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This paper presents BathDNN25, a global bathymetry model developed using gravity data derived from wide‐swath altimetry collected by the Surface Water and Ocean Topography (SWOT) mission, with shipborne bathymetry serving as training data in a deep neural network (DNN) framework. BathDNN25 integrates multiple geophysical inputs, including gravity anomalies (G)$(G)$ , vertical gravity gradients (VGG)$(VGG)$ , their band‐pass filtered forms VGGBP$\\left(VG{G}_{BP}\\right)$ , the north and east components derived from the deflection of the vertical (SN$SN$ , SE$\\mathrm{SE}$ ), their band‐pass versions (SNBP$S{N}_{BP}$ , SEBP${\\mathrm{SE}}_{BP}$ ), low‐pass filtered bathymetry BLP$\\left({B}_{LP}\\right)$ , and both low‐pass and band‐pass filtered gravity (GLP${G}_{LP}$ , GBP${G}_{BP}$ ), to capture both large‐scale trends and fine‐scale bathymetric features. A key innovation lies in its use of multi‐scale geophysical features, enabling enhanced sensitivity to morphological complexity such as ridges, escarpments, and seamounts, while adapting well to varying geological conditions and data sparsity. Model performance was assessed using residual statistics against independent data sets, including global shipborne soundings and seamount summits, with BathDNN25 achieving residual standard deviations of 99 and 167 m, respectively. Compared to existing methods (Harper & Sandwell, 2024, https://doi.org/10.1029/2023ea003199), this represents reductions in residual error of over 51% and 113%. SHAP analysis across 14 regions and ablation tests using four model variants further confirmed the complementary value of SWOT‐derived gravity features. Overall, BathDNN25 demonstrates accuracy, robustness, and scalability, underscoring the importance of high‐quality geophysical inputs and the potential of SWOT‐derived data and artificial intelligence in advancing global bathymetric modeling. Plain Language Summary Understanding the shape and depth of the ocean floor is essential for navigation, climate modeling, and tsunami forecasting. However, only a small portion of the global seafloor has been mapped using ships because the process is time‐consuming and expensive. This study presents BathDNN25, a new global bathymetry model that uses artificial intelligence to estimate seafloor depth from satellite observations. The model is trained using existing ship‐based depth data and a set of geophysical features, including gravity anomalies, vertical gravity gradients, and deflection of vertical components, that are derived from the Surface Water and Ocean Topography (SWOT) satellite mission. BathDNN25 improves upon traditional methods by more accurately detecting geological features like ridges, trenches, and seamounts. It provides a faster and more scalable solution for ocean floor mapping, helping scientists better understand Earth's underwater landscape. Key Points BathDNN25 is a global bathymetry model trained with a deep neural network using SWOT‐derived geophysical features Multiple input features (gravity anomalies, vertical gravity gradient, band‐pass filtered gravity, and the north and east components of the deflection of the vertical) provide complementary information across spatial scales, and ablation experiments confirm that using them together significantly improves prediction accuracy compared with using any single data type The model consistently outperforms classical approaches, including the SWOT‐based Nettleton method and the SWOT‐based variant of Harper and Sandwell (2024) DNN model, across diverse oceanic environments and depth ranges

The effective management and conservation of fishery resources requires knowledge of their spatial distribution and notably of their critical life history stages. Predictive modelling of the European hake (Merluccius merluccius L., 1758) distribution was developed in the south-central Mediterranean Sea by means of historical fisheries-independent databases available in the region. The study area included the international waters of the south-central Mediterranean Sea and the territorial waters of Italy, Malta, Tunisia and Libya. Distribution maps of predicted population abundance index, and probabilistic occurrence of recruits and large adults were obtained by means of generalized additive models using depth and seafloor characteristics as predictors. Presence/absence data of the two life stages was obtained using threshold values applied to the mean weight of the survey catches. Modelling results largely matched previously reported knowledge on habitat preference of the species and its critical life phases. Hake recruits showed an occurrence peak at 200 m depth with preference for soft bottoms. Large adults preferred deeper and harder bottom substrates. Prediction maps allowed to improve our knowledge on the distributional patterns of one of the most important shared stocks in the south-central Mediterranean. This knowledge is essential for an appropriate development of regional-spatial-based management plans.

Landscape-scale vulnerability assessment from multiple sources, including paleoecological site histories, can inform climate change adaptation. We used an array of lake sediment pollen and charcoal records to determine how soils and landscape factors influenced the variability of forest composition change over the past 2000 years. The forests in this study are located in northwestern Wisconsin on a sandy glacial outwash plain. Soils and local climate vary across the study area. We used the Natural Resource Conservation Service's Soil Survey Geographic soil database and published fire histories to characterize differences in soils and fire history around each lake site. Individual site histories differed in two metrics of past vegetation dynamics: the extent to which white pine ( Pinus strobus ) increased during the Little Ice Age (LIA) climate period and the volatility in the rate of change between samples at 50-120 yr intervals. Greater increases of white pine during the LIA occurred on sites with less sandy soils ( R 2 = 0.45, P < 0.0163) and on sites with relatively warmer and drier local climate ( R 2 = 0.55, P < 0.0056). Volatility in the rate of change between samples was positively associated with LIA fire frequency ( R 2 = 0.41, P < 0.0256). Over multi-decadal to centennial timescales, forest compositional change and rate-of-change volatility were associated with higher fire frequency. Over longer (multi-centennial) time frames, forest composition change, especially increased white pine, shifted most in sites with more soil moisture. Our results show that responsiveness of forest composition to climate change was influenced by soils, local climate, and fire. The anticipated climatic changes in the next century will not produce the same community dynamics on the same soil types as in the past, but understanding past dynamics and relationships can help us assess how novel factors and combinations of factors in the future may influence various site types. Our results support climate change adaptation efforts to monitor and conserve the landscape's full range of geophysical features.

Luana explores the world of mathematics with her companion Infinitum, the artificial intelligence. Together they will travel through fantastic concepts discovering the mathematics behind them.

Full-waveform (FWF) LiDAR (Light Detection and Ranging) systems have their advantage in recording the entire backscattered signal of each emitted laser pulse compared to conventional airborne discrete-return laser scanner systems. The FWF systems can provide point clouds which contain extra attributes like amplitude and echo width, etc. In this study, a FWF data collected in 2010 for Eisenstadt, a city in the eastern part of Austria was used to classify four main classes: buildings, trees, waterbody and ground by employing a decision tree. Point density, echo ratio, echo width, normalised digital surface model and point cloud roughness are the main inputs for classification. The accuracy of the final results, correctness and completeness measures, were assessed by comparison of the classified output to a knowledge-based labelling of the points. Completeness and correctness between 90% and 97% was reached, depending on the class. While such results and methods were presented before, we are investigating additionally the transferability of the classification method (features, thresholds …) to another urban FWF lidar point cloud. Our conclusions are that from the features used, only echo width requires new thresholds. A data-driven adaptation of thresholds is suggested.

We define Dry Rivers as those whose usual habitat in space and time are dry channels where surface water may interrupt dry conditions for hours or a few days, primarily after heavy rainfall events that are variable in time and that usually lead to flash floods, disconnected from groundwater and thereby unable to harbor aquatic life. Conceptually, Dry Rivers would represent the extreme of the hydrological continuum of increased flow interruption that typically characterizes the non-perennial rivers, thus being preceded by intermittent and ephemeral rivers that usually support longer wet phases, respectively. This paper aims to show that Dry Rivers are ecosystems in their own right given their distinct structural and functional characteristics compared to other non-perennial rivers due to prevalence of terrestrial conditions. We firstly reviewed the variety of definitions used to refer to these non-perennial rivers featured by a predominant dry phase with the aim of contextualizing Dry Rivers. Secondly, we analyzed existing knowledge on distribution, geophysical and hydrological features, biota and biogeochemical attributes that characterize Dry Rivers. We explored the capacity of Dry Rivers to provide ecosystem services and described main aspects of anthropogenic threats, management challenges and the conservation of these ecosystems. We applied an integrative approach that incorporates to the limnological perspective the terrestrial view, useful to gain a better understanding of Dry Rivers. Finally, we drew main conclusions where major knowledge gaps and research needs are also outlined. With this paper, we ultimately expect to put value in Dry Rivers as non-perennial rivers with their own ecological identity with significant roles in the landscape, biodiversity and nutrient cycles, and society; thus worthy to be considered, especially in the face of exacerbated hydrological drying in many rivers across the world.

This case study examines emigrants from Namoluk Atoll in the Eastern caroline islands of Micronesia, in the Western pacific. Most members of the Namoluk Community (cbon Namoluk) do not currently live there. some 60 percent of them have moved to chuuk, Guam, Hawai'i, or the mainland United states (such as Eureka, California). The question is how (and why) those expatriates contine to think of themselves as cbon Namoluk, amd behave accodingly, despite being a far-flung network of people, with inevitable erosions of shared language and culture. List of Illustrations -- List of Abbreviations and Acronyms -- Series Editor Preface -- 1 Openings -- 2 Namoluk Atoll, 1969 -- 3 Journeyings -- 4 Namoluk people, 2002 -- 5 Heading off to college -- 6 Heading off to college -- 7 Reef Crossings -- 8 Four Locations Beyond the Reef -- 9 Closings: Points of Departure -- Glossary -- Suggestions for further Reading -- References. Marshall, Mac