TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958-2015

Modelling the spatial risk of malaria through probability distribution of Anopheles maculipennis s.l. and imported cases

Malaria remains one of the most important infectious diseases globally due to its high incidence and mortality rates. The influx of infected cases from endemic to non-endemic malaria regions like Europe has resulted in a public health concern over sporadic local outbreaks. This is facilitated by the continued presence of competent Anopheles vectors in non-endemic countries.We modelled the potential distribution of the main malaria vector across Spain using the ensemble of eight modelling techniques based on environmental parameters and the Anopheles maculipennis s.l. presence/absence data collected from 2000 to 2020. We then combined this map with the number of imported malaria cases in each municipality to detect the geographic hot spots with a higher risk of local malaria transmission.The malaria vector occurred preferentially in irrigated lands characterized by warm climate conditions and moderate annual precipitation. Some areas surrounding irrigated lands in northern Spain (e.g. Zaragoza, Logroño), mainland areas (e.g. Madrid, Toledo) and in the South (e.g. Huelva), presented a significant likelihood of A. maculipennis s.l. occurrence, with a large overlap with the presence of imported cases of malaria.While the risk of malaria re-emergence in Spain is low, it is not evenly distributed throughout the country. The four recorded local cases of mosquito-borne transmission occurred in areas with a high overlap of imported cases and mosquito presence. Integrating mosquito distribution with human incidence cases provides an effective tool for the quantification of large-scale geographic variation in transmission risk and pinpointing priority areas for targeted surveillance and prevention.

Precipitation, temperature, and landcovers drive spatiotemporal variability of groundwater nitrate concentration across the Continental United States

Groundwater nitrate contamination, especially in agriculturally active regions, is a well-recognized environmental concern. Understanding how this contamination evolves across the continental USA (CONUS) and through time is important to designing effective mitigation strategies. Despite extensive research on nitrate contamination, no existing studies can accurately predict changes in groundwater nitrate concentrations over time across the CONUS. To bridge this gap, we compiled a comprehensive dataset for a systematic evaluation of the potential influence of climate dynamics, landcover changes, and crucial soil and geological properties on groundwater contamination. We employed an interpretable machine learning approach, using 293,775 groundwater nitrate observations and 12 independent variables, to estimate annual groundwater nitrate concentrations at the county level from 2001 to 2020. Our model is the first one capable of accurately forecasting temporal changes in groundwater nitrate concentration across the entire CONUS. Our analysis reveals county level groundwater nitrate concentration changes occurred over the past two decades, particularly in regions initially with high concentrations in 2001, ranging from -16.2 mg/L-N to +6.5 mg/L-N between 2001 and 2020. 27 counties in the country appeared to have new concentrations greater than or equal to the maximum concentration level (MCL) at least once during this period. We revealed direct relationships between groundwater nitrate concentrations and climate factors, including that temperature and precipitation dominate the interannual variability in groundwater nitrate concentration in 75.2 % of counties. Notably, we have established a clear correlation between groundwater nitrate concentration and precipitation. Specifically, when annual precipitation falls below a threshold of about 748 mm, an increase of precipitation can directly result in elevated nitrate concentrations in groundwater, indicating heightened vulnerability to contamination due to climate change. This study forms a pivotal foundation for forecasting groundwater nitrate concentration changes across the continent and assessing the potential impact of climate change on future groundwater nitrate concentrations.

Divergent determinants on interannual variability of terrestrial water cycle across the globe

Intensifying variability in precipitation under a changing climate is projected to amplify fluctuation in terrestrial hydrological cycle, leading to more severe water-related disasters. The connections between interannual variability of hydrological components and factors influencing these connections have not been clearly defined yet. Based on terrestrial water budget from Climate Data Record, we identify dominant factors influencing partitioning interannual variability of precipitation (P) into that of evapotranspiration (E), runoff (Q), and water storage deviation (ΔS) across the globe by employing geographical detector model (GDM). Sensitivities of the variability partitioning to dominant factors are quantified for different hydroclimate regions by linear regression model and law of total differential. Results show that dominant factors influencing precipitation variability partitioning (VP) are different across distinct hydroclimate conditions. Comparing the statistical index (q value) of the GDM, it can be seen that surface air temperature (Ta), snow water equivalent (SWE) and water storage capacity (Smax) are dominant factors of VP in humid, semi-arid and arid regions, respectively. Changes in P variability largely can transfer into Q variability in humid region. The P variability partitioned into Q variability is dramatically reduced in semi-arid region with SWE decreasing, while P variability partitioned into ΔS variability increases with Smax increasing in arid region. Joint effects of Ta and coefficient of variation of precipitation (Pcv) are found to be the most important interaction in determining VP across the globe. Furthermore, warmer temperatures in humid region cause >90 % of the change in precipitation variability to be transferred to Q variability change. In semi-arid region with snowfall, decreased SWE has strong effect on changes in ΔS (30-40 %) and Q (20-40 %) variability. Our findings imply a changing VP and more severe impacts of hydrological extremes under future climate, where intensive changes in Ta, SWE and land cover are projected.

Mapping and analyzing the spatiotemporal dynamics of forest aboveground biomass in the ChangZhuTan urban agglomeration using a time series of Landsat images and meteorological data from 2010 to 2020

In the context of global warming, there is a substantial demand for accurate and cost-effective assessment and comprehensive understanding of forest above-ground biomass (AGB) dynamics. The timeliness and low cost of optical remote sensing data enable the mapping of large-scale forest AGB dynamics. However, mapping forest AGB with optical remote sensing data presents challenges primarily due to data uncertainty and the complex nature of the forest environment. Previous studies have demonstrated the potential of meteorological data in enhancing forest AGB mapping. To accurately capture the dynamics of forest AGB, we initially acquired Landsat datasets, digital elevation model (DEM), and meteorological datasets (temperature, humidity, and precipitation) from 2010 to 2020 in Changsha-Zhuzhou-Xiangtan urban agglomeration (CZT) located in Hunan Province, China. Spectral variables (SVs), including spectral bands and vegetation indices, were extracted from Landsat images, while meteorological variables (MVs) were derived from the monthly meteorological data using the Savitzky-Golay (S-G) filtering algorithm. Additionally, terrain variables (TVs) were also extracted from the DEM data. Three modelling models, multiple linear regression (MLR), K nearest neighbor (KNN) and random forest (RF), were developed for mapping the dynamics of forest AGB in CZT. The result revealed that MVs have the potential to improve forest AGB mapping. Integration of MVs into the models resulted in a significant reduction in root mean square error (RMSE) ranging from 32.85 % to 19.25 % compared to utilizing only SVs. However, minimal improvement was observed with the inclusion of TVs due to negligible topographic relief within the study area. An upward trend of forest AGB in CZT was observed during this period, which can be attributed to the effective implementation of government environmental protection policies. It is confirmed that the meteorological data has significant contribution to forest AGB mapping, thereby endorsing advancements in forest resource monitoring and management programs.

Spatiotemporal evolution and driving mechanisms of desertification on the Mongolian Plateau

Desertification poses a severe ecological and environmental challenge in the Mongolian Plateau (MP). It is difficult to quantify desertification distribution using unified indicators in the entire MP, because of its complex physical geographic conditions and various climatic zones covered. To accurately address this challenge, the spatial distribution of desertification at a 30-m resolution from 1990 to 2020 were mapped in this study. The desertification potential occurrence zone was identified by using a moisture index on the MP firstly. The feature space model and five machine learning models were constructed to make the map based on Google Earth Engine and Landsat data. The spatiotemporal distribution of desertification were further analyzed, and the dominant drivers of desertification distribution and evolution were identified using Geodetector model. The results indicate that the potential occurrence area of desertification accounted for 83.88 % of the total land area. The gradient boosted tree model for desertification assessment has the best performance with the highest overall accuracy of 88.18 %. The year 2010 marked a pivotal transition from land degradation to land restoration in the MP. Between 2010 and 2020, desertified land continued to deteriorate extensively in the southern Mongolia, while Inner Mongolia, China, essentially entered a full recovery phase. Precipitation and land use emerged as the primary drivers of the spatial distribution of desertification on the Mongolian Plateau and Mongolia, with potential evapotranspiration and precipitation influencing the distribution of desertification in Inner Mongolia, China. Land use change was the primary driver of desertification evolution on the MP and Mongolia. This study constructs an indicator system and methodology suitable for desertification monitoring on the MP, addresses the lack of refined desertification data over a long time series, and provides scientific reference for decision-making support in combating desertification in this region, and other large arid and semi-arid areas in the world.

Spatio-temporal change of winter wheat yield and its quantitative responses to compound frost-dry events - An example of the Huang-Huai-Hai Plain of China from 2001 to 2020

Extreme climate events such as frost and drought have great influence on wheat growth and yield. Understanding the effects of frost, drought and compound frost-dry events on wheat growth and yield is of great significance for ensuring national food security. In this study, wheat yield prediction model (SCYMvp) was developed by combining crop growth model (CGM), satellite images and meteorological variables. Wheat yield maps in the Huang-Huai-Hai Plain (HHHP) during 2001-2020 were generated using SCYMvp model. Meanwhile, accumulative frost days (AFD), accumulative dry days (ADD) and accumulative frost-dry days (AFDD) in different growth periods of wheat were calculated, and the effects of frost and drought on wheat yield were quantified by the first difference method and linear mixed model. The results showed that wheat yield increased significantly, while the rising trend was obvious at more than half of the regions. Extreme climate events (ECEs) showed a relatively stable change trend, although the change trend was significant only in a few areas. Compared with frost and drought in the early growth period, ECEs in the middle growth period (spring ECEs) had more negative effects on wheat growth and yield. Wheat yield was negatively correlated with spring ECEs, and yield loss was between 4.6 and 49.8 kg/ha for each 1 d increase of spring ECEs. The effects of spring ECEs on wheat yield were ranked as AFDD > AFD > ADD. The negative effect of ADD on wheat yield in the late growth period was higher than that in the other periods. The negative effects of spring ECEs on yield in southern regions were higher than those in northern regions. Overall, due to the adverse effects of frost and drought on wheat yield in the middle and late growth periods, the mean annual yield loss was 6.4 %, among which spring AFD caused the greatest loss to wheat yield (3.1 %). The results have important guiding significance for formulating climate adaptation management strategies.

Century-long timelines of herbarium genomes predict plant stomatal response to climate change

Dissecting plant responses to the environment is key to understanding whether and how plants adapt to anthropogenic climate change. Stomata, plants' pores for gas exchange, are expected to decrease in density following increased CO2 concentrations, a trend already observed in multiple plant species. However, it is unclear whether such responses are based on genetic changes and evolutionary adaptation. Here we make use of extensive knowledge of 43 genes in the stomatal development pathway and newly generated genome information of 191 Arabidopsis thaliana historical herbarium specimens collected over 193 years to directly link genetic variation with climate change. While we find that the essential transcription factors SPCH, MUTE and FAMA, central to stomatal development, are under strong evolutionary constraints, several regulators of stomatal development show signs of local adaptation in contemporary samples from different geographic regions. We then develop a functional score based on known effects of gene knock-out on stomatal development that recovers a classic pattern of stomatal density decrease over the past centuries, suggesting a genetic component contributing to this change. This approach combining historical genomics with functional experimental knowledge could allow further investigations of how different, even in historical samples unmeasurable, cellular plant phenotypes may have already responded to climate change through adaptive evolution.

Spatiotemporally non-stationary evolution of groundwater levels in Poyang Lake Basin driven by meteorological and hydrological factors

The complex relationship between wet-dry transition in the Poyang Lake basin and groundwater storage significantly affects the lake's hydrology, downstream ecological state, and overall security along the Yangtze River in China. There is, however, a notable lack of systematic exploration into how various factors drive spatiotemporal variability in groundwater level (GWL). Using local indicators of spatial association (LISA), spatial non-stationarity models, and multi-source data, our analysis explores the spatial distribution of GWL and quantifies the influence of driving factors on its spatiotemporal non-stationarity at annual and monthly scales. We also compare driving factor contributions in hilly, plain, and local areas within the Poyang Lake basin. Our findings reveal significant local clustering of GWL, indicating substantial spatial autocorrelation and geographic heterogeneity in GWL. Influencing factors exhibit non-stationary effects on GWL at spatial and temporal scales, with precipitation (P), ground surface elevation (GSE), and soil moisture (SM) being primary contributors, generally exerting positive effects. SM contributes most during dry years and normal periods. P and the Palmer Drought Severity Index (PDSI) have greater impacts in hilly areas, while GSE shows the opposite trend. Rainfall is a source of groundwater recharge, with a lagged response observed in GWL to rainfall in this basin. The lag time is about 1-2 months. Evapotranspiration is not the dominant discharge pathway. The decrease in GWL during the dry season is mainly due to reduced precipitation recharge and increased lateral groundwater discharge from areas of high hydraulic head to areas of low hydraulic head.

Assessment of site suitability for centralized photovoltaic power stations in Northwest China's six provinces

Northwest China has abundant solar energy resources and extensive land, making it a pivotal site for solar energy development. However, restrictions on site selection and severe weather conditions have hindered the establishment and operation of photovoltaic (PV) power stations. Previous studies have not considered meteorological factors when evaluating site suitability, leading to research gaps in identifying suitable areas and establishing indicator systems. We aimed to address these gaps by considering seven factors constraining the construction of centralized PV power stations (CPPS) and developing an indicator system based on terrain, climate, soil, and economic factors. Furthermore, we conducted analyses to quantify the solar energy generation potential (SEGP), carbon emissions reduction benefits, and land utilization potential at different sites. The findings indicate that areas rated as very suitable and extremely suitable comprised the largest proportion (62.35%) of site suitability. The correlation between site suitability and electricity consumption was largely non-significant, highlighting the need for enhanced coordination. Additionally, we forecast the electricity consumption in Xinjiang, Gansu, Inner Mongolia, Qinghai, Ningxia, and Shaanxi for 2030 to be 56.62, 19.86, 54.54, 13.59, 15.96, and 33.34 ( × 1011 kWh), respectively, with corresponding carbon emissions reduction potentials of 20.2, 7.1, 19.4, 4.8, 5.7, and 11.9 ( × 109 kg). Consequently, PV carbon reduction and land utilization potential are substantial.

Among-population variation in drought responses is consistent across life stages but not between native and non-native ranges

Understanding how widespread species adapt to variation in abiotic conditions across their ranges is fundamental to ecology. Insight may come from studying how among-population variation (APV) in the common garden corresponds with the environmental conditions of source populations. However, there are no such studies comparing native vs non-native populations across multiple life stages. We examined APV in the performance and functional traits of 59 Conyza canadensis populations, in response to drought, across large aridity gradients in the native (North America) and non-native (Eurasia) ranges in three experiments. Our treatment (dry vs wet) was applied at the recruitment, juvenile, and adult life stages. We found contrasting patterns of APV in drought responses between the two ranges. In the native range, plant performance was less reduced by drought in populations from xeric than mesic habitats, but such relationship was not apparent for non-native populations. These range-specific patterns were consistent across the life stages. The weak adaptive responses of non-native populations indicate that they can become highly abundant even without complete local adaptation to abiotic environments and suggest that long-established invaders may still be evolving to the abiotic environment. These findings may explain lag times in invasions and raise concern about future expansions.

Remotely sensed crown nutrient concentrations modulate forest reproduction across the contiguous United States

Global forests are increasingly lost to climate change, disturbance, and human management. Evaluating forests' capacities to regenerate and colonize new habitats has to start with the seed production of individual trees and how it depends on nutrient access. Studies on the linkage between reproduction and foliar nutrients are limited to a few locations and few species, due to the large investment needed for field measurements on both variables. We synthesized tree fecundity estimates from the Masting Inference and Forecasting (MASTIF) network with foliar nutrient concentrations from hyperspectral remote sensing at the National Ecological Observatory Network (NEON) across the contiguous United States. We evaluated the relationships between seed production and foliar nutrients for 56,544 tree-years from 26 species at individual and community scales. We found a prevalent association between high foliar phosphorous (P) concentration and low individual seed production (ISP) across the continent. Within-species coefficients to nitrogen (N), potassium (K), calcium (Ca), and magnesium (Mg) are related to species differences in nutrient demand, with distinct biogeographic patterns. Community seed production (CSP) decreased four orders of magnitude from the lowest to the highest foliar P. This first continental-scale study sheds light on the relationship between seed production and foliar nutrients, highlighting the potential of using combined Light Detection And Ranging (LiDAR) and hyperspectral remote sensing to evaluate forest regeneration. The fact that both ISP and CSP decline in the presence of high foliar P levels has immediate application in improving forest demographic and regeneration models by providing more realistic nutrient effects at multiple scales.

Groundwater-dependent ecosystem map exposes global dryland protection needs

Groundwater is the most ubiquitous source of liquid freshwater globally, yet its role in supporting diverse ecosystems is rarely acknowledged1,2. However, the location and extent of groundwater-dependent ecosystems (GDEs) are unknown in many geographies, and protection measures are lacking1,3. Here, we map GDEs at high-resolution (roughly 30 m) and find them present on more than one-third of global drylands analysed, including important global biodiversity hotspots4. GDEs are more extensive and contiguous in landscapes dominated by pastoralism with lower rates of groundwater depletion, suggesting that many GDEs are likely to have already been lost due to water and land use practices. Nevertheless, 53% of GDEs exist within regions showing declining groundwater trends, which highlights the urgent need to protect GDEs from the threat of groundwater depletion. However, we found that only 21% of GDEs exist on protected lands or in jurisdictions with sustainable groundwater management policies, invoking a call to action to protect these vital ecosystems. Furthermore, we examine the linkage of GDEs with cultural and socio-economic factors in the Greater Sahel region, where GDEs play an essential role in supporting biodiversity and rural livelihoods, to explore other means for protection of GDEs in politically unstable regions. Our GDE map provides critical information for prioritizing and developing policies and protection mechanisms across various local, regional or international scales to safeguard these important ecosystems and the societies dependent on them.

Effects of photodegradation on the composition characteristics and metal binding behavior of sediment-derived dissolved organic matter (SDOM) in nansi lake, China

Sediment-derived dissolved organic matter (SDOM) is instrumental in the cycling of nutrients and heavy metals within lakes, influencing ecological balance and contaminant distribution. Given the influence of photodegradation on the alteration and breakdown of SDOM, further understanding of this process is essential. In this research, the properties of the SDOM photodegradation process and its metal-binding reactions in Nansi Lake were analyzed using the EEM-PARAFAC and 2D-SF/FTIR-COS techniques. Our study identified three sorts of humic-like components and one protein-like component in SDOM, with the humic-like material accounting for 71.3 ± 5.19% of the fluorescence intensity (Fmax). Photodegradation altered the abundance and structure of SDOM, with a 41.6 ± 5.82% decrease in a280 and a 29.1 ± 9.31% reduction in Fmax after 7 days, notably reducing the protein-like component C4 by 54.0 ± 5.17% and the humic-like component C2 by 48.5 ± 2.54%, which led to SDOM being formed with lower molecular weight and aromaticity. After photodegradation, the LogKCu values for humic-like and protein-like substances decreased (humic-like C2: LogKCu: 1.35 ± 0.10-1.11 ± 0.15, protein-like C4: 1.49 ± 0.14-1.29 ± 0.34), yet the preferential binding sequence of protein-like materials and specific functional groups with Cu2+ such as aliphatic C-OH, amide (I) C=O and polysaccharide C-O groups remained unaltered. Our results enhance the knowledge of light-induced SDOM alterations and offer insights into SDOM-metal interactions in aquatic ecosystems.

Predicting wheat yield from 2001 to 2020 in Hebei Province at county and pixel levels based on synthesized time series images of Landsat and MODIS

To obtain seasonable and precise crop yield information with fine resolution is very important for ensuring the food security. However, the quantity and quality of available images and the selection of prediction variables often limit the performance of yield prediction. In our study, the synthesized images of Landsat and MODIS were used to provide remote sensing (RS) variables, which can fill the missing values of Landsat images well and cover the study area completely. The deep learning (DL) was used to combine different vegetation index (VI) with climate data to build wheat yield prediction model in Hebei Province (HB). The results showed that kernel NDVI (kNDVI) and near-infrared reflectance (NIRv) slightly outperform normalized difference vegetation index (NDVI) in yield prediction. And the regression algorithm had a more prominent effect on yield prediction, while the yield prediction model using Long Short-Term Memory (LSTM) outperformed the yield prediction model using Light Gradient Boosting Machine (LGBM). The model combining LSTM algorithm and NIRv had the best prediction effect and relatively stable performance in single year. The optimal model was then used to generate 30 m resolution wheat yield maps in the past 20 years, with higher overall accuracy. In addition, we can define the optimum prediction time at April, which can consider simultaneously the performance and lead time. In general, we expect that this prediction model can provide important information to understand and ensure food security.

Tibetan Plateau Runoff and Evapotranspiration Dataset by an observation-constrained cryosphere-hydrology model

Runoff and evapotranspiration (ET) are pivotal constituents of the water, energy, and carbon cycles. This research presents a 5-km monthly gridded runoff and ET dataset for 1998-2017, encompassing seven headwaters of Tibetan Plateau rivers (Yellow, Yangtze, Mekong, Salween, Brahmaputra, Ganges, and Indus) (hereinafter TPRED). The dataset was generated using the advanced cryosphere-hydrology model WEB-DHM, yielding a Nash coefficient ranging from 0.77 to 0.93 when compared to the observed discharges. The findings indicate that TPRED's monthly runoff notably outperforms existing datasets in capturing hydrological patterns, as evidenced by robust metrics such as the correlation coefficient (CC) (0.944-0.995), Bias (-0.68-0.53), and Root Mean Square Error (5.50-15.59 mm). Additionally, TPRED's monthly ET estimates closely align with expected seasonal fluctuations, as reflected by a CC ranging from 0.94 to 0.98 when contrasted with alternative ET products. Furthermore, TPRED's annual values exhibit commendable concordance with operational products across multiple dimensions. Ultimately, the TPRED will have great application on hydrometeorology, carbon transport, water management, hydrological modeling, and sustainable development of water resources.

Case study on climate change effects and food security in Southeast Asia

Agriculture, a cornerstone of human civilization, faces rising challenges from climate change, resource limitations, and stagnating yields. Precise crop production forecasts are crucial for shaping trade policies, development strategies, and humanitarian initiatives. This study introduces a comprehensive machine learning framework designed to predict crop production. We leverage CMIP5 climate projections under a moderate carbon emission scenario to evaluate the future suitability of agricultural lands and incorporate climatic data, historical agricultural trends, and fertilizer usage to project yield changes. Our integrated approach forecasts significant regional variations in crop production across Southeast Asia by 2028, identifying potential cropland utilization. Specifically, the cropland area in Indonesia, Malaysia, Philippines, and Viet Nam is projected to decline by more than 10% if no action is taken, and there is potential to mitigate that loss. Moreover, rice production is projected to decline by 19% in Viet Nam and 7% in Thailand, while the Philippines may see a 5% increase compared to 2021 levels. Our findings underscore the critical impacts of climate change and human activities on agricultural productivity, offering essential insights for policy-making and fostering international cooperation.

Utilizing Artificial Intelligence and Remote Sensing to Detect Prosopis juliflora Invasion: Environmental Drivers and Community Insights in Rangelands of Kenya

The remarkable adaptability and rapid proliferation of Prosopis juliflora have led to its invasive status in the rangelands of Kenya, detrimentally impacting native vegetation and biodiversity. Exacerbated by human activities such as overgrazing, deforestation, and land degradation, these conditions make the spread and management of this species a critical ecological concern. This study assesses the effectiveness of artificial intelligence (AI) and remote sensing in monitoring the invasion of Prosopis juliflora in Baringo County, Kenya. We investigated the environmental drivers, including weather conditions, land cover, and biophysical attributes, that influence its distinction from native vegetation. By analyzing data on the presence and absence of Prosopis juliflora, coupled with datasets on weather, land cover, and elevation, we identified key factors facilitating its detection. Our findings highlight the Decision Tree/Random Forest classifier as the most effective, achieving a 95% accuracy rate in instance classification. Key variables such as the Normalized Difference Vegetation Index (NDVI) for February, precipitation, land cover type, and elevation were significant in the accurate identification of Prosopis juliflora. Community insights reveal varied perspectives on the impact of Prosopis juliflora, with differing views based on professional experiences with the species. Integrating these technological advancements with local knowledge, this research contributes to developing sustainable management practices tailored to the unique ecological and social challenges posed by this invasive species. Our results highlight the contribution of advanced technologies for environmental management and conservation within rangeland ecosystems.

Soil moisture alters the responses of alpine ecosystem productivity to environmental factors, especially VPD, on the Qinghai-Tibetan Plateau

Water availability, which can be represented by soil water content (SWC), plays a crucial role in plant growth and productivity across the cold and arid Qinghai-Tibetan Plateau. However, the indirect effects of SWC are less well understood, and a more comprehensive understanding of its regulating effects may enhance the recognition of its importance, as this factor is pivotal for accurately predicting the future response of alpine ecosystems to climate change. In this study, in situ eddy covariance observation data from typical alpine ecosystems and satellite data covering the Qinghai-Tibetan region were used to comprehensively reveal the effects of SWC on ecosystem productivity. The results indicated that SWC played an important role in regulating the responses of gross primary productivity (GPP) to other environmental factors over both time and space, especially in terms of the responses of GPP to vapor pressure deficit (VPD). The regulating effect can be summarized as follows: there was a specific SWC value (SWC = 0.24 m3 m-3 on the Qinghai-Tibetan Plateau) above which SWC was no longer the primary limiting factor. The responses of GPP to certain environmental factors shifted from negative to positive when the SWC increased above this value. The responses of GPP to VPD exhibited the highest sensitivity to the regulating effects of SWC, with a general response pattern found across different temporal and spatial scales. The findings revealed divergent responses of GPP to environmental factors under different SWC conditions and between arid and humid regions, emphasizing the importance of soil water conditions. These findings suggest that water conditions should be given primary consideration in global change studies.

Brain size mediates the choice of breeding strategy in the red-backed shrike Lanius collurio

The brain size of vertebrates represents a trade-off between natural selection for enhanced cognitive abilities and the energetic constraints of brain tissue production. Processing information efficiently can confer benefits, but it also entails time costs. Breeding strategies, encompassing timing of breeding onset and nest-site selection, may be related to brain size. In this study, we aim to elucidate the relationship between brain size, breeding timing, nest-site choice, and breeding success in the red-backed shrike Lanius collurio. Our findings revealed that the timing of the first egg-laying date was associated with female head size, with larger-headed females tending to lay eggs later in the breeding season. Additionally, we observed that breeding success was positively correlated with increased nest concealment. However, this relationship was stronger in males with smaller heads. In turn, nest concealment was not related to head size but primarily influenced breeding onset. These results suggest that the choice of breeding strategy may be moderated by brain size, with differences between sexes. Larger-headed females may invest more time in selecting nesting sites, leading to delayed breeding onset, while larger-headed males may compensate for suboptimal nest concealment. Our study sheds light on the intricate interplay between brain size, breeding timing, nest-site preferences, and breeding success in passerine birds, underscoring the potential role of cognitive capacity in shaping individual decision-making processes.

Do Tasmanian devil declines impact ecosystem function?

Tasmanian eucalypt forests are among the most carbon-dense in the world, but projected climate change could destabilize this critical carbon sink. While the impact of abiotic factors on forest ecosystem carbon dynamics have received considerable attention, biotic factors such as the input of animal scat are less understood. Tasmanian devils (Sarcophilus harrisii)-an osteophageous scavenger that can ingest and solubilize nutrients locked in bone material-may subsidize plant and microbial productivity by concentrating bioavailable nutrients (e.g., nitrogen and phosphorus) in scat latrines. However, dramatic declines in devil population densities, driven by the spread of a transmissible cancer, may have underappreciated consequences for soil organic carbon (SOC) storage and forest productivity by altering nutrient cycling. Here, we fuse experimental data and modeling to quantify and predict future changes to forest productivity and SOC under various climate and scat-quality futures. We find that devil scat significantly increases concentrations of nitrogen, ammonium, phosphorus, and phosphate in the soil and shifts soil microbial communities toward those dominated by r-selected (e.g., fast-growing) phyla. Further, under expected increases in temperature and changes in precipitation, devil scat inputs are projected to increase above- and below-ground net primary productivity and microbial biomass carbon through 2100. In contrast, when devil scat is replaced by lower-quality scat (e.g., from non-osteophageous scavengers and herbivores), forest carbon pools are likely to increase more slowly, or in some cases, decline. Together, our results suggest often overlooked biotic factors will interact with climate change to drive current and future carbon pool dynamics in Tasmanian forests.

Disentangling the impact of climate change, human activities, vegetation dynamics and atmospheric CO2 concentration on soil water use efficiency in global karst landscapes

Soil Water Use Efficiency (SWUE), which quantifies the carbon gain against each unit of soil moisture depletion, represents an essential ecological parameter that delineates the carbon-water coupling within terrestrial ecosystems. However, the spatiotemporal dynamics of SWUE, its sensitivity to environmental variables, and the underlying driving mechanisms across various temporal scales in the global karst region are largely uncharted. This study utilized the sensitivity algorithm of partial least squares regression, partial differential equations, and elasticity coefficients to investigate the characteristics of SWUE variations across different climatic zones in the global karst region and their responsiveness to environmental variables. Moreover, the study quantified the individual contributions of climate variability, atmospheric carbon dioxide concentration, human activities, and vegetation changes to SWUE variations. The results indicated that SWUE across different climatic zones in the global karst region demonstrated an increasing trend from 2000 to 2018, with the most notable improvement observed in the humid zone. SWUE presented regular distribution and variation characteristics across different latitudinal zones at a monthly scale. The sensitivity of SWUE to precipitation was significantly higher compared to its responsiveness to other environmental factors. Additionally, the trend in SWUE's sensitivity to precipitation demonstrated the most significant change. The sensitivity of SWUE to various environmental factors and the trend of this sensitivity in the arid zone revealed significant variation compared to other climatic zones. Gross primary productivity and soil moisture were identified as the intrinsic factors influencing SWUE changes, contributing 16 % and - 84 %, respectively. Climate variability and human activities were identified as the primary exogenous factors contributing to the increase in SWUE, accounting for 76 % and 16 %, respectively. This study advances the understanding of carbon-water coupling in karst regions, providing significant insights into the ecological management of global karst environments amidst climate variations.

Reptile responses to fire across the western Mediterranean Basin

Effects of anthropogenic activities, including climate change, are modifying fire regimes, and the dynamic nature of these modifications requires identification of general patterns of organisms' responses to fire. This is a challenging task because of the high complexity of factors involved (including climate, geography, land use, and species-specific ecology). We aimed to describe the responses of the reptile community to fire across a range of environmental and fire-history conditions in the western Mediterranean Basin. We sampled 8 sites that spanned 4 Mediterranean countries. We recorded 6064 reptile sightings of 36 species in 1620 transects and modeled 3 community metrics (total number of individuals, species richness, and Shannon diversity) as responses to environmental and fire-history variables. Reptile community composition was also analyzed. Habitat type (natural vs. afforestation), fire age class (time since the last fire), rainfall, and temperature were important factors in explaining these metrics. The total number of individuals varied according to fire age class, reaching a peak at 15-40 years after the last fire. Species richness and Shannon diversity were more stable during postfire years. The 3 community metrics were higher under postfire conditions than in unburned forest plots. This pattern was particularly prevalent in afforested plots, indicating that the negative effect of fire on reptiles was lower than the negative effect of afforestation. Community composition varied by fire age class, indicating the existence of early- and late-successional species (xeric and saxicolous vs. mesic reptiles, respectively). Species richness was 46% higher in areas with a single fire age class relative to those with a mixture of fire age classes, which indicates pyrodiverse landscapes promoted reptile diversity. An expected shift to more frequent fires will bias fire age distribution toward a predominance of early stages, and this will be harmful to reptile communities.

The simultaneous prediction of yield and maturity date for wheat-maize by combining satellite images with crop model

BACKGROUND

The simultaneous prediction of yield and maturity date has an important impact on ensuring food security. However, few studies have focused on simultaneous prediction of yield and maturity date for wheat-maize in the North China Plain (NCP). In this study, we developed the prediction model of maturity date and yield (PMMY) for wheat-maize using multi-source satellite images, an Agricultural Production Systems sIMulator (APSIM) model and a random forest (RF) algorithm.

RESULTS

The results showed that the PMMY model using peak leaf area index (LAI) and accumulated evapotranspiration (ET) has the optimal performance in the prediction of maturity date and yield. The accuracy of the PMMY model using peak LAI and accumulated ET was higher than that of the PMMY model using only peak LAI or accumulated ET. In a single year, the PMMY model had good performance in the prediction of maturity date and yield. The latitude variation in spatial distribution of maturity date for WM was obvious. The spatial heterogeneity for yield of wheat-maize was not prominent. Compared with 2001-2005, the maturity date of the two crops in 2016-2020 advanced 1-2 days, while yield increased 659-706 kg ha-1. The increase in minimum temperature was the main meteorological factor for advance in the maturity date for wheat-maize. Precipitation was mainly positively correlated with maize yield, while the increase in minimum temperature and solar radiation was crucial to the increase in yield.

CONCLUSION

The simultaneous prediction of yield and maturity can be used to guide agricultural production and ensure food security. © 2024 Society of Chemical Industry.

A crucial nexus: Phylogenetic versus ecological support of the life-cycle of Ixodes ricinus (Ixodoidea: Ixodidae) and Borrelia spp. amplification

The tick Ixodes ricinus parasitizes a wide range of vertebrates. These hosts vary in the relative contribution to the feeding of the different tick life stages, and their interplay is pivotal in the transmission dynamics of tick-borne pathogens. We aimed to know if there is a phylogenetic signal in the feeding and propagation hosts of I. ricinus, independently of other traits, as well as in the amplification of Borrelia burgdorferi (s.l.) in feeding larvae. We used a compilation of 1127 published field surveys in Europe, providing data for 96,586 hosts, resulting in 265,124 larvae, 72,080 nymphs and 37,726 adults. The load of immature ticks on hosts showed a significant phylogenetic signal towards the genera Psammodromus, Podarcis, and Lacerta (nymphs only). We hypothesize that such signal is the background hallmark of the primitive hosts associations of I. ricinus, probably in the glaciation refugia. A secondary phylogenetic signal for tick immatures appeared for some genera of Rodentia and Eulipotyphla. Results suggest the notion that the tick gained these hosts after spread from glaciation refugia. Analyses support a phylogenetic signal in the tick adults, firmly linked to Cetartiodactyla, but not to Carnivora or Aves. This study provides the first demonstration of host preferences in the generalist tick I. ricinus. We further demonstrate that combinations of vertebrates contribute in different proportions supporting the tick life-cycle in biogeographical regions of the Western Palaearctic as each region has unique combinations of dominant hosts. Analysis of the amplification of B. burgdorferi (s.l.) demonstrated that each genospecies is better amplified by competent reservoirs with which a strong phylogenetic signal exists. These vertebrates are the same along the spatial range: environmental traits do not change the reservoirs along the large territory studied. The transmission of B. burgdorferi (s.l.) is amplified by a few species of vertebrates, that share biogeographical regions with the tick vector in variable proportions.

Leveraging browse and grazing forage estimates to optimize index-based livestock insurance

African pastoralists suffer recurrent droughts that cause high livestock mortality and vulnerability to climate change. The index-based livestock insurance (IBLI) program offers protection against drought impacts. However, the current IBLI design relying on the normalized difference vegetation index (NDVI) may pose limitation because it does not consider the mixed composition of rangelands (including herbaceous and woody plants) and the diverse feeding habits of grazers and browsers. To enhance IBLI, we assessed the efficacy of utilizing distinct browse and grazing forage estimates from woody LAI (LAIW) and herbaceous LAI (LAIH), respectively, derived from aggregate leaf area index (LAIA), as an alternative to NDVI for refined IBLI design. Using historical livestock mortality data from northern Kenya as reference ground dataset, our analysis compared two competing models for (1) aggregate forage estimates including sub-models for NDVI, LAI (LAIA); and (2) partitioned biomass model (LAIP) comprising LAIH and LAIW. By integrating forage estimates with ancillary environmental variables, we found that LAIP, with separate forage estimates, outperformed the aggregate models. For total livestock mortality, LAIP yielded the lowest RMSE (5.9 TLUs) and higher R2 (0.83), surpassing NDVI and LAIA models RMSE (9.3 TLUs) and R2 (0.6). A similar pattern was observed for species-specific livestock mortality. The influence of environmental variables across the models varied, depending on level of mortality aggregation or separation. Overall, forage availability was consistently the most influential variable, with species-specific models showing the different forage preferences in various animal types. These results suggest that deriving distinct browse and grazing forage estimates from LAIP has the potential to reduce basis risk by enhancing IBLI index accuracy.

Coastal Dune Vegetation Dynamism and Anthropogenic-Induced Transitions in the Mexican Caribbean during the Last Decade

In the Mexican Caribbean, environmental changes, hydrometeorological events, and anthropogenic activities promote dynamism in the coastal vegetation cover associated with the dune; however, their pace and magnitude remain uncertain. Using Landsat 7 imagery, spatial and temporal changes in coastal dune vegetation were estimated for the 2011-2020 period in the Sian Ka'an Biosphere Reserve. The SAVI index revealed cover changes at different magnitudes and paces at the biannual, seasonal, and monthly timeframes. Climatic seasons had a significant influence on vegetation cover, with increases in cover during northerlies (SAVI: p = 0.000), while the topographic profile of the dune was relevant for structure. Distance-based multiple regressions and redundancy analysis showed that temperature had a significant effect (p < 0.05) on SAVI patterns, whereas precipitation showed little influence (p > 0.05). The Mann-Kendall tendency test indicated high dynamism in vegetation loss and recovery with no defined patterns, mostly associated with anthropogenic disturbance. High-density vegetation such as mangroves, palm trees, and shrubs was the most drastically affected, although a reduction in bare soil was also recorded. This study demonstrated that hydrometeorological events and climate variability in the long term have little influence on vegetation dynamism. Lastly, it was observed that anthropogenic activities promoted vegetation loss and transitions; however, the latter were also linked to recoveries in areas with pristine environments, relevant for tourism.

Climate correlates of bluetongue incidence in southern Portugal

Model forecasts of the spatiotemporal occurrence dynamics of diseases are necessary and can help understand and thus manage future disease outbreaks. In our study, we used ecological niche modelling to assess the impact of climate on the vector suitability for bluetongue disease, a disease affecting livestock production with important economic consequences. Specifically, we investigated the relationship between the occurrence of bluetongue outbreaks and the environmental suitability of each of the four vector species studied. We found that the main vector for bluetongue disease, Culicoides imicola, a typically tropical and subtropical species, was a strong predictor for disease outbreak occurrence in a region of southern Portugal from 2004 to 2021. The results highlight the importance of understanding the climatic factors that might influence vector presence to help manage infectious disease impacts. When diseases impact economically relevant species, the impacts go beyond mortality and have important economic consequences.

Dual effects on vegetation from urban expansion in the drylands of northern China: A multiscale investigation using the vegetation disturbance index

Drylands contribute roughly 40 % of the global net primary productivity and are essential for achieving sustainable development. Investigating the effects on vegetation from urban expansion in drylands within the context of rapid urbanization could help enhance the sustainability of dryland cities. With the use of the drylands of northern China (DNC) as an example, we applied the vegetation disturbance index to investigate the negative and positive effects on vegetation from urban expansion in drylands. The results revealed that the DNC experienced massive and rapid urban expansion from 2000 to 2020. Urban land in the entire DNC increased by 19,646 km2 from 8141 to 27,787 km2, with an annual growth rate of 6.3 %. Urban expansion in the DNC imposed both negative and positive effects on regional vegetation. The area with negative effects reached 7736 km2 and was mainly concentrated in the dry subhumid zones. The area with positive effects amounted to 5011 km2 and was comparable among the dry subhumid, semiarid, and arid zones. Land use/cover change induced by population growth significantly contributed to these negative effects, while the positive effects were largely caused by economic growth. Therefore, it is recommended to strike a balance between urban growth and vegetation conservation to mitigate the adverse effects on vegetation from urban expansion in drylands. Simultaneously, it is imperative to expand urban green spaces and build sustainable and livable ecological cities to facilitate sustainable urban development.

High-resolution mapping of essential maternal and child health service coverage in Nigeria: a machine learning approach

BACKGROUND

National-level coverage estimates of maternal and child health (MCH) services mask district-level and community-level geographical inequities. The purpose of this study is to estimate grid-level coverage of essential MCH services in Nigeria using machine learning techniques.

METHODS

Essential MCH services in this study included antenatal care, facility-based delivery, childhood vaccinations and treatments of childhood illnesses. We estimated generalised additive models (GAMs) and gradient boosting regressions (GB) for each essential MCH service using data from five national representative cross-sectional surveys in Nigeria from 2003 to 2018 and geospatial socioeconomic, environmental and physical characteristics. Using the best-performed model for each service, we map predicted coverage at 1 km2 and 5 km2 spatial resolutions in urban and rural areas, respectively.

RESULTS

GAMs consistently outperformed GB models across a range of essential MCH services, demonstrating low systematic prediction errors. High-resolution maps revealed stark geographic disparities in MCH service coverage, especially between rural and urban areas and among different states and service types. Temporal trends indicated an overall increase in MCH service coverage from 2003 to 2018, although with variations by service type and location. Priority areas with lower coverage of both maternal and vaccination services were identified, mostly located in the northern parts of Nigeria.

CONCLUSION

High-resolution spatial estimates can guide geographic prioritisation and help develop better strategies for implementation plans, allowing limited resources to be targeted to areas with lower coverage of essential MCH services.

Time series of freshwater macroinvertebrate abundances and site characteristics of European streams and rivers

Freshwater macroinvertebrates are a diverse group and play key ecological roles, including accelerating nutrient cycling, filtering water, controlling primary producers, and providing food for predators. Their differences in tolerances and short generation times manifest in rapid community responses to change. Macroinvertebrate community composition is an indicator of water quality. In Europe, efforts to improve water quality following environmental legislation, primarily starting in the 1980s, may have driven a recovery of macroinvertebrate communities. Towards understanding temporal and spatial variation of these organisms, we compiled the TREAM dataset (Time seRies of European freshwAter Macroinvertebrates), consisting of macroinvertebrate community time series from 1,816 river and stream sites (mean length of 19.2 years and 14.9 sampling years) of 22 European countries sampled between 1968 and 2020. In total, the data include >93 million sampled individuals of 2,648 taxa from 959 genera and 212 families. These data can be used to test questions ranging from identifying drivers of the population dynamics of specific taxa to assessing the success of legislative and management restoration efforts.

Woody plant encroachment drives population declines in 20% of common open ecosystem bird species

Grassy ecosystems cover more than 40% of the world's terrestrial surface, supporting crucial ecosystem services and unique biodiversity. These ecosystems have experienced major losses from conversion to agriculture with the remaining fragments threatened by global change. Woody plant encroachment, the increase in woody cover threatening grassy ecosystems, is a major global change symptom, shifting the composition, structure, and function of plant communities with concomitant effects on all biodiversity. To identify generalisable impacts of encroachment on biodiversity, we urgently need broad-scale studies on how species respond to woody cover change. Here, we make use of bird atlas, woody cover change data (between 2007 and 2016) and species traits, to assess: (1) population trends and woody cover responses using dynamic occupancy models; (2) how outcomes relate to habitat, diet and nesting traits; and (3) predictions of future occupancy trends, for 191 abundant, southern African bird species. We found that: (1) 63% (121) of species showed a decline in occupancy, with 18% (34) of species' declines correlated with increasing woody cover (i.e. losers). Only 2% (4) of species showed increasing population trends linked with increased woody cover (i.e. winners); (2) Open habitat specialist, invertivorous, ground nesting birds were the most frequent losers, however, we found no definitive evidence that the selected traits could predict outcomes; and (3) We predict open habitat loser species will take on average 52 years to experience 50% population declines with current rates of encroachment. Our results bring attention to concerning region-wide declining bird population trends and highlight woody plant encroachment as an important driver of bird population dynamics. Importantly, these findings should encourage improved management and restoration of our remaining grassy ecosystems. Furthermore, our findings show the importance of lands beyond protected areas for biodiversity, and the urgent need to mitigate the impacts of woody plant encroachment on bird biodiversity.

Critical slowing down of the Amazon forest after increased drought occurrence

Dynamic ecosystems, such as the Amazon forest, are expected to show critical slowing down behavior, or slower recovery from recurrent small perturbations, as they approach an ecological threshold to a different ecosystem state. Drought occurrences are becoming more prevalent across the Amazon, with known negative effects on forest health and functioning, but their actual role in the critical slowing down patterns still remains elusive. In this study, we evaluate the effect of trends in extreme drought occurrences on temporal autocorrelation (TAC) patterns of satellite-derived indices of vegetation activity, an indicator of slowing down, between 2001 and 2019. Differentiating between extreme drought frequency, intensity, and duration, we investigate their respective effects on the slowing down response. Our results indicate that the intensity of extreme droughts is a more important driver of slowing down than their duration, although their impacts vary across the different Amazon regions. In addition, areas with more variable precipitation are already less ecologically stable and need fewer droughts to induce slowing down. We present findings indicating that most of the Amazon region does not show an increasing trend in TAC. However, the predicted increase in extreme drought intensity and frequency could potentially transition significant portions of this ecosystem into a state with altered functionality.

Millennial changes and cooling trends in land surface warm-season temperatures during the Holocene

The scarcity of proxies and calibration models for quantitatively reconstructing millennial timescale seasonal temperature tremendously constraints our understanding of the Holocene thermal variation and its driven mechanisms. Here, we established two global warm-season temperature models by applying deep learning neural network analysis to the branched tetraether membrane lipids originating from surface soil and lacustrine sediment bacteria. We utilized these optimal models in global well-dated lacustrine, peatland, and loess profiles covering the Holocene. All reconstructions of warm-season temperatures, consistent with climate model simulations, indicate cooling trends since the early Holocene, primarily induced by decreased solar radiation in the Northern Hemisphere due to the precession peak at the early. We further demonstrated that the membrane lipids can effectively enhance the future millennial seasonal temperature research, including winter temperatures, without being restricted by geographical location and sedimentary carrier.

Spatiotemporal trends and covariates of Lyme borreliosis incidence in Poland, 2010-2019

Lyme borreliosis (LB) is the most commonly diagnosed tick-borne disease in the northern hemisphere. Since an efficient vaccine is not yet available, prevention of transmission is essential. This, in turn, requires a thorough comprehension of the spatiotemporal dynamics of LB transmission as well as underlying drivers. This study aims to identify spatiotemporal trends and unravel environmental and socio-economic covariates of LB incidence in Poland, using consistent monitoring data from 2010 through 2019 obtained for 320 (aggregated) districts. Using yearly LB incidence values, we identified an overall increase in LB incidence from 2010 to 2019. Additionally, we observed a large variation of LB incidences between the Polish districts, with the highest risks of LB in the eastern districts. We applied spatiotemporal Bayesian models in an all-subsets modeling framework to evaluate potential associations between LB incidence and various potentially relevant environmental and socio-economic variables, including climatic conditions as well as characteristics of the vegetation and the density of tick host species. The best-supported spatiotemporal model identified positive relationships between LB incidence and forest cover, the share of parks and green areas, minimum monthly temperature, mean monthly precipitation, and gross primary productivity. A negative relationship was found with human population density. The findings of our study indicate that LB incidence in Poland might increase as a result of ongoing climate change, notably increases in minimum monthly temperature. Our results may aid in the development of targeted prevention strategies.

Patterns of Grewia (Malvaceae) diversity across geographical scales in Africa and Madagascar

BACKGROUND AND AIMS

Quantifying spatial species richness is useful to describe biodiversity patterns across broad geographical areas, especially in large, poorly known plant groups. We explore patterns and predictors of species richness across Africa in one such group, the palaeotropical genus Grewia L. (Malvaceae).

METHODS

Grewia species richness was quantified by extracting herbarium records from GBIF and Tropicos and creating geographical grids at varying spatial scales. We assessed predictors of species richness using spatial regression models with 30 environmental variables. We explored species co-occurrence in Madagascar at finer resolutions using Schoener's index and compared species range sizes and International Union for Conservation of Nature status among ecoregions. Lastly, we derived a trait matrix for a subset of species found in Madagascar to characterize morphological diversity across space.

KEY RESULTS

Grewia species occur in 50 countries in Africa, with the highest number of species in Madagascar (93, with 80 species endemic). Species richness is highest in Madagascar, with ≤23 Grewia species in a grid cell, followed by coastal Tanzania/Kenya (≤13 species) and northern South Africa and central Angola (11 species each). Across Africa, higher species richness was predicted by variables related to aridity. In Madagascar, a greater range in environmental variables best predicted species richness, consistent with geographical grid cells of highest species richness occurring near biome/ecoregion transitions. In Madagascar, we also observe increasing dissimilarity in species composition with increasing geographical distance.

CONCLUSIONS

The spatial patterns and underlying environmental predictors that we uncover in Grewia represent an important step in our understanding of plant distribution and diversity patterns across Africa. Madagascar boasts nearly twice the Grewia species richness of the second most species-rich country in Africa, which might be explained by complex topography and environmental conditions across small spatial scales.

Global convergence in terrestrial gross primary production response to atmospheric vapor pressure deficit

Atmospheric vapor pressure deficit (VPD) increases with climate warming and may limit plant growth. However, gross primary production (GPP) responses to VPD remain a mystery, offering a significant source of uncertainty in the estimation of global terrestrial ecosystems carbon dynamics. In this study, in-situ measurements, satellite-derived data, and Earth System Models (ESMs) simulations were analysed to show that the GPP of most ecosystems has a similar threshold in response to VPD: first increasing and then declining. When VPD exceeds these thresholds, atmospheric drought stress reduces soil moisture and stomatal conductance, thereby decreasing the productivity of terrestrial ecosystems. Current ESMs underscore CO2 fertilization effects but predict significant GPP decline in low-latitude ecosystems when VPD exceeds the thresholds. These results emphasize the impacts of climate warming on VPD and propose limitations to future ecosystems productivity caused by increased atmospheric water demand. Incorporating VPD, soil moisture, and canopy conductance interactions into ESMs enhances the prediction of terrestrial ecosystem responses to climate change.

Climate change and land cover effects on water yield in a subtropical watershed spanning the yungas-chaco transition of Argentina

The demand for mountain water resources is increasing, and their availability is threatened by climate change, emphasizing the urgency for effective protection and management. The upper Sali-Dulce watershed holds vital significance as it contributes the majority of the Sali-Dulce water resources, supporting a densely populated dry region in Northwestern Argentina, covering an area of 24,217 km2. However, the potential impact of climate change and land use/land cover change on water yield in this watershed remains uncertain. This study employs the InVEST Annual Water Yield model to analyze the average water yield in the watershed and evaluate its potential changes under future scenarios of climate and land use/land cover change. InVEST was calibrated using data from multiple river gauges located across the watershed, indicating satisfactory performance (R2 = 0.751, p-value = 0.0054). Precipitation and evapotranspiration were the most important variables explaining water yield in the area, followed by land use. Water yield showed a notable concentration in the montane area with 40% of the watershed accounting for 80% of the water yield, underscoring the importance of conserving natural land cover in this critical zone. Climate change scenarios project an increase in water yield ranging from 21 to 75%, while the effects of land cover change scenarios on water yield vary, with reforestation scenarios leading to reductions of up to 15% and expansions in non-irrigated agriculture resulting in increases of up to 40%. Additionally, water yield distribution may become more concentrated or dispersed, largely dependent on the type of land cover. The combined scenarios highlight the pivotal role of land cover in adapting to climate change. Our findings provide valuable insights for designing future studies and developing policies aimed at implementing effective adaptation strategies to climate change within the Salí-Dulce watershed.

Climate-induced tree-mortality pulses are obscured by broad-scale and long-term greening

Vegetation greening has been suggested to be a dominant trend over recent decades, but severe pulses of tree mortality in forests after droughts and heatwaves have also been extensively reported. These observations raise the question of to what extent the observed severe pulses of tree mortality induced by climate could affect overall vegetation greenness across spatial grains and temporal extents. To address this issue, here we analyse three satellite-based datasets of detrended growing-season normalized difference vegetation index (NDVIGS) with spatial resolutions ranging from 30 m to 8 km for 1,303 field-documented sites experiencing severe drought- or heat-induced tree-mortality events around the globe. We find that severe tree-mortality events have distinctive but localized imprints on vegetation greenness over annual timescales, which are obscured by broad-scale and long-term greening. Specifically, although anomalies in NDVIGS (ΔNDVI) are negative during tree-mortality years, this reduction diminishes at coarser spatial resolutions (that is, 250 m and 8 km). Notably, tree-mortality-induced reductions in NDVIGS (|ΔNDVI|) at 30-m resolution are negatively related to native plant species richness and forest height, whereas topographic heterogeneity is the major factor affecting ΔNDVI differences across various spatial grain sizes. Over time periods of a decade or longer, greening consistently dominates all spatial resolutions. The findings underscore the fundamental importance of spatio-temporal scales for cohesively understanding the effects of climate change on forest productivity and tree mortality under both gradual and abrupt changes.

Disturbance sensitivity shapes patterns of tree species distribution in Afrotropical lowland rainforests more than climate or soil

Understanding how tropical forests respond to abiotic environmental changes is critical for preserving biodiversity, mitigating climate change, and maintaining ecosystem services in the coming century. To evaluate the relative roles of the abiotic environment and human disturbance on Central African tree community composition, we employ tree inventory data, remotely sensed climatic data, and soil nutrient data collected from 30 1-ha plots distributed across a large-scale observational experiment in forests that had been differently impacted by logging and hunting in northern Republic of Congo. We show that the composition of Afrotropical plant communities at this scale responds to human disturbance more than to climate, with particular sensitivities to hunting and distance to the nearest village (a proxy for other human activities, including tree-cutting and gathering). These findings contrast neotropical predictions, highlighting the unique ecological, evolutionary, and anthropogenic history of Afrotropical forests.

Influence of hydrometeorological risk factors on child diarrhea and enteropathogens in rural Bangladesh

BACKGROUND

A number of studies have detected relationships between weather and diarrhea. Few have investigated associations with specific enteric pathogens. Understanding pathogen-specific relationships with weather is crucial to inform public health in low-resource settings that are especially vulnerable to climate change.

OBJECTIVES

Our objectives were to identify weather and environmental risk factors associated with diarrhea and enteropathogen prevalence in young children in rural Bangladesh, a population with high diarrheal disease burden and vulnerability to weather shifts under climate change.

METHODS

We matched temperature, precipitation, surface water, and humidity data to observational longitudinal data from a cluster-randomized trial that measured diarrhea and enteropathogen prevalence in children 6 months-5.5 years from 2012-2016. We fit generalized additive mixed models with cubic regression splines and restricted maximum likelihood estimation for smoothing parameters.

RESULTS

Comparing weeks with 30°C versus 15°C average temperature, prevalence was 3.5% higher for diarrhea, 7.3% higher for Shiga toxin-producing Escherichia coli (STEC), 17.3% higher for enterotoxigenic E. coli (ETEC), and 8.0% higher for Cryptosporidium. Above-median weekly precipitation (median: 13mm; range: 0-396mm) was associated with 29% higher diarrhea (adjusted prevalence ratio 1.29, 95% CI 1.07, 1.55); higher Cryptosporidium, ETEC, STEC, Shigella, Campylobacter, Aeromonas, and adenovirus 40/41; and lower Giardia, sapovirus, and norovirus prevalence. Other associations were weak or null.

DISCUSSION

Higher temperatures and precipitation were associated with higher prevalence of diarrhea and multiple enteropathogens; higher precipitation was associated with lower prevalence of some enteric viruses. Our findings emphasize the heterogeneity of the relationships between hydrometeorological variables and specific enteropathogens, which can be masked when looking at composite measures like all-cause diarrhea. Our results suggest that preventive interventions targeted to reduce enteropathogens just before and during the rainy season may more effectively reduce child diarrhea and enteric pathogen carriage in rural Bangladesh and in settings with similar meteorological characteristics, infrastructure, and enteropathogen transmission.

A mathematical model for mapping the insecticide resistance trend in the Anopheles gambiae mosquito population under climate variability in Africa

The control of arthropod disease vectors using chemical insecticides is vital in combating malaria, however the increasing insecticide resistance (IR) poses a challenge. Furthermore, climate variability affects mosquito population dynamics and subsequently IR propagation. We present a mathematical model to decipher the relationship between IR in Anopheles gambiae populations and climate variability. By adapting the susceptible-infected-resistant (SIR) framework and integrating temperature and rainfall data, our model examines the connection between mosquito dynamics, IR, and climate. Model validation using field data achieved 92% accuracy, and the sensitivity of model parameters on the transmission potential of IR was elucidated (e.g. μPRCC = 0.85958, p-value < 0.001). In this study, the integration of high-resolution covariates with the SIR model had a significant impact on the spatial and temporal variation of IR among mosquito populations across Africa. Importantly, we demonstrated a clear association between climatic variability and increased IR (width = [0-3.78], α = 0.05). Regions with high IR variability, such as western Africa, also had high malaria incidences thereby corroborating the World Health Organization Malaria Report 2021. More importantly, this study seeks to bolster global malaria combat strategies by highlighting potential IR 'hotspots' for targeted intervention by National malria control programmes.

Intra- and interspecific diversity in a tropical plant clade alter herbivory and ecosystem resilience

Declines in biodiversity generated by anthropogenic stressors at both species and population levels can alter emergent processes instrumental to ecosystem function and resilience. As such, understanding the role of biodiversity in ecosystem function and its response to climate perturbation is increasingly important, especially in tropical systems where responses to changes in biodiversity are less predictable and more challenging to assess experimentally. Using large-scale transplant experiments conducted at five neotropical sites, we documented the impacts of changes in intraspecific and interspecific plant richness in the genus Piper on insect herbivory, insect richness, and ecosystem resilience to perturbations in water availability. We found that reductions of both intraspecific and interspecific Piper diversity had measurable and site-specific effects on herbivory, herbivorous insect richness, and plant mortality. The responses of these ecosystem-relevant processes to reduced intraspecific Piper richness were often similar in magnitude to the effects of reduced interspecific richness. Increased water availability reduced herbivory by 4.2% overall, and the response of herbivorous insect richness and herbivory to water availability were altered by both intra- and interspecific richness in a site-dependent manner. Our results underscore the role of intraspecific and interspecific richness as foundations of ecosystem function and the importance of community and location-specific contingencies in controlling function in complex tropical systems.

HiMIC-Monthly: A 1 km high-resolution atmospheric moisture index collection over China, 2003-2020

Near-surface atmospheric moisture is a key environmental and hydro-climatic variable that has significant implications for the natural and human systems. However, high-resolution moisture data are severely lacking for fine-scale studies. Here, we develop the first 1 km high spatial resolution dataset of monthly moisture index collection in China (HiMIC-Monthly) over a long period of 2003~2020. HiMIC-Monthly is generated by the light gradient boosting machine algorithm (LightGBM) based on observations at 2,419 weather stations and multiple covariates, including land surface temperature, vapor pressure, land cover, impervious surface proportion, population density, and topography. This collection includes six commonly used moisture indices, enabling fine-scale assessment of moisture conditions from different perspectives. Results show that the HiMIC-Monthly dataset has a good performance, with R2 values for all six moisture indices exceeding 0.96 and root mean square error and mean absolute error values within a reasonable range. The dataset exhibits high consistency with in situ observations over various spatial and temporal regimes, demonstrating broad applicability and strong reliability.

Using process-oriented model output to enhance machine learning-based soil organic carbon prediction in space and time

Monitoring and modelling soil organic carbon (SOC) in space and time can help us to better understand soil carbon dynamics and is of key importance to support climate change research and policy. Although machine learning (ML) has attracted a lot of attention in the digital soil mapping (DSM) community for its powerful ability to learn from data and predict soil properties, such as SOC, it is better at capturing soil spatial variation than soil temporal dynamics. By contrast, process-oriented (PO) models benefit from mechanistic knowledge to express physiochemical and biological processes that govern SOC temporal changes. Therefore, integrating PO and ML models seems a promising means to represent physically plausible SOC dynamics while retaining the spatial prediction accuracy of ML models. In this study, a hybrid modelling framework was developed and tested for predicting topsoil SOC stock in space and time for a regional cropland area located in eastern China. In essence, the hybrid model uses predictions of the PO model in unsampled years as additional training data of the ML model, with a weighting parameter assigned to balance the importance of SOC values from the PO model and real measurements. The results indicated that temporal trends of SOC stock modelled by PO and ML models were largely different, while they were notably similar between the PO and hybrid models. Cross-validation showed that the hybrid model had the best performance (RMSE = 0.29 kg m-2), with a 19 % improvement compared with the ML model. We conclude that the proposed hybrid framework not only enhances space-time soil carbon mapping in terms of prediction accuracy and physical plausibility, it also provides insights for soil management and policy decisions in the face of future climate change and intensified human activities.

Analysis of rainfall and temperature variabilities in Sidama regional state, Ethiopia

The objective of the study was to examine local-scale fluctuation in precipitation and temperature in selected districts of Sidama regional state. Specifically, it focuses on three districts-Hawassa Zuriya, Wonsho, and Hula-using precipitation and temperature records obtained from the Climate Hazards Group Infrared Precipitation with Station (CHIRPS) database which covers the period from 1981 to 2022. Various statistical measures such as mean, standard deviation, as well as coefficient of variation was employed to detect fluctuation. For trend detection, the Mann-Kendall (MK) and Sen's slope tests were also employed. Observations revealed that the average yearly precipitation spatially varied from 1331 mm in Hula, followed by 1275 mm in Wonsho, and 1013 mm at Hawassa Zuriya. Rainfall was bimodal which 53% rains in Kiremt and 33% in Belg season respectively. Annual rainfall show relatively low variability (<20%) for Hula and Wonsho districts, and moderate variability (CV˃20%) for Hawassa Zuriya respectively. The findings also revealed noticeable rising tendencies (p < 0.05) for average temperature across all three agroecosystems over the years under consideration with the highest slope at Hawassa Zuriya (0.038 °C/year), followed by Hula (0.031 °C/year), and Wonsho (0.022 °C/year) respectively. Moreover, both temperature and rainfall exhibited spatial and inter-annual variability. The results of this study necessitate farmers for systematic planning and implementing location specific crop calendar in the context of fluctuating climatic settings. Policy-makers as well as development practitioners can also utilize the finding to better devise and execute plans for adapting and minimizing the effects of climate change.

Assessing the role of drought in dust storm formation in the Tigris and Euphrates basin

Drought is a common meteorological phenomenon and one of the world's most costly natural hazards. A large part of the Tigris and Euphrates basin (TEB) is located in the arid and semi-arid regions of western Asia and suffers from drought. Drought has many destructive effects on the environment and human societies, among which the formation of dust storms, is a major global challenge. This study aims to figure out the role of different types of drought on dust storm formation in the TEB. Standardized precipitation index (SPI), Tasseled Cap greenness index, and surface water area changes based on time series of satellite remote sensing data were considered as proxies to investigate meteorological, agricultural, and hydrological droughts, respectively. Our results show that the continuation of the 5-month and 27-month meteorological droughts are followed by agricultural and hydrological droughts, respectively. In recent decades, the TEB has experienced two prominent drought periods in 2008-2012 and 2021-2022, resulting in a 214 % and 200 % increase in dust events, respectively, compared to the 23-year (2000-2022) average. Overall, 84 %, 10 %, and 6 % of the TEB dust events can be attributed to meteorological, agricultural, and hydrological droughts, respectively.

Coarse-resolution burned area datasets severely underestimate fire-related forest loss

Global coarse-resolution (≥250 m) burned area (BA) products have been used to estimate fire related forest loss, but we hypothesised that a significant part of fire impacts might be undetected because of the underestimation of small fires (<100 ha), especially in the tropics. In this paper, we analysed fire-related forest cover loss in sub-Saharan Africa (SSA) for 2016 and 2019 based on a BA product generated from Sentinel-2 data (20 m), which was observed to have significantly lower omission errors than the coarse-resolution BA products. Using these higher resolution BA datasets, we found that fires contribute to >46 % of total forest losses over SSA, more than twice the estimates from coarse-resolution BA products. In addition, burned forest areas showed more than twofold likelihood of subsequent loss compared to unburned ones. In moist tropical forests, the most fire-vulnerable biome, burning had even six times more chance to precede forest loss than unburned areas. We also found that fire-related characteristics, such as fire size and season, and forest fragmentation play a major role in the determination of tree cover fate. Our results reveal that medium-resolution BA detects more fires in late fire season, which tend to have higher impact on forests than early season ones. On the other hand, small fires represented the major driver of forest loss after fires and the vast majority of these losses occur in fragmented landscapes near forest edge (<260 m). Therefore medium-resolution BA products are required to obtain a more accurate evaluation of fire impacts in tropical ecosystems.

The effect of a political crisis on performance of community forests and protected areas in Madagascar

Understanding the effectiveness of conservation interventions during times of political instability is important given how much of the world's biodiversity is concentrated in politically fragile nations. Here, we investigate the effect of a political crisis on the relative performance of community managed forests versus protected areas in terms of reducing deforestation in Madagascar, a biodiversity hotspot. We use remotely sensed data and statistical matching within an event study design to isolate the effect of the crisis and post-crisis period on performance. Annual rates of deforestation accelerated at the end of the crisis and were higher in community forests than in protected areas. After controlling for differences in location and other confounding variables, we find no difference in performance during the crisis, but community-managed forests performed worse in post-crisis years. These findings suggest that, as a political crisis subsides and deforestation pressures intensify, community-based conservation may be less resilient than state protection.

Assessing scale-dependency of climate risks in coffee-based agroforestry systems

Agroforestry is a management strategy for mitigating the negative impacts of climate and adapting to sustainable farming systems. The successful implementation of agroforestry strategies requires that climate risks are appropriately assessed. The spatial scale, a critical determinant influencing climate impact assessments and, subsequently, agroforestry strategies, has been an overlooked dimension in the literature. In this study, climate risk impacts on robusta coffee production were investigated at different spatial scales in coffee-based agroforestry systems across India. Data from 314 coffee farms distributed across the districts of Chikmagalur and Coorg (Karnataka state) and Wayanad (Kerala state) were collected during the 2015/2016 to 2017/2018 coffee seasons and were used to quantify the key climate drivers of coffee yield. Projected climate data for two scenarios of change in global climate corresponding to (1) current baseline conditions (1985-2015) and (2) global mean temperatures 2 °C above preindustrial levels were then used to assess impacts on robusta coffee yield. Results indicated that at the district scale rainfall variability predominantly constrained coffee productivity, while at a broader regional scale, maximum temperature was the most important factor. Under a 2 °C global warming scenario relative to the baseline (1985-2015) climatic conditions, the changes in coffee yield exhibited spatial-scale dependent disparities. Whilst modest increases in yield (up to 5%) were projected from district-scale models, at the regional scale, reductions in coffee yield by 10-20% on average were found. These divergent impacts of climate risks underscore the imperative for coffee-based agroforestry systems to develop strategies that operate effectively at various scales to ensure better resilience to the changing climate.

Fusion-based approach for hydrometeorological drought modeling: a regional investigation for Iran

The objective of this study was to model a new drought index called the Fusion-based Hydrological Meteorological Drought Index (FHMDI) to simultaneously monitor hydrological and meteorological drought. Aiming to estimate drought more accurately, local measurements were classified into various clusters using the AGNES clustering algorithm. Four single artificial intelligence (SAI) models-namely, Gaussian Process Regression (GPR), Ensemble, Feedforward Neural Networks (FNN), and Support Vector Regression (SVR)-were developed for each cluster. To promote the results of single of products and models, four fusion-based approaches, namely, Wavelet-Based (WB), Weighted Majority Voting (WMV), Extended Kalman Filter (EKF), and Entropy Weight (EW) methods, were used to estimate FHMDI in different time scales, precipitation, and runoff. The performance of single and combined products and models was assessed through statistical error metrics, such as Kling-Gupta efficiency (KGE), Mean Bias Error (MBE), and Normalized Root Mean Square Error (NRMSE). The performance of the proposed methodology was tested over 24 main river basins in Iran. The validation results of the FHMDI (the compliance of the index with the pre-existing drought index) revealed that it accurately identified drought conditions. The results indicated that individual products performed well in some river basins, while fusion-based models improved dataset accuracy more compared to local measurements. The WMV with the highest accuracy (lowest NRMSE) had a good performance in 60% of the cases compared to all other products and fusion-based models. WMV also showed higher efficiency in 100% of the cases than all other fusion-based and SAI models for simultaneous hydrological and meteorological drought estimation. In light of these findings, we recommend the use of fusion-based approach to improve drought modeling.