Search Results (403)

Soil respiration (Rs), the second largest carbon flux in terrestrial ecosystems, critically regulates the turnover of soil carbon pools. However, its seasonal and annual responses to extreme events in monsoon forests remain unclear. This study used a continuous multichannel automated chamber system to monitor Rs over three years of drought (2019–2021) in an Asian monsoon forest in Taiwan. We assessed seasonal and annual Rs patterns and examined how drought influenced autotrophic (Rr) and heterotrophic (Rh) respiration through changes in soil temperature and moisture. Results showed Rs declined from 5.20 ± 2.08 to 3.86 ± 1.20 μmol CO₂ m⁻² s⁻¹, and Rh from 3.36 ± 1.21 to 3.15 ± 0.98 μmol CO₂ m⁻² s⁻¹ over the study period. Spring Rr values dropped significantly—by 29.3% in 2020 and 62.2% in 2021 compared to 2019 (p < 0.05), while Rh remained unchanged (p > 0.05). These results suggest that spring drought strongly suppresses autotrophic respiration but has minimal effect on Rh. Incorporating these dynamics into carbon models could improve predictions of carbon cycling under climate change. Our findings demonstrate that spring drought exerts a strong influence on soil carbon fluxes in Asian monsoon forests. Full article

Taiwan, located in a subtropical region, has experienced continuous warming in recent years, making the Urban Heat Island (UHI) effect one of its most pressing environmental challenges. Importantly, UHI is not confined to Taipei, the most populous city, but is also present in other metropolitan areas. This study investigates UHI effects in the five largest cities in Taiwan and examines climate-related news attention using web crawling. Cross-city comparisons are further conducted through Urban Heat Island Intensity (UHII) and correlation analysis. The results reveal that Taipei records the highest number of UHI-related news reports, particularly during summer, and its UHII is about 1.5 °C to 3 °C higher than in the other four cities. In addition, UHII in Taipei shows a marked increase between 2021 and 2023, suggesting a worsening impact on citizens’ living conditions. Meanwhile, news coverage in Taipei dominates nationwide attention, creating a spatially uneven distribution of media focus. This imbalance may undermine efforts to promote UHI mitigation and adaptation strategies in cities outside Taipei. Overall, this study highlights that UHI is not solely a problem of Taipei but a widespread issue across Taiwan’s urban areas. The findings provide useful references for policymakers and government agencies, emphasizing the need for equitable attention and broader public engagement through media channels to raise awareness and foster comprehensive climate adaptation actions. Full article

Climate change continues to threaten global biodiversity, making it essential to assess how keystone species may shift their distributions and to use these findings to inform conservation planning. This study evaluated the current and future habitat suitability of D. macropodum, an important tree species within subtropical evergreen broad-leaved forests in China, using 354 occurrence records and a suite of environmental variables. A parameter-optimized MaxEnt model (calibrated with ENMeval; RM = 4, FC = QHPT) was applied to simulate the species’ present distribution and projected changes under three climate scenarios (SSP126, SSP245, SSP585). The main factors influencing distribution were determined to be moisture and temperature seasonality, with the precipitation of the coldest quarter (Bio19, 36.3%), the mean diurnal range (Bio2, 37.5%), and the precipitation of the warmest quarter (Bio18, 14.2%) jointly contributing 88.0% of the total influence. The model projections indicated a 40.1% reduction in the total number of suitable habitats under high-emission scenarios (SSP585) by the 2090s, including a loss of over 80% of highly suitable areas. Centroid movements also diverged across the scenarios: a southwestern shift under SSP126 and SSP245 contrasted with a southeastern shift under SSP585, with each accompanied by significant habitat fragmentation. Key climate refugia were identified primarily in central Taiwan Province and the mountainous zones of Zhejiang and Fujian Provinces, which should be prioritized for conservation activities. These insights offer a foundational understanding for the conservation of D. macropodum and other ecologically similar subtropical evergreen species. However, direct extrapolation to other taxa should be made cautiously, as specific responses may vary based on differing ecological tolerances and dispersal capacities. Further research is needed to test the generalizability of these patterns across diverse plant functional types. Full article

Red bricks serve as an important material for load-bearing or enclosing structures in traditional architecture and are widely used in construction projects both domestically and internationally. Fujian red bricks, due to geographical, trade, and immigration-related factors, have spread to Taiwan and various regions in Southeast Asia, giving rise to distinctive red brick architectural complexes. To further investigate the types of damage, such as cracking and missing bricks, that occur in traditional red brick buildings due to multiple factors, including climate and human activities, this study takes Fujian red brick buildings as its research subject. It employs the YOLOv12 rapid detection method to conduct technical support research on structural assessment, type detection, and damage localization of surface damage in red brick building materials. The experimental model was conducted through the following procedures: on-site photo collection, slice marking, creation of an image training set, establishment of an iterative model training, accuracy analysis, and experimental result verification. Based on this, the causes of damage types and corresponding countermeasures were analyzed. The objective of this study is to attempt to utilize computer vision image recognition technology to provide practical, automated detection and efficient identification methods for damage types in red brick building brick structures, particularly those involving physical and mechanical structural damage that severely threaten the overall structural safety of the building. This research model will reduce the complex manual processes typically involved, thereby improving work efficiency. This enables the development of customized intervention strategies with minimal impact and enhanced timeliness for the maintenance, repair, and preservation of red brick buildings, further advancing the practical application of intelligent protection for architectural heritage. Full article

In real fisheries management practices, ecological information on the seasonal distribution patterns and characteristics of marine economic fauna and responses to climate change is necessary. In this study, we analyzed data obtained from surveys conducted between 2018 and 2019 in the southern Yellow and East China Seas, using ensemble models to predict variations in the habitat area of Metapenaeopsis provocatoria longirostris across seasons and under different climate scenarios. The highest abundances were observed at the following water temperature and salinity conditions, respectively: 18.5 °C and 34.5‰–35‰ in spring, 18.9–28 °C and 33.4–34.6‰ in summer, 18.6–21.7 °C and 33.5–34.4‰ in autumn, and 18–21.5 °C and 34‰ in winter. The major cohort was concentrated at depths of 100–110 m and 85–105 m in spring and summer, respectively, and at 80–100 m and 70–120 m in autumn and winter, respectively. In spring and winter, M. provocatoria longirostris was distributed in the continental shelf waters of the East China Sea, at wider salinity (30–35‰) and water temperature (8–26 °C) ranges, whereas in summer and autumn, the distribution shifted offshore. The values predicted for habitat loss under different climate scenarios were ranked as follows: ~70% loss under SSP585-2100 > ~50% loss under SSP370-2100 > ~30% loss under SSP245-2100 > ~15% loss under SSP126-2100 and SSP370-2050 > ~1–5% under SSP126-2050, SSP245-2050, and SSP585-2050. No great gains in habitat were predicted under any scenario. Our findings can contribute to the establishment of appropriate fisheries management schemes for the rational exploitation of M. provocatoria longirostris. Our predictions can assist in improving fisheries management practices within the context of climate change. Full article

Accurate estimation of flow velocities in torrents and steep rivers is essential for flood risk assessment, sediment transport analysis, and the sustainable management of water resources. While deep learning models are increasingly applied to such tasks, their evaluation often depends on statistical metrics that may yield conflicting interpretations. The objective of this study is to clarify how different evaluation metrics influence the interpretation of hydrological deep learning models. We analyze two models of flow velocity prediction in a torrential creek in Taiwan. Although the models differ in architecture, the critical distinction lies in the datasets used: the first model was trained on May–June data, whereas the second model incorporated May–August data. Four performance metrics were examined—root mean square error (RMSE), Nash–Sutcliffe efficiency (NSE), Willmott’s index of agreement (d), and mean absolute percentage error (MAPE). Quantitatively, the first model attained RMSE = 0.0471 m/s, NSE = 0.519, and MAPE = 7.78%, whereas the second model produced RMSE = 0.0572 m/s, NSE = 0.678, and MAPE = 11.56%. The results reveal a paradox. The first model achieved lower RMSE and MAPE, indicating predictions closer to the observed values, but its NSE fell below the 0.65 threshold often cited by reviewers as grounds for rejection. In contrast, the second model exceeded this NSE threshold and would likely be considered acceptable, despite producing larger errors in absolute terms. This paradox highlights the novelty of the study: model evaluation outcomes can be driven more by data variability and the choice of metric than by model architecture. This underscores the risk of misinterpretation if a single metric is used in isolation. For sustainability-oriented hydrology, robust assessment requires reporting multiple metrics and interpreting them in a balanced manner to support disaster risk reduction, resilient water management, and climate adaptation. Full article

Sustainable management of Portunus pelagicus is hindered by uncertain stock boundaries across rapidly changing marginal seas and culturally diverse markets. We measured 12 size-adjusted morphometrics in 525 adults from five sites (Kyushu, Xiamen, Tainan, Hong Kong, and Singapore). Canonical variate analysis resolved three robust groups that mirror oceanographic regimes: a Kuroshio–China group (Kyushu, Xiamen, and Hong Kong), a Taiwan Strait subgroup (Tainan), and a Southeast Asia group (Singapore). Permutation tests (1000 runs) showed near-zero probabilities of observing the low misclassification rates by chance (p < 0.001). A reproductive trait (female AB3W) displayed group-specific allometric slopes, consistent with local functional demands. We integrate these results with a cultural ecology lens—linking ornamental carapace valuation to selective harvest—to propose morphological management units (MMUs) and region-specific rules that can be implemented immediately and refined with genomics. This work reframes a descriptive morphometric study into a socio-ecological mechanism for climate-ready, actionable fisheries governance. Full article

Landslides pose a significant threat to Taiwan’s mountainous regions, particularly after extreme weather events such as typhoons. This study introduces a machine learning framework for post-disaster land use-land cover (LULC) classification and landslide detection in Nanfeng Village, central Taiwan, following Typhoon Khanun in August 2023. Using high-resolution Pléiades imagery and 22 environmental and spectral factors, a Random Forest classifier was developed. To address class imbalance, the Synthetic Minority Oversampling Technique (SMOTE) was systematically evaluated across multiple variants. The Distance_SMOTE method yielded the best results, increasing overall accuracy from 74% to 85% and the Kappa coefficient from 0.69 to 0.82. F1-scores for landslides, roads, and grassland improved markedly, reaching 0.97, 0.85, and 0.78, respectively. The optimized model produced accurate pre- and post-typhoon LULC maps, revealing significant expansion of landslide zones after the event. This study demonstrates the practical value of combining SMOTE-based resampling with Random Forest for rapid, reliable post-disaster assessment, offering actionable insights for disaster response and land management in data-imbalanced conditions. By enabling timely mapping of hazard-affected areas and informing targeted recovery actions, the approach supports disaster risk reduction, sustainable land use planning, and ecosystem restoration. These outcomes contribute to the Sustainable Development Goals, particularly SDG 11 (Sustainable Cities and Communities), SDG 13 (Climate Action), and SDG 15 (Life on Land), by strengthening community resilience, promoting climate adaptation, and protecting terrestrial ecosystems in hazard-prone regions. Full article

This study aims to develop a climate-resilient landscape design framework for coastal healthcare facilities by integrating Artificial Intelligence (AI)-generated design prompts with Computational Fluid Dynamics (CFD) simulations and on-site validation. Focusing on a coastal hospital in Penghu, Taiwan—a region vulnerable to strong winds, salt spray, and extreme weather—the research proposes a climate-adaptive, microclimate-responsive, and resilient design framework. Key findings demonstrate that the optimized design reduced average winter wind speed from 12 m/s to 4.5 m/s (a 62.5% reduction) and increased the three-year survival rate of salt-tolerant plant species (e.g., Pittosporum tobira, Casuarina) to 92%, significantly outperforming conventional planting strategies. The combination of water features and evapotranspiration planting reduced summer temperatures by 2.3 °C and increased humidity to 75%, with the PMV comfort index improving from +1.5 to +0.5. The program also resulted in a 15% increase in biodiversity, a 20% reduction in soil erosion, and a 40% improvement in users’ perceived aesthetic value of outdoor spaces. Furthermore, AI-based analyses to determine foundational depth led to a reduction in structural failure rates—from 40% to 5%—substantially elevating the safety and long-term durability of outdoor infrastructures. This study demonstrates that integrating AI with CFD is both feasible and highly effective for addressing complex coastal climate challenges in landscape architecture. The developed framework is parametric, evidence-based, and tailored to site-specific requirements, enabling the formulation of intelligent, climate-responsive landscape solutions for future healthcare environments in vulnerable coastal areas. Full article

This study examines the seasonal variability of surface currents in the South China Sea (SCS) and its adjacent regions, employing trajectory data from four seasonal deployments of Beidou drifters in the northern SCS. These observations are supplemented by reanalysis datasets, as well as satellite-derived sea surface wind and sea surface height data. The principal findings of this research are summarized as follows: (1) Drifter trajectories in the SCS exhibit pronounced seasonal characteristics. During autumn and winter, drifters predominantly move westward, ultimately merging with the SCS Western Boundary Current (SCSWBC). In spring, drifters are frequently entrained by mesoscale eddies. In summer, drifter trajectories generally move northeastward toward the Luzon Strait and the Taiwan Strait, with drifters subsequently returning to the SCS through these straits in autumn or winter before either joining the SCSWBC or settling in the coastal waters of Hainan. (2) The observed average drifter velocities show strong consistency with the CMEMS-reanalyzed current data during both the summer and winter seasons. (3) The surface current speeds along drifter trajectories in winter exhibit significant interannual variability, primarily driven by variations in wind speed. When the Niño 3.4 index exceeds ±0.5 °C (positive/negative phase), wind speeds and current speeds often reach their minimum (positive phase) or maximum (negative phase) values. These results enhance our understanding of the seasonal dynamics of surface currents in the SCS and their linkage to large-scale climatic variability. Full article

The survival status of Lumnitzera littorea is near threatened globally and critically endangered in China. Clarifying its global distribution pattern and its changing trends under different future climate models is of great significance for the protection and restoration of its endangered status. To build a model for this purpose, this study selected 73 actual distribution points of Lumnitzera littorea worldwide, combined with 12 environmental factors, and simulated its potential suitable habitats in six periods: the Last Interglacial (130,000–115,000 years ago), the Last Glacial Maximum (27,000–19,000 years ago), the Mid-Holocene (6000 years ago), the present (1970–2000), and the future 2050s (2041–2060) and 2070s (2061–2080). The results show that the optimal model parameter combination is the regularization multiplier RM = 4.0 and the feature combination FC (Feature class) = L (Linear) + Q (Quadratic) + P (Product). The MaxEnt model has a low omission rate and a more concise model structure. The AUC values in each period are between 0.981 and 0.985, indicating relatively high prediction accuracy. Min temperature of the coldest month, mean diurnal range, clay content, precipitation of the warmest quarter, and elevation are the dominant environmental factors affecting its distribution. The environmental conditions for min temperature of the coldest month at ≥19.6 °C, mean diurnal range at <7.66 °C, clay content at 34.14%, precipitation of the warmest quarter at ≥570.04 mm, and elevation at >1.39 m are conducive to Lumnitzera littorea’s survival and distribution. The global potential distribution areas are located along coasts. Starting from the paleoclimate, the plant’s distribution has gradually expanded, and its adaptability has gradually improved. In China, the range of potential highly suitable habitats is relatively narrow. Hainan Island is the core potential habitat, but there are fragmented areas in regions such as Guangdong, Guangxi, and Taiwan. The modern centroid of Lumnitzera littorea is located at (109.81° E, 2.56° N), and it will shift to (108.44° E, 3.22° N) in the later stage of the high-emission scenario (2070s (SSP585)). Under global warming trends, it has a tendency to migrate to higher latitudes. The development of the aquaculture industry and human deforestation has damaged the habitats of Lumnitzera littorea, and its population size has been sharply and continuously decreasing. The breeding and renewal system has collapsed, seed abortion and seedling establishment failure are common, and genetic variation is too scarce. This may indicate why Lumnitzera littorea is near threatened globally and critically endangered in China. Therefore, the protection and restoration strategies we propose are as follows: strengthen the legislative guarantee and law enforcement supervision of the native distribution areas of Lumnitzera littorea, expanding its population size outside the native environment, and explore measures to improve its seed germination rate, systematically collecting and introducing foreign germplasm resources to increase its genetic diversity. Full article

This study investigates how local governance actors in northern Taiwan navigate agricultural irrigation coordination under intensifying climate-induced water stress. Although conventional water governance models prioritize structural alignment and centralized integration, they frequently prove to be inadequate under conditions marked by institutional ambiguity and semantic volatility. Focusing on the transitional phase between early drought signaling and the formal implementation of water rationing, this research adopts Situational Grounded Theory (SGT) to examine how actors discursively interpret, negotiate, and adapt to evolving hydrological and institutional constraints. Based on unstructured interviews with irrigation officials, farmers, and public administrators, this study traces how expressions such as “under review” and “adjusting regionally” function as semantic instruments for deferral, alignment, and legitimacy building. These phrases are not merely rhetorical fillers; rather, they operate as situated mechanisms through which actors reposition their roles and recalibrate the meanings of governance. Through iterative coding, semantic clustering, and reflexive mapping grounded in SGT, this study develops the LAWFGS (Local Adaptive Water Governance under Flexible Governance Settings) framework. This tri-axial interpretive framework comprises three interrelated dimensions: (1) governance contexts, which captures the hydrological and institutional phase; (2) actor strategy roles, which reflect how actors adopt and shift their discursive positions; and (3) interpretive flexibility, which denotes the degree of semantic maneuvering exercised in response to governance tensions. The LAWFGS framework offers a situated analytical perspective for understanding how coordination is maintained through meaning-making practices under environmental pressure. The framework emphasizes the relational dynamics through which governance unfolds across shifting and often uncertain contexts. Full article

Landslides pose persistent threats to mountainous regions in Taiwan, particularly in areas such as Nanfeng Village, Nantou County, where steep terrain and concentrated rainfall contribute to chronic slope instability. This study investigates spatiotemporal patterns of vegetation change as a proxy for identifying potential landslide-prone zones, with a focus on the Tung-An tribal settlement in the eastern part of the village. Using high-resolution satellite imagery from SPOT 6/7 (2013–2023) and Pléiades (2019–2023), we derived annual NDVI layers to monitor vegetation dynamics across the landscape. Long-term vegetation trends were evaluated using the Mann–Kendall test, while spatiotemporal clustering was assessed through Emerging Hot Spot Analysis (EHSA) based on the Getis-Ord Gi* statistic within a space-time cube framework. The results revealed statistically significant NDVI increases in many valley-bottom and mid-slope regions, particularly where natural regeneration or reduced disturbance occurred. However, other valley-bottom zones—especially those affected by recurring debris flows—still exhibited declining or persistently low vegetation. In contrast, persistent low or declining NDVI values were observed along steep slopes and debris-flow-prone channels, such as the Nanshan and Mei Creeks. These zones consistently overlapped with known landslide paths and cold spot clusters, confirming their ecological vulnerability and geomorphic risk. This study demonstrates that integrating NDVI trend analysis with spatiotemporal hot spot classification provides a robust, scalable approach for identifying slope hazard areas in data-scarce mountainous regions. The methodology offers practical insights for ecological monitoring, early warning systems, and disaster risk management in Taiwan and other typhoon-affected environments. By highlighting specific locations where vegetation decline aligns with landslide risk, the findings can guide local authorities in prioritizing slope stabilization, habitat conservation, and land-use planning. Such targeted actions support the Sustainable Development Goals, particularly SDG 11 (Sustainable Cities and Communities), SDG 13 (Climate Action), and SDG 15 (Life on Land), by reducing disaster risk, enhancing community resilience, and promoting the long-term sustainability of mountain ecosystems. Full article

In the context of growing global climate change awareness and intensifying environmental degradation, technological innovation in renewable energy has become a key realization method for sustainable development. This paper uses data samples from 30 provinces, municipalities, and autonomous regions in China (excluding Tibet, Hong Kong, Macao, and Taiwan due to data availability) from 2007–2022, constructs an SFA model to measure carbon emission efficiency, and innovatively investigates the U-shaped impact of technological innovation in renewable energy on carbon emission efficiency along with the moderating effects of informatization level and fiscal decentralization. The empirical findings reveal the following: (1) Technological innovation in renewable energy demonstrates a U-shaped impact on carbon emission efficiency, with a negative impact before inflection point 2.596605 and a positive impact after the inflection point. (2) The informatization level plays a positive regulating role in the impact of technological innovation in renewable energy toward carbon emission efficiency, while fiscal decentralization exerts a negative regulating effect. (3) The impact of technological innovation in renewable energy concerning carbon emission efficiency varies depending on regional differences, industrial structure levels, and technological innovation levels in renewable energy. The conclusions of this paper are helpful for promoting the development of technological innovation in renewable energy, improving carbon emission efficiency, and advancing sustainable socio-economic development. Full article

Changes in habitat suitability are critical indicators of the ecological impacts of climate change. Syndiclis Hook. f., a rare and endangered genus endemic to montane limestone and cloud forest ecosystems in China, holds considerable ecological and economic value. However, knowledge of its current distribution and the key environmental factors influencing its habitat suitability remains limited. In this study, we employed the MaxEnt model, integrated with geographic information systems (ArcGIS), to predict the potential distribution of Syndiclis under current and future climate scenarios, identify dominant bioclimatic drivers, and assess temporal and spatial shifts in habitat patterns. We also analyzed spatial displacement of habitat centroids to explore potential migration pathways. The model demonstrated excellent performance (AUC = 0.988), with current suitable habitats primarily located in Hainan, Taiwan, Southeastern Yunnan, and along the Yunnan–Guangxi border. Temperature seasonality (bio7) emerged as the most important predictor (67.00%), followed by precipitation of the driest quarter (bio17, 14.90%), while soil factors played a relatively minor role. Under future climate projections, Hainan and Taiwan are expected to serve as stable climatic refugia, whereas the overall suitable habitat area is projected to decline significantly. Combined with topographic constraints, population decline, and limited dispersal ability, these changes elevate the risk of extinction for Syndiclis in the wild. Landscape pattern analysis revealed increased habitat fragmentation under warming conditions, with only 4.08% of suitable areas currently under effective protection. We recommend prioritizing conservation efforts in regions with habitat contraction (e.g., Guangxi and Yunnan) and stable refugia (e.g., Hainan and Taiwan). Conservation strategies should integrate targeted in situ and ex situ actions, guided by dominant environmental variables and projected migration routes, to ensure the long-term persistence of Syndiclis populations and support evidence-based conservation planning. Full article