生物多样性 ›› 2020, Vol. 28 ›› Issue (11): 1391-1404. DOI: 10.17520/biods.2020240
收稿日期:
2020-06-15
接受日期:
2020-07-26
出版日期:
2020-11-20
发布日期:
2020-09-13
通讯作者:
王少鹏
作者简介:
* E-mail: shaopeng.wang@pku.edu.cn基金资助:
Received:
2020-06-15
Accepted:
2020-07-26
Online:
2020-11-20
Published:
2020-09-13
Contact:
Shaopeng Wang
摘要:
食物网刻画了物种间通过捕食而形成的复杂网络关系。阐明食物网结构与功能之间的关系, 既是生态学的基本理论问题, 也是预测全球变化背景下生态系统响应的重要依据。早期关于食物网结构与功能的研究往往是分离的, 或是基于食物链等的简单网络模型, 而近期研究基于复杂食物网模型取得了重要理论进展。本文综述了食物网研究的理论方法和近期进展, 特别介绍了复杂食物网中的结构、多样性和功能的度量指标、结构-多样性-功能之间的关系以及全球变化对食物网结构与功能的影响。本文最后对未来的一些研究方向进行了展望, 包括与功能性状和化学计量学的整合、食物网与其他网络类型的整合以及拓展食物网研究的空间和时间尺度。
王少鹏 (2020) 食物网结构与功能: 理论进展与展望. 生物多样性, 28, 1391-1404. DOI: 10.17520/biods.2020240.
Shaopeng Wang (2020) Food web structure and functioning: Theoretical advances and outlook. Biodiversity Science, 28, 1391-1404. DOI: 10.17520/biods.2020240.
图1 复杂食物网的结构、多样性与功能间的关系。食物网结构可影响物种多样性和生态系统功能的维持, 多样性也对功能起重要作用。全球变化可通过改变个体大小、种群动态、种间作用等影响食物网结构、多样性与功能。背景食物网来自http://www.foodwebs.org/。
Fig.1 Relationships between the structure, diversity and functioning of complex food webs. Food web structure can affect species diversity and ecosystem functioning, and diversity also affects functioning. Global change can affect food webs through changing species body size, population dynamics, interspecific interaction, etc. The food web in the background was obtained from http://www.foodwebs.org/.
Box 1 复杂食物网理论模型 |
食物网建模的核心是对网络结构与种群动态的理论刻画。网络结构决定了每个物种在生态系统中的位置, 即“谁吃谁” (Who eats who?)。种群动态刻画了任一物种如何受其捕食者和猎物的影响, 即“如何吃” (How does a predator eat its prey?)。这里概述食物网结构与动态模型的一般框架。在具体应用中, 针对研究问题可有不同的具体模型。比如在复杂性-稳定性研究中, |
$\cdot$“谁吃谁”: 模拟食物网结构 |
生态学家提出了多种理论模型试图刻画食物网的网络结构。早期的理论模型包括随机模型( |
$\cdot$“如何吃”: 模拟食物网动态与功能 |
对食物网中的任一物种而言, 种群增长主要取决于其获取资源的速率, 种群下降主要取决于其被捕食的速率以及新陈代谢和自然死亡。因此, 种群动态模拟的关键是给出物种获取资源或捕食的速率。通常地, 这一速率由功能响应(functional response)函数刻画( |
$F(N)=\frac{a{{N}^{q}}}{1+ah{{N}^{q}}}$ (B1) |
其中, N表示猎物密度, F(N)表示单个捕食者的捕食速率, a表示捕食者的攻击速率(attack rate), h表示消化或处理一只猎物所需的时间(handling time)。q是一个调控密度依赖形式的参数。当q = 0时, 捕食速率F(N)是与猎物密度N无关的常数, 称为“I型”功能响应; q = 1时, F(N)在密度N较低时随N线性增长, 随后增长速率减慢, 最终达到饱和水平, 称为“II型”功能响应; q = 2时, F(N)在密度N较低时随N呈二次函数增长, 随后增长减慢, 最终达到饱和水平, 称为“III型”功能响应。很多研究者通过实验分析了猎物密度和捕食速率之间的关系, 发现很多实验系统都服从“II型”功能响应( |
基于功能响应函数, 食物网中任一植物种i(Pi)和动物种j(Aj)的种群大小的时间动态由以下方程决定( |
$\frac{d{{P}_{i}}}{dt}={{r}_{i}}{{G}_{i}}{{P}_{i}}-\underset{k}{\mathop \sum }\,{{A}_{k}}{{F}_{ki}}-{{x}_{i}}{{P}_{i}}$ (B2) |
$\frac{d{{A}_{j}}}{dt}={{e}_{1}}{{A}_{j}}\cdot \sum\limits_{i:植物资源}{{{F}_{ji}}+{{e}_{2}}{{A}_{j}}}\cdot \sum\limits_{j:动物资源}{{{F}_{jk}}-}\underset{l}{\mathop \sum }\,{{A}_{l}}{{F}_{lj}}-{{x}_{j}}{{A}_{j}}~~$. (B3) |
其中, ri 表示植物种i的最大生长速率, Gi刻画了营养元素对植物种i生长的限制(见 |
${{F}_{ji}}=\frac{{{\omega }_{ji}}{{a}_{ji}}N_{i}^{q}}{1+\mathop{\sum }_{k}{{\omega }_{jk}}{{a}_{jk}}{{h}_{jk}}N_{k}^{q}}$ (B4) |
其中Ni表示第i个猎物种的生物量, ajk和hjk表示捕食者j对猎物k的攻击速率和处理时间, ωjk表示捕食者j对猎物k的取食偏好。类似地, q表示取食速率对猎物密度的依赖关系为II型(q = 1)或III型(q = 2)功能响应。 |
为了模拟食物网功能, 后续研究通过引入物种个体大小, 将基于种群的模型拓展到基于生物量的模型, 其中关注变量不再是“种群变化”, 而是“生物量变化” ( |
Box 1 复杂食物网理论模型 |
食物网建模的核心是对网络结构与种群动态的理论刻画。网络结构决定了每个物种在生态系统中的位置, 即“谁吃谁” (Who eats who?)。种群动态刻画了任一物种如何受其捕食者和猎物的影响, 即“如何吃” (How does a predator eat its prey?)。这里概述食物网结构与动态模型的一般框架。在具体应用中, 针对研究问题可有不同的具体模型。比如在复杂性-稳定性研究中, |
$\cdot$“谁吃谁”: 模拟食物网结构 |
生态学家提出了多种理论模型试图刻画食物网的网络结构。早期的理论模型包括随机模型( |
$\cdot$“如何吃”: 模拟食物网动态与功能 |
对食物网中的任一物种而言, 种群增长主要取决于其获取资源的速率, 种群下降主要取决于其被捕食的速率以及新陈代谢和自然死亡。因此, 种群动态模拟的关键是给出物种获取资源或捕食的速率。通常地, 这一速率由功能响应(functional response)函数刻画( |
$F(N)=\frac{a{{N}^{q}}}{1+ah{{N}^{q}}}$ (B1) |
其中, N表示猎物密度, F(N)表示单个捕食者的捕食速率, a表示捕食者的攻击速率(attack rate), h表示消化或处理一只猎物所需的时间(handling time)。q是一个调控密度依赖形式的参数。当q = 0时, 捕食速率F(N)是与猎物密度N无关的常数, 称为“I型”功能响应; q = 1时, F(N)在密度N较低时随N线性增长, 随后增长速率减慢, 最终达到饱和水平, 称为“II型”功能响应; q = 2时, F(N)在密度N较低时随N呈二次函数增长, 随后增长减慢, 最终达到饱和水平, 称为“III型”功能响应。很多研究者通过实验分析了猎物密度和捕食速率之间的关系, 发现很多实验系统都服从“II型”功能响应( |
基于功能响应函数, 食物网中任一植物种i(Pi)和动物种j(Aj)的种群大小的时间动态由以下方程决定( |
$\frac{d{{P}_{i}}}{dt}={{r}_{i}}{{G}_{i}}{{P}_{i}}-\underset{k}{\mathop \sum }\,{{A}_{k}}{{F}_{ki}}-{{x}_{i}}{{P}_{i}}$ (B2) |
$\frac{d{{A}_{j}}}{dt}={{e}_{1}}{{A}_{j}}\cdot \sum\limits_{i:植物资源}{{{F}_{ji}}+{{e}_{2}}{{A}_{j}}}\cdot \sum\limits_{j:动物资源}{{{F}_{jk}}-}\underset{l}{\mathop \sum }\,{{A}_{l}}{{F}_{lj}}-{{x}_{j}}{{A}_{j}}~~$. (B3) |
其中, ri 表示植物种i的最大生长速率, Gi刻画了营养元素对植物种i生长的限制(见 |
${{F}_{ji}}=\frac{{{\omega }_{ji}}{{a}_{ji}}N_{i}^{q}}{1+\mathop{\sum }_{k}{{\omega }_{jk}}{{a}_{jk}}{{h}_{jk}}N_{k}^{q}}$ (B4) |
其中Ni表示第i个猎物种的生物量, ajk和hjk表示捕食者j对猎物k的攻击速率和处理时间, ωjk表示捕食者j对猎物k的取食偏好。类似地, q表示取食速率对猎物密度的依赖关系为II型(q = 1)或III型(q = 2)功能响应。 |
为了模拟食物网功能, 后续研究通过引入物种个体大小, 将基于种群的模型拓展到基于生物量的模型, 其中关注变量不再是“种群变化”, 而是“生物量变化” ( |
图2 食物网中的生物多样性(a)和功能(b)。在食物网中, 生物多样性可通过水平多样性(如同一营养级内的物种数)和垂直多样性(如食物网的最高营养级)度量, 功能可通过营养级或物种之间的能量流、植物或动物的总代谢量和生物量刻画。其中, 非生物资源与植物群落(绿色框)之间的能量流表征了初级生产力, 植物与动物群落(蓝色框)之间的能量流表征了次级生产力。修改自Wang & Brose (2018)和Schneider等(2016)。
Fig. 2 Food web diversity (a) and functioning (b). Biodiversity in food webs can be measured by both horizontal diversity (e.g. species richness within trophic levels) and vertical diversity (e.g. the maximum trophic level). Functioning can be measured by energy fluxes between two trophic levels or two species, total plant or animal metabolism, and total plant or animal biomass. Specifically, the energy flux from abiotic resource to plant communities (green box) represents the primary productivity, and that from plant communities to animal communities (blue box) represents the secondary productivity. Modified from Wang & Brose (2018) and Schneider et al (2016).
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