Discussion
We utilised a niche-based approach to investigate five hypotheses
relating to factors influencing community stability. Our first
hypothesis supported was that species richness will be higher if the
site was located closer to the niche centre of the species pool. This
was expected as niche-distribution modelling is dependent on the
relationship between species niche-space and the probability of
occurrence (Elith & Leathwick 2009). We add here that a simple
aggregate score was informative about the species richness of our
communities. This, however, leaves room for improvement when predicting
niche effects on richness, such as utilising individual niche models to
sum occurrence at each site, or alternatively, applying joint approaches
that utilise species covariances to improve predictive performance
(Poggiato et al. 2021). A spatial correlation structure also
accounted for differences in species richness not accounted for by
climatic niche characteristics. Other factors are, therefore, important
for predicting richness such as latitudinal gradients in species
richness (Hillebrand 2004), lower species richness of island faunas
(MacArthur & Wilson 1967), and with smaller scale effects such as
changing land cover.
That our second hypothesis was supported, lends the first large scale
support for the abundant centre hypothesis (Andrewartha & Birch, 1954;
Brown, 1984; Lawton, 1993) operating in butterfly communities. The
effect size here was modest, however, we applied a simple aggregate
metric (mean niche mismatch) and more variation in abundance might be
explained for each species by niche mismatch. Similarly, increasing the
dimensionality of our niche constructions, or accounting for other
factors such as land use may better isolate the effect of niche position
on abundance. We also found increased niche volume was associated with
increased mean abundance. This suggests that in communities where
species had larger niches, i.e. more generalist in terms of climate
association, the populations were larger. This could be a reflection of
the wide-scale declines that have been noted for specialist species
(Clavel et al. 2011). However, studies of generalist-specialist
trends in butterflies typically utilise species traits, including host
plant association, and focus on single countries (Stefanescu et
al. 2011; Dapporto & Dennis 2013; Eskildsen et al. 2015) and so
linking these results must be cautious. Nevertheless, our results
suggest that evaluating species trends in relation to climatic niches
could be valuable, particularly given the recent, and projected,
increase in extreme climatic events (Donohue et al. 2016;
Ummenhofer & Meehl 2017). Finally, a path not predicted a priori,
suggested communities with higher niche overlap had populations that
were on average more abundant. It is possible, that after accounting for
niche mismatch, a community with greater overlap means more species
situated in optimal conditions producing a positive association between
high overlap and abundance. However, we suggest further work is needed
to resolve interactions between niche overlap, volume, and mismatch.
Results from our third hypothesis showed a significant negative
association between species richness and synchrony, thereby supporting a
key diversity-stability mechanism for butterfly communities (Thibaut &
Connolly, 2013). That we found no effect of climatic niche-overlap on
synchrony is surprising as butterflies are strongly influenced by
weather (McDermott Long et al. 2017). This suggests that other
factors, such as species traits and local adaptation, may also be needed
to predict species responses to weather variation. Alternatively, the
niches constructed here using yearly average weather apply
space-for-time substitutions that might be too coarse to account for the
weather events causing changes in population size (White & Kerr 2006).
Our results suggest that other factors must generate asynchrony. One
factor could be intra-specific density dependence of population growth
rates (Roy et al. 2001) as density varies between species,
asynchrony could occur even between species with similar climatic
niches. We also noted additional associations, as niche overlap
decreased and niche mismatch increased with species richness. The
relationship with niche-overlap is perhaps expected as, so long as the
niche shapes vary between species, then adding species will reduce
overlap. The result with niche mismatch is harder to interpret, but
could be due to sites with higher species richness include more species
near the edges of the niche positions, or that the UK with a moderate
climate and lower species richness may induce a niche-mismatch and
species richness association.
Results from our fourth hypothesis were partly as predicted. Average
population stability decreasing with niche mismatch is an extension of
the abundant centre hypothesis, and natural populations at range edges
have previously been shown to be more variable (Oliver et al.2012; Mills et al. 2017). However, we expected mean niche volume
to increase average population stability as species with broader niches
should tolerate a wider range of conditions leading to higher stability.
It is possible that species with broader niches tolerate more marginal
conditions leading to lower stability at a site while still being able
to persist. Species with broad niches may also be more affected by
competition, reducing the stability of the population. By comparison,
specialists with narrower niches may only occupy favourable areas where
they are generally more stable and relatively freer from inter-specific
competition. Finally, we found no effect of mean abundance on average
stability which is surprising due to expected mean-variance
relationships (Taylor 1961; Kilpatrick & Ives 2003). However, mean
abundances take no account of species weighting, whereas species
stability was abundance weighted so that the evenness of the community
might obscuring the effect.
Finally, we hypothesised synchrony and average population stability will
explain differences in community stability (Doak et al. 1998;
Tilman et al. 1998; Thibaut & Connolly 2013; Wang & Loreau
2014) was supported. The standardised effect size (Table 1) and the
model fits (Figure 3e,f) suggest that asynchrony was, overall, having a
larger impact on community stability as expected from theoretical
considerations (Thibaut & Connolly 2013; Wang & Loreau 2014). In
butterfly communities asynchrony may be particularly important as being
r-selected – with high reproductive and interannual population growth
rates – populations are characterised by high levels of population
variability (Pianka 1970; May 1974), and community stability may be more
influenced by how the dynamics of populations combine than differences
in population stability. Mean abundance also increased community
stability. This is expected from mean-variance scaling relationships,
such as Taylor’s power law (Taylor 1961; Kilpatrick & Ives 2003) and we
show this affects the stability of butterfly communities in the
aggregate even if it was not detected at the individual level. In
addition to asynchrony, we also noted that species richness increased
stability through mechanisms not directly accounted for. This could be
related to how species richness reflects the evenness of species. For
example, if species-poor communities are dominated by one species in
terms of absolute abundance, then even with high levels of asynchrony,
the aggregate community abundance will largely reflect the population
variability of the dominant species (Grime 1998).
Our approach demonstrates that consideration of hyper-dimensional niches
and the derived metrics are useful for understanding population and
community dynamics (Barros et al. 2016). To date, they have been
applied more in plant communities (Enrique et al. 2018; Papugaet al. 2018), though their uses for predicting biogeography of
populations has been recently demonstrated for birds (Osorio‐Olveraet al. 2020). However, there are some possible weaknesses in our
approach. First, several metrics can be generated from
niche-hyper-volumes (Mammola 2019) and for some hypotheses different
metrics may have been more effective. For example, niche mismatch could
consider minimum distances from niche volumes to site locations, and
Mahalanobis distances could provide better measures than Euclidean
distances from the centroids. Similarly, niche-overlap could be
quantified using a range of metrics. However, we justify our efforts
here as applying a straightforward approach that uses a minimum number
of intuitive metrics to test key hypotheses regarding butterfly
community stability. A second limitation of the niche approach is that
the strength of any one event on any single niche dimension is not
measured as directly. Consequently, identifying the threats of any class
of extreme event such as drought (Oliver et al. 2013; De Palmaet al. 2017) may still require a single variable approach.
Combining the approaches, for example tolerance to drought would be
predicted to have lower scores on an axis related to
precipitation-aridity, could provide a way to test and generate new
hypotheses around the factors affecting the population dynamics of
species.
In conclusion, we find support for the mechanisms purported to influence
community stability operating in butterfly communities. We utilised
metrics derived by niche hyper-volumes to provide a unique overview of
the environmental drivers behind these mechanisms. Thus, our method
provides a novel test of factors affecting the stability of terrestrial
animal communities and demonstrates how considering niches can allow
consideration of mechanisms, operating at a range of scales, that
ultimately influence community stability.