Introduction
The factors affecting community stability are important to understand as
community stability ultimately underpins the stability of ecosystem
function (Millennium Ecosystem Assessment 2005; Cardinale et al.2012). A central problem is how species diversity (richness) contributes
to stability (Elton 1958; May 1972; Tilman & Downing 1994; Tilman
1999). Community stability is influenced by two properties of the
community: average population stability and asynchrony (Doak et
al. 1998; Tilman et al. 1998; Yachi & Loreau 1999; Thibaut &
Connolly 2013). Briefly, average population stability describes the
variability of each population over time and can be influenced by
factors such as mean-variance scaling, where larger populations are
relatively less variable (Taylor 1961; Kilpatrick & Ives 2003) as well
as density-dependent (e.g. pathogen-induced mortality) and
density-independent processes (e.g. mortality induced by extreme weather
events). Population asynchrony constitutes negatively correlated
population dynamics, which can be driven by species interactions (e.g.
competition) or through varying responses to environmental conditions.
Average asynchrony between the populations in a given community is
expected to increase with species richness (Thibaut & Connolly 2013),
but associations between richness and average population stability are
less obvious (Jiang & Pu 2009).
Though the theoretical background of community stability is well
developed, the mechanisms have not been tested in natural animal
communities and much experimentation has been conducted with plant or
aquatic communities (Dı́az & Cabido 2001; Craven et al. 2018; van
der Plas 2019). For example, asynchrony increases with species richness
in grassland communities (Roscher et al. 2011; Isbell et
al. 2019), but less is known about how richness impacts asynchrony or
its importance in animal communities. Previous work suggests that the
relative importance of the mechanisms can vary between taxa, with some
studies finding a prominent role for asynchrony in driving community
stability (Jucker et al. 2014; Ma et al. 2017). For
example, asynchrony is observed as key to the stability of arthropod and
plant communities between different habitat types (Blüthgen et
al. 2016), while work on intertidal and algal communities suggest
average population stability can explain differences in community
stability (Pennekamp et al. 2018; White et al. 2020).
To understand why the impact of these mechanisms varies between
communities, it is important to quantify both how asynchrony or
population stability contribute to community stability and also the
factors leading to differences in synchrony and stability. Three main
forces influence correlations among the dynamic of species populations:
intra- and inter-specific interactions, differing responses to
environmental variations, and demographic stochasticity (Roscheret al. 2011). A key factor for insect communities is the response
to environmental fluctuations (Ives et al. 1999). The
distributions of butterflies are strongly influenced by climate (Setteleet al. 2008) and population dynamics by weather (WallisDeVrieset al. 2011; Palmer et al. 2017). Consequently, responses
to environmental variation could be important in understanding
differences in stability. However, a challenge when linking weather to
synchrony in natural communities is that the importance of environmental
variables will vary among species (Roy et al. 2001; WallisDeVrieset al. 2011; McDermott Long et al. 2017).
Additionally, at local scales species richness and abundance may vary
due to the suitability of sites, or at large scales, due factors such as
latitudinal gradients in richness (Hillebrand 2004). For example, the
abundant centre hypothesis posits that species will be most abundant at
locations near the centre of their range (Andrewartha & Birch 1954;
Brown 1984; Lawton 1993), and so sites located near many species’ range
centres should have higher population stability due to mean-variance
effects (i.e. larger populations being relatively less variable over
time) (Oliver et al. 2012, 2014). The importance of asynchrony or
population stability may then vary due to community structure, e.g.
differing levels of interspecific competition (Lehman & Tilman 2000).
Finally, varying sensitivities to environmental variation (Ives et
al. 1999) may then drive differences in population variability and
contribute to the degree of asynchrony in a community.
To tackle this complexity, we apply the ecological niche concept, i.e. a
species tolerance to different environmental variables, can be
represented as an n-dimensional space (Hutchinson 1957). Fundamental to
niche modelling, at both the species and community scales (Hirzel & Le
Lay 2008; Poggiato et al. 2021), is that species occurrence under
local conditions is dictated predominantly by the niche space.
Consequently, notwithstanding nuances of extinction debt and
colonisation credit (Tilman et al. 1994; Kuussaari et al.2009), species richness should be predictable from the location of a
site relative to the niches of the species in the regional species pool.
Similarly, mean abundance should be informed by site position relative
to the niches of the species at the site (Osorio‐Olvera et al.2020). Species at the edge of their niche may also have lower average
population stability (Oliver et al. 2012; Mills et al.2017) and species with larger niche breadths may be more robust to local
environmental variation. Finally, when a site is in a different niche
position for two species, they may have differing responses to
environmental variation at the site – generating asynchrony.
We apply the niche-based approach to study 140 butterfly communities
spanning a European scale from Finland to Northeast Spain. We utilise a
computationally fast approach to generate bioclimatic niche
hyper-volumes (Blonder et al. 2014, 2018) for each species. We
then derive niche-based metrics to test potential factors affecting
first species richness, mean abundance, and then the mechanisms of
community stability. Specifically, we test five expected responses
associated with key mechanisms: 1) species richness will be higher if
the site is located at shorter distances to the niche centres of the
total species pool; 2) mean abundance will be greater when the site is
nearer to the niche centres of the species at the site; 3) average
asynchrony between pairs of species in a community will be greater if
the niches of species are more dissimilar at a site, and will also
increase with species richness; 4) average population stability will be
highest when the site is nearer the centre of the species niches, when
the average niche breadth of species is larger (i.e. more resilient to
local weather anomalies), and when the average abundance of the species
is greater; 5) asynchrony and average population stability will explain
differences in community stability.