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Theoretical basis for predicting
climate-induced abrupt shifts in
the oceans
Gregory Beaugrand
Research
Cite this article: Beaugrand G. 2015
Theoretical basis for predicting climate-induced
abrupt shifts in the oceans. Phil. Trans. R. Soc.
B 370: 20130264.
http://dx.doi.org/10.1098/rstb.2013.0264
One contribution of 16 to a Theme Issue
‘Marine regime shifts around the globe: theory,
drivers and impacts’.
Subject Areas:
environmental science
Centre National de la Recherche Scientifique, Laboratoire d’Océanologie et de Géosciences’ UMR LOG CNRS 8187,
Station Marine, Université des Sciences et Technologies de Lille 1, Lille 1 BP 80, Wimereux 62930, France
Among the responses of marine species and their ecosystems to climate change,
abrupt community shifts (ACSs), also called regime shifts, have often been
observed. However, despite their effects for ecosystem functioning and both
provisioning and regulating services, our understanding of the underlying
mechanisms involved remains elusive. This paper proposes a theory showing
that some ACSs originate from the interaction between climate-induced environmental changes and the species ecological niche. The theory predicts that a
substantial stepwise shift in the thermal regime of a marine ecosystem leads
indubitably to an ACS and explains why some species do not change during
the phenomenon. It also explicates why the timing of ACSs may differ or
why some studies may detect or not detect a shift in the same ecosystem,
independently of the statistical method of detection and simply because they
focus on different species or taxonomic groups. The present theory offers a
way to predict future climate-induced community shifts and their potential
associated trophic cascades and amplifications.
Keywords:
abrupt shift, climate, theory
Author for correspondence:
Gregory Beaugrand
e-mail: [email protected]
1. Introduction
An ecosystem regime shift is often defined as a substantial and relatively rapid
shift between two contrasting stable states [1]. A common explanation to the
origin of some shifts refers to the theory of alternative stable states [2–4]. The
theory stipulates that, for a given system, some alternative stable states or attractors are possible and that the shift from one system to another depends on the
size of the attraction basin and the strength of both positive and negative feedbacks. The transition from one state to another is difficult to predict, and the
return to initial environmental conditions is not sufficient for the system to
switch back in the case of hysteresis [5]. Although some stepwise ecosystem
changes may well be caused by the existence of several attractors, their existence
remains difficult to prove [6]. Some processes and alternative stable states leading to such a phenomenon have been identified in lakes and in some marine
ecosystems [4,5,7]. When the system is controlled by engineer species, such as
in coral-dominated ecosystems [7], or a keystone species, such as in seaweedstructured marine ecosystems [8], alternative stable states (coral/macroalgae
and seaweed/deforested states) and their causes can be well explained [4].
Algal overgrowth in Caribbean coral reefs is mainly attributed to (i) nutrient
loading, which stimulates algal growth and (ii) overfishing that reduces the
number of herbivorous fish that controls algal proliferation [7]; similar mechanisms have also been invoked to explain regime shifts in kelp forest ecosystems.
In contrast to these local benthic ecosystems, the processes and mechanisms
leading to abrupt community shifts (ACSs) in pelagic ecosystems remain difficult to both identify and understand [1,9] and scientists have mainly progressed
on the detection of patterns [10– 13].
While climate and temperature have repeatedly been assumed to be at the
origin of regime shifts in pelagic ecosystems such as those of the Pacific
Ocean [10] and the North Sea [11,14], or more recently in the northeast Atlantic
and its adjacent seas [12,13], the mechanisms by which a climate signal may
& 2014 The Author(s) Published by the Royal Society. All rights reserved.
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The ecological theory teaches us that each species can be characterized by its ecological niche. Although different definitions
of the niche concept exist [21], one common and operational
definition is the range of tolerance of a species when several
factors are taken together [22]. According to this definition,
each species can be distinguished by an n-dimensional envelope, which determines its response to spatial and temporal
environmental changes. One advantage of the concept of the
niche is that it can be seen as a macroscopic elementary property, not only integrating the molecular, physiological and
behavioural characteristics of a species but also allowing the
consideration of the emergence of new ecological properties at
the species level. In practice, the niche is assessed from the physiological or biogeographic knowledge of a species using only a
limited number of environmental dimensions [23].
At a large scale (regional to basin scale), temperature is a key
environmental driver that should affect all species, especially
ectotherms (i.e. 99.9% of all species on the Earth) [24,25].
In this paper, we will primarily focus on thermal niche, although
it is well known that other environmental parameters (e.g.
photosynthetically active radiation, bathymetry, oxygen,
mixed layer depth) may also play a role [26]. A one-dimensional
(thermal) niche with a Gaussian shape has a few remarkable
points (figure 1). When the species is at the centre of its thermal
niche (between TS1 and TS2; S for stable), the species is resistant
to climate-induced temperature changes, the resistance being
dependent on the shape of the niche, the breadth of the thermal
window, and more specifically the breadth of the optimal zone
[27,28]. When the species is present in an environment with a
thermal regime that corresponds to the edge of its thermal
niche (THV; HV for high variability), it becomes highly sensitive
to climate-induced changes in temperatures. Towards the limits
of the niche, the variability levels off (TD; D for detection) before
reaching the lethal temperature TL. At this level of abundance,
1.0
Topt
TS1
0.9
TS2
relative abundance
0.8
0.7
ts
0.6
0.5
THV
THV
0.4
0.3
0.2
0.1
0
TL
ts
TD
0
5
TD
10 15 20 25 30
sea-water temperature (°C)
TL
35
40
Figure 1. A hypothetical thermal niche. Topt, optimum temperature; TS1 and TS2
(S, stable), temperatures between which no strong alteration in the abundance
of species is expected; THV (HV, high variability), point at which large variation in
abundance is expected as a function of temperature; TD (D, detection), point
from which the relationship between temperature and the abundance of species
becomes more difficult to detect; TL (L, lethal), temperature at which the animal
dies. ts, Thermal amplitude for species s. Redrawn from [27].
the influence of climate becomes difficult to detect and
the location of TD depends on the noise-to-signal ratio [27].
Such a theory, which may be coined the Macroecological
Theory for the Arrangement of Life (METAL theory) enables
us to understand why the species’ responses to climate-induced
temperature change vary in space and time. It has been applied
to plankton (Calanus finmarchicus and Acartia tonsa), fish (the
Atlantic cod Gadus morhua) and seabirds (the little auk Alle
alle) [28–31]. It has also been used to understand the global ecogeographical pattern in biodiversity (foraminifers and
copepods) and the Rapoport’s effect (i.e. the increase in the
mean latitudinal range of a species with latitude) [32], as well
as to connect phenological and biogeographical shifts at the
species level and long-term shifts at the community level [33].
The shape of the niche was not always Gaussian in all these
studies. Some were determined by non-parametric ecological
niche models [31] and some niches were even rectangular to
relax the constraints related to the shape of the niche [32].
3. A theoretical framework
A simple model can show why ACSs may also result from the
individual response of each species to climate-induced
changes in temperature. The procedure developed in Beaugrand et al. [32] was transformed to produce a number of
hypothetical (Gaussian) niches for both eurytherms and stenotherms. The response curve of the expected abundance E
of a pseudospecies s to change in sea temperature in a
given site i and time j was modelled as follows [34]:
Ei,j,s ¼ cs e((xi,j us )
2
=2t2s )
,
(3:1)
with Ei,j,s the expected abundance of a pseudospecies s at
location i and time j; cs the maximum value of abundance
for species s fixed to one; xi,j the value of temperature at
location i and time j; us the thermal optimum and ts the
thermal amplitude for species s (figure 1).
Phil. Trans. R. Soc. B 370: 20130264
2. Rationale
2
cs
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bring about these ecosystem responses [15,16] are unclear.
Even the existence of relatively stable states has also been questioned for some systems (e.g. North Sea), which led some
authors to prefer the term abrupt community/ecosystem
shift (ACS or AES) [17]. Investigating long-term changes in
the state of calanoid assemblages in the North Sea, Beaugrand
& Ibanez [18] revealed an abrupt shift in the 1980s between
two apparent stable states. As the length of the time series
increased, however, the existence of stable states became less
apparent [17] and long-term changes in the community state
were more comparable to climatic vacillations [19]. This led
Beaugrand et al. [19] to propose to base the detection of these
shifts by the change in the multi-scale multivariate (multispecies) temporal variance of the community, the approach
removing the necessity of having a stable state; the higher the
variance signature, the greater the magnitude of the shift [20].
This paper shows that ACSs do not always originate from
the transition from one alternative stable state to another but
may instead result from the interaction between climateinduced environmental changes and the species ecological
niche at the community level. A theory is proposed to explain
in detail how an ACS may be instigated and some generalizations are propounded. The theory is then tested in the North
Sea using data on copepods sampled by the Continuous
Plankton Recorder (CPR) survey.
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1.0
0.8
0.6
0.4
0.2
cold
0
index of abundance
1.0
0.8
index of abundance
increase
of 1°C
0.6
0.4
0.2
0
(c)
thermal gradient
time
1.0
0.8
0.6
0.4
0.2
0
1950 1960 1970 1980 1990 2000 2010
first principal component
(arb. units)
(d )
6
4
1988
(p < 0.01)
2
0
–2
–4
–6
–8
1950 1960 1970 1980 1990 2000 2010
year
Figure 2. An example showing how climate-induced shift in temperature, by its
interaction with the thermal niche of each species, may trigger an ACS. (a) Eight
pseudospecies’ niches with thermal optima of us ¼ 128C (black) and us ¼ 148C
(blue) for more psychrophiles, us ¼ 188C (green), us ¼ 228C (orange) and us ¼
248C (red) for more thermophiles. The thermal amplitude for more stenotherms
was ts ¼ 28C (dashed line) and ts ¼ 48C for more eurytherms (filled line). (b)
Idealized shifts triggered by a rise in the thermal regime of 18C at the middle of
the time series. (c) Predicted long-term changes in the abundance of each pseudospecies from the knowledge of the thermal niche and sea surface temperature.
(d) Long-term expected changes in the pseudocommunity inferred from the first
principal component of a principal component analysis (PCA) performed on the
table 53 years (1958–2010) 8 pseudospecies. The rectangles in (b) highlight
that the stenotherm species are more sensitive than more eurytherm species.
Figure 2 shows some examples of niches that can be generated from equation (3.1) along a thermal gradient. Eight
niches were created in this example with thermal optima of
3
Phil. Trans. R. Soc. B 370: 20130264
(b)
warm
us ¼ 128C (black) and us ¼ 148C (blue) for more psychrophiles, us ¼ 188C (green), us ¼ 228C (orange) and us ¼ 248C
(red) for more thermophiles. The thermal amplitude was
ts ¼ 28C for more stenotherms (dashed line) and ts ¼ 48C
for more eurytherms (filled line). Substantial shifts in the
abundance of these pseudospecies, expected from the knowledge of their thermal niche, took place when temperature was
artificially increased from 17.58C to 18.58C (middle of time
series). A single degree of temperature rise was sufficient to
trigger abrupt changes in the abundance of some eurytherms
and stenotherms, especially those with an initial thermal
regime close to THV (figure 2a,b; an increase for more thermophiles in orange and a decrease for more psychrophiles in
blue). No shift was observed for pseudospecies with an initial
thermal regime close to Topt (green). The magnitude of the
change was greater for stenotherms because they are more
sensitive to temperature (figure 2a,b). However, stenotherms
with an initial thermal regime close to the edge of their thermal niche (outside TD) exhibited only minor changes in
abundance (figure 2a,b; black and red).
The same expectations emerged when the abundance of
pseudospecies were assessed from a time series of temperature
having the same variability as long-term changes in temperatures observed in the North Sea for the period 1958–2010 but
a different average (mean average ¼ 17.58C versus 10.28C in
the North Sea; figure 2c). When the eight pseudospecies were
combined and their long-term changes analysed by standardized principal component analysis (PCA on the table
years pseudospecies), the first principal component (PC,
91.4% of the total variance) revealed major changes in this
pseudocommunity, including a stepwise and rapid shift at
the end of the 1980s (figure 2d). A change-point analysis [35]
detected a shift in 1988 (figure 2d; in red), corresponding to
the timing of the regime shift originally proposed in the
North Sea [11]. In such ecosystems, the framework predicts
that the response of the community is directly a function
of the magnitude of the thermal shift and the degree of
stenothermy in the community.
To demonstrate that a community composed of stenotherms should be theoretically more sensitive to long-term
changes in temperature than eurytherms, we created strict stenothermic (thermal amplitude ts varying from 18C to 28C,
every 0.28C) and eurythermic (thermal amplitude ts varying
from 98C to 108C, every 0.28C) pseudocommunities with different thermal optima (figure 3). For a thermal optimum varying
between 8 and 128C (figure 3a(i),b(i)), long-term changes in
expected abundance varied more for stenotherms (figure
3a(ii)) than eurytherms (figure 3b(ii)). Despite this, a PCA performed on each pseudocommunity gave similar results (first
PC in figure 3a(iii),b(iii)). Similar results were found for pseudocommunities with a thermal optimum varying between 5 and
178C (figure 3c,d) and 0 and 258C (figure 3e,f). In particular,
long-term changes in the expected abundance of stenothermic
species crossed each other (figure 3e(ii)) at the time of two
ACSs detected by change-point analysis ca 1988 and 1998
(figure 3e(iii)). The two first PCs were also similar, although
the second shift (1998) was more apparent for the stenothermic
than the eurythermic pseudocommunity (figure 3e versus
3f(iii)). Note also that the second shift was not detected in the
simplified pseudocommunity of the first example (figure 2d).
The amplitude of year-to-year changes or first differences
(d(2iþ1)/2 ¼ xiþ1 2 xi with i varying between 1 and t 2 1) were
then calculated for all long-term changes in the expected
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index of abundance
(a)
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(a)
us = [8:1:12]; ts = [9:0.2:10]
(b)
(i)
us = [8:1:12]; ts = [1:0.2:2]
4
expected
abundance
0.5
0
(ii) 1.0 0
5
10
15
20
25
30
35
40 (ii) 0
5
10
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30
35
40
0.5
(iii) 60 65 70 75 80 85 90 95 00 05
0
–10
60 65 70 75 80 85 90 95 00 05
(c)
(d)
(i)
us = [5:1:17]; ts = [1:0.2:2]
(i) 1.0
60 65 70 75 80 85 90 95 00 05
us = [5:1:17]; ts = [9:0.2:10]
expected
abundance
0.5
0
5
(ii) 1.0
10
15
20
25
30
35
40
(ii)
5
10
15
20
25
30
35
40
0.5
PC
0
60 65 70 75 80 85 90 95 00 05
(iii) 20
(iii)
60 65 70 75 80 85 90 95 00 05
0
–20
60 65 70 75 80 85 90 95 00 05
(e)
(i) 1.0
60 65 70 75 80 85 90 95 00 05
(f)
(i)
us = [0:1:25]; ts = [1:0.2:2]
us = [0:1:25]; ts = [9:0.2:10]
expected
abundance
0.5
0
5
(ii) 1.0
10
15
20
25
30
35
40
(ii)
5
10
15
20
25
30
35
40
PC
0.5
0
(iii) 20 60 65 70 75 80 85 90 95 00 05
88
0
98
–20
60 65 70 75 80 85 90 95 00 05
year
(iii) 60 65 70 75 80 85 90 95 00 05
88
98
60 65 70 75 80 85 90 95 00 05
year
Figure 3. Theoretical demonstration of the sensitivity of a community to the degree of steno/eurythermy. (a) Community composed of stenotherms (thermal
optimum us varying from 88C to 128C, every 18C; thermal amplitude ts varying from 18C to 28C, every 0.28C). (i) all thermal niches; (ii) long-term changes
(1958 – 2009) in expected abundance; (iii) first PC of the table years species of expected abundance. (b) Community composed of eurytherms (thermal optimum
us varying from 88C to 128C, every 18C; thermal amplitude ts varying from 98C to 108C, every 0.28C). (i)-(iii) as in (a). (c) Community composed of stenotherms
(thermal optimum us varying from 58C to 178C, every 18C; thermal amplitude ts varying from 18C to 28C, every 0.28C). (i)-(iii) as in (a). (d ) Community composed
of eurytherms (thermal optimum us varying from 58C to 178C, every 18C; thermal amplitude ts varying from 98C to 108C, every 0.28C). (i)-(iii) as in (a).
(e) Community composed of stenotherms (thermal optimum us varying from 08C to 258C, every 18C; thermal amplitude ts varying from 18C to 28C, every
0.28C). (i)-(iii) as in (a). (f ) Community composed of eurytherms (thermal optimum us varying from 08C to 258C, every 18C; thermal amplitude ts
varying from 98C to 108C, every 0.28C). (i)-(iii) as in (a). In (e,f ), the red line is the result of a change-point analysis. Two stepwise shifts are detected:
1988 and 1998.
abundance of stenotherms (figure 3e(ii)) and eurytherms
(figure 3f(ii)) to estimate the theoretical sensitivity of these
two pseudocommunities to changes in temperature (figure 4).
Only species with an average expected abundance above 0.1
was considered in this analysis. The year-to-year amplitude of
the stenothermic pseudocommunity (ranging between 20.6
and 0.6) was higher than the year-to-year amplitude of the
eurythermic pseudocommunity (ranging between 20.1 and
0.1). This analysis demonstrates that the sensitivity of a community should depend upon the degree of stenothermy/
eurythermy of species.
The application of the METAL theory in the context of
regime shift leads to several predictions: (i) a stepwise shift in
temperature leads to an ACS; (ii) a long-term shift in temperature
Phil. Trans. R. Soc. B 370: 20130264
PC
0
(iii) 10 60 65 70 75 80 85 90 95 00 05
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(i) 1.0
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(b) Comparison with long-term changes in the
abundance of copepods
250
200
150
100
150
0
4000
absolute frequency
3500
3000
2500
2000
1500
1000
500
0
–1.0
–0.5
0
0.5
1.0
species year-to-year amplitude
sensitivity
Figure 4. Sensitivity of two pseudocommunities to long-term changes in
temperature. The analysis was performed on a pseudocommunity only composed of (a) stenotherms and (b) eurytherms. Sensitivity was assessed by
calculating the first differences on long-term changes in expected abundance
for stenotherms (figure 3e(iii)) and eurytherms (figure 3f (iii)). Species with a
mean expected abundance (1958– 2009) inferior to 0.1 were removed from
the analysis.
(c) Comparison with long-term changes in calanoid
assemblage
A second comparison between long-term expected and
observed community changes was carried out using the first
PC from a PCA performed on calanoid assemblages [38,39] in
the North Sea for the period 1958–1999 [18].
5. Results
will be amplified at the community scale by the interaction
between the thermal niche of a species and the thermal
regime, this being more prominent when the number of stenotherms is higher in the community or when the thermal
regime of the region is close to the point THV of eurytherms
(figures 1 and 2); (iii) some species will not exhibit a stepwise
response to change in temperature during an ACS (e.g. species
close to their optimum and species at the edge of their thermal
niche outside TD).
4. Test of the theoretical framework for the
North Sea ecosystem
(a) Generation of the pseudocommunities
As in Beaugrand et al. [32], we produced pseudospecies that
were able to colonize the North Sea (48 W, 108 E, 518 N, 608 N),
so long as they could withstand monthly changes in sea surface
temperatures (dataset ERSST_V3). All species had a unique
thermal niche after the principle of competitive exclusion of
Gause [36]. The generation of pseudospecies was made using
equation (3.1) for the period 1958–2009. Many pseudospecies
were produced, each having a unique thermal niche with an
optimum temperature ranging from 48C to 258C by 48C increment and a thermal amplitude ranging from 0.18C to 108C by
28C increment. However, only pseudospecies with an annual
relative (i.e. expressed as percentage) abundance greater than
0.01 and a presence greater than 10% for all years of the
period 1958–2009 were kept [37].
(a) Predicted long-term changes from the model
A total of 10 000 expected long-term community states were
generated (figure 5; curves in black). Positive values reflected
a warmer dynamic regime and negative values a colder
dynamic regime. Most long-term expected patterns exhibited
a warm dynamic regime between 1958 and 1962 and then a
colder dynamic regime until the end of the 1980s, periods
well identified in previous studies [18]. The cold-ocean climate anomaly of 1978–1982 was also correctly reproduced
[40]. After an abrupt shift at the end of the 1980s, the
system remained in a warm dynamic regime and a second
abrupt shift was detected at the end of the 1990s [12,41].
(b) Comparison with long-term changes in the
abundance of copepods
When long-term changes in copepod abundance were examined by a PCA (figure 5a; in red), the first (approx. 1988) and
second (approx. 1998) shifts were well synchronous to expected patterns. The first PC explained 25.96% of the total
variance. Patterns of changes predicted by the theoretical
framework were closely related to observed long-term changes
(figure 5a). The observed warm dynamic regime of the period
1958–1962 was, however, less apparent. The correlation coefficients between the 10 000 long-term expected patterns and
observed long-term community shifts in copepod abundance
were rmean ¼ 0.72 (rmin ¼ 0.68 and rmax ¼ 0.76). All correlations
were significant after accounting for temporal autocorrelation
at the threshold p , 0.05 [42].
Phil. Trans. R. Soc. B 370: 20130264
(b)
A first comparison was performed for the time period 1958–
2009. The annual mean of all North Sea copepods sampled
from the CPR survey was calculated. A total of 52 species,
which had an annual relative (i.e. expressed as percentage)
abundance greater than 0.01 and a presence greater than 10%
for all years of the period 1958–2009 [37], were retained. As
described above, changes in the community state were inferred
from the calculation of the first PC of a standardized PCA performed on the abundance (log-transformed) matrix 52 years 52 species or taxa. The first PCs from the PCA performed on
pseudospecies and copepods were then compared.
As more pseudospecies were generated than observed copepods (86 pseudospecies versus 52), we chose 52 pseudospecies
at random with which to examine long-term changes in the
pseudocommunity state in the North Sea. The pseudocommunity state was assessed by the first PC of a standardized PCA
on the table 52 years (1958–2009) 52 pseudospecies. We
repeated the selection of the pseudospecies 10 000 times and
recalculated each time the first PC on pseudospecies.
5
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absolute frequency
(a)
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expected (black) and observed
(red) community state
1.0
0.8
0.6
0.4
0.2
0
–0.2
–0.4
–0.6
–0.8
–1.0
1960
10.5
10.0
9.5
1970
1980 1990
years
2000
sea surface temperature (°C)
11.0
2010
Figure 5. Test of the hypothesis that climate-caused ACSs may be triggered
by the interaction between changes in temperature and the thermal niche of
each species composing a community. A total of 10 000 long-term expected
changes was simulated (curves in black) and compared to (a) the first component of long-term copepod abundance changes (1958 – 2009; curve in red)
and (b) the first component of long-term calanoid assemblage changes
(1958 – 1999; curve in red).
(c) Comparison with long-term changes in calanoid
assemblages
It is remarkable that the pattern predicted by the theoretical
framework was also closely related to observed long-term
changes ( percentage of explained variance of the first PC:
32.46%) in calanoid assemblages (shift in 1962, similar oscillations during the colder dynamic regime; figure 5b). The
predicted 1988 shift was observed earlier when calanoid
assemblages were examined, however (figure 5a versus 5b).
The correlation coefficients between the 10 000 long-term
expected patterns and observed long-term community shifts
in calanoid assemblages were rmean ¼ 0.47 (rmin ¼ 0.43 and
rmax ¼ 0.55). All correlations were significant after accounting
for temporal autocorrelation at the threshold p , 0.05.
6. Discussion
Current theories suggest that shifts from one ecosystem state
to another may be related to the presence of attractors [2,6].
Although they are difficult to identify in the field, there is
evidence that attractors play a key role in the catastrophic
shifts of some natural systems [4]. For example, attractors
have been suggested for lakes where the system alternates
between clear and algae bloom phases and in some marine
systems, e.g. shifts from a cod- to clupeid-dominated
system state in the Baltic Sea [9]. In the benthic realm,
regime shifts are also frequently reported at a local scale
and some are well explained by referring to the theory of
alternative stable states [5]. However, using a simple framework, this paper shows that another mechanism may be at
the origin of some climate-induced ACSs reported in the
scientific literature (e.g. the North Sea), without the need to
invoke the theory of alternative stable states.
Here I propose that climate-induced temperature changes
may also cause ACSs through the interaction between temperature changes and the thermal niche of each species
composing a community. From the simple framework used
in this study, two theorems can be proposed. The first theorem states that ‘providing that the niche has a Gaussian
shape and that there is no species interaction, a substantial
increase (e.g. a 18C of mean annual temperature) in the thermal regime of an ecosystem triggers an ACS. The magnitude
of the community shift depends on the extent of the temperature change and the degree of eurythermy (stenothermy) of
the species composing a community. A higher degree of stenothermy increases the sensitivity of the community to
temperature change’. In practice, the first theorem should
be robust from departure to the condition on the niche
shape. It is well known that the shape of the niche of some
species is not Gaussian and may exhibit large differences in
shape [34]. However, the niche must be unimodal. The magnitude of the observed shift is likely to strongly depend on
the noise-to-signal ratio, which is influenced by the quality
of the data (e.g. sampling and species identification). The
noise-to-signal ratio influences the location of the point TD
along the thermal niche (figure 1). This phenomenon may
also be at the origin of substantial changes in the timing of
a shift. Such a problem is more likely to happen when the
change in the thermal regime is small.
The second theorem states that ‘during a climate-driven
ACS, the response of species is individualistic, depending
upon the characteristics of their thermal niche, the initial thermal regime and the magnitude of the thermal shift. It follows
that not all species are expected to show a shift (e.g. species
located around Topt or outside TD) and that some may react
earlier (stenotherms for an initial thermal regime close to
THV) than others (eurytherms or species with an initial thermal regime close to Topt or outside TD)’. This second theorem
has strong practical implications. According to species or
taxonomic group, the magnitude and the timing of the shift
may vary, independently of the type of statistical procedures
that also influence the timing [43]. In the North Sea, it has
been suggested that the timing of the shift varies according
to the selection of species, taxonomic group and species assemblage [11,14,17,18]. It is also possible that some studies focusing
on the same ecosystem find a shift, whereas others do not, independently of other issues related to statistical technics or
sampling programmes. This prediction was observed in this
study. Using a small number of pseudospecies, we found a
unique shift (ca 1988; figure 2d). By contrast, when a higher
number of pseudospecies was examined, we detected two
shifts (ca 1988 and 1998, figure 3e,f ).
Two further assertions are proposed here. The first assertion stipulates that climate-caused ACSs are highly correlated
to temperature changes, the intensity of the correlation
depending on species composition and the noise-to-signal
ratio. Although this first assertion needs to be better evaluated, it is expected that if patterns of temperature change
exhibit a long-term trend, a cyclical variability and a strong
year-to-year variability (very common pattern), long-term
changes in the community state may show vacillations
6
Phil. Trans. R. Soc. B 370: 20130264
(b)
11.5
1.0
0.8
0.6
0.4
0.2
0
–0.2
–0.4
–0.6
–0.8
–1.0
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expected (black) and observed
(red) community state
(a)
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7. Conclusion
This paper provides evidence that some climate-induced
ACSs may result from the nonlinear interaction of the thermal
niche of species with temperature change at the community
level. Because temperatures are expected to rise rapidly
with global climate change in the next decades, these ACSs
are likely to increase in both frequency and intensity. The
term regime shift has often been used in the past to describe
stepwise changes in the ecosystem/community state. Because
time series were relatively short (i.e. a few decades), two
full dynamic regimes (apparently stable states) and a shift
were frequently observed [10,11,18]. Since then, time series
have increased in length and have started to reveal more complex temporal patterns [17]. The deterministic theory outlined
in this paper explains why such complex patterns forms.
Soon, it may be become apparent that we are observing
7
Phil. Trans. R. Soc. B 370: 20130264
changes. These environmentally induced trophic cascades
should not be confounded with anthropogenic trophic cascades related to pollution or overfishing [9] for which the
proposed theory is not relevant. The likelihood of natural trophic cascades may increase at times of substantial temperature
change. The proposed framework suggests that the null hypothesis of a common response of all species to climate-induced
changes in temperature should also be tested when climateinduced trophic cascades are hypothesized [49]. Kirby &
Beaugrand [48] proposed the term trophic amplification,
when climate-induced temperature changes have a significant influence on each species, and that this influence is also
exacerbated through the trophic food web.
Other mechanisms have been proposed to explain
observed regime shifts in some exploited marine ecosystems
[52]. These hypotheses generally invoke fishing, which may
unbalance species interactions and lead to persistent ecosystem changes. The prey-to-predator loop is based on the fact
that many prey feed on the eggs or larval stages of their
predators [52]. When the predator declines because of an
external force such as fishing, this can lead to the outbreak of
its prey, which may in turn prevent the recovery of the predator
because of predation pressure at the egg or larval stage. A key
assumption here is the reduction of the predator by fishing.
Note that this hypothesis may also hold if the predator has
been affected by adverse environmental conditions. The predator pit loop is a hypothesis that is based on the empirical
observation that two opposite states seem to occur for forage
(prey) fish population, one characterized by a low abundance
(refuge) and another characterized by outbreak abundance
[52]. This hypothesis invokes prey–predator interactions.
When the prey becomes low, the predator searches for alternative preys and the specific mortality rate of the prey diminishes.
This virtual refuge allows the species to survive and to
subsequently recover. When the prey becomes more abundant,
the predator will only catch a constant fraction corresponding
to the satiation point and the prey is likely to continue to
increase. In some circumstance, this can lead to an explosive
increase. These two hypotheses explain the persistence of an
ecosystem shift but are less effective in explaining the origin
of the change, especially in the absence of fishing. Trophic
interactions could be implemented in the present theory and
would probably lead to an increased sensitivity of the system
to temperature change.
rstb.royalsocietypublishing.org
characterized by a succession of periods of low and higher temporal variance [19]. An example of such vacillations is shown in
figure 5a (black and red curves). When only a relatively small
number of years are considered, these vacillations may look
like a (community) regime shift; a community regime shift
can be defined as a special case of ACS between two stable
community states (i.e. state with no temporal autocorrelation).
The second assertion stipulates that the sharpness of the
ACS depends not only on the abruptness of the temperature
change but also on the degree of eurythermy (stenothermy) of
species. A high degree of stenothermy may lead to a more stepwise shift whereas a large degree of eurythermy may lead to a
longer shift. This was shown in figure 3e,f and suggested in
figure 4. Here also, the noise-to-signal ratio can interact and
move the exact theoretical timing of the shift [43].
Other mechanisms have been proposed to explain how
climate-induced temperature change may affect aquatic ecosystems [44,45]. In shallow lakes, North Atlantic Oscillationinduced changes in temperature can affect the occurrence of
clear water phases [44]. Using a periodically forced algae–
zooplankton model, Scheffer et al. [44] proposed that both
plankton and food web dynamics can be modulated by temperature by altering the seasonal patterns and the biomass of
phytoplankton and their interactions with zooplankton and
fish. It is, however, unknown whether such a mechanism can
be translated to the marine environment. Using time series investigated to characterize the 1976/1977 Pacific regime shift, Hsieh
et al. showed that whereas time series of physical variables (e.g.
Pacific Decadal Oscillation index, Southern Oscillation Index)
were linear stochastic, biological time series (e.g. California
Cooperative Oceanic Fisheries Investigations copepods, sockeye
salmon, chum salmon) exhibited a nonlinear signature. These
results supported the idea of a nonlinear amplification of stochastic hydro-climatic forcing. However, the authors did not provide
any mechanisms to explain how the nonlinear amplification takes
place. The theory proposed here shows that ACSs may arise
from the nonlinear interaction between the species niche and
the year-to-year fluctuations in temperature.
The theory outlined in this study is simple. It is well
known that ecosystems and their biodiversity are influenced by a multitude of environmental parameters, not only
temperature. The proposed theory is here tested using a
one-dimensional (thermal) niche. Although this thermal
dimension is probably important for all species [24], it is by
no means the only dimension that conditions the species’
response to environmental (even climate) changes [26].
Climate also has a strong influence through photosynthetically active radiation, the degree of stability of the water
column, oxygen concentration and the amount of both
macro- and micro-nutrients [46,47]. Therefore, changes in
atmospheric forcing, which also affect oceanic circulation
at many spatial scales, may have a strong effect on the ecosystem state [48]. It is likely that the consideration of all these
dimensions will reinforce the theory rather than weakening
it, although this point requires more investigation.
The proposed theory does not consider species interaction
yet. Species interactions are often involved in the positive feedbacks that shift a system from one state to another [4]. Because
of these species interactions, climate-induced species shifts
may propagate through the food web [41,49]. Both bottomup or top-down controls have been suggested in the pelagic
environment [50,51]. Trophic cascades may be observed
if a key or engineer species is affected by temperature
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states. The present theory offers a way to predict future climate-induced ACSs that may be at the origin of trophic
cascades and amplifications.
References
1.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
22.
23.
24.
25.
26.
27.
28.
Wadden Sea. Mar. Ecol. Prog. Ser. 298, 21 –39.
(doi:10.3354/meps298021)
Mackas DL et al. 2012 Changing zooplankton
seasonality in a changing ocean: comparing time series
of zooplankton phenology. Prog. Oceanogr. 97–100,
31–62. (doi:10.1016/j.pocean.2011.11.005)
Perry AI, Low PJ, Ellis JR, Reynolds JD. 2005
Climate change and distribution shifts in marine
fishes. Science 308, 1912 –1915. (doi:10.1126/
science.1111322)
Beaugrand G, Edwards M, Brander K, Luczak C,
Ibañez F. 2008 Causes and projections of abrupt
climate-driven ecosystem shifts in the North
Atlantic. Ecol. Lett. 11, 1157– 1168. (doi:10.1111/j.
1461-0248.2008.01218.x)
Beaugrand G, Ibanez F. 2004 Monitoring marine
plankton ecosystems. II. Long-term changes in
North Sea calanoid copepods in relation to hydrometeorological variability. Mar. Ecol. Prog. Ser. 284,
35 –47. (doi:10.3354/meps284035)
Hufty A. 2005 Introduction à la climatologie. Québec,
Canada: de Boeck.
Carpenter SR, Brock WA. 2006 Rising variance: a
leading indicator of ecological transition. Ecol.
Lett. 9, 311– 318. (doi:10.1111/j.1461-0248.
2005.00877.x)
Chase JM, Leibold MA. 2003 Ecological niches:
linking classical and contemporary approaches.
Chicago, IL: University of Chicago.
Hutchinson GE. 1957 Concluding remarks. Cold
Spring Harb. Symp. Quant. Biol. 22, 415– 427.
(doi:10.1101/SQB.1957.022.01.039)
Helaouët P, Beaugrand G. 2009 Physiology, ecological
niches and species distribution. Ecosystems 12,
1235–1245. (doi:10.1007/s10021-009-9261-5)
Brown JH, Gillooly JF, Allen AP, Savage VM, West GB.
2004 Toward a metabolic theory of ecology. Ecology
85, 1771–1789. (doi:10.1890/03-9000)
Gillooly JF, Brown JA, West GB, Savage VM, Charnov
EL. 2001 Effects of size and temperature on
metabolic rate. Science 293, 2248–2251. (doi:10.
1126/science.1061967)
Beaugrand G, Mackas D, Goberville E. 2013 Applying
the concept of the ecological niche and a
macroecological approach to understand how climate
influences zooplankton: advantages, assumptions,
limitations and requirements. Prog. Oceanogr. 111,
75–90. (doi:10.1016/j.pocean.2012.11.002)
Beaugrand G. 2012 Unanticipated biological
changes and global warming. Mar. Ecol. Prog. Ser.
445, 293 –301. (doi:10.3354/meps09493)
Beaugrand G, Kirby RR. 2010 Spatial changes in the
sensitivity of Atlantic cod to climate-driven effects
in the plankton. Clim. Res. 41, 15 –19. (doi:10.
3354/cr00838)
29. Helaouët P, Beaugrand G, Edwards M. 2012
Understanding long-term changes in species
abundance using a niche-based approach. PLoS ONE
8, e79186. (doi:10.1371/journal.pone.0079186)
30. Fort J, Beaugrand G, Grémillet D, Phillips RA. 2012
Biologging, remotely-sensed oceanography and the
continuous plankton recorder reveal the environmental
determinants of a seabird wintering hotspot. PLoS ONE
7, e41194. (doi:10.1371/journal.pone.0041194)
31. Chalaali A, Beaugrand G, Raybaud V, Goberville E, David
V, Boët P, Sautour B. 2013 Climatic facilitation of the
colonisation of an estuary by Acartia tonsa. PLoS ONE 8,
1–11. (doi:10.1371/journal.pone.0074531)
32. Beaugrand G, Rombouts I, Kirby RR. 2013 Towards
an understanding of the pattern of biodiversity in
the oceans. Glob. Ecol. Biogeogr. 22, 440–449
(doi:10.1111/geb.12009)
33. Beaugrand G, Goberville E, Luczak C, Kirby RR. 2014
Marine biological shifts and climate. Proc. R. Soc. B
281, 20133350. (doi:10.1098/rspb.2013.3350)
34. Ter Braak CJF. 1996 Unimodal models to relate
species to environment. Wageningen, The
Netherlands: DLO-Agricultural Mathematics Group.
35. Taylor W. 2000 Change-point analysis: a powerful
new tool for detecting changes. Available at: www.
variation.com/cpa/tech/changepoint.html.
36. Gause GF. 1934 The struggle for coexistence.
Baltimore, MD: Williams and Wilkins.
37. Ibanez F, Dauvin J-C. 1998 Shape analysis of
temporal ecological processes: long-term changes in
English Channel macrobenthic communities.
Coenoses 13, 115– 129.
38. Beaugrand G, Ibañez F, Lindley JA, Reid PC. 2002
Diversity of calanoid copepods in the North Atlantic
and adjacent seas: species associations and
biogeography. Mar. Ecol. Prog. Ser. 232, 179 –195.
(doi:10.3354/meps232179)
39. Beaugrand G, Reid PC, Ibañez F, Lindley JA, Edwards
M. 2002 Reorganisation of North Atlantic marine
copepod biodiversity and climate. Science 296,
1692– 1694. (doi:10.1126/science.1071329)
40. Edwards M, Beaugrand G, Reid PC, Rowden AA,
Jones MB. 2002 Ocean climate anomalies and the
ecology of the North Sea. Mar. Ecol. Prog. Ser. 239,
1–10. (doi:10.3354/meps239001)
41. Luczak C, Beaugrand G, Lindley JA, Dewarumez J-M,
Dubois PJ, Kirby RR. 2012 North Sea ecosystem
changes from swimming crabs to seagulls. Biol.
Lett. 8, 821 –824. (doi:10.1098/rsbl.2012.0474)
42. Beaugrand G, Brander KM, Lindley JA, Souissi S,
Reid PC. 2003 Plankton effect on cod recruitment in
the North Sea. Nature 426, 661 –664. (doi:10.1038/
nature02164)
43. Beaugrand G. 2004 The North Sea regime shift:
evidence, causes, mechanisms and consequences.
Phil. Trans. R. Soc. B 370: 20130264
2.
de Young B, Barange M, Beaugrand G, Harris R,
Perry RI, Scheffer M. 2008 Regime shifts in marine
ecosystems: detection, prediction and management.
Trends Ecol. Evol. 23, 402 –409. (doi:10.1016/j.tree.
2008.03.008)
Holling CS. 1973 Resilience and stability of
ecological systems. Annu. Rev. Ecol. Syst. 4, 1– 23.
(doi:10.1146/annurev.es.04.110173.000245)
Scheffer M, Carpenter SR. 2003 Catastrophic regime
shifts in ecosystems: linking theory to observation.
Trends Ecol. Evol. 18, 648 –656. (doi:10.1016/j.tree.
2003.09.002)
Scheffer M. 2009 Critical transitions in nature and
society. Princeton, NJ: Princeton University Press.
Scheffer M, van Nes EH. 2004 Mechanisms for
marine regime shifts: can we use lakes as
microcosms for oceans? Prog. Oceanogr. 60,
303–319. (doi:10.1016/j.pocean.2004.02.008)
Seekell DA, Cline TJ, Carpenter KR, Pace ML.
2013 Evidence of alternate attractors from a
whole-ecosystem regime shift experiment.
Theor. Ecol. 6, 385 –394. (doi:10.1007/s12080013-0183-7)
Nystrom M, Folke C, Moberg F. 2000 Coral reef
disturbance and resilience in a human-dominated
environment. Trends Ecol. Evol. 15, 413 –417.
(doi:10.1016/S0169-5347(00)01948-0)
Carpenter RC. 1990 Mass mortality of Diadema
antillarum. I. Long-term effects on sea urchin
population-dynamics and coral reef algal communities.
Mar. Biol. 104, 67–77. (doi:10.1007/BF01313159)
Casini M, Hjelma J, Molinero J-C, Lövgren J, Cardinale
M, Bartolino V, Belgrano A, Kornilovs G. 2009 Trophic
cascades promote threshold-like shifts in pelagic
marine ecosystems. Proc. Natl Acad. Sci. USA 106,
197–202. (doi:10.1073/pnas.0806649105)
Hare SR, Mantua NJ. 2000 Empirical evidence for
North Pacific regime shifts in 1977 and 1989. Prog.
Oceanogr. 47, 103–145. (doi:10.1016/S0079-6611
(00)00033-1)
Reid PC, Borges M, Svenden E. 2001 A regime shift
in the North Sea circa 1988 linked to changes in the
North Sea horse mackerel fishery. Fish. Res. 50,
163–171. (doi:10.1016/S0165-7836(00)00249-6)
Luczak C, Beaugrand G, Jaffré M, Lenoir S. 2011
Climate change impact on Balearic shearwater
through a trophic cascade. Biol. Lett. 7, 702 –705.
(doi:10.1098/rsbl.2011.0225)
Beaugrand G, McQuatters-Gollop A, Edwards M,
Goberville E. 2012 Long-term changes in calcifying
plankton in the North Atlantic and climate.
Nat. Clim. Change 3, 263 –267. (doi:10.1038/
NCLIMATE1753)
Weijerman M, Lindeboom H, Zuur AF. 2005 Regime
shifts in marine ecosystems of the North Sea and
8
rstb.royalsocietypublishing.org
ecosystem/community state vacillations, where ACSs alternate with periods of more relative stability. These periods
of relative stability may not be confounded with stable
Downloaded from http://rstb.royalsocietypublishing.org/ on June 15, 2017
50. Richardson AJ, Schoeman DS. 2004 Climate impact
on plankton ecosystems in the northeast Atlantic.
Science 305, 1609– 1612. (doi:10.1126/science.
1100958)
51. Frank KT, Petrie B, Choi JS, Leggett WC. 2005
Trophic cascades in a formerly cod-dominated
ecosystem. Science 308, 1621–1623. (doi:10.1126/
science.1113075)
52. Bakun A, Weeks SJ. 2006 Adverse feedback
sequences in exploited marine systems: are
deliberate interrruptive actions warranted? Fish
Fish. 7, 316–333. (doi:10.1111/j.1467-2979.2006.
00229.x)
9
Phil. Trans. R. Soc. B 370: 20130264
Exp. Mer. 18, 287– 295. (doi:10.1093/icesjms/
18.3.287)
47. Behrenfeld MJ. 2010 Abandoning Sverdrup’s critical
depth hypothesis on phytoplankton blooms. Ecology
91, 977– 989. (doi:10.1890/09-1207.1)
48. Reid PC, Edwards M, Beaugrand G, Skogen M,
Stevens D. 2003 Periodic changes in the
zooplankton of the North Sea during the twentieth
century linked to oceanic inflow. Fish. Oceanogr. 12,
260 –269. (doi:10.1046/j.1365-2419.2003.00252.x)
49. Kirby RR, Beaugrand G. 2009 Trophic amplification
of climate warming. Proc. R. Soc. B 276,
3053 –3062. (doi:10.1098/rspb.2009.1320)
rstb.royalsocietypublishing.org
Prog. Oceanogr. 60, 245 –262. (doi:10.1016/j.
pocean.2004.02.018)
44. Scheffer M, Straile D, van Nes EH, Hosper H. 2001
Climatic warming causes regime shifts in lake food
webs. Limnol. Oceanogr. 1780 –1783. (doi:10.4319/
lo.2001.46.7.1780)
45. Hsieh C-H, Glaser SM, Lucas AJ, Sugihara G. 2005
Distinguishing random environmental fluctuations
from ecological catastrophes for the North Pacific
Ocean. Nature 435, 336–340. (doi:10.1038/
nature03553)
46. Sverdrup HU. 1953 On conditions for the vernal
blooming of phytoplankton. J. Cons. Perm. Int.