Does functional trait diversity predict above-ground biomass and productivity of tropical forests? Testing three alternative hypotheses
Does functional trait diversity predict above-ground biomass and productivity of tropical forests? Testing three alternative hypotheses
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DOI：
10.1111/1365-2745.12346
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发表时间：
2015-01-01
影响因子：
5.5
通讯作者：
Poorter, Lourens
中科院分区：
文献类型：
--
作者：
Finegan, Bryan;Pena-Claros, Marielos;Poorter, Lourens
1.热带雨林在全球具有重要意义，但尚不清楚生物多样性是否能加强热带雨林中的碳储存和固存。我们测试了这种关系，重点放在功能性状生物多样性的组成部分作为预测因素。提供了玻利维亚、巴西和哥斯达黎加三个热带雨林的数据。估计了树木直径为10 cm时，幸存者、新兵和幸存者+新兵的初始地上生物量和生物量增量(总计)。在62个1.0公顷和21个1.0公顷样地中。测定了生物量增量与初始现存量(AGB(I))、8个功能性状的生物量加权群落平均值(CWM)和4个功能性状多样性指数(功能丰富度、功能均匀度、功能多样性和功能分散度)的关系。采样的森林连续体范围从组织坚韧、AGB(I)高的‘慢’林分到以叶软、营养丰富、木材较轻、AGB(I)低的树为主的‘快’林分。我们检验了AGB(I)和生物量增量是否与系统中优势种的CWM性状值(生物量比率假说)、功能性状值的多样性(生态位互补假说)相关，或者在生物量增量的情况下，仅仅与初始现存量相关(绿汤假说)。CWM是AGB(I)和生物量增量的合理的二元预测因子，其中CWM比叶面积SLA、CWM叶含氮量、CWM撕叶力、CWM最大成体高度H-max和CWM木材比重最重要。AGB(I)也是三种生物量增量的合理预测因子。在最佳拟合多元回归模型中，CWM H-max是AGB(I)初始蓄积量最重要的预测因子。在生物量增长的最佳模型中，只有叶片性状被选择；CWM SLA是最重要的预测因子，与预期的正相关。功能多样性指数与生物量增量之间不存在相关性，AGB(I)是唯一能预测招募生物量增量的因子。综合。我们没有发现支持生态位互补假说，而支持绿汤假说，只支持新兵的生物量增量。我们强烈支持生物质比假说。CWM H-max是生态系统生物量、碳储量和CWM SLA的强大驱动因素，而其他CWM叶片性状对生物量增量和碳固存尤为重要。
1. Tropical forests are globally important, but it is not clear whether biodiversity enhances carbon storage and sequestration in them. We tested this relationship focusing on components of functional trait biodiversity as predictors.2. Data are presented for three rain forests in Bolivia, Brazil and Costa Rica. Initial above-ground biomass and biomass increments of survivors, recruits and survivors + recruits (total) were estimated for trees >= 10 cm d.b.h. in 62 and 21 1.0-ha plots, respectively. We determined relationships of biomass increments to initial standing biomass (AGB(i)), biomass-weighted community mean values (CWM) of eight functional traits and four functional trait variety indices (functional richness, functional evenness, functional diversity and functional dispersion).3. The forest continuum sampled ranged from 'slow' stands dominated by trees with tough tissues and high AGB(i), to 'fast' stands dominated by trees with soft, nutrient-rich leaves, lighter woods and lower AGB(i).4. We tested whether AGB(i) and biomass increments were related to the CWM trait values of the dominant species in the system (the biomass ratio hypothesis), to the variety of functional trait values (the niche complementarity hypothesis), or in the case of biomass increments, simply to initial standing biomass (the green soup hypothesis).5. CWMs were reasonable bivariate predictors of AGB(i) and biomass increments, with CWM specific leaf area SLA, CWM leaf nitrogen content, CWM force to tear the leaf, CWM maximum adult height H-max and CWM wood specific gravity the most important. AGB(i) was also a reasonable predictor of the three measures of biomass increment. In best-fit multiple regression models, CWM H-max was the most important predictor of initial standing biomass AGB(i). Only leaf traits were selected in the best models for biomass increment; CWM SLA was the most important predictor, with the expected positive relationship. There were no relationships of functional variety indices to biomass increments, and AGB(i) was the only predictor for biomass increments from recruits.6. Synthesis. We found no support for the niche complementarity hypothesis and support for the green soup hypothesis only for biomass increments of recruits. We have strong support for the biomass ratio hypothesis. CWM H-max is a strong driver of ecosystem biomass and carbon storage and CWM SLA, and other CWM leaf traits are especially important for biomass increments and carbon sequestration.