The High Arctic deserts of remote northern Greenland are expected to become warmer and wetter due to climate change. Precipitation changes will increase fluctuations in surface soil salinity, and the same happens for thawed permafrost soil where stable salt concentrations are replaced with fluctuating salinity during annual freeze-thaw cycles. Both have unknown effects on the microbial communities and their emissions of microbial volatile organic compounds (MVOCs). These compounds are produced from various pathways mainly as secondary metabolites and have ecological and climatic implications when released into the environment and the atmosphere. Thus, it is important to explore the effects of environmental changes, such as changes in salinity, on soil microbial communities and their MVOC emissions. Here, we characterize the MVOC production of three novel bacterial isolates from northern Greenland throughout their growth period under low, moderate, and high salt concentrations. We demonstrate that salinity significantly alters both the quantity and composition of MVOCs emitted by all three strains, including changes in the emissions of sulphur- and nitrogen-containing compounds, potentially leading to ecosystem nutrient loss. The observed changes in MVOC profiles suggest that changes in soil salinity due to climate change could alter microbial metabolism and MVOC emissions, with potential implications for Arctic nutrient cycling and atmospheric chemistry.

Abstract East Asian monsoon shows strong annual cycle with the wet season in summer and dry in winter. In addition to annual cycle, some monsoon regions exhibit distinct climatological sub-seasonal variations such as fast annual cycle, climatological intraseasonal oscillations (CISOs) and climate singularities. In this paper a significant cyclic-type outgoing longwave radiation (OLR) singularity during the spring to summer transition period over the South China Sea (SCS) is identified based on the daily OLR data from 1979 to 2023. The quasi-CISO in each individual year is invented as an analogue to CISO. Its relationship with Madden–Julian Oscillation (MJO) and the SCS summer monsoon onset (SCSSM) is analyzed. The quasi-CISO shows large interannual variability. A group of years with the quasi-CISO amplitudes larger than the medium of all sample years are selected to form the quasi-CISO mode. Among them 72% of the quasi-CISO cyclic valley coincides with the SCSSM onset time represented by the persistent intensification of the monsoonal westerlies, which are the SCS-CISO years. The MJO activity during the sixteen SCS-CISO years shows three different levels. Composite results of eight SCS-CISO years with strong MJO show clear synchronized planetary- and regional-scale convection and circulation progressions where strong MJOs over the equatorial Indian Ocean appeared two weeks before SCSSM onset. Strong convection over the SCS associated with the SCSSM onset process can be triggered by the enhanced low-level southerly flow by MJO’s subsidence leg over the equatorial maritime continent through increasing the moisture transport. This study provides observational-based evidence that supports earlier findings about CISO, CMJO and SCSSM onset. The findings can be applied to evaluate the Sub-seasonal to Seasonal forecast model products for better understanding of the extended-range weather and climate predictability.

Abstract Long‐term monitoring of bird populations across scales is important in evaluating conservation targets and creating effective conservation strategies. For nearly six decades, the Breeding Bird Survey (BBS) has served as the primary broad‐scaled source of relative abundance trends of swallows and martins in North America. Recently, however, it has become possible to obtain breeding population trends using semi‐structured eBird community science data. Moreover, weather surveillance radar data of swallow and martin roosting populations yield a third complementary source of trend information. Using results from these three approaches, we propose a novel method of spatially combining estimates of percent change per year into a probability of directional agreement and/or disagreement that describes (1) the direction of the trend within a given region, (2) the amount of evidence associated with the estimate and (3) how much uncertainty surrounds it. We focus our efforts on an area of high Hirundinidae concentration in the North American Great Lakes region and predict trends from 2012 to 2022. We found a high probability of agreement between all three sources about observed declines in swallow and martin trends in the region surrounding Lake Ontario and to the west of Lake Michigan. Focusing future research on these regions could improve our understanding of these declines and help build more targeted conservation initiatives. Synthesis and applications . Our data integration methodology allows managers to identify regions that accumulate evidence of concerning trends across multiple wildlife monitoring schemes. These regions can thus be prioritized in conservation and management efforts. This approach can be generalized to other sources of long‐term monitoring data of different species, at different stages of their annual cycle, in any geographic location.

This study analyzed the relationship between Suspended Sediment Concentration (SSC), rainfall, and water discharge in four large basins that drain entirely cratonic areas: Negro, Branco, and Tapajós, in the Amazon basin, and Congo, in Central Africa. We analyzed 1,323 samples of suspended sediment concentration (SSC) combined with precipitation and discharge data. A combination of statistical correlation parameters, time lag analysis, and hysteresis evaluation were calculated to identify the main controls on the SSC of cratonic rivers. Results indicate strong heterogeneity in sediment dynamics among the rivers. The Branco River displayed linear relationships between SSC and hydrological variables, moderate clockwise hysteresis and no lag, confirming a hydrologically reactive system with rapid sediment mobilization. In contrast, the Negro and Tapajós Rivers showed low linear correlations and moderate to high lag values, suggesting non-linear sediment responses associated with dilution, storage and geomorphological controls. However, the Negro River exhibited moderate anticlockwise hysteresis (HI = −0.20), negative lag values and low partial correlation, suggesting delayed sediment response, floodplain storage and dilution during high flows. The Congo River exhibited strong partial correlation with rainfall but lacked hysteresis and presented long lag values (−8 to −10 months), indicating that SSC responds predominantly at interannual climatic scales rather than to annual discharge cycles. Geological differences among basins proved crucial: rivers draining deeply weathered cratonic areas (Negro, Tapajós and Congo) showed dissipated sediment signals, whereas the Branco River, partially draining erodible Quaternary deposits, exhibited strong hydrological coupling. By combining multiple approaches, the study provides a framework for understanding sediment transport in cratonic rivers and contributes to sediment budgeting, climate-change assessments and geomorphological modeling in tropical basins.

Abstract. Atmospheric sulfate formation influences climate and air quality, yet its chemical pathways remain difficult to constrain. This study utilizes the oxygen isotope anomaly (Δ17O) of sulfate aerosol (ASO4) as a tracer to distinguish formation processes. This work presents a simulation of Δ17O(ASO4) within the contiguous United States, conducted over full annual cycles, which enables the quantification of seasonal and spatial patterns of sulfate oxidation pathways and their response to major emission reductions, for the first time at this scale and temporal coverage. In 2019, Δ17O(ASO4) values were predicted to be below 1 ‰ in the Gulf Coast, indicating acidic, ASO4-rich conditions dominated by S(IV) + H2O2 oxidation, while values above 2 ‰ in the West suggested less acidic conditions, leading to enhanced ASO4 production via S(IV) + O3 oxidation. Peak Δ17O(ASO4) values of ∼4.5 ‰ in April across the Western US reflected O3-driven ASO4 formation during high ammonia (NH3) emissions from fertilization. Between 2006 and 2019, mean Δ17O(ASO4) was predicted to increase by up to 2 ‰, driven by declining sulfur dioxide (SO2) emissions from regulatory measures. Model comparisons with historical measurements show reasonable agreement in the acidic southeastern US (RMSE = 0.20 ‰, Baton Rouge, LA). However, the model overpredicts Δ17O(ASO4) in the Western US with RMSE values of 0.36 ‰ (La Jolla, CA) and 1.9 ‰ (White Mountain Research Center, CA). This overestimation suggests an excessive model response to aqueous S(IV) + O3 reactions. These findings underscore the diagnostic potential of Δ17O(ASO4) for assessing sulfate formation mechanisms and pinpointing shortcomings in chemical transport models. However, Δ17O(ASO4) observations across the United States remain exceedingly limited, with most available data dating back to the late 1990s and early 2000s, highlighting the need for renewed measurement efforts.

The Grijalva basin is of great relevance in southern Mexico because it receives the highest precipitation and has the most extensive hydroelectric system in the country. In addition, the lower basin has been impacted by extreme flooding in recent years. It is the source of water for several million people and the regional industry. For this study, the basin was divided into four sub-basins: Angostura, Chicoasén, Malpaso, and Peñitas. Each of these sub-basins has a dam that helps regulate the water flow and generate hydroelectric energy. To better understand the region’s climatology, this study uses long-term rainfall observations from sub-basins to describe precipitation patterns and their variability. Various statistics are computed to describe the annual precipitation cycle for each sub-basin. The results show that Angostura, Chicoasén, and Malpaso share a common climatology, with precipitation peaks in June and September and a mid-summer drought (MSD) in July. Peñitas receives considerably more precipitation throughout the year, with the highest values in October-November. In all sub-basins, La Niña (El Niño) years are characterized by increased (decreased) precipitation in the rainy season. The study demonstrates that the extreme precipitation observed during La Niña years in late summer and autumn is mainly due to an increased number of tropical cyclones over the western Caribbean Sea and the Gulf of Mexico. The interquartile range and other percentile values of monthly precipitation provide additional information that may be useful for dam management.

Understanding the residency and movement patterns of at-risk species is critical for effective conservation and management. Due to declining sea ice, Arctic skate Amblyraja hyperborea could be a potentially vulnerable bycatch species in expanding Arctic fisheries targeting Greenland halibut Reinhardtius hippoglossoides . Multi-year acoustic telemetry (2011-2012 and 2014-2016) at depths of 400-1200 m in Cumberland Sound (Nunavut, Canada) revealed that most Arctic skates remained resident or displayed fidelity (29 individuals, 69%) to the location where they were released. Dispersal was generally limited (<20 km; n = 31; 74% of detected individuals), although 11 skates (26%) exhibited higher mobility which could exceed 100 km. Resident skates were present across an annual cycle, experiencing varying bottom water temperatures (0.5-2.6°C), dissolved oxygen levels (2.63-3.99 μmol l -1 ), and sea ice concentrations (0-100%). These findings suggest that Arctic skate distribution in Cumberland Sound is influenced more by sedentary behaviour than by hydrodynamic conditions, with regional residency maintained over multiple years. Concurrent tracking of Greenland halibut revealed contrasting movement patterns: while Arctic skates exhibited residency, Greenland halibut displayed seasonal mobility. Sedentary behaviour increases the vulnerability of Arctic skates to bycatch, as Greenland halibut migrations overlap with resident Arctic skates across Cumberland Sound. Consequently, growth of Greenland halibut fisheries will heighten Arctic skate bycatch risk; thus, local extirpation risks are a concern, requiring fisheries to consider (1) bycatch monitoring strategies, (2) improved handling practices, and (3) an assessment of depth-based mortality risk following capture and release.

Climate-driven ocean warming is profoundly reshaping marine ecosystems, with cascading effects on biodiversity and trophic interactions. For migratory marine predators such as seabirds, demographic responses to warming depend on when and where populations are exposed across the annual cycle. Therefore, integrating demographic monitoring and tracking data, across broad geographic and temporal scales, is essential, given the spatial and seasonal variability in ocean warming. Here, we integrated long-term demographic data, seasonal distributions, and sea surface temperatures (SSTs) for 26 populations of five seabird species breeding in the North-East Atlantic to assess the effects of SSTs on reproduction, survival, and population growth trajectories. Demographic responses varied widely among populations and seasons, but negative effects were most consistently associated with warming during the autumn period postbreeding, particularly in the Barents and East Greenland Seas. Winter warming also corresponded to reduced survival, while breeding-season SSTs showed fewer significant effects on reproductive rates. Populations with dual responses to warming in both the breeding and nonbreeding seasons had the lowest projected population growth rates under future SSTs given a high emissions scenario. These results demonstrate that population vulnerability reflects the interaction between seabirds' year-round distributions and regional ocean warming. This underlines the need to integrate year-round tracking and long-term monitoring to inform conservation strategies and marine spatial planning to ensure climate-resilient marine ecosystems.

BackgroundClimate change is expected to reshape malaria transmission dynamics in tropical and subtropical regions. Stratospheric Aerosol Injection (SAI), a proposed solar geoengineering strategy to reduce global warming, could have unintended consequences for vector-borne diseases such as malaria. This study investigates how SAI, through the Stratospheric Aerosol Geoengineering Large Ensemble (GLENS-SAI) scenario, could alter malaria transmission patterns across seven South Asian countries—Afghanistan, Bangladesh, Bhutan, Iran, India, Nepal, and Pakistan—compared with an unmitigated warming scenario over coming decades.MethodsUsing the VECTRI malaria model, malaria transmission dynamics were simulated from 2020 to 2097 under two climate pathways: the GLENS-SAI simulations, designed to stabilize global temperatures at 2020 levels, and the high-emissions Representative Concentration Pathway (RCP) 8.5—the control scenario (CTRL), representing unmitigated climate change. The model incorporated climatic and demographic factors to simulate vector density, Entomological Inoculation Rate (EIR), and malaria cases. Spatial patterns were assessed using distribution maps, while temporal variability was examined through time-series analysis. Statistical comparisons employed regional averages, anomaly detection, and significance testing.ResultsThe findings reveal a redistribution of malaria transmission dynamics under the GLENS-SAI scenario, reflected in variations in vector density, EIR, and malaria cases. Compared to CTRL, the GLENS-SAI scenario reduces malaria transmission intensity across South Asia, though spatial heterogeneity persists. Significant declines in EIR are observed in India, Nepal, Bangladesh, northern Pakistan, southern Iran, and the Afghanistan-Pakistan border region, indicating the suppressive effect of the GLENS-SAI scenario on malaria transmission. However, localized increases in EIR are projected in southeastern Pakistan, western Afghanistan, north-central and eastern Iran, and northern Nepal. These shifts are likely driven by SAI-induced changes in temperature and precipitation, influencing mosquito survival and reproductive dynamics. Additionally, the annual malaria transmission cycle shortens in amplitude and duration across several endemic areas, suggesting a shift in seasonal transmission patterns and altered windows of disease risk throughout South Asia.ConclusionsWhile the GLENS-SAI scenario may reduce malaria transmission across much of South Asia, localized increases highlight the need for region-specific public health strategies. These findings underscore the importance of incorporating GLENS-SAI scenario impacts into malaria control planning to address spatially varied effects.

Objective: To assess the recent advances in the ecological niche dynamics of two invasive grass species and their interactions with two native grasses from Mexico. Design/methodology/approach: In 2024, sites exhibiting the presence of Bouteloua gracilis, Bouteloua curtipendula, Melinis repens, and Pennisetum ciliarewere identified. Occurrence data were collected, incorporating 19 bioclimatic variables, to conduct correlation analyses, select relevant variables, and generate ecological niche models and distribution maps. Key selected variables included BIO 1 (mean annual temperature) and BIO 12 (annual precipitation), among others. Results: The native species and Melinis repens (natal redtop grass) demonstrated broad ecological adaptability across Mexico. The bioclimatic variables of shared significance across all species were BIO 12 and BIO 1, which are critical to their growth and development. The invasive species exhibited an annual biological cycle, influenced by precipitation seasonality (BIO 15) and fluctuations in minimum temperature (BIO 6). Melinis repens displayed the largest potential niche area, spanning 57 million hectares, followed by Bouteloua curtipendula (sideoats grama) with 51 million hectares. The most substantial ecological niche overlap, 22 million hectares, was observed between sideoats grama and natal redtop grass. The findings highlight the need for targeted management strategies and technological solutions to effectively control invasive species, restore grassland ecosystems, and enhance forage quality in Mexico. Limitations/implications: The study focused exclusively on four grass species, thereby excluding potential interactions with other Poaceae members and plant families. Findings/conclusions: Both native and introduced grasses exhibit considerable economic, social, and ecological relevance in Mexico, underscoring the necessity for comprehensive assessments and the implementation of sustainable management plans to safeguard vital grassland ecosystems.

Abstract Accurate estimation of precipitable water vapor (PWV) is critical for evaluating the feasibility of millimeter- and submillimeter-wave astronomical observations. We assess ERA5 and MERRA-2 reanalysis datasets at the Nanshan 26-meter Radio Telescope (NSRT) and propose a Digital Elevation Model (DEM)-Weighted Inverse Distance Weighting (IDW) interpolation method that accounts for both horizontal distance and elevation difference. Compared with nearest-neighbor and bilinear interpolation, the DEM-Weighted approach substantially improves PWV estimation, with MERRA-2 achieving over 26% lower RMSE than its bilinear interpolation. Seasonal evaluation identifies MERRA-2 with DEM-Weighted interpolation (M2-DEM) as the most accurate, with slightly higher summer bias than ERA5 DEM-Weighted IDW (E5-DEM). A 30-year PWV climatology (1995–2024) shows a pronounced annual cycle, stable interannual variability, and no significant long-term trend. Winter minima (typically below 3 mm) define the primary high-frequency observing window, while summer maxima arise from elevated temperatures and monsoonal moisture transport. Comparisons with QTT, GBT, and Effelsberg, along with atmospheric transmission modeling, confirm NSRT’s favorable K- and Q-band transparency and partial 3 mm accessibility under optimal conditions. These results establish M2-DEM as the preferred dataset–method combination for site-specific PWV evaluation and provide a strong basis for expanding NSRT’s high-frequency astronomical capabilities.

The aim of this study was to develop functional food with high antioxidant potential and to examine its effect on oxidative–antioxidant markers in the blood of athletes in an intervention study. The study population consisted of 40 athletes—long-distance runners who were divided into a study group (SG) and a control group (CG). Before and after the dietary intervention in the blood, the following were determined: total antioxidant status (TAS), antioxidant enzymes (superoxide dismutase—SOD, glutathione peroxidase—GPx, catalase—CAT) and total oxidative stress (TOS). Additionally, the oxidative stress index (OSI) was calculated. It was shown that in the SG after the dietary intervention, the TOS (p < 0.001) and OSI (p = 0.029) decreased, while the TAS increased (p < 0.001). However, no significant differences were found in the level of antioxidant enzymes in the SG. In the CG, dietary intervention did not affect the level of the assessed parameters. This study demonstrated that functional food in the form of a bar with high antioxidant potential, rich in polyphenols, dietary fiber, vitamin E, selenium, and magnesium, can support the body’s endogenous antioxidant system and restore oxidative–antioxidant homeostasis in athletes. However, further studies are needed, including a larger group of athletes, longer intervention times, and different periods of the annual training cycle.

Empirical relationships explore and understand an advanced effectively methodology for predicting maximum temperature (Tm) in regions with different climatic conditions based on solar irradiance (SI) readings taken from NASA/POWER for the period 2014–2024. Using seasonal and annual daily means, the SI-Tm correlations during the four seasons (winter, spring, summer, and autumn) and annual cycle were investigated in six cities (Basra, Shanafiya, Baghdad, Rutba, Kirkuk and Shakhan) in Iraq using quadrate and cubic polynomial regression models. These models provided strong fit lines between the data points for SI and Tm, which effectively capture the seasonal (R2>0.4 in winter and summer and >0.8 in spring and autumn) and annual daily Tm (R 2 > 0.9) as a predictor variable. The results show that Iraq experiences high SI and extreme Tm, especially in the southern regions. In contrast, the northern provinces have lower SI and Tm values due to higher altitudes and increased weather activity. This study offers valuable information for policymakers and planners to highlight the potential for solar energy generation and distribution in accordance to climatic conditions.

Plasma estradiol 17β (E2) and gonadosomatic index (GSI) are reliable biomarkers for assessing gonadal maturity in fish. This study investigated seasonal variations in GSI and plasma E2 concentrations and their correlation with ovarian development stages in female golden trevally (Gnathanodon speciosus), a high-value fish species for marine aquaculture, over an annual reproductive cycle. The broodfish were 900 ± 200 g and 40 ± 5 cm in body weight and total length, respectively. From November to February, plasma E2 levels and GSI remained low, followed by a significant increase from March to October, with E2 peaking during vitellogenesis. Ovarian histology revealed asynchronous oocyte development from March to October, with multiple oocyte stages coexisting, confirming G. speciosus as a multiple-spawning species with a reproductive season spanning March to October. Elevated plasma E2 levels during oocyte growth underscore its pivotal role in driving vitellogenesis. These findings enhance understanding of G. speciosus reproductive physiology, providing valuable insights for optimizing broodstock management, conditioning protocols, and strategic planning for artificial reproduction and seed production for marine aquaculture.

Abstract Untangling the spatial and temporal processes that influence the population dynamics of migratory species is challenging, because changes in abundance are shaped by variation in demographic rates across differing environments throughout the annual cycle. Population and demographic data available on migratory species are often fragmentary, providing only local, season‐specific perspectives. To be effective, conservation strategies require range‐wide, full‐annual‐cycle knowledge. We developed a novel approach to link data from different segments of the population in a unified framework using stage‐ and season‐specific population growth rates to assess population trends and conservation interventions for the New Zealand endemic migratory tōrea (South Island pied oystercatcher, Haematopus finschi ). We combined demographic data collected at breeding and non‐breeding sites between 1980 and 2022 in an integrated population model and population viability analysis to (a) determine population dynamics over time, (b) identify underlying drivers of change by estimating stage‐ and season‐specific demographic rates and (c) evaluate future conservation interventions and interacting threats that are likely to primarily affect survival by comparing future population trajectories through a range of scenarios. The tōrea population has been declining over the past 42 years by an average of 1.8% annually. Summer survival probabilities were generally higher than winter survival probabilities. Adult survival probability fluctuated less over time than the other demographic rates. Variation in population growth rates was most strongly associated with subadult winter survival ( r = 0.29). The population viability analysis showed that conservation interventions aimed at improving productivity would only be beneficial if survival probabilities remain at current levels. It is unlikely that additional mortality due to future, increased threats on survival can be offset by increased productivity. Synthesis and applications. The tōrea population decline suggests the need for a higher IUCN threat classification and a conservation strategy (e.g. habitat protection and restoration) targeting multiple demographic rates across the annual cycle of tōrea. Our novel approach using population growth rates to link fragmented local, season‐specific data to model range‐wide full‐annual‐cycle dynamics has the potential to guide the conservation of migratory species where existing data are similarly fragmented.

Third-party risk management (TPRM) reaches an inflection point, with artificial intelligence (AI) capabilities meeting pressing demands for real-time vendor risk oversight of increasingly complex digital ecosystems. Conventional assessment methodologies resting on manual questionnaires, annual review cycles, and document-centric evaluations are poorly matched to the pace and interconnectedness driving modern technology. This article analyzes how intelligent automation is remaking basic processes in vendor governance, from optimization of questionnaires through semantic modeling to predictive monitoring allowed through continuous data synthesis. Unstructured vendor control documentation is now parsed by natural language models to extract control metadata and produce risk assessments that must be validated, rather than created, by humans. Algorithmic integrity is tackled with multi-model verification architectures that employ parallel processing pipelines where ensemble methods quantify confidence levels and flag gaps in the vendor control environment for risk subject matter expert review. Brain-inspired computing principles underpin system design, with hierarchical feature extraction possible, along with adaptive learning from assessment outcomes. Technical debt becomes a critical governance factor, particularly in the context of data dependencies and configuration management across model lifecycles. Explainable artificial intelligence provides transparency that is vital to regulatory recognition, allowing risk officers to trace decision pathways and understand feature attributions underlying automated recommendations. Convergence of distributed ledger technology with intelligent risk systems unlocks opportunities for tamper-proof audit trails and privacy-preserving attestations in support of cross-organizational governance frameworks framed by emerging digital resilience mandates.

Aims The global prevalence of cardio-renal-metabolic (CRM) conditions, including chronic kidney disease, heart failure, type 2 diabetes in interconnected pathophysiology, is rapidly increasing. This presents health/economic consequences for Asia-Pacific (APAC) countries experiencing substantial disease burden. We aimed to forecast the regional clinical/economic burden of comorbid CRM conditions and evaluate the potential impact of sodium-glucose cotransporter 2 (SGLT2) inhibitors on burden reduction, with empagliflozin as the modeled intervention. Methods A Markov model (10-year time horizon [2024–2033]; annual cycle length) was developed and adapted for selected Southeast Asian countries (Malaysia/the Philippines/Thailand/Vietnam/Singapore), Australia, and South Korea using published local epidemiological and economic data. Future CRM disease prevalence, as well as the modeled SGLT2 inhibitor empagliflozin’s potential in reducing clinical burden (prevalence/mortality) and associated healthcare resource use (HCRU) costs, were estimated. Empagliflozin cost was excluded from HCRU calculations to isolate/evaluate the economic burden of CRM conditions. Results Projecting the current disease trends (without SGLT2 inhibitors), the total number of patients with comorbid CRM conditions and deaths in Southeast Asia/Australia/South Korea would increase ∼3-fold from 19.0 million/968,000/2.6 million (2024) to 63.4 million/3.1 million/7.1 million (2033). Guideline-recommended use of SGLT2 inhibitor empagliflozin is estimated to prevent 925,000/19,100/105,000 patients from developing comorbid conditions and ∼1.9 million/50,000/174,000 deaths in Southeast Asia/Australia/South Korea by 2033. After excluding empagliflozin cost, these reductions translate to discounted cumulative cost savings for Southeast Asia (Malaysia: RM16.9 billion/the Philippines: ₱775.6 billion/Thailand: ฿973.7 billion/Vietnam: 148.0 trillion ₫/Singapore: S$1.1 billion), Australia (AU$23.2 billion), and South Korea (₩42.4 trillion). Varying the parameters of the deterministic sensitivity analysis resulted in upper and lower estimates of economic burden that differed by no more than 10% from the mean economic burden of each health state across the seven APAC countries. Limitations Underreporting/variability in local epidemiological data potentially affected burden projection accuracy. Insufficient available data on comorbid CRM conditions across countries necessitated model input assumptions, which may have introduced uncertainties. Conclusions Substantial increases in comorbid CRM disease prevalence and associated healthcare resource strain in APAC are expected over the next decade. Guideline-directed SGLT2 inhibitor use, with empagliflozin as an example, may alleviate regional clinical and economic burden.

Biological invasions can strongly disrupt ecosystems, reshaping their structure and functioning. We investigate how two widespread invasive parrots –the rose-ringed parakeet Psittacula krameri and the monk parakeet Myiopsitta monachus- affect plant-bird interaction networks using a multilayer framework. Field data were collected over a full annual cycle in an area with both species, accumulating 288 h of observations and tracking 24,561 fruits from 576 plants. Parakeets modified networks by introducing novel interactions, increasing species turnover and altering modularity and nestedness. Acting as both seed predators and dispersers, they became central connectors, enabling native birds to access previously unavailable resources and increasing rare dispersal mechanisms. Their activities increased antagonisms and generated new interspecific interactions with numerous plant species. By exploiting plants not previously used by local birds, parakeets heightened the risk of secondary invasions and the spread of exotic plants. These findings underscore their dual roles in disrupting and restructuring ecological networks and stress the need to reassess their contributions in native and invaded ecosystems. Understanding their potential to facilitate exotic plant expansion is critical, as their ecological impacts will likely intensify with population growth and geographic spread. Comprehensive assessments are essential to predict and mitigate these far-reaching consequences.

The aim of the research is to conduct a systematic statistical analysis of the long-term dynamics of crop biomass formation in Ukraine based on the Normalized Difference Vegetation Index (NDVI) and to establish quantitative patterns of the infl uence of key meteorological factors on the state of vegetation over twenty years (2001–2020). The relevance of the work is driven by the need to adapt agricultural technologies to global climate change and to develop accurate predictive models of crop productivity based on remote sensing data. Research Methodology. The study is based on data from the Global Agriculture Monitoring (GLAM) system, derived from 8-day NDVI composites of MODIS satellites (Terra and Aqua). A retrospective database covering 24 administrative regions of Ukraine was created, containing approximately 23,000 records. Trend analysis using linear regression models was applied to assess long-term tendencies. The complex impact of meteorological conditions was evaluated by calculating correlation coeffi cients and constructing multiple regression dependencies between NDVI values and indicators of average monthly air temperature, precipitation, and cumulative climatic water balance (CWB). Results. It was established that within the annual cycle, maximum NDVI values (0.61–0.67) are achieved from May to July, with the most intensive biomass growth (42%) occurring in April–May. Long-term analysis revealed a stable positive trend of index growth during the cold period of the year (0.42–0.55% per year), while growth rates are minimal in the summer months. A signifi cant decrease in NDVI was recorded in September–October (by 13.9–15.6% over 20 years), which is explained by the shift in winter crop sowing dates and accelerated crop maturation due to warming. Spatial analysis confi rmed profound zonal differentiation: the steppe zone is characterized by the lowest biomass potential (0.47–0.66) and critically high interannual variability (32–50%), which is 4-5 times higher than in Polissya. An inertial character of the temperature regime’s infl uence was identifi ed: the temperature of the current month correlates most signifi cantly with the state of vegetation in the subsequent period (r reaches 0.80 in March–April). Conclusions. Remote monitoring using the NDVI indicator objectively refl ects the transformation of Ukraine’s agrocenoses under the infl uence of climate change. It has been proven that comprehensive consideration of the temperature regime and cumulative water balance allows for predicting crop status with high accuracy (multiple correlation coeffi cient R = 0.85). The results obtained provide a scientifi c basis for optimizing irrigation systems and implementing climate-smart agriculture methods in the most vulnerable southern regions of Ukraine.

The present work is an endeavour to study seasonal variations in various blood parameters of fresh water teleostean mud eel; Macrognathus aculeatus (Bloch). Marked seasonal variations were observed in RBC counts, WBC counts, Hb content, PCV, MCV, MCHC, Cell size, N/C ratio, surface area of erythrocytes, TCDC and oxygen capacity in different months of the year with a peak value in May/June which concide well with the breeding periods of the fishes.. KEYWORDS :Blood parameters, Fish, Annual cycle.