Department of Forestry, Michigan State University, East Lansing, Michigan, 48840, USA.
Ecol Appl. 2019 Jan;29(1):e01817. doi: 10.1002/eap.1817. Epub 2018 Nov 21.
Tree leaf mass is a small, highly variable, but critical, component of forest ecosystems. Estimating leaf mass on standing trees with models is challenging because leaf mass varies both within and between tree species and at different locations and points in time. Typically, models for estimating tree leaf mass are species specific, empirical models that predict intraspecific variation from stem diameter at breast height (dbh). Such models are highly limited in their application because there are many other factors beyond tree girth and species that cause leaf mass to vary and because such models provide no way to predict leaf mass for species for which data are not available. We conducted destructive sampling of 17 different species in Michigan, covering multiple life history traits and sizes, to investigate the potential for using a single, "trans-species" model for predicting leaf mass for all the trees in our study. Our results show the most important predictors of tree leaf mass are dbh, five-year basal area increment, crown class, and competition index, none of which are species specific. Species-specific variation could be captured by leaf longevity and shade tolerance. Wood specific gravity was a statistically significant, but marginally important predictor. Together, these variables describing tree size, life-history traits, and competitive environment allowed us to develop a generalized leaf mass model applicable to a diverse set of species, without having to develop species-specific equations.
树木叶片质量是森林生态系统中一个较小但非常重要的组成部分。用模型来估算立木上的叶片质量是具有挑战性的,因为叶片质量在不同树种、不同地点和不同时间都存在差异。通常,估算树木叶片质量的模型是基于物种特异性的经验模型,这些模型通过胸径(dbh)预测种内变异。这些模型在应用上受到很大限制,因为除了树木的周长和物种之外,还有许多其他因素会导致叶片质量发生变化,而且这些模型无法预测没有数据的物种的叶片质量。我们在密歇根州对 17 个不同的物种进行了破坏性采样,涵盖了多个生活史特征和大小,以研究使用单一的“跨物种”模型来预测我们研究中所有树木的叶片质量的可能性。我们的结果表明,树木叶片质量最重要的预测因子是 dbh、五年基面积增量、冠层等级和竞争指数,这些都不是物种特异性的。物种特异性变异可以通过叶片寿命和耐荫性来捕捉。木材比重是一个具有统计学意义但边际重要的预测因子。这些描述树木大小、生活史特征和竞争环境的变量使我们能够开发出一种适用于多种物种的通用叶片质量模型,而无需开发特定于物种的方程。