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Results for “Plant trait prediction”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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NASA NTRS2017

The Impact of Alternative Trait-Scaling Hypotheses for the Maximum Photosynthetic Carboxylation Rate (V (sub cmax)) on Global Gross Primary Production

The maximum photosynthetic carboxylation rate (V (sub cmax)) is an influential plant trait that has multiple scaling hypotheses, which is a source of uncertainty in predictive understanding of global gross primary production (GPP). Four trait-scaling hypotheses (plant functional type, nutrient limitation, environmental filtering, and plant plasticity) with nine specific implementations were used to predict global V(sub cmax) distributions and their impact on global GPP in the Sheffield Dynamic Global Vegetation Model (SDGVM). Global GPP varied from 108.1 to 128.2 petagrams of Carbon (PgC) per year, 65 percent of the range of a recent model intercomparison of global GPP. The variation in GPP propagated through to a 27percent coefficient of variation in net biome productivity (NBP). All hypotheses produced global GPP that was highly correlated (r equals 0.85-0.91) with three proxies of global GPP. Plant functional type-based nutrient limitation, underpinned by a core SDGVM hypothesis that plant nitrogen (N) status is inversely related to increasing costs of N acquisition with increasing soil carbon, adequately reproduced global GPP distributions. Further improvement could be achieved with accurate representation of water sensitivity and agriculture in SDGVM. Mismatch between environmental filtering (the most data-driven hypothesis) and GPP suggested that greater effort is needed understand V(sub cmax) variation in the field, particularly in northern latitudes.

chlorophyll fluorescence↗

NASA NTRSDate not supplied

Game-Theoretic Modeling of Vegetation Composition, Structure, and Dynamics: Physical Constraints, Fundamental Processes, and Emergent Properties

Vegetation structural and compositional dynamics emerge from plant physiological and demographic processes, individual-based competition, vegetation-soil feedbacks, and environmental variations and disturbance events. Predicting long-term changes in vegetation requires scaling plant individual behavior to large scale ecosystem processes. In this presentation, we summarize our studies in the modeling of vegetation demographic processes, competitively dominant plant traits, plant hydraulic processes, and stochastic disturbance effects on ecosystems, and illustrate the roles of the underlying ecological processes and eco-evolutionary optimization in vegetation modeling. With the case studies of evolutionarily stable strategy of allocation, leaf traits, and plant hydraulic processes, we show how the ecosystem processes and vegetation dynamics are determined by the individual-based plant competition and variations of soil and climate conditions. The predictions of ecosystem carbon dynamics can be greatly different with those from the traditional “single-tree” models. We also discuss the tradeoffs of plant traits and evolutionarily optimal strategies in the modeling of terrestrial ecosystem dynamics in an Earth system model.

vegetation models↗

NASA NTRS2022

New Directions in Tropical Phenology

Earth’s most speciose biomes are in the tropics, yet tropical plant phenology remains poorly understood. Tropical phenological data are comparatively scarce and viewed through the lens of a ‘temperate phenological paradigm’ expecting phenological traits to respond to strong, predictably annual shifts in climate (e.g., between subfreezing and frost-free periods). Digitized herbarium data greatly expand existing phenological data for tropical plants; and circular data, statistics, and models are more appropriate for analyzing tropical (and temperate) phenological datasets. Phylogenetic information, which remains seldom applied in phenological investigations, provides new insights into phenological responses of large groups of related species to climate. Consistent combined use of herbarium data, circular statistical distributions, and robust phylogenies will rapidly advance our understanding of tropical – and temperate – phenology.

tropical phenology↗

NASA NTRS2020

Plant responses to rising vapor pressure deficit

Recent decades have been characterized by increasing temperatures worldwide, resulting in an exponential climb in vapor pressure deficit (VPD). VPD has been identified as an increasingly important driver of plant functioning in terrestrial biomes and has been established as a major contributor in recent drought‐induced plant mortality independent of other drivers associated with climate change. Despite this, few studies have isolated the physiological response of plant functioning to high VPD, thus limiting our understanding and ability to predict future impacts on terrestrial ecosystems. An abundance of evidence suggests that stomatal conductance declines under high VPD and transpiration increases in most species up until a given VPD threshold, leading to a cascade of subsequent impacts including reduced photosynthesis and growth, and higher risks of carbon starvation and hydraulic failure. Incorporation of photosynthetic and hydraulic traits in ‘next‐generation’ land‐surface models has the greatest potential for improved prediction of VPD responses at the plant‐ and global‐scale, and will yield more mechanistic simulations of plant responses to a changing climate. By providing a fully integrated framework and evaluation of the impacts of high VPD on plant function, improvements in forecasting and long‐term projections of climate impacts can be made.

mortality↗

NASA NTRS2024

Effects of Water Limitation and Competition on Tree Carbon Allocation in an Earth System Modeling Framework

Earth system models (ESMs) have a limited capacity to represent plant functional diversity and shifts in trait distributions. Approaches to improving the representation of this complexity in ESMs include (i) optimality-based approaches that predict trait–environment responses and (ii) explicitly modelling coexistence and community assembly. These approaches are expected to converge only when optimality-based approaches identify competitively dominant strategies, which often differ from strategies that maximize ecosystem functioning or fitness components in monoculture. We used two models, LM3-PPA (a vegetation demographic model designed as an ESM component) and BiomeE (a computationally efficient analog for LM3-PPA), to explore how water limitation affects carbon allocation strategies of canopy trees. We compared competitive allocation strategies and those that maximize biomass or productivity in monoculture. We did not explicitly model coexistence or community assembly. Rather, we used model experiments to identify competitive and maximizing strategies in a two-dimensional trait space under different precipitation and mortality scenarios. At 10 eastern US locations, we simulated historical, wet and dry climate scenarios, novel drought and three different mortality scenarios (low, medium or high sensitivity to water deficit). For each site and scenario, we identified the competitive strategy and three maximizing strategies (maximum biomass, productivity or drought-tolerance). Root: leaf ratios tended to increase and leaf area tended to decrease with increasing water stress (increasing water limitation and its effects on mortality). However, relative to maximizing strategies, competitive strategies shifted towards greater allocation to roots and leaves with increasing water stress. Competitive overinvestments (greater allocation to roots and leaves by competitive strategies compared with maximizing strategies) were robust across different modelling contexts, including vegetation parameter sets (Acer vs. Populus), models (LM3-PPA vs. BiomeE) and uncalibrated vs. calibrated BiomeE versions. Synthesis: The theoretical prediction that competitive and maximizing allocation strategies differ under water limitation is confirmed for a demographic model designed as an ESM component. Optimality-based trait predictions can simplify representing trait diversity in ESMs but do not always correspond to competitive outcomes. Explicitly modelling coexistence and community assembly in ESMs is challenging but is likely the most general approach to representing trait diversity.

vegetation demographic model↗

NASA NTRS2024

Unveiling the Transferability of PLSR Models for Leaf Trait Estimation: Lessons from a Comprehensive Analysis with a Novel Global Dataset

Leaf traits are essential for understanding many physiological and ecological processes. Partial least-squares regression (PLSR) models with leaf spectroscopy are widely applied for trait estimation, but their transferability across space, time and plant functional types (PFTs) remains unclear. We compiled a novel dataset of paired leaf traits and spectra, with 47,393 records for >700 species and eight PFTs at 101 globally-distributed locations across multiple seasons. Using this dataset, we conducted an unprecedented comprehensive analysis to assess the transferability of PLSR models in estimating leaf traits. While PLSR models demonstrate commendable performance in predicting chlorophyll content, carotenoid, leaf water and leaf mass per area prediction within their training data space, their efficacy diminishes when extrapolating to new contexts. Specifically, extrapolating to locations, seasons, and PFTs beyond the training data leads to reduced R 2 (0.12-0.49, 0.15-0.42, and 0.25-0.56) and increased NRMSE (3.58-18.24%, 6.27-11.55% and 7.0-33.12%) compared to nonspatial random cross-validation (NRCV). The results underscore the importance of incorporating greater spectral diversity in model training to boost its transferability. These findings highlight potential errors in estimating leaf traits across large spatial domains, diverse PFTs and time due to biased validation schemes and provide guidance for future field sampling strategies and remote sensing applications.

Leaf traits↗