Sustainable Ecosystem Research Articles

Deciphering the role of Moringa oleifera seeds and probiotic bacteria on mitigation of biogas production from ruminants

Maintaining cleaner and more sustainable ecosystems by mitigating greenhouse gas (GHG) emissions from livestock through dietary manipulation is in demand. This study was aimed to assess the effect of Moringa oleifera seeds and probiotics (Pediococcus acidilactici BX-B122 and Bacillus coagulans BX-B118) as feed supplements on GHG production and fermentation profile from steers and sheep. The treatments included diets containing 0, 6, 12, and 18% of M. oleifera seeds meal and a mixture of probiotic bacteria (0.2 ml/g of diet). Total biogas production, CH4, CO, and H2S emission from animals (up to 48 h), rumen fermentation profile, and CH4 conversion efficiency were recorded using standard protocols. Results showed interaction among M. oleifera seeds and probiotics on asymptotic biogas production and total biogas production up to 48 h (P < 0.05). The rate of CH4 emission in steers was reduced from 0.1694 to 0.0447 ml/h using 6 and 18% of M. oleifera seeds (P < 0.05). Asymptotic CO and the rate of CO production were increased (P < 0.05) by supplementing different doses of M. oleifera seeds and probiotics. Adding 12% of M. oleifera seeds and probiotics reduced H2S production from 0.0675 to 0.0112 ml H2S/g DM (at 48 h of fermentation) in steers. In sheep, the additives mitigated H2S production from 0.0364 to 0.0029 ml H2S/g DM (at 48 h of fermentation), however there were not interaction (P = 0.7744). In addition, M. oleifera seeds and probiotics reduced the pH level and dry matter degradability (DMD) in steers and sheep (P < 0.0001) showing a positive impact on CH4:ME and CH4:OM (in steers) and CH4:SCFA (in sheep), while the interaction was not significant (P > 0.05) for CH4:SCFA (in steers) and CH4:ME and CH4:OM (in sheep). In conclusion, the interaction of M. oleifera seeds and probiotics in the feeding diet reduced GHG emissions and affected the fermentation profile of steers and sheep.

Ecosystem risk from human use of ocean space and resources: A case study from the Norwegian coast

Coastal and adjacent shelf waters are generally highly productive ecosystems harboring important ecological processes and exposed to a range of anthropogenic pressures from land-based and marine sectors. Ensuring that the cumulative pressures from human activities do not cause unacceptable, permanent harm to the ecosystem is challenging but crucial for sustainable management of these regions. Linkage frameworks and ecological risk assessments have proven to be useful tools for holistic evaluations of cumulative human pressures as a guide to managers and policy makers for prioritization of risk factors. Here, we present the first holistic assessment of ecosystem risk from human activities along the Norwegian coast. Pressures from coastal sectors are identified and weighted by the exposure to and potential impact on ecosystem components following the ODEMM (Options for Delivering Ecosystem-based Marine Management) framework. We focus on four coastal regions with contrasting scales of human activities. Two southern regions with multiple anthropogenic activities are associated with higher cumulative risk of negative impacts compared to northern areas where less extensive activities have a lower potential of harming the coastal ecosystems. Despite latitudinal differences in human use of the coastline, the pressures and ecosystem components associated with the greatest risk of cumulative impacts are relatively similar between the regions. Contaminants and underwater noise stand out as high-risk pressures, associated with multiple sectors with a high spatiotemporal footprint and with the potential to negatively impact a range of ecosystem components. Nevertheless, a confidence assessment also highlights the need for more in-depth analysis on the input, spread and effect of these pressures on coastal ecosystems. We discuss strengths and weaknesses of the risk assessment framework and suggest new directions which may enhance the utility and uptake of such assessments for sustainable management of coastal ecosystems.

Evaluating the ecological impact of fisheries management strategies in Georgia, USA: A review on current practices and future directions

Effective fisheries management is crucial for maintaining the ecological balance and sustainability of marine ecosystems. This review paper examines the current fisheries management strategies employed in the state of Georgia, USA, and evaluates their ecological impact. The review synthesizes existing research on the implementation and outcomes of various management approaches, including gear restrictions, catch limits, seasonal closures, and marine protected areas. It analyzes the efficacy of these strategies in conserving target species, mitigating bycatch, and preserving the overall health of coastal and estuarine environments. Furthermore, the paper identifies emerging challenges, such as the impacts of climate change and shifting species distributions, and explores potential future directions for fisheries management in Georgia. The findings of this review aim to inform policymakers, resource managers, and stakeholders about the strengths and limitations of current practices, and provide recommendations for developing more comprehensive and adaptive management frameworks to ensure the long-term ecological resilience of Georgia's marine ecosystems.

Optimization of multiple ecological infrastructures across the land–sea interface for coordination management: A case study around Laizhou Bay in China

Ecological infrastructure (EI), providing ecosystem services across the land-sea interface, has been proposed as a key element in sustainable terrestrial-marine ecosystem coordinated governance. Terrestrial and marine ecosystems should be regarded as an integrated unit for guaranteeing coastal ecological security. However, the existing EI construction framework focused on terrestrial ecosystems, and few studies consider the composite characteristics of the terrestrial-marine ecosystem in coastal areas. In the case study of Laizhou Bay, China, this study proposes an optimization method for multiple ecological infrastructures (MEIs) across the land-sea interface. The method is oriented towards achieving trans-regional scale cohesion, enhancing terrestrial-riverine-marine linkages, providing adequate pathways for marine ecological protection, and promoting coordinated conservation of terrestrial and marine ecosystems. The results showed that: (1) The new optimization framework synthetically considering the terrestrial multi-scale EI networks cohesion, hydrological corridors, and marine conservation network is available. (2) The preliminary ecological sources (PESs) are mainly distributed in the eastern mountainous areas, the estuary of the Yellow River, and six marine protected areas. The spatial imbalance of EI resulted in four marine protected areas in the southwest of the Bohai Sea insufficiently connected between sea-to-sea ecological sources. (3) The integrated MEIs includes four newly added ecological sources (two each for land and sea), eight trans-regional ecological corridors, 17 hydrological corridors, and 11 marine ecological corridors. Through optimization, the MEIs avoid fragmentation across multi-scale terrestrial regions, promote river-based connectivity between land and sea, and increase pathways for marine ecological protection, thereby ensuring effective circulation of regional ecological materials. (4) MEIs-conserved priority areas include 12.4 km2 ecological pinch points and 6.39 km2 marine biological protective points. Focusing on these conserved priority areas provides spatial references for the implementation planning of MEIs. Compared with traditional respective ecosystem networks, the MEIs across land-sea interface optimization approach is feasible for terrestrial-marine ecosystem coordinated management.

Promoting financial inclusion for SMEs: Leveraging AI and data analytics in the banking sector

Financial inclusion is crucial for the growth and sustainability of small and medium-sized enterprises (SMEs), which are significant contributors to economic development and job creation. However, traditional banking models often fall short in serving the unique needs of SMEs due to perceived high risks and insufficient credit history. Leveraging artificial intelligence (AI) and data analytics, the banking sector can transform its approach to financial inclusion, offering tailored financial products and services that cater specifically to SMEs. AI and data analytics enable banks to analyze vast amounts of data from diverse sources, including transactional histories, social media activity, and market trends. This holistic view of an SME's financial health and business potential allows for more accurate risk assessment and credit scoring. Machine learning algorithms can identify patterns and predict creditworthiness, enabling banks to extend credit to SMEs that may have been overlooked by traditional models. Additionally, AI-driven insights facilitate the development of customized financial products, such as flexible loan terms and dynamic interest rates, that align with the cash flow cycles and operational realities of SMEs. Automated processes and AI-powered chatbots enhance customer service, providing SMEs with timely support and financial advice, thereby improving their banking experience. Data analytics also play a critical role in fraud detection and prevention, ensuring the security of transactions and building trust among SME clients. By continuously monitoring and analyzing transaction data, banks can quickly identify and mitigate fraudulent activities, protecting SMEs from financial losses. Moreover, AI and data analytics support the creation of financial literacy programs tailored to the specific needs of SMEs, empowering business owners with the knowledge and tools to make informed financial decisions. This educational aspect is vital in fostering a sustainable financial ecosystem for SMEs. In conclusion, the integration of AI and data analytics in the banking sector holds significant promise for promoting financial inclusion among SMEs. By providing more accessible, customized, and secure financial services, banks can support the growth and success of SMEs, ultimately contributing to broader economic development and financial stability.

Determining key mammalian species and food web robustness across different land cover vegetation using network analysis

Abstract. Risdiyanto I, Santosa Y, Santoso N, Sunkar A. 2024. Determining key mammalian species and food web robustness across different land cover vegetation using network analysis. Biodiversitas 25: 3175-3190. The ecosystem's food web depicts the intricate energy flow and complex interactions among its diverse organisms. Organisms are not confined to single trophic levels or food chains; they establish multiple food connections with other organisms within the ecosystem. This study aims to identify critical mammalian species and assess the robustness of various ecosystem types, considering the diversity of species presence, through food web analysis. Computational graphic-based network analysis is employed to achieve this goal. Ecosystem types, categorized by their land cover, include plantation/agricultural forests, Bushes and shrubss, forests, and mixed landscapes. Network centrality metrics such as degree, closeness, betweenness, and eigenvector centrality are utilized to evaluate the presence of key species and ecosystem robustness. The relative contribution of mammalian species as connectors and regulators of energy flow in the food web ranges from 8-23% of all nodes involved. Key mammalian species are classified into ecosystem stability and sustainability keys. In Bushes and shrubss ecosystems, key species predominantly consist of mammalian predator species that are crucial for maintaining ecosystem stability through population control. Conversely, in other ecosystems, key species are primarily connectors, ensuring the sustained energy flow. The most resilient food webs are observed in Bushes and shrubss and mixed ecosystems due to their higher biomass growth rates and abundant presence of mammalian species. Utilizing food web analysis can significantly contribute to species and ecosystem conservation efforts by offering a comprehensive understanding of interspecies interactions, food web structures, and ecosystem dynamics.

Mathematical modelling and control approach for sustainable ecosystems in mitigating the impact of pollutants on aquatic species in rivers

This paper focuses on the urgent issue of minimising the impact of pollutants on aquatic life in river ecosystems. Our innovative approach involves the integration of mathematical modelling and strategic control methods to counteract the negative consequences of industrial and agricultural activities. The model, developed in a one-dimensional context, captures the complex dynamics of species population and pollutant concentration. Using an optimisation framework, we strive to achieve a harmonious balance that limits pollution, enhances species diversity and optimises control expenditure. Ultimately, we seek to harmonise industrial progress with ecological vitality, promoting the sustainability of river ecosystems for generations to come.

Modelling of the above-ground biomass and ecological composition of semi-natural grasslands on the strenght of remote sensing data and machine learning algorithms

Semi-natural grasslands represent a vital source of forage and fodder for livestock farming, fulfilling an essential ecological function.This study upholded the efficacy of Sentinel-2 time series data in conjunction with ground data on aboveground biomass (AGB), ecological composition, and floristic composition for the development of a prediction model to determine AGB yield and species distribution in a semi-natural grassland. Two machine learning (ML) approaches, namely K-means clustering, and Random Forest (RF) classification were employed in a semi-natural grassland in Southern Europe. The study was subjected to a qualitative ground survey that identified two main groups. The first group (Cluster 1) was composed of nitrophilous species or nitrophilous species combined with xerophilous species and some hygrophilous species.The second group, on the other hand, consisted of only hygrophilous species or hygrophilous species associated with Ranunculus acris, Bromus hordeaceus, Brassica nigra, Lolium perenne and Trifolium sp. (Cluster 2).Using the above-mentioned algorithms, the study identified thetwo distinctive areas based on AGB estimates in the period beginning in March (vegetative growth) and extending to the first half of May. The R2 value for K-means classification was 0.59, with a root mean square error (RMSE) value of 1 t ha−1.The R2 value for the RF classification was 0.69, with an RMSE value of 0.59 t ha−1. The integration of ground-based data and ML classification techniques enables the generation of accurate, dynamic information on grassland, core attributes that play an integral role in sustainable ecosystem management and conservation.

Cleantech incubators within the sustainable entrepreneurial ecosystem: Fundraising sources, income generation strategies, and the role of public support

Cleantech incubators within the sustainable entrepreneurial ecosystem: Fundraising sources, income generation strategies, and the role of public support

Plant diversity conservation issues and challenges: A review

Significant conceptual changes have occurred over the past years in plant conservation, with major methodological advances incorporating modern techniques, technology, and disciplines. The global, national, and regional loss of biodiversity is a multifaceted issue affecting social, economic, organizational, political, scientific, and communicational scenarios. The identification of root causes is key to preventing biodiversity loss. India's diverse ecosystem supports various needs, including food, medicine, clothing, and shelter. Spatial biodiversity assessment is important for prioritizing and monitoring mechanisms and efforts in the context of land use, land cover change, unprecedented overexploitation, uncontrolled pollution, and alien species invasion. Odisha, an eastern Indian state, is rich in natural resources, with tropical moist, and dry deciduous forests covering remote areas of many districts, north-south-western hills, plateaus, and isolated pockets. Conservation efforts have expanded with protected areas, Red Lists, ex-situ facilities, wild plant accessions, and some developed species recovery programs. The conservation community is concerned about how to respond to the continuously depleting bioresources. Conservation prioritization focuses on protecting socioeconomically, medicinally, and environmentally significant species and living communities, regions of high species richness and endemism, and active functional ecosystems. The present review explores how to address sustainable biodiversity conservation practices and natural ecosystems aligning with UN Sustainable Development Goals No. 13 and 15.

Urban greenspaces shape soil nematode community across soil depth gradients: Belowground life at The Ohio State University

Urban greenspaces face significant anthropogenic transformation, impacting soil ecosystems, multifunctionality, and global biodiversity. With increasing population and urbanization, understanding the drivers influencing soil nematode communities in urban greenspaces is crucial for sustainable urban ecosystem management. We chose the campus of The Ohio State University (OSU) due to its unique urban settings with minimally disturbed both turf and non-turf ecosystems. This study focuses on nematodes, the often-overlooked ecological engineers which play diverse roles in ecosystem functions. Nematodes were collected from 99 sampling locations across three soil depths to represent two ecosystem types (i.e., turf and non-turf) of the OSU campus. Among plant parasitic nematodes (PPN), Helicotylenchus and Pratylenchus populations were above damage threshold limits. No specific pattern of community composition was observed in the spatial variation map. The presence of rare PPN genera in the lower soil layers had a significant impact on beta diversity. Trophic group abundances displayed distinct patterns, with turf ecosystems exhibiting higher PPN as well as total nematode abundance decreasing with soil depths. In the subsurface layer (10–30 cm), both bacterivores and fungivores were higher in the non-turf than turf ecosystem. Fungal-dominated decomposition of organic matter was observed in both ecosystem types. Soil physiochemical properties, specifically, total organic carbon and soil texture, had a significant impact on PPN community composition. However, nematode trophic group composition was more altered by ecosystem type than edaphic factors followed by soil depths. Together these three explanatory variables explained 27.5 % of the total variance in trophic group composition. Overall, this study provides insights into the complex interactions between PPN, trophic groups, soil properties, and urban ecosystem characteristics, contributing valuable knowledge for sustainable urban greenspace management.

Evaluating genomic inbreeding of two Chinese yak (Bos grunniens) populations

BackgroundYaks are a vital livestock in the Qinghai-Tibetan Plateau area for providing food products, maintaining sustainable ecosystems, and promoting cultural heritage. Because of uncontrolled mating, it is impossible to estimate inbreeding level of yak populations using the pedigree-based approaches. With the aims to accurately evaluate inbreeding level of two Chinese yak populations (Maiwa and Jiulong), we obtained genome-wide single nucleotide polymorphisms (SNPs) by DNA sequencing and calculated five SNP-by-SNP estimators (\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:{F}_{HOM}$$\\end{document}, \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:{F}_{L\\&H}$$\\end{document}, \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:{F}_{VR1}$$\\end{document}, \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:{F}_{VR2}$$\\end{document}, and \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:{F}_{YAN}$$\\end{document}), as well as two segment-based estimators of runs of homozygosity (ROH, \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:{F}_{ROH}$$\\end{document}) and homozygous-by-descent (HBD, \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:{F}_{HBD}$$\\end{document}). Functional implications were analyzed for the positional candidate genes located within the related genomic regions.ResultsA total of 151,675 and 190,955 high-quality SNPs were obtained from 71 Maiwa and 30 Jiulong yaks, respectively. Jiulong had greater genetic diversity than Maiwa in terms of allele frequency and nucleotide diversity. The two populations could be genetically distinguished by principal component analysis, with the mean differentiation index (Fst) of 0.0054. The greater genomic inbreeding levels of Maiwa yaks were consistently supported by all five SNP-by-SNP estimators. Based on simple proportion of homozygous SNPs (\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:{F}_{HOM}$$\\end{document}), a lower inbreeding level was indicated by three successfully sequenced old leather samples that may represent historical Maiwa yaks about five generations ago. There were 3304 ROH detected among all samples, with mean and median length of 1.97 Mb and 1.0 Mb, respectively. A total of 94 HBD segments were found among all samples, whereas 92 of them belonged to the shortest class with the mean length of 10.9 Kb. Based on the estimates of \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:{F}_{ROH}$$\\end{document} and \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:{F}_{HBD}$$\\end{document}, however, there was no difference in inbreeding level between Maiwa and Jiulong yaks. Within the genomic regions with the significant Fst or enriched by ROH, we found several candidate genes and pathways that have been reported to be related to diverse production traits in farm animals.ConclusionsWe successfully evaluated the genomic inbreeding level of two Chinese yak populations. Although different estimators resulted in inconsistent conclusions on their genomic inbreeding levels, our results may be helpful to implement the genetic conservation and utilization programs for the two yak populations.

Mapping available water capacity as a soil production capital metric in Australia

This study aims to identify a key soil property influencing farmland prices as a soil production capital indicator for soil security. There is generally a strong relationship between farmland price and production capital, as well as with biomass production. From a soil-centric point of view, under dryland agriculture in Australia, we hypothesise that soil available water capacity (AWC) is a key soil property influencing farmland prices and consequently can act as a soil production capital indicator. Here, we relate soil AWC of Australia to median farmland prices using digital soil maps of AWC. The results revealed that AWC from 0 to 100 cm is significantly associated with farmland prices (r = 0.31, p-value<0.05), providing a linear regression to estimate the soil production capital. As long as the AWC (mm) is above 67.6, the coefficient suggests that a 1 mm increase in AWC (mm) results in an increment of 293.6 $/ha in farmland prices in Australia. Additionally, an assessment of the anthropological impact on soils was conducted through phenosoil-genosoil differentiation analysis. We concluded that despite the shared formation characteristics of each pedogenon, soils with the highest AWC were not necessarily chosen for agricultural purposes since European settlement. Furthermore, our findings indicate that phenosoils in Queensland and Tasmania have higher levels of insecurity, likely due to intensified and inappropriate farming that may result in AWC reductions. This analysis provides another perspective to quantify the capital dimension of soil security, valuing its critical role in sustainable food systems and ecosystem services.

Multi-Scenario Simulation of Land Use Change and Ecosystem Service Value Based on the Markov–FLUS Model in Ezhou City, China

Changes in land use patterns, types, and intensities significantly impact ecosystem services. This study follows the time series logic from history to the expected future to investigate the spatial and temporal characteristics of land use changes in Ezhou and their potential impacts on the ecosystem services value (ESV). The results show that the Markov–FLUS model has strong applicability in predicting the spatial pattern of land use, with a Kappa coefficient of 0.9433 and a FoM value of 0.1080. Between 2000 and 2020, construction land expanded continuously, while water area remained relatively stable, and other land types experienced varying degrees of contraction. Notably, the area of construction land expanded significantly compared to 2000, and it expanded by 70.99% in 2020. Moreover, the watershed area expanded by 9.30% from 2000 to 2010, but there was very little change in the following 10 years. Under the three scenarios, significant differences in land use changes were observed in Ezhou City, driven by human activities, particularly the strong expansion of construction land. In the inertial development scenario, construction land expanded to 313.39 km2 by 2030, representing a 38.30% increase from 2020. Conversely, under the farmland protection scenario, construction land increased to 237.66 km2, a 4.89% rise from 2020. However, in the ecological priority development scenario, the construction land area expanded to 253.59 km2, a 10.13% increase from 2020. Compared to 2020, the ESV losses in the inertia development and farmland protection scenarios were USD 4497.71 and USD 1072.23, respectively, by 2030. Conversely, the ESV under the ecological protection scenario increased by USD 2749.09, emphasizing the importance of prioritizing ecological protection in Ezhou City’s development. This study may provide new clues for the formulation of regional strategies for sustainable land use and ecosystem restoration.

Changes in soil bacterial community diversity and its network stability under different grazing intensities in plateau rangelands

Grassland degradation is a significant ecological challenge, it changes the grassland environment, including the structure of soil microbial communities. Grazing is a critical factor in this process, yet the impact of different grazing intensities on soil bacterial communities, particularly in unique environments like the Qinghai-Tibet Plateau, remains poorly understood. This study utilized a full-length 16 S rRNA gene sequencing technology combined with environmental factors to assess the impacts of the Holba sheep grazing on soil bacterial community diversity in Zhongba County, Shigatse City, Qinghai-Tibet Plateau. The specific aim was to explore the changes in soil bacterial community diversity and stability under contrasting grazing intensities: prohibited grazing, light grazing, moderate grazing, and heavy grazing. The results showed that grazing activities significantly altered the diversity of bacterial communities, particularly under light and heavy grazing. The spatial dissimilarity in soil bacterial communities was mainly driven by species turnover (i.e., species replacement), which was more pronounced under heavy grazing. The number of keystone species increased with the grazing intensity of the bacterial co-occurrence network. Under heavy grazing, bacterial communities showed the highest number of keystone species and the community stability. Environmental factors such as exchangeable magnesium (EM), soil available kalium (SAK), and soil pH were the most important environmental variables impacting changes in bacterial community structure by influencing community α-diversity and stability. This study enhances our understanding of the response mechanisms of soil microbial communities to grazing intensities and other environmental stresses in grassland ecosystems. In addition, this study also provides important references and guidance for microbial diversity research in extreme environments. These findings are crucial for promoting the sustainable management and conservation of grassland ecosystems in climatically harsh areas such as the Qinghai-Tibet Plateau.

A trait-based approach to quantify ecosystem services delivery potentials in the Sundarbans mangrove forest of Bangladesh

An ecosystem’s potential to deliver goods and services, primarily determined by the functional traits of the species, is crucial for assessing its health and conservation value. This study uses a trait-based approach to evaluate the spatial and temporal variation in the ecosystem services delivery potentials (ESDPs) of the Sundarbans mangrove. We analyzed abundance data from 81 permanent sample plots (PSPs), spread over different saline zones, collected between 1986 and 2014, paired with species-specific plant functional traits. Seventeen traits were examined, with species-level data sourced from secondary literature, linking traits to ecosystem services. Study revealed that plant height, specific leaf area, and root length are key traits for assessing ESDPs. The Sundarbans’ ESDPs varied spatially across different saline zones but remained stable over time. Notably, ESDPs were significantly higher in low saline zones compared to ESDPs in high saline zones. This spatial variation is partly explained by differences in plant species composition among saline zones and the relatively stable species composition over time. Results from mixed effect models showed that PSP as a random factor for all four models is attributed to the nested effect. The study highlights the value of functional traits in ecosystem service assessments, providing a robust method to predict how changes in biodiversity and environmental conditions affect service delivery. The observed trend of decreasing ESDPs with increasing salinity offers insights into the potential impacts of sea-level rise on mangrove forest functions. These findings are essential for developing conservation strategies and policies to preserve the Sundarbans’ ecological integrity and support local livelihoods. This research advocates for a trait-based framework as a critical tool for the sustainable management of mangrove ecosystems globally.

Revitalizing Indonesian Tourism: Insights from Gunungkidul Regency's Communication Strategy

Tourism is a crucial part of Indonesia's economy, thanks to the country's natural beauty and attractions. However, the COVID-19 pandemic has presented significant challenges to the sector, requiring innovative strategies to revive it. This study examines the communication strategy of the Gunungkidul Regency Tourism Office, focusing on how it navigates the complexities of the new normal era. The study uses a qualitative descriptive approach and centers on the Gunungkidul Regency Tourism Office. Data was gathered through observation and documentation of tourist sites and interviews with tourism officials, site management personnel, and tourists from June to July 2021. The study found that the Gunungkidul Regency Tourism Office diligently adopted key communication strategy phases, including audience profiling, message formulation, method selection, and media choice. They also provided fundamental components of tourism development, such as attraction, accessibility, amenities, and ancillary, to cater to tourist demands. The strategy emphasizes effective communication to engage diverse stakeholders, using audience segmentation and engagement and multimedia platforms to ensure maximal outreach and resonance. The findings highlight the importance of crisis communication best practices, audience segmentation, and evaluating tourism community resilience, which are essential for crafting a resilient and sustainable tourism ecosystem.

Coping with extremes: Responses of Quercus robur L. and Fagus sylvatica L. to soil drought and elevated vapour pressure deficit

Climate change, particularly droughts and heat waves, significantly impacts global photosynthesis and forest ecosystem sustainability. To understand how trees respond to and recover from hydrological stress, we investigated the combined effects of soil moisture and atmospheric vapour pressure deficit (VPD) on seedlings of the two major European broadleaved tree species Fagus sylvatica (FS) and Quercus robur (QR). The experiment was conducted under natural forest gap conditions, while soil water availability was strictly manipulated. We monitored gas exchange (net photosynthesis, stomatal conductance and transpiration rates), nonstructural carbohydrates (NSC) concentration in roots and stomatal morphometry (size and density) during a drought period and recovery. Our comparative empirical study allowed us to distinguish and quantify the effects of soil drought and VPD on stomatal behavior, going beyond theoretical models. We found that QR conserved water more conservatively than FS by reducing transpiration and regulating stomatal conductance under drought. FS maintained higher stomatal conductance and transpiration at elevated VPD until soil moisture became critically low. QR showed higher intrinsic water use efficiency than FS. Stomata density and size also likely played a role in photosynthetic rate and speed of recovery, especially since QR with its seasonal adjustments in stomatal traits (smaller, more numerous stomata in summer leaves) responded and recovered faster compared to FS. Our focal species showed different responses in NSC content under drought stress and recovery, suggesting possible different evolutionary pathways in coping with stress. QR mobilized soluble sugars, while FS relied on starch mobilization to resist drought. Although our focal species often co-occur in mixed forests, our study showed that they have evolved different physiological, morphological and biochemical strategies to cope with drought stress. This suggests that ongoing climate change may alter their competitive ability and adaptive potential in favor of one of the species studied.

Technological innovations in accounting for food supply chain management

The food supply chain management sector has undergone significant transformations in recent years, largely driven by technological innovations aimed at enhancing efficiency, transparency, and sustainability. This review delves into the burgeoning realm of technological advancements in accounting within the food supply chain management domain. Technological innovations such as blockchain, Internet of Things (IoT), artificial intelligence (AI), and big data analytics have revolutionized traditional accounting practices in the food supply chain. Blockchain technology, for instance, has emerged as a promising solution for enhancing transparency and traceability throughout the supply chain by providing an immutable ledger of transactions. This ensures the authenticity of data related to food products, from origin to consumption, thus mitigating risks associated with fraud and food safety incidents. Moreover, the integration of IoT devices enables real-time monitoring of various parameters such as temperature, humidity, and location, thereby ensuring optimal conditions for food transportation and storage. This not only minimizes wastage but also enhances the overall quality and freshness of food products. Artificial intelligence and machine learning algorithms play a pivotal role in optimizing inventory management and demand forecasting processes. By analyzing vast amounts of historical data and current market trends, these technologies facilitate accurate predictions of consumer demand, thereby reducing stockouts and overstocking scenarios. Furthermore, big data analytics enables stakeholders to derive valuable insights from large datasets generated across the supply chain. These insights aid in identifying inefficiencies, optimizing resource allocation, and making data-driven decisions to enhance profitability and sustainability. However, despite the numerous benefits offered by technological innovations in accounting for food supply chain management, challenges such as data security, interoperability, and the need for skilled personnel remain significant hurdles to widespread adoption. Addressing these challenges will be crucial in harnessing the full potential of technology to create a more resilient, transparent, and sustainable food supply chain ecosystem. In conclusion, technological innovations in accounting have emerged as key enablers in revolutionizing the food supply chain management landscape. By leveraging blockchain, IoT, AI, and big data analytics, stakeholders can unlock new opportunities for enhancing efficiency, transparency, and sustainability throughout the food supply chain. Keywords: Accounting, Food, Supply Chain, Management, Technology, Innovation, Review.

Technological innovations in accounting for food supply chain management

The food supply chain management sector has undergone significant transformations in recent years, largely driven by technological innovations aimed at enhancing efficiency, transparency, and sustainability. This review delves into the burgeoning realm of technological advancements in accounting within the food supply chain management domain. Technological innovations such as blockchain, Internet of Things (IoT), artificial intelligence (AI), and big data analytics have revolutionized traditional accounting practices in the food supply chain. Blockchain technology, for instance, has emerged as a promising solution for enhancing transparency and traceability throughout the supply chain by providing an immutable ledger of transactions. This ensures the authenticity of data related to food products, from origin to consumption, thus mitigating risks associated with fraud and food safety incidents. Moreover, the integration of IoT devices enables real-time monitoring of various parameters such as temperature, humidity, and location, thereby ensuring optimal conditions for food transportation and storage. This not only minimizes wastage but also enhances the overall quality and freshness of food products. Artificial intelligence and machine learning algorithms play a pivotal role in optimizing inventory management and demand forecasting processes. By analyzing vast amounts of historical data and current market trends, these technologies facilitate accurate predictions of consumer demand, thereby reducing stockouts and overstocking scenarios. Furthermore, big data analytics enables stakeholders to derive valuable insights from large datasets generated across the supply chain. These insights aid in identifying inefficiencies, optimizing resource allocation, and making data-driven decisions to enhance profitability and sustainability. However, despite the numerous benefits offered by technological innovations in accounting for food supply chain management, challenges such as data security, interoperability, and the need for skilled personnel remain significant hurdles to widespread adoption. Addressing these challenges will be crucial in harnessing the full potential of technology to create a more resilient, transparent, and sustainable food supply chain ecosystem. In conclusion, technological innovations in accounting have emerged as key enablers in revolutionizing the food supply chain management landscape. By leveraging blockchain, IoT, AI, and big data analytics, stakeholders can unlock new opportunities for enhancing efficiency, transparency, and sustainability throughout the food supply chain. Keywords: Accounting, Food, Supply Chain, Management, Technology, Innovation, Review.