Global Environmental Change Research Articles

Artificial river flow regulation triggered spatio-temporal changes in marine macrobenthos of the Yellow River Estuary

The Water Sediment Discharge Regulation (WSR) in the Yellow River transports a vast quantity of freshwater and materials to the Bohai Sea within 20 days, significantly altering the ambient environment of the estuary. To elucidate the ecological impacts of this typically artificial flood event, we investigated the benthic habitats and macrobenthic biodiversity within the Affected Core Area (ACA) influenced by this discharge. Our results show that: (1) The discharge created an area with extreme environmental conditions, extending from the southern estuary to Laizhou Bay. This led to a rapid transformation of the habitat, as evidenced by a significant increase in turbidity, ammonium, and silicate levels. Among these factors, nitrogen nutrients and pH were the dominant drivers of environmental filtration, shaping the macrobenthos community structure; (2) The changing habitat triggered spatial shifts in macrobenthos abundance based on the distance from the estuary. Compared to the northern estuary, species composition and C-diversity in the southern area decreased significantly. These changes collectively established a short-term biodiversity front in the estuary region; (3) Community stability declined, as evidenced by a 24.20% reduction in niche width for generalist species and a 90.91% shift in specialist species. Furthermore, the connectivity between species decreased, and the average path length of the network increased, resulting in a more fragmented community structure. Notably, some ecological patches dominated by generalist species (e.g. Alpheus distinguendus) emerged. These findings enhance our understanding of marine ecological responses to artificial flood events within the context of global environmental changes.

Distributions of non‐native and native plants are not determined by the same environmental factors

Abstract Global environmental change will cause shifts in species communities, with non‐native species likely replacing native ones at an unprecedented rate. This will have consequences for biodiversity and ecosystem services, in addition to the ecological and economic damage caused by those non‐native species that are invasive. Understanding general patterns driving distributions of native and non‐native species is therefore vital, but no study has compared yet whether environmental variables that correlate with a species' presence differ between the two groups other than at local scale and often with very limited sample size. In this study, we focus on 141 native and non‐native congeneric plant species pairs at the scale of Switzerland. In the framework of correlative species distribution models, we used newly developed methods for efficient automated selection of a parsimonious number of predictor environmental variables to determine which ones, out of a large candidate set in eight classes, have the strongest explanatory power for both species groups. Our results indicated that variables influence the two groups in significantly different ways. Climate was by far the strongest determinant of both native and non‐native species distributions, although it had significantly more explanatory power in native species models. Models for non‐native species were significantly more influenced by anthropogenic factors, land use variables and forest cover. The presence of non‐native species was also associated with habitats with a significantly lower mean naturality value than native species. These findings provide novel empirical evidence for the different environmental factors driving native and non‐native plants' distributions and guidance for non‐native species management. Practical implications: species distribution models are an increasingly frequently advised tool for conservation management and our results provide guidelines on which covariates should be specifically considered to assess the habitat suitability of non‐native versus native species. The distributions of the former group of species are particularly important to research, as, in time, they may turn invasive. In addition, areas close to infrastructure should be scanned regularly for incipient colonizations by non‐native species, especially in as yet uninvaded areas, such as high mountains.

Animal personality in multiple stressor environments: the evolutionary ecology of among-individual differences in responses to stressor suites.

Animal personality differences may have evolved as alternative strategies for negotiating multiple stressor landscapes. Indeed, ecologists are increasingly recognizing that interactions among multiple stressors can transform selective landscapes and behavioural and physiological responses to stress regimes. Yet, evaluating this hypothesis poses challenges, as most studies involving relationships between personality variation and the environment consider single stressors. Here, we review the literature to explore the theory and evidence that multiple stressor environments may mediate personality variation. We consider effects on evolution of personality variation, as influenced by life-history, energetic and behavioural trade-offs, and effects on phenotypic expression of personality traits. We then explore how personality variation may modulate behavioural and physiological responses to multiple stressors, and how differential responses may be affected by personality-dependent movement ecology and cognitive strategies. Among-individual differences in responses to multiple stressors are critical to elucidate, as multi-stress interactions may transform animal behavioural and physiological responses relative to those predicted under single stressor scenarios, and because among-individual variation comprises the basis for evolutionary shifts in stress responsiveness and population resiliency to global environmental change.

Lower grass stomatal conductance under elevated CO2 can decrease transpiration and evapotranspiration rates despite carbon fertilization.

Anthropogenic increase in carbon dioxide (CO2) affects plant physiology. Plant responses to elevated CO2 typically include: (1) enhanced photosynthesis and increased primary productivity due to carbon fertilization and (2) suppression of leaf transpiration due to CO2-driven decrease in stomatal conductance. The combined effect of these responses on the total plant transpiration and on evapotranspiration (ET) has a wide range of implications on local, regional, and global hydrological cycles, and thus needs to be better understood. Here, we investigated the net effect of CO2-driven perennial ryegrass (Lolium perenne) physiological responses on transpiration and evapotranspiration by integrating physiological and hydrological (water budget) methods, under a controlled environment. Measurements of the net photosynthetic rate, stomatal conductance, transpiration rate, leaf mass per area, aboveground biomass, and water balance components were recorded. Measured variables under elevated CO2 were compared with those of ambient CO2. As expected, our results show that elevated CO2 significantly decreases whole-plant transpiration rates (38% lower in the final week) which is a result of lower stomatal conductance (57% lower in the final week) despite a slight increase in aboveground biomass. Additionally, there was an overall decline in evapotranspiration (ET) under elevated CO2, indicating the impact of CO2-mediated suppression of transpiration on the overall water balance. Although studies with larger sample sizes are needed for more robust conclusions, our findings have significant implications for global environmental change. Reductions in ET from ryegrass-dominated grasslands and pastures could increase soil moisture and groundwater recharge, potentially leading to increased surface runoff and flooding.

Enhancing long-term vegetation monitoring in Australia: a new approach for harmonising the Advanced Very High Resolution Radiometer normalised-difference vegetation (NVDI) with MODIS NDVI

Abstract. Long-term, reliable datasets of satellite-based vegetation condition are essential for understanding terrestrial ecosystem responses to global environmental change, particularly in Australia, which is characterised by diverse ecosystems and strong interannual climate variability. We comprehensively evaluate several existing global Advanced Very High Resolution Radiometer (AVHRR) normalised-difference vegetation index (NDVI) products for their suitability for long-term vegetation monitoring in Australia. Comparisons with the MODIS NDVI highlight significant deficiencies, particularly over densely vegetated regions. Moreover, all the assessed products failed to adequately reproduce the interannual variability in the pre-MODIS era as indicated by Landsat NDVI anomalies. To address these limitations, we propose a new approach to calibrating and harmonising NOAA's Climate Data Record of AVHRR NDVI to the MODIS MCD43A4 NDVI for Australia using a gradient-boosting decision tree ensemble method. Two versions of the datasets are developed, one incorporating climate data in the predictors (“AusENDVI-clim”: Australian Empirical NDVI-climate) and another that is independent of climate data (“AusENDVI-noclim”). These datasets, spanning 1982–2013 at a spatial resolution of 0.05° and with a monthly time step, exhibit strong correlations (r2=0.89–0.94) and low mean errors compared with MODIS MCD43A4 NDVI (mean absolute error (MAE) = 0.014–0.028, RMSE = 0.021–0.046), accurately reproducing seasonal cycles over densely vegetated regions. Furthermore, they closely replicate the interannual variability in vegetation condition in the pre-MODIS era. A reliable method for gap-filling the AusENDVI record is also developed that leverages climate, atmospheric CO2 concentration, and woody-cover fraction predictors. The resulting synthetic NDVI dataset shows excellent agreement with the MODIS MCD43A4 NDVI and the recalibrated AVHRR NDVI time series (r2=0.82–0.95, MAE = 0.016–0.029, RMSE = 0.039–0.041). Finally, we provide a complete 41-year dataset where the gap-filled AusENDVI-clim from January 1982 to February 2000 is joined with the MODIS MCD43A4 NDVI from March 2000 to December 2022. Analysing 40-year per-pixel trends in Australia's annual maximum NDVI revealed increasing values, and shifts in the timing, of the annual peak NDVI across most of the continent, underscoring the dataset's potential to address crucial questions regarding the changing vegetation phenology and its drivers. The AusENDVI dataset can be used for studying Australia's changing vegetation dynamics and downstream impacts on the terrestrial carbon and water cycles, and it provides a reliable foundation for further research into the drivers of vegetation change. AusENDVI is open access and available at https://doi.org/10.5281/zenodo.10802703 (Burton et al., 2024).

Transferability of ecological forecasting models to novel biotic conditions in a long-term experimental study.

Ecological forecasting models play an increasingly important role for managing natural resources and assessing our fundamental knowledge of processes driving ecological dynamics. As global environmental change pushes ecosystems beyond their historical conditions, the utility of these models may depend on their transferability to novel conditions. Because species interactions can alter resource use, timing of reproduction, and other aspects of a species' realized niche, changes in biotic conditions, which can arise from community reorganization events in response to environmental change, have the potential to impact model transferability. Using a long-term experiment on desert rodents, we assessed model transferability under novel biotic conditions to better understand the limitations of ecological forecasting. We show that ecological forecasts can be less accurate when the models generating them are transferred to novel biotic conditions and that the extent of model transferability can depend on the species being forecast. We also demonstrate the importance of incorporating uncertainty into forecast evaluation with transferred models generating less accurate and more uncertain forecasts. These results suggest that how a species perceives its competitive landscape can influence model transferability and that when uncertainties are properly accounted for, transferred models may still be appropriate for decision making. Assessing the extent of the transferability of forecasting models is a crucial step to increase our understanding of the limitations of ecological forecasts.

Unleashing the Potential of Artificial Intelligence (AI) Tools in Phytogeographical studies

Phytogeography, the study of the geographic distribution of plants, is important for understanding ecosystem dynamics, biodiversity, and ecological processes. Over the past few years, advances in technology, especially artificial intelligence (AI), have revolutionized various scientific fields, including ecology and environmental science. In recent years, AI techniques have been increasingly applied in phytogeography, providing new opportunities to increase our understanding of plant distribution patterns and improve conservation efforts. The study of the role of artificial intelligence in phytogeography focuses on how AI techniques such as machine learning, remote sensing, and spatial analysis are being used to analyse large-scale plant distribution data. By leveraging AI, researchers can gain valuable insights from vast and complex datasets, identify patterns and predict future changes in plant distributions with greater accuracy. Furthermore, AI-driven approaches have the potential to address important challenges in phytogeography, such as species distribution modelling, habitat mapping, and biodiversity conservation. By integrating AI with traditional ecological methods, more effective strategies can be developed to manage and conserve plant species and their habitats. AI-driven phytogeography research, provides an overview of recent progress, discusses potential applications of AI techniques in ecological studies, and the opportunities and challenges associated with the use of AI in understanding and conserving plant biodiversity. Ultimately, the integration of AI with phytogeography has the potential to revolutionize our understanding of plant distributions and inform more sustainable conservation practices in the face of global environmental change.

Photoinhibition and recovery of primary photosynthesis in Antarctic and subantarctic lichens. Analysis of interspecific differences

This study meticulously investigates the dynamics of photoinhibition and the mechanisms of primary photosynthetic activity recovery in lichens found in Antarctica and the sub-Antarctic regions. Advanced methodologies were utilised, such as Kautsky's kinetic analysis and the OJIP test. The study carefully details the response of various lichen species to intense light stress, outlining both immediate effects and subsequent recovery processes. Our findings reveal that these lichens employ a range of adaptive strategies, specific to each species, to mitigate the effects of photoinhibition, thereby emphasizing their remarkable resilience and ecological importance in harsh environments. Notably, the investigation reveals the sophisticated interplay between inherent photoprotective mechanisms and the ecological adaptations that enable these lichens to thrive under such harsh conditions. The study not only advances our knowledge of plant physiology under stress but also enriches our insights into the survival strategies of terrestrial organisms facing global environmental changes. Three types of photoinhibitory treatments differing in their duration and strength were applied to 7 lichen species from Antarctica and South America (Isla Navarino). The lichens responded with a decrease in photosynthetic processes in photosystem II (FV/FM and ΦPSII declined), although they showed almost complete recovery in the following 5 h. This was attributed to the activation of photoprotective mechanisms, non-photochemical quenching (NPQ) in particular, during photoinhibitory treatments. Chlorophyll fluorescence parameters derived from slow Kautsky kinetics were correlated with those derived from the OJIP curve. Our study presents data that supports the conclusion of significant photoresistance of the studied lichen species in the hydrated state to photoinhibition induced by high doses of photosynthetically active radiation (PAR).

Mathematical model of oxygen minimum zones in the vertical distribution of oxygen in the ocean.

Processes determining the amount and spatial distribution of dissolved oxygen in the ocean have been a focus of intense research over the last two decades. Anomalies known as Oxygen Minimum Zones (OMZs) have been attracting growing attention, in particular because their growth is believed to be a result of the global environmental change. Comprehensive understanding of factors contributing to and/or controlling the emergence and evolution of OMZs is still lacking though. OMZs are usually thought to result from an interplay between the oxygen transport through the water column from the ocean surface and variable oxygen solubility at different water temperature. In this paper, we suggest a different, novel mechanism of the OMZ formation relating it to the oxygen production in phytoplankton photosynthesis in a stratified ocean. We consider a simple, conceptual model of the coupled phytoplankton-oxygen dynamics and show that the model predictions are in qualitative agreement with some relevant field observations.

The multiscale response of global cropland cropping intensity to urban expansion

CONTEXTUrban expansion(UE) and multiple cropping(MC) are key factors in anthropogenic impacts on global environmental change. However, the multi-scale response patterns of UE and MC have not yet been revealed, and how urbanization affects cropland intensification is still not deeply explored. OBJECTIVEThis study examines the spatial and temporal trends in global UE and MC and analyses the multi-scale response patterns of both. METHODSThe GEE (Google Earth Engine) platform was used to count global cropland cropping intensity (CCI) and impervious surface rasters on an image-by-image basis, while GIS was employed for spatial analyses, and the generalized additive model (GAM) was applied to inscribe variable response trajectories. RESULTS AND CONCLUSIONThe global multiple cropping index(MCI) increased significantly (by 4.1 %) over the period 2001–2019, with growth in double- and triple-cropped cropland dominating this change. Double cropping, as a widespread global farming strategy, has led to a shift towards the intensification of agriculture, with countries in the northern hemisphere contributing more. Global UE significantly expands to twice the baseline level at an average annual growth rate of 2. 42 × 104 km2 over the period 2001–2019, with the expansion of world-class urban agglomerations in China, the United States and Europe dominates this trend. The spatial clustering of MC and UE has continued to intensify, with high-intensity cropping strategies progressively clustering towards areas of significant UE, and this tendency has a clear decreasing urban-rural gradient effect. The global CCI grows significantly and non-linearly with UE, but an important inflection point in the growth trajectory has occurred under the influence of threshold effects. Developed countries tend to be more flexible in their cropping intensity strategies as they move forward with urbanization. There is a clear increasing effect of the urban-rural gradient in the degree of non-linearity in the response of urbanization and cropping intensity. SIGNIFICANCEThe results of the study contribute to the understanding of the complex spatial and temporal coupling mechanisms between UE and MC, and provide useful insights for the development of trade-offs between urbanization and maturity strategies.

The impact of population change on social-ecological systems’ vulnerability: A case of the Qinling-Daba Mountains of Southern Shaanxi in China

Human activities have exacerbated the contradiction between ecosystem functionality and social-economic growth. Understanding and managing social-ecological systems' changes under the dual drivers of intensive human activities and rapid global environmental changes are crucial for achieving coordinated and sustainable development. While the vulnerability framework provides a new perspective for human-environment relationship studies, limited research has focused on the impact mechanisms of population change on social-ecological systems' vulnerability (SESV). Therefore, this study selects the Qinling-Daba Mountains of Southern Shaanxi in China (QMSSC), one of the areas with the most prominent human-environment contradictions in China, as a case study. Using the Vulnerability Scoping Diagram (VSD) assessment framework and the Spatially Explicit Resilience-Vulnerability (SERV) model, we divide SESV into three dimensions: exposure, sensitivity, and adaptive capacity. We select indicators, calculate weights, and quantitatively assess the spatiotemporal distribution characteristics, evolution trends, and spatial correlation features of SESV in the QMSSC from 2000 to 2020. Furthermore, we reveal the impact mechanisms of population change on SESV. The results indicate that the impact of population outflow on SESV depends on the spatiotemporal distribution of SESV. Gender ratio's impact on SESV is closely linked to land use and economic development. The proportion of elderly population affects SESV through adaptive capacity. In other words, population change often indirectly drives SESV by affecting different dimensions or elements of SESV. This necessitates policymakers to deploy population strategies across dimensions and elements. This study aims to provide reference for population development policies and spatial governance directions in globally underdeveloped mountainous regions through a typical case analysis of China's mountainous areas.

Impact of Land Use/Cover Change on Soil Erosion and Future Simulations in Hainan Island, China

Soil erosion (SE) is a critical threat to the sustainable development of ecosystem stability, agricultural productivity, and human society in the context of global environmental and climate change. Particularly in tropical island regions, due to the expansion of human activities and land use/cover changes (LUCCs), the risk of SE has been exacerbated. Combining the RUSLE with machine learning methods, SE spatial patterns, their driving forces and the mechanisms of how LUCCs affect SE, were illustrated. Additionally, the potential impacts of future LUCCs on SE were simulated by using the PLUS model. The main results are as follows: (1) Due to LUCCs, the average soil erosion modulus (SEM) decreased significantly from 108.09 t/(km2·a) in 2000 to 106.75 t/(km2·a) in 2020, a reduction of 1.34 t/(km2·a), mainly due to the transformation of cropland to forest and urban land. (2) The dominant factor affecting the spatial pattern of SE is the LS factor (with relative contributions of 43.9% and 45.17%), followed by land use/cover (LUC) (the relative contribution is 28.46% and 34.89%) in 2000 and 2020, respectively. (3) Three kinds of future scenarios simulation results indicate that the average SEM will decrease by 2.40 t/(km2·a) under the natural development scenario and by 1.86 t/(km2·a) under the ecological protection scenario by 2060. However, under the cropland protection scenario, there is a slight increase in SEM, with an increase of 0.08 t/(km2·a). Sloping cropland erosion control remains a primary issue for Hainan Island in the future.

Species interactions drive continuous assembly of freshwater communities in stochastic environments

Understanding the factors driving the maintenance of long-term biodiversity in changing environments is essential for improving restoration and sustainability strategies in the face of global environmental change. Biodiversity is shaped by both niche and stochastic processes, however the strength of deterministic processes in unpredictable environmental regimes is highly debated. Since communities continuously change over time and space—species persist, disappear or (re)appear—understanding the drivers of species gains and losses from communities should inform us about whether niche or stochastic processes dominate community dynamics. Applying a nonparametric causal discovery approach to a 30-year time series containing annual abundances of benthic invertebrates across 66 locations in New Zealand rivers, we found a strong negative causal relationship between species gains and losses directly driven by predation indicating that niche processes dominate community dynamics. Despite the unpredictable nature of these system, environmental noise was only indirectly related to species gains and losses through altering life history trait distribution. Using a stochastic birth-death framework, we demonstrate that the negative relationship between species gains and losses can not emerge without strong niche processes. Our results showed that even in systems that are dominated by unpredictable environmental variability, species interactions drive continuous community assembly.

Effects of precipitation changes and warming on vegetation–soil–microbial relationships in desert grasslands

Vegetation–soil–microbial relationships significantly affect global climatic and environmental changes. Fluctuations in temperature and precipitation caused by climate change are the driving forces of dynamic changes in vegetation–soil–microbial relationships. Our study was conducted in the desert grassland of Ningxia, China, and involved five levels of annual precipitation (33 % (R33), 66 % (R66), 100 % (RCK), 133 % (R133), and 166 % (R166)) and two temperature levels (within and outside the open-top chamber). Our objective was to determine how vegetation–soil–microbial relationships in desert grasslands respond to changes in precipitation and warming. Our results showed that precipitation was positively correlated with above-ground living and root biomass, and soil organic carbon. R166 had the strongest effect on the correlation between vegetation, soil, and microbes, whereas R33 had the weakest effect. Temperature was positively correlated with soil microbial α-diversity, and the effect of warming on the correlation among vegetation, soil, and microbes was less significant than under natural temperature. The combination of precipitation and warming was positively correlated with the above-ground living biomass and soil respiration. The correlation effects of the combination of precipitation and warming on vegetation, soil, and microbes were more substantial than those of precipitation and warming. Our findings provide a theoretical basis for the formulation of reasonable response strategies for desert steppe ecosystems.

Environmental regulation and energy consumption transition of rural residents: A case of China

With rising global temperatures and complex environmental changes, it has become particularly important to reduce carbon emissions and promote clean energy transition, while energy transition in rural areas is difficult. Environmental regulation, as an important policy enforcement tool, plays an important role in protecting the environment, but its impact and mechanism on the energy consumption transition of rural residents are not clear. In this regard, this paper analyses the impact of environmental regulations on the energy consumption transition of rural residents in 30 provinces in China, using provincial panel data from 2005 to 2021. The main conclusions are as follows: (1) Environmental regulation can significantly promote the transition of rural residents' clean energy consumption, and the mechanism of action shows that environmental regulation can influence the transition of rural residents' energy consumption through the spread of environmental knowledge, technological progress, and the enhancement of residents' health awareness. (2) Heterogeneity analyses showed that the results of the impact of environmental regulations on the rural residents' energy consumption transition differ by geographic region, level of energy consumption, and temperature. This paper hopes to provide experiences and insights for effectively promoting energy consumption transition and upgrading and alleviating energy poverty in rural areas of China.

Exploring sustainable food system transformation options in China: An integrated environmental-economic modelling approach based on the applied general equilibrium framework

Our food system drives global environmental change, and differences in environmental concerns of consumers may cause negative environmental ‘spillover effects’ in less concerned countries. While food system transformation is increasingly recognised as crucial for mitigating such negative environmental spillovers, possible unintended negative environmental consequences in other regions and/or economic sectors have received less attention. Using an integrated environmental-economic modelling framework and scenario analyses, we explored options for more sustainable food systems and to mitigate the negative environmental spillovers from trading partners to China. We found that doubling novel soy-based food (soy-based meat) consumption while reducing pork consumption in China decreased Chinese economy-wide emissions of greenhouse gases (GHGs) by 1 % and acidification pollutants by 3 %. However, it increased Chinese economy-wide emissions of eutrophication pollutants by 2 %, driven by the increased production of soy-based food and other food with relatively high emission intensities of eutrophication pollutants. Combining a dietary shift with the adoption of cleaner cereals production technology for half of the current resources used for cereals production decreased Chinese economy-wide emissions of GHGs by 1 %, acidification pollutants by 7 %, and eutrophication pollutants by 3 %, but required capital reallocation from other sectors. Implementing a unilateral environmental policy in China (i.e., implementing incentive-based emission permits to reduce emissions of all pollutants by 3 % annually) increased economy-wide emissions of GHGs in trading partners by 2 %. This ‘carbon leakage’ emerges due to the shift of production of products with relatively high emission intensities (i.e., nitrogen fertiliser and livestock) from China to its trading partners through international trade. We demonstrate that indirect environmental impacts are crucial to consider when analysing the economy-wide consequences of food system transformations, as these indirect impacts may inadvertently affect other regions and/or economic sectors that were not initially targeted. Our study offers policymakers insights into designing effective policies for more sustainable food systems and sheds light on trade-offs among competing environmental and economic goals.

Population asynchrony within and between trophic levels have contrasting effects on consumer community stability in asubtropical lake.

Clarifying the effects of biodiversity on ecosystem stability in the context of global environmental change is crucial for maintaining ecosystem functions and services. Asynchronous changes between trophic levels over time (i.e. trophic community asynchrony) are expected to increase trophic mismatch and alter trophic interactions, which may consequently alter ecosystem stability. However, previous studies have often highlighted the stabilising mechanism of population asynchrony within a single trophic level, while rarely examining the mechanism of trophic community asynchrony between consumers and their food resources. In this study, we analysed the effects of population asynchrony within and between trophic levels on community stability under the disturbances of climate warming, fishery decline and de-eutrophication, based on an 18-year monthly monitoring dataset of 137 phytoplankton and 91 zooplankton in a subtropical lake. Our results showed that species diversity promoted community stability mainly by increasing population asynchrony both for phytoplankton and zooplankton. Trophic community asynchrony had a significant negative effect on zooplankton community stability rather than that of phytoplankton, which supports the match-mismatch hypothesis that trophic mismatch has negative effects on consumers. Furthermore, the results of the structural equation models showed that warming and top-down effects may simultaneously alter community stability through population dynamics processes within and between trophic levels, whereas nutrients act on community stability mainly through the processes within trophic levels. Moreover, we found that rising water temperature decreased trophic community asynchrony, which may challenge the prevailing idea that climate warming increases the trophic mismatch between primary producers and consumers. Overall, our study provides the first evidence that population and trophic community asynchrony have contrasting effects on consumer community stability, which offers a valuable insight for addressing global environmental change.

Global patterns of plant functional traits and their relationships to climate

Plant functional traits (FTs) determine growth, reproduction and survival strategies of plants adapted to their growth environment. Exploring global geographic patterns of FTs, their covariation and their relationships to climate are necessary steps towards better-founded predictions of how global environmental change will affect ecosystem composition. We compile an extensive global dataset for 16 FTs and characterise trait-trait and trait-climate relationships separately within non-woody, woody deciduous and woody evergreen plant groups, using multivariate analysis and generalised additive models (GAMs). Among the six major FTs considered, two dominant trait dimensions—representing plant size and the leaf economics spectrum (LES) respectively—are identified within all three groups. Size traits (plant height, diaspore mass) however are generally higher in warmer climates, while LES traits (leaf mass and nitrogen per area) are higher in drier climates. Larger leaves are associated principally with warmer winters in woody evergreens, but with wetter climates in non-woody plants. GAM-simulated global patterns for all 16 FTs explain up to three-quarters of global trait variation. Global maps obtained by upscaling GAMs are broadly in agreement with iNaturalist citizen-science FT data. This analysis contributes to the foundations for global trait-based ecosystem modelling by demonstrating universal relationships between FTs and climate.

Spatial pattern of forest aboveground biomass and its environmental influencing factors in Qinling-Daba Mountains, central China

Accurate estimation of forest aboveground biomass (AGB) is crucial for understanding and managing forest ecosystems in the context of global environmental change, and also provides a scientific basis for national and regional ecological planning and carbon emission reduction policies. In order to investigate the regional pattern of forest AGB and its influencing factors in central China, a total of 469 sample plots were measured along four sample transects and on six mountains in field survey. The results showed that: 1) Two longitudinal distribution patterns of forest AGB were found, and one was that the AGB in the Qinling Mountains and the Daba Moutains gradually decreased from east to west, and the other was that the AGB in the areas between the two mountains gradually increased from east to west. The latitudinal distribution pattern of the forest AGB showed an "increasing–decreasing-increasing–decreasing" trend from south to north. The altitudinal distribution pattern showed a "first increasing-then decreasing" pattern with increasing altitude. 2) The influence of each factor on the spatial pattern of forest AGB was manifested as temperature > precipitation > HAI > topography, indicating that the spatial pattern of forest AGB in central China was the result of the interaction of climate, human activities and natural factors.

Drought reduces nitrogen supply and N2O emission in coastal bays

Severe droughts are increasingly prevalent under global climate change, disrupting watershed hydrology and coastal nitrogen cycling. However, the specific effects of drought on nitrogen transport from land to sea and subsequent nitrogen dynamics remain inadequately understood. In this study, we evaluated the consequences of the 2020–2022 drought on nitrogen supply and N2O emissions in Xiamen Bay, Southeast China. The results showed that drought significantly reduced annual NH4N, NO2N, and NO3N concentrations in Xiamen Bay by 49.4 %, 32.1 %, and 40.3 %, respectively, compared with the pre-drought year of 2019. The decline in NH4N concentration was mainly attributed to reduced surface runoff across all seasons. NO3N and NO2N concentrations declined only during spring and summer, primarily due to increased potential evapotranspiration (PET) hindering nitrogen supply via groundwater and concurrently enhancing land denitrification. Annual N2O emission from Xiamen Bay decreased by 40.0∼72.7 % during the drought, highly correlated with the decline in the concentrations of NO3N, DIN, and DTN (p < 0.001). Comparative analysis revealed that NO3N concentration exhibited consistent negative linear regressions with PET and declined as evaporative demand drought conditions worsened across Xiamen Bay, Sansha Bay, and Chesapeake Bay throughout 2010–2022. NH4N concentration showed a positive regression with river discharge in Xiamen Bay, but negative regressions in the other two bays. Our results indicates that drought reduces N2O emission primarily driven by nitrate substrate reduction in the bay. This study provides new insights for predicting coastal nitrogen dynamics and greenhouse gas emissions under global environmental change.