Bayesian phylogenetic analysis of Philippine languages supports a rapid migration of Malayo-Polynesian languages.

The Philippines are central to understanding the expansion of the Austronesian language family from its homeland in Taiwan. It remains unknown to what extent the distribution of Malayo-Polynesian languages has been shaped by back migrations and language leveling events following the initial Out-of-Taiwan expansion. Other aspects of language history, including the effect of language switching from non-Austronesian languages, also remain poorly understood. Here we apply Bayesian phylogenetic methods to a core-vocabulary dataset of Philippine languages. Our analysis strongly supports a sister group relationship between the Sangiric and Minahasan groups of northern Sulawesi on one hand, and the rest of the Philippine languages on the other, which is incompatible with a simple North-to-South dispersal from Taiwan. We find a pervasive geographical signal in our results, suggesting a dominant role for cultural diffusion in the evolution of Philippine languages. However, we do find some support for a later migration of Gorontalo-Mongondow languages to northern Sulawesi from the Philippines. Subsequent diffusion processes between languages in Sulawesi appear to have led to conflicting data and a highly unstable phylogenetic position for Gorontalo-Mongondow. In the Philippines, language switching to Austronesian in 'Negrito' groups appears to have occurred at different time-points throughout the Philippines, and based on our analysis, there is no discernible effect of language switching on the basic vocabulary.

The evolutionary dynamics of how languages signal who does what to whom.

Languages vary in how they signal "who does what to whom". Three main strategies to indicate the participant roles of "who" and "whom" are case, verbal indexing, and rigid word order. Languages that disambiguate these roles with case tend to have either verb-final or flexible word order. Most previous studies that found these patterns used limited language samples and overlooked the causal mechanisms that could jointly explain the association between all three features. Here we analyze grammatical data from a Grambank sample of 1705 languages with phylogenetic causal graph methods. Our results corroborate the claims that verb-final word order generally gives rise to case and, strikingly, establish that case tends to lead to the development of flexible word order. The combination of novel statistical methods and the Grambank database provides a model for the rigorous testing of causal claims about the factors that shape patterns of linguistic diversity.

Machine learning predicts which rivers, streams, and wetlands the Clean Water Act regulates.

We assess which waters the Clean Water Act protects and how Supreme Court and White House rules change this regulation. We train a deep learning model using aerial imagery and geophysical data to predict 150,000 jurisdictional determinations from the Army Corps of Engineers, each deciding regulation for one water resource. Under a 2006 Supreme Court ruling, the Clean Water Act protects two-thirds of US streams and more than half of wetlands; under a 2020 White House rule, it protects less than half of streams and a fourth of wetlands, implying deregulation of 690,000 stream miles, 35 million wetland acres, and 30% of waters around drinking-water sources. Our framework can support permitting, policy design, and use of machine learning in regulatory implementation problems.