搜索结果：Climate dynamics

Climate variability and extreme weather events pose an increasingly serious threat to agricultural productivity in Europe. Although many studies have focused on the mean yield response to climate change, relatively few have attempted to offer a comprehensive, multi-dimensional view of agricultural resilience, separating long-term productivity trends from short-term climate-induced fluctuations. In this paper, a data-driven Climate Resilience Index is proposed to assess the resilience of European cereal systems under climate stress. Annual yield time series are decomposed into trend and anomaly components to identify interannual deviations driven by climate variability. Machine learning and deep learning algorithms, including RF, CatBoost, CNN, LSTM, TCN, and a TCN-LSTM hybrid model, are used to assess the predictive validity of climate extreme indices and persistence patterns. Among the evaluated models, the TCN-LSTM architecture achieved the highest predictive performance with 0.8347 R2, outperforming standalone temporal and ensemble-based models. The PCA-based exposure framework explained 68.4% of the total climate variability within the first principal component, indicating a highly concentrated climate-stress structure. Feature importance analyses further revealed that lagged yield anomalies and rolling volatility indicators were the dominant predictors of resilience dynamics. The findings show that temporal models perform better than traditional ensemble methods, emphasizing the significance of multi-year recovery mechanisms in anomaly evolution. To provide a consistent measure of exposure, principal component analysis is used to reduce correlated climate indicators into a multi-dimensional climate stress index. The CRI captures these three essential elements: climate exposure, yield variability, and recovery. The results at the country level show substantial variability across Europe, indicating that while exposure intensity is important, recovery dynamics are also critical to understanding resilience. The results also show that there are structurally distinct resilience profiles, and robustness tests indicate that country rankings are not sensitive to different weights and recovery functions. The results clearly show that recovery and adaptive capacity are critical to agricultural sustainability amid growing climate uncertainty. The CRI developed in this paper provides a clear, empirically valid framework for comparing resilience in agricultural systems.

Climate change is increasing the frequency of compound drought and heat events, threatening forest stability worldwide. While genomics has helped identify resilient genotypes, our ability to characterize adaptive traits - phenotyping - has not kept pace. This creates a bottleneck: we can sequence trees faster than we can understand how they physically respond to stress. Moving away from single-sensor monitoring, the field is now embracing multi-sensor data fusion, in which thermal imaging, Solar-Induced Fluorescence (SIF), hyperspectral remote sensing, and LiDAR are combined on platforms ranging from Unmanned Aerial Vehicles (UAVs) to ground-based robotic systems. These integrated approaches are proving effective for detecting physiological stress - such as changes in stomatal conductance - before visible damage appears. Deep learning models, meanwhile, are beginning to outperform traditional vegetation indices for specific tasks such as tree-crown segmentation and stress classification, although their performance remains constrained by overfitting, limited transferability, and domain shift across forest types in analyzing complex forest canopies. A major limitation remains, however: most high-throughput phenotyping (HTP) focuses on the canopy, largely ignoring the root system and the soil-plant-atmosphere continuum (SPAC), which are critical for drought resilience. In this review, we argue that developing climate-resilient forests requires looking below the canopy. We propose a constraint-based framework that couples aerial sensor data with eco-hydrological approaches and process-based modeling to narrow the range of plausible root functional strategies-rather than to directly identify root phenotypes, while critically evaluating the assumptions and validation challenges inherent in this approach. Future research should focus on standardized protocols, open benchmark datasets, and Explainable AI (XAI) to strengthen the link between above-ground signals and below-ground traits.

Climate change is increasingly disrupting ecological processes across arid and mountainous biomes, with profound implications for the reproductive phenology of large herbivores. These species are especially climate-sensitive, as their breeding cycles are tightly coupled with vegetation dynamics driven by seasonal temperature and precipitation. Yet, in biodiversity-rich regions such as eastern Iran, where climate variability is acute and data are sparse, long-term phenological responses remain poorly understood. Here, we examine how reproductive timing in urial sheep (Ovis vignei), a mountain herbivore, responds to climatic variation across six protected areas, as climate-driven mismatches between birth timing and peak forage availability may reduce neonate survival and ultimately affect population viability and connectivity. Climate data (temperature, precipitation, snowfall, and humidity) from the nearest weather station to each study area, along with latitude and mean elevation of each habitat, were integrated using generalized linear mixed models (GLMMs) to assess phenological responses to environmental variables. Our results reveal clear regional differences in mating and lambing time. Mating time was significantly influenced by latitude, summer temperature, and autumn precipitation, with higher latitudes and autumn rainfall delaying mating, while warmer summers advanced it. In contrast, lambing timing was largely dictated by study area-level random effects, which accounted for the majority of variance, whereas fixed effects such as January temperature, snowfall, and latitude contributed only minimally, highlighting the dominant role of spatial differences among study areas in shaping lambing phenology. These findings, over the past decade, underscore the role of climate and latitude in shaping reproductive timing and highlight the urgent need to incorporate phenological data into adaptive wildlife management and habitat-specific climate resilience planning in vulnerable arid mountain ecosystems.

Climate change is accelerating spatially complex transformations in land, water, coasts, cryosphere, and ecosystems, creating a critical need for reliable, scalable, and timely monitoring based on Earth observation imagery. Conventional approaches that rely on sparse in-situ measurements, manual image interpretation, and simple spectral indices or thresholding often fail to capture subtle, heterogeneous, and multiscale changes, and they do not scale to today's multi-sensor, multi-temporal satellite archives. This review synthesizes image processing and AI techniques applied to optical, SAR, thermal, and hyperspectral remote sensing for climate change detection, covering classical change detection methods, machine learning classifiers, deep learning architectures (including Siamese and segmentation networks), spatio-temporal models for satellite image time series, and multi-sensor fusion, across application domains such as land use/land cover (LULC) and deforestation, hydrology and flooding, coastal and mangrove dynamics, cryospheric change, urban heat, ecosystems, and natural hazards. In addition, we analyze how these methods are evaluated using common performance metrics-Overall Accuracy (OA), precision, recall, F1-score, Intersection over Union (IoU), Kappa coefficient, and error measures such as RMSE-and discuss key challenges related to data quality and annotation, domain shift and generalization, computational and operational constraints, interpretability, and integration with climate and impact models. The distinctive contribution of this review is a unified method-application taxonomy that explicitly links algorithm families to specific climate monitoring tasks, a systematic comparison of reported performance metrics that clarifies trade-offs between techniques under different data and class-imbalance conditions, and a practical decision framework to guide researchers and practitioners in selecting appropriate image processing and AI approaches for given sensors, regions, and operational requirements, while outlining promising future directions such as foundation models, standardized benchmarks, and interoperable climate decision-support systems. Across the reviewed literature, deep learning approaches consistently demonstrate higher accuracy (e.g., improved IoU and F1-scores) in complex and heterogeneous environments, while classical methods remain effective for large-scale and data-scarce applications. However, significant gaps persist in model generalization across regions, availability of labeled datasets, and integration of multi-sensor time-series data.

As one of the most widespread anthropogenic activities in grasslands, livestock grazing exerts profound and complex influences on soil organic carbon (SOC) dynamics. However, the responses of SOC dynamics to grazing intensity in alpine grassland are still unclear. We manipulated a grazing intensity (including no grazing, CK; moderate grazing, MG; and heavy grazing, HG) experiment from 2019 to 2024 on the Tibetan Plateau. Plant community characteristics, i.e., above-ground biomass (AGB), cover, and height, as well as SOC and soil nutrient characteristics at topsoil (0-10 cm) and subsoil (10-20 cm) layers, were systematically measured. Climate variables from 2019 to 2024 were derived from the fifth-generation global climate atmospheric reanalysis data. We found that SOC content declined temporally in both soil layers. The temporal stability of SOC under MG was lowest in the topsoil compared with CK and HG. Under MG, plants positively promote SOC accumulation through biomass and coverage; however, under HG, this synergy is disrupted, and plant height becomes the core proxy indicator for characterizing the negative effects of grazing. Precipitation and soil available nutrients have a stronger direct positive effect on topsoil SOC, while the negative impact of grazing on subsoil SOC is mainly achieved through the indirect path of suppressing plant height. Concluding, grazing intensity drives SOC dynamics by altering plant community traits, with the net effect contingent on soil depth and coupled with climate and soil nutrient availability. These findings can provide scientific data support for the sustainable management of alpine grasslands and soil carbon sinks.

The COVID-19 pandemic significantly affected the transmission of respiratory pathogens worldwide. This study aimed to describe the epidemiological characteristics of respiratory infections among children in Lanzhou, China, during the post-pandemic period and to investigate how meteorological factors influence the transmission of respiratory pathogens in the Lanzhou region. During the study period, 42.5% of children tested positive for respiratory pathogens (32.8% single, 9.7% mixed infections). Predominant pathogens were influenza virus (IFV, 12.3%), adenovirus (HAdV, 8.2%), rhinovirus (RV, 7.5%), and respiratory syncytial virus (RSV, 6.7%). RSV and IFV peaked in infants <1 year (14.1% and 13.5%), while Mycoplasma pneumoniae (MP) was highest in school-aged children (8.5%). Mixed infections increased with age (2.8% to 11.6%), with no sex differences. Seasonal patterns varied: IFV peaked in winter (26.9%), enteroviruses in summer (18.9%), and RSV showed bimodal winter-spring distribution. IFV negatively correlated with temperature (r = -0.50) and positively with atmospheric pressure (r = 0.37). RSV negatively correlated with temperature (r = -0.49), while MP positively correlated with humidity and pressure.Nonlinear lag modeling showed immediate meteorological effects (0-week lag). IFV showed elevated risk under extreme low temperatures and high pressure (RR: 10.395 and 18.597), though wide confidence intervals warrant caution. HAdV and MP risks increased similarly. Conversely, HPIV and RV showed protective associations at low temperatures or high wind speeds. Wind speed appeared protective against most pathogens, but this observational finding requires interventional validation. Pediatric respiratory pathogen prevalence in Lanzhou exhibited distinct age-dependent and seasonal characteristics, with pathogen-specific meteorological associations. Detection risks for IFV, RSV, HAdV, and MP increased with low temperatures and high pressure, while RV and HPIV showed opposite patterns. The protective wind speed effect suggests ventilation improvements may reduce transmission risk, though this hypothesis-generating conclusion requires further validation. These findings inform age-stratified, seasonally-adapted prevention strategies and meteorological early warning systems.

Biotic resistance, the reduction in invasion success caused by native communities, plays an important role in the long-term dynamics of biological invasions. A large body of empirical research on biotic resistance has accumulated since the last comprehensive review on the subject 20&#x2009;years ago, enabling us to achieve a refined understanding of biotic resistance and its dynamics. Here, we aim to reshape research on biotic resistance to alien plant invasions by (i) synthesizing existing evidence on biotic resistance and (ii) exploring the so far rarely considered interplay between biotic resistance mechanisms (i.e. competition, aboveground and belowground antagonisms, and diversity-invasibility effects) and the potential eco-evolutionary changes in biotic resistance over time. To address the first aspect, we conducted a global meta-analysis of 240 experimental studies to assess the mechanisms by which and the extent to which biotic resistance of native communities affects the performance of alien plant species. We show that competition with native plant species, aboveground antagonism (e.g. herbivores) and diversity-invasibility effects significantly reduced alien plant performance, whereas there was no evidence for consistent effects of belowground antagonism (e.g. soil pathogens). Competition exerted the strongest biotic resistance, followed by aboveground antagonism. However, the strength of biotic resistance also depended on the alien plant performance measure considered (vegetative performance, survival, reproductive performance, or population growth). From the small set of studies that considered more than one biotic resistance mechanism, we did not detect an overall synergistic effect of combined mechanisms. The meta-analysis results also revealed that biotic resistance first decreased with the residence time of the alien plant species but increased again after approximately 200&#x2009;years. In a subset of studies directly comparing species of different origin, we did not detect a difference in biotic resistance to alien versus native species. To address the second aspect, we expanded the limited empirical evidence on temporal dynamics by presenting a conceptual causal network and an accompanying mathematical model to explore the eco-evolutionary dynamics of biotic resistance mechanisms. Our conceptual and mathematical models highlight that biotic resistance is determined by both the attributes of the alien species (i.e. invasiveness) and of the recipient community (i.e. invasibility). Both factors can change over time as inter- and/or intraspecific selection cause changes in the composition and overall density of the native community and the alien species. As invaders evolve and the successful ones persist, biotic resistance initially decreases, then increases again due to intra- and interspecific adaptation of the native community. Using the findings from the comprehensive synthesis of empirical studies and our modelling approach, we highlight research avenues to better understand the temporal dynamics of biotic resistance to plant invasions, including how biotic resistance depends on multiple mechanisms and performance measures, how it may differently affect alien versus native species and crucially, how it changes over time.

Monsoon-dominated agricultural regions face increasing hydro-climatic stress driven by climate change, yet a critical methodological gap persists: no integrated, spatially explicit framework simultaneously links drought dynamics, crop water stress, and system-level stability under future climate projections. South Korea exemplifies this challenge, where seasonal water deficits, rising evaporative demand, and dependence on monsoon rainfall create compounding vulnerabilities for agricultural resilience. Here we develop a multi-index framework combining the Standardized Precipitation Evapotranspiration Index (SPEI), a modified Crop Water Stress Index (CWSI), and a Reliability-Resilience-Vulnerability (RRV) stability metric to quantify spatio-temporal patterns of water stress and resilience. Observations (1985-2014) were used to evaluate and bias-correct multiple CMIP6 models under SSP2-4.5 and SSP5-8.5. Continuous climate fields were interpolated, and Crop Water Zones (CWZs) were derived using a percentile-based 3&#x2009;&#xd7;&#x2009;3 classification that combines interpolated RRV and CWSI. Results show increased precipitation variability and higher evaporative demand, intensifying seasonal water stress, particularly in summer and autumn. Differences between scenarios are modest in the near term, but both indicate expansion of high-risk CWZs. Bias correction improves agreement with observations; however, performance metrics are interpreted cautiously given methodological constraints. Spatial patterns indicate a shift from stable/resilient to more critical/high-risk conditions, revealing emerging vulnerabilities in agricultural systems. This framework provides a transferable approach for assessing climate-driven water stress and supports adaptive water management in monsoon-influenced regions.

Climate change disproportionately affects poorer countries like Uganda, intensifying poverty and livelihood stress, which can escalate gender-based violence (GBV). Although the parent study was not designed to focus on GBV, GBV emerged repeatedly during interviews and focus groups; this paper presents a GBV-focused thematic analysis of those narratives. Particularly, we examine how GBV interconnects with poverty, shifting gender roles, alcoholism, environmental stress, and family planning dynamics. Between April and July 2021, we conducted an exploratory qualitative research study that comprised 28 focus group discussions (FGDs), comprising six-eight participants each, stratified by sex and age (18-25, 25-49, and mixed 50&#x2009;+&#x2009;groups). Additionally, 40 key informant interviews (KIIs) were held in Rukiga district, Uganda. Purposive sampling was applied. Data were organised in NVivo 12 and analysed thematically. Participants perceived GBV, including intimate partner violence, non-partner sexual violence, child abuse, and early marriage, as widespread and normalised. Two main interconnected driver clusters emerged. First, poverty, male alcohol use, and shifting gender norms contributed to household instability. As men abandoned provider roles, women assumed more responsibilities, provoking conflict and sometimes violence from disempowered male partners. Second, environmental degradation and climate-related stressors (droughts, floods, soil erosion) worsened economic hardship, tensions, and GBV. Population growth and limited land access further strained livelihoods. While family planning was generally supported, male opposition sometimes triggered conflict. Climate change impacts are gendered, with GBV pathways shaped by shifting gender roles, norms, and relations destabilised by environmental and livelihood pressures. Addressing GBV in climate-affected communities requires gender-transformative environmental and livelihood programmes. This should include strengthened social and structural resilience to challenge inequitable gender norms and power imbalances.

Global warming has become an increasingly urgent issue that must be addressed within a limited timeframe, as the debate over the overshoot pathway intensifies. Existing climate and socio-economic models inform our understanding but are too complex for timely action and non-expert use. To address these challenges, our stochastic framework leverages a one-box Ornstein-Uhlenbeck model with colored-noise forcing to characterize variance scaling in detrended temperatures and links this behavior to an empirical Hurst coefficient. The observed weakening of anti-persistence, as summarized by this coefficient, is then used to quantify changes in temperature variance relative to the pre-industrial era. Our empirical Hurst maps reveal patterns consistent with findings from global climate models, highlighting greater changes in temperature variance in equatorial regions, such as southeastern Amazonia and western Indonesia, than in high-latitude regions. Furthermore, seasonal analyses of African temperature records reveal pronounced heterogeneity in variance changes, identifying vulnerable regions that are masked when variability is assessed at annual timescales. By linking our results to climate solutions that consider fairness in burden-sharing and unequal risk-bearing capacity, we anticipate that the empirical Hurst coefficient will support more equitable and effective climate action.

Submediterranean marcescent oak forests form a climatic ecotone highly exposed to increasing aridity across the Mediterranean Basin. Understanding how these vulnerable taxa were affected by past climatic shifts can help contextualize their sensitivity to ongoing changes. Here we used ensemble Species Distribution Models (SDMs) to infer the distribution dynamics of eight marcescent oak species from the Heinrich Stadial (~&#x2009;17&#xa0;ka) to the present, and to explore their potential future trajectories for 2070 and 2100 under three SSP scenarios. Models calibrated with 12,450 filtered occurrences and high-resolution paleoclimate and CHELSA datasets performed well (AUC&#x2009;>&#x2009;0.97), with precipitation and temperature seasonality emerging as key predictors. Hindcasts revealed contrasting east-west Quaternary histories, including episodes of expansion, contraction and partial stability linked to abrupt climate transitions such as the Heinrich Stadial and Younger Dryas. Future projections indicate widespread northward shifts and substantial suitability losses, especially under SSP5-8.5, with pronounced impacts on narrowly distributed taxa. By comparing past-to-present and present-to-future range shifts, we identify temporal coherence in species responses, showing that taxa with strong historical fluctuations tend to exhibit larger projected changes. Integrating past range dynamics provides an essential ecological baseline to interpret species-specific sensitivity and regional asymmetries. Our results refine the identification of potential climatic refugia and high-risk zones, offering a framework to prioritize conservation strategies for transitional oak forests in a rapidly warming Mediterranean Basin.

Amaranth (Amaranthus spp.) is increasingly recognized as a promising opportunity crop for improving food and nutritional security in sub-Saharan Africa under climate change. This review synthesizes current knowledge on amaranth utilization in Africa, with particular emphasis on how drought stress influences nutrient dynamics, yield, and quality, and the implications for breeding sustainable leafy vegetable systems. Evidence from both controlled and field studies indicates that moderate drought can enhance the accumulation of minerals, proteins, and secondary metabolites in leaves, whereas severe stress reduces growth, yield, and nutritional stability; in contrast, grain nutritional quality remains relatively stable under moderate water deficit. These responses are underpinned by coordinated morphological, physiological, biochemical, and molecular mechanisms, including osmotic adjustment, antioxidant defense, ABA-mediated gene regulation, and genotype-specific accumulation of protective proteins and osmolytes. The review further evaluates the feasibility of breeding for drought tolerance alongside stable nutrient composition, considering genetic diversity, trait heritability, and genotype &#xd7; environment interactions. Findings suggest that environmental effects strongly influence nutrient-related traits, limiting the effectiveness of selection based solely on genetics. While breeding exclusively for nutritional traits may yield modest gains, integrating drought-resilient genotypes with water-efficient agronomic practices and improved value-chain management offers a more robust pathway. Overall, this review highlights key drought adaptation mechanisms in Amaranthus and underscores its potential as a climate-resilient crop for sustainable and affordable leafy vegetable production in sub-Saharan Africa.

The Rawdah ecosystem in arid regions is characterized by its unique vegetation and biodiversity that support wildlife, buffer climate, and provide ecosystem services that benefit humans and nature. Understanding the floristic composition of the Rawdah ecosystem and its ecological dynamics is crucial for informing sustainable management. Rawdhat Khuraym, a vegetation hotspot zone in the Riyadh region of Saudi Arabia, has recently become part of the Imam Abdulaziz bin Mohammed Royal Reserve. For 23 years, the vegetation composition of this cultural and ecological important rawdhat has not been studied; hence, to inform suitable conservation and effective restoration strategies this study aims to (1) update the floristic composition of Rawdhat Khuraym in the dry and wet seasons, (2) determine the dominant plant communities within distinct parts of Rawdhat Khuraym, (3) determine the soil variables controlling the various plant communities, (4) assess the extent to which the reserve's protection since 2020 has impacted plant communities, and (5) explore how these trajectories may alter in response to future climatic changes. Three distinct zones were identified in Rawdhat Kkuraym, and based on a random stratified sampling design, a total of 21 plots were selected. Surveys were conducted in the dry and wet season. In each plot, three replicate quadrat surveys were conducted, and soil samples were collected. For historical vegetation cover, satellite-based NDVI and climatological variables were analysed. The present study revealed a rich plant diversity in Rawdhat Khuraym, in particular, the dense growing trees and shrubs that support the plant communities in this ecosystem during the dry season, such as Ziziphus nummularia, Capparis decidua, Vachellia. gerrardi, Capparis spinosa. During the wet season, grasses like Cynodon dactylon and herbs like Malva parviflora and Calendula arvensis flourished after rains and became dominant. The floristic analysis showed 89 plant species (57.3% annuals and 42.4% perennials), belonging to 30 families, where Asteraceae, Poaceae, Fabaceae, and Brassicaceae were prominent (50.6%). Six life forms were recorded, with therophyte as dominant (49.4%). The Saharo-Arabian element was the most represented (55.1%), followed by the Irano-Turanian element (31.5%). In the wet winter-spring season, the northern part of Rawdhat Khuraym had three main plant communities (two dominated by C. dactylon and one by Z. nummularia). The central part also had three communities, two dominated by M. parviflora and one dominated by C. arvensis. The southern part showed four plant communities (Zilla spinosa, V. gerrardi, Rhazya stricta, and C. dactylon community). As can be expected, in the dry summer-fall season, the number of plant communities within each part became more limited; the northern part attained two communities (Z. nummularia and C. spinosa-C. dactylon), the central part showed one community (C. spinosa-C. dactylon), while the southern part had three communities (Z. spinosa, V. gerrardi, and R. stricta). Soil variables controlling the plant community differed in the three distinct parts. in the northern part communities showed a positive correlation with HCO3, Mg, Ca, salinity, SO4, clay, Na, NO3, K, porosity, field capacity, and NH4. In the central part, communities showed a positive correlation with silt, CaCO3, and pH. In the southern part, communities revealed a positive correlation with sand and the bulk density of the soil. The comparative analysis between the previous studies and the present study, 5 years after it became a protected area, revealed a recovery of perennial species, and most of the identified species are drought-resistant and show moderate salinity resistance. The NDVI analysis showed that the northern part had stable vegetation cover (average 63%) from 1986 to 2025, while central and southern parts attained lower levels of 38 and 40%, respectively. The fluctuation of NDVI can be attributed to water availability, as it showed a positive correlation with the soil wetness index, relative humidity, and precipitation. To predict future trajectories of plant communities, it is recommended to perform a detailed modelling study of the vegetation dynamics in correlation to the environmental and regional predicted future climatic conditions to be able to account for future changes in the vegetation composition of the Rawdhat Khuraym in restoration programs.

Climatic fluctuations can play a crucial role in shaping large-scale patterns of biodiversity and biogeography. This study examines how climatic gradients and their variability influence family-level temporal ecological processes that govern the balance between stochastic and deterministic processes that underlie community dynamics of riverine insects. This comparison, done across multiple continents but mostly in the Northern Hemisphere, used long-term data from 302 sites mainly in Europe, ranging from 10 to 37&#x2009;years, and with 4751 observations of family-level riverine insect assemblages. We analyzed spatial patterns in family richness, family-level temporal beta diversity, and associated ecological processes. Generalized additive models revealed high spatial heterogeneity, with family-level richness declining with latitude, but total family-level temporal beta-diversity decreasing with increasing latitude and elevation across the continents studied. The cross-continental results from generalized additive and dissimilarity models further showed spatial shifts in the proxy indicators of ecological processes (e.g., environmental variables and time-series decomposition), with stochastic processes (e.g., ecological drift) increasing in importance towards higher latitudes. In contrast, deterministic processes (e.g., temperature) declined. At a cross-continental perspective, these findings highlight how spatial asymmetry under fluctuating climates may shape riverine insect community dynamics and improve adaptive conservation strategies.

Vegetation, though central to terrestrial ecosystems, remains highly vulnerable to fluctuations in climate and human-induced activities. Such combined influence on vegetation health dynamics necessitates the application of robust remote sensing-based vegetation indices, such as the Normalized Difference Vegetation Index (NDVI). The latter enables the detection of subtle structural and functional changes, offering an early warning of plant stress before visible symptoms appear. It is therefore crucial to predict vegetation activities by modelling NDVI that is able to detect and attribute the climate change impacts on vegetation growth through its temporal and spatial variations. For this reason, in this paper we introduce an advanced combined deep learning method (bidirectional long short-term memory and convolutional neural network model, BiLSTM-CNN) for temporal-spatial modelling of NDVI informed by meteorological and soil moisture data. BiLSTM-CNN is a composite progressive processing model that can investigate potential trends of vegetation alterations that may be abrupt and barely obvious, localized and extensive, happening over short or long-time scales. Our proposed BiLSTM-CNN forecasting method has been evaluated and compared with state-of-the-art techniques, and the experimental results have shown clearly that our proposed method is competitive with the existing relevant NDVI deep learning predicting models.

Temperate grasslands are essential for sustainable ruminant production, yet their seasonal stability and nutritional balance are increasingly threatened by climate shifting. While high-sugar ryegrass (HSG) cultivars and grass-legume mixtures offer solutions to these constraints, their integrated performance across productivity, quality, and stability dimensions requires holistic evaluation. This study conducted a field experiment in Aberystwyth, UK, employing a two-factor design with five grass components (three HSG cultivars, a tri-mixture, and a standard control) and two sowing modes (pure grass vs. white clover mixtures). Across nine harvests, we assessed temporal dynamics in dry matter (DM) yield, community composition, and nutritive value. Additionally, the Coupling Coordination Degree, food equivalent unit (FEU), and food equivalent unit productivity (PFEU) were comprehensively evaluated as independent indices to quantify trade-offs and synergies. The pure-sown HSG cultivar AberMagic (AM) maximized early-season DM yield (9144.48 kg&#xb7;ha-1) but experienced significant mid-season declines. Conversely, grass-clover mixtures mitigated this "summer slump" via temporal niche differentiation, significantly improving yield stability and biological weed suppression (grass weeds< 0.5%). Nutritional analysis revealed a physiological trade-off: pure HSG stands delivered superior energy density through elevated water-soluble carbohydrates (WSC), whereas mixtures provided a more balanced nutritional profile, significantly increasing late-season crude protein (CP) to 22.20%-28.05% and reducing neutral detergent fiber (NDF). Management models must align with specific agronomic goals. Pure AM is the premier choice for intensive systems prioritizing maximum total output. For long-term grazing requiring a stable feed supply, the AberAvon (AA) and white clover (WC) mixture is optimal due to its exceptional late-season recovery and excellent synergistic performance across indicators. Furthermore, the consistent underperformance of standard varieties underscores the absolute necessity of utilizing improved traits. Synergizing the ecological resilience of multi-species mixtures with the improved traits establishes an optimal framework for sustainable and highly coordinated forage systems.

Mangrove xylem development is shaped by multiple interacting environmental drivers, yet it remains unclear whether vessel traits primarily follow a salinity-dominated pattern or a more integrated climatic-edaphic framework. Here, we combined field surveys, controlled pot experiments, and structural equation modeling (SEM), together with transcriptomic profiling, to disentangle the relative contributions of climate, soil properties, and developmental regulation to xylem architecture in Aegiceras corniculatum. We found that temperature and precipitation regimes significantly modified soil chemistry-particularly by driving acidification and salinization-which jointly accounted for most of the variation in wood anatomical traits. Contrary to the prevailing "salinity-centric" view, soil pH emerged as the dominant driver of vessel diameter and vessel area, whereas vessel density responded to the combined effects of salinity and nutrient dynamics. These shifts produced clear seasonal differentiation in growth rings and a coordinated adjustment of vessel diameter and density. Transcriptome analyses further revealed a set of candidate transcription factors (including WRKY, ABF, and NAC/MYB families) that integrate stress perception with secondary cell-wall biosynthesis, suggesting a mechanistic basis for anatomical plasticity. Together, our findings highlight a soil-mediated pathway through which climatic factors influence mangrove hydraulic strategies, offering new insights into the adaptive coordination between vessel architecture, transcriptional regulation, and the heterogeneous intertidal environment.

The assessment of freshwater ecosystem health is essential for sustainable water management amid increasing environmental stress. Aquatic macroinvertebrates and fishes serve as key bioindicators, providing integrated insights into the impacts of water quality and habitat degradation. This study investigated the spatial and seasonal patterns of aquatic invertebrate and fish communities in relation to water quality variation in two contrasting sites of the Nile Valley, Egypt (Kafr Saad and Abu Rawash areas). Sampling was conducted across different freshwater habitats, including canals, irrigation channels, ditches, and pools. A total of 73 aquatic taxa belonging to 30 families and 11 orders were recorded. Community composition and total abundance differed markedly between sites, with the Kafr Saad area exhibiting higher diversity and a greater representation of pollution-intolerant taxa, whereas pollution-tolerant groups dominated Abu Rawash. Seasonal variation significantly influenced aquatic community structure, with higher abundance and diversity observed during the warmer season. Fish taxa, particularly Poeciliidae, were more prevalent in habitats characterized by normal and good water quality. Canonical correspondence analysis (CCA) revealed that plant communities, temperature, nitrate concentration, and electrical conductivity were the main environmental drivers structuring aquatic communities. Based on integrated physicochemical and biological indicators, water quality was classified as good in the Kafr Saad area and moderately good in the Abu Rawash area. These findings highlight the value of combining aquatic macroinvertebrates and fishes in spatiotemporal biomonitoring frameworks for freshwater quality assessment in the Nile Valley.

The capacity of living organisms to withstand stress may be energetically expensive, thereby placing a demand on body nutrient stores and any nutrients that might be linked to coping with that stress. Here, we tested the hypothesis that desiccation-resistant Ceratitis capitata (Diptera: Tephritidae) and desiccation-sensitive Ceratitis rosa differentially oxidize nutrients to counter desiccation stress and facilitate recovery. Lines of flies from both species had their body macronutrient stores experimentally enriched with one of three 13C tracers: Leucine (enriches proteins), palmitic acid (enriches lipids), and glucose (enriches primarily carbohydrates), and were then subjected to a regimen of desiccation stress followed by recovery. We used flow-through respirometry and 13C-breath testing to detect changes in metabolic rates and macronutrient oxidation. Ceratitis capitata, unlike C. rosa, showed highly flexible metabolic rates and nutrient oxidation. The former upregulated oxidation of both carbohydrates and lipids whereas protein oxidation was only upregulated during recovery from desiccation. This provides novel support for biochemical pathway (macronutrient) flexibility enhancing the survival of a desiccation-resistant invasive species over a desiccation-sensitive species.