CAS Key Laboratory of Tropical Forest Ecology, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Mengla, Yunnan, China.
Department of Biology, University of Maryland, College Park, MD, USA.
Ann Bot. 2021 Mar 24;127(4):533-542. doi: 10.1093/aob/mcaa085.
The composition and dynamics of plant communities arise from individual-level demographic outcomes, which are driven by interactions between phenotypes and the environment. Functional traits that can be measured across plants are frequently used to model plant growth and survival. Perhaps surprisingly, species average trait values are often used in these studies and, in some cases, these trait values come from other regions or averages calculated from global databases. This data aggregation potentially results in a large loss of valuable information that probably results in models of plant performance that are weak or even misleading.
We present individual-level trait and fine-scale growth data from >500 co-occurring individual trees from 20 species in a Chinese tropical rain forest. We construct Bayesian models of growth informed by theory and construct hierarchical Bayesian models that utilize both individual- and species-level trait data, and compare these models with models only using individual-level data.
We show that trait-growth relationships measured at the individual level vary across species, are often weak using commonly measured traits and do not align with the results of analyses conducted at the species level. However, when we construct individual-level models of growth using leaf area ratio approximations and integrated phenotypes, we generated strong predictive models of tree growth.
Here, we have shown that individual-level models of tree growth that are built using integrative traits always outperform individual-level models of tree growth that use commonly measured traits. Furthermore, individual-level models, generally, do not support the findings of trait-growth relationships quantified at the species level. This indicates that aggregating trait and growth data to the species level results in poorer and probably misleading models of how traits are related to tree performance.
植物群落的组成和动态源于个体水平的人口统计结果，而这些结果则是由表型与环境之间的相互作用所驱动的。可以跨植物测量的功能特征经常被用于模拟植物的生长和存活。也许令人惊讶的是，这些研究中经常使用物种平均特征值，而在某些情况下，这些特征值来自其他地区或从全球数据库中计算得出的平均值。这种数据聚合可能会导致大量有价值信息的丢失，这可能导致植物表现模型较弱甚至具有误导性。
我们提出了来自中国热带雨林中 20 个物种的 500 多个共存个体树木的个体水平特征和细尺度生长数据。我们构建了基于理论的生长贝叶斯模型，并构建了利用个体和物种水平特征数据的分层贝叶斯模型，然后将这些模型与仅使用个体水平数据的模型进行比较。
我们表明，个体水平上测量的特征-生长关系因物种而异，使用常用特征测量时通常较弱，并且与物种水平上的分析结果不一致。然而，当我们使用叶面积比近似值和综合表型构建个体水平的生长模型时，我们生成了树木生长的强预测模型。
在这里，我们已经表明，使用综合特征构建的树木生长个体水平模型始终优于使用常用特征测量的树木生长个体水平模型。此外，一般来说，个体水平模型不支持在物种水平上量化的特征-生长关系的发现。这表明，将特征和生长数据聚合到物种水平会导致特征与树木性能之间关系的模型变差，并且可能具有误导性。
