Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset

Trade-offs in biodiversity and ecosystem services between edges and interiors in European forests

Forest biodiversity and ecosystem services are hitherto predominantly quantified in forest interiors, well away from edges. However, these edges also represent a substantial proportion of the global forest cover. Here we quantified plant biodiversity and ecosystem service indicators in 225 plots along forest edge-to-interior transects across Europe. We found strong trade-offs: phylogenetic diversity (evolutionary measure of biodiversity), proportion of forest specialists, decomposition and heatwave buffering increased towards the interior, whereas species richness, nectar production potential, stemwood biomass and tree regeneration decreased. These trade-offs were mainly driven by edge-to-interior structural differences. As fragmentation continues, recognizing the role of forest edges is crucial for integrating biodiversity and ecosystem service considerations into sustainable forest management and policy.

Intellligent sustainable agricultural water practice using multi sensor spatiotemporal evolution

The amount of water taken from non-renewable resources such as aquifers to fulfill irrigation requirements is rarely monitored, putting sustainable agriculture under threat in the face of changing climate. In the present research, an attempt was made to apply multi-sensor (Landsat suite, GRACE, GRACE-FO) satellite data to monitor spatiotemporal evolution of agriculture for the Al-Qassim region, Kingdom of Saudi Arabia (KSA). For this purpose, time series of NDVI (Normalized Difference Vegetation Index), SAVI (Soil-Adjusted Vegetation Index), and MSAVI2 (Modified Soil-Adjusted Vegetation 2) was utilized to assess vegetation pattern change in the study area. The present investigation used High-resolution Planetscope (PS) nanosatellite data to validate the vegetation results. Mann Kendall trend analysis and linear regression were performed to study the temporal pattern, and the relationship between vegetation, GRACE, and climate variables was performed from 1984 to 2020. Water extraction based on the averaged value of JPL GWS and CSR GWS showed a decreasing trend of -10.24 ± 1.4 mm/year from 2003-2020. The annual rainfall showed a decreasing trend, while the annual temperature showed an increasing trend from 1982-2020. The correlation of vegetation indices with rainfall of one-month lag showed a significantly better relationship of 0.74, 0.74, and 0.75, respectively, for NDVI, SAVI, and MSAVI2. The correlation between temperature and all three vegetation indices is a strong negative correlation: -0.85 for NDVI and -0.9 for SAVI and MSAVI.

Climate velocities and species tracking in global mountain regions

Mountain ranges contain high concentrations of endemic species and are indispensable refugia for lowland species that are facing anthropogenic climate change1,2. Forecasting biodiversity redistribution hinges on assessing whether species can track shifting isotherms as the climate warms3,4. However, a global analysis of the velocities of isotherm shifts along elevation gradients is hindered by the scarcity of weather stations in mountainous regions5. Here we address this issue by mapping the lapse rate of temperature (LRT) across mountain regions globally, both by using satellite data (SLRT) and by using the laws of thermodynamics to account for water vapour6 (that is, the moist adiabatic lapse rate (MALRT)). By dividing the rate of surface warming from 1971 to 2020 by either the SLRT or the MALRT, we provide maps of vertical isotherm shift velocities. We identify 17 mountain regions with exceptionally high vertical isotherm shift velocities (greater than 11.67 m per year for the SLRT; greater than 8.25 m per year for the MALRT), predominantly in dry areas but also in wet regions with shallow lapse rates; for example, northern Sumatra, the Brazilian highlands and southern Africa. By linking these velocities to the velocities of species range shifts, we report instances of close tracking in mountains with lower climate velocities. However, many species lag behind, suggesting that range shift dynamics would persist even if we managed to curb climate-change trajectories. Our findings are key for devising global conservation strategies, particularly in the 17 high-velocity mountain regions that we have identified.

Climate change drove the route shift of the ancient Silk Road in two distinct ways

Although climate change has convincingly been linked to the evolution of human civilization on different temporal scales, its role in influencing the spatial patterns of ancient civilizations has rarely been investigated. The northward shift of the ancient Silk Road (SR) route from the Tarim Basin (TB) to the Junggar Basin during ∼420-850 CE provides the opportunity to investigate the relationship between climate change and the spatial evolution of human societies. Here, we use a new high-resolution chironomid-based temperature reconstruction from arid China, combined with hydroclimatic and historical datasets, to assess the possible effects of climate fluctuations on the shift of the ancient SR route. We found that a cooling/drying climate in the TB triggered the SR route shift during ∼420-600 CE. However, a warming/wetting climate during ∼600-850 CE did not inhibit this shift, but instead promoted it, because of the favorable climate-induced geopolitical conflicts between the Tubo Kingdom and the Tang Dynasty in the TB. Our findings reveal two distinct ways in which climate change drove the spatial evolution of human civilization, and they demonstrate the flexibility of societal responses to climate change.

Impact of Preseason Climate Factors on Vegetation Photosynthetic Phenology in Mid-High Latitudes of the Northern Hemisphere

During the period preceding the vegetation growing season (GS), temperature emerges as the pivotal factor determining phenology in northern terrestrial ecosystems. Despite extensive research on the impact of daily mean temperature (Tmean) during the preseason period, the influence of diurnal temperature range (DTR) on vegetation photosynthetic phenology (i.e., the impact of the plant photosynthetic cycle on seasonal time scale) has largely been neglected. Using a long-term vegetation photosynthetic phenology dataset and historical climate data, we examine vegetation photosynthetic phenology dynamics and responses to climate change across the mid-high latitudes of the Northern Hemisphere from 2001 to 2020. Our data reveal an advancing trend in the start of the GS (SOS) by -0.15 days per year (days yr-1), affecting 72.1% of the studied area. This is particularly pronounced in western Canada, Alaska, eastern Asia, and latitudes north of 60°N. Conversely, the end of the GS (EOS) displays a delaying trend of 0.17 days yr-1, impacting 62.4% of the studied area, especially northern North America and northern Eurasia. The collective influence of an earlier SOS and a delayed EOS has resulted in the notably prolonged length of the GS (LOS) by 0.32 days yr-1 in the last two decades, affecting 70.9% of the studied area, with Eurasia and western North America being particularly noteworthy. Partial correlation coefficients of the SOS with preseason Tmean, DTR, and accumulated precipitation exhibited negative values in 98.4%, 93.0%, and 39.2% of the study area, respectively. However, there were distinct regional variations in the influence of climate factors on the EOS. The partial correlation coefficients of the EOS with preseason Tmean, DTR, and precipitation were positive in 58.6%, 50.1%, and 36.3% of the region, respectively. Our findings unveil the intricate mechanisms influencing vegetation photosynthetic phenology, holding crucial significance in understanding the dynamics of carbon sequestration within terrestrial ecosystems amidst climate change.

Negative correlation between soil salinity and soil organic carbon variability

Soil organic carbon (SOC) is vital for terrestrial ecosystems, affecting biogeochemical processes, and soil health. It is known that soil salinity impacts SOC content, yet the specific direction and magnitude of SOC variability in relation to soil salinity remain poorly understood. Analyzing 43,459 mineral soil samples (SOC < 150 g kg-1) collected across different land covers since 1992, we approximate a soil salinity increase from 1 to 5 dS m-1 in croplands would be associated with a decline in mineral soils SOC from 0.14 g kg-1 above the mean predicted SOC ([Formula: see text] = 18.47 g kg-1) to 0.46 g kg-1 below [Formula: see text] (~-430%), while for noncroplands, such decline is sharper, from 0.96 above [Formula: see text] = 35.96 g kg-1 to 4.99 below [Formula: see text] (~-620%). Although salinity's significance in explaining SOC variability is minor (<6%), we estimate a one SD increase in salinity of topsoil samples (0 to 7 cm) correlates with respective [Formula: see text] declines of ~4.4% and ~9.26%, relative to [Formula: see text] and [Formula: see text]. The [Formula: see text] decline in croplands is greatest in vegetation/cropland mosaics while lands covered with evergreen needle-leaved trees are estimated with the highest [Formula: see text] decline in noncroplands. We identify soil nitrogen, land cover, and precipitation Seasonality Index as the most significant parameters in explaining the SOC's variability. The findings provide insights into SOC dynamics under increased soil salinity, improving understanding of SOC stock responses to land degradation and climate warming.

Investigating the Yanomami malaria outbreak puzzle: surge in mining during Bolsonaro's government triggered peak in malaria burden

The Yanomami, an Indigenous people from the Amazon, confront multifaceted challenges endangering their health and cultural integrity. Of immediate concern is the surge in malaria cases in their territory during Bolsonaro's government. We investigated the impact of land use on malaria incidence among the Yanomami leveraging satellite imagery and ran difference-in-differences analyses to ask whether the Yanomami suffered disproportionately from malaria when illegal mining was rising in the region (2016-2022). We show a remarkable ~300% rise in malaria from 2016 to 2022 and point to mining as the primary driver of malaria among the Yanomami; when mining increases by 1%, malaria increases by 31%. After mining unfolded, the burden of malaria among the Yanomami was disproportionately higher, up to 15%, than in non-indigenous communities. Our findings underscore the impact of illegal mining on the high malaria burden suffered by the Yanomami and the importance of rainforest conservation and land sovereignty for Indigenous health.

Dynamic pathway linking Pakistan flooding to East Asian heatwaves

In July to August 2022, Pakistan suffered historic flooding while record-breaking heatwaves swept southern China, causing severe socioeconomic impacts. Similar extreme events have frequently coincided between two regions during the past 44 years, but the underlying mechanisms remain unclear. Using observations and a suite of model experiments, here, we show that the upper-tropospheric divergent wind induced by convective heating over Pakistan excites a barotropic anomalous anticyclone over eastern China, which further leads to persistent heatwaves. Atmospheric model ensemble simulation indicates that this dynamic pathway linking Pakistan flooding and East Asian heatwaves is intrinsic to the climate system, largely independent of global sea surface temperature forcing. This dynamic connection is most active during July to August when convective variability is large over Pakistan and the associated divergent flow excites barotropic Rossby waves that propagate eastward along the upper troposphere westerly waveguide. This robust waveguide and the time delay offer hopes for improved subseasonal prediction of extreme events in East Asia.

Coupled Pacific Rim megadroughts contributed to the fall of the Ming Dynasty's capital in 1644 CE

Historical documents provide evidence for regional droughts preceding the political turmoil and fall of Beijing in 1644 CE, when more than 20 million people died in northern China during the late Ming famine period. However, the role climate and environmental changes may have played in this pivotal event in Chinese history remains unclear. Here, we provide tree-ring evidence of persistent megadroughts from 1576 to 1593 CE and from 1624 to 1643 CE in northern China, which coincided with exceptionally cold summers just before the fall of Beijing. Our analysis reveals that these regional hydroclimatic extremes are part of a series of megadroughts along the Pacific Rim, which not only impacted the ecology and society of monsoonal northern China, but likely also exacerbated external geopolitical and economic pressures. This finding is corroborated by last millennium reanalysis data and numerical climate model simulations revealing internally driven Pacific sea surface temperature variations and the predominance of decadal scale La Niña-like conditions to be responsible for precipitation decreases over northern China, as well as extensive monsoon regions in the Americas. These teleconnection patterns provide a mechanistic explanation for reoccurring drought spells during the late Ming Dynasty and the environmental framework fostering the fall of Beijing in 1644 CE, and the subsequent demise of the Ming Dynasty.

Spatial spillovers of violent conflict amplify the impacts of climate variability on malaria risk in sub-Saharan Africa

Africa carries a disproportionately high share of the global malaria burden, accounting for 94% of malaria cases and deaths worldwide in 2019. It is also a politically unstable region and the most vulnerable continent to climate change in recent decades. Knowledge about the modifying impacts of violent conflict on climate-malaria relationships remains limited. Here, we quantify the associations between violent conflict, climate variability, and malaria risk in sub-Saharan Africa using health surveys from 128,326 individuals, historical climate data, and 17,429 recorded violent conflicts from 2006 to 2017. We observe that spatial spillovers of violent conflict (SSVCs) have spatially distant effects on malaria risk. Malaria risk induced by SSVCs within 50 to 100 km from the households gradually increases from 0.1% (not significant, P>0.05) to 6.5% (95% CI: 0 to 13.0%). SSVCs significantly promote malaria risk within the average 20.1 to 26.9 °C range. At the 12-mo mean temperature of 22.5 °C, conflict deaths have the largest impact on malaria risk, with an approximately 5.8% increase (95% CI: 1.0 to 11.0%). Additionally, a pronounced association between SSVCs and malaria risk exists in the regions with 9.2 wet days per month. The results reveal that SSVCs increase population exposure to harsh environments, amplifying the effect of warm temperature and persistent precipitation on malaria transmission. Violent conflict therefore poses a substantial barrier to mosquito control and malaria elimination efforts in sub-Saharan Africa. Our findings support effective targeting of treatment programs and vector control activities in conflict-affected regions with a high malaria risk.

Ozone pollution contributes to the yield gap for beans in Uganda, East Africa, and is co-located with other agricultural stresses

Air quality negatively impacts agriculture, reducing the yield of staple food crops. While measured data on African ground-level ozone levels are scarce, experimental studies demonstrate the damaging impact of ozone on crops. Common beans (Phaseolus vulgaris), an ozone-sensitive crop, are widely grown in Uganda. Using modelled ozone flux, agricultural surveys, and a flux-effect relationship, this study estimates yield and production losses due to ozone for Ugandan beans in 2015. Analysis at this scale allows the use of localised data, and results can be presented at a sub-regional level. Soil nutrient stress, drought, flood risk, temperature and deprivation were also mapped to investigate where stresses may coincide. Average bean yield losses due to ozone were 17% and 14% (first and second growing season respectively), equating to 184 thousand tonnes production loss. However, for some sub-regions, losses were up to 27.5% and other crop stresses also coincided in these areas. This methodology could be applied widely, allowing estimates of ozone impact for countries lacking air quality and/or experimental data. As crop productivity is below its potential in many areas of the world, changing agricultural practices to mitigate against losses due to ozone could help to reduce the crop yield gap.

Density-dependent species interactions modulate alpine treeline shifts

Species interactions such as facilitation and competition play a crucial role in driving species range shifts. However, density dependence as a key feature of these processes has received little attention in both empirical and modelling studies. Herein, we used a novel, individual-based treeline model informed by rich in situ observations to quantify the contribution of density-dependent species interactions to alpine treeline dynamics, an iconic biome boundary recognized as an indicator of global warming. We found that competition and facilitation dominate in dense versus sparse vegetation scenarios respectively. The optimal balance between these two effects was identified at an intermediate vegetation thickness where the treeline elevation was the highest. Furthermore, treeline shift rates decreased sharply with vegetation thickness and the associated transition from positive to negative species interactions. We thus postulate that vegetation density must be considered when modelling species range dynamics to avoid inadequate predictions of its responses to climate warming.

An enhanced drought forecasting in coastal arid regions using deep learning approach with evaporation index

Coastal arid regions are similar to deserts, where it receives significantly less rainfall, less than 10 cm. Perhaps the world's worst natural disaster, coastal area droughts, can only be detected using reliable monitoring systems. Creating a reliable drought forecast model and figuring out how well various models can analyze drought factors in coastal arid regions are two of the biggest obstacles in this field. Different time-series methods and machine-learning models have traditionally been utilized in forecasting strategies. Deep learning is promising when describing the complex interplay between coastal drought and its contributing variables. Considering the possibility of enhancing our understanding of drought features, applying deep learning approaches has yet to be tried widely. The current investigation employs a deep learning strategy. Coastal Drought indices are commonly used to comprehend the situation better; hence the Standard Precipitation Evaporation Index (SPEI) was used since it incorporates temperatures and precipitation into its computation. An integrated coastal drought monitoring model was presented and validated using convolutional long short-term memory with self-attention (SA-CLSTM). The Climatic Research Unit (CRU) dataset, which spans 1901-2018, was mined for the drought index and predictor data. To learn how LSTM forecasting could enhance drought forecasting, we analyzed the findings regarding numerous drought parameters (drought severity, drought category, or geographic variation). The model's ability to predict drought intensity was assessed using the Coefficient of Determination (R2), the Root Mean Square Error (RMSE), and the Mean Absolute Error (MAE). Both the SPEI 1 and SPEI 3 examples had R2 values more than 0.99 for the model. The range of predicted outcomes for each drought group was analyzed using a multi-class Receiver Operating Characteristic based Area under Curves (ROC-AUC) method. The research showed that the AUC for SPEI 1 was 0.99 and for SPEI 3, 0.99. The study's results indicate progress over machine learning models for one month in advance, accounting for various drought conditions. This work's findings may be used to mitigate drought, and additional improvement can be achieved by testing other models.

Spatial variability in herbaceous plant phenology is mostly explained by variability in temperature but also by photoperiod and functional traits

Whereas temporal variability of plant phenology in response to climate change has already been well studied, the spatial variability of phenology is not well understood. Given that phenological shifts may affect biotic interactions, there is a need to investigate how the variability in environmental factors relates to the spatial variability in herbaceous species' phenology by at the same time considering their functional traits to predict their general and species-specific responses to future climate change. In this project, we analysed phenology records of 148 herbaceous species, which were observed for a single year by the PhenObs network in 15 botanical gardens. For each species, we characterised the spatial variability in six different phenological stages across gardens. We used boosted regression trees to link these variabilities in phenology to the variability in environmental parameters (temperature, latitude and local habitat conditions) as well as species traits (seed mass, vegetative height, specific leaf area and temporal niche) hypothesised to be related to phenology variability. We found that spatial variability in the phenology of herbaceous species was mainly driven by the variability in temperature but also photoperiod was an important driving factor for some phenological stages. In addition, we found that early-flowering and less competitive species characterised by small specific leaf area and vegetative height were more variable in their phenology. Our findings contribute to the field of phenology by showing that besides temperature, photoperiod and functional traits are important to be included when spatial variability of herbaceous species is investigated.

Impacts of climate warming and atmospheric deposition on recent shifts in chironomid communities in two alpine lakes, eastern China

Climate warming and atmospheric deposition are altering alpine lake ecosystems at unprecedented rates, whereas their direct and indirect effects on primary consumer communities are unclear. This study presents sedimentary multi-proxy records including chironomids, diatoms, elements and stable isotopes of carbon and nitrogen in 210Pb-dated cores from two alpine lakes located above the timberline in the Taibai Mountain, eastern China. Before ∼2000 CE, chironomid communities were co-dominated by Heterotrissocladius marcidus-type and Micropsectra atrofasciata-type in the two lakes. Thereafter, Tanytarsus glabrescens-type increased rapidly to be a dominant species. Redundancy analyses (RDAs) revealed that chironomid fauna shifts were significantly correlated with rising diatom concentrations in both lakes, declining Ti content in the upstream lake and δ13C depletion in the downstream lake. Although temperature, precipitation and δ15N were not significant explanatory variables in RDAs, climate warming and atmospheric deposition likely promoted terrestrial and aquatic primary production, indicated by synchronous increases in organic matter contents and diatom concentrations in the two sediment cores. Since diatoms contain essential polyunsaturated fatty acids that are essential for chironomids, rising diatom concentrations can promote food quantity and quality. In addition, increased primary production would create organic substrates for chironomid larvae. Recent shifts in chironomid fauna driven by indirect effects of global warming and atmospheric deposition might be a widespread phenomenon in alpine lakes, probably triggering regime shifts in headwater lake ecosystems.

Decadal-scale variability and warming affect spring timing and forest growth across the western Great Lakes region

The Great Lakes region of North America has warmed by 1-2 °C on average since pre-industrial times, with the most pronounced changes observable during winter and spring. Interannual variability in temperatures remains high, however, due to the influence of ocean-atmosphere circulation patterns that modulate the warming trend across years. Variations in spring temperatures determine growing season length and plant phenology, with implications for whole ecosystem function. Studying how both internal climate variability and the "secular" warming trend interact to produce trends in temperature is necessary to estimate potential ecological responses to future warming scenarios. This study examines how external anthropogenic forcing and decadal-scale variability influence spring temperatures across the western Great Lakes region and estimates the sensitivity of regional forests to temperature using long-term growth records from tree-rings and satellite data. Using a modeling approach designed to test for regime shifts in dynamic time series, this work shows that mid-continent spring climatology was strongly influenced by the 1976/1977 phase change in North Pacific atmospheric circulation, and that regional forests show a strengthening response to spring temperatures during the last half-century.

Nitrogen and phosphorus trends in lake sediments of China may diverge

The brief history of monitoring nutrient levels in Chinese lake waters limits our understanding of the causes and the long-term trends of their eutrophication and constrains effective lake management. We therefore synthesize nutrient data from lakes in China to reveal the historical changes and project their future trends to 2100 using models. Here we show that the average concentrations of nitrogen and phosphorus in lake sediments have increased by 267% and 202%, respectively since 1850. In the model projections, 2030-2100, the nitrogen concentrations in the studied lakes in China may decrease, for example, by 87% in the southern districts and by 19% in the northern districts. However, the phosphorus concentrations will continue to increase by an average of 25% in the Eastern Plain, Yunnan-Guizhou Plateau, and Xinjiang. Based on this differentiation, we suggest that nitrogen and phosphorus management in Chinese lakes should be carried out at the district level to help develop rational and sustainable environmental management strategies.

Temperature anomalies undermine the health of reproductive-age women in low- and middle-income countries

Climate change is expected to undermine population health and well-being in low- and middle-income countries, but relatively few analyses have directly examined these effects using individual-level data at global scales, particularly for reproductive-age women. To address this lacuna, we harmonize nationally representative data from the Demographic and Health Surveys on reproductive health, body mass index (BMI), and temporary migration from 2.5 million adult women (ages 15 to 49) in approximately 109,000 sites across 59 low- and middle-income countries, which we link to high-resolution climate data. We use this linked dataset to estimate fixed-effect logistic regression models of demographic and health outcomes as a function of climate exposures, woman-level and site-level characteristics, seasonality, and regional time trends, allowing us to plausibly isolate climate effects from other influences on health and migration. Specifically, we measure the effects of recent exposures to temperature and precipitation anomalies on the likelihood of having a live birth in the past year, desire for another child, use of modern contraception, underweight (BMI < 18.5), and temporary migration, and subsequently allow for nonlinearity as well as heterogeneity across education, rural/urban residence, and baseline climate. This analysis reveals that exposures to high temperatures increase live births, reduce desire for another child, increase underweight, and increase temporary migration, particularly in rural areas. The findings represent clear evidence that anthropogenic temperature increases contribute to temporary migration and are a significant threat to women's health and reproductive autonomy in low- and middle-income countries.

An evolutionary innovation for mating facilitates ecological niche expansion and buffers species against climate change

One of the drivers of life's diversification has been the emergence of "evolutionary innovations": The evolution of traits that grant access to underused ecological niches. Since ecological interactions can occur separately from mating, mating-related traits have not traditionally been considered factors in niche evolution. However, in order to persist in their environment, animals need to successfully mate just as much as they need to survive. Innovations that facilitate mating activity may therefore be an overlooked determinant of species' ecological limits. Here, we show that species' historical niches and responses to contemporary climate change are shaped by an innovation involved in mating-a waxy, ultra-violet-reflective pruinescence produced by male dragonflies. Physiological experiments in two species demonstrate that pruinescence reduces heating and water loss. Phylogenetic analyses show that pruinescence is gained after taxa begin adopting a thermohydrically stressful mating behavior. Further comparative analyses reveal that pruinose species are more likely to breed in exposed, open-canopy microhabitats. Biogeographic analyses uncover that pruinose species occupy warmer and drier regions in North America. Citizen-science observations of Pachydiplax longipennis suggest that the extent of pruinescence can be optimized to match the local conditions. Finally, temporal analyses indicate that pruinose species have been buffered against contemporary climate change. Overall, these historical and contemporary patterns show that successful mating can shape species' niche limits in the same way as growth and survival.

Neighbourhood species richness and drought-tolerance traits modulate tree growth and δ13 C responses to drought

Mixed-species forests are promoted as a forest management strategy for climate change adaptation, but whether they are more resistant to drought than monospecific forests remains contested. In particular, the trait-based mechanisms driving the role of tree diversity under drought remain elusive. Using tree cores from a large-scale biodiversity experiment, we investigated tree growth and physiological stress responses (i.e. increase in wood carbon isotopic ratio; δ13 C) to changes in climate-induced water availability (wet to dry years) along gradients in neighbourhood tree species richness and drought-tolerance traits. We hypothesized that neighbourhood species richness increases growth and decreases δ13 C and that these relationships are modulated by the abiotic (i.e. climatic conditions) and the biotic context. We characterised the biotic context using drought-tolerance traits of focal trees and their neighbours. These traits are related to cavitation resistance versus resource acquisition and stomatal control. Tree growth increased with neighbourhood species richness. However, we did not observe a universal relief of water stress in species-rich neighbourhoods. The effects of neighbourhood species richness and climate on growth and δ13 C were modulated by the traits of focal trees and the traits of their neighbours. At either end of each drought-tolerance gradient, species responded in opposing directions during dry and wet years. We show that species' drought-tolerance traits can explain the strength and nature of biodiversity-ecosystem functioning relationships in experimental tree communities experiencing drought. Mixing tree species can increase growth but may not universally relieve drought stress.

Subnational estimates of vitamin A supplementation coverage in children: a geospatial analysis of 45 low- and middle-income countries

OBJECTIVES

Vitamin A supplementation (VAS) can protect children from the adverse health consequences of vitamin A deficiency. Granular data on VAS coverage can guide global and national efforts to achieve universal VAS coverage. To provide geographically precise targeting of VAS programs and to monitor progress in reducing geographic disparities, we aimed to create high-resolution (5 × 5 km2) maps of VAS coverage in children under 5 years across VAS priority countries.

STUDY DESIGN

We used cross-sectional data from the Demographic and Health Surveys (DHS) program.

METHODS

We used data from the DHS program for United Nations Children's Fund -designated VAS priority countries between 2000 and 2017 with data available from 2005 or later. The outcome variable was the proportion of children under 5 years who received a vitamin A dose in each sampled cluster. We applied a Bayesian geostatistical approach incorporating geographic, climatic, and nutritional covariates to estimate VAS coverage for each cell. We estimated and mapped absolute VAS coverage, Bayesian uncertainty intervals, and exceedance probabilities.

RESULTS

Our sample included countries from Latin America and the Caribbean, Asia, and Africa. Most countries had estimated VAS coverage levels <70%, and our exceedance probabilities indicated high certainty that our estimates fell below this threshold in most grid cells. International variations were most notable in the Latin America and the Caribbean region and Africa. Intranational variations were greatest in some South Asian and West and Central African countries.

CONCLUSIONS

These prevalence and exceedance maps, especially used with data on indicators of VAS need, could help to improve equity.

Analysis of environmental factors influencing lumpy skin disease outbreak seasonality and assessment of its spread risk in the Saratovskaya oblast of Russia

Background and Aim

Lumpy skin disease (LSD) is a transboundary viral disease of cattle that causes serious economic losses due to a significant decrease in meat and milk productivity. This study analyzed the influence of natural and anthropogenic environmental factors on LSD spread seasonality and assessed the risk of LSD outbreaks in the Saratovskaya oblast of the Russian Federation.

Materials and Methods

Data on LSD outbreaks and environmental factors during different seasons were collected for the period 2011-2020 in the Balkan Peninsula, Middle East, and Russia. Risk assessment was performed using mathematical modeling with generalized linear regression and maximum entropy.

Results

Fourteen statistically significant environmental factors influencing LSD spread were identified. The analysis of MaxEnt models built using the selected factors showed that the presence of the pathogen is mostly exerted by: the density of susceptible cattle (an increased risk is observed at a density above 10 and 20 heads/10 km2 in winter and autumn, with a permanent risk in spring and summer), the density of water bodies (the risk is increased at any density in winter and autumn, in the range of 13-23.5 m2/km2 in spring, in the ranges of 0-8 and over 14.5 m2/km2 in summer), and average monthly precipitation rate (the most risky are 105-185 mm/month in winter, 35 mm in spring, 15-105 mm in summer, and above 50 mm in autumn).

Conclusion

LSD tends to spread during the warm season. Compared with other test zones, the Saratovskaya oblast has a negligible risk of disease spread (in winter), low risk (in spring), or medium risk (in summer and autumn). The annual risk is low to medium.

Validation of global precipitation time series products against tree ring records and remotely sensed vegetation greenness

Global interpolated climate products are widely used in ecological research to investigate biosphere-climate interactions and to track ecological response to climate variability and climate change. In turn, biological data could also be used for an independent validation of one aspect of climate data quality. All else being equal, more variance explained in biological data identifies the better climate data product. Here, we compare seven global precipitation time series products, including gauge-based datasets (CRU-TS, UDEL-TS, GPCC), re-analysis products (ERA5, CHELSA), a satellite-based dataset (PERSIANN) and a multi-source product that draws on gauge, re-analysis, and satellite data (MSWEP). We focus on precipitation variables, because they are more difficult to interpolate than temperature, and show larger divergence among gridded data products. Our validation is based on 20 years of remotely sensed vegetation greenness (MODIS-EVI) and 120 years of tree ring records from the International Tree Ring Data Bank (ITRDB). The results for the 20-year EVI based validation shows that all gauge and re-analysis data products performed similarly, but were outperformed by the multi-source MSWEP product, especially in regions with low weather station coverage, such as Africa. For analyzing long 120-year time-series, UDEL-TS showed superior performance prior to the 1940s, with especially large margins for northern Asia and the Himalayas region. For other regions, CRU-TS and GPCC could be recommended. We provide maps that can guide the best regional choice of climate product for research involving time series of biological response to historic climate variability and climate change.

Modern anthropogenic drought in Central Brazil unprecedented during last 700 years

A better understanding of the relative roles of internal climate variability and external contributions, from both natural (solar, volcanic) and anthropogenic greenhouse gas forcing, is important to better project future hydrologic changes. Changes in the evaporative demand play a central role in this context, particularly in tropical areas characterized by high precipitation seasonality, such as the tropical savannah and semi-desertic biomes. Here we present a set of geochemical proxies in speleothems from a well-ventilated cave located in central-eastern Brazil which shows that the evaporative demand is no longer being met by precipitation, leading to a hydrological deficit. A marked change in the hydrologic balance in central-eastern Brazil, caused by a severe warming trend, can be identified, starting in the 1970s. Our findings show that the current aridity has no analog over the last 720 years. A detection and attribution study indicates that this trend is mostly driven by anthropogenic forcing and cannot be explained by natural factors alone. These results reinforce the premise of a severe long-term drought in the subtropics of eastern South America that will likely be further exacerbated in the future given its apparent connection to increased greenhouse gas emissions.

The effects of urban thermal heterogeneity and feather coloration on oxidative stress and metabolism of pigeons (Columba livia)

Urbanization stands out as a significant anthropogenic factor, exerting selective pressures on ecosystems and biotic components. A notable outcome of urbanization is thermal heterogeneity where the emergence of Urban Heat Islands is characterized by elevated air and surface temperatures compared to adjacent rural areas. Investigating the influence of thermal heterogeneity on urban animals could offer insights into how temperature variations can lead to phenotypic shifts. Urban pigeons (Columba livia) serve as an excellent model for studying urban thermal effects, given the melanism variations, which are associated with the pleiotropy of the melanocortin system. To examine the development of physiological plasticity in response to urban thermal variations, we conducted a study on pigeons in Santiago, Chile, during the rainy season. We assessed the influence of habitat on physiological traits related to metabolism and antioxidant capacities, which are theoretically affected by feather coloration. Our findings reveal that variations in melanism significantly impact pigeon physiology, affecting both antioxidant capacities and the mitochondrial activity of red blood cells. It was found that higher urban temperatures, from both the current sampling month and the prior sampling month (from CRU TS dataset), were negatively and strongly associated with lower antioxidant and metabolic activities. This suggests that elevated urban temperatures likely benefit the energetic budgets of pigeon populations and mitigate the negative effects of oxidative metabolism, with differential effects depending on feather colorations.

Riparian vegetation plastic monitoring: A harmonized protocol for sampling macrolitter in vegetated riverine habitats

Many studies highlighted that rivers transported land-based plastics to the sea. However, most of the litter remains stuck in the fluvial ecosystem, also blocked by vegetation. To date, research on riverine macrolitter focused on floating and riverbank monitoring, thus methods to sample riverbank and floating litter have been developed. Concerning rivers, few recent studies highlighted the role of riparian vegetation in entrapping plastics. Given that vegetation represents a large part of riverine ecosystems and that the dynamics of plastics entrapped by vegetation are neglected, it appears pivotal to study in more detail how vegetation contributes to plastic retention. However, as current protocols and guidelines considered only floating and riverbank plastics without providing standardized and updated strategies to monitor litter in vegetation, here we aimed to develop a new standardized protocol and tools to assess plastics in vegetation. Specifically, we focused on unveiling the three-tridimensional structure of vegetation in relation to plastic occurrence, while considering seasonal and hydromorphological aspects. To investigate the trapping effect of vegetation, we developed a three-dimensional vegetation structure index (3DVI) related to plastics. The 3DVI index considers plant structure (i.e., number of branches) and diversity (i.e., species). To test the 3DVI, we conducted an in-situ case study in central Italy. We found that both primary and secondary riparian vegetation blocked plastic litter. In detail, 3DVI correlated with the number of plastics, highlighting that the densest and most diverse communities trap more plastics. Furthermore, we provided for the first time the assessment of seasonality for the macroplastic entrapment by riparian vegetation and a preliminary quantification of wind-blown plastics. Our results should be of interest to promote the development of standardized and harmonized monitoring strategies for riparian habitat management and conservation.

Early warning signals indicate climate change-induced stress in Norway spruce in the Eastern Carpathians

Climate change is affecting forest ecosystems globally, in particular through warming as well as increases in the frequency and intensity of extreme events. Norway spruce (Picea abies (L.) Karst.) is one of the most important coniferous tree species in Europe. In recent extremely dry years in Central Europe, spruce suffered and large dieback has been observed. In parts of Eastern Europe, however, no large-scale decline in spruce has been reported so far, though anticipated changes in climate pose the question how the future of these forests may look like. To assess the current state of spruce forests in Eastern Europe, we established a tree-ring network consisting of 157 Norway spruce chronologies (from >3000 trees) of different ages distributed along elevational transects in the Eastern Carpathians, Romania. We evaluated early warning signals of climate change-induced stress, i.e. (1) growth decline, (2) increased sensitivity of tree growth (assessed over the statistics first-order autocorrelation and standard deviation), and (3) increased growth synchrony. A pronounced growth decline was observed over the last two decades, which was strongest in younger stands and at lower elevations. However, growth sensitivity and synchrony did not show consistent patterns, suggesting that forest decline may not be immediately imminent. Overall, our findings highlight an increased vulnerability of spruce in the Eastern Carpathians. With ongoing climate change, spruce dieback may be expected in this part of Europe as well.

Human or climate? Differentiating the anthropogenic and climatic drivers of lake storage changes on spatial perspective via remote sensing data

Lakes are an essential part of the terrestrial water system in which storage changes are controlled by water balance and human impact. Although there are some attempts to define storage changes on a global scale, examination of spatial relations is poorly quantified. In this study, therefore, lake storage changes have been investigated using remote-sensing-derived data around the globe. Hence, 372 artificial/natural lakes were obtained, covering between 1992 and 2019. Watersheds belong to river was extracted via HydroSHED data. Based on watershed, dominant climate types were determined via Köppen-Geiger classification. Similarly, the areal average CRU TS v.4.05 monthly gridded precipitation time series and human footprint data based on watersheds were obtained to understand the drivers of lake storage changes. The nonparametric Mann-Kendall and Sen's slope trend analyses were applied to the lake storage change and precipiation values in order to determine long-term increases and decreases. A bivariate map was constructed between storage changes trend vs precipitation trend and human footprint to reveal the drivers of lake storage changes in terms of spatial aspects. The trend analysis and bivariate map results show that North America, the East African Highlands, and the Tibet plateau are important increasing hotspots, where precipitation is a significant driver for storage oscillations, except for the Tibet plateau. Besides, the Brazilian Highlands, Pacific Mountain System, and Intermontane of conterminous USA are other decreasing hotspots in which human footprint and decreasing precipitation collectively affect these changes. Furthermore, results clearly show that anthropogenic influence is low in the northern and mountainous areas, and storage changes have a linear relationship with precipitation. In contrast, intense human climate interaction influences lake changes in plains areas and arid/temperate climates.

Climatic drivers of cork growth depend on site aridity

Cork is one of the main non-timber forest products in the world. Most of its production is concentrated in the Iberian Peninsula, a climate change hotspot. Climate warming may lead to increased aridification and reduce cork production in that region. However, we still lack assessments of climate-cork relationships across ample geographical and climatic gradients explicitly considering site aridity. We quantified cork growth by measuring cork ring width and related it to climate variables and a drought index using dendrochronology. Four cork oak (Quercus suber) forests located from north eastern Spain to south western Morocco (31.5-41.5° N) and subjected to different aridity levels were sampled. Warm conditions in spring to early summer, when cork is formed, reduced cork width, whereas high precipitation in winter and spring enhanced it. The response of cork to increased water availability in summer peaked (r = 0.89, p = 0.00002) in the most arid and continental site considering 14-month long droughts. A severe drought caused a disproportionate loss of cork production in this site, where for every five-fold decrease in the drought index, the cork-width index declined by a factor of thirteen. Therefore, site aridity determines the responses of cork growth to the soil water availability resulting from accumulated precipitation during winter and spring previous to cork growth and until summer. In general, this cumulative water balance, which is very dependent on temperature and evapotranspiration rate, is critical for cork production, especially in continental, dry sites. The precipitation during the hydrological year can be used as a proxy of cork production in similar sites. Assessments of climate-cork relationships in the western Mediterranean basin could be used as analogues to forecast the impacts of aridification on future cork production.

Transition from positive to negative indirect CO2 effects on the vegetation carbon uptake

Although elevated atmospheric CO2 concentration (eCO2) has substantial indirect effects on vegetation carbon uptake via associated climate change, their dynamics remain unclear. Here we investigate how the impacts of eCO2-driven climate change on growing-season gross primary production have changed globally during 1982-2014, using satellite observations and Earth system models, and evaluate their evolution until the year 2100. We show that the initial positive effect of eCO2-induced climate change on vegetation carbon uptake has declined recently, shifting to negative in the early 21st century. Such emerging pattern appears prominent in high latitudes and occurs in combination with a decrease of direct CO2 physiological effect, ultimately resulting in a sharp reduction of the current growth benefits induced by climate warming and CO2 fertilization. Such weakening of the indirect CO2 effect can be partially attributed to the widespread land drying, and it is expected to be further exacerbated under global warming.

Relationship between the variations in glacier features classified on a large scale with climate variables: a case study of Gangotri Glacier

Changes in glacier area, glacial lakes, debris cover, and geomorphological features such as debris fans have a significant impact on glacial dynamics. Therefore, precise and timely observation and tracking of glacier surface changes is a necessity. The availability of high spatial resolution remote sensing images has made it viable to analyse the glacier surface changes at a local level. However, with an increase in spatial resolution, the spectral variability increases, giving rise to additional challenges (such as false changes and misregistration) in the change detection process. These challenges can preferably be dealt with using an object-based change detection (OBCD) approach rather than the conventional pixel-based change detection approach. Therefore, this study has proposed an OBCD methodology using high-spatial-resolution remote sensing images to detect changes in glacier features. Variability in glacier features has been further analysed by associating it with important climate variables, that is, air temperature and precipitation. As a case study, the changes in Gangotri Glacier (Uttarakhand Himalayas in India) features have been studied using high-spatial-resolution WorldView-2 and Linear Imaging Self-Scanning System (LISS)-4 images for a 3-year period 2011-2014. The spectral correspondences between glacier surface and non-glacier surface have been handled by considering brightness temperature and slope as ancillary data to improvise their distinction. A change detection accuracy of ~ 84% has been obtained using the OBCD approach. Results further show that the variations in glacier features are in congruence with the climatic observations.

Mapping the global distribution of C4 vegetation using observations and optimality theory

Plants with the C4 photosynthesis pathway typically respond to climate change differently from more common C3-type plants, due to their distinct anatomical and biochemical characteristics. These different responses are expected to drive changes in global C4 and C3 vegetation distributions. However, current C4 vegetation distribution models may not predict this response as they do not capture multiple interacting factors and often lack observational constraints. Here, we used global observations of plant photosynthetic pathways, satellite remote sensing, and photosynthetic optimality theory to produce an observation-constrained global map of C4 vegetation. We find that global C4 vegetation coverage decreased from 17.7% to 17.1% of the land surface during 2001 to 2019. This was the net result of a reduction in C4 natural grass cover due to elevated CO2 favoring C3-type photosynthesis, and an increase in C4 crop cover, mainly from corn (maize) expansion. Using an emergent constraint approach, we estimated that C4 vegetation contributed 19.5% of global photosynthetic carbon assimilation, a value within the range of previous estimates (18-23%) but higher than the ensemble mean of dynamic global vegetation models (14 ± 13%; mean ± one standard deviation). Our study sheds insight on the critical and underappreciated role of C4 plants in the contemporary global carbon cycle.

Perceptions of environmental changes among a climate-vulnerable population from Bangladesh

Effective climate change adaptation requires a thorough understanding of whether and how affected populations perceive climatic and environmental changes. Existing research has been inconclusive regarding the consistency of these perceptions compared to objective meteorological indicators. Moreover, no systematic comparison has been done for the perception of discrete environmental events such as floods or erosion. This study relies on novel panel survey data of approximately 1700 households residing along the Jamuna River in Bangladesh as well as on unique individual-level, satellite-based erosion data. It compares respondents' perceptions of environmental events, namely riverbank erosion, and three climate change indicators, specifically long-term temperature change and changes in precipitation during wet and dry seasons, to objective measurements using satellite imagery and climatic time-series data (CRU TS). I find that long-term temperature change is perceived more accurately than long-term changes in precipitation. Given that educational attainment and climate change literacy among the study population are low, this indicates that global temperature increases are felt even by remote populations who have never heard the term climate change. Erosion is strongly overestimated, especially by those respondents who had been personally affected by it. Since human behavior is guided by perceptions rather than objective data, this has important policy implications, underlining the importance of considering people's perceptions if the goal is to assist them in adapting to environmental changes.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10584-024-03678-6.

Global spatially explicit yield gap time trends reveal regions at risk of future crop yield stagnation

Yield gaps, here defined as the difference between actual and attainable yields, provide a framework for assessing opportunities to increase agricultural productivity. Previous global assessments, centred on a single year, were unable to identify temporal variation. Here we provide a spatially and temporally comprehensive analysis of yield gaps for ten major crops from 1975 to 2010. Yield gaps have widened steadily over most areas for the eight annual crops and remained static for sugar cane and oil palm. We developed a three-category typology to differentiate regions of 'steady growth' in actual and attainable yields, 'stalled floor' where yield is stagnated and 'ceiling pressure' where yield gaps are closing. Over 60% of maize area is experiencing 'steady growth', in contrast to ∼12% for rice. Rice and wheat have 84% and 56% of area, respectively, experiencing 'ceiling pressure'. We show that 'ceiling pressure' correlates with subsequent yield stagnation, signalling risks for multiple countries currently realizing gains from yield growth.

Rising minimum temperatures contribute to 50 years of occupancy decline among cold-adapted Arctic and boreal butterflies in North America

Global climate change has been identified as a potential driver of observed insect declines, yet in many regions, there are critical data gaps that make it difficult to assess how communities are responding to climate change. Poleward regions are of particular interest because warming is most rapid while biodiversity data are most sparse. Building on recent advances in occupancy modeling of presence-only data, we reconstructed 50 years (1970-2019) of butterfly occupancy trends in response to rising minimum temperatures in one of the most under-sampled regions of North America. Among 90 modeled species, we found that cold-adapted species are far more often in decline compared with their warm-adapted, more southernly distributed counterparts. Furthermore, in a post hoc analysis using species' traits, we find that species' range-wide average annual temperature is the only consistent predictor of occupancy changes. Species with warmer ranges were most likely to be increasing in occupancy. This trend results in the majority of butterflies increasing in occupancy probability over the last 50 years. Our results provide the first look at macroscale butterfly biodiversity shifts in high-latitude North America. These results highlight the potential of leveraging the wealth of presence-only data, the most abundant source of biodiversity data, for inferring changes in species distributions.

Quantifying the drought sensitivity of grassland under different climate zones in Northwest China

Grassland is essential for maintaining the stability and functionality of terrestrial ecosystems. Although previous research has explored how grassland responds to drought, the drought sensitivity of grassland (DSG) across climate zones and aridity gradients remains uncertain. In this study, we conducted a comprehensive investigation spanning 1982 to 2015 in Northwest China. To assess the time-cumulative effect (TCE) and the time-lag effect (TLE) of drought on grassland, we employed Spearman rank correlation analysis, utilizing long-term datasets of the normalized difference vegetation index (NDVI) and the standardized precipitation evapotranspiration index (SPEI). This analysis allowed us to quantify the DSG in the region and further examine its variations across climate zones and aridity gradient. Our results revealed that 81.2 % and 99.7 % of the grassland in Northwest China was influenced by the TCE and TLE of drought, respectively, with 38.2 % and 60.9 % of these effects being statistically significant (p < 0.05). The mean accumulated and lagged timescales of drought on grassland were 7.89 and 9.41 months, respectively. Remarkably, the highest DSG was observed in the semi-arid zone (0.58), followed by the arid (0.54), sub-humid (0.51), and humid (0.44) zones. Furthermore, we identified significant nonlinear variation patterns of DSG along the aridity gradient, characterized by several discernible trend breaks. These findings contribute to our understanding of the impacts of drought on vegetation, particularly in ecologically fragile regions.

Forest understorey flowering phenology responses to experimental warming and illumination

Species are altering their phenology to track warming temperatures. In forests, understorey plants experience tree canopy shading resulting in light and temperature conditions, which strongly deviate from open habitats. Yet, little is known about understorey phenology responses to forest microclimates. We recorded flowering onset, peak, end and duration of 10 temperate forest understorey plant species in two mesocosm experiments to understand how phenology is affected by sub-canopy warming and how this response is modulated by illumination, which is related to canopy change. Furthermore, we investigated whether phenological sensitivities can be explained by species' characteristics, such as thermal niche. We found a mean advance of flowering onset of 7.1 d per 1°C warming, more than previously reported in studies not accounting for microclimatic buffering. Warm-adapted species exhibited greater advances. Temperature sensitivity did not differ between early- and later-flowering species. Experimental illumination did not significantly affect species' phenological temperature sensitivities, but slightly delayed flowering phenology independent from warming. Our study suggests that integrating sub-canopy temperature and light availability will help us better understand future understorey phenology responses. Climate warming together with intensifying canopy disturbances will continue to drive phenological shifts and potentially disrupt understorey communities, thereby affecting forest biodiversity and functioning.

Dynamics of rhizosphere microbial structure and function associated with the biennial bearing of moso bamboo

Moso bamboo (Phyllostachys edulis) is a valuable nontimber forestry product with a biennial cycle, producing abundant bamboo shoots within one year (on-year) and few shoots within the following year (off-year). Moso bamboo plants undergo clonal reproduction, resulting in similar genetic backgrounds. However, the number of moso bamboo shoots produced each year varies. Despite this variation, the impact of soil nutrients and the root microbiome on the biennial bearing of moso bamboo is poorly understood. We collected 139 soil samples and determined 14 major physicochemical properties of the rhizosphere, rhizoplane, and bulk soil in different seasons (i.e., the growing and deciduous seasons) and different years (i.e., on- and off-years). Based on 16S rRNA and metagenomic sequencing, major variations were found in the rhizospheric microbial composition during different seasons and years in the moso bamboo forest. Environmental driver analysis revealed that essential nutrients (i.e., SOC, TOC, TN, P, and NH4+) were the main drivers of the soil microbial community composition and were correlated with the on- and off-year cycles. Moreover, 19 MAGs were identified as important biomarkers that could distinguish on- and off-years. We found that both season and year influenced both the microbial community structure and functional pathways through the biosynthesis of nutrients that potentially interact with the moso bamboo growth rhythm, especially the on-year root-associated microbiome, which had a greater abundance of specific nutrients such as gibberellins and vitamin B6. This work provides a dynamic perspective of the differential responses of various on- and off-year microbial communities and enhances our understanding of bamboo soil microbiome biodiversity and stability.

Interannual Variations in Terrestrial Net Ecosystem Productivity and Climate Attribution in the Southern Hilly Region of China

The vegetation ecosystem in the southern hilly region of China (SHRC) plays a crucial role in the country's carbon reservoir. Clarifying the dynamics of net primary productivity (NPP) in this area and its response to climate factors in the context of climate change is important for national forest ecology, management, and carbon neutrality efforts. This study, based on remote sensing and meteorological data spanning the period 2001 to 2021, aims to unveil the spatiotemporal patterns of vegetation productivity and climate factors in the southern hilly region, explore interannual variation characteristics of vegetation productivity with altitude, and investigate the response characteristics of NPP to various climate factors. The results indicate that from 2001 to 2021, the annual average NPP in the southern hilly region had a significant increasing trend of 2.13 ± 0.78 g m-2 a-1. The trend of NPP varies significantly with altitude. Despite a general substantial upward trend in vegetation NPP, regions at lower elevations exhibit a faster rate of increase, suggesting a diminishing difference in the NPP of different elevation ranges. The overall rise in average temperature has positive implications for the southern hilly region, while the impact of precipitation on vegetation NPP demonstrates noticeable spatial heterogeneity. Regions in which vegetation NPP is significantly negatively correlated with precipitation are mainly concentrated in the southern areas of Guangdong, Fujian, and Jiangxi provinces. In contrast, other regions further away from the southeastern coast tend to exhibit a positive correlation. Over the past two decades, there has been an asymmetry in the diurnal temperature variation in the SHRC, with the nighttime warming rate being 1.8 times that of the daytime warming rate. The positive impact of daytime warming on NPP of vegetation is more pronounced than the impact of nighttime temperature changes. Understanding the spatiotemporal patterns of NPP in the SHRC and the characteristics of its response to climate factors contributes to enhancing our ability to protect and manage vegetation resources amidst the challenges of global climate change.

Modification of the RothC model to evaluate the inconsistent effect of conservation tillage on SOC stock and a suggestion of a national-scale assessment framework

Simulation of conservation tillage effect on soil organic carbon (SOC) stock on the national scale is essential for Tier 3 level greenhouse gas inventory in the agricultural sector. However, the conservation tillage effects varied depending on different soil conditions, potentially leading to inaccurate national assessments. This study aimed to propose a framework for estimating the national scale impact of conservation tillage on SOC. As even in the most commonly used SOC dynamic model, the Rothamsted Carbon Model (RothC), does not reflect the conservation tillage effect in an explicit way, we modified it by developing the tillage rate modifiers (TRMs). First, we investigated the conditions for the inconsistent conservation tillage effects using the decision tree analysis based on 210 field experiment data from the mid-latitude region. The results highlighted that soil sand content and the existing SOC stock were the main factors driving the inconsistencies. After we categorized into four distinctive conditions, the TRMs for each condition were parameterized using a genetic algorithm. The average TRMs were 0.88 in the soils with sand content >37.6 % and 1.58 in the soils with sand content ≤37.6 %, indicating that conservation tillage is more effective in coarse-textured soil, and there is a risk of decreasing SOC stock in the latter condition. Using the modified RothC model, a three-step national-scale simulation framework was suggested: compiling country-specific data, establishing baseline and conservation tillage scenarios, and modeling conservation tillage effects with uncertainty analysis. Our approach also defined the maximum conservation tillage area, factoring in local cropping systems and soil conditions. Our refined RothC model with TRMs provides a nuanced understanding of conservation tillage effects, emphasizing the role of soil characteristics. The proposed national-scale simulation framework offers a reliable tool for evaluating conservation tillage impact on SOC, ensuring more accurate greenhouse gas inventories in agriculture.

High-resolution climate projection dataset based on CMIP6 for Peru and Ecuador: BASD-CMIP6-PE

Here, we present BASD-CMIP6-PE, a high-resolution (1d, 10 km) climate dataset for Peru and Ecuador based on the bias-adjusted and statistically downscaled CMIP6 climate projections of 10 GCMs. This dataset includes both historical simulations (1850-2014) and future projections (2015-2100) for precipitation and minimum, mean, and maximum temperature under three Shared Socioeconomic Pathways (SSP1-2.6, SSP3-7.0, and SSP5-8.5). The BASD-CMIP6-PE climate data were generated using the trend-preserving Bias Adjustment and Statistical Downscaling (BASD) method. The BASD performance was evaluated using observational data and through hydrological modeling across Peruvian and Ecuadorian river basins in the historical period. Results demonstrated that BASD significantly reduced biases between CMIP6-GCM simulations and observational data, enhancing long-term statistical representations, including mean and extreme values, and seasonal patterns. Furthermore, the hydrological evaluation highlighted the appropriateness of adjusted GCM simulations for simulating streamflow, including mean, low, and high flows. These findings underscore the reliability of BASD-CMIP6-PE in assessing regional climate change impacts on agriculture, water resources, and hydrological extremes.

PigBiobank: a valuable resource for understanding genetic and biological mechanisms of diverse complex traits in pigs

To fully unlock the potential of pigs as both agricultural species for animal-based protein food and biomedical models for human biology and disease, a comprehensive understanding of molecular and cellular mechanisms underlying various complex phenotypes in pigs and how the findings can be translated to other species, especially humans, are urgently needed. Here, within the Farm animal Genotype-Tissue Expression (FarmGTEx) project, we build the PigBiobank (http://pigbiobank.farmgtex.org) to systematically investigate the relationships among genomic variants, regulatory elements, genes, molecular networks, tissues and complex traits in pigs. This first version of the PigBiobank curates 71 885 pigs with both genotypes and phenotypes from over 100 pig breeds worldwide, covering 264 distinct complex traits. The PigBiobank has the following functions: (i) imputed sequence-based genotype-phenotype associations via a standardized and uniform pipeline, (ii) molecular and cellular mechanisms underlying trait-associations via integrating multi-omics data, (iii) cross-species gene mapping of complex traits via transcriptome-wide association studies, and (iv) high-quality results display and visualization. The PigBiobank will be updated timely with the development of the FarmGTEx-PigGTEx project, serving as an open-access and easy-to-use resource for genetically and biologically dissecting complex traits in pigs and translating the findings to other species.

Increasing tropical cyclone intensity in the western North Pacific partly driven by warming Tibetan Plateau

The increase in intense tropical cyclone (TC) activity across the western North Pacific (WNP) has often been attributed to a warming ocean. However, it is essential to recognize that the tropical WNP region already boasts high temperatures, and a marginal increase in oceanic warmth due to global warming does not exert a significant impact on the potential for TCs to intensify. Here we report that the weakened vertical wind shear is the primary driver behind the escalating trend in TC intensity within the summer monsoon trough of the tropical WNP, while local ocean surface and subsurface thermodynamic factors play a minor role. Through observational diagnoses and numerical simulations, we establish that this weakening of the vertical wind shear is very likely due to the increase in temperature of the Tibetan Plateau. With further warming of the Tibetan Plateau under the Representative Concentration Pathway 4.5 scenario, the projected TCs will likely become stronger.

Simulating long-term wildfire impacts on boreal forest structure in Central Yakutia, Siberia, since the Last Glacial Maximum

Background

Wildfires are recognized as an important ecological component of larch-dominated boreal forests in eastern Siberia. However, long-term fire-vegetation dynamics in this unique environment are poorly understood. Recent paleoecological research suggests that intensifying fire regimes may induce millennial-scale shifts in forest structure and composition. This may, in turn, result in positive feedback on intensifying wildfires and permafrost degradation, apart from threatening human livelihoods. Most common fire-vegetation models do not explicitly include detailed individual-based tree population dynamics, but a focus on patterns of forest structure emerging from interactions among individual trees may provide a beneficial perspective on the impacts of changing fire regimes in eastern Siberia. To simulate these impacts on forest structure at millennial timescales, we apply the individual-based, spatially explicit vegetation model LAVESI-FIRE, expanded with a new fire module. Satellite-based fire observations along with fieldwork data were used to inform the implementation of wildfire occurrence and adjust model parameters.

Results

Simulations of annual forest development and wildfire activity at a study site in the Republic of Sakha (Yakutia) since the Last Glacial Maximum (c. 20,000 years BP) highlight the variable impacts of fire regimes on forest structure throughout time. Modeled annual fire probability and subsequent burned area in the Holocene compare well with a local reconstruction of charcoal influx in lake sediments. Wildfires can be followed by different forest regeneration pathways, depending on fire frequency and intensity and the pre-fire forest conditions. We find that medium-intensity wildfires at fire return intervals of 50 years or more benefit the dominance of fire-resisting Dahurian larch (Larix gmelinii (Rupr.) Rupr.), while stand-replacing fires tend to enable the establishment of evergreen conifers. Apart from post-fire mortality, wildfires modulate forest development mainly through competition effects and a reduction of the model's litter layer.

Conclusion

With its fine-scale population dynamics, LAVESI-FIRE can serve as a highly localized, spatially explicit tool to understand the long-term impacts of boreal wildfires on forest structure and to better constrain interpretations of paleoecological reconstructions of fire activity.

Supplementary Information

The online version contains supplementary material available at 10.1186/s42408-023-00238-8.

Projected soil carbon loss with warming in constrained Earth system models

The soil carbon-climate feedback is currently the least constrained component of global warming projections, and the major source of uncertainties stems from a poor understanding of soil carbon turnover processes. Here, we assemble data from long-term temperature-controlled soil incubation studies to show that the arctic and boreal region has the shortest intrinsic soil carbon turnover time while tropical forests have the longest one, and current Earth system models overestimate intrinsic turnover time by 30 percent across active, slow and passive carbon pools. Our constraint suggests that the global soils will switch from carbon sink to source, with a loss of 0.22-0.53 petagrams of carbon per year until the end of this century from strong mitigation to worst emission scenarios, suggesting that global soils will provide a strong positive carbon feedback on warming. Such a reversal of global soil carbon balance would lead to a reduction of 66% and 15% in the current estimated remaining carbon budget for limiting global warming well below 1.5 °C and 2 °C, respectively, rendering climate mitigation much more difficult.

Negative interaction effect of heat and drought stress at the warm end of species distribution

Geographic range limits of species are often a reflection of their ecological niche limits. In many organisms, important niche limits that coincide with distribution limits are warm and warm-dry conditions. We investigated the effects of heat and drought, as they can occur at the warm end of distribution. In a greenhouse experiment, we raised North American Arabidopsis lyrata from the centre of its distribution as well as from low- and high-latitude limits under average and extreme conditions. We assessed plant growth and development, as well as leaf and root functional traits, and tested for a decline in performance and selection acting on growth, leaf, and root traits. Drought and heat, when applied alone, lowered plant performance, while combined stress caused synergistically negative effects. Plants from high latitudes did not survive under combined stress, whereas plants originating from central and low latitudes had low to moderate survival, indicating divergent adaptation. Traits positively associated with survival under drought, with or without heat, were delayed and slowed growth, though plastic responses in these traits were generally antagonistic to the direction of selection. In line, higher tolerance of stress in southern populations did not involve aspects of growth but rather a higher root-to-shoot ratio and thinner leaves. In conclusion, combined heat and drought, as can occur at southern range edges and presumably more so under global change, seriously impede the long-term persistence of A. lyrata, even though they impose selection and populations may adapt, though under likely interference by considerable maladaptive plasticity.

Pathways and drivers of canopy accession across primary temperate forests of Europe

Canopy accession strategies reveal much about tree life histories and forest stand dynamics. However, the protracted nature of ascending to the canopy makes direct observation challenging. We use a reconstructive approach based on an extensive tree ring database to study the variability of canopy accession patterns of dominant tree species (Abies alba, Acer pseudoplatanus, Fagus sylvatica, Picea abies) in temperate mountain forests of Europe and elucidate how disturbance histories, climate, and topography affect canopy accession. All four species exhibited high variability of radial growth histories leading to canopy accession and indicated varying levels of shade tolerance. Individuals of all four species survived at least 100 years of initial suppression. Fir and particularly beech, however, survived longer periods of initial suppression, exhibited more release events, and reached the canopy later on average, with a larger share of trees accessing the canopy after initially suppressed growth. These results indicate the superior shade tolerance of beech and fir compared to spruce and maple. The two less shade-tolerant species conversely relied on faster growth rates, revealing their competitive advantage in non-suppressed conditions. Additionally, spruce from higher-elevation spruce-dominated forests survived shorter periods of initial shading and exhibited fewer releases, with a larger share of trees reaching the canopy after open canopy recruitment (i.e. in absence of suppression) and no subsequent releases compared to spruce growing in lower-elevation mixed forests. Finally, disturbance factors were identified as the primary driver of canopy accession, whereby disturbances accelerate canopy accession and consequently regulate competitive interactions. Intensifying disturbance regimes could thus promote shifts in species composition, particularly in favour of faster-growing, more light-demanding species.

1 km monthly precipitation and temperatures dataset for China from 1952 to 2019 based on new baseline climatology surfaces

Long-term climate data and high-quality baseline climatology surface with high resolution are essential to investigate climate change and its effect on hydrological processes and ecosystem functioning. However, large uncertainties remain in the climate products in China owing to lacking of high-density distribution network of weather stations. Here, the thin plate spline (TPS) algorithm was used to produce new baseline climatology surfaces (ChinaClim_baseline) using >2000 freely available weather stations. Then, climatologically aided interpolation (CAI) was employed to generate a 1 km monthly precipitation and temperatures dataset for China during 1952-2019 (ChinaClim_time-series) via superimposing ChinaClim_baseline and monthly anomaly surface. Our finding showed that ChinaClim_baseline performed exceptionally well in four climatic regions, with RMSEs for precipitation and temperature element estimation of 1.276-28.439 mm and 0.310-2.040 °C, respectively. The correlations among ChinaClim_baseline and WorldClim2 and CHELSA were high, but there were clearly spatial differences. For ChinaClim_time-series, precipitation and temperature elements had average RMSEs between 7.502- 52.307 mm, and 0.461-0.939 °C for all months, respectively. In comparison to Peng's climatic surface and CHELSAcruts, R2 increased by ~7 %, RMSE and MAE dropped by ~17 % for precipitation; R2 hardly increased, while RMSE and MAE decreased by ~50 % for temperature elements. Our findings indicated that ChinaClim_baseline improved the accuracy of time-series climatic elements estimation obviously, and the satellite-driven data can greatly improve the accuracy of time-series precipitation estimation, but not the accuracy of time-series temperature estimation. Overall, ChinaClim_baseline as an excellent baseline climatology surface can be used for obtaining high-quality and long-term climate datasets from past to future. Meantime, ChinaClim_time-series of 1 km spatial resolution is appropriate for investigating the spatial-temporal climate changes and their impacts on eco-environmental systems in China.

Climate warming enhances chemical weathering in permafrost-dominated eastern Siberia

Understanding the impacts of climate warming on hydrogeochemical processes, particularly in areas dominated by permafrost, is crucial. However, the natural background levels of chemical components in eastern Siberian rivers from permafrost-dominated regions and their responses to climate warming have not been adequately quantified. This study aims to address this knowledge gap by using a comprehensive river water chemistry database (n = 1264) spanning from 1940 to 2019. Our results reveal that the concentration of total dissolved solids (TDS), a key parameter of drinking water quality, in river basins predominantly free of permafrost (194.6 ± 256.4 mg/L) is approximately 2.3 times higher than in permafrost-dominated river basins (83.7 ± 35.8 mg/L) in eastern Siberia. These observations imply potential shifts in freshwater quality resulting from permafrost degradation. We further detect that carbonate weathering, which plays a fundamental role in the global carbon cycle, is a predominant process controlling hydrogeochemical cycles. (Ca2+ + Mg2+) concentrations as a proxy for carbonate weathering intensity are sensitive to climate warming, increasing at a rate of 0.10 mmol/(L·°C). This finding provides evidence that the current acceleration of carbonate weathering, driven by climate warming, is already influencing local water quality. Additionally, (Ca2+ + Mg2+) concentrations and TDS are highly interrelated with temperature-dependent variables (e.g., active layer thickness of permafrost and leaf area index) and basin erosion-controlling factors (e.g., precipitation, elevation and slope of basin). Under a warming climate, river chemical fluxes (e.g., export of TDS and major ions) increase notably, especially during the winter, indicating an increase in mineral-laden groundwater discharge to rivers due to permafrost degradation. Our results demonstrate that climate warming is accelerating hydrogeochemical processes in permafrost-dominated Arctic basins.

A deep learning model for reconstructing centenary water storage changes in the Yangtze River Basin

Since 2002, the Gravity Recovery and Climate Experiment (GRACE) and its Follow-On mission (GRACE-FO) have facilitated highly accurate observations of changes in total water storage anomalies (TWSA). However, limited observations of TWSA derived from GRACE in the Yangtze River Basin (YRB) have hindered our understanding of its long-term variability. In this paper, we present a deep learning model called RecNet to reconstruct the climate-driven TWSA in the YRB from 1923 to 2022. The RecNet model is trained on precipitation, temperature, and GRACE observations with a weighted mean square error (WMSE) loss function. The performance of the RecNet model is validated and compared against GRACE data, water budget estimates, hydrological models, drought indices, and existing reconstruction datasets. The results indicate that the RecNet model can successfully reconstruct historical water storage changes, surpassing the performance of previous studies. In addition, the reconstructed datasets are utilized to assess the frequency of extreme hydrological conditions and their teleconnections with major climate patterns, including the El Niño-Southern Oscillation, Indian Ocean Dipole, Pacific Decadal Oscillation, and North Atlantic Oscillation. Independent component analysis is employed to investigate individual climate patterns' unique or combined influence on TWSA. We show that the YRB exhibits a notable vulnerability to extreme events, characterized by a recurrent occurrence of diverse extreme dry/wet conditions throughout the past century. Wavelet coherence analysis reveals significant coherence between the climate patterns and TWSA across the entire basin. The reconstructed datasets provide valuable information for studying long-term climate variability and projecting future droughts and floods in the YRB, which can inform effective water resource management and climate change adaptation strategies.