doi:10.3402/polar.v34.23947


R E S E A R C H / R E V I E W A R T I C L E

The effect of temperature on egg development rate and hatching
success in Calanus glacialis and C. finmarchicus
Agata Weydmann,1,2 Adrian Zwolicki,3 Krzysztof Muś4 & Sławomir Kwaśniewski1

1 Institute of Oceanology, Polish Academy of Sciences, Powstancow Warszawy St. 55, PL-81-712 Sopot, Poland
2

Institute of Oceanography, University of Gdansk, Piłsudskiego 46, PL-81-378, Gdynia, Poland
3 Department of Vertebrate Ecology and Zoology, University of Gdansk, Wita Stwosza 59, PL-80-308, Gdansk, Poland
4

Bird Migration Research Station, University of Gdansk, Wita Stwosza 59, PL-80-308, Gdansk, Poland

Keywords

Zooplankton; Arctic; climate change;

tipping point; Bĕlehrádek’s temperature

function; Bayesian statistics.

Correspondence

Agata Weydmann, Institute of Oceanography,

University of Gdansk, Piłsudskiego 46,

PL-81-378, Gdynia, Poland.

E-mail: agataw@ug.edu.pl

Abstract

The pelagic copepods Calanus glacialis and C. finmarchicus are important com-

ponents of Arctic marine ecosystems. Projected climate warming may influence

the roles they play in the ecosystem. Arctic C. glacialis and boreal C. finmarchicus

eggs were incubated at temperatures of 0, 2.5, 5, 7.5 and 108C to investigate the
effects of increasing temperature on egg development rate and hatching success.

The effect of increasing temperature on median development time, described by

Bĕlehrádek’s temperature function, was examined using a Bayesian approach.

For the studied temperature range, we observed the increase of egg develop-

ment rates with the increasing temperature, although there was no change in

hatching success. Calanus finmarchicus eggs hatched significantly faster than C.

glacialis above approximately 28C; the difference was progressively larger at
higher temperatures. This may indicate that the boreal species have physiolo-

gical advantages in areas where ambient temperatures increase, which may lead

to C. finmarchicus outcompeting the Arctic species in situations where timing

is important, for example, in relation to spring bloom dynamics. Development

time to hatching (DH) was evaluated using Bĕlehrádek’s model and a set of

different assumptions. The models that best fitted our data were those with

species-specific parameters: DH (h) �5940 (T�9.7)�1.63 for C. finmarchicus and
DH (h) �14168 (T�14)�1.75 for C. glacialis.

Calanus species are among the key components of Arctic

ecosystems on account of their high biomass and their

role in food chains. They are predominant grazers and

important prey items for other zooplankton species, fish

and seabirds such as the little auk (Alle alle) (Karnovsky

et al. 2003; Falk-Petersen et al. 2007). Calanus fin-

marchicus (Gunnerus 1770) is a boreal species that is

widely distributed in the North Atlantic and European

Arctic (Jaschnov 1970; Planque & Batten 2000; Weydmann

et al. 2014). In the Barents and Norwegian seas, as well

as in Disko Bay, it has a one-year life cycle, and spawns

during or just after the spring phytoplankton bloom,

which occurs in these areas between March and May

(Niehoff et al. 2002; Madsen et al. 2008). Its sibling

species Calanus glacialis (Jaschnov 1955), which is an

Arctic species, spawns on shelves, along continental

slopes and in fjords (Jaschnov 1970; Conover 1988;

Weydmann et al. 2013). It uses its internal lipid reserves

to develop gonads before the bloom, but requires energy

from either ice-algae or phytoplankton blooms to max-

imize egg production rates (Hirche & Kattner 1993;

Madsen et al. 2008; Søreide et al. 2010).

If the present trend of climate warming continues,

current knowledge predicts this will have a substantial

impact on the functioning of marine ecosystems in the

Arctic (Wassmann 2011). Temperature affects every aspect

of zooplankton life, from cellular processes to behaviour,

development and fitness (Huntley & Lopez 1992; Clarke

2003), and warming beyond a species’ thermal tolerance

could modify these aspects. For example, changes in

Polar Research 2015. # 2015 A. Weydmann et al. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0
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original work is properly cited.

1

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development time can lead to a tipping point in the

structure and functioning of Arctic marine ecosystems

(Carstensen & Weydmann 2012). Increasing temperatures

may especially influence the Arctic species C. glacialis,

whose egg production rate was found to stabilize at 2.58C
(Kjellerup et al. 2012) and eventually decrease in tem-

peratures above 58C (Pasternak et al. 2013). At the same
time, the presence and biomass of this species in the

Nordic seas was observed to decrease with ambient

temperatures above the critical threshold of ca. 68C
(Carstensen et al. 2012). Additionally, a positive balance

between ingestion and respiration was found to cease

above the same (58C) temperature threshold (Alcaraz
et al. 2013). None of the above effects was observed in

parallel studies concerning the boreal C. finmarchicus at

these temperatures (Carstensen et al. 2012; Pasternak

et al. 2013).

The early development of Calanus species has been

studied in different aspects, such as the influence of

temperature (McLaren et al. 1969; Corkett et al. 1986);

both temperature and food availability (Campbell et al.

2001; Cook et al. 2007) and pyrene exposure and tem-

perature (Grenvald et al. 2013), although the authors did

not focus on the problem of egg developmental time and

hatching success under extreme temperatures. Devel-

opment rates are routinely described by Bĕlehrádek’s

(1935) temperature function: DH �a (T � a)b, where DH
(development time to hatching) is the egg development

time to hatching, T is the temperature and a, b and a
are fitted parameters defining the properties of species

response curves.

The aim of this study was to investigate the effects of

temperature on Arctic C. glacialis and boreal C. finmar-

chicus egg development rate and hatching success, in

order to examine the implications of a warming Arctic

scenario. Another goal was to examine the correctness

of Bĕlehrádek’s temperature function parameters, known

from earlier studies, when used for the experimentally

obtained data set over a relatively large temperature

range.

Materials and methods

Zooplankton collection and experiments

Zooplankton were collected from vertical tows with a

Multi Plankton Sampler (Hydro-Bios; Kiel, Germany) or

a WP-2 type net (Hydro-Bios) using a large volume cod

end bucket (both nets with 0.180 mm mesh size) during

the cruise of the RV Jan Mayen (currently RV Helmer

Hanssen) to the north-west Barents and north-east

Greenland seas in June 2009 (four sampling stations).

In April�May 2010 we used a WP-2 net (mesh size
0.180 mm) in Hornsund, a fjord located at the south-

western tip of Spitsbergen. In 2009, the mean water

temperatures at the zooplankton sampling depths ranged

from 1.288C (station ATP 3; 778 08.6?N 288 11.0?E;
190 m) to 2.668C (station ATP 6; 798 56.0?N 148 43.1?E;
123 m), whereas in 2010 in Hornsund it was 0.38C.
The mean chlorophyll a concentrations in the 0�100 m
water layer in 2009, ranged from 0.89 (station ATP 7;

768 29.7?N 288 02.0?E; 142 m) to 1.17 mg m�3 at station
ATP 6. In 2010 the samples were collected from 50 m to

the surface at two stations, and chlorophyll a was not

measured.

The experiments in 2009 and 2010 were performed

aboard the RV Jan Mayen and at the Polish Polar Station

in Hornsund, respectively, as a series of incubations at

temperatures of 08C, 2.58C, 58C, 7.58C and 108C. Females
of Calanus glacialis and C. finmarchicus, sorted out from the

zooplankton samples at the given stations, were trans-

ferred simultaneously to 250 ml beakers filled with

the filtered seawater. The beakers were equipped with

suspended inserts, which had mesh on the bottom,

allowing the eggs to pass through, while keeping the

females away from them and were reared in tempera-

tures close to ambient (in 2009 T �2.58C, in 2010
T �08C) for egg shedding. In 2009 we collected eggs
from 69 females of C. finmarchicus and 196 of C. glacialis,

while in 2010 it was 19 and 32, respectively. Usually five

females were placed in one beaker; they were not fed and

kept until eggs were produced. The healthy-looking eggs

were collected from the rearing beakers with females

every 12 h, transported gently with a pipette to separate

Petri dishes and incubated in laboratory coolers (type

CHL 1B) at the experimental temperatures. To reduce

the effects of females or in situ environmental conditions

on the results, the eggs collected from groups of females

from the same station were mixed and split into separate

dishes (25 eggs per dish for C. finmarchicus and 30 eggs per

dish for C. glacialis), which were set at all experimental

temperatures. In total, we used 54 Petri dishes of 60 ml

volume for C. finmarchicus (14 in 2009 and 40 in 2010)

and 104 for C. glacialis (31 in 2009 and 73 in 2010); they

were equally distributed among temperatures and treated

as replicates in the following statistical analyses. The time

‘‘0’’ in our experiment is understood as the beginning of

the egg incubation period, not as the exact time of egg

release by females. The state of the reared eggs and the

number of nauplii and unhatched eggs were checked

daily, if possible, every 12 h. Incubations were continued

until all the eggs hatched or showed signs of decay, which

usually took around a week (168 h).

Calanus egg development rate and hatching success A. Weydmann et al.

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Statistical analysis

To properly evaluate all sources of uncertainty when

applying Bĕlehrádek’s temperature function to the experi-

mentally obtained data set, we decided to employ Bayesian

statistics (Brooks 2003; Clark 2005). As a programming

tool, we chose WinBUGS software, which is commonly

used in ecological studies (Lunn et al. 2000; Kery 2010;

Lunn et al. 2012).

To assess hatching success in the experimental tem-

peratures, a Bayesian linear model with three main

factors*year (Y), species (S) and temperature (T)*
and their interactions*year�species interaction (YS),
temperature�species interaction (TS), temperature�year
interaction (TY)*was performed. The dependent variable
was defined as logit(P), P �(K�1)/(N�2), where P is
the mean value of hatching success probability, K is the

number of hatched nauplii, and N is the total egg number.

In total 10 000 samples of the joint posterior distribution

from the Reversible Jump Markov Chain Monte Carlo

(RJ MCMC) simulation in three independent chains was

generated in WinBUGS software (Lunn et al. 2006; Lunn

et al. 2008). In addition, a Brooks�Gelman�Rubin (B�G�
R) test of chain convergence was successfully performed

(Brooks & Gelman 1998).

Differences in egg development times between species

and experimental temperatures were calculated for med-

ian hatching time, which was defined as the time at which

half of the eggs had hatched. All 141 time-to-hatching

log-median data were divided into 10 species-temperature

cells (two species, five experimental temperatures), and

the Jarque�Bera test of normality (Jarque & Bera 1987)
was performed for each cell. In six cells, the p-value of the

test was below or near 0.05, and six clear outliers were

removed. Repeated Jarque�Bera tests for the remaining
135 data values resulted in p-values of approximately

0.1 and above.

For each model configuration, a number of models

NM �2NP were tested in WinBUGS. NP represents the
number of free (estimated) parameters in a particular

configuration. For each parameter P, two cases were

tested: models with common values of this parameter

for both species (symbol P) and models with different

values for each species C. finmarchicus and C. glacialis

(symbol PP). For example, there are 2
NP �4 models for

NP �2 estimated parameters a and b, with symbols: aa�bb,
a�bb, aa�b and a�b.

To compare different models and select the most

plausible model, the Deviance Information Criterion,

DIC(m), was estimated using WinBUGS (Spiegelhalter

et al. 2002) for each model m of NM number of models,

following the calculation of ‘‘penalized’’ likelihood

L(m) �exp{ �0.5 �DIC(m)}. Posterior probability of model
m equals:

P mð Þ¼ L mð Þ=
XNM

m¼1
L mð Þ

Evidence in favour of model m against all other NM�1
models was calculated as follows (Jeffreys 1961):

Evid mð Þ¼ 2 � log10
P mð Þ

1 � P mð Þ

 !

Jeffreys’ qualitative scale for evidence (where evidence

50: bad model quality, �0 and B1: not substantial, ]1

and B2: substantial, ]2 and B3: strong, ]3 and B4:

very strong, ]4: decisive evidence) was also adopted

(see Lunn et al. 2012, chapter 8). The decisive level of

evidence of a certain model indicates that all remaining

models can be rejected from further analysis.

To compare the results between species with other

published data Bĕlehrádek’s (1935) temperature function

DH �a (T � a)b was used, where DH is median time to
hatching in hours, T is temperature in Celsius degrees,

and a, b and a are fitted parameters. The parameters in
Bĕlehrádek’s function define properties of species re-

sponse curves to temperature: a describes differences in

slope, b is associated with differences in monotonic cur-

velinearity within natural temperature ranges and a is a
species’ ‘‘biological zero’’ temperature (McLaren et al. 1969).

A nonlinear regression was performed using a log trans-

formed expression: loge DHð Þ¼ loge a þ b � loge T � að Þ;
parameter a was ‘‘recovered’’ by exponentiation.

The fit of Bĕlehrádek’s temperature function to the

experimental data was performed in three configurations:

(i) with forced b � �2.05 as suggested by McLaren et al.
(1969), which resulted in four models: a-( �2.05)-a,
aa-( �2.05)-a, a-( �2.05)-aa, and aa-( �2.05)-aa; (ii)
with a common parameter b estimated from data in four

models: a�b�a, aa�b�a, a�b�aa, and aa�b�aa; (iii) with
all parameters free, to determine the function that best

fitted our data in eight models: a�b�a, aa�b�a, a�b�aa,
aa�b�aa, a�bb�a, aa�bb�a, a�bb�aa, and aa�bb�aa. In
each configuration, the most probable model was selected

for further evaluation by the DIC described above. The

ratio of hatching times between species as a function of

temperature was also estimated for the model results that

gave the best model fit.

Due to a very strong correlation among the estimated

Bĕlehrádek’s model parameters, which resulted in sub-

stantial autocorrelations in most of the MCMC chains, in

each of the total 12 simulations, the following calculation

regime was applied: 20 000 initial iterations followed by

one million main iterations recorded every 100 iterations

A. Weydmann et al. Calanus egg development rate and hatching success

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(the ‘‘thin’’ value was 100), in three independent chains.

The B�G�R test of convergence was always performed.

Results

The total number of 1261 eggs of Calanus finmarchicus (501

in 2009 and 760 in 2010) and 3323 eggs of C. glacialis

(1477 in 2009 and 1846 in 2010) were selected from the

mixed egg pool and used for the following incubation ex-

periments (Table 1). Hatching occurred at all experimental

temperatures with a considerably high success (Fig. 1).

The proportion of hatched nauplii for C. finmarchicus

ranged from 74.5% at 58C in 2010 to 95.4% in the
highest temperature in 2009, whereas in C. glacialis it

ranged from 71.3% at 108C in 2009 to 92.6% at 2.58C in
2010 (Table 1). According to the Bayesian comparison

of the linear models with three main factors and the

interactions between them, which was performed using

RJ MCMC, the most probable, at almost a decisive level

of evidence (evidence �3.9, Table 2), was the null model
with no parameters. According to this analysis, the

hatching success was the same at all temperatures, and

for both years and species (see Fig. 1).

In the first configuration of the tested Bĕlehrádek’s models,

with forced b� �2.05, DH (S)�a(S) [T � a(S)]�2.05,
a reasonable fit was achieved only by the model type

aa-( �2.05)-aa (Table 3). According to this model, the
experimental results were fitted to the following hatch-

ing time functions: DH (h) �28420 (T�13)�2.05 for
C. finmarchicus and DH (h) �45838 (T�16.9)�2.05 for
C. glacialis. Similarly, in the second configuration DH

(S) �a(S) [T � a(S)]b with the value of a common para-
meter b estimated as b � �1.73 based on the data from
our experiments, only the model type aa�b�aa was quali-
fied as decisive (Table 3). In this case, the developmental

functions were DH (h) �8629 (T�10.5) �1.73 for
C. finmarchicus and DH (h) �12977 (T�13.7)�1.73 for

C. glacialis. Model aa�b�aa was much more probable than
model aa-(�2.05)-aa, with the decisive evidence criterion
value above 4.5 (difference in DIC is 10.5 in favour

of the model with common parameter b; see Table 3).

The comparison of Bĕlehrádek’s models with different

fitted parameters a, b and a resulted in the highest satis-
factory evidence (Table 3) only for the model with all

free parameters aa�bb�aa: DH (S) �a(S) [T � a(S)]b(S).
According to this model, the experimental results were

best fitted to the following hatching time functions:

DH (h) �5940 (T�9.7)�1.63 for C. finmarchicus and DH
(h) �14168 (T�14)�1.75 for C. glacialis (Fig. 2). The ratio
of hatching time between species based on the model

aa �bb�aa (Fig. 3) showed the higher rates for the Arctic
species in the lowest experimental temperatures, i.e.,

from 0 to ca. 18C. With the increasing temperature,
C. finmarchicus eggs hatched faster; taking the995%

Bayesian credibility interval into account, the difference

between species was evident above 2.68C. These results
are supported by the evidence of differences in hatching

times between species calculated for temperature inter-

vals (Table 4), which confirmed that for temperatures

between 0 and 0.78C C. glacialis egg hatching time was
shorter, whereas C. finmarchicus eggs hatched substan-

tially faster in temperatures above 2.08C; however, the
evidence was decisive only above 3.68C.

Discussion

The egg development times for both of the sibling copepod

species Calanus finmarchicus and C. glacialis, which co-

occur in the Arctic, was shorter with increasing tem-

perature but did not lead to decreased hatching success,

even at the maximum experimental temperature. Hatch-

ing was faster in higher temperatures, similar to the results

of earlier studies. The hatching rates in C. glacialis obtained

by McLaren et al. (1969) were mostly within the 995%

Table 1 Number of eggs and hatching success of Calanus finmarchicus and C. glacialis in the experimental temperatures.

Species Temperature (8C)
No. of eggs

2009

Hatching success (%)

2009

No. of eggs

2010

Hatching success (%)

2010

C. finmarchicus 0 81 84.7 101 93.1

2.5 124 93.4 138 90.6

5 85 95.2 145 74.5

7.5 126 89.4 171 76.0

10 85 95.4 205 87.8

Total N 501 91.4 760 83.8

C. glacialis 0 234 74.8 301 86.0

2.5 322 79.5 378 92.6

5 318 77.9 425 85.6

7.5 281 82.8 380 81.3

10 322 71.3 362 89.5

Total N 1477 77.6 1846 87.0

Calanus egg development rate and hatching success A. Weydmann et al.

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Bayesian credibility interval of this study (Fig. 2); hence,

they do not differ substantially. However, Grenvald et al.

(2013), in the control of their pyrene exposure experi-

ments, measured earlier median hatching times for both

species. In the case of C. finmarchicus (Marschall & Orr

1953 cited in McLaren et al. 1969), our observations

of median hatching time resulted in slightly higher but

comparable values, and this discrepancy might be due to

small differences in experimental temperatures because

the time ‘‘0’’ in our experiment was understood as the

beginning of the egg incubation period, not as the exact

time of egg shedding by females. Other factors that might

have influenced our results were: the temperature in

which females were maintained to shed eggs (2.58C
in 2009), which could accelerate egg development espe-

cially at lower temperatures, and the lower number of

C. finmarchicus females and, consequently, eggs, than of

C. glacialis.

Interestingly, we did not notice substantial differences

in egg development rates and hatching success between

Calanus females collected from different ambient condi-

tions (open sea in 2009, fjord in 2010). In addition, the

ranges in hatching times and success did not differ sub-

stantially between replicates (see the whiskers in Fig. 2).

Therefore, we believe that the locations of the sampling

stations, sea temperature, and in situ chlorophyll con-

centrations did not have any substantial effect on the

studied processes. However, since the number of eggs

produced at each station varied, similarly as measured

chlorophyll a concentrations, the results of egg-hatching

experiments could have been biased towards stations

where egg production rates were higher due to higher

food availability (Hirche & Kattner 1993).

Kjellerup et al. (2012), who examined the effects of

temperature and food for C. finmarchicus and C. glacialis

during three phases of the phytoplankton spring bloom

in Disko Bay (western Greenland), found that C. glacialis

sampled before the bloom, and incubated with excess

food, exhibited high specific egg production at tempera-

tures between 0 and 2.58C, whereas egg production for
C. finmarchicus more than tripled between 2.5 and 58C.
The authors suggested that a future warmer ocean would

reduce the advantage of early spawning by C. glacialis and

that C. finmarchicus would become increasingly prevalent.

In our experiments, the egg development time of Arctic

C. glacialis eggs was substantially longer than that of

boreal C. finmarchicus above 2.08C and on the decisive
level of evidence above 3.68C. Grenvald et al. (2013) also

Fig. 1 Relationship between the mean hatching success (%) and time (hours) in C. finmarchicus and C. glacialis in 2009 and 2010.

Table 2 Bayesian linear model of hatching success, with three main

factors and interactions performed by Reversible Jump Markov Chain

Monte Carlo. All remaining models had a zero posterior probability.

Model Probability Evidence Model quality

Null model 0.9889 3.9 Very strong

Year*species 0.0031 �5.0 Bad
Year 0.0030 �5.0 Bad
Species 0.0018 �5.5 Bad
Temperature*species 0.0015 �5.6 Bad
Temperature 0.0011 �5.9 Bad
Temperature*year 0.0005 �6.5 Bad

A. Weydmann et al. Calanus egg development rate and hatching success

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demonstrated faster median hatching times in C. glacialis

at lower temperatures (08C) and in C. finmarchicus at
higher temperatures (108C); the authors explained this
result by the different origin and specific temperature

tolerance of each species.

The observed retarding of C. glacialis egg develop-

ment rate at higher temperatures in comparison to

C. finmarchicus, may have crucial and accumulating effects

on the subsequent developmental stages. The reproduc-

tion and growth of C. glacialis is connected with the timing

of ice algal and phytoplankton blooms, therefore the

change in the current primary production regime due to

global warming, coupled with higher sea temperatures,

may lead to a mismatch between the timing of blooms

and the reproductive cycle of this key Arctic grazer, while

C. finmarchicus, which develops faster in higher tempera-

tures, may take advantage of this situation.

In comparison to other developmental stages, eggs are

particularly vulnerable to predation because they do not

have the ability to escape, and reduced hatching times

could lead to reduced predation rates and higher popula-

tion abundances. Lower egg production rates with in-

creasing temperatures (Kjellerup et al. 2012; Pasternak

et al. 2013), coupled with longer egg development could

eventually lead to decreasing numbers of the Arctic

species and shift zooplankton community structure to-

wards a smaller zooplankton size spectrum that is pre-

dominated by C. finmarchicus, with lower energy content

per individual (Falk-Petersen et al. 2007). Such changes

in the zooplankton community may have consequences

for both terrestrial and marine lipid-driven Arctic ecosys-

tems (Falk-Petersen et al. 2007; Stempniewicz et al. 2007;

Zwolicki et al. 2013).

Calanus glacialis was reported to exhibit a tipping

point at an ambient temperature of 5�68C in its oceanic
distribution (Carstensen et al. 2012), as well as in the rate

of metabolic processes (Alcaraz et al. 2013). Pasternak

et al. (2013) measured the egg production of both species,

at the same experimental temperatures as used in

this study and found that the egg production rates of

Table 3 The comparison of Bĕlehrádek’s models: with a common parameter b (forced b � �2.05 and estimated b � �1.73), with evidence �0
according to Jeffreys’ qualitative scale for evidence; and with different configurations of fitted parameters a, b and a. The best model is in boldface.

Model Deviance Information Criterion Weight Probability Odds Evidence Model quality

aa-( �2.05)-aa �215.8 266 0.9941 167 4.4 Decisive
aa-( �1.73)-aa �226.3 774 0.9970 337 5.1 Decisive
aa�bb�aa �231.5 538 0.8494 5.64 1.5 Substantial
aa�bb�a �224.1 13.2 0.0209 0.0214 �3.3 Bad
aa�b�aa �226.3 40.5 0.0640 0.0683 �2.3 Bad
aa�b�a �214.6 0.117 0.0002 0.000185 �7.5 Bad
a�bb�aa �226.3 41.1 0.0648 0.0693 �2.3 Bad
a �bb�a �217.3 0.454 0.0007 0.000717 �6.3 Bad
a�b�aa �206.6 0.0021 0.0000 3.31e�006 �11.0 Bad
a�b�a �204.5 0.000755 0.0000 1.19e�006 �11.8 Bad

Fig. 2 Relationship between median hatching time and temperature

based on the best model aa�bb�aa, with 995% Bayesian credibility
interval shown by dotted lines, and the comparison of the obtained

hatching rates with other studies. Whiskers indicate ranges between

replicates.

Fig. 3 Ratio of hatching time (HT) for C. glacialis (Cg) to HT for

C. finmarchicus (Cf) based on the best model aa�bb�aa, with 995%
Bayesian credibility interval.

Calanus egg development rate and hatching success A. Weydmann et al.

6
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Citation: Polar Research 2015, 34, 23947, http://dx.doi.org/10.3402/polar.v34.23947

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http://dx.doi.org/10.3402/polar.v34.23947


C. finmarchicus increased with temperature over the entire

tested range, but for C. glacialis the rates increased only in

the range of 0�58C and decreased with further tempera-
ture increases. Our experiments support the above find-

ings, indicating that the temperature range of 5�68C can
be suggested as a threshold value for the Arctic C. glacialis

in yet another biological characteristic that depends on

temperature, the egg development time.

The above-mentioned studies and our results identify-

ing the potential temperature tipping point are based

mainly on Calanus individuals collected in the Svalbard

area, whereas some of the identified differences between

Calanus populations, e.g., by single nucleotide polymor-

phism in C. finmarchicus from the North Atlantic (Unal

& Bucklin 2010), may have an effect on the results

obtained for individuals of populations from different

locations. It is not unlikely that these population differ-

ences could be the reason for lower median hatching

times obtained for C. glacialis and C. finmarchicus from

western Greenland (Grenvald et al. 2013).

From the tested models of Bĕlehrádek’s temperature

function, with different combination of parameters, the

model with three different parameters for each species

proved to be the most accurate. Interestingly, parameter

a, which is regarded as the species’ ‘‘biological zero’’
temperature (McLaren et al. 1969), was 3�48C lower for
the Arctic sibling in all versions of the model that we

used. The commonly used model with a forced parameter

b � �2.05 (e.g., Corkett et al. 1986; Campbell et al.
2001), which was postulated by McLaren et al. (1969) to

be applied to all copepod species, gave the worst fit to

our experimental data, especially for C. finmarchicus. We

propose new, species-specific values for parameter b in

Bĕlehrádek’s temperature function for C. finmarchicus and

C. glacialis egg hatching time, and we highlight the need

to verify the parameters of important biological func-

tions models. This issue is especially important due to

the possibility of differences in temperature adaptations

among populations of a single species over their distribu-

tion ranges, especially between regions where ambient

temperatures are notably different. It is also worth noting

that the use of Bayesian methods allowed us to not only

to compare different models describing limited experi-

mental data but also select the best-supported model

based on its statistical confidence. The methods also

allowed us to yield a more accurate estimation of the

critical value of an important biological variable.

Acknowledgements

This research was funded by the Arctic Tipping Points pro-

ject, contract no. 226248 of the Framework Program

7 of the European Union, the Polish State Committee

for Scientific Research grant no. N N304 119834 and

the Ministry of Science and Higher Education grant no.

1313/7. PR UE/2010/7.

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http://dx.doi.org/10.3402/polar.v34.23947