Integrating Genomics, Collections, and Community Science to Reveal Speciation in a Variable Monitor Lizard (Varanus tristis)

Abstract

--The accurate characterization of species diversity is a vital prerequisite for ecological and evolutionary research, as well as conservation. Thus, it is necessary to generate robust hypotheses of species limits based on the inference of evolutionary processes. Integrative species delimitation, the inference of species limits based on multiple sources of evidence, can provide unique insight into species diversity and the processes behind it. However, the application of integrative approaches in non-model organisms is often limited by the amount of data that is available. Here, we show how data relevant for species delimitation can be bolstered by incorporating information from tissue collections, museum specimens, and observations made by the wider community. We show how to integrate these data under a hypothesis-driven, integrative framework by identifying the processes generating genetic and phenotypic variation in Varanus tristis, a widespread and variable complex of Australian monitor lizards. Using genomic, morphometric (linear and geometric), coloration, spatial, and environmental data we show that disparity in this complex is inconsistent with intraspecific variation and instead suggests that speciation has occurred. Based on our results, we identify the environmental factors that may have been responsible for the geographic sorting of variation. Our workflow provides a guideline for the integrative analysis of several types of data to identify the occurrence and causes of speciation. Furthermore, our study highlights how community science and machine learning--two tools used here--can be used to accelerate taxonomic research.