Animal species exhibit characteristic diurnal or nocturnal activity patterns, as a result of adaptations to the daily light cycle. However, we do not fully understand the evolutionary causes or consequences of these activity patterns. We have previously investigated temporal activity patterns across ~4000 species of fish through meta-analysis of the literature, and compared these to the activity patterns of ~5000 species of tetrapods. We demonstrated that nocturnality conferred an evolutionary advantage during mass extinctions, and that frequent nocturnal-to-diurnal transitions facilitated post-extinction diversification across vertebrates (Shafer et al, biorXiv, 2023). However, almost nothing is known about the evolution of this behaviour across the most diverse animal phyla, invertebrates (insects, mollusks, and cnidaria) of which there may be as many as 10 million species worldwide.

The SUDS scholar will extend our macro-analyses, and reconstruct the tempo and evolution of nocturnality and diurnality across invertebrates. Using machine learning assisted text mining, they will perform a systematic literature survey to identify the temporal activity patterns for thousands of species, and use statistical phylogenetic modeling and ancestral reconstruction to compare the evolution of nocturnality and diurnality across the vertebrate and invertebrate animal kingdoms.

Animal species exhibit circadian activity patterns in response to the rotation and light cycle on Earth. However, we do not understand the evolutionary causes or consequences of this variation; for example, why are moths nocturnal, while butterflies are diurnal? Research in our lab has suggested that nocturnality may confer an evolutionary advantage during mass extinction events (Shafer, et. al., 2023), and transitions between activity patterns might drive speciation (Nichols & Shafer, et. al., 2024). However, we only have information on the activity patterns of ~12% of vertebrate species, and no systematic information is available on the activity patterns of invertebrates, which represent >97% of all animal species. Given the scale of missing information, we aim to leverage citizen science to fill in the gap. iNaturalist is a popular application that allows users to post observations of organisms along with metadata for their location/timing, spawning a new generation of digital naturalists, and generating huge databases of scientific-grade observations of Earth’s biodiversity. We propose to mine >200 million observations of ~500,000 species by >8 million users from around the world. The SUDS scholar will dereminte the activity pattern for millions of species by identifying patterns in this data using data science techniques.