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

Thriving arid oasis urban agglomerations: Optimizing ecosystem services pattern under future climate change scenarios using dynamic Bayesian network

The effects of global climate change and human activities are anticipated to significantly impact ecosystem services (ESs), particularly in urban agglomerations of arid regions. This paper proposes a framework integrating the dynamic Bayesian network (DBN), system dynamics (SD) model, patch generation land use simulation (PLUS) model, and the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model for predicting land use change and optimizing ESs spatial patterns that is built upon the SSP-RCP scenarios from CMIP6. This framework is applied to the oasis urban agglomeration on the northern slope of the Tianshan Mountains in Xinjiang (UANSTM), China. The findings indicate that both the SD model and PLUS model can accurately forecast the distribution of future land use. The SD model shows a relative error of less than 2.32%, while the PLUS model demonstrates a Kappa coefficient of 0.89. The land use pattern displays obvious spatial heterogeneity under different climate scenarios. The expansion of cultivated land and construction land is the main form of land use change in UANSTM in the future. The DBN model proficiently simulates the interactive relationships between ESs and diverse factors. The classification error rates for net primary productivity (NPP), habitat quality (HQ), water yield (WY), and soil retention (SR) are 20.04%, 3.47%, 4.45%, and 13.42%, respectively. The prediction and diagnosis of DBN determine the optimal ESs development scenario and the optimal ESs region in the study area. It is found that the majority of ESs in UANSTM are predominantly influenced by natural factors with the exception of HQ. The socio-economic development plays a minor role in such urban agglomerations. This study offers significant insights that can contribute to the fields of ecological protection and land use planning in arid urban agglomerations worldwide.

Ecosystem multifunctionality and soil microbial communities in response to ecological restoration in an alpine degraded grassland

Linkages between microbial communities and multiple ecosystem functions are context-dependent. However, the impacts of different restoration measures on microbial communities and ecosystem functioning remain unclear. Here, a 14-year long-term experiment was conducted using three restoration modes: planting mixed grasses (MG), planting shrub with Salix cupularis alone (SA), and planting shrub with Salix cupularis plus planting mixed grasses (SG), with an extremely degraded grassland serving as the control (CK). Our objective was to investigate how ecosystem multifunctionality and microbial communities (diversity, composition, and co-occurrence networks) respond to different restoration modes. Our results indicated that most of individual functions (i.e., soil nutrient contents, enzyme activities, and microbial biomass) in the SG treatment were significantly higher than in the CK treatment, and even higher than MG and SA treatments. Compared with the CK treatment, treatments MG, SA, and SG significantly increased the multifunctionality index on average by 0.57, 0.23 and 0.76, respectively. Random forest modeling showed that the alpha-diversity and composition of bacterial communities, rather than fungal communities, drove the ecosystem multifunctionality. Moreover, we found that both the MG and SG treatments significantly improved bacterial network stability, which exhabited stronger correlations with ecosystem multifunctionality compared to fungal network stability. In summary, this study demonstrates that planting shrub and grasses altogether is a promising restoration mode that can enhance ecosystem multifunctionality and improve microbial diversity and stability in the alpine degraded grassland.

Impacts of human pressure and climate on biodiversity-multifunctionality relationships on the Qinghai-Tibetan Plateau

Many studies have investigated the effects of environmental context on biodiversity or multifunctionality in alpine regions, but it is uncertain how human pressure and climate may affect their relationships. Here, we combined the comparative map profile method with multivariate datasets to assess the spatial pattern of ecosystem multifunctionality and further identify the effects of human pressure and climate on the spatial distribution of biodiversity-multifunctionality relationships in alpine ecosystems of the Qinghai-Tibetan Plateau (QTP). Our results indicate that at least 93% of the areas in the study region show a positive correlation between biodiversity and ecosystem multifunctionality across the QTP. Biodiversity-multifunctionality relationships with increasing human pressure show a decreasing trend in the forest, alpine meadow, and alpine steppe ecosystems, while an opposite pattern was found in the alpine desert steppe ecosystem. More importantly, aridity significantly strengthened the synergistic relationship between biodiversity and ecosystem multifunctionality in forest and alpine meadow ecosystems. Taken together, our results provide insights into the importance of protecting and maintaining biodiversity and ecosystem multifunctionality in response to climate change and human pressure in the alpine region.

Influence of Anthropogenic Activities and Major Natural Factors on Vegetation Changes in Global Alpine Regions

Understanding vegetation changes and their driving forces in global alpine areas is critical in the context of climate change. We aimed to reveal the changing trend in global alpine vegetation from 1981 to 2015 using the least squares regression method and Mann-Kendall (MK) test. The area-of-influence dominated by anthropogenic activity and natural factors was determined in an area with significant vegetation change by residual analysis; the primary driving force of vegetation change in the area-of-influence dominated by natural factors was identified using the partial correlation method. The results showed that (1) the vegetation in the global alpine area exhibited a browning trend from 1981 to 2015 on the annual scale; however, a greening trend was observed from May to July on the month scale. (2) The influence of natural factors was greater than that of anthropogenic activities, and the positive impact of natural factors was greater than the negative impact. (3) Among the factors that were often considered as the main natural factors, the contribution of albedo to significant changes in vegetation were greater than that of temperature, precipitation, soil moisture, and sunshine duration. This study provides a scientific basis for the protection of vegetation and sustainable development in alpine regions.

Mapping Forest Restoration Probability and Driving Archetypes Using a Bayesian Belief Network and SOM: Towards Karst Ecological Restoration in Guizhou, China

To address ecological threats such as land degradation in the karst regions, several ecological restoration projects have been implemented for improved vegetation coverage. Forests are the most important types of vegetation. However, the evaluation of forest restoration is often uncertain, primarily owing to the complexity of the underlying factors and lack of information related to changes in forest coverage in the future. To address this issue, a systematic case study based on the Guizhou Province, China, was carried out. First, three archetypes of driving factors were recognized through the self-organizing maps (SOM) algorithm: the high-strength ecological archetype, marginal archetype, and high-strength archetype dominated by human influence. Then, the probability of forest restoration in the context of ecological restoration was predicted using Bayesian belief networks in an effort to decrease the uncertainty of evaluation. Results show that the overall probability of forest restoration in the study area ranged from 22.27 to 99.29%, which is quite high. The findings from regions with different landforms suggest that the forest restoration probabilities of karst regions in the grid and the regional scales were lower than in non-karst regions. However, this difference was insignificant mainly because the ecological restoration in the karst regions accelerated local forest restoration and decreased the ecological impact. The proposed method of driving-factor clustering based on restoration as well as the method of predicting restoration probability have a certain reference value for forest management and the layout of ecological restoration projects in the mid-latitude ecotone.