Catalina Pimiento

Robust evidence for modest neoselachian diversity loss across the K/Pg: Response to Guinot et al.

The paper reanalyzes neoselachian diversity through the K/Pg boundary using new data and methods, finding no diversity collapse during the K/Pg and robust results across different data treatments.

Temporal resolution reshapes dynamics of inferred community structure and extinction selectivity across the Permian-Triassic mass extinction

This study shows that high temporal resolution reveals abrupt ecological changes and shifts in extinction selectivity during the Permian-Triassic mass extinction, which coarse resolution obscures.

No global collapse of food webs across the Permian-Triassic Mass Extinction

This study reconstructs marine food webs across the Permian-Triassic Mass Extinction to show that trophic collapse was not global and was influenced by environmental factors and species traits.

Integrating functional and phylogenetic perspectives reveals new priorities for bird and mammal conservation

This paper compares two conservation metrics, EDGE and FUSE, and introduces a new metric EFUSE that combines evolutionary and functional distinctiveness to improve conservation prioritization.

Global gaps and priorities for shark and ray conservation: Integrating threat, function, and evolutionary distinctiveness

The paper identifies global conservation priorities for sharks and rays by analyzing threat levels, functional roles, and evolutionary distinctiveness, revealing protection deficits and conflicts with fishing pressures.

FINS - A global occurrence dataset of fossil neoselachians from the Cretaceous to the Quaternary

The paper presents FINS, a comprehensive dataset of fossil neoselachian occurrences from the Cretaceous to the Quaternary, doubling the number of records in the Paleobiology Database.

Benchmarking imputation methods for discrete biological data

This study benchmarks imputation methods for discrete biological traits using a pipeline combining phylogenetic, machine learning, and deep learning approaches, finding an ensemble method to be most accurate.