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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