Discussion:
By the end of the experiment, the initial state of the community had
ceased to be a principal determinant of composition for all species
(Fig. 3). A large contribution of initial state would indicate either
that species frequencies had not changed, or that change was contingent
on history, either through drift (for example, because of the increased
risk of extinction when a species becomes rare) or sorting (for example,
through facilitation, inhibition, or competitive intransitivity). The
dominant process depended on the focal species, as the dynamics of Lm
and Wc were governed predominantly by sorting, while those of Lt and Sp
were governed largely by drift (Fig. 3). Overall, Lm benefited at the
expense of Wc, while Lt and Sp remained largely static with a moderate
amount of stochastic variation around their mean state. A modest
contribution of sorting (and therefore a large relative contribution of
drift) could be caused either by weak competition or because the
equilibrium frequency of a species fell close to its average initial
frequency over all community types (as was the case for Sp, whose
initial and equilibrium relative abundances were both 0.25). Conversely,
the dynamics of the species whose equilibrium frequencies are the
furthest from their average initial frequencies (Lm and Wc) were most
dominated by species sorting.
The strong effect of sorting and the absence of any strong effect of
initial state suggests that the community tends towards an equilibrium
composition, either through competitive exclusion or stable coexistence.
Given a relatively constant and spatially homogeneous environment (May
1973, Chesson 2017), the distance from equilibrium which an actual
community lies is determined by the balance of sorting and drift.
Whether or not this equilibrium community involves the stable
coexistence of several species can be determined by considering how the
frequency of a species changes as a function of its current value (Fig.
5). The presence of a large negative correlation for all four species is
evidence of negative frequency-dependent selection acting as a stable
coexistence mechanism (Chesson 2000, Adler et al. 2007). The slope of
this correlation indicates the strength of the frequency-dependence, and
its intercept with zero change in frequency indicates the equilibrium
frequency of that species. This negative frequency-dependence is
probably a widespread coexistence mechanism in natural communities of
floating freshwater macrophytes (Barrat-Segretain and Elger 2004, Gérard
and Triest 2018, Armitage and Jones 2019, Hart et al. 2019) responsible
for maintaining local diversity. Any real community will deviate from
the ideal equilibrium composition through ecological drift.
The balance of sorting, drift and initial state shifted in a simple and
predictable way over time. At the beginning of the experiment, since no
change has yet occurred, all variation in community composition is due
to initial state (Fig. 4). As time progresses, the relative contribution
of initial state diminishes for all species, indicating the lessening
contribution of initial state to community dynamics. Both species
sorting and ecological drift increase in importance, and as the
community nears equilibrium, roughly balance each other. This balance is
however species-specific, due to the strength of competition. The
further away a species’ frequency is from its equilibrium value, the
stronger species sorting will act to bring it closer. Thus, both sorting
and drift are acting on all species at all times, but how they combine
depends on the species and will be determined by its competitive
advantage and how far it is from its equilibrium frequency.
The standard approach to quantify stochastic effects on community
structure is with the use of pairwise beta-diversity indices (eg.
Bray-Curtis) (Anderson et al. 2011). Compositional dissimilarity is
calculated for all pairwise combinations of sites in the same conditions
and averaged to produce an estimate of among site (beta) diversity due
to stochasticity, ranging from 0 to 1 (Gilbert and Levine 2017, Ron et
al. 2018). Our approach of using an Anova framework allows us to
partition the contribution stochastic processes into those due to
ecological drift (variation in vital rates among individuals of the same
species) and priority effects (stochastic variation in order of
colonisation), and allows us to compare these numerically with the
contribution from species sorting. Furthermore, obtaining estimates for
each species separately can reveal how these fundamental processes may
operate distinctly for different species in a community. The weakness of
such an approach is that the contribution of sorting is highly sensitive
to the initial frequencies (and their distances from the equilibrium
frequencies). Consequently, estimates of the proportion of the total sum
of squares due to stochasticity will also be sensitive to an arbitrary
initial frequency. To work around this, it may also be informative to
consider the contributions of stochastic processes as the raw
within-group (drift) and among-group (initial state) sums of squares. We
compared these measures to the more commonly used Bray-Curtis
beta-diversity, calculated among replicate communities of each community
type, at each time point, and found that both methods captured largely
the same variability in species frequencies (Fig. 6). Equating this
variability to drift of course assumes that the experiment is perfectly
controlled, since any unintentional biotic and abiotic variation among
replicate mesocosms contributes to our estimates of drift.
We conclude that the dynamics of our experimental communities shifted
over time but were eventually dominated by species sorting, which
resulted in the predictable and more or less deterministic shift towards
an equilibrium state. The contribution of initial state declined
consistently over time, but the balance between species sorting and
ecological drift varied among species because, as in any community, some
species were closer than others to their equilibrium frequency.
Acknowledgments: We thank Julia Kossakowski and Isabel
Fernandez-McAuley, who helped with data collection. This experiment was
supported by a Discovery Grant from the Natural Science and Engineering
Research Council of Canada to GB and an Alexander Graham Bell Canada
Graduate Scholarship from the Natural Science and Engineering Research
Council of Canada to MDJ.