Pub Date : 2025-11-26DOI: 10.1016/j.ecolmodel.2025.111413
Yu-Jie Hu , Ren-Jie Cao
Ecosystem carbon sinks play a crucial role in mitigating climate change by sequestering carbon within ecosystems, thereby reducing atmospheric CO₂ concentrations. There is a lack of a unified theoretical framework and applicability research on methods for assessing carbon sinks. Therefore, this study comprehensively analyzes all mainstream carbon sink assessment methods. The selection of the Carnegie-Ames-Stanford approach (CASA) model known for its robust applicability, is made after evaluating principles, heterogeneity, and practicality. Then based on CASA model, through systematic literature analysis, we construct a three-parameter synergistic improvement theoretical framework. And taking China as a case study, this study develops the carbon sink assessment methodology applicable to sub-regional implementation. This will reduce the uncertainty in China's carbon sink assessment and provide a basis for the implementation of carbon sink management strategies to combat climate change.
{"title":"A theoretical framework for the methodology of carbon sink assessment in China: A literature review","authors":"Yu-Jie Hu , Ren-Jie Cao","doi":"10.1016/j.ecolmodel.2025.111413","DOIUrl":"10.1016/j.ecolmodel.2025.111413","url":null,"abstract":"<div><div>Ecosystem carbon sinks play a crucial role in mitigating climate change by sequestering carbon within ecosystems, thereby reducing atmospheric CO₂ concentrations. There is a lack of a unified theoretical framework and applicability research on methods for assessing carbon sinks. Therefore, this study comprehensively analyzes all mainstream carbon sink assessment methods. The selection of the Carnegie-Ames-Stanford approach (CASA) model known for its robust applicability, is made after evaluating principles, heterogeneity, and practicality. Then based on CASA model, through systematic literature analysis, we construct a three-parameter synergistic improvement theoretical framework. And taking China as a case study, this study develops the carbon sink assessment methodology applicable to sub-regional implementation. This will reduce the uncertainty in China's carbon sink assessment and provide a basis for the implementation of carbon sink management strategies to combat climate change.</div></div>","PeriodicalId":51043,"journal":{"name":"Ecological Modelling","volume":"513 ","pages":"Article 111413"},"PeriodicalIF":3.2,"publicationDate":"2025-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145594847","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-22DOI: 10.1016/j.ecolmodel.2025.111422
Gokul Govind , Martin Lange , Volker Grimm , Karin Frank , Jürgen Groeneveld
Honeybees are vital pollinators but face growing stress from weather, land-use change, and parasites. Detailed simulation models like BEEHAVE help explore these impacts but are slow, limiting large-scale applications. To address this, we developed machine learning metamodels that emulate BEEHAVE outputs. We ran BEEHAVE simulations using a new, faster Go implementation and generated millions of synthetic weather scenarios with our SynHr weather generator. Using these data, we trained two metamodels, a Neural Network and an XGBoost model, providing a comparison between a slower training method and a faster one. Applied to historical weather data across Germany, both metamodels accurately reproduce BEEHAVE’s annual honey yield predictions. Our spatial and temporal simulations confirmed a positive linear relationship between foraging hours and honey production that saturates at high foraging hours. The worker bee population peaked at intermediate foraging levels and declined beyond that. This work demonstrates how weather influences colony performance and shows that metamodeling can effectively complement mechanistic models, enabling scalable digital twin applications for environmental research.
{"title":"A machine learning-derived metamodel of BEEHAVE predicts how honey yield depends on weather and region across Germany","authors":"Gokul Govind , Martin Lange , Volker Grimm , Karin Frank , Jürgen Groeneveld","doi":"10.1016/j.ecolmodel.2025.111422","DOIUrl":"10.1016/j.ecolmodel.2025.111422","url":null,"abstract":"<div><div>Honeybees are vital pollinators but face growing stress from weather, land-use change, and parasites. Detailed simulation models like BEEHAVE help explore these impacts but are slow, limiting large-scale applications. To address this, we developed machine learning metamodels that emulate BEEHAVE outputs. We ran BEEHAVE simulations using a new, faster Go implementation and generated millions of synthetic weather scenarios with our SynHr weather generator. Using these data, we trained two metamodels, a Neural Network and an XGBoost model, providing a comparison between a slower training method and a faster one. Applied to historical weather data across Germany, both metamodels accurately reproduce BEEHAVE’s annual honey yield predictions. Our spatial and temporal simulations confirmed a positive linear relationship between foraging hours and honey production that saturates at high foraging hours. The worker bee population peaked at intermediate foraging levels and declined beyond that. This work demonstrates how weather influences colony performance and shows that metamodeling can effectively complement mechanistic models, enabling scalable digital twin applications for environmental research.</div></div>","PeriodicalId":51043,"journal":{"name":"Ecological Modelling","volume":"512 ","pages":"Article 111422"},"PeriodicalIF":3.2,"publicationDate":"2025-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145617780","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-21DOI: 10.1016/j.ecolmodel.2025.111415
Adam Shaaban Mgana , Aidi Huo , Yilu Zhao , Padam Raj Joshi , Abderrahim Firaouni
Crop growth models (CGMs) are essential tools for simulating plant growth, optimizing management, and supporting climate-resilient agriculture, yet selecting appropriate models and ensuring accurate predictions remain challenging due to data limitations, model structure constraints, and diverse agroecological conditions. This narrative review synthesizes CGM development, applications, and limitations, emphasizing systematic model selection, calibration, and validation to enhance prediction reliability, alongside best practices in sensitivity analysis, uncertainty assessment, and data integration. A major focus is on model integration and coupling, linking CGMs with hydrological, climate, economic, and spatial models to capture complex biophysical and socio-economic interactions, while multi-model approaches; including ensembles, modular frameworks, and cross-model comparisons, address single-model limitations, improve predictive reliability, and enable scenario-based analyses. Emerging technologies such as remote sensing, real-time sensors, AI, and digital twins further enhance adaptive modeling, automated calibration, and actionable decision support, supporting sustainable management, informing climate adaptation, and enabling evidence-based policy development. By combining systematic model selection, model coupling, and technological innovations, this review provides a roadmap for advancing CGMs toward more comprehensive, accurate, and actionable simulations, bridging the gap between modeling science and practical agricultural decision-making.
{"title":"Advancing crop growth modeling: Challenges, model integration, and pathways for improved decision support","authors":"Adam Shaaban Mgana , Aidi Huo , Yilu Zhao , Padam Raj Joshi , Abderrahim Firaouni","doi":"10.1016/j.ecolmodel.2025.111415","DOIUrl":"10.1016/j.ecolmodel.2025.111415","url":null,"abstract":"<div><div>Crop growth models (CGMs) are essential tools for simulating plant growth, optimizing management, and supporting climate-resilient agriculture, yet selecting appropriate models and ensuring accurate predictions remain challenging due to data limitations, model structure constraints, and diverse agroecological conditions. This narrative review synthesizes CGM development, applications, and limitations, emphasizing systematic model selection, calibration, and validation to enhance prediction reliability, alongside best practices in sensitivity analysis, uncertainty assessment, and data integration. A major focus is on model integration and coupling, linking CGMs with hydrological, climate, economic, and spatial models to capture complex biophysical and socio-economic interactions, while multi-model approaches; including ensembles, modular frameworks, and cross-model comparisons, address single-model limitations, improve predictive reliability, and enable scenario-based analyses. Emerging technologies such as remote sensing, real-time sensors, AI, and digital twins further enhance adaptive modeling, automated calibration, and actionable decision support, supporting sustainable management, informing climate adaptation, and enabling evidence-based policy development. By combining systematic model selection, model coupling, and technological innovations, this review provides a roadmap for advancing CGMs toward more comprehensive, accurate, and actionable simulations, bridging the gap between modeling science and practical agricultural decision-making.</div></div>","PeriodicalId":51043,"journal":{"name":"Ecological Modelling","volume":"512 ","pages":"Article 111415"},"PeriodicalIF":3.2,"publicationDate":"2025-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145571292","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-21DOI: 10.1016/j.ecolmodel.2025.111416
Sonja Radosavljevic , Francesca Acotto , Quanli Wang , Jie Su , Alexandros Gasparatos
Despite the promise of inland small-scale aquaculture for improving food security and alleviating poverty, the long-term sustainability of such production systems remains poorly understood, particularly in contexts where economic and ecological processes reinforce each other. This paper develops a stylized social–ecological model that captures feedbacks between producer wealth, fish biomass, and nutrient dynamics in inland pond-based small-scale aquaculture systems. The model reveals how these intertwined feedbacks shape the long-term dynamics of the system and lead to monostability, bistability, or multistability. These regimes correspond to a collapse, a high-yield but high-risk, and a sustainable equilibrium in fish production. Using bifurcation and stability analysis, we identify six dynamic scenarios: Balanced efficiency, Overload, Flux, Knife-edge, Tipping pond and Decay, that represent qualitatively different long-term outcomes. Rather than predicting specific outcomes, the model gives a structural understanding of small-scale aquaculture system dynamics and highlights the importance of local context and producers’ heterogeneity in shaping the outcomes. It also provides a theoretical foundation for scenario-based management and empirical model development.
{"title":"Pathways to sustainability or collapse in inland small-scale aquaculture systems: insights from a social–ecological systems model","authors":"Sonja Radosavljevic , Francesca Acotto , Quanli Wang , Jie Su , Alexandros Gasparatos","doi":"10.1016/j.ecolmodel.2025.111416","DOIUrl":"10.1016/j.ecolmodel.2025.111416","url":null,"abstract":"<div><div>Despite the promise of inland small-scale aquaculture for improving food security and alleviating poverty, the long-term sustainability of such production systems remains poorly understood, particularly in contexts where economic and ecological processes reinforce each other. This paper develops a stylized social–ecological model that captures feedbacks between producer wealth, fish biomass, and nutrient dynamics in inland pond-based small-scale aquaculture systems. The model reveals how these intertwined feedbacks shape the long-term dynamics of the system and lead to monostability, bistability, or multistability. These regimes correspond to a collapse, a high-yield but high-risk, and a sustainable equilibrium in fish production. Using bifurcation and stability analysis, we identify six dynamic scenarios: Balanced efficiency, Overload, Flux, Knife-edge, Tipping pond and Decay, that represent qualitatively different long-term outcomes. Rather than predicting specific outcomes, the model gives a structural understanding of small-scale aquaculture system dynamics and highlights the importance of local context and producers’ heterogeneity in shaping the outcomes. It also provides a theoretical foundation for scenario-based management and empirical model development.</div></div>","PeriodicalId":51043,"journal":{"name":"Ecological Modelling","volume":"512 ","pages":"Article 111416"},"PeriodicalIF":3.2,"publicationDate":"2025-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145571295","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-20DOI: 10.1016/j.ecolmodel.2025.111418
Jesse B. Alderliesten , Mark P. Zwart , J.Arjan G.M. de Visser , Arjan Stegeman , Egil A.J. Fischer
Mathematical models that are used to study the persistence and spread of antibiotic resistance plasmids usually describe conjugation within a single bacterial species or neglect ecological interactions and differences in conjugation rates between species. To increase our understanding of the role of differences between species in plasmid dynamics in a bacterial community, we extend the generalised Lotka-Volterra model with plasmid-bearing populations and density-dependent conjugation. With this model, we investigate how ecological interactions between species with distinct conjugation rates affect the invasion and spread of a conjugative plasmid in a bacterial community. We find that the possibility for plasmid invasion mediated by a species that was already present in the modelled plasmid-free communities is mainly determined by the intraspecies conjugation rate of the initially plasmid-bearing species and the fitness costs of bearing a plasmid. In contrast, the possibility for plasmid invasion mediated by a species that was not yet present in the plasmid-free community is mainly determined by ecological interspecies interactions involving the initially plasmid-bearing species. Nevertheless, decreased interspecies conjugation rates to and from the initially plasmid-bearing species reduce the spread of a plasmid through the community. In both scenarios, weaker competitive and stronger mutualistic ecological interspecies interactions reduce the spread of a plasmid through the community, limiting the creation of new plasmid-host combinations. Our results indicate that ecological interactions and differences in conjugation rates between species should be taken into account when modelling the invasion and spread of plasmids.
{"title":"The effects of ecological interactions and distinct conjugation rates on the invasion of a conjugative plasmid in bacterial communities","authors":"Jesse B. Alderliesten , Mark P. Zwart , J.Arjan G.M. de Visser , Arjan Stegeman , Egil A.J. Fischer","doi":"10.1016/j.ecolmodel.2025.111418","DOIUrl":"10.1016/j.ecolmodel.2025.111418","url":null,"abstract":"<div><div>Mathematical models that are used to study the persistence and spread of antibiotic resistance plasmids usually describe conjugation within a single bacterial species or neglect ecological interactions and differences in conjugation rates between species. To increase our understanding of the role of differences between species in plasmid dynamics in a bacterial community, we extend the generalised Lotka-Volterra model with plasmid-bearing populations and density-dependent conjugation. With this model, we investigate how ecological interactions between species with distinct conjugation rates affect the invasion and spread of a conjugative plasmid in a bacterial community. We find that the possibility for plasmid invasion mediated by a species that was already present in the modelled plasmid-free communities is mainly determined by the intraspecies conjugation rate of the initially plasmid-bearing species and the fitness costs of bearing a plasmid. In contrast, the possibility for plasmid invasion mediated by a species that was not yet present in the plasmid-free community is mainly determined by ecological interspecies interactions involving the initially plasmid-bearing species. Nevertheless, decreased interspecies conjugation rates to and from the initially plasmid-bearing species reduce the spread of a plasmid through the community. In both scenarios, weaker competitive and stronger mutualistic ecological interspecies interactions reduce the spread of a plasmid through the community, limiting the creation of new plasmid-host combinations. Our results indicate that ecological interactions and differences in conjugation rates between species should be taken into account when modelling the invasion and spread of plasmids.</div></div>","PeriodicalId":51043,"journal":{"name":"Ecological Modelling","volume":"512 ","pages":"Article 111418"},"PeriodicalIF":3.2,"publicationDate":"2025-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145571294","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-18DOI: 10.1016/j.ecolmodel.2025.111420
Viviane M. Oliveira , Fernando Fagundes Ferreira , Ediline L.F. Nguessap , Paulo R.A. Campos
Populations that remain at critical low sizes are acutely sensitive to random fluctuations in birth–death processes and to the accumulation of mildly deleterious mutations. We begin with a deterministic consumer–resource model featuring seasonally forced inputs, and based on this framework, we develop an individual-based, discrete-generation stochastic simulation to capture demographic and mutational randomness. The phase diagram of the deterministic model reveals the existence of three distinct regimes: one in which the population goes extinct, a second regime in which the population remains at a finite size, and finally a third non-physical regime observed at low mortality rates. Our simulation outcomes address the second regime. Two easily measured statistics – the minimum influx and the fraction of each cycle spent below a critical threshold, – are predictors of extinction. We demonstrate that enhancing long-term population persistence by increasing the peak of resource influx, reducing oscillation amplitude, or phase-shifting multiple resources out of sync is possible.
{"title":"Persistence of small populations under seasonal resource fluctuations","authors":"Viviane M. Oliveira , Fernando Fagundes Ferreira , Ediline L.F. Nguessap , Paulo R.A. Campos","doi":"10.1016/j.ecolmodel.2025.111420","DOIUrl":"10.1016/j.ecolmodel.2025.111420","url":null,"abstract":"<div><div>Populations that remain at critical low sizes are acutely sensitive to random fluctuations in birth–death processes and to the accumulation of mildly deleterious mutations. We begin with a deterministic consumer–resource model featuring seasonally forced inputs, and based on this framework, we develop an individual-based, discrete-generation stochastic simulation to capture demographic and mutational randomness. The phase diagram of the deterministic model reveals the existence of three distinct regimes: one in which the population goes extinct, a second regime in which the population remains at a finite size, and finally a third non-physical regime observed at low mortality rates. Our simulation outcomes address the second regime. Two easily measured statistics – the minimum influx <span><math><msub><mrow><mi>s</mi></mrow><mrow><mo>min</mo></mrow></msub></math></span> and the fraction of each cycle spent below a critical threshold, <span><math><mrow><mi>τ</mi><mo>/</mo><mi>T</mi></mrow></math></span> – are predictors of extinction. We demonstrate that enhancing long-term population persistence by increasing the peak of resource influx, reducing oscillation amplitude, or phase-shifting multiple resources out of sync is possible.</div></div>","PeriodicalId":51043,"journal":{"name":"Ecological Modelling","volume":"512 ","pages":"Article 111420"},"PeriodicalIF":3.2,"publicationDate":"2025-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145571290","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-16DOI: 10.1016/j.ecolmodel.2025.111414
Mahdi Sedighkia , Bithin Datta
This study introduces a novel hybrid framework combining Biogeography-Based Optimization (BBO) with a Mamdani fuzzy inference system (FIS) to simulate physical habitat suitability in riverine ecosystems. The approach was developed to overcome two critical limitations in habitat modelling: the reliance on expert-defined fuzzy rules and the need for extensive datasets, both of which are often unavailable in many aquatic ecosystems. The model was tested using field data, where key physical habitat parameters—flow depth, velocity, and bed particle size—were measured alongside normalized Brown Trout population data to assess habitat suitability. Three modelling approaches were compared: a univariate model, a multiple linear regression (MLR) model, and the proposed BBO-FIS model. The univariate and MLR models failed to reliably replicate observed suitability patterns due to their inability to account for complex ecological interactions. In contrast, the BBO-FIS model generated optimized membership functions and fuzzy rules directly from limited data, significantly improving prediction accuracy. Evaluation using statistical metrics—root mean square error (RMSE) and Nash–Sutcliffe efficiency (NSE)—confirmed the superior performance of the BBO-FIS framework. By integrating fuzzy logic with evolutionary optimization, the model successfully captured nonlinear and uncertain relationships among habitat variables, offering a more ecologically realistic simulation. The results highlight the potential of the BBO-FIS framework for use in ecological flow assessment, habitat conservation, and riverine ecosystem management. This hybrid approach provides a promising solution for robust habitat modelling in data-scarce and complex aquatic ecosystems in rivers.
{"title":"Hybrid biogeography-based optimization and Mamdani fuzzy modelling for physical habitat suitability modelling under limited data conditions","authors":"Mahdi Sedighkia , Bithin Datta","doi":"10.1016/j.ecolmodel.2025.111414","DOIUrl":"10.1016/j.ecolmodel.2025.111414","url":null,"abstract":"<div><div>This study introduces a novel hybrid framework combining Biogeography-Based Optimization (BBO) with a Mamdani fuzzy inference system (FIS) to simulate physical habitat suitability in riverine ecosystems. The approach was developed to overcome two critical limitations in habitat modelling: the reliance on expert-defined fuzzy rules and the need for extensive datasets, both of which are often unavailable in many aquatic ecosystems. The model was tested using field data, where key physical habitat parameters—flow depth, velocity, and bed particle size—were measured alongside normalized Brown Trout population data to assess habitat suitability. Three modelling approaches were compared: a univariate model, a multiple linear regression (MLR) model, and the proposed BBO-FIS model. The univariate and MLR models failed to reliably replicate observed suitability patterns due to their inability to account for complex ecological interactions. In contrast, the BBO-FIS model generated optimized membership functions and fuzzy rules directly from limited data, significantly improving prediction accuracy. Evaluation using statistical metrics—root mean square error (RMSE) and Nash–Sutcliffe efficiency (NSE)—confirmed the superior performance of the BBO-FIS framework. By integrating fuzzy logic with evolutionary optimization, the model successfully captured nonlinear and uncertain relationships among habitat variables, offering a more ecologically realistic simulation. The results highlight the potential of the BBO-FIS framework for use in ecological flow assessment, habitat conservation, and riverine ecosystem management. This hybrid approach provides a promising solution for robust habitat modelling in data-scarce and complex aquatic ecosystems in rivers.</div></div>","PeriodicalId":51043,"journal":{"name":"Ecological Modelling","volume":"512 ","pages":"Article 111414"},"PeriodicalIF":3.2,"publicationDate":"2025-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145571291","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-15DOI: 10.1016/j.ecolmodel.2025.111419
Mona Zevika , S. Khoirul Himmi , Anita Triska , Juni Wijayanti Puspita
Hyperparasitoids, which parasitize primary parasitoids, undermine classical biological control and pose a major challenge to integrated pest management (IPM). This study proposes a novel four-species model comprising a pest (host), a parasitoid, a hyperparasitoid, and a hypothetical predator of the hyperparasitoid. The model captures essential trophic interactions in agroecosystems, with parameters informed by cabbage pest systems, and reveals how predator introduction can reinforce pest suppression when parasitoid-based control is disrupted by hyperparasitoids. Through analytical investigation, we establish model feasibility and stability, and identify bifurcation thresholds that highlight limitations of classical control under hyperparasitoid pressure. An optimal impulsive control framework is then developed using the state-dependent Riccati equation (SDRE), incorporating seasonal fluctuations in pest growth. Two control strategies are evaluated: direct suppression via parasitoid release and indirect suppression through predator release targeting hyperparasitoids, with particular attention given to the effects of varying impulsive control intervals. Simulations reveal that combining both strategies achieves effective pest suppression while highlighting a trade-off between control intensity and outcome, shaped by intervention timing and seasonal dynamics. While daily control yields stronger suppression, it demands greater operational effort. In contrast, weekly control aligned with pest seasonality maintains pest levels below the economic threshold with much lower intervention frequency. These findings underscore the importance of accounting for ecological seasonality to avoid underestimating or overestimating control requirements, and to design pest management strategies with efficient timing and resilient intervention levels.
{"title":"Parasitoid-led control in seasonal multitrophic systems: Impulsive strategies under hyperparasitoid disruption","authors":"Mona Zevika , S. Khoirul Himmi , Anita Triska , Juni Wijayanti Puspita","doi":"10.1016/j.ecolmodel.2025.111419","DOIUrl":"10.1016/j.ecolmodel.2025.111419","url":null,"abstract":"<div><div>Hyperparasitoids, which parasitize primary parasitoids, undermine classical biological control and pose a major challenge to integrated pest management (IPM). This study proposes a novel four-species model comprising a pest (host), a parasitoid, a hyperparasitoid, and a hypothetical predator of the hyperparasitoid. The model captures essential trophic interactions in agroecosystems, with parameters informed by cabbage pest systems, and reveals how predator introduction can reinforce pest suppression when parasitoid-based control is disrupted by hyperparasitoids. Through analytical investigation, we establish model feasibility and stability, and identify bifurcation thresholds that highlight limitations of classical control under hyperparasitoid pressure. An optimal impulsive control framework is then developed using the state-dependent Riccati equation (SDRE), incorporating seasonal fluctuations in pest growth. Two control strategies are evaluated: direct suppression via parasitoid release and indirect suppression through predator release targeting hyperparasitoids, with particular attention given to the effects of varying impulsive control intervals. Simulations reveal that combining both strategies achieves effective pest suppression while highlighting a trade-off between control intensity and outcome, shaped by intervention timing and seasonal dynamics. While daily control yields stronger suppression, it demands greater operational effort. In contrast, weekly control aligned with pest seasonality maintains pest levels below the economic threshold with much lower intervention frequency. These findings underscore the importance of accounting for ecological seasonality to avoid underestimating or overestimating control requirements, and to design pest management strategies with efficient timing and resilient intervention levels.</div></div>","PeriodicalId":51043,"journal":{"name":"Ecological Modelling","volume":"512 ","pages":"Article 111419"},"PeriodicalIF":3.2,"publicationDate":"2025-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145520790","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-14DOI: 10.1016/j.ecolmodel.2025.111402
Nishan Nazer, Bimal P, Chithra K
Rapid urbanization in Kozhikode district, Kerala, from 2003 to 2023 has significantly altered land use and land cover (LULC), impacting water-related ecosystem services (WES). This study proposed a novel integration of pixel-level coupling coordination degree (CCD) analysis, Random Forest (RF) and geographical detector modeling to examine the complex LULC-WES interactions. The methodology employs high-resolution, multi-source spatial data to identify key LULC conversions driving WES changes and map hotspots (high WES degradation) and coldspots (stable/improved WES) at pixel-level resolution. The results reveal weak to moderate coordination between LULC and WES, with negative impacts concentrated in urbanizing southern coastal areas and Western Ghats foothills, driven by forest-to-agriculture and coconut plantation-to-agriculture conversions. Positive WES contributions stem from retaining coconut plantations and converting to rubber agroforestry systems. This GIS-based decision support system, supported by spatially explicit raster outputs, enables policymakers to design precise land use policies and simulate future scenarios for sustainable urban land management. The approach’s data efficiency and adaptability make it a valuable tool for micro-level land and water management, with potential for future integration of socioeconomic drivers and broader ecosystem services to further optimize urban LULC changes in rapidly developing regions.
{"title":"A GIS-based methodology to identify priority areas for land use regulations - Case study of Kozhikode","authors":"Nishan Nazer, Bimal P, Chithra K","doi":"10.1016/j.ecolmodel.2025.111402","DOIUrl":"10.1016/j.ecolmodel.2025.111402","url":null,"abstract":"<div><div>Rapid urbanization in Kozhikode district, Kerala, from 2003 to 2023 has significantly altered land use and land cover (LULC), impacting water-related ecosystem services (WES). This study proposed a novel integration of pixel-level coupling coordination degree (CCD) analysis, Random Forest (RF) and geographical detector modeling to examine the complex LULC-WES interactions. The methodology employs high-resolution, multi-source spatial data to identify key LULC conversions driving WES changes and map hotspots (high WES degradation) and coldspots (stable/improved WES) at pixel-level resolution. The results reveal weak to moderate coordination between LULC and WES, with negative impacts concentrated in urbanizing southern coastal areas and Western Ghats foothills, driven by forest-to-agriculture and coconut plantation-to-agriculture conversions. Positive WES contributions stem from retaining coconut plantations and converting to rubber agroforestry systems. This GIS-based decision support system, supported by spatially explicit raster outputs, enables policymakers to design precise land use policies and simulate future scenarios for sustainable urban land management. The approach’s data efficiency and adaptability make it a valuable tool for micro-level land and water management, with potential for future integration of socioeconomic drivers and broader ecosystem services to further optimize urban LULC changes in rapidly developing regions.</div></div>","PeriodicalId":51043,"journal":{"name":"Ecological Modelling","volume":"512 ","pages":"Article 111402"},"PeriodicalIF":3.2,"publicationDate":"2025-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145520793","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-14DOI: 10.1016/j.ecolmodel.2025.111398
Pengfei Wang , Junguo Liu , Shuyu Zhang , Shuman Liu , Soon Keat Tan
This study quantified the hydrological impacts of changes in the proportion of green-grey-blue infrastructures in the Maozhou River Basin following urban expansion in the basin from 1990 to 2019. This study found that, as the proportion of green area decreased, both runoff and runoff coefficient exhibited increasing trends, whereas evapotranspiration showed a decreasing trend. The changes in the magnitude of the relevant hydrologic processes were found to be non-linear and strongly correlated with variations in the areal extent of the green-grey-blue infrastructures. From 1990 to 2011, the reduction in green infrastructure area was the primary cause of increased runoff, runoff coefficient, and decreased evapotranspiration. After 2012, and following the shift of urban expansion focus on sustainable development, the areal expansion of grey infrastructure became the dominant factor influencing the hydrologic processes, much larger than the effects of green infrastructure. The impact of changes in green-grey-blue infrastructure areas on hydrologic processes is significant, albeit at a slower rate compared to that in 2012, and underscores a critical shift in the hydrological regime of the Maozhou River Basin.
{"title":"Hydrological responses to changes in the proportion of green-grey-blue infrastructures following urban expansion","authors":"Pengfei Wang , Junguo Liu , Shuyu Zhang , Shuman Liu , Soon Keat Tan","doi":"10.1016/j.ecolmodel.2025.111398","DOIUrl":"10.1016/j.ecolmodel.2025.111398","url":null,"abstract":"<div><div>This study quantified the hydrological impacts of changes in the proportion of green-grey-blue infrastructures in the Maozhou River Basin following urban expansion in the basin from 1990 to 2019. This study found that, as the proportion of green area decreased, both runoff and runoff coefficient exhibited increasing trends, whereas evapotranspiration showed a decreasing trend. The changes in the magnitude of the relevant hydrologic processes were found to be non-linear and strongly correlated with variations in the areal extent of the green-grey-blue infrastructures. From 1990 to 2011, the reduction in green infrastructure area was the primary cause of increased runoff, runoff coefficient, and decreased evapotranspiration. After 2012, and following the shift of urban expansion focus on sustainable development, the areal expansion of grey infrastructure became the dominant factor influencing the hydrologic processes, much larger than the effects of green infrastructure. The impact of changes in green-grey-blue infrastructure areas on hydrologic processes is significant, albeit at a slower rate compared to that in 2012, and underscores a critical shift in the hydrological regime of the Maozhou River Basin.</div></div>","PeriodicalId":51043,"journal":{"name":"Ecological Modelling","volume":"512 ","pages":"Article 111398"},"PeriodicalIF":3.2,"publicationDate":"2025-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145520789","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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