Evidence map›Paper›PMID 39294171›Full record

ArticleNature communications2024

Number and dissimilarity of global change factors influences soil properties and functions.

Mohan Bi, Huiying Li, Peter Meidl, Yanjie Zhu, Masahiro Ryo, Matthias C Rillig

Abstract read
In one paragraph

Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

15 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Impacts of pollution on the soil microbiome.Nature reviews. Microbiology · 2026
    Review
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Crops under stress: can we mitigate the impacts of climate change on agriculture and launch the 'Resilience Revolution'?Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2025
    Article
  15. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Mohan Bi *Freie Universität Berlin, Institute of Biology, Berlin, Germany.
Huiying Li *Freie Universität Berlin, Institute of Biology, Berlin, Germany.
Peter MeidlFreie Universität Berlin, Institute of Biology, Berlin, Germany.
Yanjie ZhuFreie Universität Berlin, Institute of Biology, Berlin, Germany.
Masahiro RyoLeibniz Centre for Agricultural Landscape Research (ZALF), Müncheberg, Germany.ORCID http://orcid.org/0000-0002-5271-3446
Matthias C RilligFreie Universität Berlin, Institute of Biology, Berlin, Germany. rillig@zedat.fu-berlin.de.ORCID http://orcid.org/0000-0003-3541-7853

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Soil biota and functions are impacted by various anthropogenic stressors, including climate change, chemical pollution or microplastics. These stressors do not occur in isolation, and soil properties and functions appear to be directionally driven by the number of global change factors acting simultaneously. Building on this insight, we here hypothesize that co-acting factors with more diverse effect mechanisms, or higher dissimilarity, have greater impacts on soil properties and functions. We created a factor pool of 12 factors and calculated dissimilarity indices of randomly-chosen co-acting factors based on the measured responses of soil properties and functions to the single factors. Results show that not only was the number of factors important, but factor dissimilarity was also key for predicting factor joint effects. By analyzing deviations of soil properties and functions from three null model predictions, we demonstrate that higher factor dissimilarity and a larger number of factors could drive larger deviations from null models and trigger more frequent occurrence of synergistic factor net interactions on soil functions (decomposition rate, cellulase, and β-glucosidase activity), which provides mechanistic insights for understanding high-dimensional effects of factors. Our work highlights the importance of considering factor similarity in future research on interacting factors.

Identifiers

PMID39294171
PMCPMC11410830

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.