Rapid and large-scale characterizations of forest canopies using remote sensing and modelling techniques are necessary, but the extent to which leaf-level traits and spectra can be upscaled to validate larger scale remote sensing data and vice versa is still not well understood. For one, spatial variations of leaf-level traits across tree crowns and canopies can bias results. While differences in extreme crown variations (e.g., shade and sun leaves) have been observed, across crown variation (i.e., sunlit leaves from top and sides of crown), which is of particular importance to increasingly higher resolution optical airborne and satellite remote sensing applications, have not been as widely documented.

Here, we investigated the across-crown and canopy variations of leaf-level plant functional traits and spectral reflectances and transmittances in a temperate forest in northern Japan. Our study period spanned the growing and senescence periods from July to October 2023 as well as deciduous broadleaf and evergreen conifer tree species. For each tree, we collected and processed sunlit leaves from two different crown positions visible to overhead aircraft: the top of the tree crown (TC) and at the tree crown periphery (CP).

We found differences in certain trait values (e.g., carbon (C) concentration,  d¹³C, leaf mass per area (LMA), chlorophyll, and total carotenoids) and spectra between TC (average height of 16.7 m above ground level) and CP (average height of 12.5 m above ground level) positions. We also show that differences between trait values and spectra between crown positions varied across tree species and over the growing and senescence periods. Overall reflectance and transmittance spectra across species showed increasing difference between TC and CP, especially in the near-infrared (NIR) region, from the growing to the senescence periods. Collectively, results suggest that crown sampling positions should be considered in the estimation of functional traits from spectral information, reflecting the dynamics of crown architecture. Particularly in spectral regions such as the NIR or short-wave infrared (SWIR), as well as during periods of senescence, where greater difference between crown positions were found.

How to cite: Lai, R., Nakaji, T., Akitsu, T. K., Hyodo, F., Noda, H., and Kobayashi, H.: Across-crown and canopy variations of plant functional traits and spectra for deciduous broadleaf and evergreen coniferous species in a temperate forest, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4475, https://doi.org/10.5194/egusphere-egu24-4475, 2024.

Relationships between plant functional traits and environmental variables have been intensively studied in the ecological community due to their importance for applications such as generating upscaled trait maps and predicting trait responses due to climate change. However, such relationships have been found to be relatively weak for various potential reasons.

We analyzed global-scale trait-environment relationships using plot-level trait estimates based on the sPlotOpen and TRY databases. In addition to the commonly used community weighted mean (CWM), we considered a top-of-canopy weighted mean (TWM) metric that excludes understory vegetation. For both trait metrics, we quantified the change in trait-environment relationships when considering the dominant plant functional type (PFT) of the plot. 

We found that, overall, TWM combined with PFT had the strongest correlations to environmental variables and TWM also had the strongest increase in correlation when adding PFTs. CWM, in contrast, tended to show slightly higher correlations than TWM when not adding PFTs, but the correlations for CWM combined with PFTs were also substantially higher than CWM without PFTs. Overall, we found stronger trait-environment relationships compared to the existing literature. Our findings confirm the relevance of considering PFT-specific trait-environment relationships and demonstrate the considerable impact of different plot-level trait metrics. The choice of the most suitable trait metric depends on the application and the availability of ancillary data that can be used as weighting factors in CWM.

How to cite: Dechant, B., Pavlick, R., Kattge, J., Schneider, F., Sabatini, F. M., Moreno-Martinez, A., Kattenborn, T., Bruehlheide, H., and Townsend, P. A.: Global-scale plant trait-environment relationships based on sPlotOpen and TRY data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15797, https://doi.org/10.5194/egusphere-egu24-15797, 2024.

Exploring the intricate interplay between global biodiversity patterns and the looming impact of climate change stands as a paramount inquiry within the realm of earth system science. Furthermore, the acknowledgment of shifts in plant functional diversity emerges as a key catalyst, wielding substantial influence over pivotal ecosystem processes like the carbon cycle. Various essential plant traits, intricately tied to vegetation function—ranging from photosynthesis to carbon storage and water/nutrient uptake—underscore the significance of comprehensive global trait maps. These maps prove indispensable for unraveling environmental interactions, identifying threats to the biosphere, and fostering a profound understanding of our planet's intricacies. However, the sparse and non-representative nature of current trait observations poses a formidable challenge. Presently, global maps of vegetation traits are constructed by bridging observational gaps, primarily relying on empirical or statistical relationships between trait observations, climate and soil data, and remote sensing information. However, these approaches exhibit limited explanatory power, struggle to encompass a myriad of traits, and face constraints in ensuring ecological consistency in their extrapolations.

The VESTA (Vegetation Spatialization of Traits Algorithm) project emerges as a groundbreaking initiative aimed at refining our grasp on global above and belowground plant traits. This endeavor involves integrating a trait-based dynamic global vegetation model (DGVM) with Earth observation (EO) data. Trait-based DGVMs, rooted in a process-based foundation, forge a direct nexus between the environment, plant ecology, and emerging vegetation patterns. Leveraging insights from contemporary global trait databases, the model is initialized to mirror real-world conditions. Subsequently, EO data enters the equation to fine-tune the model through a calibration process, adjusting trait relationship curves having as reference satellite measurements of vegetation structure and productivity.

Drawing parallels to prior methods used in climate reanalysis, EO-constrained trait-based DGVMs yield a multivariate, spatially comprehensive, and coherent record of global vegetation traits. The resultant dataset encapsulates trait distributions, offering detailed insights into plant functional diversity metrics—mean, variance, skewness, and kurtosis—at specific locations. Notably, these trait maps extend beyond mere snapshots, evolving into a temporal series that affords a nuanced comprehension of the prevailing state of functional diversity and its temporal shifts. Ultimately, the fruition of this project manifests as an invaluable EO product, showcasing leaf, wood, and root traits and their change through time.

How to cite: Dantas de Paula, M. and Hickler, T.: Towards global maps of vegetation trait change - the VESTA project, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5574, https://doi.org/10.5194/egusphere-egu24-5574, 2024.

The continuous rising of atmospheric carbon dioxide (CO₂) concentration is undoubtedly affecting the resilience of tropical forests worldwide. However, the magnitude of such effects is poorly known, limiting our capacity to assess the vulnerability of tropical forests and to improve their representation by models. Functional diversity (FD) is an important component of biodiversity enhancing ecosystem resilience, as high FD can provide higher response diversity and capacity to buffer against climate change. How FD is represented by different Dynamic Global Vegetation Models (DGVMs) may affect how such models predict the impacts of environmental changes on hyperdiverse ecosystems. We compared simulations of five trait-based DGVMs (i.e., with flexible, variable traits) constrained with data from the Amazon rainforest in the scope of the AmazonFACE project. Simulations were conducted considering initial high or low diversity scenarios under ambient and elevated CO₂ (400 ppm and 600 ppm, respectively). We searched for correspondence between the functional identity of simulated plant strategies and their ecophysiological performances under elevated CO₂. As models take different approaches to simulating functional trait distributions and they differ in their structure and in the trade-offs implemented, we found important intermodel differences in simulated results. Nevertheless, we took advantage of these differences in order to assess the most likely scenarios in terms of functional composition under elevated CO₂, as well as to give feedback for better harmonization of model inputs and outputs and future model improvements. In the face of the pessimistic scenarios that project a continuous increase in CO₂ levels, resolving the divergent responses among model predictions is critical, given the global importance of the Amazon rainforest's biodiversity and climate regulation, as well as the approximately 30 million people that directly or indirectly depend on the forest for their well-being.

How to cite: Blanco, C. C., Rius, B. F., Darela-Filho, J. P., Cardeli, B., Aleixo, I., Scheiter, S., Langan, L., Joshi, J., Hofhansl, F., Singh, S., De Paula, M. D., Hickler, T., Ongole, S., Higgins, S., Fleischer, K., Rammig, A., Lichstein, J., and Lapola, D. M.: Non-matching predictions from different models simulating the effects of elevated atmospheric CO2 on the Amazon forest’s functional diversity, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-22186, https://doi.org/10.5194/egusphere-egu24-22186, 2024.

Leaf photosynthetic and respiratory processes are important for the terrestrial carbon cycle. Leaf physiological traits, such as the maximum carboxylation rate and leaf respiration rate at 25˚C (V_cmax,25, R₂₅), the key parameters affecting photosynthesis and respiration rate, acclimate to environmental changes. However, many land surface models (LSMs) assume a constant R₂₅ and V_cmax,25 by plant functional types (PFTs) due to limited understanding of plant acclimation processes. Here, we incorporated the acclimation of photosynthesis and leaf respiration into a land surface model (Noah MP) using the Eco-Evolutionary Optimality principle (Noah MP-EEO), and evaluated the performances of the EEO and standard schemes to simulate photosynthesis and respiration using global plant trait measurements and data from FLUXNET. We demonstrate that R₂₅ and V_cmax,25 varied temporally and spatially within the same PFT (C.V. >20%). This behaviour is captured by the EEO scheme (R² =0.69 and 0.62 for temporal and spatial variations) but ignored by the standard scheme. At the FLUXNET sites, the standard scheme underestimates gross primary production (GPP) but this is reduced in the EEO scheme. The EEO scheme explains 66% of the variation of site-annual GPP compared to 55% in the standard scheme. The EEO scheme also simulates the variation of leaf respiration (R_leaves) better than the standard scheme (R² increases from 0.45 to 0.77). The EEO scheme shows less temperature sensitivity than the standard scheme because of acclimation. This study indicates that adopting EEO approaches that do not require PFT-specific parameters improves carbon cycle predictions and could be used in Earth system models for better understanding the climate-carbon feedback.

How to cite: Ren, Y., Wang, H., Harrison, S., Prentice, C., Mengoli, G., Zhao, L., and Yang, K.: Incorporating the acclimation of photosynthesis and leaf respiration in the Noah-MP land surface model: model development and evaluation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8705, https://doi.org/10.5194/egusphere-egu24-8705, 2024.

Quantifying leaf transpiration and photosynthesis is crucial for modeling global vegetation in a changing climate, but deriving general relationships and responses to environmental drivers across time scales remains challenging. A promising new approach to predict general leaf trait responses is the P-model (Prentice et al., 2014), which is based on eco-evolutionary optimality (EEO) theory. Leaf-level optimality is defined as the optimal ratio of leaf internal CO₂ partial pressure to ambient CO₂ partial pressure, resulting in maximum assimilation while minimizing respiration and transpiration costs. This process is coordinated via changes in both stomatal conductance and photosynthetic biochemistry, and results in a fundamental model with a strong dependency on climatic variables including temperature, relative humidity, CO₂ levels, and light quantity. The P-model currently predicts instantaneous changes in leaf-level traits of photosynthesis and gas exchange without explicitly considering timescales of adaptation and acclimation, and the associated ranges of phenotypic plasticity of individual plants. It is also limited/confined to leaf-level traits, without considering whole-plant processes, like resource allocation. Thus, it is uncertain how leaf-level optimality and their related costs translate to organ level carbon and nitrogen allocation.

Our work focuses on further developing and evaluating the P-model by incorporating time scales of acclimation and adaptation, as well as upscaling leaf-level traits to whole-plant traits. To achieve these aims, we first developed a theoretical framework which allows the distinction of different timescales. Using a literature review, we identified and highlighted the tight relationship between leaf traits across timescales, and we also identified constraints on key leaf-level EEO optimality traits, and the timescales at which these constraints occur. We then propose a new framework to separate these responses at physiological, developmental, and evolutionary timescales. Second, we performed experimental work in order to evaluate the link between leaf-level optimality and whole-plant acclimation. Our experiments showed a strong response in whole-plant resource allocation to nutrient availability while leaf-level optimality was unresponsive to the nutrient treatments. This indicates that while leaf-level optimality is regulated mainly by climatic variables, whole-plant performance is strongly influenced by below-ground resource availability.

This research is a way forward in bridging plant ecophysiology and vegetation modeling, while acknowledging timescales and plasticity ranging from meteorology to deep time climate research. This work can be used to further develop EEO-modeling, and specifically the P-model.

How to cite: Rebel, K., Odé, A., Lankhorst, J., and de Boer, H.: Temporal and plant-structural constraints for eco-evolutionary optimality models, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17097, https://doi.org/10.5194/egusphere-egu24-17097, 2024.

While roots have long been considered the sole pathway for plant nutrient uptake, a surprising discovery reveals a hidden source: foliar dust. This study demonstrates that plants can directly acquire crucial minerals like P, Fe, and Ni from deposited dust particles, bypassing the root system altogether. In our experiments, we had applied two types of dust on plant foliage and examined biomass, P content, and ionome. Remarkably, utilizing radiogenic Nd isotopes, we show that this foliar pathway surpasses root uptake in just weeks, contributing over 60% of a plant's nutrient profile under elevated CO2 conditions.

These findings shed light on a previously unrecognized adaptation that could be critical for plant survival in a CO2-rich world. As eCO2 levels are predicted to decrease soil nutrient availability, the dust-as-nutrient phenomenon offers a potential lifeline for plants and ecosystems. Moreover, understanding this alternative pathway could pave the way for novel agricultural strategies to combat "hidden hunger" malnutrition triggered by CO2-induced nutrient deficiencies.

How to cite: Palchan, D., Lokshin, A., Golan, E., Erel, R., and Fox, S.: Beyond roots: Foliar dust as a vital nutrient source for plants under elevated CO2, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2371, https://doi.org/10.5194/egusphere-egu24-2371, 2024.

Nutrient resorption from senescing leaves is a critical process of plant nutrient cycling that can significantly affect plant nutrient status and growth, making it essential for land surface models in order to predict long-term primary productivity. Most models assume leaf resorption to be a fixed value of 50% for N and P partially because we lack the knowledge of what drives this process, being unknown its implications when simulating nutrient cycling. Based on our own analysis of global patterns of nutrient resorption from trait data, we developed a dynamic scheme of nutrient resorption for nitrogen and phosphorus driven by leaf structure, longevity and environmental factors, to be implemented in the QUINCY model. We present the concept behind this novel scheme through ecophysiological traits trade-offs, as well as first implications for ecosystem functioning. We show that we can better predict plant and soil nutrient dynamics at steady state and crucially, under altered climate and CO2 conditions. Plant internal nutrient cycling has cascading implications for ecosystem nutrient pools and fluxes, being an essential process in ecosystem models, that allows us to improve our predictions of the future and furthers our understanding of nutrient cycling processes.

How to cite: Sophia, G., Caldararu, S., Stocker, B., and Zaehle, S.: Ecological trade-offs between leaf structure and plant nutrient demand to predict nutrient resorption in a terrestrial biosphere model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5264, https://doi.org/10.5194/egusphere-egu24-5264, 2024.

Plant nutritional traits (or nutritional values) are a fundamental property of the food web and also important for human beings' health. Much research on nutritional values has focused on a small selected group of plants and primarily crops, but their broader importance for wild animals has been neglected. In particular, the effect of climate change on the nutritional quality of plants is poorly understood even though it may have major implications for the food web and ecosystems. Here we use a dataset covering > 1450 plant species to identify the factors driving plants nutritional properties and develop global projections. We reveal that plant type, CO₂, and solar radiation are major drivers controlling nutritional properties. Projections for 2050 show a decline in nutritional quality (-8%, on average at the global scale), measured as the protein to fiber ratio, a strong decrease in minerals (-18%), and a small decrease in digestibility (-3%). Plants in arid and tropical areas will experience the largest decline in quality, which will decline minimally in temperate areas and improve in cold, and polar regions. Quality trends will be opposite in grasses. These results have important implications for human's health, livestock management, and wildlife conservation.

How to cite: Berzaghi, F. and Makowski, D.: Better integration of nutritional quality of plants in ecological research, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5592, https://doi.org/10.5194/egusphere-egu24-5592, 2024.

How to cite: Maxwell, T. L., Stefaniak, E., Hofhansl, F., and Joshi, J.: Incorporating roots into Plant-FATE, a dynamic eco-evolution trait-based vegetation model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5706, https://doi.org/10.5194/egusphere-egu24-5706, 2024.

Understanding how crops contribute to carbon, water and nitrogen cycling under different fertiliser regimes will be crucial for improving ecosystem models and predicting future yields. Synthetic fertilisers hugely boost crop yields, but excessive application often leads to negative environmental impacts including increased nitrous oxide emissions (about c. 300x more potent than CO₂). To maximise crop yields and optimise fertiliser and water application, rapid retrieval of plant traits and fluxes will be critical. Here, we explore the effectiveness of optical (trait-based) and thermal (flux-based) remotely-sensed data collected from ground-based and drone platforms for quantifying differences in plant physiological performance and overall yield in field-grown wheat under different nitrogen, sulphur and or sugar treatments.

Research was undertaken at a winter wheat variable nutrient field trial in North Yorkshire, UK during June, 2021. Across 24 treatment plots (3 plot replicates per treatment), leaf level hyperspectral reflectance data was obtained using a Spectral Evolution PSR+ 3500 Spectroradiometer which was paired with stomatal conductance (g_sw) measurements (collected using a LI-COR LI-600 porometer) and photosynthetic capacity (V_cmax) measurements (collected using a Li-6800 portable infra-red gas analyser). Plant thermal images were captured using a handheld FLIR T650-C thermal imaging camera (640x480). Field-assessed leaves were destructively harvested for leaf chlorophyll and nitrogen content analysis. Drone flights were conducted using a DJI Matrice M200 with a MicaSense RedEdge-Mx multispectral imaging sensor (1456 x 1088) and a Parrot Analfi thermal drone (160 x 120) at a 10 m altitude above ground.

Results show that plants fertilised with sulphur and nitrogen had the highest or equal-highest leaf chlorophyll values (c. 60-70 µg/cm²), followed by plants that only received nitrogen (c. 40-55 µg/cm²), with unfertilised controls having the lowest chlorophyll values (c. 15-20 µg/cm²). Sugar did not significantly affect leaf chlorophyll values but an interaction was detectable between sugar and fertiliser at the plot level (Two-way ANOVA, p < 0.05). Strong relationships were found between the MERIS terrestrial chlorophyll index (MTCI) spectral vegetation index, calculated from drone-acquired optical reflectance, and both leaf chlorophyll content (R² = 0.76; p < 0.0001) and crop yield (R²= 0.90; p < 0.0001). V_cmax assessment also revealed a strong relationship with chlorophyll across fertiliser treatments (R² = 0.77, p < 0.0001), with sulphur and nitrogen application again producing the highest trait values. Plants receiving nitrogen or nitrogen and sulphur had c. 50% higher g_sw, with leaf temperatures that were c. 1-3 °C cooler than unfertilised controls. Sugar did not significantly affect leaf g_sw or temperature. Using ground-based and drone-mounted thermal cameras, strong correlations were shown between leaf temperature and g_sw (R² = 0.64; p < 0.0001  and R² = 0.6; p < 0.001 respectively).  Data captured during the drone flights enabled the production of spatial maps of V_cmax (~3 cm spatial resolution) across the field trails to reveal clear differences in photosynthetic capacity both across and within nutrient treatments. Overall, remote sensing data accurately captured subtle differences in plant traits and water fluxes paving the way for field-scale mapping of crop physiological, biochemical and structural traits.

How to cite: Caine, R., Berry, P., Storer, K., and Croft, H.: Modelling crop productivity, water fluxes and yield in winter wheat from remotely-sensed drone data under differential sulphur, nitrogen and or sugar application., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17600, https://doi.org/10.5194/egusphere-egu24-17600, 2024.

Dusty secrets: REE unveil hidden trends in foliar plant nutrition under rising CO₂

Anton Lokshin^1,2, Avner Gross², Daniel Palchan¹

¹Department of Civil Engineering, Ariel University, Ariel 40700, Israel

² Department of Geography and Environmental Development, Ben Gurion University of the Negev, Beer Sheva 8410501, Israel

The emerging phenomenon of direct foliar nutrient uptake, where dust deposits replenish plant ionome under stress, holds potential as a significant adaptation in a changing world. Dust's rapid leaf deposition, bypassing soil contact, offers a unique nutrient source, particularly relevant in soils of limited fertility. Rising CO₂, known to hinder root nutrient uptake, along with an expected decrease in soil fertility, further underscores the potential importance of this pathway. Here, we present findings from laboratory experiments in which chickpea plants were treated with atmospheric particles used as natural fertilizers. These particles, including fire ash, volcanic ash, and desert dust, were applied to plant foliage under ambient and elevated CO₂ conditions. We examined the foliar nutrient pathway from the Rare Earth Elements (REE) perspective and demonstrated that REEs serve as an excellent tool for its exploration. Analysis of the REE within the treated plants showed enrichment in Light REE compared to Heavy REE concurrent with higher biomass and improved carbon assimilation – proving the nutrients were assimilated into the plant rather than just surface retention. Finally, our results elucidate a couple of trends in the foliar nutrient uptake pathway: (1) under elevated CO₂ levels, the foliar uptake is larger, and (2) nutrient transport from dust to plant is in the following order- volcanic ash > desert dust > fire ash. Analysis of REE patterns and ratios, alongside other biological parameters, provides novel insights into the extent and dynamics of foliar nutrient uptake across dust types and CO₂ levels, shedding light on previously unexplored aspects of this crucial adaptation.

How to cite: Lokshin, A.: Dusty secrets: REE unveil hidden trends in foliar plant nutrition under rising CO2, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1697, https://doi.org/10.5194/egusphere-egu24-1697, 2024.

The tree height–diameter at breast height (H–DBH) and crown radius–DBH (CR–DBH) relationships as well as wood density are key for forest carbon/biomass estimation, parameterization in vegetation models and vegetation–atmosphere interactions. Although the H–DBH relationship has been widely investigated on site or regional scales, and a small amount of studies have involved CR–DBH relationships based on plot-level data, few studies have quantitatively verified the universality of these two relationships on a global scale. Moreover, in current Earth System Models/Dynamic Global Vegetation Models (ESMs/DGVMs), wood density is also oversimplified, being defined either as a uniform constant or as plant functional type-dependent (PFT-dependent) constants worldwide. Such oversimplifications may lead to simulation biases in morphology of woody PFTs, ecosystem transition and vegetation-atmosphere interactions.

In our study, the ability of 29 functions to fit the H–DBH and CR–DBH relationships for six different plant functional types (PFTs) are evaluated on a global scale, based on a global plant trait database. Then, the relationships between H-DBH, CR-DBH, wood density and climate are investigated. This work provides a valuable foundation for parameterization improvements in vegetation models, and some clues to forest field investigations.

How to cite: Song, X., Li, J., and Zeng, X.: Adaptions of tree individual morphology and stem wood density to multiple climate and soil characteristics gradients, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4927, https://doi.org/10.5194/egusphere-egu24-4927, 2024.

During drought, when trees have lost access to soil moisture, the survival time of trees is intimately linked to leaf minimum water conductance (g_min), which determines the residual water loss after a tree has fully closed its stomates. Large differences in g_min are known among different tree species from contrasting climates. In addition, g_min typically exhibits strong and highly species-specific thermal sensitivity (T) with rising temperatures. Within a species, the genetic variability (G) and phenotypic plasticity (P) of g_min, and especially the G and P of the thermal sensitivity, in natural tree populations remains unknown. Here we examined the thermal sensitivity of four tree species (Acer pseudoplatanus, Fagus sylvatica, Picea abies, and Pseudotsuga menziesii) and assessed G, P, and the interaction of G x P of g_min and T in a provenience trial. Additionally, we determined the relative distance plasticity index (RDPI) among populations for each species and how leaf cuticular and stomatal traits are related to the intraspecific variation in g_min. The trees that we investigated were grown in three trials with different hydroclimatic conditions in Switzerland. Our results show a strong effect of T on g_min, which increased by a factor of two to seven when the temperature increased from 30 to 50 °C for all studied species. Importantly, g_min in two deciduous broadleaf tree species displayed strong G, with g_min values being higher for genotypes originating from wet climates than those of trees originating from dry climates. In contrast, there was little G in g_min for two evergreen conifers. On the other hand, significant P of g_min was found in all tested tree species, with higher g_min values for trees grown in wet rather than dry environments. RDPI was typically low across provenances for all studied tree species, suggesting the limited absolute P of g_min. Interestingly, there was a dramatic interaction of P x T for Fagus, showing stronger temperature responses under wet growing conditions rather than dry growing conditions. Interestingly, G and P of g_min could not be simply explained by leaf stomatal and cuticular traits. Our study provides novel insights into the long-term evolution and short-term adaptation of g_min, suggesting that g_min may be capable of acclimating to future hotter and drier environments in some but not all species. Our findings provide practical measurements for improving European forest management in the context of global-change-type drought.

How to cite: Wang, S., Hoch, G., Hopf, S., and Kahmen, A.: Genotypic variability and phenotypic plasticity of leaf minimum conductance, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16156, https://doi.org/10.5194/egusphere-egu24-16156, 2024.

Terrestrial climate and vegetation exhibit distinct zonal patterns in relation to longitude, latitude, and altitude, a phenomenon known as three-dimensional zonality. Accelerated warming rates since the industrial revolution, along with changes in latitude and altitude, have the potential to influence the geographical distribution of vegetative phenology. To comprehend and accurately predict changes in terrestrial ecosystems, it is critical to understand the three-dimensional zonal variations in global vegetation phenology. Using the PEP725, USA-NPN, and CPON phenological network datasets, we examined and compared the spatiotemporal dynamics of longitudinal, latitudinal, and elevational gradients at the beginning (SOS) and end (EOS) of the growing season over the last forty years in different regions and identified the potential mechanisms underlying these variations. It is revealed that the changes in latitudinal, longitudinal, and altitudinal gradients of SOS vary by location. Between 1980-1990 and 2008-2018, the latitudinal gradient of SOS in Europe decreased threefold, from 1.38 days/degree to 0.37 days/degree. In China, the longitudinal, latitudinal, and altitudinal gradients of SOS have all decreased, indicating that SOS is becoming more synchronized across longitude, latitude, and elevation. Unlike SOS, the latitudinal, longitudinal, and altitudinal gradients of EOS differ from species. For example, in Europe, the correlation between EOS and latitude has weakened for Aesculus hippocastanum and Betula pendula, indicating a reduced latitudinal gradient in EOS for these two tree species. In contrast, the correlation between latitude and EOS for Fagus sylvatica has strengthened, suggesting an increased latitudinal gradient in EOS for this species. In North America, due to the limited observation period, the changes in latitudinal gradients of plant phenological periods are not yet clear. These findings of mixed three-dimensional zonal variations in global vegetation phenology pose challenges for mitigating the possible adverse impacts of climate change.

How to cite: Gao, M.: Mixed three-dimensional zonal variations in vegetation phenology based on global site observations, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17553, https://doi.org/10.5194/egusphere-egu24-17553, 2024.