Plant-soil feedbacks (PSFs) have been shown to strongly affect plant performance under controlled conditions, and PSFs are thought to have far reaching consequences for plant population dynamics and the structuring of plant communities. However, thus far the relationship between PSF and plant species abundance in the field is not consistent. Here, we synthesize PSF experiments from tropical forests to semiarid grasslands, and test for a positive relationship between plant abundance in the field and PSFs estimated from controlled bioassays. We meta-analyzed results from 22 PSF experiments and found an overall positive correlation (0.12 ≤ ▁(r ̅ ) ≤ 0.32) between plant abundance in the field and PSFs across plant functional types (herbaceous and woody plants) but also variation by plant functional type. Thus, our analysis provides quantitative support that plant abundance has a general albeit weak positive relationship with PSFs across ecosystems. Overall, our results suggest that harmful soil biota tend to accumulate around and disproportionately impact species that are rare. However, data for the herbaceous species, which are most common in the literature, had no significant abundance-PSFs relationship. Therefore, we conclude that further work is needed within and across biomes, succession stages and plant types, both under controlled and field conditions, while separating PSF effects from other drivers (e.g. herbivory, competition, disturbance) of plant abundance to tease apart the role of soil biota in causing patterns of plant rarity versus commonness.
The plant-soil feedback and abundance data provided in "PSF_data_2020.csv" were used to determine effect sizes (correlation coefficients per experiment) with the R script (see "R script.docx"). Background information on the plant-soil feedback and plant abundance data can be understood by cross-referencing the meta-data descriptions per study provided at the beginning of "R script.docx" file and Table A1 of the main manuscript.
Selected and mean effects sizes (i.e. correlation coefficients) were then aggregated into a second data file (meta-analysis parameters2_2020.csv) and analyzed with the subsequent script in the same R script file.