Plant traits - morphological, anatomical, biochemical, physiological or phenological features measurable at the level of individuals or their component organs or tissues - reflect the outcome of evolutionary and community assembly processes responding to abiotic and biotic environmental constraints. Therefore, measurements of plant traits and trait syndromes (consistent associations of plant traits) are valuable observations to evaluate models based on eco-evolutionary optimality (EEO) principles. In 2007 the TRY database project (https://www.try-db.org/) was initiated to improve the empirical basis for trait-based ecological studies, trying to bring together the different plant trait databases worldwide. As a result, the TRY Plant Trait Database has constantly been growing and has accomplished unprecedented coverage. Since 2019 the data are publicly available under a CC BY license. This presentation is supposed to provide an update on recent developments in the context of the TRY initiative, i.e. the recently released new version of the TRY database (version 6), the release of the 'Global Spectrum of Plant Form and Function Dataset', and the 'rtry' R package to support preprocessing of trait data retrieved from the TRY database.

How to cite: Kattge, J., Bönisch, G., Lam, O. H. Y., Schellenberger Costa, D., Diaz, S., Lavorel, S., Prentice, I. C., Leadley, P., and Wirth, C. and the TRY Consortium: Recent developments in the context of the TRY Plant Trait Database, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-908, https://doi.org/10.5194/egusphere-egu23-908, 2023.

Plants acclimate to increasing CO₂ by reducing leaf nutrient allocation and photosynthetic capacity at the leaf level, a response that often occurs alongside growth stimulation at the whole plant level. Nutrient limitation has been hypothesized to be the primary mechanism driving leaf and whole plant acclimation responses to CO₂, as nutrient availability commonly limits primary productivity and may decrease with increasing CO₂ over time. However, recent work leveraging photosynthetic least-cost theory indicates that these acclimation responses may instead be the result of optimal resource investment toward photosynthetic capacity, which maximizes nutrient allocation to whole plant growth. Acclimation responses to CO₂ may also vary in species with different nutrient acquisition strategies, but few studies have examined these responses across a soil nitrogen availability gradient and in species with different nutrient acquisition strategies. To test whether nutrient limitation or optimal leaf resource investment controls leaf and whole plant acclimation responses to CO₂ and how nutrient acquisition strategy modifies these responses, we grew Glycine max L. (Merr) seedlings under two atmospheric CO₂ levels, with and without Bradyrhizobium japonicum inoculation, and across nine soil nitrogen fertilization treatments in a full factorial growth chamber experiment. After seven weeks, G. max demonstrated a strong downregulation in leaf nitrogen content, V_cmax25, and J_max25 under elevated CO₂, patterns that were not causally linked to changes in soil nitrogen fertilization or inoculation treatment. A relatively stronger downregulation in leaf nitrogen content than V_cmax25 increased the proportion of leaf nitrogen content allocated to photosynthesis, while a relatively stronger downregulation in V_cmax25 than J_max25 stimulated J_max25:V_cmax25 under elevated CO₂. These leaf acclimation responses to elevated CO₂ corresponded with strong stimulations in total leaf area and total biomass, a pattern that was generally stronger with increasing fertilization and in inoculated pots. Whole plant acclimation responses to CO₂ were driven by reductions in the cost of acquiring nitrogen with increasing fertilization and inoculation. Overall, these results provide strong support for patterns expected from photosynthetic least-cost theory, showing that optimal resource investment is the primary mechanism governing G. max acclimation responses to elevated CO₂.

How to cite: Perkowski, E. A. and Smith, N. G.: Leaf acclimation to elevated CO2 is independent of soil nitrogen fertilization and rhizobial inoculation, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3518, https://doi.org/10.5194/egusphere-egu23-3518, 2023.

A better understanding of the litter decay process is critical for improved predictions of terrestrial carbon (C) exchange between above and below-ground C reservoirs. Furthermore, developing well-constrained decomposition models with explicit representation of microorganisms is becoming more crucial for improving our understanding of nutrient recycling between soils and plants, greenhouse gas emissions, and the contribution of litter to soil organic matter formation.

Litter is typically characterized by structural and non-structural pools—structural components representing the lignin like compounds and non-structural representing soluble and holocellulose organic compounds. Initial litter chemical composition has a strong control on its decomposition. In fact, empirical studies show that a higher initial lignin content in a litter is associated with slower decomposition of holocellulose and implies an increased cost of oxidative enzyme production to break the lignin cross-linked compounds, thereby decreasing microbial community carbon use efficiency. In mathematical models describing litter dynamics, the decomposition rates of these pools are given by the assumed kinetics, either first-order or Monod type, with time-invariant kinetic parameters. This approach neglects possible temporal changes in microbial traits that reflect how decomposer communities adapt to litter chemical properties.

Here, we have taken an optimal control approach that does not fix kinetic parameters, but instead finds the decomposition rate constant of the structural (lignin) pool by maximizing the microbial growth (i.e., maximum fitness as a result of natural selection) while taking into account the effect of litter chemistry on microbial metabolism. In this formulation, we combine the soluble and holocellulose C into a non-structural C pool and assume first-order kinetics of decomposition of both structural and non-structural pools. Our results predict a time-varying decomposition rate constant for the lignin pool. This means that optimally adapted microbes would start decomposing lignin at different times as a function of initial lignin content. Further, we provide a case study testing the performance of our model against observed litter decomposition data from a boreal forest. With this contribution, we aim to highlight the applications of eco-evolutionary approaches as an alternate parametrization scheme for litter decomposition models by utilizing microbial life strategy as the main driving factor.

How to cite: Chakrawal, A., Lindahl, B., and Manzoni, S.: Optimal lignin decomposition during litter decay, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5128, https://doi.org/10.5194/egusphere-egu23-5128, 2023.

Leaf phenology, often measured by the seasonal dynamics of leaf area index (LAI), is a key control on the exchanges of CO₂ and energy between land ecosystems and the atmosphere. It is therefore also a key target process for dynamic vegetation models. However, there is no agreement on how leaf phenology should be modelled. Much research has focused on the specific triggers for budburst– and, to a lesser extent, leaf senescence– in biomes characterized by distinct cold or dry seasons. Recent theoretical developments however suggest the existence of a more general, global relationship between leaf phenology and the seasonal time course of “steady-state LAI”: the LAI would be in equilibrium with GPP if weather conditions were held constant. This can be predicted from the time course of gross primary production (GPP) because LAI and GPP are mutually related, via the Beer’s law dependence of GPP on LAI, and the requirement for GPP to support LAI development. In our current research we are developing a new global phenology model, by combining this new theoretical approach with a terrestrial photosynthesis model (the P-model) that avoids the multiplicity of parameters required by more complex models, while achieving good fit to GPP derived from flux towers in all biomes. But whereas P-model applications to date have exploited satellite-derived green vegetation cover indices as input, our current research aims to predict the seasonal time course of both LAI and GPP. This is done in two steps. First, we predict seasonal maximum LAI as the lesser of an energy-limited value that maximizes GPP, and a water-limited value that allows vegetation to transpire a fraction of annual precipitation. Second, we model the time-course of LAI assuming that its derivative tracks the difference between current and steady-state LAI with some lag. We are testing this approach with data from a global phenocam network and using remotely sensed LAI. Results so far are promising, but point to challenges, especially in representing interannual variability and trends.

How to cite: Zhou, B., Zhu, Z., Cai, W., and Prentice, I. C.: Towards a global leaf phenology model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6407, https://doi.org/10.5194/egusphere-egu23-6407, 2023.

Understanding the distribution of assimilated carbon (C) among different plant parts is essential in explaining the responses of multiple functional traits to climate change. C allocation is not adequately represented by current ecosystem models, and the general explanatory framework of C allocation with environmental conditions is fragmented. Machine learning approaches applied to large data sets have failed to reveal general principles underlying C allocation. Here, we analyse a large global set of data derived from several previous compilations to test eco-evolutionary optimality hypotheses that potentially account for the environmental controls on root:shoot biomass ratios (R:S) in both woody and herbaceous plants. These controls are expressed in terms of statistical predictors describing aspects of the environment relevant to plant stimuli. Thus, for example, we consider growing-season temperatures rather than annual means; and we include modelled gross primary production (GPP) and root-zone water capacity (RZWC) among the candidate predictors. We hypothesize that increasing gross primary productivity (GPP) permits increased C allocation to stems, automatically reducing R:S. Demand for C allocation to roots is less in warmer climates because of faster nutrient turnover in warmer soils. On acid soils, the need for roots to take up nutrients is reduced due to more open stomata and thus lower optimal photosynthetic capacity. More C is allocated to roots in climates with seasonal mismatches between water supply and demand, where increased RZWC is required to maintain water availability during the dry season. On sandy soils, low water-holding capacity implies a need for further investment in roots for water uptake. Our analysis broadly supports these hypotheses, and an ordinary least-squares multiple linear regression model explains nearly three-quarters of the observed global variation in R:S. However, the allocation strategies of woody and herbaceous plants differ. The expected negative relationship of R:S to growth temperature, and the positive relationship of R:S to sand content, are shown only in woody plants; while the expected positive relationship of R:S to soil pH is shown only in herbaceous plants. These findings constitute a first step towards a theory of C allocation response to resource availability, and a parsimonious model for inclusion in next-generation C cycle models.

How to cite: Ding, R., Nóbrega, R., and Prentice, I. C.: Towards a theory of carbon allocation based on eco-evolutionary optimality principles, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6459, https://doi.org/10.5194/egusphere-egu23-6459, 2023.

There exists a long going discussion on how many parameters are needed within complex ecological, ecosystem and earth system models. Overparameterization is an often-used term to describe an overshoot in parameter space dimensionality, which sometimes may lead to better results, but goes hand in hand with a loss in generality. Oversimplification of parameter space dimensions - on the other hand - may lead to results that may be correct in the mean, but incorrect in each single case.

So, one question arises: How can we determine the number of parameters needed to describe a system with the desired accuracy and precision for a given application?

A possible answer lies in the relationship between the correlation among, and the respective information content within, some given data or model outputs. Alfréd Rényi provided dimensional descriptors for this issue, i.e. the correlation dimension and the information dimension embedded in a given data series.

When both dimensions are equal, the most simple model description with least parameters is best. When information content exceeds the correlation, then a higher dimensional parameter space is needed to achieve accurate results.

We will use two examples to demonstrate this principle: temperate alpine ecosystems and tropical lowland ecosystems, both modelled with BGC-MAN. The degree of difference between correlation and information will show the differences in parameter space needed to get an accurate and precise description of the modelled system.

How to cite: Pietsch, S.: Paradigm Shifts in Parameter Space, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10880, https://doi.org/10.5194/egusphere-egu23-10880, 2023.

Next generation ecosystem models based on a fundamental physiological trait based approach are promising. At leaf level, Eco-Evolutionary Optimality (EEO) approaches are useful as a base for these kinds of models. The tradeoff between the cost of maintaining photosynthetic capacity and the cost of transpiration in different environments can be used effectively and modelled accurately for many different environments. The utility of EEO principles in these models is based on the link between photosynthesis and Gross Primary Production (GPP). Plant available nutrients are one of the largest constraints in ecosystem productivity so recently efforts to include this in EEO theory have been made. A crucial uncertainty in current EEO theory is how the carbon cost to acquire nutrients should be parametrized and to which extend this costs is relatively conservative or changes dynamically under different environmental conditions and between species. We hypothesize that the carbon cost to acquire nutrients increases for a plant grown in a poor soil, requiring more root system to obtain a similar amount of nutrients compared to a plant grown in a rich soil. The effect of soil microbial activity on this cost is less intuitively. Plants grown in reciprocal altruistic symbiosis with mycorrhizal fungi, for example, are known to "trade" carbon for nutrients or water, altering this carbon cost. Soil microbial pathogens can be costly from a plants perspective without any gain, but a generic representation is not yet incorporated in the optimality framework.

To test this hypothesis, we conducted a greenhouse pot experiment with two plant species grown in three different nutrient treatments in sand, and compared them to plants of the same species grown in either natural soil or sterilized soil, both without additional nutrient treatment. The plant available nitrogen (N) and phosphorus (P) in the middle nutrient treatment were set to correspond closely to the available nutrients in the natural and sterilized soils

Initial results show a positive correlation between photosynthetic capacity at leaf level and total plant dry weight (DW), both increasing with increasing nutrient availability in sand. In soils however, leaf level photosynthetic capacity and total plant DW react in opposite directions when comparing natural versus sterilized soils. Total plant DW was high in sterilized soils with a relatively low leaf level photosynthetic capacity, while the opposite was found in natural soils. Elemental analyses will be used to (I) analyse carbon allocated to root systems and the correlation with whole plant nitrogen and (I) to extrapolate leaf level photosynthetic capacity to whole plant photosynthetic capacity and examine the relation between plant photsynthetic capacity and total plant DW.

Investigating plant carbon allocation under varying soil environments could provide a link between the well formulated leaf level EEO theory and the more cryptic soil influences.

How to cite: Lankhorst, J., Rebel, K., Odé, A., and de Boer, H.: Soil microbial communities influence plant carbon cost to acquire nutrients, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14808, https://doi.org/10.5194/egusphere-egu23-14808, 2023.

Eco-evolutionary optimality (EEO) states that plants adapt or acclimate to their environment, thereby eliminating uncompetitive plant strategies by natural selection. EEO has been proven successful for developing hypotheses and models of the terrestrial biosphere. On a plant leaf level, EEO theory is used to analyze and model plant processes including photosynthesis, gas exchange, and stomatal behavior. Plants regulate their gas exchange by dynamically adjusting their stomata on a short term time scale (opening and closing) and long term time scale (stomatal size and density), which also influences photosynthetic capacity. The operational stomatal conductance (Gop) is determined by the opening state of the stomata during typical growth conditions. The anatomical maximum stomatal conductance (Gsmax) results from the maximum stomatal aperture, stomatal density and pore depth. According to the work of McElwain et al. (2016), plants operate at the conservative  Gop:Gsmax ratio of ~0.25, which means that they utilize only a fraction of their anatomical potential. Yet, it is currently unknown whether conservation of Gop:Gsmax can be explained from EEO theory.

To further investigate this interesting coupling between leaf physiology and morphology in an EEO context, we conducted an experiment to gain insight into the differences in gas exchange, photosynthesis, morphology and Gop:Gsmax ratio resulting from acclimation to shifts in atmospheric CO₂ growth conditions. Plants of six common crop species were grown in ambient (400ppm) and elevated (1000ppm) CO₂ growth chambers. Species include four eudicots (including one woody species) and two monocots (one C3 and one C4 photosynthesis species), enabling an assessment of adaptation in species with different photosynthetic mechanisms and stomatal morphologies. For all species, a diurnal cycle, leaf mass per area, ACi response curves, light response curves, and Gop were measured. Additionally, imprints of the leaves were taken to derive Gsmax from microscope analysis.

Preliminary results show that exposure to elevated CO₂ leads to a decline in Gop, Gsmax and photosynthetic capacity, in-line with EEO theory. Results of one C3 eudicot showed the expected lower Vcmax, Jmax, and stomatal density at elevated atmospheric CO₂ concentrations. There was also a small decrease in Gop compared to the ambient group for this species. Overall, the Gop:Gsmax ratio of the elevated atmospheric CO₂ treatment was slightly higher than at ambient levels. Combining gas exchange and the ACi curves shows a shift of Gop towards the high sensitivity region where small changes in leaf internal CO2 concentration result in a relatively large change in net photosynthesis rate. Further analysis, including an assessment of adaption to atmospheric CO₂ in the other species, will reveal the overall responses of the small but diverse group of plants in this experiment, and potential differences in strategy between species with different photosynthetic mechanisms and stomatal traits. This will improve our understanding of EEO theory across different species and environmental conditions.

How to cite: Odé, A., Drake, P., Lankhorst, J., Veneklaas, E., Rebel, K., and de Boer, H.: Testing the responses and interplay of leaf physiological and morphological traits at elevated CO2 levels in six common crop species, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15215, https://doi.org/10.5194/egusphere-egu23-15215, 2023.