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EDI
Plant traits, adaptation, and biogeochemical cycles – from measurements to models

Plant traits extend the range of earth observations to the level of individual organisms, providing a link to ecosystem function and modelling in the context of rapid global changes. However, overcoming the differences in temporal and spatial scales between plant trait data and biogeochemical cycles remains a challenge.

This session will address the role of plant traits, biodiversity, acclimation, and adaptation in the biogeochemical cycles of water, carbon, nitrogen, and phosphorus. We welcome conceptual, observational, experimental and modelling approaches and studies from the local to the global scale, including in-situ or remote sensing observations.

Convener: Jens Kattge | Co-conveners: Michael Bahn, Oskar Franklin, Han WangECSECS
Presentations
| Thu, 26 May, 08:30–11:37 (CEST)
 
Room 2.95

Thu, 26 May, 08:30–10:00

Chairpersons: Jens Kattge, Michael Bahn, Oskar Franklin

08:30–08:37
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EGU22-426
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ECS
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Virtual presentation
Nathielly Martins et al.

In large parts of the Amazon rainforest low soil phosphorus availability may prevent the stimulation of forest growth in response to elevated atmospheric CO2 (eCO2). One strategy of plants could be to increase the relative allocation of the extra C belowground to their root systems to enhance nutrient acquisition and alleviate the potential phosphorus limitation, but little is known about the responses of tropical lowland forest species. We hypothesized that in tropical understory plants will trigger a first a fast upregulation of fine root phosphatase activities, followed by changes in fine root productivity and adaptions of morphological parameters, such as specific root length (SRL), specific root area (SRA) and root tissue density (RTD) to enhance phosphorus mobilization, increase its availability and exploit a larger soil and litter volume.

We tested our hypothesis in the first CO2 enrichment experiment in Central Amazonia at a low soil phosphorus site, increasing CO2 levels by 200 ppm relative to CO2 ambient (aCO2) concentrations using open top chambers (OTC) in the forest understory. We monitored potential root phosphatase activity, root productivity, and morphological traits in the soil with ingrowth cores (0-15 cm) and in the litter layer, as well as root biomass stocks in 0-5 and 5-10 cm of depth.

In contrast to our hypothesis, we observed a reduction in fine root productivity (<1mm diameter), from 0.038 ± 0.01 mg cm2 day-1 under aCO2 to 0.013 ± 0.004 mg cm2 day-1 after 12 months of eCO2. On the other hand, the fine root biomass stock (<2mm diameter) increased at 5-10 cm from 0.86 ± 0.18 at aCO2 to 1.74 ± 0.65 mg-1 cm2 with eCO2, but there was no effect of eCO2 on fine root biomass in the litter layer. However, roots growing in the litter layer significantly increased their SRL and showed a strong tendency of higher SRA in response to eCO2 (SRL: 4.66 ± 1.08 and 9.58 ± 2.12 cm mg-1; SRA:  0.63 ± 0.18 and 1.0 ± 0.25 cm2 mg-1 with aCO2 and eCO2, respectively), but we did not observe changes in root morphological parameters in the soil, only a tendency towards decreasing RTD. Moreover, we found a strong trend towards an increase in potential root phosphatase activity with eCO2 in the litter by 20.0 % (aCO2: 66.16 ± 10.4; eCO2: 79.39 ± 20.8 nmol mg-1 dry root h-1) and soil by 45.61% (aCO2: 97.42 ± 30.76; eCO2:141.86 ± 34.04 nmol mg-1 dry root h-1).

Our initial results suggest that understory plants intensified the investment in fine root dynamics in litter layer as response to eCO2 (e.g., increase in SRL and potential root phosphatase activity) Furthermore, with a potential increase in root phosphatases exudation (litter and soil) in the first year with eCO2, our results reinforce the importance of this mechanism to mobilize inorganic P. Our results provide an initial understanding of nutrient mechanisms acquisition under eCO2 in a tropical forest, which can be incorporated into ecosystem models to allow more reliable predictions of forest productivity under eCO2.

How to cite: Martins, N., F. Lugli, L., J. Valverde-Barrantes, O., Takeshi, B., Pires, M., G. Menezes, J., Sales Pereira, I., Guedes, A., R. Ferrer, V., R. Santos, Y., U. Neves, G., C. M. Moraes, A., Caroline Miron, A., P. Hartley, I., J. Norby, R., A. Quesada, C., and Fuchslueger, L. and the AmazonFACE team: Initial responses of fine root dynamics of understory plants to elevated CO2 in a Central Amazon rainforest, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-426, https://doi.org/10.5194/egusphere-egu22-426, 2022.

08:37–08:44
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EGU22-1556
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ECS
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Highlight
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On-site presentation
Quan Pan et al.

Multi-traits covariation and plant trait-network have become a hot issue in the current research. The adaptation of plants to environmental changes depends on the coordinated changes of multiple traits, and ecosystem processes and functions also rely on the combined effects of multiple traits. Recent studies show that the combination of traits in different organs of plants has great potential to study the adaptation strategies of plants to their environment, but such assertions have not been proven on a larger scale. For exploring plant adaptation strategies worldwidely,we collected 268680 trait records with environmental background information from TRY database after pre-processing, including 4 taxonomic traits and 22 continuous traits from different organs of 25014 species. In this research, we consider species as basic units,as they are the units for responding to environmental change and ecosystem management. This presentation mainly covers the adaptation strategies embodied by different plant trait-network and the main environmental factors that drive the change of plant trait-network. Revealing the interdependence of plant traits could not only advance our understanding of the adaptive strategies of plants, but also helps to optimize the vegetation dynamic models. At the end,  this presentation will prospect for potential and ideas for research on plant trait-network for vegetation model improvement and ecosystem management measures.

How to cite: Pan, Q., Peaucelle, M., Bauters, M., and Verbeeck, H.: Trait-network reveals the adaptation strategies of plants, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1556, https://doi.org/10.5194/egusphere-egu22-1556, 2022.

08:44–08:51
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EGU22-2008
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Virtual presentation
Congwen Wang et al.

Ecosystem functions play crucial role in maintaining human well-being. In recent years, more studies have focused on multiple ecosystem functions (ecosystem multifunctionality, EMF) in terrestrial ecosystems. Biotic and abiotic factors mediated by climate change and human activities have important influence on regulating EMF. However, their relative roles are unclear in grassland ecosystems. We conducted a transect survey across grassland ecosystems of northern China to illustrate the relative effects of biotic (including plant diversity, plant traits, and soil microbial diversity) and abiotic (including climatic and soil variables) factors on EMF (including 9 functions, i.e. aboveground biomass, aboveground litter biomass, soil organic carbon, total carbon, total nitrogen, total microbial biomass, bacterial biomass, fungal biomass, and arbuscular mycorrhizal fungi biomass) in 2018. Structural equation modeling indicated that soil sand content and plant diversity had direct effects on EMF, and soil fungal diversity (including main functional guild diversity) indirectly affected EMF through regulating plant diversity. Functional richness of leaf dry matter content had direct effects on EMF, while functional richness of stem density indirectly regulated EMF through affecting functional richness of leaf dry matter content. Variance partitioning analysis showed that biotic and abiotic factors together explained 85% of the variance in EMF, and biotic and abiotic factors explained 77% and 34%, respectively, and combined explained 26%. The random forest algorithm detected that soil sand content and plant diversity were the important abiotic and biotic variables in predicting EMF. These findings have contributed to comprehensive unraveling the effects of biotic and abiotic factors on EMF, highlighting particularly the importance of soil texture and plant diversity in regulating EMF. Land degradation (increased soil sand content) and biodiversity loss induced by on-going climate change and human activities will cause detrimental effects on grassland ecosystem functions. Our findings suggest that integrated management of aboveground and belowground ecosystems contributes to better restoration of degraded grassland ecosystem functions and services.

How to cite: Wang, C., Yu, W., Ye, X., Ma, L., Wang, R., Huang, Z., and Liu, G.: Soil texture and plant diversity are important abiotic and biotic factors regulating ecosystem multifunctionality across grasslands of northern China, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2008, https://doi.org/10.5194/egusphere-egu22-2008, 2022.

08:51–08:58
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EGU22-4767
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Highlight
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Virtual presentation
Silvia Caldararu et al.

Leaf nutrient contents, in particular nitrogen (N) and phosphorus (P), are key plant traits, linking to processes such as photosynthesis and respiration. Traditionally, plant traits are considered to be constant in time, but there is ample observational evidence that nutrient content varies with changes in environmental conditions. Specifically, increased atmospheric CO2 drives increased plant growth and potentially increases in plant nutrient limitation, while anthropogenic N deposition further drives an imbalance in the N and P available to plants. Therefore, being able to dynamically, and accurately, represent leaf N and P content in land surface models (LSMs) is critical to predicting future ecosystem response to global change.

Most LSMs that include dynamic leaf N and P do so through a set of empirical functions that balance out demand and supply of nutrients. However, such representations do not take into account the differing physiological roles of the two nutrients, with N being directly linked to photosynthetic compounds, while P has a much stronger control on new biomass growth and respiration. 

Using the QUINCY land surface model we represent physiologically-realistic dynamic leaf N and P content, using optimality theory, which assumes that plants alter their structure and function in order to maximize growth. We test the model using data from ICP Forests, a spatially extensive European network of sites with repeated, standardized measurements of leaf N and P content covering the period 1990 - present. We show that the new model representation performs better than the standard empirical functions, both in terms of spatial distribution of leaf nutrient content and its change through time, being able to reproduce the observed shift towards P limitation caused by increased N deposition. Most importantly, the model does not rely on the law of the minimum principle, being able to represent true co-limitation and allow leaf N and P to vary with different physiological and environmental pressures, thus creating more robust and realistic predictions.

How to cite: Caldararu, S., Yu, L., Nair, R., Fleischer, K., and Zaehle, S.: Dynamic leaf nitrogen and phosphorus under increasing nutrient co-limitation in a land surface model, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4767, https://doi.org/10.5194/egusphere-egu22-4767, 2022.

08:58–09:05
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EGU22-5357
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ECS
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Highlight
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On-site presentation
Louis Krieger et al.

Commonly, xylem hydraulic conductance is measured by applying a positive pressure (above atmospheric) to push water through a twig. To imitate flow in twig samples under natural conditions, we developed a method that applies a controlled flow rate using suction, similar to transpiration-driven flow in plants.

The setup consists of a syringe pump to control water flow, where a twig is inserted in the flow path and hydraulic conductivity is calculated from measurements using pressure sensors and a flow meter. The syringe pump can be used to generate controlled flow rates in both directions and a series of bypasses can be used to self-calibrate the sensors and reverse flow directions through the twig while the syringe pump is either pushing or pulling. In this way, we were able to compare our suction method with the more conventional pushing method and assess the effect of flow direction on hydraulic conductance measurements. We found a reproducible pattern in measured conductivity values, where measurements using suction resulted in a 50% lower conductivity than when flow was induced by pushing. The direction of flow (root-shoot vs. shoot-root) also had a strong influence, with suction in root-shoot direction resulting in the lowest conductivity measurements, but repeated reversals of flow revealed an intricate pattern of loss and partial restoration of conductivity, implicating the existence of particles that move with the flow and accumulate at the vessel ends.

Here we present the intriguing results and propose an explanation capable of explaining the reproducible patterns in observed conductivity dynamics during the experiments. The explanation involves nanobubbles that shrink and swell depending on the liquid pressure and surface tension, move with the flow and reduce conductivity as they accumulate at vessel ends.

How to cite: Krieger, L., Schymanski, S., and Jansen, S.: Xylem hydraulic conductivity measurements during flow-controlled experiments suggest the presence of nanobubbles that move with the flow and accumulate at vessel ends, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5357, https://doi.org/10.5194/egusphere-egu22-5357, 2022.

09:05–09:12
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EGU22-5458
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ECS
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Virtual presentation
Tatiana Reichert et al.

Phosphorus (P) is one of the main limiting nutrients for forest productivity in Amazonia. To meet P needs, plants invest resources in different strategies which may increase their P-use efficiency, e.g., by resorbing P from senescing organs, or increase their P-acquisition efficiency, e.g., by acclimating fine root traits (architectural, morphological, physiological, and symbiotic). P-acquisition strategies can be categorized into foraging strategies related to the uptake of plant-available P or mining strategies related to the mobilization and uptake of less available forms of P. However, little is known about the effects of soil P on plant P-use and -acquisition strategies in Amazonia. Therefore, we have conducted a literature review and synthesized the current knowledge on the variation of different P-use and -acquisition strategies across soil P fertility gradients and their response to P fertilization in Amazonia and other tropical forests (Reichert et al., in press). We provide a conceptual framework on the distribution of these strategies in Amazonia and propose that, at the plant community level, foraging strategies (via fine roots and arbuscular mycorrhizas) are more prevalent and may contribute most for plant P uptake in soils with intermediate to high P availability, and leaf P resorption and mining strategies (via root exudation of acid phosphatases and organic acids) in soils with intermediate to low P availability (Reichert et al., in press). Here, we suggest that the investment in different P-acquisition strategies may be partially explained by the energy cost per unit P acquired. Based on the assumption that this cost varies with strategy and the form of P and its concentration in the soil (Raven et al., 2018), we have developed a stand-alone theoretical model to predict plant investments in P acquisition and test our conceptual framework. We constrain the model with field observations on forest growth and soil nutrients from sites in Amazonia and explore possible shifts in P-acquisition strategies along soil P fertility gradients.

Raven JA, Lambers H, Smith SE, Westoby M. 2018. Costs of acquiring phosphorus by vascular land plants: patterns and implications for plant coexistence. New Phytologist 217(4): 1420-1427.

Reichert T, Rammig A, Fuchslueger L, Lugli LF, Quesada CA, Fleischer K. In press. Plant phosphorus-use and -acquisition strategies in Amazonia. New Phytologist.

How to cite: Reichert, T., Rammig, A., Fuchslueger, L., F. Lugli, L., A. Quesada, C., Papastefanou, P., Gregor, K., and Fleischer, K.: Plant phosphorus-use and -acquisition strategies and energy costs in Amazonia, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5458, https://doi.org/10.5194/egusphere-egu22-5458, 2022.

09:12–09:19
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EGU22-5920
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ECS
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Highlight
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On-site presentation
Mateus Dantas de Paula et al.

Community assembly in highly diverse tropical rainforests is poorly understood and advancing our understanding is crucial for predicting ecosystem responses to future environmental change. Dynamic vegetation models which include plant trait variation are able to produce realistic plant communities by ecological filtering under climatic and edaphic drivers. Building upon more than a decade of field research in the biodiversity hotspot of the mountain rainforest in southern Ecuador, we implemented plant trait variability and improved soil organic matter dynamics in a widely used regional to global dynamic vegetation model (LPJ-GUESS) in order to explore how nutrients may influence community trait assembly and productivity along an altitudinal gradient.

In the new model version LPJ-GUESS-NTD (where NTD stands for nutrient-trait dynamics), each plant individual can possess different trait combinations which determine their demand for nutrients (N and P) and competiveness along conservative – acquisitive strategies.   Nutrient availability is determined by the stoichiometry of the source organic matter and edaphic constraints to its decomposition, producing a feedback between vegetation traits and their drivers. Final community trait composition emerges via ecological sorting. Further model developments include mycorrhizal nutrient uptake.

The new model version reproduced the main observed community trait shift and related vegetation processes along the elevational gradient, but only if nutrient limitations to plant growth were activated. In turn, when traits were fixed, low productivity communities emerged due to reduced nutrient-use efficiency. The results strongly suggest that interactions between plant traits and nutrient limitations are crucial for community assembly and ecosystem functioning in our study area and probably in other systems where water is not limiting. Future studies based on the LPJ-GUESS-NTD model will include further traits based on the belowground carbon economy and collaboration with mycorrhiza, and may provide important insights concerning the role of functional diversity for ecosystem resilience under climate change and increased anthropogenic nutrient deposition.

How to cite: Dantas de Paula, M., Forrest, M., Langan, L., Bendix, J., Homeier, J., Velescu, A., Wilcke, W., and Hickler, T.: Nutrient cycling and plant trait variation - two crucial processes for simulating the community assembly and productivity of tropical forests, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5920, https://doi.org/10.5194/egusphere-egu22-5920, 2022.

09:19–09:26
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EGU22-6108
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ECS
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On-site presentation
Josias Gloy et al.

Cryptic refugia enable tree species to survive outside their current range in an area occupied during a previous glacial/interglacial period. Once the climatic conditions are more favorable the populations can expand from these refugia. This can impact the resulting tree composition as it can enable species to dominate that are computationally weaker. The smaller population size of a cryptic refugium, the isolation and adaptation to unfavorable conditions can however also lead to the loss in genetic diversity and weaker populations.

One type current populations that might finds its origin in those refugia are the larches of Eastern Siberia that are dominating vast areas. While it is known and apparent from observation that once established they present a currently stable ecosystem it is now known what factors lead to their establishment in contrast to Northern America and Europe where other conifers dominate the landscapes.

We are using an individual-based model that includes trait adaptation and will be modified to allow for inbreeding depression effects. This model will be used to simulate the refugia and the connections between them during the glacial to assess the fitness and genetic diversity that long isolation can cause. Furthermore we are going to perform breakout simulations to simulate the migration into the area in the interglacial period.

This results could give insights into the adaptations and genetic diversity in refugia and how these impact the colonization and untimely shape the ecosystem. This knowledge could be used to make stronger predictions about future developments and the possibility of regeneration of the ecosystem should it be further hanged by climate change.

How to cite: Gloy, J., Kruse, S., and Herzschuh, U.: Simulating traits adaptation of trees in refugia , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6108, https://doi.org/10.5194/egusphere-egu22-6108, 2022.

09:26–09:33
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EGU22-7411
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On-site presentation
Yinon Bar-On and Ron Milo

Photosynthetic carbon assimilation enables energy storage in the living world and produces most of the biomass in the biosphere. Rubisco (D-ribulose 1,5-bisphosphate carboxylase/oxygenase), is responsible for the vast majority of global carbon fixation, and has been previously claimed to be the most abundant protein on Earth. Here, we provide an updated and rigorous estimate for the total mass of Rubisco on Earth, concluding it is ≈0.7 Gt, more than an order of magnitude higher than previously thought. We find that >90% of Rubisco enzymes are found in the ≈2x1014 m2 of leaves of terrestrial plants, and that Rubisco accounts for ≈3% of the total mass of leaves which we estimate at ≈30 Gt dry weight. We use our estimate for the total mass of Rubisco to derive the effective time-averaged catalytic rate of Rubisco and find that it is ≈0.03 s-1 on land and ≈0.6 s-1 in the ocean. In comparison to the maximal catalytic rate observed in vitro at 25℃, the effective rate in the wild is ≈100-fold slower on land and ≈7-fold slower in the ocean. The lower ambient temperature, and Rubisco not working at nighttime, are enough to explain most of the difference from lab conditions in the ocean, which implies that in the ocean Rubisco is working close to its maximal catalytic capacity. This is not the case for land Rubiscos, and therefore motivates future quantification of many more factors on a global scale.

How to cite: Bar-On, Y. and Milo, R.: The Global Mass and Average Rate of Rubisco, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7411, https://doi.org/10.5194/egusphere-egu22-7411, 2022.

09:33–09:40
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EGU22-7684
On the features of seasonal water storage and heavy metals accumulation in the leaf phytomass of the genus Acer L. plants
(withdrawn)
Lyudmila Kavelenova et al.
09:40–09:47
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EGU22-7960
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ECS
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Virtual presentation
Changti Zhao et al.

As important components of leaf economic spectrum (LES), specific leaf area (SLA), chlorophyll (Chl) content, and foliar nutrient content are crucial plant functional traits (PFT) and essential parameters in most earth system models. Among those, SLA, Chl, foliar carbon (C), nitrogen (N), phosphorus (P) content and their stoichiometry are key indicators which are frequently focused on due to their application in predicting vegetation dynamics and ecosystem productivity in response to anthropogenic perturbations, especially atmospheric N deposition increase. With the hotspot of global N deposition transferred to subtropical and tropical regions, how forest ecosystem changes in these ecoregions response to N deposition has attracted great attention during the past decades. Hence, we established a network of nutrient enrichment experiments in eastern China's forests (NEECF) for exploring the effects of N deposition in 2010.

To evaluate the effect of long-term N addition on foliar traits, we conducted field sampling of the dominant tree species (i.e., Castanopsis eyrie and Castanopsis sclerophylla) in two subtropical forests on the platform of NEECF in August, 2020. 100 kg N ha-1 yr-1 were applied in each forest with 3 replications of plots, respectively. The adults and seedlings of the two dominant species were sampled to make a contrast. Through the subsequent detection and analysis, we found that: (1) leaf-trait syndrome of the dominant species in two subtropical forests followed the predictions of global LES, and the growth strategy of the old-aged C.eyrie forest was more conservative than the middle-aged C.sclerophylla forest; (2) N addition had no significant effect on leaf N contents and C:N ratios of both species, but significantly reduced SLA and Chl content of C. eyrie adults and increased C content of C. sclerophylla seedlings. Moreover, both species showed a more consistent trend of decreasing P content and a corresponding increase of C:P and N:P ratios. (3) N addition shifted the C~P scaling relationship of both species and SLA~P scaling relationship of C. sclerophylla.

Our results verified the existence of LES patterns among closely related species at the local scale. Moreover, we found that N addition showed varied effects on different leaf traits and trait-pairs relationship of subtropical evergreen plants. 10 years’ N addition of high dosage significantly aggravated P limitation in subtropical evergreen forests, which led to a more conservative growth strategy, especially in middle-aged C.sclerophylla forest. Our work through site-level case study provided data support for connecting foliar functional traits with earth system models, which will contribute to enhance the predictions of ecosystem function and vegetation dynamics in the context of increasing global N deposition.

How to cite: Zhao, C., Lin, Q., and Tian, D.: Effects of nitrogen addition on foliar traits of the dominant tree species in two subtropical evergreen forests in eastern China, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7960, https://doi.org/10.5194/egusphere-egu22-7960, 2022.

09:47–09:54
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EGU22-8348
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ECS
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On-site presentation
Loeka Jongejans et al.

With ongoing climate warming, ice-rich permafrost, such as late Pleistocene Yedoma permafrost, is especially vulnerable to rapid and deep thaw processes. Such permafrost sediments contain a large organic matter storage that becomes increasingly accessible to microbes upon thaw. Only a few studies analysed organic matter in deep (>10 m) permafrost and thawed permafrost sediments. We studied Yedoma sediments from four sites in Yakutia in the Russian Federation: at the Arctic Ocean (Bykovsky Peninsula), inside the Lena Delta (Sobo-Sise Cliff), close to the northern hemisphere’s cold pole (Batagay) and in central Yakutia (Yukechi Alas). We measured biomarker concentrations of sediment cores taken from below thermokarst lakes and sediment samples taken from the headwall of a coastal bluff and a retrogressive thaw slump. In addition, we carried out incubation experiments to quantify greenhouse gas production in thawing permafrost. Here, we present the first molecular biomarker distributions (alkanes and fatty acids) and organic carbon turnover (anaerobic CO2 and CH4 production) data as well as insights in organic matter decomposition processes in deep frozen and thawed Yedoma sediments. We show that biomarker proxies are useful to assess the source and degree of degradation of permafrost organic matter. Furthermore, the organic matter in frozen Pleistocene Yedoma sediments was better preserved than in thawed Holocene sediments. These findings show the relevance of studying organic matter in deep permafrost sediments.

How to cite: Jongejans, L., Mangelsdorf, K., Liebner, S., Grosse, G., Grigoriev, M., Fedorov, A., and Strauss, J.: Molecular biomarkers and carbon turnover data in ice-rich permafrost in Yakutia, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8348, https://doi.org/10.5194/egusphere-egu22-8348, 2022.

Thu, 26 May, 10:20–11:50

Chairpersons: Jens Kattge, Michael Bahn, Oskar Franklin

10:20–10:27
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EGU22-9450
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ECS
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On-site presentation
Haozhi Ma et al.

Forest leaf habit and leaf type largely affect the structure and functioning of ecosystems, driving spatial variation in carbon, water, and nutrient cycles. To address spatial variation of leaf habit and leaf type across global forests, we combined a global scale forest inventory dataset with leaf habit and leaf type information from the TRY database, allowing us to generate a spatial understanding of the environmental controls of the global forest functional type distribution. Our analyses reveal large gradients of broadleaved evergreen, broadleaved deciduous and needle-leaved forest across the globe, which can be attributed to climatic, soil and anthropogenic features. In agreement with local experimental studies, hot and humid climates with acidic soil favor broadleaved evergreen species, whereas broadleaved deciduous species dominate in regions with intermediate rainfall, and needle-leaved trees dominate in nutrient-poor sites with cold or dry climate. By integrating our forest functional type maps with a recent global assessment of tree density, we estimate that 29.6%, 28.9% and 41.5% of the ~3 trillion global trees presently existing are broadleaved evergreen, broadleaved deciduous and needle-leaved. Based on the analysis of forest-type climate envelopes, we predict that 22–37% of the forest area is likely to experience a future change in climate envelope, with the evergreen forest climate envelope declining and the deciduous forest climate envelope increasing in area worldwide. By quantifying the present distribution of trees with different leaf habit and leaf type and highlighting regions where climate change will increase the climatic stress experienced by the present forest, our results are valuable to improve predictions of global terrestrial carbon cycling now, and in the future.

How to cite: Ma, H., Zohner, C. M., Mo, L., Maynard, D. S., van den Hoogen, J., and Crowther, T. W.: The biogeographic distribution of forest functional types based on ground-sourced inventory data, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9450, https://doi.org/10.5194/egusphere-egu22-9450, 2022.

10:27–10:34
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EGU22-10154
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ECS
Benjamin Dechant and the sTRAITS synthesis working group

Foliar traits such as leaf nitrogen and phosphorus content (LNC, LPC) as well as specific leaf area (SLA) are key components of the leaf economic spectrum and hence important to characterize ecosystem functioning and functional diversity. However, up to now, global-scale maps of these traits have been produced using rather indirect approaches: either statistical upscaling on the basis of large plant trait databases or process-based modeling. Although there are more direct approaches to estimate such leaf traits from remote sensing, their applicability is still limited in coverage due to the sparsity of suitable ground reference data and satellite or airborne imagery.

Here, we report a comprehensive intercomparison of the currently available global maps of LNC, LPC, and SLA. In total, we examined global plant trait maps from seven different upscaling approaches. Here we categorize these different upscaling approaches and analyze the spatial patterns in the trait maps at different scales.

Overall, global foliar trait maps show considerable differences in both the distribution of values and spatial patterns. Major differences in spatial patterns among products were related to differences in the use of plant functional type (PFT) categories from land cover maps in the upscaling. While some of the upscaling approaches did not rely on PFT information at all, others used it in one or several steps of the upscaling. Similarities in spatial patterns emerged when the foliar trait maps are subset according to whether PFT information was used or not. Only the maps that used PFT information showed similarities in spatial patterns at smaller scales.

Future upscaling approaches should take into account new remote sensing data sources, such as hyperspectral reflectance from upcoming satellite missions, and provide sufficient details on the upscaling methodology as well as the intended purpose of the resulting maps.

How to cite: Dechant, B. and the sTRAITS synthesis working group: Intercomparing global foliar trait maps: upscaling approaches and spatial patterns , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10154, https://doi.org/10.5194/egusphere-egu22-10154, 2022.

10:34–10:41
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EGU22-10248
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ECS
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On-site presentation
Laura Martínez-Ferrer et al.

Gross primary production (GPP) represents the amount of carbon captured via vegetation photosynthesis,  being this process one of the main drivers of climate regulation. Due to its importance, GPP is routinely estimated at global scales using different operational algorithms combining remotely-sensed data from medium spatial resolution sensors and ancillary meteorological information. There are numerous processes at multiple spatio temporal scales that result in GPP variability. Since these processes occur simultaneously at finer resolutions but also across large areas there is a need for GPP products that meet these specifications. The estimation of GPP requires consistent mosaics and long time series of high spatial resolution satellite information, which are often plagued by data record gaps as a result of cloud contamination, radiometric differences across sensors, scene overlaps, and their inherent sensor noise. To overcome these constraints, we used the HIghly Scalable Temporal Adaptive Reflectance Fusion Model (HISTARFM) algorithm that fuses spectral data from Landsat and MODIS and produces monthly gap free surface reflectance data at 30m over large areas with associated well-calibrated data uncertainties. Combining this monthly high resolution data with daily meteorological information, along with in-situ eddy covariance GPP estimates leads us to be able to create accurate and continuous high spatial resolution GPP estimates and their corresponding uncertainties (aleatoric and epistemic) using machine learning approaches. The processing pipeline is implemented in the Google Earth Engine (GEE) to produce a long time series (20 years) of continuous GPP estimates over Europe at 30m. This work enables more precise carbon studies and understanding of land-atmosphere interactions, as well as the possibility of deriving other carbon, heat and energy fluxes at an unprecedented spatio-temporal resolution.

How to cite: Martínez-Ferrer, L., Moreno-Martínez, Á., Kimball, J. S., Running, S. W., Clinton, N., and Camps-Valls, G.: Carbon fluxes estimation with aleatoric and epistemic uncertainties at high spatial resolution over large areas, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10248, https://doi.org/10.5194/egusphere-egu22-10248, 2022.

10:41–10:48
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EGU22-10409
Roshanak Darvishzadeh et al.

Canopy biophysical properties play an important role in understanding forest health and productivity. Among these parameters, forest leaf area index (LAI), canopy cover fraction, and canopy chlorophyll content describe the vegetation abundance, photosynthetic capacity and primary productivity of forest stands. The new generation of remote sensing satellites such as Sentinel-2 with high spatial and temporal resolutions has provided vast opportunities for monitoring these parameters and assessing their interrelationships over vast forest landscapes. In this research, temporal Sentinel-2 data between 2017-2019 in the temperate mixed forest ecosystem of the Bavarian Forest National Park, Germany, was used to retrieve forest canopy biophysical variables. INFORM radiative transfer model was used to retrieve LAI and canopy chlorophyll content while the fraction of vegetation functional types were calculated using phenological parameters and empirical approaches. A recent landcover map of the Bavarian Forest National Park was applied to retrieve considered variables pursuant to the different land cover classes. The retrieved variables were validated using in situ measurements of LAI and canopy chlorophyll content. Primary productivity was then calculated using (i) vegetation index universal pattern decomposition approach and (ii) the process-based dynamic vegetation-terrestrial ecosystem model LPJ-GUESS model. The relationships between calculated productivities and estimated biophysical variables were then studied. Our results showed that there is a good agreement between primary productivities calculated from LPG GUESS and the decomposition approach. Among studied parameters, canopy chlorophyll content, which represents pigments and vegetation abundance within the canopy, showed a strong direct relationship with both calculated primary productivities and hence may be used to explain plant functioning. Our results also revealed that remotely sensed vegetation biophysical parameters- that are becoming more and more readily available due to the availability of Earth observation data- can be used as proxies for estimation of the primary productivity calculated using either approach. Calculation of primary productivity usually needs information about canopy life-cycle and geometry, which are often not available at large scales. The results of our study support our findings in the myVARIABLE pilot of the EuroGEOSS Showcases initiative (e-shape) on developing primary productivity as a remotely sensed- essential biodiversity variable describing ‘Ecosystem function.’

How to cite: Darvishzadeh, R., Nienavaz, E., Huesca, M., Skidmore, A., Nieuwenhuis, W., Fernandez, N., and Wårlind, D.: On the relationship of primary productivity and remotely sensed canopy biophysical variables , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10409, https://doi.org/10.5194/egusphere-egu22-10409, 2022.

10:48–10:55
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EGU22-11298
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ECS
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Virtual presentation
Doroteja Bitunjac et al.

Coarse woody debris (CWD), aside its contribution to forest ecosystem productivity and biodiversity, has a significant role in nutrient cycling and carbon storage. Still, CWD chemical features, as well as its density, are less studied in comparison to other plant traits. This is evident from the statistics of the most recent version 5 of TRY Plant Trait Database where CWD traits are underrepresented. Nevertheless, also due to the carbon (C) accounting requirements under UNFCCC and EU regulations, where dead wood (DW) is recognized as one of five mandatory C pools in forest ecosystems, interest in CWD has been increasing. The aim of this research was to provide national DW biomass conversion factors, i.e. dead wood densities (DWD) and C concentrations, which can be used for reporting on C stocks in DW pool. We hypothesize that there are differences in DW biomass conversion factors with respect to tree species group (ring-porous, diffuse-porous, non-porous) and biogeographical region. Additionally, we explored the content of N, K, Ca, P, Mg mineral macronutrients in CWD of different decay classes and tree species. 

The research was conducted on ten forest tree species that represent main forest ecosystems in Croatia located in three biogeographical regions, i.e. Continental, Alpine and Mediterranean. In the field, stem discs were sampled from lying CWD, with diameter between 5 and 30 cm, that was categorized into five decay classes, from 0 (raw wood) to 4 (very decayed dead wood). In total, we collected 446 CWD samples evenly distributed between tree species and decay classes. All samples were analysed for density, C and N content, with selected 165 samples analysed further on K, Ca, P, Mg content. Overall, DWD, as expected, showed decreasing trend with respect to decay class, while for C, N and mineral macronutrients no trend regarding decay classes was observed. For each tree species group, DWDs by decay classes were compared between different biogeographical regions. In ring-porous species a significant difference was observed in DWD between samples collected in Continental and Mediterranean biogeographical region for decay classes 0-2, while in non-porous species DWD between samples collected in Alpine and Mediterranean biogeographical region were significantly different for decay classes 2-4. Results on C, N and mineral macronutrients were were compared with those published in TRY database and other available sources.

How to cite: Bitunjac, D., Ostrogović Sever, M. Z., Sever, K., and Marjanović, H.: Coarse woody debris density and elemental components by decay classes for ten tree species in Croatia, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11298, https://doi.org/10.5194/egusphere-egu22-11298, 2022.

10:55–11:02
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EGU22-11459
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ECS
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Highlight
Di Tian et al.

As an important aspect of the leaf economic spectrum, leaf nitrogen (N) and phosphorus (P) concentrations are key traits that shape plant growth and function and reflect evolutionary history. Their patterns across the globe have been interpreted as being reflective of controls by climate, soil fertility, and atmospheric N and P deposition. Yet, recent research has emphasized an overriding importance of species identity and phylogeny in determining stoichiometry. This limits the applicability of methods for predicting global patterns in foliar stoichiometry from environmental covariates and implies that stoichiometry responses to global environmental change are limited by the rate of species replacement.

Here, we investigated these contrasting views. We established a comprehensive global data set of 36,413 paired observations of leaf N and P concentrations with their specific phylogenetic taxonomy and 46 environmental covariates. For leaf N, we identified the most important predictors being N deposition, irradiance, temperature of the coldest month, atmospheric CO2, elevation, mean annual vapor pressure deficit, and aluminum saturation of the soil solution. For leaf P, the predictors are N deposition, temperature of the coldest month, aridity index, atmospheric CO2, soil phosphorus concentration, annual mean ratio of actual over potential evapotranspiration, multi-day average stomatal conductance, available water storage capacity and precipitation of the driest month. Together, the predictors explain 46% and 33% of leaf N and P variations respectively in the data aggregated by sites, using a Random Forest model. Using linear models on the full non-aggregated data and not accounting for interactions between predictors, species identity explains the largest portion of observed variations in individual-level foliar N (50.4%) and P (33.9%), while environmental covariates explain only 3.8%, and 2.8%, respectively.

A trait gradient analysis reconciles these contrasting results and suggests more within-species variations in response to the environment as would be expected from the results of the linear models. We found (i) that the genetic variation or environmentally-induced plasticity in foliar N and P within species is substantial for most species, (ii) that species’ distributions cover a wide range of environments as expressed by site-level mean leaf N and P, and (iii) that variations of leaf N and P stoichiometry across sites are not merely emerging as a result of distinct species present at different sites. This challenges the notion of a fixed biogeochemical niche occupied by individual species and suggests that plants mediate their stoichiometry in response to their environment, and potentially to global environmental change. This insight also provides the basis for a robust spatial upscaling of foliar N and P, using global fields of environmental covariates as predictors. Our global predictions also suggest that the highest foliar N concentration occurs in cold and dry climates and at high-elevation.

How to cite: Tian, D., Hufkens, K., Kattage, J., Yan, Z., Schmid, B., and Stocker, B. D.: Deciphering climate, soil and phylogenetic controls on leaf nitrogen and phosphorus stoichiometry of terrestrial plants-V1, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11459, https://doi.org/10.5194/egusphere-egu22-11459, 2022.

11:02–11:09
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EGU22-11710
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Highlight
Ivika Ostonen and Iuliia Burdun

The estimation of root traits and root growth dynamics is still a major challenge in plant belowground ecology. The urgent need for root data has stimulated the development of methodologies that led to an explosive increase in root trait data in recent years. To understand the challenges raising from rapid innovation, we did a systematic review 157 articles, published from 2000 to 2021, where the root traits measurements were performed using RGB images.  Our goal was to explore 1) how the imaging technologies used in root research have progressed in recent 20-years, 2) what are the dominating sensors and software used for imaging and image processing, 3) what are the challenges on our way towards machine readability of root traits. We discussed our results of the timeline dynamics analysis of root traits measurements based on RGB images, utilised sensors, type of root images and software used for image processing, but also the developments in the national and international collaborations in root research. Finally, and most importantly, we explored what are the most examined functional groups of plants, average number of species included in the study and the age of plants which roots were studied. We identified the most measured root traits and analysed the variety of terms used for definition of root traits. In the light of this, we discussed the challenges in root trait terminology on our way towards machine-readable root data.

How to cite: Ostonen, I. and Burdun, I.: Towards a world of machine-readable belowground data: technological innovation brings new challenges to root terminology, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11710, https://doi.org/10.5194/egusphere-egu22-11710, 2022.

11:09–11:16
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EGU22-12504
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ECS
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Highlight
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On-site presentation
Lidong Mo et al.

Wood density is an important functional trait, linked to tree growth and carbon storage in forests. Studying biogeographic variation in this trait along with the environmental and anthropogenic drivers of this variation is therefore critical to improve present and future carbon storage estimates of global forests. We combined a global database of ~1.1 million forest inventory plots with wood density records from 10,703 tree species to quantify the global wood density distribution and its drivers. In a first step, we tested the phylogenetic imprint on wood density variation among species and communities. Using information on environmental and anthropogenic covariates, we then created a global map of wood density at ~1km resolution. By integrating this wood density map with an existing map of growing stock volume and biome-level biomass expansion factors, we estimate that 403 Gt C are presently stored in the world’s forests, which largely agrees with previous estimates. Our analysis also allowed us to explore wood density variation along human disturbance and fire frequency gradients at different spatial scales to show that disturbance effects on wood density vary between forest types, biomes and environmental conditions. This study contributes to a better understanding of terrestrial biomass distribution patterns and the effects of human and ecological disturbances on forest structure.

How to cite: Mo, L., Zohner, C., Ma, H., Maynard, D., and Crowther, T.: The imprint of disturbance on the global wood density distribution, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12504, https://doi.org/10.5194/egusphere-egu22-12504, 2022.

11:16–11:23
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EGU22-12645
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Highlight
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On-site presentation
Philipp Porada

Non-vascular vegetation, such as lichens, mosses, or terrestrial algae and cyanobacteria, carry out key functions in various ecosystems world-wide.
These functions include effects on hydrological processes, net exchange of CO2, and the nutrient budget of ecosystems. The impact of the organisms on ecosystem functions, however, is not uniform, but it may strongly depend on the distribution of traits in individual non-vascular communities. Hence, to estimate large scale effects of non-vascular vegetation on biogeochemical cycles, it is crucial to predict their spatially and temporally dynamic community composition with respect to key traits.

The few currently available large-scale estimates do not examine systematically the role of traits for the biogeochemical effects of non-vascular vegetation, A better assessment of the patterns of key eco-physiological traits is, however, urgently needed, since climate change will likely substantially affect non-vascular community composition and, consequently, ecosystem functions.

Here, I will present a process-based modeling approach which provides quantitative estimates on the trait distributions of non-vascular vegetation communities, and relates these to biogeochemical functions. The so-called LiBry model has been extensively applied to simulate effects of non-vascular organisms on global biogeochemical cycles, focusing on cycles of carbon, water, and nitrogen. The model is, however, applicable at site scale as it accounts for key ecophysiological processes which control the carbon balance and the growth of an individual organism. By simulating at each location a large number of individuals which differ broadly with regard to 12 key physiological trait values, the model explicitly represents physiological diversity of non-vascular communities. The long-term carbon balance of each individual is then used as the criterion for relative success in the process of natural selection, shaping the trait distribution of communities at different locations. In this way, effects of environmental conditions on trait distributions and their consequences for ecosystem functions of non-vascular vegetation can be represented in a quantitative way.

I will provide an overview on large-scale biogeochemical impacts of non-vascular vegetation, derived by the LiBry model. In particular, I will show examples of how patterns in key traits, and also dynamic changes in community mean trait values, affect the biogeochemical functions of the organisms.

How to cite: Porada, P.: The role of trait variation of non-vascular vegetation for their impact on biogeochemical cycles, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12645, https://doi.org/10.5194/egusphere-egu22-12645, 2022.

11:23–11:30
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EGU22-13006
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ECS
Cesar Hinojo Hinojo et al.

With thousands of plant functional trait observations across the world, there is still a lack of spatially and temporally explicit estimates of traits that help inform how biodiversity, ecological and biogeochemical processes are changing across the globe. The Leaf Mass per Area (LMA) is a key trait that influences plant ecological strategies, and it is strongly correlated with leaf photosynthesis, plant growth, vegetation primary production and decomposition rates. Based on biophysical principles of the radiative transfer in canopies, we designed a new multispectral remote sensing index that is highly sensitive to the community weighted mean of LMA. We called this index iLMA, as an acronym for “index for LMA''. We tested and calibrated this index with ground data of a wide range of forest types and herbaceous communities  collected at 510 plots from 77 sites located in the American continent (R2 = 0.64). Using Landsat imagery, iLMA and the resulting calibrating equation, we made a 30m resolution global map of LMA. The LMA spatial pattern is consistent with our current understanding of LMA variation across major terrestrial biomes but at an unprecedented high-resolution. Then, we used Landsat imagery from 1985 to 2019 to produce yearly global estimates of LMA and map its rate of change. This map of change in LMA indicates that there has been a widespread decrease in LMA across the globe over the last 35 years, with the fastest and strongest declines happening in evergreen conifer forests in boreal and mountainous regions of the world, and tropical evergreen broadleaf forests in Africa and Asia. We discuss potential causes of such widespread decrease in LMA, including climate change and widespread changes in vegetation composition and structure, and its potential consequences for biogeochemical processes.

How to cite: Hinojo Hinojo, C., Bohner, T., Chacon-Labella, J., Falco, N., Frazier, A., Hemingway, B., Nikolopoulos, E. I., Wainwright, H., and Enquist, B.: Mapping 35 years of change in Leaf Mass per Area across the globe from multispectral satellite data, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13006, https://doi.org/10.5194/egusphere-egu22-13006, 2022.

11:30–11:37
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EGU22-13251
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ECS
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Olee Hoi Ying Lam et al.

From evolutionary biology, functional ecology, earth system modelling to landscape management, plant trait data are used to determine how the plants respond to the environmental factors and can act as indicators of ecosystem functions. In 2007, the TRY initiative was founded as an integrated database of trait data and all additional attributes relevant to understanding and interpreting a given trait value. Since then, the TRY database has integrated more than 400 datasets, including both original datasets and collective databases.

Due to the unique long table structure, the relevant information (e.g. trait names, species names, ancillary data representing context information, units of trait data, and identifiers for duplicates and outliers) for trait data filtering is stored at different places of the released TRY data. This makes the process to find all relevant information to select or remove trait data not straightforward without knowledge of the inherent data structure.

The ‘rtry’ package is an R package that provides a set of easily applicable functions for the basic steps of data preprocessing and is designed in particular to support the data exploration and removal of the plant trait data, taking advantage of the features of trait data released from the TRY database. This package is supposed to be applicable without advanced knowledge of the data structure released from TRY or the R software. Most importantly, despite the ‘rtry’ package being developed to support the application of plant trait data received via the TRY database, it is also applicable to other trait data.

 

How to cite: Lam, O. H. Y., Tautenhahn, S., Walther, G., Boenisch, G., Baddam, P., and Kattge, J.: The ‘rtry’ R package for preprocessing plant trait data, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13251, https://doi.org/10.5194/egusphere-egu22-13251, 2022.