Search Results (141)

Ants play a central role in seed dispersal and predation, shaping plant recruitment, yet their foraging behavior is increasingly influenced by anthropogenic food subsidies. In human-modified landscapes, processed food waste may disrupt ant–seed interactions by diverting foragers or altering activity patterns, but the extent and mechanisms of these effects across habitats remain unclear. We conducted three field experiments in Panama to test how common food residues affect seed removal by ants in urban and forest environments. Using oat seeds as standardized diaspores, we (1) tested whether potato chips surrounding seed depots reduced removal, (2) evaluated the effects of adjacent chips or cookies on removal rates, ant activity, and species composition, and (3) manipulated the distance between chips and seeds (0, 30, 60 cm) to distinguish behavioral distraction from physical obstruction. Across experiments, seeds near food residues were removed significantly more slowly than controls, approximately half as fast in both habitats, despite differences in ant assemblages. Ant activity near seeds declined in the presence of food, particularly in the urban site. Suppression of seed removal occurred at close range but disappeared at 60 cm. These findings indicate that food waste disrupts ant-mediated seed removal through fine-scale behavioral shifts across contrasting habitats. Full article

Yarn tenacity is one of the vital quality parameters that determine the performance, fabric durability and end use suitability. The tenacity of yarn is largely influenced by the fibre characteristics used. The physical properties of jute fibres, including root content, defect, bundle strength, and fineness, exert a significant influence on yarn tenacity. This study utilized metaheuristic optimized random forest regression (RFR) to predict jute yarn tenacity from fibre parameters. The hyperparameters of the RFR models were optimized using four metaheuristic algorithms: whale optimization algorithm (WOA), grey wolf optimization (GWO), beetle antennae search (BAS) and ant colony optimization (ACO). The model utilized a dataset comprising 414 experimental data with 70% data for training and 30% for testing the model, using input variables such as bundle strength (g/tex), defects (%), root content (%) and fineness (tex) to predict yarn tenacity (cN/tex). The developed models effectively predicted yarn tenacity. However, RFR–GWO achieved slightly better performance with R² of 1.0 for training set and 0.96 for test set. Regarding execution time, RFR–GWO is the fastest requiring only 14.25 s. SHAP analysis revealed that bundle strength and root content of jute fibre are the most influential factors, whereas defect and fineness exert the least influence on model’s prediction. The best model RFR–GWO was deployed into an interactive Streamlit web application, offering an intuitive and user-friendly platform for the real-time estimation of yarn tenacity. Full article

Liquidambar formosana Hance, a tree species in subtropical broad-leaved forests, exhibits a striking autumn leaf coloration. However, how drought stress during this period influences leaf color change remains poorly understood. In this study, two-year-old seedlings were subjected to four drought gradients. Leaf color parameters, pigment contents, cellular structure, photosynthetic physiology, and hydraulic properties were systematically measured throughout the leaf color transition period. The results show that, with increasing drought severity, leaf red-green coordinate a* increased significantly during early-to-middle stress (S1–S3), while lightness L* and yellow-blue coordinate b* increased at late stress (S4). Chlorophyll (Chl) content continuously decreased, anthocyanins (Ant) peaked at mid-stress, and carotenoids (Car) became enriched at late stress. Leaf cellular structure and hydraulic parameters declined, photosynthetic function was inhibited, and antioxidant enzyme activities showed an initial increase followed by a decrease. Correlation analysis and Random Forest models revealed that L* was strongly associated with superoxide dismutase (SOD) activity, carotenoid-to-chlorophyll (Car/Chl) ratio, and net photosynthetic rate (P_n); a* was closely linked to osmotic potential at full saturation (Ψ_sat), relative water content at the turgor loss point (RWCtlp), SOD activity, Car/Chl ratio, anthocyanin-to-chlorophyll (Ant/Chl) ratio, Ant content, transpiration rate (T_r), P_n, and main vein thickness (Mvt), while b* was primarily correlated with Ψ_sat, Car/Chl ratio, SOD activity, Ant/Chl ratio, and P_n. These statistical associations suggest multiple physiological processes are involved in leaf color change. Based on these findings, we propose a hypothetical sequence: drought initially disrupts leaf water status, leading to structural atrophy and hydraulic decline, followed by photosynthetic inhibition, activated antioxidant defense, and altered pigment accumulation, which are correlated with the sequential leaf color transition from green to red to yellow-orange in this species. Full article

Soil salinity severely threatens global ecosystems and agriculture, making accurate monitoring an ongoing priority. Currently, efficiently utilizing multi-source datasets to enhance monitoring accuracy while minimizing computational resources remains a critical challenge. This study evaluated several modeling strategies, including full-dataset modeling, variance inflation factor (VIF), Boruta, particle swarm optimization, ant colony optimization and recursive feature elimination (RFE), and validated results across diverse regions (Almaty, Kazakhstan; Shandong, China). We further validated the results using multiple algorithms, including linear regression, partial least squares regression, extreme gradient boosting, k-nearest neighbor and random forest (RF), with topsoil (0–20 cm) electrical conductivity inverted via the optimal method. Results indicate that input feature numbers substantially impact model performance: regional-scale feature selection is indispensable, with RFE outperforming full-dataset modeling (R² improves by up to 0.28, while RMSE decreases by 2.21 dS m⁻¹) and VIF performing the worst. Transferability is also demonstrated in Almaty and Shandong. Additionally, the RF algorithm shows superior performance in soil salinity mapping (overall accuracy = 0.73; kappa coefficient = 0.65). And, the RFE and SHAP results highlight CRSI, BI, and MSAVI2 as particularly important predictors for estimating soil salinity in our study area. Collectively, this study highlights the critical importance of feature optimization and interpretability in soil attribute mapping through the integration of multi-source remote sensing data. Full article

This study investigates the predictability of Environmental, Social, and Governance (ESG) performance using financial fundamentals within the context of Taiwan, a prominent small open economy integrated into global value chains. As global markets transition toward mandatory sustainability reporting, identifying the financial ante-cedents of ESG outcomes is critical for risk management and regulatory oversight. Uti-lizing a decade of firm-level data (2014–2023) from the Taiwan Economic Journal (TEJ), we employ supervised machine learning (ML) architectures-including Decision Tree, Random Forest, and Extreme Gradient Boosting (XGBoost)-to classify firms into ESG performance tiers based on indicators such as profitability, valuation, and scale. Our empirical results provide robust support for the Slack Resources Hypothesis, identifying Return on Assets (ROA) and Firm Size (SIZE) as the most consistent predictors of ESG excellence across the semiconductor, cement, and steel sectors. Conversely, mar-ket-based indicators (Tobin’s Q) dominate predictive models for the financial industry. Methodologically, XGBoost delivers superior predictive calibration for the financial sector, while Decision Trees offer highly interpretable threshold-based logic for risk screening. Our study contributes a transparent “early-warning” framework, enabling investors and regulators to identify sustainability risks through auditable financial benchmarks. The findings suggest that while financial latitude is a structural prerequisite for ESG engagement, it is not its sole determinant, pointing toward a “virtuous circle” of financial health and managerial quality. Full article

Fire refugia are critical for post-disturbance recovery, yet microhabitats such as stones remain understudied despite their ubiquity and thermal persistence. This study tested whether the depth- and area-dependent refugial capacity of stones previously demonstrated in Mediterranean oak forests also operates in intensively managed plantations and how forest type and management modulate this capacity. Immediate wildfire effects (1–8 days post-fire) on ground-dwelling macroinvertebrates were quantified under 660 stones across burnt and unburnt native maritime pine and exotic eucalypt plantations following a medium- to high-severity wildfire. Stones acted as thermal refugia in both plantation types, with burial depths greater than 5 cm and surface areas greater than 500 cm² predicting survival. Despite severe impacts (richness declined by 56% in pine and 63% in eucalypt; overall mortality exceeding 50%), diverse taxa persisted under stones, particularly ground spiders, ants, centipedes, rock bristletails, and harvestmen, while plant-associated and moisture-dependent groups suffered the highest losses. Native pine supported a higher abundance and richness per stone than exotic eucalypt in both burnt and unburnt conditions, reflecting management-driven differences in stone size, depth, and availability. These findings show that retaining sufficiently large, deeply buried stones during plantation establishment can enhance post-fire biodiversity recovery in increasingly fire-prone production landscapes. Full article

Illegal logging is a major driver of tropical deforestation, accounting for the majority of timber harvested in many tropical countries and degrading many protected areas, due to both weak law enforcement capacity and corruption. Commercial logging is illegal in Peru’s Allpahuayo-Mishana National Reserve, a state protected area, but clandestine logging operations persist and affect its biodiversity, including the endemic bird species associated with its rare Amazonian white-sand forests. We examined the effects of illegal logging operations on white-sand forest understory bird communities as sentinels of biodiversity. We sampled birds with mist nets at 12 study sites in unlogged forest and forest regenerating between 1 and 10 years after timber harvest, capturing and releasing 348 birds representing 54 species in 16 families. Forest structure differed significantly between forest treatments, with canopy cover in logged forest significantly lower than in unlogged forest. All avian foraging guilds tested (including ant followers, other insectivores, frugivores, granivores, and nectarivores) responded significantly to changes in one or more forest structure characteristics we measured. The abundance of ant followers and other insectivores was positively correlated with canopy cover, while granivore abundance was positively correlated with subcanopy cover, and both frugivore and nectarivore abundance was negatively correlated with the numbers of trees in white-forest stands. We also took a rare opportunity to compare avian foraging guilds and relative abundance using capture data collected at the same white-sand forest sites in both 2005 and 2023. Over this 18-year period, the total number of understory birds and ant followers in particular declined, whereas other insectivores increased with time since logging. Our results demonstrate that logging has significant influences on white-sand forest habitat structure and bird community dynamics for decades after logging events. Illegal logging threatens forests and wildlife in many tropical protected areas, and we recommend their managers prioritize both preventing illegal logging and mitigating its negative effects to effectively conserve biodiversity. Full article

The stability of ice structures in cold regions and polar environments has been increasingly challenged by global warming and climate change, making the accurate estimation of ice flexural properties essential. However, the flexural failure process of ice is highly complex, and the calculated flexural properties are influenced by multiple factors. Hence, several data-driven artificial intelligence models were developed to predict flexural strength, using classification and regression tree (CART), AdaBoost, and Random Forest methods, while the Elitist Ant System (EAS) was applied to optimize model parameters. The EAS procedure converged rapidly within ten iterations and effectively enhanced overall model performance. Compared with the single CART model, ensemble approaches exhibited higher prediction accuracy and better generalization, with AdaBoost achieving the best performance (R² = 0.736). Feature-importance analysis indicated that the testing method and specimen geometry had the greatest influence on the results, highlighting the importance of careful control of experimental conditions. The proposed ensemble–metaheuristic framework provides an efficient tool for predicting the mechanical behavior of ice and offers useful support for stability assessments of ice structures under changing climatic conditions. Full article

Habitat fragmentation profoundly alters ecological processes such as seed predation and dispersal. Ants play a central role as seed removers and dispersers, yet the effects of fragmentation on seed-carrying ant assemblages in dry tropical forests remain insufficiently studied. In this work, we examined the influence of forest fragmentation on seed-carrying ants in the Chaco forests of central Argentina. Ants were sampled across nine forest fragments of varying sizes and two continuous forests within an agroecosystem landscape, and species richness, composition, and occurrence were analyzed. Our results revealed that species richness did not vary significantly with fragment size; however, fragmentation caused pronounced shifts in species composition, with clear distinctions between continuous forests and fragments. Large-bodied specialist harvester ants declined in fragments, whereas small- to medium-sized generalist species from genera such as Pheidole and Solenopsis persisted. These compositional changes suggest that although overall seed removal rates may remain stable, the functional quality of seed dispersal likely diminishes. This study highlights the sensitivity of seed-carrying ant assemblages to habitat fragmentation and underscores the need for further research that integrates behavioral and landscape-scale approaches to better assess impacts on seed removal, dispersal, and forest regeneration in fragmented dry forests. Full article

The transition of Türkiye’s energy market toward net-zero emissions by 2053 requires modeling approaches capable of capturing complex interactions and long-term uncertainties. In this study, a long-term agent-based modeling (ABM) framework was developed, integrating econometric demand forecasting with a seasonal autoregressive integrated moving average (SARIMA) model and machine learning (ML)-based day-ahead market (DAM) price prediction. Of the ML models tested, CatBoost achieved the highest accuracy, outperforming XGBoost and Random Forest, and supported investment analysis through net present value (NPV) calculations. The framework represents major market actors—including generation units, investors, and the market operator—while also incorporating the impact of Türkiye’s first nuclear power plant (NPP) under construction and the potential introduction of a carbon emissions trading scheme (ETS). All model components were validated against historical data, confirming robust forecasting and market replication performance. Hourly simulations were conducted until 2053 under alternative policy and demand scenarios. The results show that renewable generation expands steadily, led by onshore wind and solar photovoltaic (PV), while nuclear capacity, ETS implementation, and demand assumptions significantly reshape prices, generation mix, and carbon emissions. The nuclear plant lowers market prices, whereas an ETS substantially raises them, with both policies contributing to emission reductions. These scenario results were connected to actionable policy recommendations, outlining how renewable expansion, ETS design, nuclear development, and energy efficiency measures can jointly support Türkiye’s 2053 net-zero target. The proposed framework provides an ex-ante decision-support framework for policymakers, investors, and market participants, with future extensions that can include other energy markets, storage integration, and enriched scenario design. Full article

The genus Proceratium comprises rare but ecologically significant cryptobiotic predators of temperate and tropical forest litter. The group is known for its abdomen which can curl dorsally >90° relative to the body axis. Proceratium includes 130 described species worldwide. In China, seven species have been recorded, yet recent surveys repeatedly reveal morphologically distinctive undescribed taxa, indicating a still-underestimated diversity of Proceratium. In this study, seven known Chinese species of the ant genus Proceratium Roger, 1863 are reviewed: P. bruelheidei, P. itoi, P. japonicum, P. kepingmai, P. longmenense, P. shohei and P. zhaoi. The species P. longigaster is removed from the ant fauna of China. Additionally, seven new species are described: P. crassopetiolum sp. nov., P. digitospinum sp. nov., P. planodorsum sp. nov., P. quandratinodum sp. nov., P. rugiceps sp. nov., P. shanyii sp. nov., and P. spinosubum sp. nov. An illustrated key to Chinese species based on workers is also provided. Full article

The Argentine ant (Linepithema humile) is a highly invasive species with a widespread global distribution. However, the dynamics of its recent expansion into southwestern Argentina remain unclear. We evaluated its spread, niche shifts, and genetic diversity using field surveys, distribution models, and mitochondrial DNA analyses. Our results revealed a strong expansion from Pampas into anthropogenic habitats (multiple urban and rural sites) in the Monte Desert, but not into natural habitats. The Argentine ant’s presence declined westward along the Río Negro Valley in the Monte Desert, and was virtually absent from the Patagonian Steppe, where it was found only in urban sites in Bariloche (ecotone with the Patagonian Forest). The distribution models identified isothermality and water balance as the key drivers of suitability. Thus, urbanization and irrigated agriculture seem to have facilitated its establishment in the Río Negro Valley by buffering climate extremes. Genetic analyses revealed widespread and novel haplotypes, which are consistent with multiple introductions and potential regional differentiation. This southward expansion underscores the critical role of urban areas and irrigated agriculture as refuges and stepping stones that facilitate Argentine ant survival in otherwise inhospitable environments. Continued monitoring of transitional zones in northern Patagonia is crucial to determining whether this front will remain stable or shift further south. Full article

The red imported fire ant (RIFA; Solenopsis invicta) is an invasive species that severely threatens ecology, agriculture, and public health in Taiwan. In this study, the feasibility of applying multispectral imagery captured by unmanned aerial vehicles (UAVs) to detect red fire ant mounds was evaluated in Fenlin Township, Hualien, Taiwan. A DJI Phantom 4 multispectral drone collected reflectance in five bands (blue, green, red, red-edge, and near-infrared), derived indices (normalized difference vegetation index, NDVI, soil-adjusted vegetation index, SAVI, and photochemical pigment reflectance index, PPR), and textural features. According to analysis of variance F-scores and random forest recursive feature elimination, vegetation indices and spectral features (e.g., NDVI, NIR, SAVI, and PPR) were the most significant predictors of ecological characteristics such as vegetation density and soil visibility. Texture features exhibited moderate importance and the potential to capture intricate spatial patterns in nonlinear models. Despite limitations in the analytics, including trade-offs related to flight height and environmental variability, the study findings suggest that UAVs are an inexpensive, high-precision means of obtaining multispectral data for RIFA monitoring. These findings can be used to develop efficient mass-detection protocols for integrated pest control, with broader implications for invasive species monitoring. Full article

Tropical rainforests are rapidly disappearing due to human activities, particularly land-use changes, resulting in a heterogeneous mosaic of landscapes that substantially contribute to global terrestrial biodiversity loss. We investigated how changes in land-use affect species richness, composition, and functional guilds of ground-dwelling ants within various land-use systems at a local scale in the Amazonian rainforest. Our focus was to respond to the following: (i) How do local species richness and community composition reflect differences among land-use systems? (ii) Are ground-dwelling ants, especially specialists, negatively impacted by intensified land-use changes? We surveyed 55 sites representing five land-use systems: primary forest, secondary forest, forest corridor, selective logging, and Eucalyptus plantation. We registered 150 ant species, and species richness ranged from 43 to 94. Richness varies according to the land-use systems, likely influenced by differences in habitat structural complexity both vertically and horizontally. Ant species composition and guilds distribution also varied among land-use systems studied. Environments characterized by reduced structural complexity or higher disturbed levels, such as Eucalyptus plantations, tend to support lower resource availability, which may lead to decreased species richness. However, the surrounding matrix appears to play a key role in maintaining regional biodiversity, as evidenced by the absence of differences in ground-dwelling ants diversity across all land-use systems studied. Full article

Ants are key drivers of biodiversity in both tropical and temperate forests, though the underlying mechanisms of this remain debated. In tropical lowland rainforests, ants dominate the canopy as opportunistic predators, shaping arthropod abundance and community structure. By contrast, few arboreal ant species exist in temperate forests due to climatic constraints, and predation pressure is generally low. This changes when ground-nesting Formica species enter the canopy to forage. Using insecticidal knockdown, we collected arthropod communities from trees with high and low ant abundance in both tropical and temperate forests and in different seasons. We found consistently higher arthropod abundances on trees with strong ant dominance, including preferred prey taxa such as Diptera, Psocoptera, and Lepidoptera. In temperate forests, high arthropod densities may be driven by aphid-produced honeydew, whereas in tropical rainforests, the absence of large hemipteran aggregations suggests that other mechanisms are involved. Consequently, this mechanism fails to explain high arthropod abundance in tropical primary forests. In contrast, secondary tropical forests host structurally and compositionally altered ant communities, resulting in reduced predation pressure and a marked increase in the abundance of individual species, including potential pest species. These findings suggest that biodiversity maintenance in the canopy depends on intact, diverse ant communities. Recolonization from nearby primary forests is essential for recovery, yet even after five decades, secondary forests remain ecologically distinct, rendering full restoration to primary forest conditions unlikely within a management-relevant timeframe. Full article