Biology subjects

Weibel, R.

Publications and source records attributed to Weibel, R..

3 recordsLinked to original sources

bioRxiv2021

Contact-tracing in cultural evolution: a Bayesian mixture model to detect geographic areas of language contact

When speakers of two or more languages interact, they are likely to influence each other: contact leaves traces in the linguistic record, which in turn can reveal geographic areas of past human interaction and migration. However the complex, multi-dimensional nature of contact has hindered the development of a rigorous methodology for detecting its traces. Specifically, other factors may contribute to similarities between languages. Inheritance (a property is passed from an ancestor to several descendant languages), and universal preference (a property is universally preferred), may both overshadow contact signals. How can we find geographic contact areas in language data, while accounting for the confounding effects of inheritance and universal preference? We present sBayes, an algorithm for Bayesian clustering in the presence of confounding effects. The algorithm learns which similarities in a set of features are better accounted for by confounders, and which are due to contact effects. Contact areas are free to take any shape or size, but an explicit geographic prior ensures their spatial coherence. We test the clustering method on simulated data and apply it in two case studies to reveal language contact in South America and the Balkans. Our results are supported by --mostly qualitative-- findings from previous studies. While we focus on the specific problem of language contact, the method can also be used to uncover other traces of shared history in cultural evolution, and more generally, to reveal latent spatial clusters in the presence of confounders.

evolutionary biology↗

bioRxiv2020

Can Bayesian phylogeography reconstruct migrations and expansions in human history?

Bayesian phylogeography aims to reconstruct migrations in evolutionary processes. This methodological framework has been used for the reconstruction of homelands and historical expansions of various language families, but its reliability for language diversification research has remained unclear. We contribute to this discussion with a simulation study where we distinguish two types of spatial processes: migration and expansion. By migration we denote long-distance movement of whole populations, leaving their previous habitat empty. Expansions are small-scale movements of speakers or inclusions of new speakers into the language community, cumulatively contributing to a gradual spread into new territories. We simulate migrations, in the form of directional random walks, and expansions, in the form of a grid-based region-growing process. We run both simulation scenarios with varying degrees of directional trends and evaluate the performance of state-of-the-art phylogeographic methods. Our results show that phylogeography fails to reconstruct migrations, but works surprisingly well on expansions, even under severe directional trends. We demonstrate that migrations and expansions have typical phylogenetic and spatial patterns, which in the one case inhibit and in the other facilitate phylogeographic reconstruction. Furthermore, we propose descriptive statistics to identify whether a real sample of languages (Bantu), their relationship and spatial distribution, better fits a migration or an expansion scenario. Bringing together the results of the simulation study and theoretical arguments, we make recommendations for judging the adequacy of phylogeographic models to reconstruct the spatial evolution of languages.

evolutionary biology↗

bioRxiv2017

Integrating animal movement with habitat suitability for estimating dynamic landscape connectivity

ContextHigh-resolution animal movement data are becoming increasingly available, yet having a multitude of empirical trajectories alone does not allow us to easily predict animal movement. To answer ecological and evolutionary questions at a population level, quantitative estimates of a species potential to link patches or populations are of importance.\n\nObjectivesWe introduce an approach that combines movement-informed simulated trajectories with an environment-informed estimate of the trajectories plausibility to derive connectivity. Using the example of bar-headed geese we estimated migratory connectivity at a landscape level throughout the annual cycle in their native range.\n\nMethodsWe used tracking data of bar-headed geese to develop a multi-state movement model and to estimate temporally explicit habitat suitability within the species range. We simulated migratory movements between range fragments, and calculated a measure we called route viability. The results are compared to expectations derived from published literature.\n\nResultsSimulated migrations matched empirical trajectories in key characteristics such as stopover duration. The viability of the simulated trajectories was similar to that of the empirical trajectories. We found that, overall, the migratory connectivity was higher within the breeding than in wintering areas, corresponding to previous findings for this species.\n\nConclusionsWe show how empirical tracking data and environmental information can be fused for meaningful predictions of animal movements throughout the year and even outside the spatial range of the available data. Beyond predicting connectivity, our framework will prove useful for modelling ecological processes facilitated by animal movement, such as seed dispersal or disease ecology.

ecology↗