Pub Date : 2026-01-30DOI: 10.1016/j.ecoinf.2026.103624
Diogo F. Oliveira , Gonçalo M. Marques , Filipe M.P. Santos , Laure Pecquerie , João M.C. Sousa , Tiago Domingos
Dynamic Energy Budget (DEB) theory is a general theory that describes how organisms utilize the energy in food for maintenance, growth, development, and reproduction. DEB models have been widely applied in fields such as conservation biology, aquaculture and ecotoxicology, due to their ability to simulate how organisms respond to changing environmental conditions. To obtain a DEB model, the calibration problem must be solved: find the parameters that minimize the deviation between observed data and model predictions. While DEB model calibration is largely automated, the selection of initial parameters remains a key unresolved step, since the only automated method – the bijection method – often fails to produce a feasible initial parameter set. Consequently, modelers resort to trial-and-error to find parameters to seed the estimation. To bridge this gap, we propose using machine learning to initialize the calibration. We develop two models: a neural network and a 1-nearest-neighbor. Both models are built with a focus on feasibility, directly integrating parameter constraints into their structure. We train and evaluate our methods on the 5000+ DEB models in the Add-my-Pet database. Both methods generate feasible parameter sets in 99% of cases — compared to only 40% for the bijection method. The neural network initialization leads to improved DEB model calibration, achieving a calibration loss three times lower, on average, when compared to other methods. To support broader adoption, we have open-sourced our code and our models are available as initialization options within DEBtool, the primary software for parameter calibration.
{"title":"Reliable machine learning initialization methods for the calibration of Dynamic Energy Budget models","authors":"Diogo F. Oliveira , Gonçalo M. Marques , Filipe M.P. Santos , Laure Pecquerie , João M.C. Sousa , Tiago Domingos","doi":"10.1016/j.ecoinf.2026.103624","DOIUrl":"10.1016/j.ecoinf.2026.103624","url":null,"abstract":"<div><div>Dynamic Energy Budget (DEB) theory is a general theory that describes how organisms utilize the energy in food for maintenance, growth, development, and reproduction. DEB models have been widely applied in fields such as conservation biology, aquaculture and ecotoxicology, due to their ability to simulate how organisms respond to changing environmental conditions. To obtain a DEB model, the calibration problem must be solved: find the parameters that minimize the deviation between observed data and model predictions. While DEB model calibration is largely automated, the selection of initial parameters remains a key unresolved step, since the only automated method – the bijection method – often fails to produce a feasible initial parameter set. Consequently, modelers resort to trial-and-error to find parameters to seed the estimation. To bridge this gap, we propose using machine learning to initialize the calibration. We develop two models: a neural network and a 1-nearest-neighbor. Both models are built with a focus on feasibility, directly integrating parameter constraints into their structure. We train and evaluate our methods on the 5000+ DEB models in the Add-my-Pet database. Both methods generate feasible parameter sets in 99% of cases — compared to only 40% for the bijection method. The neural network initialization leads to improved DEB model calibration, achieving a calibration loss three times lower, on average, when compared to other methods. To support broader adoption, we have open-sourced our code and our models are available as initialization options within <span>DEBtool</span>, the primary software for parameter calibration.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"94 ","pages":"Article 103624"},"PeriodicalIF":7.3,"publicationDate":"2026-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146173894","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2026-01-29DOI: 10.1016/j.ecoinf.2026.103629
Juan M. Requena-Mullor , Estefanía Rodríguez , Mónica González , Antonio J. Castro , Enrica Garau , Irene Pérez-Ramírez , Álvaro Peláez-Pérez , Pablo Barranco
Understanding pest dynamics beyond greenhouse boundaries is critical for anticipating outbreaks and guiding sustainable management. Despite the central role of insect pest ecology in Integrated Pest Management, landscape-scale research on external greenhouse environments is limited. This knowledge gap constrains the development of spatially informed early-warning systems and green infrastructure to intercept pest movement. We introduce a spatially explicit simulation framework designed to model pest abundance in the peri-greenhouse landscape by integrating high-resolution data on greenhouse density, landscape structure, and resistance to pest dispersal. We used Barrier models to assess the comparative performance of cluster versus simple random sampling across varying spatial scenarios and sample sizes. Our results demonstrate that greenhouse spatial arrangement significantly mediates sampling efficiency. Cluster sampling consistently outperformed simple random sampling in scarcely and densely fragmented landscapes, reflecting its effectiveness in capturing strong spatial continuity. Crucially, in moderately dense landscapes, both methods showed comparable performance, suggesting that intermediate fragmentation disrupts the necessary spatial aggregation for cluster sampling's efficiency. These findings highlight the necessity of matching sampling design to spatial landscape features and pest management goals. The proposed framework is a customizable and scalable tool for simulating pest dynamics and optimizing field monitoring strategies. By bridging geostatistical modeling and ecological simulation, it provides a transferable workflow that advances landscape-scale ecological modeling and supports the design of adaptive pest management strategies. It strengthens the integration of ecological informatics into decision-making by enabling scenario testing under diverse spatial configurations, offering practical insights for spatially informed early-warning systems in agricultural landscapes.
{"title":"Accounting for physical barriers in insect pest modeling: A spatially explicit simulation-based approach","authors":"Juan M. Requena-Mullor , Estefanía Rodríguez , Mónica González , Antonio J. Castro , Enrica Garau , Irene Pérez-Ramírez , Álvaro Peláez-Pérez , Pablo Barranco","doi":"10.1016/j.ecoinf.2026.103629","DOIUrl":"10.1016/j.ecoinf.2026.103629","url":null,"abstract":"<div><div>Understanding pest dynamics beyond greenhouse boundaries is critical for anticipating outbreaks and guiding sustainable management. Despite the central role of insect pest ecology in Integrated Pest Management, landscape-scale research on external greenhouse environments is limited. This knowledge gap constrains the development of spatially informed early-warning systems and green infrastructure to intercept pest movement. We introduce a spatially explicit simulation framework designed to model pest abundance in the peri-greenhouse landscape by integrating high-resolution data on greenhouse density, landscape structure, and resistance to pest dispersal. We used Barrier models to assess the comparative performance of cluster versus simple random sampling across varying spatial scenarios and sample sizes. Our results demonstrate that greenhouse spatial arrangement significantly mediates sampling efficiency. Cluster sampling consistently outperformed simple random sampling in scarcely and densely fragmented landscapes, reflecting its effectiveness in capturing strong spatial continuity. Crucially, in moderately dense landscapes, both methods showed comparable performance, suggesting that intermediate fragmentation disrupts the necessary spatial aggregation for cluster sampling's efficiency. These findings highlight the necessity of matching sampling design to spatial landscape features and pest management goals. The proposed framework is a customizable and scalable tool for simulating pest dynamics and optimizing field monitoring strategies. By bridging geostatistical modeling and ecological simulation, it provides a transferable workflow that advances landscape-scale ecological modeling and supports the design of adaptive pest management strategies. It strengthens the integration of ecological informatics into decision-making by enabling scenario testing under diverse spatial configurations, offering practical insights for spatially informed early-warning systems in agricultural landscapes.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"94 ","pages":"Article 103629"},"PeriodicalIF":7.3,"publicationDate":"2026-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146173896","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2026-01-29DOI: 10.1016/j.ecoinf.2026.103633
Ludovic Crochard , Léa Mariton , Colin Fontaine , Mathilde Baude , Maxime Ragué , Didier Bas , Sabrina Gaba , Vincent Bretagnolle , Romain Julliard , Yves Bas
Animal pollination is involved in the reproduction of 90% of flowering plants. Approximately 70% of crops at global scale rely on pollinators, and growing concerns about insect decline highlight the need for effective monitoring of their activity. Traditional monitoring methods are often time-consuming and destructive. Technological advances now allow the development of passive techniques, such as computer vision and acoustic recording, combined with machine learning. These methods offer improved spatial and temporal coverage for biodiversity monitoring. Passive acoustic monitoring is particularly promising for tracking pollinators but remains underutilized and often relies on outdated machine learning approaches. Recently, deep learning methods—originally designed for image analysis—have begun to be applied to spectrograms of acoustic monitoring of various taxa, including flying insects. In this study, we propose a method for quantifying pollinator activity in sunflower fields based on the automatic detection of wingbeat sounds. We tested both a random forest and a deep learning algorithm using a new open-access software tool for acoustic biodiversity monitoring, TadariDeep. Our results show that deep learning outperforms random forest algorithms in classifying pollinator flight sounds. Comparisons with a standard visual observation protocol confirm the validity of the acoustic approach. Moreover, acoustic monitoring provides a more continuous and accurate assessment of pollinator activity than visual methods. Therefore, combining passive acoustic monitoring with deep learning presents a reliable way to assess pollinator activity at broad spatial and temporal scales. Nonetheless, further refinement is needed to improve species-level identification.
{"title":"Buzzy bees: Improving the monitoring of pollinator activity in sunflower fields with continuous acoustic recording and deep learning","authors":"Ludovic Crochard , Léa Mariton , Colin Fontaine , Mathilde Baude , Maxime Ragué , Didier Bas , Sabrina Gaba , Vincent Bretagnolle , Romain Julliard , Yves Bas","doi":"10.1016/j.ecoinf.2026.103633","DOIUrl":"10.1016/j.ecoinf.2026.103633","url":null,"abstract":"<div><div>Animal pollination is involved in the reproduction of 90% of flowering plants. Approximately 70% of crops at global scale rely on pollinators, and growing concerns about insect decline highlight the need for effective monitoring of their activity. Traditional monitoring methods are often time-consuming and destructive. Technological advances now allow the development of passive techniques, such as computer vision and acoustic recording, combined with machine learning. These methods offer improved spatial and temporal coverage for biodiversity monitoring. Passive acoustic monitoring is particularly promising for tracking pollinators but remains underutilized and often relies on outdated machine learning approaches. Recently, deep learning methods—originally designed for image analysis—have begun to be applied to spectrograms of acoustic monitoring of various taxa, including flying insects. In this study, we propose a method for quantifying pollinator activity in sunflower fields based on the automatic detection of wingbeat sounds. We tested both a random forest and a deep learning algorithm using a new open-access software tool for acoustic biodiversity monitoring, TadariDeep. Our results show that deep learning outperforms random forest algorithms in classifying pollinator flight sounds. Comparisons with a standard visual observation protocol confirm the validity of the acoustic approach. Moreover, acoustic monitoring provides a more continuous and accurate assessment of pollinator activity than visual methods. Therefore, combining passive acoustic monitoring with deep learning presents a reliable way to assess pollinator activity at broad spatial and temporal scales. Nonetheless, further refinement is needed to improve species-level identification.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"94 ","pages":"Article 103633"},"PeriodicalIF":7.3,"publicationDate":"2026-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146173895","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2026-01-28DOI: 10.1016/j.ecoinf.2026.103622
Amina Ben Salah , Bouchra Zellou , Mohammed Seaid , Mofdi El Amrani , Nabil El Mocayd
Sea surface temperature (SST) and chlorophyll-a (Chl-a) are key indicators of marine ecosystem productivity, particularly for small pelagic species that are sensitive to climate-driven environmental changes. This study investigates the coupled dynamics of SST and Chl-a in two ecologically distinct regions, the Alboran Sea (AS) and the North Atlantic Moroccan Ocean (NAMO), to better understand their response under future climate scenarios. Historical satellite observations from MODIS-Aqua and projections from six Coupled Model Intercomparison Project Phase VI (CMIP6) General Circulation Models (GCMs) are analyzed under two Shared Socioeconomic Pathways (SSP2-4.5 and SSP5-8.5). Multivariate bias correction (MBCp) is performed to correct systematic biases in the model outputs. While GCMs effectively capture SST trends, they show significant limitations in simulating Chl-a variability. To address this issue, we introduce a conditional copula-based inference framework that links SST and Chl-a distributions based on their joint probabilistic behavior. In addition, marginal distributions are identified using goodness-of-fit tests, AIC and BIC. The copula families have been selected based on AIC, taking into account regional and seasonal variability. Conditional simulations from fitted copulas, informed by future SST projections, are used to predict Chl-a levels under climate change. However, the method is initially validated during the historical period using historical SST models, confirming the robustness of the approach. Results highlight a pronounced divergence between the optimistic and the pessimistic scenarios, suggesting a consistent reduction of the most productive phases that sustain higher trophic levels. This decline in productivity indicates that the far future ocean will not merely be a warmer version of the present system but a biogeochemically altered and less resilient ocean, characterized by lower productivity and reduced variability. Regionally, the NAMO region is projected to undergo a gradual yet persistent weakening. In contrast, the AS region is projected to face two contrasting futures, partial resilience under an optimistic scenario or a potential catastrophic ecological transition under a pessimistic scenario.
{"title":"Projecting Chlorophyll-a distributions under climate change using Copula-based inference and SST projections","authors":"Amina Ben Salah , Bouchra Zellou , Mohammed Seaid , Mofdi El Amrani , Nabil El Mocayd","doi":"10.1016/j.ecoinf.2026.103622","DOIUrl":"10.1016/j.ecoinf.2026.103622","url":null,"abstract":"<div><div>Sea surface temperature (SST) and chlorophyll-a (Chl-a) are key indicators of marine ecosystem productivity, particularly for small pelagic species that are sensitive to climate-driven environmental changes. This study investigates the coupled dynamics of SST and Chl-a in two ecologically distinct regions, the Alboran Sea (AS) and the North Atlantic Moroccan Ocean (NAMO), to better understand their response under future climate scenarios. Historical satellite observations from MODIS-Aqua and projections from six Coupled Model Intercomparison Project Phase VI (CMIP6) General Circulation Models (GCMs) are analyzed under two Shared Socioeconomic Pathways (SSP2-4.5 and SSP5-8.5). Multivariate bias correction (MBCp) is performed to correct systematic biases in the model outputs. While GCMs effectively capture SST trends, they show significant limitations in simulating Chl-a variability. To address this issue, we introduce a conditional copula-based inference framework that links SST and Chl-a distributions based on their joint probabilistic behavior. In addition, marginal distributions are identified using goodness-of-fit tests, AIC and BIC. The copula families have been selected based on AIC, taking into account regional and seasonal variability. Conditional simulations from fitted copulas, informed by future SST projections, are used to predict Chl-a levels under climate change. However, the method is initially validated during the historical period using historical SST models, confirming the robustness of the approach. Results highlight a pronounced divergence between the optimistic and the pessimistic scenarios, suggesting a consistent reduction of the most productive phases that sustain higher trophic levels. This decline in productivity indicates that the far future ocean will not merely be a warmer version of the present system but a biogeochemically altered and less resilient ocean, characterized by lower productivity and reduced variability. Regionally, the NAMO region is projected to undergo a gradual yet persistent weakening. In contrast, the AS region is projected to face two contrasting futures, partial resilience under an optimistic scenario or a potential catastrophic ecological transition under a pessimistic scenario.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"94 ","pages":"Article 103622"},"PeriodicalIF":7.3,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174421","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2026-01-25DOI: 10.1016/j.ecoinf.2026.103625
Qiqi Ding , Haojun Xi , Pinjian Li , Hongzhe Fang , Yibin Yuan , Tianhong Li
Rapid urbanisation intensifies multiple pollution pressures on river ecosystems, generating heterogeneous and nonlinear spatiotemporal water quality dynamics that challenge conventional evaluation methods. Although machine-learning (ML) models are increasingly used for water-quality assessment and prediction, most applications remain evaluation-centric and rarely link their outputs to receptor-based source apportionment or explicit spatial zoning, which limits interpretability and management relevance. Here, we proposed an integrated framework that couples ML-assisted water quality index (WQI) optimisation with receptor modelling and unsupervised spatial clustering. Using monthly observations of ten water quality indicators from 19 sites in the Jinma River Basin (2018–2022), we trained an eXtreme Gradient Boosting (XGBoost) model to derive optimised weights of water quality indicators for WQI aggregation. We then applied positive matrix factorisation (PMF) to resolve latent source factors and quantify their contributions, and used self-organising maps (SOM) to cluster monitoring sites into spatially coherent zones based on both WQI status and source composition. Four dominant contributors to the basin water pollution were identified: seasonal hydrological influences (28.16%), domestic sewage (27.36%), agricultural runoff (27.30%) and industrial emissions (17.18%). Integrating the XGBoost-optimised WQI, PMF-resolved source contributions, and SOM clusters delineated three functional management zones, specifically, forested headwaters with high WQI and minimal anthropogenic influence, midstream transition reaches dominated by nutrient-enriched agricultural runoff, and urban downstream corridors affected by combined industrial and domestic inputs. This modular, code-driven workflow translates routine multi-indicator monitoring data into management outputs and can be retrained for other river basins facing complex pollution regimes.
{"title":"Linking water quality assessment to source apportionment with machine learning-assisted WQI, PMF, and SOM: A case study of the Jinma River basin","authors":"Qiqi Ding , Haojun Xi , Pinjian Li , Hongzhe Fang , Yibin Yuan , Tianhong Li","doi":"10.1016/j.ecoinf.2026.103625","DOIUrl":"10.1016/j.ecoinf.2026.103625","url":null,"abstract":"<div><div>Rapid urbanisation intensifies multiple pollution pressures on river ecosystems, generating heterogeneous and nonlinear spatiotemporal water quality dynamics that challenge conventional evaluation methods. Although machine-learning (ML) models are increasingly used for water-quality assessment and prediction, most applications remain evaluation-centric and rarely link their outputs to receptor-based source apportionment or explicit spatial zoning, which limits interpretability and management relevance. Here, we proposed an integrated framework that couples ML-assisted water quality index (WQI) optimisation with receptor modelling and unsupervised spatial clustering. Using monthly observations of ten water quality indicators from 19 sites in the Jinma River Basin (2018–2022), we trained an eXtreme Gradient Boosting (XGBoost) model to derive optimised weights of water quality indicators for WQI aggregation. We then applied positive matrix factorisation (PMF) to resolve latent source factors and quantify their contributions, and used self-organising maps (SOM) to cluster monitoring sites into spatially coherent zones based on both WQI status and source composition. Four dominant contributors to the basin water pollution were identified: seasonal hydrological influences (28.16%), domestic sewage (27.36%), agricultural runoff (27.30%) and industrial emissions (17.18%). Integrating the XGBoost-optimised WQI, PMF-resolved source contributions, and SOM clusters delineated three functional management zones, specifically, forested headwaters with high WQI and minimal anthropogenic influence, midstream transition reaches dominated by nutrient-enriched agricultural runoff, and urban downstream corridors affected by combined industrial and domestic inputs. This modular, code-driven workflow translates routine multi-indicator monitoring data into management outputs and can be retrained for other river basins facing complex pollution regimes.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"94 ","pages":"Article 103625"},"PeriodicalIF":7.3,"publicationDate":"2026-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146080075","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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}
Planning resilient plant communities for ecological restoration under climate change requires tools that integrate functional trait data with explicit climatic constraints. This study presents a multi-objective optimisation framework that identifies species assemblages balancing hydraulic safety (drought resistance) with functional diversity. We apply this approach to a Mediterranean forest system using three key traits, xylem vulnerability (P50), specific leaf area (SLA), and leaf dry matter content (LDMC), to represent species' physiological performance and resource-use strategies. Climatic filtering is included by deriving community-weighted P50 targets from the Standardised Precipitation Evapotranspiration Index (SPEI), classified into drought categories. We report two representative scenarios—Near Normal (–0.99 < SPEI<0.99) and Extra Dry (SPEI<-2.0)—thereby aligning species selection with scenario-specific drought conditions. Functional diversity is quantified using Rao's quadratic entropy, which captures trait dissimilarity across communities. Using the Non-dominated Sorting Genetic Algorithm II (NSGA-II), the model generates Pareto fronts describing the trade-offs between hydraulic alignment and functional divergence. Across climatic scenarios, the increasing drought severity progressively constrains the solution space and promotes the selection of moderately drought-tolerant and functionally distinct species, shifting community-weighted P50 towards more negative values. In the Near Normal scenario (target P50 ≈ −2.0 MPa), the Pareto front spans P50 ≈ −4.0 to −2.0 MPa and Rao's Q ≈ 0.36–5.3. In contrast, in the Extra Dry scenario (target P50 ≈ −3.8 MPa), P50 narrows to ≈ −4.3 to −3.8 MPa while diversity remains high (Rao's Q ≈ 5.0–5.4). Kernel density estimation and pairwise overlap analyses across 500 optimisation runs demonstrate a strong convergence, particularly under extreme drought (in the Extra Dry scenario, 80.5% of solutions fall within the top 5% kernel-density region). Compositional similarity to field communities, measured using Bray-Curtis' dissimilarity, corroborates this pattern, with a lower median dissimilarity under Extra Dry than Near Normal (median BC = 0.365 vs 0.478). This framework provides a robust, flexible, and scalable method for trait-based restoration planning. By explicitly modelling trade-offs and uncertainty, it enhances the ecological relevance and reproducibility of species selection under future climate scenarios, offering practical support for data-informed restoration strategies.
{"title":"Functional trait-based multi-objective optimisation of plant communities for ecological restoration under climate change","authors":"Kristina Micalizzi, Danilo Lombardi, Giulia Bardino, Marcello Vitale","doi":"10.1016/j.ecoinf.2026.103623","DOIUrl":"10.1016/j.ecoinf.2026.103623","url":null,"abstract":"<div><div>Planning resilient plant communities for ecological restoration under climate change requires tools that integrate functional trait data with explicit climatic constraints. This study presents a multi-objective optimisation framework that identifies species assemblages balancing hydraulic safety (drought resistance) with functional diversity. We apply this approach to a Mediterranean forest system using three key traits, xylem vulnerability (P50), specific leaf area (SLA), and leaf dry matter content (LDMC), to represent species' physiological performance and resource-use strategies. Climatic filtering is included by deriving community-weighted P50 targets from the Standardised Precipitation Evapotranspiration Index (SPEI), classified into drought categories. We report two representative scenarios—Near Normal (–0.99 < SPEI<0.99) and Extra Dry (SPEI<-2.0)—thereby aligning species selection with scenario-specific drought conditions. Functional diversity is quantified using Rao's quadratic entropy, which captures trait dissimilarity across communities. Using the Non-dominated Sorting Genetic Algorithm II (NSGA-II), the model generates Pareto fronts describing the trade-offs between hydraulic alignment and functional divergence. Across climatic scenarios, the increasing drought severity progressively constrains the solution space and promotes the selection of moderately drought-tolerant and functionally distinct species, shifting community-weighted P50 towards more negative values. In the Near Normal scenario (target P50 ≈ −2.0 MPa), the Pareto front spans P50 ≈ −4.0 to −2.0 MPa and Rao's Q ≈ 0.36–5.3. In contrast, in the Extra Dry scenario (target P50 ≈ −3.8 MPa), P50 narrows to ≈ −4.3 to −3.8 MPa while diversity remains high (Rao's Q ≈ 5.0–5.4). Kernel density estimation and pairwise overlap analyses across 500 optimisation runs demonstrate a strong convergence, particularly under extreme drought (in the Extra Dry scenario, 80.5% of solutions fall within the top 5% kernel-density region). Compositional similarity to field communities, measured using Bray-Curtis' dissimilarity, corroborates this pattern, with a lower median dissimilarity under Extra Dry than Near Normal (median BC = 0.365 vs 0.478). This framework provides a robust, flexible, and scalable method for trait-based restoration planning. By explicitly modelling trade-offs and uncertainty, it enhances the ecological relevance and reproducibility of species selection under future climate scenarios, offering practical support for data-informed restoration strategies.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"94 ","pages":"Article 103623"},"PeriodicalIF":7.3,"publicationDate":"2026-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146080648","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2026-01-23DOI: 10.1016/j.ecoinf.2026.103610
Javier Lopatin , Rocío Araya-López , Iryna Dronova
Plant phenology is often used as an indicator of ecological processes and responses to changing environmental conditions. Remote sensing enables phenological monitoring across space and time, yet separating vegetation composition or environmental drivers remains challenging in heterogeneous tidal marshes. We analyzed Sentinel-2 EVI time series to derive phenological metrics, grouped pixels into phenological types via clustering, and linked these to vegetation composition and environmental variation in Suisun Marsh, California. Using phenology metrics alone, a PLS-DA classifier achieved an overall accuracy of 0.69 (per-class balanced accuracy of 0.50–0.81), demonstrating that phenology captures meaningful community patterns. However, transition zones exhibited a complex interplay among vegetation, phenology, elevation, and hydrology: mean mixing rates ranged from 1 to 45%, with class-specific error structures (sensitivity = 0–0.80), indicating limited separability where inundation and salinity covary with phenology. The variation in the timing and magnitude of greenness, alongside the differing proportions of vegetation types across phenological types, suggests that these interacting drivers jointly shape seasonal vegetation cycles. Core phenology metrics (start, peak, end of season) effectively distinguished wetland communities with similar aboveground function and aided delineation of wetland–upland transitions. Yet, despite ecological differences, several vegetation types expressed similar phenological behavior, likely due to shared hydrologic and microclimatic regimes and, potentially, spectral mixing at moderate spatial resolution. We provide a comprehensive work that combines management and vegetation classes to disentangle the complex interplay between wetland communities and remotely sensed phenology predictions.
{"title":"Remotely sensed phenology reveals environmental and management controls on coastal wetland plant communities","authors":"Javier Lopatin , Rocío Araya-López , Iryna Dronova","doi":"10.1016/j.ecoinf.2026.103610","DOIUrl":"10.1016/j.ecoinf.2026.103610","url":null,"abstract":"<div><div>Plant phenology is often used as an indicator of ecological processes and responses to changing environmental conditions. Remote sensing enables phenological monitoring across space and time, yet separating vegetation composition or environmental drivers remains challenging in heterogeneous tidal marshes. We analyzed Sentinel-2 EVI time series to derive phenological metrics, grouped pixels into phenological types via clustering, and linked these to vegetation composition and environmental variation in Suisun Marsh, California. Using phenology metrics alone, a PLS-DA classifier achieved an overall accuracy of 0.69 (per-class balanced accuracy of 0.50–0.81), demonstrating that phenology captures meaningful community patterns. However, transition zones exhibited a complex interplay among vegetation, phenology, elevation, and hydrology: mean mixing rates ranged from 1 to 45%, with class-specific error structures (sensitivity = 0–0.80), indicating limited separability where inundation and salinity covary with phenology. The variation in the timing and magnitude of greenness, alongside the differing proportions of vegetation types across phenological types, suggests that these interacting drivers jointly shape seasonal vegetation cycles. Core phenology metrics (start, peak, end of season) effectively distinguished wetland communities with similar aboveground function and aided delineation of wetland–upland transitions. Yet, despite ecological differences, several vegetation types expressed similar phenological behavior, likely due to shared hydrologic and microclimatic regimes and, potentially, spectral mixing at moderate spatial resolution. We provide a comprehensive work that combines management and vegetation classes to disentangle the complex interplay between wetland communities and remotely sensed phenology predictions.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"94 ","pages":"Article 103610"},"PeriodicalIF":7.3,"publicationDate":"2026-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146080647","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2026-01-22DOI: 10.1016/j.ecoinf.2026.103627
Seyd Teymoor Seydi , Mojtaba Sadegh
Hyperspectral change detection (HCD) is a critical remote sensing approach for monitoring land surface changes. Despite notable progress, state-of-the-art HCD methodologies encounter difficulties in modeling the high-dimensional, nonlinear spectral characteristics of hyperspectral imagery when comparing pre- and post-change imagery. To address these limitations, we propose a novel deep learning architecture, termed Cross Kolmogorov–Arnold Network (CrossKAN), for accurate and interpretable HCD. The CrossKAN model is predicated on the functional decomposition theory of Kolmogorov–Arnold Networks, a theoretical framework that enables compact and mathematically grounded modeling of complex spectral relationships. A Siamese architecture is employed to process bi-temporal image patches, enabling robust feature extraction by KAN layers based on Chebyshev polynomials. Next, deep features are fused in the CrossKAN layer, and are fed to the subsequent KAN layers to discriminate between change and no-change locations. CrossKAN's performance was assessed using four benchmark datasets in different geographical locations with divergent context and change classes. CrossKAN outperformed state-of-the-art HCD models, including SSTFormer, DBS3TAN, ML-EDAN, and MSDFFN, and achieved an overall accuracy of >94%. Low missed detection and false alarm rates demonstrate CrossKAN's superior effectiveness and generalization in complex regions.
{"title":"CrossKAN: A bivariate cross KAN model for hyperspectral change detection","authors":"Seyd Teymoor Seydi , Mojtaba Sadegh","doi":"10.1016/j.ecoinf.2026.103627","DOIUrl":"10.1016/j.ecoinf.2026.103627","url":null,"abstract":"<div><div>Hyperspectral change detection (HCD) is a critical remote sensing approach for monitoring land surface changes. Despite notable progress, state-of-the-art HCD methodologies encounter difficulties in modeling the high-dimensional, nonlinear spectral characteristics of hyperspectral imagery when comparing pre- and post-change imagery. To address these limitations, we propose a novel deep learning architecture, termed Cross Kolmogorov–Arnold Network (CrossKAN), for accurate and interpretable HCD. The CrossKAN model is predicated on the functional decomposition theory of Kolmogorov–Arnold Networks, a theoretical framework that enables compact and mathematically grounded modeling of complex spectral relationships. A Siamese architecture is employed to process bi-temporal image patches, enabling robust feature extraction by KAN layers based on Chebyshev polynomials. Next, deep features are fused in the CrossKAN layer, and are fed to the subsequent KAN layers to discriminate between change and no-change locations. CrossKAN's performance was assessed using four benchmark datasets in different geographical locations with divergent context and change classes. CrossKAN outperformed state-of-the-art HCD models, including SSTFormer, DBS<sup>3</sup>TAN, ML-EDAN, and MSDFFN, and achieved an overall accuracy of >94%. Low missed detection and false alarm rates demonstrate CrossKAN's superior effectiveness and generalization in complex regions.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"94 ","pages":"Article 103627"},"PeriodicalIF":7.3,"publicationDate":"2026-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146173892","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2026-01-22DOI: 10.1016/j.ecoinf.2026.103628
Ying Liu , Sihan Wang , Zhaohua Liu , Dongmei Lyu , Sijia Li , Bingxue Zhu , Ge Liu , Kaishan Song
Orchard plantations play a crucial role in the rural economy of southern China, making accurate orchard surveys essential for effective management and resource allocation. Owing to the distinct seasonal growth patterns of orchards, extracting phenological features from multi-temporal remote sensing data has become a primary approach for obtaining orchard information. However, the subtropical monsoon climate of southern China brings frequent cloud cover and rainfall. This poses major challenges to constructing continuous, high-resolution optical remote sensing datasets. To overcome these limitations, this study integrates high-temporal-resolution MODIS data with medium-spatial-resolution Landsat imagery to generate monthly composite images that capture key stages of orchard growth. Based on more than 9000 sampling sites across the province, phenological information was extracted from three conventional features, including spectral reflectance, vegetation indices, and texture features, to build multiple machine learning classification models for high-precision orchard mapping. The results demonstrate that the proposed multi-feature fusion framework yields a substantial accuracy gain of up to 17 percentage points compared to traditional methods. While the baseline method relying solely on single-phase spectral features achieved 72.2% accuracy, the optimal combination of spectral, texture, and phenological features using the LightGBM model reached an accuracy of 89.2% (F1-score: 88.6%).Furthermore, SHAP analysis enhanced model interpretability by revealing the key factors influencing the decision-making process. The results indicate that orchards in Hunan Province are primarily distributed in hilly regions, where large- and small-scale orchards coexist, with Huaihua and Yongzhou containing the largest orchard areas. From 1995 to 2022, the province's orchard area expanded significantly, growing from approximately 45,000 ha to nearly 140,000 ha, which represents an increase of more than 200%. This study demonstrates the effectiveness of spatiotemporal data fusion in mitigating cloud-related challenges in subtropical regions and underscores the novel role of texture features in capturing key phenological information. It provides a reliable framework for large-scale orchard mapping and supports protective land utilization strategies and sustainable agricultural development in the region.
{"title":"Orchard plantation mapping using remote sensing phenological feature fusion and interpretable ML algorithms in Hunan Province, China","authors":"Ying Liu , Sihan Wang , Zhaohua Liu , Dongmei Lyu , Sijia Li , Bingxue Zhu , Ge Liu , Kaishan Song","doi":"10.1016/j.ecoinf.2026.103628","DOIUrl":"10.1016/j.ecoinf.2026.103628","url":null,"abstract":"<div><div>Orchard plantations play a crucial role in the rural economy of southern China, making accurate orchard surveys essential for effective management and resource allocation. Owing to the distinct seasonal growth patterns of orchards, extracting phenological features from multi-temporal remote sensing data has become a primary approach for obtaining orchard information. However, the subtropical monsoon climate of southern China brings frequent cloud cover and rainfall. This poses major challenges to constructing continuous, high-resolution optical remote sensing datasets. To overcome these limitations, this study integrates high-temporal-resolution MODIS data with medium-spatial-resolution Landsat imagery to generate monthly composite images that capture key stages of orchard growth. Based on more than 9000 sampling sites across the province, phenological information was extracted from three conventional features, including spectral reflectance, vegetation indices, and texture features, to build multiple machine learning classification models for high-precision orchard mapping. The results demonstrate that the proposed multi-feature fusion framework yields a substantial accuracy gain of up to 17 percentage points compared to traditional methods. While the baseline method relying solely on single-phase spectral features achieved 72.2% accuracy, the optimal combination of spectral, texture, and phenological features using the LightGBM model reached an accuracy of 89.2% (F1-score: 88.6%).Furthermore, SHAP analysis enhanced model interpretability by revealing the key factors influencing the decision-making process. The results indicate that orchards in Hunan Province are primarily distributed in hilly regions, where large- and small-scale orchards coexist, with Huaihua and Yongzhou containing the largest orchard areas. From 1995 to 2022, the province's orchard area expanded significantly, growing from approximately 45,000 ha to nearly 140,000 ha, which represents an increase of more than 200%. This study demonstrates the effectiveness of spatiotemporal data fusion in mitigating cloud-related challenges in subtropical regions and underscores the novel role of texture features in capturing key phenological information. It provides a reliable framework for large-scale orchard mapping and supports protective land utilization strategies and sustainable agricultural development in the region.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"94 ","pages":"Article 103628"},"PeriodicalIF":7.3,"publicationDate":"2026-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146080649","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2026-01-22DOI: 10.1016/j.ecoinf.2026.103601
Ricardo H.G. Furiati , Filipe Sacchetto , Simon Malinowski , Zenilton Kleber G. do Patrocínio Jr. , Felipe D. Cunha , Cristiana B. Maia , Silvio Jamil F. Guimarães
To enable the study of solar behavior without installing expensive sensor equipment, machine learning time-series models can be highly useful. In this study, we forecast future values of solar radiation incident on a horizontal surface by comparing five different models: Holt-Winters, LSTM, SARIMAX, SVM, and XGBoost, using a comprehensive dataset of satellite meteorological observations from NASA spanning over 30 years. 90 points in the Brazilian southeast (in the state of Minas Gerais and its surroundings) were analyzed using two different cross-validation methods (Fixed Start and Rolling Window) and compared using the Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE) metrics. Our analysis revealed that the Holt-Winters model yielded the lowest error, with an MAE of 0.302 kWh/m/day, followed by the LSTM (0.314), SARIMAX (0.338), SVM (0.39), and XGBoost (0.336) models. The statistical analysis of the cross-validation methods revealed that although the fixed start method yields lower error metrics, it requires substantially longer training times (due to the increased input data) and is only slightly superior to the rolling window method. The most significant divergence between the models and the actual solar radiation values was observed along the eastern border of the state. An exploratory analysis of solar behavior showed that greater data variability (standard deviation and variance) is associated with worse forecasting performance. Given the worldwide availability of the data, the methodology presented in our work can be replicated to make solar radiation predictions anywhere, facilitating new developments in sustainable renewable energy production.
{"title":"Solar radiation times-series forecasting in southern Brazil: A comprehensive analysis","authors":"Ricardo H.G. Furiati , Filipe Sacchetto , Simon Malinowski , Zenilton Kleber G. do Patrocínio Jr. , Felipe D. Cunha , Cristiana B. Maia , Silvio Jamil F. Guimarães","doi":"10.1016/j.ecoinf.2026.103601","DOIUrl":"10.1016/j.ecoinf.2026.103601","url":null,"abstract":"<div><div>To enable the study of solar behavior without installing expensive sensor equipment, machine learning time-series models can be highly useful. In this study, we forecast future values of solar radiation incident on a horizontal surface by comparing five different models: Holt-Winters, LSTM, SARIMAX, SVM, and XGBoost, using a comprehensive dataset of satellite meteorological observations from NASA spanning over 30 years. 90 points in the Brazilian southeast (in the state of Minas Gerais and its surroundings) were analyzed using two different cross-validation methods (Fixed Start and Rolling Window) and compared using the Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE) metrics. Our analysis revealed that the Holt-Winters model yielded the lowest error, with an MAE of 0.302 kWh/m<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span>/day, followed by the LSTM (0.314), SARIMAX (0.338), SVM (0.39), and XGBoost (0.336) models. The statistical analysis of the cross-validation methods revealed that although the fixed start method yields lower error metrics, it requires substantially longer training times (due to the increased input data) and is only slightly superior to the rolling window method. The most significant divergence between the models and the actual solar radiation values was observed along the eastern border of the state. An exploratory analysis of solar behavior showed that greater data variability (standard deviation and variance) is associated with worse forecasting performance. Given the worldwide availability of the data, the methodology presented in our work can be replicated to make solar radiation predictions anywhere, facilitating new developments in sustainable renewable energy production.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"94 ","pages":"Article 103601"},"PeriodicalIF":7.3,"publicationDate":"2026-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146173893","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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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