Cross-disciplinary information for understanding macroevolution

作者：Lee Hsiang Liow, Josef C. Uyeda, Gene Hunt · 发表于：Trends in Ecology & Evolution · 年份：2022 · DOI：10.1016/j.tree.2022.10.013 · 被引用次数：25 · 研究领域：Evolution and Paleontology Studies、Evolution and Genetic Dynamics

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