Functional traits and demography: how can they address global environmental change challenges in plant communities?

全球环境变化正在对生态系统功能产生广泛影响。对于植物群落来说，气候变化、外来物种的引入和森林砍伐是一些主要威胁，导致全球五分之一的植物物种面临灭绝风险（皇家植物园，2016）。了解植物群落如何应对环境压力是一个重要的科学和社会问题。能够预测反应将极大地有助于有效地分配财政和人力资源来管理植物群落。利用功能性状预测植物群落动态变化已引起人们极大的兴趣。这些是个体生物的可测量特征，可以是形态学的、物候学的或生理的（Violle et al. 2007）。对于植物，例子包括离地高度、叶面积或种子的平均质量。在生态和环境科学中，功能性状已被用来解释生物入侵（van Kleunen et al. 2010）或物种共存机制测试（Adler et al. 2013）等现象。然而，很少使用通过重要的生命过程（生存、生长和繁殖）对种群的贡献与个体适应性相关的特征（Salguero-Gómez et al. 2018; Middleton et al. in prep）。功能特征中“功能性”的脱钩导致实证研究在解释和预测能力方面的结果好坏参半（Yang et al. 2018）。

Global environmental change is having widespread effects on ecosystem functioning. For plant communities, climate change, introduction of non-native species and deforestation are some major threats, resulting in the extinction risk of 1 in 5 plant species globally (Royal Botanic Gardens, 2016). Understanding how plant communities respond to environmental stressors is a major scientific and societal concern. Being able to predict responses would greatly help to efficiently allocate financial and human resources to manage plant communities. A method that has gained great interest for predicting changes in plant community dynamics is the use of functional traits. These are measurable features of an individual organism that can be morphological, phenological or physiological in nature (Violle et al. 2007). For plants, examples include height above ground, leaf area or average mass of seeds. Functional traits have been adopted across the ecological and environmental sciences to explain phenomena like biological invasions (van Kleunen et al. 2010), or testing for species coexistence mechanisms (Adler et al. 2013). However, rarely is the use of traits linked to individual fitness through its contributions to the population through important life processes: survival, growth and reproduction (Salguero-Gómez et al. 2018; Middleton et al. in prep). This decoupling of the "functionality" in functional traits, has resulted in mixed results in empirical studies in terms of their explanatory and predictive power (Yang et al. 2018).