Partitioning changes in ecosystem productivity by effects of species interactions in biodiversity experiments.

IF 6.4 1区 生物学 Q1 BIOLOGY
eLife Pub Date : 2025-10-09 DOI:10.7554/eLife.98073
Jing Tao, Charles A Nock, Eric B Searle, Shongming Huang, Rongzhou Man, Hua Yang, Grégoire T Freschet, Cyrille Violle, Ji Zheng
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引用次数: 0

Abstract

Species interactions affect ecosystem productivity. Positive interactions (resource partitioning and facilitation) increase productivity while negative interactions (species interference) decrease productivity relative to the null expectations defined by monoculture yields. Effects of competitive interactions (resource competition) can be either positive or negative. Distinguishing effects of species interactions is therefore difficult, if not impossible, with current biodiversity experiments involving mixtures and full density monocultures. To partition changes in ecosystem productivity by effects of species interactions, we modify null expectations with competitive growth responses, i.e., proportional changes in individual size (biomass or volume) expected in mixture based on species differences in growth and competitive ability. We use partial density (species density in mixture) monocultures and the competitive exclusion principle to determine maximum competitive growth responses and full density monoculture yields to measure species ability to achieve maximum competitive growth responses in mixture. Deviations of observed yields from competitive expectations represent the effects of positive/negative species interactions, while the differences between competitive and null expectations reflect the effects of competitive interactions. We demonstrate the effectiveness of our competitive partitioning model in distinguishing effects of species interactions using both simulated and experimental species mixtures. Our competitive partitioning model enables meaningful assessments of species interactions at both species and community levels and helps disentangle underlying mechanisms of species interactions responsible for changes in ecosystem productivity and identify species mixtures that maximize positive effects.

生物多样性实验中物种相互作用对生态系统生产力的影响。
物种间的相互作用影响着生态系统的生产力。积极的相互作用(资源分配和促进)提高了生产力,而消极的相互作用(物种干扰)相对于单一栽培产量定义的零期望降低了生产力。竞争互动(资源竞争)的影响可以是积极的,也可以是消极的。因此,区分物种相互作用的影响是困难的,如果不是不可能的话,目前的生物多样性实验涉及混合和全密度单一栽培。为了通过物种相互作用的影响来划分生态系统生产力的变化,我们用竞争性生长响应来修改零期望,即基于物种生长和竞争能力差异的混合中预期的个体大小(生物量或体积)的比例变化。我们使用部分密度(混合中物种密度)单一栽培和竞争排斥原理来确定最大竞争生长响应,使用全密度单一栽培产量来衡量物种在混合中实现最大竞争生长响应的能力。观察到的产量与竞争预期的偏差代表了正/负物种相互作用的影响,而竞争预期与零预期之间的差异反映了竞争相互作用的影响。我们用模拟和实验的物种混合证明了我们的竞争分配模型在区分物种相互作用效应方面的有效性。我们的竞争划分模型能够在物种和群落水平上对物种相互作用进行有意义的评估,并有助于理清导致生态系统生产力变化的物种相互作用的潜在机制,并确定最大化积极效应的物种混合。
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来源期刊
eLife
eLife BIOLOGY-
CiteScore
12.90
自引率
3.90%
发文量
3122
审稿时长
17 weeks
期刊介绍: eLife is a distinguished, not-for-profit, peer-reviewed open access scientific journal that specializes in the fields of biomedical and life sciences. eLife is known for its selective publication process, which includes a variety of article types such as: Research Articles: Detailed reports of original research findings. Short Reports: Concise presentations of significant findings that do not warrant a full-length research article. Tools and Resources: Descriptions of new tools, technologies, or resources that facilitate scientific research. Research Advances: Brief reports on significant scientific advancements that have immediate implications for the field. Scientific Correspondence: Short communications that comment on or provide additional information related to published articles. Review Articles: Comprehensive overviews of a specific topic or field within the life sciences.
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