Ecosystem Model Research Articles

Predicting the effect of climate change and management on net carbon sequestration in the forest ecosystems of the European part of Russia with the complex of models

The objectives of sustainable development require the ability to estimate potential changes in net carbon sequestration in forest ecosystems (including changes in biomass, mortmass, and soil organic matter) under possible climate change and different forest management strategies. Such estimates require consideration of different ecosystem components and processes. To achieve this goal, we have integrated several ecological models (dynamic stand model FORRUS-S, soil organic matter model Romul_Hum, statistical climate generator SCLISS and process-based forest ecosystem model EFIMOD3) to simulate the ecosystem dynamics at the regional level in several study areas within the forest zone of the European part of Russia. The simulation results reflected both the direct effects of climate change and forest management actions on ecosystem carbon pools, and the indirect effects through changes in species composition. The simulation experiments were spatially detailed at the level of individual forest management units, thereby revealing the influence of habitat conditions on the rate of carbon sequestration under the influence of environmental factors. We found that net carbon sequestration in all major ecosystem pools was mostly positive, ranging between 0 and 1.45 t ha year−1 depending on study area and simulation scenario. Higher accumulation was typically observed in medium-rich and rich habitat types with mesic moistening. In the long term, the effect of climate change was found to be comparable to the effect of management interventions.

Research on the Impact of Lamprey's Sex Ratio on Ecosystems Based on the Lotka-Volterra Model

The aim of this study is to explore the impact of the population of the Lamprey on the ecosystem, particularly through the regulation of sex ratio, in order to provide effective strategies for environmental control. The study first improved the traditional Lotka Volterra model by introducing predator-prey relationships and environmental limiting factors. A Lotka Volterra predator environment model was established, and the stable environment of lampreys and their parasitic fish was simulated using this model. The results indicate that lower gender ratios (male population/overall population) can cause greater harm to the environment. The change in gender ratio can help control the food chain and may also exacerbate resource competition. Further integrating the relationship between gender ratio and growth rate into the Lotka Volterra predator environment model to form a gender Lotka Volterra predator environment model, the study found that a smaller gender ratio can lead to a decrease in ecological stability. When the gender ratio reaches a critical value, the ecosystem collapses. In addition, a dynamic ecosystem model was constructed for five species of organisms, including lampreys and parasites. Simulation results showed that reducing the sex ratio of lampreys can inhibit parasites and promote the prosperity of other organisms. The model has high robustness and stability. This study indicates that by regulating the sex ratio of lampreys, their impact on the ecosystem can be effectively controlled, providing a feasible environmental intervention strategy.

Digital Green Finance Architecture in the Context of Green Deal Implementation: Modern Fintech Tools and Solutions Under the Sustainable Development Concept

Objective: The purpose of the article is to develop a scientific and applied toolkit, which is a set of a logically informationally and algorithmically interconnected means of the information-analytical support of green financing within the Green Deal under the sustainable development concept. Theoretical Framework: The study is based on the Paris Climate Agreement, the European Green Agreement, and the concept of sustainable development. Method: To achieve the defined goal and solve the set tasks, a system of general scientific and special research methods was used. Results and Discussion: The author’s vision of the ecosystem model of the green financing is presented. The main trends of the global green bond market are identified; the parameters of the digital financing markets are estimated. The management decisions on accelerating the capital attraction and activation of green financing, ensuring the balance of the regional financing opportunities in the face of the global challenges are systematized. Research Implications: The important role of the state impact on the creation of the favourable conditions for the climate finance expansion by reducing the political, regulatory, control, macroeconomic and business risks has been established. It was established that the political stability, the consistent and clear regulatory framework, the agreed green financing policy and the openness to the international investors create the attractive conditions for the climate finance conditions. Originality/Value: Applying the author’s developments will create a basis for making the substantiated managerial decisions to the accelerated involvement of the capital in the green financing under the sustainable development concept.

Coastal generalized ecosystem model (CGEM) 1.0: Flexible model formulations for simulating complex biogeochemical processes in aquatic ecosystems

The Coastal Generalized Ecosystem Model (CGEM) is a biogeochemical model developed to study regulating processes of water-column optical properties, water-column and benthic carbon, oxygen, and nutrient cycles, and phytoplankton and zooplankton dynamics. CGEM offers numerous formulations for important rate processes, providing users flexibility in altering model structure. This flexibility also provides a means for evaluating model structural uncertainty and impacts on simulations, which are rarely evaluated with numerical ecosystem models. As an open-source model, CGEM also offers users the option to implement new formulations or modify existing routines. We also provide a full description of the model formulations, state variables, and model parameters in CGEM. Using two published case studies, we explore how different formulations for light attenuation, phytoplankton temperature growth response, and sediment processes impact simulations. We discuss CGEM's role as a new ecosystem model within the modeling community and opportunities to address current and future water quality issues.

Web application of an integrated simulation for aquatic environment assessment in coastal and estuarine areas

This paper introduces the web application-type Graphical User Interface that has been developed and also presents an application example. The introduced simulator conducts hydrodynamics and ecosystems in coastal and estuarine areas. It consists of (1) a hydrodynamic model that can simulate the current velocity, water temperature, salinity, and water level; (2) an ecosystem model that can simulate dissolved oxygen, phytoplankton, zooplankton, nutrients, fish, and bivalves; and (3) a benthic ecosystem model that can simulate elution. Web GUI is the first web system of aquatic environment simulation system that can both prepare calculation conditions and visualize them. Another significant feature is that it requires no installation and can be easily used by anyone to perform calculations. Thus, the proposed system helps fill the expertise gap experienced by potential users of the model. The use of standard systems, such as those discussed in this study, will facilitate evidence-based policymaking (EBPM).

The paradigm logic of blockchain governance

Blockchain technology is mainly composed of public-key cryptography, hash algorithms and consensus mechanisms. Blockchain solves the two trust problems inherent in human interaction: the Byzantine Generals Problem and the Prisoner's Dilemma, which allows blockchain to replace the function of public power as a centre of trust to a certain extent, and also enables it to become a kind of decentralised large-scale collaborative infrastructure for human beings. Blockchain itself has a strong escape from public power, and the blockchain order is also non-rational. Its emergence has impacted the traditional constructivist-rationalism governance paradigm. Current research exploring blockchain governance issues at the level of governance paradigms is still limited. This study mainly adopts a normative analysis methodology, firstly outlining the decentralised technology model of blockchain and the economic ecosystem model built on top of this technology model. Secondly, this study explores the ontology of blockchain technology and its economic model on a philosophical level as well as the philosophical roots of the limitations of the traditional governance paradigm when confronted with blockchain governance. On this basis, this study further proposes ideas for shifting the blockchain governance paradigm. Specifically, it includes that a judicature-based governance paradigm should be established while the executive power should shift to focus on the issue of equality on the blockchain, and in the context of weakly centralised legislation, soft law governance should be allowed to play more of a role. Considering the popularity and development of blockchain technology, this study provides insights into the theoretical level of blockchain governance.

Using text and image mining to study how actor engagement creates value in the sharing economy

PurposeThis paper aims to explore the concept of “actor engagement” within the context of the sharing economy, a novel and dynamic business model. Specifically, it investigates the formation of actor engagement and its relationship with value creation within this business model.Design/methodology/approachDrawing on Storbacka et al. (2016)actor engagement framework and service-dominant logic service ecosystem model, unstructured data (text and images) from the Airbnb platform in seven countries and text- and image-mining techniques such as machine learning are used to measure the research variables and test the model by PLS-SEM.FindingsThe results indicate that affective engagement has a more significant impact on behavioural engagement than cognitive engagement for multidimensional actor engagement. Service providers’ engagement – directly, and through customer engagement – influences value creation for service providers (i.e. performance). Moreover, national-level moderator (i.e. economic, competitiveness, technology, social and political factors) plays a significant moderating role in our model.Research limitations/implicationsThis study encourages future research to explore how actor engagement leads to value creation for all actors on the different sharing economy platforms.Practical implicationsThe findings provide practical insights for service providers to engage their customers and platform managers, especially in an international context, on managing their relationships with both customers and service providers in different countries.Originality/valueThis study advances the current literature on actor engagement and its role in value creation by providing a better understanding of the role of the national context in this process through unstructured data analysis.

Simulation psychological safety ecosystem: using constructivist grounded theory to explore nurses’ experiences with prelicensure simulation

Introduction Learning through simulation requires psychological safety where participants feel comfortable engaging to their fullest extent, speaking up or asking questions without fear of embarrassment. While professional simulation organizations provide recommendations on fostering psychological safety, anecdotal stories from newly graduated nurses demonstrated variability in their experiences. The study explored nurses’ experience with psychological safety in their prelicensure curricula. Methods Using constructivist grounded theory methodology, the researchers conducted 17 semi-structured interviews with nurses who had experienced simulation in their prelicensure curriculum. They collected and analysed data iteratively using constant comparison to identify categories and explore their relationships. They used theoretical sampling in later stages until data sufficiency was achieved. Results The constructed theory, Simulation Psychological Safety Ecosystem, expresses the variability of nurses’ experiences. Psychological safety is a dynamic, complex process with a spectrum of outcomes from feeling psychologically safe to suffering psychological harm. Factors that influence this outcome include clarification of Expectations, Facilitation from the instructor during scenarios, experience with Observation, and structure and quality of Debriefing/Feedback. Psychological safety is also influenced by nurses’ existing relationships with faculty and peers, and their desire to achieve meaningful clinical learning. Discussion Implications for nursing education reinforce clarifying expectations for equipment and roles, instructor presence, and observation. The findings suggest the benefits of establishing peer and faculty relationships prior to simulation-based education implementation and providing guidelines for faculty and peer feedback. Potential areas for future research include clarifying the ecosystem model and exploring the impact of faculty and peer relationships on psychological safety.

Advancing projections of crown-of-thorns starfish to support management interventions

Outbreaks of corallivorous Crown of Thorns Starfish (Acanthaster spp.; CoTS) cause substantial coral mortality throughout the Indo-Pacific, particularly on the Great Barrier Reef (GBR). Refining CoTS population density modelling and understanding the disparities between real-world observations and model predictions is crucial for developing effective control strategies. Using a spatially explicit ecosystem model of the GBR, we compared CoTS density model predictions to observations and incorporated a new zone-specific mortality rate to account for differences in predation of CoTS between fished and protected reefs. We found high congruence between predictions and observations: ∼81 % of categorical reef level CoTS densities matched or only differed by one category. However, underpredictions increased with higher observed densities. Zone-specific CoTS mortality reduced severe underpredictions from 7.1 % to 5.6 %, which is critical for managers as underpredictions indicate missing outbreaks where targeted culling is necessary, but also lead to underestimated coral loss attributed to CoTS outbreaks. Reef protection status affected prediction accuracy, highlighting the importance of further research on in situ CoTS mortality rates. The location of a reef inside or outside the “initiation box”, a speculative area of primary outbreaks (i.e., initial abrupt population increases) on the GBR, also influenced accuracy, with exact predictions more likely outside. Accurately modelling initiation box dynamics is challenging due to limited empirical data on CoTS outbreaks, highlighting the need for focussed research on outbreak dynamics to enhance predictive accuracy. Spatial factors, such as region and shelf position, contributed to the variance between observations and predictions, underscoring the importance of the spatial-temporal context of each observation. Observations of CoTS can help refine model predictions, guide targeted control measures, and contribute to effective ecosystem management for the long-term resilience of the GBR and other reefs targeted by CoTS throughout the Indo-Pacific.

Soil aggregate size distribution mediates microbial responses to prolonged acid deposition in a subtropical forest in south China

Extended exposure to acid rain has vastly limited soil microbial activity with the consequences for soil carbon (C) storage, but less is known about the microbial responses within soil aggregates that to some extent determine soil C stabilization. Here, we investigated the main microbial group compositions and the relevant potential enzyme activities within different soil aggregates sizes (microaggregates (<250 μm), small macroaggregates (250–2000 μm), and microaggregates (>2000 μm)) in a subtropical forest with decade-long simulated acid rain (SAR) treatments. Four SAR treatments were set by irrigating plots with water of different pH values (i.e., 3.0, 3.5, 4.0, and 4.5 as a control). Results showed that the SAR treatment significantly inhibited microbial activities, specifically decreasing both bacterial and fungal abundances, leading to declines in C-degrading potential enzyme activities. Conversely, potential enzyme activities related to phosphorus (P) and nitrogen (N) mineralization as well as the enzyme stoichiometry for P/N ratio significantly increased under the SAR treatment. The SAR treatment showed no significant differences in microbial abundance across the three soil aggregate sizes. However, it had a more pronounced effect on potential enzyme activities in their optimal aggregate sizes, such as hydrolytic enzymes like β-glucosidase in macroaggregates and oxidases like phenol oxidase and peroxidase in microaggregates. Overall, C-degrading potential enzyme activities were more strongly decreased in the microaggregates than in macroaggregates, and the distribution in aggregates was significantly altered, transforming from large to small sizes under the SAR treatment, which together may boost SOC stabilization and accumulation. Additionally, our findings indicate that prolonged acid rain also caused soil nutrient limitation and imbalance, particularly for P, in subtropical forests. This study highlights the importance of soil aggregate size in regulating microbial responses to acid rain, which should be integrated into ecosystem models to predict soil biogeochemical cycles under future climate conditions.

Toward A Process Model of a Harmonious Retail Platform Ecosystem

Toward A Process Model of a Harmonious Retail Platform Ecosystem

Reflecting on the symmetry of ecosystem tipping points: The influence of trait dissimilarity and environmental driver dynamics in a simple ecosystem model.

Our understanding of the similarity in trajectories of ecosystem changes during different directions of environmental change is limited. For example, do the dominant organisms exhibit the same responses to different directions of environmental change, that is, do they exhibit symmetric responses? Here, we explore whether such response symmetry is determined and controlled by the symmetry in the features of the underlying biological system (i.e., system symmetry), such as in the network and strength of biotic and abiotic processes, and in symmetry of the environmental change (i.e., environmental symmetry). For this exploration, we developed and used a simple mathematical model of a microbial ecosystem driven by mutual inhibition in which we could vary the amount of system and environmental symmetry. Our results show that perfect system and environmental symmetry indeed produce perfect response symmetry. Moreover, introducing asymmetry in biological systems or in the environment proportionally increases response asymmetry. These findings suggest using symmetries in ecosystem structure and interaction strength to better understand and predict similarities in degradation and restoration phases of environmental change.

Modularization of Activities in Ecosystems and Business Models

Modularization of Activities in Ecosystems and Business Models

Identifying and explaining resilience in ecological networks.

Resilient ecological systems are more likely to persist and function in the Anthropocene. Current methods for estimating an ecosystem's resilience rely on accurately parameterized ecosystem models, which is a significant empirical challenge. In this paper, we adapt tools from biochemical kinetics to identify ecological networks that exhibit 'structural resilience', a strong form of resilience that is solely a property of the network structure and is independent of model parameters. We undertake an exhaustive search for structural resilience across all three-species ecological networks, under a generalized Lotka-Volterra modelling framework. Out of 20,000 possible network structures, approximately 2% display structural resilience. The properties of these networks provide important insights into the mechanisms that could promote resilience in ecosystems, provide new theoretical avenues for qualitative modelling approaches and provide a foundation for identifying robust forms of ecological resilience in large, realistic ecological networks.

Using System‐Inspired Metrics to Improve Water Quality Prediction in Stratified Lakes

AbstractDespite the growing use of Aquatic Ecosystem Models for lake modeling, there is currently no widely applicable framework for their configuration, calibration, and evaluation. Calibration is generally based on direct data comparison of observed versus modeled state variables using standard statistical techniques, however, this approach may not give a complete picture of the model's ability to capture system‐scale behavior that is not easily perceivable in observations, but which may be important for resource management. The aim of this study is to compare the performance of “naïve” calibration and a “system‐inspired” calibration, an approach that augments the standard state‐based calibration with a range of system‐inspired metrics (e.g., thermocline depth, metalimnetic oxygen minima), to increase the coherence between the simulated and natural ecosystems. A coupled physical‐biogeochemical model was applied to a focal site to simulate two key state‐variables: water temperature and dissolved oxygen. The model was calibrated according to the new system‐inspired modeling convention, using formal calibration techniques. There was an improvement in the simulation using parameters optimized on the additional metrics, which helped to reduce uncertainty predicting aspects of the system relevant to reservoir management, such as the occurrence of the metalimnetic oxygen minima. Extending the use of system‐inspired metrics when calibrating models has the potential to improve model fidelity for capturing more complex ecosystem dynamics.

Key Uncertainties and Modeling Needs for Managing Living Marine Resources in the Future Arctic Ocean

AbstractEmerging fishing activity due to melting ice and poleward species distribution shifts in the rapidly‐warming Arctic Ocean challenges transboundary management and requires proactive governance. A 2021 moratorium on commercial fishing in the Arctic high seas provides a 16‐year runway for improved scientific understanding. Given substantial knowledge gaps, characterizing areas of highest uncertainty is a key first step. Marine ecosystem model ensembles that project future fish distributions could inform management of future Arctic fisheries, but Arctic‐specific variation has not yet been examined for global ensembles. We use the Fisheries and Marine Ecosystem Intercomparison Project ensemble driven by two Earth System Models (ESMs) under two Shared Socioeconomic Pathways (SSP1‐2.6 and SSP5‐8.5) to illustrate the current state of and uncertainty among biomass projections for the Arctic Ocean over the duration of the moratorium. The models generally project biomass increases in more northern Arctic ecosystems and decreases in southern ecosystems, but wide intra‐model variation exceeds projection means in most cases. The two ESMs show opposite trends for the main environmental drivers. Therefore, these projections are currently insufficient to inform policy actions. Investment in sustained monitoring and improving modeling capacity, especially for sea ice dynamics, is urgently needed. Concurrently, it will be necessary to develop frameworks for making precautionary decisions under continued uncertainty. We conclude that researchers should be transparent about uncertainty, presenting these model projections not as a source of scientific “answers,” but as bounding for plausible, policy‐relevant questions to assess trade‐offs and mitigate risks.

Dining in danger: Resolving adaptive fish behavior increases realism of modeled ecosystem dynamics.

Animals occupying higher trophic levels can have disproportionately large influence on ecosystem structure and functioning, owning to intricate behavioral responses to their environment, but the effects of behavioral adaptations on aquatic ecosystem dynamics are underrepresented, especially in model studies. Here, we explore how adaptive behavior of fish can affect the dynamics of aquatics ecosystems. We frame fish behavior in the context of the central trade-off between feeding and predation, calculating the optimal level of feeding determined by ambient food availability and predation risk. To explore whole-ecosystem consequences of fish behavior, we embed our behavioral model within the Water Ecosystems Tool (WET), a contemporary end-to-end aquatic ecosystem model. The principle of optimality provides a robust and mechanistic framework for representing animal behavior that is relevant for complex models, and can provide a stabilizing effect on model dynamics. The model predicts an emergent functional response similar to Holling type III, but with richer dynamics and a more rigorous theoretical foundation. We show how adaptive fish behavior works to stabilize food web dynamics compared to a control model with no optimal behavior, and how changing the strength of the underlying trade-off has profound effects on trophic control and food web structure. Furthermore, we demonstrate how including fish behavior allows for an overall more realistic response of the model system to environmental perturbation in the form of nutrient enhancement. We discuss the structuring effects of behavioral adaptations in real ecosystems, and how approaches like this one may benefit aquatic ecological modeling. Our study further highlights how a mechanistic approach based on concepts from theoretical ecology can be successfully implemented in complex operational models resulting in improved dynamics and descriptive power.

Reconciling the EU forest, biodiversity, and climate strategies.

Forests provide important ecosystem services (ESs), including climate change mitigation, local climate regulation, habitat for biodiversity, wood and non-wood products, energy, and recreation. Simultaneously, forests are increasingly affected by climate change and need to be adapted to future environmental conditions. Current legislation, including the European Union (EU) Biodiversity Strategy, EU Forest Strategy, and national laws, aims to protect forest landscapes, enhance ESs, adapt forests to climate change, and leverage forest products for climate change mitigation and the bioeconomy. However, reconciling all these competing demands poses a tremendous task for policymakers, forest managers, conservation agencies, and other stakeholders, especially given the uncertainty associated with future climate impacts. Here, we used process-based ecosystem modeling and robust multi-criteria optimization to develop forest management portfolios that provide multiple ESs across a wide range of climate scenarios. We included constraints to strictly protect 10% of Europe's land area and to provide stable harvest levels under every climate scenario. The optimization showed only limited options to improve ES provision within these constraints. Consequently, management portfolios suffered from low diversity, which contradicts the goal of multi-functionality and exposes regions to significant risk due to a lack of risk diversification. Additionally, certain regions, especially those in the north, would need to prioritize timber provision to compensate for reduced harvests elsewhere. This conflicts with EU LULUCF targets for increased forest carbon sinks in all member states and prevents an equal distribution of strictly protected areas, introducing a bias as to which forest ecosystems are more protected than others. Thus, coordinated strategies at the European level are imperative to address these challenges effectively. We suggest that the implementation of the EU Biodiversity Strategy, EU Forest Strategy, and targets for forest carbon sinks require complementary measures to alleviate the conflicting demands on forests.

Explainable deep learning insights into the history and future of net primary productivity in China

Net primary productivity (NPP) is a crucial component of the terrestrial carbon cycle and plays a crucial role in assessing ecological security. Although machine learning models have been widely used in research on ecosystem modelling, however, the performance of deep learning models with interpretability in predicting NPP are still very limited. In this study, we developed an interpretable deep learning predictive model to uncover the historical drivers and future changes of NPP in China using remote sensing data, climate data and topography. The results show that more than three quarters (88 %) of China’s regional NPP have shown an increasing trend over the past 20 years, with an overall trend of 2.46 gC/m2/yr. This extensive range of dynamic processes is a nonlinear response to climatic conditions and topographic factors. The constructed convolutional neural network (CNN) model showed good predictive skill for historical NPP, and revealed that temperature is the main controlling factor, highlighting its importance in vegetation growth in China. Compared to the historical period (2001–2020), in the context of future climate change, the risk of carbon sinks decreasing in China is highly likely in the imminent near term (2021–2039), especially under high emission scenarios. These results suggest that interpretable deep learning method is a useful tool for revealing vegetation productivity drivers and estimating vegetation productivity under a nonstationary climate background.

Synphytoindication models of the anthropogenic transformation of ecosystems

Human activity related to the exploitation or protection of environmental elements requires the creation of forecasts of the consequences involved. Such forecasts are not possible without the construction of models of ecosystems and the processes occurring in them. One of the critical elements in modern forecasting of the dynamics of ecosystems is the modelling of their anthropogenic block. Our research aims to develop methods for assessing the anthropogenic transformation of ecosystems necessary for modelling their state and dynamics. In accordance with the goal, we set ourselves the following tasks: to determine the limits of anthropotolerance of plant species; to evaluate the hemeroby of plant groups by the methods of synphytoindication; to create models of mutual dependence between anthropogenic transformation and natural dynamics. To implement the tasks, we used standard geobotanical methods (creating a geobotanical description, classification of plant communities, and synphytoindication). Hemeroby of plant communities can be used as an indicator of the anthropogenic transformation of ecosystems. To do this, we measure the boundaries of anthropotolerance of the plant species that make up these groupings. The range of anthropotolerance of individual plant species can be determined by the 12 most common types of human activity. The strength of their impact on ecosystems was determined by the induced changes in above-ground phytomass. The transition from anthropotolerance of individual species to hemeroby of plant communities was carried out according to the classical synphytoindication technique. We created an 18-point scale by assigning three points to each classic type of hemoroby. The use of the synphytoindication scale allows modelling of the interdependence between anthropogenic transformation and the measurable characteristics of the ecosystem. An inverse linear relationship with a probability of approximation of 0.2 and a correlation coefficient of 0.45 is an indicator of natural dynamics and the level of anthropogenic transformation. The correlation coefficient is 0.29 for ecosystems that are not suitable for economic use, and 0.85 for the rest.