Continuous tea cropping degrades the ecological functions of soil. However, studies on the influence of long-term tea cropping on soil environment are scarce, and understanding of whether short-term intercropping hedgerows can improve the soil environment is unclear. Tea intercropping mint (TM), tea intercropping rosemary (TR), tea and fertilization (TF), tea monoculture (TC), and natural grassland (NG) were used as research objects to analyze the environmental characteristics, and to comprehensively evaluate the soil environmental quality. Compared with NG, TF and TC had prominent acidification. Alpha diversity of soil bacteria in TF and TC were decreased, whereas soil fungi increased. In contrast to TF and TC, sucrase and catalase activities in TM and TR were enhanced. The structure of the soil microbial community in TM and TR was close to that in NG. According to the TOPSIS method, the order of soil quality was TM > TR > NG > TF > TC. In summary, continuous tea cropping for the long-term reduced soil environmental quality, whereas short-term cropping of mint and rosemary can lead to improvement in soil properties.

The ecological conservation redline (ECR) strategy is a new model for China’s ecological environmental protection that has attracted increasing attention. Ecological compensation is an effective means to promote ECR management. Although China’s ECR policy has been fully implemented, the ecological compensation mechanism remains underdeveloped, and there is an urgent need to establish a compensation standard accounting framework that meets the requirements of ECR management. This study proposed an ecological compensation accounting framework based on the protection requirements of “no reduction in function, no transformation in nature, and no reduction in area” and took Jiangsu Province, China, as a case study to estimate the ECR compensation standard. The results indicate that ECR policy effectively promotes the protection and restoration of important key ecosystems in this important ecological region. Influenced by factors such as ecosystem service value (ESV) variation, ECR distribution, and socioeconomic considerations, ecological compensation standards differed among cities during the same period. In the long term, ecological compensation standards increased with the ESV net gain and displayed a gradually increasing trend under ECR policy. Currently, the ecological compensation model is mainly organized by the government, and the sources of ecological compensation funds are limited. Government fiscal funds are limit to meet the great need of compensation, and there are serious deficiencies in inter-regional compensation especially for market oriented compensation approach between regions. It is difficult to meet the needs of ecological compensation using existing compensation methods, and diversified compensation methods such as tax exemption compensation, resident resettlement compensation, project construction compensation, resource sharing compensation, and market-oriented compensation should be adopted to improve the ecological compensation mechanism.

Plant residues serve as a critical source of soil organic matter and can modulate the mineralization of soil organic carbon via the priming effect. This process is crucial for the storage capacity and stability of organic carbon in terrestrial soils. Most studies have investigated the priming effect of plant residues, but still do not clearly understand whether priming effects alter the soil carbon balance. Therefore, this study employed stable isotope tracing technology and carried out a 200-day microcosm simulation experiment to explore the impacts of six types of plant residues with different qualities on soil priming effects and carbon balance. The results showed that only stems of the Acer truncatum showed a negative priming effect (-75.70 mg CO2 kg−1 soil); other plant residues all triggered positive priming effects (50.71–141.27 mg CO2 kg−1 soil). While the Pinus sylvestris root triggered a net soil carbon output of 6.58 mg C kg⁻¹ , other plant residues all resulted in a net carbon input (34.11–156.04 mg C kg−1). The priming intensity was significantly positively correlated with soil fungal biomass, cellulose concentration, and was significantly negatively correlated with plant lignin content (p < 0.05). Overall, our findings indicate that the decomposition of plant residues disrupts the balance of microbial available nutrients, which serves as a key determinant of the priming effect. Longer decomposition times of plant residues tended to shift the soil carbon balance toward net input. Our study demonstrates that plant residues generally promote soil carbon sequestration. This finding provides a scientific basis for future assessment of how exogenous organic matter affects carbon sequestration.

The 30×30 commitment outlined in the Kunming-Montreal Global Biodiversity Framework (KM-GBF) offers a critical opportunity for enhancing global biodiversity conservation. However, KM-GBF's efforts to address climate change impacts remain limited. We developed 1-km-resolution hotspot maps for climate change vulnerability with the exposure-sensitivity-adaptation framework, species distribution for 4 terrestrial vertebrate taxa, and carbon stock capacity including organic and biomass carbon, for 2030. Then, we developed a systematic conservation planning approach that, beyond the 3 conservation features mentioned, also considered human activities, connectivity, and Shared Socioeconomic Pathways. The plan included the identification of conservation priorities and gaps for China and the Association of Southeast Asian Nations region (China-ASEAN) at regional, national, and biogeographical scales. We found that 6.59% of the land in China-ASEAN overlapped all 3 hotspots, primarily in Indonesia, Malaysia, and Cambodia. Across all 3 spatial scales, newly identified conservation priorities were concentrated in low-elevation areas, particularly between 10° S and 10° N at the regional scale. Currently, protected areas cover 15.49% of China-ASEAN's land, representing 7.00% of climate change vulnerability hotspots, 12.45% of species distribution potential hotspots, and 14.56% of carbon stock capacity hotspots for 2030. If the 30×30 commitment is realized at a regional scale, these percentages are expected to increase to 22.93%, 33.15%, and 34.75%, respectively. Areas of conservation priority identified with our framework were significantly affected by the scale of protection coordination, yet they remained stable across Shared Socioeconomic Pathways, indicating their effectiveness in diverse future scenarios. The biogeographical scale had the smallest average conservation gap for all 12 countries (13.14%). Financial challenges are highest for Indonesia at the regional scale and for Malaysia at the national and biogeographical scales. Precise conservation based on appropriate scales is essential to achieving the 30×30 commitment and maximizing its conservation effectiveness under climate change.

With the advancement of artificial intelligence (AI) technologies, vehicle‐mounted mobile monitoring systems have become increasingly integrated into wildlife monitoring practices. However, images captured through these systems often present challenges such as low resolution, small target sizes, and partial occlusions. Consequently, detecting animal targets using conventional deep‐learning networks is challenging. To address these challenges, this paper presents an enhanced YOLOv7 model, referred to as YOLOv7(sr‐sm), which incorporates a super‐resolution (SR) reconstruction module and a small object optimization module. The YOLOv7(sr‐sm) model introduces a super‐resolution reconstruction module that leverages generative adversarial networks (GANs) to reconstruct high‐resolution details from blurry animal images. Additionally, an attention mechanism is integrated into the Neck and Head of YOLOv7 to form a small object optimization module, which enhances the model's ability to detect and locate densely packed small targets. Using a vehicle‐mounted mobile monitoring system, images of four wildlife taxa—sheep, birds, deer, and antelope —were captured on the Tibetan Plateau. These images were combined with publicly available high‐resolution wildlife photographs to create a wildlife test dataset. Experiments were conducted on this dataset, comparing the YOLOv7(sr‐sm) model with eight popular object detection models. The results demonstrate significant improvements in precision, recall, and mean Average Precision (mAP), with YOLOv7(sr‐sm) achieving 93.9%, 92.1%, and 92.3%, respectively. Furthermore, compared to the newly released YOLOv8l model, YOLOv7(sr‐sm) outperforms it by 9.3%, 2.1%, and 4.5% in these three metrics while also exhibiting superior parameter efficiency and higher inference speeds. The YOLOv7(sr‐sm) model architecture can accurately locate and identify blurry animal targets in vehicle‐mounted monitoring images, serving as a reliable tool for animal identification and counting in mobile monitoring systems. These findings provide significant technological support for the application of intelligent monitoring techniques in biodiversity conservation efforts.

Grassland canopy cover acts as an essential metric for gauging the vitality and ecological functions of grassland. Unmanned aerial vehicles (UAVs) provide stable and reliable data for estimating grassland canopy cover. However, conventional approaches primarily rely on samples from ground surveys and visual assessments, where data consistency is often affected by variations in survey techniques and personnel expertise. By contrast, UAVs provide consistent multi-scale grassland canopy data. Thus, effectively harnessing the strengths of multi-scale UAV imagery can markedly improve the efficiency and precision of canopy cover estimation. This study uses high-resolution UAV imagery for semantic segmentation to derive precise quadrat-scale canopy cover as ground truth. Subsequently, a deep regression network is developed using UAV orthophotos to estimate canopy cover at the plot scale. The findings indicate that semantic segmentation models leveraging deep learning techniques provide accurate vegetation segmentation and canopy cover estimation at the quadrat level, with UNet++ delivering the highest performance, marked by a mean intersection over union (MIoU) of 0.81 and an F1-score of 0.88. The canopy cover results derived from UNet++ segmentation exhibit a coefficient of determination (R2) of 0.98 and a root mean square error (RMSE) under 3.6 %, surpassing conventional methods like Canopeo and Random Forest (RF). At plot scale, models based on convolutional neural networks (CNNs) and vision transformer (ViT) architecture show enhanced capabilities in predicting canopy cover, with the Swin transformer-based model achieving the greatest accuracy (R2 = 0.90, RMSE = 5.48 %). In meadow, typical, and desert steppe, the Swin tansformer-based model consistently delivers high-precision canopy cover estimates. This study highlights the potential of integrating multi-scale UAV imagery with advanced deep learning techniques for efficient and accurate grassland vegetation monitoring. Future research should focus on optimizing model performance, extending applications to diverse ecosystems, and incorporating additional data sources to enhance robustness and precision.

China's terrestrial ecosystem carbon sink (TCS) is crucial for the global carbon budget. However, little is known how the enhanced human disturbances and increased extreme climate events may potentially destabilize TCS under warming climate. Using three process-based ecosystem models, we simulated the spatiotemporal variations of China's terrestrial net ecosystem productivity (NEP) from 2000 to 2020. We found that 26.7 % of the land area exhibit simultaneous increases in NEP temporal variability and autocorrelation during this period, indicating an increasing risk of TCS destabilization. Particularly, the southeastern subtropical monsoon region in China emerged as a hot-spot of potentially increasing NEP instability, despite its high carbon sink capacity, both NEP temporal variability and autocorrelation in this area exhibit a notable upward trend. Climate change, notably increasing precipitation and its temporal variation, appeared to be the primary driver of this instability. This harbinger implies that a regime shift in carbon sink capacity may occur as the warming climate continues to push it to the verge of stability.

Ecological restoration zoning is a critical component of ecological restoration planning, providing spatial guidance for the layout of restoration projects. However, no consensus on a standardized technical process for ecological restoration zoning has been reached. This study proposes a multi-level ecological restoration zoning framework from a “patterns-ecosystems-humans” perspective to offer differentiated and targeted guidance. First-level zones were initially delineated based on the natural geographical pattern and the provincial ecological restoration zoning plan. Subsequently, second-level zones were identified using various quantitative models and machine learning methods to define dominant ESs and key ecological challenges. Finally, the second-level zones were further subdivided into third-level zones based on the degree of human interference. This framework was applied in a case study of Sanmenxia City, delineating 3 first-level zones, 12 seconds-level zones, and 3 third-level zones, including ecological conservation zones, key restoration zones, and general restoration zones. Ecological conservation and key restoration zones accounted for 40.84 % and 29.93 % of the city's area, respectively. According to the ecosystem service index proposed in this study, five ecological restoration zones in Sanmenxia City exhibit significant multifunctionality, encompassing 53.3 % of the city's area. Overall, this study presents a clear and practical paradigm for ecological restoration zoning that aligns with provincial spatial ecological protection patterns while effectively guiding differentiated restoration efforts. This framework offers a valuable reference for ecological restoration in China's urban scale.

Aims:The classification and identification of grassland plants is an essential part of grassland resource surveillance and biodiversity monitoring.Rapid advancements in computer vision and deep learning have created opportunities for automating this process,however,there is currently a shortage of datasets and models specifically tailored for the identification of grassland plants. Methods:This study established a dataset comprising images of 831 species of native grassland plants in northern China.Employing state-of-the-art image classification architectures based on convolutional neural networks(CNN)and vision transformers(ViT),we trained models for the recognition of grassland plant images.Four models(Eva-02,ResNet_RS,MobileNetV3,and MobileViTv2)were evaluated for accuracy,recognition speed,and size. Results:Regarding model recognition accuracy,the Topl accuracy of the Eva-02,MobileViTv2,ResNet_RS,and MobileNetV3 models on the test set were 96.78％,94.29％,95.57％,and 91.53％,respectively.The Top5 accuracy on the test set were 99.17％,98.93％,98.79％,and 97.56％,respectively.In terms of model size and recognition speed,the MobileNetV3 model exhibited the smallest parameter size and fastest recognition speed,followed by MobileViTv2,making these models suitable for deployment on mobile devices.Conversely,the Eva-02 model had the largest parameter size and the slowest detection speed.Comparing with Pl@ntNet,HuaBanLv,and Baidu-Shitu,all four models developed in this study outperform these three recognition systems. Conclusion:The plant recognition models trained in this study can recognize the largest number of natural grassland plant species with the highest accuracy compared to other popular recognition systems.The four models strike a balance between model recognition accuracy and performance that is suitable for deployment on both desktop and mobile platforms.They also fulfill the requirements for indoor and outdoor application scenarios.

EDITORIAL article Front. Ecol. Evol., 25 January 2024Sec. Environmental Informatics and Remote Sensing Volume 12 - 2024 | https://doi.org/10.3389/fevo.2024.1367840

Artemisia frigida, as an important indicator species of grassland degradation, holds significant guidance significance for understanding grassland degradation status and conducting grassland restoration. Therefore, conducting rapid surveys and monitoring it is crucial. In this study, to address the issue of insufficient identification accuracy due to the large density and small size of Artemisia frigida in UAV images, we improved the YOLOv7 object detection algorithm to enhance the performance of the YOLOv7 model in Artemisia frigida detection. We applied the improved model to the detection of Artemisia frigida across the entire experimental area, achieving spatial mapping of Artemisia frigida distribution. The results indicate: In comparison across different models, the improved YOLOv7 + Biformer + wise-iou model exhibited the most notable enhancement in precision metrics compared to the original YOLOv7, showing a 6% increase. The mean average precision at intersection over union (IoU) threshold of 0.5 (mAP@.5) also increased by 3%. In terms of inference speed, it ranked second among the four models, only trailing behind YOLOv7 + biformer. The YOLOv7 + biformer + wise-iou model achieved an overall detection precision of 96% and a recall of 94% across 10 plots. The model demonstrated superior overall detection performance. The enhanced YOLOv7 exhibited superior performance in Artemisia frigida detection, meeting the need for rapid mapping of Artemisia frigida distribution based on UAV images. This improvement is expected to contribute to enhancing the efficiency of UAV-based surveys and monitoring of grassland degradation. These findings emphasize the effectiveness of the improved YOLOv7 + Biformer + wise-iou model in enhancing precision metrics, overall detection performance, and its applicability to efficiently map the distribution of Artemisia frigida in UAV imagery for grassland degradation surveys and monitoring.

To better understand the chemical characteristics and the potential source regions of PM2.5 measured from 18 January until 22 January 2016 in Shijiazhuang, China, PM2.5 was measured continuously and integrated daily sampling using mid-volume samplers was conducted at the three sites. The mean concentration of PM2.5 at the three sites reached 113, 131 and 119 µg m−3 during the sampling period, the higher concentrations occurred at early morning and noon, similar variation trends were found in the three sites. The concentrations of OC were higher than EC at three sampling sites and the OC/EC ratios ranged from 9.09 to 12.4 with a daily mean value of 10.8 during a haze pollution episode (HPE), which suggested that carbonaceous compositions might be from same source. The total concentration of water soluble inorganic ions (WSII) at the sites ranged from 72.2 to 100.0 µg m−3 with a mean of 84.3 µg m−3. The dominant species were NO3−, SO42−, NH4+, Cl−, accounting for 88.4

The biodiversity of grasslands is important for ecosystem function and health.The protection and mana-gement of grassland biodiversity requires the collection of the information on plant diversity.Hyperspectral remote sensing,with its unique advantages of extensive coverage and high spectral resolution,offers a new solution for long-term monitoring of plant diversity.We first reviewed the development history of hyperspectral remote sensing technology,emphasized its advantages in monitoring grassland plant diversity,and further analyzed its specific applications in this field.Finally,we discussed the challenges faced by hyperspectral remote sensing technology in its applications,such as the complexity of data processing,accuracy of algorithms,and integration with ground-based remote sensing data,and proposes prospects for future research directions.With the advancement of remote sensing technology and the integrated application of multi-source data,hyperspectral remote sensing would play an increasingly important role in grassland ecological monitoring and biodiversity conservation,which could provide scientific basis and technical support for global ecological protection and sustainable development.

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The Three North Shelterbelt Forest (TNSF) region in northern China is the largest artificial afforestation area in the world. Vegetation quality in the TNSF region has been improved greatly in recent years. This article presents a new approach to characterize vegetation quality by coupling vegetation productivity and coverage, examining the trend of vegetation quality from 2000 to 2021 at 1 km x 1 km spatial resolution by the linear regression trend method, and analyzing the driving forces of that trend. The results revealed a significant spatial difference of vegetation quality. Higher vegetation quality was found in the east and southeast of the TNSF region. Improvement of vegetation quality was found in approximately 80% area of the TNSF region, at a rate of 0 similar to 52 g C m(-2)yr(-1) with alpha = 0.05 significance between 2000 and 2021. Vegetation quality deteriorated in a few areas. The increase in CO2 concentrations and annual precipitation facilitated the improvement of vegetation quality, but also human efforts in ecological protection and restoration accelerated the improvement of vegetation quality under the current climate change background. The results might contribute to designing future ecological projects and the scientific adjustment of vegetation restoration strategies.

Plant litter decomposition is a significant ecosystem function that regulates nutrient cycling, soil fertility, and biomass production. It is heavily regulated by nutrient intake. The effects of exogenous nutrients on litter decomposition are not yet fully understood. To determine how Eriobotrya japonica litter decomposition responds to adding nutrients, we used the decomposition litter bag method in the laboratory for 180 days. There were five different nutrient treatment levels were used: control (no addition), low nitrogen addition (LN; 100 kg N·ha−1·year−1), high nitrogen addition (HN; 200 kg N·ha−1·year−1), phosphorus addition (P; 50 kg P·ha−1·year−1), and micronutrient addition (M; 50 kg M·ha−1·year−1). According to a repeated-measures analysis of variance, adding N reduced the remaining mass (p < 0.01) by 4.1% compared to the CK group. In contrast, adding M increased the remaining mass (p < 0.01) by 6.8% compared to the CK group. Adding P had no significant effect on the remaining mass. Although the amount of residual carbon (C) was unaffected, adding N increased the level of residual N in the litter. Litter C content, K content, N concentration, and C/N ratio were linearly correlated to the remaining litter (p < 0.01). Although adding nutrients decreased soil enzyme activity later in the decomposition process, no significant correlation was detected between enzyme activity and the remaining mass. N fertilization treatments decreased the soil microbial diversity index. The addition of nitrogen and micronutrients reduced the abundance of Acidobacteria, while HN addition increased the abundance of Actinobacteria. The addition of micronutrients increased the abundance of Proteobacteria. These results imply that N-induced alterations in the element content of the litter regulated the effects of nutrient inputs on litter decomposition. This study can be a reference for the fertilization-induced decomposition of agricultural waste litter.

Driven by climate change and ecological restoration, the contradiction between rising vegetation water consumption and declining terrestrial water availability becomes more apparent in non-humid regions in China, putting significant strain on the stability of water balance pattern. Thus, evaluating scientifically the suitability of plant coverage and types from the standpoint water balance is crucial to preserve or construct a relatively stable pattern of “carbon-water balance”, while which has not been investigated thoroughly. Here the study quantified the appropriate vegetation coverage of forest, shrubland and grassland, and identified unsuitable vegetation types over China in 2018 under 2 constraints of water balance (land-atmosphere water balance and terrestrial water storage stability). The findings revealed that, majority of China was across a stable state of "carbon-water balance" in 2018, but there was still a space to lower plant coverage in about 345,052 km2 (14.87%) of China with the declines mostly falling within 0.3, in 8.94% of which vegetation types were unsustainable; vegetation adjustment is advised to be prioritized in the locations (final hotspot regions, accounting for 7.43% of vegetation area of China) with water deficit, descending terrestrial water storage and the artificial vegetation areas. This study proposed a new quantitative assessment method for vegetation suitability aiming at “carbon-water balance” and provided scientific basis and practical value for maintaining the balance between the vegetation restoration and sustainable utilization of water resources.