Seasonal time constraints reduce genetic variation in

life-history traits along a latitudinal gradient

Szymon Sniegula1*, Maria J. Golab1, Szymon M. Drobniak2 and Frank Johansson3

1Department of Ecosystem Conservation, Institute of Nature Conservation, Polish Academy of Sciences, al.

Mickiewicza 33, 31-120 Cracow, Poland; 2Population Ecology Group, Institute of Environmental Sciences, Jagiellonian

University, Gronostajowa 7, 30-387 Cracow, Poland; and 3Department of Ecology and Genetics, Uppsala University,

SE-751 05 Uppsala, Sweden

Summary

1. Time constraints cause strong selection on life-history traits, because populations need to

complete their life cycles within a shorter time. We therefore expect lower genetic variation in

these traits in high- than in low-latitude populations, since the former are more time-

constrained.

2. The aim was to estimate life-history traits and their genetic variation in an obligately uni-

voltine damselfly along a latitudinal gradient of 2730 km.

3. Populations were grown in the laboratory at temperatures and photoperiods simulating

those at their place of origin. In a complementary experiment, individuals from the same fam-

ilies were grown in constant temperature and photoperiod that mimicked average conditions

across the latitude.

4. Development time and size was faster and smaller, respectively, and growth rate was

higher at northern latitudes. Additive genetic variance was very low for life-history traits, and

estimates for egg development time and larval growth rate showed significant decreases

towards northern latitudes. The expression of genetic effects in life-history traits differed con-

siderably when individuals were grown in constant rather than simulated and naturally vari-

able conditions.

5. Our results support strong selection by time constraints. They also highlight the impor-

tance of growing organisms in their native environment for correct estimates of genetic vari-

ance at their place of origin. Our results also suggest that the evolutionary potential of life-

history traits is very low at northern compared to southern latitudes, but that changes in cli-

mate could alter this pattern.

Key-words: additive variance, climate change, development time, growth rate, heritability,

maternal effect, phenotypic plasticity, photoperiod, temperature

Introduction

The presence of additive genetic variation in quantitative

traits can lead to evolutionary change (Stearns 1992),

while its absence can create a constraint for the evolu-

tionary response to selection (Barton & Partridge 2000).

Variation in the degree of standing additive genetic vari-

ance in life-history traits is very common among popula-

tions and species and is assumed to be due to the

interplay of several factors, for example mutations that

generate new variants, differing degrees of environmental

variation interacting with genetic effects, and directional

and stabilizing selection that reduces variation (Barton &

Keightley 2002). While past directional selection suggests

that additive variance should be depleted for quantitative

traits (Futuyma 2009), we still have few examples of low

heritability and selection limits from natural systems,

where we expect strong directional selection (Kellermann

et al. 2006; Bridle, Gavaz & Kennington 2009; McFarlane

et al. 2014). The absence of such findings could be due to

many factors (Blows & Hoffmann 2005). For example, a

focus on traits unrelated to species abundance and distri-

bution or estimates of genetic trait variation done under

conditions not simulating those occurring in the native

range could contribute to incorrect estimates of additive*Correspondence author. E-mail: szymon.sniegula@gmail.com

© 2015 The Authors. Journal of Animal Ecology © 2015 British Ecological Society

Journal of Animal Ecology 2015 doi: 10.1111/1365-2656.12442

genetic variance in life-history traits (Stearns 1992; Flatt

& Heyland 2011). Therefore, it is important to estimate

the genetic variance in quantitative traits under condi-

tions mirroring the natural ones, because environmental

conditions have a strong impact on the expression of

genetic variance (Hoffmann & Merilä 1999; Shama et al.

2011).

A large effective population size and a high level of

gene flow should lead to high genetic variance (Bridle &

Vines 2007). Hence, it is in the centre of the distribution

of a species, where environmental conditions are assumed

to be optimal, that we expect to find the highest genetic

variance (Eckert, Samis & Lougheed 2008). In contrast,

populations situated close to the margins of a species’

geographic range experience suboptimal conditions. This

should result in smaller population size and a decrease in

genetic variation due to genetic drift, lack of incoming

migrants and steeper selection gradients at range margins

associated with physiological limitations (Hoffmann &

Parsons 1997). Although these predictions are supported

by previous studies based on neutral genetic markers,

few studies have examined whether the same patterns

exist with regard to additive genetic variance in quantita-

tive traits (Eckert, Samis & Lougheed 2008; van Heer-

waarden et al. 2009; Hoffmann & Sgr�o 2011; Berger

et al. 2013).

Seasonal time constraints and suboptimal ambient tem-

peratures are important abiotic variables that strongly

influence key life-history traits of ectotherms (Gotthard

2001) and may limit a species’ geographic distribution

(Hoffmann & Merilä 1999; Eckert, Samis & Lougheed

2008; Gaston 2009). Examples of such key life-history

traits are time of and size at emergence, factors which

have a strong impact on fitness in adults (Sokolovska,

Rowe & Johansson 2000; De Block & Stoks 2005). In the

most extreme case, at high-latitude range margins, there is

an extremely short time period available for growth and

development, which should result in depletion of additive

genetic variation in these life-history traits (Hoffmann &

Parsons 1997). Estimates of variation in mean values of

intrinsic growth rates in different latitudes indicate that

time- and temperature-constrained individuals have been

selected for rapid growth and development and that addi-

tive genetic variance in these traits existed in the past

(Dmitriew 2011). In addition, artificial selection experi-

ments have indicated that evolutionary changes in growth

rate and other life-history traits in response to time and

temperature constraints can occur rapidly given high ini-

tial genetic variances (Partridge et al. 1994; Teuschl, Reim

& Blanckenhorn 2007). Though many researchers have

studied genetic variation in life-history traits along a lati-

tudinal gradient, few have found a decrease in genetic

variation at higher latitudes (but see Etterson 2004; Pujol

& Pannell 2008). One reason for the absence of the

expected pattern could be that the environmental condi-

tions used in such experiments did not mirror those expe-

rienced by organisms in nature (Schlichting & Pigliucci

1998; Angilletta 2009).

The main objective of this study was to estimate genetic

variance in life-history traits along a latitudinal gradient

of populations in the damselfly Lestes sponsa (Hanse-

mann), in conditions that mimicked the native conditions

of the populations as well as in constant environmental

conditions that mimicked average conditions across the

latitude. Lestes sponsa has a strongly time-constrained 1-

year life cycle, meaning that it must complete its develop-

ment before the season is over. It is therefore an excellent

model system for the study of time constraints, since life-

history responses are not confounded by life cycle length.

We estimated egg size, egg development time, larval

growth rate, larval development time and size at emer-

gence along a latitudinal gradient differing in time con-

straints. We predicted that high-latitude populations

would express faster growth and development, and lower

genetic variation in life-history traits than core and south-

ern populations, because the former populations are more

time-constrained. In addition, since many studies tend to

simplify their experimental designs by applying constant

average conditions to individuals collected in different

geographical locations (Gotthard 2001; Shama et al. 2011;

Nilsson-Örtman et al. 2012), we decided to check the

robustness of our analyses under constant, average condi-

tions. We did this by means of an additional experiment

in which we raised damselflies from our study populations

at a constant temperature and photoperiod that simulated

intermediate latitudinal temperatures and photoperiod

conditions across the relevant latitude. Then, we deter-

mined whether there was a genetic correlation between

trait expressions in constant (novel) and changing thermo-

photoperiods (native) within and among studied popula-

tions. A genetic correlation would suggest that conclu-

sions obtained in constant conditions would reflect

patterns observed in native conditions. In contrast, lack

of a genetic correlation would suggest that it is difficult to

predict genetic variation from one environment to another

based on results in only one of them.

Materials and methods

We used the damselfly L. sponsa, which has an adult terrestrial

stage and an aquatic larval stage. Most of the mass gain occurs

in the larvae stage and larvae emerge at a fixed size, and hence,

no size increase occurs after emergence (Corbet 1999). We esti-

mated life-history variables under the natural light and tempera-

ture conditions that these damselfly larvae populations experience

in their native range along the latitudinal gradient examined.

Lestes sponsa is an obligatorily univoltine species, seasonally

time-stressed in temperate regions: eggs are deposited in summer

and then overwinter; the aquatic larvae hatch in spring and lar-

vae emerge and mature in summer (Dijkstra 2006; Śniegula &

Johansson 2010). Time of and size at emergence have been shown

to affect fitness components such as mating success in Lestes spe-

cies, including L. sponsa (Stoks 2000; De Block & Stoks 2005).

© 2015 The Authors. Journal of Animal Ecology © 2015 British Ecological Society, Journal of Animal Ecology

2 S. Sniegula et al.

Lestes sponsa has a wide latitudinal distribution, and studies

suggest that northern populations have experienced strong direc-

tional selection for fast growth and development, since there is

adaptive latitudinal differentiation in the mean values of life-his-

tory traits (Śniegula & Johansson 2010; Śniegula et al. 2014).

However, previous studies have not examined additive genetic

variation across populations at native temperatures and photope-

riods.

The methods are described in full detail in Supporting Infor-

mation 1; here, we will give a brief description only. To estimate

growth and development in the egg and larval stages, we col-

lected eggs from adult females in three geographic regions cover-

ing a distance of 2730 km: northern Sweden (66°N, alt.

220 mamsl and 10 mamsl), north-western Poland (54°N, alt. 140

mamsl) and southern France (43°N, alt. 0 mamsl) (Fig. 1a), here-

after called northern, central and southern populations or

regions.

We reared two populations per latitude, for a total of six pop-

ulations. Earlier investigations of L. sponsa and other damselfly

species report that the differences among replicate populations

within a latitude are much smaller than the effects of latitude on

life-history traits (Stoks & De Block 2011; De Block & Stoks

2012; Nilsson-Örtman et al. 2012; Śniegula et al. 2014). A sum-

mary of the environmental variables at the sampling sites, includ-

ing information on latitude estimated population size, length of

the growth season, mean shallow water temperature throughout

the growth season and degree-days is given in the Table S1. We

collected eggs from paternal half-siblings by separating initially

copulating pairs and saving females for egg laying. The male

mating with the first female was thereafter enclosed in a small

insectary together with a new single female, which resulted in a

second mated female (Fig. 1b). Although there is no data avail-

able on L. sponsa, studies on damselflies indicate that the propor-

tion of the female’s offspring sired by the last male with which

she copulated rarely falls below 95% in the majority of damselfly

species (Corbet 1999). However, it has been shown that in some

species, the proportion may vary from 44% to over 90% (Fincke

1984; Cooper, Miller & Holland 1996).

Using this method, we produced the following number of

paternal half-sib families: a northern site, 10 males, each mated

with two females, resulting in a total of 20 families; central sites,

16 and four males, each mated with two females, resulting in

totals of 32 and eight families, respectively; and southern sites, 18

and nine males, each mated with two females, resulting in totals

of 36 and 18 families, respectively. Damselflies were too scarce in

one of the northernmost populations for us to be able to obtain

males for a second mating. We therefore have 16 full-sib families

and no half-sib families for this population, and these were

included for estimates of broad-sense heritabilities, overall genetic

variance, variation in mean trait values across regions and in

experiment 2 based on full-sib design (in these analysis the two

northern populations were merged into one region), but not for

estimates of additive genetic variance, that is half-sib/full-sib

Fig. 1. (a) European distribution of Lestes sponsa (grey shading

on the map), and sampling sites (black dots) at northern (N),

central (C) and southern (S) latitudes. Arrows denote how larvae

of full/half-sib breeding design were grown under northern, cen-

tral, southern and constant environmental condition in climate

chambers (see b for breeding design). (b) Breeding design where

h represents the half-sib design (each male mated to two females)

and f the full-sib design (each male mated to one female); full/

half-sib design was used in experiment 1 and full-sib design was

used in experiment 2. (c) Results from experiment 1 (N, C and S)

and experiment 2 (Const) showing temporal distribution of egg

(above axis) and larval (below axis) development time.

© 2015 The Authors. Journal of Animal Ecology © 2015 British Ecological Society, Journal of Animal Ecology

Seasonal constraints and heritable variance 3

design (experiment 1; described below). In addition, from the

northern, one central and one southern population where we

sampled half-sibs, we also sampled the following numbers of

adult females that produced full-sib families: eight, nine and one,

respectively. These families were included in the variance parti-

tioning analysis (see below).

The collected eggs were then transported to a laboratory in the

Institute of Nature Conservation PAS in Cracow, Poland, where

an experiment on development and growth was performed in four

climate chambers (Fig. 1a). In three separate chambers, we reared

the northern, central and southern populations at programmed

temperatures and photoperiods (thermo-photoperiods) simulating

those experienced by the damselflies in their natural conditions

(experiment 1, Fig. 1a). A fourth chamber, with a mean thermo-

photoperiod averaged over all sampled regions and growth sea-

sons, was used to rear individuals from all three study regions

(experiment 2. Fig. 1a). These individuals originated from the

same families as in experiment 1, that is family effects are crossed

with experimental groups in experiment 2. Both experiments ran

in parallel.

experiment 1

In this experiment, we determined genetic variance in life-history

traits simulating natural temperature and photoperiod regimes by

using the half-sib/full-sib design based on the northern, central

and southern populations (Fig. 1a, Fig. S1).

Upon arrival at the laboratory, the northern, central and

southern eggs were placed in light–dark conditions simulating

those occurring under native conditions during this time of year

(late summer). After 2–3 weeks in late-summer conditions, we

simulated winter conditions by lowering the temperature to 5 °C

and switching the lights off. We kept the eggs in winter condi-

tions for 4 weeks. Thereafter, we simulated spring conditions by

setting the thermo-photoperiods to the dates when the tempera-

tures exceeded 12 °C at each population’s origin. For the north-

ern region, this corresponded to 30 May (temp. 14 °C), for the

central region 25 April (temp. 13�3 °C) and for the southern

region 4 April (temp. 13�8 °C). During these spring conditions,

all larvae hatched. Then, we simulated natural (weekly) changes

in temperature until 25 July in a chamber holding the northern

populations (week 9), 15 August in a chamber holding the cen-

tral populations (week 17) and 12 September in a chamber

holding the southern populations (week 24) (Fig. S1). On these

dates, when temperatures start to decrease slowly in nature,

there were still individuals that had not emerged. We therefore

maintained the temperature the larvae experience at these dates

until all individuals had emerged in northern populations

(Fig. S1). Photoperiods followed weekly changes until the end

of the experiment (Fig. S1).

Larvae were grown individually in round plastic containers

(diameter 7 cm, height 4 cm) and fed daily with laboratory-

reared brine shrimp Artemia salina. Ten individuals were raised

from each female, resulting in 440 northern, 490 central and 550

southern individuals, which resulted in a total of 1480 individu-

als at the start of the experiment. However, for the additive

genetic analysis, 160 individuals were excluded from the north-

ern region since these originated from a population where

full-sib families only were sampled. We estimated the following

life-history traits: egg volume, egg development time, larval

development time, larval size at last instar (F0) and larval

growth rate. Larval development time was estimated as the

number of days between hatching and emergence. Larval growth

rate was estimated as final instar larval head width divided by

the number of days needed for larval development, that is

between hatching and emergence dates. We used head widths

for growth rate estimates, as this measurement significantly cor-

relates with other body size measurements and is commonly

used for adult size and growth rate estimates (Corbet 1999). In

addition, using head width instead of weight at emergence

enabled us to use a larger sample size for growth rate, since it

was impossible to accurately estimate dry weight on some

emerging individuals. Head width of final larval instars is

strongly correlated with adult weight in this species with an r2

value > 0�70 (Mikolajewski, Johansson & Brodin 2004).

Statistical methods, experiment 1

We employed a full-sib/half-sib design, where each sire was

mated with two dams and offspring were measured in each

full-sib family. Thus, full-sibs for each dam were also paternal

half-sibs (PHS). In such a breeding design, the covariance

between paternal half-sibs is equal to the variance between

sires (V(s)) and approximates one-quarter of the total additive

genetic variance V(a) (Lynch & Walsh 1998). Observed variance

between dams V(d) is the sum of several components: ¼

additive genetic variance, ¼ dominance variance, plus several

terms related to epistatic effects and maternal effects if present

(both genetic and environmental). The remaining sources of

variation (e.g. environmental) form the unexplained residual

component of the variance (V(e)). Heritability can thus be

approximated as 4tPHS, where the intraclass correlation between

PHS (tPHS) is defined as V(s)/V(z), that is the fraction of total

phenotypic variance V(z) = V(s) + V(d) + V(e) explained by sire

effects (V(s)).

Data were analysed using the linear mixed model in

ASReml-R v. 3.0 (Butler et al. 2009) and the R computing

environment (R Development Core Team 2014). Prior to anal-

ysis, all response variables were standardized (to mean = 0 and

SD = 1). In all analyses, we inspected residual plots to ensure

that the models fitted the data correctly. In all models, we

included sire and dam identity as random effects, and the

region of sampling as a fixed effect. Preliminary analyses,

including population identities, indicated no population-related

differences in estimated parameters within regions. We thus

decided to remove the population effect from all models to

increase the power of comparisons. We included offspring sex

(male/female/unknown) as a fixed variable; however, we later

removed it, as it proved insignificant. In total, we analysed five

response variables: egg volume, egg developmental time, larval

growth rate, larval developmental time and last instar (F0) lar-

val head width.

To test for the presence of genetic variance and its partitioning

among regions, we employed a hierarchy of mixed models of suc-

cessively greater complexity. A detailed description of the testing

procedure and results can be found in Supporting Information 2,

but in short, we relaxed constraints placed on the covariance

matrices and fitted all random effects as square 3 9 3 covariance

matrices. Testing of respective variances and their differences was

performed using the likelihood-ratio test.

In half-sib/full-sib cases, heritabilities (h2) were calculated as

4 (V(s)/V(z)), except in cases where the between-sires and between-

© 2015 The Authors. Journal of Animal Ecology © 2015 British Ecological Society, Journal of Animal Ecology

4 S. Sniegula et al.

dams variances were approximately equal. In the latter case, heri-

tability was calculated as 4 ((V(s) + V(d))/V(z)) (i.e. using the

covariance between full-sibs as the proxy of ¼Va; Lynch & Walsh

1998). We also calculated fractions of total variance explained by

the dam effect m2 = V(d)/V(z). Standard errors of all variance

functions were calculated using the delta method (Lynch & Walsh

1998). All values of h2 and m2 were calculated from models with

the highest likelihood.

Since we did not gather data on half-sibs in one of the two

northern populations, it was impossible to separately estimate

dam and sire components of variance with these individuals

included. The structure of random effects in these models without

half-sibs was therefore different as it did not include sire effect.

Broad-sense genetic variance was approximated in these models

by the dam (i.e. family) effect. In purely full-sib analyses, broad-

sense heritabilities for all traits were calculated as 2*V(d)/V(z)

(Lynch & Walsh 1998).

To estimate regional phenotypic differences in egg volume,

egg development time and larval development time, F0 larval

size and larval growth rate (F0 larval size/larval development

time in days), we used a linear mixed model function imple-

mented in the package for R nlme, where full-sib families and

populations were random effects. Initially, we included egg size

as a covariate in these analyses but since it did not affect

growth rate, larval size or development time significantly

(P = 0�98, 0�69 and 0�99, respectively), we excluded egg size

from all models.

experiment 2

The main aim was to compare the difference in genetic variance

expressed in non-native average conditions and at the simulated

natural temperature and photoperiod used in experiment 1. We

determined genetic variance in egg development time, larval

development time, larval size and growth rate using a constant

temperature and photoperiod, which we then used to compare

with results from experiment 1. We used a full-sib design, and

hence included data on all sampled populations (Fig. 1a). Space

limitations in the climate chamber did not allow for a half-sib

design with a sufficient number of replicates; hence, the half-sibs

were not included. After the winter simulation in experiment 1

had been terminated (see above), we randomly chose six eggs

from eight randomly chosen full-sib families from the northern,

central and southern regions. This gave us a total of 144 larvae,

which were then placed in a chamber with a constant tempera-

ture of 21�9 °C and a photoperiod corresponding to the maxi-

mum day length during the growth season at the middle

latitude along the transect of our study regions (55°N, 10°E), L

19 : 25, D 04 : 35. We set this temperature because (i) earlier

studies indicated that larvae have the lowest mortality when

reared at this temperature (Johansson et al. 2001; Stoks, De

Block & McPeek 2006a; Śniegula & Johansson 2010) and (ii)

this temperature is experienced by all study regions in natural

conditions for at least several hours during the day at the peak

of the growth season. We used a constant temperature and pho-

toperiod because we wanted to estimate whether the amount of

genetic variance in the studied traits changed as the individuals

were grown in constant and changing native temperatures and

photoperiods, respectively. In this experiment, we estimated the

same life-history parameters as in experiment 1, except that we

did not measure egg volume.

Statistical methods, experiment 2

To test whether family effects (i.e. broad-sense genetic effects, G)

are correlated between two contrasting environments (E), simu-

lated natural thermo-photoperiods (experiment 1) and a constant

mean thermo-photoperiod for all regions (experiment 2), we fitted

an additional set of mixed models in which, for each response vari-

able, we included region and experimental group (simulated vs.

constant conditions) as a fixed effect. The random family effect was

fitted in the form of four different (co)variance structures:

1. Homogenous (equal) variances.

2. Heterogeneous variances and family-wise correlation between

treatments equal to unity.

3. Heterogeneous variances and family-wise correlation between

treatments equal to zero.

4. Heterogeneous variances and family-wise correlation uncon-

strained.

All models were fitted in ASReml-R (Butler et al. 2009). Signif-

icance of the interaction between genetic effects and conditions

(i.e. the presence or absence of genetic correlation between simu-

lated and constant conditions) was tested using a likelihood-ratio

test. Comparison of models 1 and 2 tests the presence of G 9 E

interaction due to uneven genetic variances; comparison of mod-

els 2–3 and 3–4 tested for G 9 E due to cross-environmental cor-

relations of genetic effects being less than one. For visualization

purposes, we extracted BLUPs (best linear unbiased predictors)

of the genetic family effect (Robinson 1991) from all best-fitting

models. BLUPs were used solely for graphing purposes.

Results

experiment 1

Differences across latitudes

Egg volume differed between latitudes (v2 = 164�91,d.f. = 2, P < 0�001) and was largest in the northern region

and smaller in the southern and central regions (Table 1).

Egg development time was short in the northern region

and progressively longer in the central and southern

regions (v2 = 47�36, d.f. = 2, P < 0�001; Table 1, Fig. 1c).

Also larval development time differed between latitudes

(v2 = 39�41, d.f. = 2, P < 0�001) and was shortest in the

northern region and longer in the central and southern

regions (Table 1, Fig. 1c). The northern larvae were char-

acterized by the smallest final instar size, which became

progressively larger further south (Table 1), and there was

a significant effect of latitude (v2 = 565�55, d.f. = 2,

P < 0�001). Finally, there was a significant effect of lati-

tude on growth rate (v2 = 5�83, d.f. = 2, P < 0�05), and

the northern larvae showed a higher growth rate than

central and southern larvae (Table 1). Within region, pop-

ulation-specific mean trait values are shown in Table S2.

In summary, with the exception of egg volume, pheno-

typic difference in life-history traits showed consistent

increasing or decreasing patterns with regard to latitude,

suggesting stronger selection for a rapid life history in the

north. We note, however, that growth rate did not differ

© 2015 The Authors. Journal of Animal Ecology © 2015 British Ecological Society, Journal of Animal Ecology

Seasonal constraints and heritable variance 5

between south and central regions, but that it was highest

in the north.

Genetic variation in life-history traits across latitudes

We found support for our prediction that genetic variance

should be lower in the north. Initial analysis of raw total

phenotypic variances (including two northern popula-

tions) indicated a substantial difference in variance

between the three sampled regions in all five measured

traits (Table 1, Table S3). On average, the northern

region tends to be less variable compared to southern

(V(z) and CV), with respect to the measured traits (this

pattern is least apparent in the case of egg volume and F0

larval head width (size); Table 1, Table S3). Full-sib anal-

yses indicated that substantial amounts of broad-sense

genetic variance (V(family) and H2) were present in most

traits (Table 1). Note that these broad-sense heritabilities

are mixtures of purely additive genetic effects and other

sources of parent–offspring resemblance (maternal effects,

dominance).

Half-sib/full-sib analyses indicated that in three of the

studied life-history traits, there was a significant additive

genetic component as shown by a significant variance

among sires (egg volume: LRT = 3�35, d.f. = 1, P = 0�03;developmental time of eggs: LRT = 8�17, d.f. = 1,

P < 0�001; growth rate: LRT = 3�38, d.f. = 1, P = 0�009).Genetic effects were non-significant for larval develop-

ment time (P = 0�13) and F0 larval size (head width;

P = 0�59). Further analyses of the three traits with signifi-

cant variances among sires supported our prediction of a

lower genetic variance in the north: genetic variance in

egg developmental time and growth rate was lowest in the

northern, intermediate in the central and highest in the

southern region (Table 2, Table S3). In contrast, the vol-

ume of the eggs exhibited a different pattern. The central

region turned out to have the lowest genetic variance,

whereas the northern and southern regions exhibited

Table 1. Summary statistics (means ŷ, phenotypic variance V(z), coefficients of variation CV, genetic variance V(family) and broad-sense

heritability H2) for all five analysed traits in experiment 1, divided by northern (N), central (C) and southern (S) regions. These estimates

include all sampled populations, including two northern populations. Presented are values for raw, non-standardized traits. Region-speci-

fic mean trait values and genetic parameters are presented with their standard errors.

Traitb Region ŷ V(z) CV V(family) H2

Egg volume (mm3) N 0�080 � 0�0003 3�510a 0�08 0�23 � 0�05 0�93 � 0�13P < 0�0001 C 0�063 � 0�0002 1�861a 0�07 0�14 � 0�14 1�08 � 0�12

S 0�069 � 0�0002 2�801a 0�07 0�23 � 0�05 1�19 � 0�11Egg devel. time (days) N 9�75 � 0�110 5�33 0�24 0�003 � 0�001 0�29 � 0�09P < 0�0001 C 31�79 � 0�458 99�08 0�31 0�11 � 0�03 0�65 � 0�11

S 39�37 � 0�765 311�76 0�45 0�23 � 0�06 0�45 � 0�10Larval devel. time (days) N 77�88 � 0�496 49�38 0�09 0�06 � 0�03 0�27 � 0�13P < 0�001 C 86�30 � 0�529 88�29 0�11 0�09 � 0�04 0�27 � 0�10

S 87�93 � 0�733 151�52 0�14 0�07 � 0�06 0�11 � 0�10Larval head width (mm) N 3�32 � 0�006 0�010 0�03 0�11 � 0�05 0�35 � 0�14P < 0�0001 C 3�49 � 0�005 0�011 0�03 0�13 � 0�05 0�33 � 0�11

S 3�59 � 0�005 0�012 0�03 0�08 � 0�07 0�12 � 0�10Growth rate (mm day�1) N 0�043 � 0�0003 1�450a 0�09 0�05 � 0�03 0�26 � 0�11P < 0�0001 C 0�041 � 0�0003 1�981a 0�11 0�09 � 0�03 0�47 � 0�11

S 0�042 � 0�0003 3�331a 0�14 0�08 � 0�03 0�37 � 0�10aAll values 10�5.bP values for differences between regions in mean values (ŷ).

Table 2. Sire (V(s)) and dam (V(d)) variance components divided by regions in the three traits showing significant genetic variance in

experiment 1. Heritabilities (h2) and proportions of dam variance (m2) are presented with standard errors.

Trait Region V(s) h2 V(d) m2

Egg volume (mm3) N 0�11 � 0�06 1�10 � 0�44 0�04 � 0�03 0�09 � 0�07C <0�001a <0�001a 0�14 � 0�03 0�54 � 0�06S 0�08 � 0�05 0�78 � 0�47 0�16 � 0�05 0�40 � 0�11

Egg devel. time (days) N <0�001a <0�001a 0�003 � 0�001 0�31 � 0�08C 0�01 � 0�03 0�15 � 0�37 0�09 � 0�03 0�28 � 0�10S 0�19 � 0�07 0�74 � 0�23 0�04 � 0�04 0�04 � 0�03

Growth rate (mm day�1) N 0�007 � 0�06 0�05 � 0�42 0�03 � 0�07 0�05 � 0�12C 0�05 � 0�06 0�26 � 0�30 0�08 � 0�06 0�10 � 0�07S 0�12 � 0�07 0�36 � 0�19 <0�001a <0�001a

Abbreviations as in Table 1.aEstimates of variance were very low and therefore can be seen as fixed at the parameter–space boundary (i.e. 0).

© 2015 The Authors. Journal of Animal Ecology © 2015 British Ecological Society, Journal of Animal Ecology

6 S. Sniegula et al.

intermediate and the highest genetic variance, respectively

(Table 2, Table S3). The observed differences in genetic

variances (Vs) between regions were closely mirrored by

estimated narrow-sense heritabilities (h2) (Table 1).

The trait-dependent patterns of differences in genetic

variances are unlikely to result from the smaller sample size

in the north. First, genetic variances in general closely mir-

rored raw values of overall trait variances (Tables 1 and 2).

Secondly, if these patterns were due to lower statistical

power, we would expect increased errors of estimates rather

than decreases in the overall effect size of variance esti-

mates.

Maternal effects

In Fig. 2, the proportions of the variance explained by the

dam, sire and residual variance are given for each region

and for the traits with substantial genetic component. The

general pattern is that the relative contribution of the dam

and sire variance to egg development time and growth rate

decreases as one moves from the south to the north.

Interestingly, we found differences not only in genetic

variances but also in the maternal components of variance

(Table 2). In several cases (growth rate in the northern, egg

developmental time in the central and northern regions, egg

volume in all regions), the observed variance in dams mark-

edly exceeded that in sires. Since the dam variance is com-

posed mainly of ¼ VA (i.e. sire variance) plus a quarter of

dominance variance and maternal effects, we hypothesize

that the main drivers of these differences are maternal

effects.

experiment 2

Genetic correlations between simulated natural and

constant thermo-photoperiods

We predicted that the expressed genetic variance should

differ depending on whether animals were reared under

natural simulated environmental conditions or constant

environmental conditions. We found support for this pre-

diction because egg development time and growth rate

showed significant family (genetic) and treatments interac-

tion (simulated natural vs. constant thermo-photoperiod

conditions; Fig. 3) effects. Variance between families in

egg development time and growth rate differed between

regions, as shown by a significant difference in broad-

sense heritabilities (inferred from the likelihood-ratio test):

egg developmental time P < 0�001; growth rate P = 0�007.In support of this, family effects were expressed differ-

ently between rearing conditions as indicated by genetic

correlations less than unity. There was a significant nega-

tive genetic correlation for egg development time

(r = �0�67, P = 0�007). For growth rate, the genetic corre-

lation was less than unity (P = 0�024), but not different

from zero (P = 0�80), indicating no correlation of family

effects and crossing of reaction norms. These results are

visualized in Fig. 3, where it can be seen that reaction

norms cross and that the degree of genetic variation dif-

fers between environments. For example, the low vari-

ances observed in native rearing conditions for the

northern population were not evident when these popula-

tions were reared in constant conditions.

We do not report results for larval development time or

head width because both traits had non-significant additive

genetic variation (Table 2, Table S4). However, for both

traits, mixed models supported different variances in simu-

lated and constant conditions (larval development time

P = 0�025; larval head width P < 0�0001), which supports

our prediction that expressed genetic variance should differ

depending on whether animals were reared under natural

simulated environmental conditions or constant environ-

mental conditions. No support was found for the crossing

of reaction norms (for the null hypothesis that rg = 1: larval

development time P = 0�06; larval head width P = 0�57).These values should be treated with caution; both variances

and correlations in these models were fixed at the parame-

ter–space boundary (zero for variance and unity for corre-

lation), preventing the models from reaching convergence.

These computational problems are likely to be caused by

Fig. 2. Proportion of variance explained

by dam, residual and sire effects received

in experiment 1. Random effects used to

calculate the ratios were extracted from

the highest likelihood mixed models ana-

lysing the half-sibling genetic data. S,

southern region; C, central region; N,

northern region.

© 2015 The Authors. Journal of Animal Ecology © 2015 British Ecological Society, Journal of Animal Ecology

Seasonal constraints and heritable variance 7

the smaller sample size of these two traits due to the sur-

vival of only some of the larvae until emergence.

Discussion

In this study, we estimated life-history traits and their

genetic variation in a damselfly along a latitudinal gradi-

ent of 2730 km. We predicted that time constraints at

northern latitudes would results in higher growth and fas-

ter development in northern compared to southern popu-

lations of L. sponsa, when reared at natural changes in

photoperiod and temperature. We found support for this

since growth and development was higher at northern lati-

tudes compared to southern ones. A strong selection on

growth and development at northern latitudes should

result in a low genetic variance in life-history traits in

northern populations compared to southern ones, for

which time constraints are less pronounced. We also

found support for this prediction, since northern

L. sponsa populations expressed low, central intermediate

and southern high relative levels of additive genetic vari-

ance in egg development time and larval growth rate.

When we used a simpler natural condition, not including

natural changes in photoperiod and temperature, we

found that the genetic variances expressed, changed con-

siderably compared to those in natural environmental

conditions. Hence, our study also highlights the need to

estimate genetic variance in nearly natural environmental

conditions in order to make accurate estimates at the

source of origin of a population.

latitude and life-history traits

In a high-latitude environment, where the climate condi-

tions are highly unfavourable and the growth season is

short, growth and development are constrained. We found

that northern populations compensated for a short

growth season with accelerated development time and an

increasing growth rate. However, larvae could not com-

pensate fully, since they were characterized by smaller

size. This kind of cost for development and growth and

response to time constraints is common in many organ-

isms (Stoks et al. 2006b; De Block et al. 2008; Conover,

Duffy & Hice 2009; Śniegula & Johansson 2010; Śniegula,Johansson & Nilsson-Örtman 2012a; Śniegula et al. 2014).

The key environmental factor driving this compensation

in insects is the photoperiod (Bradshaw & Holzapfel

2007; Śniegula et al. 2014), because a photoperiod simu-

lating a late date or a high-latitude photoperiod during

the growth season accelerates development and growth

rate, which, in turn, affects emergence dates and adult

body size: key fitness components in many organisms

(Banks & Thompson 1987; Stearns 1992; Sokolovska,

Rowe & Johansson 2000; De Block & Stoks 2005; Śnieg-ula et al. 2014).

We found a decrease in expressed additive genetic vari-

ance from south to north in two of the life-history traits:

egg development time and larval growth rate. We suggest

that the major cause for this pattern is a relatively long

and strong history of selection for synchronized emer-

gence and maturity at an optimal breeding date at high

latitudes. At temperate latitudes, a population’s larval

growth and breeding are restricted to a short time win-

dow. Such strong selection on emergence at the right time

and optimal size should reduce genetic variance (Stearns

1992; Flatt & Heyland 2011). Very few studies have found

support for strong directional selection on quantitative

traits at range limits such as at the northern end of a spe-

cies’ distribution, and those that have found this did not

focus on time constraints per se (Wilson et al. 1991; Palo

Fig. 3. Illustration of genetic variation in phenotypic plasticity of egg development time and larval growth rate when measured at con-

stant (experiment 2) and natural simulated temperature and photoperiod conditions (experiment 1) in full-sibling families from popula-

tions from different latitudes. Lines represent the best linear unbiased predictors of family effects for the height and slope of thermal

reaction norms extracted from the highest likelihood mixed models testing for genetic correlations between treatments: mean reaction

norm for each family in constant and simulated temperature and photoperiod conditions. The y-axis represents deviations from mean

trait values, so that zero corresponds to the mean values of both egg development time and growth rate.

© 2015 The Authors. Journal of Animal Ecology © 2015 British Ecological Society, Journal of Animal Ecology

8 S. Sniegula et al.

et al. 2003; Etterson 2004; Pujol & Pannell 2008; Bridle,

Gavaz & Kennington 2009; Shama et al. 2011). One

explanation for the absence of such a trend could be that

organisms experience different thermal variation within

and among generations across a latitudinal gradient (Ber-

ger et al. 2013) and that many studies do not explore how

such thermal variation affects the thermal variation

organisms experience. For example, many organisms have

a shift in voltinism along a latitudinal gradient which

affects the thermal variation experienced within and

among generations (Nilsson-Örtman et al. 2012), and

therefore species or populations that have a 1- or a 2-year

life cycle would differ in how time-constrained they are.

An alternative non-exclusive explanation for the absence

of a strong pattern in past studies could be the different

environmental conditions used during experiments. The

additive genetic variance expressed is partly determined

by the environmental conditions experienced by the

organisms during development, as suggested by the sec-

ond experiment in our study (Hoffmann & Merilä 1999).

Hence, we suggest that accurate estimates of genetic varia-

tion should be made in natural conditions.

Traits that expressed significant values of additive

genetic variance (egg volume, egg development time and

larval growth rate) showed typical values of narrow-sense

heritability as for traits associated with fitness (Mousseau

& Roff 1987; Visscher, Hill & Wray 2008). For two of

our life-history traits, larval development time and size at

emergence, we found no significant genetic variance. Low

genetic variance in life-history traits is not unexpected

(Mousseau & Roff 1987; Price & Schulter 1991; Keller-

mann et al. 2006; McFarlane et al. 2014), and since

L. sponsa has an obligatory 1-year life cycle, strong selec-

tion on size and development time is expected. We cannot

exclude the possibility that inbreeding and genetic drift

have eroded genetic variance in some traits, especially in

the most constrained and least dense northern popula-

tions. However, the observed patterns of decreasing vari-

ance from the south towards the north are consistent with

the predictions of life-history theory we proposed. Our

analyses were based on a reasonable sample size with

appropriate replication, and in all fitted mixed models, all

effects were identifiable and not confounded with other

terms. Thus, the low power of applied statistical tech-

niques is unlikely to bias the reported estimates of genetic

parameters.

Northern females oviposited larger eggs than southern

and central females. These results agree with previous

studies on egg size of ectothermic animals experiencing

different thermal conditions (Azevedo, French & Par-

tridge 1996; Blanckenhorn 2000; Fischer et al. 2004;

Śniegula, Nilsson-Örtman & Johansson 2012b). Larger

eggs might provide more resource availability, especially

during early larval growth and development (Fischer

et al. 2004; Van Doorslaer & Stoks 2005). This should

be important in species that overwinter in the egg stage

for relatively long periods, as in northern L. sponsa pop-

ulations. It has also been shown that larger eggs better

prevent embryos from dehydration (Hercus & Hoffmann

1999), and this could explain why eggs laid by southern

females had intermediate size and those laid by central

females the smallest size. Surprisingly, there was very

low additive genetic variance in egg size in the central

populations, but we have no good explanation for this.

Nevertheless, phenotypic variance in this trait was

strongly mediated by maternal effects, and we found evi-

dence that maternal effects seem to be greater for egg

volume and egg development time than for growth rate.

Egg traits are measured early in ontogeny and are there-

fore more likely to have stronger maternal effects (Mous-

seau & Fox 1998). The maternal influence on offspring

egg development and hatching might be adaptive, since

the offspring’s environmental conditions are predictable

from the mother’s past environmental conditions, based

on development stage and size in relation to the pho-

toperiod of the growth season (Bradshaw, Zani & Hol-

zapfel 2004; Uller 2008).

temperature variat ion and gene flow

Apart from different degrees of selection pressure caused

by time constraints, there are at least three additional fac-

tors that could shape the current difference in standing

genetic variance between the regions studied. First, the

variation around the mean temperature during the growth

season decreases with latitude (Fig. S1, see also Nilsson-

Örtman et al. 2012). This is because there is a very sudden

increase in temperature at northern latitudes as the

growth season starts and there are very few days with ‘in-

termediate’ temperatures for growth in spring and

autumn. This indicates that the level of intrageneration

thermal variation differs across studied populations, being

higher in the south. Hence, more variable environmental

conditions might maintain higher additive genetic variance

in the south. Secondly, temperature variation between

years is higher in the north compared to the south along

the gradient explored here (see Fig. 3a in Nilsson-Örtman

et al. (2012)). This variation among years should probably

add more genetic variation in the north but its impact

seems not to be stronger than the short growth season

and the low within season temperature variation since we

found a lower genetic variation in the north. Thirdly,

northern populations are relatively young compared to

southern ones due to the last glacial maximum, when ice

covered the northern and central parts of Europe. It is

therefore possible that northern populations have had a

relatively short time to accumulate genetic variance in

development and growth (Eckert, Samis & Lougheed

2008). Although we did not investigate whether ‒ and, if

so, to what extent ‒ there is a gene flow between studied

populations, molecular data based on neutral genetic

markers on other damselflies indicated its significance

even across the distance used in this study (Johansson

et al. 2013). Together with results from other studies, our

© 2015 The Authors. Journal of Animal Ecology © 2015 British Ecological Society, Journal of Animal Ecology

Seasonal constraints and heritable variance 9

results imply that gene flow does not inhibit local adapta-

tion to heterogeneous environments in life-history traits

(Garant, Forde & Hendry 2007; Richter-Boix et al. 2010;

Shama et al. 2011; Hassall et al. 2014), and therefore, this

third explanation seems unlikely.

novel environmental condit ions

Our second experiment showed that the degree of

expressed genetic variance differed depending on environ-

mental conditions. For example, the genetic variance for

egg development time was very low in the northern popu-

lations in native temperature and light conditions; con-

trastingly, it was much higher when constant

environmental conditions were used for these populations.

Intriguingly, southern populations showed the opposite

pattern, with genetic variation being high in their native

temperature and light conditions, but much lower in con-

stant temperature and light conditions. The constant light

conditions simulated a late season for the central and

southern populations, which stimulated much faster and

more synchronized egg development time, because the

eggs experienced the time constraint of lateness of the sea-

son. These results highlight the need to perform estimates

of genetic variation in nearly natural conditions if we wish

to establish the current genetic variance in traits. They

also highlight the need to perform estimates of genetic

variation in a novel environment if we wish to learn how

such an environment will affect the selection of traits in

focus. This point has been made several times before

(Weigensberg & Roff 1996; Hoffmann & Merilä 1999),

but here we show how genetic variation is affected at the

level of a latitudinal cline that differs in temperature and

light conditions.

Even though the northern populations harbour little

genetic variation, they possess the potential for genetic

change assuming environmental change. Had we used a

constant temperature and photoperiod, we would have

obtained completely different results with regard to

genetic variance. Such conditions may lead to the expres-

sion of hidden or cryptic genetic variance. The presence

of this genetic variance can, in some cases, facilitate adap-

tation to a changing environment (Flatt 2005), but cannot

reflect the standing genetic variance in natural conditions.

Moreover, it has been shown that the heritabilities of phe-

notypic traits can correlate with selection differentials,

suggesting that more variation may be available in years

when selection is stronger (Husby, Visser & Kruuk 2011).

Thus, although selection can limit genetic variation at

range margins, these extreme environments can also reveal

genetic variation for selection to act upon. Nevertheless,

the response to selection is dependent upon the degree of

genetic variation present, and low genetic variation at

northern range margins can result in a slower response to

selection, which may in turn limit the evolutionary

response of high-latitude populations to rapid climate

change.

conclusion

Based on the half-sib experimental design, we have shown

that presumably intense selection for rapid development

and growth at northern latitudes caused northern popula-

tions of the strictly univoltine damselfly L. sponsa to

express low additive genetic variance in life-history traits,

while central and southern populations expressed interme-

diate and high variances, respectively. Past and ongoing

selection on these traits was reflected in patterns of mean

phenotypic trait values among studied populations which

showed linear positive or negative relationships with lati-

tude, depending on the traits studied. Our accompanying

results, based on a full-sib experimental design, imply that

overall genetic variance may vary substantially with envi-

ronmental changes. Hence, artificial experiments on

genetic variance in life-history traits should be performed

in more natural regimes, preferably at naturally changing

temperatures and photoperiods.

Acknowledgements

We thank The Nature Reserve Marais du Vigueirat, France, for sampling

permission and Philippe Lambret and Viktor Nilsson-Örtman for informa-

tion about French populations. Thanks to Viktor Nilsson-Örtman for

updating the model for the simulation of water temperature. We thank

David Berger, Martin Lind and Robby Stoks for comments on the first

draft of the manuscript. S.Ś. was supported by National Science Centre

(Grant 2012/05/N/NZ8/00981 and a doctoral scholarship Etiuda 2014/12/

T/NZ8/00522) and the Institute of Nature Conservation, Polish Academy

of Sciences. M.J.G. was partially supported by the Institute of Nature

Conservation, Polish Academy of Sciences. S.M.D. was partially sup-

ported by the Jagiellonian University within the SET project co-financed

by the European Union. F.J. was supported by the Swedish Research

Council.

Data accessibility

The data are archived at the Institute of Nature Conservation, Polish

Academy of Sciences, Mickiewicza 33, 31-120 Cracow, Poland. Data avail-

able from the Dryad Digital Repository http://dx.doi.org/10.5061/

dryad.1qr1s (Sniegula et al. 2015).

References

Angilletta, M.J. (2009) Thermal Adaptation: A Theoretical and Empirical

Synthesis. Oxford University Press, New York, NY, USA.

Azevedo, R.B., French, V. & Partridge, L. (1996) Thermal evolution of

egg size in Drosophila melanogaster. Evolution, 50, 2338–2345.Banks, M. & Thompson, D. (1987) Lifetime reproductive success of

females of the damselfly Coenagrion puella. The Journal of Animal Ecol-

ogy, 56, 815–832.Barton, N.H. & Keightley, P.D. (2002) Understanding quantitative genetic

variation. Nature Reviews Genetics, 3, 11–21.Barton, N. & Partridge, L. (2000) Limits to natural selection. BioEssays,

22, 1075–1084.Berger, D., Postma, E., Blanckenhorn, W.U. & Walters, R.J. (2013) Quan-

titative genetic divergence and standing genetic (co)variance in thermal

reaction norms along latitude. Evolution, 67, 2385–2399.Blanckenhorn, W.U. (2000) Temperature effects on egg size and their fit-

ness consequences in the yellow dung fly Scathophaga stercoraria. Evolu-

tionary Ecology, 14, 627–643.Blows, M. & Hoffmann, A. (2005) A reassessment of genetic limits to evo-

lutionary change. Ecology, 86, 1371–1384.Bradshaw, W. & Holzapfel, C. (2007) Evolution of animal photoperi-

odism. Annual Review of Ecology, Evolution, and Systematics, 38,

1–25.

© 2015 The Authors. Journal of Animal Ecology © 2015 British Ecological Society, Journal of Animal Ecology

10 S. Sniegula et al.

Bradshaw, W., Zani, P. & Holzapfel, C. (2004) Adaptation to temperate

climates. Evolution, 58, 1748–1762.Bridle, J.R., Gavaz, S. & Kennington, W.J. (2009) Testing limits to adap-

tation along altitudinal gradients in rainforest Drosophila. Proceedings

of the Royal Society B: Biological Sciences, 276, 1507.

Bridle, J.R. & Vines, T.H. (2007) Limits to evolution at range margins:

when and why does adaptation fail? Trends in Ecology & Evolution, 22,

140–147.Butler, D.G., Cullis, B.R., Gilmour, A.R. & Gogel, B.J. (2009) ASReml-R

Reference Manual. VSNI International, Hemel Hempstead, UK.

Conover, D.O., Duffy, T.A. & Hice, L.A. (2009) The covariance between

genetic and environmental influences across ecological gradients. Annals

of the New York Academy of Sciences, 1168, 100–129.Cooper, G., Miller, P.L. & Holland, P.W.H. (1996) Molecular genetic

analysis of sperm competition in the damselfly Ischnura elegans (Vander

Linden). Proceedings of the Royal Society of London Series B-Biological

Sciences, 263, 1343–1349.Corbet, P. (1999) Dragonflies: Behavior and Ecology of Odonata. Harley

Books, Colchester, UK.

De Block, M. & Stoks, R. (2005) Fitness effects from egg to reproduction:

bridging the life history transition. Ecology, 86, 185–197.De Block, M. & Stoks, R. (2012) Phosphoglucose isomerase genotype

effects on life history depend on latitude and food stress. Functional

Ecology, 26, 1120–1126.De Block, M., Slos, S., Johansson, F. & Stoks, R. (2008) Integrating life

history and physiology to understand latitudinal size variation in a

damselfly. Ecography, 31, 115–123.Dijkstra, K.-D.B. (2006) Field Guide to the Dragonflies of Britain and Eur-

ope. British Wildlife Publishing, Gillingham, UK.

Dmitriew, C.M. (2011) The evolution of growth trajectories: what limits

growth rate? Biological Reviews, 86, 97–116.Eckert, C.G., Samis, K.E. & Lougheed, S.C. (2008) Genetic variation

across species geographical ranges: the central-marginal hypothesis and

beyond. Molecular Ecology, 17, 1170–1188.Etterson, J.R. (2004) Evolutionary potential of Chamaecrista fasciculata in

relation to climate change. II. Genetic architecture of three populations

reciprocally planted along an environmental gradient in the Great

Plains. Evolution, 58, 1459–1471.Fincke, O. (1984) Sperm competition in the damselfly Enallagma hageni

Walsh (Odonata, Coenagrionidae) – benefits of multiple mating to

males and females. Behavioral Ecology and Sociobiology, 14, 235–240.Fischer, K., Bot, A.N.M., Zwaan, B.J. & Brakefield, P.M. (2004) Genetic

and environmental sources of egg size variation in the butterfly Bicyclus

anynana. Heredity, 92, 163–169.Flatt, T. (2005) The evolutionary genetics of canalization. Quarterly

Review of Biology, 80, 287–316.Flatt, T. & Heyland, A. (2011) Mechanisms of Life History Evolution: The

Genetics and Physiology of Life History Traits and Trade-Offs, 1st edn.

Oxford University Press, New York, NY, USA.

Futuyma, D. (2009) Evolution. Sinauer Associates Inc., Sunderland, MA,

USA.

Garant, D., Forde, S.E. & Hendry, A.P. (2007) The multifarious effects of

dispersal and gene flow on contemporary adaptation. Functional Ecol-

ogy, 21, 434–443.Gaston, K.J. (2009) Geographic range limits: achieving synthesis. Proceed-

ings of the Royal Society B-Biological Sciences, 276, 1395–1406.Gotthard, K. (2001) Growth strategies of ectothermic animals in temper-

ate environments. Environment and Animal Development: Genes, Life

Histories and Plasticity (eds D. Atkinson & M. Thorndyke), pp. 287–303. Bios Scientific Publisher Limited, Oxford, UK.

Hassall, C., Keat, S., Thompson, D.J. & Watts, P.C. (2014) Bergmann’s

rule is maintained during a rapid range expansion in a damselfly. Global

Change Biology, 20, 475–482.van Heerwaarden, B., Kellermann, V., Schiffer, M., Blacket, M., Sgr�o,

C.M. & Hoffmann, A.A. (2009) Testing evolutionary hypotheses about

species borders: patterns of genetic variation towards the southern bor-

ders of two rainforest Drosophila and a related habitat generalist. Pro-

ceedings of the Royal Society B: Biological Sciences, 276, 1517–1526.Hercus, M.J. & Hoffmann, A.A. (1999) Desiccation resistance in inter-

specific Drosophila crosses: genetic interactions and trait correlations.

Genetics, 151, 1493–1502.Hoffmann, A. & Merilä, J. (1999) Heritable variation and evolution under

favourable and unfavourable conditions. Trends in Ecology & Evolution,

14, 96–101.

Hoffmann, A. & Parsons, P. (1997) Extreme Environmental Change and

Evolution, 1st edn. Cambridge University Press, Cambridge, UK.

Hoffmann, A.A. & Sgr�o, C.M. (2011) Climate change and evolutionary

adaptation. Nature, 470, 479–485.Husby, A., Visser, M.E. & Kruuk, L.E.B. (2011) Speeding up microevolu-

tion: the effects of increasing temperature on selection and genetic vari-

ance in a wild bird population. PLoS Biology, 9, e1000585.

Johansson, F., Stoks, R., Rowe, L. & De Block, M. (2001) Life history

plasticity in a damselfly: effects of combined time and biotic constraints.

Ecology, 82, 1857–1869.Johansson, H., Stoks, R., Nilsson-Ortman, V., Ingvarsson, P.K. & Jo-

hansson, F. (2013) Large-scale patterns in genetic variation, gene flow

and differentiation in five species of European Coenagrionid damselfly

provide mixed support for the central-marginal hypothesis. Ecography,

36, 744–755.Kellermann, V.M., Heerwaarden, B.V., Hoffmann, A.A. & Sgro, C.M.

(2006) Very low additive genetic variance and evolutionary potential in

multiple populations of two rainforest Drosophila species. Evolution, 60,

1104–1108.Lynch, M. & Walsh, B. (1998) Genetics and Analysis of Quantitative Traits.

Sinauer Associates Inc., Sunderland, MA, USA.

McFarlane, S.E., Gorrell, J.C., Coltman, D.W., Humphries, M.M., Bou-

tin, S. & McAdam, A.G. (2014) Very low levels of direct additive

genetic variance in fitness and fitness components in a red squirrel pop-

ulation. Ecology and Evolution, 4, 1729–1738.Mikolajewski, D., Johansson, F. & Brodin, T. (2004) Condition-dependent

behaviour among damselfly populations. Canadian Journal of Zoology,

82, 653–659.Mousseau, T.A. & Fox, C.W. (1998) The adaptive significance of maternal

effects. Trends in Ecology & Evolution, 13, 403–407.Mousseau, T.A. & Roff, D.A. (1987) Natural selection and the heritability

of fitness components. Heredity, 59, 181–197.Nilsson-Örtman, V., Stoks, R., De Block, M. & Johansson, F. (2012) Gen-

eralists and specialists along a latitudinal transect: patterns of thermal

adaptation in six species of damselflies. Ecology, 93, 1340–1352.Palo, J.U., O’Hara, R.B., Laugen, A.T., Laurila, A., Primmer, C.R. &

Merila, J. (2003) Latitudinal divergence of common frog (Rana tempo-

raria) life history traits by natural selection: evidence from a comparison

of molecular and quantitative genetic data. Molecular Ecology, 12,

1963–1978.Partridge, L., Barrie, B., Fowler, K. & French, V. (1994) Thermal evolu-

tion of pre-adult life history traits in Drosophila melanogaster. Journal

of Evolutionary Biology, 7, 645–663.Price, T. & Schulter, D. (1991) On the low heritability of life-history traits.

Evolution, 45, 853–861.Pujol, B. & Pannell, J.R. (2008) Reduced responses to selection after spe-

cies range expansion. Science, 321, 96–96.R Development Core Team (2014) R: A Language and Environment for

Statistical Computing. The R Foundation for Statistical Computing, Vi-

enna, Austria.

Richter-Boix, A., Teplitsky, C., Rogell, B. & Laurila, A. (2010) Local

selection modifies phenotypic divergence among Rana temporaria popu-

lations in the presence of gene flow. Molecular Ecology, 19, 716–731.Robinson, G.K. (1991) That BLUP is a good thing: the estimation of ran-

dom effects. Statistical Science, 6, 15–32.Schlichting, C.D. & Pigliucci, M. (1998) Phenotypic Evolution: A Reaction

Norm Perspective, 1st edn. Sinauer Associates Inc., Sunderland, MA,

USA.

Shama, L., Campero-Paz, M., Wegner, K., De Block, M. & Stoks, R.

(2011) Latitudinal and voltinism compensation shape thermal reaction

norms for growth rate. Molecular Ecology, 20, 2929–2941.Śniegula, S. & Johansson, F. (2010) Photoperiod affects compensating

developmental rate across latitudes in the damselfly Lestes sponsa. Eco-

logical Entomology, 35, 149–157.Śniegula, S., Johansson, F. & Nilsson-Örtman, V. (2012a) Differentiation

in developmental rate across geographic regions: a photoperiod driven

latitude compensating mechanism? Oikos, 121, 1073–1082.Śniegula, S., Nilsson-Örtman, V. & Johansson, F. (2012b) Growth pattern

responses to photoperiod across latitudes in a northern damselfly. PLoS

One, 7, e46024.

Śniegula, S., Drobniak, S.M., Gołazb, M.J. & Johansson, F. (2014) Pho-

toperiod and variation in life history traits in core and peripheral popu-

lations in the damselfly Lestes sponsa. Ecological Entomology, 39, 137–148.

© 2015 The Authors. Journal of Animal Ecology © 2015 British Ecological Society, Journal of Animal Ecology

Seasonal constraints and heritable variance 11

Sniegula, S., Golab, M.J., Drobniak, S.M. & Johansson, F. (2015) Data

from: Seasonal time constraints reduce genetic variation in life history

traits along a latitudinal gradient. Dryad Digital Repository, http://dx.

doi.org/10.5061/dryad.1qr1s.

Sokolovska, N., Rowe, L. & Johansson, F. (2000) Fitness and body size in

mature odonates. Ecological Entomology, 25, 239–248.Stearns, S.C. (1992) The Evolution of Life Histories. Oxford University

Press, New York, NY, USA.

Stoks, R. (2000) Components of lifetime mating success and body size in

males of a scrambling damselfly. Animal Behaviour, 59, 339–348.Stoks, R. & De Block, M. (2011) Rapid growth reduces cold resistance:

evidence from latitudinal variation in growth rate, cold resistance and

stress proteins. PLoS One, 6, e16935.

Stoks, R., De Block, M. & McPeek, M. (2006a) Physiological costs of

compensatory growth in a damselfly. Ecology, 87, 1566–1574.Stoks, R., De Block, M., Slos, S., Van Doorslaer, W. & Rolff, J. (2006b)

Time constraints mediate predator-induced plasticity in immune func-

tion, condition, and life history. Ecology, 87, 809–815.Teuschl, Y., Reim, C. & Blanckenhorn, W.U. (2007) Correlated responses

to artificial body size selection in growth, development, phenotypic plas-

ticity and juvenile viability in yellow dung flies. Journal of Evolutionary

Biology, 20, 87–103.Uller, T. (2008) Developmental plasticity and the evolution of parental

effects. Trends in Ecology & Evolution, 23, 432–438.Van Doorslaer, W. & Stoks, R. (2005) Growth rate plasticity to tempera-

ture in two damselfly species differing in latitude: contributions of beha-

viour and physiology. Oikos, 111, 599–605.Visscher, P.M., Hill, W.G. & Wray, N.R. (2008) Heritability in the geno-

mics era – concepts and misconceptions. Nature Reviews Genetics, 9,

255–266.Weigensberg, I. & Roff, D.A. (1996) Natural heritabilities: can they be

reliably estimated in the laboratory? Evolution, 50, 2149–2157.Wilson, J., Yin, R., Mark, A. & Agnew, A. (1991) A test of the low mar-

ginal variance (LMV) theory, in Leptospermum scoparium (Myrtaceae).

Evolution, 45, 780–784.

Received 4 February 2015; accepted 26 August 2015

Handling Editor: Jason Chapman

Supporting Information

Additional Supporting Information may be found in the online version

of this article.

Fig. S1. Mean weekly temperatures derived from the Lake Model

FLake (a), and photoperiods including morning and evening civil

twilights (b) for the three studied regions.

Table S1. Coordinates for sampled populations, estimated popula-

tion size, number of days within a year when the shallow water

temperature exceeds 10�C, mean shallow water temperature within

a growth season, number of degree days for each of the studied

populations.

Table S2.Mean (�1 SE) values of egg volume, egg development time,larval development time, larval head width and larval growth rate acrossstudied populations.

Table S3. Comparisons of linear mixed models with various

constrained (C) and unconstrained (U) structures for respective

random effects (S – sire, D – dam, R – residual).

Table S4. Comparisons of linear mixed models with various

constrained (C) and unconstrained (U) structures for respective

random effects (D – dam, R – residual).

Appendix S1. Supporting information and reference on study

organism, field sampling method, experimental set-up and statisticalanalyses.

Appendix S2. Supporting information on genetic variance and its

partitioning among studied regions.

© 2015 The Authors. Journal of Animal Ecology © 2015 British Ecological Society, Journal of Animal Ecology

12 S. Sniegula et al.