The population structure of A. mantiqueira between
mountains ranges and implications for conservation
The best number of clusters detected by the clustering analyses for all
samples was K = 2. This K value, however, did not indicate any
population structure for A. mantiqueira through its distribution.
Nonetheless, phylogenetic inference and DAPC analysis revealed some
intraspecific structure, implying that a population structure exists
that was not detected by cluster analyses using the optimal K = 2. All
methods proposed to determine genetic structure a posteriori will
face detection limits when differentiation between groups is low, and
some factors such as uneven sampling and the number of individuals may
affect the optimal K value (Miller et al., 2020). In simulations to test
the efficiency of DAPC to assess population genetic structure, it was
observed that there is considerable inaccuracy in the face of low levels
of differentiation (FST values <0.1) (Miller
et al., 2020). This may explain the inability to detect intraspecific
population structure in A. mantiqueira since the mean
FST within populations is 0.0122.
Clustering methods depict a simplified version of a complicated reality
that does not always correspond to existing population genetic models
(Jombart, 2012). For this reason, it is important to interpret
clustering results with caution and consider additional genetic and
demographic factors that may influence population structure.
Additionally, incorporating information from multiple clustering methods
or conducting further analyses can provide a more comprehensive
understanding of genetic diversity within populations. Therefore, other
K values were investigated, and the K = 4 value recovered the same
clustering pattern in all analyses, regardless of the different
assumptions underlying these methods. Furthermore, this clustering
pattern was preferred because it is congruent with A. mantiqueiramountain range distribution, implying biological relevance. Indeed, a
process of differentiation between the populations from Serra da Bocaina
and the inland populations may be ongoing, as evidenced by the slight
variations in wing patterns that exist between the two.
The phylogeographic break recovered between Serra da Mantiqueira and
Serra do Mar Mountain ranges has been observed in Atlantic Forest
orchids, and dispersal is a more plausible explanation than vicariance
for the origin of this population structure (Pinheiro et al., 2013). The
lowland regions between these mountains were potentially phylogeographic
barriers to dispersal for endemic birds as well (Chaves et al., 2015).
Singularly, the sample of A. mantiqueira from Poços de Caldas
(MG) stands out from the rest of the Serra da Mantiqueira. The Poços de
Caldas plateau is a circular structure of Mesozoic age comprising a
suite of alkaline volcanic and plutonic rocks with elevations up to
1500–1600 m above sea level in its borders. Its original vegetation
coverage consisted of the Atlantic Forest biome, but this region has
suffered high degradation, losing areas of native vegetation to pastures
(Grohmann et al., 2007; RadamBrasil, 1983; Schorscher & Shea, 1992).
The highlands of Poços de Caldas also have a distinct geology when
compared to the rest of the Serra da Mantiqueira complex (Schorscher &
Shea, 1992), making them a distinct and representative area of endemism
for the region’s anurans (Neves et al., 2018). Because of the strong
influence of a different domain (the Cerrado savannas) in this location,
phytophisiognomy factors may explain some of the observed genetic
discontinuity. Furthermore, this location is one of the most
geographically distant from the others in Serra da Mantiqueira and may
be under greater isolation by distance (IBD) effect.
The discontinuity between mountain ranges may be partially due to
historical processes, such as the founder effect, which involves the
colonization of new mountains through dispersal. By this model, new
populations would be established by few individuals from a large
ancestral population, suffering the effects of genetic drift after
establishment and large changes in allele frequencies due to sampling
error (Templeton, 2008). Another potential outcome is the reduction in
genetic diversity caused by genetic drift following the disruption of
gene flow between distinct mountain ranges. In this case, the ancestral
population would have been distributed over the entire sampled extent,
and later, climatic barriers during interglacial periods may have caused
isolation in sub-populations. Natural selection may also have influenced
these polymorphisms in different locations, selecting those more adapted
to the ecological, climatic, and niche variations of these mountain
ranges, promoting faster differentiation among populations (Matsubayashi
& Fujiyama, 2016). Our results suggests that gene flow between
populations of A. mantiqueira may be limited, leading to
potential genetic divergence within the species. Therefore, the same
factors that lead to allopatric speciation in A. mantiqueira andA. alalia may also operate at the intrapopulation level inA. mantiqueira .
Climate change is a potential danger to the survival of these species
that inhabit mountainous regions. This is particularly true for the
Atlantic Forest, a region of high biodiversity that is currently facing
habitat fragmentation as a result of urbanisation and human activities,
leaving only 12% of its original vegetation intact. (SOS Mata
Atlântica, 2019). According to the niche models generated in the present
study, the fragments suitable for these species will become scarcer as
the mean temperature rises. Habitat fragmentation may reduce dispersal
and, consequently, genetic connectivity among these populations
(Cunningham & Moritz, 1998; Dayanandan et al., 1999; Gerlach & Musolf,
2000). This major landscape alteration is likely to change gene flow and
promote genetic drift in natural populations (Dixo et al., 2009;
Vandergast et al., 2006). The loss of genetic connectivity may be
detrimental to long-term species persistence (Gilpin & Soulé, 1986;
Templeton et al., 1990). For this reason, more phylogeographic, climate
modeling, and demographic studies on the fauna of MAF will be crucial
for the conservation of the biodiversity of this hotspot biome in
scenarios of habitat fragmentation and climate change.