at Google ScholarTitle data
Zhang, Pengfei ; Seabloom, Eric W. ; Foo, Jasmine ; MacDougall, Andrew S. ; Harpole, W. Stanley ; Adler, Peter B. ; Hautier, Yann ; Eisenhauer, Nico ; Spohn, Marie ; Bakker, Jonathan D. ; Lekberg, Ylva ; Young, Alyssa L. ; Carbutt, Clinton ; Risch, Anita C. ; Peri, Pablo L. ; Smith, Nicholas G. ; Stevens, Carly J. ; Prober, Suzanne M. ; Knops, Johannes M. H. ; Wardle, Glenda M. ; Dickman, Christopher R. ; Ebeling, Anne ; Roscher, Christiane ; Martinson, Holly M. ; Martina, Jason P. ; Power, Sally A. ; Niu, Yujie ; Ren, Zhengwei ; Du, Guozhen ; Virtanen, Risto ; Tognetti, Pedro ; Tedder, Michelle J. ; Jentsch, Anke ; Catford, Jane A. ; Borer, Elizabeth T.:
Dominant species predict plant richness and biomass in global grasslands.
In: Nature Ecology & Evolution.
Vol. 9
(2025)
Issue 6
.
- pp. 924-936.
ISSN 2397-334X
DOI: https://doi.org/10.1038/s41559-025-02701-y
Abstract in another language
The bidirectional relationship between plant species richness and community biomass is often variable and poorly resolved in natural grassland ecosystems, impeding progress in predicting impacts of environmental changes. Most biological communities have long-tailed species abundance distributions (for example, biomass, cover, number of individuals), a general property that may provide predictive power for species richness and community biomass. Here we show mathematical relationships between community characteristics and the abundance of dominant species arising from long-tailed distributions and test these predictions using observational and experimental data from 76 grassland sites across 6 continents. We find that community biomass provides little predictive ability for community richness, consistent with previous findings. By contrast, the relative abundance of dominant species quantitatively predicts species richness, whereas their absolute abundance quantitatively predicts community biomass under both ambient and altered environmental conditions, as expected mathematically. These results are robust to the type of abundance measure used. Three types of simulated data further show 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 conditions.