Dominant species predict plant richness and biomass in global grasslands

物种丰富度 生物量(生态学) 草原 环境科学 植物种类 生态学 地理 农林复合经营 生物
作者
Pengfei Zhang,Eric W. Seabloom,Jasmine Foo,Andrew S. MacDougall,W. Stanley Harpole,Peter B. Adler,Yann Hautier,Nico Eisenhauer,Marie Spohn,Jonathan D. Bakker,Ylva Lekberg,Alyssa Young,Clinton Carbutt,Anita C. Risch,Pablo Luís Peri,Nicholas G. Smith,Carly J. Stevens,Suzanne M. Prober,Johannes M. H. Knops,Glenda M. Wardle
出处
期刊:Research Square - Research Square
标识
DOI:10.21203/rs.3.rs-4941047/v1
摘要

Abstract The bidirectional relationship between plant species richness and community biomass is often variable and poorly resolved in natural grassland ecosystems1–3, impeding progress in predicting impacts of environmental changes. In contrast, most biological communities have lognormal species abundance distributions (e.g., biomass, cover, number of individuals)4–7, a general property that may provide predictive power for species richness and community biomass. Here, we demonstrate mathematical relationships between community characteristics and the abundance of dominant species arising from the lognormal distribution and test these predictions using observational and experimental data from 76 grassland sites across six continents. We find that community biomass provides little predictive ability for community richness, consistent with previous findings3,8. In contrast, the relative abundance of dominant species quantitatively predicts species richness, whereas the absolute abundance of dominant species quantitatively predicts community biomass under both ambient and altered environmental conditions, as expected mathematically. These results are robust to the type of abundance measure used9. Simulated data further demonstrate the generality of these results. Our integrative framework, arising from a few dominant species and mathematical properties of species abundance distributions, fills a persistent gap in our ability to predict community richness and biomass under ambient and anthropogenically altered conditions1,10.
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