Population genetic predictors of phylogeographic structure
Testing for whether population size, stability or dispersal limitation
predicted the scale of phylogeographic structure, geographically
restricted lineages had higher Ho (AIC = -75.664; p = 0.005), but
there was no significant relationship between range size and either
Tajima’s D (AIC= 24.025; p= 0.081) or the slope of IBD (AIC= -56.6; p =
0.212). When we add Tajima’s D as a covariate (to control for possible
reduction in Ho with range expansion) the negative relationship
of heterozygosity and range size remained significant (p = 0.049). As in
some taxa our estimates of IBD and Tajima’s D could still be impacted by
sampling across historically isolated lineages rather than
metapopulations (Battey et al. 2020), we removed such potentially
composite taxa (Carlia munda ; Diporiphora bilineata ,D. perplexa ; Gehyra gemina , G. koira , G.
nana 4; Heteronothia binoei TE; see Suppl. Mat. Info and
Figure S10) and repeated tests. Excluding these taxa, more localised
lineages now had less negative Tajima’s D values (AIC= 52.351; p =
0.005); other results remained the same. Older clades (mostlyGehyra ) have stronger IBD (Figure 4), but there is no
relationship with either Tajima’s D or Ho . When the above taxa
are excluded, Tajima’s D values did not differ across genera, region of
occurrence or habitat specialization (p>0,05).
Discussion
Our SNP datasets support and extend previous evidence for strong
phylogeographic structuring within these tropical lizard species
complexes in the AMT. Compared with the smaller scale datasets available
through traditional multilocus sequencing or exon capture, SNPs provide
much higher resolution for studying the influence of environmental
features on genetic divergence and population structure and admixture
(Georges et al., 2018; Melville et al., 2017).
Intrinsic dispersal limitation, represented by the IBD slopes, was not
related to the habitat specialization, genus or aridity. While
populations of most of the rock-specialist lineages can be found on
widely-dispersed flat rocks and open sandstone platforms (Melville et
al., 2019; Oliver et al., 2019), only Gehyra koira and G.
lapistola are associated with the more sparsely distributed tall rock
escarpments (Oliver et al., 2019). That even these taxa, and especially
the widely distributed G. koira , did not have stronger IBD than
generalists is surprising. Similarly, there were no consistent
differences among genera, which implicitly represent broader divergence
in ecological traits. More insight into causes of the three-fold
variation in observed intra-lineage IBD could come from inclusion of
additional natural history attributes, such as body size and
physiological tolerances, as well as more intensive sampling at finer
spatial scales.
Isolation by Environment emerged as a stronger predictor of
landscape-scale genetic divergence than Isolation by Distance or
Isolation by Resistance. As IBE represents an interaction between
species traits and abiotic elements of the landscape (Myers et al.,
2019; Paz, Ibáñez, Lips, & Crawford, 2015), we infer that local
adaptation could be the principal driver of genomic divergence in these
AMT lizards. Previous comparative studies showed that species-specific
characteristics can be the main determinant of landscape genetic
patterns (Myers et al., 2019; Reid, Mladenoff, & Peery, 2017; Robertson
et al., 2018). But here, Gehyra and Heteronotia(Gekkonidae) have different determinants of population structure.
Similarly, there is no overall differences in landscape genetic patterns
between rock-specialists and generalist species. Yet, the distantly
related Diporiphora magna KIM and Gehyra nana4 lineages
share a widely sympatric distribution and also have the same variables
fall out as most important variables in explaining their genetic
diversity - Average Annual Temperature and Average Daytime Range. These
species have little in common and it is unclear why they might exhibit
similar responses to environmental heterogeneity - they belong to
different lizard families (Agamidae and Gekkonidae), and have different
ecological requirements: D. magna KIM is a daytime habitat
generalist dragon, while G. nana 4 is a nocturnal, rock-related
gecko. Overall, the expected association between species traits and both
landscape genetics and phylogeographic patterns (Zamudio et al., 2016)
is elusive here.
Spatial variation in both temperature and precipitation emerged as
important influences on landscape-scale diversification of these
lizards. This supports findings that the environmental constraints are
important drivers of genome-wide neutral differentiation patterns
(Bothwell et al., 2017; Orsini, Vanoverbeke, Swillen, Mergeay, & De
Meester, 2013; Sexton, Hangartner, & Hoffmann, 2014). This result is
expected for ectotherm lizards, in which ecological traits are
responsive to climate variation in space and time (Camargo, Sinervo, &
Sites Jr, 2010; Pianka & Vitt, 2003). Additionally, the strong
seasonality of rainfall across the AMT (Figure 1B) has direct impact on
geographic (e.g. changes in the river regimes; Woinarski et al., 2007)
and physiological barriers for organisms and further dispersal
abilities. Spatial variation in seasonality variables were significant
predictors of genetic distance within about half of the lineages
examined here. This is not the first time seasonal variation in climatic
condition is reported as a principal driver of genetic differentiation
(Bothwell et al., 2017; Cushman et al., 2014; Yang, Cushman, Song, Yang,
& Zhang, 2015), and should be investigated in the AMT in more detail.
Other factors not considered in the present work are biotic
interactions, such as competition, that could influence the resilience
of the species/lineages in determined regions (Harvey, Aleixo, et al.,
2017; Riginos et al., 2014), and spatial variation in long-term climatic
stability (Potter et al., 2018), wherein past climate events affected
species distribution and genetic structure (Vasconcellos et al., 2019).
While spatial variation in habitat suitability (IBR) is a significant
predictor of genetic differentiation in some cases, it was never the
best predictor, revealing that landscape features play a small role on
differentiation of our co-distributed taxa. Rather, IBE, alone or in
combination with IBD or IBR, was generally the best predictor. A deeper
look into the IBD graphs shows that, for widely distributed taxa (e.g.D. bilineata and G. gemina; Figures S6 and S7), there is
substantial population structure within lineages as landscape features
(such as topographic variation and rivers) are not limiting the
intrinsic dispersal. This does not necessarily result in formation of
new phylogeographic lineages, as IBD is related to earlier stages of
differentiation (Avise, 2000; Singhal et al., 2018). Yet, for other
lineages, the structure within lineages is not clear on the IBD graphs,
and environmental and landscape features could have a bigger influence
on genetic divergence (Singhal et al., 2018).
While macroevolutionary patterns are presumably generated by population
level (microevolutionary) dynamics (Harvey et al. 2019), it has proved
challenging to discern the processes that connect the two scales (Li et
al., 2018; Singhal et al., 2018; Harvey, Seeholzer, et al., 2017). A
promising result here is that geographically restricted lineages had
higher heterozygosity and (when removing taxa with cryptic lineage
structure) lower deviations from mutation-drift equilibrium within
lineages. This points to larger local effective population size and
greater population stability as determinants of fine-scale
phylogeogaphic structure, whereas the strength of IBD had no influence.
Thus, population persistence, more than dispersal limitation, could be
key to development of fine-scale phylogeographic structure and potential
speciation. Finally, while other studies have shown that ecological
preference can predict genetic diversity and divergence (Harvey, Aleixo,
et al., 2017), this was not observed in our co-distributed taxa,
emphasizing again that the divergence is species-specific and that
effects of other ecological dimensions, such as physiological
specialization and biotic interactions (e.g. range bloking) should be
investigated. Methods incorporating microevolution into
macroevolutionary analyses (and vice versa) are still limited, but
recent developments promise a better integration of both fields (Harvey,
Seeholzer, et al., 2017; Li et al., 2018; Price et al., 2014; Rabosky &
Matute, 2013).
Conclusion
Our study on co-occurring taxa and well-sampled SNP datasets allows us
to explore what intrinsic and extrinsic factors influence the
development of fine-scale phylogeographic structuring within species.
Population genetic statistics indicate the importance of population size
and stability in promoting fine-scale phylogeographic structure. Local
adaptation seems to be the strongest driver of phylogeographic structure
in the AMT lizards, rather than family, habitat restriction and region
of occurrence. Further examination of species-specific characteristics
should help to elucidate which biogeographic and environmental features
promote persistence and isolation between populations (Zamudio, Bell, &
Mason, 2016). The strong seasonality of the AMT could also impact the
propensity for genetic divergence and future studies should focus on
local dispersal restriction across dry and wet seasons as these
influence physiological and physical barriers.
Acknowledgements
JF thanks Coordenação de Aperfeiçoamento de Pessoal de Nível Superior –
CAPES for the PhD scholarship. We would like to thank Renee Catullo and
Rebecca Laver for comments and Ian Brennan for advice on figure 2. We
also thank the Australian Museum, Northern Territories Museum, South
Australian Museum, Queensland Museum, and Western Australian Museum for
access to the tissues. This research was supported by an ARC Laureate
Fellowship to CM.
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