Ecological Management System Research Articles

Research on the Development and Management of Ecological Agriculture Characteristic Tourism Based on Artificial Intelligence Technology for Water Resources

In order to improve the effectiveness of ecological agriculture tourism development and management of water resources, this article combines artificial intelligence technology to study the development and management of ecological agriculture tourism. This article conducts a hierarchical analysis based on the characteristics of tourism symbol aggregation and spatial distribution patterns within a certain period of time, determines the threshold for extracting the optimal tourism behavior boundary, and strives to solve the problems caused by the complexity and dynamism of tourism behavior. In addition, this article also developed an intelligent water resources agricultural characteristic tourism management system to improve the development effect of agricultural characteristic tourism and promote precise control of water conservancy irrigation systems in ecological agricultural tourism areas. Finally, this article combines the proposed algorithm model to construct an ecological agriculture characteristic tourism development and management system. Simulation evaluation shows that the ecological agriculture characteristic tourism development and management model based on artificial intelligence technology proposed in this paper has good effects.

An intelligent system for determining the degree of tree bark beetle damage based on the use of generative-adversarial neural networks.

The increasing prevalence of tree bark beetle infestations poses a significant threat to forest ecosystems, leading to detrimental impacts on biodiversity, carbon storage, and timber resources. This study addresses the pressing need for innovative solutions to enhance forest management practices through early detection and assessment of tree health. We developed an intelligent system leveraging multispectral images and generative-adversarial networks (GANs) to accurately determine the extent of bark beetle damage. Recognizing the challenges posed by traditional neural networks, which require vast amounts of labeled training data, we proposed a novel approach that utilizes a GAN architecture. In this system, a discriminator functions as a classifier, effectively trained on both real and synthetically generated data. Our methodology not only reduces the dependency on extensive labeled datasets but also enhances the robustness of the classification process. The results indicate a classification accuracy of 87.5%, demonstrating significant promise for improving detection capabilities even with limited training resources. The implications of this research are profound, offering potential benefits for the forestry industry through optimized management strategies and economic gains. Furthermore, our findings contribute to the preservation of critical ecosystem services by providing a means to monitor forest health effectively. However, the responsible implementation of this technology is paramount. Continuous refinement of the model, integration with traditional ecological knowledge, and ensuring transparency and equitable access to the developed system are essential for maximizing societal benefits. Additionally, addressing risks such as misinterpretation of data, overreliance on technology, and privacy concerns is crucial to minimize unintended consequences. Overall, this research presents a significant advancement in the field of forest health monitoring and establishes a foundation for future developments in intelligent ecological management systems.

The AI-assisted Traditional Design Methods for the Construction Sustainability: A Case Study of the Lisu Ethnic Minority Village

In recent decades, economic and technical advancements have surged tremendously, accompanied by non-regulatory and unsustainable use of non-renewable assets. The ecological field of toxicology has garnered considerable focus on elucidating the impact of harmful substances on people's wellness. Environmental hazardous chemicals induce many illnesses, posing a heightened danger to youngsters, pregnant women, the elderly, and anyone with preexisting health conditions. Air pollution adversely impacts human health, leading to heightened morbidity and death, necessitating intensified toxicological investigations on industrial air pollution affecting the general populace. It is necessary to develop a computerized Ecological Toxicology-based Air Pollution Management System. Constructive social development is driven by cultural education. It can guarantee a sustainable future, protect national history, and unleash community potential. This study examines Chinese architects' perspectives and understanding of sustainability in village revitalization and conservation. It incorporates traditional Lisu building techniques and materials in Hunan, China. To close research gaps, this study looks at the contributions made by architects to traditional practice integration and environmentally conscious architectural design. This study proposes an IoT-enabled Environmental Toxicology platform for air pollution surveillance, utilizing artificial intelligence (AI) techniques to mitigate the limits of standard monitoring systems and lower total costs, enhancing human wellness. The research examined the intricate interaction between architects' understanding of sustainability, Lisu building techniques and materials, and sustainable architectural designs in village preservation and revitalization programs using path analysis on data from 326 architects in Hunan Province. Lisu building practices and materials strongly correlate with architects' awareness of and attitudes toward sustainability. According to the research, these characteristics are necessary for sustainable architectural solutions that support village revival and preservation. This study has significant theoretical and practical ramifications for academics, lawmakers, and architects. To evaluate the air quality monitoring efficacy of the suggested approach, a comprehensive series of simulation analyses is conducted, and the results are assessed at durations of 5, 15, 30, and 60 minutes. The experimental results demonstrate the superior efficacy of the suggested model compared to current methodologies. These findings could lead to the creation of sustainable design solutions that advance rural development. A thorough overview of upcoming studies on sustainable design approaches in Chinese contexts rounds out this talk.

Coupling Coordination and Influencing Factors Between Digital Village Development and Agricultural and Rural Modernization: Evidence from China

With the increasing application of new digital technologies in rural areas, digital village development has become a crucial pathway for achieving agricultural and rural modernization. This study develops a comprehensive measurement index system to assess the levels of digital village development and agricultural modernization. Then, the coupling coordination model is adopted to measure the coupling coordination development levels of digital villages and agricultural modernization across 266 prefecture-level cities in China from 2014 to 2020. Additionally, the driving mechanisms of the coupling coordination degree are also explored based on the geographic detector model. The results show that the overall levels of digital village development and agricultural and rural modernization in China are on an upward trend. The coupling coordination degree of the two systems has shifted from being on the verge of imbalance to primary coordination. Additionally, absolute regional differences have widened, while relative differences have narrowed. Ecological livability, management systems, living standards, informatization, and mechanization levels are identified as the key factors driving the coupling coordination between digital village development and agricultural modernization. These results offer valuable insights for both theoretical research and practical applications in advancing digital villages and agricultural modernization efforts.

Improving the Air Quality Monitoring Framework Using Artificial Intelligence for Environmentally Conscious Development

This study aims to significantly improve air quality monitoring through the innovative application of Artificial Intelligence (AI). Introducing the Artificial Intelligence Kualitas Udara (AIKU) model, this research offers a novel approach by integrating advanced machine learning algorithms with environmental sensors to predict air quality in real-time more accurately than traditional methods. The novelty of the AIKU model lies in its sophisticated data analytics framework, which processes high-frequency environmental data to assess air quality changes dynamically. The technique employs calibrating and deploying the AIKU model across various urban and suburban settings and analyzing its performance against conventional monitoring systems such as the Internet of Things (IoT) and Wireless Sensor Networks (WSNs). The results demonstrate that AIKU significantly outperforms these traditional systems in both accuracy and speed of response, highlighting its effectiveness in real-time environmental monitoring. Furthermore, the AIKU model's scalability and adaptability are tested, showing promising potential for application in densely populated urban areas and less populated rural settings. This research contributes to environmental monitoring by demonstrating how AI can transform traditional methodologies into more effective, scalable, and intelligent ecological management systems. This research provides substantial evidence that the AIKU model can serve as a powerful tool for sustainable and smart development worldwide, enhancing the ability of governments and organizations to respond to environmental challenges promptly and effectively. Doi: 10.28991/HIJ-2024-05-03-017 Full Text: PDF

Quality Parameters of Plum Orchard Subjected to Conventional and Ecological Management Systems in Temperate Production Area

Environmental protection, global food security, and nutritional quality are critical issues for worldwide sustainable development. Plums (Prunus domestica L.), well-known for their rich nutritional content and distinct phytochemical profile, have received increased attention due to their potential health benefits. The present study evaluates the ecological and conventional management systems of quality parameters in three plum varieties—Tuleu Gras, Record, and Centenar—and establishes suitable practices to improve fruit quality and yields. The fruit morphometric features (color, firmness, soluble solid content, titratable acidity, and total sugar) were analyzed during their raw fruit state, while different fruit-sample extracts were assessed for phytochemical compounds and heavy metal concentrations, specifically, zinc, copper, nickel, and cadmium. The results show a wide variability in the examined characteristics among management practices that differentially accumulated throughout the ripening phase and significantly influenced the nutritional value of the plum fruit. The application of an ecological management practice yielded small plum fruits (38 g) and maintained a more consistent and vigorous fruit color compared with the conventional system (83 g). Moreover, ecological plums have higher antioxidant activities, total polyphenols, and flavonoids (21.70–25.54 µM Trolox/g of dw, 3.89–7.76 mg GAE/ g of dw, and 1.45–3.65 mg CE/g of dw, respectively). Among the content of metals in the plum fruit, copper exhibited the highest concentrations (0.83–1.21 mg/kg), while cadmium was detected at the lowest levels (0.006–0.009 mg/kg). The potential health implications of heavy metals, by calculating their estimated daily intake (EDI), target hazard quotient (THQ), and hazard index (HI), for both adults and children were also analyzed. The EDI values of accumulated elements in the plum fruits followed the order of Cu > Zn > Ni > Cd with no concern for human health (THQ and HI < 1). According to this study’s findings, fruit quality parameters are significantly higher in ecological plums, providing a compelling argument for adopting sustainable agricultural practices. These results highlight the importance of selecting sustainable agricultural practices, not only to safeguard the environment but also to ensure high-quality products suitable for human consumption.

Развитие экологического туризма в регионе: холистический подход к управлению стратегическими коммуникациями

In recent decades, there has been a change in the system of tourism development and its gradual integration into the socio-economic space of the regions of the Russian Federation, which requires comprehensive work with goals, risks, and target audiences and involves the use of strategic communication based on a holistic approach. The purpose of the article is to present the concept of the strategy and practice of regional ecological tourism development from the point of view of holistic marketing and strategic management. The issue reflects increased competition between Russian regions for leading positions in the domestic market and the attraction of incoming tourists. The empirical basis of the study is based on holistic marketing and analysis of ecological tourism indicators in the regions over time. The authors of the article conducted a study of modern approaches to the use of strategic communication in the development of ecological tourism in the Ural Federal District of the Russian Federation, as well as the identification of possible development vectors. In the article, the researchers assessed the impact of strategic communication on the ecological tourism management system at the regional level and also determined the role of strategic communication in management decision-making, as well as the use of technologies and tools to minimize risks. Based on the analysis carried out by the authors of the article, a model of strategic communication was developed to achieve the key goal, i.e., the effective development of ecological tourism in the constituent entities of the Russian Federation.

Analysis of Long-Term Vegetation Trends and Their Climatic Driving Factors in Equatorial Africa

Understanding vegetation seasonality and its driving mechanisms improves decision-making in the management of ecological systems in a warming global climate. Using multiple statistical methods (i.e., trend analysis, abrupt changes, and partial correlation analysis), this study analyzed the spatiotemporal variations in the Normalized Difference Vegetation Index (NDVI) in the Equatorial Africa (EQA) region and their responses to climate factors from 1982 to 2021. The NDVI values declined at a rate of 0.00023 year−1, while the precipitation (P) and mean temperature (TMEAN) values increased at rates of 0.22 mm year−1 and 0.22 °C year−1, respectively. The mean minimum temperature (TMIN) had a higher rate of 0.2 °C year−1 than the mean maximum temperature (TMAX) at 0.02 °C year−1. An abrupt change analysis showed that the TMAX, P, and NDVI breakpoints occurred in 2000, 2002, and 2009, respectively; TMEAN and TMIN breakpoints occurred in 2001. The NDVI trends declined in forest and cropland areas but increased in shrubland and grassland areas. The summer NDVI trends declined for all vegetation types and were reversed in the winter season. The NDVI positively correlated with the P (r = 0.50) and TMEAN (r = 0.60). All seasonal analyses varied across four seasons. A temporal analysis was conducted using partial correlation analysis (PCR), and the results revealed that TMIN had a greater impact on the NDVI (PCR = −0.45), followed by the TMAX (PCR = 0.31) and then the P (PCR = −0.19). The annual trend showed that areas with significant greening were consistent with stronger wetter and weaker warming trends. Both precipitation and temperature showed a positive relationship with vegetation in semi-arid and arid regions but a negative relationship with humid regions. Our findings improve our insight into scientific knowledge on ecological conservation.

Monitoring and management of green information in water ecological environment based on sensors and big data

With the continuous development of industrial production, the pollution of water resources is becoming more and more serious, and the damage to aquatic organisms is also increasing. Therefore, strengthening water environment monitoring has become a key measure to prevent and solve this problem. Aiming at the limitation of traditional water environment monitoring methods, a water ecological environment monitoring scheme is proposed. This study uses advanced sensor technology, including water quality sensor, water level sensor, weather sensor, etc., to monitor the indicators of water in real time. Through data acquisition and storage technology, the data obtained by the sensor is integrated and analyzed. At the same time, big data analysis method is used to predict and simulate the change trend of water ecological environment. This scheme designs and develops a sensor network monitoring system, which can collect water temperature data in real time at the monitoring point, transmit the sampled information to the aggregation node through the sensor network, and finally transmit to the information intelligent monitoring equipment through GPRS, so as to realize timely display and early warning. At the same time, combined with the water quality monitoring instrument, it can realize the remote query and processing of monitoring data. The experimental results show that the water ecological environment monitoring and management system based on sensor and big data technology can efficiently and accurately monitor the indicators of water. Through the comprehensive monitoring of the water ecological environment, abnormal situations can be found in time, and corresponding measures can be taken to protect and repair. The results of big data analysis provided by the system can provide scientific basis and guidance for decision makers.

A Benchmark Data Set for Long-Term Monitoring in the eLTER Site Gesäuse-Johnsbachtal

This paper gives an overview over all currently available data sets for the European Long-term Ecosystem Research (eLTER) monitoring site Gesäuse-Johnsbachtal. The site is part of the LTSER platform Eisenwurzen in the Alps of the province of Styria, Austria. It contains both protected (National Park Gesäuse) and non-protected areas (Johnsbachtal). Although the main research focus of the eLTER monitoring site Gesäuse-Johnsbachtal is on inland surface running waters, forests and other wooded land, the eLTER whole system (WAILS) approach was followed in regard to the data selection, systematically screening all available data in regard to its suitability as eLTER’s Standard Observations (SOs). Thus, data from all system strata was included, incorporating Geosphere, Atmosphere, Hydrosphere, Biosphere and Sociosphere. In the WAILS approach these SOs are key data for a whole system approach towards long term ecosystem research. Altogether, 54 data sets have been collected for the eLTER monitoring site Gesäuse-Johnsbachtal and included in the Dynamical Ecological Information Management System – Site and Data Registry (DEIMS-SDR), which is the eLTER data platform. The presented work provides all these data sets through dedicated data repositories for FAIR use. This paper gives an overview on all compiled data sets and their main properties. Additionally, the available data are evaluated in a concluding gap analysis with regard to the needed observation data according to WAILS, followed by an outlook on how to fill these gaps.

Problems Encountered in Marketing Sustainable Products and Suggested Solutions in Turkey

The growth of the consumer economy in the last century has led to several significant issues that endanger human life. Overexploitation of nature and soils, the use of pesticides and growth stimulants have had a negative impact on the environment and water resources. The current situation has necessitated that societies worldwide adopt new production approaches to immediately halt soil degradation and improve water quality. One production approach that aims to protect public health and the environment is sustainable production. Sustainable production is an ecological management system that promotes biodiversity, biological cycles, and soil biological activity. The farming system is based on minimal use of off-farm inputs and management practices that restore, maintain or enhance ecological harmony. The aim of this study is to explain the importance of sustainable products and production, to address the problems and suggestions encountered in the marketing of sustainable products in Turkey, and to propose solutions.

Evaluation of the impact of climate change on the ecological resistance and ecological corridors based on set pair analysis theory

Climate change significantly impacts ecological resistance and ecological corridors. However, the difficulty in evaluating this impact lies in two crucial issues: (i) the uncertainty arising from spatial heterogeneity, and (ii) the diversity of impacts arising from this uncertainty. A set pair analysis (SPA) model is designed to assess the impact of climate change on ecological resistance and ecological corridors for addressing the aforementioned problems. First, the ecological resistance and corridors under different climate change scenarios are simulated. Then, the five-element connection number is used to reflect changes in ecological resistance and corridors influenced by climate change and uncertainty. Finally, the influence indices are constructed to quantify the three distinct impacts of climate change on them. The evaluation results for Jiangxi Province are as follows. (i) The province’s UR index values are 0.085, 0.127, 0.351, 0.501, and 0.468 under five climate change scenarios, respectively. The impact of climate change on ecological resistance is rapidly deteriorating, and the central region is the high-value center for future ecological resistance. (ii) The values of UC in the central and southern regions range from − 0.568 to − 0.320 and from − 0.816 to − 0.446, respectively. In the two regions, the assessments of the impact of climate change on ecological corridors are negative, which results in a reduction in connectivity. (iii) The crucial meteorological risks with the greatest impact on ecological resistance and corridors are floods and high-temperature disasters. (iv) The proposals for Jiangxi Province to address climate change are as follows: enhancing flood prevention, improving high-temperature monitoring, and establishing an ecological management system. (v) The SPA model can effectively assess the different impacts of climate change with spatial uncertainty on ecological resistance and corridors.

A model free method of predicting transient dynamics in anaerobic digestion.

Transient dynamics pose unique challenges when dealing with predictions and management of ecological systems yet little headway has been made on understanding when an ecological system might be in a transient state. As a start we consider a specific model, here focusing on a canonical model for anaerobic digestion. Through a series of simplifications, we analyse the potential of the model for transient dynamics, and the driving mechanisms. Using a stochastic analogue of this model, we create synthetic ecological data. Thus, combining our understanding of the deterministic transient dynamics with the use of empirical dynamical modelling, we propose several new metrics to indicate when the synthetic time series is leaving a transient state.

The Limitations of EMSs in Comparison with the SDGs When Considering Infrastructure Sustainability: The Case of the Terzo Valico Dei Giovi, Italy

Infrastructure plays the largest role in the amount of annual emissions, so much so that investments promoted in the European Union must be subjected to a careful assessment of the sustainability of projects. The current landscape for assessing the sustainability of infrastructure is varied and complex. Considering the object of the assessment methodologies (such as the Environmental Impact Assessment or the Ecological Management System) and specific tools such as Envision, there is a shift from the infrastructure in itself and the company’s actions to promoting sustainable development. This article introduces a methodology to examine how tools used in environmental impact assessments of transport infrastructure projects, regardless of the actor implementing them, align with different sustainable development objectives. Moreover, it identifies the Sustainable Development Goals (SDGs) as a reference point that can be used in estimating the validity of these instruments. This paper also validates the methodology proposed in our study, by comparing the results obtained on the Envision model with those obtained from its application in a case study regarding the Terzo Valico dei Giovi, a railway infrastructure in Italy. The article shows that although the final target is in many respects the same, the nuances with which actors pursue sustainability through the different instruments vary.

Mobilising Long-Term Natural Environment and Biodiversity Data and Exposing it for Federated, Semantic Queries

Biodiversity and ecosystems cannot be studied without assessing the impacts of changing environmental conditions. Since the 1980s, the U.S. National Science Foundation's Long Term Ecological Research (LTER) Network has been a major force in the field of ecology to better understand ecosystems. In Europe, the LTER developments are led by the the Integrated European Long-Term Ecosystem, critical zone and socio-ecological system Research Infrastructure (eLTER RI), a currently project-based infrastructure initiative with the aim to facilitate high impact research and catalyse new insights about the compounded impacts of climate change, biodiversity loss, soil degradation, pollution, and unsustainable resource use on a range of European ecosystems and socio-ecological systems. The European LTER network, which forms the basis for the up-coming eLTER RI, is active in 26 countries and has 500 registered sites that provide legacy data e.g., historical time-series data about the environment (not only biodiversity). Its site information and dataset metadata with the measured variables are available to be searched at the Dynamic Ecological Information Management System - Site and dataset registry (DEIMS-SDR, Wohner et al. 2019). While DEIMS-SDR data models utilize parts of the Ecological Metadata Language (EML) schema 2.0.0, location information follows the European INSPIRE specification. The future eLTER data is planned to consist of site-based, long-term time-series of ecological data. The eLTER projects have defined eLTER Standard Observations (SO), which will include the minimum set of variables as well as the associated method protocols that can characterise adequately the state and future trends of the Earth's systems. (Masó et al. 2020, Reyers et al. 2017). The current eLTER network consists of sites that differ in terms of infrastructure maturity or environment type and may focus on one or several of the future SOs or they are not yet executing any holistic monitoring scheme. The main objective is to convert the eLTER site network into a distributed research infrastructure that incorporates a clearly outlined mandatory monitoring program. Essential to this effort are the suggested variables for eLTER SOs and the corresponding methods and protocols for relevant habitat types according to the European Nature Information System (EUNIS) in each domain. eLTER variables are described by using the eLTER thesaurus "EnvThes". These descriptions are currently enhanced by the use of the InteroperAble Descriptions of Observable Property Terminology (I-ADOPT, Magagna et al. 2022) framework to provide the necessary level of detail required for seamless data discovery and integration. Variables and their associated methods and protocols will be formalised to enable automatic site classifications, by building on existing observation representations such as the Extensible Observation Ontology (OBOE), Open Geospatial Consortium's Observation and Measurement, and the future eLTER Standard Observation ontology. DEIMS-SDR will continue to be used as a core service with an RDF representation of its assets (sites, sensors, activities, people) currently being implemented. This action is synced with the Biodiversity Digital Twin (BioDT) project to ensure maximum findability, accessibility, interoperability and re-usability (FAIRness; Wilkinson et al. 2016) of data through FAIR Digital Objects (FDO). Other (digital) assets such as datasets, models and analytical workflows will be documented in the Digital Asset Register (DAR) alongside semantic mapping and crosswalk techniques, to provide machine-actionable metadata (Schultes and Wittenburg 2019, Schwardmann 2020). The Biodiversity Digital Twin (BioDT) project is bringing together biodiversity and natural environment data from seven thematic use cases for modeling. BioDT prototypes rely on openly available data that comes from multiple heterogeneous sources using a multitude of standards and formats. In the pilot phase, merging data requires "hand picking" from selected sources, and automation of workflows would still require many additional steps. There are ongoing efforts in both the BioDT and eLTER projects to find best ways and practices to bring the raw data together by using suitable standards but also to harmonise the other environment variables by referring to vocabularies and possibly express the data as FDOs. Currently both the EML schema and Darwin Core standard (Darwin Core Task Group 2009; with registered extensions) allow referring to external schemas and vocabularies, which give flexibility but may still prove to be too narrow for the multitude of data types and formats the natural environment data requires. We welcome discussion about how to create good practices for enriching and harmonising natural environment data and species occurrence data in a meaningful way. GBIF's new data model and enriching the raw data with semantic artefacts may prove to be the way to provide thematic data products that combine data from multiple sources.

Spatial and temporal driving mechanisms of ecosystem service trade-off/synergy in national key urban agglomerations: A case study of the Yangtze River Delta urban agglomeration in China

Clarifying the ecosystem service (ES) trade-off/synergy is a prerequisite for scientific implementation and optimization of integrated ecological system management strategies, especially in highly urbanized areas with declining eco-environmental carrying capacity. This study took China's Yangtze River Delta urban agglomeration (YRDUA) as an example. First, we used a pixel-by-pixel analysis based on dynamic spatial correlation to quantify the trade-off/synergy among the 6 primary ESs' change amounts from 2005 to 2020 (habitat quality, soil retention, carbon sequestration, water yield, food production, leisure and recreation). Then, we integrated the multiple ES trade-off/synergy and explored their driving factors by spatial cluster analysis and geographically and temporally weighted regression (GTWR). The results showed synergy between supporting and regulating services predominated while regulating services were mostly trade-offs with regulating and cultural services. The trade-off/synergy of the 15 ES functional pairs within YRDUA can be downscaled into 4 ES trade-off and synergy bundles. Furthermore, the climate drivers (annual highest temperature (TH), annual total precipitation (PRE), annual total solar radiation (SRT)) significantly influenced the spatial heterogeneity of the 6 ESs in the YRDUA. The socio-economic drivers (night light index (NLI), gross domestic product (GDP)) and land use drivers (proportion of arable land (ALP), proportion of construction land (CLP), proportion of woodland (WLP)) had a more remarkable power of explanation for the ES temporal variation and ES trade-off/synergy. YRDUA and other urban agglomerations can use the study's findings to develop differentiated integrated ecological management strategies.

Temperate forest understory vegetation shifts after 40 years of conservation

Understanding how vegetation composition and diversity respond to global changes is crucial for effective ecosystem management and conservation. This study evaluated shifts in understory vegetation after 40 years of conservation within Drawa National Park (NW Poland), to check which plant communities changed the most, and whether vegetation shifts reflect global change symptoms (climate change and pollution) or natural forest dynamics. Using ordination and generalized mixed-effects linear models, we assessed changes in alpha diversity metrics, accounting for taxonomic, functional, and phylogenetic aspects within 170 quasi-permanent plots, surveyed in 1973–85 and resurveyed in 2015–19. We found an overall homogenization of forest vegetation and specific shift patterns in certain forest associations. In coniferous and nutrient-poor broadleaved forests, the overall number of species increased due to the replacement of functionally distinct or specialized species with more ubiquitous species that could exploit increased resource availability. In riparian forests and alder carrs we found either shifts from riparian forest to alder carrs or to mesic broadleaved forests. The most stable communities were fertile broadleaved forests. Our study quantified shifts in taxonomic, functional, and phylogenetic diversity after 40 years of conservation and provides important insights into the shifts in vegetation composition in temperate forest communities. In coniferous and nutrient-poor broadleaved forests we found an increase in species richness and replacement of functionally distinct or specialized species by ubiquitous species, indicating increased resource availability. Shifts between wet broadleaved forests and transition into mesic forests suggest water limitation, which can be related to climate change. The most stable were fertile broadleaved forests fluctuating due to natural stand dynamics. The findings highlight the need for ongoing monitoring and management of ecological systems to preserve their diversity and functionality in the face of global changes.

Traditional ecological knowledge-based calendar system for sustainable seasonal grazing in the Pamir Mountains

Indigenous mountain communities are on the cutting edge of environmental threats, their responses, and the preservation of traditional knowledge that ensures the harmony between the environment and sustainable resource use. In the context of seasonal grazing, a key livelihood activity in the eastern Pamir Mountains, this study aimed to document a traditional ecological calendar-like management system and scientifically validate its effectiveness. Through an ethno-ecological survey comprising focus group discussions and semi-structured interviews, we examined the variations in seasonal grazing practices based on ecological calendars. The study quantified and evaluated the indicators' efficacy in guiding these seasonal activities by analyzing time series satellite data of Normalized Difference Vegetation Index (NDVI). Additionally, the research assessed the potential alterations required in the future under anticipated climatic scenarios (SSP126 and SSP585) using the random forest algorithm. The findings underscored the alignment between seasonal migration patterns, grazing as the primary seasonal activity, and spatiotemporal variations in vegetation phenology. Our analysis revealed that in the future local herders may need to spend more or shorter time in pastures at different elevation compared to present because of possible change in the phenology. Our findings demonstrate the high validity of this calendar system in local resource management, and with modification it would be equally important in the future under new climatic scenarios.

Driving Forces on the Distribution of Urban Ecosystem’s Non-Point Pollution Reduction Service

In the context of increasing urbanization and worsening environmental pollution, nonpoint source pollution during high-frequency rainfall has become a major ecological problem that endangers residents in cities. This study takes Shenzhen as an example. On the basis of a large number of soil sample test data, and combined with relevant environmental variables, it has drawn the high-resolution, high-precision spatial distribution maps of soil attributes within the city. In addition, this paper combines the revised universal soil loss equation and the GeoDetector model to evaluate the supply capacity of nonpoint source reduction services in the city’s ecological space and the main driving factors of spatial distribution characteristics for different types of land. The study found that increasing soil point density and combining environmental variables can help improve the accuracy of spatial mapping for soil attributes. The ME, MSE, ASE, RMSE, and RMSSE of spatial mapping all meet the accuracy evaluation criteria and are better than many existing studies; the spatial distribution characteristics of soil attributes and nonpoint source reduction services show significant differences among the whole city, secondary administrative regions, and different types of land; the GeoDetector results show that among the three main types of land use (forested land, industrial land, and street town residential land), topographic factors, habitat-quality factors, and ecosystem types have the greatest impact on the spatial differentiation characteristics of nonpoint source reduction services. Among climate factors, only precipitation factors have the greatest impact on the spatial differentiation characteristics of services. Facing the above factors, the q-values calculated by the GeoDetector are all higher than 10%. The results of this study can provide information for making better decisions on regional ecological system management and soil protection and on restoration work aimed at improving nonpoint source reduction services.

Development of ecological management system for planted forest based on ELM deep learning algorithm

Plantations play a central and lever role in maintaining the ecological balance of the earth, maintaining the overall function of the terrestrial ecosystem, and promoting the coordinated development of economic society and ecological construction. In order to strengthen the ecological management of plantation forests and improve the ecological level of forest region, the C/S framework is taken as the basic structure, and the programming mode of business model-user interface controller is used, on J2EE platform. The ecological management system of a planted forest is constructed by the evaluation module, the principal component comprehensive analysis module of ecological function value and the demand prediction module of planted forest based on extreme learning machine and deep learning algorithm, and runs under the support of windows system, oracle 15G and above database software. The indexes and factors affecting the ecological function of plantation forests were evaluated and analyzed, and the final management decision was given by the prediction module. The results showed that the plant density significantly affected plant biomass, organic carbon storage, water content and nutrient accumulation, and the comprehensive evaluation indexes of four ecological functions increased from 32.69, 31.84, 33.71 and 35.46 to 86.18, 89.46, 89.83 and 88.76, respectively. Although the degree of influence of the system on lemon strip plants, herbaceous plants, surface litter and soil varies, it still has good feasibility, effectiveness and practicality, and can assist the scientific ecological management of artificial plantation forests.