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IntroductionMethodsKey results and conclusionsMarsupella profunda Lindb. is known from Britain, Portugal and Macaronesia. Its population status and ecology in Britain were investigated.An inventory of records of Marsupella profunda was prepared, and surveys were conducted at a subset of sites. Population size was measured by counts of ‘individual-equivalents’, defined as occupied 1 m grid cells. Habitat and community composition were recorded by relevés.Marsupella profunda is confined to habitat created by china clay quarrying, and mostly occurs on kaolinised granite. It is one of the earliest colonists of this substrate. The longevity of habitat patches is limited mainly by competitive exclusion by other bryophytes and excessive shade from scrub development. It has been recorded from six sites in West Cornwall but presently survives at only one of these, where it will likely become extinct within the next 20 years due to vegetation succession. In East Cornwall, an enormous complex of active and disused quarries within the St Austell China Clay Region supports a large population (10,000–20,000 individual-equivalents), but this is declining because china clay production has decreased greatly over the past 30 years. Attempts to conserve the species by the special protection of areas of land have failed because the quarrying process that originally supplied fresh suitable substrate no longer operates in these areas, habitat management measures have been insufficient, and scrub development has progressed. The future of the species in Britain depends on the uncertain future of commercial quarrying in areas of kaolinised granite in Cornwall. [ABSTRACT FROM AUTHOR]

Complex sensory information arrives in the brain from an animal's first-person ('egocentric') perspective. However, animals can efficiently navigate as if referencing map-like ('allocentric') representations. The postrhinal (POR) and retrosplenial (RSC) cortices are thought to mediate between sensory input and internal maps, combining egocentric representations of physical cues with allocentric head direction (HD) information. Here we show that neurons in the POR and RSC of female Long-Evans rats are tuned to distinct but complementary aspects of local space. Egocentric bearing (EB) cells recorded in square and L-shaped environments reveal that RSC cells encode local geometric features, while POR cells encode a more global account of boundary geometry. Additionally, POR HD cells can incorporate egocentric information to fire in two opposite directions with two oppositely placed identical visual landmarks, while only a subset of RSC HD cells possess this property. Entorhinal grid and HD cells exhibit consistently allocentric spatial firing properties. These results reveal significant regional differences in the neural encoding of spatial reference frames. Whether and how the postrhinal (POR) and retrosplenial (RSC) cortices interact with each other and impact downstream allocentric representations are not fully understood. Here authors present single neuron recordings from freely moving rats exploring different environments to reveal distinct egocentric (self-centered) and allocentric (world-centered) coding frameworks for landmarks and boundaries in interconnected cortical regions. [ABSTRACT FROM AUTHOR]

AbstractAltimetry missions designed for ocean monitoring can also evaluate the Antarctic ice sheet’s mass balance by measuring surface height. However, the returned radar echoes (waveforms) have complex shapes influenced by various snow and ice surface properties at multiple scales. Radar altimeters, operating in different modes and frequencies, interact with these surface properties in diverse ways. In this study, we developed a method to assess Antarctic surface properties using data from SARAL/AltiKa and Sentinel-3A & 3B altimeters for June 2021 and January 2023, respectively. We normalized and averaged the observed waveforms in 5 × 5 km2 grid cells. These were then used to create false-color images, with each grid cell’s average power and time gate represented as different ‘bands’. False-color images were analyzed using Principal Component Analysis (PCA) and ISODATA unsupervised classification to identify regions of Antarctica with similar surface properties. We achieved a percent variance of 73% and 84% for the first three Principal Components (PCs), and 96% and 98% for the first ten PCs with the Sentinel-3 and SARAL/AltiKa datasets, respectively. Finally, we analyzed the Ka and Ku band altimeter waveform shapes for each identified class to better understand snow and ice sensitivity to waveforms. [ABSTRACT FROM AUTHOR]

Landscape connectivity operates at a variety of scales, depending on the geography of the area in question and the focal species or ecological process under consideration. Most connectivity studies, however, are typically focused on a single scale, which in the case of resistance‐based connectivity modelling, is often the entire landscape or protected area (PA) network. This large, single‐scale focus may miss areas that are important for connectivity at smaller scales and that can be documented via observed animal movements without resorting to landscape‐wide statistical modelling and extrapolation approaches. Here, we characterize landscape connectivity at three different scales (local/micro, inter‐PA, and landscape‐wide/macro), using observed animal movements rather than conventional resistance surface models, to produce a connectivity conservation blueprint for African elephants (Loxodonta africana) in the Kavango‐Zambezi transfrontier conservation area (KAZA) in southern Africa. This analysis is based on an extensive, high‐resolution GPS tracking database comprising approximately 4 million GPS locations from nearly 300 tagged elephants and their associated herds. Our results show that high‐fidelity elephant use of micro‐corridors is typically—though not exclusively—related to directed movements towards water, often amidst heavy anthropogenic presence. Movement pathways that connected KAZA's core protected areas were longer and variable, with some channelled into narrow areas of use and others more dispersed across larger sub‐landscapes. At the largest scale, a network analysis incorporating all used landscape grid cells revealed several clusters of large‐scale movement corridors that connected distant parts of KAZA. Synthesis and applications: Our three scales of analyses reveal disparate geographical priorities for connectivity conservation that collectively could help ensure the functional connectivity of KAZA for its largest inhabitants. Each scale will require its own set of inter‐related conservation interventions, while further research into areas with sparse data collection, and other species of conservation concern, could reveal additional connectivity priorities at each scale. [ABSTRACT FROM AUTHOR]

Accurate population data is crucial for assessing exposure in disaster risk assessments. In recent years, there has been a significant increase in the development of spatially gridded population datasets. Despite these datasets often using similar input data to derive population figures, notable differences arise when comparing them with direct ground-level observations. This study evaluates the precision and accuracy of flood exposure assessments using both known and generated gridded population datasets in Sweden. Specifically focusing on WorldPop and GHSPop, we compare these datasets against official national statistics at a 100 m grid cell resolution to assess their reliability in flood exposure analyses. Our objectives include quantifying the reliability of these datasets and examining the impact of data aggregation on estimated flood exposure across different administrative levels. The analysis reveals significant discrepancies in flood exposure estimates, underscoring the challenges associated with relying on generated gridded population data for precise flood risk assessments. Our findings emphasize the importance of careful dataset selection and highlight the potential for overestimation in flood risk analysis. This emphasises the critical need for validations against ground population data to ensure accurate flood risk management strategies. [ABSTRACT FROM AUTHOR]

This article presents the C-grid implementation of the CICE sea ice model, including the C-grid discretization of the momentum equation, the boundary conditions (BCs), and the modifications to the code required to use the incremental remapping transport scheme. To validate the new C-grid implementation, many numerical experiments were conducted and compared to the B-grid solutions. In idealized experiments, the standard advection method (incremental remapping with C-grid velocities interpolated to the cell corners) leads to a checkerboard pattern. A modal analysis demonstrates that this computational noise originates from the spatial averaging of C-grid velocities at corners. The checkerboard pattern can be eliminated by adjusting the departure regions to match the divergence obtained from the solution of the momentum equation. We refer to this novel approach as the edge flux adjustment (EFA) method. The C-grid discretization with edge flux adjustment allows for transport in channels that are one grid cell wide – a capability that is not possible with the B-grid discretization nor with the C-grid and standard remapping advection. Simulation results match the predicted values of a novel analytical solution for one-grid-cell-wide channels. [ABSTRACT FROM AUTHOR]

Key Biodiversity Areas (KBAs) represent the largest global network of sites critical to the persistence of biodiversity, which have been identified against standardised quantitative criteria. Sites that hold very high biodiversity value or potential are given specific attention on site‐based conservation targets of the Kunming‐Montreal Global Biodiversity Framework (GBF), and KBAs are already used in indicators for the GBF and the Sustainable Development Goals. However, most of the species that trigger KBA status are birds and to maximise benefits for biodiversity under the actions taken to fulfil the GBF, countries need to update their KBAs to represent important sites across multiple taxa. Here we introduce KBAscope, an R package to identify potential KBAs using multiple taxonomic groups. KBAscope provides flexible, user‐friendly functions to edit species data (population, range maps, area of occupancy, area of habitat and localities); apply KBA criteria; and generate outputs to support the delineation and validation of KBAs. The details of the analysis – such as the spatial units tested or the KBA criteria applied – can be decided according to the scope of the analysis. We demonstrate the functionality of KBAscope by using it to identify potential KBAs in Greece based on multiple terrestrial taxonomic groups and four sizes of grid cells (4 km2, 25 km2, 100 km2, 225 km2). [ABSTRACT FROM AUTHOR]

Uncovering how mental representations acquire, recall, and decode spatial information about relative locations and environmental attributes (cognitive map) involves different challenges. This work is geared towards theoretical discussions on the controversial issue of cognitive scalability for understanding cognitive map emergence from place and grid cells at the intersection between neuroscience and artificial intelligence. In our view, different place maps emerge from parallel and hierarchical neural structures supporting a global cognitive map. The mechanisms sustaining these maps do not only process sensory input but also assign the input to a location. Contentious issues are presented around these concepts and provide concrete suggestions for moving the field forward. We recommend approaching the described challenges guided by AI-based theoretical aspects of encoded place instead of based chiefly on technological aspects to study the brain. SIGNIFICANCE: A formal difference exists between the concepts of spatial representations between experimental neuroscientists and computer scientists and engineers in the so-called neural-based autonomous navigation field. From a neuroscience perspective, we consider the position of an organism's body to be entirely determined by translational spatial information (e.g., visited places and velocities). An organism predicts where it is at a specific time using continuous or discrete spatial functions embedded into navigation systems. From these functions, we infer that the concept of place has emerged. However, from an engineering standpoint, we represent structured scaffolds of behavioral processes to determine movements from the organism's current position to some other spatial locations. These scaffolds are certainly affected by the system's designer. Therefore, the coding of place, in this case, is predetermined. The contrast between emergent cognitive map through inputs versus predefined spatial recognition between two fields creates an inconsistency. Clarifying this tension can inform us on how the brain encodes abstract knowledge to represent spatial positions, which hints at a universal theory of cognition. [ABSTRACT FROM AUTHOR]

When subjects navigate through spatial environments, grid cells exhibit firing fields that are arranged in a triangular grid pattern. Direct recordings of grid cells from the human brain are rare. Hence, functional magnetic resonance imaging (fMRI) studies proposed an indirect measure of entorhinal grid-cell activity, quantified as hexadirectional modulation of fMRI activity as a function of the subject’s movement direction. However, it remains unclear how the activity of a population of grid cells may exhibit hexadirectional modulation. Here, we use numerical simulations and analytical calculations to suggest that this hexadirectional modulation is best explained by head-direction tuning aligned to the grid axes, whereas it is not clearly supported by a bias of grid cells toward a particular phase offset. Firing-rate adaptation can result in hexadirectional modulation, but the available cellular data is insufficient to clearly support or refute this option. The magnitude of hexadirectional modulation furthermore depends considerably on the subject’s navigation pattern, indicating that future fMRI studies could be designed to test which hypothesis most likely accounts for the fMRI measure of grid cells. Our findings also underline the importance of quantifying the properties of human grid cells to further elucidate how hexadirectional modulations of fMRI activity may emerge. [ABSTRACT FROM AUTHOR]

Background: Gambling and its harmful effects on human health and well-being represent a significant public health concern in many countries, with electronic gambling machines (EGMs) recognized as one of the most detrimental forms of gambling. Previous research has established an association between EGM accessibility, expenditure, gambling harm, and the socioeconomic status (SES) of neighborhoods. However, there is limited understanding of the direct impact of SES and EGM accessibility on individual player expenditures. Prior estimations of expenditure often rely on self-reported data or venue-level revenue statistics. This study uses high spatial resolution socioeconomic data together with individual-level account-based location and expenditure (point of sales) data (71,669 players, 745 EGM venues) to explore the association between EGM accessibility and neighborhood SES and to examine whether the EGM expenditure of neighborhood residents is associated with EGM accessibility and neighborhood SES. Data and methods: Player account data include information on the home location and expenditure of the entire EGM gambling population across every EGM venue located in the Helsinki region, Finland. High-resolution (250 × 250 m) grid-level data on socioeconomic variables were used to obtain the local socioeconomic conditions of the players. EGM accessibility was estimated for every grid cell using a calibrated gravity model derived from the player account data. Statistical analyses included correlation analysis, spatial autocorrelation analysis, and regression models. Results: First, significantly higher levels of EGM accessibility were found in areas with lower local SES. Second, regression analysis revealed that both higher EGM accessibility and lower local SES were associated with higher annual losses per adult. These results, in combination with visual and spatial autocorrelation analyses, revealed that accessibility to EGM gambling is highly concentrated, especially in lower socioeconomic neighborhoods with higher levels of EGM expenditure. Conclusions: The results lay the groundwork for future spatial research on gambling harm, expenditure, accessibility, and SES utilizing detailed account data on the interaction between players and venues. The results underscore the importance of spatial restrictions when regulating EGM accessibility, particularly in areas with vulnerable populations, as a crucial measure for public health and harm prevention. The results also enable targeted gambling harm prevention actions at the local level. [ABSTRACT FROM AUTHOR]

The entorhinal cortex represents allocentric spatial geometry and egocentric speed and heading information required for spatial navigation. However, it remains unclear whether it contributes to the prediction of an animal's future location. We discovered grid cells in the medial entorhinal cortex (MEC) that have grid fields representing future locations during goal-directed behavior. These predictive grid cells represented prospective spatial information by shifting their grid fields against the direction of travel. Predictive grid cells discharged at the trough phases of the hippocampal CA1 theta oscillation and, together with other types of grid cells, organized sequences of the trajectory from the current to future positions across each theta cycle. Our results suggest that the MEC provides a predictive map that supports forward planning in spatial navigation. [ABSTRACT FROM AUTHOR]

Automatically adaptive grid resolution is a common way of improving simulation accuracy while keeping computational efficiency at a manageable level. In space physics, adaptive grid strategies are especially useful as simulation volumes are extreme, while the most accurate physical description is based on electron dynamics and hence requires very small grid cells and time steps. Therefore, many past global simulations encompassing, for example, near-Earth space have made tradeoffs in terms of the physical description and laws of magnetohydrodynamics (MHD) used that require less accurate grid resolutions. Recently, using supercomputers, it has become possible to model the near-Earth space domain with an ion-kinetic hybrid scheme going beyond MHD-based fluid dynamics. These simulations, however, must develop a new adaptive mesh strategy beyond what is used in MHD simulations. We developed an automatically adaptive grid refinement strategy for ion-kinetic hybrid-Vlasov schemes, and we implemented it within the Vlasiator global solar wind–magnetosphere–ionosphere simulation. This method automatically adapts the resolution of the Vlasiator grid using two indices: one formed as a maximum of dimensionless gradients measuring the rate of spatial change in selected variables and the other derived from the ratio of the current density to the magnetic field density perpendicular to the current. Both these indices can be tuned independently to reach a desired level of refinement and computational load. We test the indices independently and compare the results to a control run using static refinement. The results show that adaptive refinement highlights relevant regions of the simulation domain and keeps the computational effort at a manageable level. We find that the refinement shows some overhead in the rate of cells solved per second. This overhead can be large compared to the control run without adaptive refinement, possibly due to resource utilization, grid complexity, and issues in load balancing. These issues lay out a development roadmap for future optimizations. [ABSTRACT FROM AUTHOR]

The decline of Arctic sea ice in the global warming era has received much attention as a contributing factor to the changes in the weather/climate in the Arctic and beyond. The coverage of Arctic sea ice (i.e., sea ice concentration (SIC)) has been monitored since 1972 using satellite passive microwave (PMW) measurements because of their extensive coverage and all-weather capability. However, the fundamental basis of algorithms for estimating SIC has not improved much since the early days due to the lack of reference SIC data, leading to discrepancies between existing PMW SIC algorithms. To overcome this issue, this study aims to construct data records of reference SIC over Arctic sea ice using 30 m resolution imagery from the Operational Land Imager (OLI) onboard Landsat-8. In order to collect relatively bright and clear scenes, thresholds of solar elevation > 15° and cloud cover < 10 % were applied in this study. Clouds in each Landsat-8 scene were masked using the cloud masking array provided in Landsat data. Due to the poor accuracy of the cloud masking array over ice-covered surface types, an additional step of visually inspecting the state of cloud mask using the true-color image was designated in this study. Each Landsat-8 scene was sorted into four categories depending on the state of cloud mask. Normalized Difference Snow Index and OLI band 5 reflectivity were used to differentiate between ice and open water within each selected Landsat-8 pixel. The classified data were projected onto a 6.25 km polar stereographic grid, and SIC for each grid cell was obtained by counting ice-classified pixels within the grid. SIC was only computed for grid cells with more than 99 % of its area covered with Landsat-8 pixels to limit the uncertainty in SIC arising from grids that are not fully concentrated with Landsat-8 pixels. Uncertainty in the produced SIC was 1~4 %, inferred using the Gaussian error propagation method. Out of 15,286 collected Landsat-8 images, 14,297 images were translated into SIC maps, and a total of 2,934,399 Landsat-8 SIC grid cells were generated. Evaluation of Landsat-8 SIC with SIC from ice charts revealed a good linear relationship (correlation coefficient of 0.96) between the two products as well as a mean negative bias which fell within the uncertainty range of Landsat-8 SIC. SIC based on Landsat-8 can be used as reference SIC to evaluate existing SIC products and thus one can improve SIC products as well as the use of the improved SIC for other applications such as data assimilation and retrieval studies. The vast amount of Landsat-8 SIC generated in this study may also be used to train deep learning models for estimation of Arctic SIC coverage. The Landsat-8 SIC dataset can be publicly accessed at https://zenodo.org/doi/10.5281/zenodo.10973297 (Jung et al., 2024), and the python codes for production and evaluation of the Landsat-8 SIC dataset is accessible at https://zenodo.org/doi/10.5281/zenodo.12754603 (Jung, 2024). [ABSTRACT FROM AUTHOR]

Continental shelves experience many human pressures with demersal fisheries central to disturbing the ocean floor. However, ecological processes such as predation rates of benthos and their relationship with bottom fishing are often unknown for large marine ecosystems. We examined the amount of benthos consumed by 14 benthivorous fishes, the overlap between benthos predation and bottom fishing (dredge and trawl gear), and temporal trends in benthivorous fish abundance and the number of fishing trips on the northeast US continental shelf. Mean annual predation (biomass of prey removed) and 95% confidence intervals ranged from .0002 (.0001–.0003) to 3967 (1761–7112) t per 10‐min area squared grid cell and prey taxa for these benthivorous fishes. Predation and bottom fishing had overlapping footprints of activity, which were slightly stronger for dredge gear. Trophic success (ratio of prey biomass eaten to the biomass of the benthivore community per grid cell) revealed more Bivalvia, Gammaridea, and Polychaeta eaten in areas targeted by trawling with more fish biomass. In contrast, dredging did not target fish biomass, but these areas had increased (Echinoidea, Gammaridea, and other benthos) or decreased (Ophiuroidea) trophic success relative to dredging footprint, suggesting habitat preferences for benthic prey and demersal fisheries have converged rather than diverged. Despite declines in bottom fishing, recent increases in benthivorous fish abundance and growing interest in ocean floor use suggest that fisheries managers should ensure benthivores have sufficient prey resources relative to their community size and human pressures to promote long‐term sustainability of demersal fisheries and healthy ecosystems. [ABSTRACT FROM AUTHOR]

Advancements in global satellite missions have revolutionized the assessment of Surface Soil Moisture (SSM) at global to local scales. However, spatio-temporal data discontinuities in specific regions remain a challenge. This study proposes a Machine Learning (ML)-based framework to extend the Soil Moisture Ocean Salinity (SMOS) SSM product both in the spatial and temporal domains, over Canada. In the first phase of the proposed framework, ML models based on Random Forest (RF) and Convolutional Neural Networks (CNN) are trained and validated with SMOS SSM as target and SSM-relevant climatic variables and geophysical variables, obtained from fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis data (ERA5), for the 2011–2020 period, as predictors. Developed models, when tested on unseen data for the years 2021–2022, suggest slightly better performance of the RF model compared to CNN, with root mean square error (RMSE) of 0.033 and 0.056 respectively; prediction biases mostly noted for regions with large inter-annual variability. The spatial filling of SSM for grid cells that were excluded during the training process, with similar land types as those in the SMOS training data, yields reasonable performance, with RF (RMSE = 0.013) performing better than CNN (RMSE = 0.064). In the second phase, the RF model is selected to extend the SMOS dataset for the 2008–2010 period. The temporal correlation between extended SMOS and ASCAT (Advanced Scatterometer) SSM demonstrates a reasonable association, with correlation coefficient exceeding 0.6. Additionally, spatial correlation analysis reveals similar patterns between the two datasets, with smaller values for the summer season owing to the importance of local processes on SSM during this period. However, extending SMOS SSM spatially for surface types that were not included in the training process such as peatlands, remains a challenge warranting additional studies. The developed framework is robust and can address spatio-temporal discontinuities in other SSM products. [ABSTRACT FROM AUTHOR]

Viewshed analysis is an important research content of digital terrain analysis. The terrain viewshed refers to the range that can be seen at the current position, which varies with the nature of the observer. When the observer is a wireless signal tower, it is the communication viewshed, which refers to the area consisted of grid cells where the receiving antennas can receive the signals from a transmitting antenna set up on a grid cell of terrain. The core of base station location problem includes two aspects: combinatorial optimization and the calculation of the coverage rate of signal. The calculation of communication viewshed is an important research content for determining signal coverage range. In this article, we propose an accurate communication viewshed computation algorithm for wireless signal (CVCWS) using the projection curve of 3D Fresnel zone analysis based on DEM. The CVCWS method can calculate the signal reception quality at different locations more precisely. Besides, a signal attenuation model is proposed to compute the theoretical attenuation value according to the signal receiving quality index. The proposed algorithm is compared with the existing DEM‐based communication viewshed algorithms, and the theoretical attenuation values are compared with the measured values. The experimental results show that the theoretical values gained by the CVCWS algorithm are close to the measured values, indicating high accuracy of the CVCWS algorithm. The proposed method can provide theoretical support for communication tower location planning and other related applications. [ABSTRACT FROM AUTHOR]

The last few decades have presented a significant increase in hydrological disasters, such as floods. In some countries, most of the environmental, socioeconomic, and biodiversity losses are caused by floods. Thus, flood forecasting is crucial to support an efficient disaster warning system. This work proposes a model for hydrological forecasting based on a neural network with a geographically aligned input named GeoNN. It employs weather radar data to obtain accumulated rainfall in each grid cell of the watershed and make 15‐ and 120‐min predictions of the outlet river level. An uncertainty propagation analysis was performed for GeoNN from a collection of test cases obtained by either using different schemes of the dataset partitioning or introducing different additive‐noise rates to the input data to provide a probability of flood occurrence and also an ensemble prediction. Both this probability and the ensemble were able to detect occurrences of river levels exceeding a given flood threshold. [ABSTRACT FROM AUTHOR]

Deep neural networks have made tremendous gains in emulating human-like intelligence, and have been used increasingly as ways of understanding how the brain may solve the complex computational problems on which this relies. However, these still fall short of, and therefore fail to provide insight into how the brain supports strong forms of generalization of which humans are capable. One such case is out-of-distribution (OOD) generalization - successful performance on test examples that lie outside the distribution of the training set. Here, we identify properties of processing in the brain that may contribute to this ability. We describe a two-part algorithm that draws on specific features of neural computation to achieve OOD generalization, and provide a proof of concept by evaluating performance on two challenging cognitive tasks. First we draw on the fact that the mammalian brain represents metric spaces using grid cell code (e.g., in the entorhinal cortex): abstract representations of relational structure, organized in recurring motifs that cover the representational space. Second, we propose an attentional mechanism that operates over the grid cell code using determinantal point process (DPP), that we call DPP attention (DPP-A) - a transformation that ensures maximum sparseness in the coverage of that space. We show that a loss function that combines standard task-optimized error with DPP-A can exploit the recurring motifs in the grid cell code, and can be integrated with common architectures to achieve strong OOD generalization performance on analogy and arithmetic tasks. This provides both an interpretation of how the grid cell code in the mammalian brain may contribute to generalization performance, and at the same time a potential means for improving such capabilities in artificial neural networks. [ABSTRACT FROM AUTHOR]

The representation of subgrid‐scale orography is a challenge in the physical parameterization of orographic gravity‐wave sources in weather forecasting. A significant hurdle is encoding as much physical information with as simple a representation as possible. Other issues include scale awareness, that is, the orographic representation has to change according to the grid cell size and usability on unstructured geodesic grids with non‐quadrilateral grid cells. This work introduces a novel spectral analysis method approximating a scale‐aware spectrum of subgrid‐scale orography on unstructured geodesic grids. The dimension of the physical orographic data is reduced by more than two orders of magnitude in its spectral representation. Simultaneously, the power of the approximated spectrum is close to the physical value. The method is based on well‐known least‐squares spectral analyses. However, it is robust to the choice of the free parameters, and tuning the algorithm is generally unnecessary. Numerical experiments involving an idealized setup show that this novel spectral analysis performs significantly better than a straightforward least‐squares spectral analysis in representing the physical energy of a spectrum. Studies involving real‐world topographic data are conducted, and reasonable error scores within ±10% error relative to the maximum physical quantity of interest are achieved across different grid sizes and background wind speeds. The deterministic behavior of the method is investigated along with its principal capabilities and potential biases, and it is shown that the error scores can be iteratively improved if an optimization target is known. Discussions on the method's limitations and broader applicability conclude this work. Plain Language Summary: Wind flow over terrain has wide‐ranging influences on atmospheric processes. Meteorologists want to include this effect in their weather forecasts but encounter computational limitations. For global weather forecasting, terrain features are relatively small and usually not explicitly represented in forecast models. An ongoing research question is how best to represent these small‐scale features in forecast models. However, a few difficulties arise: (a) We want to encode as much information about the terrain with as simple a representation as possible. (b) Some forecast models represent the globe with an icosahedron. Therefore, terrain information must be encoded within the triangular or hexagonal cells. (c) The information encoded has to change if the size of the triangles or hexagons changes. In this work, we present a novel method to overcome the three difficulties mentioned above. We can compress terrain information from over 50,000 to below 100 data points that can be used for climate simulations. When the relative effect of the encoded terrain on atmospheric processes is considered, the method has good accuracy despite the severe constraints and data compression, with a 10% error bound. Apart from representing terrain in weather forecasting, this method has potential applications for generic data analysis. Key Points: We introduce a novel scale‐aware spectral approximation method for subgrid‐scale orographic data on non‐quadrilateral geodesic gridsThe method yields physically sound results and achieves reasonable error scores when approximating real‐world orographic spectraThe method features over two orders of magnitude compression in complexity and has potential applications in generic spectral analyses [ABSTRACT FROM AUTHOR]

The visibility of retail frontages is critical for earning profits from spontaneous traffic visits to retail shops located along a street. The urban tree canopy plays a crucial role in enhancing the street-side environment, yet more is not always better when considering the placement of retail shops behind trees with big canopies. Related evidence in the literature is rarely provided, and an unclear relationship has been reported to exist between the number of shops for a specific retail type and the quantified ratio of the canopy shade in a street view. In this study, both big data crawling and deep learning were employed to unravel this relationship for retail shops in Changchun, Northeast China. The entire study area was divided into 6037 grid cells with a side length of ~0.6 km, wherein the number of shops of five retail types (food and beverage, shopping, life services, entertainment, and hotel) were quantified by computer counting their points of interest (POIs). The canopy shade was evaluated using the green view index (GVI) quantified through the ratio of canopy pixels divided by all the pixels in a street view image obtained through an online map API. A neighboring road network was categorized into four classes: class I road density mainly reduced the number of retail shops, and the road densities of classes III and IV accounted for more retail shops. The relationship between the number of retail shops and the GVI could be fitted with positive skewness curves for class II roads, where the critical peak of the GVI was estimated to be about 3.27%. The optimization scheme indicated that more retail shops should be placed along class I and II roads. In conclusion, more retail shops for food and beverage, shopping, and life services should be placed in the landscape neighboring big canopies. [ABSTRACT FROM AUTHOR]

With the operation of the first route in Xi'an City, the matching relationship between the metro networks and the urban population is a root factor affecting the utilization of rail transit facilities. The mismatch between the metro networks and the urban population has led to an imbalance between the supply and demand for rail transport, resulting in wasted urban infrastructure. Based on this issue, the research objective is to focus on the spatiotemporal variations of the matching relationship. Firstly, the topological network model abstractly extracted metro spatial distribution features, and the spatial autocorrelation model was adopted to identify the evolution characteristics of the metro networks and urban population. Secondly, this paper adopted a time-lagged regression model to demonstrate the action relationship from 2011 to 2021. Then, the compositive coordination index was utilized to assess the variation of the global matching relationship. Finally, the paper explored spatial heterogeneity through the coupling coherence degree attached to grid cells. The research results indicate that the Moran's I value of metro elements decreased from 0.782 to 0.510 with the further complexity of topological networks, while the population was consistently high in spatial dependence with a Moran's I value of around 0.75 during the decade. Based on the regression coefficients and significance, this paper verified the hypothesis that the metro networks and urban population had a positive time-lagged feedback effect in urban development. From 2011 to 2021, the compositive coordination index symbolizing the global matching relationship increased from 0.29 to 0.90, but the coupling coherence degree shows significant spatial heterogeneity in different grid units. Differentiated spatial planning strategies were proposed for varied areas to efficiently utilize rail transit, which may provide a reference for other cities with the same reality problem. [ABSTRACT FROM AUTHOR]

In Flanders, beavers have reappeared since 2000 and their numbers have expanded rapidly over the last decade, challenging impact management. To help identify areas with increased vulnerability to beaver damage we developed a species distribution model (SDM). In this model, we combined a MaxEnt-based approach to estimate habitat suitability and potential future spread with simulated dispersal using the SiMRiv R-package. We constructed our model in 2020 based on 2019 distribution data of beavers in Flanders and used it to predict population distribution probability up to 2022. To validate the combined model output, we compared the results for each 1 km² grid cell to the observed distribution of beavers in Flanders in 2020–2022. Our model predicted possible beaver occupancy in 4,361 out of 14,343 1 km² grid cells in Flanders. In 51.4% of these grid cells predicted probability of occupancy was accidental, in 29.3% it was low, in 13.0% medium and in 6.4% high. In 2020–2022 beavers were observed in 908 1 km² grid cells in Flanders. Comparison with the model output showed that the model performed well overall (AUC 0.825), although the negative predictive power was much higher than the positive predictive power since the model tends to overestimate actual occupancy over this three-year period. However, the accuracy rises with the projected level of occupancy probability. Although the overestimation fits the intended risk management purposes of the model, expanding it beyond the borders of Flanders and strengthening the backbone of the model with a more detailed population model could further improve the output and help improve the positive predictive power. [ABSTRACT FROM AUTHOR]

Kolonnawa marsh (KM) is an important wetland ecosystem in Colombo district, Sri Lanka that provides essential ecosystem services, and has undergone significant changes over recent decades due to continuous exploitation and reclamation. The values of wetlands are disregarded by decision-makers, despite the fact that they are crucial for improving the quality of water and offer chances for relaxation and amusement in metropolitan areas. Underestimation of the value of wetlands contributes to their continuing deterioration and inevitable loss. Investigating the changes in wetlands can provide crucial information for decision-making. This study aimed to monitor the spatiotemporal land-cover dynamics of KM with the prospect prediction as reduced total extent of KM gradually with time and marsh area being transformed into terrestrial vegetation with time. The collective images from Google Earth (2000 to 2021) and drone data (2022) were analyzed with the GIS application. Subsequently, 50-m2 grid squares with unique cell IDs are designed to link among land cover maps for spatiotemporal land-cover change analysis. Then, we calculate land cover category: surface water, marsh, and terrestrial vegetation proportions for each map in 50-m2 grid cells. Statistical comparison of the land cover changes in grid square cells shows that each land cover category has significant change with the time. The results showed that the reduction of KM marsh resulting in land cover changes has a positive implication on wetland degradation. Thus, interventions should be made for the restoration and sustainable management of KM. [ABSTRACT FROM AUTHOR]

The majority of existing numerical simulations of the effect of bridge piers on water's movement are based on a limited number of bridge piers at a laboratory scale. Furthermore, some 2D numerical simulations for actual bridge projects have deficiencies, including the use of overly large meshes and an inadequate treatment of bridge piers. In this study, we compare three methods (structural mesh encryption, suspension mesh, and non-structural mesh) based on Delft3D, and we apply the optimisation scheme to a real bridge project. It is demonstrated that optimal results can be achieved by utilising a grid size comparable to the pier diameter (Dp) in the region away from the pier. In the vicinity of the pier, the grid cell size should be no larger than 1/9 Dp. The suspended grid technique (DD Boundary) can yield results consistent with those obtained using a full-area high-resolution grid, provided that the total grid number can be reduced and the computational time is considerably reduced. In this study, the unstructured mesh (Delft3D Flexible Mesh) scheme was unable to capture the oscillations in the wake flow behind the bridge piers. However, the application of the optimised scheme in bridge engineering demonstrated its practical value. The findings of this study on mesh resolution and suspension mesh schemes can be applied to the Delft3D software and are also useful for other numerical simulation work. [ABSTRACT FROM AUTHOR]

Accurate subsurface soil moisture (SM) estimation is critical for vegetation growth, drought monitoring, and climate change mitigation, yet remains a significant challenge. Previous data assimilation (DA) approaches are limited to only surface SM assimilation. In this study, we utilized the proxy subsurface SM estimated via the exponential filter method (ExpF) as another assimilation variable in our 2‐dimensional DA. Meanwhile, the dual updating DA scheme was implemented to simultaneously update model parameters and states. The two DA pathways were incorporated into the proposed framework (DA_1E_D), which enhanced the subsurface SM accuracy, with the effects of 2‐dimensional assimilation being more significant. Under 2‐dimensional DA, the information transfer between layers was more accurately characterized, leading to overall improvements with unbiased root‐mean‐square error (ubRMSE) reductions of 0.015 and 0.005 m3 · m−3, and Kling–Gupta efficiency (KGE) increases of 0.248 and 0.067 for surface and subsurface SM, respectively, across five SM networks. The soil thickness (d2) and hydraulic conductivity exponent (expt2) are the most influential parameters affecting subsurface SM dynamics through model propagation. DA_1E_D also outperformed ExpF in subsurface SM accuracy, particularly in SM networks with weak surface‐subsurface correlation, achieving an average ubRMSE reduction of 0.003 m3 · m−3 and an average KGE increase of 0.202. It was also applied to Soil Moisture Active Passive data at regional scale, demonstrating significant improvements. The model surface‐subsurface SM coupling was adjusted toward the actual coupling after subsurface assimilation and dual updating. This study may provide new insights into the diagnosis and refinements of the model representation of surface‐subsurface processes. Plain Language Summary: Soil moisture (SM) is a key variable in the fields of hydrology, ecology, and agriculture, and in characterizing Earth's climate. Particularly, accurate subsurface SM estimation remains challenging for the Earth science community. Here, we used the empirical subsurface SM estimates derived from an exponential filter method (ExpF) as another assimilation variable, in combination with the data assimilation (DA) technique which updates model states and parameters simultaneously, to constrain the model estimates. We found these two pathways lead to significant improvements in subsurface SM estimation. The accuracy of subsurface SM was improved with biases (ubRMSE) significantly reduced by 0.005 m3 · m−3 on average over the modeling grid cells, due to the assimilation of empirical subsurface SM. The effects of parameter updating were relatively smaller but also positive. The proposed DA framework demonstrated superior accuracy to ExpF, with an average ubRMSE reduction of 0.003 m3 · m−3 and an average KGE increase of 0.202. This framework was also applicable to regional scale using satellite products. Moreover, both pathways proved effective in adjusting the physical surface‐subsurface correlation in the land surface model toward actual coupling. Our analysis has important implications for future studies on estimating subsurface SM using land surface models at larger spatiotemporal scales. Key Points: A 2‐dimensional data assimilation (DA) framework which accounted for a dual state‐parameter updating scheme was proposedThe accuracy of subsurface soil moisture (SM) was improved by assimilating empirical subsurface estimates and dual updatingThe proposed DA framework can also be applied to Soil Moisture Active Passive data to improve subsurface SM estimation at regional scale [ABSTRACT FROM AUTHOR]

The outbreak of infectious diseases in swine, such as classical swine fever (CSF), has become a significant concern in the pig‐farming industry. In Japan, after the re‐emergence of CSF in 2018, farms are now exposed to the risk of transmission from infected wild boar and CSF‐contaminated farms. This study aimed to identify biosecurity measures that were effective for the prevention of CSF introduction into farms during the period from the beginning of the CSF epidemic to the implementation of a vaccination campaign for domestic pigs at risk. The probability of virus introduction was assumed to be increased by the elevated risk from CSF‐infected wild boar and infected farms around the farm. The risk from infected wild boar was represented by the prevalence of CSF in wild boar or the occupancy of 1‐km grid cells with infected wild boar within 10‐km radii from a pig farm and the occurrence of CSF outbreaks on neighboring farms. Conversely, the probability of virus introduction was assumed to decrease in response to on‐farm biosecurity measures being implemented on each farm. The implementation of biosecurity measures on the farms and farm attributes were obtained through a questionnaire survey. Analyses were performed on each farm under the weekly situations where infected wild boar were both absent and present in the vicinity using a binomial generalized linear model. On farms where infected wild boar were not present around farms, daily washing and disinfecting of work clothing in pig houses was identified as the main measure to reduce the risk of CSF introduction into farms. On farms with infected wild boar in the vicinity, the absence of public roads on the farm and preventing wildlife intrusion into the areas where pig carcasses were stored were demonstrated to be effective in preventing CSF introduction. Based on the assumption that strict and comprehensive biosecurity measures are required to prevent CSF introduction, the implementation of these potentially effective measures is worth being prioritized. [ABSTRACT FROM AUTHOR]

Grid‐ or pixel‐based models, used across various scientific disciplines from microscopic to planetary scales, contain an unquantified error that bias our interpretation of the data. The error is produced by projecting 3D data onto a 2D grid. For Digital Terrain Models (DTMs) the projection error affects all slope‐dependent topographic metrics, like surface area or slope angle. Due to the proportionality of the error to the cosine of the slope, we can correct for it. We quantify the error and test the correction using synthetic landscapes for which we have analytical solutions of their metrics. Application to real‐world landscapes in California, reveal the systematic underestimation of surface area by up to a third, and mean slope angles by up to 10° in steep topography in current DTMs. Correcting projection errors allow for true estimates of surface areas and slope distributions enabling physics‐based models of surface processes at any spatial scale. Plain Language Summary: Hiking up a steep mountain slope feels longer than the horizontal distance measured on a map. The slope angle is calculated by taking the height over the horizontal distance. The length of a (mountain) slope is always greater than the horizontal plane. This mistake is also very common in Digital Terrain Models (DTMs). Here landscapes images captured in a bird's eye view are projected onto horizontal gridded surfaces. In the model, each grid cell has the same length and contains the height of the landscape. Due to the view, the slopes are only represented by the horizontal distance, which is shorter. We call this the projection error. Because the error depends on the slope, we use it to fix it. We test the correction on different landscapes with steep and gentle topography. We find that uncorrected models underestimate surface area and length by up to a third. Fixing the projection error shows that mountain slopes are much steeper and longer than usually reported. Knowing the true length of a mountain slope, we can use the topographic data to better understand and maybe predict processes and volumes, like landslides or fluid transport. Key Points: The projection error in gridded models biases our view of surface topography at all scalesCorrecting for the projection error allows exploring physics‐based erosion and transport lawsThe correction enables better sourcing of the topographic data we have access to [ABSTRACT FROM AUTHOR]

Aim Location Taxon Methods Results Main Conclusion We used two fungal data sources for occurrence records (fruitbodies and roots) to (1) test the influence of data source on estimating the environmental niche of ectomycorrhizal (ECM) fungi and (2) compare the differences in estimated niche area and density for ECM fungal species with conspicuous (easily observed, i.e. mushrooms) versus inconspicuous (difficult to observe and/or usually overlooked, i.e. crusts and truffles) fruitbodies.Europe.Sixty‐six ectomycorrhizal fungi.We used fungal records obtained from fruitbody and root data of 66 common ECM fungal species in European forests to estimate their environmental niches. The fruitbody data were extracted from public databases (GBIF, UNITE), while the root data (from individual ectomycorrhizas) were obtained from a dataset of 136 ICP Forests long‐term intensive monitoring plots. We estimated the niches for combined data sources (fruitbody and root data) and for each individual data source using six key environmental variables for ECM fungal community composition. We then examined how estimated niche overlap and area (number of cells in niche grid) varied for the two data sources between conspicuous and inconspicuous species.We found that although the niches estimated using combined data from the two data sources had high overlap with the niches estimated from fruitbody data, the niches estimated from fruitbody data had low or medium overlap with the niches estimated using root data for most ECM fungi. The overlap between the two data sources for conspicuous species was significantly larger than that for inconspicuous species. Root data were important for estimating the niche of inconspicuous species, which had a high ratio of root data to fruitbody data.Our results indicate that although fruitbody data suffice for estimating the environmental niche for most conspicuous ECM fungi, combined datasets including fruitbody and root data can improve the accuracy of estimated niches and should be used. Root data for inconspicuous species are particularly useful, and thus, adopting root data in niche estimation will better infer the niches of ECM fungi. Inferring niches along environmental variables can guide future sampling and conservation of fungi. [ABSTRACT FROM AUTHOR]

Racial geography studies the spatial distributions of multiracial populations. Technical challenges arise from the fact that US Census data, upon which all US-based studies rely, is only available in the form of spatial aggregates at a few levels of granularity. This negatively affects spatial analysis and, consequently, the quantification of racial segregation, especially on a smaller length scale. A recent methodology called the Racial Landscape (RL) stochastically disaggregates racial data at the level of census block aggregates into a grid of monoracial cells. RL-transformed racial data makes possible pattern-based, zoneless analysis, and visualization of racial geography. Here, we introduce the National Racial Geography Dataset 2020 (NRGD2020)—a collection of RL-based grids calculated from the 2020 census data and covering the entire conterminous US. It includes a virtual image layer for a bird's-eye-like view visualization of the spatial distribution of racial sub-populations, numerical grids for calculating racial diversity and segregation within user-defined regions, and precalculated maps of racial diversity and segregation on various length scales. NRGD2020 aims to facilitate and extend spatial analyses of racial geography and to make it more interpretable by tightly integrating quantitative analysis with visualization (mapping). [ABSTRACT FROM AUTHOR]

Studies on the neural correlates of navigation in 3D environments are plagued by several issues that need to be solved. For example, experimental studies show markedly different place cell responses in rats and bats, both navigating in 3D environments. In this study, we focus on modelling the spatial cells in rodents in a 3D environment. We propose a deep autoencoder network to model the place and grid cells in a simulated agent navigating in a 3D environment. The input layer to the autoencoder network model is the HD layer, which encodes the agent's HD in terms of azimuth (θ) and pitch angles (ϕ). The output of this layer is given as input to the Path Integration (PI) layer, which computes displacement in all the preferred directions. The bottleneck layer of the autoencoder model encodes the spatial cell-like responses. Both grid cell and place cell-like responses are observed. The proposed model is verified using two experimental studies with two 3D environments. This model paves the way for a holistic approach using deep neural networks to model spatial cells in 3D navigation. [ABSTRACT FROM AUTHOR]

This paper presents a new mesh data layout for parallel I/O of linear unstructured polyhedral meshes. The new mesh representation infers coherence across entities of different topological dimensions, i.e., grid cells, faces, and points. The coherence due to cell-to-face and face-to-point connectivities of the mesh is formulated as a tree data structure distributed across processors. The mesh distribution across processors creates consecutive and contiguous slices that render an optimized data access pattern for parallel I/O. A file format using the coherent mesh representation, developed and tested with OpenFOAM, enables the usability of the software at unprecedented scales. Further implications of the coherent and sliceable mesh representation arise due to simplifications in partitioning and diminished pre- and post-processing overheads. [ABSTRACT FROM AUTHOR]

Recent advancements in simultaneous localization and mapping (SLAM) have significantly improved the handling of dynamic objects. Traditionally, SLAM systems mitigate the impact of dynamic objects by extracting, matching, and tracking features. However, in real-world scenarios, dynamic object information critically influences decision-making processes in autonomous navigation. To address this, we present a novel approach for incorporating dynamic object information into map representations, providing valuable insights for understanding movement context and estimating collision risks. Our method leverages on-site mobile robots and multiple object tracking (MOT) to gather activation levels. We propose a multimodal map framework that integrates occupancy maps obtained through SLAM with Gaussian process (GP) modeling to quantify the activation levels of dynamic objects. The Gaussian process method utilizes a map-based grid cell algorithm that distinguishes regions with varying activation levels while providing confidence measures. To validate the practical effectiveness of our approach, we also propose a method to calculate additional costs from the generated maps for global path planning. This results in path generation through less congested areas, enabling more informative navigation compared to traditional methods. Our approach is validated using a diverse dataset collected from crowded environments such as a library and public square and is demonstrated to be intuitive and to accurately provide activation levels. [ABSTRACT FROM AUTHOR]

Identifying of the spatial distribution of the extreme rainfall in various regions of Algeria is a crucial element over the last four decades provides essential information for infrastructure development, assessing risks of flooding and drought, agricultural planning, and resource management. In the present study, the 30-day annual maximum rainfall spanning a period from 1979 to 2012 in Algeria was analyzed for each grid cell, where a "grid" denotes a spatial framework used to organize and analyze rainfall data across the country's geographical extent. The proposed percentiles such as 5th, 50th, 75th and 95th of the extreme precipitation for each grid cell were computed. Moran's index, Universal Kriging method, Hierarchical cluster analysis (HCA) were used in order to undestandat the spatial distribution of the extreme rainfall over Algeria country. The results obtained reveal a distinct spatial distribution of extreme rainfall events across Algeria, with higher amounts observed in the northern part of the country, followed by smaller amounts in the northeast, northwest and central regions. In contrast, the southern part, with a few exceptions in the Adrar region, experiences lower amounts of extreme rainfall. Various factors such as to-pography, water systems, prevailing winds and climate change likely influence this sptial pattern. Understanding these factors is essential to effectively manage and mitigate the impacts of extreme rainfall events in different regions of Algeria country. [ABSTRACT FROM AUTHOR]

With the requirement of a macroscopic approach to understanding the relationship between water resources and hydrological phenomena, such as severe droughts under climate change, this study investigates South Korea's long-term terrestrial water storage (TWS) using GRACE satellite data. A detailed analysis of water balance in TWS, based on the GLDAS model outputs in Google Earth Engine, reveals the following results: (1) TWS anomaly shows an average decrease of −33.5 mm year−1 from 2003 to 2016; (2) spatial shifts in TWS anomaly (−1.176 to −0.424 cm) unveil regional water storage dynamics, indicating negative temporal slope changes per grid cell (−0.393 to −0.225); (3) contributions of precipitation to TWS are not always straightforward, due to runoff inefficiencies affecting water storage and groundwater; (4) accessible water, integrating surface water and groundwater linked only to the shallow layer's soil moisture, constrains deep groundwater accounting, emphasizing the need for local groundwater surveys in further research. [ABSTRACT FROM AUTHOR]

Pathfinding for autonomous vehicles in large-scale complex terrain environments is difficult when aiming to balance efficiency and quality. To solve the problem, this paper proposes Hierarchical Path-Finding A* based on Multi-Scale Rectangle, called RHA*, which achieves efficient pathfinding and high path quality for large-scale unequal-weighted maps. Firstly, the original map grid cells were aggregated into fixed-size clusters. Then, an abstract map was constructed by aggregating equal-weighted clusters into rectangular regions of different sizes and calculating the nodes and edges of the regions in advance. Finally, real-time pathfinding was performed based on the abstract map. The experiment showed that the computation time of real-time pathfinding was reduced by 96.64% compared to A* and 20.38% compared to HPA*. The total cost of the generated path deviated no more than 0.05% compared to A*. The deviation value is reduced by 99.2% compared to HPA*. The generated path can be used for autonomous vehicle traveling in off-road environments. [ABSTRACT FROM AUTHOR]

Traditional one-dimensional system thermal-hydraulic analysis has been widely applied in the nuclear industry for licensing purposes because of its numerical efficiency. However, such tools have inherently limited opportunities for modeling multiscale multidimensional flows in large reactor enclosures. Recent interest in three-dimensional coarse grid (CG) simulations has shown their potential in improving the predictive capability of system-level analysis. At the same time, CGs do not allow one to accurately resolve and capture turbulent mixing and stratification, whereas implemented in CG solvers relatively simple turbulence models exhibit large model form uncertainties. Therefore, there is a strong interest in further advances in CG modeling techniques. In this work, two high-to-low data-driven (DD) methodologies (and their combination) are explored to reduce grid and model-induced errors using a case study based on the Texas A&M upper plenum of a high-temperature gas-cooled reactor facility. The first approach relies on the use of a DD turbulence closure [eddy viscosity predicted by a neural network (NN)]. A novel training framework is suggested to consider the influence of grid cell size on closure. The second methodology uses a NN to predict velocity errors to improve low-fidelity results. Both methodologies and their combination have shown the potential to improve CG simulation results by using data with higher fidelity. [ABSTRACT FROM AUTHOR]

In recent years discontinuous Galerkin (DG) methods have received increased interest from the geophysical community. In these methods the solution in each grid cell is approximated as a linear combination of basis functions. Ensemble data assimilation (DA) aims to approximate the true state by combining model outputs with observations using error statistics estimated from an ensemble of model runs. Ensemble data assimilation in geophysical models faces several well‐documented issues. In this work we exploit the expansion of the solution in DG basis functions to address some of these issues. Specifically, it is investigated whether a DA–DG combination (a) mitigates the need for observation thinning, (b) reduces errors in the field's gradients, and (c) can be used to set up scale‐dependent localisation. Numerical experiments are carried out using stochastically generated ensembles of model states, with different noise properties, and with Legendre polynomials as basis functions. It is found that strong reduction in the analysis error is achieved by using DA–DG and that the benefit increases with increasing DG order. This is especially the case when small scales dominate the background error. The DA improvement in the first derivative is, on the other hand, marginal. We think this to be a counter‐effect of the power of DG to fit the observations closely, which can deteriorate the estimates of the derivatives. Applying optimal localisation to the different polynomial orders, thus exploiting their different spatial length, is beneficial: it results in a covariance matrix closer to the true covariance than the matrix obtained using traditional optimal localisation in state space. [ABSTRACT FROM AUTHOR]

Simple Summary: Bumblebee species are crucial pollinators for both crops and natural ecosystems. Managing diversity and securing the services they provide requires sufficient data to manage these systems effectively. In this study, seventeen environmental factors were selected to understand their contribution to shaping species assemblages and to provide baseline information for understanding drivers of community assembly. The study focused on Gansu Province because it has some of the highest richness of bumblebees within China, and China represents a major data gap for bee distribution data. Net primary productivity followed by water vapor pressure were top-ranked drivers of species distributions. The results enhance our understanding of the importance of environmental factors in shaping bumblebee community assemblages. Understanding the influence of factors responsible for shaping community assemblage is crucial for biodiversity management and conservation. Gansu is one of the richest regions for bumblebee species in the world. We explored the distribution data of 52 bumblebee species collected in Gansu and its surroundings between 2002 and 2022, predicting habitat suitability based on 17 environmental variables using MaxEnt. The factors influencing community assemblage were assessed using canonical correspondence analysis. Net primary productivity, water vapor pressure, temperature seasonality, annual precipitation, and precipitation seasonality were some of the most influential drivers of species distributions. Based on Ward's agglomerative cluster analysis, four biogeographic zones are described: the Southern humid zone, the Western Qilian snow mountain zone, the Eastern Loess plateau zone, and the Western dry mountain zone. In the clusters of grid cells based on beta diversity values, the Southern humid zone comprised 42.5% of the grid cells, followed by the Eastern Loess plateau zone (32.5%), the Western dry mountain zone (20%), and the Western Qilian snow mountain zone (5%). Almost all the environmental factors showed a significant contribution to the assemblages of bumblebees of different groups. Our findings highlight the need for better data to understand species biogeography and diversity patterns, and they provide key baseline data for refining conservation strategies. [ABSTRACT FROM AUTHOR]

In cloud/fog-based environments, resource management is an important and challenging process. The deadline-based workflow scheduling mechanism is a common practice in such systems to overcome the complexities of resource management. However, many proposed approaches suffer from resource overloading/underloading, ignoring volunteer and volatile resources, and acting reactively. This paper presents a load-balancing method for IoT/Fog/Cloud environments integrated with local schedulers based on predicting workload and the presence of volatile mobile nodes (as dynamic resources). The proposed approach, firstly, turns the environment into a grid of equal-sized cells to reduce the system's complexity. Then, the overall status of intra-cell resources (overloaded, underloaded, or normal) is estimated. This estimation is done according to the workload prediction and available dynamic resources. Finally, an exhaustive search is applied to dispatch extra workflows from an overloaded cell to an underloaded one in such a way as to avoid missing workflow deadlines and improve system performance. The proposed method is intended to be scalable and decentralized by nature, allowing it to be used in large-scale settings such as smart cities. Extensive software simulation is used to evaluate and compare the proposed method to with two recently published works. The simulation results show that the proposed method outperforms others regarding job completion rate, workload variances, and time-related parameters. [ABSTRACT FROM AUTHOR]

Aim: To evaluate the performance of species distribution models in predicting observed colonisations, persistences and extirpations in response to changes in climate and land use over a multi‐decadal period. Location: Sweden. Methods: We use historical (early 20th century) land use and climate data to build species distribution models for 84 plant species across three provinces of Sweden. Model performance was then evaluated internally using a subset of the historical data for cross‐validation, as well as by using the models to project occurrences to the modern day and validating them with observed occurrences from 1990 to 2020. We then analysed predicted and observed occurrences in the modern period in terms of persistence, extirpation (local extinction) and colonisation in relation to species' habitat and climate associations. Results: We found overall high agreement between evaluation methods, although internal evaluation gave consistently higher values for model performance (using true skill statistic, TSS). Overall, extirpations were worst predicted, with on average fewer than one‐third of each species' extirpations being foreseen by the models. Colonisations were better predicted, while persistences were relatively well‐predicted. Predictive accuracy of colonisations was higher for species with relatively warmer temperature associations (climate‐driven expansion), while extirpations were better predicted in cool‐related species (retractions at cool edges). Colonisations of forest‐associated species were more common than predicted (underpredicted), despite widespread patterns of afforestation. Assessing grid‐cell level turnover, we found that in grid cells that experienced the largest changes in terms of climate and land use, predicted extirpations were less likely to have happened. Main Conclusions: We found that commonly applied modelling approaches have limited ability to predict observed changes in species occurrences, especially extirpations. This suggests that we should take predictions of future biodiversity loss very seriously. However, the ability for species to (at least temporarily) persist in unsuitable conditions could be an opportunity for biodiversity conservation. [ABSTRACT FROM AUTHOR]

The activity patterns of grid cells form distinctively regular triangular lattices over the explored spatial environment and are largely invariant to visual stimuli, animal movement, and environment geometry. These neurons present numerous fascinating challenges to the curious (neuro)scientist: What are the circuit mechanisms responsible for creating spatially periodic activity patterns from the monotonic input-output responses of single neurons? How and why does the brain encode a local, nonperiodic variable—the allocentric position of the animal—with a periodic, nonlocal code? And, are grid cells truly specialized for spatial computations? Otherwise, what is their role in general cognition more broadly? We review efforts in uncovering the mechanisms and functional properties of grid cells, highlighting recent progress in the experimental validation of mechanistic grid cell models, and discuss the coding properties and functional advantages of the grid code as suggested by continuous attractor network models of grid cells. [ABSTRACT FROM AUTHOR]

Off-road route planning plays a vital role across various domains, including civil transportation, earthquake relief and civil production. Off-road route planning requires the consideration of a diverse range of environmental constraints, which is a considerable challenge compared with conventional route planning along existing networks. We aimed to present a multi-objective route planning method for off-road environments. Specifically, we designed a heuristic function incorporating multiple characteristics to generate reliable route planning solutions based on Pareto optimality. We also constructed a hierarchical hexagonal grid model by merging similar grid cells and reconstructing adjacency relationships with other grid cells to achieve high efficiency. The proposed method was compared with conventional route planning methods using single- and multi-objective approaches in traditional and hierarchical hexagonal grid models. The proposed method can effectively plan off-road routes, improve optimal route distance and trafficability, and provide more efficient route solutions. It reduced the planning time by 97.1% compared with multi-objective route planning based on a traditional hexagonal grid model. [ABSTRACT FROM AUTHOR]

The super-droplet method (SDM) is a Lagrangian particle-based numerical scheme for cloud microphysics. In this work, a series of simulations based on the DYCOMS-II (RF02) setup with different horizontal and vertical resolutions are conducted to explore the grid convergence of the SDM simulations of marine stratocumulus. The results are compared with the double-moment bulk scheme (SN14) and model intercomparison project (MIP) results. In general, all SDM and SN14 variables show a good agreement with the MIP results and have similar grid size dependencies. The stratocumulus simulation is more sensitive to the vertical resolution than to the horizontal resolution. The vertical grid length DZ ≪ 2.5 m is necessary for both SDM and SN14. The horizontal grid length DX < 12.5 m is necessary for the SDM simulations. DX ≤ 25 m is sufficient for SN14. We also assess the numerical convergence with respect to the super-droplet numbers. The simulations are well converged when the super-droplet number concentration (SDNC) is larger than 16 super-droplets per cell. Our results indicate that the super-droplet number per grid cell is more critical than that per unit volume at least for the stratocumulus case investigated here. Our comprehensive analysis not only offers guidance on numerical settings essential for accurate stratocumulus cloud simulation but also underscores significant differences in liquid water content and cloud macrostructure between SDM and SN14. These differences are attributed to the inherent modeling strategies of the two schemes. SDM's dynamic representation of aerosol size distribution through wet deposition markedly contrasts with SN14's static approach, influencing cloud structure and behavior over a 6 h simulation. Findings reveal sedimentation's crucial role in altering aerosol distributions near cloud tops, affecting the vertical profile of cloud fraction (CF). Additionally, the study briefly addresses numerical diffusion's potential effects, suggesting further investigation is needed. The results underscore the importance of accurate aerosol modeling and its interactions with cloud processes in marine stratocumulus simulations, pointing to future research directions for enhancing stratocumulus modeling accuracy and predictive capabilities. [ABSTRACT FROM AUTHOR]

Discovered as an apparent pattern, a universal relation between geometry and information called the holographic principle has yet to be explained. This relation is unfolded in the present paper. As it is demonstrated there, the origin of the holographic principle lies in the fact that a geometry of physical space has only a finite number of points. Furthermore, it is shown that the puzzlement of the holographic principle can be explained by a magnification of grid cells used to discretize geometrical magnitudes such as areas and volumes into sets of points. To wit, when grid cells of the Planck scale are projected from the surface of the observable universe into its interior, they become enlarged. For that reason, the space inside the observable universe is described by the set of points whose cardinality is equal to the number of points that constitute the universe's surface. [ABSTRACT FROM AUTHOR]

The Amazon rainforest (ARF) is threatened by deforestation and climate change, which could trigger a regime shift to a savanna‐like state. Whilst previous work has suggested that forest resilience has declined in recent decades, that work was based only on local resilience indicators, and moreover was potentially biased by the employed multi‐sensor and optical satellite data and undetected anthropogenic land‐use change. Here, we show that the average correlation between neighboring grid cells' vegetation time series, which is referred to as spatial correlation, provides a more robust resilience indicator than local estimations. We employ it to measure resilience changes in the ARF, based on single‐sensor Vegetation Optical Depth data under conservative exclusion of human activity. Our results show an overall loss of resilience until around 2019, which is especially pronounced in the southwestern and northern Amazon for the time period from 2002 to 2011. The results from the reliable spatial correlation indicator suggest that in particular the southwest of the ARF has experienced pronounced resilience loss over the last two decades. Plain Language Summary: The Amazon rainforest (ARF) is Earth's most biodiverse ecosystem, crucial for regulating global and regional climate through water recycling and carbon uptake. Several studies suggest that ongoing deforestation, land‐use changes, and climate change induced shifts in rainfall patterns could trigger an abrupt regime shift from the current rainforest to a low‐treecover state. Here, we investigate the resilience of the ARF vegetation based on remotely sensed Vegetation Optical Depth, particularly useful in high‐biomass areas such as the ARF. The main feedback mechanism, essential for potential tipping, is moisture recycling. The trade winds bring in moisture from the Atlantic Ocean to South America's east, where it precipitates. Much of this moisture re‐enters the atmosphere via evapo‐transpiration and is transported further west, thereby establishing a moisture regime supporting the rainforest. The induced spatial coupling results in the spatial correlation being a reliable indicator of resilience. We compare changes in the spatial correlation to changes in the two classic indicators. The greatest resilience loss is detected in the southwestern Amazon, which has been identified as a highly‐coupled sub‐system since it is reliant on recycled moisture from upstream the trade winds, hence making the spatial correlation a particularly suitable and trustworthy resilience indicator there. Key Points: We quantify changes in Amazon resilience by investigating satellite data that has been carefully selected to avoid known sources of biasSpatial correlation of vegetation time series reliably shows resilience changes in spatially coupled systems such as the Amazon RainforestThe Amazon forest is losing resilience, especially in the southwestern Amazon, a region which has high demand on recycled moisture [ABSTRACT FROM AUTHOR]

Land surface hydrologic models adeptly capture crucial terrestrial processes with a high level of spatial detail. Typically, these models incorporate numerous uncertain, spatially varying parameters, the specification of which can profoundly impact the simulation capabilities. There is a longstanding tradition wherein parameter calibration has served as the conventional procedure to enhance model performance. However, calibrating distributed land surface hydrologic models presents a great challenge, often resulting in uneven spatial performance due to the compression of information inherent in model outputs and observations into a single‐value objective function. To address this problem, we propose a novel Generative Adversarial Network‐based Parameter Optimization (GAN‐PO) method. By leveraging a deep neural network to discern model spatial biases, we train a generative network to produce spatially coherent parameter fields, minimizing distinctions between simulations and observations. By leveraging neural network‐based surrogate models to make the physical model differentiable, we employ GAN‐PO to calibrate the Variable Infiltration Capacity (VIC) model against evapotranspiration (ET) over China's Huaihe basin. The results show that GAN‐PO can diminish errors in simulated ET derived from default parameters across nearly all grid cells within the study region, surpassing the conventional calibration approach based on the parameter regionalization technique. Ablation analysis indicates that relying solely on the traditional loss could lead to deteriorated model performance, underscoring the crucial role of the discriminator. Notably, due to the discriminator's explicit identification of model spatial biases, GAN‐PO excels in maintaining spatial consistency, outperforming the state‐of‐the‐art differentiable parameter learning (dPL) method in terms of model spatial performance. Key Points: A novel generative adversarial network‐based parameter estimation method is proposed to calibrate distributed land surface hydrologic modelsBy employing a discriminator to identify model spatial biases, this method contributes to effective and spatially coherent parameter estimationThis method can substantially reduce model simulated errors at grid scale and achieve consistent spatial performance [ABSTRACT FROM AUTHOR]

Since the launch of TROPOMI on the Sentinel-5 Precursor (S5P) satellite, NO 2 observations have become available with a resolution of 3.5× 5 km, which makes monitoring NO x emissions possible at the scale of city districts and industrial facilities. For Europe, emissions are reported on an annual basis for country totals and large industrial facilities and made publicly available via the European Environment Agency (EEA). Satellite observations can provide independent and more timely information on NO x emissions. A new version of the inversion algorithm DECSO (Daily Emissions Constrained by Satellite Observations) has been developed for deriving emissions for Europe on a daily basis, averaged to monthly mean maps. The estimated precision of these monthly emissions is about 25 % for individual grid cells. These satellite-derived emissions from DECSO have been compared to the officially reported European emissions and spatial–temporal disaggregated emission inventories. The country total DECSO NO x emissions are close to the reported emissions and the emissions compiled by the Copernicus Atmosphere Monitoring Service (CAMS). Comparison of the spatially distributed NO x emissions of DECSO and CAMS showed that the satellite-derived emissions are often higher in cities, while they are similar for large power plants and slightly lower in rural areas. [ABSTRACT FROM AUTHOR]

Environmental mapping serves as a crucial element in Simultaneous Localization and Mapping (SLAM) algorithms, playing a pivotal role in ensuring the accurate representation necessary for autonomous robot navigation guided by SLAM. Current SLAM systems predominantly rely on grid-based map representations, encountering challenges such as measurement discretization for cell fitting and grid map interpolation for online posture prediction. Splines present a promising alternative, capable of mitigating these issues while maintaining computational efficiency. This paper delves into the efficiency disparities between B-Spline surface mapping and discretized cell-based approaches, such as grid mapping, within indoor environments. B-Spline Online SLAM and FastSLAM, utilizing Rao-Blackwellized Particle Filter (RBPF), are employed to achieve range-based mapping of the unknown 2D environment. The system incorporates deep learning networks in the B-Spline curve estimation process to compute parameterizations and knot vectors. The research implementation utilizes the Intel Research Lab benchmark dataset to conduct a comprehensive qualitative and quantitative analysis of both approaches. The B-Spline surface approach demonstrates significantly superior performance, evidenced by low error metrics, including an average squared translational error of 0.0016 and an average squared rotational error of 1.137. Additionally, comparative analysis with Vision Benchmark Suite demonstrates robustness across different environments, highlighting the effectiveness of B-Spline SLAM for real-world applications. [ABSTRACT FROM AUTHOR]