Discussion
Using a model of anoxic-oxic regime shifts, we found three key results:
first, not all functional groups had positive diversity effects on the
resilience of their preferred ecosystem state (Fig. 3a-f). Second, the
effect of diversity was often smaller or absent when a functional group
was on the recovery trajectory rather than on the collapse trajectory
(Figure 3a-f). And third, diversity effects were often reduced or erased
when multiple groups varied in traits, except when a functional group
had no individual effect on resilience (Figure 3g-o). From a management
perspective, our results suggest that higher biodiversity in a
functional group that dominates a desired ecosystem state may prevent or
delay the shift to an undesired state but does not necessarily promote
recovery. A more promising approach to facilitate recovery may be to
identify functional groups in the undesired state (here: phototrophic
sulfur bacteria) that promote their own collapse and therefore the
recovery of the desired state.
Diversity did not always increase
resilience
Greater diversity in cyanobacteria or sulfate-reducing bacteria
increased the resilience of the state they dominated, whereas greater
diversity in phototrophic sulfur bacteria reduced resilience of their
preferred state (Figure 3a-f). These contrasting diversity effects
reflect differences in interactions: whereas cyanobacteria and
sulfate-reducing bacteria interact in a strictly antagonistic way,
phototrophic sulfur bacteria have both positive and negative effects on
the other two functional groups (Figure 2). By oxidizing sulfide,
phototrophic sulfur bacteria indirectly increase the level of oxygen
because the lower sulfide concentration (i) promotes cyanobacteria and
therefore oxygen production, and (ii) leads to less oxygen consumption
during abiotic oxidation of sulfide (Supplementary Report Section 10.1).
Via these two pathways, phototrophic sulfur bacteria deteriorate their
own environment and promote the shift to the oxic state. High diversity
in this group even resulted in failure to exclude cyanobacteria likely
because the fastest-growing strain lowered the sulfide concentration to
such an extent that cyanobacteria were able to grow despite low oxygen
diffusivity (Supplementary Report Section 10.2).
Stimulating the growth of groups that have negative diversity effects
may be used as a management tool to promote the recovery of a desired
ecosystem state. Such an approach would require identifying functional
groups that co-dominate an undesired ecosystem state and negatively
affect it. Microbes may be particularly useful for this approach because
(i) they often dominate undesired ecosystem states (e.g. turbid lake
state, anoxic state), and (ii) they often interact via modification of
the chemical environment (Ratzke et al. 2018; Ratzke & Gore
2018), including even microbes that drive themselves extinct by
deteriorating their environment (Ratzke et al. 2018).
Diversity effects were often smaller or absent on recovery
trajectories
In two functional groups, the effect of trait diversity on resilience
was very small (cyanobacteria; Figure 3b) or absent (phototrophic sulfur
bacteria; Figure 3 f) when the group was on the recovery trajectory
rather than on the collapse trajectory. For example, except for very
high diversity levels, cyanobacteria diversity had no effect on the
shift from the anoxic to the oxic state likely due to the abrupt change
of the inhibiting substrate: sulfide was maintained by sulfate-reducing
bacteria at a constantly high level such that not even the most tolerant
cyanobacteria strain was able to grow (Figure 4a, e). At the tipping
point to the oxic state, sulfide dropped abruptly to a level at which
all cyanobacteria strains would be able to grow, resulting in immediate
dominance of the strain with lowest sulfide tolerance and highest
maximum growth rate (Figure 4a). Previous work showed that evolutionary
trait change can even delay recovery when organisms adapt to the
alternative state and then have reduced performance once environmental
conditions return to original (Chaparro-Pedraza et al. 2021). Our
study adds a further mechanism for why diversity might not aid recovery
of a suppressed group: the greater abruptness of change of the
inhibiting substrate on recovery trajectories might hinder attempts to
restore ecosystems by introducing tolerant strains.
Diversity effects were often reduced or erased when multiple
groups varied in
traits
Antagonistic diversity effects were to be expected in a system where
organisms interact via mutual inhibition. Surprisingly, however,
opposing diversity effects were not necessarily caused by groups that
dominated alternative ecosystem states but also by two groups that
dominated the same state and interacted via facilitation. Contrary to
other examples where functional groups that interact via facilitation
enhance each other’s diversity effects (Eisenhauer et al. 2012),
variation in sulfate-reducing bacteria erased the diversity effect of
phototrophic sulfur bacteria. Apparently, when both groups varied in
traits, the positive effect of sulfate-reducing bacteria on sulfide
outweighed the negative effect of phototrophic sulfur bacteria,
precluding the earlier shift to the oxic state. Simultaneous variation
did usually not alter diversity effects when a functional group had no
diversity effect on resilience, which was often the case on recovery
trajectories. If it is a general phenomenon that diversity plays out
less on recovery trajectories, the positive diversity effect on
resilience of the dominant group would not be dampened by diversity in
the suppressed group. Collectively, our results illustrate that
predicting the outcome of simultaneous diversity effects of multiple
functional groups can be difficult in systems where organisms interact
via a multitude of complex interactions.
In natural systems, the alternative states of an ecosystem are often
dominated by functional groups that strongly differ in intra- and
interspecific diversity, and in how quickly new variation arises. In
some of the most prominent examples of ecosystems with alternative
states, macro-organisms dominate the desired state and micro-organisms
the undesired state. For example, benthic macro-organisms and bacteria
dominate the oxic and anoxic state of coastal ecosystems, respectively
(Diaz & Rosenberg 2008), and macrophytes and phytoplankton dominate the
clear and turbid state of shallow lakes, respectively (Scheffer et al.
1993). Prokaryotic and eukaryotic micro-organisms are both characterized
by tremendous diversity (de Vargas et al. 2015; Locey & Lennon 2016),
including diversity in functional traits (Litchman & Klausmeier 2008;
Escalas et al. 2019). Furthermore, microorganisms might adapt more
rapidly to new environmental conditions than macro-organisms due to
their short generation times, high population sizes, and low complexity
(Orr 2000; Barraclough 2015). Such greater standing variation and higher
ability to adapt to stressful conditions of micro-organisms may pose an
additional challenge to reversing shifts to an undesired ecosystem state
dominated by micro-organisms.
Ecology, evolution, or
both?
The observed replacement of strains along the gradient of oxygen
diffusivity may be interpreted as changes in the abundances of species
(an ecological process) or of genotypes (an evolutionary process).
Furthermore, models such as ours are interpreted in both of two ways
(Govaert et al. 2019): as representing ecological processes
(Ceulemans et al. 2019; Wojcik et al. 2021) or
eco-evolutionary feedbacks (Jones et al. 2009). To be clear, this
modelling approach only includes sorting of standing heritable
variation. Including mutation and recombination could be a useful next
step to investigate how these evolutionary processes influence regime
shifts. Chaparro-Pedraza et al. (2021) used a quantitative genetics
approach to show that changes in macrophyte shade tolerance can shift
the tipping point between the clear and turbid state of a lake to higher
levels of stress. Despite its different approach, our study yielded
similar results for two of the three functional groups. This gives some
credence to interpreting models such as ours as usefully informing about
the effects of evolution on ecosystem resilience.
Limitations and future
directions
Naturally, our model is a simplification of natural ecosystems with
assumptions that might have influenced our results. The model system
focuses on three groups of bacteria; however, other functional groups
(e.g. eukaryotic phytoplankton, colorless sulfur bacteria) might also
have relevant effects on oxic-anoxic regime shifts (Lavik et al.2009). We demonstrated that including more than two functional groups
can reveal counter-intuitive diversity effects; expanding models such as
ours to include even more functional groups may therefore give further
useful insights into the response of ecosystems to environmental change.
Furthermore, we did not vary the values of parameters other than those
of maximum growth rate and environmental tolerance. For the remaining
parameters we used the same values as Bush et al. (2017) because
the authors found high consistency of their modelling results and
empirical observations. Also, we did not vary the shape of the trade-off
between environmental tolerance and maximum growth rate. We speculate
that such changes to the trade-off would have altered the pattern of
strain replacement but not the position of tipping points.
An interesting avenue for future studies would be the analysis of
transient dynamics, that is, the response of the system to temporal
environmental change. Because we addressed questions about alternative
stable states, we focused on responses of stable states, and therefore
ran simulations for 1,000,000 hours at each level of oxygen diffusivity.
However, in natural systems environmental conditions would not remain
constant over such long timescales, and new trait variation could arise
by mutation and recombination. Most of our results remained
qualitatively similar when we reduced simulation length to 10,000 hours,
but a further reduction in simulation length considerably changed the
results (Supplementary Report Section 6). Investigation of transient
dynamics in a model of shallow lakes showed that the capacity of
evolution to prevent a regime shift depended on the relative rates of
environmental change and trait change (Chaparro-Pedraza et al.2021). It would be interesting to test with our system if the capacity
of functional diversity to increase resilience is contingent on a slow
rate of environmental change. Possibly, however, the rate of change is
less influential in systems such as ours because sorting of standing
variation might allow a faster response to environmental change than if
relevant variation has yet to arise.
Conclusions
Our results illustrate that greater trait diversity of the functional
group that dominates a desired ecosystem state may increase the ability
of the system to absorb environmental change before tipping to the
alternative, undesired state. However, once the system has shifted to an
undesired state, recovery of a desired ecosystem state might be more
easily achieved by managing functional groups that dominate the
undesired state than by managing groups that dominate the desired state.
Trying to facilitate recovery by bringing in more stress tolerant
strains of the functional group that dominates the desired state might
be unsuccessful because of the abrupt change of the inhibiting substrate
on the recovery trajectory. A more promising management approach could
be to stimulate the growth of functional groups that co-dominate the
undesired state but have negative effects on it. To identify such
groups, it is useful to consider more than two functional groups in
models of systems with alternative stable states. Collectively, our
results highlight the importance of considering multiple interacting
groups when predicting the response of ecosystems with alternative
states to environmental change.