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Summary It has been 60 years since the discovery of C₄photosynthesis, an event that rewrote our understanding of plant adaptation, ecosystem responses to global change, and global food security. Despite six decades of research, one aspect of C₄photosynthesis that remains poorly understood is how the pathway fits into the broader context of adaptive trait spectra, which form our modern view of functional trait ecology. The C₄CO₂‐concentrating mechanism supports a general C₄plant phenotype capable of fast growth and high resource‐use efficiencies. The fast‐efficient C₄phenotype has the potential to operate at high productivity rates, while allowing for less biomass allocation to root production and nutrient acquisition, thereby providing opportunities for the evolution of novel trait covariances and the exploitation of new ecological niches. We propose the placement of the C₄fast‐efficient phenotype near the acquisitive pole of the world‐wide leaf economic spectrum, but with a pathway‐specific span of trait space, wherein selection shapes both acquisitive and conservative adaptive strategies. A trait‐based perspective of C₄photosynthesis will open new paths to crop improvement, global biogeochemical modeling, the management of invasive species, and the restoration of disturbed ecosystems, particularly in grasslands. 

Abstract Plant responses to water stress is a major uncertainty to predicting terrestrial ecosystem sensitivity to drought. Different approaches have been developed to represent plant water stress. Empirical approaches (the empirical soil water stress (or Beta) function and the supply‐demand balance scheme) have been widely used for many decades; more mechanistic based approaches, that is, plant hydraulic models (PHMs), were increasingly adopted in the past decade. However, the relationships between them—and their underlying connections to physical processes—are not sufficiently understood. This limited understanding hinders informed decisions on the necessary complexities needed for different applications, with empirical approaches being mechanistically insufficient, and PHMs often being too complex to constrain. Here we introduce a unified framework for modeling transpiration responses to water stress, within which we demonstrate that empirical approaches are special cases of the full PHM, when the plant hydraulic parameters satisfy certain conditions. We further evaluate their response differences and identify the associated physical processes. Finally, we propose a methodology for assessing the necessity of added complexities of the PHM under various climatic conditions and ecosystem types, with case studies in three typical ecosystems: a humid Midwestern cropland, a semi‐arid evergreen needleleaf forest, and an arid grassland. Notably, Beta function overestimates transpiration when VPD is high due to its lack of constraints from hydraulic transport and is therefore insufficient in high VPD environments. With the unified framework, we envision researchers can better understand the mechanistic bases of and the relationships between different approaches and make more informed choices. 

Abstract Graphite is a commonly used raw material across many industries and the demand for high‐quality graphite has been increasing in recent years, especially as a primary component for lithium‐ion batteries. However, graphite production is currently limited by production shortages, uneven geographical distribution, and significant environmental impacts incurred from conventional processing. Here, an efficient method of synthesizing biomass‐derived graphite from biochar is presented as a sustainable alternative to natural and synthetic graphite. The resulting bio‐graphite equals or exceeds quantitative quality metrics of spheroidized natural graphite, achieving a RamanI_D/I_Gratio of 0.051 and crystallite size parallel to the graphene layers (L_a) of 2.08 µm. This bio‐graphite is directly applied as a raw input to liquid‐phase exfoliation of graphene for the scalable production of conductive inks. The spin‐coated films from the bio‐graphene ink exhibit the highest conductivity among all biomass‐derived graphene or carbon materials, reaching 3.58 ± 0.16 × 10⁴S m⁻¹. Life cycle assessment demonstrates that this bio‐graphite requires less fossil fuel and produces reduced greenhouse gas emissions compared to incumbent methods for natural, synthesized, and other bio‐derived graphitic materials. This work thus offers a sustainable, locally adaptable solution for producing state‐of‐the‐art graphite that is suitable for bio‐graphene and other high‐value products. 

Transfer learning refers to the transfer of knowledge or information from a relevant source domain to a target domain. However, most existing transfer learning theories and algorithms focus on IID tasks, where the source/target samples are assumed to be independent and identically distributed. Very little effort is devoted to theoretically studying the knowledge transferability on non-IID tasks, e.g., cross-network mining. To bridge the gap, in this paper, we propose rigorous generalization bounds and algorithms for cross-network transfer learning from a source graph to a target graph. The crucial idea is to characterize the cross-network knowledge transferability from the perspective of the Weisfeiler-Lehman graph isomorphism test. To this end, we propose a novel Graph Subtree Discrepancy to measure the graph distribution shift between source and target graphs. Then the generalization error bounds on cross-network transfer learning, including both cross-network node classification and link prediction tasks, can be derived in terms of the source knowledge and the Graph Subtree Discrepancy across domains. This thereby motivates us to propose a generic graph adaptive network (GRADE) to minimize the distribution shift between source and target graphs for cross-network transfer learning. Experimental results verify the effectiveness and efficiency of our GRADE framework on both cross-network node classification and cross-domain recommendation tasks.