Journal of
plankt.oxfordjournals.org
J. Plankton Res. (2015) 37(2): 285– 292. First published online February 27, 2015 doi:10.1093/plankt/fbv009
Patterns of thermal limits of
phytoplankton
BINGZHANG CHEN*
STATE KEY LABORATORY OF MARINE ENVIRONMENTAL SCIENCE AND FUJIAN PROVINCIAL JOINT KEY LABORATORY FOR COASTAL ECOLOGY AND ENVIRONMENTAL
STUDIES, XIAMEN UNIVERSITY, XIAMEN, FUJIAN, P. R. CHINA
*CORRESPONDING AUTHOR: [email protected]
Received October 31, 2014; accepted February 8, 2015
Corresponding editor: John Dolan
The latitudinal patterns of the optimal, minimal and maximal growth temperatures of phytoplankton are analyzed
using linear mixed-effect models, and whether environmental temperature plays a role in affecting these thermal traits
is tested. The optimal, minimal and maximal growth temperatures of phytoplankton decrease with latitude for marine
taxa; whereas the minimal and maximal growth temperatures are relatively invariant with latitude for freshwater
phytoplankton. The thermal breadth, defined as the range between the maximal and minimal growth temperature, is
larger for freshwater than marine phytoplankton. In contrast to Jenzen’s rule, there is no increasing trend of thermal
breadth with increasing latitude. For most phytoplankton, the minimal growth temperatures are lower than the lowest
environmental temperatures and the maximal growth temperatures are higher than the highest environmental temperatures. In marine phytoplankton, there is a strong phylogenetic signal in the minimal growth temperature and
hence the thermal breadth. After controlling other variables (i.e. latitude and maximal growth rate) constant, the
minimal growth temperatures of cyanobacteria and dinoflagellates are significantly higher than that of diatoms. The
thermal breadths of cyanobacteria and dinoflagellates are narrower than of diatoms. The maximal growth rate is positively correlated with thermal breadth for marine but not freshwater phytoplankton.
KEYWORDS: phytoplankton; temperature; macroecology; Janzen’s rule
I N T RO D U C T I O N
Temperature is an important factor affecting distribution
and productivity of phytoplankton (Behrenfeld et al., 2006;
Finkel et al., 2010). Species distributions often track the
changes of isotherms (Walther et al., 2002; Gregory et al.,
2009). The limits of species distribution may be determined
by physiological tolerance to temperature extremes or by indirect factors such as solar radiation, nutrients or biotic interaction that covary with temperature in aquatic ecosystems
(Brown, 1995).
Knowledge of the temperature tolerance curves or
thermal reaction norms is essential for predicting how
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organisms and ecosystems will respond to future climate
change (Follows et al., 2007; Thomas et al., 2012). While
there have been numerous studies on the effect of temperature on phytoplankton growth rate, information on
temperature limits has not been adequately synthesized.
Classic functions describing the dependence of phytoplankton growth rate on temperature are often monotonous (e.g. exponential or power law) (Eppley, 1972; Brown
et al., 2004), without taking into account the effect of temperature extremes. While some studies focus on the
optimal growth temperature of phytoplankton (Thomas
et al., 2012), there is little information on the temperature
limits of phytoplankton although its importance has long
been recognized (Li, 1980, 1985). Analogous to optimal
growth temperatures, temperature limits can also be viewed
as species functional traits that might be useful in understanding ecological responses to environmental factors.
In this paper, I focus on the latitudinal distributions of
temperature limits of phytoplankton, by analyzing a
dataset compiled from laboratory culture experiments
using linear mixed-effect models. I evaluate three hypotheses related to the patterns of phytoplankton thermal
limits. The first is whether the environmental temperatures affect phytoplankton thermal limits. Secondly,
Janzen’s rule states that the thermal breadth often
increases with latitude owing to the fact that the minimal
growth temperature decreases with latitude and upper
temperature limits are relatively constant with latitude
(Addo-Bediako et al., 2000; Deutsch et al., 2008; Gaston
et al., 2009). Li (Li, 1985) used a small dataset to show
that the thermal breadth of phytoplankton photosynthetic rate seems to increase with increasing latitude. It
remains unknown whether phytoplankton will follow
similar patterns in larger, more comprehensive datasets.
The third question is whether there is a tradeoff between
maximal growth rate and thermal breadth. A reasonable
hypothesis, based on the supposed tradeoff between the
evolution of an optimal enzyme structure under the optimal
temperature and enzyme functions under non-optimal temperatures, is that a generalist that can survive under a wide
range of temperatures should grow slower than a specialist
at the optimal temperature of the specialist (Levins, 1964).
However, Huey and Hertz (Huey and Hertz, 1984) show a
counter-example in lizards whereby the fastest species can
also tolerate the broadest thermal ranges. What patterns
exist for phytoplankton remains an interesting problem.
METHOD
I compiled from the literature the data of phytoplankton
growth rates cultured at different temperatures under
optimal light and nutrient conditions (see Supplementary
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Appendix). This dataset merged four previous data
compilations (López-Urrutia et al., 2006; Rose and
Caron, 2007; Bissinger et al., 2008; Thomas et al., 2012),
with additional data from recently published literature.
The optimal growth temperature was roughly determined as the temperature corresponding to the highest
growth rate when there is a unimodal relationship
between temperature and growth rate. The minimal
growth temperature (i.e. the lower end of the temperature
limit) was determined as the highest temperature above
which the growth rate was positive and the maximal
growth temperature (i.e. the upper temperature limit) was
determined as the lowest temperature below which the
growth rate was positive. This definition minimizes the
problem that there could be a sharp increase (or decrease) of growth rate from zero (or a significant positive
value) to a significant positive value (or zero) between two
adjacent experimental temperatures. The thermal breadth
of phytoplankton was determined as the range between
maximal and minimal growth temperature. I also recorded
the coordinates of the location where the species were isolated (Fig. 1) and estimated the annual mean temperature
as well as minimum and maximum temperatures of these
locations based on the World Ocean Atlas 2009 (http
://www.nodc.noaa.gov/OC5/WOA09/pr_woa09.html)
with the method of k-nearest neighbor classification
using the function “knn” in the R package “class”. Cell
volume was also recorded. When cell volume was not
reported in the primary literature, the volume of the
same species was taken from the secondary literature.
I used the latitude, cell volume and phylum as fixedeffect predictors for the optimal, minimal and maximal
growth temperatures, collectively referred to as “thermal
traits” in the following text. Because the within-species
variations of the thermal traits (i.e. multiple measurements for one species are common) are significantly
lower than among-species based on F-tests (P , 0.05),
mixed-effect linear regression models were applied to
allow random variations among species, which were
implemented with the function “lme” in the R package
“nlme” (Pinheiro and Bates, 2000). I used a second-order
polynomial to fit the relationship between the optimal,
minimal and maximal temperatures and latitude, in
addition to the linear effects of log-transformed cell
volume and phylum (Table I). Higher orders of polynomials were also tried but were found unnecessary. I used
a mixed-effect generalized additive model with a penalized cubic regression spline to fit the latitudinal distribution of thermal breadth that was more complex than the
simple unimodal pattern. This was implemented with
the function “gamm” in the R package “mgcv” (Wood,
2006). I also tried a fourth-order polynomial, which gave
similar results. The maximal growth rate was also considered
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Fig. 1. A map showing the locations where the phytoplankton taxa were originally isolated. The filled circles are for marine species and the open
triangles are for freshwater species. The contours indicate the annual mean sea surface temperature (8C) estimated from the World Ocean Atlas
(WOA) 2009 climatology.
Table I: Regression models and relevant statistics for the optimal (Top, 8C), minimal (Tmin, 8C) and
maximal (Tmax, 8C) growth temperatures and derived thermal breadths ( Tb ¼ Tmax 2 Tmin, 8C).
Optimal temperature
Minimal temperature
Maximal temperature
Thermal breadth
Models
N
SDR
Marine: Top(i,j) ¼ 20.7 þ 0.077Lat – 0.0033Lat2 þ 0.66 ln(Vol) þ Phylumj þ 1i,sp
Marine: Top(i,j) ¼ 1.1 þ 2.51Ta – 0.118Ta2 þ 0.002Ta3 þ 0.64 ln(Vol) þ Phylumj þ 1i,sp
Freshwater: Top ¼ 27.0–0.0043Lat – 0.0015Lat2
Freshwater: Top( j) ¼ 13.6 þ 0.46Ta þ Phylumj
Marine: Tmin(i,j) ¼ 9.5 þ 0.035Lat – 0.003Lat2 þ Phylumj þ 1i,sp
Marine: Tmin(i,j) ¼ 0.35 þ 0.40Tl þ Phylumj þ 1i,sp
Marine: Tmax(i,j) ¼ 33.6 þ 0.068Lat – 0.005Lat2 þ Phylumj þ 1i,sp
Marine: Tmax(i) ¼ 8.7 þ 1.6Th – 0.028T2h þ 1i,sp
Freshwater: Tmax ¼ 26.9 þ 0.21Th þ Phylumj
Marine: Tb(i,j) ¼ s(Lat) þ Phylumj þ 1.49 ln(mm) þ 1i,sp
272
272
64
64
125
125
158
158
45
93
3.5
3.4
3.3
2.8
4.1
3.9
0.4
Only three significant regression models (two for optimal growth temperature and one for maximal temperature) are given for freshwater phytoplankton.
N, number of observations; Sdr, standard deviation of the random effect; Ta, environmental annual mean temperature; Tl, the lowest environmental
temperature; Th, the highest environmental temperature; Top(i,j), the optimal growth temperature of a taxon belonging to the ith species of the jth phylum.
Similar meanings apply to Tmin(i,j); Tmax(i,j), and Tb(i,j). 1i,sp, the random deviation from the mean of the ith species; Vol, cell volume (mm3).
as a predictor for thermal breadth based on the consideration of the potential tradeoff between maximal growth
rate and thermal breadth mentioned above. All the analysis and graph plotting were conducted with R 3.1.1 (R
Core Team, 2014).
R E S U LT S
In total, I compiled 339 phytoplankton taxa including 275
marine and 64 freshwater. The marine taxa include 116
diatoms, 56 dinoflagellates, 44 cyanobacteria, 28 haptophytes, 14 raphidophytes, 10 chlorophytes and a few others.
The freshwater ones were dominated by cyanobacteria (44
taxa).
The optimal growth temperatures were highly correlated with maximal growth temperatures (Pearson r ¼
0.56 and 0.92, respectively, for freshwater and marine
taxa, P , 1024), but were only weakly or insignificantly
correlated with minimal growth temperature (Pearson
r ¼ 0.06 and 0.18 and P ¼ 0.76 and 0.05, respectively,
for freshwater and marine taxa). In marine taxa, the
thermal breadth was more strongly correlated with
minimal growth temperature (Pearson r ¼ -0.72, P ,
10215) than with maximal growth temperature (Pearson
r ¼ 0.29, P , 1022), implying that it is the minimal temperature rather than the maximal temperature that more
likely determines thermal breadth. In marine phytoplankton, thermal breadth was highly positively correlated with maximal growth rate (r ¼ 0.68, P , 10214).
Cell size was not correlated with any of the thermal traits.
The optimal growth temperatures of both marine and
freshwater phytoplankton decreased from the equator
to high latitudes and this decreasing trend of marine
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phytoplankton was more drastic than that of freshwater
phytoplankton (Fig. 2A). There was a positive trend
between optimal temperature and cell volume for marine
taxa when controlling latitude constant (Table I; P ,
0.01). For marine phytoplankton, after controlling latitude and cell volume constant, the average optimal temperature of cyanobacteria was significantly higher than
that of diatoms (t ¼ 2.77, P , 0.05), while the optimal
temperatures of other phyla were not significantly different from that of diatoms. In freshwater phytoplankton,
no significant effects of cell size or phylum on optimal
temperature could be found. The likelihood ratio test
suggested that the random variations of optimal temperature were significant for marine taxa (likelihood ratio ¼
25.8, P , 0.001), but not for freshwater taxa (P . 0.05).
The latitudinal patterns of minimal and maximal
growth temperatures of marine phytoplankton were
similar to those of optimal temperatures (Fig. 2B and D).
Cell size did not play a role in affecting minimal and
maximal growth temperatures of marine or freshwater
phytoplankton. The signal of phyla was evident for
minimal growth temperatures of marine phytoplankton
(Table I). After controlling latitude constant, the minimal
growth temperatures of dinoflagellates and cyanobacteria
were significantly higher than those of diatoms (P ,
0.001; Fig. 2C). The maximal growth temperature of
chlorophytes was also significantly higher than that of
diatoms (P , 0.001). For freshwater phytoplankton, the
patterns were not evident. Visual inspection suggested
that the maximal growth temperatures of freshwater
phytoplankton were often higher than those of marine
ones (Fig. 2D). The minimal and maximal growth temperatures of freshwater phytoplankton were relatively invariant with latitude (Fig. 2B and D).
The thermal breadth of marine phytoplankton did not
evidently increase with latitude as Jenzen’s rule predicts
(Fig. 2E). When thermal breadth was calculated as the
difference between minimal and maximal temperatures
that were predicted based on fixed effects of the linear
mixed-effect models in Table I, there was even a trend
that the thermal breadth peaked at the equator and
decreased to high latitudes (Fig. 2F).
Maximal growth rate increased with thermal breadth
irrespective of whether latitude and phylum were controlled (Fig. 2G). Holding latitude and maximal growth
rate constant, the thermal breadths of dinoflagellates,
cyanobacteria and haptophytes were significantly higher
than those of diatoms (P , 0.001; Fig. 2H).
Freshwater phytoplankton could tolerate wider temperature ranges than marine taxa (Fig. 2E). The positive
relationship between maximal growth rate and thermal
breadth did not exist in freshwater phytoplankton
(Fig. 2F). I also examined the relationships between
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environmental temperature extremes and thermal limits
of phytoplankton growth to assess to what extent the
thermal limits could be affected by environmental temperature extremes (Fig. 3). The optimal growth temperatures of both marine and freshwater phytoplankton
increased with environmental annual mean temperature
(Table I; Fig. 3A) with the trend more pronounced for
marine phytoplankton. The relationship between optimal
temperature of marine phytoplankton and environmental mean temperature was well fit by a three-order polynomial, confirming the conclusion that species optimal
growth temperatures could be lower than annual mean
temperature in the tropics (Thomas et al., 2012) (Fig. 3A).
The minimal growth temperatures of marine taxa
increased linearly with, and were usually lower than, environmental lowest temperatures. The phylogenetic
signal was still evident in the minimal growth temperatures. When controlling environmental lowest temperatures constant, the average minimal growth temperatures
of marine chlorophytes, cyanobacteria, dinoflagellates
and raphidophytes were all significantly higher than
those of diatoms (P , 0.01). The maximal growth temperatures of both marine and freshwater phytoplankton
increased significantly with environmental highest temperature, with the trend of marine phytoplankton much
more drastic than of freshwater ones (Fig. 3C). The
maximal growth temperatures of freshwater phytoplankton were significantly higher than marine ones and were
much higher than environmental highest temperatures,
particularly for polar taxa (Fig. 3C). There were no clear
trends of thermal breadths of phytoplankton with environmental temperature ranges (Fig. 3D). Again, the phylogenetic differences were pronounced in the thermal
breadth of marine phytoplankton. The thermal breadths
of cyanobacteria, dinoflagellates, and haptophytes were
significantly lower than those of diatoms (P , 0.001).
DISCUSSION
This study provides a macroscopic pattern of thermal
limits of phytoplankton growth, which has not been
thoroughly investigated previously. Although this dataset
is compiled from the datasets of four comprehensive
studies, there are still limitations associated with this dataset. There are insufficient observations of freshwater taxa,
particularly in the tropics. And the freshwater taxa are
over-represented by cyanobacteria. Therefore, the analysis
and discussion mainly focuses on marine taxa.
The null hypothesis of the first hypothesis is that the
environment does not affect the thermal traits of phytoplankton. The fact that the optimal, minimal and
maximal growth temperatures are highly correlated with
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Fig. 2. (A) Optimal growth temperatures versus latitude. The solid line represents the regression line for a marine diatom with a cell volume of
500 mm3 and the dashed line is for freshwater cyanobacteria. The thin dotted lines represent 95% confidence intervals. (B) Minimal growth
temperatures versus latitude. (C) Taxonomic differences of minimal growth temperatures at 08N. The circles represent average values for each group
and the horizontal lines represent 95% confidence intervals. Chloro, chlorophytes. Cyano, cyanobacteria; Dino, dinoflagellates; Hapto,
haptophytes; Raphi, raphidophytes. (D) Maximal growth temperatures versus latitude. (E) Thermal breadth versus latitude. The thick solid line
represents a cubic spline fitted for a marine diatom with maximal growth rate of 1 d21 (F) Thermal breadth versus maximal growth rate. The fitted
line is for a marine diatom at 08N. (G) Taxonomic differences of thermal breadth when fixing latitude at 08N. (H) Estimated latitudinal patterns of
thermal breadth based on modeled minimal and maximal growth temperatures for three marine phyla.
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Fig. 3. Scatterplots of (A) optimal growth temperature versus environmental annual mean temperature, (B) minimal growth temperature versus
environmental lowest temperature, (C) maximal growth temperature versus environmental highest temperature, and (D) thermal breadth versus
environmental temperature range. Other symbols similar to Fig. 2.
latitude (Fig. 2) rejects the null hypothesis and suggests
that the environmental temperature of the isolation
locale should be an important factor shaping phytoplankton physiology. It is noteworthy that this evident pattern
exists for phytoplankton taxa that have been possibly cultivated in the laboratory after multiple generations, suggesting that the thermal characteristics can be inherited
for a relatively long period. We need to recognize that the
thermal limits obtained from temperature tolerance curves
represent the most extreme temperatures that phytoplankton species can tolerate; while in nature, phytoplankton experience high mortality due to grazing, sinking and viral
attack (Lehman and Sandgren, 1985; Calbet and Landry,
2004), which suggests that, even at the lowest or highest environmental temperatures, the intrinsic growth rate of
phytoplankton should be positive to balance mortality.
This can well explain why we observe that the thermal
breadths tend to encompass the environmental temperature range (Fig. 3).
It is unclear why phytoplankton do not follow “Jenzen’s
rule” in the same way as terrestrial organisms (AddoBediako et al., 2000; Deutsch et al., 2008; Gaston et al.,
2009). One speculation might be because the highest temperature–latitude relationship differs between the land
and the ocean. The maximal environmental temperatures
are relatively invariant with latitude on the land so that environmental temperature ranges broaden at high latitudes
(Addo-Bediako et al., 2000). However, this is not the case
for the ocean, which has large heat storage, and the temperature variations are less abrupt than on the land.
Another factor might be also related with the high mortality rate of phytoplankton in the ocean, in which environmental temperature extremes are only correlated with, but
do not directly determine, the thermal limits of phytoplankton. We can see from Fig. 2 that the maximal growth
temperatures seem to decrease more rapidly than minimal
temperatures with increasing latitude, leading to the plausible pattern that thermal breadth of marine phytoplankton
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decreases with latitude (Fig. 2F). However, the random
effects and errors cannot be overlooked (Table I). This
pattern needs to be corroborated by more data and
requires a reasonable explanation if it really exists.
The positive correlation between maximal growth rate
and thermal breadth of marine phytoplankton, which holds
both within and among phyla, suggests that fast-growing
species have the capacity to tolerate a wider range of temperatures. One possible explanation might be that the
growth rate is determined by the concentrations of the ratelimiting enzymes [e.g. the ribulose-1,5-bisphosphate carboxylase/oxygenase (RubisCO) enzyme]. A fast-grower tends
to have higher concentrations of the key enzyme. Compared
with the slower growers, even if the same proportion of the
enzyme is denatured at extreme temperatures, it still has
more enzymes to support positive growth.
Significant differences of minimal growth temperature
and thermal breadth are found between diatoms and
other algae including cyanobacteria and dinoflagellates
(see also Moore et al., 1995; Staal et al., 2003). It is well
known that diatoms thrive in cold, turbulent and
nutrient-rich environments, and dinoflagellates and cyanobacteria are well adapted to warm and stratified oligotrophic conditions (Margalef, 1978; Li, 2002, 2009). While
it is often believed that resource availability plus grazing
impact play the dominant role in determining phytoplankton community structure and the effects of temperature are
marginal or even nonexistent, sometimes the effect of temperature in modulating the interactions between species
cannot be overlooked (Tilman et al., 1981). It is also possible
that, for diatoms, the ability to tolerate cold environments is
an evolutionary outcome of long-term adaptation to the
nutrient-rich waters that are also cold at the same time.
There are remarkable differences between freshwater
and marine taxa. Much of the difference relates to one
study (Tang et al., 1997), which has already noted the differences of thermal tolerance between polar freshwater
cyanobacteria and marine diatoms. They attribute this
difference to the relative stable marine environment and
variable freshwater environment. They also speculate
that the polar freshwater algae might have been dispersed
into the polar areas so that their thermal characteristics
are similar to the temperate taxa. This remains to be
further investigated.
The patterns reported in this study are useful for modeling phytoplankton diversity and functional groups
(Smith et al., 2014). Many ecosystem models do not take
into account the thermal limits of phytoplankton growth.
In a global modeling project, Follows et al. (Follows et al.,
2007) generate random temperature – growth curves for a
number of phytoplankton taxa. This approach is powerful but requires high computing resources. The patterns
found in this study can be used to simplify the model,
with the implicit assumption that phytoplankton can
acclimate/adapt to the thermal environment. The adaptive capacity of phytoplankton is not limited to temperature. For example, the positive correlations between
half-saturation constants of nitrate uptake and ambient
nitrate concentrations suggest phytoplankton adaptation
to the nutrient conditions (Collos et al., 2005; Smith et al.,
2009). Therefore, modelers should take into account the
adaptive capacities of organisms and be careful of assigning “constants” to represent traits of organisms across
large scales (Smith et al., 2011).
One important difference between laboratory data
and the field situation is that the growth of phytoplankton
in situ is often nutrient-limited. While the complex nutrient effects on the temperature sensitivity of phytoplankton growth are beyond the scope of this study that mainly
focuses on the thermal limits of phytoplankton growth, it
is noteworthy that there are very few data of thermal
limits of in situ phytoplankton assemblages. Hence, at
present we have to rely on the laboratory data to obtain
the patterns of phytoplankton thermal limits. More
studies are needed to relate the species temperature tolerance curves with in situ distributional ranges.
AC K N OW L E D G E M E N T S
I sincerely thank one anonymous reviewer and W. K. W.
Li for constructive comments on an earlier version of the
manuscript.
FUNDING
This work was supported by National Basic Research
Program (“973” Program) of China (2015CB954003),
National Science Foundation of China (41106119 and
41376130) and Program for New Century Excellent
Talents of Xiamen University.
REFERENCES
Addo-Bediako, A., Chown, S. L. and Gaston, K. J. (2000) Thermal tolerance, climatic variability and latitude. Proc. R. Soc. B Biol. Sci., 267,
739 –745.
Behrenfeld, M. J., O’malley, R. T., Siegel, D. A. et al. (2006)
Climate-driven trends in contemporary ocean productivity. Nature,
444, 752– 755.
Bissinger, J. E., Montagnes, D. J., Sharples, J. et al. (2008) Predicting
marine phytoplankton maximum growth rates from temperature:
Improving on the Eppley curve using quantile regression. Limnol.
Oceanogr., 53, 487.
Brown, J. H. (1995) Macroecology, The University of Chicago Press, Chicago.
291
JOURNAL OF PLANKTON RESEARCH
j
VOLUME 37
Brown, J. H., Gillooly, J. F., Allen, A. P. et al. (2004) Toward a metabolic
theory of ecology. Ecology, 85, 1771– 1789.
Calbet, A. and Landry, M. R. (2004) Phytoplankton growth, microzooplankton grazing, and carbon cycling in marine systems. Limnol.
Oceanogr., 49, 51–57.
Collos, Y., Vaquer, A. and Souchu, P. (2005) Acclimation of nitrate
uptake by phytoplankton to high substrate levels. J. Phycol., 41,
466–478.
Deutsch, C. A., Tewksbury, J. J., Huey, R. B. et al. (2008) Impacts of
climate warming on terrestrial ectotherms across latitude. Proc. Natl
Acad. Sci. USA, 105, 6668–6672.
Eppley, R. W. (1972) Temperature and phytoplankton growth in the sea.
Fishery Bull., 70, 1063– 1085.
Finkel, Z. V., Beardall, J., Flynn, K. J. et al. (2010) Phytoplankton in a
changing world: cell size and elemental stoichiometry. J. Plankton Res.,
32, 119–137.
Follows, M. J., Dutkiewicz, S., Grant, S. et al. (2007) Emergent biogeography of microbial communities in a model ocean. Science, 315,
1843– 1846.
Gaston, K. J., Chown, S. L., Calosi, P. et al. (2009) Macrophysiology: a
conceptual reunification. Am. Nat., 174, 595–612.
Gregory, B., Christophe, L. and Martin, E. (2009) Rapid biogeographical plankton shifts in the North Atlantic Ocean. Global Change Biol.,
15, 1790– 1803.
Huey, R. B. and Hertz, P. E. (1984) Is a jack-of-all-temperatures a
master of none? Evolution, 38, 441–444.
Lehman, J. T. and Sandgren, C. D. (1985) Species-specific rates of
growth and grazing loss among freshwater algae. Limnol. Oceanogr., 30,
34– 46.
j
NUMBER 2
j
PAGES 285 – 292
j
2015
López-Urrutia, Á., San Martin, E., Harris, R. P. et al. (2006) Scaling the
metabolic balance of the oceans. Proc. Natl Acad. Sci. USA, 103,
8739–8744.
Margalef, R. (1978) Life-forms of phytoplankton as survival alternatives
in an unstable environment. Oceanologica Acta, 1, 493– 509.
Moore, L. R., Goericke, R. and Chisholm, S. W. (1995) Comparative
physiology of Synechococcus and Prochlorococcus: influence of light and
temperature on growth, pigments, fluorescence and absorptive properties. Mar. Ecol. Prog. Ser., 116, 259–275.
Pinheiro, J. C. and Bates, D. M. (2000) Mixed-effects Models in S and
S-PLUS. Springer, New York, USA.
R Core Team. (2014) R: A language and environment for statistical
computing. R Foundation for Statistical Computing, Vienna, Austria.
URL http://www.R-project.org/.
Rose, J. M. and Caron, D. A. (2007) Does low temperature constrain the
growth rates of heterotrophic protists? Evidence and implications for
algal blooms in cold waters. Limnol. Oceanogr., 52, 886– 895.
Smith, S. L., Merico, A., Wirtz, K. W. et al. (2014) Leaving misleading
legacies behind in plankton ecosystem modeling. J. Plankton Res., 36,
613 –620.
Smith, S. L., Pahlow, M., Merico, A. et al. (2011) Optimality-based
modeling of planktonic organisms. Limnol. Oceanogr., 56, 2080– 2094.
Smith, S. L., Yamanaka, Y., Pahlow, M. et al. (2009) Optimal uptake
kinetics: physiological acclimation explains the pattern of nitrate
uptake by phytoplankton in the ocean. Mar. Ecol. Prog. Ser., 384, 1–12.
Staal, M., Meysman, F. J. and Stal, L. J. (2003) Temperature excludes
N2-fixing heterocystous cyanobacteria in the tropical oceans. Nature,
425, 504– 507.
Levins, R. (1964) Evolution in Changing Environments. Princeton University
Press, Princeton.
Tang, E. P. Y., Tremblay, R. and Vincent, W. F. (1997) Cyanobacterial
dominance of polar freshwater ecosystems: are high-latitude matformers adapted to low temperature? J. Phycol., 33, 171– 181.
Li, W. K. W. (1980) Temperature adaptation in phytoplankton: cellular
and photosynthetic characteristics. In Primary Productivity in the Sea.
Springer, USA, pp. 259–279.
Thomas, M. K., Kremer, C. T., Klausmeier, C. A. et al. (2012) A global
pattern of thermal adaptation in marine phytoplankton. Science, 338,
1085–1088.
Li, W. K. W. (1985) Photosynthetic response to temperature of marine
phytoplankton along a latitudinal gradient (168N to 748N). Deep-Sea
Res I, 32, 1381– 1391.
Tilman, D., Mattson, M. and Langer, S. (1981) Competition and nutrient
kinetics along a temperature gradient: An experimental test of a mechanistic approach to niche theory. Limnol. Oceanogr., 26, 1020–1033.
Li, W. K. W. (2002) Macroecological patterns of phytoplankton in the
northwestern North Atlantic Ocean. Nature, 419, 154–157.
Walther, G.-R., Post, E., Convey, P. et al. (2002) Ecological responses to
recent climate change. Nature, 416, 389–395.
Li, W. K. W. (2009) From cytometry to macroecology: a quarter century
quest in microbial oceanography. Aquat. Microb. Ecol., 57, 239– 251.
Wood, S. N. (2006) Generalized Additive Models: An Introduction with R.
Chapman and Hall, Boca Raton, Florida, USA.
292