A direct comparison of ecological theories for predicting the relationship between plant traits and growth
A direct comparison of ecological theories for predicting the relationship between plant traits and growth
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DOI：
10.1002/ecy.3986
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发表时间：
2023-03-06
期刊：
影响因子：
4.8
通讯作者：
Sparks, Jed P.
中科院分区：
文献类型：
--
作者：
Goud, Ellie M.;Agrawal, Anurag A.;Sparks, Jed P.
尽管对植物生态策略进行分类的理论由来已久，但有限的数据直接将有机特征与全植物生长率(GR)联系起来。我们基于三个重要的理论对性状-生长关系进行了比较：生长分析、Grime的竞争胁迫耐受三角(CSR)和叶片经济谱(LES)。在这些方案下，生长分别由与相对生物量投资、叶片结构或气体交换相关的性状来预测。我们还考虑了这些理论中没有包含的特征，但这可能会为增长提供潜在的替代最佳预测因素。对30种不同的黄连属(Asclepias spp.)21个形态和生理性状，GR(每日产生的总生物量)变化50倍，最好的预测是生物量分配给叶片(通过生长分析预测)，以及叶片大小和叶片干物质含量的CSR性状。总叶面积(LA)和株高也是全株GRS的良好预测因子。尽管LES有两个与生长相关的性状(基于质量的叶片氮含量和基于面积的叶片磷含量)，但这两个性状与LES预测的结果相反，即N和P含量越高，生长越慢。其余的LES性状(例如，叶片气体交换)不能预测植物的GR。总体而言，GR的差异更多地受到生物量分数和总LA等全株性状的影响，而不是单个叶水平性状的影响，如光合作用速率或比叶面积。我们的结果与经典的生长分析最一致--将叶片性状与全株分配相结合，以最好地预测生长。然而，考虑到破坏性的生物量措施通常是不可行的，应用与CSR分类相关的易于测量的叶片性状似乎比LES性状更能预测整个植物的生长。在更多的分类群中测试这一结果的普遍性将进一步提高我们根据不同规模的功能性状预测全植物生长的能力。
Despite long-standing theory for classifying plant ecological strategies, limited data directly link organismal traits to whole-plant growth rates (GRs). We compared trait-growth relationships based on three prominent theories: growth analysis, Grime's competitive-stress tolerant-ruderal (CSR) triangle, and the leaf economics spectrum (LES). Under these schemes, growth is hypothesized to be predicted by traits related to relative biomass investment, leaf structure, or gas exchange, respectively. We also considered traits not included in these theories but that might provide potential alternative best predictors of growth. In phylogenetic analyses of 30 diverse milkweeds (Asclepias spp.) and 21 morphological and physiological traits, GR (total biomass produced per day) varied 50-fold and was best predicted by biomass allocation to leaves (as predicted by growth analysis) and the CSR traits of leaf size and leaf dry matter content. Total leaf area (LA) and plant height were also excellent predictors of whole-plant GRs. Despite two LES traits correlating with growth (mass-based leaf nitrogen and area-based leaf phosphorus contents), these were in the opposite direction of that predicted by LES, such that higher N and P contents corresponded to slower growth. The remaining LES traits (e.g., leaf gas exchange) were not predictive of plant GRs. Overall, differences in GR were driven more by whole-plant characteristics such as biomass fractions and total LA than individual leaf-level traits such as photosynthetic rate or specific leaf area. Our results are most consistent with classical growth analysis-combining leaf traits with whole-plant allocation to best predict growth. However, given that destructive biomass measures are often not feasible, applying easy-to-measure leaf traits associated with the CSR classification appear more predictive of whole-plant growth than LES traits. Testing the generality of this result across additional taxa would further improve our ability to predict whole-plant growth from functional traits across scales.