Data from: Refinement of a theoretical trait space for North American trees via environmental filtering

We refer to a theoretical trait space (TTS) as an n-dimensional hypervolume (“hypercube”) characterizing the range of values and covariations among multiple functional traits, in the absence of explicit filtering mechanisms. We previously constructed a 32-dimensional TTS for North American trees by fitting the Allometrically Constrained Growth and Carbon Allocation (ACGCA) model to USFS Forest Inventory and Analysis (FIA) data. Here, we sampled traits from this TTS, representing different individual “trees,” and subjected these trees to a series of gap dynamics simulations resulting in different annual light levels to explore the impact of environmental filtering (light stress) on the trait space. Variation in light limitation led to non-random mortality and a refinement of the TTS. We investigated potential mechanisms underlying such filtering processes by exploring how traits and the environment relate to mortality rates at the tree, phenotype (a specific set of trait values), and stand (a specific gap scenario) levels. The average light level at the forest floor explained 42% of the stand-level mortality, while phenotype- and tree-level mortality were best explained by six functional traits, especially radiation-use efficiency, maximum tree height, and xylem conducting area to sapwood area ratio (ΥX). These six “mortality” traits and six traits related to the leaf and wood economics spectra were used to construct trait hypercubes represented by trees that died or that survived each gap scenario. For trees that survived, the volume of their refined trait space decreased linearly with increasing stand-level mortality (up to ~50% mortality); the location also shifted, as indicated by non-zero distances between the hypercube centroids of surviving trees compared to dead trees and the original TTS. Overall, the patterns were consistent with empirical studies of functional traits, in terms of which traits predict mortality and the direction of the relationships. This work, however, also identified potentially important functional traits that are not commonly measured in empirical studies, such as ΥX and senescence rates of relatively long-lived tissues.

本研究将理论性状空间（theoretical trait space, TTS）定义为一个n维超体积（即“超立方体”），用于表征无明确过滤机制时多功能性状的取值范围与协变关系。此前我们通过将异速约束生长与碳分配（Allometrically Constrained Growth and Carbon Allocation, ACGCA）模型拟合至美国林业局（USFS）森林清查与分析（Forest Inventory and Analysis, FIA）数据，构建了适用于北美树木的32维理论性状空间。本研究从该理论性状空间中采样性状以代表不同的个体“树木”，并对这些树木开展一系列林隙动态模拟，设置不同的年光照水平，以此探究环境过滤（光照胁迫）对性状空间的影响。光照限制的差异会导致非随机的个体死亡，并使理论性状空间得到优化。我们通过探究性状与环境在个体、表型（特定性状值组合）以及林分（特定林隙情景）三个层级上与死亡率的关联，解析了此类环境过滤过程的潜在机制。林地表平均光照水平可解释42%的林分层级死亡率，而表型层级与个体层级的死亡率则可通过6个功能性状得到最佳解释，其中尤以辐射利用效率、最大树高以及木质部导水面积与边材面积之比（ΥX）最为关键。我们选取这6个“死亡关联性状”以及与叶片和木材经济谱相关的6个性状，基于各林隙情景下死亡和存活的树木分别构建性状超立方体。对于存活树木而言，其优化后性状空间的体积随林分层级死亡率的升高呈线性下降（死亡率最高可达约50%）；同时其空间位置也发生偏移，表现为存活树木与死亡树木的超立方体质心、以及存活树木与原始理论性状空间的超立方体质心之间存在非零距离。整体而言，在性状预测死亡率及其关联方向方面，本研究的结果与功能性状的实证研究结论一致。但本研究同时也识别出了一些在实证研究中通常未被测量的潜在重要功能性状，例如ΥX以及相对长寿组织的衰老速率。