Ecological geography, a crucial interdisciplinary branch of geography and ecology, primarily focuses on the interactions and patterns between ecosystems and environmental factors in geographical spaces. This paper first reviews the theoretical foundation and historical development of ecological geography, emphasizing the evolution of its research paradigms. It systematically summarizes the main research topics and hotspots in the field, including the spatial distribution and dynamic changes of ecosystems, interaction mechanisms between ecological processes and geographical patterns, the impacts of human activities on ecosystems, and ecological responses and adaptations under global change. Furthermore, it outlines the core research methods and technological advancements in ecological geography, particularly the applications of remote sensing, geographic information systems, big data analytics, artificial intelligence, and multi-perspective simulation methods. Finally, the paper explores ecological geography’s practical applications and future directions, such as biodiversity conservation, land use optimization, climate change mitigation, and ecological restoration strategies. This paper aims to provide theoretical support and practical guidance for researchers in the field of ecological geography and to offer scientific foundations and research prospects for its continued development.

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The study of Earth’s internal structure and dynamic processes is a fundamental scientific issue for understanding the evolution of the Earth system and the nature of geodynamic activities. This paper discusses the layered characteristics of Earth’s interior and the dominant dynamic processes, covering key research areas such as seismic tomography, gravity and magnetic field exploration, mantle convection, material cycling, and heat transfer. Seismic tomography reveals the detailed three-dimensional structure from the crust to the inner core, while gravity and geomagnetic observations provide critical constraints on the density distribution and conductivity structure of Earth’s deep interior, aiding in the identification of fluid dynamics in the mantle and outer core. Mantle convection is widely regarded as the primary driving force of plate tectonics and surface structural movements, and deep material cycling plays a crucial role in maintaining Earth’s long-term thermal state and chemical evolution. Research on internal heat transfer mechanisms helps to elucidate the distribution of deep heat sources and their control over global tectonic processes. With the continuous advancement of high-resolution geophysical imaging technologies, deep Earth experiments, geochemical tracing, and numerical simulation methods, studies on Earth’s internal structure and evolution are moving toward higher precision, multi-scale coupling, and interdisciplinary integration. In the future, the integration of seismology, geodynamics, geomagnetic and gravity exploration, and advanced simulation techniques is expected to deepen our understanding of internal Earth processes and provide theoretical support for planetary science, Earth system modeling, and resource and environmental research.

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As a critical technological support for energy and resource development, geophysical exploration faces the dual challenges of traditional resource depletion and the transition to clean energy. This paper systematically reviews six key research directions in geophysical exploration for energy and resources: (1) development of advanced techniques for the detailed exploration of oil, gas, and traditional mineral resources; (2) geophysical innovations driven by the development of unconventional energy sources such as shale gas and gas hydrates; (3) high-precision characterization and dynamic monitoring methods of reservoir structures and physical properties; (4) integrated detection and engineering applications of geothermal resources; (5) high-resolution identification and sustainable management of groundwater resources; and (6) intelligent approaches and green mining models for mineral resource exploration. The paper highlights the integration and innovation of core technologies such as high-resolution seismic imaging, electromagnetic exploration, multi-physics coupling methods, artificial intelligence algorithms, and digital geophysical systems in practical exploration. Finally, it envisions future development trends of geophysical exploration technologies toward intelligence, automation, and low-carbonization, aiming to provide systematic theoretical references and technical guidance for scientific research and engineering practices in the energy and resource sectors.

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The frequent occurrence of natural disasters poses significant challenges to human societal security, economic development, and ecosystem stability, making effective forecasting and scientific disaster reduction core topics in geosciences and disaster prevention research. This paper systematically reviews the occurrence mechanisms and evolutionary processes of typical geological disasters such as earthquakes, volcanic eruptions, landslides, and tsunamis, with a particular focus on recent key advances in monitoring technologies, including seismic network deployment, remote sensing, InSAR, GNSS observations, and deep-sea pressure sensors. Furthermore, it explores the application potential of disaster identification and early warning algorithms based on big data integration, machine learning, and artificial intelligence in improving forecasting timeliness and spatial accuracy. Regarding the construction of integrated early warning systems, the importance of multi-hazard and multi-source data monitoring platforms is emphasized, and the establishment of regional-to-global linkage mechanisms is highlighted as crucial for addressing disaster chains and compound disasters. Looking ahead, with the intelligent upgrading of sensor networks and the deep integration of cloud computing and digital twin technologies, natural disaster forecasting and response are expected to become more efficient, collaborative, and intelligent. This study aims to provide theoretical support and technical reference for disaster science research and offer strategic insights for building a globally coordinated disaster prevention and reduction framework.

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Forest soil is a significant source of atmospheric nitrous oxide (N₂O), and its emission processes are strongly influenced and regulated by nitrogen input. This review systematically summarizes the primary mechanisms of N₂O production in forest soils, including nitrification, denitrification, and non-biological pathways, and elucidates the role of environmental factors, such as soil physicochemical properties and climatic conditions, in regulating N₂O emissions. It highlights the biological pathways through which nitrogen input affects N₂O emissions via plants and soil microorganisms, including plant nitrogen uptake and allocation, litter decomposition processes, the regulatory role of arbuscular mycorrhizae, and the contribution of soil microbial functional gene abundance and diversity to N₂O production pathways. Additionally, the review analyzes the indirect effects of nitrogen input on critical factors such as soil moisture and substrate concentration and compares the differences and driving mechanisms of various nitrogen forms on N₂O production pathways. This study aims to deepen the understanding of how nitrogen input regulates forest soil N₂O emissions through multidimensional pathways, providing a scientific basis for optimizing nitrogen management in forest ecosystems and developing greenhouse gas mitigation strategies.

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With the increasing complexity of oilfield development environments, traditional water injection strategies relying on empirical rules and simple numerical simulations have become ineffective in addressing reservoir heterogeneity, dynamic variations, and geological uncertainties, thereby limiting injection control accuracy and production efficiency. This paper explores the application of intelligent optimization algorithms, including deep reinforcement learning, genetic algorithms, and particle swarm optimization, in optimizing water injection strategies. It further examines the role of data fusion, real-time monitoring, and dynamic optimization techniques—such as digital twins, long short-term memory time series prediction, and Kalman filtering—in enhancing the scientific rigor and reliability of water injection decisions. Additionally, this paper analyzes the advantages of multi-objective optimization algorithms, such as NSGA-II, in balancing recovery rates, energy consumption, and economic benefits. The study aims to provide valuable academic insights for developing an efficient, precise, and low-carbon intelligent oilfield management system.

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Intelligent reservoir modeling and high-precision numerical simulation integrate artificial intelligence, machine learning, and big data analytics to provide efficient and accurate solutions for reservoir modeling, parameter optimization, and uncertainty assessment. Traditional reservoir modeling relies on geostatistics and physical numerical simulation methods, which are computationally expensive and struggle to accurately characterize complex reservoir structures and fluid migration patterns. Intelligent reservoir modeling leverages technologies such as deep learning, reinforcement learning, and surrogate modeling to extract key features from vast historical production data and geological information, enabling automated optimization of reservoir parameters and rapid predictions. Additionally, multi-scale numerical simulation combined with uncertainty quantification enhances the stability and computational efficiency of reservoir modeling, offering data-driven decision support for reservoir development optimization. The advancement of this research field will contribute to improving recovery rates, reducing oilfield development costs, and promoting the intelligent and efficient utilization of oil and gas resources.

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The screening and analysis of growth-promoting mechanisms of core rhizosphere strains in salt-alkali tolerant rice are crucial for enhancing rice tolerance to saline-alkali stress, optimizing saline-alkali soil environments, and promoting sustainable agriculture. This study explores the screening methods of salt-alkali tolerant plant growth-promoting bacteria, with a particular focus on the application of multi-omics technologies, including metagenomics, transcriptomics, and metabolomics, in core strain selection and functional analysis. The study reveals the mechanisms by which these strains promote rice growth and alleviate saline-alkali stress through nitrogen fixation, phosphorus solubilization, plant hormone synthesis, and exopolysaccharide production. Furthermore, based on field trial progress, the study evaluates the application potential of core strains in saline-alkali rice cultivation and discusses strategies for symbiotic enhancement using synthetic microbiomes, as well as optimization schemes for biofertilizer application to improve strain colonization and growth-promoting effects in the field. These findings provide theoretical support for the development of salt-alkali tolerant microbial fertilizers and offer a scientific basis for rice cultivation in saline-alkali soils and the sustainable development of ecological agriculture.

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As an important branch of geography, hydrological geography primarily studies the spatial and temporal distribution of water bodies, hydrological processes, and their complex interactions with the natural environment and human activities. In recent years, intensified global climate change and human activities have driven the field toward a deeper exploration of hydrological cycle dynamics, sustainable water resource utilization, and the mechanisms underlying extreme hydrological events. This paper systematically reviews the theoretical framework of hydrological geography, with a focus on hydrological cycle theory, watershed system theory, and their interdisciplinary integration. It summarizes key research areas, including watershed hydrological processes, the impacts of human activities on hydrological systems, the spatial and temporal distribution of water resources and their optimal allocation, and risk assessment of hydrological hazards. Based on recent research advancements, the paper highlights the mechanisms of hydrological responses to global change, innovative applications of big data and artificial intelligence in hydrological geography, and breakthroughs in surface water–groundwater coupling studies. Additionally, it discusses the practical value of hydrological geography in integrated watershed management, ecosystem protection, and transboundary water resource governance. Finally, the paper identifies current research challenges and outlines future directions, emphasizing the importance of data-driven model development, climate change adaptation studies, and interdisciplinary collaboration in advancing the field. By comprehensively summarizing the theories and practices of hydrological geography, this paper aims to provide scientific references and practical insights for the discipline’s development and the resolution of global water resource and environmental issues.

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The China-based AI model “DeepSeek AI” is revolutionizing the industry by achieving high-performance AI without relying on expensive chips or massive investments. This technological innovation promotes the democratization of AI, enabling small and medium-sized enterprises as well as individuals to leverage cutting-edge technology. At the same time, it highlights the importance of ethics and risk management in an increasingly fast-paced competitive landscape. The emergence of DeepSeek has the potential to create new market opportunities and drive the restructuring of technology and regulatory frameworks.

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Wheat saline-alkali stress is one of the major challenges facing global agriculture, particularly in arid and semi-arid regions, significantly affecting crop growth, yield, and quality. Researchers have conducted in-depth studies in four key areas to enhance wheat salt tolerance and achieve sustainable agriculture. In soil improvement, microbial remediation techniques, novel soil amendments, and smart agricultural technologies effectively enhance soil conditions. Agronomic management practices, such as precise water and fertilizer management, optimized crop rotation and intercropping systems, and plant signal molecule regulation, improve wheat growth conditions. Salt-tolerant variety breeding, supported by genomics, gene editing, and phenomics, accelerates the identification and application of salt-tolerant genes. Biological and chemical regulation approaches, including the utilization of rhizosphere microbes, exogenous regulatory substances, and RNA-based mechanisms, provide critical support for improving wheat salt tolerance. In the future, integrating interdisciplinary technologies with precision management strategies is expected to enhance wheat resistance to saline-alkali stress further and unlock the agricultural potential of saline-alkali lands.

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As an interdisciplinary field, environmental geography aims to reveal the complex interactions and dynamic mechanisms between the natural environment and human activities, providing a theoretical foundation and decision-making basis for understanding environmental changes, formulating scientific policies, and promoting sustainable development. In globalization, urbanization, and climate change, this field is transitioning from traditional descriptive studies to quantitative analysis, dynamic simulation, and comprehensive evaluation. Its research scope encompasses global change and regional environmental responses, human-environment interactions and sustainable development, environmental risk assessment and emergency management, ecosystem services and environmental value quantification, urban environmental geography and smart city development, the application of new technologies in environmental monitoring and simulation, multi-scale and multi-factor coupling mechanisms, and environmental governance and ecological civilization construction. Leveraging modern technologies such as big data, remote sensing, GIS, and artificial intelligence, research in this field continues to deepen the understanding of complex environmental systems, significantly enhancing the monitoring, prediction, and management of environmental changes. This progress provides a solid scientific foundation for government decision-making and sustainable societal development, effectively addressing global environmental challenges.

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Plant growth-promoting bacteria (PGPB) are microorganisms capable of enhancing plant growth through various mechanisms, including rhizosphere bacteria, endophytic bacteria, and certain fungal organisms. This paper systematically summarizes the main functions and mechanisms of PGPB, encompassing the synthesis of plant growth regulators (such as auxins, cytokinins, and gibberellins), nitrogen fixation, activation of nutrients like phosphorus and potassium, mitigation of abiotic stresses (e.g., drought and salinity), and defense against biotic stresses (e.g., pathogenic infections). Additionally, it highlights the critical roles of PGPB in regulating plant root system architecture and shaping the rhizosphere microbial community. On the application side, this paper reviews the screening and preparation technologies of PGPB, including strain selection, carrier development, and preservation techniques, along with diverse inoculation methods such as seed coating, soil application, foliar spraying, and root drenching. The multifunctionality and potential of PGPB in microbial inoculants, agricultural biopesticides, and biofertilizers are discussed through typical application cases, emphasizing their pivotal role in promoting sustainable agriculture. This study provides theoretical support for understanding the functional mechanisms of PGPB and their applications in addressing climate change, improving agricultural productivity, and enhancing resource use efficiency. It also outlines future research and practical directions in this field.

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Soil organic carbon (SOC) is a core indicator for evaluating soil quality and health, playing a crucial role in the functionality and sustainability of agricultural ecosystems. Under the context of global climate change and intensive agricultural development, especially in arid and semi-arid regions, the accumulation and stability of SOC face significant challenges. This study focuses on the arid regions of northwest China as a typical case, systematically assessing the effects of different tillage practices (no-tillage vs. conventional tillage) and rotation systems (wheat/maize, wheat-winter rapeseed, and wheat) on SOC and its components. Field experiments were conducted to analyze the dynamic changes in key parameters such as soil respiration rate, organic carbon content, microbial biomass carbon, water-soluble organic carbon, and particulate organic carbon. The results demonstrated that no-tillage significantly reduced soil respiration rates while substantially increasing SOC accumulation in the surface soil layer (0–5 cm). Crop rotation systems exhibited distinct impacts on SOC, with the “wheat-winter rapeseed → maize” rotation showing the best performance in enhancing SOC accumulation. Furthermore, the combined management of no-tillage and optimized rotation significantly improved soil carbon storage characteristics. Particularly in arid environments, optimizing tillage and rotation systems effectively enhanced soil health and ecosystem stability. This study provides scientific evidence for sustainable agricultural development in northwest China, emphasizing the importance of optimizing tillage and rotation strategies to promote SOC accumulation, thereby achieving long-term stability and sustainability of agricultural ecosystems.

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The 2024 Nobel Prize in Physics, awarded to Geoffrey Hinton and his colleagues, highlights the transformative impact of AI research, particularly in deep learning, on science and society. This milestone represents a paradigm shift, with AI driving interdisciplinary innovation in fields such as medicine, climate science, and education. While celebrating these achievements, it is essential to address ethical challenges to ensure AI’s benefits are shared equitably. This recognition underscores the growing importance of computational methodologies in tackling complex global issues, heralding a new era of scientific discovery and collaboration.

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The discovery of dark oxygen can bring innovations in multiple fields, including reconsidering the Earth’s oxygen supply mechanisms, a deeper understanding of deep-sea ecosystems, and exploring new energy sources. This phenomenon is believed to occur when polymetallic nodules electrolyze seawater, generating oxygen, and it is suggested that it may play a crucial role in deep-sea material cycles and biological activities. Furthermore, applying this principle could pave the way for its utilization as a renewable energy source. However, many aspects of its generation mechanism and its impact on deep-sea ecosystems remain unknown. In particular, careful research and the development of environmentally friendly technologies are necessary to assess the potential effects of deep-sea mining on dark oxygen supply and marine ecosystems.

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Soil health and water management are crucial for agricultural production and ecosystems. Healthy soil regulates water, while scientific management optimizes its dynamics. Their synergy influences microbial activity, nutrient cycling, and carbon storage. In the face of climate change and water scarcity, integrated strategies can enhance agricultural productivity, and resource use efficiency, and strengthen ecosystem resilience and food security. This paper systematically reviews five key research frontiers in this field: (1) the response mechanisms and adaptability of soil microbial communities to water stress; (2) the relationship between soil aggregate stability and water retention capacity; (3) the development of high-precision models for soil moisture dynamics monitoring and prediction; (4) the synergistic effects of organic matter management on soil moisture regulation; and (5) the construction of dynamic models for soil-plant-water interactions. These studies provide a theoretical foundation and technical support for optimizing soil management strategies, improving water use efficiency, and promoting the sustainable development of agriculture and ecosystems.

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This study explores the frontier research directions of soil-plant-climate interactions, with a focus on the following aspects: (1) the coupling mechanisms of the soil-plant-atmosphere carbon cycle under climate change; (2) intelligent regulation of crop growth environments and precision agricultural decision-making; (3) impact assessment of extreme climate events on soil health and crop productivity; (4) multi-scale interactions between soil microbiomes and plant adaptation; and (5) integration and modeling of cross-scale soil-plant-climate data. This study aims to reveal the interaction mechanisms among soil, plants, and climate, providing theoretical support and practical references for sustainable agriculture, enhanced ecosystem resilience, and responses to global climate change.

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The China-based AI model “DeepSeek AI” is bringing significant transformation to the AI industry. By achieving performance equivalent to OpenAI’s “o1” at a lower cost and partially open-sourcing its technology, it accelerates the democratization of AI development. This has made AI adoption more accessible for small and medium-sized enterprises and startups. Furthermore, its unique ability to “visualize the thought process” holds the potential to advance AI understanding and improvement to a new level. The rise of DeepSeek AI is also impacting major players such as AI chip manufacturers like NVIDIA and industry giants like OpenAI, intensifying price competition and reshaping the industry’s structure. Additionally, the emergence of low-cost, high-performance AI models promotes technological efficiency and widespread adoption, further accelerating the AI race. DeepSeek AI is expected to be a turning point that profoundly influences the future of AI technology and society as a whole.

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As an important interdisciplinary field integrating geography and economics, trade geography focuses on the spatial distribution characteristics of trade activities and their driving mechanisms, serving as a crucial tool for understanding global and regional economic development patterns. This paper systematically reviews the theoretical framework of trade geography, including spatial economics, location theory, comparative advantage theory, and economic globalization theory. It provides an in-depth analysis of global and regional trade patterns' main characteristics and formation mechanisms. Based on this, the paper examines the impacts and underlying mechanisms of key factors such as natural resource distribution, transportation network layout, policy environment, and technological progress on trade geography patterns. The paper further highlights research hotspots and frontier areas in trade geography, including the evolution of regionalization trends in the context of globalization, practices of green trade and sustainable development, the constraints and reshaping of trade flows by geopolitics, and the profound influence of the digital economy on traditional trade geography patterns. Through case studies, it demonstrates the application of geographic research in areas such as regional economic integration, logistics network optimization, and the construction of free trade zones, revealing the practical value of trade geography in promoting regional economic coordination and international economic cooperation. This paper aims to provide a comprehensive review of the current state of trade geography, offer a scientific outlook on future research directions, and advance the further development of the discipline’s theories and practices.

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Agricultural geography not only aids in understanding agricultural production patterns and land use dynamics across different regions but also reveals the impacts of agricultural activities on the environment and ecosystems. With increasing population growth and resource pressures, agricultural geography provides critical support for formulating scientific agricultural policies and optimizing resource allocation. This paper highlights the importance of agricultural geography in predicting the impacts of climate change on agricultural production and promoting the development of ecological and green agriculture. Future research directions include studying the effects of climate change, advancing precision agriculture technologies, analyzing land use changes and sustainability, protecting agricultural ecosystems and biodiversity, and exploring solutions to global food security and agricultural production patterns.

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Soil acidification and carbon sequestration are central issues in addressing global soil degradation and climate change. Soil acidification, driven by natural processes and human activities, has significant negative impacts on soil physicochemical properties, agricultural productivity, and ecosystem functions. In contrast, soil carbon sequestration, as a critical approach to mitigating climate change, not only enhances soil fertility but also improves ecosystem functionality. This paper systematically outlines the mechanisms underlying soil acidification and its effects on soil properties and the environment, while delving into the processes and influencing factors of soil carbon sequestration. It emphasizes the evaluation of major technologies for ameliorating acidic soils, including traditional lime amendments, novel materials such as biochar, and biological approaches. The paper also highlights the application of carbon sequestration techniques in improving acidic soils, such as optimizing agricultural management, applying amendments, and promoting vegetation restoration. By analyzing the synergistic mechanisms between acidic soil amelioration and carbon sequestration technologies, it elucidates how these approaches effectively enhance soil carbon stocks and overall soil health. Finally, the paper identifies the limitations of existing research and challenges in technology adoption, proposes future research directions and critical breakthroughs, and envisions the broad prospects of integrated technical applications. This paper aims to provide a theoretical foundation and technical reference for synergistic research on acidic soil amelioration and carbon sequestration, fostering scientific progress and practical applications in this field.

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Drought stress is a key environmental factor limiting peanut production and nitrogen use efficiency. This study was conducted at an agricultural experimental station in Sichuan Province, employing the 15N tracer technique. Under normal water supply and mild drought stress conditions during the pegging and pod-setting stages, combined with different nitrogen fertilizer levels (0, 90, and 180 kg N/ha), the comprehensive effects of drought stress, and nitrogen fertilizer levels on peanut yield, nitrogen absorption and accumulation, distribution, and utilization efficiency were systematically analyzed. The results showed that drought stress significantly reduced peanut yield and nitrogen accumulation in plants, with the yield inhibition caused by drought during the pegging stage being significantly higher than during the pod-setting stage. In terms of nitrogen distribution, the nitrogen accumulated in kernels constituted the majority of the total nitrogen; however, drought stress significantly affected nitrogen distribution and transport efficiency among plant organs. Analysis of nitrogen fertilizer levels indicated that the medium nitrogen treatment (90 kg N/ha) under drought stress exhibited a higher nitrogen fertilizer recovery rate (38.8%) and lower nitrogen loss rate (12.6%), significantly improving nitrogen use efficiency. This study elucidates the regulatory mechanisms of drought stress on nitrogen utilization in peanuts, providing scientific insights for efficient peanut cultivation and fertilizer management in drought-prone regions.

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This paper explores the potential applications of generative AI in addressing various challenges faced by local communities and proposes specific solutions along with their impacts. It highlights concrete examples such as enhancing recognition of local specialty products through regional resource utilization, reducing social isolation among the elderly, and improving recycling rates to tackle environmental issues. Additionally, it discusses the potential of generative AI in diverse fields, including providing personalized learning materials to address educational disparities, optimizing public transportation, and disseminating disaster information to enhance regional safety. Furthermore, the introduction of AI technologies to support the preservation of local culture and promote health activities is suggested to contribute to raising community awareness and revitalizing the region as a whole. Through these initiatives, generative AI emerges as a critical tool with the potential to effectively resolve regional challenges and improve the quality of life for residents.

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Climate change-induced drought and environmental stresses challenge maize production. Artificial intelligence (AI) and big data show great potential in maize breeding for drought resistance. Intelligent breeding platforms using multi-source data and deep learning improve drought resistance prediction models and uncover gene-environment relationships. Explainable AI reveals the biological mechanisms behind drought resistance, aiding breeding decisions. Technologies like drones, multispectral imaging, and LiDAR enable high-resolution phenotypic data collection for water use efficiency and genotype screening. Regional breeding databases support genotype selection and climate adaptability simulations. Integrating gene editing with precision breeding further enhances drought resistance, supporting sustainable global agriculture.

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Soil salinization is a critical environmental issue restricting agricultural production and ecosystem health globally, making its effective management a core topic in agricultural and environmental science research. With the continued expansion of saline-alkali land, developing innovative remediation technologies and integrated management strategies is very important. This paper systematically reviews key research directions and major challenges in soil salinization management: (1) the dynamic changes in microbial community structure and function in saline-alkali soils, with a focus on the interaction mechanisms between microbes and plant roots and their potential in soil remediation; (2) the molecular basis of plant salt tolerance, analyzing the regulatory networks and metabolic pathways of salt tolerance genes under saline-alkali stress, providing theoretical support for breeding salt-tolerant crops; (3) the synergistic effects and optimization strategies of bioremediation combined with physicochemical treatment technologies; (4) the development and application of novel saline-alkali soil amendments, emphasizing their roles in improving soil structure, ionic balance, and water retention; (5) innovative water management techniques for saline-alkali soils, investigating the impact of precise water regulation on crop yield and quality; and (6) the interactions between heavy metals and salinity in saline-alkali soils and their ecological risk assessment, uncovering potential threats to soil ecological functions and sustainable agriculture while proposing scientific management strategies. These advancements provide technical support for the efficient use and sustainable management of saline-alkali land and contribute significantly to global food security and environmental improvement.

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Phytogeography, as a core discipline studying the distribution patterns of plants on Earth and their environmental driving mechanisms, has made remarkable progress over the past decades. This paper systematically reviews the classification and distribution characteristics of global flora. It explores the driving roles of environmental factors such as climate, soil, and geology in the formation and evolution of plant flora. It highlights the dynamic changes and historical pathways of plant distribution patterns across different regions. Additionally, the paper emphasizes modern research methods in phytogeography, including the application of remote sensing technology, geographic information systems, and the development of flora models for future distribution predictions. In the context of biodiversity conservation and global climate change, the study discusses the potential applications of phytogeography in ecosystem management and sustainable development. It also identifies key directions and challenges for future research. By uncovering plant distribution's spatial patterns and environmental responses, phytogeography provides essential theoretical foundations and practical insights for understanding ecosystem dynamics and addressing global environmental changes.

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