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的功能产生广泛影响。对于植物群落来说，气候变化、非本土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年）。了解植物群落如何应对环境压力是一个重大的科学和社会问题。能够预测反应将大大有助于有效地分配财政和人力资源来管理植物群落。一种方法，已经获得了很大的兴趣，用于预测植物群落动态的变化是使用功能性状。这些是个体生物的可测量特征，可以是形态学、物候学或生理学性质的（Violle等人，2007年）。对于植物，实例包括离地高度、叶面积或种子的平均质量。生态和环境科学已经采用功能性状来解释生物入侵等现象（货车Kleunen et al. 2010），或测试物种共存机制（Adler et al. 2013）。然而，很少使用与个体适应性相关的性状，这些性状通过重要的生命过程对种群的贡献：生存，生长和繁殖（Salguero-Gómez et al. 2018; Middleton et al. in prep）。这种功能性特征中“功能性”的脱钩导致了实证研究在解释和预测能力方面的混合结果（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).