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Is climate warming more consequential towards poles?The phenology of Lepidoptera in FinlandANU VALTONEN* † , R E IMA LE INONEN ‡ , J UHA P €OYRY § , HE IKK I RO IN INEN* ,
JUKKA TUOMELA ¶ and MATTHEW P. AYRES†
*Department of Biology, University of Eastern Finland, P.O. Box 111, Joensuu FI 80101, Finland, †Department of Biological
Sciences, Dartmouth College, Hanover, New Hampshire 03755, USA, ‡Rauhalantie 14 D 12, Nakertaja FI 87830, Finland,
§Finnish Environment Institute, Research Programme of Biodiversity, Helsinki FI 00251, Finland, ¶Department of Physics and
Mathematics, University of Eastern Finland, P.O. Box 111, Joensuu FI 80101, Finland
Abstract
The magnitude and direction of phenological shifts from climate warming could be predictably variable across the
planet depending upon the nature of physiological controls on phenology, the thermal sensitivity of the developmen-
tal processes and global patterns in the climate warming. We tested this with respect to the flight phenology of adult
nocturnal moths (3.33 million captures of 334 species) that were sampled at sites in southern and northern Finland
during 1993–2012 (with years 2005–2012 treated as an independent model validation data set). We compared eight
competing models of physiological controls on flight phenology to each species and found strong support for thermal
controls of phenology in 66% of the species generations. Among species with strong thermal control of phenology in
both the south and north, the average development rate was higher in northern vs. southern populations at 10 °C,but about the same at 15 and 20 °C. With a 3 °C increase in temperature (approximating A2 scenario of IPPC for
2090–2099 relative to 1980–1999) these species were predicted to advance their phenology on average by 17
(SE � 0.3) days in the south vs. 13 (�0.4) days in the north. The higher development rates at low temperatures of
poleward populations makes them less sensitive to climate warming, which opposes the tendency for stronger
phenological advances in the north from greater increases in temperature.
Keywords: climate change, light trap, moth, photoperiod, temperature, thermal sensitivity, thermal sum
Received 14 April 2013 and accepted 13 August 2013
Introduction
Many observations of climate change responses in
natural systems come from shifts in phenology (Parme-
san, 2006), that is, the seasonal timing of (periodic) life
cycle events (Rathcke & Lacey, 1985). Two quantitative,
globally comprehensive meta-analyses have both
shown stronger phenological shifts towards earlier
spring events at higher vs. lower latitudes (Root et al.,
2003; Parmesan, 2007). Stronger phenological shifts at
higher latitudes could arise because temperatures are
increasing more towards the poles (IPCC, 2007), or
because an equivalent increase in average temperatures
make a larger proportional contribution towards
annual thermal sums in high latitude systems where
the summers are already short and cool. Also, the
phenology of high latitude species could be generally
more sensitive to climate because the costs of inappro-
priate phenology are probably higher than at lower lati-
tude systems with less seasonality (Pau et al., 2011).
However, there could be additional, and possibly
opposing, effects from latitudinal trends in the physio-
logical sensitivity of organisms to temperature. For
example, climate warming could increase the metabolic
rates of tropical species more than that of high latitude
species (Dillon et al., 2010; see also Deutsch et al., 2008;
Tewksbury et al., 2008).
Biological responses to temperature arise from ther-
modynamic control of biochemical rates (e.g. van der
Have & de Jong, 1996). Many biological processes,
including development rates of poikilotherms, exhibit
similar unimodal thermal responses where rates
increase more than linearly from low to moderate
temperatures, then approximately linearly through
moderate to warm temperatures, until finally decelerat-
ing to a maximum beyond which rates decline rapidly
(e.g. Logan et al., 1976; Fig. 1a). Thus, there is almost
universally a range of cool to at least moderately warm
temperatures where increasing temperature yields
increasing development rate of poikilotherms. Other
things being equal, increasing development rate yields
decreased development time, and therefore tends to
advance the seasonal timing of life history events (e.g.
the time of reproduction in insects or flowering
in plants). Therefore, there is a strong theoreticalCorrespondence: Anu Valtonen, tel. + 35 84 075 16885,
fax +35 81 325 135 90, e-mail: [email protected]
© 2013 John Wiley & Sons Ltd 1
Global Change Biology (2013), doi: 10.1111/gcb.12372
expectation for climate warming to yield advanced
phenology in general, and, other things being equal,
greater advances in phenology at high latitudes where
there is greater warming. However, this general
tendency could be modified by latitudinal patterns in
physiological responses to temperature.
Developmental responses to temperature vary
broadly among species (Hon�ek, 1996; Trudgill et al.,
2005) and populations within species (e.g. Lonsdale &
Levinton, 1985; Ayres & Scriber, 1994). For example,
poleward populations of Papilio canadensis in Alaska
develop faster, especially at low temperatures, than
midlatitude populations in Michigan (Ayres & Scriber,
1994). Genetically based variation in the form of
thermal response can include vertical shifts
(‘faster-slower’), horizontal shifts (‘hotter-cooler’), or
‘generalist-specialist trade-offs’, that is, variation in the
width of the thermal response function (sensu Izem &
Kingsolver, 2005; Fig. 1b-d). Such genetic variation can
enable populations to adapt to different temperature
regimes (Miller & Castenholz, 2000). If as a result there
are predictable latitudinal patterns in physiological
responses to temperature, this could amplify, weaken,
or reverse (depending on the details) the tendency from
global temperature trends alone to advance phenology
more at high latitudes than at low latitudes. Three sim-
ple expectations can be derived from considering the
effects of changes in temperature and thermal response
functions within the range of temperatures where
response functions are approximately linear (Fig. 1e–h):(i) because development time is the inverse of develop-
ment rate, equivalent warming has larger effect on
development time when the baseline temperatures are
cooler (warming scenario 12–>15 °C) vs. warmer
(15–>18 °C); (ii) if poleward populations (HL) tend to
have more rapid development rates than midlatitude
(a)
(e) (f)
(g) (h)
(b)
(c)
(d)
Fig. 1 (a) General function of dependence of biological processes on temperature, and (b–d) three forms of genetically based variation
in the thermal response. Development rate (1 days�1), development time (days) and predicted shift in in phenology of two theoretical
populations with same (e–f) or different (g–h) slope of temperature response, across a range of temperatures.
© 2013 John Wiley & Sons Ltd, Global Change Biology, doi: 10.1111/gcb.12372
2 A. VALTONEN et al.
populations (ML), especially at cool temperatures
(Fig. 1e–f), then their development times would be less
sensitive to warming, especially at cooler temperatures
(smaller change in HL development time vs. ML in
both warming scenarios); (iii) if poleward populations
have relatively higher development rates at cooler
temperatures and concomitant relative decreases in
development rates at warmer temperatures (Fig. 1g–h),as predicted by ‘generalist-specialist trade-offs’ in ther-
mal responses, this also has the effect of decreasing the
effects of warming on development time.
In addition to temperature, photoperiod is the other
prominent environmental modifier of developmental
schedules and therefore phenology in poikilotherms
(Bradshaw & Holzapfel, 2010). It can cue the termina-
tion of diapause (Tauber et al., 1986; Danks, 1987), and
influence development rates during active life stages
(Nylin et al., 1989). In general, photoperiodic controls
attenuate phenological plasticity. Interannual pheno-
logical variation would be zero in a population with
complete photoperiodic control of life history sched-
ules. Thus, latitudinal patterns in the strength of photo-
periodic controls would influence latitudinal patterns
in phenological responses to climatic variation: for
example, if poleward populations tended to have
weaker photoperiodic controls than temperate popula-
tions, then poleward populations would be more
responsive than otherwise to climatic warming, and
vice versa.
Studies addressing intra and interspecific variation in
phenological controls (Hodgson et al., 2011; Valtonen
et al., 2011) have been rare because long-term data sets
covering wide latitudinal ranges and a high number of
species are difficult to collect. Here, we compare the lati-
tudinal differences in the phenological sensitivity to
temperature of annual flight times in Finnish nocturnal
moths. The spatially replicated time series included 362
species-generation combinations, 12 years, and
>1010 km of latitude (from 60 to 68°N). The models fits
were cross-validated with additional 8 years of data
from the same sites. We examined whether moth
communities in northern vs. southern Finland differed
in the strength and form of controls on phenology from
temperature and photoperiod. With the best-fit models
of empirical responses of temperature, we compared the
responses of northern and southern moth communities
to the same forcing from scenarios of climate warming.
Material and methods
Moth data
Our analyses were based on moth captures from a network of
light traps (in total 208 traps) located in forested areas across
Finland during 1993–2012 (Finnish Moth Monitoring Scheme
Nocturna, coordinated by the Finnish environmental adminis-
tration). Only trap data covering the entire flight season of
macrolepidoptera were selected, and in other cases species fly-
ing in missing months were excluded from the data (details in
Appendix S1). Data from traps located less than 10 km apart
were combined and treated as the same sites, resulting in 94
sites and 620 site–year combinations. For modelling purposes,
the sites had to be divided into two groups. Because thermal
conditions in Finland are affected both by latitude and the
maritime/continental effect, we decided to divide the sites
approximating the +3 °C isocline of yearly average tempera-
tures in Finland (Finnish Meteorological Institute, 2013b).
Altogether 50 sites were in southern Finland (59.8–63.1°N)
and 44 in northern Finland (62.0–68.9°N; Fig. 2).
For the model fitting phase, data of years 1993–2004 were
used, including 701 species of macro moths. We excluded one
species that has two geographically overlapping subspecies
with differing phenologies, one species that can have either 2
or 1-year development time with different phenologies, and
one species that partly lives inside houses, as well as 14 migra-
tory species (Huld�en et al., 2000). Of the remaining 684
species, 453 were univoltine with a nonadult overwintering
stage, 20 species were univoltine with adult overwintering
stage, 210 species were bivoltine, and one species produces
three generations per year. For all species that overwinter as
Fig. 2 Map of locations of the 50 southern study sites, 44
northern study sites and 50 weather stations.
© 2013 John Wiley & Sons Ltd, Global Change Biology, doi: 10.1111/gcb.12372
THE PHENOLOGY OF LEPIDOPTERA IN FINLAND 3
adults and for 89 bivoltine species with distinct generations,
we were able to assign a cut-off date based on literature
(Huld�en et al., 2000), dividing the individuals into two genera-
tions (cf. P€oyry et al., 2011). This left us with 562 species and
660 species-generation combinations. Finally, only species
generations with more than five individuals per site year and
more than nine site years per geographical zone (south or
north) during the period 1993–2004 were selected. These arbi-
trary thresholds were selected to ensure informative data
while not excluding all rare species from the analyses. In the
south, this left 322 species, 348 species generations and
1 576 473 individuals, and in the north, 195 species, 211
species generations, and 1 076 056 individuals (197 species
generations occurred in both south and north). Species
belonged to the superfamilies Lasiocampoidea, Bombycoidea,
Geometroidea, Noctuoidea and Hepialoidea. Systematics and
nomenclature follow Kullberg et al. (2008).
For the cross-validation phase, data from the same species
generations and sites from the years 2005–2012 were used.
Only species generations with more than five individuals per
site year and more than two site years were selected, leaving
us 279 species generations and 417 670 individuals in the
south, and 172 species generations, and 262 684 individuals in
the north.
For analyses, we scored the day of capture as the middle
date of the weekly trapping period and converted this to days
from the last winter solstice (details in Appendix S1).
Temperature data and thermal sum calculations
We obtained daily minimum and maximum temperatures
from 50 stations of the Finnish Meteorological Institute
(Fig. 2). We then estimated the daily minimum and maximum
temperatures at each study site by fitting a trend surface over
a grid (with least squares method) using the daily weather sta-
tion observations. The trend surface was fitted with package
‘spatial’ (Ripley, 2010) in R version 2.12.0 (R Development
Core Team, 2008) using a 4th order polynomial (after evaluat-
ing 1st to 6th order polynomials). For each trap site, we then
downscaled daily maximum and minimum temperatures to
hourly temperatures for use in model fitting (details in
Appendix S1). The hourly temperatures, instead of daily aver-
age temperatures, were used in the analyses, because in many
spring days the daily average temperature can be below the
developmental threshold, although some hourly temperatures
rise above the base (Ruel & Ayres, 1999).
Competing models for environmental controls ofphenology
We compared the ability of eight alternative theoretical mod-
els to predict the peak flight dates for species generations
across all years (1993–2004) and sites of each geographical
zone. Peak flight dates were calculated as the median date of
capture for each species generation in each year and site.
1. The null model against which all other models were
compared was the solar day (Sday) model, under which
the peak flight date across years and sites was predicted to
be the average day of peak flight across years and sites
(Fig. 3a). If all sites experience the same photoperiodic
regime (e.g. for species present only in the southernmost
part of the country), Sday model describes photoperiodic
control of phenology. When sites span different photoperi-
odic regimes, the Sday model represents the theoretical
possibility that individuals are adapted to fly on about the
same calendar date even when distributed across a range
of thermal and photoperiodic environments.
2. For the photoperiod (Photo) model, we predicted the peak
flight date across years and sites from a threshold day
length, representing minimum or maximum required day
length for species generations with peak flight before or
after midsummer respectively (Fig. 3a–b). In this model,
the predicted day length of peak flight was the average of
day lengths observed at peak flight day of each year and
site (details in Appendix S2).
3. For the model based on thermal sum (Tsum), we solved for
the best-fit model to predict the peak flight dates across
years and sites from a threshold thermal sum, starting to
accumulate at previous winter solstice (Fig. 3c–d). The
thermal sum for each hour (th) was calculated as the cumu-
lative sum of rates of forcing:
Sf ¼Xtht1
Rf ðxtÞ; ð1Þ
where t1 = first h after winter solstice and xt = the estimated
hourly temperature. The rate of forcing in this model is given
as
Rf ¼0; if xt\Tb
ðxt � TbÞ=24; if xt �Tb
�; ð2Þ
where Tb is the parameter value for lower developmental
threshold (i.e. base temperature). This involved optimizing
the parameter value for Tb using function ‘fmincon’ in MAT-
LAB (R2010a-R2012a, The MathWorks Inc., Natick, MA, 2000).
The details of the optimization process are given in Appendix
S2.
4. For the model based on thermal sum and solar day
(Tsum∩Sday), we solved for the best-fit model to predict
the peak flight date across years and sites from a threshold
thermal sum, starting to accumulate after a threshold num-
ber of hours had elapsed from the previous winter solstice
(t1 of formula 1). This involved optimizing the parameter
values for Tb and start hour.
5. In insects, there can be multiple pathways to diapause com-
pletion in the spring and sometimes it can be stimulated by
high temperatures (Hodek & Hodkov�a, 1988). For the
model based on thermal sum and high temperature thresh-
old terminating the diapause (Tsum∩HiT), we solved for
the best-fit model to predict the peak flight date across years
and sites from a threshold thermal sum, starting to accumu-
late after a threshold high temperature had been achieved
(t1 of formula 1). This involved optimizing the parameter
values for Tb and high temperature threshold (Thigh).
6. For the model based on nonlinear effect of temperature on
poikilotherm development rate (Tsum.nl; e.g. Logan et al.,
© 2013 John Wiley & Sons Ltd, Global Change Biology, doi: 10.1111/gcb.12372
4 A. VALTONEN et al.
1976), we solved for the best-fit model to predict the peak
flight across years and sites from a threshold thermal time,
where the rate of forcing followed a logistic sigmoid func-
tion (H€anninen, 1990):
Rf ¼ ð 28:4
1þ ebðxt�cÞÞ=24; ð3Þ
where parameter b < 0 and c > 0. This involved optimizing
the parameter values for b and c.
7. For the model based on nonlinear effect of temperature and
solar day (Tsum.nl∩Sday), we solved for the best-fit model
to predict the peak flight across years and sites from a
threshold thermal time in the same way as in Tsum.nl
model, but the thermal time started to accumulate after
3000 h (125 days, ~ April 25) had elapsed from the previ-
ous winter solstice (t1 of formula 1), which approximates
the median of optimized start values in Tsum∩Sday mod-
els across species generations. We could not optimize the
value for start hour, because the method used allowed us
to optimize only two parameters (here b and c).
8. For some insect species, the adult flight could also be cued
by onset of low temperatures in the fall (Masaki, 1980). For
the low temperature model (LoT), fitted only to species
generations with peak flight in September or later, we
predicted the peak flight date across years and sites from
threshold cumulative minimum temperature, starting to
accumulate after midsummer.
Statistical analyses
The overall responsiveness of moth communities to thermal
conditions of the season was tested with ANCOVA, where the
annual averages of peak flight days (calculated across all
species generations in a community) at each site (censused
during the entire season) was the response variable, site a
fixed factor, and thermal sum (base +5 °C) accumulating until
midsummer, or until the end of season, a covariate.
We compared the ability of the alternative theoretical mod-
els to predict peak flight time using the corrected Akaike
Information Criteria (AICc; Anderson, 2007). If the difference
in AICc between the highest and the second highest ranked
model (=DAICc) was ≥2, the highest ranked model was
assigned as the most likely (top) model for the species genera-
tion. Species generations were further classified into four
(a) (b)
(c) (d)
Fig. 3 Graph showing how the model predicted peak flight days were determined for Sday, Photo and Tsum models of Abraxas grossu-
lariatus (southern population). (a) The average solar day (horizontal line) of peak flight days across years and sites (circles) is the
predicted day of Sday model, (b) the solar day (vertical arrow) when the average length-of-day of peak flights across years and sites
(vertical line in a) is reached is the predicted day of Photo model, (c–d) the solar day (vertical arrow in d) when the average thermal
sum of peak flights across years and sites (vertical line in c) is reached is the predicted day of Tsum model.
© 2013 John Wiley & Sons Ltd, Global Change Biology, doi: 10.1111/gcb.12372
THE PHENOLOGY OF LEPIDOPTERA IN FINLAND 5
phenological classes: (i) Photoperiodic control of phenology if
Sday or Photo model was selected as the top model or if
together they were the two most likely models (with
DAICc < 2, but to the other models ≥2); (ii) Thermal∩Photope-riodic control of phenology if Tsum∩Sday or Tsum.nl∩Sdaymodel was selected as the top model or they together were the
two most likely models; (iii) Thermal control of phenology if
Tsum or Tsum.nl or Tsum∩HiT was selected as the top model
or some combination of thermal models (Tsum, Tsum∩Sday,Tsum∩HiT, Tsum.nl or Tsum.nl∩Sday) were the most likely
models and (4) Other (in all other cases). For all models we
calculated root mean square errors (RMSE), which describes
the accuracy of the estimate in days. Averages in RMSEs of
the four phenological classes were compared with ANOVAS and
Tukey’s post hoc multiple comparisons. For evaluation of
thermal models, we also estimated the proportion of variance
explained (R2adj) relative to the solar day -model. All formulae
are given in Appendix S2.
We tested for differences in the frequency of the four
phenological classes between northern and southern Finland.
We also conducted randomization tests to ask whether the
four phenological classes were randomly distributed among
the species (details in Appendix S3). To evaluate the patterns
in thermal sensitivity, we selected the highest ranked models
of species generations classified to Thermal∩Photoperiodic or
Thermal control of phenology, but excluded those having start
hour less than 1 week before the earliest observed peak of
flight across years and sites (because these models could
describe the effect of high temperatures enhancing flight activ-
ity of adults (e.g. Battisti et al., 2006) rather than increasing the
development rate of earlier life stages). We then calculated the
development rate (proportion of development/day) at 10, 15
and 20 °C. For Tsum, Tsum∩Sday, and Tsum∩HiT models
the development rate at temperature t was calculated as
(t - Tb)/model predicted thermal sum at peak flight. For
Tsum.nl and Tsum.nl∩Sday models, the development rate
at temperature t was (28.4/(1 + exp(b * (t - c))))/model pre-
dicted thermal sum at peak flight.
Finally, to compare northern and southern Finland with
respect to the expected phenological shift in moth flight times
from climate warming, we used the historical hourly tempera-
tures and the highest ranked Thermal or Thermal∩Photoperi-odic models to calculate the average peak flight dates before
and after incrementing hourly temperatures by 3 °C (approxi-
mates A2 scenario for 2090–2099 relative to 1980–1999; IPCC,
2007).
Cross-validation of model fits
We also tested how well the highest ranked models were able
to predict the peak flight days in an independent, temporally
nonoverlapping data set, covering the years 2005–2012. For
each species generation, year and site, the predicted peak
flight day was calculated based on observed temperatures and
the highest ranked model. For each species generation, the
model accuracy was then estimated by RMSE. All analyses
were conducted using program R 2.12.0 (R Development Core
Team, 2008).
Results
Variation in peak flight days
The observed variation in timing of flight (years 1993–2004) was generally high, which provided a basis for
comparing the alternative models. The root mean
square error (RMSE) of the null model (Sday) averaged
9.1 (range from 4.0 to 20.9) days in southern Finland
(n = 348 species generations) and 8.7 (2.4–21.9) days in
northern Finland (n = 211). The phenological range
from early years and sites to late years and sites was
similarly high throughout Finland: averaged across
species, the difference between earliest and latest peak
flight day in southern vs. northern Finland was
41.9 days (range 14–105) vs. 40.3 (8–96) respectively.At the community level, moth phenology was
responsive to the thermal conditions of the season
(Fig. 4). The annual averages of peak flight days, at
sites censused the entire season, were negatively associ-
ated with the thermal sums at midsummer, at the same
sites (years 1993–2012; ANCOVA; effect of thermal sum in
south F1, 104 = 101.9, P < 0.0001; in north F1, 350 = 264.5,
P < 0.0001). The moth phenology was significantly, but
(a)
(b)
Fig. 4 Relationship between the annual average of peak flight
days (calculated across all species generations in a community)
and thermal sum accumulating until midsummer (at sites cens-
used the entire season) in (a) north and (b) south (data of years
1993–2012 combined).
© 2013 John Wiley & Sons Ltd, Global Change Biology, doi: 10.1111/gcb.12372
6 A. VALTONEN et al.
more weakly, associated with thermal sums accumulat-
ing over the entire season (effect of thermal sum in
south F1, 104 = 11.2, P = 0.001; in north: F1, 350 = 184.7,
P < 0.0001).
Competing models of controls on phenology
There was a relatively good success in identifying top
models for phenological control. In 330 (59%) of the 559
species generations modelled, one competing model
emerged as a better fit than the alternatives (Table 1).
Models (or combination of models producing coarsely
equal fit) including some form of thermal control of
phenology were by far the most common: 367 (66%) of
the species generations studied. The low-temperature
threshold (LoT) was the only model that did not
emerge as a top model to any of the species generations
studied.
The highest ranked models fitted generally well to
the observed data, the average RMSE being 7.3 days
(range from 3.0 to 20.9) in southern data and 7.1 days
(2.4–21.9) in northern data. The averages of RMSEs
differed among the four phenological classes both in
southern (one-way ANOVA F3, 344 = 18.4, P < 0.001) and
in northern data (F3, 207 = 8.6, P < 0.001; Fig. 5a–b).Both in the south and the north, the models including
thermal control of phenology fitted significantly better
to data compared to Photoperiodic models (see Tukey’s
post hoc multiple comparisons of means in Fig. 5a–b).Further details of model comparison results and sum-
maries of optimized parameter values of the top mod-
els are in Appendix S4. For example, the lower
developmental threshold was significantly lower in top
Tsum∩Sday models (average = �2.9) than in Tsum
models (5.0) in southern data (Welch two sample t-test;
t = 8.2, df = 68.0, P < 0.001) but not in northern data
(t = 2.2, df = 11.2, P = 0.054).
Phenological classes
There were no statistical differences in the frequency of
the four phenological classes (Photoperiodic/Ther-
mal∩Photoperiodic/Thermal/Other) between south
and north (v2 = 5.7, df = 3, P = 0.13). Altogether 107
(54%) species generations, out of 197, were classified to
the same phenological class in the two geographical
zones (Table 2). However, in both the south and the
north, there was evidence of phylogenetic structure to
phenological classes from randomization test with
phylogenetic trees (Appendix S3).
Differences in development rates between southern vs.northern populations
The average development rate (proportion of devel-
opment/day) was significantly higher in northern
compared to southern populations of the same
species at 10 °C (n = 105; paired samples t-test;
t = 4.3, df = 104, P < 0.0001), but not at 15 °C(t = 0.4, df = 104, P = 0.66), or at 20 °C (t = 0.5,
df = 104, P = 0.62; Fig. 6).
When poorly fitting models (RMSE > 10 days) were
removed, and the analyses were repeated (n = 99), the
direction or significance of the results did not change:
the average development rate was significantly higher
in northern populations at 10 °C (t = 3.9, df = 98,
P = 0.0002), but not at 15 °C (t = 0.4, df = 98, P = 0.67),
or at 20 °C (t = 0.4, df = 98, P = 0.67).
Predicted phenological response to increase of 3 °C
The average estimated phenological shift of the 232
southern species generations classified to either
Thermal∩Photoperiodic or Thermal control of
phenology was 16.7 � 0.3 days (range from 3 to 27)
and of the 135 species generations in north
13.2 � 0.4 days (3–26; Fig. 7). Of the 105 species
generations with Thermal or Thermal∩Photoperiodiccontrol both in the south and the north, the average
estimated shift was 2.8 � 0.5 days higher in south-
ern vs. northern populations (t = 5.1, df = 104, P <0.0001). When poorly fitting models (RMSE
> 10 days) were removed, and the analyses were
repeated (n = 99), the direction or significance of the
results did not change: the average estimated shift
was still significantly higher in southern populations
(t = 4.9, df = 98, P < 0.0001).
Table 1 Frequency (and % of total) of species-generation
combinations with different top models for control of flight
phenology
Top models for control
of flight phenology South North
Sday 61 (18%) 39 (18%)
Photo 7 (2%) 7 (3%)
Tsum* 34 (10%) 11 (5%)
Tsum∩Sday* 52 (15%) 54 (26%)
Tsum∩HiT* 9 (3%) 11 (5%)
Tsum.nl* 13 (4%) 8 (4%)
Tsum.nl∩Sday* 17 (5%) 7 (3%)
LoT 0 (0%) 0 (0%)
Sday or Photo 20 (6%) 13 (6%)
Tsum∩Sday or Tsum.nl∩Sday 8 (2%) 0 (0%)
Thermal (=other combination of *) 99 (28%) 44 (21%)
Other combinations of models 28 (8%) 17 (8%)
Total 348 211
*Model including thermal control of phenology.
© 2013 John Wiley & Sons Ltd, Global Change Biology, doi: 10.1111/gcb.12372
THE PHENOLOGY OF LEPIDOPTERA IN FINLAND 7
Cross-validation of model fits
The highest ranked models fitted relatively well to
the cross-validation data (2005–2012), the average
RMSEC-V being 8.4 days (range from 2.3 to 26.5) in
southern data (n = 279) and 8.5 days (2.8–25.1) in
northern data (n = 172; Fig. 5). The RMSEC-V
differed among the four phenological classes both in
southern (one-way ANOVA F3, 275 = 2.7, P = 0.048) and
in northern data (F3, 168 = 2.8, P = 0.042; but no pair-
wise differences were identified in Tukey’s post hoc
multiple comparisons in either geographical zone;
Fig. 5c–d).When models fitting poorly to cross-validation data
(RMSEC-V > 10 days) were excluded, and the analyses
were repeated (n = 62), the average development rate
remained to be significantly higher in northern popula-
tions at 10 °C (t = 3.2, df = 61, P = 0.002), but not at
15 °C (t = 0.5, df = 61, P = 0.60), or at 20 °C (t = 0.2,
df = 61, P = 0.83), and the estimated shift remained sig-
nificantly higher in southern populations (t = 5.0,
df = 61, P < 0.0001).
(a) (b)
(c) (d)
Fig. 5 Boxplots showing the variation in model fits (RMSE) among four phenological classes (Photoperiodic/Thermal∩Photoperiodic/Thermal/Other) for (a–b) years 1993–2004 for which the models were fitted, and (c–d) for the cross-validation years 2005–2012. Pairs of
models with significant differences in mean RMSE are shown with different letter symbol (Tukey’s post hoc multiple comparisons).
Table 2 Frequency of the four phenological classes for the 197 species generations with data from both south and north
South
North
Photoperiodic Thermal∩Photoperiodic Thermal Other
Photoperiodic 35 6 4 1
Thermal∩Photoperiodic 7 31 14 8
Thermal 11 20 41 7
Other 3 2 7 0
© 2013 John Wiley & Sons Ltd, Global Change Biology, doi: 10.1111/gcb.12372
8 A. VALTONEN et al.
Discussion
Interspecific variation in phenological controls
One fundamental challenge of phenological studies is
to understand which environmental controls or cues
are important in dictating the phenology of different
species. Based on our results, approximately two-thirds
of the 183 Lepidoptera species studied here expressed
some kind of thermal control of phenology. Similarly,
Valtonen et al. (2011) found 51% in analyses of a subset
of the same Finnish data, and 60% of the 15 butterfly
species in Britain displayed phenology that varied with
thermal conditions across years and sites (Hodgson
et al., 2011). A literature review, covering mostly spring
phenophases of plant species from tropics to arctic,
revealed that thermal control of phenology was impor-
tant in 86% of the >300 species (Pau et al., 2011).
In addition to distinguishing between thermal and
photoperiodic controls of phenology, our results
revealed a range in the forms of thermal control, both
linear and nonlinear. The wide interspecific variation in
phenological control mechanisms and thermal sensitiv-
ity suggest that the future climate warming is likely to
shift the phenology of most, but not all, species and
those that do shift their phenology will display variable
responses. This will ensure that the composition of
‘phenological communities’ will change with warming
climate. The capacity for adaptive phenotypic adjust-
ments of phenology can influence population sizes,
population persistence and community structure (e.g.
Møller et al., 2008). If interacting species respond differ-
ently to climate change this can affect predation and
plant–herbivore interactions (e.g. Visser & Holleman,
2001), competition (Gange et al., 2011), parasitism (Møl-
ler, 2010), pollination (Memmott et al., 2007) and seed
dispersal (Warren et al., 2011). If species at higher
trophic levels tend to have higher thermal sensitivity
than species at lower trophic levels, as hypothesized by
Berggren et al. (2009), we could predict that plants will
advance their phenology less than herbivores, and her-
bivores less than their (poikilotherm) predators. Consis-
tent with this, a meta-analysis indicated that butterflies
and birds have shown more than three times larger
phenological shifts compared to plants (Parmesan,
2007).
Intraspecific variation in phenological controls
A second fundamental challenge of phenological stud-
ies is to understand the intraspecific variation in pheno-
logical controls and how these could contribute to the
degree of phenological shifts in the future. About half
of the southern and northern populations of the same
species differed in the form of their phenological
controls. Northern populations with strong thermal
(a)
(b)
Fig. 6 (a) Average (�SE) development rate (proportion of
development/day) and (b) average development time (1/devel-
opment rate) in three temperatures for the 105 species classified
to Thermal∩Photoperiodic or Thermal control of phenology
both in southern and northern data.
(a)
(b)
(c)
Fig. 7 Predicted phenological shift after an increase of 3 °C in
(a) south (n = 232) and (b) in north (n = 135) among species
generations with strong thermal control of phenology. (c) Differ-
ence in predicted shift between south and north among species
with strong thermal control of phenology in both zones
(n = 105).
© 2013 John Wiley & Sons Ltd, Global Change Biology, doi: 10.1111/gcb.12372
THE PHENOLOGY OF LEPIDOPTERA IN FINLAND 9
control of phenology expressed higher development
rates in low (10 °C) but not in higher temperatures.
This explains the smaller phenological shifts predicted
in north vs. south (our theoretical predictions 2 and 3).
If no differences in development rates had been found,
the equivalent warming (+3 °C in both south and north,
in our case) would have been predicted to lead to larger
changes in northern compared to southern populations
(prediction 1).
Therefore, our initial predictions of how differences
in development rates can shape differences in pheno-
logical shifts (Fig. 1) were consistent with our
process-based model projections for phenological
shifts across latitudes. This provides a basis for gen-
eral predictions regarding phenological plasticity
when there is knowledge of patterns in thermal sen-
sitivity among species, clades, assemblages, or tro-
phic levels. For example, if it is general that lower
latitude species have higher thermal sensitivity (see
also Amarasekare & Savage, 2012), we could antici-
pate larger phenological shifts than in analogous high
latitude species given the same climatic warming.
This opposes the effect of a general pattern in greater
warming at higher latitudes. The opposition of these
two effects could be why the meta-analyses of Par-
mesan (2007) found surprisingly weak empirical
trends for an effect of latitude on advancing phenol-
ogy (<4% of the variation in phenological shifts).
Differences in development rates can also shed light
on the evolutionary pressures faced by populations at
different latitudes. Higher development rates of cold
source populations at low, but not at high tempera-
tures, as found here and in some other poikilotherm
species (Lonsdale & Levinton, 1985; Ayres & Scriber,
1994), matches most closely the outcome of the ‘gener-
alist–specialist trade-off’ in thermal performance curves
(sensu Izem & Kingsolver, 2005). Differences in devel-
opment rates are important also in another fundamen-
tal way: thermal sensitivity of development, fecundity
and mortality (all with different thermal performance
relationships) together determine the thermal sensitiv-
ity of fitness and population growth rates (Amarasek-
are & Savage, 2012).
Predicted phenological response to increase of 3 °C
A third fundamental challenge of phenological studies
is to predict the degree of phenological shifts in the
future. Based on our results, among the two-thirds of
the species generations with strong thermal control of
phenology, the average phenological shift after a 3 °Cincrease in temperatures is predicted to be 17 days in
south and 13 days in north, but the rest of the species
(one-third) are not predicted to shift their phenology at
all, or the degree or direction of phenological shifts in
these species is uncertain.
The predicted phenological shifts are well within the
historical variation in peak flight dates across years and
sites (average range = 40–42 days in north and south
respectively). The length of the growing season (when
the mean temperatures exceed 5 °C) in Finland varies
between 100 and 180 days (Finnish Meteorological
Institute, 2013a) and is projected to lengthen by
>40 days by the end of the century (A2 scenario; IPCC,
2007; Ruosteenoja et al., 2011). If an equal lengthening
of growing season both in spring and fall is assumed,
the 20 days earlier start in the spring would match well
with our predicted shifts in moth phenology. Our esti-
mates are, however, larger than what could be
predicted by extrapolating from the phenological
changes oberved so far in Europe. Over the past dec-
ades, spring or summer phenology of plants and ani-
mals across Europe have, on average, advanced
2.5 days per 1 °C increase in temperatures (Menzel
et al., 2006). However, in North America the change in
spring leaf phenology has been, on average 4.8 days
per 1 °C increase in temperatures (Groffman et al.,
2012). Our predicted shifts are also opposite to the phe-
nological changes observed so far, which have been
(marginally) stronger towards higher latitudes (Parme-
san, 2007), but they are in line with the observation by
P€oyry et al. (2011) of the highest increase in moth multi-
voltinism in southern Finland compared to more north-
ern areas in response to the recent warming.
Statistical models predicting shifts in insect phenol-
ogy have produced results very similar to ours. Mem-
mott et al. (2007) estimated the future phenological shift
of plants and pollinators by extrapolating from pheno-
logical shifts observed so far, predicting 1–3 weeks
earlier phenology by the end of the 21st century. Simi-
larly, the spring phenology of British butterflies was
estimated to shift by 10–30 days after a 3 °C increase in
temperatures (Sparks & Yates, 1997).
However, process-based models taking into account
winter cold temperatures have produced very different
estimates of future phenological shifts in North Ameri-
can tree species, for which the lack of sufficient chilling
in the winter delays the break of bud dormancy (Morin
et al., 2009). These models predict that some, but not
all, species will consistently advance their phenology
more towards higher latitudes (Morin et al., 2009). Low
temperatures in winter can sometimes be influential for
termination of insect diapause (Gray et al., 2001), but
the chilling effect was not included into our models
because in the present climate the demand of low
temperatures is almost surely fulfilled across Finland
where mean temperatures are below 0 °C 100–200 days
annually (Finnish Meteorological Institute, 2013a).
© 2013 John Wiley & Sons Ltd, Global Change Biology, doi: 10.1111/gcb.12372
10 A. VALTONEN et al.
To predict the future phenological shifts by using
process-based phenological models, it is important to
evaluate, whether the temperature variability in the
study years was representative of temperature variabil-
ity that is expected to occur over the long term. During
the 12 years (1993–2004) used to parameterize our mod-
els, the variation in daily temperatures was high: the
estimated daily minimum and maximum temperatures
of our study sites ranged between �52 and +33 °C,which represents well the temperature variation
recorded at all weather stations across Finland between
1960s and 2013 (from �52 to 37 °C; Finnish Meteorolog-
ical Institute, 2013a). For simplicity, possible long-term
changes in temperature variability were not included in
our analyses predicting phenological response to
increase of 3 °C, although slight increases in interannu-
al summer temperature variability are possible in the
future (R€ais€anen, 2002).
Strengths and weaknesses of our modelling approach
In this study, we used the high interannual and spatial
variation in species’ phenologies to fit process-based
phenological models. This allowed us to compare the
differences in phenological controls among species and
populations, and to predict the future phenological
shifts under climate change, for a large number of spe-
cies. Therefore, our results are not affected by the possi-
ble trends in temperatures during the study period (or
lack of it).
In this study, we used a novel way of optimizing the
parameter values for phenological process-based mod-
els. Our approach allowed us to inspect visually each
2D or 3D landscape showing the difference between
observed and model predicted days of peak flight with
different combinations of parameter values (examples
in Appendix S2). This allowed us to ensure that the
global instead of local minimum was found and that an
approximate estimate for parameter values was found
also when the surface of observed values was very rug-
ged. The disadvantage of this method is that we were
able to optimize only two parameters at a time. In addi-
tion, the sites had to be divided into two groups (south-
ern vs. northern), to produce a sufficient number of
site–year combinations for modelling purposes (with
12 years of data it was not feasible to fit the models
separately for each site to model the thermal sensitivity
as a function of latitude as a continuous predictor).
Species-specific experimental studies are needed, for
example, to investigate the variation in phenological
controls between different life stages and to study what
cue (or alternative cues) is used to terminate the dia-
pause in the spring. We can only assume that the start
hours in our models reflect a photoperiodically cued
termination of diapause, but there could also be alter-
native physiological mechanisms producing the same
outcome (Ti et al., 2004). Experimental studies are also
needed to understand in detail the form of thermal con-
trol under low temperatures. Our analyses produced
some top models in which the estimated base for the
lower developmental threshold was <�5 °C (Appendix
S4), even though meaningful development at such low
temperatures seems improbable (Pritchard et al., 1996).
The most obvious explanation is the nonlinearity of
thermal responses, which even our Tsum.nl models
were not able to capture. Alternatively, in early spring,
the snow or soil cover protecting the individuals could
provide warmer conditions than air temperatures
suggest, and therefore some development could take
place even on coldest hours.
Anticipating how climate change will affect phenol-
ogy across latitudes requires that we can compare the
relative strengths of latitudinal trends in (i) tempera-
ture increases and (ii) thermal sensitivities of the
species that are present. This task will be aided by
increasingly sophisticated regional projections of
increase in temperatures, rate of climate change and
shifts in seasonal timing of temperatures (Burrows
et al., 2011). Analyses such as reported here also
contribute via the development and parameterization
of process-based models that can capture the diversity
of physiological controls on phenological responses
within and among biological communities.
Acknowledgements
We thank Liisa Tuominen-Roto and Guy S€oderman (FinnishEnvironment Institute) for their help with Nocturna database,Matti Rousi and Hanni Sikanen for allowing us to use thehourly temperature data from Punkaharju, Tommi Nyman,Sanna Lepp€anen, Seppo Neuvonen and the anonymous review-ers for help and comments to this manuscript. We are especiallygrateful to the voluntary Finnish lepidopterists for maintainingthe traps and identifying the moth samples. This study wasfunded by Joensuun Yliopiston Tukis€a€ati€o.
References
Amarasekare P, Savage V (2012) A framework for elucidating the temperature depen-
dence of fitness. American Naturalist, 179, 178–191.
Anderson DR (2007) Model Based Inference in the Life Sciences. A Primer on Evidence.
Springer, New York.
Ayres MP, Scriber JM (1994) Local adaptation to regional climates in Papilio canadensis
(Lepidoptera: Papilionidae). Ecological Monographs, 64, 465–482.
Battisti A, Stastny M, Buffo E, Larsson S (2006) A rapid altitudinal range expansion in
the pine processionary moth produced by the 2003 climatic anomaly. Global
Change Biology, 12, 662–671.
Berggren �A, Bj€orkman C, Bylund H, Ayres MP (2009) The distribution and abun-
dance of animal populations in a climate of uncertainty. Oikos, 118, 1121–1126.
BradshawWE, Holzapfel CM (2010) Light, time, and the physiology of biotic response
to rapid climate change in animals. Annual Review of Physiology, 72, 147–166.
Burrows MT, Schoeman DS, Buckley LB et al. (2011) The pace of shifting climate in
marine and terrestrial ecosystems. Nature, 334, 652–655.
© 2013 John Wiley & Sons Ltd, Global Change Biology, doi: 10.1111/gcb.12372
THE PHENOLOGY OF LEPIDOPTERA IN FINLAND 11
Danks HV (1987) Insect Dormancy: An Ecological Perspective. Biological Survey of Can-
ada, National Museum of Natural Sciences, Ottawa.
Deutsch CA, Tewksbury JJ, Huey RB, Sheldon KS, Ghalambor CK, Haak DC, Martin
PR (2008) Impacts of climate warming on terrestrial ectotherms across latitude.
Proceedings of the National Academy of Sciences of the United States of America, 105,
6668–6672.
Dillon ME, Wang G, Huey RB (2010) Global metabolic impacts of recent climate
warming. Nature, 467, 704–707.
Finnish Meteorological Institute (2013a) Seasons in Finland. Available at: http://en.
ilmatieteenlaitos.fi/seasons-in-finland (accessed 7 April 2013).
Finnish Meteorological Institute (2013b) Ilmastollinen vertailukausi 1981-2010. Avail-
able at: http://ilmatieteenlaitos.fi/ilmastollinen-vertailukausi-1981-2010 (accessed
7 April 2013).
Gange AC, Gange EG, Mohammad AB, Boddy L (2011) Host shifts in fungi caused
by climate change? Fungal Ecology, 4, 184–190.
Gray DR, Ravlin FW, Braine JA (2001) Diapause in the gypsy moth: a model of inhibi-
tion and development. Journal of Insect Physiology, 47, 173–184.
Groffman PM, Rustad LE, Templer PH et al. (2012) Long-term integrated studies
show complex and surprising effects of climate change in the northern hardwood
forest. BioScience, 62, 1056–1066.
H€anninen H (1990) Modeling dormancy release in trees from cool and temperate
regions. In: Process Modeling of Forest Growth Responses to Environmental Stress (eds
Dixon RK, Meldahl RS, Ruark GA, Warren WG), pp. 159–165. Timber press, Port-
land, Oregon.
van der Have TM, de Jong G (1996) Adult size in ectotherms: temperature effects on
growth and differentiation. Journal of Theoretical Biology, 183, 329–340.
Hodek I, Hodkov�a M (1988) Multiple role of temperature during insect diapause: a
review. Entomologia Experimentalis et Applicata, 49, 153–165.
Hodgson JA, Thomas CD, Oliver TH, Anderson BJ, Brereton TM, Crone EE (2011)
Predicting insect phenology across space and time. Global Change Biology, 17,
1289–1300.
Hon�ek A (1996) Geographical variation in thermal requirements for insect develop-
ment. European Journal of Entomology, 93, 303–312.
Huld�en L, Albrecht A, It€amies J, Malinen P, Wettenhovi J (2000) Atlas of Finnish Mac-
rolepidoptera. Suomen Perhostutkijain seura, Luonnontieteellinen keskusmuseo,
Helsinki.
IPCC (2007) Climate Change 2007: The Physical Science Basis. Contribution of Working
Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate
Change. Cambridge University Press, Cambridge.
Izem R, Kingsolver JG (2005) Variation in continuous reaction norms: quantifying
directions of biological interest. The American Naturalist, 166, 277–289.
Kullberg J, Albrecht A, Kaila L, Varis V (2008) Checklist of Finnish Lepidoptera - An
Updated Version. Available at: http://www.fmnh.helsinki.fi/elainmuseo/hyonteiset/
perhoset/ (accessed 1 December 2012).
Logan JA, Wollkind DJ, Hoyt SC, Tanigoshi LK (1976) An analytic model for descrip-
tion of temperature dependent rate phenomena in Arthropods. Environmental
Entomology, 5, 1133–1140.
Lonsdale DJ, Levinton JS (1985) Latitudinal differentiation in copepod growth: an
adaptation to temperature. Ecology, 66, 1397–1407.
Masaki S (1980) Summer diapause. Annual Review of Entomology, 25, 1–25.
Memmott J, Craze PG, Waser NM, Price MV (2007) Global warming and the disrup-
tion of plant-pollinator interactions. Ecology Letters, 10, 710–717.
Menzel A, Sparks TH, Estrella N et al. (2006) European phenological response to
climate change matches the warming pattern. Global Change Biology, 12, 1969–1976.
Miller SR, Castenholz RW (2000) Evolution of thermotolerance in hot spring Cyano-
bacteria of the genus Synechococcus. Applied and Environmental Microbiology, 66,
4222–4229.
Møller AP (2010) Host-parasite interactions and vectors in the barn swallow in rela-
tion to climate change. Global Change Biology, 16, 1158–1170.
Møller AP, Rubolini D, Lehikoinen E (2008) Populations of migratory bird species
that did not show a phenological response to climate change are declining.
Proceedings of the National Academy of Sciences USA, 42, 16195–16200.
Morin X, Lechowicz MJ, Augspurger C, O’Keefe J, Viner D, Chuine I (2009) Leaf phe-
nology in 22 North American tree species during the 21st century. Global Change
Biology, 15, 961–975.
Nylin S, Wickman PO, Wiklund C (1989) Seasonal plasticity in growth and develop-
ment of the specled wood butterfly, Pararge aegeria (Satyrinae). Biological Journal of
the Linnean Society, 38, 155–171.
Parmesan C (2006) Ecological and evolutionary responses to recent climate change.
Annual Review of Ecology, Evolution, and Systematics, 37, 637–669.
Parmesan C (2007) Influences of species, latitudes and methodologies on estimates
of phenological response to global warming. Global Change Biology, 13,
1860–1872.
Pau S, Wolkovich EM, Cook BI et al. (2011) Predicting phenology by integrating ecol-
ogy, evolution and climate science. Global Change Biology, 17, 3633–3643.
P€oyry J, Leinonen R, S€oderman G, Nieminen M, Heikkinen RK, Carter TR (2011)
Climate-induced increase of moth multivoltinism in boreal regions. Global Ecology
and Biogeography, 20, 289–298.
Pritchard G, Harder LD, Mutch RA (1996) Development of aquatic insect eggs in rela-
tion to temperature and strategies for dealing with different thermal environ-
ments. Biological Journal of the Linnean Society, 58, 221–244.
R Development Core Team (2008) R: A Language and Environment for Statistical
Computing. R Foundation for Statistical Computing, Vienna, Austria.
R€ais€anen J (2002) CO2-induced changes in interannual temperature and precipitation
variability in 19 CMIP2 experiments. Journal of Climate, 15, 2395–2411.
Rathcke B, Lacey EP (1985) Phenological patterns of terrestrial plants. Annual Review
of Ecology, Evolution, and Systematics, 16, 179–214.
Ripley B (2010) Package ‘spatial’. Available at: http://cran.r-project.org/web/
packages/spatial/spatial.pdf (accessed 30 December 2010).
Root TL, Price JT, Hall KR, Schneider SH, Rosenzweig C, Pounds JA (2003) Finger-
prints of global warming on wild animals and plants. Nature, 421, 57–60.
Ruel JJ, Ayres MP (1999) Jensen’s inequality predicts effects of environmental varia-
tion. Trends in Ecology and Evolution, 14, 361–366.
Ruosteenoja K, R€ais€anen J, Pirinen P (2011) Projected changes in thermal seasons and
the growing season in Finland. International Journal of Climatology, 31, 1473–1487.
Sparks TH, Yates TJ (1997) The effect of spring temperature on the appearance of Brit-
ish butterflies 1883-1993. Ecography, 20, 368–374.
Tauber MJ, Tauber CA, Masaki S (1986) Seasonal Adaptations of Insects. Oxford Univer-
sity Press, New York.
Tewksbury JJ, Huey RB, Deutsch C (2008) Climate warming puts the heat on tropical
ectotherms. Science, 320, 1296–1297.
Ti X, Tuzuki N, Tani N, Morigami E, Isobe M, Kai H (2004) Demarcation of
diapause development by cold and its relation to time-interval activation of
TIME-ATPase in eggs of the silkworm, Bombyx mori. Journal of Insect Physiology,
50, 1053–1064.
Trudgill DL, Honek A, Li D, van Straalen NM (2005) Thermal time – concepts and
utility. Annals of Applied Biology, 146, 1–14.
Valtonen A, Ayres MP, Roininen H, P€oyry J, Leinonen R (2011) Environmental con-
trols on the phenology of moths: predicting plasticity and constraint under climate
change. Oecologia, 165, 237–248.
Visser ME, Holleman LJM (2001) Warmer springs disrupt the synchrony of oak and
winter moth phenology. Proceedings of the Royal Society B, 268, 289–294.
Warren RJ, Bahn V, Bradford MA (2011) Temperature cues phenological synchrony
in ant-mediated seed dispersal. Global Change Biology, 17, 2444–2454.
Supporting Information
Additional Supporting Information may be found in theonline version of this article:
Appendix S1. Details of source data.Appendix S2. Details of competing models.Appendix S3. Effect of phylogeny on phenological controls.Appendix S4. Model comparison results.
© 2013 John Wiley & Sons Ltd, Global Change Biology, doi: 10.1111/gcb.12372
12 A. VALTONEN et al.