Discussion
Patterns of genetic variation often reflect spatial variation in gene
flow which can be influenced in two important ways (Wang & Summers,
2013): Spatially separated populations may experience
isolation-by-distance (IBD; Wright 1943) in which landscape barriers and
geographical distances cause restricted gene flow; and
isolation-by-environment (IBE; Wang & Summers, 2010) in which gene flow
among populations inhabiting different ecological environments is
limited either by selection against dispersers moving between them or by
individual preference to remain in a particular environment due to local
adaptation (Dobzhansky, 1937). IBE predicts a correlation between
genetic divergence and environmental dissimilarity because greater
environmental differences between populations are expected to be
associated with stronger divergent selection and reduction in the
success of dispersers (Crispo et al., 2006; Lee & Mitchell-Olds, 2011).
Of course, geographical and environmental isolation do not exclude each
other, and spatial genetic divergence can be associated to gene flow
reduction due to both geographical and ecological factors (e.g., Coyne
& Orr, 2004; Crispo et al., 2006; Thorpe et al., 2008). Furthermore,
population genetic theory predicts that genetic distances among
individuals will increase with increasing geographical distance
(Allendorf & Luikart, 2007).
Our investigation of the genetic vs. geographic distances within species
across multiple guilds of different ecological traits and taxa (95
Coleoptera families) revealed that there are only few cases in which
this relationship resulted to be significant and thus predictable, i.e.,
for which an increase in geographic intraspecific distances was followed
by an increase in genetic distances, or the other way around, and most
of all, showing a sufficient sampling (in terms of number of samples to
examine) to support such trends statistically. However, observed
patterns of infraspecific genetic and geographical distances among most
of the central European Coleoptera species and ecological guilds
examined were neither uniform nor entirely different among each other.
Thus, the unlikely hypothesis that all species increase their genetic
diversity with the distance of their record, which could be interpreted
as a signal of gradual dispersion and genetic differentiation in
progress (Allendorf & Luikart, 2007), could be not universally
confirmed, despite the study area suffered an almost entire biodiversity
loss during the Pleistocene and was reoccupied afterwards from external
founder populations (e.g., Hewitt, 2000; Hofreiter & Stewart, 2009;
Abellán et al. 2011; Birks & Tinner, 2016).
Besides limited sampling, cases, in which species did not show a
positive relation between genetic and geographical distances, might be
explained in two ways: 1) the geographic expansion of the species was
not followed by an equal genetic diversification (intraspecific DNA
distances are smaller than geographic distances), i.e., occurring in
species with high dispersal capabilities with continuous genetic mixing
and in a well interconnected area. Or, 2) the genetic diversification in
the study area was independent from the geographic scale, occurring
mainly in species with a potentially different phylogeographic origin of
their populations. It is well known that Central Europe experienced
post-glacial recolonization events from different Mediterranean and
extra-Mediterranean refugia located all across the continent (e.g.,
Ahrens et al., 2013; Kühne et al., 2017, Schmitt & Varga, 2012).
Freijeiro & Baselga (2016) suggested that dispersal-based processes in
European beetles were probably taxon-dependent, but also depended on
dispersal ability and ecological traits (Gómez-Rodríguez et al., 2015).
Although patterns appear not very clear due to widely lacking
significance, we here found several patterns in genetic/geographic
relationships among ecological preference classes which might fit more
ecology-dependent dispersal and differentiation (Papadopoulou et al.,
2008). Indeed, causalities are expected to depend both on environmental
and ecological processes in the species range. The distribution area as
well as the relation genetic distance vs. geographic distance of a
species depends on several factors: the paleo-biogeographic and
biogeographic history (i.e., glacial expansion dynamics, glacial refugia
presence, postglacial climatic gradients, and postglacial species
expansion) (Stewart et al., 2010) could have been the major cause of
such genetic trend in diversification. It is widely accepted that
present distribution patterns in Central Europe are related to post
glacial recolonization dynamics (e.g., Schuldt & Assmann, 2009;
Schmitt, 2009) beside other also important factors (Baselga et al.,
2012). In this context, geographic, climatic and ecological exogenous
factors (i.e., climatic gradients, habitat fragmentation or
presence/absence of corridors) and ecological endogenous factors (i.e.,
potential niche or dispersal capabilities due to physiological
properties, level of adaptation) play a crucial role to determine these
patterns (e.g., Schmitt et al., 2009; Rundell et al., 2009). So far,
distribution patterns and genetic differentiation have been studied for
mainly selected cases in the framework of phylogeographic studies (taxa
or/and study sites; e.g., Múrria et al., 2020; Garcia-Raventós et al.,
2021; Domènech et al., 2022), while only some studies with wider
taxonomic and geographical scope exist (e.g., Baselga et al., 2013,
2015; Joly et al., 2014; Fujisawa et al., 2014; Dapporto et al., 2019).
With the upcoming barcode data, a vast amount of data is becoming
available to address such questions routinely at large scale, and to
uncover particularly responses at population level regarding many
ecological and climatic factors which have so far been explored with
limited systematic sampling.
Here, deeper going conclusions lack statistic support since barcode
data/ libraries are generally not designed to explore phylogeographic
patterns in the context of species ecology. At this stage we expect
sampling bias since data were generated with the scope of collecting and
barcoding as many species as possible for future species identification.
However, the amount of available data on central European beetle species
was good enough to start to enquire the relationships between ecological
properties of the species and their intraspecific patterns of genetic
differentiation and to look at patterns that go beyond a single guild or
species group (Baselga et al., 2013, 2015; Fujisawa et al., 2014). In
fact, we faced severe problems due to the available number of sampling
localities and of individuals per species. Therefore, we included all
the sampling variables in the linear models excluding all the species
with poor number of specimens and sampling sites. Furthermore, we
investigated for the role of the sampling variables, such as number of
individuals per species and number of localities, on the final results
of statistic and significance scores as well as for an eventual
implication of geographic distances of arbitrary chosen sample sites.
PCA and NMDS techniques captured different information compared to the
Mantel test. Thus, the PCA technique was the least efficient in
describing the relation of genetic and geographic distances in terms of
amount of resulting significant species, followed by NMDS method and
Mantel test. Results of both were similar but partially different from
those obtained from Mantel test (Figure S5). This discordance limited
more general conclusions. It is likely that the different algorithms
behind the analytical techniques behaved differently in the presence of
high level of noise in the data caused by spatial autocorrelation
(Diniz-Filho et al., 2003; Legendre et al., 2015; although not tested
here) and lacking sufficient geographical sampling (due to financial
constraint of the Barcoding initiative, which did not allow higher
samples numbers). Because different methods may emphasize different
aspects of the data, using different data analyses techniques (Figure 2)
may reveal more aspects of the data structure than a single method
(Kenkel & Orloci, 1986). The Mantel test was considered to handle the
limited number of available samples per species best which was
disadvantageous for the ordination technique, which better read and
converted the data matrices in more readable and efficient row data
(Legendre et al., 2015) for the further Procrustes analysis. Ordination
techniques are known to work better when dealing with big amount of
data. On the other hand, minimal sampling size in our data was below the
generally suggested amount to robustly investigate phenomena depending
on spatial scale (at least 20 sampling localities; Dale & Fortin,
2014). Being first applied in population genetics by Sokal (1979), the
Mantel test is currently one of the most commonly used methods to
evaluate the relationship between geographic distance and genetic
divergence (Mantel, 1967; see Manly, 1985, 1997; Diniz-Fhilo et al.,
2013) – despite recent controversy and criticism about its statistical
performance (e.g., Harmon & Glor, 2010; Legendre & Fortin, 2010;
Guillot & Rousset, 2013; Castellano & Balletto, 2002) and the
existence of more sophisticated and complex approaches to analyze
spatial multivariate data (Diniz-Fhilo et al., 2013). In our case study,
the mean number was only five sampling localities per species. Even
though ordination methods are better suited, less prone to type I error
and better in describing patterns (Legendre & Fortin, 2010; Legendre et
al., 2015; C. Wang et al., 2010; I.J. Wang et al., 2013), results were
not congruent with those of the Mantel tests. Nevertheless, PCNM methods
combined to genetic information should be considered an alternative to
the Mantel test and further analysis on a richer dataset could then
possibly lead to clearer ecological conclusions.
It is known that the occupied habitat type has significant effects on
both extent of the species range and latitudinal distribution (Ribera &
Vogler, 2000; Hof et al., 2006, Fujisawa et al., 2014). This extends by
some aspects the results of Fujisawa et al (2014) who found
infraspecific genetic variation of COI in water beetles
positively correlated with occupancy (numbers of sites of species
presence; i.e., a similar but not identical measure to geographical
distance) and negatively with latitude, whereas substitution rates
across species (which we did not examine here) was influenced mainly by
habitat types; specialized species of more stable environments, such as
running water, had the highest rate. Baselga et al. (2015) expected
dispersal to be high in aquatic beetles (of standing waters) because of
the need for movement between ephemeral water bodies, while dispersal of
leaf beetles do not require long-range movement for population
persistence due to more stable conditions in vegetation. This is also
reflected by our findings for species using vegetation as habitat. Our
data thus seem to confirm the habitat stability hypothesis (Ribera et
al., 2003) which sees in Pleistocene glacial events and the following
climatic stability the major causes in producing equilibrium conditions,
either with environmental factors due to niche-based processes or with
spatial distributions from long-term stochastic dispersal.
Our data suggested higher dispersal tendencies and lower infraspecific
variation of mtDNA for more ephemeral food resources (dung, dead
animals), or habitats (fungi/ mushrooms). However, low number of species
in these guilds and a similar pattern for eurytopic species (Tab. 1)
might indicate that this observed pattern could be also a result of
sampling bias. Specimens’ body size does not provide an answer to this
question, as generally divergent patterns of infraspecific genetic vs
geographical distances between smaller (x_s, s, m) and larger species
(l, x_l) (Figure 3) are contrasted by rather uniform correlation
statistics between the size classes (Figure 4). Studies on ground
beetles have shown a generally higher genetic diversity across larger
species independent from their sample site distance (Assmann et al.,
2010; Schuldt & Assmann, 2011) which explain this pattern by lack of
interconnection among populations due to their very specific habitat
requirements, the habitat quality, and respective morphological
adaptations (e.g., wing reduction; Jelaska & Durbešić, 2009). According
to Freijeiro & Baselga (2016), the presence or absence of wings is an
important factor for a better understanding of the geographical/genetic
scale relationship. Indeed, habitat fragmentation is considered a major
factor limiting gene flow in ground beetle populations (Liebherr, 1988).
Our results, beside yet still enormous sampling gaps and
underrepresentation of many species, indicate that ecological niches and
preferences may play a major role in geographic dispersal and genetic
differentiation within species even though we did not consider here
environmental, climatic factors or
longitudinal\latitudinal gradients which are also known
to have a fundamental role in explaining population dynamics (Rosindell
et al., 2011; Baselga et al., 2013, 2015; Frejeiro & Baselga, 2016).
These results can be extremely helpful to further develop conservation
strategies, from a simple species conservation approach towards the
conservation of genetic diversity in habitats or landscapes (e.g.,
Hedrick, 2001; Vellend et al., 2014). Therefore, further molecular
screening would be needed, with particular focus on more geographical
sampling to cover more in detail the genetic variation within the study
area and to uncover causalities of such patterns (i.e., extending the
Barcoding towards population level). Indeed, our results showed: an
increase in number of sampling localities was usually followed by a
related increase in statistic score and thus increase the explanatory
power of barcode data to explain infraspecific genetic patterns among
different ecological guilds.
The screening at the diversity patterns of the entire entomological
fauna in such vast territory as Central Europe is a demanding task which
request efficiency and great sampling efforts, but in the light of the
emergency of current trends of insect decline (e.g., Hallmann et al.,
2017; Wagner et al., 2021) it becomes an important issue for deeper
understanding of its causes. The German Barcoding of Life campaign
(Hendrich et al., 2015; Rulik et al., 2017) and resulting database
contributed to resolve these issues. Nevertheless, a denser geographic
sampling that will also result from future monitoring studies or
metabarcoding projects will enhance the number of sampled localities and
specimens, while concerted actions would be desirable. This will
strengthen statistic results and allow bolder conclusions regarding
biodiversity in a study area rather than simple species numbers.