Evidence map›Paper›PMID 41497787›Full record

ArticleEcology and evolution2026

Precipitation Dominates Forest Net Primary Productivity Variations With Distinct Regional Differences in Yunnan Province, China.

Xiaofang Yang, Kun Yang, Shaohua Zhang, Wenxia Zeng, Jing Liu, Yan Rao, Yan Ma, Changyou Bi

Abstract read
In one paragraph

Article in Ecology and evolution, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

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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

8 authors.

Xiaofang YangFaculty of Geography Yunnan Normal University Kunming China.
Kun YangFaculty of Geography Yunnan Normal University Kunming China.ORCID https://orcid.org/0009-0002-6845-6381
Shaohua ZhangFaculty of Geography Yunnan Normal University Kunming China.
Wenxia ZengFaculty of Geography Yunnan Normal University Kunming China.
Jing LiuFaculty of Geography Yunnan Normal University Kunming China.
Yan RaoFaculty of Geography Yunnan Normal University Kunming China.
Yan MaFaculty of Geography Yunnan Normal University Kunming China.
Changyou BiFaculty of Geography Yunnan Normal University Kunming China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In the topographically and climatically diverse region of Yunnan, clarifying the driving mechanisms and threshold effects of factors influencing forest net primary productivity (NPP) is crucial for managing forest carbon sinks. In this study, we established a comparative analysis framework for the stable forest (SF) and the changing forest (CF). Yunnan was then divided into five subregions based on topography and climate. Using a random forest model and the SHAP method, we systematically analyzed drivers of spatiotemporal NPP variations. The results show that CF exhibits greater NPP variation and spatial heterogeneity than SF and maintains stronger trend persistence. At the provincial scale, elevation and precipitation are the main drivers of NPP in SF and CF, respectively, while at the subregional scale, dominant factors differ and include solar radiation, temperature, and forest age, indicating clear scale-dependent effects. The explanatory power of multiple-factor interactions for NPP variations is generally higher than that of single factors, with precipitation showing particularly strong synergistic effects. Human activities also have a pronounced impact on NPP in CF. Key driving factors exhibit nonlinear threshold responses, with NPP in SF most suitable under temperature (14°C-20°C), precipitation (100-125 mm), elevation (~2000 m), and forest age (50-70 years), whereas CF shows broader response ranges, reflecting greater adaptability. This study highlights the nonlinear responses and threshold characteristics of forest NPP to multiple driving factors in complex mountainous environments. It also emphasizes the importance of interactions and scale effects in ecological modeling.

Indexed as

forest NPPmulti‐scale analysisnonlinear threshold responsesstable forest and changing forest

Identifiers

PMID41497787
PMCPMC12765817

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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.