The three major axes of terrestrial ecosystem function

Metagenomic approaches and opportunities in arid soil research

Arid soils present unique challenges and opportunities for studying microbial diversity and bioactive potential due to the extreme environmental conditions they bear. This review article investigates soil metagenomics as an emerging tool to explore complex microbial dynamics and unexplored bioactive potential in harsh environments. Utilizing advanced metagenomic techniques, diverse microbial populations that grow under extreme conditions such as high temperatures, salinity, high pH levels, and exposure to metals and radiation can be studied. The use of extremophiles to discover novel natural products and biocatalysts emphasizes the role of functional metagenomics in identifying enzymes and secondary metabolites for industrial and pharmaceutical purposes. Metagenomic sequencing uncovers a complex network of microbial diversity, offering significant potential for discovering new bioactive compounds. Functional metagenomics, connecting taxonomic diversity to genetic capabilities, provides a pathway to identify microbes' mechanisms to synthesize valuable secondary metabolites and other bioactive substances. Contrary to the common perception of desert soil as barren land, the metagenomic analysis reveals a rich diversity of life forms adept at extreme survival. It provides valuable findings into their resilience and potential applications in biotechnology. Moreover, the challenges associated with metagenomics in arid soils, such as low microbial biomass, high DNA degradation rates, and DNA extraction inhibitors and strategies to overcome these issues, outline the latest advancements in extraction methods, high-throughput sequencing, and bioinformatics. The importance of metagenomics for investigating diverse environments opens the way for future research to develop sustainable solutions in agriculture, industry, and medicine. Extensive studies are necessary to utilize the full potential of these powerful microbial communities. This research will significantly improve our understanding of microbial ecology and biotechnology in arid environments.

Growing-Season Precipitation Is a Key Driver of Plant Leaf Area to Sapwood Area Ratio at the Global Scale

Leaf area to sapwood area ratio (AL/AS) influences carbon sequestration, community composition, and ecosystem functioning in terrestrial vegetation and is closely related to leaf economics and hydraulics. However, critical predictors of AL/AS are not well understood. We compiled an AL/AS data set with 1612 species-site combinations (1137 species from 285 sites worldwide) from our field experiments and published literature. We found the global mean AL/AS to be 0.63 m2 cm-2, with its variation largely driven by growing-season precipitation (Pgs), which accounted for 18% of the variation in AL/AS. Species in tropical rainforests exhibited the highest AL/AS (0.82 m2 cm-2), whereas desert species showed the lowest AL/AS (0.16 m2 cm-2). Soil factors such as soil nitrogen and soil organic carbon exhibited positive effects on AL/AS, whereas soil pH was negatively correlated with AL/AS. Tree density accounted for 7% of the variation in AL/AS. All biotic and abiotic predictors collectively explained up to 45% of the variation in AL/AS. Additionally, AL/AS was positively correlated to the net primary productivity (NPP) of the ecosystem. Our study provides insights into the driving factors of AL/AS at the global scale and highlights the importance of AL/AS in ecosystem productivity. Given that Pgs is the most critical driver of AL/AS, alterations in global precipitation belts, particularly seasonal precipitation, may induce changes in plant leaf area on the branches.

The Ecosystem as Super-Organ/ism, Revisited: Scaling Hydraulics to Forests under Climate Change

Classic debates in community ecology focused on the complexities of considering an ecosystem as a super-organ or organism. New consideration of such perspectives could clarify mechanisms underlying the dynamics of forest carbon dioxide (CO2) uptake and water vapor loss, important for predicting and managing the future of Earth's ecosystems and climate system. Here, we provide a rubric for considering ecosystem traits as aggregated, systemic, or emergent, i.e., representing the ecosystem as an aggregate of its individuals or as a metaphorical or literal super-organ or organism. We review recent approaches to scaling-up plant water relations (hydraulics) concepts developed for organs and organisms to enable and interpret measurements at ecosystem-level. We focus on three community-scale versions of water relations traits that have potential to provide mechanistic insight into climate change responses of forest CO2 and H2O gas exchange and productivity: leaf water potential (Ψcanopy), pressure volume curves (eco-PV), and hydraulic conductance (Keco). These analyses can reveal additional ecosystem-scale parameters analogous to those typically quantified for leaves or plants (e.g., wilting point and hydraulic vulnerability) that may act as thresholds in forest responses to drought, including growth cessation, mortality, and flammability. We unite these concepts in a novel framework to predict Ψcanopy and its approaching of critical thresholds during drought, using measurements of Keco and eco-PV curves. We thus delineate how the extension of water relations concepts from organ- and organism-scales can reveal the hydraulic constraints on the interaction of vegetation and climate and provide new mechanistic understanding and prediction of forest water use and productivity.

Characterizing the structural complexity of the Earth's forests with spaceborne lidar

Forest structural complexity is a key element of ecosystem functioning, impacting light environments, nutrient cycling, biodiversity, and habitat quality. Addressing the need for a comprehensive global assessment of actual forest structural complexity, we derive a near-global map of 3D canopy complexity using data from the GEDI spaceborne lidar mission. These data show that tropical forests harbor most of the high complexity observations, while less than 20% of temperate forests reached median levels of tropical complexity. Structural complexity in tropical forests is more strongly related to canopy attributes from lower and middle waveform layers, whereas in temperate forests upper and middle layers are more influential. Globally, forests exhibit robust scaling relationships between complexity and canopy height, but these vary geographically and by biome. Our results offer insights into the spatial distribution of forest structural complexity and emphasize the importance of considering biome-specific and fine-scale variations for ecological research and management applications. The GEDI Waveform Structural Complexity Index data product, derived from our analyses, provides researchers and conservationists with a single, easily interpretable metric by combining various aspects of canopy structure.

Community assembly influences plant trait economic spectra and functional trade-offs at ecosystem scales

Plant functional traits hold the potential to greatly improve the understanding and prediction of climate impacts on ecosystems and carbon cycle feedback to climate change. Traits are commonly used to place species along a global conservative-acquisitive trade-off, yet how and if functional traits and conservative-acquisitive trade-offs scale up to mediate community and ecosystem fluxes is largely unknown. Here, we combine functional trait datasets and multibiome datasets of forest water and carbon fluxes at the species, community, and ecosystem-levels to quantify the scaling of the tradeoff between maximum flux and sensitivity to vapor pressure deficit. We find a strong conservative-acquisitive trade-off at the species scale, which weakens modestly at the community scale and largely disappears at the ecosystem scale. Functional traits, particularly plant water transport (hydraulic) traits, are strongly associated with the key dimensions of the conservative-acquisitive trade-off at community and ecosystem scales, highlighting that trait composition appears to influence community and ecosystem flux dynamics. Our findings provide a foundation for improving carbon cycle models by revealing i) that plant hydraulic traits are most strongly associated with community- and ecosystem scale flux dynamics and ii) community assembly dynamics likely need to be considered explicitly, as they give rise to ecosystem-level flux dynamics that differ substantially from trade-offs identified at the species-level.

Arbuscular mycorrhizal fungal interactions bridge the support of root-associated microbiota for slope multifunctionality in an erosion-prone ecosystem

The role of diverse soil microbiota in restoring erosion-induced degraded lands is well recognized. Yet, the facilitative interactions among symbiotic arbuscular mycorrhizal (AM) fungi, rhizobia, and heterotrophic bacteria, which underpin multiple functions in eroded ecosystems, remain unclear. Here, we utilized quantitative microbiota profiling and ecological network analyses to explore the interplay between the diversity and biotic associations of root-associated microbiota and multifunctionality across an eroded slope of a Robinia pseudoacacia plantation on the Loess Plateau. We found explicit variations in slope multifunctionality across different slope positions, associated with shifts in limiting resources, including soil phosphorus (P) and moisture. To cope with P limitation, AM fungi were recruited by R. pseudoacacia, assuming pivotal roles as keystones and connectors within cross-kingdom networks. Furthermore, AM fungi facilitated the assembly and composition of bacterial and rhizobial communities, collectively driving slope multifunctionality. The symbiotic association among R. pseudoacacia, AM fungi, and rhizobia promoted slope multifunctionality through enhanced decomposition of recalcitrant compounds, improved P mineralization potential, and optimized microbial metabolism. Overall, our findings highlight the crucial role of AM fungal-centered microbiota associated with R. pseudoacacia in functional delivery within eroded landscapes, providing valuable insights for the sustainable restoration of degraded ecosystems in erosion-prone regions.

A shift in transitional forests of the North American boreal will persist through 2100

High northern latitude changes with Arctic amplification across a latitudinal forest gradient suggest a shift towards an increased presence of trees and shrubs. The persistence of change may depend on the future scenarios of climate and on the current state, and site history, of forest structure. Here, we explore the persistence of a gradient-based shift in the boreal by connecting current forest patterns to recent tree cover trends and future modeled estimates of canopy height through 2100. Results show variation in the predicted potential height changes across the structural gradient from the boreal forest through the taiga-tundra ecotone. Positive potential changes in height are concentrated in transitional forests, where recent positive changes in cover prevail, while potential change in boreal forest is highly variable. Results are consistent across climate scenarios, revealing a persistent biome shift through 2100 in North America concentrated in transitional landscapes regardless of climate scenario.

Enhancing ecosystem productivity and stability with increasing canopy structural complexity in global forests

Forest canopy structural complexity (CSC) plays a crucial role in shaping forest ecosystem productivity and stability, but the precise nature of their relationships remains controversial. Here, we mapped the global distribution of forest CSC and revealed the factors influencing its distribution using worldwide light detection and ranging data. We find that forest CSC predominantly demonstrates significant positive relationships with forest ecosystem productivity and stability globally, although substantial variations exist among forest ecoregions. The effects of forest CSC on productivity and stability are the balanced results of biodiversity and resource availability, providing valuable insights for comprehending forest ecosystem functions. Managed forests are found to have lower CSC but more potent enhancing effects of forest CSC on ecosystem productivity and stability than intact forests, highlighting the urgent need to integrate forest CSC into the development of forest management plans for effective climate change mitigation.

Functional diversity enhances dryland forest productivity under long-term climate change

Short-term experimental studies provided evidence that plant diversity increases ecosystem resilience and resistance to drought events, suggesting diversity to serve as a nature-based solution to address climate change. However, it remains unclear whether the effects of diversity are momentary or still hold over the long term in natural forests to ensure that the sustainability of carbon sinks. By analyzing 57 years of inventory data from dryland forests in Canada, we show that productivity of dryland forests decreased at an average rate of 1.3% per decade, in concert with the temporally increasing temperature and decreasing water availability. Increasing functional trait diversity from its minimum (monocultures) to maximum value increased productivity by 13%. Our results demonstrate the potential role of tree functional trait diversity in alleviating climate change impacts on dryland forests. While recognizing that nature-based climate mitigation (e.g., planting trees) can only be partial solutions, their long-term (decadal) efficacy can be improved by enhancing functional trait diversity across the forest community.

Spatial patterns and drivers of ecosystem multifunctionality in China: Arid vs. humid regions

Ecosystem multifunctionality (EMF) refers to an ecosystem's capacity to simultaneously uphold multiple ecological functions or services. In terrestrial ecosystems, the potential patterns and processes of EMF remain largely unexplored, limiting our comprehension of how ecosystems react to various driving factors. We collected environmental, soil and plant nutrient data, investigate the spatial distribution characteristics of EMF in China's terrestrial ecosystems, differentiating between arid and humid regions and examining the underlying drivers. Our findings reveal substantial spatial heterogeneity in the distribution of EMF across China's terrestrial ecosystems, with pronounced variations between arid and humid regions. In arid regions, the EMF index predominantly falls within the range of -1 to 1, including approximately 66.8 % of the total area, while in humid regions, the EMF index primarily falls within the range of 0 to 2, covering around 55.2 % of the total area. Climate, soil, and vegetation factors account for 61.4 % and 51.9 % of the total EMF variation in arid and humid regions, respectively. Notably, climate emerges as the dominant factor governing EMF variation in arid regions, whereas soil physicochemical properties take precedence in humid regions. Specifically, mean annual temperature (MAT) emerges as the primary factor influencing EMF variation in arid regions, while the normalized difference vegetation index (NDVI) and soil biodiversity index (SBI) play pivotal roles in regulating EMF variation in humid regions. Indeed, climate can exert both direct and indirect influences on EMF. In summary, our study not only compared the disparities in the spatial distribution of EMF in arid and humid regions but also unveiled the distinct controlling factors that govern EMF changes in these different regions. Our research has contributed novel insights for evaluating the drivers responsible for mediating EMF in diverse ecosystems, shedding light on the adaptability and response mechanisms of ecosystems under varying environmental conditions.

Combining different species in restoration is not always the right decision: Monocultures can provide higher ecological functions than intercropping in a desert ecosystem

Vegetation restoration in deserts is challenging due to these ecosystems' inherent fragility and harsh environmental conditions. One approach for active restoration involves planting native species, which can accelerate the recovery of ecosystem functions. To ensure the effectiveness of this process, carefully selecting species for planting is crucial. Generally, it is expected that a more diverse mix of species in the plantation will lead to the recovery of a greater number of ecosystem functions, especially when the selected species have complementary niche traits that facilitate maximum cooperation and minimize competition among them. In this study, we evaluated the planting of two native species from the hyper-desert of Taklamakan, China, which exhibit marked morpho-physiological differences: a phreatophytic legume (Alhagi sparsifolia) and a halophytic non-legume (Karelinia caspia). These species were grown in both monoculture and intercrop communities. Monoculture of the legume resulted in the highest biomass accumulation. Intercropping improved several ecosystem functions in the 50 cm-upper soil, particularly those related to phosphorus (P), carbon (C), and sulfur (S) concentrations, as well as soil enzyme activities. However, it also increased soil sodium (Na+) concentration and pH. Halophyte monocultures enhanced ecological functions associated with nitrogen concentrations in the upper soil and with P, S, C, and cation concentrations (K+, Ca2+, Mg2+, Cu2+, Fe2+, Zn2+, Co2+, Ni2+), along with enzyme activities in the deep soil. It also maximized Na+ accumulation in plant biomass. In summary, we recommend legume monoculture when the primary goal is to optimize biomass accumulation. Conversely, halophyte monoculture is advisable when the objective is to extract sodium from the soil or enhance ecosystem functions in the deep soil. Intercropping the two species is recommended to maximize the ecosystem functions of the upper soil, provided there is no salinization risk. When planning restoration efforts in desert regions, it is essential to understand the impact of each species on ecosystem function and how complementary species behave when intercropped. However, these interactions are likely species- and system-specific, highlighting the need for more work to optimize solutions for different arid ecosystems.

Vegetation canopy structure mediates the response of gross primary production to environmental drivers across multiple temporal scales

Gross primary production (GPP) is a critical component of the global carbon cycle and plays a significant role in the terrestrial carbon budget. The impact of environmental factors on GPP can occur through both direct (by influencing photosynthetic efficiency) and indirect (through the modulation of vegetation structure) pathways, but the extent to which these mechanisms contribute has been seldom quantified. In this study, we used structural equation modeling and observations from the FLUXNET network to investigate the direct and indirect effects of environmental factors on terrestrial ecosystem GPP at multiple temporal scales. We found that canopy structure, represented by leaf area index (LAI), is a crucial intermediate factor in the GPP response to environmental drivers. Environmental factors affect GPP indirectly by altering canopy structure, and the relative proportion of indirect effects decreased with increasing LAI. The study also identified different effects of environmental factors on GPP across time scales. At the half-hourly time scale, radiation was the primary driver of GPP. In contrast, the influences of temperature and vapor pressure deficit took on greater prominence at longer time scales. About half of the total effect of temperature on GPP was indirect through the regulation of canopy structure, and the indirect effect increased with increasing time scale (GPPNT-based models: 0.135 (half-hourly) vs. 0.171 (daily) vs. 0.189 (weekly) vs. 0.217 (monthly); GPPDT-based models: 0.139 vs. 0.170 vs. 0.187 vs. 0.215; all values were reported in gC m-2 d-1 °C-1, P < 0.001); while the indirect effect of radiation on GPP was comparatively lower, accounting for less than a quarter of the total effect. Furthermore, we observed a direct, negative-to-positive impact of precipitation on GPP across timescales. These findings provide crucial information on the interplay between environmental factors and LAI on GPP and enable a deeper understanding of the driving mechanisms of GPP.

A slow-fast trait continuum at the whole community level in relation to land-use intensification

Organismal functional strategies form a continuum from slow- to fast-growing organisms, in response to common drivers such as resource availability and disturbance. However, whether there is synchronisation of these strategies at the entire community level is unclear. Here, we combine trait data for >2800 above- and belowground taxa from 14 trophic guilds spanning a disturbance and resource availability gradient in German grasslands. The results indicate that most guilds consistently respond to these drivers through both direct and trophically mediated effects, resulting in a 'slow-fast' axis at the level of the entire community. Using 15 indicators of carbon and nutrient fluxes, biomass production and decomposition, we also show that fast trait communities are associated with faster rates of ecosystem functioning. These findings demonstrate that 'slow' and 'fast' strategies can be manifested at the level of whole communities, opening new avenues of ecosystem-level functional classification.

Environmental DNA and remote sensing datasets reveal the spatial distribution of aquatic insects in a disturbed subtropical river system

Biodiversity datasets with high spatial resolution are critical prerequisites for river protection and management decision-making. However, traditional morphological biomonitoring is inefficient and only provides several site estimates, and there is an urgent need for new approaches to predict biodiversity on fine spatial scales throughout the entire river systems. Here, we combined the environmental DNA (eDNA) and remote sensing (RS) technologies to develop a novel approach for predicting the spatial distribution of aquatic insects with high spatial resolution in a disturbed subtropical Dongjiang River system of southeast China. First, we screened thirteen RS-based vegetation indices that significantly correlated with the eDNA-inferred richness of aquatic insects. In particular, the green normalized difference vegetation index (GNDVI) and normalized difference red-edge2 (NDRE2) were closely related to eDNA-inferred richness. Second, using the gradient boosting decision tree, our data showed that the spatial pattern of eDNA-inferred richness could achieve a high spatial resolution to 500 m reach and accurate prediction of more than 80%, and the prediction efficiency of the headwater streams (Strahler stream order = 1) was slightly higher than the downstream (Strahler stream order >1). Third, using the random forest algorithm, the spatial distribution of aquatic insects could reach a prediction rate of over 70% for the presence or absence of specific genera. Overall, this study provides a new approach to achieving high spatial resolution prediction of the distribution of aquatic insects, which supports decision-making on river diversity protection under climate changes and human impacts.

Wood-density has no effect on stomatal control of leaf-level water use efficiency in an Amazonian forest

Forest disturbances increase the proportion of fast-growing tree species compared to slow-growing ones. To understand their relative capacity for carbon uptake and their vulnerability to climate change, and to represent those differences in Earth system models, it is necessary to characterise the physiological differences in their leaf-level control of water use efficiency and carbon assimilation. We used wood density as a proxy for the fast-slow growth spectrum and tested the assumption that trees with a low wood density (LWD) have a lower water-use efficiency than trees with a high wood density (HWD). We selected 5 LWD tree species and 5 HWD tree species growing in the same location in an Amazonian tropical forest and measured in situ steady-state gas exchange on top-of-canopy leaves with parallel sampling and measurement of leaf mass area and leaf nitrogen content. We found that LWD species invested more nitrogen in photosynthetic capacity than HWD species, had higher photosynthetic rates and higher stomatal conductance. However, contrary to expectations, we showed that the stomatal control of the balance between transpiration and carbon assimilation was similar in LWD and HWD species and that they had the same dark respiration rates.

Climate and soil properties regulate the vertical heterogeneity of minor and trace elements in the alpine topsoil of the Hengduan Mountains

Soil minor and trace elements are vital regulators of ecological processes that sustain alpine ecosystem functions. In this study, the vertical pattern and driving factors of element concentrations in alpine soils of the Tibetan Plateau were investigated. Three snow mountains (Meili, Baima, and Haba) part of the Hengduan Mountain range, were selected as the study area to determine the vertical distribution of 12 typical elements (Cr, Ni, Cu, Fe, Mn, Zn, Cd, Pb, Ca, Sr, As, and Se) in topsoil with increasing and decreasing elevation, as well as the dominant driving factors of their spatial heterogeneity. Results showed that all elements, except Se, showed strong vertical heterogeneity, among which Cr, Ni, Cu, and Fe showed peak concentrations at 2700-3000 m; the highest concentrations of Mn and Zn were at 3200 m and 2700 m, with Cd and Pb at 2500 m. Ca and Sr levels gradually decreased with increasing elevation. According to the structural equation model and random forest analysis, the vertical heterogeneity of soil elements is directly regulated by the variability of climate and soil properties due to changes in elevation. A three-way PERMANOVA further quantized the contributions of climate and soil properties on vertical heterogeneity of all soil elements, which were 35.2 % and 50.5 %, respectively. This study used various statistical tools to reveal the dominant factors affecting the vertical heterogeneity of soil elements. These findings provided a scientific overview of element distribution on the Tibetan Plateau and significant references for the vertical distribution of elements in the topsoil of other snow mountains worldwide.

Three main dimensions reflected by national SDG performance

Unraveling the complexity of the 17 interacting sustainable development goals (SDGs) is crucial for their achievement. Empirically revealing the dimensions of the SDGs helps generalize the dominant features of SDGs and better understand their drivers. Here, using a database of 166 countries' progress toward achieving each individual SDG, we found that about 70% of the variability of national SDG performance can be captured by three dimensions: socioeconomic development at the expense of resource and climate, the environment, and development at the expense of equality. Moreover, these dimensions are mainly affected by the economy; as gross domestic product (GDP) per capita increases, the first dimension increases monotonically, the environment dimension decreases and then increases, and the inequality dimension increases and then decreases. Our findings indicate a dim prospect of eventually achieving all SDGs because of the conflicts between economic growth and resource and climate goals under the current development paradigm, highlighting the importance of sustainable transformation.

A global assessment of the Raunkiaeran shortfall in plants: geographic biases in our knowledge of plant traits

This article is part of the Special Collection ‘Global plant diversity and distribution’. See https://www.newphytologist.org/global-plant-diversity for more details.

Leveraging research infrastructure co-location to evaluate constraints on terrestrial carbon cycling in northern European forests

Integrated long-term, in-situ observations are needed to document ongoing environmental change, to "ground-truth" remote sensing and model outputs and to predict future Earth system behaviour. The scientific and societal value of in-situ observations increases with site representativeness, temporal duration, number of parameters measured and comparability within and across sites. Research Infrastructures (RIs) can support harmonised, cross-site data collection, curation and publication. Integrating RI networks through site co-location and standardised observation methods can help answers three questions about the terrestrial carbon sink: (i) What are present and future carbon sequestration rates in northern European forests? (ii) How are these rates controlled? (iii) Why do the observed patterns exist? Here, we present a conceptual model for RI co-location and highlight potential insights into the terrestrial carbon sink achievable when long-term in-situ Earth observation sites participate in multiple RI networks (e.g., ICOS and eLTER). Finally, we offer recommendations to promote RI co-location.

A high-resolution canopy height model of the Earth

The worldwide variation in vegetation height is fundamental to the global carbon cycle and central to the functioning of ecosystems and their biodiversity. Geospatially explicit and, ideally, highly resolved information is required to manage terrestrial ecosystems, mitigate climate change and prevent biodiversity loss. Here we present a comprehensive global canopy height map at 10 m ground sampling distance for the year 2020. We have developed a probabilistic deep learning model that fuses sparse height data from the Global Ecosystem Dynamics Investigation (GEDI) space-borne LiDAR mission with dense optical satellite images from Sentinel-2. This model retrieves canopy-top height from Sentinel-2 images anywhere on Earth and quantifies the uncertainty in these estimates. Our approach improves the retrieval of tall canopies with typically high carbon stocks. According to our map, only 5% of the global landmass is covered by trees taller than 30 m. Further, we find that only 34% of these tall canopies are located within protected areas. Thus, the approach can serve ongoing efforts in forest conservation and has the potential to foster advances in climate, carbon and biodiversity modelling.

Changes in mass allocation play a more prominent role than morphology in resource acquisition of the rhizomatous Leymus chinensis under drought stress

BACKGROUND AND AIMS

Plants can respond to drought by changing their relative investments in the biomass and morphology of each organ. The aims of this study were to quantify the relative contribution of changes in morphology vs. allocation and determine how they affect each other. These results should help us understand the mechanisms that plants use to respond to drought events.

METHODS

In a glasshouse experiment, we applied a drought treatment (well-watered vs. drought) at early and late stages of plant growth, leading to four treatment combinations (well-watered in both early and late periods, WW; drought in the early period and well-watered in the late period, DW; well-watered in the early period and drought in the late period, WD; drought in both early and late periods, DD). We used the variance partitioning method to compare the contribution of organ (leaf and root) biomass allocation and morphology to the leaf area ratio, root length ratio and root area ratio, for the rhizomatous grass Leymus chinensis (Trin.) Tzvelev.

KEY RESULTS

Compared with the continuously well-watered treatment, the leaf area ratio, root length ratio and root area ratio showed increasing trends under various drought treatments. The contribution of leaf mass allocation to leaf area ratio differed among the drought treatments and was 2.1- to 5.3-fold greater than leaf morphology, and the contribution of root mass allocation to root length ratio was ~2-fold greater than that of root morphology. In contrast, root morphology contributed more to the root area ratio than biomass allocation under drought in both the early and late periods. There was a negative correlation between the ratio of leaf mass fraction to root mass fraction and the ratio of specific leaf area to specific root length (or specific root area).

CONCLUSIONS

This study suggested that organ biomass allocation drove a larger proportion of variation than morphological traits for the absorption of resources in this rhizomatous grass. These findings should help us understand the adaptive mechanisms of plants when they are confronted with drought stress.

Spatial variations and mechanisms for the stability of water use efficiency in China

A clearer understanding of the stability of water use efficiency (WUE) and its driving factors contributes to improving water use efficiency and strengthening water resource management. However, the stability of WUE is unclear. Based on the EEMD method, this study analyses the spatial variations and mechanisms for the stability of WUE in China, especially in the National Forest Protection Project (NFPP) areas. It is found that the stable WUE was dominated by non-significant trends and increasing trends in China, accounting for 33.59% and 34.19%, respectively. The non-significant trend of stable WUE was mainly located in the Three-North shelterbelt program area, and the increasing trend of stable WUE was in Huaihe and Taihu, Taihang Mountains, and Pearl River shelterbelt program areas. Precipitation and soil moisture promoted the stable WUE in these project areas. The unstable WUE was dominated by positive reversals or negative reversals of WUE trends. The positive reversals of unstable WUE were mainly located in the Yellow River shelterbelt program areas, which was promoted by temperature and radiation, while the negative reversals of unstable WUE were mainly distributed in the Yangtze River and Liaohe shelterbelt program areas, which were mainly induced by saturation water vapor pressure difference (VPD). Our results highlight that some ecological restoration programs need to be improved to cope with the negative climate impact on the stability of WUE.

AmeriFlux BASE data pipeline to support network growth and data sharing

AmeriFlux is a network of research sites that measure carbon, water, and energy fluxes between ecosystems and the atmosphere using the eddy covariance technique to study a variety of Earth science questions. AmeriFlux's diversity of ecosystems, instruments, and data-processing routines create challenges for data standardization, quality assurance, and sharing across the network. To address these challenges, the AmeriFlux Management Project (AMP) designed and implemented the BASE data-processing pipeline. The pipeline begins with data uploaded by the site teams, followed by the AMP team's quality assurance and quality control (QA/QC), ingestion of site metadata, and publication of the BASE data product. The semi-automated pipeline enables us to keep pace with the rapid growth of the network. As of 2022, the AmeriFlux BASE data product contains 3,130 site years of data from 444 sites, with standardized units and variable names of more than 60 common variables, representing the largest long-term data repository for flux-met data in the world. The standardized, quality-ensured data product facilitates multisite comparisons, model evaluations, and data syntheses.

Joint optimization of land carbon uptake and albedo can help achieve moderate instantaneous and long-term cooling effects

Both carbon dioxide uptake and albedo of the land surface affect global climate. However, climate change mitigation by increasing carbon uptake can cause a warming trade-off by decreasing albedo, with most research focusing on afforestation and its interaction with snow. Here, we present carbon uptake and albedo observations from 176 globally distributed flux stations. We demonstrate a gradual decline in maximum achievable annual albedo as carbon uptake increases, even within subgroups of non-forest and snow-free ecosystems. Based on a paired-site permutation approach, we quantify the likely impact of land use on carbon uptake and albedo. Shifting to the maximum attainable carbon uptake at each site would likely cause moderate net global warming for the first approximately 20 years, followed by a strong cooling effect. A balanced policy co-optimizing carbon uptake and albedo is possible that avoids warming on any timescale, but results in a weaker long-term cooling effect.

Leaf-level coordination principles propagate to the ecosystem scale

Fundamental axes of variation in plant traits result from trade-offs between costs and benefits of resource-use strategies at the leaf scale. However, it is unclear whether similar trade-offs propagate to the ecosystem level. Here, we test whether trait correlation patterns predicted by three well-known leaf- and plant-level coordination theories - the leaf economics spectrum, the global spectrum of plant form and function, and the least-cost hypothesis - are also observed between community mean traits and ecosystem processes. We combined ecosystem functional properties from FLUXNET sites, vegetation properties, and community mean plant traits into three corresponding principal component analyses. We find that the leaf economics spectrum (90 sites), the global spectrum of plant form and function (89 sites), and the least-cost hypothesis (82 sites) all propagate at the ecosystem level. However, we also find evidence of additional scale-emergent properties. Evaluating the coordination of ecosystem functional properties may aid the development of more realistic global dynamic vegetation models with critical empirical data, reducing the uncertainty of climate change projections.

Browning of vegetation in efficient carbon sink regions of India during the past two decades is driven by climate change and anthropogenic intrusions

Accurate estimation of carbon cycle is a challenging task owing to the complexity and heterogeneity of ecosystems. Carbon Use Efficiency (CUE) is a metric to define the ability of vegetation to sequester carbon from the atmosphere. It is key to understand the carbon sink and source pathways of ecosystems. Here, we quantify CUE using remote sensing measurements to examine its variability, drivers and underlying mechanisms in India for the period 2000-2019, by applying the principal component analyses (PCA), multiple linear regression (MLR) and causal discovery. Our analysis shows that the forests in the hilly regions (HR) and northeast (NE), and croplands in the western areas of South India (SI) exhibit high (>0.6) CUE. The northwest (NW), Indo-Gangetic plain (IGP) and some areas in Central India (CI) show low (<0.3) CUE. In general, the water availability as soil moisture (SM) and precipitation (P) promote higher CUE, but higher temperature (T) and air organic carbon content (AOCC) reduce CUE. It is found that SM has the strongest relative influence (33%) on CUE, followed by P. Also, SM has a direct causal link with all drivers and CUE; reiterating its importance in driving vegetation carbon dynamics (VCD) for the cropland dominated India. The long-term analysis reveals that the low CUE regions in NW (moisture induced greening) and IGP (irrigation induced agricultural boom) have an increasing trend in productivity (greening). However, the high CUE regions in NE (deforestation and extreme events) and SI (warming induced moisture stress) exhibit a decreasing trend in productivity (browning), which is a great concern. Our study, therefore, provides new insights on the rate of carbon allocation and the need of proper planning for maintaining balance in the terrestrial carbon cycle. This is particularly important in the context of drafting policy decisions for the mitigation of climate change, food security and sustainability.

Machine learning assisted remote forestry health assessment: a comprehensive state of the art review

Forests are suffering water stress due to climate change; in some parts of the globe, forests are being exposed to the highest temperatures historically recorded. Machine learning techniques combined with robotic platforms and artificial vision systems have been used to provide remote monitoring of the health of the forest, including moisture content, chlorophyll, and nitrogen estimation, forest canopy, and forest degradation, among others. However, artificial intelligence techniques evolve fast associated with the computational resources; data acquisition, and processing change accordingly. This article is aimed at gathering the latest developments in remote monitoring of the health of the forests, with special emphasis on the most important vegetation parameters (structural and morphological), using machine learning techniques. The analysis presented here gathered 108 articles from the last 5 years, and we conclude by showing the newest developments in AI tools that might be used in the near future.

Global change and plant-ecosystem functioning in freshwaters

Freshwater ecosystems are of worldwide importance for maintaining biodiversity and sustaining the provision of a myriad of ecosystem services to modern societies. Plants, one of the most important components of these ecosystems, are key to water nutrient removal, carbon storage, and food provision. Understanding how the functional connection between freshwater plants and ecosystems is affected by global change will be key to our ability to predict future changes in freshwater systems. Here, we synthesize global plant responses, adaptations, and feedbacks to present-day and future freshwater environments through trait-based approaches, from single individuals to entire communities. We outline the transdisciplinary knowledge benchmarks needed to further understand freshwater plant biodiversity and the fundamental services they provide.

The bright side of ecological stressors

Ecological stressors are considered to negatively affect biological systems; however, corresponding responses to stressors can be complex, depending on the ecological functions and the number and duration of the stressors. Mounting evidence indicates potential benefits of stressors. Here, we develop an integrative framework to understand stressor-induced benefits by clarifying three categories of mechanisms: seesaw effects, cross-tolerance, and memory effects. These mechanisms operate across various organizational levels (e.g., individual, population, community) and can be extended to an evolutionary context. One remaining challenge is to develop scaling approaches for linking stressor-induced benefits across organizational levels. Our framework provides a novel platform for predicting the consequences of global environmental changes and informing management strategies in conservation and restoration practices.

The effect of the vertical gradients of photosynthetic parameters on the CO2 assimilation and transpiration of a Panamanian tropical forest

Terrestrial biosphere models (TBMs) include the representation of vertical gradients in leaf traits associated with modeling photosynthesis, respiration, and stomatal conductance. However, model assumptions associated with these gradients have not been tested in complex tropical forest canopies. We compared TBM representation of the vertical gradients of key leaf traits with measurements made in a tropical forest in Panama and then quantified the impact of the observed gradients on simulated canopy-scale CO₂ and water fluxes. Comparison between observed and TBM trait gradients showed divergence that impacted canopy-scale simulations of water vapor and CO₂ exchange. Notably, the ratio between the dark respiration rate and the maximum carboxylation rate was lower near the ground than at the top-of-canopy, leaf-level water-use efficiency was markedly higher at the top-of-canopy, and the decrease in maximum carboxylation rate from the top-of-canopy to the ground was less than TBM assumptions. The representation of the gradients of leaf traits in TBMs is typically derived from measurements made within-individual plants, or, for some traits, assumed constant due to a lack of experimental data. Our work shows that these assumptions are not representative of the trait gradients observed in species-rich, complex tropical forests.

Eukaryotes contribute more than bacteria to the recovery of freshwater ecosystem functions under different drought durations

Global climate change mostly impacts river ecosystems by affecting microbial biodiversity and ecological functions. Considering the high functional redundancy of microorganisms, the unknown relationship between biodiversity and ecosystem functions obstructs river ecological research, especially under the influence of increasing weather extremes, such as in intermittent rivers and ephemeral streams (IRES). Herein, dry-wet alternation experiments were conducted in artificial stream channels for 25 and 90 days of drought, both followed by 20 days of rewetting. The dynamic recovery of microbial biodiversity and ecosystem functions (represented by ecosystem metabolism and denitrification rate) were determined to analyse biodiversity-ecosystem-function (BEF) relationships after different drought durations. There was a significant difference between bacterial and eukaryotic biodiversity recovery after drought. Eukaryotic biodiversity was more sensitive to drought duration than bacterial, and the eukaryotic network was more stable under dry-wet alternations. Based on the establishment of partial least squares path models, we found that eukaryotic biodiversity has a stronger effect on ecosystem functions than bacteria after long-term drought. Indeed, this work represents a significant step forward for further research on the ecosystem functions of IRES, especially emphasizing the importance of eukaryotic biodiversity in the BEF relationship.

Integrating multiple plant functional traits to predict ecosystem productivity

Quantifying and predicting variation in gross primary productivity (GPP) is important for accurate assessment of the ecosystem carbon budget under global change. Scaling traits to community scales for predicting ecosystem functions (i.e., GPP) remain challenging, while it is promising and well appreciated with the rapid development of trait-based ecology. In this study, we aim to integrate multiple plant traits with the recently developed trait-based productivity (TBP) theory, verify it via Bayesian structural equation modeling (SEM) and complementary independent effect analysis. We further distinguish the relative importance of different traits in explaining the variation in GPP. We apply the TBP theory based on plant community traits to a multi-trait dataset containing more than 13,000 measurements of approximately 2,500 species in Chinese forest and grassland systems. Remarkably, our SEM accurately predicts variation in annual and monthly GPP across China (R² values of 0.87 and 0.73, respectively). Plant community traits play a key role. This study shows that integrating multiple plant functional traits into the TBP theory strengthens the quantification of ecosystem primary productivity variability and further advances understanding of the trait-productivity relationship. Our findings facilitate integration of the growing plant trait data into future ecological models.

Autumn nitrogen enrichment destabilizes ecosystem biomass production in a semiarid grassland

Nitrogen (N) deposition decreases the temporal stability of ecosystem aboveground biomass production (ecosystem stability). However, little is known about how the responses of ecosystem stability differ based on seasonal N enrichment. By adding N in autumn, winter, or growing season, from October 2014 to May 2020, in a temperate grassland in northern China, we found that only N addition in autumn resulted in a significantly positive correlation between ecosystem mean aboveground net primary productivity (ANPP) and its standard deviation and significantly reduced ecosystem stability. Autumn N-induced reduction in ecosystem stability was associated with the vanished negative effect of community-wide species asynchrony (asynchronous dynamics among populations to environmental perturbations) on the standard deviation of ecosystem ANPP in combination with the emerged positive effect of dominance (Simpson's dominance index that indicates the relative weight of dominant species in a community). Our findings indicate that autumn N addition might overestimate the negative effect of annual atmospheric N deposition on ecosystem stability, suggesting that to better evaluate the influence of N deposition in temperate grasslands, both field experiments and global modeling should consider not only the annual N load but also its seasonal dynamics. Moreover, further studies should pay more attention to the alteration in the ecosystem temporal deviations, which might be more sensitive to human-induced environmental changes.

Rethinking experiments that explore multiple global change factors

Our current capacity to predict the responses of ecosystem functions under global change factors is limited. We propose new and more efficient approaches to experimental design and modeling that utilize interactions between ecosystem functions to improve our understanding of their sensitivity to global change factors.

Low precipitation due to climate change consistently reduces multifunctionality of urban grasslands in mesocosms

Urban grasslands are crucial for biodiversity and ecosystem services in cities, while little is known about their multifunctionality under climate change. Thus, we investigated the effects of simulated climate change, i.e., increased [CO2] and temperature, and reduced precipitation, on individual functions and overall multifunctionality in mesocosm grasslands sown with forbs and grasses in four different proportions aiming at mimicking road verge grassland patches. Climate change scenarios RCP2.6 (control) and RCP8.5 (worst-case) were simulated in walk-in climate chambers of an ecotron facility, and watering was manipulated for normal vs. reduced precipitation. We measured eight indicator variables of ecosystem functions based on below- and aboveground characteristics. The young grassland communities responded to higher [CO2] and warmer conditions with increased vegetation cover, height, flower production, and soil respiration. Lower precipitation affected carbon cycling in the ecosystem by reducing biomass production and soil respiration. In turn, the water regulation capacity of the grasslands depended on precipitation interacting with climate change scenario, given the enhanced water efficiency resulting from increased [CO2] under RCP8.5. Multifunctionality was negatively affected by reduced precipitation, especially under RCP2.6. Trade-offs arose among single functions that performed best in either grass- or forb-dominated grasslands. Grasslands with an even ratio of plant functional types coped better with climate change and thus are good options for increasing the benefits of urban green infrastructure. Overall, the study provides experimental evidence of the effects of climate change on the functionality of urban ecosystems. Designing the composition of urban grasslands based on ecological theory may increase their resilience to global change.

Can metabolic traits explain animal community assembly and functioning?

All animals on Earth compete for free energy, which is acquired, assimilated, and ultimately allocated to growth and reproduction. Competition is strongest within communities of sympatric, ecologically similar animals of roughly equal size (i.e. horizontal communities), which are often the focus of traditional community ecology. The replacement of taxonomic identities with functional traits has improved our ability to decipher the ecological dynamics that govern the assembly and functioning of animal communities. Yet, the use of low-resolution and taxonomically idiosyncratic traits in animals may have hampered progress to date. An animal's metabolic rate (MR) determines the costs of basic organismal processes and activities, thus linking major aspects of the multifaceted constructs of ecological niches (where, when, and how energy is obtained) and ecological fitness (how much energy is accumulated and passed on to future generations). We review evidence from organismal physiology to large-scale analyses across the tree of life to propose that MR gives rise to a group of meaningful functional traits - resting metabolic rate (RMR), maximum metabolic rate (MMR), and aerobic scope (AS) - that may permit an improved quantification of the energetic basis of species coexistence and, ultimately, the assembly and functioning of animal communities. Specifically, metabolic traits integrate across a variety of typical trait proxies for energy acquisition and allocation in animals (e.g. body size, diet, mobility, life history, habitat use), to yield a smaller suite of continuous quantities that: (1) can be precisely measured for individuals in a standardized fashion; and (2) apply to all animals regardless of their body plan, habitat, or taxonomic affiliation. While integrating metabolic traits into animal community ecology is neither a panacea to disentangling the nuanced effects of biological differences on animal community structure and functioning, nor without challenges, a small number of studies across different taxa suggest that MR may serve as a useful proxy for the energetic basis of competition in animals. Thus, the application of MR traits for animal communities can lead to a more general understanding of community assembly and functioning, enhance our ability to trace eco-evolutionary dynamics from genotypes to phenotypes (and vice versa), and help predict the responses of animal communities to environmental change. While trait-based ecology has improved our knowledge of animal communities to date, a more explicit energetic lens via the integration of metabolic traits may further strengthen the existing framework.

Recent global decline in rainfall interception loss due to altered rainfall regimes

Evaporative loss of interception (Ei) is the first process occurring during rainfall, yet its role in large-scale surface water balance has been largely underexplored. Here we show that Ei can be inferred from flux tower evapotranspiration measurements using physics-informed hybrid machine learning models built under wet versus dry conditions. Forced by satellite and reanalysis data, this framework provides an observationally constrained estimate of Ei, which is on average 84.1 ± 1.8 mm per year and accounts for 8.6 ± 0.2% of total rainfall globally during 2000-2020. Rainfall frequency regulates long-term average Ei changes, and rainfall intensity, rather than vegetation attributes, determines the fraction of Ei in gross precipitation (Ei/P). Rain events have become less frequent and more intense since 2000, driving a global decline in Ei (and Ei/P) by 4.9% (6.7%). This suggests that ongoing rainfall changes favor a partitioning towards more soil moisture and runoff, benefiting ecosystem functions but simultaneously increasing flood risks.

Drivers of Decadal Carbon Fluxes Across Temperate Ecosystems

Long-running eddy covariance flux towers provide insights into how the terrestrial carbon cycle operates over multiple timescales. Here, we evaluated variation in net ecosystem exchange (NEE) of carbon dioxide (CO₂) across the Chequamegon Ecosystem-Atmosphere Study AmeriFlux core site cluster in the upper Great Lakes region of the USA from 1997 to 2020. The tower network included two mature hardwood forests with differing management regimes (US-WCr and US-Syv), two fen wetlands with varying levels of canopy sheltering and vegetation (US-Los and US-ALQ), and a very tall (400 m) landscape-level tower (US-PFa). Together, they provided over 70 site-years of observations. The 19-tower Chequamegon Heterogenous Ecosystem Energy-balance Study Enabled by a High-density Extensive Array of Detectors 2019 campaign centered around US-PFa provided additional information on the spatial variation of NEE. Decadal variability was present in all long-term sites, but cross-site coherence in interannual NEE in the earlier part of the record became weaker with time as non-climatic factors such as local disturbances likely dominated flux time series. Average decadal NEE at the tall tower transitioned from carbon source to sink to near neutral over 24 years. Respiration had a greater effect than photosynthesis on driving variations in NEE at all sites. Declining snowfall offset potential increases in assimilation from warmer springs, as less-insulated soils delayed start of spring green-up. Higher CO₂ increased maximum net assimilation parameters but not total gross primary productivity. Stand-scale sites were larger net sinks than the landscape tower. Clustered, long-term carbon flux observations provide value for understanding the diverse links between carbon and climate and the challenges of upscaling these responses across space.

Critical soil moisture thresholds of plant water stress in terrestrial ecosystems

Plant water stress occurs at the point when soil moisture (SM) limits transpiration, defining a critical SM threshold (θ_crit). Knowledge of the spatial distribution of θ_crit is crucial for future projections of climate and water resources. Here, we use global eddy covariance observations to quantify θ_crit and evaporative fraction (EF) regimes. Three canonical variables describe how EF is controlled by SM: the maximum EF (EF_max), θ_crit, and slope (S) between EF and SM. We find systematic differences of these three variables across biomes. Variation in θ_crit, S, and EF_max is mostly explained by soil texture, vapor pressure deficit, and precipitation, respectively, as well as vegetation structure. Dryland ecosystems tend to operate at low θ_crit and show adaptation to water deficits. The negative relationship between θ_crit and S indicates that dryland ecosystems minimize θ_crit through mechanisms of sustained SM extraction and transport by xylem. Our results further suggest an optimal adaptation of local EF-SM response that maximizes growing-season evapotranspiration and photosynthesis.

Global patterns and abiotic drivers of ecosystem multifunctionality in dominant natural ecosystems

The potential patterns and processes of ecosystem multifunctionality (EMF) across global ecosystems are largely unknown, which limits our understanding of how ecosystems respond to drivers. Here we compile a global dataset that consists of 973 unique sites across the forest, grassland, and shrub ecosystems. We identify a critical global pattern of hump-shaped EMF relationship with mean annual precipitation at a threshold of ∼671 mm, where low and high precipitation patterns are discriminated. We find that climatic and soil factors jointly drive the EMF in low precipitation areas, and climatic factors dominate the EMF in high precipitation regions. However, when comparing across the three dominant ecosystems and precipitation regions, the key driver in EMF differs substantially. Specifically, climatic and soil factors dominate the EMF of low and high precipitation regions across forest ecosystems, respectively. Climatic drivers dominate the EMF under different precipitation conditions across grassland and shrub ecosystems. Overall, our findings highlight the importance of climatic and soil drivers on EMF, which should be considered in ecosystem stability models in response to global climate and land-use change scenarios.

An improved representation of the relationship between photosynthesis and stomatal conductance leads to more stable estimation of conductance parameters and improves the goodness-of-fit across diverse data sets

Stomata play a central role in surface-atmosphere exchange by controlling the flux of water and CO₂ between the leaf and the atmosphere. Representation of stomatal conductance (g_sw ) is therefore an essential component of models that seek to simulate water and CO₂ exchange in plants and ecosystems. For given environmental conditions at the leaf surface (CO₂ concentration and vapor pressure deficit or relative humidity), models typically assume a linear relationship between g_sw and photosynthetic CO₂ assimilation (A). However, measurement of leaf-level g_sw response curves to changes in A are rare, particularly in the tropics, resulting in only limited data to evaluate this key assumption. Here, we measured the response of g_sw and A to irradiance in six tropical species at different leaf phenological stages. We showed that the relationship between g_sw and A was not linear, challenging the key assumption upon which optimality theory is based-that the marginal cost of water gain is constant. Our data showed that increasing A resulted in a small increase in g_sw at low irradiance, but a much larger increase at high irradiance. We reformulated the popular Unified Stomatal Optimization (USO) model to account for this phenomenon and to enable consistent estimation of the key conductance parameters g₀ and g₁ . Our modification of the USO model improved the goodness-of-fit and reduced bias, enabling robust estimation of conductance parameters at any irradiance. In addition, our modification revealed previously undetectable relationships between the stomatal slope parameter g₁ and other leaf traits. We also observed nonlinear behavior between A and g_sw in independent data sets that included data collected from attached and detached leaves, and from plants grown at elevated CO₂ concentration. We propose that this empirical modification of the USO model can improve the measurement of g_sw parameters and the estimation of plant and ecosystem-scale water and CO₂  fluxes.

Temperature and Rainfall Patterns Constrain the Multidimensional Rewilding of Global Forests

The long-term contribution of global forest restoration to support multiple dimensions of biodiversity and ecosystem function remains largely illusive across contrasting climates and forest types. This hampers the capacity to predict the future of forest rewilding under changing global climates. Here, 120 studies are synthesized across five continents, and it is found that forest restoration promotes multiple dimensions of biodiversity and ecosystem function such as soil fertility, plant biomass, microbial habitat, and carbon sequestration across contrasting climates and forest types. Based on global relationship between stand age and soil organic carbon stock, planting 350 million hectares of forest under the UN Bonn Challenge can sequester >30 Gt soil C in the surface 20 cm over the next century. However, these findings also indicate that predicted increases in temperature and reductions in precipitation can constrain the positive effects of forest rewilding on biodiversity and ecosystem function. Further, important tradeoffs are found in very old forests, with considerable disconnection between biodiversity and ecosystem function. Together, these findings provide evidence of the importance of the multidimensional rewilding of forests, suggesting that on-going climatic changes may dampen the expectations of the positive effects of forest restoration on biodiversity and ecosystem function.

Downwelling longwave radiation and sensible heat flux observations are critical for surface temperature and emissivity estimation from flux tower data

Land surface temperature (LST) is a preeminent state variable that controls the energy and water exchange between the Earth’s surface and the atmosphere. At the landscape-scale, LST is derived from thermal infrared radiance measured using space-borne radiometers. In contrast, plot-scale LST estimation at flux tower sites is commonly based on the inversion of upwelling longwave radiation captured by tower-mounted radiometers, whereas the role of the downwelling longwave radiation component is often ignored. We found that neglecting the reflected downwelling longwave radiation leads not only to substantial bias in plot-scale LST estimation, but also have important implications for the estimation of surface emissivity on which LST is co-dependent. The present study proposes a novel method for simultaneous estimation of LST and emissivity at the plot-scale and addresses in detail the consequences of omitting down-welling longwave radiation as frequently done in the literature. Our analysis uses ten eddy covariance sites with different land cover types and found that the LST values obtained using both upwelling and downwelling longwave radiation components are 0.5–1.5 K lower than estimates using only upwelling longwave radiation. Furthermore, the proposed method helps identify inconsistencies between plot-scale radiometric and aerodynamic measurements, likely due to footprint mismatch between measurement approaches. We also found that such inconsistencies can be removed by slight corrections to the upwelling longwave component and subsequent energy balance closure, resulting in realistic estimates of surface emissivity and consistent relationships between energy fluxes and surface-air temperature differences. The correspondence between plot-scale LST and landscape-scale LST depends on site-specific characteristics, such as canopy density, sensor locations and viewing angles. Here we also quantify the uncertainty in plot-scale LST estimates due to uncertainty in tower-based measurements using the different methods. The results of this work have significant implications for the combined use of aerodynamic and radiometric measurements to understand the interactions and feedbacks between LST and surface-atmosphere exchange processes.

Soil phosphorus drives plant trait variations in a mature subtropical forest

Earth system models are implementing soil phosphorus dynamic and plant functional traits to predict functional changes in global forests. However, the linkage between soil phosphorus and plant traits lacks empirical evidence, especially in mature forests. Here, we examined the soil phosphorus constraint on plant functional traits in a mature subtropical forest based on observations of 9943 individuals from 90 species in a 5-ha forest dynamic plot and 405 individuals from 15 species in an adjacent 10-year nutrient-addition experiment. We first confirmed a pervasive phosphorus limitation on subtropical tree growth based on leaf N:P ratios. Then, we found that soil phosphorus dominated multidimensional trait variations in the 5-ha forest dynamic plot. Soil phosphorus content explained 44% and 53% of the variance in the traits defining the main functional space across species and communities, respectively. Lastly, we found much stronger phosphorus effects on most plant functional traits than nitrogen at both species and community levels in the 10-year nutrient-addition experiment. This study provides evidence for the consistent pattern of soil phosphorus constraint on plant trait variations between the species and community levels in a mature evergreen broadleaf forest in the East Asian monsoon region. These findings shed light on the predominant role of soil phosphorus on plant functional trait variations in mature subtropical forests, providing new insights for models to incorporate soil phosphorus constraint in predicting future vegetation dynamics.

Hydroclimatic adaptation critical to the resilience of tropical forests

Forest and savanna ecosystems naturally exist as alternative stable states. The maximum capacity of these ecosystems to absorb perturbations without transitioning to the other alternative stable state is referred to as 'resilience'. Previous studies have determined the resilience of terrestrial ecosystems to hydroclimatic changes predominantly based on space-for-time substitution. This substitution assumes that the contemporary spatial frequency distribution of ecosystems' tree cover structure holds across time. However, this assumption is problematic since ecosystem adaptation over time is ignored. Here we empirically study tropical forests' stability and hydroclimatic adaptation dynamics by examining remotely sensed tree cover change (ΔTC; aboveground ecosystem structural change) and root zone storage capacity (S_r ; buffer capacity towards water-stress) over the last two decades. We find that ecosystems at high (>75%) and low (<10%) tree cover adapt by instigating considerable subsoil investment, and therefore experience limited ΔTC-signifying stability. In contrast, unstable ecosystems at intermediate (30%-60%) tree cover are unable to exploit the same level of adaptation as stable ecosystems, thus showing considerable ΔTC. Ignoring this adaptive mechanism can underestimate the resilience of the forest ecosystems, which we find is largely underestimated in the case of the Congo rainforests. The results from this study emphasise the importance of the ecosystem's temporal dynamics and adaptation in inferring and assessing the risk of forest-savannah transitions under rapid hydroclimatic change.

Variability of ecosystem carbon source from microbial respiration is controlled by rainfall dynamics

Soil heterotrophic respiration (R _h) represents an important component of the terrestrial carbon cycle that affects whether ecosystems function as carbon sources or sinks. Due to the complex interactions between biological and physical factors controlling microbial growth, R _h is uncertain and difficult to predict, limiting our ability to anticipate future climate trajectories. Here we analyze the global FLUXNET 2015 database aided by a probabilistic model of microbial growth to examine the ecosystem-scale dynamics of R _h and identify primary predictors of its variability. We find that the temporal variability in R _h is consistently distributed according to a Gamma distribution, with shape and scale parameters controlled only by rainfall characteristics and vegetation productivity. This distribution originates from the propagation of fast hydrologic fluctuations on the slower biological dynamics of microbial growth and is independent of biome, soil type, and microbial physiology. This finding allows us to readily provide accurate estimates of the mean R _h and its variance, as confirmed by a comparison with an independent global dataset. Our results suggest that future changes in rainfall regime and net primary productivity will significantly alter the dynamics of R _h and the global carbon budget. In regions that are becoming wetter, R _h may increase faster than net primary productivity, thereby reducing the carbon storage capacity of terrestrial ecosystems.

Ecosystem effects of environmental extremes

[Figure: see text].