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Understanding the origins of diversity and the factors that drive some clades to be more diverse than others are important issues in evolutionary biology. Sophisticated SSE (state-dependent speciation and extinction) models provide insights into the association between diversification rates and the evolution of a trait. The empirical data used in SSE models and other methods is normally imperfect, yet little is known about how this can affect these models. Here, we evaluate the impact of common phylogenetic issues on inferences drawn from SSE models. Using simulated phylogenetic trees and trait information, we fitted SSE models to determine the effects of sampling fraction (phylogenetic tree completeness) and sampling fraction mis-specification on model selection and parameter estimation (speciation, extinction, and transition rates) under two sampling regimes (random and taxonomically biased). As expected, we found that both model selection and parameter estimate accuracies are reduced at lower sampling fractions (i.e., low tree completeness). Furthermore, when sampling of the tree is imbalanced across sub-clades and tree completeness is ≤ 60%, rates of false positives increase and parameter estimates are less accurate, compared to when sampling is random. Thus, when applying SSE methods to empirical datasets, there are increased risks of false inferences of trait dependent diversification when some sub-clades are heavily under-sampled. Mis-specifying the sampling fraction severely affected the accuracy of parameter estimates: parameter values were over-estimated when the sampling fraction was specified as lower than its true value, and under-estimated when the sampling fraction was specified as higher than its true value. Our results suggest that it is better to cautiously under-estimate sampling efforts, as false positives increased when the sampling fraction was over-estimated. We encourage SSE studies where the sampling fraction can be reasonably estimated and provide recommended best practices for SSE modeling. [Trait dependent diversification; SSE models; phylogenetic tree completeness; sampling fraction.].
Assuntos
Especiação Genética , Filogenia , FenótipoRESUMO
Wallacea-the meeting point between the Asian and Australian fauna-is one of the world's largest centers of endemism. Twenty-three million years of complex geological history have given rise to a living laboratory for the study of evolution and biodiversity, highly vulnerable to anthropogenic pressures. In the present article, we review the historic and contemporary processes shaping Wallacea's biodiversity and explore ways to conserve its unique ecosystems. Although remoteness has spared many Wallacean islands from the severe overexploitation that characterizes many tropical regions, industrial-scale expansion of agriculture, mining, aquaculture and fisheries is damaging terrestrial and aquatic ecosystems, denuding endemics from communities, and threatening a long-term legacy of impoverished human populations. An impending biodiversity catastrophe demands collaborative actions to improve community-based management, minimize environmental impacts, monitor threatened species, and reduce wildlife trade. Securing a positive future for Wallacea's imperiled ecosystems requires a fundamental shift away from managing marine and terrestrial realms independently.
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The mass mortality of oak trees has been prevalent in Japan since the late 1980s. The fungus Raffaelea quercivora is transmitted by an ambrosia beetle, Platypus quercivorus, which causes mortality. The beetle is able to bore galleries into the sapwood of most Fagaceae trees in Japan; however, the level of mortality caused by R. quercivora and P. quercivorus differs greatly among tree species. Previous studies by our research group have demonstrated that the virulence of R. quercivora differs among isolates when inoculated into Quercus serrata logs. However, interactions between the virulence of R. quercivora isolates and the susceptibility of other fagaceous species have yet to be elucidated. In this study, we inoculated the fresh logs of 11 fagaceous species with isolates of low and high virulence, and measured the tangential widths of discolored sapwoods 3 weeks after inoculation. Although the discoloration widths of Q. crispula sapwood were similar among all isolates, those of Q. serrata and Q. acutissima tended to increase with the more virulent isolates. Sapwood discoloration in Q. glauca, Q. acuta, Q. salicina, Lethocarpus edulis, and Castanopsis sieboldii was greatly increased by highly virulent isolates. Discoloration in Fagus japonica was not influenced by any of the isolates. The logs of Q. crispula and Q. serrata but not Q. glauca were significantly more discolored by a low-virulence isolate compared with standing trees. The various virulent isolates induced unique sapwood discoloration characteristics in each species, which may explain species-specific differences in mortality rates.
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The relative contribution of speciation and extinction into current diversity is certainly unknown, but mathematical frameworks that use genetic information have been developed to provide estimates of these processes. To that end, it is necessary to reconstruct molecular phylogenetic trees which summarize ancestor-descendant relationships as well as the timing of evolutionary events (i.e., rates). Nevertheless, diversification models show poor fit when assuming that single rate of speciation/extinction is constant over time and across lineages: species exhibit such a great variation in features that it is unlikely they give birth and die at the same pace. The state-dependent diversification framework (SSE) reconciles the species phenotypic variation with heterogeneous rates of diversification observed in a clade. This family of models allows testing contrasting hypotheses on mode of speciation, trait evolution, and its influence on speciation/extinction regimes. Although microbial species richness outnumbers diversity in plants and animals, diversification models are underused in microbiology. Here, we introduce microbiologists to models that estimate diversification rates and provide a detailed description of SSE models. Besides theoretical principles underlying the method, we also show how SSE analysis should be set up in R. We use pH evolution in Thaumarchaeota to explain its evolutionary dynamic in the light of SSE model. We hope this chapter spurs the study of trait evolution and evolutionary outcomes in microorganisms.