Small Landscape Research Articles

Navigating Green Growth Strategies in the Art Industry's Small Business Landscape

Navigating Green Growth Strategies in the Art Industry's Small Business Landscape

Bioinspired hierarchical and dual-morphology humic-acid/pectin/chitosan composite aerogels for efficient removal of pollutants from wastewater

How to solve the contradiction between the efficiency and adsorption rate of porous materials in adsorbing pollutants has always been one of the focus issues. In this study, the small landscape cypress trees structure like biomimetic of a hierarchical and dual morphology 3D porous HA-based aerogel was designed and synthesized to use humic acid (HA), pectin (PE) and chitosan (CTS) as raw materials, which it was formed by the disorderly overlapping of lamella composed of fiber networks in 3D space. Due to its special microstructure, it can be used like separation membrane, which allowing for rapid adsorption of pollutants in the water while the water flow passes through quick. In general, this work provides a new concept for owning fast adsorption rate and efficient adsorption of porous materials of preparation to use green method.

Archaeological LiDAR in Mediterranean Karst Landscapes. A Multiproxy Dating Method for Archaeological Landscape and a Case Study From Prehistoric Kras Plateau (Slovenia)

ABSTRACTThe case study area is a small but typical prehistoric landscape in the Kras Plateau on the north coast of the central Mediterranean. The Late Bronze and Iron Age Kras Plateau was an emblematic Mediterranean archaeological landscape dotted with numerous hillforts. Since the mid‐20th century, the landscape had been overgrown with some of the most archaeology‐hostile vegetation, severely impeding landscape archaeology until archaeological LiDAR revealed thousands of archaeological features that attest to a carefully constructed and managed agro‐pastoral landscape. However, these discoveries were hampered by insecure chronology typical of any LiDAR guided analysis. This case study meticulously documented two prehistoric hillforts and a previously unknown agro‐pastoral landscape with hundreds of archaeological features. The focus of the article, however, was on establishing a more precise and objective dating method. We proposed a multiproxy method to date the archaeological landscape. It combines relative dating using remote sensing data and historical maps; dating based on historical context; relative stratigraphic dating; indirect dating based on associated archaeological finds; and dating by association. Particularly, we focused on the association‐based dating of archaeological features. We proposed a method based on the concept of taskscapes that relies on kernel density estimation. Using these methods, we objectively demonstrated that agro‐pastoral landscape features documented with archaeological LiDAR were contemporaneous with Late Bronze Age and Iron Age hillforts and have no connection to the post‐medieval landscape. The latter has important methodological implications for the prehistoric archaeology of Mediterranean karst landscapes, where backdating post‐Medieval landscapes is a common practise.

Loss of ADAR1 protein induces changes in small RNA landscape in hepatocytes.

In recent years, numerous evidence has been accumulated about the extent of A-to-I editing in human RNAs and the key role ADAR1 plays in the cellular editing machinery. It has been shown that A-to-I editing occurrence and frequency are tissue-specific and essential for some tissue development, such as the liver. To study the effect of ADAR1 function in hepatocytes, we have created Huh7.5 ADAR1 KO cell lines. Upon IFN treatment, the Huh7.5 ADAR1 KO cells show rapid arrest of growth and translation, from which they do not recover. We analyzed translatome changes by using a method based on sequencing of separate polysome profile RNA fractions. We found significant changes in the transcriptome and translatome of the Huh7.5 ADAR1 KO cells. The most prominent changes include negatively affected transcription by RNA polymerase III and the deregulation of snoRNA and Y RNA levels. Furthermore, we observed that ADAR1 KO polysomes are enriched in mRNAs coding for proteins pivotal in a wide range of biological processes such as RNA localization and RNA processing, whereas the unbound fraction is enriched mainly in mRNAs coding for ribosomal proteins and translational factors. This indicates that ADAR1 plays a more relevant role in small RNA metabolism and ribosome biogenesis.

Wide-Scale Identification of Small Woody Features of Landscape from Remote Sensing

Small landscape features (i.e., trees outside forest, small woody features) and linear vegetation such as hedgerows, riparian vegetation, and green lanes are vital ecological structures in agroecosystems, enhancing the biodiversity, landscape diversity, and protecting water bodies. Therefore, their monitoring is fundamental to assessing a specific territory’s arrangement and verifying the effectiveness of strategies and financial measures activated at the local or European scale. The size of these elements and territorial distribution make their identification extremely complex without specific survey campaigns; in particular, remote monitoring requires data of considerable resolution and, therefore, is very costly. This paper proposes a methodology to map these features using a combination of open-source or low-cost high-resolution orthophotos (RGB), which are typically available to local administrators and are object-oriented classification methods. Additionally, multispectral satellite images from the Sentinel-2 platform were utilized to further characterize the identified elements. The produced map, compared with the other existing layers, provided better results than other maps at the European scale. Therefore, the developed method is highly effective for the remote and wide-scale assessment of SWFs, making it a crucial tool for defining and monitoring development policies in rural environments.

Wild lions in small, fenced reserves in South Africa conform to a meta‐population

Abstract Lions (Panthera leo) are declining across their range, mainly due to human‐induced habitat fragmentation and prey depletion. However, the South African lion population continues to grow. Unlike other range states, South Africa actively manages wild lions across a continuum of landscapes and ecological constraints. Many of these lions are in small, fenced reserves where managers seek to mimic ecological processes in small landscapes. However, the effectiveness of this management approach has not been evaluated against meta‐population criteria. Given that meta‐population dynamics allows species living in fragmented habitats to persist, we evaluated how South Africa's lion population complies with meta‐population criteria using national audit data between 2010 and 2019 from 49 fenced, wild lion reserves. The small, fenced reserves holding wild lions fulfil the criteria for meta‐population functionality. However, this functionality was achieved through haphazard and uncoordinated management actions and not through a coordinated approach. Our main recommendation is to consider implementing a more coordinated meta‐population management approach. At the very least, meta‐population management guidelines should be reviewed and updated on a regular basis, regular audits should be conducted and periodic genetic evaluation of the meta‐population (every 10 years) should be instituted. We recommend incentivising lion managers to enhance the conservation of lions in South Africa within a meta‐population framework. We also recommend a focus on improving decision making and policy procedures that facilitates compliance with relevant legislation aimed at achieving high levels of lion conservation‐governance efficiency. South Africa's meta‐population approach to wild lion management in small, fenced reserves is effective at conserving lions and contributing to lion conservation more broadly. In an increasingly fragmented landscape, the need for human management actions to ensure persistence of large carnivores is likely to increase. A managed meta‐population approach of fenced (or unfenced, but geographically isolated) populations is a useful tool for conservationists to consider worldwide.

Exploration of the potential of a boosted sterile insect technique to control fruit flies in mango orchards.

An innovative version of the sterile insect technique (SIT) for pest control, called boosted SIT, relies on the use of sterile males coated with a biocide to control a target wild pest population of the same species. The objective of the present study was to assess the relevance of such technology to control the fruit fly Bactrocera dorsalis and fruit losses in mango orchards using. An agent-based simulation model named BOOSTIT was used to explore the reduction of fruit losses thank to sterile male fruit flies control and economic benefits according to different strategies of sterile male release. The simulation considered a landscape of 30.25 ha made up of four mango orchards. The SIT and the boosted SIT reduced fruit losses when releases were made before the mango fruiting period. According to model simulations, releases should be performed at least seven times at 2-week intervals and with a sterile/wild male ratio of at least 10:1. Considering the benefit/cost ratio (BCR), few releases should be done with a late start date. The BCR showed economic gains from the two control methods, the number of saved fruits and BCR being higher for SIT. Our simulations showed that SIT would have better results than the boosted SIT to contribute to an effective control of Bactrocera dorsalis at the scale of a small landscape. We highlight the need for laboratory studies of other types of pathogen to find a suitable one with higher incubation time and lower cost. © 2024 The Author(s). Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

STATE SUPPORT MEASURES FOR SMALL BUSINESS IN UZBEKISTAN

This article explores the diverse range of state support measures available to small businesses in Uzbekistan. By analyzing these initiatives and their impact, it seeks to provide insights into how government policies are shaping the small business landscape and fostering an environment conducive to entrepreneurship. Understanding these dynamics is crucial for policymakers, business owners, and stakeholders aiming to promote sustainable economic development and inclusive growth in Uzbekistan.

3D Modeling of rural environments from multiscale aerial imagery

Given the increasing attention to environmental preservation and sustainable development, the digitization of rural landscapes stands out as a pivotal strategy for effective environmental management and sustainability, land use planning, and preservation of cultural heritage. This work proposes a novel methodology for generating 3D models of rural landscapes by integrating multiscale data sources. Although Unmanned Aerial Vehicles (UAV) simplify the acquisition of multi-source data, their coverage is typically restricted to small landscapes due to their limited range and flight time. On the other hand, although the use of aerial images provides a broader view of the terrain, it is important to note that the low resolution of these images interferes with the task of accurate 3D modeling. Given these challenges, we propose a methodology that combines UAV data and high-resolution aerial imagery provided by the Spanish National Orthophoto Program (PNOA). This multi-source data integration is crucial to generating detailed and accurate 3D models of rural environments. The proposed methodology involves three steps: (1) semantic segmentation of aerial images identifying features such as vegetation, ground, and human-made structures, (2) estimation of the Digital Elevation Model (DEM), and (3) 3D modeling of rural environments using the point clouds generated from UAV images. The conducted experiments demonstrate the effectiveness of our approach identifying and representing previously mentioned features. Thus, this work presents advances in 3D representation techniques for real scenarios, contributing to the coordination of land utilization and environmental sustainability in rural landscapes.

Local and landscape factors shape alpha and beta trophic interaction diversity in urban gardens.

Promoting urban green spaces is an effective strategy to increase biodiversity in cities. However, our understanding of how local and landscape factors influence trophic interactions in these urban contexts remains limited. Here, we sampled cavity-nesting bees and wasps and their natural enemies within 85 urban gardens in Zurich (Switzerland) to identify factors associated with the diversity and dissimilarity of antagonistic interactions in these communities. The proportions of built-up area and urban green area at small landscape scales (50 m radius), as well as the management intensity, sun exposure, plant richness and proportion of agricultural land at the landscape scale (250 m radius), were key drivers of interaction diversity. This increased interaction diversity resulted not only from the higher richness of host and natural enemy species, but also from species participating in more interactions. Furthermore, dissimilarity in community structure and interactions across gardens (beta-diversity) were primarily influenced by differences in built-up areas and urban green areas at the landscape scale, as well as by management intensity. Our study offers crucial insights for urban planning and conservation strategies, supporting sustainability goals by helping to understand the factors that shape insect communities and their trophic interactions in urban gardens.

Green treasures: Investigating the biodiversity potential of equine yards through the presence and quality of landscape features in the Netherlands.

At a time of mounting ecological crises and biodiversity loss, there is an urgent need for nature-based solutions. Equestrian properties cover a considerable proportion of the European rural and peri-urban landscape and provide much potential for integrating ecosystem services, such as the inclusion of small landscape features. The aim of this study was to investigate the presence and quality of landscape features (LF) to help determine how the equine sector can contribute to the agro-ecological transition. Using a citizen science approach, 87 commercial and 420 private yard owners reported the type, frequency and geometric dimension of LFs and additional biodiversity enhancing features. A hierarchical multivariate regression was used to determine how equine property characteristics explain variation in the Percentage Property Coverage (PPC) of LFs. The model explained 47% of the variation of PPC. The variables that explained significant variation in PPC included Yard size, Number of LFs, Tree rows, Fruit orchard, Wild hedges, Flowering strips, Buffer strips, Embankments and Cluttered corners. Commercial yards are significantly larger with significantly more horses and on average only 9% (±13.87%) of the property was covered by LFs whilst private yards had significantly more coverage of LFs with on average 12% (±14.77%). These findings highlight the substantial yet untapped potential of equine yards in fostering biodiversity, suggesting that the equine sector could play an important role in the agro-ecological transition. To encourage more biodiverse-inclusive yard designs, tailored strategies should consider the diverse factors influencing equine yard design, including existing knowledge, client demands, financial considerations, and equine health and welfare.

A decision support system for effective implementation of agro-environmental measures targeted at small woody landscape features: The case study of Slovenia

Years of agricultural intensification across Europe’s plains, valleys and hills had left behind a more or less generalized land use pattern dominated by profitable land cover types. Small landscape features with no direct (financial) benefit to the farmer, have been, in many cases, removed and converted to arable land. This study investigates the possibilities to integrate small woody landscape features (SWLF) into the new (2023–2027) CAP measures, which are in conjunction with the EU Green Deal ambitions and support biodiversity preservation in agroecosystems. A spatial SWLF preservation/restoration algorithm, based on the most recent European (SWF2018) and improved national level (SWLF2015) status databases, was developed and then applied to identify priority SWLF management regions and areas in Slovenia. In addition, a self-standing (LPIS independent) information, monitoring and decision support system has been designed for a target priority area (Goričko) in different spatial scales (from regional, 1 km, 300 m, AH to parcel level) to bypass existing gaps in SWLF-targeted CAP measure implementation strategies in Slovenia. Such results are urgently needed across all European countries to easier, faster and better realize CAP national strategic plans.

Estimating Sugarcane Aboveground Biomass and Carbon Stock Using the Combined Time Series of Sentinel Data with Machine Learning Algorithms

Accurately mapping crop aboveground biomass (AGB) in a timely manner is crucial for promoting sustainable agricultural practices and effective climate change mitigation actions. To address this challenge, the integration of satellite-based Earth Observation (EO) data with advanced machine learning algorithms offers promising prospects to monitor land and crop phenology over time. However, achieving accurate AGB maps in small crop fields and complex landscapes is still an ongoing challenge. In this study, the AGB was estimated for small sugarcane fields (<1 ha) located in the Kumphawapi district of Udon Thani province, Thailand. Specifically, in order to explore, estimate, and map sugarcane AGB and carbon stock for the 2018 and 2021 years, ground measurements and time series of Sentinel-1 (S1) and Sentinel-2 (S2) data were used and random forest regression (RFR) and support vector regression (SVR) applied. Subsequently, optimized predictive models used to generate large-scale maps were adapted. The RFR models demonstrated high efficiency and consistency when compared to the SVR models for the two years considered. Specifically, the resulting AGB maps displayed noteworthy accuracy, with the coefficient of determination (R2) as 0.85 and 0.86 with a root mean square error (RMSE) of 8.84 and 9.61 t/ha for the years 2018 and 2021, respectively. In addition, mapping sugarcane AGB and carbon stock across a large scale showed high spatial variability within fields for both base years. These results exhibited a high potential for effectively depicting the spatial distribution of AGB densities. Finally, it was shown how these highly accurate maps can support, as valuable tools, sustainable agricultural practices, government policy, and decision-making processes.

Viral and host small RNA transcriptome analysis of SARS-CoV-1 and SARS-CoV-2-infected human cells reveals novel viral short RNAs

RNA viruses have been shown to express various short RNAs, some of which have regulatory roles during replication, transcription, and translation of viral genomes. However, short viral RNAs generated from SARS-CoV-1 and SARS-CoV-2 genomic RNAs remained largely unexplored, possibly due limitations of the widely used library preparation methods for small RNA deep sequencing and corresponding data processing. By analyzing publicly available small RNA sequencing datasets, we observed that human Calu-3 cells infected by SARS-CoV-1 or SARS-CoV-2 accumulate multiple previously unreported short viral RNAs. In addition, we verified the presence of the five most abundant SARS-CoV-2 short viral RNAs in SARS-CoV-2-infected human lung adenocarcinoma cells by quantitative PCR. Interestingly, the copy number of the observed SARS-CoV-2 short viral RNAs dramatically exceeded the expression of previously reported viral microRNAs in the same cells. We hypothesize that the reported SARS-CoV-2 short viral RNAs could serve as biomarkers for early infection stages due to their high abundance. Furthermore, unlike SARS-CoV-1, the SARS-CoV-2 infection induced significant (Benjamini-Hochberg-corrected p-value <0.05) deregulation of Y-RNA, transfer RNA, vault RNA, as well as more than 300 endogenous short RNAs that aligned predominantly to human protein-coding and long noncoding RNA transcripts. In particular, more than 20-fold upregulation of reads derived from Y-RNA (and several transfer RNAs) have been documented in RNA-seq datasets from SARS-CoV-2 infected cells. Finally, a significant proportion of short RNAs derived from full-length viral genomes also aligned to various human genome (hg38) sequences, suggesting opportunities to investigate regulatory roles of short viral RNAs during infection. Further characterization of the small RNA landscape of both viral and host genomes is clearly warranted to improve our understanding of molecular events related to infection and to design more efficient strategies for therapeutic interventions as well as early diagnosis.

Integrating wind disturbances into forest planning: a stochastic programming approach

Forest disturbances challenge our ability to carefully plan for sustainable use of forest resources. As forest disturbances are stochastic, we cannot plan for the disturbance at any specific time or location. However, we can prepare for the possibility of a disturbance by integrating its potential intensity range and frequency when developing forest management plans. This study uses stochastic programming to integrate wind intensity (wind speed) and wind event frequency (number of occurrences) into the forest planning process on a small coastal Finnish forest landscape. We used a mechanistic model to quantify the critical wind speed for tree felling, with a Monte Carlo approach to include wind damage and salvage logging into forest management alternatives. We apply a stochastic programming model to explore two objectives: maximizing the expected forest net present value or maximizing the even-flow of income. To assess the effects of improper wind risk assumptions in planning, we compare the results when optimizing for correct versus incorrect wind intensity and frequency assumptions. When maximizing for net present value, the impacts of misidentifying wind intensity and frequency are minor, likely due to harvests planned immediately as trees reach maturity. For the case when maximizing even-flow of income, incorrectly identifying wind intensity and frequency severely impacts the ability to meet the required harvest targets and reduces the expected net present value. The specific utility of risk mitigation therefore depends on the planning problem. Overall, we show that incorporating wind disturbances into forest planning can inform forest owners about how they can manage wind risk based on their specific risk preferences.

Molecular and Genetic Advances in Small Cell Lung Cancer Landscape: From Homogeneity to Diversity.

Small cell lung cancer (SCLC) has been historically considered a homogeneous disease and thus approached as a single entity when it comes to clinical studies design and new treatments developments. However, increasing knowledge in the genetic and molecular landscape of this disease challenges this concept, opening the possibility that different subtypes might show differential vulnerability to treatments. In this narrative review, we gather the most relevant advances in genetic and molecular characterization of SCLC, focusing on how these discoveries may be used to design the path for a personalized treatment approach. Indeed, we discuss the new classification based on differential protein expression, the prevalence and significance of oncogenic drivers (e.g., EGFR mutations and ALK rearrangements) in SCLC, the genetic characteristics of SCLC in patients with no smoking history, and the existing evidence supporting the use of liquid biopsy for capturing the heterogeneity of the disease. We use the keywords "small cell lung cancer", "SCLC", "EGFR", "ALK", "histological transformation", and "transcriptional factors" to identify original research manuscripts, clinical trials, case reports, and case series from PubMed.

Identifying landscape hot and cold spots of soil greenhouse gas fluxes by combining field measurements and remote sensing data

Abstract. Upscaling chamber measurements of soil greenhouse gas (GHG) fluxes from point scale to landscape scale remain challenging due to the high variability in the fluxes in space and time. This study measured GHG fluxes and soil parameters at selected point locations (n=268), thereby implementing a stratified sampling approach on a mixed-land-use landscape (∼5.8 km2). Based on these field-based measurements and remotely sensed data on landscape and vegetation properties, we used random forest (RF) models to predict GHG fluxes at a landscape scale (1 m resolution) in summer and autumn. The RF models, combining field-measured soil parameters and remotely sensed data, outperformed those with field-measured predictors or remotely sensed data alone. Available satellite data products from Sentinel-2 on vegetation cover and water content played a more significant role than those attributes derived from a digital elevation model, possibly due to their ability to capture both spatial and seasonal changes in the ecosystem parameters within the landscape. Similar seasonal patterns of higher soil/ecosystem respiration (SR/ER–CO2) and nitrous oxide (N2O) fluxes in summer and higher methane (CH4) uptake in autumn were observed in both the measured and predicted landscape fluxes. Based on the upscaled fluxes, we also assessed the contribution of hot spots to the total landscape fluxes. The identified emission hot spots occupied a small landscape area (7 % to 16 %) but accounted for up to 42 % of the landscape GHG fluxes. Our study showed that combining remotely sensed data with chamber measurements and soil properties is a promising approach for identifying spatial patterns and hot spots of GHG fluxes across heterogeneous landscapes. Such information may be used to inform targeted mitigation strategies at the landscape scale.

Small mammal associations with habitat composition, configuration, and management in tallgrass prairies: a review

Abstract Tallgrass prairie is among the most threatened ecosystems but is often fragmented and surrounded by human‐modified landscapes. Small mammals are integral components of tallgrass prairies. However, little is known about how landscape composition, configuration, and management impact small mammals in tallgrass prairies. We conducted a systematic literature review to identify species‐specific and community associations with three broad topics: landscape composition, landscape configuration, and management practices. We identified 61 studies that assessed our variables of interest. We categorised the location, species assessed, variables monitored, and results by species and for the community. The majority of studies (64%) were conducted in two states, Illinois and Kansas. Deer mice (Peromyscus maniculatus), prairie voles (Microtus ochrogaster), and white‐footed mice (Peromyscus leucopus) showed specific associations with landscape variables, with deer mice preferring bare ground and recently burned plots, and prairie voles preferring thatch and negatively associated with prescribed fire. White‐footed mice were frequently associated with wooded areas. Small mammal biodiversity was positively associated with patchy habitats containing greater diversity in vegetative composition and management regime. Management and land composition were both relatively well studied for several species; habitat configuration was understudied. We identified significant gaps in our understanding of small mammal landscape ecology in tallgrass prairies. With tallgrass prairie restoration a growing trend in this region, a greater understanding of drivers of small mammal populations will be crucial to successful restoration efforts. Future research should focus on understudied areas and species, and examine how habitat heterogeneity impacts small mammal biodiversity.

The contributions of flower strips to wild bee conservation in agricultural landscapes can be predicted using pollinator habitat suitability models

Abstract Sowing flower strips along field edges is a widely adopted method for conserving pollinating insects in agricultural landscapes. To maximize the effect of flower strips given limited resources, we need spatially explicit tools that can prioritize their placement, and for identifying plant species to include in seed mixtures. We sampled bees and plant species as well as their interactions in a semi‐controlled field experiment with roadside/field edge pairs with/without a sown flower strip at 31 sites in Norway and used a regional spatial model of solitary bee species richness to test if the effect of flower strips on bee species richness was predictable from the modelled solitary bee species richness. We found that sites with flower strips were more bee species rich compared to sites without flower strips and that this effect was greatest in areas that the regional solitary bee species richness model had identified to be particularly important for bees. Spatial models revealed that even within small landscapes there were pronounced differences between field edges in the predicted effect of sowing flower strips. Of the plant species that attracted the most bee species, the majority mainly attracted bumblebees and only few species also attracted solitary bees. Considering both the taxonomic diversity of bees and the species richness of bees attracted by plants we suggest that seed mixes containing Hieracium spp. such as Hieracium umbellatum, Pilosella officinarum, Taraxacum spp., Trifolium repens, Lotus corniculatus, Stellaria graminea and Achillea millefolium would provide resources for diverse bee communities in our region. Spatial prediction models of bee diversity can be used to identify locations where flower strips are likely to have the largest effect and can thereby provide managers with an important tool for prioritizing how funding for agri‐environmental schemes such as flower strips should be allocated. Such flower strips should contain plant species that are attractive to both solitary and bumblebees, and do not need to be particularly plant species rich as long as the selected plants complement each other.

Analysis of the Dong bao Ye as sacred landscape and its Putative therapeutic mechanisms.

Humans have innately established close and profound ties with the, and through these relationships shaped many kinds of landscapes. Among these are sacred landscapes, which have drawn the attention of researchers due to their cultural significance. In the field of health geography, large-sized sacred regional landscapes are now the focus of studies for their therapeutic properties. However, few scholars have focused on small sacred landscape systems at the community level (constructed by local communities) or the physical and psycological health benefits that these landscapes offer to the local residents. These small-sized and widespread, but often hidden, sacred landscapes are closely tied to people's daily lives and work. They have evolved and grown over millennia to become critical sociocultural phenomena. This study takes the sacred bao ye landscape of the Dong people of China as the research subject. By adopting the case study approach, field research, semi-structured interviews, and textual analysis, it summarizes the types, geographical distribution, rituals and processes of bao ye as a sacred landscape of the Huanggang village in Guizhou Province, and concludes with an analysis of motivation and health benefits to the bao ye worship. In this paper we argue that bao ye is a sacred landscape system focusing on the healthy development of children, and constitutes a local belief developed in an isolated environment lacking medical resources, which remains in practice. The sacred landscape of bao ye offers a therapeutic environment, providing children with increased opportunities to engage with and build deep connections to nature. Thruogh this process children may develop a bond with nature that inspires them to protect nature on their own accord. We argue that bao ye offers an important case study for understanding the landscape-people-healing interactivity at the community level.