Earth & Environmental Sciences

Understanding how leaf gas exchange responds to changes in vapor pressure deficit (VPD) is key to predicting tropical forest resilience to climate change. Stomata regulate leaf water and CO2 diffusion, and respond to changes in temperature and relative humidity, two drivers of VPD. At high temperatures, the cuticular pathway may also become significant and participate in the overall leaf conductance. Here, we measured gas exchange under light and dark conditions to investigate the stomatal and cuticular responses to temperature and relative humidity on detached branches in five tropical tree species. Leaf conductance in the dark, when stomata are essentially closed, was not markedly impacted by temperature and relative humidity, suggesting a minimal response of the cuticular pathway to these conditions. We compared six steady-state conductance models incorporating different effects of photosynthesis and evaporative demand on stomatal control. All models performed well (RMSE < 0.025 mol m-2 s-1), but the best-fitting model (RMSE = 0.017 mol m-2 s-1) used a nonlinear relationship between photosynthesis and stomatal conductance and incorporated relative humidity rather than vapor pressure difference. All models overestimated the steady-state conductance at high relative humidity. Furthermore, leaf conductance immediately increased after a decrease in relative humidity (wrong-way response), but not after an increase in leaf temperature. This suggests that the mechanisms underlying stomatal response to VPD need further investigation. The non-linear coupling between photosynthetic rate and stomatal conductance indicates a sharper physiological response than previously acknowledged.

The majority of plant biomass is located belowground in Arctic ecosystems and plant roots are responsible for the uptake of the nutrients that constrain plant growth in these infertile ecosystems. Despite performing a crucial role connecting primary producers to the soil, roots are relatively understudied in the Arctic and their functional response to a rapidly warming and increasingly variable climate is unknown. We assessed whether one growing season with elevated temperatures would have an impact on nutrient uptake and allocation by applying a warming technique that increased daily air temperatures by 3.2 °C. Destructive sampling was performed at the peak of the growing season to quantify biomass pools of carbon (C) and nitrogen (N), root traits, and uptake of a 15N tracer (15NH4+) for the dominant plant species, Arctagrostis latifolia. We found that soil nutrient availability increased with short-term warming, but A. latifolia NH4+ uptake remained unchanged. Fine-root length density and root biomass within the soil profile, however, were both reduced by warming. N allocation patterns across plant tissues were also altered by warming. NH4+ uptake was best fit with a logistic model that captured the spatial relationship between roots and soil (NH4+ uptake expressed per length fine root and NH4+ availability expressed per unit soil volume) rather than a traditional Michaelis–Menten model. Our results indicate that short-term experimental warming can shift plant–soil interactions, suggesting that the tundra’s belowground response to elevated temperatures may be more dynamic than previously recognized.

Snowmelt triggers a soil microbial bloom and crash that affects nitrogen (N) export in high-elevation watersheds. The mechanisms underlying these microbial dynamics are uncertain, making soil nitrogen processes difficult to predict as snowpack declines globally. Here, integration of genome-resolved metagenomics, metatranscriptomics and metabolomics in a high-elevation watershed revealed ecologically distinct soil microorganisms linked across the snowmelt time-period by their unique nitrogen cycling capacities. The molecular properties and transformations of dissolved organic N suggested that degradation or recycling of microbial biomass provided N for biosynthesis during the microbial bloom. Winter-adapted Bradyrhizobia spp. oxidized amino acids anaerobically and had the highest gene expression for denitrification during the microbial bloom. A pulse of nitrate was driven by spring-adapted Nitrososphaerales after snowmelt, but dissimilatory nitrate reduction to ammonia (DNRA) gene expression indicated significant nitrate retention potential. These findings inform our understanding of nitrogen cycling in environments sensitive to snowpack decline due to global change.

The expansion of shrubs in the Arctic tundra fundamentally modifies land-atmosphere interactions. However, it remains unclear how shrub distribution and expansion differ across key species due to challenges with discriminating tundra plant species at regional scales. Here, we combined multi-scale, multi-platform remote sensing and in situ trait measurements to elucidate the distribution patterns and primary controls of two representative deciduous-tall-shrub (DTS) genera, Alnus and Salix, in low-Arctic tundra. We show that topographic features were a key control on DTSs, creating heterogeneous, but predictable distributions of Alnus and Salix fractional cover (fCover). Alnus was more tolerant of elevation and slope and was found on hilly uplands (slope >10°) within a specific elevational band (200-400 m above sea level [MSL]). In contrast, Salix occurred at lower elevations (50-300 m MSL) on gentler slopes (3-10°) and required adequate soil moisture associated with its profligate water use. We also show that niche differentiation between Alnus and Salix changed with patch size, where larger patches were more specialized in resource requirements than individual plants of Alnus and Salix. To understand what constrains the growth of DTSs at locations with low fCover, we developed environmental limiting factor models, which showed that topography limits the upper bound of Alnus and Salix fCover in 69.2% and 48.7% of the landscape, respectively. These findings highlight a critical need to better understand and represent topography-controlled processes and functional traits in regulating shrub distribution, as well as a need for more detailed species classification to predict shrubification in the Arctic.

Abstract. Accurate assessment of leaf functional traits is crucial for a diverse range of applications from crop phenotyping to parameterizing global climate models. Leaf reflectance spectroscopy offers a promising avenue to advance ecological and agricultural research by complementing traditional, time-consuming gas exchange measurements. However, the development of robust hyperspectral models for predicting leaf photosynthetic capacity and associated traits from reflectance data has been hindered by limited data availability across species and environments. Here we introduce the Global Spectra-Trait Initiative (GSTI), a collaborative repository of paired leaf hyperspectral and gas exchange measurements from diverse ecosystems. The GSTI repository currently encompasses over 7500 observations from 397 species and 41 sites gathered from 36 published and unpublished studies, thereby offering a key resource for developing and validating hyperspectral models of leaf photosynthetic capacity. The GSTI database is developed on GitHub (https://github.com/plantphys/gsti, last access: 4 January 2026) and published to ESS-DIVE https://doi.org/10.15485/2530733, Lamour et al., 2025). It includes gas exchange data, derived photosynthetic parameters, and key leaf traits often associated with traditional gas exchange measurements such as leaf mass per area and leaf elemental composition. By providing a standardized repository for data sharing and analysis, we present a critical step towards creating hyperspectral models for predicting photosynthetic traits and associated leaf traits for terrestrial plants.

BACKGROUND AND AIMS: Tropical forests exchange more carbon dioxide (CO2) with the atmosphere than any other terrestrial biome. Yet, uncertainty in the projected carbon balance over the next century is roughly three times greater for the tropics than other for ecosystems. Our limited knowledge of tropical plant physiological responses, including photosynthetic, to climate change is a substantial source of uncertainty in our ability to forecast the global terrestrial carbon sink. METHODS: We used a meta-analytic approach, focusing on tropical photosynthetic temperature responses, to address this knowledge gap. Our dataset, gleaned from 18 independent studies, included leaf-level light-saturated photosynthetic (Asat) temperature responses from 108 woody species, with additional temperature parameters (35 species) and rates (250 species) of both maximum rates of electron transport (Jmax) and Rubisco carboxylation (Vcmax). We investigated how these parameters responded to mean annual temperature (MAT), temperature variability, aridity and elevation, as well as also how responses differed among successional strategy, leaf habit and light environment. KEY RESULTS: Optimum temperatures for Asat (ToptA) and Jmax (ToptJ) increased with MAT but not for Vcmax (ToptV). Although photosynthetic rates were higher for 'light' than 'shaded' leaves, light conditions did not generate differences in temperature response parameters. ToptA did not differ with successional strategy, but early successional species had ~4 °C wider thermal niches than mid/late species. Semi-deciduous species had ~1 °C higher ToptA than broadleaf evergreen species. Most global modelling efforts consider all tropical forests as a single 'broadleaf evergreen' functional type, but our data show that tropical species with different leaf habits display distinct temperature responses that should be included in modelling efforts. CONCLUSIONS: This novel research will inform modelling efforts to quantify tropical ecosystem carbon cycling and provide more accurate representations of how these key ecosystems will respond to altered temperature patterns in the face of climate warming.

Abstract Snow thermal insulation is a critical factor influencing ground thermal dynamics and associated biogeochemical processes. We analyzed the spatiotemporal variability of snow accumulation, melting, and thermal insulation dynamics using spatially dense, collocated snow depth and ground interface temperature time series over two consecutive years. We demonstrated that considering late‐winter snow depth alone was insufficient to fully capture the complexity in snow and insulation dynamics. The influence of vegetation and topography on snow depth distribution varied over the season, across sites and years. We found that deep snow with a long melting period had a substantial impact on thawing n ‐factors. To better predict snow insulation effects, we proposed a new weighted snow depth metric that integrates mean daily snow depth and air temperature throughout the cold season. Our results provide insights for developing space‐time remote sensing products and evaluating the representation of snow and permafrost processes in Earth system models. Plain Language Summary In Arctic regions, the snow plays an important role in regulating ground temperatures and influencing different earth processes. Acting like a blanket, the snow keeps the ground warm during the cold season and prevents heat from moving into ground during snowmelt. Snow depth and timing of snow accumulation and melt events can vary greatly from place to place, leading to significant differences in ground temperatures. We analyzed snow depth and ground interface temperatures collected continuously for 112 locations at two small subarctic sites in Alaska over two years. Our results showed that vegetation and topography strongly influenced snow depth, but their relationships changed over time and varied between sites. We also found that differences in late‐winter snow depth led to variable snow‐free dates, and local air temperature further complicated this. Finally, we developed a new metric to better estimate how snow insulated the ground, incorporating daily snow depth and air temperature throughout the entire cold season. This research advances our understanding of snow dynamics and insulation effects over space and time, which is vital for evaluating how well we are capturing snow and permafrost processes in Earth system models. Key Points Unprecedented high‐resolution monitoring of snow depth and temperature reveals fine temporal dynamics in snow distribution and insulation Vegetation and topography drive spatial variability in snow accumulation, with effects varying over season, across years and sites Incorporating mean daily snow depth and air temperature changes along with snowmelt timing improves snow insulation quantification

Despite its importance for determining global carbon fluxes, leaf respiration remains poorly constrained in land surface models (LSMs). We tested the sensitivity of the Energy Exascale Earth System Model Land Model - Functionally Assembled Terrestrial Ecosystem Simulator (ELM-FATES) to variation in the canopy gradients of leaf maintenance respiration (R_dark). We ran global and point simulations varying the canopy gradient of R_dark to explore the impacts on forest structure, composition, and carbon cycling. In global simulations, steeper canopy gradients of R_dark lead to increased understory survival and leaf biomass. Leaf area index (LAI) increased up to 77% in tropical regions compared with the default parameterization, improving alignment with remotely sensed benchmarks. Global vegetation carbon varied from 308 Pg C to 449 Pg C across the ensemble. In tropical forest simulations, steeper gradients of R_dark had a large impact on successional dynamics. Results show the importance of canopy gradients in leaf traits and fluxes for determining plant carbon budgets and emergent ecosystem properties such as competitive dynamics, LAI, and vegetation carbon. The high-model sensitivity to canopy gradients in R_dark highlights the need for more observations of how leaf traits and fluxes vary along light micro-environments to inform critical dynamics in LSMs.

Leaf dark respiration (R_dark), an important yet rarely quantified component of carbon cycling in forest ecosystems, is often simulated from leaf traits such as the maximum carboxylation capacity (V_cmax), leaf mass per area (LMA), nitrogen (N) and phosphorus (P) concentrations, in terrestrial biosphere models. However, the validity of these relationships across forest types remains to be thoroughly assessed. Here, we analyzed R_dark variability and its associations with V_cmax and other leaf traits across three temperate, subtropical and tropical forests in China, evaluating the effectiveness of leaf spectroscopy as a superior monitoring alternative. We found that leaf magnesium and calcium concentrations were more significant in explaining cross-site R_dark than commonly used traits like LMA, N and P concentrations, but univariate trait-R_dark relationships were always weak (r² ≤ 0.15) and forest-specific. Although multivariate relationships of leaf traits improved the model performance, leaf spectroscopy outperformed trait-R_dark relationships, accurately predicted cross-site R_dark (r² = 0.65) and pinpointed the factors contributing to R_dark variability. Our findings reveal a few novel traits with greater cross-site scalability regarding R_dark, challenging the use of empirical trait-R_dark relationships in process models and emphasize the potential of leaf spectroscopy as a promising alternative for estimating R_dark, which could ultimately improve process modeling of terrestrial plant respiration.

Grazing exclusion and land abandonment are commonly adopted to restore degraded ecosystems in semiarid and arid regions worldwide. However, the temporal variation in the soil- versus root-associated microbiome over plant species turnover during secondary succession has rarely been quantified. Using the chronosequence restored from fenced grassland and abandoned farmlands on the Loess Plateau of China, we characterized the dynamics of the soil- and root-associated microbiome of host plant with different dominance statuses during secondary succession from 0 to 40 years. Our results revealed that the root microhabitat, the host plant and their interactions were the main contributors to the bacterial community shift (R² = 15.5%, 8.1%, and 22.3%, respectively), and plant interspecies replacement had a greater effect on the shift in the root-associated microbial community than intraspecies replacement did during succession. The root-associated bacterial community of pioneer plants was particularly responsive to succession, especially the endosphere community. Endosphere microbial diversity was positively correlated with host plant coverage change, and the diversity and abundance of taxon recruitment into the endosphere of pioneer plants from the surrounding environment decreased as succession progressed. The community assembly processes also indicated that the endosphere microbiota are strongly selected in younger host plants, whereas stochastic processes dominate in aged host plants. Our study provides evidence of the unique response of the root-associated microbiome to the replacement of plant species during secondary succession, and the function of endosphere microbes should be considered when studying plant-microbe feedback.