated standard errors,
and 95% credible intervals for the selected model on the association
between weather conditions and SIN growth rates
Covariate Est. coef. Est. SE
95% Cr.I.
Lower Upper
Intercept 0.023 0.032 0.086 0.040
T
D
0.119 0.060 0.238 0.001
T
Mar
0.058 0.057 0.171 0.054
T
Apr
0.026 0.048 0.069 0.119
T
May
0.065 0.044 0.021 0.151
T
Jun
0.095 0.059 0.023 0.209
T
Jul
0.153 0.052 0.254 0.051
T
Aug
0.041 0.058 0.154 0.072
P
D
0.094 0.073 0.049 0.239
P
Mar
0.114 0.049 0.017 0.209
P
Apr
0.146 0.051 0.047 0.247
P
May
0.401 0.067 0.269 0.533
P
Jun
0.128 0.051 0.027 0.229
P
Jul
0.162 0.069 0.027 0.299
P
Aug
0.058 0.059 0.172 0.057
AR[1] 0.132 0.042 0.214 0.050
r
(SIN intercept)
0.051 0.039 0.002 0.143
r
res
0.932 0.025 0.885 0.981
AR[1]: first-order autocorrelation term; P
D
: Average diapause period pre-
cipitation; P
Mon
: Monthly precipitation; T
D
: average diapause period tem-
perature; T
Mon
: monthly average temperature; r
(SIN intercept)
: standard
deviation of random intercepts; r
res
: residual standard deviation.
KAHILAINEN ET AL.
|
7
differ from zero (Table 3). However, the 90% Cr.I. does not include
zero (Lower: 0.022; Upper: 0.912) suggesting that a relationship
between the two synchronies exists. The relationship does not seem
to depend on the distance class, as the interaction between residual
weather synchrony and distance class does not differ from zero (nei-
ther 95% nor 90% Cr.I; Table S6).
3.4
|
Temporal trends in population connectivity,
colonization dynamics, and parasitoid distribution
We did not observe increasing trends in our proxies of dispersal over
time (Figure 2, Table 4). In fact, if anything, the proportion of the
population within a SIN representing colonizations of patches unoc-
cupied in the previous time step has decreased. Similarly, there were
no apparent increasing or decreasing trends in the parasitoid C. meli-
taearum distribution between or within SINs (Figure 3, Table 4).
4
|
DISCUSSION
We observed that the previously reported temporal increase in the
synchrony of the Glanville fritillary butterfly, M. cinxia, (Hanski &
Meyke, 2005; Tack et al., 2015) metapopulation is a result of
increased synchrony across all distances, and that the increase is
paralleled by a temporal increase in synchrony of weather conditions
(Figure 1). Furthermore, we show that other potential explanations
for the increasing synchrony—namely increasing dispersal and
changes in trophic interactions with an influential specialist para-
sitoid—do not exhibit trends matching that of increasing
(a)
(b)
(c)
FIGURE 1 Fisher’sz-transformed cross-correlation between (a)
SIN annual population growth rates over different distance classes
and (b) in weighted median weather conditions across time, and (c)
the residual relationships between the two after accounting for
distance class and temporal trend
TABLE 3 Estimated coefficients, their estimated standard errors,
and 95% credible intervals for models of the effects of time and
distance on average synchrony in SIN growth rates and weighted
averaged weather conditions
Covariate Est. coef. Est. SE
95% Cr.I.
Lower Upper
SIN growth rate synchrony (Fisher’s z-score)
Intercept 0.048 0.156 0.217 0.289
Time window 0.205 0.056 0.123 0.305
Dist. class 0.041 0.033 0.013 0.096
Time window : Dist. class 0.026 0.010 0.042 0.010
AR[1] 0.762 0.154 0.461 0.944
r
res
0.163 0.032 0.115 0.220
Weather synchrony (weighted average Fisher’s z-score)
Intercept 0.437 0.079 0.310 0.567
Time window 0.101 0.022 0.065 0.138
Dist. class 0.228 0.012 0.247 0.209
AR[1] 0.782 0.121 0.557 0.938
r
res
0.078 0.022 0.045 0.116
Residual SIN growth rate synchrony vs. residual weather synchrony
Intercept 0.046 0.037 0.120 0.026
Residual weather synchrony 0.460 0.272 0.077 0.999
r
res
0.189 0.035 0.125 0.262
AR[1]: first-order autocorrelation term; r
res
: residual standard deviation.
8
|
KAHILAINEN ET AL.
metapopulation synchrony, and are therefore unlikely drivers of syn-
chrony of M. cinxia. We will elaborate on these below.
4.1
|
Alternative explanations for increased
synchrony
An increase in synchrony could be driven by increased dispersal
between local populations and SINs over time (Gyllenberg et al.,
1993; Kendall, Bjørnstad, Bascompte, Keitt, & Fagan, 2000; Liebhold
et al., 2004). However, ecological long-term datasets most often do
not allow for characterization of dispersal dynamics and hence disen-
tangling the effects of dispersal and environmental conditions on
population synchrony is notoriously difficult. Therefore, dispersal as
a driver of population synchrony has been ruled out in situations
where one can be sure that no dispersal between study regions
occurs, for example, due to geographic barriers (Grenfell et al.,
1998), or using comparative data on sets of species that differ in
their dispersal abilities (Peltonen, Liebhold, Bjørnstad, & Williams,
2002). With extensive surveys of metapopulation dynamics (i.e.,
patch level extinctions and recolonizations), we can derive proxies
that reflect temporal trends in dispersal within the system. We esti-
mated both connectivity between local populations and proportion
of the population representing colonizing events. Neither showed an
increasing trend and, in fact, colonizations seem to have decreased
over time (Figure 2 and Table 4). Hence, there is no evidence sug-
gesting that the increased metapopulation synchrony would be asso-
ciated with increased dispersal, and it may even be the opposite.
Another frequently reported driver of population synchrony is a
spatially correlated predator that can drive cyclic populations into
the same phase (Liebhold et al., 2004; Vasseur & Fox, 2009). Alter-
natively, synchrony may increase if parts of a metapopulation are
(a)
(b)
FIGURE 2 Temporal trends in (a) the connectivity between local
populations and (b) the proportion of overwintering Melitaea cinxia
nests within each SIN representing colonization of new patches. The
boxplots illustrate the variability between SINs in different years and
the trend line illustrates the temporal trend derived from a binomial
GLM (+-95% Cr.I.)
TABLE 4 Estimated coefficients, their estimated standard errors,
and 95% credible intervals for models of the temporal trends in
Melitaea cinxia population connectivity, proportion of colonizing
overwintering nests, proportion of SINs, and patches within SINs
occupied by C. melitaearum
Covariate Est. coef. Est. SE
95% Cr.I.
Lower Upper
Temporal trend in log(population connectivity)
Intercept 0.824 0.384 0.187 1.446
Year 0.002 0.031 0.047 0.053
AR[1] 0.412 0.064 0.310 0.522
r
(S intercept)
1.994 0.316 1.521 2.553
r
(Year|SIN slope)
0.142 0.031 0.093 0.194
r
res
1.227 0.044 1.157 1.303
Temporal trend in the proportion of colonizing overwintering nests
Intercept 1.252 0.003 0.886 1.639
Year 0.052 0.000 0.080 0.024
Prop. patches occupied
(t-1)
5.759 0.005 6.547 4.971
AR[1] 0.218 0.001 0.090 0.336
r
(SIN intercept)
0.685 0.002 0.453 0.970
r
(Year|SIN slope)
0.037 0.000 0.003 0.074
r
res
0.975 0.001 0.890 1.070
Temporal trend in the proportion of SINs occupied by C. melitaearum
Intercept 3.505 0.005 4.352 -2.734
Year 0.004 0.001 0.079 0.086
AR[1] 0.714 0.002 0.364 0.986
r
(SIN intercept)
0.607 0.006 0.039 1.693
r
(Year|SIN slope)
0.082 0.001 0.019 0.190
r
res
0.906 0.002 0.694 1.162
Temporal trend in proportion of patches within each SIN occupied by C.
melitaearum
Intercept 1.181 0.006 2.222 0.287
Year 0.016 0.000 0.087 0.061
AR[1] 0.421 0.003 0.125 0.927
r
res
0.758 0.002 0.451 1.203
AR[1]: first-order autocorrelation term; r
(SIN intercept)
: standard deviation
of random intercepts; r
(Year|SIN slope)
: standard deviation of random slopes
of the temporal trend; r
res
: residual standard deviation.
KAHILAINEN ET AL.
|
9
released from predation as spatially restricted predator can alter the
density dependence locally, creating asynchrony (Walter et al.,
2017). Previous studies have documented that the braconid para-
sitoid wasp C. melitaearum can impact the population dynamics of M.
cinxia and in some cases even drive local populations into extinction
(Lei & Hanski, 1997). Cotesia melitaearum is rather sedentary and is
typically restricted to few SINs in the M. cinxia metapopulation (van
Nouhuys & Ehrnsten, 2004; van Nouhuys & Hanski, 2002, 2004).
Therefore, any change in the distribution—be it a decrease or an
increase—could potentially alter the metapopulation synchrony of M.
cinxia. However, our results suggest no trend in either the propor-
tion of SINs or patches within SINs related to C. melitaearum. There-
fore, the hypothesis that synchrony could be driven by a change in
density dependence due to a change in the extent of C. melitaearum
is not supported. Admittedly, our surveys do not systematically
account for natural enemies other than C. melitaearum so we have
not analyzed them. However, the observed predators are broad gen-
eralists that do not appear to systematically use M. cinxia as prey
(van Nouhuys & Hanski, 2004). Over many years of field studies of
all life stages, we have not observed substantial changes in predator
community. While they may respond to M. cinxia density within a
local population under some conditions (van Nouhuys & Kraft,
2012), we do not expect individuals of these taxa to drive synchrony
by moving in the landscape in response to local density of M. cinxia.
4.2
|
Evidence for a Moran effect
Population growth rate synchrony increases in parallel with the syn-
chrony of weather conditions and, even if there is some uncertainty
in the estimate, there is an indication of an association between
population dynamics synchrony and weather synchrony even after
the removal of the temporal trend and the effect of distance. It is
worth noting that our estimate of the association is conservative as
detrending synchronies with respect to time and distance prior to
analyzing the relationship between them may underestimate their
association (Chevalier et al., 2015). The paralleled trends in metapop-
ulation and weather synchronies and the residual relationship
between the two point toward a Moran effect, in which correlated
environmental conditions force populations into synchrony (Liebhold
et al., 2004; Moran, 1953).
The increase in weather synchrony can be seen in different
weather components individually (Figures S2 and S3), but more
importantly, it is also evident when combining the weather condi-
tions according to their importance to M. cinxia population growth
rate (Figure 1b). To this end, precipitation-related variables are par-
ticularly influential for M. cinxia population growth rate (Table 2). Of
these, May precipitation—the time corresponding to postdiapause
larval development and pupation (Murphy et al., 2004)—has the
strongest positive association. The importance of precipitation is
concordant with a recent study suggesting a central role for precipi-
tation in global natural selection patterns (Siepielski et al., 2017).
Although in-depth discussion of the specific mechanisms by which
the different weather variables influence the M. cinxia metapopula-
tion growth rate is beyond the scope of this study, the fact that
May precipitation stands out as influential makes perfect sense: The
larvae consume much more host plant biomass per capita during the
postdiapause than during the prediapause phase, and can be forced
to compete for resources with their siblings, which there can be
hundreds of (Kuussaari & Singer, 2017; Kuussaari et al., 2004). As
the habitats of M. cinxia are dry meadows characterized by shallow
soils, host plant growth can be very limited in the absence of precip-
itation during spring.
In a previous study, Tack et al. (2015) suggested that the syn-
chrony of M. cinxia in
Aland was driven by an increase in the fre-
quency of late summer drought events, while our results suggest
that it is the overall increase in synchrony of weather conditions,
with spring and early summer weather being most important, and
late summer conditions playing less of a role (Table 2). Our analysis
included many weather variables that had not been considered pre-
viously, and therefore, it is not too surprising that our findings are
different from those of Tack et al. (2015). Indeed, population growth
rates of several butterfly species have been reported to be sensitive
to weather conditions across their life cycle (Mills et al., 2017;
(a)
(b)
FIGURE 3 Temporal trends in the occurrence of a specialist
parasitoid Cotesia melitaearum at the level of different (a) SINs and
(b) habitat patches within SINs. The boxplots illustrate the variability
between SINs in different years and the trend line illustrates the
temporal trend derived from a binomial GLM (95% Cr.I.)
10
|
KAHILAINEN ET AL.
Radchuk, Turlure, & Schtickzelle, 2013). This being said, the results
of the current study are concordant with those of Tack et al. (2015)
in the sense that July precipitation was observed to be positively
associated with M. cinxia growth rate in both.
4.3
|
Population synchrony is increasing across
various systems and scales
The results at hand add to the growing pool of evidence that change
in climatic conditions is likely to drive synchrony in populations
dynamics across systems (Allstadt et al., 2015; Defriez & Reuman,
2017; Defriez, Sheppard, Reid, & Reuman, 2016; Hansen et al., 2013;
Koenig & Liebhold, 2016; Sheppard, Bell, Harrington, & Reuman,
2015; Shestakova et al., 2016). However, few studies (if any) have
reported a weather synchrony driven increase in a highly dynamic
metapopulation system characterized by frequent local extinctions
and recolonizations. In such dynamic systems with high local turnover,
synchrony can have large effects for long-term metapopulation viabil-
ity, as habitat recolonization can be reduced due to synchronous pop-
ulation declines or extinctions (Hanski & Woiwod, 1993). Additionally,
whether synchrony increases extinction risk or not is dependent on
the source of synchrony: Increased dispersal can maintain high levels
of habitat recolonization even if it increases synchrony, but environ-
ment induced synchrony is likely more detrimental as simultaneous
population declines or local extinctions are less likely to be balanced
out by dispersal (Hanski & Woiwod, 1993; Heino et al., 1997).
Furthermore, whereas previous studies have reported climate dri-
ven increase in population synchrony on large scale patterns ranging
from regional (e.g., within the area of a country; Sheppard et al.,
2015; Defriez et al., 2016; Shestakova et al., 2016) to continental
(Defriez & Reuman, 2017; Hansen et al., 2013; Koenig & Liebhold,
2016), our results are at a smaller spatial scale, suggesting generaliz-
ability of the phenomenon across scales. In cyclic populations, Moran
effect has been suggested to work primarily on shorter spatial scales,
whereas phase locking due to dispersal and/or predators is a likely
driver of synchrony at longer distances (Fox et al., 2011). Our results
and the findings of Fox et al. (2011) would suggest that the impact
of climate change on population dynamics can be prevalent on rela-
tively small spatial scales.
With increasing habitat fragmentation and advancing climate
change there is a need for understanding the interactions between
the two, and the ways one facet of global change might influence
that of the other (Holyoak & Heath, 2016; Oliver & Morecroft,
2014). Our results suggest that climate change can alter the dynam-
ics of spatially structured populations occupying fragmented land-
scapes. Although the M. cinxia in Finland exhibits classical
metapopulation dynamics with recurrent extinction and colonization
events, our results should apply to other spatially structured systems
with limited dispersal between local populations. Another important
aspect of climate change that our results highlight is that in addition
to changes in average weather conditions, changes in the spatial
variability of weather conditions can have important consequences
for natural populations and should not be overlooked.
ACKNOWLEDGEMENTS
We are grateful to all those who have participated in collecting the
presented long-term data throughout the years, and to Etsuko Non-
aka, Veijo Kaitala, Petri Niemel€
a and three anonymous reviewers for
valuable comments on conceptual and methodology aspects of the
presented work. The study was funded by European Research Coun-
cil (Independent starting grant no. 637412 ‘META-STRESS’to MS)
and by Academy of Finland (Decision numbers 213457, 273098 &
265461).
ORCID
Aapo Kahilainen http://orcid.org/0000-0001-9180-6998
Saskya van Nouhuys http://orcid.org/0000-0003-2206-1368
Torsti Schulz http://orcid.org/0000-0001-8940-9483
Marjo Saastamoinen http://orcid.org/0000-0001-7009-2527
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SUPPORTING INFORMATION
Additional supporting information may be found online in the
Supporting Information section at the end of the article.
How to cite this article: Kahilainen A, van Nouhuys S, Schulz
T, Saastamoinen M. Metapopulation dynamics in a changing
climate: Increasing spatial synchrony in weather conditions
drives metapopulation synchrony of a butterfly inhabiting a
fragmented landscape. Glob Change Biol. 2018;00:1–14.
https://doi.org/10.1111/gcb.14280
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