Dynamics of Grassland Vegetation Composition across different Land-use Types on the Qinghai Tibet Plateau: Implications to Combat Grassland Degradation

The constant biotic and abiotic interventions on the Qinghai Tibet Plateau (QTP) are seriously degrading the grasslands and, at the same time, restricting the active ecosystem function and grassland vegetation distribution on the plateau. This research analyses the dynamics of grassland vegetation composition across three land uses and counties. The degree of grassland degradation was divided into four land-use types based, i.e., healthy grassland (HG), restored grassland (RG), moderately degraded (MD) grassland, and severely degraded (SD) grassland. About 32 plant species were recorded in Tiebujia county, 28 in Maqin county, and 18 in Maduo county. Results showed Poa crymophila, Polygonum sibiricum, Leontopodium nanum and Oxytropis falcatabunge as the most abundant grassland species in all land-uses and counties. The richness of species ranged from 8 to 12 species per land-use, suggesting low richness and diversity in restored and degraded grassland. A positive non-significantly mean change (p<0.05) was detected for richness and evenness indices while a negative mean change (p<0.05) was detected for Simpson and Shannon indices in the alpine meadow and steppe in both Maqin and Maduo county. The results imply that degradation affects grassland vegetation, health, and distribution across the QTP. Plant total cover for the healthy grassland covered far more areas than other land-uses. Urgent mitigation measures to halt grassland degradation and decline in plant vegetation composition on the plateau should be adopted.

Selection of r-K strategies by soil bacterial communities in response to grassland degradation

Numerous studies have investigated bacterial community structure in grassland ecosystems and bacterial community responses to human management at various spatial and temporal scales; however, research on soil bacterial community assembly dynamics in the course of grassland degradation is limited. Here, the authors investigate the response and assembly processes of bacterial communities adopted in two grasslands with different degrees of degradation. Stochastic processes dominated bacterial community assembly processes in response to grassland degradation, with the bacterial diversity decreasing; however, functional gene diversity increased. Furthermore, different phyla exhibited distinct response strategies: Proteobacteria and Bacteroidetes, as r-strategists, exhibited positive responses, with increases in diversity, abundance, and niche width with an increase in grassland degradation, enhancing biodiversity and productivity; other phyla (mainly Acidobacteria) exhibited greater phylogenetic dispersion and functional redundancy, and less niche overlap, highlighting the role of K-strategy in improving community resource-use efficiency in response to resource loss in degraded grasslands. The transition from K- to r- strategy in bacterial communities following grassland degradation could help communities adapt to environmental disturbance in the form of nutrient loss. The results of the present study enhance our understanding of how nutrient loss in natural grassland ecosystems leads to shifts in bacterial community composition and assembly processes mediated by different response strategies of different phyla.

Applying Bayesian Belief Networks to Assess Alpine Grassland Degradation Risks: A Case Study in Northwest Sichuan, China

Grasslands are crucial components of ecosystems. In recent years, owing to certain natural and socio-economic factors, alpine grassland ecosystems have experienced significant degradation. This study integrated the frequency ratio model (FR) and Bayesian belief networks (BBN) for grassland degradation risk assessment to mitigate several issues found in previous studies. Firstly, the identification of non-encroached degraded grasslands and shrub-encroached grasslands could help stakeholders more accurately understand the status of different types of alpine grassland degradation. In addition, the index discretization method based on the FR model can more accurately ascertain the relationship between grassland degradation and driving factors to improve the accuracy of results. On this basis, the application of BBN not only effectively expresses the complex causal relationships among various variables in the process of grassland degradation, but also solves the problem of identifying key factors and assessing grassland degradation risks under uncertain conditions caused by a lack of information. The obtained result showed that the accuracies based on the confusion matrix of the slope of NDVI change (NDVIs), shrub-encroached grasslands, and grassland degradation indicators in the BBN model were 85.27, 88.99, and 74.37%, respectively. The areas under the curve based on the ROC curve of NDVIs, shrub-encroached grasslands, and grassland degradation were 75.39% (P &lt; 0.05), 66.57% (P &lt; 0.05), and 66.11% (P &lt; 0.05), respectively. Therefore, this model could be used to infer the probability of grassland degradation risk. The results obtained using the model showed that the area with a higher probability of degradation (P &gt; 30%) was 2.22 million ha (15.94%), with 1.742 million ha (78.46%) based on NDVIs and 0.478 million ha (21.54%) based on shrub-encroached grasslands. Moreover, the higher probability of grassland degradation risk was mainly distributed in regions with lower vegetation coverage, lower temperatures, less potential evapotranspiration, and higher soil sand content. Our research can provide guidance for decision-makers when formulating scientific measures for alpine grassland restoration.