Abstract:

Baird’s tapir (Tapirus bairdii) plays an important ecological role in Mesoamerican forests as a browser and seed disperser, earning it the nickname of “gardener of the forest”. However, knowledge of its diet composition remains scattered. We reviewed and analyzed the available literature of diet composition of Baird’s tapir throughout its geographic distribution. We compiled evidence from 25 studies related to these topics. Baird’s tapir was found to consume 511 plant taxa belonging to 407 genera and 122 families. Five types of dietary components have been identified: fibre (stems), leaf, fruit, bark and flowers. The influence of seasonality on the tapir’s diet is unclear due to the underestimation of some components (fruit). We identified limitations in the techniques used to determine diet components and study designs. Future research should focus on develop novel techniques to improve the quantification of dietary components. Additionally, the direct and indirect effects of Baird’s tapir’s diet and plant consumption on ecosystem dynamics should be investigated to clearly understand the functional role of this species.

Abstract: Climate fluctuations are expected to drive a decline in the growth of many conifer and broadleaf species, especially in the Mediterranean region, where these species grow at or very near the southern limits of their distribution. Such trends have important im-plications not only for forest productivity but also for plant diversity, as shifts in spe-cies performance may alter competitive interactions and long-term community com-position. Using tree-ring data sourced from two Abies cephalonica stands with different elevation in Mount Parnassus in Central Greece, we evaluate the growth responses of the species to climatic variability employing a dendroecological approach. We hy-pothesize that radial growth at higher elevations is more strongly influenced by cli-mate variability than at lower elevations. Despite the moderate to relatively good common signal indicated by the expressed population signal (EPS: 0.645 for the high-altitude stand and 0.782 for the low-altitude stand), the chronologies for both sites preserve crucial stand-level growth patterns, providing an important basis for ecological insights. The calculation of the Average Tree-Ring Width Index (ARWI) for both sites revealed that fir in both altitudes exhibited a decline in growth rates from the late 1980s to the early 1990s, followed by a general recovery and increase throughout the late 1990s. They also both experienced a significant decline in growth between approximately 2018 and 2022. The best-fit model for annual ring-width vari-ation at lower elevations was a simple autoregressive model of order one (AR1), where growth was driven exclusively by the previous year’s growth (p < 0.001). At the higher elevation, a more complex model emerged: while previous year’s growth remained significant (p < 0.001), other variables such as maximum growing season temperature (p = 0.041), annual temperature (inverse effect, p = 0.039), annual precipitation (p = 0.017), and evapotranspiration (p = 0.039) also had a statistically significant impact on tree growth. Our results emphasize the prominent role of carry-over effects in shaping their annual growth patterns.

Abstract: We present an improved algorithm based on the POlarization LIdar PHOtometer Networking (POLIPHON) method to retrieve cloud condensation nuclei (CCN) concentration profiles from spaceborne lidar observations. Our previous paper, which was the first study to demonstrate the feasibility of using measurements from Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) to retrieve CCN, is revisited. Our results focus on the Evaluation of CALIPSO’s Aerosol Classification scheme over Eastern Mediterranean (ACEMED) research campaign that took place over Thessaloniki, Greece, in September 2011. We compare our results with our earlier retrievals, discussing the critical changes that have been made and the importance of using the proper conversions factors. We also demonstrate the use of conversion factors acquired based on CALIPSO aerosol typing for CCN retrievals. The analysis highlights the strong influence of smoke on CCN concentrations and shows that the assumed aging state of the smoke can significantly alter the retrieval outcome.

Abstract: River discharge is a pivotal metric in hydrological and water resources management. To address limitations in traditional hydrological monitoring stations, such as sparse distribution and high data acquisition costs, this study focuses on the Fuyang River LHK hydrological station in Handan City, Hebei Province, China, and proposes a synergistic estimation method for river discharge using multi-source remote sensing data. The approach first extracts river water bodies from Sentinel-1 SAR imagery and Sentinel-2 optical imagery via EN-OTSU and MNDWI-OTSU algorithms, respectively. Subsequently, river width is calculated using the water area-to-length ratio method to reduce errors caused by edge effects. Finally, a power-law discharge estimation model is developed by fitting river width to discharge data. For water body extraction, the Sentinel-2 MNDWI-OTSU method achieves the highest accuracy (overall accuracy: 95.31%, Kappa coefficient: 0.90), followed by the Sentinel-1 EN-OTSU method (overall accuracy: 92.55%, Kappa coefficient: 0.89). For discharge estimation, both data sources exhibit robust inversion performance, with the Sentinel-1-based model showing superior error stability (NSE=0.83, R²=0.83, RRMSE=0.24) and the Sentinel-2-based model marginally better theoretical fit (NSE=0.84, R²=0.84, RRMSE=0.26). Compared with traditional in situ measurements and single-sensor approaches, this method enables a shift from point-based to basin-wide dynamic monitoring, resolving data scarcity in ungauged regions; it integrates the high boundary delineation precision of optical remote sensing with the all-weather penetration of radar, effectively countering interruptions from cloudy and rainy conditions; and it reduces reliance on ground infrastructure, providing a cost-effective, reliable framework for river monitoring and informed water resource allocation.

Abstract: This study investigates how a Long Short-Term Memory (LSTM) model inter-nally represents baseflow contributions in snowmelt-driven, semi-arid mountain basins with heterogeneous geologic characteristics. Five basins in the Sangre de Cristo Mountains of northern New Mexico, spanning fractured Precambrian bedrock and sedimen-tary-volcanic terrain, were used to evaluate both model performance and interpretability. Baseflow dynamics were inferred post hoc using the Baseflow Index (BFI) and a two-reservoir HEC-HMS (Hydrologic Engineering Center’s Hydrologic Modeling System) model. Although baseflow was not explicitly included in model training, internal cell state activations showed strong correlations with both shallow and deep baseflow com-ponents derived from the HEC-HMS model. To better understand how these relationships may change under climatic stress, BFI-based baseflow patterns were further analyzed un-der pre-drought and drought conditions. Results indicate that the LSTM learned to inter-nally distinguish between short- and long-residence flowpaths, encoding physically meaningful hydrologic behavior. This work demonstrates the potential for LSTM models to offer valuable insights into baseflow generation and groundwater–surface water inter-actions, particularly critical in water-scarce regions facing increasing drought frequency.

Abstract:

Accurate snow monitoring is critical for understanding hydrological processes and managing water resources. However, traditional snow sensing networks in the United States, such as the United States Department of Agriculture’s (USDA) SNOwpack TELemetry (SNOTEL) system, are costly and limited in spatial coverage. This study presents the design and deployment of a lower-cost, open-source snow sensing station aimed at improving the accessibility and affordability of snow hydrology monitoring. The system integrates research-grade environmental sensors with an Arduino-based Mayfly datalogger, providing high temporal resolution measurements of snow depth, radiation fluxes, air and soil temperatures, and soil moisture. Designed for adaptability, the station supports multiple sensor types, various power configurations—including solar and battery-only setups—multiple telemetry options, and capability for diverse deployment environments, including forested and open terrain. A multi-site case study at Tony Grove Ranger Station in northern Utah, USA demonstrated the station’s performance across different physiographic conditions. Results show that the system significantly reduces costs while increasing the spatial resolution of data, offering a scalable solution for enhancing snow monitoring networks. This study contributes an open-source hardware and software design that facilitates replication and adaptation by other researchers, supporting advancements in snow hydrology research.

Abstract: Hazard risk monitoring of groundwater depletion and land subsidence due to excessive groundwater extraction is crucial for groundwater resource development, especially in densely populated, small-island developing sites. The island of Bali, Indonesia, represents such an urban environment at risk of land subsidence arising from groundwater depletion. The total percentage of groundwater depletion was calculated and interpolated spatially using measurements of groundwater level from 2008 to 2017 at 18 monitoring well sites available in the area. Furthermore, time-series synthetic-aperture radar (SAR) interferometry processing was applied to estimate the temporal change in land displacement using the Phased Array type L-band SAR (PALSAR) data from 2007 to 2010. The result of downward displacement, signifying subsidence, corresponded with the Global Navigation Satellite System (GNSS) data measurements at stations distributed in the observed subsided areas, i.e., CDNP and CPBI. The displacement varied consistently with changes in groundwater level. In regard to maintaining groundwater utilization, the hazard–risk relation of the groundwater depletion, i.e., low (0–25%), moderate (25–50%), and high (>50%), and the presence/absence of subsidence were utilized to classify groundwater conservation into safe, vulnerable, critical, and damaged zones. This application can be considered effective in providing spatial information for sustainable groundwater management.

Abstract: As Akasofu noted, no two geomagnetic storms are identical, yet the storm that occurred between November 12 and 14, 2025, stands out as an exceptional phenomenon. Its impact was evident across multiple layers of the ionosphere and numerous parameters, making it essential to conduct a comprehensive multi-parameter analysis of this event. Such an analysis relied upon data from the four LAERT topside sounders mounted aboard the recently-launched Ionosfera-M satellites. Global ionospheric dynamics was thoroughly examined during the storm period, particularly focusing on the polar and auroral zones, along with the equatorial anomaly region. Notable features included sharp electron density gradients, widespread F-layer disturbances, and the formation of giant plasma bubbles. These elements collectively contributed to the dynamic picture of the ionospheric storm captured through multi-parameter measurements by the LAERT sounders.

Abstract:

The ecological status of lakes based on ichthyofauna, as defined by the Water Framework Directive, is assessed using intercalibrated methods. However, the methods adopted (in Poland, the Lake Fish Index LFI-EN method, based on results of one-off fishing with multi-mesh gillnets) are labor-intensive and do not allow for frequent repeat testing. Therefore, the concept of a simple model describing changes in the relative number of single traces in the vertical profile (according to the TS target strength distribution) in a lake is presented, as well as an index (the sum of deviations from such a model), enabling quantification of the similarity of TS distributions in lakes with this model. Preliminary analyses were conducted on acoustic data collected in Lake Dejguny. This lake—the condition of which could be estimated based on historical data using the relationships between LFI and the degree of lake eutrophication (expressed by Carlson’s TSI)—was assessed as having a good status in 2006, whereas in 2021, (based on LFI-EN) it had a moderate status. The study tested the TS distribution model, calculated as the arithmetic mean of the relative number of single traces in 2 m-thick layers. It was also shown that the proposed indicator can effectively signal deterioration of ecological status—the sum of the absolute values of the TS distribution deviations in 2021 (moderate status) from the model was more than seven times greater than the sum of the deviations of the distributions from which the model was built (good status). The obtained results confirmed the hypothesis about the possibility of determining a characteristic distribution of single traces in the vertical profile when the lake was classified as being in good condition.

Abstract: Video synthetic aperture radar (SAR) enables observation of moving targets by leveraging temporal information across successive frames. In particular, dynamic shadows in video SAR image sequences provide critical cues for detecting moving objects whose energy is smeared or Doppler-shifted. To achieve high-resolution imaging at a high frame rate for effective dynamic scene monitoring, video SAR systems typically operate at extremely high frequencies or even in the terahertz band, rather than the microwave band. However, terahertz video SAR suffers from significant signal attenuation due to atmospheric absorption. We present a deep learning framework for high-frame-rate and high-resolution imaging with microwave video SAR system. In this framework, the problem of microwave video SAR imaging is formulated as an image super-resolution reconstruction task for low-resolution yet high-frame-rate image sequences from microwave video SAR. We develop a simple yet effective image super-resolution reconstruction network that is completely built upon convolutional neural networks. The designed network takes a low-resolution image sequence and the corresponding high-resolution image with blurred shadows as input, and then produces a high-resolution image sequence where shadows are clearly visible. Furthermore, the network is trained in a self-supervised manner and thus does not require desired high-resolution image sequences as ground truth, which is appealing to practical applications. Processing results of real data from two different video SAR systems have shown good performance of the proposed approach with convincing generalization ability.

Abstract: The Philippines experiences intense rainfall but has limited ground-based monitoring infrastructure for flood prediction. Satellite rainfall products provide broad coverage but contain systematic biases that reduce operational usefulness. This study evaluated three correction methods—Quantile Mapping (QM), Random Forest (RF), and Hybrid Ensemble—for improving Satellite Rainfall Monitor (SRM) estimates in the Cagayan de Oro River Basin, Northern Mindanao. When trained on comprehensive 2019-2020 data, Random Forest and Hybrid Ensemble substantially outperformed Quantile Map-ping, achieving excellent calibration accuracy (R² = 0.71 and 0.76 versus R² = 0.25 for QM). However, when tested on an independent year with substantially different rain-fall patterns (2021: 120% higher mean rainfall, 33% increase in rainy-day frequency), performance rankings reversed completely. Quantile Mapping maintained satisfactory operational performance (R² = 0.53, RMSE = 5.23 mm), showing improvement over training conditions, while Random Forest and Hybrid Ensemble both failed dramati-cally, with R² dropping to 0.46 and 0.41 respectively despite their excellent training performance. This highlights that training accuracy alone poorly predicts operational reliability under changing rainfall regimes. Quantile Mapping's percentile-based cor-rection naturally adapts when rainfall patterns shift without requiring recalibration, while machine learning methods learned magnitude-specific patterns that failed when conditions changed. For flood early warning in basins with limited data, equipment failures, and variable rainfall, only Quantile Mapping proved operationally reliable. This has practical implications for disaster risk reduction across the Philippines and similar tropical regions where standard validation approaches may systematically mislead model selection by measuring calibration performance rather than operational transferability.

Abstract: Africa is a land of paradox. It is home to the world’s largest uncultivated arable land. Yet, food insecurity remains a pervasive in urban centers. With increasing rural-urban migration, hunger and obesity will continue to pose considerable threats to urban residents. It is in that respect that the paper rehabilitates the notion of urban agriculture (UA) as a buffer against uncertainties in food access, both in terms of quality and quantity. Despite the numerous potentials of this practice, few urban residents in Africa engage in producing their own food. While a plethora of contemporary literature has explored the challenges undercutting UA, there are still unanswered questions. Lingering questions concern what the underlying cause of limited participation in participation in the practice is. The paper discovered that a substantial percentage of African cities lack a comprehensive urban policy. To that end, there is no comprehensive guideline in the allocation of land and logistical support for potential farmers. The paper argues that, since UA has the potential to enhance the resilience of urban residents to climate change and ultimately, food insecurity, it is imperative for states to frame urban policies that recognize UA as an essential component of urban development.

Abstract: Methanogens, also known as methanogenic archaea, are among the most ancient and widespread microorganisms, despite their particular requirements for growth. These oxygen-sensitive microorganisms have impacted climate and biogeochemical cycles throughout Earth’s history, although their specific roles in the long-term carbon cycle remain little explored. Methanogens evolved early during Earth’s history, likely during the Archaean Eon, in layered benthic microbial communities called microbial mats. These ancient mats, when lithified, form microbialites that represent some of the earliest evidence of life in the fossil record dating back > 3.5 Gy. Contemporary microbial mats experience a wide range of fluctuating conditions, including dramatic diel shifts in oxygen, sulfide, redox, temperature, salinity and pH. Methanogens are an integral part of marine and freshwater microbial mats and have been identified in the oxic zone of these sedimentary ecosystems; however, their adaptations to apparently unfavorable conditions and their role in long-term CO2 sequestration through precipitation of carbonate are unclear. Furthermore, the importance and coevolution of methanogens and microbial mats may explain the global role these organisms had on Earth’s major climate events during the Archean and Proterozoic eons, notably in the ending of icehouse periods and recovery of mats following mass extinctions – often in conditions with low or no oxygen. In addition to an important role in the evolution of our planet, methanogens may also produce biosignatures that are relevant for astrobiology research [and space exploration]. This review will discuss the diversity, physiology, and ecology of methanogens in order to clarify their role in biogeochemical processes through geologic time.

Abstract: Tropical cyclones pose major risks to life and property, especially as coastal populations grow and climate change increases the likelihood of intense storms, making seasonal prediction of tropical cyclones an important scientific and societal goal. This study uses HURDAT best-track records from 1950–2024 to quantify annual tropical cyclone, hurricane, and major hurricane counts across the Atlantic basin, Caribbean Sea, and Gulf of Mexico. These nine targets are paired with 34 monthly climate predictors from NOAA and NASA GISS—including SST and ENSO indices, Main Development Region (MDR) wind and pressure fields, and latent heat flux empirical orthogonal functions—evaluated under nine predictor-set configurations. Four forecasting approaches are developed and tested under operationally realistic conditions: Lasso regression, K-nearest neighbors (KNN), an artificial neural network (ANN), and XGBoost, using a 30-year sliding-window cross-validation design and a Poisson log-likelihood skill score relative to climatology. Lasso performs reliably with concise, physically interpretable predictors, while XGBoost provides the most consistent overall skill, particularly for basin-wide total cyclone and hurricane counts. The skill of ANN is limited by small sample sizes, and KNN offers only marginal improvements. Forecast skill is the highest for basin-wide storm totals and decreases for regional and major-hurricane targets due to lower event frequencies and stronger predictability limits.

Abstract: Seed wings are well-documented as morphological adaptations for seed dispersal and environmental persistence in angiosperms, but their functional significance in gymnosperms, which dominate temperate and subalpine forest ecosystems, remains poorly understood. This study examines the germination ecology of Smith fir (Abies georgei var. smithii), a species whose seeds possess membranous, translucent wings. We tested the germination responses of three seed treatments—intact, mixed (de-winged seeds mixed with the detached wings), and de-winged seeds under two light conditions (12 hours light/12 hours dark and continuous darkness) and three temperature regimes (5/1°C, 15/2°C, and 25/5°C) to assess the interactive effects of light, temperature, and seed-wing conditions on germination. Smith fir seeds showed optimal germination between 15 and 25°C, with light exposure significantly enhancing germination under cooler conditions (&lt; 5 ℃). De-winged seeds germinated significantly better than intact seeds (P &lt; 0.001), confirming that seed wings inhibit germination. The germination percentages of intact and mixed seed were comparably low and significantly lower than those of de-winged seeds, suggesting that the inhibitory effect is more likely attributable to chemical inhibitors associated with the wings rather than to mechanical restriction. Smith fir seeds, dispersed in October, exhibit conditional physiological dormancy, with wing-derived inhibitors delaying germination until favorable spring conditions. These findings provide insights into the adaptive strategies of gymnosperms in regulating germination timing in responses to seasonal environmental cues in temperate mountain ecosystems.

Abstract: The principles of nonequilibrium thermodynamics are briefly discussed, with a focus on entropy. For the first time, the energy consumption and entropy production of CO2 final storage and utilization (CCS and CCU) are quantitatively analyzed and interpreted. This shows that the final storage and chemical utilization of CO2 are not sustainable processes for solving the climate crisis. Building on this, a new proposal for a quantitative criterion for sustainability is presented: entropy. In addition, a relatively simple indicator is presented that is a helpful (and easier to calculate) indicator for the entropy production of various processes or products.

Abstract:

Heracleum sosnowskyi Manden. (commonly known as Sosnowsky’s hogweed or giant hogweed; family Apiaceae, formerly Umbelliferae), an invasive species introduced to Europe as an ornamental plant in the early 20th century and to European Russia in the mid-20th century as a potential forage crop, has become widespread in many countries by the late 20th century. While some researchers focus on eradication and control strategies for this plant, others investigate its potential for producing valuable products, such as sugars, alcohols, biofuels, paperboard, and essential oils. In this study, we analyzed the elemental composition of various plant parts (roots, leaves, stems, and fruits) collected from H. sosnowskyi populations in the Leningrad region (Vyborg district). Using instrumental neutron activation analysis (INAA), we determined the concentrations of 32 elements, encompassing major and trace elements (Na, Mg, Al, Cl, K, Ca, Sc, Ti, V, Mn, Fe, Co, Ni, Zn, As, Se, Br, Rb, Sr, Mo, Sb, Cs, Ba, La, Ce, Sm, Tb, Hf, Ta, Th, U). Our findings indicate that many potentially toxic elements exhibit no bioaccumulation and are present at lower concentrations in the plant tissues compared to the surrounding soil.

Abstract:

Open biomass burning (OBB) plays a vital role in adverse effects on air quality, climate systems, and human public health. Large-scale OBB, including forest fires and crop residue burning, is detected in Southeast Asia (SEA), a region with agrarian countries. The characteristics of OBB have been widely studied in SEA; however, the daytime and nighttime variations in fire and the effects of fire production remain limited. Particulate matter (PM) is released in significant amounts, burying open biomass during the episode. This study uses the Visible Infrared Imaging Radiometer Suite (VIIRS) to detect active fires during daytime and nighttime from OBB in Chiang Mai, Thailand, during March-April 2020, and investigates the mass concentration of size-specific PM down to PM_0.1. The results showed that hot spots occur more often at night than during the day. The VIIRS fire detection data provides better response to small fires and better mapping of extensive fire perimeters. PM_1.0–0.5 showed the highest mass concentration among particle sizes. Moreover, the fire hotpots are the highest correlated with PM_0.5-0.1 during daytime and PM_1.0–0.5 during nighttime. The large OBB in Chiang Mai significantly contributes to ambient PM. This study offers crucial insights into particulate pollution from biomass burning.

Abstract: Cloud detection is an important procedure for the processing of remote sensing images. A cloud detection scheme driven by the spectral and the temporal features is presented in this paper, where an unsupervised hierarchy clustering approach is proposed for large scale image segmentation. The potential cloudy pixels are identified by means of the spectral matching, in which the spectral data of the clustering centers are compared to the patterns in the spectral dataset of ground covers. The matched pixels are regarded as cloudless pixels, whose category can be recognized accordingly. In contrast, the bright temperatures corresponding to the unmatched pixels are used to exclude the interference of the occasional hotspots, enabling the final cloud detection result. Landsat 8, Sentinel-2, and MODIS satellite data are used in the validation to demonstrate the precision and stability of the proposed scheme for the data at different spatial resolutions.

Abstract: Based on 402 micro-survey data from water conservancy, environmental protection and other departments in the middle reaches of the Yangtze River Basin, the characteristics of digital technology are identified from three dimensions: tools, power and capacity. Combined with factor analysis and mediating effect model, the impact and mechanism of digital technology on inter-provincial ecological coordination in the basin are empirically tested. Research shows that: (1) Digital technology has a significant positive driving effect on inter-provincial ecological coordination in the middle reaches of the Yangtze River. After robustness tests such as re-measurement by digital technology and elimination of interference from smart water conservancy pilot projects, the conclusion still holds. (2) The mechanism path shows that central government support and public participation play a partial mediating role in the impact of digital technology on inter-provincial ecological coordination, and both have a masking mediating effect on the sustainability of inter-provincial ecological coordination-digital technology indirectly enhances the level of inter-provincial ecological coordination by strengthening central policy and financial support and broadening public participation channels. (3) In empirical tests, the dependent variable mostly relies on subjective scores, lacking cross-validation of objective indicators, and the logical chain of mechanism transmission is not clear enough. Based on this, targeted suggestions such as improving the policy system for digital collaboration in river basins and building a digital and intelligent public participation platform are put forward, providing practical references for the ecological collaborative governance of the Yangtze River Basin.