Ecosystem Model Research Articles

Bifurcation Analysis and Multiobjective Nonlinear Model Predictive Control of Sustainable Ecosystems

<i>Objective: </i>All optimal control work involving ecological models involves single objective optimization. In this work, we perform multiobjective nonlinear model predictive control (MNLMPC) in conjunction with bifurcation analysis on an ecosystem model. <i>Methods: </i>Bifurcation analysis was performed using the MATLAB software MATCONT while the multi-objective nonlinear model predictive control was performed by using the optimization language PYOMO. <i>Results: </i>Rigorous proof showing the existence of bifurcation (branch) points is presented along with computational validation. It is also demonstrated (both numerically and analytically) that the presence of the branch points was instrumental in obtaining the Utopia solution when the multiobjective nonlinear model prediction calculations were performed. <i>Conclusions: </i>The main conclusions of this work are that one can attain the utopia point in MNLMPC calculations because of the branch points that occur in the ecosystem model and the presence of the branch point can be proved analytically. The use of rigorous mathematics to enhance sustainability will be a significant step in encouraging sustainable development. The main practical implication of this work is that the strategies developed here can be used by all researchers involved in maximizing sustainability The future work will involve using these mathematical strategies to other ecosystem models and food chain models which will be a huge step in developing strategies to address problems involving nutrition.

Beyond MRV: Combining remote sensing and ecosystem modeling for geospatial monitoring and attribution of forest carbon fluxes over Maryland, USA

Abstract Members of the U.S. Climate Alliance, a coalition of 24 states committed to achieving the emissions reductions outlined in the 2015 Paris Agreement, are considering policy options for inclusion of forest carbon in climate mitigation plans. These initiatives are generally limited by a lack of relevant data on forest carbon stocks and fluxes past-to-future. Previously, we developed a new forest carbon modeling system that combines high-resolution remote sensing, field data, and ecological modeling to estimate contemporary above-ground forest carbon stocks, and projected future forest carbon sequestration potential for the state of Maryland. Here we extended this work to provide a consistent geospatial approach for monitoring changes in forest carbon stocks over time. Utilizing the same data and modeling system developed previously for planning, we integrated historical input data on weather and disturbance to reconstruct the history of vegetation dynamics and forest above-ground carbon stocks annually over the period 1984-2016 at 30 m resolution and provided an extension to 2023. Statewide, forested land had an average annual net above ground carbon sink of 1.37 TgC yr-1, comparable to prior estimates. However, unlike the prior estimates, there was considerable variation around this mean. The statewide net above ground flux ranged interannually from -0.65 - 2.77 Tg C yr-1. At the county scale, the average annual net above ground flux ranged spatially from 0.01 - 0.13 Tg C yr-1 and spatiotemporally from -0.43 - 0.24 Tg C yr -1. Attribution analyses indicate the primary importance of persistent and regrowing forests, vegetation structure, local disturbance, and rising CO2 to the mean flux, and the primary importance of weather to the large-scale interannual variability. These results have important implications for state climate mitigation planning, reporting and assessment. With this approach, it is now possible to monitor changes in forest carbon stocks spatiotemporally over policy relevant domains with a consistent framework that is also enabled for future planning.

Innovation Reefs (I-Reef): Innovation Ecosystems Focused on Regional Sustainable Development

The creation of successful innovation ecosystems, like Silicon Valley, is challenging due to significant cultural, infrastructural, and resource differences between regions. In this context, the Innovation Reef (I-Reef) model emerges as a promising alternative, offering an approach for regions with limited resources to develop successful innovation ecosystems based on cooperation and mutual benefit among participants. This model has great potential to promote regional development, especially due to its focus on retaining and sharing the value generated. However, the role of I-Reef in sustainable regional development still needs to be further explored. Thus, the objective of this study is to deepen the theoretical understanding of the I-Reef model by analyzing its contribution to sustainable development. To achieve this, a comparison was made between I-Reef and established models such as business, innovation, knowledge, and entrepreneurial ecosystems. A systematic literature review conducted on Scopus found 704 articles published in the last three decades. The purpose was to identify the similarities and differences between the models of innovation business ecosystem models. The results show that there is alignment between I-Reef and the different ecosystems on several points. A central aspect of I-Reef is that it relies on a strong network of mutually beneficial relationships, much more oriented to sustainable development than the other models, which is a key factor in generating competitive advantage and development for the region. This characteristic is either not addressed or not placed at the core of the ecosystems discussed in the literature. For future research, empirical studies and validation of the I-Reef model with experts are suggested, as this theoretical study lays the foundation for more in-depth analyses.

Projected changes in forest fire season, the number of fires, and burnt area in Fennoscandia by 2100

Abstract. Forest fire dynamics are expected to alter due to climate change. Despite the projected increase in precipitation, rising temperatures will amplify forest fire risk from the present to the end of the century. Here, we analysed changes in fire season, the number of fires, and burnt area in Fennoscandia from 1951 to 2100. Regional simulations from the JSBACH–SPITFIRE ecosystem model (where SPITFIRE stands for SPread and InTensity of FIRE) were performed under two climate change forcing scenarios (Representative Concentration Pathway (RCP) 4.5 and RCP 8.5) and three global climate driver models (CanESM2, CNRM-CM5, and MIROC5) with a 0.5° resolution. Simulations were forced by downscaled and bias-corrected EURO-CORDEX data. Generally, as a consequence of the projected longer fire season and drier fuel, the probability of fires is projected to increase. However, changes in fire season, the number of fires, and burnt area are highly dependent on climate projections and location. The fire season is estimated to increase on average from 20 ± 7 to 52 ± 12 d, starting from 10 ± 9 to 23 ± 11 d earlier and ending from 10 ± 10 to 30 ± 16 d later, compared to the reference period (1981–2010), by the end of the century (2071–2100). The results for Finland indicate a change in the number of fires, ranging from −7 ± 4 % to 98 ± 56 %, and a change in burnt area, ranging from −19 ± 24 % to 87 ± 42 %. These findings contribute to a better understanding of potential changes in the future fire seasons of northern Europe.

Developing Prototype Digital Twins for biodiversity conservation and management: achievements, challenges and perspectives

The BioDT project, funded by the European Union Horizon Europe Programme, seeks to investigate and push the boundaries of methodological, intellectual, data, technical and relevance aspects of the digital twin (DT) approach for understanding and predicting biodiversity patterns and processes. In this special issue, we present the reasoning and goals of the project and the overall advancements made towards the development of ten prototype Digital Twins (pDTs). Based on models of ecosystems, species and biological processes, digital twins integrate diverse data sources with advanced modelling and simulation techniques to provide a comprehensive, dynamic and data-driven understanding of biodiversity. Leveraging the EuroHPC LUMI infrastructure and data and expertise from GBIF, eLTER, DiSSCo and LifeWatch ERIC research infrastructures, the project aims to develop hybrid models combining the strengths of statistical and process-based models for improved predictive capabilities. While there are specific challenges and limitations to biodiversity DTs, the pDTs of BioDT lay the foundation for model-data fusions that will offer potential benefits to a wide range of end-users, from researchers and conservation organisations to policy-makers, land managers, educators and private sector companies. By fostering global interoperability in biodiversity data and research, BioDT paves the way for future collaborative efforts in biodiversity conservation and management.

Modeling carbon dynamics from a heterogeneous watershed in the mid-Atlantic USA: A distributed-calibration and independent verification (DCIV) approach

The terrestrial ecosystem plays a vital role in regulating regional and global carbon budgets. Ecosystem models are extensively employed to estimate carbon fluxes across different spatial scales. However, there remains a need to reduce the uncertainties associated with model parameterization and input data. To address these limitations, we assessed a distributed-calibration and independent-verification (DCIV) approach that uses (1) remotely sensed net primary production (NPP) and evapotranspiration (ET) data from the Moderate Resolution Imaging Spectroradiometer (MODIS), (2) multi-site eddy covariance net ecosystem exchange (NEE) data; and (3) field sampling of soil organic carbon (SOC) and aboveground biomass (ABG) data to improve the overall predictability of carbon fluxes for the different land use and land cover (LULC) types at a watershed scale. The DCIV approach was applied to an advanced version of the Soil and Water Assessment Tool (SWAT)-Carbon (or SWAT-C), equipped with Century-based SOC algorithms to simulate carbon dynamics for watersheds with heterogeneous vegetation. The objective of the modeling effort was to assess carbon stocks and fluxes under different land management scenarios for a 3000-acre experimental farm and forest preserve in the northeastern United States. Our study showed that a large SOC stock of at least 100 tons ha−1 is stored under mixed forest, deciduous, shrubland, and floodplain (grass). Our study also showed that converting floodplain (grass) to deciduous forest has the potential to increase CO2 uptake (-NEE) by an order of three magnitude and ABG by 77 %, leading to an increased SOC stock of 23 % after twenty years. Similarly, we found that converting ungrazed grassland to grazed pasture leads to a non-statistically decreasing trend of SOC, especially in the 0–30 cm soil layer. Thus, the methodology used in this study can be applied to improve carbon dynamic prediction from a heterogeneous watershed at a regional scale.

Spiralling Inverse Method: A New Inverse Method to Estimate Ocean Mixing

Abstract Here, we introduce the Spiralling Inverse Method (SIM) that provides estimates of the small-scale and mesoscale mixing strength. The SIM uses a vertical integral over a balance between the water mass transformation equation and the thermal wind equation. The result is an equation where all terms, except for the mixing strengths, can be obtained from hydrographic data of temperature and salinity. As an advantage, the SIM estimates the mixing strengths without the need for further knowledge of a reference velocity or streamfunction. Here, we apply the SIM to a small region in the North Atlantic. We find that the estimates obtained by the SIM compare well to observations and other (inverse) estimates of the mixing strength. The SIM therefore has the potential to improve and constrain parameterizations used for climate and ecosystem modeling using readily available hydrographic data. Significance Statement Ocean mixing is a combination of many different physical processes over a large range of scales in time (from seconds to years) and space (from millimeters to 100 km). Most of these processes are too small to compute in climate models and need to be simplified (parameterized). These parameterizations have a strong influence on climate projections, and their shape and magnitude need to be constrained using observations and indirect estimates of ocean mixing strength. The Spiralling Inverse Method (SIM) is a new method to obtain such constraints of mixing strengths using readily available observations of temperature and salinity. We here test the SIM and confirm its potential to improve mixing estimates and therewith ultimately climate simulations.

Decline in Coupling Between Vegetation Photosynthesis and Greening in Northern Ecosystems During the Photosynthesis-Up Period.

The maximum seasonal vegetation photosynthesis (Phomax) is crucial to regulating the global carbon dynamics. Of particular importance are the seasonal increments in vegetation photosynthesis (ΔPho), which provide key insights into understanding Phomax. However, the interannual variability of ΔPho within the photosynthesis-up period (PUP) and its influencing factors remain unclear. To address this gap, we identified PUP and quantified the multi-year characteristics of ΔPho using satellite-derived solar-induced chlorophyll fluorescence. We further investigated the response of ΔPho in northern ecosystems to climate change and vegetation greening by integrating climate data and the normalized difference vegetation index. In the northern ecosystems, longer PUP often spatially correlated with a higher ΔPho. An increasing trend was evident regarding the multi-year variations in ΔPho, suggesting enhanced vegetation photosynthesis within the PUP. This phenomenon is primarily driven by increased solar radiation and intensified vegetation greening. Additionally, based on the results derived from satellite data, we found three pieces of evidence for the decoupling trend between vegetation photosynthesis and greening under the influence of climate change: first, the inconsistent trends between ΔPho and greening; second, the declining moving trend in the correlation coefficient between ΔPho and greening, approximately 9.17 × 10-4; and third, the weakened dominant role of greening on ΔPho. These findings were further supported by results from ecosystem model simulations. In summary, this study provides insights into the interannual variability of ΔPho and its influencing factors and indicates that vegetation dynamics and terrestrial carbon cycle are likely to become more complex under future climate change scenarios.

Blue Carbon Potential of Mangrove Ecosystems and Its Management to Promote Climate Change Mitigation in Indonesia

This research aimed to analyze blue carbon potential and mangrove ecosystem management in promoting climate change mitigation in Indonesia. The collected data included mangrove area data obtained through the Indonesian National Ecosystem Monitoring System (SIMONTANA). This research calculated the carbon stock using the global average of mangrove carbon stock. The results showed that the total blue carbon stored in mangrove ecosystems reached 3,267.87 Megaton Carbon (MtC). Papua and Kalimantan regions contributed the most significant potential, with 1,707.22 MtC and 608.17 MtC, respectively. The absorption of carbon dioxide (CO2) reached 11,982.21 MtCO2e. The most significant CO2 gas emissions absorption was observed in the Papua region, amounting to 6,259.80 MtCO2e, followed by Sumatra with 2,118.59 MtCO2e. This high potential value correlated with mangrove ecosystem management policies, such as conservation, restoration, and blue carbon monetization. Mangrove management for climate change mitigation was carried out through national and international cooperation to achieve the 26% to 40% emission reduction target. The existing management model of mangrove ecosystems focused on improving ecological sustainability with less orientation on improving community welfare. Implementing blue carbon monetization at the district level by involving the community as the owners or managers of the mangrove ecosystems is crucial.

A Comprehensive Model for Innovation Ecosystems

Abstract Research on innovation ecosystems is mostly approached from the point of view of institution-oriented aspects, which typically focus on the structural elements of innovation systems. However, this perspective has limitations to describe a really functioning innovation system, as it does not take into account its complex nature of dynamics and the functional features. The authors run research on models of innovation ecosystems based on complexity theory principles. The presented perspective, based on complexity theory, takes into account not only the structural and the complex but also the dynamic feature of the innovation ecosystems, still using the triple helix approach as a basis. The objective of the article is to present a system model framework for innovation ecosystems, that can be used for development of technology and science parks management models. The approach of the research is based on literature review and discussion of relevant results, followed by explanation and concluding a possible innovation ecosystem framework model, finally demonstrated its interpretation through a specific case. In its first part, the article gives a general overview on complex systems from point of related findings on innovation ecosystems. Based on literature review, the preliminary analysis of the authors in the subject is given. Then, the relevant model features are described, then and the proof of the concept is explained according to the research. The descriptive features of complex systems based on the referenced papers are used to interpret the proposed model. The main outcome of the article is a comprehensive innovation ecosystem model as a tool to understand and analyse such systems based on actors, functions and key factors. The authors demonstrated in the article that features of complex systems not only help describing innovation ecosystems but also support defining key model elements. An empiric case of ZalaZONE Technology Park was shown related to the presented innovation ecosystem model. Finally, the next steps of the model development are outlined and advice is concluded for application of the model in the practice and in further research. The novelty of the article is pointing out that the simple model of triple helix and the complexity approach as a view for modelling the real circumstances, can be well-combined to outline a comprehensive model for innovation ecosystems.

Exploring novel North Water Polynya ecosystems under climate change

Climate change is rapidly reshaping species distributions in the Arctic, which could profoundly impact ecosystem structure and function. While considerable effort has focused on projecting future species distributions, assessing the impacts of range-shifting species on recipient communities and subsequent disruptions to food webs remains largely unstudied. Here, we address this gap by combining species distribution models and ecosystem models to explore the emergence of novel ecosystems in the North Water Polynya. The North Water Polynya is an open-water area between Greenland and Canada, surrounded by sea ice and one of the world’s most productive ocean ecosystems. Using existing literature and projections from species distribution models of four marine species, we develop six plausible future ecosystem scenarios for the North Water Polynya. These scenarios include changing biomass of primary producers, changing biomass and size structure of copepods, shifting abundances of forage fish species, and the establishment of killer whales. We find that the biomass of higher trophic levels show pronounced decreases in response to the decrease in pelagic primary producers, with polar bear biomass halving compared to present conditions. Changes in the copepod size structure has the largest impact on the entire ecosystem compared to the other novel ecosystem scenarios, suggesting a strong reliance of higher trophic levels on large, lipid-rich copepods. We further show that increasing capelin with a simultaneous decrease in Arctic cod biomass causes large decreases in the biomass of marine mammals such as polar bear, beluga and ringed seal. Finally, we show the establishment of killer whales as a key novel predator could have cascading top-down effects on the North Water Polynya ecosystem. The framework presented here provides an approach for exploring the emergence of novel ecosystems and highlights how climate change could disrupt a high Arctic ecosystem.

Estimation of carbon stocks in the areas of a seasonally dry tropical forest in the Brazilian semi-arid region

In addition to its ecological importance, the Caatinga biome, one of the most extensive seasonally dry tropical forests (SDTF) in the world, has a relevant socioeconomic role as it is used as a primary natural resource by local communities. However, inadequate ecosystem management practices have resulted in gradual loss of natural vegetation in this ecosystem. Carbon stock estimation is a parameter that can contribute as a support tool for managing and maintaining the few remaining natural vegetated areas. In this study, we calibrated and validated the CENTURY model to simulate carbon stocks in areas of the Caatinga, in the state of Pernambuco, and compared the predictive capacity of the CENTURY model with available estimates. In the validation dataset, the average for biomass stocks was 33.1 Mg C ha−1, this value is close to those observed in the literature for the region. The model also performed well when estimating carbon stocks in the soil (r2 = 0.79, p = 0.017). Ecosystem modeling combined with Geographic Information Systems (GIS) is a promising tool for estimating carbon stocks in the Caatinga, where field sampling campaigns are generally expensive and have scarce research funding opportunities. Furthermore, it also allows the evaluation of the effect of environmental changes on C stocks in long-term studies, which is essential for creating and implementing public policies to mitigate and adapt to the impacts of climate change on the ecosystem. However, additional efforts are needed to improve C estimates, especially in areas with a strongly negative water balance.

Dynamics of ecosystem services along ecological network seascapes

Abstract Human societies depend on services provided by ecosystems, from local needs such as clean water and pest control to global services like the ozone layer and the ocean biological pump. Ecosystem services are linked to the states of the ecosystem, which are, in turn, governed by a web of ecological interactions. These interactions, along with the services they support, are under threat from environmental changes driven by human activities. Therefore, safeguarding these vital services requires an understanding of how the structure and dynamics of ecological interactions are affected by environmental change. A critical step towards this goal is the development of a theoretical framework that can elucidate how ecosystem services are sustained or impaired by interactions within ecosystems in fluctuating environments. Recent years have seen progress in characterizing the organization and dynamics of ecological networks. However, linking temporally varying network structure in fluctuating environments, the seascapes of ecological networks, and their impact on services remains a challenge. We propose an approach based on merging ecological network analysis with Boolean functions and modeling of fluctuating environments to address how services are affected by environmental change. We review aspects of Boolean Network models and illustrate the approach using biologically inspired Boolean rules that involve predator-prey cycles, trophic cascades, and mutualisms formed by plants and their frugivores. This approach aims to contribute to the study of how the organization of ecological interactions affects the persistence of ecosystem services. Specifically, we discuss how this approach can provide new insights into how environmental change affects the relationship between ecological networks and ecosystem services. The combination of information on the natural history of species interactions and ecosystem services, Boolean networks, and models for fluctuating environments may contribute to conservation strategies for preserving biodiversity and ecosystem services in the face of ongoing environmental change.

Turbulent Energy and Carbon Fluxes in an Andean Montane Forest—Energy Balance and Heat Storage

High mountain rainforests are vital in the global energy and carbon cycle. Understanding the exchange of energy and carbon plays an important role in reflecting responses to climate change. In this study, an eddy covariance (EC) measurement system installed in the high Andean Mountains of southern Ecuador was used. As EC measurements are affected by heterogeneous topography and the vegetation height, the main objective was to estimate the effect of the sloped terrain and the forest on the turbulent energy and carbon fluxes considering the energy balance closure (EBC) and the heat storage. The results showed that the performance of the EBC was generally good and estimated it to be 79.5%. This could be improved when the heat storage effect was considered. Based on the variability of the residuals in the diel, modifications in the imbalances were highlighted. Particularly, during daytime, the residuals were largest (56.9 W/m2 on average), with a clear overestimation. At nighttime, mean imbalances were rather weak (6.5 W/m2) and mostly positive while strongest underestimations developed in the transition period to morning hours (down to −100 W/m2). With respect to the Monin–Obukhov stability parameter ((z − d)/L) and the friction velocity (u*), it was revealed that the largest overestimations evolved in weak unstable and very stable conditions associated with large u* values. In contrast, underestimation was related to very unstable conditions. The estimated carbon fluxes were independently modelled with a non-linear regression using a light-response relationship and reached a good performance value (R2 = 0.51). All fluxes were additionally examined in the annual course to estimate whether both the energy and carbon fluxes resembled the microclimatological conditions of the study site. This unique study demonstrated that EC measurements provide valuable insights into land-surface–atmosphere interactions and contribute to our understanding of energy and carbon exchanges. Moreover, the flux data provide an important basis to validate coupled atmosphere ecosystem models.

The economic impacts of living habitat changes in the Virginia Middle Peninsula, Chesapeake Bay

Living habitats support ecologically and commercially important species but are threatened by anthropogenic stressors, including climate change. The populations of two living habitats in the Chesapeake Bay, Eastern Oyster (Crassostrea virginica) and Eelgrass (Zostera marina), are depleted compared to historical levels, but recent Oyster restoration efforts have created new reef habitat and enhanced existing Oyster reefs. Other animals (e.g., commercially important fish and invertebrates) consume organisms that associate with these living habitats, suggesting the regional economies that rely on the commercial fishing industry may be impacted by changes in living habitat coverage. Ecosystem models were developed for two rivers that have undergone Oyster restoration in the Virginia Middle Peninsula, and simulations were conducted to estimate the potential influence that changes to living habitats may have on commercial fisheries harvests. Additionally, commercial fishers were interviewed to document expenditures needed to link ecological model estimates to an economic input-output model via IMPLAN that estimated the regional economic impacts of these changes. Increases to Oyster and Eelgrass populations are predicted to enhance commercial fisheries populations and in turn, harvests, while decreases to living habitats are predicted to have negative effect on fisheries harvests. The driving factor behind landed value trends is changes with the lucrative Blue Crab (Callinectes sapidus) fishery. The primary expenses of watermen are repairs/maintenance, fuel, and bait, but greater than 37 % of the annual revenue is retained as proprietor and crew income. Oyster reef restoration alone is predicted to increase the regional economic impact of the commercial fishing industry by $1.1 M yr-1 and support 12 more full-time jobs at the end of a 20-year simulation. Oyster restoration in combination with a continued decline of Eelgrass led to a negligible increase (∼$150 K yr-1) in the regional economic impact. Eelgrass restoration to the current management goal without the influence of Oyster restoration is anticipated to elevate the regional economic impact to $16.8 M yr-1 and 152 full-time jobs, and smaller increases to the Eelgrass population, relative to current levels, will still have a positive impact on the regional economy. Combined Oyster and Eelgrass restoration are predicted to enhance economic impacts to the greatest extent, while continued Eelgrass decline is likely to minimize these benefits.

Methods, Progress and Challenges in Global Monitoring of Carbon Emissions from Biomass Combustion

Global biomass burning represents a significant source of carbon emissions, exerting a substantial influence on the global carbon cycle and climate change. As global carbon emissions become increasingly concerning, accurately quantifying the carbon emissions from biomass burning has emerged as a pivotal and challenging area of scientific research. This paper presents a comprehensive review of the primary monitoring techniques for carbon emissions from biomass burning, encompassing both bottom-up and top-down approaches. It examines the current status and limitations of these techniques in practice. The bottom-up method primarily employs terrestrial ecosystem models, emission inventory methods, and fire radiation power (FRP) techniques, which rely on the integration of fire activity data and emission factors to estimate carbon emissions. The top-down method employs atmospheric observation data and atmospheric chemical transport models to invert carbon emission fluxes. Both methods continue to face significant challenges, such as limited satellite resolution affecting data accuracy, uncertainties in emission factors in regions lacking ground validation, and difficulties in model optimization due to the complexity of atmospheric processes. In light of these considerations, this paper explores the prospective evolution of carbon emission monitoring technology for biomass burning, with a particular emphasis on the significance of high-precision estimation methodologies, technological advancements in satellite remote sensing, and the optimization of global emission inventories. This study aims to provide a forward-looking perspective on the evolution of carbon emission monitoring from biomass burning, offering a valuable reference point for related scientific research and policy formulation.

The biogeochemical model Biome-BGCMuSo v6.2 provides plausible and accurate simulations of the carbon cycle in central European beech forests

Abstract. Process-based ecosystem models are increasingly important for predicting forest dynamics under future environmental conditions, which may encompass non-analogous climate coupled with unprecedented disturbance regimes. However, challenges persist due to the extensive number of model parameters, scarce calibration data, and trade-offs between the local precision and the applicability of the model over a wide range of environmental conditions. In this paper, we describe a protocol that allows a modeller to collect transferable ecosystem properties based on ecosystem characteristic criteria and to compile the parameters that need to be described in the field. We applied the procedure to develop a new parameterisation for European beech (Fagus sylvatica L.) for the Biome-BGCMuSo model, the most advanced member of the Biome-BGC family. For model calibration and testing, we utilised multiyear forest carbon data from 87 plots distributed across five European countries. The initial values of 48 new ecophysiological parameters were defined based on a literature review. The final values of six calibrated parameters were optimised for single sites as well as for multiple sites using generalised likelihood uncertainty estimation (GLUE) and model output conditioning that ensured plausible simulations based on user-defined ranges of carbon stock output variables (carbon stock in aboveground wood biomass, soil, and litter) and finding the intersections of site-specific plausible parameter hyperspaces. To support the model use, we tested the model performance by simulating aboveground tree wood, soil, and litter carbon across a large geographical gradient of central Europe and evaluated the trade-offs between parameters tailored to single plots and parameters estimated using multiple sites. Our findings indicated that parameter sets derived from single sites provided an improved local accuracy of simulations of aboveground wood, soil, and litter carbon stocks by 35 %, 55 %, and 11 % in comparison to the a priori parameter set. However, their broader applicability was very limited. A multi-site optimised parameter set, on the other hand, performed satisfactorily across the entire geographical domain studied here, including on sites not involved in the parameter estimation, but the errors were, on average, 26 %, 35 % and 9 % greater for the aboveground wood, soil, and litter carbon stocks than those obtained with the site-specific parameter sets. Importantly, model simulations demonstrated plausible responses across large-scale environmental gradients, featuring a clear production optimum of beech that aligns with empirical studies. These findings suggest that the model is capable of accurately simulating the dynamics of European beech across its range and can be used for more comprehensive experimentations.

Correction: Spours, K. From Learning Ecologies to a Social Ecosystem Model for Learning and Skills. Systems 2024, 12, 324

In the published article [...]

Ecological‐economic modeling of nutrient enrichment in multispecies fisheries

AbstractIn fisheries economics, multispecies or ecosystem models typically focus on interactions among fish species, often overlooking the flow of nutrients. However, biologists widely acknowledge that nutrients–Âİsuch as nitrogen, phosphorus, potassium, and calcium–Âİare vital for organisms and play crucial roles in ecosystem dynamics. This paper delves into a bioeconomic predator‐prey model incorporating nutrient enrichment through Holling Type II and Michaelis–Menten functional forms. Initially, we examine the system's dynamics before formulating an optimal time‐dependent harvest policy using the Pontryagin maximum principle. Our findings reveal that heightened nutrient enrichment can lead to the breakdown of a positive stable equilibrium, resulting in the extinction of both prey and predator. The stability of the ecosystem may also be influenced by the interplay of nutrient enrichment and predator harvest.

Phytoplankton Spring Bloom Inhibited by Marine Heatwaves in the North‐Western Mediterranean Sea

AbstractMarine heatwaves (MHWs) represent anomalously warm temperature conditions of seawater that may affect marine life and ocean biogeochemistry. Under such conditions, phytoplankton communities may modify their structure and functions, and their resilience is not assured. This study characterizes the impact of MHWs on the phytoplankton spring bloom in the North‐Western Mediterranean Sea. Here, we synergistically combine autonomous observations from BioGeoChemical‐Argo floats, satellite‐based and marine ecosystem model data, and show that MHW events occurring during winter drastically inhibit phytoplankton carbon biomass in spring by up to 70%. Such reduction is related to the enhanced stratification of the water column under MHWs which hinders the renewal of nutrients from deep‐ocean reservoirs, thus preventing surface phytoplankton from blooming. This process negatively impacts particulate organic carbon stocks within the mixed layer, while severe events cause an earlier shift of phytoplankton phenology that provokes changes in zooplankton biomass distribution.