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The rate of environmental change as an important driver across scales in ecology

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Abstract

Global change has been predominantly studied from the prism of ‘how much’ rather than ‘how fast’ change occurs. The paradigm underlying the former assumes that a smooth change in an environmental driver can force a regime shift between alternative states (Bifurcation-tipping). This presupposes that environmental conditions change at a rate which allows the ecological entity to track them and thus reach equilibrium. However, current rates of environmental change are often too fast for this paradigm to apply, necessitating a shift in approach to improve predictions on the impacts of rapid environmental change. The theory of rate-induced tipping (Rate-tipping) demonstrates how rates of environmental change can cause tipping phenomena even in the absence of alternative states. We illustrate how Rate-tipping can apply to a range of ecological scenarios and explore the literature for properties which increase the sensitivity to rates of change. Further, we discuss how targeted empirical studies can investigate the ecological and evolutionary mechanisms through which rate-induced phenomena can propagate across levels of organisation. Finally, we argue for the inclusion of Rate-tipping in the study of global change as the first step towards the theoretical synthesis necessary to account for multiple stressors impacting ecological entities simultaneously.
Title: The rate of environmental change as an important driver across scales in ecology
Authors: Alexis D. Synodinos 1, 2, 11 *, Rajat Karnatak 3, 11, Carlos A. Aguilar-Trigueros 4, 11,
Pierre Gras 5, 11, Tina Heger 3, 4, 6, 7, 11, Danny Ionescu 8, 11, Stefanie Maaß 1, 11, Camille L.
Musseau 3, 4, 11, Gabriela Onandia 9, 11, Aimara Planillo 5, 11, Lina Weiss 1, 11, Sabine Wollrab 3,
11**, Masahiro Ryo 9 , 10, 11**
Affiliations:
1 University of Potsdam, Plant Ecology and Nature Conservation, Potsdam, Germany
2 French National Centre for Scientific Research (CNRS), Theoretical and Experimental
Ecology Station (SETE), Moulis, France
3 Leibniz-Institute of Freshwater Ecology and Inland Fisheries (IGB), Berlin, Germany
4 Freie Universität Berlin, Institute of Biology, Berlin, Germany.
5 Leibniz Institute for Zoo and Wildlife Research (IZW), Department of Ecological Dynamics,
Berlin, Germany
6 University of Potsdam, Biodiversity Research/Botany, Potsdam, Germany
7 Technical University of Munich, Restoration Ecology, Freising, Germany
8 Leibniz Institute of Freshwater Ecology and Inland Fisheries (IGB), Neuglobsow, Germany
9 Leibniz Centre for Agricultural Landscape Research (ZALF), Research Platform Data
Analysis and Simulation, Muencheberg, Germany
10 Brandenburg University of Technology Cottbus-Senftenberg, Environment and Natural
Sciences, Cottbus, Germany
11 Berlin-Brandenburg Institute of Advanced Biodiversity Research (BBIB), Berlin, Germany
* Correspondence to: Alexios Synodinos, email: alexios.synodinos@sete.cnrs.fr
** Joint last authors
Article Type: Perspective
Keywords: climate change, ecological communities, ecosystem response, buffering
mechanisms, r-tipping, temporal ecology, global change
Authorship statement: ADS conceived the study. Discussions with RK, CA, PG, TH, DI, SM,
CM, GO, AP, LW, SW and MR provided feedback to develop and refine the original ideas.
ADS, CA, TH, PG, RK, SM, CM, GO, AP, LW and MR performed the literature review. ADS
wrote the first version of the manuscript, RK, CA, PG, TH, DI, SM, CM, GO, AP, LW, SW
and MR contributed to the writing of all subsequent versions. The authors declare no conflict
of interest.
Data accessibility statement: This study produced no new data. Details of the studies used in
the literature review are provided in the Supplementary Information.
Abstract
Global change has been predominantly studied from the prism of ‘how much’ rather than ‘how
fast’ change occurs. The paradigm underlying the former assumes that a smooth change in an
environmental driver can force a regime shift between alternative states (Bifurcation-tipping).
This presupposes that environmental conditions change at a rate which allows the ecological
entity to track them and thus reach equilibrium. However, current rates of environmental change
are often too fast for this paradigm to apply, necessitating a shift in approach to improve
predictions on the impacts of rapid environmental change. The theory of rate-induced tipping
(Rate-tipping) demonstrates how rates of environmental change can cause tipping phenomena
even in the absence of alternative states. We illustrate how Rate-tipping can apply to a range of
ecological scenarios and explore the literature for properties which increase the sensitivity to
rates of change. Further, we discuss how targeted empirical studies can investigate the
ecological and evolutionary mechanisms through which rate-induced phenomena can propagate
across levels of organisation. Finally, we argue for the inclusion of Rate-tipping in the study of
global change as the first step towards the theoretical synthesis necessary to account for multiple
stressors impacting ecological entities simultaneously.
Introduction
Understanding how ecosystems will respond to ongoing anthropogenic changes (e.g.,
increasing mean temperatures, atmospheric CO2 and nitrogen enrichment, habitat loss and
fragmentation) is a formidable challenge in ecology (Steffen et al. 2005; Rillig et al. 2019; Sage
2020). Related theory has focused on so-called tipping phenomena, where the (eco)system
undergoes a significant, and sometimes catastrophic, change. Three types of tipping have been
identified: Bifurcation-, Noise- and Rate-tipping (Ashwin et al. 2012) (Box 1, Fig. 1).
Bifurcation-tipping or B-tipping presupposes the existence of stable states, with transitions
occurring due to change in the magnitude of a parameter exceeding a threshold (Fig. 1, green
lines). B-tipping has formed the dominant paradigm in ecology and beyond; the notion of
tipping points and catastrophic transitions between alternative states guides policy in the fight
against the global ecological crisis (Rockström et al. 2009; Steffen et al. 2015). Noise-tipping
or N-tipping is related to noise driven regime shifts, where noisy fluctuations in a driving factor
can lead the system dynamics to switch from one stable state to an alternate one (Fig. 1, yellow
lines). Rate-tipping or R-tipping occurs when the rate of change of a forcing parameter exceeds
a critical threshold (Fig. 1, red lines). R-tipping leads to a situation where the system dynamics
are unable to track the corresponding changes in the dynamical attractor, thereby departing from
the stable state. R-tipping does not require the existence of alternative states.
In this work, we emphasise the ecological relevance of R-tipping to the ongoing ecological
crisis, which is partially driven by unprecedented rates of environmental change (Waters et al.
2016; Pattyn et al. 2018; Ceballos et al. 2020). Significantly, B- and R-tipping occur due to
different causal factors; exceeding a critical magnitude versus exceeding a critical rate of
change in an external parameter, respectively. Therefore, R-tipping can be triggered by
continuously increasing rates of change in an external factor like temperature, atmospheric CO2
concentrations or habitat loss (which are known drivers of the current ecological crisis). Hence,
we argue for the application of R-tipping theory to improve our understanding of ecological
dynamics under the current ecological crisis.
BOX 1: Tipping
B-tipping: Bifurcation-induced tipping occurs when the system shifts to an alternate state once
a critical threshold in magnitude of an external driver is passed.
N-tipping: Noise-induced tipping occurs when a stochastic noisy event in the external forcing
parameter can lead to a departure from the current state to an alternate state.
R-tipping: Rate-induced tipping occurs when the dynamics of the system are unable to track
the changes in the attractor due to an increased rate of change in the external driver/forcing
parameter. The system exhibits a tipping response when the rate of change exceeds a critical
threshold.
Figure 1. Illustration of how a change in an external driver can induce tipping in the state of
an ecological entity: Bifurcation-induced tipping (B-tipping), noise-induced tipping (N-
tipping), and rate-induced tipping (R-tipping). A) Different ways in which the external driver
can change; B) the three corresponding types of tipping (the colours of the curves in panel A
correspond to the same-colour responses in panel B). An equilibrium state changes over time
due to a continuous directional change in an external condition. Even if the change is slow so
that the ecological entity can track the moving equilibrium, it can tip into an alternative basin
of attraction if the change exceeds a critical magnitude (B-tipping, green curves) or if
consecutive stochastic perturbations force it beyond the current attractor’s basin boundary (N-
tipping, brown curves). If the change is too fast, the ecological entity cannot keep track of the
equilibrium trajectory, and the gap eventually causes tipping behaviour (R-tipping, red curves).
Note that R-tipping can happen without necessarily
Bifurcation theory in ecology
The occurrence of drastic regime shifts in response to an external driver crossing a critical
threshold has drawn a lot of attention in ecology. Prominent examples are shallow lake systems
exhibiting shifts between a clear water state dominated by submerged plants and a turbid state
dominated by algae caused by eutrophication (Scheffer et al. 1993; Scheffer & Carpenter 2003).
Other examples are the shift of coastal systems to an alternative depauperate state caused by
the over-exploitation of top predators (sea otters) (Estes & Palmisano 1974; Jackson et al. 2001;
Estes et al. 2011), the occurrence of cyanobacterial blooms in lakes with climate warming and
eutrophication causing drastic shifts in the community composition of aquatic systems
(Wilkinson et al. 2018), or the woody encroachment of grasslands (Ratajczak et al. 2014; Sala
& Maestre 2014).
These transitions correspond to catastrophic regime shifts mediated by a bifurcation in the
underlying system, referred to as B-tipping (Scheffer 2009; Ashwin et al. 2012). While such a
regime shift in principle can also be smooth - the system state changing smoothly with a
continuous shift in the forcing parameter (Kéfi et al. 2013) - when talking about tipping, we
refer to abrupt or catastrophic regime shifts. These lead to a drastic functional and structural re-
organisation of the ecological entity. Additionally, once a critical threshold in the magnitude of
the forcing parameter is crossed, the transition might not be easily reversed (Scheffer &
Carpenter 2003; Hughes et al. 2013), leading to hysteresis (Scheffer et al. 2009).
The necessity of a “bifurcation” between alternate states in the underlying dynamics limits the
general applicability of the B-tipping formalism. This is emphasized by a lack of evidence for
well-defined alternate states and corresponding regime shifts (Capon et al. 2015; Montoya et
al. 2018; Hillebrand et al. 2020). Moreover, there is a pressing need to further develop an
understanding on how ecosystems will respond to temporal changes in critical drivers.
Consequently, an important question is how the rate of environmental change will impact
populations, communities and ecosystems (Joos & Spahni 2008; Trusel et al. 2018; Pinek et al.
2020). Therefore, we require a paradigm shift to consider this aspect of change in ecological
predictions (Williams et al. 2021).
Glossary
Alternative stable state: When more than one dynamical attractor coexist for a fixed set of
system parameters. This parameter range is called hysteresis area.
Bifurcation: When small, smooth changes to the parameter values of the system cause a sudden,
qualitative change in its dynamics due to changes in the stability properties of an attractor.
Dynamical system: A differential/difference (time continuous/discrete) equation system,
defining the dynamical rules of the changes of all state variables over time. State variables can,
for example, be the biomass of an individual, of populations or of multiple species representing
a functional group or a community. Dynamical systems provide predictions on the immediate
future values of all state space variables on the basis of their present values.
Ecological entity: The object of study which can range from an individual organism to a whole
ecosystem.
External driver: The general environment where the system is located. Typically, there exists
no feedback between the ecological entity and the abiotic conditions. Hence, factors describing
the abiotic conditions which influence system dynamics are considered as external.
Regime shift: A qualitative change in the system state. Regime shifts can be smooth, i.e., the
quantitative behaviour changes smoothly with respect to the parameter, or abrupt/catastrophic,
i.e., the behaviour changes abruptly, so even a small change in the environmental parameter
can result in a large change in the dynamic behaviour.
Stable state/attractor: The dynamical regime/state a dynamical system settles on in the long
term, after the transient dynamics phase. Examples of such attractors are a fixed point
equilibrium, a limit cycle (periodic oscillations), or a strange attractor.
Significance of rates of change
The theory of R-tipping describes how high rates of parameter change cause the departure of
the dynamical system from the stable state, when this rate exceeds a critical value (Ashwin et
al. 2012). It is important to note that the occurrence of R-tipping does not necessitate the
presence of an alternate state. This makes R-tipping even more concerning: it can lead to a
catastrophic loss of the current system state, in the absence of an alternative state that the system
can settle onto. A complete loss of any equilibrium state (or the ability to reach any equilibrium
state) would have drastic consequences for the underlying ecosystem, possibly triggering a
cascade of species extinctions. Below we present studies which have explored the potential
ramifications of R-tipping in simple ecological settings.
In consumer-resource models, the coupling of fast and slow processes can lead to a collapse in
the community (Siteur et al. 2016; Vanselow et al. 2019). In one case, the reduction in the
growth rate of the resource over time caused a lag in the response of the consumers (Siteur et
al. 2016). When the rate of reduction was too high, the depleted resources were overconsumed
by the slow-responding consumers, causing the community to eventually crash. A similar
approach identified a critical threshold in the rate of reduction in the resource carrying capacity
(Vanselow et al. 2019). Below this threshold, dynamics tracked the equilibrium with a temporal
lag; once the reduction rate threshold was crossed, the community collapsed to extremely low
densities. Significantly, the R-tipping occurred in a parameter space of stable consumer-
resource coexistence under the assumption of static parameters. This implied that no regime
shift in the classical sense was possible, i.e., the response was caused by the rate of change in
the carrying capacity rather than by passing a critical threshold in the magnitude of carrying
capacity. The prey population abruptly collapsed (fast dynamics) and remained at extremely
low densities (slow dynamics), before eventually recovering and converging to the equilibrium.
In a natural setting, this long phase of extremely low resource densities would make the
community vulnerable to stochastic extinctions; hence the significance of such a collapse
should not be underestimated, and its irreversibility not excluded.
In a different system, it has been described how the rate of increasing soil temperature can cause
an explosive release of soil carbon into the atmosphere (Luke & Cox 2011; Wieczorek et al.
2011). Increasing soil temperatures (e.g. through climate change) increase microbial soil
respiration, and vice versa in a self-reinforcing cycle (positive feedback). Meanwhile, soil
carbon and soil respiration are regulated by a negative feedback; increasing soil carbon
increases respiration, which in turn limits soil carbon, keeping both quantities in check. The so-
called ‘compost-bomb instability’ arises when the rate of increasing atmospheric temperature
causes the positive feedback to overpower the negative feedback, forcing the release of soil
carbon into the atmosphere in large amounts.
Though simplified in their ecological conception, these studies seem both relevant and
alarming. Therefore, it is necessary to apply this theory more broadly within ecology, where it
currently remains largely understudied (Pinek et al. 2020), with some exceptions. The
framework of climate velocity has helped to quantify the impacts of the rate of temperature
increases relative to the rate of species range shifts on ecosystem dynamics (Loarie et al. 2009;
Burrows et al. 2011; Garciá Molinos et al. 2016). Recently the potential for R-tipping in a
marine food-web was demonstrated (Gil et al. 2020); a rapid increase in top predator density
causing the collapse of herbivores and the proliferation of algae. In a broader application, a
framework for ecological conservation based on rates of environmental change identified three
potential outcomes (Williams et al. 2021): (1) ecological change tracks climate change, (2)
ecological change lags behind, creating an extinction or evolutionary debt, or (3) ecological
change fails to track the rate of climate change leading to abrupt or catastrophic regime shifts.
The authors found that the realised outcome will depend on specific traits, such as body size,
topographic heterogeneity or the number of interspecific interactions and their effects on
ecosystem dynamics.
Here, we bring together the mathematical theory of R-tipping (Ashwin et al. 2012) and a broad
ecological perspective (Williams et al. 2021) to help fill the gap in the study of rate-induced
phenomena in ecology (Pinek et al. 2020), while preserving the rigour of the original theory.
R-tipping takes place when a directional change in external conditions occurs at a rate which
induces an ecological or evolutionary response even though the magnitude of change alone
would not (Fig. 1). When conditions occur slower than a critical rate (Fig. 1A, red curve for
𝑡 < 𝑡𝑅), the ecological entity will “track” the equilibrium - the deviations from the equilibrium
are bounded (Fig. 1B, red curve for 𝑡 < 𝑡𝑅). However, when external or environmental change
exceeds the critical rate (Fig. 1A, red curve for 𝑡 ≥ 𝑡𝑅), the deviation is unbounded, and the
ecological entity moves rapidly away from the equilibrium (Fig. 1B, red curve for 𝑡 ≥ 𝑡𝑅).
Manifestations of R-tipping may already exist. For example, increasing atmospheric CO2
concentrations cause the acidification of the oceans (Cao & Caldeira 2008), adversely affecting
coral reefs (Anthony et al. 2008; Kiessling & Simpson 2011; Pandolfi et al. 2011; Albright et
al. 2016). Besides the increase in the amount of CO2 (magnitude of change), the rate of increase
presents a threat in itself (Connell 1997): coral reefs may not be able to adapt to the rate of
increasing CO2 (Hoegh-Guldberg et al. 2007) and as a result suffer damage even at pH levels
which would not otherwise be considered harmful (Fabricius et al. 2011). In a further example,
species will have to shift their ranges in order to follow climatic changes (Burrows et al. 2011;
Sunday et al. 2015). Both marine and terrestrial species have been shifting their ranges towards
higher elevations or latitudes as they fail to adapt to the rapidly changing conditions (Parmesan
& Yohe 2003; Chen et al. 2011; Pecl et al. 2017). However, this only works if species can
physically move at a rate which tracks the climatic shifts (Thomas et al. 2004); many species
such as certain tropical Andean trees (Feeley et al. 2011; Fadrique et al. 2018), European
butterflies (Parmesan et al. 1999) or French birds (Devictor et al. 2008) cannot. The mismatch
between optimal conditions and those experienced by slower-moving species lead to a loss of
fitness and a so-called extinction debt or result in direct extinction (Williams et al. 2021).
Mismatches in range shifts of migrating species can also generate new interactions (Ordonez &
Williams 2013) and alter the functioning of ecosystems (Garciá Molinos et al. 2016), which
can be considered a shift to a novel regime.
What increases sensitivity to rates of change?
To seek the properties that make ecological entities sensitive to rates of change, we conducted
a narrative literature review (based on keywords), selecting only studies which explicitly
investigated the impact of the rate of change on a target variable. We found 22 studies met our
selection criteria (see the Supplementary Information for a list of these studies and a description
of the review process). Within certain studies multiple target organisms or target variables were
investigated; we treated these as independent data points which increased the total number of
data points to 30. This low number of studies explicitly aimed at sensitivities to rates of change
is congruent with the findings of a recent review (Pinek et al. 2020) and highlights the
knowledge gap that needs to be addressed. From our review, we created the following
categorical predictive variables with sufficient repetition between the studies to allow for an
indicative statistical analysis: level of ecological organisation (organism, population,
community, ecosystem/biome), kingdom (bacteria, fungi, plants, animals), ecosystem type
(terrestrial, aquatic) and study type (experimental, observation, model). We defined the
response variable as ‘no tipping’ (dynamics track the equilibrium linear response) or ‘tipping’
(dynamics cannot track the equilibrium nonlinear response), and used the conditional
inference tree algorithm of machine learning (Hothorn et al. 2006; Ryo & Rillig 2017), which
classified the studies into subgroups based on the categorical variables (Fig. 2).
Figure 2. Conditional inference tree algorithm of machine learning for
22
studies analysing
rate-induced changes, leading to
30
data points due to multiple experiments in certain studies.
Level of organisation was the strongest predictor to split distinguish ‘tipping (nonlinear)’
(community, ecosystem/biome) from ‘no tipping (linear)’ (organism, population) outcomes. At
a secondary level of prediction, ‘tipping’ occurred at
85%
of the cases at the community and
55%
of the cases at the ecosystem/biome level.
Level of ecological organisation was the only predictor, among the other variables, to explain
the consequence (tipping or no tipping) of the rate of environmental change: higher levels
(community, ecosystem/biome) were most likely to undergo ‘tipping’, while at lower levels
(organism and population) ‘no tipping’ dominated the outcomes. It is intuitive that higher levels
of ecological organisation are sensitive to tipping, since these will contain a higher number of
interactions (Williams et al. 2021) and more likely include fast-slow processes, which increase
the likelihood of R-tipping (Ashwin et al. 2012; Siteur et al. 2016). Within the first group,
community had a higher prevalence of ‘tipping’ (85% of cases) than ecosystem/biome (55%
of cases). We should note that the very limited sample size (n=30) does not allow for
generalisations based on these results, and therefore, we do not consider the relatively high p-
value (e.g., p=0.25) as a lack of evidence. Rather, we regard this emergent pattern as an
important perspective that needs further investigation.
Testing rate-sensitivity across levels of organisation
To this point, we have elucidated how R-tipping can manifest itself in a broad ecological context
(Fig. 1) and demonstrated the potential significance of different levels of organisation (Fig. 2).
Here we discuss how empirical studies can help advance the theory on R-tipping phenomena.
Study design
It is vital that empirical approaches share a common definition of what constitutes rapid
environmental change, by seeking thresholds along a continuum rather than by picking extreme
scenarios of slow and rapid change (Pinek et al. 2020). It is also critical to disentangle
transitions driven by the magnitude of change from rate-sensitive responses, as transitions that
have been attributed to the former could have been induced by the rapidity of change (Vanselow
et al. 2019). Since the rate of change equals the magnitude of change divided by time (i.e., the
duration of the treatment), one could seek existing studies where both a treatment’s magnitude
and its duration have been recorded. Alternatively, new experiments could investigate rates of
change by varying both the magnitude and the duration of a treatment, essentially producing a
full factorial design (Fig. 3). Statistical analyses can then help to reveal whether it is the
magnitude or the rate of change, the latter modelled as the interaction of magnitude with
duration, driving the response.
Figure 3. A visualisation of how to investigate increasing rates of change in environmental
conditions. The rate of change in the treatment (i.e., environmental conditions) equals the
change in magnitude over the duration of the treatment. One can investigate the same increase
in the rate of change by either increasing the magnitude of change (B) or by reducing the
treatment duration (C). A) The control: the change in conditions is
δC = C2-C1
within a time
of
δt = tj-ti
. B) Increasing the rate of change by increasing the magnitude of the treatment,
δC
= C3-C1
, while keeping the duration
δt
constant. C) Increasing the rate of change by reducing
the treatment duration
δt = tj-t1
and preserving the magnitude of
δC
. Direct comparison of the
effect of the different approaches to increasing the rate of change over a range of different rates
of change can help disentangle the effect of the magnitude of change from the rate of change.
Mechanistic approach
Any environmental change will directly impact individual organisms and indirectly cause
change across levels of organisation. Thus, to understand R-tipping propagation across levels
of organisation, experiments and field observations can start with studying the responses at the
lower levels (organism, population) focusing on a single stressor and only pair-wise interacting
species. A mechanistic understanding of the given system can help produce generalisable
results. Below we present different mechanisms known to buffer against environmental change
at different levels of organisation and give simplified examples of potential experiments to test
their susceptibility to rates of change.
At the organismal level, plasticity (Parmesan 2006; Allen et al. 2012, 2016; Moyano et al. 2017;
Radchuk et al. 2019) or microevolution, sensu local adaptation (Hendry & Kinnison 1999;
Ignacio et al. 2013), can buffer against environmental change. Alternatively, well-adapted
species (e.g., thermal tolerance of heat shocks) can produce ecological responses (e.g.,
increased foraging due to higher metabolic demands with rapid warming). Observations of
individual species can follow the study design described above (Fig. 3) to identify any
physiological, morphological, phenological, life-history or other response induced by the rate
of changing conditions. A slightly more complex experimental design could allow for
individuals to disperse to account for another buffering mechanism against rapid change
(Higgins et al. 2003; Stefan et al. 2015).
It should be noted that even when dynamics at one level track the equilibrium with some delay,
this lag can indicate a suboptimal fit to current conditions (Williams et al. 2021). Such
mismatches may cause evolutionary or extinction debt, ultimately impacting higher levels of
organisation. Therefore, even if experiments at the organism (or population) level show that the
ecological entity can track the rate of environmental change, it is critical to evaluate the impact
of this response on higher levels of organisation. Such an investigation would benefit from a
mechanistic approach: identifying which aspects of higher-level interactions could be impacted
by the organismal level responses to the rates of change, and developing associated hypotheses.
For example, a decrease in the body size of predators can lead to lower per-capita consumption
of prey with consequences for top-down control. A corresponding hypothesis could be used to
test how total prey and predator biomasses or population fluctuations will be affected.
At the community level, in particular in competitive communities, species can achieve stable
coexistence by occupying different niches for resource acquisition (Macarthur & Levins 1967;
MacArthur 1970; Tilman et al. 1997). High rates of environmental change can induce responses
in individual species which can alter the competitive balance of the community. In drylands,
for example, rainfall events will become more intense, with yearly rainfall concentrated in
fewer time windows and droughts more prolonged (Ma et al. 2015). If we only focus on the
change in rainfall, in communities of competing herbaceous and woody plants (e.g., savannas),
extreme individual rainfall events could accelerate woody seed germination causing woody
vegetation recruitment pulses and ultimately woody encroachment (Morrison et al. 2018).
Alternatively, grasses can respond fastest to produce growth pulses of their own, causing the
exclusion of woody vegetation (Xu et al. 2015). These opposing outcomes rely on different
mechanisms: root-niche separation benefiting trees which can access water from deeper soils
and a so-called temporal niche (i.e., fast responsiveness) benefiting grasses, respectively.
Experiments can establish how isolated populations of grasses and woody plants respond to
different rates of increasing rainfall intensity (i.e., rainfall amount per individual rainfall event
without altering the total amount of rainfall in a given ‘year’) (Fig. 4). By identifying the
mechanisms responsible for the observed responses at the population level, hypotheses
regarding the response patterns of competitive plant communities to altered rainfall intensity
patterns can be developed and tested at the community level.
Figure 4. Testing how the rate of increasing rainfall intensity (i.e., rainfall amount per
individual event while maintaining the total yearly’ amount constant) can alter competitive
dryland plant communities, starting at the organismal level and working upwards. In this setup,
we assume the rate of rainfall intensity as the single driver of the dynamics, so we will observe
and hypothesise based on the associated mechanisms known from the literature. Increased
rainfall intensity can be applied either by increasing the rainfall amount and maintaining the
duration of the individual rainfall events or by preserving the rainfall amount and reducing the
duration of the rainfall events (as explained in Fig. 3). At the organism level, the resource
allocation of individuals differs between woody plants and grasses. The former expand their
roots before investing in above-ground biomass accumulation, the latter increase above-
ground biomass and reproduce. For trees this translates into access to water in the deeper soil
layers (root-niche separation). Grasses, in the absence of inter-specific competition, can
rapidly colonise the available space in the topsoil layer (so-called ‘temporal niche’). With more
intense rainfall events, water percolates faster into the deeper soil layer.. Given these
hypotheses (separate niches), if in the presence of tree competition, grasses begin to lose out
(trees take up water from the upper and lower soils, shading negatively affects grasses, etc.)
and woody species expand their population, we can stipulate the root-niche competition causes
R-tipping in the form of woody encroachment. Extensions to the experiment should consider
other drivers of tree sapling establishment bottlenecks such as the interaction between drought
duration and increased rainfall amounts (Geissler & Blaum, unpublished data) or the rate of
increase in atmospheric CO2 with respect to the benefits C3 and C4 photosynthetic pathways.
As a second example at the community level, trophic interactions will also be impacted by rates
of environmental change. Different evolutionary or ecological rates of resource and consumer
can increase the risk of consumer extinction through long phases of low densities (Yoshida et
al. 2003), lead to community collapse due to overconsumption (Siteur et al. 2016) or create
resource-dominated communities (Scheffer et al. 2008), among others. An experimental setup
which studies the potential for local adaptations of isolated prey and predator populations can
generate hypotheses about potential rate-induced mismatches at the community-level (Fig. 5).
The spatial extension of this example to include more patches (i.e., a metacommunity) or the
addition of another trophic level (i.e., food chain) would be an important extension as it may
yield unexpected results (Faillace et al. 2021).
Figure 5. An illustration of how fast rates of warming can propagate from low to higher levels
of organisation in predator-prey interaction. Many eco-evolutionary feedbacks are possible,
but we can assume the following: algae can respond to rapid warming by increasing their
tolerance to heat shocks, through a trade-off with reproduction, i.e., improved survival at the
cost of reduced reproduction (evolutionary response). The daphnia cannot adapt so they reduce
their mobility to increase survival (ecological response). Already, this can be considered a form
of R-tipping which propagates to the population level: algae can reproduce even as warming
occurs at high rates, while daphnia populations shrink. This leads to a community where algae
can grow almost uninhibited, since the ineffective daphnia population does not create a top-
down control. This example illustrates only on a single aspect of increased warming rates;
more complex eco-evolutionary feedbacks are possible, particularly in the context of a
metacommunity (i.e., additional patches) (Faillace et al. 2021).
In a spatial context, the rate of fragmentation or habitat destruction could benefit habitat
generalists over habitat specialists, as the latter could struggle to locate their preferred prey in
fragmented landscapes (Ryall & Fahrig 2006) or to colonise new, undisturbed patches (Warren
et al. 2001; Travis 2003). In cases where such patterns have been observed, the R-tipping
framework could help develop related questions which can be tested in the same systems. For
example, given a critical dispersal rate below which a species would go locally extinct, what
trait trade-offs (e.g., competition-colonisation) are feasible and can buffer against extinction?
R-tipping constitutes a temporal process; therefore, it is vital to understand the interplay of
interacting timescales (e.g., eco-evolutionary feedbacks, predator-prey lifecycles, ‘competing’
positive and negative feedbacks). Species with short generation timescales relative to their
competitors or predators can evolve to track the rate of environmental change, inducing R-
tipping at higher levels of organisation and setting off eco-evolutionary feedbacks.
Alternatively, fast-responding species (i.e., with traits well-adapted to fast change) can
immediately proliferate, thus altering their environment. This change will then select for
different traits in the community, forcing eco-evolutionary feedbacks. Given that empirical
and theoretical - research on R-tipping in ecology remains in its infancy, the approaches we
discussed focus on simple pairwise species interactions being driven by a single environmental
factor. It is clear that in the future we should aim to incorporate a second or even more if
possible stressor, which will impose trade-offs in the responses of individual species.
A paradigm shift is necessary
R-tipping alone will not, and indeed should not, replace the application of B-tipping theory. In
fact, different mechanisms can co-occur under global change, where multiple stressors can act
simultaneously (Rillig et al. 2019). Interactions between B- and R-tipping have been
theoretically explored, yielding potentially complex dynamics (Arumugam et al. 2020, 2021;
O’Keeffe & Wieczorek 2020). The proposed ‘critical rate hypothesis’ combines extreme
external events, alternative states and a rapidly responding ecological entity (Scheffer et al.
2008). The sustained macro-algal bloom in Caribbean coral reefs in the 1990s was explained
based on this hypothesis. According to this, long-term eutrophication and overfishing of
grazers, though not sufficient to cause a regime shift, pushed the system close to bifurcation,
thereby making it more susceptible to a transition. An abrupt loss of sea urchins (grazers) due
to a disease outbreak combined with a spike in nutrient availability from a hurricane, triggered
a rapid, uninhibited algal recruitment event. The authors argue that this ‘critical rate of change’
induced a regime shift as the algae colonised the coral reef, before grazers could re-establish
effective top-down control.
Successful biological invasions can follow a similar pattern. After initial establishment in the
novel environment, the exotic species will rapidly expand once physical conditions provide a
window of opportunity (e. g. ‘boom-bust’ dynamic (Strayer et al. 2017)). San Francisco Bay
was invaded by zebra mussels at initially low densities (Paine et al. 1998). Following a flood
which removed the resident estuarine community, the zebra mussels’ population exploded and
within two years had permanently replaced the resident communities. A similar dynamic has
been observed in tropical woodlands, where high rainfall years trigger mass recruitment events
of woody vegetation (Holmgren & Scheffer 2001; Holmgren et al. 2013), which can cause a
transition to an encroached state (Scheffer et al. 2008; Kulmatiski & Beard 2013; Synodinos et
al. 2018).
However, temporal events are often recurrent, and thus the frequency of extreme events needs
to be additionally considered due to ecological memory (Ryo et al. 2019). If the frequency of
such events were to increase, as predicted (Rahmstorf & Coumou 2011; Myhre et al. 2019),
existing communities’ traits could become ill-suited to cope. This could yield novel
communities through invasions, extinctions or a switch in competitive balance (Paine et al.
1998). Such considerations will inevitably overlap with studies on climate-induced changes in
disturbance regimes (Johnstone et al. 2016; Hart et al. 2018; Sarneel et al. 2019; de Bello et al.
2021), providing fertile ground for theoretical synthesis.
As we have stressed above, multiple drivers acting simultaneously are impacting ecological
entities from individuals to ecosystems. One theory cannot cover them all. Even though the
paradigm of bifurcations and alternative states has yielded valuable insights, it cannot deal with
the temporal aspect of environmental change. Given the mounting evidence on the increasing
rate of change and variability in climatic regimes and environmental conditions (Waters et al.
2016; Pattyn et al. 2018; Ceballos et al. 2020), we propose that alternative paradigms, such as
R-tipping, are explored to provide appropriate predictions. Specifically, we argue for a shift of
focus to rates of change as important drivers across scales in ecology. Coupling existing theory
to empirical data can be used to develop and test hypotheses and improve our predictive
capabilities. We believe that anticipating and understanding rate-induced phenomena will
become a major challenge for ecology as we enter a future with no analogues in the past. We
hope our work will facilitate the applicability of R-tipping theory in ecology, which combined
with more conservation-oriented approaches (Williams et al. 2021) should increase our ability
to predict and mitigate adverse impacts of high rates of environmental change. Moreover,
theoretical synthesis through the integration of multiple conceptual models (B-, N- and R-
tipping) will become necessary. Challenging times lie ahead, and we must make sure that theory
and the resulting conservation actions do not become outpaced by the rate of ecological crises.
Acknowledgements
ADS is thankful to Katja Geissler, Volker Grimm and Bart Haegeman for their helpful
comments. All authors received funding from the German Federal Ministry of Education and
Research within the Collaborative Project “Bridging in Biodiversity Science” (grant no.
01LC1501A and 01LC1501B). ADS received funding through FRAGCLIM Consolidator
Grant, funded by the European Research Council under the European Union’s Horizon 2020
research and innovation programme (Grant Agreement Number 726176). Figures 3, 4 and 5
were created with BioRender.com.
Data availability statement
No new datasets were generated for this study. All data used for the literature review was
available in the original studies. A list of the reviewed literature is provided in the
Supplementary Information.
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