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Abstract

Aim: Ecological and evolutionary forces shape the functional traits of species within and across environments, generating biogeographical patterns in traits. We aimed to: (1) determine the extent to which temperature traits of phytoplankton are adapted to their local environment, and (2) detect and explain differences in patterns of adaptation between functional groups (reflecting evolutionary history) and across ecosystems (freshwater versus marine). Location: We used laboratory-measured data on phytoplankton strains isolated from marine (76° N to 75° S) and freshwater ecosystems (80° N to 78° S). Methods We studied variation in five temperature traits: optimum temperature for growth (Topt), maximum and minimum persistence temperature (Tmax, Tmin), temperature niche width and maximum growth rate, estimated in 439 strains from over 200 species. We tested whether these traits change along environmental temperature gradients (across latitude and ecosystems) and also investigated differences in trait–environment relationships related to evolutionary history (functional group identity). We used mixed models to evaluate our hypotheses while accounting for intraspecific variation. Results: We identified three patterns caused by adaptation and community assembly: (1) Topt, Tmax and Tmin decline sharply with latitude; (2) Topt, Tmax and Tmin are similar across all functional groups at the equator, where temperature variation is low; and (3) Topt and Tmax are higher in freshwater locations than marine locations at similar latitudes. Additionally, evolutionary history explained substantial variation in all traits: functional groups differ strongly in their niche widths and maximum growth rates, as well as their Topt, Tmax, and Tmin relationships with latitude. Main conclusions: Globally, phytoplankton temperature traits are well adapted to local conditions, changing across ecosystems and latitude. Functional groups differ strongly in their patterns of adaptation: traits are similar in hot tropical environments, but diverge at temperate latitudes. These differences reflect two possible evolutionary constraints: cyanobacterial inability to adapt to low temperatures and differences in nutrient requirements between groups.
RESEARCH
PAPER
Environment and evolutionary history
determine the global biogeography of
phytoplankton temperature traits
Mridul K. Thomas1,2*, Colin T. Kremer1,3† and Elena Litchman1,2
1W. K. Kellogg Biological Station, Michigan
State University, Hickory Corners, MI 49060,
USA, 2Department of Integrative Biology,
Michigan State University, East Lansing, MI
48824, USA, 3Department of Plant Biology,
Michigan State University, East Lansing, MI
48824, USA
ABSTRACT
Aim Ecological and evolutionary forces shape the functional traits of species
within and across environments, generating biogeographical patterns in traits. We
aimed to: (1) determine the extent to which temperature traits of phytoplankton
are adapted to their local environment, and (2) detect and explain differences in
patterns of adaptation between functional groups (reflecting evolutionary history)
and across ecosystems (freshwater versus marine).
Location We used laboratory-measured data on phytoplankton strains isolated
from marine (76° N to 75° S) and freshwater ecosystems (80° N to 78° S).
Methods We studied variation in five temperature traits: optimum temperature
for growth (Topt), maximum and minimum persistence temperature (Tmax,Tmin),
temperature niche width and maximum growth rate, estimated in 439 strains from
over 200 species. We tested whether these traits change along environmental tem-
perature gradients (across latitude and ecosystems) and also investigated differ-
ences in trait–environment relationships related to evolutionary history (functional
group identity). We used mixed models to evaluate our hypotheses while account-
ing for intraspecific variation.
Results We identified three patterns caused by adaptation and community assem-
bly: (1) Topt,Tmax and Tmin decline sharply with latitude; (2) Topt,Tmax and Tmin are
similar across all functional groups at the equator, where temperature variation is
low; and (3) Topt and Tmax are higher in freshwater locations than marine locations at
similar latitudes. Additionally, evolutionary history explained substantial variation
in all traits: functional groups differ strongly in their niche widths and maximum
growth rates, as well as their Topt,Tmax, and Tmin relationships with latitude.
Main conclusions Globally, phytoplankton temperature traits are well adapted
to local conditions, changing across ecosystems and latitude. Functional groups
differ strongly in their patterns of adaptation: traits are similar in hot tropical
environments, but diverge at temperate latitudes. These differences reflect two
possible evolutionary constraints: cyanobacterial inability to adapt to low tempera-
tures and differences in nutrient requirements between groups.
Keywords
Adaptation, community assembly, environmental filtering, evolutionary
constraint, functional traits, phytoplankton, selection, temperature,
trait–environment relationships.
*Correspondence: Mridul K. Thomas,
Department of Aquatic Ecology, Eawag: Swiss
Federal Institute of Aquatic Science and
Technology, Überlandstrasse 133, 8600
Dübendorf, Switzerland.
E-mail: mridul.thomas@eawag.ch
Present address: Department of Ecology and
Evolutionary Biology, Yale University, PO
Box 208106, New Haven, CT 06520, USA.
INTRODUCTION
Functional traits characterize how organisms interact with their
environment and each other, providing a firm foundation for
predictive community ecology (Lavorel & Garnier, 2002; McGill
et al., 2006; Litchman & Klausmeier, 2008; Webb et al., 2010).
They consist of measurable properties of organisms (usually
physiological or morphological) that strongly influence fitness.
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Global Ecology and Biogeography, (Global Ecol. Biogeogr.) (2016) 25, 75–86
© 2015 John Wiley & Sons Ltd DOI: 10.1111/geb.12387
http://wileyonlinelibrary.com/journal/geb 75
Consequently, they explain major ecological phenomena such
as species distributions (Cornwell & Ackerly, 2009; Edwards
et al., 2013a), seasonal succession (Edwards et al., 2013b) and
responses to global environmental change (Soudzilovskaia et al.,
2013). Environmental variation shapes trait variation, generat-
ing trait–environment relationships through a combination of
ecological and evolutionary processes. Ecologically, species may
be excluded from an environment if their traits do not allow
them to survive (abiotic filtering) or if they are outcompeted by
species with better-suited traits (biotic filtering). These pro-
cesses, jointly referred to as community assembly, favour the
presence of species with traits that are optimal for the local
environment (HilleRisLambers et al., 2012). In terms of evolu-
tion, selection shifts species’ traits towards an optimum for the
local environment. Species are impeded from reaching optimal
trait values by evolutionary constraints on adaptation, including
trade-offs between traits, limited genetic variation and genetic
correlations (Ackerly, 2003; Hendry & Gonzalez, 2008;
HilleRisLambers et al., 2012).
Trait–environment relationships can reveal both optimal
trait values for an environment and the evolutionary con-
straints that influence a taxon’s ability to reach the optimum.
Differences in these relationships can occur between species
(Prunier et al., 2012) or between higher taxonomic groups (e.g.
angiosperms and gymnosperms differ in drought resistance;
Choat et al., 2012). These differences may emerge due to dis-
tinct physiological and evolutionary constraints related to the
evolutionary histories of different taxa. For example, distantly
related taxa may occupy separate positions along a trade-off
curve, or the genetic changes required to reach the optimum
may be more complex, and therefore improbable, in some.
Trait–environment matching arising from community assem-
bly and natural selection (together referred to hereafter as
‘adaptation’) can therefore help us develop a mechanistic
understanding of ecological communities.
We focus on traits relating to temperature, a fundamental
driver of biological processes that strongly influences fitness,
causes large changes in communities and determines the
responses of organisms to climate change (Kingsolver, 2009;
Kordas et al., 2011;Poloczanska et al., 2013).The effects of tem-
perature on ectotherms are characterized by thermal reaction
norms, unimodal and negatively skewed functions that describe
how fitness changes with temperature (Fig. S1 in Appendix S1 in
Supporting Information; Martin & Huey, 2008; Kingsolver,
2009). Thermal reaction norms may be described by tempera-
ture traits, of which we consider five here – optimum tempera-
ture for growth (Topt, the temperature at which population
growth rate is highest), maximum persistence temperature
(Tmax, the temperature above which population growth rate
becomes negative), minimum persistence temperature (Tmin, the
temperature below which population growth rate becomes
negative), temperature niche width (Tmax Tmin) and maximum
population growth rate (or maximum fitness, Fig. S1 in Appen-
dix S1). Studies examining traits such as these have shown that
a variety of taxa are adapted to their local temperature condi-
tions, albeit imperfectly (Deutsch et al., 2008; Huey et al., 2009;
Clusella-Trullas et al., 2011; Sunday et al., 2011; Sunday et al.,
2012; Thomas et al., 2012; Araújo et al., 2013; Boyd et al., 2013).
This body of work has indicated that changes in temperature
regimes are likely to drive community re-organization either
through migration or extinction, particularly in tropical taxa.
Understanding how evolutionary constraints have shaped
current trait–environment relationships in multiple groups may
help us refine predictions of how environmental change will
affect the diversity and composition of communities.
Phytoplankton are particularly sensitive to changes in their
environment and play a critical role in global biogeochemical
cycles and aquatic food webs (Falkowski et al., 1998; Field et al.,
1998). They are a diverse community of autotrophs belonging to
evolutionarily distinct functional groups. These groups are not
merely united by functional similarities but are paraphyletic
and defined taxonomically, albeit at different levels of the
taxonomic hierarchy (e.g. phylum Cyanobacteria versus class
Coccolithophyceae). We therefore follow convention by refer-
ring to them as functional groups, though we use them as
proxies for shared evolutionary history. Functional groups
exhibit exceptional divergence in functional traits, ecological
strategies and contributions to biogeochemical cycling and food
webs (Table S1 in Appendix S1; Reynolds, 2006; Litchman et al.,
2007; Edwards et al., 2012). In previous work on the Topt of
marine species, we showed that phytoplankton are adapted to
local temperatures (Thomas et al., 2012). However, we do not
know if this is also true in freshwater ecosystems, which experi-
ence far greater temperature variation, or if adaptation also
influences the biogeography of other temperature traits. Most
importantly, we do not know whether functional groups differ
in their response to temperature. Studies of seasonal succession
patterns and physiology have suggested that cyanobacteria are
adapted to high temperatures (Robarts & Zohary, 1987; Kosten
et al., 2012) and that diatoms are adapted to low temperatures.
But a recent analysis of freshwater phytoplankton found no
support for differences in temperature traits between groups
(Lürling et al., 2013). A key limitation of these past studies is
that they failed to consider the influence of both environmental
gradients and evolutionary history simultaneously (but see
Thomas, 2013; Chen, 2015), possibly confounding the two.
Because some of the major phytoplankton functional groups
thrive in both marine and freshwater ecosystems, and transi-
tions between the two ecosystems are rare (Logares et al., 2009),
trait patterns in these two ecosystems are the result of semi-
independent processes of adaptation. The question of how envi-
ronmental warming will drive community re-organization in
the phytoplankton is therefore an open one. It may occur
through species replacement within functional groups or shifts
in the dominance of particular groups, with consequent changes
to biogeochemical cycling and food webs.
To understand how adaptation and evolutionary history
have jointly influenced the global biogeography of temperature
traits, we analysed the traits of 439 phytoplankton strains
using over 6000 laboratory-measured growth rates extracted
from published papers. These strains belong to approximately
240 species and were isolated across a wide range of latitudes
M. K. Thomas et al.
Global Ecology and Biogeography,25, 75–86, © 2015 John Wiley & Sons Ltd76
(80° N to 78° S; Fig. 1), two aquatic ecosystems (marine and
freshwater), and multiple functional groups (primarily
cyanobacteria, diatoms and green algae, but also dinoflagellates,
coccolithophores, non-calcifying haptophytes, desmids,
chrysophytes and raphidophytes). We examined how these three
variables interacted to determine temperature traits, using lati-
tude and ecosystem as proxies for the local temperature regime
(because temperature estimates are unavailable for most fresh-
water bodies) and functional group for evolutionary history
(due to their distinct evolutionary backgrounds and the absence
of a usable phylogeny for the phytoplankton). We expected
that the following environmental temperature patterns would
drive selection on temperature traits: (1) temperatures (mean,
maximum and minimum) decline nonlinearly from the equator
to the poles (mean temperature is a quadratic function of lati-
tude, R2=0.86, Fig. S2 in Appendix S1; Reynolds et al., 2007);
(2) temperature variability is highest at mid-latitudes and lowest
in the tropics and at the poles (Fig. S2 in Appendix S1; Reynolds
et al., 2007); and (3) at any given latitude, temperature variabil-
ity is higher in freshwater bodies than the ocean (higher
maximum and lower minimum annual temperatures), due to
the smaller size of the former. Together, these lead to eight
specific predictions relating global patterns of temperature trait
variation to environmental differences (Table 1). We tested each
of these predictions, while simultaneously examining how evo-
lutionary history has influenced adaptation.
MATERIALS AND METHODS
Data collection, trait estimation and quality control
We assembled a data set containing 6899 published laboratory
measurements of phytoplankton growth rates at different tem-
peratures from the published literature. The complete dataset, as
well as parameters describing the thermal reaction norm model
fits, trait estimates, and procedures for data collection and tem-
perature trait estimation, is included in Appendix S2. The pro-
cedures followed Thomas et al. (2012), but with additional
quality control measures undertaken to account for the diffi-
culty of estimating niche width, Tmin and Tmax values. After initial
quality control, we had data on 439 isolates belonging to
approximately 240 species from at least 261 unique isolation
locations (47 were from unknown locations). 199 isolates were
from freshwater ecosystems and 240 were marine. The isolation
locations ranged in latitude from 80° N to 78° S (Fig. 1). As
additional quality control criteria were imposed for each trait,
the sample size for different analyses ranged from 179 (for niche
width) to 392 (for maximum growth rate).
Apart from the five traits described, we also investigated curve
skewness but found no systematic variation across latitude or
functional groups. A small difference between ecosystems was
driven by differences in the average niche width between eco-
systems (Fig. S3 in Appendix S1). Methods concerning this trait
can be found in Appendix S1.
Model comparison and parameter estimation
To estimate how environment and evolutionary history influ-
enced the spatial distributions of temperature traits, we quanti-
fied the effects of functional group, ecosystem (marine versus
freshwater) and latitude on each trait. To detect differences
between traits in marine and freshwater ecosystems, we first
constructed models explaining variation in the traits of the
three functional groups well-represented in both ecosystems:
cyanobacteria, green algae and diatoms. Other functional groups
were represented almost exclusively in one ecosystem and were
excluded initially. If we detected no effect of ecosystem on a trait,
we re-ran the analyses after dropping the ecosystem term and
including data on the additional functional groups. If differences
between ecosystems were detected, we constructed separate
models for each of the additional groups. These models used the
best model identified with the three major groups but excluded
the ecosystem and group covariates, focusing solely on group
intercepts and the influence of latitude. Finally, if no relationship
between latitude and a trait was detected, we included additional
data from isolates with unknown isolation locations.
We fit mixed models to the trait data, using a random inter-
cept to account for instances where multiple strains of the same
Figure 1 Isolation locations of 392
phytoplankton strains in our dataset (an
additional 47 strains were from
unknown locations). Freshwater
locations (triangles) range from 80° N to
78° S and marine locations (circles) from
76° N to 75° S.
Phytoplankton temperature trait biogeography
Global Ecology and Biogeography,25, 75–86, © 2015 John Wiley & Sons Ltd 77
species were measured. Trait variation within species showed no
systematic trends with latitude, even in the taxon with the largest
number of measured strains (Fig. S4 in Appendix S1), indicating
that a random slope term was unnecessary. To determine the
importance and significance of covariates, we first fitted a full
model (containing interactions between latitude, group and
ecosystem) to data for each trait. We chose to only include terms
that were biologically plausible in this model. Specifically, we
excluded a linear latitude term for models involving Topt,Tmax
and Tmin, as we have strong reason to expect these to peak
around the equator, driven by the latitudinal trends in mean,
maximum and minimum temperature (Fig. S2 in Appendix S1).
However, we included the linear parameter in model compari-
son for niche width and maximum growth rate, as a peak away
from the equator was plausible in these cases.
Starting with this full model, successive comparisons were
made between complex models and simpler,nested models gen-
erated by removing one term at a time, beginning with the most
complex interaction. At each step of this process, complex and
nested models were compared using a likelihood ratio test
(LRT). The significance of the LRT was determined using para-
metric bootstrapping with 10,000 samples, thereby avoiding
assumptions of large sample sizes and asymptotic normality
required by the typical χ2distribution approximation (Halekoh
& Højsgaard, 2014). When a term had a non-significant boot-
strap P-value (>0.05), it was dropped. After considering all
interaction terms, we examined the importance of main effects,
retaining them only if they were significant or included in a
significant interaction. We examined the fit of the resulting best
model to the trait data, checking for outliers, influential points
and deviations from normality assumptions. When identified,
problematic points were removed and the model comparison
was re-run. In all cases, the best model remained the same and
parameter estimates remained very similar after the removal of
these points; we therefore retained these points and present our
original findings. With the final model for each trait determined,
we obtained 95% confidence intervals on all parameter esti-
mates using parametric bootstrapping. Lastly, we calculated the
marginal and conditional R2of the final models, which quantify
the explanatory power of the fixed effects and the combined
fixed and random effects, respectively (Nakagawa & Schielzeth,
2013). Conditional and marginal R2values for the best models
for all traits, and bootstrap P-values for their parameters are
shown in Table 2.
All analyses were performed in the R statistical environment
(R Core Team, 2013). Details of the software tools used in the
analyses may be found in Appendix S1.
RESULTS
Environment (latitude and ecosystem) and evolutionary history
(functional group) explained a large proportion of the variation
Table 1 Testing hypotheses of how selection and community assembly (abiotic and biotic filtering) shape temperature traits across
environmental gradients.
Hypothesis Prediction Supported? Figures
The position (Topt,Tmin,Tmax) of the thermal
reaction norm is influenced by the
minimum, mean and maximum
temperatures at a location through selection
and community assembly
1. Topt,Tmax and Tmin will decline with distance from
the equator, in concert with mean temperature
Yes 2–4
2. Topt and Tmax will be higher in freshwater locations
than marine ones at the same latitude, because mean
and maximum temperatures are higher in the former
Yes 2, 3
3. Tmin will be lower in freshwater locations than
marine ones at the same latitude, because minimum
temperatures are lower in the former
Only in one of three groups
(cyanobacteria)
4
The width of the thermal reaction norm
(temperature niche width) is driven to
increase in locations with more variable
temperatures through selection and
community assembly. Alternatively, in
variable environments, selection and
community assembly will favour
combinations of specialists having curves
with different Tmin,Topt and Tmax
4. Topt,T
max and Tmin will be similar across taxa in the
tropics, where temperatures are fairly constant
Yes 2–4
5. Freshwater taxa will possess wider niches than
marine taxa, as they experience more temperature
variation on average
Only in one of three groups
(cyanobacteria)
6
6. Taxa at temperate latitudes will possess wider niches
than tropical or polar taxa, because temperature
variation peaks in temperate areas
No 6
The height of the thermal reaction norm
(maximum growth rate) is always favoured to
increase. However, the response to selection
is constrained by metabolic limits and by the
lack of sufficient nutrients to support high
growth rates
7. Maximum growth rate will be highest in the tropics,
where metabolic constraints are weak
Yes (P=0.018), but weak
support for the contrasting
hypothesis below
(P=0.09)
5
8. Maximum growth rate will be highest at temperate
latitudes, where nutrients are plentiful
Weak, statistically
insignificant support
(P=0.09)
5
M. K. Thomas et al.
Global Ecology and Biogeography,25, 75–86, © 2015 John Wiley & Sons Ltd78
in all five traits and interacted to influence trait values in all but
one case. Across traits, these fixed effects explained between 28%
and 51% of the variance in our data (Table 2, marginal R2;
Nakagawa & Schielzeth, 2013). Along with the species-level
random effects, they captured 69–90% of the variance (Table 2,
conditional R2; Nakagawa & Schielzeth, 2013). The unexplained
variance may be attributable to unaccounted-for phylogenetic
relatedness, differences in study methodology, trade-offs with
traits that do not exhibit clear latitudinal patterns and error due
to the use of proxies for the thermal environment. Details of the
models, including parameter estimates and bootstrap-based
confidence intervals, can be found in Tables S2–S6 in Appendix
S1. In the subsequent sections we summarize the results of these
models, starting with the main effects of latitude, ecosystem and
functional group, and then describing interactions between
factors.
Influence of latitude
Topt,Tmax and Tmin were most strongly affected by the latitudinal
temperature gradient (Figs 2–4, Table 1). These traits are highest
at the equator and decline substantially towards the poles
(results are similar in marine locations if mean local tempera-
ture is used instead of latitude as a proxy; Fig. S5 in Appendix
S1). Their mean values decrease between 15 and 30 °C across the
latitudinal range of the data, depending on trait and functional
group. Latitude had no detectable effect on niche width (Fig. 5),
while maximum growth rate declined slightly with distance
from the equator (P=0.018; Fig. 6, Table 2). However, we
cannot rule out the possibility that maximum growth rate peaks
slightly away from the equator. Our model selection procedure
gave weak support to a linear latitude term (P=0.09); if
retained, this term implies that maximum growth rates are
highest in the subtropics. In either case, maximum growth rate
is lowest at polar latitudes.
Influence of ecosystem
The Topt and Tmax values of isolates from freshwater ecosystems
were 4 °C higher on average than from marine ones at the same
latitude (Figs 2 & 3, Table 2), consistent with our predictions
(Table 1). Tmin and niche width varied less strongly between
ecosystems. In both cases, only cyanobacteria exhibited differ-
ences between ecosystems, and these were also consistent with
our predictions (Figs 4 & 5, Tables 1 & 2). Maximum growth
rate did not differ between ecosystems in any functional group.
Influence of functional group
Functional group explains most of the variance in maximum
growth rate, with threefold variation between groups in their
average maximum growth rate (Fig. 6). Groups also differ in
niche width. Most groups have mean niche widths between 20
and 30 °C, although marine cyanobacteria have narrower niches
(16 °C) and desmids wider ones (32 °C) (Fig. 5). Topt,Tmax and
Tmin show complex interactions between functional group and
environment; these are discussed below.
Interactions between evolutionary history
and environment
Functional group (a proxy for evolutionary history) interacts
strongly with latitude to determine patterns of Topt,Tmax and
Tmin. Cyanobacteria exhibit a far smaller decline in these three
traits with increasing absolute latitude than the other two
groups (Figs 2–4). Topt values of all groups converge in the
tropics and diverge at temperate latitudes, with those of
cyanobacteria being far higher than green algae and diatoms at
high latitudes (Fig. 2). Diatoms exhibit greater declines with
latitude than both cyanobacteria and green algae in the case of
Topt and Tmin (Figs 2 & 4), while their decline in Tmax is similar to
Table 2 Summaries of the best models for all five traits. Marginal R2refers to the variance explained by fixed effects alone while
conditional R2refers to the variance explained by fixed and random effects together (see Materials and Methods). Where interactions were
found to be important, associated main effects were retained in the model but were not assessed for significance and are not displayed here.
Additional details of the models, including all parameter estimates and bootstrap-based confidence intervals, can be found in Tables S2–S6
in Appendix S1.
Trait
Marginal R2
(fixed effects)
Conditional R2
(fixed +random effects)
Model parameters in final
model Bootstrap P-value
Topt 0.51 0.84 Group ×quadratic latitude 0.002
Ecosystem <0.001
Tmax 0.47 0.90 Group ×quadratic latitude 0.001
Ecosystem 0.003
Tmin 0.49 0.82 Group ×quadratic latitude 0.01
Group ×ecosystem 0.011
Niche width 0.35 0.78 Group ×ecosystem 0.007
Maximum growth rate 0.28 0.69 Group <0.001
Quadratic latitude 0.018
Phytoplankton temperature trait biogeography
Global Ecology and Biogeography,25, 75–86, © 2015 John Wiley & Sons Ltd 79
the green algae (Fig. 3). The interaction between functional
group and ecosystem was also a significant predictor of Tmin and
niche width (Figs 4 & 5), largely due to the cyanobacteria, which
exhibit considerably higher Tmin (and hence narrower niche
widths) in marine ecosystems.
Several functional groups are poorly represented in either
marine or freshwater ecosystems and could not be included in
models looking for interactions between functional group and
ecosystem. The traits of these groups decline with increasing
latitude, as with the groups that occur in both ecosystems
described above (except for the chrysophytes, for which we have
limited data) (Figs 2–4). They also exhibit considerable differ-
ences from each other: marine coccolithophores and freshwater
desmids display the smallest declines with latitude, comparable
to the cyanobacteria, while the marine non-calcifying
haptophytes show the largest decline.
DISCUSSION
Globally, phytoplankton temperature traits have been deter-
mined by local water temperatures as well as evolutionary con-
straints; the latter become apparent only when comparing traits
of different functional groups across broad spatial scales. The
substantial differences in trait–environment relationships
between phytoplankton groups indicate that evolutionary
history has played an important role in shaping how species
adapt to their thermal environment, and will shape how these
communities respond to global change.
Temperature trait–environment relationships in phytoplank-
ton functional groups have been investigated recently (Thomas,
2013; Chen, 2015), facilitated by a compilation of temperature
trait data (Thomas et al., 2012). Chen (2015) found that func-
tional groups exhibited different relationships with latitude and
ecosystem, although no interactions were explored. However,
that study had several weaknesses that our analysis avoids. First,
it lacked clear ecological and evolutionary hypotheses (which we
present in Table 1), and therefore found statistically significant
but biologically implausible patterns. These include a fourth-
order polynomial relationship between niche width and latitude
that made different predictions for the two polar oceans.
Second, the dataset in Chen (2015) was heavily biased towards
marine species (275 out of 339 taxa), severely limiting the power
of its freshwater analyses and comparisons between freshwater
and marine patterns. Finally, of the 64 freshwater taxa, 27 were
benthic mat-forming cyanobacteria from polar environments,
not phytoplankton (data from Tang et al., 1997). These values
strongly bias the freshwater regression estimates because benthic
species experience different thermal environments, and because
these samples represent extreme latitudes. We focus on our own
results hereafter.
Out of eight predictions describing how environmental vari-
ation should affect phytoplankton temperature traits (Table 1),
we found strong support for three across functional groups, all
concerning Topt,Tmax and Tmin (Table 1, Figs 2–4). These traits
declined strongly with latitude, were more similar across taxa in
the tropics and the first two were higher in freshwater ecosys-
010203040
Diatoms
Green algae
Cyanobacteria
(a)
Diatoms
Green algae
Cyanobacteria
(b)
0 20406080
010203040
Dinoflagellates
Haptophytes
Coccolithophores
Raphidophytes
(c)
020406080
Desmids
Chrysophytes
(d)
Absolute latitude (°)
Topt (°C)
Marine Freshwater
Figure 2 Optimum temperature for
growth (Topt) decreases towards the
poles, but the rate of this decline differs
strongly between functional groups
(n=387). Also, freshwater Topt values are
4 °C higher on average than those of
marine species at the same latitude. The
best model was identified using groups
common to both ecosystems, shown in
(a) and (b). Data spread around these
regression lines indicates the proportion
of variance explained by the fixed effects
(marginal R2=0.51), but our model
explains considerably more variance by
accounting for intraspecific variation
with a random intercept (conditional
R2=0.84). Groups for which we had
data largely from one ecosystem are
shown in (c) and (d); model parameters
for these groups were estimated
separately.
M. K. Thomas et al.
Global Ecology and Biogeography,25, 75–86, © 2015 John Wiley & Sons Ltd80
tems, consistent with our hypotheses (Figs 2–4). Topt and Tmax
changed by more than 20 °C across the latitudinal gradient in
some groups, in contrast to studies suggesting that Tmax is
strongly phylogenetically conserved (Araújo et al., 2013). The
decline in Tmin across latitude was much smaller,which suggests
that there is at most a weak physiological cost to having a low
Tmin. These three traits are strongly correlated with each other,
suggesting that they are not under independent selection. Topt is
more strongly correlated with Tmax (r=0.88) than with Tmin
(r=0.65), lending support to the hypothesis that Tmin is more
evolutionarily labile than Tmax (Fig. S6 in Appendix S1; Araújo
et al., 2013; Hoffmann et al., 2013). Positive correlations
between Topt and Tmin (Figs S6 & S7 in Appendix S1), and be-
tween Tmax and Tmin (Fig. S7), may be viewed as trade-offs
between high and low temperature tolerance. However, we
believe it is better characterized as a constraint on niche width,
for reasons we discuss later. In either view, the presence of both
trait correlations and environmental correlations (high mean
temperatures are associated with high maximum and minimum
temperatures; Fig. S2 in Appendix S1) complicates attempts to
draw causal links. For example, it is difficult to address whether
distinct aspects of environmental temperature regimes drive
selection on different traits (such as maximum environmental
temperature selecting on Tmax and minimum selecting on Tmin)
or whether physiological correlations are responsible for the
similarities in pattern between traits. Both factors may play a
role, and disentangling these will require evolution experiments.
Two additional predictions were supported only among the
cyanobacteria: freshwater isolates have lower Tmin and larger
niche widths (Figs 4 & 5, Table 1). Apart from the cyanobacteria,
most taxa exhibit niche widths of 20–30 °C regardless of the
annual temperature variation they experience, which in the
tropics may be as low as 2 °C. Marine cyanobacteria may face a
cost to maintaining wide niches that the eukaryotic groups avoid
because of their large physiological differences.
Finally,our two contrasting predictions relating to maximum
growth rate each found support, though one was stronger.
Maximum growth rates are lowest at high latitudes (P=0.018)
and appear highest in the tropics, although they may actually
peak in the subtropics (P=0.09; Fig. 6, Table 1). This statistical
uncertainty means we cannot distinguish with a high degree of
confidence whether trends in maximum growth rate are driven
more by reduced metabolic constraints in the tropics or by a
combination of low nutrients in the tropics and a trade-off
between maximum growth rate and nutrient competitive ability
(Table 1). Future work may build upon our findings by model-
ling the effects of temperature and nutrient availability directly,
rather than using latitude as a proxy.
Evolutionary history is often as important in determining
traits as selection and community assembly (encompassing
abiotic and biotic filtering). Trait–environment relationships dif-
fered between functional groups in two ways: groups had distinct
mean trait values in some cases, and in others their traits differed
in the rate of change with latitude.
10 20 30 40
Diatoms
Green algae
Cyanobacteria
(a)
Diatoms
Green algae
Cyanobacteria
(b)
020406080
10 20 30 40
Dinoflagellates
Haptophytes
Coccolithophores
Raphidophytes
(c)
0 20406080
Desmids
Chrysophytes
(d)
Absolute latitude (°)
Tmax (°C)
Marine Freshwater
Figure 3 Maximum persistence
temperature (Tmax) decreases with
increasing latitude, but the rate of this
decrease differs strongly between
functional groups (n=311).
Furthermore, Tmax is higher in freshwater
strains than in marine strains at the
same latitude. The best model was
identified using groups common to both
ecosystems, shown in (a) and (b). Data
spread around these regression lines
indicates the proportion of variance
explained by the fixed effects (marginal
R2=0.47), but our model explains
considerably more variance by
accounting for intraspecific variation
with a random intercept (conditional
R2=0.90). Groups for which we had
data largely from one ecosystem are
shown in (c) and (d); model parameters
for these groups were estimated
separately.
Phytoplankton temperature trait biogeography
Global Ecology and Biogeography,25, 75–86, © 2015 John Wiley & Sons Ltd 81
Niche width and maximum growth rate fall into the first
category. Groups differ substantially in niche width (the niches
of marine diatoms are 8–10 °C wider than those of marine
cyanobacteria), which may contribute to performance
differences, particularly in variable environments. However,
niches are not wider in more variable environments (with the
exception of the ecosystem differences in cyanobacteria), con-
trary to the findings of Boyd et al. (2013), using a smaller
dataset. The absence of a latitudinal pattern is surprising,
because increases in niche width are always beneficial to an
organism: wider niches imply an increase in performance at all
temperatures on either side of Topt. On its own, this absence
might suggest that gene flow is preventing local adaptation, but
the strong latitudinal relationships we find with other traits
makes this unlikely. This suggests that there is no cost to main-
taining a wide niche, or in other words, that there is no trade-off
between niche width and any other important trait. Instead,
niche widths likely experienced positive selection until evolu-
tionary constraints prevented further increases. This is also why
we believe that the correlation between Tmax and Tmin is better
viewed as a constraint on niche width (Fig. S7 in Appendix S1).
Variation in maximum growth rate is also largely explained by
differences in group means (Fig. 6), consistent with earlier
studies (Edwards et al., 2012); latitude plays a minor role. In
addition to the functional group differences we have identified
here, maximum growth rate also scales with cell size. We will
explore the joint influences of temperature and cell size on
maximum growth rate in forthcoming work.
Evolutionary history influenced the form of trait-environment
relationships for Topt,Tmax and Tmin through an interaction
between functional group and latitude. Although these traits are
similar across groups in the tropics (where relatively constant
temperatures should select for a narrow range of traits), they
diverge rapidly with increasing latitude, especially in the case of
Topt.
If there is a single adaptive peak (i.e. a single best trait value)
for an environment across all groups, functional group differ-
ences in trait–environment relationships may reflect different
levels of maladaptation. These may be caused by inhibition of
local adaptation by dispersal and gene flow, or differences in
the rate of adaptation as a result of differences in generation
time or mutation rates. Neither of these explanations appears
reasonable in the case of the phytoplankton. A third mecha-
nism is more plausible: groups may have distinct evolutionary
constraints that limit their ability to adapt to low or high tem-
peratures. A genetic constraint preventing cold adaptation in
cyanobacteria would explain their elevated Topt,Tmax and Tmin
at high latitudes. Cyanobacteria are common in polar and
alpine ecosystems, and previous work has also shown that
benthic polar cyanobacteria exhibit high Topt values, typically
between 15 and 35 °C (Tang et al., 1997; Nadeau & Castenholz,
2000). Since these Topt values are far higher than the maximum
environmental temperatures at polar latitudes and we do not
see similar values in other functional groups, these high trait
values most likely represent an evolutionary constraint specific
to cyanobacteria.
−5 0 5 10 15 20 25
Diatoms
Green algae
Cyanobacteria
(a) Diatoms
Green algae
Cyanobacteria
(b)
0 20406080
−5 0 5 10 15 20 25
Dinoflagellates
Haptophytes
Coccolithophores
Raphidophytes
(c)
020406080
Desmids
Chrysophytes
(d)
Absolute latitude (°)
Tmin (°C)
Marine Freshwater
Figure 4 Minimum persistence
temperature (Tmin) decreases towards the
poles, but this decrease also differs
between functional groups and
ecosystems (n=222). Unlike in the case
of Topt and Tmax, only cyanobacteria
exhibited clear differences between
ecosystems. Negative temperatures below
1.8°C are a result of extrapolation from
the fitted thermal reaction norms; as
part of quality control, we limited
analysis to Tmin values within 5 °C of
measured temperatures. The best model
was identified using groups common to
both ecosystems, shown in (a) and (b).
Data spread around these regression
lines indicates the proportion of
variance explained by the fixed effects
(marginal R2=0.49), but our model
explains considerably more variance by
accounting for intraspecific variation
with a random intercept (conditional
R2=0.82). Groups for which we had
data largely from one ecosystem are
shown in (c) and (d); model parameters
for these groups were estimated
separately.
M. K. Thomas et al.
Global Ecology and Biogeography,25, 75–86, © 2015 John Wiley & Sons Ltd82
Alternatively, if there are multiple adaptive peaks in the envi-
ronment, selection may drive groups towards different trait–
environment relationships if there are fundamental differences
in their physiologies. For example, temperature traits may be
evolutionarily constrained by group-specific trade-offs, either
between temperature traits or with other functional traits. We
found no evidence for the former (Fig. S8 in Appendix S1) and
lack the data to test for trade-offs with other traits (for which no
clear hypotheses exist). A second possibility is that selection
favours different temperature traits in different groups because
of evolutionary constraints on other important functional traits.
For example, traits that favour the proliferation of different
groups during different seasons would lead to selection favour-
ing temperature traits that suit the conditions each group
experiences when it is abundant. This is similar to temporal
niche partitioning, with the proviso that an explicit evolutionary
constraint is needed to maintain the partitioning over the time-
scales at play here. This mechanism is consistent with our
results: Topt values of the major groups at high latitudes reflect
patterns of seasonal succession in phytoplankton. Diatoms peak
in the spring, followed by green algae and then cyanobacteria
later in summer (Sommer et al., 1986; Reynolds, 2006; Alvain
et al., 2008). Succession is often driven by differences in nutrient
requirements (diatoms can store large quantities of nutrients,
excelling in pulsed or well-mixed environments; Litchman et al.,
2009; Edwards et al., 2012), survival in stratified waters (many
cyanobacteria regulate their buoyancy) and resistance to preda-
tion (Sommer et al., 2012). If the timing of peak abundance was
originally determined by one of these other traits, species may
have subsequently adapted to the corresponding temperatures,
leading to the patterns we see (Figs 2–4). It therefore appears
likely that group differences in temperature trait–environment
relationships have arisen as a result of evolutionary constraints:
either on the temperature traits directly (in high-latitude
cyanobacteria) or on another trait that previously determined
their temporal niches (with temperature trait differences arising
as a secondary effect), or both together.
Comparative analyses of trait–environment relationships can
advance our understanding of the ecology, evolution and physi-
ology of different organisms, but this approach presents several
challenges. It requires data on the traits of many taxa across
broad ranges of environmental conditions. However, the infer-
ences that can be drawn are sufficiently powerful to make this a
worthwhile endeavour, at least for research communities (Boyd
et al., 2013). Measurements at the most extreme portions of a
taxon’s range (as defined by environmental parameters) are par-
ticularly valuable. In the phytoplankton, measurements of polar
cyanobacteria and tropical diatoms are urgently needed, as well
as the understudied picoeukaryotes that play a large role in
global primary productivity (Vaulot et al., 2008). And though
10 15 20 25 30 35
Temperature niche width (°C)
Marine
Freshwater
Haptophytes
Coccolithophores
Raphidophytes
Desmids
Chrysophytes
Diatoms
Green algae
Cyanobacteria
Dinoflagellates
Figure 5 Niche widths differed between functional groups and,
in the case of the cyanobacteria, between ecosystems as well
(n =179; marine taxa are represented by circles, freshwater taxa
by triangles). Diatoms, green algae, and cyanobacteria are
represented in both marine and freshwater ecosystems in our
dataset; other groups are represented in only one ecosystem.
Error bars represent 95% confidence intervals. The fixed
effects explain one-third of the variance (marginal R2=0.35),
but our model explains considerably more by accounting for
intraspecific variation with a random intercept (conditional
R2=0.78).
Maximum specific growth rate (per day)
Absolute latitude (°)
0.1 0.2 0.5 1 2 5
0 20406080
Diatoms
Green algae
Cyanobacteria
Dinoflagellates
Haptophytes
Coccolithophores
Raphidophytes
Desmids
Chrysophytes
Figure 6 Maximum growth rate differs between functional
groups and decreases weakly towards the poles (n=392). As there
were no detectable differences between ecosystems, all groups
were modelled together. Data spread around the regression lines
indicates the proportion of variance explained by the fixed effects
(marginal R2=0.28), but our model explains considerably more
variance by accounting for intraspecific variation with a random
intercept (conditional R2=0.69).
Phytoplankton temperature trait biogeography
Global Ecology and Biogeography,25, 75–86, © 2015 John Wiley & Sons Ltd 83
we have focused on temperature, many environmental factors
and traits play a role in phytoplankton ecology and evolution
(Litchman & Klausmeier, 2008). Examining these additional
dimensions will reveal a more complex adaptive landscape than
can be considered at present.We hope that our work will stimu-
late two types of experiments that can shed light on this:
factorial experiments manipulating environmental conditions
while tracking community change and rapid evolution, and evo-
lution experiments designed to test the limits of the ability of
species to adapt, and how this varies across taxa.
Our findings advance the field of thermal biology by demon-
strating that some temperature traits are more evolutionarily
labile than others, using the most comprehensive dataset on
thermal reaction norms available in terms of taxonomic breadth
and spatial coverage. Our results are consistent across two dis-
tinct ecosystems with similar phytoplankton functional groups
and temperature gradients, thereby forming semi-independent
tests of our hypotheses. They also improve our understanding of
a group of organisms that are critical to global biogeochemical
cycles and aquatic food webs. We have shown that diatoms
exhibit Topt and Tmax values comparable to other groups in the
tropics and may therefore be more resilient to the direct effects
of warming than previously thought. This has important impli-
cations for predictions of aquatic ecosystem productivity and
carbon sequestration, to which diatoms contribute a large pro-
portion (Nelson et al., 1995; Field et al., 1998). Rising tempera-
tures are expected to increase the frequency of harmful
cyanobacterial blooms, in part because eukaryotic phytoplank-
ton are thought to be incapable of exhibiting Topt values as high
as those of cyanobacteria (Paerl & Huisman, 2009). We have
shown that eukaryotes can achieve similar Topt values, suggesting
that the effect of temperature on growth rate alone will not drive
increases in bloom frequency.
Finally, our results highlight the importance of considering
evolutionary processes when predicting how communities will
respond to changing environments. Because many systems will
experience novel patterns of environmental covariation in the
future (Williams et al., 2007), the power of ecological predic-
tions based on statistical associations between current commu-
nity composition and environmental conditions is inherently
limited. In contrast, a mechanistic understanding of the con-
straints and trade-offs that underpin functional trait variation
will allow us to model community change in novel conditions
(Litchman et al., 2007; Litchman & Klausmeier, 2008). Spatial
patterns in temperature traits indicate that tropical organisms
are especially vulnerable to environmental warming in both
terrestrial and marine ecosystems (Deutsch et al., 2008; Dillon
et al., 2010; Sunday et al., 2012; Thomas et al., 2012). Our work
shows how ecological and evolutionary processes have formed
these spatial patterns. It demonstrates that we can infer the
nature of important evolutionary constraints from trait–
environment relationships, and that predicting ecological
outcomes from these constraints is a realistic goal. We hope
that these insights will aid efforts to understand present-day
communities and predict future ecological and evolutionary
responses to global change.
ACKNOWLEDGEMENTS
This research was supported by NSF grants DEB-0845932 to
E.L. and OCE-0928819 to E.L. and C. A. Klausmeier. C.T.K. was
supported by NSF GRFP fellowship and NSF PRFB fellowship
1402074. We are grateful to K. F. Edwards for statistical advice,
and K. F. Edwards, C. A. Klausmeier, G. G. Mittelbach, J. A. Lau,
M. Stockenreiter, D. O’Donnell and three anonymous referees
for helpful feedback on the manuscript. This is Kellogg Biologi-
cal Station contribution number 1764.
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SUPPORTING INFORMATION
Additional Supporting Information may be found in the online
version of this article:
Appendix S1 Supporting methods, figures, tables and
references.
Appendix S2 Dataset of phytoplankton population growth rates
compiled from literature measurements and temperature trait
estimates derived using these growth rates.
BIOSKETCHES
Mridul K. Thomas is a post-doctoral researcher at
Eawag, the Swiss Federal Institute for Aquatic Science
and Technology. He is interested in building and testing
mechanistic trait-based models of ecological and
evolutionary dynamics.
http://mridulkthomas.weebly.com/.
Colin T. Kremer is a post-doctoral fellow at Yale
University and Princeton University. He explores
interactions between ecology and evolution in
communities exposed to fluctuating environments,
linking theory with empirical data.
http://colinkremer.wordpress.com.
Elena Litchman is a professor at Michigan State
University. She is a community ecologist interested in
developing trait-based approaches to phytoplankton
and other microbes and identifying the mechanisms
that structure microbial communities.
Editor: Janne Soininen
M. K. Thomas et al.
Global Ecology and Biogeography,25, 75–86, © 2015 John Wiley & Sons Ltd86
... According to the modern approaches to water resources management, their characterization should be based on the basin principle [15], the priority role is given to the biotic component, which, along with hydromorphological and hydrochemical indicators, can be used to establish the ecological status of a water body within a certain basin. As one of the biological quality elements, phytoplankton is proposed as a sensitive indicator of the environmental changes in the aquatic ecosystems, including changes in hydrological conditions, nutrient loads and other environmental conditions [9,[16][17][18][19][20][21][22][23][24]. Thus, the aim for this work was to analyze available information to find reliable unpolluted sites in the Siversky Donets river basin, which can be used as a reference sites. ...
... The sites merged together in two groups according to the river's sizes: small (S) + medium (M)-2, 3, 6,7,8,9,10,12,15,16,19,20,21,24 and large (L) + extra-large (XL)-1, 4,5,11,13,14,17,18,22,23, which revealed similar features among the group and between different groups. As can be noted in Figure 3, the higher divisions number as well as species correspond to the rivers with smaller size, while bigger size rivers possessed the lesser divisions' numbering. ...
... The overall low ratio of abundance to biomass is indicative of an increased proportion of small-celled algae. (20) 19(20) 14(15) 25 21 14 16 20 18 19 12 10 10 12 9 12 11 13(14) 10 10 35 11 13 The sites merged together in two groups according to the river's sizes: small (S) + medium (M)-2, 3,6,7,8,9,10,12,15,16,19,20,21,24 and large (L) + extra-large (XL)-1, 4,5,11,13,14,17,18,22,23, which revealed similar features among the group and between different groups. As can be noted in Figure 3, the higher divisions number as well as species correspond to the rivers with smaller size, while bigger size rivers possessed the lesser divisions' numbering. ...
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The river basin of Siversky Donets is of great scientific interest since this river runs through a territory with heavy industry (in particular, coal mining, chemical processing and metal industries). Within the basin, rivers of different sizes were explored (small, medium, large and extra-large) that flow through siliceous and calcareous rocks on the same elevation (lowland—below 200 m a.s.l.). Phytoplankton, as one of the Biological Quality Element, was used to perform the assessment of ecological status of the water bodies within the Siversky Donets river basin in 2019. The state monitoring program based on the updated approaches has been implemented in the river basin for the first time. The composition of phytoplankton species in the basin comprised 167 species (168 intraspecies taxa), mainly Bacillariophyta (63%) and Chlorophyta (22%) with the presence of other species (Cyanobacteria, Charophyta, Chrysophyta, Dinophyta and Euglenophyta). High species diversity and divisions amount are a distinctive property of the smaller rivers, while the bigger rivers show lower number of divisions. The “bloom” events, which are important ecological factors, were not detected in the Siversky Donets river basin. Algal species composition in plankton samples of the basin was identified and series of ecological parameters, such as habitat preferences, temperature, pH, salinity, oxygenation and organic water pollution according to Watanabe and Sládeček’s index of saprobity (S) trophic state and nitrogen uptake metabolism were analyzed. The ecological conclusions were also verified by a canonical correspondence analysis (CCA). The significance of the Canonical Correspondence Analysis (CCA) results was estimated of by a Monte-Carlo permutation test. The high concentrations of inorganic phosphorus compounds (permanganate index (CODMn)) and nitrite ions favored the diversity of Chlorophyta and Cyanobacteria diversity correlated with the levels of bicarbonate and CODMn. High diversity of diatoms was facilitated by the total amount of dissolved solids and chemical oxygen demand (COD). It was found that low water quality could be associated with conditions leading to predominant growth of the mentioned groups of algae. According to the analysis, the highest water quality was characterized by balanced phytoplankton composition and optimal values of the environmental variables. The sites with reference conditions are proposed for future monitoring.
... See Supplementary information for details of search and data. [36]. CO 2 response curves at different temperatures are scarce, so expectations of temperature-CO 2 response surfaces cannot be generated. ...
... There is substantial variation in responses to changes in temperature and CO 2 both within and between functional groups (and within and between species) [12, 36,38,44,45]. While studying response surfaces for model organisms from each functional group will produce general insights, estimates of how much taxonomic variation exists for response surfaces are also needed. ...
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Climate change is an existential threat, and our ability to conduct experiments on how organisms will respond to it is limited by logistics and resources, making it vital that experiments be maximally useful. The majority of experiments on phytoplankton responses to warming and CO2 use only two levels of each driver. However, to project the characters of future populations, we need a mechanistic and generalisable explanation for how phytoplankton respond to concurrent changes in temperature and CO2. This requires experiments with more driver levels, to produce response surfaces that can aid in the development of predictive models. We recommend prioritising experiments or programmes that produce such response surfaces on multiple scales for phytoplankton.
... We cannot address this suggestion with data because temperature-growth curves including low early-winter temperatures are not available for the taxa from our experiment. Yet among the three taxa that together accounted for the lion's share of total algal biovolume during the first month of the experiment (>75% and 91%, respectively, in the ambient and warmed mesocosms), M. minutum has a considerably higher maximum growth rate than S. obliquus and Cyclotella meneghiniana (Thomas et al., 2016). Typically, a high maximum growth rate at optimum temperature comes at the expense of a low growth rate at low temperatures (Eppley, 1972;Norberg, 2004;Thomas et al., 2016), giving some credibility to the hypothesis that ...
... Yet among the three taxa that together accounted for the lion's share of total algal biovolume during the first month of the experiment (>75% and 91%, respectively, in the ambient and warmed mesocosms), M. minutum has a considerably higher maximum growth rate than S. obliquus and Cyclotella meneghiniana (Thomas et al., 2016). Typically, a high maximum growth rate at optimum temperature comes at the expense of a low growth rate at low temperatures (Eppley, 1972;Norberg, 2004;Thomas et al., 2016), giving some credibility to the hypothesis that ...
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In many ecosystems, consumers respond to warming differently than their resources, sometimes leading to temporal mismatches between seasonal maxima in consumer demand and resource availability. A potentially equally pervasive, but less acknowledged threat to the temporal coherence of consumer‐resource interactions is mismatch in food quality. Many plant and algal communities respond to warming with shifts towards more carbon‐rich species and growth forms, thus diluting essential elements in their biomass and intensifying the stoichiometric mismatch with herbivore nutrient requirements. Here we report on a mesocosm experiment on the spring succession of an assembled plankton community in which we manipulated temperature (ambient vs. +3.6°C) and presence vs. absence of two types of grazers (ciliates and Daphnia), and where warming caused a dramatic regime shift that coincided with extreme stoichiometric mismatch. At ambient temperatures, a typical spring succession developed, where a moderate bloom of nutritionally adequate phytoplankton was grazed down to a clear‐water phase by a developing Daphnia population. While warming accelerated initial Daphnia population growth, it speeded up algal growth rates even more, triggering a massive phytoplankton bloom of poor food quality. Consistent with the predictions of a stoichiometric producer‐grazer model, accelerated phytoplankton growth promoted the emergence of an alternative system attractor, where extremely low phosphorus content of abundant algal food eventually drove Daphnia to extinction. Where present, ciliates slowed down the phytoplankton bloom and the deterioration of its nutritional value, but this only delayed the regime shift. Eventually, phytoplankton grew out of grazer control also in presence of ciliates, and the Daphnia population crashed. To our knowledge, the experiment is the first empirical demonstration of the ‘paradox of energy enrichment’ (= grazer starvation in an abundance of energy‐rich but nutritionally imbalanced food) in a multi‐species phytoplankton community. More generally, our results support the notion that warming can exacerbate the stoichiometric mismatch at the plant‐herbivore interface and limit energy transfer to higher trophic levels.
... Temperature response curves exist for >100 species representing all major phytoplankton functional groups [32]. CO2 response curves at different temperatures are scarce, so expectations of temperature-CO2 response surfaces cannot be generated. ...
... There is substantial variation in responses to changes in temperature and CO2 both within and between functional groups (and within and between species) [10, 32,34,40,41]. While studying response surfaces for model organisms from each functional group will produce general insights, estimates of how much taxonomic variation exists for response surfaces are also needed. ...
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Climate change is an existential threat, and our ability to conduct experiments on how organisms will respond to it is limited by logistics and resources, making it vital that experiments be maximally useful. The majority of experiments on phytoplankton responses to warming and CO2 use only two levels of each driver. However, to project the characters of future populations, we need a mechanistic and generalizable explanation for how phytoplankton respond to concurrent changes in temperature and CO2. This requires experiments with more driver levels, to produce response surfaces that can aid in the development of predictive models. We recommend prioritizing experiments or programmes that produce such response surfaces on multiple scales for phytoplankton.
... The compilations of temperature traits of different phytoplankton taxa allow us to determine if diatom temperature traits differ from other groups. Thomas et al. (2016) found that diatoms tend to have lower T opt , T max , and T min than other taxonomic groups, but this difference is significant only for the taxa from temperate regions, and in the tropics, different taxa have more similar T opt and T max . Interestingly, T min of marine diatoms is lower than T min of marine cyanobacteria even in the tropics, suggesting that tropical diatoms may have wider thermal niches compared to cyanobacteria . ...
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... How and why the diversity of life on earth increased over time are key research questions in ecology and biogeography (Blanquart et al., 2013;Cox et al., 2016;Futuyma & Antonovics, 1992;Savolainen et al., 2013;Thomas et al., 2016). Genetic and ecological speciation can occur in different parts of an ancestral species' range in which contrasting environmental conditions lead directly or indirectly to the evolution of reproductive isolation (Faulkes et al., 2004;Rundle & Nosil, 2005;Schluter, 2001). ...
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The process of phenotypic adaptation to the environments is widely recognized. However, comprehensive studies integrating phylogenetic, phenotypic, and ecological approaches to assess this process are scarce. Our study aims to assess whether local adaptation may explain intraspecific differentiation by quantifying multidimensional differences among populations in closely related lucanid species, Platycerus delicatulus and Platycerus kawadai, which are endemic saproxylic beetles in Japan. First, we determined intraspecific analysis units based on nuclear and mitochondrial gene analyses of Platycerus delicatulus and Platycerus kawadai under sympatric and allopatric conditions. Then, we compared differences in morphology and environmental niche between populations (analysis units) within species. We examined the relationship between morphology and environmental niche via geographic distance. P. kawadai was subdivided into the "No introgression" and "Introgression" populations based on mitochondrial COI gene - nuclear ITS region discordance. P. delicatulus was subdivided into "Allopatric" and "Sympatric" populations. Body length differed significantly among the populations of each species. For P. delicatulus, character displacement was suggested. For P. kawadai, the morphological difference was likely caused by geographic distance or genetic divergence rather than environmental differences. The finding showed that the observed mitochondrial-nuclear discordance is likely due to historical mitochondrial introgression following a range of expansion. Our results show that morphological variation among populations of P. delicatulus and P. kawadai reflects an ecological adaptation process based on interspecific interactions, geographic distance, or genetic divergence. Our results will deepen understanding of ecological specialization processes across the distribution and adaptation of species in natural systems.
... Our focus here is, as for the GR, on cyanobacteria. After Thomas et al. (2016) and based on the latitude of the study site, we set the base temperature at 10°C and the upper limit for growth at 37°C. This results in considering, for the calculation of the GDD, only temperatures that yield to a GR above 0.2 d −1 (see figure 5.1). ...
Thesis
The ecological state of freshwater ecosystems worldwide has deteriorated along the past decades. Anthropogenic pressures have altered their physical and biogeochemical dynamics, acting both within their watershed and on the climatic conditions. Eutrophication and climate change contributed to the increase of algal blooms, and in particular of toxic cyanobacteria blooms , which currently constitute one of the main concern in the management of water resources.With the advance of urbanization, an increasing number of lakes are located in metropolitan areas. The high loads of nutrients and pollutants coming from the watershed often lead urban lakes to eutrophic conditions and cyanobacteria blooms, that cause bathing bans and restrictions for aquatic sports. Responsive surveys and long-term climate change impact studies are essential for the management of such sites, but rarely addressed.In this respect, modelling tools are of central importance to better understand the functioning of aquatic ecosystems, the factors triggering harmful algal blooms, and to support the management of water resources. However, aquatic ecological models are often complex and highly parametrized, and their implementation and calibration are challenging. Automated strategies for parameters calibration are available but are rarely applied. Furthermore, data from traditional periodical limnological survey do not allow to test the models on dynamics quicker than the span between two successive campaigns, and to thoroughly assess the uncertainty of their outcomes.In this context, this PhD thesis focuses on the use of deterministic models to reproduce the thermal dynamics and phytoplankton dynamics, notably cyanobacteria, in a small and shallow urban lake on different time-scales. To do so, two coupled hydrodynamic and biogeochemical three-dimensional (3D) models are implemented and analysed. The models used here are the FLOW and BLOOM modules from the Delft3D modelling suite, and the biogeochemical library Aquatic EcoDynamics coupled with the hydrodynamic model TELEMAC3D. The models are applied on Lake Champs-sur-Marne, an urban lake located in the East of Paris that suffers from strong cyanobacterial blooms and for which an extensive data set is available.This work aims to address in detail three strategic elements in lake ecosystem modelling:•The impact of climate change on the thermal regime of small and shallow lakes, and its relation to cyanobacterial growth. This is assessed through long-term 3D hydrodynamic simulations that allowed to hindcast the evolution of the study site during the past six decades.• The applicability and the benefits of automated calibration for complex biogeochemical models. This is done through an innovative methodology for parameter estimation: Approximate Bayesian Computation (ABC), tested here for the first time on a complex, highly-parametrized model.•The coupling and the feedbacks between hydrodynamic and biogeochemical models focusing on different time scales, and the importance of an extensive data set, that includes continuous high-frequency observations.The results show that the impact of climate change on small and shallow lakes can be severe, with consequences on the stratification dynamics and that thermal conditions increasingly favourable for cyanobacterial growth have established over time in the study site. This suggests that cyanobacteria dominance could become a widespread issue in the near future, if such trends are confirmed. Furthermore, this work proves that automated calibration strategies, and ABC in particular, can be profitably applied to complex physically-based biogeochemical models in order to improve their results over the period chosen for calibration. Eventually, this work also highlights the importance of an extensive data set to set-up a coupled 3D hydrodynamic / biogeochemical model, and analyse and exploit its results over different time scales.
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En este este capítulo se revisan algunos elementos de psicología cognitiva relevantes para el aprendizaje, se hace una revisión crítica de la pedagogía constructivista y se identifican prácticas que han demostrado ser eficaces para mejorar los resultados de la enseñanza. Se discuten también diferentes sesgos cognitivos que tienen profundas implicaciones en el aprendizaje y la práctica de la ciencia, y en nuestra visión del mundo.
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