Cross-disciplinary information for understanding macroevolution

Trends in Ecology & Evolution, Год журнала: 2022, Номер 38(3), С. 250 - 260

Опубликована: Ноя. 29, 2022

Many different macroevolutionary models can produce the same observations. Despite efforts in building more complex and realistic models, it may still be difficult to distinguish processes that have generated biodiversity we observe. In this opinion argue make new progress by reaching out across disciplines, relying on independent data theory constrain inference. Using mainly paleontological insights data, illustrate how eliminate less plausible or implausible and/or parts of parameter space, while applying comparative phylogenetic approaches. We emphasize such cross-disciplinary drawn between many other disciplines relevant macroevolution. urge training, collaboration using common-use databases as a platform for increasing our understanding.

Язык: Английский

Analysing planktonic foraminiferal growth in three dimensions with foram3D: an R package for automated trait measurements from CT scans

Journal of Micropalaeontology, Год журнала: 2022, Номер 41(2), С. 149 - 164

Опубликована: Ноя. 1, 2022

Abstract. Foraminifera are one of the few taxa that preserve their entire ontogeny in fossilised remains. Revealing this through micro-computed tomography (CT) fossil planktonic foraminifera has greatly improved our understanding life history and allows accurate quantification total shell volume, growth rates developmental constraints throughout an individual's life. Studies using CT scans currently mainly focus on chamber size, but wealth three-dimensional data generated by potential to reconstruct complete trajectories. Here we present open-source R package analyse space. Using only centroid xyz coordinates every chamber, functions determine sequence check chambers correct order. Once order been verified, calculate distances angles between subsequent chambers, number whorls final whorl at time each was built, and, for first time, quantify trochospirality. The applications will enable repeatable analysis large sets key taxonomic traits ultimately provide new insights into effects evolution.

Язык: Английский