Individual-Based Modeling of Amazon Forests Suggests That Climate Controls Productivity While Traits Control Demography
Individual-Based Modeling of Amazon Forests Suggests That Climate Controls Productivity While Traits Control Demography
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DOI：
10.3389/feart.2019.00083
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发表时间：
2019-04-30
影响因子：
2.9
通讯作者：
Malhi, Yadvinder
中科院分区：
文献类型：
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作者：
Fauset, Sophie;Gloor, Manuel;Malhi, Yadvinder
气候、物种组成和土壤被认为控制着亚马逊森林的碳循环和森林结构。本文在最近开发的非人口统计学模型——基于性状的森林模拟器（TFS）中加入了一个人口统计学方案（树木招募、生长和死亡），以探索气候和植物性状在控制森林生产力和结构中的作用。我们比较了两个不同气候（季节性和季节性降水）和植物性状的地点。通过初始验证模拟，我们利用功能特征、结构和气候数据集评估了模型是否收敛于观测到的森林特性（生产力、人口统计学和结构变量），以模拟两个地点的碳循环。在第二组模拟中，我们在TFS框架内测试了气候和植物性状对森林特性的相对重要性，使用两个地点的气候，假设性状分布代表两个功能变化轴（“快”vs“慢”）。“缓慢”的叶片特征，以及木材密度的高低)。经人口统计学调整的模型再现了观测到的总（GPP）和净（NPP）初级产量和呼吸的变化。然而，植物器官（叶、茎和根）水平的NPP和呼吸模拟较差。死亡率和招募率被低估了。平衡森林结构不同于对树干数量的观测，这表明森林目前不是处于平衡状态，或者模型中缺少机制。第二组模拟的结果表明，生产力的差异是由气候而不是植物特性驱动的。与预期相反，不同叶片性状对GPP没有影响。模拟森林结构的驱动因素是复杂的，其中木材密度与树木死亡率之间的联系介导了关键作用。模型死亡率和招募率仅与植物性状有关，未考虑与干旱有关的死亡率。未来的模型开发应着眼于改善林下乔木的分配、死亡率、器官呼吸、模拟林下乔木和增加水力学性状。如果我们能够模拟热带森林对全球变化情景的反应，这种结合了多种树木策略、详细的森林结构和现实生理学的模型是必要的。
Climate, species composition, and soils are thought to control carbon cycling and forest structure in Amazonian forests. Here, we add a demographics scheme (tree recruitment, growth, and mortality) to a recently developed non-demographic model-the Trait-based Forest Simulator (TFS)-to explore the roles of climate and plant traits in controlling forest productivity and structure. We compared two sites with differing climates (seasonal vs. aseasonal precipitation) and plant traits. Through an initial validation simulation, we assessed whether the model converges on observed forest properties (productivity, demographic and structural variables) using datasets of functional traits, structure, and climate to model the carbon cycle at the two sites. In a second set of simulations, we tested the relative importance of climate and plant traits for forest properties within the TFS framework using the climate from the two sites with hypothetical trait distributions representing two axes of functional variation ("fast" vs. "slow" leaf traits, and high vs. low wood density). The adapted model with demographics reproduced observed variation in gross (GPP) and net (NPP) primary production, and respiration. However, NPP and respiration at the level of plant organs (leaf, stem, and root) were poorly simulated. Mortality and recruitment rates were underestimated. The equilibrium forest structure differed from observations of stem numbers suggesting either that the forests are not currently at equilibrium or that mechanisms are missing from the model. Findings from the second set of simulations demonstrated that differences in productivity were driven by climate, rather than plant traits. Contrary to expectation, varying leaf traits had no influence on GPP. Drivers of simulated forest structure were complex, with a key role for wood density mediated by its link to tree mortality. Modeled mortality and recruitment rates were linked to plant traits alone, drought-related mortality was not accounted for. In future, model development should focus on improving allocation, mortality, organ respiration, simulation of understory trees and adding hydraulic traits. This type of model that incorporates diverse tree strategies, detailed forest structure and realistic physiology is necessary if we are to be able to simulate tropical forest responses to global change scenarios.