Discussion
This study compared microbial community stability components from
different successional stages of terrestrial ecosystems and offered
direct empirical evidence for the role interactions play in governing
RSRs.
We found a rough trend that forests had higher resilience, but lower
resistance, than shrub, grassland, and bare soil (Fig. 1A), which was
opposite to the trend displayed by plant communities where complex
systems were likely to have higher resistance (Isbell et al.2015). We suggest that such differences arise from the reproductive
rates, physiological resistance, and differences in richness between
plants and microbes (Curtis 2006; Konopka 2006). Compared to microbes,
plants have much lower reproductive rates and richness (Curtis 2006;
Konopka 2006). When faced with short term high temperature stress, on
the order of only several days, the dominant forest plant keystone
population was likely to remain unchanged unless large scale regional
death occurred, and thus appearing to have high resistance (Curtis 2006;
Konopka 2006). However, microbes have extremely high richness as
compared to plants in terrestrial ecosystems, and are able to reproduce
in a timespan as short as hours or even minutes (Curtis 2006; Konopka
2006). These indicate that competitive functional groups with different
temperature range tolerances could potentially replace the original
dominant groups and thus led to lower resistance of the microbial
community (Pinsky 2019). However, with keystone species dominated by
organisms with high stress tolerance, such as the communities from bare
soil (Fig. 1A) that are exposed to high day-night temperature variation,
dry-wet alteration, and high ultra-violate, could exhibit high
resistance because those keystone species were not likely to change
(Remias et al. 2012; Harrison & LaForgia 2019).
We found a strong negative linear relationship between microbial
community resistance and resilience, which indicated a trade-off between
resistance and resilience of microbial communities in the ecosystems
studied here (Fig. 1B and Fig. 1C). This is plausible and inevitable
from both basic logic and an evolutionary perspective. The essence of
resistance and resilience is the ability of altering the relative
abundance of species as conditions change. Thus, a microbial community
that is more readily prone to change simultaneously has less resistance
and higher resilience, and vice versa (Miller & Chesson 2009; Griffiths
& Philippot 2013). From an evolutionary perspective, communities need
to coordinate the functions of different components to ensure the
continuation of key ecological processes for survival of the community
under variable environmental conditions. This can be realized by
assigning key functions to a few stress tolerant functional species
(such as the high resistance community in bare soil) (Craine et
al. 2013), or to alternative functional groups composed of different
stress tolerant members (like the low resistance community in forests)
(Whitham et al. 2006; Walworth et al. 2020), that can be
treated as the K-strategy and r-strategy of community evolution or
succession. However, to simultaneously possess K- and r-strategy wastes
energy and is an evolutionary dead-end (Whitham et al. 2006; Liet al. 2020; Walworth et al. 2020). Thus, the RSRs varied
according to the stability components that we observed.
Most importantly, our results confirmed the role that interaction types
play in governing RSRs. Richness offers a basic available species pool
from which the community derived, while interactions offer the basic
functional organizational patterns for species to form the community
(Whitham et al. 2006; Montesinos-Navarro et al. 2017;
Walworth et al. 2020). Our results showed that only under
approximately balanced proportions of positive and negative interactions
did richness increase resistance and decrease resilience, and that an
exceedingly high proportion of positive interactions caused richness to
decrease resistance and increase resilience (Fig. 3). An extremely high
proportion of positive interactions is theoretically unfavorable for
resistance because the extinction of one species would threaten the
survival of other cooperative or mutualistic species that rely on it,
even though they maybe insensitive to the stress (May & MacDonald 1978;
Damore & Gore 2012). The higher the richness, the greater the
possibility for species co-extinction through positive interaction.
Thus, when under exceedingly high proportions of positive interactions,
the higher richness and the lower the community resistance (May &
MacDonald 1978; Damore & Gore 2012). However, negative interactions
compensate for the effect of the positive interactions on RSRs. Negative
interactions include predation and competition, with competition the
main consideration in bacterial and fungal community networks (Deng &
Zhou 2015). Competition indicates an overlap of function and niche among
community members (Pianka 1981;
Pinsky 2019). Groups which conduct the same functions are capable of
replacing species that had been killed off by stress, and protect other
members from further extinction due to the disappearance of their
functional partner(s) (Montesinos-Navarro et al. 2017; Qian &
Akçay 2020). Thus, with appropriate proportions of positive and negative
interaction, species can coexist in a manner without total dependency.
The higher the richness, the higher the functional redundancy and
increased community resistance (Pianka 1981; Pinsky 2019).
Theoretical ecologists have considered interaction types as one factor
that influence RSRs in models, even though there has been no empirical
evidence from previous field and laboratory studies to support this
idea. Models constructed based on different constraints and underlying
theories generate significantly different results, while our empirical
results were supported in part by recent modelling studies which
indicated that the appropriate ratio of positive and negative
interactions facilitate RSRs
(Mougi & Kondoh 2012; Qian & Akçay 2020). The constraints to meet
these demands were, firstly, the quantitatively comparable and balanced
effects on community from both positive and negative interactions and,
secondly, the decreasing interaction strength with increasing
interactions (Kondoh & Mougi 2015). This indicated that interaction
strength and distribution were also potential governors of RSRs, and
were capable of influencing the effects exerted by the interaction
types. However, whether or not such constraints commonly exist in
natural ecosystems is still an open question and requires further
investigation to test the effect of interaction strength on RSRs. There
are also other models that suggest the mixture of appropriate positive
and negative interactions of inter-trophic community cannot promote
positive RSRs (Suweis et al. 2013). Considering that most
microbial networks cannot be divided into different trophic groups
according to recent methods, further research is needed to test whether
our conclusions are applicable to macro communities or not. According to
the theoretical predication, exceedingly high proportions of negative
interactions are also an unbalanced state and could possibly contribute
to a negative richness-resistance relationship, similar to an
exceedingly positive unbalanced state (Mougi & Kondoh 2012; Qian &
Akçay 2020). Because under an exceedingly negative interaction state, it
is possible that a large number of competitors that use similar
resources and conduct similar functions may become simultaneously
extinct due to environmental stress (Pianka 1981; Pinsky 2019), and thus
the higher the richness, the greater the number of species that could
potentially become extinct, and the lower the resistance. Unfortunately,
microbial communities in our sampling fields, like many terrestrial
ecosystems (Shang et al. 2018), were dominated by positive interactions,
and thus, our results were incapable of directly supporting the
conjecture that under an overly negative unbalanced state, richness also
reduces resistance. Therefore, we suggest to test the idea in other
negative interaction dominated ecosystems, such as aquatic or artificial
ecosystems.