Coarse Scale Research Articles

The Impact of Scale on Extracting Individual Mobility Patterns from Location-Based Social Media.

Understanding human movement patterns is crucial for comprehending how a city functions. It is also important for city planners and policymakers to create more efficient plans and policies for urban areas. Traditionally, human movement patterns were analyzed using origin-destination surveys, travel diaries, and other methods. Now, these patterns can be identified from various geospatial big data sources, such as mobile phone data, floating car data, and location-based social media (LBSM) data. These extensive datasets primarily identify individual or collective human movement patterns. However, the impact of spatial scale on the analysis of human movement patterns from these large geospatial data sources has not been sufficiently studied. Changes in spatial scale can significantly affect the results when calculating human movement patterns from these data. In this study, we utilized Weibo datasets for three different cities in China including Beijing, Guangzhou, and Shanghai. We aimed to identify the effect of different spatial scales on individual human movement patterns as calculated from LBSM data. For our analysis, we employed two indicators as follows: an external activity space indicator, the radius of gyration (ROG), and an internal activity space indicator, entropy. These indicators were chosen based on previous studies demonstrating their efficiency in analyzing sparse datasets like LBSM data. Additionally, we used two different ranges of spatial scales-10-100 m and 100-3000 m-to illustrate changes in individual activity space at both fine and coarse spatial scales. Our results indicate that although the ROG values show an overall increasing trend and the entropy values show an overall decreasing trend with the increase in spatial scale size, different local factors influence the ROG and entropy values at both finer and coarser scales. These findings will help to comprehend the dynamics of human movement across different scales. Such insights are invaluable for enhancing overall urban mobility and optimizing transportation systems.

Variability of rainy season onsets over East Africa

AbstractOver the East Africa region forecasts of the onset of the rainy seasons have the potential to support decision‐making, especially in the largely rain‐fed agricultural sector. However, the understanding of key features of onset remains limited. Here, we analyse the variability of onset and associated drivers at interannual and subseasonal timescales, using several onset definitions. Results show that the onset date is especially variable from year to year in some of the high‐potential agricultural areas (standard deviation >20 days), which has implications for agricultural risk management. The choice of onset definition metric matters; agronomic definitions have limited applicability at the regional scale and are also highly sensitive to the spatial scale of analysis and to the choice of rainfall data. Onset information provided at coarse scales should be used with caution for decision‐making at the local scale; the “hit rate” of coarse‐scale tercile onset information at the local scale is less than 40% on average. To varying degrees, onset is related to total seasonal rainfall and thus to dominant interannual drivers of rainfall, including the Indian Ocean Dipole and ENSO modes in October–December and the western Pacific “V‐gradient” pattern in March–May. However, by analysing the dominant proportion of onset variance unrelated to total rainfall during the climatological season we show a substantial influence of subseasonal drivers, notably the Madden–Julian Oscillation. As such, there is an opportunity for rainfall onset information to be provided across seasonal and subseasonal timescales. Our work reinforces the need for enhanced co‐production of such onset information with stakeholders, especially regarding the choice of metric, alignment of forecasts with livelihood calendars, interpretation of the credibility of information content for local‐level decision‐making, as well as appropriate strategies for staggered risk management interventions informed by forecasts over “seamless” lead times.

A 3-D extension of the Multiscale Control Volume method for the simulation of the single-phase flow in anisotropic and heterogeneous porous media

The level of detail on modern geological models requires higher resolution grids that may render the simulation of multiphase flow in porous media intractable. Moreover, these models may comprise highly heterogeneous media with phenomena taking place in different scales. Scale transferring techniques allow for the solution of such problems in a lower resolution scale at reduced computational cost. Among these techniques, the Multiscale Finite Volume (MsFV) method constructs a set of numerical operators in order to map quantities from the fine-scale mesh to a coarser one and vice-versa while maintaining flux conservation on both scales. However, the MsFV formulation, as originally stated, is only consistent on k-orthogonal grids since it uses a linear Two-point Flux Approximation (TPFA) method and may struggle to generate consistent primal-dual coarse grids pairs on unstructured grids. The Multiscale Restriction Smoothed-Basis method (MsRSB) improves on the MsFV by introducing a new iterative procedure to find the multiscale operators and the concept of support regions which reduces the method's complexity when applied to unstructured fine and coarse grids. The original version was only consistent on k-orthogonal fine grids due to the TPFA discretization, but filtering methods have been developed to also enable consistent multipoint schemes on the fine scale. Meanwhile, the Multiscale Control Volume method (MsCV) replaces the TPFA by the Multipoint Flux Approximation with a Diamond stencil (MPFA-D) scheme on the fine-scale while further enhancing the generation of the geometric entities to allow unstructured grids on the fine and coarse scales for two-dimensional simulations. In this work we propose an extension to three-dimensional geometries of both the MsCV and the algorithm to obtain the multiscale geometric entities based on the concept of a background grid, a coarser grid used as a proxy for the primal coarse grid. We modify the original MPFA-D method to use the very robust Global Least Squares (GLS) interpolation technique to obtain the required auxiliary nodal unknowns. We also introduce an enhanced version of the 3-D MsCV with the incorporation of the enhanced MsRSB (E-MsRSB) to enforce M-matrix properties and improve convergence. Finally, we employ the MsCV operators in a two-stage smoother to show how it can be used as a good iterative procedure to recover the fine-scale solution. We show that the 3-D MsCV method produces good results employing unstructured grids on both scales to handle the simulation of the single-phase flow in anisotropic, and heterogeneous porous media and that the smoothing procedure is able to accurately retrieve the fine-scale solution within a few iterations.

Thermodynamically consistent hybrid computational models for fluid-particle interactions

We introduce a novel computational framework designed to explore the dynamic interactions between fluid and solid particles or structures immersed in a viscous fluid medium adhering to the generalized Onsager principle. This innovative framework harnesses the power of the phase-field-embedding method, in which each solid component, whether rigid or elastic, is characterized by a volume-preserving phase field. The unified velocity within the fluid-solid ensemble governs the movement of both solid particles and the surrounding fluid, specifically for passive particles. Active particles, however, are not only influenced by this unified velocity but are also driven by their self-propelling velocities. To capture exclusive volume interactions among particles and between particles and boundaries, we employ repulsive potential forces at a coarser scale. These forces effectively model repulsion and collision effects. Rigid particles maintain structural integrity by enforcing a zero velocity gradient tensor within their spatial domains, necessitating the introduction of a constraining stress tensor. In contrast, elastic particles are governed by a quasi-linear constitutive equation describing the elastic stress within their domains, allowing for accurate modeling of their deformations. The motion of solid particles is tracked by monitoring the dynamics of their centers of mass. This approach facilitates the development of a hybrid, thermodynamically consistent hydrodynamic model applicable to both rigid and elastic particles. To numerically solve this thermodynamically consistent model for elastic particles, we present a structure-preserving numerical algorithm. Notably, in the limit of an infinite elastic modulus, this algorithm converges to the one employed for modeling rigid particles. Finally, we substantiate the effectiveness, accuracy, and stability of our proposed scheme through a series of numerical experiments. These experiments not only validate the computational framework but also showcase its capabilities, reinforcing the reliability of our approach.

The heritability and structural correlates of resting-state fMRI complexity

The complexity of fMRI signals quantifies temporal dynamics of spontaneous neural activity, which has been increasingly recognized as providing important insights into cognitive functions and psychiatric disorders. However, its heritability and structural underpinnings are not well understood. Here, we utilize multi-scale sample entropy to extract resting-state fMRI complexity in a large healthy adult sample from the Human Connectome Project. We show that fMRI complexity at multiple time scales is heritable in broad brain regions. Heritability estimates are modest and regionally variable. We relate fMRI complexity to brain structure including surface area, cortical myelination, cortical thickness, subcortical volumes, and total brain volume. We find that surface area is negatively correlated with fine-scale complexity and positively correlated with coarse-scale complexity in most cortical regions, especially the association cortex. Most of these correlations are related to common genetic and environmental effects. We also find positive correlations between cortical myelination and fMRI complexity at fine scales and negative correlations at coarse scales in the prefrontal cortex, lateral temporal lobe, precuneus, lateral parietal cortex, and cingulate cortex, with these correlations mainly attributed to common environmental effects. We detect few significant associations between fMRI complexity and cortical thickness. Despite the non-significant association with total brain volume, fMRI complexity exhibits significant correlations with subcortical volumes in the hippocampus, cerebellum, putamen, and pallidum at certain scales. Collectively, our work establishes the genetic basis and structural correlates of resting-state fMRI complexity across multiple scales, supporting its potential application as an endophenotype for psychiatric disorders.

High-Resolution Estimation of Soil Saturated Hydraulic Conductivity via Upscaling and Karhunen–Loève Expansion within DREAM(ZS)

Quantification of the soil hydraulic conductivity is key to the study of water flow and solute transport in unsaturated soils. Rapid advances in measurement technology have provided a large number of observations at different scales, offering unprecedented opportunities and challenges for the estimation of hydraulic parameters. This paper proposes an inverse estimation method for downscaling of observations on coarse scales to estimate hydraulic parameters on high-resolution scales. Due to the significant spatial heterogeneity, the inversion faces the problems of dynamics-based integration of data at different scales, model uncertainty due to hundreds and thousands of parameters, and computational consumption due to the large number of forward simulations. To overcome these problems, this paper uses an efficient Bayesian optimization DREAM(ZS) as an inverse framework, and incorporates an analytical upscaling method and Karhunen–Loève (KL) expansion to infer finer-scale saturated hydraulic conductivity distribution conditioned on coarse-scale measurements. The efficient upscaling method is used to link measurements and hydraulic parameters at different scales, and Karhunen–Loève (KL) expansion is incorporated to greatly reduce the dimension of the parameter to be estimated. To further improve the efficiency of the inversion, a locally one-dimensional (LOD) algorithm is used to solve the multidimensional water flow model at coarse scales. The proposed inverse model is applied in a series of numerical experiments to demonstrate its applicability and effectiveness under different flow boundary conditions, different levels of ratio between coarse- and fine-scale grids, different densities of observation points, and different degrees of statistic heterogeneity of soil mediums.

Super-localization of spatial network models

Spatial network models are used as a simplified discrete representation in a wide range of applications, e.g., flow in blood vessels, elasticity of fiber based materials, and pore network models of porous materials. Nevertheless, the resulting linear systems are typically large and poorly conditioned and their numerical solution is challenging. This paper proposes a numerical homogenization technique for spatial network models which is based on the super-localized orthogonal decomposition (SLOD), recently introduced for elliptic multiscale partial differential equations. It provides accurate coarse solution spaces with approximation properties independent of the smoothness of the material data. A unique selling point of the SLOD is that it constructs an almost local basis of these coarse spaces, requiring less computations on the fine scale and achieving improved sparsity on the coarse scale compared to other state-of-the-art methods. We provide an a posteriori analysis of the proposed method and numerically confirm the method’s unique localization properties. In addition, we show its applicability also for high-contrast channeled material data.

Single porosity model: Exploring the spatial resolution limits in complex urban patterns

When modeling large-scale urban floods, the Porosity Nonlinear Shallow Water Equations (PNSWE) emerge as an interesting and consistent subgrid approach to reduce computational time while preserving the solution structure, allowing for computational efficiency at the cost of some loss in the accuracy of the results. Porosity accounts for changes in storage and exchanges due to obstacles in urban areas, and it introduces an extra source term proportional to the porosity gradient into the momentum equations. However, no systematic analyses on the effects of grid size have been performed in real domains, when this model is used to represent fine-scale topographic information at a coarser scale. In this study, we analyze how accuracy is affected by gradually increasing grid resolution in a generalized porosity approach computed at the cell-level. The Single Porosity model (SP) in Cartesian coordinates is employed to simulate a real-world urban flooding event, with resolution transitioning from fine- to macro-scale. At an intermediate scale, the meso-scale, where cell size approximates street width and computational time is significantly reduced, the model captures main preferential flow paths by means of the porosity gradient within the urban area. Good agreement with refined classical model solutions is observed at this scale for flood extension and hazard maps, providing valuable information for early-warning systems. Numerical results underscore the importance of porosity models in rapidly assessing flow properties during an event, enhancing real-time decision-making with reliable information.

Total CO2 budget estimate and degassing dynamics for an active stratovolcano: Turrialba Volcano, Costa Rica

Distributions and concentrations of carbon dioxide being emitted from active volcanoes elucidate the subsurface controls on gas ascent from the source and provide important information regarding the extent and state of the magmatic system. The main goal of this study was to determine if degassing followed open- or closed-system dynamics, and to define a baseline for eruption monitoring of degassing across the volcanic edifice through the CO2 budget estimate from the combined results of two CO2 gas emission surveys from 2021 and 2022 on Turrialba volcano, Costa Rica. This was accomplished by utilizing a new method for estimating total carbon flux on and around this persistently degassing and intermittently erupting volcano by integrating fine and coarse spatial scales of measurements; including an analysis of carbon isotopes to determine the source contributions to the gas emissions. Approximately 99% (2287 ± 1719 t CO2 day−1) of magma-derived degassing activity is advective and concentrated at the summit crater, with a smaller, continuous component of ∼1% (23.73 ± 6.65 tonnes CO2 day−1) flank diffuse soil degassing. As the majority of the gas emissions from Turrialba are concentrated in the summit plume, the system is likely experiencing open-system degassing dynamics through one dominant degassing pathway. Though at relatively low levels, the locations and distributions of diffuse degassing on the volcanic flanks allow us to delineate subsurface features that likely reveal the extent of the magmatic system of the volcano. Volcanic CO2 outputs at Turrialba primarily concentrate along faults and fractures near the summit and across the flanks where permeable zones allow gas ascent, and with limited emissions elsewhere. The results of this study provide a baseline for monitoring future changes in the Turrialba magmatic system and demonstrate the potential for applying this method to other volcanic complexes, particularly those that are poorly monitored or where there is a greater prevalence of diffuse and distal degassing.

Vegetation biomass and topography are associated with seasonal habitat selection and fall translocation behavior in Arctic hares.

Habitat selection theory suggests that environmental features selected at coarse scales reveal fundamental factors affecting animal fitness. When these factors vary across seasons, they may lead to large-scale movements, including long-distance seasonal migrations. We analyzed the seasonal habitat selection of 25 satellite-tracked Arctic hares from a population on Ellesmere Island (Nunavut, Canada) that relocated over 100 km in the fall. Since no other lagomorph is known to perform such extensive movements, this population offered an ideal setting to test animal movement and habitat selection theory. On summer grounds hares selected low elevation areas, while on winter grounds they selected high vegetation biomass, high elevation, and steep slopes. During fall relocation, they alternated between stopover and traveling behavioral states (ratio 2:1). Stopover locations were characterized by higher vegetation heterogeneity and lower rugosity than traveling locations, while vegetation biomass and elevation interacted to explain stopover locations in a more complex way. The selected combination of environmental features thus varied across seasons and behavioral states, in a way broadly consistent with predictions based on the changing food and safety needs of hares. Although causality was not demonstrated, our results improve our understanding of long-distance movements and habitat selection in Arctic hares, as well as herbivore ecology in the polar desert. Results also provide strong support to animal movement and habitat selection theory, by showing how some important hypotheses hold when tested in a species phylogenetically distinct from most animal models used in this research field.

Three near-complete genome assemblies reveal substantial centromere dynamics from diploid to tetraploid in Brachypodium genus

BackgroundCentromeres are critical for maintaining genomic stability in eukaryotes, and their turnover shapes genome architectures and drives karyotype evolution. However, the co-evolution of centromeres from different species in allopolyploids over millions of years remains largely unknown.ResultsHere, we generate three near-complete genome assemblies, a tetraploid Brachypodium hybridum and its two diploid ancestors, Brachypodium distachyon and Brachypodium stacei. We detect high degrees of sequence, structural, and epigenetic variations of centromeres at base-pair resolution between closely related Brachypodium genomes, indicating the appearance and accumulation of species-specific centromere repeats from a common origin during evolution. We also find that centromere homogenization is accompanied by local satellite repeats bursting and retrotransposon purging, and the frequency of retrotransposon invasions drives the degree of interspecies centromere diversification. We further investigate the dynamics of centromeres during alloploidization process, and find that dramatic genetics and epigenetics architecture variations are associated with the turnover of centromeres between homologous chromosomal pairs from diploid to tetraploid. Additionally, our pangenomes analysis reveals the ongoing variations of satellite repeats and stable evolutionary homeostasis within centromeres among individuals of each Brachypodium genome with different polyploidy levels.ConclusionsOur results provide unprecedented information on the genomic, epigenomic, and functional diversity of highly repetitive DNA between closely related species and their allopolyploid genomes at both coarse and fine scale.

Mechanisms of Pore-Clogging Using a High-Resolution CFD-DEM Colloid Transport Model

Colloidal transport and clogging in porous media is a phenomenon of critical importance in many branches of applied sciences and engineering. It involves multiple types of interactions that span from the sub-colloid scale (electrochemical interactions) up to the pore-scale (bridging), thus challenging the development of representative modelling. So far published simulation results of colloidal or particulate transport are based on either reduced set of forces or spatial dimensions. Here we present an approach enabling to overcome both computational and physical limitations posed by a problem of 3D colloidal transport in porous media. An adaptive octree mesh is introduced to a coupled CFD and DEM method while enabling tracking of individual colloids. Flow fields are calculated at a coarser scale throughout the domain, and at fine-scale around colloids. The approach accounts for all major interactions in such a system: elastic, electrostatic, and hydrodynamic forces acting between colloids, as well as colloids and the collector surface. The method is demonstrated for a single throat model made of four spherical segments, and the impact of clogging is reported in terms of the evolution of the critical path diameter for percolation and permeability. We identified four stages of clogging development depending on position and time of individual colloid entrapment, which in turn correlates to a cluster evolution and local transport.

One-shot procedures for efficient minimum compliance topology optimization

In this paper, a one-shot approach for minimum compliance topology optimization is investigated. In the convex case of variable thickness sheet optimization, an optimality criteria scheme with a single step of an iterative state solver converges to the same solutions as an accurate procedure. This remarkable behavior is explained by the crucial role of the geometric multigrid preconditioner that generates accurate design sensitivities on a coarse scale. When applied to SIMP-based topology optimization, a one-shot procedure yields designs with the same primary load-transferring features as an accurate procedure. The difference is in the absence and presence of thin features that have a relatively minor impact on the objective. Several practical remedies are suggested that can close the small performance gaps between the one-shot procedure and an accurate one. The solution scheme is directly applicable to large-scale problems executed on parallel computers, allowing for significant computational savings.

DTT-CGINet: A Dual Temporal Transformer Network with Multi-Scale Contour-Guided Graph Interaction for Change Detection

Deep learning has dramatically enhanced remote sensing change detection. However, existing neural network models often face challenges like false positives and missed detections due to factors like lighting changes, scale differences, and noise interruptions. Additionally, change detection results often fail to capture target contours accurately. To address these issues, we propose a novel transformer-based hybrid network. In this study, we analyze the structural relationship in bi-temporal images and introduce a cross-attention-based transformer to model this relationship. First, we use a tokenizer to express the high-level features of the bi-temporal image into several semantic tokens. Then, we use a dual temporal transformer (DTT) encoder to capture dense spatiotemporal contextual relationships among the tokens. The features extracted at the coarse scale are refined into finer details through the DTT decoder. Concurrently, we input the backbone’s low-level features into a contour-guided graph interaction module (CGIM) that utilizes joint attention to capture semantic relationships between object regions and the contour. Then, we use the feature pyramid decoder to integrate the multi-scale outputs of the CGIM. The convolutional block attention modules (CBAMs) employ channel and spatial attention to reweight feature maps. Finally, the classifier discriminates change pixels and generates the final change map of the difference feature map. Several experiments have demonstrated that our model shows significant advantages over other methods in terms of efficiency, accuracy, and visual effects.

Most Probable Flows for Kunita SDEs

We identify most probable flows for Kunita Brownian motions, i.e. stochastic flows with Eulerian noise and deterministic drifts. Such stochastic processes appear for example in fluid dynamics and shape analysis modelling coarse scale deterministic dynamics together with fine-grained noise. We treat this infinite dimensional problem by equipping the underlying domain with a Riemannian metric originating from the noise. The resulting most probable flows are compared with the non-perturbed deterministic flow, both analytically and experimentally by integrating the equations with various choice of noise structures.

Specialist or refugee: Microhabitat use and competition between two sympatric woodland salamanders

AbstractCo‐occurrence between species may be mediated by ecological differences or competitive interactions, but the nature of these interactions can vary across spatial scales. At coarse scales, species may appear to broadly co‐occur, but at fine scales, particularly for species with small home ranges, species may utilize different aspects of the microhabitat such that co‐occurrence is limited and competitive interactions are reduced or eliminated. We investigated the microhabitat use of two morphologically and ecologically similar woodland salamanders (the Red‐backed Salamander, Plethodon cinereus, and the Northern Ravine Salamander, P. electromorphus) that are distributed throughout the Alleghany Plateau in eastern North America to determine whether they partition habitat at finer scales. We also tested if P. electromorphus is a specialist on habitat types that allow it to exclude P. cinereus, or if P. electromorphus uses lower quality habitat as a refuge to avoid P. cinereus. At a site of microsympatry, we repeatedly sampled plots during two seasons, each within a different area of the landscape, and used occupancy and abundance modeling to assess the extent of co‐occurrence and test whether the species use different microhabitat conditions. Plethodon cinereus greatly outnumbered P. electromorphus and occupied a greater proportion of plots, most plots where we detected P. electromorphus we also detected P. cinereus. In contrast to patterns documented in some montane species pairs, in which a high‐elevation habitat specialist excludes a low‐elevation generalist, we found that P. electromorphus primarily used microhabitat conditions favorable for both species, such as north‐facing slopes, yet did not have a negative effect on the abundances of P. cinereus. In the presence of an abundant competitor, microhabitat use of the less abundant species may narrow toward optimal conditions with sufficient resources to facilitate co‐occurrence.

Mapping the diversity of land uses following deforestation across Africa

African forest are increasingly in decline as a result of land-use conversion due to human activities. However, a consistent and detailed characterization and mapping of land-use change that results in forest loss is not available at the spatial-temporal resolution and thematic levels suitable for decision-making at the local and regional scales; so far they have only been provided on coarser scales and restricted to humid forests. Here we present the first high-resolution (5 m) and continental-scale mapping of land use following deforestation in Africa, which covers an estimated 13.85% of the global forest area, including humid and dry forests. We use reference data for 15 different land-use types from 30 countries and implement an active learning framework to train a deep learning model for predicting land-use following deforestation with an F1-score of 84±0.7\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$84\\pm 0.7$$\\end{document} for the whole of Africa. Our results show that the causes of forest loss vary by region. In general, small-scale cropland is the dominant driver of forest loss in Africa, with hotspots in Madagascar and DRC. In addition, commodity crops such as cacao, oil palm, and rubber are the dominant drivers of forest loss in the humid forests of western and central Africa, forming an “arc of commodity crops” in that region. At the same time, the hotspots for cashew are found to increasingly dominate in the dry forests of both western and south-eastern Africa, while larger hotspots for large-scale croplands were found in Nigeria and Zambia. The increased expansion of cacao, cashew, oil palm, rubber, and large-scale croplands observed in humid and dry forests of western and south-eastern Africa suggests they are vulnerable to future land-use changes by commodity crops, thus creating challenges for achieving the zero deforestation supply chains, support REDD+ initiatives, and towards sustainable development goals.

Lost and found: Patterns of marine litter accumulation on the remote Island of Santa Luzia, Cabo Verde

Santa Luzia, an uninhabited island in the archipelago of Cabo Verde, serves as a natural laboratory and important nesting site for loggerhead turtles Carettacaretta. The island constitutes an Integral Natural Reserve and a Marine Protected Area. We assessed marine litter accumulation on sandy beaches of the island and analysed their spatial patterns using two sampling methods: at a fine scale, sand samples from 1 × 1 m squares were collected, identifying debris larger than 1 mm; at a coarse scale, drone surveys were conducted to identify visible marine debris (>25 mm) in aerial images. We sampled six points on three beaches of the island: Achados (three points), Francisca (two points) and Palmo Tostão (one point). Then, we modelled the abundance of marine debris using topographical variables as explanatory factors, derived from digital surface models (DSM). Our findings reveal that the island is a significant repository for marine litter (>84% composed of plastics), with up to 917 plastic items per m2 in the sand samples and a maximum of 38 macro-debris items per m2 in the drone surveys. Plastic fragments dominate, followed by plastic pellets (at the fine-scale approach) and fishing materials (at the coarse-scale approach). We observed that north-facing, higher-elevation beaches accumulate more large marine litter, while slope and elevation affect their spatial distribution within the beach. Achados Beach faces severe marine debris pollution challenges, and the upcoming climate changes could exacerbate this problem.

Tropical tree ectomycorrhiza are distributed independently of soil nutrients.

Mycorrhizae, a form of plant-fungal symbioses, mediate vegetation impacts on ecosystem functioning. Climatic effects on decomposition and soil quality are suggested to drive mycorrhizal distributions, with arbuscular mycorrhizal plants prevailing in low-latitude/high-soil-quality areas and ectomycorrhizal (EcM) plants in high-latitude/low-soil-quality areas. However, these generalizations, based on coarse-resolution data, obscure finer-scale variations and result in high uncertainties in the predicted distributions of mycorrhizal types and their drivers. Using data from 31 lowland tropical forests, both at a coarse scale (mean-plot-level data) and fine scale (20 × 20 metres from a subset of 16 sites), we demonstrate that the distribution and abundance of EcM-associated trees are independent of soil quality. Resource exchange differences among mycorrhizal partners, stemming from diverse evolutionary origins of mycorrhizal fungi, may decouple soil fertility from the advantage provided by mycorrhizal associations. Additionally, distinct historical biogeographies and diversification patterns have led to differences in forest composition and nutrient-acquisition strategies across three major tropical regions. Notably, Africa and Asia's lowland tropical forests have abundant EcM trees, whereas they are relatively scarce in lowland neotropical forests. A greater understanding of the functional biology of mycorrhizal symbiosis is required, especially in the lowland tropics, to overcome biases from assuming similarity to temperate and boreal regions.

A quasi-meshfree method for constructing boundary-aware reproducing bases on geometrically complex domains using manifold geodesics

In many applications, physical domains are geometrically complex making it challenging to perform coarse-scale approximation. A defeaturing process is often used to simplify the domain in preparation for approximation and analysis at the coarse scale. Herein, a methodology is presented for constructing a coarse-scale reproducing basis on geometrically complex domains given an initial fine-scale mesh of the fully featured domain. The initial fine-scale mesh can be of poor quality and extremely refined. The construction of the basis functions begins with a coarse-scale covering of the domain and generation of weighting functions with local support. Manifold geodesics are used to define distances within the local support for general applicability to non-convex domains. Conventional moving least squares is used to construct the coarse-scale reproducing basis. Applications in quasi-interpolation and linear elasticity are presented.