Community organization and network complexity and stability: contrasting strategies of prokaryotic versus eukaryotic microbiomes in the Bohai Sea and Yellow Sea by Xiaoxiao Wang, Hualong Wang, Yantao Liang, Andrew McMinn, Min Wang

“…Environmental heterogeneity structures microbial communities into distinct spatial patterns, leading to complex direct/indirect relationships and broader ecological niches of bacterioplankton compared to microeukaryotic communities. …”
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Utilizing δ15N of biomonitors to assess N emission sources and deposition chemistry? by Meaghan I. Petix, Michael D. Bell, David G. Williams, R. Dave Evans

“…Measuring and monitoring deposition is time intensive and expensive, but biomonitors, such as mosses and lichens, can be used to evaluate finer-scale deposition patterns and source attribution using stable isotope analysis. …”
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Prevalence and ecological factors affecting the distribution of plant-parasitic nematodes in Prunus groves in Spain by Ilenia Clavero-Camacho, Antonio Archidona-Yuste, Carolina Cantalapiedra-Navarrete, Pablo Castillo, Juan Emilio Palomares-Rius

“…Seven soil physico-chemical (C, Mg, N, Na, OM, P, pH and clay, loamy sand and sandy loam texture classes), four climate (Bio04, Bio05, Bio13 and Bio14) and four agricultural management variables (grove-use history less than 10 years, irrigation, apricot seedling rootstock, and Montclar rootstock) were identified as the most influential variables driving spatial patterns of PPNs communities. In particular, younger plantations showed higher values for species richness and diversity indices than groves cultivated for more than 20 years with Prunus spp. …”
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Dynamic climate graph network and adaptive climate action strategy for climate risk assessment and low-carbon policy responses by Fang Zhou, Yan Shi, Pengfei Zhao, Zhengzhao Gu, Ye Li

“…DCGN utilizes graph-based learning to model spatial dependencies and temporal feature extraction to analyze evolving climate patterns. …”
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Recommendations for planning UAV flight missions for geodata collection by Hutsul Taras, Lysko Bohdan, Zhezhera Ivan, Tkach Vladyslav, Tsvyk Tetyana

“…The analysis of geographic information makes it possible to obtain qualitatively new information and reveal previously unknown patterns. Modern data collection methods are divided into three distinct groups: terrestrial, cartographic, and remote. …”
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Estimation of Relative Sea Level Change in Locations Without Tide Gauges Using Artificial Neural Networks by Heeryun Kim, Young Il Park, Wansik Ko, Taehyun Yoon, Jeong-Hwan Kim

“…Unlike conventional forecasting approaches focused on future time series prediction, the proposed model is designed to learn spatial distribution patterns and temporal rates of sea level change from a fusion of satellite altimetry and tide gauge data. …”
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Investigating Catching Hotspots of Fishing Boats: A Framework Using BeiDou Big Data and Deep Learning Algorithms by Fen Wang, Xingyu Liu, Tanxue Chen, Hongxiang Feng, Qin Lin

“…Post-moratorium, fishing hotspots expanded explosively, peaking in October and clustering in Yushan, Zhoushan, and Yangtze River estuary fishing grounds. Quarterly patterns identified autumn–winter 2021 as peak fishing seasons, with hotspots covering >80% of East China Sea grounds. …”
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Downscaling of Urban Land Surface Temperatures Using Geospatial Machine Learning with Landsat 8/9 and Sentinel-2 Imagery by Ratovoson Robert Andriambololonaharisoamalala, Petra Helmholz, Dimitri Bulatov, Ivana Ivanova, Yongze Song, Susannah Soon, Eriita Jones

“…Satellite-derived land surface temperature (LST) plays a vital role in analysing urban thermal patterns. However, current satellite thermal infrared (TIR) sensors have a low spatial resolution, making it difficult to accurately capture the complex thermal variations within urban areas. …”
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Meteorological Drivers and Agricultural Drought Diagnosis Based on Surface Information and Precipitation from Satellite Observations in Nusa Tenggara Islands, Indonesia by Gede Dedy Krisnawan, Yi-Ling Chang, Fuan Tsai, Kuo-Hsin Tseng, Tang-Huang Lin

“…Furthermore, our monthly and 8-day TVDI estimation models could capture spatial drought patterns consistent with MODIS, with coefficient of determination (R²) values of more than 0.64. …”
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Once Common, Long in Decline: Dynamics of Traditional Orchards in a Central European Landscape by André Große-Stoltenberg, Andreas Hanzl, Mojdeh Safaei, Till Kleinebecker

“…Despite their high level of ecological importance, they are in decline, and comprehensive spatial data over broad extents, which could enable a trend analysis, are lacking. …”
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Exploring the influence of urban green space and urban morphology on urban heat Islands using street view and satellite imagery by Ziteng Huang, Lian Duan, Yimin Xu, Siyi Yang, Zhiyan Lin, Han Yue, Jinxin Yang

“…Distinct statistical and spatial distribution patterns of NDVI and GVI were identified among districts in Guangzhou, China. …”
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Decreasing soil erosion in South China with uncertainties driven by NDVI estimates by Xinqing Lu, Yulian Liang, Tongtiegang Zhao, Xudong Zhu, Zhangcai Qin

“…However, the spatiotemporal patterns exhibited marked variations depending on NDVI datasets, particularly in interannual fluctuations and spatial discrepancies. …”
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Home Range and Habitat Selection of Blue-Eared Pheasants <i>Crossoptilon auritum</i> During Breeding Season in Mountains of Southwest China by Jinglin Peng, Xiaotong Shang, Fan Fan, Yong Zheng, Lianjun Zhao, Sheng Li, Yang Liu, Li Zhang

“…Using satellite-tracking data from six individuals (five males tracked at 4 h intervals; one female tracked hourly) in Wanglang National Nature Reserve (WLNNR), Sichuan Province during breeding seasons 2018–2019, we quantified their home ranges via Kernel Density Estimation (KDE) and examined the female movement patterns using a Hidden Markov Model (HMM). The results indicated male core (50% KDE: 21.93 ± 16.54 ha) and total (95% KDE: 158.30 ± 109.30 ha) home ranges, with spatial overlap among individuals but no significant temporal variation in home range size. …”
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Estimation of Forest Leaf Area Index Based on GEE Data Fusion Method by Xinyi Liu, Li He, Zhengwei He, Yun Wei

“…The estimation of forest parameters is of great significance in understanding regional and global climate change patterns, and the Forest Leaf Area Index (LAI) is a crucial parameter. …”
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Double Stage Transfer Learning for Brain&#x2013;Computer Interfaces by Yunyuan Gao, Mengting Li, Yun Peng, Feng Fang, Yingchun Zhang

“…Lastly, after extracting spatial features with common spatial patterns (CSP), transfer component analysis (TCA) was adopted to reduce the differences between different domains further. …”
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Assessment of hydrological loading displacement from GNSS and GRACE data using deep learning algorithms by Changshou Wei, Maosheng Zhou, Zhixing Du, Lijing Han, Hao Gao

“…An in-depth analysis of terrestrial water storage and loading displacement from 2019 to 2022 in Yunnan Province revealed distinct seasonal fluctuations, primarily driven by dominant annual and semi-annual cycles, and these periodic signals accounted for over 90% of the variance. The spatial distribution of terrestrial water loading displacement is strongly associated with regional precipitation patterns, showing smaller amplitudes in the northeast and northwest and larger amplitudes in the southwest. …”
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Optimizing Urban Greenery for Climate Resilience: A Case Study in Perth, Australia by Xiaoqi Ma, Boon Lay Ong

“…This study investigates the spatiotemporal dynamics of vegetation coverage and its interplay with climatic factors and surface thermal patterns in Perth, Australia, from 2014 to 2023, leveraging multi-source remote sensing data, geostatistical modeling, and spatial analysis. …”
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Shifting perspectives: exploring land tenure transformation through six community-based solar energy projects in the global south (Ethiopia, Uganda, Kenya, Namibia, Indonesia, and... by Bouchra El Houda Lamhamedi, Walter Timo de Vries

“…This study addresses this gap by examining how small-scale community-based renewable energy systems influence land tenure transformations and whether shifts in land tenure patterns drive changes in renewable energy adoption and management strategies. …”
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Hydrothermal Signatures Discovered in Outcropping Rocks of the Los Humeros Geothermal Field (Mexico): A Geochemometric Exploration Case Study by D. Yáñez-Dávila, E. Santoyo, E. González-Partida, Kailasa Pandarinath, G. Santos-Raga, Sumit Mishra, Z. G. Gómez-Salgado

“…Chemical component and element mobilities were determined by using the Gresens–Grant equation for mass balances. A spatial distribution of component and element mobility patterns was carried out through the mineral characterization, hydrothermal alteration, and whole-rock elemental analysis. …”
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Impacts of Urbanization and Habitat Characteristics on the Human Risk of West Nile Disease in the United States by Jian Ma, Nuo Xu, Ying Xu, Zheng Y. X. Huang, Chuanwu Chen, Yingying X. G. Wang

“…Since its initial identification in 1999, West Nile virus has spread rapidly throughout North America, exhibiting high spatial heterogeneity. Previous studies exploring the spatial patterns of the human risk of West Nile Disease (WND) in the United States have demonstrated the important roles of landscape and climatic factors. …”
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