Is climate warming more consequential towards poles? The phenology of Lepidoptera in Finland

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.


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.,



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.



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).



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.



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



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).



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).



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.

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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.



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