Model Ecosystem Research Articles

Sustainable ecosystem of business models: experience and lessons of the development project

Sustainable ecosystem of business models: experience and lessons of the development project

Multiscale morphological trajectories to support management of free-flowing rivers: the Vjosa in South-East Europe

Free-flowing rivers (FFRs) are fundamental references for river management, providing the opportunity to investigate river functioning under minimal anthropic disturbance. However, large free-flowing rivers are rare in Europe and worldwide, and knowledge of their dynamics is often scarce due to a lack of data and baseline studies. So far, their characterization is mainly grounded in the longitudinal connectivity assessment, with scarce integration of further hydro-morphological aspects, particularly concerning the processes and drivers of changes in their morphology over time scales of management relevance. This work aims to broaden the characterization of FFRs by reconstructing their catchment-scale morphological evolutionary trajectories and understanding their driving causes, to support their management better. This is achieved by integrating freely available global data including Landsat imagery and climatic reanalysis with the few locally available quantitative and qualitative information. The analysis of possible drivers of change at the catchment and reach scale assesses hydrological variability, flow regulation, land use change, sediment mining and bank protection works. We applied this approach to the Vjosa River (Albania), a model ecosystem of European significance and one of the few FFRs in Europe. The Vjosa was recently declared a Wild River National Park. We investigated its catchment-scale morphological changes over 50 years, considering four reaches of the Vjosa and four reaches of its main tributaries. Satellite imagery was analyzed taking advantage of Google Earth Engine cloud computing platform. The analysis reveals a catchment-scale response to climatic fluctuations, especially in the most natural reaches, with a significant narrowing of the active river corridor, following a flood-intense period in the early 1960s. The narrowing rate gradually decreased, from 35% before 1985 to 24% between 1985 and 2000, reaching a new equilibrium from 2000 to 2020. However, the recent trajectories of the lowland reaches have been impacted by human pressures, particularly sediment mining, which intensified after the 1990s, suggesting that these reaches may instead be far from equilibrium and adjusting to such persistent stressor. Identifying the key drivers of change and building catchment-scale knowledge of geomorphic change can inform the management of riverine protected areas, and the proposed integrated approach is a promising tool to help overcome the data scarcity typical of the limited remaining large FFRs.

Jump around: Selecting Markov Chain Monte Carlo parameters and diagnostics for improved food web model quality and ecosystem representation

Capturing ecological data variability in food web models is an important step for improving model representation of empirical systems. One approach is to use linear inverse modelling and Markov Chain Monte Carlo (LIM-MCMC) techniques to set up an inverse LIM problem using empirical data constraints, and then sample multiple plausible food webs from the inverse problem using an MCMC algorithm. We describe the set of plausible food webs as an ‘ensemble’ of solutions to the inverse problem sampled with the LIM-MCMC algorithm. The extent of data variability eventually integrated into an ensemble depends on how well the LIM-MCMC algorithm samples the solution space. Algorithm quality can be adjusted via user-defined parameters describing starting points, jump sizes, and number of iterations or food webs produced. However, little information exists on how each LIM-MCMC algorithm parameter affects the degree of empirical data variability introduced into the ensemble. Further, post hoc algorithm quality diagnostics with commonly used trace plots and the coefficient of variation (CoV) rarely address critical aspects of algorithm quality, such as (1) if the returned ensemble successfully targeted the solution space distribution (stationarity), (2) correlation between ensemble solutions (mixing), and (3) if the ensemble contains enough solutions to adequately capture input data variability (sampling efficiency). Therefore, we used several established MCMC convergence diagnostics to (1) quantify how algorithm parameters affect ensemble flow values and if these differences propagate to ecological indicators and (2) evaluate algorithm quality and compare to current evaluation and ecosystem modelling methods. We applied 30 LIM-MCMC algorithm combinations of varying starting points, jump sizes, and number of iterations to solve food web ensembles from a single food web model. We analysed ensembles with Ecological Network Analysis (ENA) to calculate indicators describing system function. Results show that LIM-MCMC algorithm parameters, in particular the jump size, affect ensemble flow values, which propagate to ecological indicators describing different ecosystem function of the same model. Thereafter, comparisons of post hoc diagnostics show that MCMC convergence diagnostics provided more robust estimates of algorithm quality than trace plots and CoV. Together, these findings underpin several novel recommendations to enhance LIM-MCMC algorithm parameter selection and quality assessments applicable to any ecological ensemble network study.

Rhizobium Inoculant and Seed-Applied Fungicide Effects Improve the Drought Tolerance of Soybean Plants as an Effective Agroecological Solution under Climate Change Conditions.

Rhizobial inoculation in combination with fungicidal seed treatment is an effective solution for improving soybean resistance to modern climate changes due to the maximum implementation of the plant's stress-protective antioxidant properties and their nitrogen-fixing potential, which will contribute to the preservation of the environment. Model ecosystems at different stages of legume-rhizobial symbiosis formation, created by treatment before sowing soybean seeds with a fungicide (fludioxonil, 25 g/L) and inoculation with an active strain of Bradyrhizobium japonicum (titer 109 cells per mL), were subjected to microbiological, biochemical, and physiological testing methods in controlled and field conditions. Seed treatment with fungicide and rhizobia showed different patterns in the dynamics of key antioxidant enzymes in soybean nodules under drought conditions. Superoxide dismutase activity increased by 32.7% under moderate stress, while catalase increased by 90.6% under long-term stress. An increase in the antioxidant enzyme activity induced the regulation of lipoperoxidation processes during drought and after the restoration of irrigation. Regeneration after stress was evident in soybean plants with a combination of fungicide seed treatment and rhizobial inoculant, where enzyme levels and lipoperoxidation processes returned to control plant levels. Applying seed treatment with fungicide and Rhizobium led to the preservation of the symbiotic apparatus functioning in drought conditions. As proof of this, molecular nitrogen fixation by nodules has a higher efficiency of 25.6% compared to soybeans without fungicide treatment. In the field, fungicidal treatment of seeds in a complex with rhizobia inoculant induced prolongation of the symbiotic apparatus functioning in the reproductive period of soybean ontogenesis. This positively affected the nitrogen-fixing activity of soybeans during the pod formation stage by more than 71.7%, as well as increasing soybean yield by 12.7% in the field. The application of Rhizobium inoculant and fungicide to seeds contributed to the development of antioxidant protection of soybean plants during droughts due to the activation of key enzymatic complexes and regulation of lipoperoxidation processes, which have a positive effect on nitrogen fixation and productivity of soybeans. This is a necessary element in soybean agrotechnologies to improve plant adaptation and resilience in the context of modern climate change.

Predicting time series of vegetation leaf area index across North America based on climate variables for land surface modeling using attention-enhanced LSTM

ABSTRACT Ecosystem process modeling is vital for a broad range of applications. It requires a key metric to characterize vegetation canopies: the leaf area index (LAI). Many land surface models utilize satellite-derived LAI for modeling ecosystem processes. The use of satellite-derived LAI constrains the models for predicting ecosystem processes when observational data are unavailable. Enriching prognostic models for predicting the LAI is favorable to land surface studies for forecasting vegetation-related processes. An attention-enhanced long and short memory (AELSTM) model was developed to predict the vegetation LAI time series based on climatic data. The AELSTM outperformed comparative machine learning models when assessed using satellite and flux tower data. The LAI predicted by AELSTM are temporally and spatially consistent with satellite-derived LAI. The R2 values attained 0.86–0.93 across biomes. AELSTM can be coupled with the land surface models to predict gross primary productivity. Our proposed model was demonstrated to be effective in predicting the LAI time series under different Shared Socioeconomic Pathways in CMIP6. Our study demonstrated that deep learning approaches are capable of modeling and characterizing the spatial and temporal patterns of key vegetation variables such as LAI. Moreover, the study has provided references for vegetation processes in land surface studies.

Local-manifold-distance-based regression: an estimation method for quantifying dynamic biological interactions with empirical time series.

Quantifying species interactions based on empirical observations is crucial for ecological studies. Advancements in nonlinear time-series analyses, particularly S-maps, are promising for high-dimensional and non-equilibrium ecosystems. S-maps sequentially perform a local linear model fitting to the time evolution of neighbouring points on the reconstructed attractor manifold, and the coefficients can approximate the Jacobian elements corresponding to interaction effects. However, despite that the advantages in nonlinear forecasting with noise-contaminated data, these methodologies have a limitation in the Jacobian estimation accuracy owing to non-equidistantly stretched local manifolds in the state space. Herein, we therefore introduced a local manifold distance (LMD) concept, a non-equidistant measure based on the multi-faceted state dependency. By integrating LMD with advanced computation techniques, we presented a robust and efficient analytical method, LMD-based regression (LMDr). To validate its advantages in prediction and Jacobian estimation, we analysed synthetic time series of model ecosystems with different noise levels and applied it to an experimental protozoan predator-prey system with established biological information. The robustness to noise was the highest for LMDr, which also showed a better correspondence to expected predator-prey interactions in the protozoan system. Thus, LMDr can be applied to study complex ecological networks under dynamic conditions.

A Systematic Review of Paradigm Approaches to Research and Modelling of Educational Ecosystems

This paper presents the results of a qualitative study based on a systematic scoping review that aimed to identify and analyse approaches to explore and model ecosystem dimensions of education. Seventy-six publications indexed in the Scopus database from the period 1999-2024 were selected and analysed. The results demonstrate that the study of educational ecosystems is based on mixed paradigm approaches combining constructivist and connectivist principles and constructs, and their modelling integrates two sets of characteristics - structural and value-functional - in a leading way. Structurally, the underlying ecosystem centres on school and higher education, lifelong learning and business-oriented learning. In value-functional terms, the guiding priorities are digitalisation, inclusion, good governance, innovation and sustainability.

Federated learning secure model: A framework for malicious clients detection

The Federated Learning Secure Model Repository presents a novel paradigm to ensure the trustworthiness of machine learning models generated through federated learning. It employs advanced algorithms for real-time detection of malicious contributions, supplemented by varied model auditing methods. By leveraging continuous monitoring, the repository guarantees the integrity and security of shared models, thereby mitigating the risks posed by malicious actors. This innovative approach fortifies the collaborative nature of federated learning while safeguarding against potential model poisoning and maintaining the reliability of the shared model ecosystem.

The development of information processing systems for forests and forest resources

Information systems are applied in the forestry sector to address a wide range of tasks. The process of their development has led to the emergence of a heterogeneous set of systems and methods. The aim of the research is to determine their further development and formulate a work plan. To achieve this, the author conducted a review of publications on the subject, including both theoretical works and existing systems and processing methods. The guiding principle of selection was to provide a comprehensive and thorough coverage of the subject area. The methods were classified according to the tasks they solve and presented in logical order, starting from the widely used ones and ending with experimental ones. As a result, it was found that analytical methods are developing significantly, which will lead to the emergence of new data models in the future. Geographic information systems, specifically distributed network-based ones, are also evolving. The problem of modeling forest vegetation is generally part of the task of modeling ecosystems as a whole. The author concluded that integrated information systems are a relevant direction for the industry. A project for further development was proposed, decomposing the task of building information systems. The author sees the forest inventory system as the main system, upon which all other systems depend and can be presented as separate services. The research contributes to the development of theoretical concepts of forest information processing systems. It is useful for specialists and researchers in the field of forestry, as well as developers of specialized software.

Dynamic model of changes in the trophic structure of an ecosystem affected by chronic radiation exposure

The conceptual dynamic ecosystem model was developed to evaluate the self-organization of trophic structure in ecosystems during the course of biogenic succession. This model was applied to analyze the possible changes in the ecosystem under impact of the anthropogenic physical stressor – chronic exposure to ionizing irradiation. The model predicts that amount of the limiting biogenic nutrient in the environment can modify the ecological effects of ionizing radiation. Negative effects of the chronic exposure are less significant in ecosystems with high food supply. The model does not show presence of any ecological effect of radiation at the exposure rates less than the derived consideration reference levels, obtained by International Commission on Radiological Protection for individual nature organisms. If the dose rates are higher than those levels, radiation exposure can affect ecological interactions between species. The model shows that environmental hormesis can exist in the ecosystems, impacted by the chronic radiation exposure. The reason of this effect is change of the ecological coefficients (for example, decrease of the predation rate), which in the certain range of parameters leads to the increase of biomasses of all species at the same amount of the limiting biogenic nutrient in ecosystem. Trigger regimes exist in the model ecosystem with mixed-feeding consumers. Within the trigger area, the realization of a particular trophic structure depends on initial species biomasses. A hysteresis phenomenon exists in such ecosystems, which means that the successive changes in the trophic structures realized following the increase of the influencing factor are not reproduced in the same order if the influencing factor was gradually decreased back to its previous values. The model predicts for this case, that the radioactively contaminated ecosystem does not necessarily return to its initial trophic structure, despite the dose rate decreases to the initial levels.

The Impact of Positivity in Multi Business Model Innovation in a World of VUCA – How Can Positive Emotions Stimulate the MBMI Process?

The importance of right speed, effectiveness, efficiency and learning in Multi Business Model Innovation (MBMI) is vital to meet today’s Volatile, Uncertainty, Complex and Ambiguous (VUCA) challenges to survive as a Business in Business Model EcosystemS (BMES). Coronavirus pandemic, war in Ukraine, volatile global material and resource prices and deliveries, cyberattacks make big external VUCA events to Businesses. VUCA can also come from the inside of the Business as management style change, interventions or a burn down of the Business. How can Businesses under these VUCA conditions still do high quality MBMI? How can MBMI participants and teams in these processes increase their MBMI output? This paper investigates the impact of positive emotions, relatedness and creativity on the outcome of three different MBMI processes in three different Business Model Ecosystems influenced by VUCA. Positivity, relatedness and creativity are hypothesized as components to strengthen the individuals’ search for meaning in VUCA and for their motivation to act in MBMI processes influenced by VUCA. The study is conducted as a mix method research project in the view of MBMI and psychological theory – particularly the concept of positive emotions and relatedness as mental states for learning and knowledge creation. Interviews and observations show that individuals in networks and teams with a mood of positivity strengthen MBMI and the MBMI learning and creation process. Thus, our intention for further research is twofold: Training in positivity in teams can stimulate the courage to make disruptive solutions sufficient for VUCA. Secondly, in searching for the MBMI mindset psychological traits will be studied by the Big Five profile test. This opens for recruitment of participants to optimizing MBMI teams. Main contributions are: • Positive emotions and relatedness strengthen the urge to learn and to create in VUCA – searching for meaning and motivation to act is related to this concept. • Learning and creative processes increase, since the basis of positivity and relatedness in individuals, teams and networks are strengthened. • Innovative outcome seems of a higher quality when based on positive emotions. Research in positive emotions in Business Model Innovation is rare. • Positive emotion is the core concept and by training of positivity, it may strengthen the MBMI processes and the VUCA competences. • The outcomes and solution of the MBMI processes in the case study show mainly incremental and radical MBMI – only one is disruptive.

Modelling an ecosystem of business models in a circular value chain: the circular business ecosystem model canvas

AbstractTo advance the circular economy, there is a need to take an ecosystem view of business models for circularity in which different actors interact dynamically to create economic, environmental and social value. This research introduces the Circular Business Ecosystem Model Canvas, a novel method to prototype a circular ecosystem of business models. The case of ferric chloride, an inorganic coagulant for wastewater treatment, is used to demonstrate the new canvas and show how it supports the development of a more holistic perspective on sustainability-oriented business model innovation.

A conceptual model of artificial intelligence effects on circular economy actions

AbstractThe circular economy (CE) presents a contemporary approach to integrating economic activity and environmental well‐being sustainably, opposing the linear open‐ended system. However, implementing this paradigm poses challenges, needing a fundamental shift in companies' strategies and business models. Emerging technology can support companies in transition toward CE and in particular artificial intelligence (AI) can play a crucial role in facilitating this transition by supporting companies not only in specific CE activities but also in strategically redefining their entire business models. Recognizing the increasing importance of both AI and CE in management literature, our paper seeks to integrate these two distinct streams of literature. Starting from existing studies, it tries to provide a framework able to understand the role of AI technology into enhancing CE. Through a systematic literature review using the Preferred Reporting Items for Systematic Review and Meta‐Analysis protocol, using keywords research on three databases, we identified 63 articles that concurrently address both CE and AI topics. Subsequent co‐occurrence and content analyses revealed how AI is utilized to bolster CE efforts leveraging the ReSOLVE framework (from the acronymous of Regenerate, Share, Optimise, Loop, Virtualise, and Exchange)—an operational tool for CE. The last step consists into the development of a conceptual framework outlining four stages of AI's engagement with the CE: namely System optimization, System redesign, Business Model redesign, and Ecosystem innovation. From our study emerges that while AI is already recognized for enhancing specific activities within CE, its potential as a strategic planning tool for business model redesign and ecosystem innovation remains largely unexplored.

Methodology for forming a digital farming management system

The paper presents methodological approaches for the creation of a digital farming management system (DFMS). A convergent approach, based on cognitive (conceptual) analysis methods, is employed in the research and applied to the formation of adaptive landscape farming systems. The fundamental principles of organizing DFMS include crop and environmental monitoring (in situ, remote sensing); the formation of farming system archetypes based on the analysis of long-term field experiments; spatial object modeling and land typology using GIS; planning and support for agrotechnologies to adapt to natural and economic conditions; modeling ecosystem services and biodiversity; assessing the impact on the sustainability and economics of crop production. The system is implemented using geoinformation models in a specific geographic coordinate. DFMS involves conducting a "inventory" of natural and production resources, as well as identifying limits of climatic, soil, and agrolandscape parameters at different levels of land use intensity. At each stage of organizing system blocks, methods of intelligent data analysis and machine learning are used, with the core of the system relying on the use of knowledge bases and logical rules of the subject area. A key element of the system is the scaling of the results of long-term field experiments and accumulated knowledge in different management areas based on the parameterization of the multi-level variability of farming systems and the formation of their archetypes. The practical implementation of the main provisions of DFMS allows approaching the solution of key issues related to reducing the level of uncertainty and associated risks in agriculture. This is achieved through scientifically justified organization of rational land use, increasing the resilience of crop production in different land use conditions, and providing information support to rural producers.

An Intervention Model for School Bullying Based on the UNESCO s Report and Its Application*

UNESCO s School Violence and Bullying: Global Status Report provides a comprehensive worldwide overview of violence and bullying and recommends priority action. These suggestions can serve as a reference and guidance for campus bullying. It discusses establishing the corresponding intervention model of School bullying based on the report s content. Especially for the psychological mechanism construction of teachers and students in the model system, it reviews the basis and ways of improving and exerting the eff ectiveness of the intervention model from the perspective of a case study of school bullying. Bullying incidents within the model ecosystem and the infl uential groups involved are realized, and psychological analysis is emphasized as part of the model construction and operation mechanism. Thus, a new intervention Model of whole-school participation is constructed.

Many-Species Ecological Fluctuations as a Jump Process from the Brink of Extinction

Highly diverse ecosystems exhibit a broad distribution of population sizes and species turnover, where species at high and low abundances are exchanged over time. We show that these two features generically emerge in the fluctuating phase of many-variable model ecosystems with disordered species interactions, when species are supported by migration from outside the system at a small rate. We show that these and other phenomena can be understood through the existence of a scaling regime in the limit of small migration, in which large fluctuations and long timescales emerge. We construct an exact analytical theory for this asymptotic regime that provides scaling predictions on timescales and abundance distributions that are verified exactly in simulations. In this regime, a clear separation emerges between rare and abundant species at any given time, despite species moving back and forth between the rare and abundant subsets. The number of abundant species is found to lie strictly below a well-known stability bound, maintaining the system away from marginality. At the same time, other measures of diversity, which also include some of the rare species, go above this bound. In the asymptotic limit where the migration rate goes to zero, trajectories of individual species abundances are described by non-Markovian jump-diffusion processes, which proceeds as follows: A rare species remains so for some time, then experiences a jump in population sizes after which it becomes abundant (a species turnover event) and later sees its population size gradually decreasing again until rare, due to the competition with other species. The asymmetry of abundance trajectories under time reversal is maintained at a small but finite migration rate. These features may serve as fingerprints of endogenous fluctuations in highly diverse ecosystems. Published by the American Physical Society 2024

Complete genome sequences of six duckweed-associated bacterial strains for studying community assembly in synthetic plant microbiome.

We report the complete genome sequences of six bacterial strains isolated from a floating macrophyte, duckweed. These six strains, representing the six dominant families of the natural duckweed microbiome, establish a simple model ecosystem when inoculated onto sterilized duckweed. Their genomes would provide insights into community assembly in plant microbiome.

Drought resistance and resilience of rhizosphere communities in forest soils from the cellular to ecosystem scale - insights from 13C pulse labeling.

The link between above- and belowground communities is a key uncertainty in drought and rewetting effects on forest carbon (C) cycle. In young beech model ecosystems and mature naturally dry pine forest exposed to 15-yr-long irrigation, we performed 13C pulse labeling experiments, one during drought and one 2 wk after rewetting, tracing tree assimilates into rhizosphere communities. The 13C pulses applied in tree crowns reached soil microbial communities of the young and mature forests one and 4 d later, respectively. Drought decreased the transfer of labeled assimilates relative to the irrigation treatment. The 13C label in phospholipid fatty acids (PLFAs) indicated greater drought reduction of assimilate incorporation by fungi (-85%) than by gram-positive (-43%) and gram-negative bacteria (-58%). 13C label incorporation was more strongly reduced for PLFAs (cell membrane) than for microbial cytoplasm extracted by chloroform. This suggests that fresh rhizodeposits are predominantly used for osmoregulation or storage under drought, at the expense of new cell formation. Two weeks after rewetting, 13C enrichment in PLFAs was greater in previously dry than in continuously moist soils. Drought and rewetting effects were greater in beech systems than in pine forest. Belowground C allocation and rhizosphere communities are highly resilient to drought.

Business model innovation of 3D-printing garment enterprises in digital transformation: business model innovation canvas approach

Purpose3D printing has been warmly welcomed by clothing enterprises for its customization capacity in recent years. However, such clothing enterprises have to face the digital transformation challenges brought by 3D printing. Since the business model is a competitive weapon for modern enterprises, there is a research gap between business model innovation and digital transformation challenges for 3D-printing garment enterprises. The aim of the paper is to innovate a new business model for 3D-printing garment enterprises in digital transformation.Design/methodology/approachA business model innovation canvas (BMIC), a new method for business model innovation, is used to innovate a new 3D-printing clothing enterprises business model in the context of digital transformation. The business model canvas (BMC) method is adopted to illustrate the new business model. The business model ecosystem is used to design the operating architecture and mechanism of the new business model.FindingsFirst, 3D-printing clothing enterprises are facing digital transformation, and they urgently need to innovate new business models. Second, mass customization and distributed manufacturing are important ways of solving the business model problems faced by 3D-printing clothing enterprises in the process of digital transformation. Third, BMIC has proven to be an effective tool for business model innovation.Research limitations/implicationsThe new mass deep customization-distributed manufacturing (MDC-DM) business model is universal. As such, it can provide an important theoretical reference for other scholars to study similar problems. The digital transformation background is taken into account in the process of business model innovation. Therefore, this is the first hybrid research that has been focused on 3D printing, garment enterprises, digital transformation and business model innovation. On the other hand, business model innovation is a type of exploratory research, which means that the MDC-DM business model’s application effect cannot be immediately observed and requires further verification in the future.Practical implicationsThe new business model MDC-DM is not only applicable to 3D-printing garment enterprises but also to some other enterprises that are either using or will use 3D printing to enhance their core competitiveness.Originality/valueA new business model, MDC-DM, is created through BMIC, which allows 3D-printing garment enterprises to meet the challenges of digital transformation. In addition, the original canvas of the MDC-DM business model is designed using BMC. Moreover, the ecosystem of the MDC-DM business model is constructed, and its operation mechanisms are comprehensively designed.

Diversity and ecotones in a model ecosystems of sessile species.

Sessile species compete for space and accessible light, with directed interactions evident in one species overgrowing another and with multispecies systems characterized by nontransitive relationships. Such patterns are observed in coral reefs or lichens on rock surfaces. Open systems with episodic invasions of such species have been predicted to exhibit a stable high-diversity state when the interaction probability is below a certain critical threshold. Here, we explore this metastable high-diversity state and find that the diversity in the high-diversity state scales with the square root of the system area. When introducing two different environments, we predict a hugely increased diversity along mutual environment border. Further, the presence of spatially segregated environments is predicted to allow for increased robustness of the high-diversity state.