Application of ecological network analysis in nitrogen cycling in agroecosystems: Progress and prospects
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摘要: 农业生态系统氮循环直接关系到粮食安全和生态环境保护, 受到国内外的广泛关注。生态系统氮循环包括氮在生态系统各个组分间迁移和转化的全部过程, 具有整体性和复杂性。然而, 现有研究大多集中在氮循环的单一或局部过程, 难以从全局水平上研究农业生态系统氮循环的变化规律。作为一种系统分析工具, 生态网络分析通过构建可以模拟复杂系统中物质或者能量流动结构的生态网络分室模型, 进而可以从全局的视角分析生态系统的内在、整体属性及其变化规律。因此, 利用生态网络分析从整体上审视农业生态系统氮素循环规律具有良好的应用及发展前景。鉴于此, 该文介绍了生态网络分析方法的基本原理、作者在生态网络分析方法研究中取得的新进展, 包括基于自主提出的网络粒子追踪法(network particle tracking, 简称NPT)将生态网络分析的应用范围由稳态系统扩展至动态系统和新提出两个性能更优的系统评价指标。此外, 分析了生态网络分析方法主要优势、实现步骤及应用案例, 指出了阻碍生态网络分析在农业生态系统氮循环研究中应用的主要问题以及应对策略, 展望了生态网络分析在农业生态系统氮循环研究中的可能应用。Abstract: Nitrogen cycling in agroecosystems is directly related to food security and environmental protection and has received worldwide attention. Ecosystem nitrogen cycling involves the migration and transformation of nitrogen among various ecosystem components, characterized as integrity and complexity. However, existing studies have mostly focused on partial cycling processes and provide limited information about the changes in nitrogen cycling at the system level. By constructing an ecological network that simulates the flow of material or energy in a complex system, ecological network analysis (ENA) analyzes within-system interactions and system-wide properties from a holistic perspective. Therefore, applying ENA to examine agroecosystem nitrogen cycling as a whole has great application and development prospects. In this paper, we first introduce the principle and recent development of ENA, including the development of network particle tracking (NPT), which extends the application of ENA from steady-state to dynamic ecosystem models, and the application of NPT in developing new system-wide indicators, including the indirect effect index (IEI) and storage-based cycling index (SCI). Then, we introduce the main advantages of ENA and the primary procedures in the application of ENA and further demonstrate a case study of applying ENA to study nitrogen cycling in a rice field. Finally, we point out that the main problems hindering the application of ENA in agroecosystems are the difficulty and high cost of gathering empirical data required to build the network models and the scarcity of scientists who are knowledgeable in both ENA and the nitrogen cycling of agroecosystems. Regarding these problems, we provide possible coping strategies (e.g., building long-term experiments for data collection, making the best of historical data, developing more effective methods to facilitate network construction, and encouraging interdisciplinary cooperation) and further discuss the prospects of applying ENA in nitrogen cycling in agroecosystems.
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Key words:
- Ecological network analysis /
- Nitrogen cycling /
- Agroecosystems /
- Ecological modeling
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图 1 具有流量和储量信息的三分室生态网络概念模型[34]
模型包括3个分室, 分别为生产者、消费者和营养库, 各自对应的储量(x)分别为50 units (x1)、20 units (x2)和5 units (x3)。z、y和F 分别表示外部环境向分室的输入流速率、分室向外部环境的输出流速率和分室之间的流量速率。
Figure 1. A hypothetical three-compartment ecosystem model with flow and stock information[34]
This model consists of producers, consumers, and nutrient pool with stocks x1 = 50 units, x2 = 20 units and x3 = 5 units, respectively. z, y and F represent rate of environmental input to compartment, rate of environmental output from compartment and rate of direct flow between compartments, respectively.
图 3 西班牙埃布罗河三角洲稻田氮循环的生态网络分室模型
PHY: 浮游植物; DON: 溶解有机氮; DIN: 溶解无机氮; DET: 碎屑+水生异养生物; SAV: 沉水水生植物; SED: 沉积物; STR: 水稻秸秆; RICE: 水稻。网络分室模型图采用EcoNet[43]制作, 构建模型所需数据源于Fores和Christian[31]。
Figure 3. General ecological network compartmental model of nitrogen cycling in rice fields of the Ebro River Delta
PHY: phytoplankton; DON: dissolved organic nitrogen; DIN: dissolved inorganic nitrogen; DET: detritus plus aquatic heterotrophs; SAV: submersed aquatic vegetation; SED: sediment; STR: rice straw; RICE: rice. The diagram was generated in EcoNet[43] based on data from Fores and Christian[31].
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