Evidence map›Paper›PMID 42627836›Full record

ArticlePloS one2026

Successional Shifts in Soil C:N:P stoichiometry and its environmental controls during forest restoration in Huize County, Qujing, Yunnan Province.

Bo Hu, Wei Li

Abstract read
In one paragraph

Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

What it found

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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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

2 authors.

Bo HuCollege of Soil and Water Conservation, Southwest Forestry University, Kunming, China.ORCID https://orcid.org/0009-0003-6717-5307
Wei LiCollege of Soil and Water Conservation, Southwest Forestry University, Kunming, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Forest restoration reshapes nutrient cycling and soil stoichiometric regulation in fragile plateau ecosystems. To clarify variations in soil C:N:P stoichiometry and its environmental controls during ecosystem recovery on the southwestern China plateau, this study investigated three representative forest types along a restoration gradient, including Pinus yunnanensis forest, mixed conifer broadleaf forest, and secondary evergreen broadleaved forest. Soil samples were collected from three depths of 0-20 cm, 20-50 cm, and 50-80 cm, together with dominant tree leaf samples, and a total of 27 composite soil samples and 81 undisturbed soil cores were analyzed. Heatmap analysis, Mantel tests, redundancy analysis, and random forest modeling were used to evaluate stoichiometric characteristics and regulatory mechanisms. The results showed that soil organic carbon or total organic carbon and total nitrogen were the dominant factors controlling soil stoichiometry across forest types, whereas the effects of soil layer, iron and aluminum oxides, pH, and nutrient fractions varied with restoration stage. In the Pinus yunnanensis forest, available K and available Al were the primary predictors of C:P, and total nitrogen was the primary predictor of N:P. In the mixed conifer broadleaf forest, total organic carbon became the primary predictor of C:P and C:N ratios, while nitrogen fractions strongly regulated N:P variation. In the evergreen broadleaved forest, C:P was co-regulated by total P, available Al, TOC, and nitrate nitrogen, while N:P was predominantly governed by total P and total N. The explanatory power of environmental variables increased markedly along the restoration gradient, and nutrient heterogeneity in the 0-20 cm soil layer intensified during later restoration stages.Mixed‑effects models confirmed that forest type × depth interactions significantly modulated Al, Fe, and P stoichiometry, corroborating the successional shift from physical to biogeochemical controls. These results indicate that forest restoration progressively strengthens plant-soil nutrient associations and corresponds to a transition from available K/Al and bulk density constraints in the early-stage plantation toward absolute nutrient pools and organo-mineral interactions in the mature forest.

Indexed as

CarbonForestsNitrogenPhosphorusSoilChinaEcosystemPinusTreesCarbonNitrogenPhosphorusSoil

Identifiers

PMID42627836
PMCPMC13496410

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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.