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

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.

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Publication Details

Journal
eLife
Published
2025-10-09
DOI
https://doi.org/10.7554/elife.98073.4
Primary Topic
Ecology and Vegetation Dynamics Studies
Type
article
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article

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

Hua Yang, Ji Zheng, Jing Tao, Eric B. Searle et al.
eLife
Ecology and Vegetation Dynamics Studies
article

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

Hua Yang, Ji Zheng, Jing Tao, Eric B. Searle, Grégoire T. Freschet, Cyrille Violle, Charles A. Nock, Rongzhou Man, Shongming Huang
article en

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.

eLifeVol. 13
Ministry of Natural Resources and Forestry (CA), University of Alberta (CA), Shanghai Jiao Tong University (CN), Ontario Forest Research Institute (CA), Beijing Forestry University (CN), Government of Alberta (CA), State Forestry and Grassland Administration (CN), Jilin Provincial Academy of Forestry Science (CN), Station d’Écologie Théorique et Expérimentale (FR)
Openalex Percentile: Top 72%
Ecology and Vegetation Dynamics Studies
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