Assessing the influence of simulated environmental gradients on the spatial heterogeneity of landscape patterns in the Tibetan Plateau.

Landscape patterns are pivotal in the realms of land use planning and ecological development, yet there remains a dearth of comprehensive research pertaining to the prediction of changes in landscape pattern characteristics. Within this study, we adopt the PLUS-CA-Markov and Fragstats models to forecast landscape patterns on the Tibetan Plateau spanning the period from 2030 to 2050. Through qualitative and quantitative analyses, we explore the spatiotemporal characteristics of landscape pattern changes between 2000 and 2050, concurrently identifying correlations among landscape pattern indices. Moreover, acknowledging the distinctive environmental gradients encompassing the plateau, notably elevation, slope, temperature, and precipitation, we investigate their implications on landscape pattern changes. Our findings indicate that: (1) Grassland degradation exhibited the utmost severity between 2000 and 2020, primarily attributed to overgrazing and climate-induced glacial melt. In contrast, cropland, forest, and water showcased divergent trends from 2020 to 2050 when compared to the preceding two decades, indicative of the efficacy of climate change control measures. (2) The distribution of landscape patterns on the Tibetan Plateau exhibited a considerable level of instability, marked by a decline in aggregation, reduced diversity and complexity, and amplified ecological connectivity between 2000 and 2020, signifying a partial amelioration in ecological quality. Between 2020 and 2050, landscape aggregation decreased alongside landscape fragmentation and the number of connectivity paths, signifying a discernible degradation of the plateau's ecosystem. (3) The most significant trade-off relationship was observed between landscape division index and largest patch index, while the synergistic relationship between landscape shape index and mean shape index was more pronounced. (4) Landscape aggregation, division, and largest patch index demonstrated non-linear quadratic trends in relation to elevation and temperature. Landscape shape index and patch density exhibited irregular non-linear effects. Largest patch index was predominantly influenced by slope, whereas division index was most affected by precipitation.

Scale effects and spatial heterogeneity of driving factors in ecosystem services value interactions within the Tibet autonomous region.

Widespread land development, deforestation, and wetland degradation have disrupted the physical integrity and functional capacity of ecosystems, leading to a reduction in ecosystem service values (ESV). However, comprehensive research addressing ESV interactions that represent various ecosystem services from multifaceted angles is limited. Moreover, the relative significance and spatiotemporal diversity of natural and socio-economic variables influencing ESV demand further investigation. This study conducts both quantitative and qualitative assessments of the spatiotemporal dynamics and interrelationships of ESV in the Tibet autonomous region from 2000 to 2020. Geographical detector and geographically weighted regression models are applied to ascertain the relative importance and spatial heterogeneity of diverse ESV determinants. The findings reveal the following key insights: (1) Barren lands experienced the most substantial expansion from 2000 to 2020, indicating an exacerbation of desertification in the Tibet autonomous region. (2) Over the two decades, ESV exhibited an overall upward trajectory, with regulation of water flows, water bodies, and forests making the most significant contributions to ESV and its growth. (3) The quantitative and qualitative assessment of ESV interrelations has identified the number of trade-offs and synergies, along with spatial occurrences, offering a detailed foundation for the scientific management of ecosystems. Specifically, quantitative results portray ESV correlations as positive or negative, qualitative spatial mapping elucidates intricate local interactions among ESV. (4) The primary driver of ESV in the Tibet autonomous region is NDVI (0.072), with elevation following closely behind, underscoring the predominant influence of natural factors relative to socio-economic variables. This research serves as a scientific underpinning for the development of ecological conservation policies and the execution of ecological restoration initiatives.

Water-related ecosystem services interactions and their natural-human activity drivers: Implications for ecological protection and restoration.

Sustainable development faces the crucial challenge of safeguarding water-related ecosystem services, particularly in arid regions. However, scale-dependent interactions and their influencing factors remain unclear. This study addresses this local gap on the regional level by focusing on ecologically vulnerable mountain areas, employing a comprehensive quantitative and spatial analysis approach, utilizing Spearman coefficient, trade-off/synergy index, and trade-off/synergy criterion, to examine water-related ecosystem services interactions across scales in arid area. Additionally, a Geographical detector was used to identify dominant natural and human activity factors. Finally, we determined ecologically optimal and worst areas and proposed spatial planning and management recommendations for ecological protection and restoration. Key results indicate that: (1) From 1995 to 2015, water yield and nutrient delivery ratio exhibited a declining trend, while soil retention showed an increasing trend, with the weakest nutrient delivery ratio function in the reserve. (2) At the grid scale, there were 2 trade-offs among water-related ecosystem services in 1995, which decreased to 1 trade-off in 2005 and 2015. The synergistic was most prominent near Qinghai Lake, while the trade-off was most obvious in the western mountainous areas. Conversely, the county scale demonstrated synergy. (3) NDVI, slope, and precipitation dominantly influence the spatial heterogeneity patterns of soil retention_water yield, soil retention_nutrient delivery ratio, and water yield_nutrient delivery ratio, respectively, with natural factors outweighing human activities in impacting water-related ecosystem services. This study contributes to the improvement and optimization of ecological environment management decisions.

How much carbon storage will loss in a desertification area? Multiple policy scenario analysis from Gansu Province.

Carbon storage plays a pivotal role in addressing climate change, maintaining ecological equilibrium, and fostering sustainable development. Gansu Province, situated in the arid to semi-arid region of Northwestern China, is confronted with substantial carbon storage losses as a result of ongoing ecological land desertification processes. However, studies on carbon storage loss under various scenarios in desertified regions are seldom reported. In this study, we delineated the ecological red line using quantified indicators encompassing multiple ecosystem service functions and ecological vulnerability sensitivity. Furthermore, we projected future land use and carbon storage transformations under multiple policy scenarios employing the patch-generating land use simulation (PLUS) model. Lastly, we unveiled spatial disparities in the driving factors behind alterations in carbon storage by geographically weighted regression model. Our findings suggest that: (1) The delineated ecological red line covers an area of 11.8 × 104 km2, approximately 27 % of the total land area of Gansu Province. (2) Quantitative findings reveal that the overall accuracy of the PLUS model reached an impressive 0.975, accompanied by a Kappa coefficient of 0.964, thus affirming the model's exceptional applicability. (3) Under the base line scenario, Gansu Province's carbon storage witnesses a consistent decline from 2000 to 2050, with a substantial total loss of 1.62 × 107 t over the ensuing three decades. The ecological red line scenario, by controlling 27 % of the land area in Gansu Province, achieves a 61.7 % effect of the global ecological scenario by 2050, thus reversing the declining trend in carbon storage. (4) Natural factors primarily influence carbon storage in the southeastern region, while human activity factors are distributed in the central region. This study offers scientifically robust policy recommendations to facilitate the attainment of carbon neutrality objective.

Multi-scenario simulation of carbon budget balance in arid and semi-arid regions.

The carbon budget has emerged as a central focus in global carbon cycle research. The limited understanding of carbon budget balance dynamics has led to an increasing imbalance between ecological and socio-economic benefits. Building upon a comprehensive analysis of carbon storage and emission in Lanzhou from 2000 to 2020, this study develops a novel deep learning model (CNN-LSTM) to simulate carbon budget under various scenarios from 2030 to 2050. Additionally, scientifically grounded recommendations for carbon compensation are provided. The results demonstrate several key findings: (1) The deep learning model exhibits outstanding performance, with an average overall accuracy exceeding 0.93. The coupled model outperforms individual models, underscoring the significance and necessity of incorporating both temporal and spatial features in land use simulation. (2) Under the ecological protection redline scenario from 2030 to 2050, a noteworthy augmentation in carbon storage and a proficient constraint on carbon emissions are observed. This substantiates the effectiveness of ecological protection interventions. (3) Carbon compensation payment areas are predominantly concentrated in built-up land, with the extent of these areas expanding over time. (4) The disparities in carbon balance effects of forest were more conspicuous than that of built-up land across diverse temporal and scenarios.

Scenario modeling of ecosystem service trade-offs and bundles in a semi-arid valley basin.

Amidst the rapid urbanization process, substantial transformations have emerged within ecosystem services, exerting profound ramifications on the sustainability of ecosystems. Nevertheless, an existing dearth persists in delineating the intricate interplay of trade-offs and synergies, as well as ecosystem services bundles under diverse future scenarios. This study harnesses the Convolutional neural network-Long and short-term memory-Cellular automata model to prognosticate and dissect the temporal and spatial dynamics of four distinct ecosystem services (soil retention, water yield, carbon storage, and habitat quality) across the semi-arid valley city of Lanzhou from 2000 to 2030 under multiple scenarios. The SPSS model methodically quantifies the intricate trade-offs and synergies interwoven among these services, while cluster analysis reveals ecosystem services bundles across varying scales. Notably, (1) convolutional neural network-long and short-term memory-cellular automata model demonstrates remarkable proficiency in accurately forecasting land use, boasting an elevated precision level of 0.93. (2) The trajectories of carbon storage and water yield demonstrate a diminishing pattern between 2000 and 2020, against the ascending trends observed in soil retention and habitat quality. Within the ecological priority scenario for 2030, water yield experiences a sluggish decrease, while the abatement of soil retention, carbon storage, and habitat quality degradation is maximized. (3) The trade-offs (3 pairs) and synergies (3 pairs) among ecosystem services within the study locale exhibit a state of relative equilibrium. Noteworthy among these interactions is the prominent trade-off correlation (-0.101) between soil conservation and carbon storage within the city priority scenario. (4) Four discernible ecosystem services bundles manifest at the grid scale from 2000 to2030. Conversely, three bundles at the county scale in 2000, eventually decrease to two bundles between 2010 and 2030, hinting at a diminishing trend concerning the number of ecosystem services bundles as spatial scales coarsen. Furthermore, whereby these bundles progressively aggregate spatially over time.

Understanding the intricate tradeoffs among ecosystem services in the Beijing-Tianjin-Hebei urban agglomeration across spatiotemporal features.

Revealing the intricate interactions between ecosystem services and their values is essential for the comprehensive management of diverse ecosystems. However, understanding tradeoffs among various ecosystem services and their influencing factors, especially at different spatial scales, remains challenging, primarily due to the difficulty in quantifying cultural services. In this study, we conducted a comprehensive analysis of the ecosystem service value (ESV) at both grid and county scales in the Beijing-Tianjin-Hebei urban agglomeration from 2000 to 2020, considering the representation of different ecosystem services. Our investigation aimed to elucidate tradeoffs among ecosystem services and identify key natural-social-economic-climate drivers. The key findings are as follows: (1) Over the study period, the overall ESV in the Beijing-Tianjin-Hebei urban agglomeration exhibited an upward trend, with regulation of waterflows, water body, and forest land making the largest contributions. (2) At the grid scale, there were 3 tradeoffs and 52 synergies among multiple ecosystem services in 2000, which increased to 18 tradeoffs in 2020, indicating a considerable degradation of numerous ecosystem services. Conversely, at the county scale, all ecosystem services exhibited a synergistic relationship. (3) The most significant synergistic effect was observed between regulation of waterflows and maintenance of soil fertility, while the most prominent tradeoff effect was identified between food production and waste treatment. (4) Social-economic factors exerted a more substantial influence on ESV, followed by climate factors, whereas the impact of natural factors was limited. GDP emerged as the primary single driver of total ESV, while farmland production potential played a crucial role in shaping the 11 ecosystem services. Notably, GDP-temperature and GDP-farmland production potential output were identified as the most influential dual factors. These findings underscore the importance of simultaneously enhancing economic development, controlling temperature rise, and improving food production as essential measures to enhance ESV.