Ecology and Restoration of Grassland—2nd Edition

A special issue of Diversity (ISSN 1424-2818). This special issue belongs to the section "Plant Diversity".

Deadline for manuscript submissions: 30 June 2026 | Viewed by 127

Special Issue Editor


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Guest Editor
Qinghai Provincial Key Laboratory of Restoration Ecology for Cold Region, Northwest Institute of Plateau Biology, Chinese Academy of Sciences, Xining, China
Interests: grassland degradation; hydrologic process; water cycle; water conservation function
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Special Issue Information

Dear Colleagues,

Grasslands are a vital component of terrestrial ecosystems, covering approximately 40% of the global vegetated area. They play an irreplaceable role in critical ecological processes such as biodiversity conservation, water and soil resource regulation, sustainable livestock production, and global carbon and nitrogen cycles. However, overgrazing, climate change, and intensified anthropogenic disturbances are accelerating grassland degradation worldwide, triggering cascading ecological effects, including retrogressive vegetation succession, the deterioration of soil physicochemical properties, and a decline in hydrological regulation functions. Current research on degraded grassland restoration predominantly focuses on aboveground vegetation reconstruction techniques, with insufficient attention paid to key scientific issues such as belowground soil–microbial interaction mechanisms and root–soil–water coupling processes. Moreover, there is a lack of a systematic theoretical framework integrating multi-trophic interactions and ecosystem multifunctionality.

This Special Issue aims to establish an interdisciplinary research platform, with a focus on soliciting cutting-edge contributions in the following areas:

(1) Cascading response mechanisms of vegetation–soil–water systems during grassland degradation;

(2) Adaptive restoration techniques for degraded grasslands across different climate zones;

(3) Aboveground–belowground synergistic recovery mechanisms driven by microbiomes;

(4) Restoration effectiveness evaluation based on ecosystem multifunctionality enhancement;

(5) Innovative applications of artificial intelligence and remote sensing in degradation monitoring.

We particularly encourage research on fundamental theories such as ecohydrological processes in arid and semi-arid regions, soil carbon pool stability maintenance, and plant–microbial interaction networks, as well as systemic solutions that integrate ecological restoration practices with sustainable development goals. By synthesizing multidimensional research findings, we hope to provide new theoretical foundations and technical paradigms for the ecological restoration of degraded grasslands worldwide.

Dr. Xiaowei Guo
Guest Editor

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Keywords

  • grasslands
  • ecosystem degradation
  • biodiversity conservation
  • soil–microbial interactions
  • restoration techniques
  • ecosystem multifunctionality
  • climate change
  • ecohydrological processes
  • remote sensing monitoring

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Published Papers (1 paper)

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Research

17 pages, 2404 KiB  
Article
Geographically Weighted Regression Enhances Spectral Diversity–Biodiversity Relationships in Inner Mongolian Grasslands
by Yu Dai, Huawei Wan, Longhui Lu, Fengming Wan, Haowei Duan, Cui Xiao, Yusha Zhang, Zhiru Zhang, Yongcai Wang, Peirong Shi and Xuwei Sun
Diversity 2025, 17(8), 541; https://doi.org/10.3390/d17080541 (registering DOI) - 1 Aug 2025
Abstract
The spectral variation hypothesis (SVH) posits that the complexity of spectral information in remote sensing imagery can serve as a proxy for regional biodiversity. However, the relationship between spectral diversity (SD) and biodiversity differs for different environmental conditions. Previous SVH studies often overlooked [...] Read more.
The spectral variation hypothesis (SVH) posits that the complexity of spectral information in remote sensing imagery can serve as a proxy for regional biodiversity. However, the relationship between spectral diversity (SD) and biodiversity differs for different environmental conditions. Previous SVH studies often overlooked these differences. We utilized species data from field surveys in Inner Mongolia and drone-derived multispectral imagery to establish a quantitative relationship between SD and biodiversity. A geographically weighted regression (GWR) model was used to describe the SD–biodiversity relationship and map the biodiversity indices in different experimental areas in Inner Mongolia, China. Spatial autocorrelation analysis revealed that both SD and biodiversity indices exhibited strong and statistically significant spatial autocorrelation in their distribution patterns. Among all spectral diversity indices, the convex hull area exhibited the best model fit with the Margalef richness index (Margalef), the coefficient of variation showed the strongest predictive performance for species richness (Richness), and the convex hull volume provided the highest explanatory power for Shannon diversity (Shannon). Predictions for Shannon achieved the lowest relative root mean square error (RRMSE = 0.17), indicating the highest predictive accuracy, whereas Richness exhibited systematic underestimation with a higher RRMSE (0.23). Compared to the commonly used linear regression model in SVH studies, the GWR model exhibited a 4.7- to 26.5-fold improvement in goodness-of-fit. Despite the relatively low R2 value (≤0.59), the model yields biodiversity predictions that are broadly aligned with field observations. Our approach explicitly considers the spatial heterogeneity of the SD–biodiversity relationship. The GWR model had significantly higher fitting accuracy than the linear regression model, indicating its potential for remote sensing-based biodiversity assessments. Full article
(This article belongs to the Special Issue Ecology and Restoration of Grassland—2nd Edition)
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