Ecological Modelling, Faculty of Biology, University of Duisburg-Essen, Universitätsstraße 2, 45117 Essen, Germany.
University of Konstanz, Aquatic Ecology and Evolution, Limnological Institute University of Konstanz Mainaustraße 252 78464, Konstanz/Egg, Germany.
Trends Ecol Evol. 2023 Aug;38(8):760-772. doi: 10.1016/j.tree.2023.03.011. Epub 2023 Jun 26.
While the reciprocal effects of ecological and evolutionary dynamics are increasingly recognized as an important driver for biodiversity, detection of such eco-evolutionary feedbacks, their underlying mechanisms, and their consequences remains challenging. Eco-evolutionary dynamics occur at different spatial and temporal scales and can leave signatures at different levels of organization (e.g., gene, protein, trait, community) that are often difficult to detect. Recent advances in statistical methods combined with alternative hypothesis testing provides a promising approach to identify potential eco-evolutionary drivers for observed data even in non-model systems that are not amenable to experimental manipulation. We discuss recent advances in eco-evolutionary modeling and statistical methods and discuss challenges for fitting mechanistic models to eco-evolutionary data.
尽管生态和进化动态的相互影响已被越来越多地认为是生物多样性的一个重要驱动因素，但检测这种生态进化反馈、其潜在机制及其后果仍然具有挑战性。生态进化动态发生在不同的空间和时间尺度上，并可能在不同的组织层次（例如基因、蛋白质、特征、群落）留下痕迹，这些痕迹往往难以检测。统计方法的最新进展结合替代假设检验为识别观察数据中潜在的生态进化驱动因素提供了一个很有前途的方法，即使在不适合实验操作的非模型系统中也是如此。我们讨论了生态进化建模和统计方法的最新进展，并讨论了将机械模型拟合到生态进化数据中的挑战。
