Evidence map›Paper›PMID 41815433›Full record

ArticleFrontiers in plant science2026

Spatiotemporal interaction and attribution analysis of ecosystem health and human activities in the Daxing'anling forest-grassland Ecotone, China.

Wanlong Pang, Yuexin Zhang, Xiuzhi Ma, Yanan Ma

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

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Wanlong PangCollege of Forestry, Inner Mongolia Agricultural University, Hohhot, China.
Yuexin ZhangCollege of Forestry, Inner Mongolia Agricultural University, Hohhot, China.
Xiuzhi MaCollege of Forestry, Inner Mongolia Agricultural University, Hohhot, China.
Yanan MaCollege of Forestry, Inner Mongolia Agricultural University, Hohhot, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: The Daxing'anling forest-grassland ecotone is a critical ecological barrier in northern China and plays an essential role in maintaining regional ecological security. In recent decades, intensified regional development has increased human-environment conflicts; however, the spatiotemporal feedback mechanisms between ecosystem health (EHI) and human activity intensity (HAI) remain insufficiently understood. Methods: This study proposes a multi-scale Assessment-Coupling-Driving framework. Ecosystem health was quantified using the Vitality-Organization-Resilience-Service (VORS) model, while HAI was constructed by integrating multi-source socioeconomic and environmental datasets. Spatial statistical analyses and XGBoost-SHAP machine learning were applied to investigate spatiotemporal interactions and driving mechanisms of the human-environment system from 2000 to 2023. Results: From 2000 to 2023, EHI increased by 25.1%, yet strong spatial heterogeneity persisted. Higher EHI was observed in northern primary forest areas, whereas ecological degradation hotspots were concentrated in the southern agro-pastoral ecotone, largely driven by urbanization and agricultural expansion. HAI increased by 16.3%, exhibiting a high-in-plains and low-in-mountainous-areas spatial pattern, indicating strong topographic constraints on human activities. A significant negative spatial correlation was detected between HAI and EHI (Moran's I = -0.62). Coupling coordination degree analysis revealed a 6.9% expansion of ecological protection zones with low coordination levels, identifying the region as a major conflict zone requiring urgent governance. Potential conservation areas accounted for 48.7% of the total area, highlighting their importance in maintaining regional ecological security. XGBoost-SHAP results demonstrated nonlinear threshold effects of key drivers. Potential evapotranspiration (PET) and low temperatures suppressed EHI, whereas plant available water enhanced ecological stability. Temperatures above 0°C promoted EHI and, together with high GDP, intensified HAI, while PET exceeding 850 mm inhibited HAI. Temperature and precipitation erosive forces dominated coupling coordination, and GDP exhibited a weak positive effect that became negative after exceeding a critical threshold. Discussion: The findings reveal clear spatial trade-offs and nonlinear threshold effects governing the coupled evolution of ecosystem health and human activities in the Daxing'anling ecotone. The proposed framework provides a scientific basis for coordinated optimization of ecological protection and regional development through process analysis, threshold identification, and zoning-based regulation.

Indexed as

coupling coordination degree modelecosystem healthhuman activity intensitySHAP modelvigor-organization-resilience-services (VORS) model

Identifiers

PMID41815433
PMCPMC12971407

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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.