| 英文摘要: | The unprecedented amount of DNA sequence data made available by recent technological advances is changing how biologists identify species. Such data have great power to reveal the boundaries separating species. However, with increased amounts of sequence data, the genetic differences that are detected are not just associated with species boundaries, but include genetic differences among populations within species. As a consequence, species boundaries are misidentified, which has profound implications across biology because species are the basic unit of reference for framing biological questions. This project data will develop analytical methods to avoid conflating the boundaries of species with the local population structure within them. It will use computer simulations to evaluate key data properties affecting the accuracy of species detection, as well as the performance of the new method, especially when different processes give rise to the formation of new species. These simulations will be informed by examples from nature, assuring that biological realities, not just theoretical ideals, are jointly considered. A diverse team of researchers with complementary skill sets will advance both the scientific goals and outreach activities, which range from developing educational instruments for the public and policy makers to the training of students involved in biodiversity research.
Current genetic-based species delimitation methods can potentially lead to mass overestimates of biodiversity by treating population and species divergence as statistically equivalent. The proposed research will address these limitations. Under the new modeling approach for species delimitation, for the first time speciation is modeled as an extended process, as opposed to being treated as an instantaneous event, and as such, this approach can be used to gain insights about the diversification process itself as well. Specifically, the new approach will couple the multispecies coalescent with different diversification models for Bayesian statistical inference to avoid conflating population and species distinctiveness. The approach will be disseminated in a free software package DELIMIT and developed with reference to existing empirical data, specifically, genomic datasets of Australian squamates. Many of these genera show extraordinarily deep phylogeographic structure across thousands of loci within currently recognized species, suggesting substantial underestimation of species diversity, whereas other genera represent recent adaptive radiations, such that the empirical systems span a range of speciation processes. This context will be used to assess how robust inferred species boundaries are to different diversification processes, but also validate inferences from DELIMIT regarding the speciation process itself by testing for a general correspondence with non-genetic information about the speciation process. |