Understory bryophytes play unique and disproportionately important roles in water retention,biogeochemical cycling,and biodiversity conservation,and serve as bioindicators of environmental health in forest ecosystems....Understory bryophytes play unique and disproportionately important roles in water retention,biogeochemical cycling,and biodiversity conservation,and serve as bioindicators of environmental health in forest ecosystems.However,biogeographical research on the biomass of forest bryophytes is inadequately studied and has been limited to elevational gradients.We conducted a systematic cross-regional survey of bryophyte biomass across 413 forest sites in Sichuan Province,China.We analyzed how each environmental variable,including climatic and atmospheric factors,overstory covers,and soil nutrients,relates to bryophyte biomass and quantified their relative contributions.The results indicate that,largely similar to previous local investigations and experiments,at a large scale,bryophytes are abundant in forests with lower temperature,nitrogen deposition,vapor pressure deficit,and tree and herb covers,as well as higher light availability.Moreover,bryophyte biomass is positively associated with soil carbon and nitrogen content.These environmental variables are closely related and jointly influence bryophyte biomass,with mean annual temperature being the most significant factor(accounting for 83%of the relative contribution).The biogeographical patterns of bryophyte biomass contribute to deepening our understanding of their adaptations to multiple environmental variables and enable us to predict their responses to global climate change.These patterns also provide essential evidence for establishing more accurate terrestrial vegetation ecosystem models.展开更多
This paper explores the evolution of geoscientific inquiry,tracing the progression from traditional physics-based models to modern data-driven approaches facilitated by significant advancements in artificial intellige...This paper explores the evolution of geoscientific inquiry,tracing the progression from traditional physics-based models to modern data-driven approaches facilitated by significant advancements in artificial intelligence(AI)and data collection techniques.Traditional models,which are grounded in physical and numerical frameworks,provide robust explanations by explicitly reconstructing underlying physical processes.However,their limitations in comprehensively capturing Earth’s complexities and uncertainties pose challenges in optimization and real-world applicability.In contrast,contemporary data-driven models,particularly those utilizing machine learning(ML)and deep learning(DL),leverage extensive geoscience data to glean insights without requiring exhaustive theoretical knowledge.ML techniques have shown promise in addressing Earth science-related questions.Nevertheless,challenges such as data scarcity,computational demands,data privacy concerns,and the“black-box”nature of AI models hinder their seamless integration into geoscience.The integration of physics-based and data-driven methodologies into hybrid models presents an alternative paradigm.These models,which incorporate domain knowledge to guide AI methodologies,demonstrate enhanced efficiency and performance with reduced training data requirements.This review provides a comprehensive overview of geoscientific research paradigms,emphasizing untapped opportunities at the intersection of advanced AI techniques and geoscience.It examines major methodologies,showcases advances in large-scale models,and discusses the challenges and prospects that will shape the future landscape of AI in geoscience.The paper outlines a dynamic field ripe with possibilities,poised to unlock new understandings of Earth’s complexities and further advance geoscience exploration.展开更多
基金supported by the National Natural Science Foundation of China(No.31600316)the Sichuan Science and Technology Program(2023NSFSC0198)。
文摘Understory bryophytes play unique and disproportionately important roles in water retention,biogeochemical cycling,and biodiversity conservation,and serve as bioindicators of environmental health in forest ecosystems.However,biogeographical research on the biomass of forest bryophytes is inadequately studied and has been limited to elevational gradients.We conducted a systematic cross-regional survey of bryophyte biomass across 413 forest sites in Sichuan Province,China.We analyzed how each environmental variable,including climatic and atmospheric factors,overstory covers,and soil nutrients,relates to bryophyte biomass and quantified their relative contributions.The results indicate that,largely similar to previous local investigations and experiments,at a large scale,bryophytes are abundant in forests with lower temperature,nitrogen deposition,vapor pressure deficit,and tree and herb covers,as well as higher light availability.Moreover,bryophyte biomass is positively associated with soil carbon and nitrogen content.These environmental variables are closely related and jointly influence bryophyte biomass,with mean annual temperature being the most significant factor(accounting for 83%of the relative contribution).The biogeographical patterns of bryophyte biomass contribute to deepening our understanding of their adaptations to multiple environmental variables and enable us to predict their responses to global climate change.These patterns also provide essential evidence for establishing more accurate terrestrial vegetation ecosystem models.
基金supported by National Natural Science Foundation of China(T2225019,41925007,62372470,U21A2013,42201415,42022054,42241109,42077156,52121006,42090014,and 42325107)the National Key R&D Programme of China(2022YFF0500)+2 种基金the Youth Innovation Promotion Association CAS(2023112)the Strategic Priority Research Program of CAS(XDA23090303)the RECLAIM Network Plus(EP/W034034/1).
文摘This paper explores the evolution of geoscientific inquiry,tracing the progression from traditional physics-based models to modern data-driven approaches facilitated by significant advancements in artificial intelligence(AI)and data collection techniques.Traditional models,which are grounded in physical and numerical frameworks,provide robust explanations by explicitly reconstructing underlying physical processes.However,their limitations in comprehensively capturing Earth’s complexities and uncertainties pose challenges in optimization and real-world applicability.In contrast,contemporary data-driven models,particularly those utilizing machine learning(ML)and deep learning(DL),leverage extensive geoscience data to glean insights without requiring exhaustive theoretical knowledge.ML techniques have shown promise in addressing Earth science-related questions.Nevertheless,challenges such as data scarcity,computational demands,data privacy concerns,and the“black-box”nature of AI models hinder their seamless integration into geoscience.The integration of physics-based and data-driven methodologies into hybrid models presents an alternative paradigm.These models,which incorporate domain knowledge to guide AI methodologies,demonstrate enhanced efficiency and performance with reduced training data requirements.This review provides a comprehensive overview of geoscientific research paradigms,emphasizing untapped opportunities at the intersection of advanced AI techniques and geoscience.It examines major methodologies,showcases advances in large-scale models,and discusses the challenges and prospects that will shape the future landscape of AI in geoscience.The paper outlines a dynamic field ripe with possibilities,poised to unlock new understandings of Earth’s complexities and further advance geoscience exploration.
