Spatial Scales Research Articles

Spatial pattern analysis reveals intra-specific competition and fractal characteristics of Pinus sylvestris var. mongolica populations in Xiaotaojia Gulley Watershed

The imprudent cultivation of Pinus sylvestris var. mongolica has led to degradation in the watersheds surrounding Chongli County. This study investigates the artificial forests of P. sylvestris var. mongolica in the Xiaotaojiagou watershed, Chongli County. Twenty-five subplots measuring 20 m × 20 m were established at upper, upper-middle, middle, and lower positions within the watershed. Spatial distribution patterns and fractal characteristics of P. sylvestris var. mongolica were analyzed using univariate pair correlation function g(r), marked correlation functions (marked correlation function Kmm(r), marked variation function γm(r), and Schlather’s Im(r) function), and fractal dimensions (box-counting dimension, information dimension). The findings are as follows: (1) P. sylvestris var. mongolica in the middle section of the watershed displayed the largest average diameters at breast height and tree height, while those in the lower section exhibited the largest average crown width. (2) P. sylvestris var. mongolica populations in the upstream, middle, and downstream sections of the watershed exhibited three alternating states: aggregated distribution, uniform distribution, and random distribution. However, with increasing spatial scale, the distribution gradually tended towards randomness. P. sylvestris var. mongolica in the upper-middle section of the watershed showed aggregated distribution at scales of 1.5–32.5 m, transitioning to random distribution and then to uniform distribution with increasing spatial scale. (3) Individual attributes of P. sylvestris var. mongolica (diameter at breast height, tree height, and crown width) exhibited high correlation and facilitation in the upstream and upper-middle sections at small-to-medium scales, whereas negative correlations and inhibitions were observed in the middle and lower sections. These attributes tended to be uncorrelated with increasing spatial scale. (4) Fractal dimensions indicated that the degree of aggregation of P. sylvestris var. mongolica population was lowest in the downstream section of the watershed. In conclusion, spatial distribution patterns and intraspecies relationships of P. sylvestris var. mongolica populations are influenced by spatial scale and environmental conditions, particularly in more resource-competitive downstream areas. Effective management and protection of P. sylvestris var. mongolica populations require targeted measures such as adjusting afforestation density, improving soil conditions, and implementing differentiated ecological restoration strategies to alleviate intraspecies competitive pressures and promote population health and sustainable development.

Decision Support Indicators (DSIs) and their role in hydrological planning

Decision Support Indicators (DSIs) are metrics designed to inform local and regional stakeholders about the characteristics of a predicted (or ongoing) event to facilitate decision-making. In this paper, the DSI concept was developed to clarify the different aims of different kinds of indicators by naming them, and a framework was developed to describe and support the usage of such DSIs. The framework includes three kinds of DSI: hydroclimatic DSIs which are easy to calculate but hard to understand by non-experts; impact-based DSIs which are often difficult to calculate but easy to understand by non-experts; and event-based DSIs, which compare a current or projected state to a locally well-known historical event, where hydroclimatic and impact-based DSIs are currently mainly used. Tables and figures were developed to support the DSI development in collaboration with stakeholders. To develop and test the framework, seven case studies, representing different hydrological pressures on three continents (South America, Asia, and Europe), were carried out. The case studies span several temporal and spatial scales (hours-decades; 70–6,000 km2) as well as hydrological pressures (pluvial and riverine floods, drought, and water scarcity), representing different climate zones. Based on stakeholder workshops, DSIs were developed for these cases, which are used as examples of the conceptual framework. The adaptability of the DSI framework to this wide range of cases shows that the framework and related concepts are useful in many contexts.

Unified control of temporal and spatial scales of sensorimotor behavior through neuromodulation of short-term synaptic plasticity.

Neuromodulators have been shown to alter the temporal profile of short-term synaptic plasticity (STP); however, the computational function of this neuromodulation remains unexplored. Here, we propose that the neuromodulation of STP provides a general mechanism to scale neural dynamics and motor outputs in time and space. We trained recurrent neural networks that incorporated STP to produce complex motor trajectories-handwritten digits-with different temporal (speed) and spatial (size) scales. Neuromodulation of STP produced temporal and spatial scaling of the learned dynamics and enhanced temporal or spatial generalization compared to standard training of the synaptic weights in the absence of STP. The model also accounted for the results of two experimental studies involving flexible sensorimotor timing. Neuromodulation of STP provides a unified and biologically plausible mechanism to control the temporal and spatial scales of neural dynamics and sensorimotor behaviors.

Spatiotemporal organization of prefrontal norepinephrine influences neuronal activity.

Norepinephrine (NE), a neuromodulator released by locus coeruleus (LC) neurons throughout cortex, influences arousal and learning through extra-synaptic vesicle exocytosis. While NE within cortical regions has been viewed as a homogenous field, recent studies have demonstrated heterogeneous axonal dynamics and advances in GPCR-based fluorescent sensors permit direct observation of the local dynamics of NE at cellular scale. To investigate how the spatiotemporal dynamics of NE release in the prefrontal cortex (PFC) affect neuronal firing, we employed in vivo two-photon imaging of layer 2/3 of PFC in order to observe fine-scale neuronal calcium and NE dynamics concurrently. In this proof of principle study, we found that local and global NE fields can decouple from one another, providing a substrate for local NE spatiotemporal activity patterns. Optic flow analysis revealed putative release and reuptake events which can occur at the same location, albeit at different times, indicating the potential to create a heterogeneous NE field. Utilizing generalized linear models, we demonstrated that cellular Ca2+ fluctuations are influenced by both the local and global NE field. However, during periods of local/global NE field decoupling, the local field drives cell firing dynamics rather than the global field. These findings underscore the significance of localized, phasic NE fluctuations for structuring cell firing, which may provide local neuromodulatory control of cortical activity.Significance Statement NE is a neuromodulator which plays a critical role in learning and arousal, but understanding its spatial scale has been limited by technical barriers. Here, we utilized two-photon imaging of GPCR-based sensors, light sheet imaging, and computational modeling to gain insight into the fine scale organization of NE in PFC. We found that NE can influence neuronal activity at a local scale within cortex, which has not been shown before, and we developed new computational approaches to analyzing two-photon imaging of GPCR based fluorescent sensors. This insight will facilitate improved understanding of NE's role in motivated behaviors, as well as new approaches for understanding local neurotransmitter function.

Ecosystem Resilience Monitoring and Early Warning Using Earth Observation Data: Challenges and Outlook

AbstractAs the Earth system is exposed to large anthropogenic interferences, it becomes ever more important to assess the resilience of natural systems, i.e., their ability to recover from natural and human-induced perturbations. Several, often related, measures of resilience have been proposed and applied to modeled and observed data, often by different scientific communities. Focusing on terrestrial ecosystems as a key component of the Earth system, we review methods that can detect large perturbations (temporary excursions from a reference state as well as abrupt shifts to a new reference state) in spatio-temporal datasets, estimate the recovery rate after such perturbations, or assess resilience changes indirectly from stationary time series via indicators of critical slowing down. We present here a sequence of ideal methodological steps in the field of resilience science, and argue how to obtain a consistent and multi-faceted view on ecosystem or climate resilience from Earth observation (EO) data. While EO data offers unique potential to study ecosystem resilience globally at high spatial and temporal scale, we emphasize some important limitations, which are associated with the theoretical assumptions behind diagnostic methods and with the measurement process and pre-processing steps of EO data. The latter class of limitations include gaps in time series, the disparity of scales, and issues arising from aggregating time series from multiple sensors. Based on this assessment, we formulate specific recommendations to the EO community in order to improve the observational basis for ecosystem resilience research.

Understanding the drivers of near-surface winds in Adélie Land, East Antarctica

Abstract. Near-surface winds play a crucial role in the climate of Antarctica, but accurately quantifying and understanding their drivers is complex. They result from the contribution of two distinct families of drivers: the large-scale pressure gradient and surface-induced pressure gradients known as katabatic and thermal wind. The extrapolation of vertical potential temperature above the boundary layer down to the surface enables us to separate and quantify the contribution of these different pressure gradients in the momentum budget equations. Using this method applied to outputs of the regional atmospheric model MAR at a 3-hourly resolution, we find that the seasonal and spatial variability in near-surface winds in Adélie Land is dominated by surface processes. On the other hand, high-frequency temporal variability (3-hourly) is mainly controlled by large-scale variability everywhere in Antarctica, except on the coast. In coastal regions, although the katabatic acceleration surpasses all other accelerations in magnitude, none of the katabatic or large-scale accelerations can be identified as the single primary driver of near-surface wind variability. The angle between the large-scale acceleration and the surface slope is a key factor in explaining strong wind speed events: the highest-wind-speed events happen when the katabatic and large-scale forcing are aligned, although each acceleration, when acting alone, can also cause strong wind speed. This study underlines the complexity of the drivers of Antarctic surface winds and the value of the momentum budget decomposition to identify drivers at different spatial and temporal scales.

Validation and analysis of the Polair3D v1.11 chemical transport model over Quebec

Abstract. Air pollution is a major health hazard, and while air quality overall has been improving in industrialized nations, pollution is still a major economic and public health issue, with some species, such as ozone (O3), still exceeding the standards set by governing agencies. Chemical transport models (CTMs) are valuable tools that aid in our understanding of the risks of air pollution both at local and regional scales. In this study, the Polair3D v1.11 CTM of the Polyphemus air quality modeling platform was set up over Quebec, Canada, to assess the model's capability in predicting key air pollutant species over the region, at seasonal temporal scales and at regional spatial scales. The simulation by the model included three nested domains, at horizontal resolutions of 9 km by 9 km and 3 km by 3 km, as well as two 1 km by 1 km domains covering the cities of Montréal and Québec. We find that the model captures the spatial variability and seasonal effects and, to a lesser extent, the hour-by-hour or day-to-day temporal variability for a fixed location. The model at both the 3 km and the 1 km resolution struggled to capture high-frequency temporal variability and showed large variabilities in correlation and bias from site to site. When comparing the biases and correlation at a site-wide scale, the 3 km domain showed slightly higher correlation for carbon monoxide (CO), nitrogen dioxide (NO2), and nitric oxide (NO), while ozone (O3), sulfur dioxide (SO2), and PM2.5 showed slight increases in correlation at the 1 km domain. The performance of the Polair3D model was in line with other models over Canada and comparable to Polair3D's performance over Europe.

Unraveling near real-time spatial dynamics of population using geographical ensemble learning

Dynamic gridded population data are crucial in fields such as disaster reduction, public health, urban planning, and global change studies. Despite the use of multi-source geospatial data and advanced machine learning models, current frameworks for population spatialization often struggle with spatial non-stationarity, temporal generalizability, and fine temporal resolution. To address these issues, we introduce a framework for dynamic gridded population mapping using open-source geospatial data and machine learning. The framework consists of (i) delineation of human footprint zones, (ii) construction of muliti-scale population prediction models using automated machine learning (AutoML) framework and geographical ensemble learning strategy, and (iii) hierarchical population spatial disaggregation with pycnophylactic constraint-based corrections. Employing this framework, we generated hourly time-series gridded population maps for China in 2016 with a 1-km spatial resolution. The average accuracy evaluated by root mean square deviation (RMSD) is 325, surpassing datasets like LandScan, WorldPop, GPW, and GHSL. The generated seamless maps reveal the temporal dynamic of population distribution at fine spatial scales from hourly to monthly. This framework demonstrates the potential of integrating spatial statistics, machine learning, and geospatial big data in enhancing our understanding of spatio-temporal heterogeneity in population distribution, which is essential for urban planning, environmental management, and public health.

Evidence of a toroidal magnetic field in the core of 3C 84

The spatial scales of relativistic radio jets, probed by relativistic magneto-hydrodynamic (RMHD) jet launching simulations and by most very long baseline interferometry (VLBI) observations differ by an order of magnitude. Bridging the gap between these RMHD simulations and VLBI observations requires selecting nearby active galactic nuclei (AGN), the parsec-scale region of which can be resolved. The radio source is a nearby bright AGN fulfilling the necessary requirements: it is launching a powerful, relativistic jet powered by a central supermassive black hole, while also being very bright. Using 22\,GHz globe-spanning VLBI measurements of we studied its sub-parsec region in both total intensity and linear polarisation to explore the properties of this jet, with a linear resolution of $ We tested different simulation set-ups by altering the bulk Lorentz factor Gamma of the jet, as well as the magnetic field configuration (toroidal, poloidal helical). We confirm the persistence of a limb brightened structure, which reaches deep into the sub-parsec region. The corresponding electric vector position angles (EVPAs) follow the bulk jet flow inside but tend to be orthogonal to it near the edges. Our state-of-the-art RMHD simulations show that this geometry is consistent with a spine-sheath model, associated with a mildly relativistic flow and a toroidal magnetic field configuration.

Formation of low-mass protostars and their circumstellar disks

Understanding circumstellar disks is of prime importance in astrophysics; however, their birth process remains poorly constrained due to observational and numerical challenges. Recent numerical works have shown that the small-scale physics, often wrapped into a sub-grid model, play a crucial role in disk formation and evolution. This calls for a combined approach in which both the protostar and circumstellar disk are studied in concert. We aim to elucidate the small-scale physics and constrain sub-grid parameters commonly chosen in the literature by resolving the star-disk interaction. We carried out a set of very high resolution 3D radiative-hydrodynamics simulations that self-consistently describe the collapse of a turbulent, dense molecular cloud core to stellar densities. We studied the birth of the protostar, the circumstellar disk, and its early evolution ($<6\ yr $ after protostellar formation). Following the second gravitational collapse, the nascent protostar quickly reaches breakup velocity and sheds its surface material, thus forming a hot ($ K $), dense, and highly flared circumstellar disk. The protostar is embedded within the disk such that material can flow without crossing any shock fronts. The circumstellar disk mass quickly exceeds that of the protostar, and its kinematics are dominated by self-gravity. Accretion onto the disk is highly anisotropic, and accretion onto the protostar mainly occurs through material that slides on the disk surface. The polar mass flux is negligible in comparison. The radiative behavior also displays a strong anisotropy, as the polar accretion shock was shown to be supercritical, whereas its equatorial counterpart is subcritical. We also find a remarkable convergence of our results with respect to initial conditions. These results reveal the structure and kinematics in the smallest spatial scales relevant to protostellar and circumstellar disk evolution. They can be used to describe accretion onto regions commonly described by sub-grid models in simulations studying larger-scale physics.

Abstract PS03-08: Spatial biomechanics determines fate in breast cancer

Abstract Background: With the recent advancements of spatial omics, it has become increasingly recognized that spatial analysis the of tumor architecture is key to decipher the heterogenous intratumoral relationships between different tumor components and provide better understanding of cancer therapy resistance, as well as help to identify potential targets for personalized therapy. The challenge of the discovery and implementation of such biomarkers still lays in the technological and methodological difficulties, and their translation into clinical practice. In parallel a major advancement in cancer research has been the recognition of the importance of cancer biomechanical properties in cancer progression, metastasis, and therapy response. Methods: We developed a new computational platform to identify biomechanical drivers of cancer outcome from spatial omics data. We used it to identify recurring spatial patterns of these markers within the tumor microenvironment, and define the spatial scale of heterogeneity of such patterns. Our dataset consists in 700 primary breast cancer baseline samples analyzed with two 30-marker imaging mass cytometry panels to identify cancer, immune, and stromal cells and structures and their biomechanical states. Our patient cohort includes 20+ years of follow up, with up to 6 samples per patient from 64 patients alive 12 years post diagnosis, and 49 patients lost to breast cancer deaths. Patients were treated with surgery, radiation, chemotherapy, endocrine therapy, or combinations of these post-surgery. Based on this approach, we derived a new three-parameter quantitative metric of preferential spatial co-localization between different cells or structures, and we use this metric to stratify patients into responders and non-responders with the aid of single and multivariate survival analysis. Finally, we include a correlation with Atomic Force Microscopy to identify the mechanical signature of these driving patterns in breast cancer. Results: Our work enabled the identification of tumor-immune-stromal spatial patterns that drive breast cancer outcome and their spatial scale. Among our results, we were able to further define spatial regions of hypoxia-driven EMT. We found these regions to be usually on the scale of 50 mm radius, and enriched in presence of unspecified immune cells. They are also more frequent in ER+ areas, with high NaKATPase activity. Our analysis also shed light on the controversy on the role of fibroblasts. We have found that the presence of a strong, spatially structured stroma, in fibroblast-rich regions is a strong predictor of positive outcome. Similarly, in our analysis collagen cross-linking emerges as a positive control factor in Vimentin-rich microenvironments, offering new interpretation to the role of the ECM structure. Importantly, our first principle approach allows for the identification of driving structures beyond heterogeneity, and thus enables easy extension to additional datasets. Finally, when correlating these patterns with Atomic force microscopy measurements, we can clearly see that patterns predictive of poor survival present a clear heterogeneous stiffness signature, as opposed to a very homogenous good survival pattern signature. Conclusion: These results confirm the importance of spatial distribution of biomechanical drivers in cancer, offering new avenues of physics-based therapeutic targets. Our results also offer a solid base to inform machine learning algorithms on how to significantly parametrize spatial patterns in breast cancer for clinical significance. Furthermore, we demonstrate for the first time the use of Atomic Force Microscopy as a single biomarker for these patterns in breast cancer with a direct correlation based on pathology matching. Citation Format: Sara Nizzero, Maria Pelaez Soni, Gregory Zaugg, Mariam Gachechiladze, Yitian Xu, Licheng Zhang, Junjun Zheng, Brian Menegaz, Lee B Jordan, Colin A Purdie, Philip R Quinlan, Chandandeep Nagi, Karla A Sepulveda, Philipp Oertle, Tobias A Appenzeller, Marko Loparic, Zhihui Wang, Shu-Hsia Chen, Vittorio Cristini, Marija Plodinec, Alastair Thompson. Spatial biomechanics determines fate in breast cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PS03-08.

Salmon shark seasonal site fidelity demonstrates the influence of scale on identifying potential high-use areas and vulnerabilities

Considering habitat use throughout the whole range of a highly mobile marine species is necessary to understand life history, identify vulnerabilities, and inform effective management. We used satellite tagging data from 128 adult female salmon sharks Lamna ditropis to identify seasonal hotspots of activity in an extended California Current region (ECCR; encompassing the California Current Large Marine Ecosystem), an area far away from their well-described primary habitat in the Alaska Downwelling Region where they have been documented, but whose utility has been poorly understood. Tag track durations had a mean of 447.7 ± 381 d, and 88 sharks (68.8%) visited the ECCR, comprising 33.6% of 28019 total daily Argos detections. Tracking data revealed that the timing and duration of migrations to the ECCR varied, but salmon shark distribution within the ECCR displayed consistent latitudinal shifts in accordance with regional oceanographic seasons. High site fidelity across multi-year tracks to high-productivity features, such as sea banks, and previously published knowledge of salmon shark life history suggest that the ECCR provides important foraging habitat which may be linked to reproductive success. The data reveal high overlap of salmon shark distribution with cumulative fishing effort collected by Global Fishing Watch for 2012-2019, particularly around seasonal hotspots, suggesting that female salmon sharks might be at risk of fisheries encounters. Collectively, our findings emphasize the importance of the ECCR in salmon shark life history and demonstrate the influence of spatial and temporal scale on interpretation of large movement data sets and identification of critical habitat outside of well-studied regions.

Proton-pumping rhodopsins promote the growth and survival of phytoplankton in a highly variable ocean.

Proton-pumping rhodopsins (PPRs) utilize sunlight to produce cellular energy. They are widely distributed in marine phytoplankton and were recently shown to occur in the vacuolar membrane of a marine diatom, making the vacuole a second light transducing organelle. Of course, the first, the chloroplast, is where photosynthesis occurs. However, the two light-driven sources of cellular energy are quite different and, in many ways, complement one another. Photosynthesis works best at low to intermediate light intensities, and is inhibited at high light, while PPR is predicted to work best at high light intensities. And photosynthetic rates decrease with decreasing temperature and are subject to iron limitation, while PPR photochemistry is not directly limited by iron, and is unaffected by temperature. Thus, the two phototrophic systems are favored under different sets of conditions. Placing PPR in the vacuole may benefit this complementary situation where one or the other phototrophic process is favored depending on the environmental conditions. And here, the presence of PPR in the vacuole may be especially beneficial for growth and survival as that organelle often acts as a storage site for cellular energy in the form of the phosphate anhydride bonds of polyphosphates. We hypothesize that this complementary behavior, along with the ability to store excess energy produced by PPR in the vacuole as high energy polyphosphates, represents an important survival strategy in the ocean, where light, iron levels, and temperature vary widely on a variety of spatial and temporal scales.

Considering scale effects in water quality analysis to enhance the precision of influencing factor response analysis

Accurately identifying the optimal spatial scales of effect analysis of influencing factors on water quality is crucial for effective water environment management. To address this, we propose a framework that consists of mix scale division (watersheds, riparian buffers, and circular buffers), and conduct a case study in the Yuanjiang River Basin (YJRB). Scale effects and non-linear impacts of various influencing factors on water quality were identified. The case study results revealed the variations of these water quality indicators were predominated by different influencing factors at various sales, such as by slope (SL) at the scale of riparian buffer (w) = 500 m and circular buffer (r) = 15 km (with a contribution percentage of 16.6 %), mean annual temperature (TE) at the watershed scale (23.4 %), annual precipitation (PR) at the scale of w = watershed and r = 15 km etc. The percentage of cultivated land (CL) > 28 % at the scale (w = 500 m, r = 50 km) will lead to increase in total phosphorus (TP) and SL > 26° at the scale (w = 500 m, r = 15 km) will lead to increase in pondus hydrogenii (pH). While, POP > 30 person per unit area at the scale (w = 100 m, r = 1 km), SL > 24° at the scale (w = 500 m, r = 15 km), NDVI > 0.48 at the scale (w = 500 m, r = 5 km) and PR > 1300 mm at the scale (w = watershed, r = 15 km) will lead to decrease in ammonia nitrogen (NH3-N), total nitrogen (TN), electrical conductivity (EC) and turbidity (NTU) respectively. Results indicated that the precision can be improved by regulating influencing factors within scale effects and considering the non-linear effects of factors on water quality.

Efficient distributed association management method of data, model, and knowledge for digital twin railway

ABSTRACT Digital twin railway is a pivotal foundation for the intelligent construction and maintenance of railway engineering projects within extensive open spaces. Its essence is the integrated representation and association management of multi-granularity spatiotemporal data, executable analysis models, and professional knowledge. These elements are characterized by the prominent characteristics of multi-source, heterogeneity, and massive volume. However, current decentralized and independent management strategies often neglect the dynamic coupling relationships between them, and numerous multi-path joins and conversion aggregation operations exist across various spatial scale applications. Consequently, this results in challenges such as the inability to dynamically couple data-model-knowledge and conduct global association retrieval, thereby limiting the potential for real-time analysis and intelligent application capabilities. To address these problems, we first constructed a tripartite graph model ( DMK − Graph model ) that explicitly associates temporal, spatial, and interactive relationships. Subsequently, an association management architecture was proposed, accompanied by a global association graph index ( DMK − Graph index ) and a global-local indexing mechanism. Finally, a prototype system for railway data-model-knowledge association management was developed. The effectiveness of the distributed association management method was demonstrated by employing a case study of high-temperature safety risk analysis in railway tunnel engineering with multi-physics field coupling.

Macrofaunal diversity patterns in coastal marine sediments: re-examining common metrics and methods

Complex biodiversity patterns arise in marine systems due to overlapping ecological processes, including organism interactions, resource distribution, and environmental conditions. Despite the importance of documenting these patterns, describing diversity in natural ecosystems remains challenging. Here, we investigate 3 nearshore sub-Arctic sites to describe benthic macroinfaunal taxa and biological traits, with the ultimate aim of determining whether common diversity metrics and typical sampling efforts adequately capture community composition in these systems. First, we assess how diversity relates to sediment depth and examine relationships among commonly used taxonomic and functional diversity indices. Second, using a power analysis, we explore how sampling effort influences the interpretation of diversity patterns in coastal systems. We report significant variation in community composition among sites, even across small spatial scales of km, and find that taxonomically diverse communities do not necessarily correspond to high functional diversity. We further find that although environmental factors such as sediment depth consistently affect macroinfaunal diversity, the direction and magnitude of these relationships are site-dependent. Finally, we demonstrate that typical sampling effort for coastal benthic studies (for example, <5 push cores of ~7 cm diameter) may not capture macroinfaunal community composition adequately, potentially obscuring hotspots in common diversity metrics such as taxonomic or functional richness. However, indices such as Simpson’s diversity may be well-suited to resource-limited studies with restricted sampling capacity. Our results highlight the importance of adopting multi-pronged approaches to biodiversity assessment and determining optimal sample sizes for marine benthic systems, particularly in the context of biodiversity monitoring for conservation purposes.

Psychometric properties of the spatial skills self-efficacy scale: A validity and reliability study

The aim of this study was to develop a valid and reliable spatial skills self-efficacy scale for middle school students. To this end, the study sampled 5th-, 6th-, 7th-, and 8-grade students in the spring term of the 2021–2022 academic year. The data were collected from 1369 students for the pilot study and the validity and reliability analysis to investigate the psychometric properties of the scale. Twenty-five subject matter experts were asked for opinions and Lawshe's content validity ratio (1975) was used to measure the content validity of the scale. The results of the exploratory factor analysis conducted for the construct validity yielded a four-factor structure consisting of seventeen items, which accounted for 41.37 % of the total variance. The factor loadings of the scale items ranged from 0.41 to 0.75. When the fit indices of the model were examined as a result of Confirmatory Factor Analysis, it was determined that the firstorder multifactor structure were within the excellent or acceptable value ranges. The Cronbach's alpha coefficient was found to be 0.77 for the total scale. The results of the independent samples t-test for the upper and lower 27 % groups showed a significant difference between the groups for all scale items. The correlation coefficients of the subscales with one another and with the total scale showed significant positive moderate and high correlations. The test–retest reliability coefficients also showed significant positive moderate and high correlations. These results indicate that the Spatial Skills Self-Efficacy Scale is a measurement tool that produces valid and reliable measurements and can be used to determine the spatial skills self-efficacy of middle school students.

Assessing Tidal Models in the Ryukyu Islands Region Using the ENOPF Method

Abstract Satellite remote sensing can monitor sea level changes at temporal and spatial scales, plays an important role in the study of tides, and is widely used in numerical tidal models. However, these tidal models are usually computationally expensive. The equidistant nodes orthogonal polynomial fitting (ENOPF) method may overcome that drawback. This study evaluates the accuracy of the ENOPF method in fitting the major tidal constituents in the region near the Ryukyu Islands, where the water depth on either side of the islands varies significantly. The results show that the ENOPF method can accurately fit the major tidal constituents in the presence of complex topography. Furthermore, this approach can also be used to generate reasonable cotidal charts and provide valuable tidal information for hydrodynamic model simulations in the East China Sea. For the high-resolution hydrodynamic model of the East China Sea in particular, reasonable open boundary conditions can be provided by the ENOPF method.

On the Mechanisms Driving Latent Heat Flux Variations in the Northwest Tropical Atlantic

AbstractThe Northwest Tropical Atlantic (NWTA) is a region of complex surface ocean circulation. The most prominent feature is the North Brazil Current (NBC) and its retroflection at 8°N, which leads to the formation of numerous mesoscale eddies known as NBC rings. The NWTA also receives the outflow of the Amazon River, generating freshwater plumes that can extend up to 100,000 km2. We show that these two processes influence the spatial variability of the region's surface latent heat flux (LHF). On the one hand, the presence of surface freshwater modifies the vertical stratification of the ocean, the mixed layer heat budget, and thus the air‐sea heat exchanges. On the other hand, NBC rings create a highly heterogeneous mesoscale sea surface temperature (SST) field that directly influences the near‐surface atmospheric circulation. These effects are illustrated by observations from the ElUcidating the RolE of Cloud‐Circulation Coupling in ClimAte ‐ Ocean Atmosphere (EUREC4A‐OA) and Atlantic Tradewind Ocean‐Atmosphere Interaction Campaign (ATOMIC) experiments, satellite and reanalysis data. We decompose the LHF budget into several terms controlled by different atmospheric and oceanic processes to identify the mechanisms leading to LHF changes. We find LHF variations of up to 160 W m2, of which 100 W m2 are associated with wind speed changes and 40 W m2 with SST variations. Surface currents or heat release associated with stratification changes remain as second‐order contributions with LHF variations of less than 10 W m2 each. This study highlights the importance of considering these three components to properly characterize LHF variability at different spatial scales, although it is limited by the scarcity of collocated observations.

Does 2D and 3D built environment matter for the spreading of COVID-19 at early stages in Wuhan, China: A spatial and multi-scale analysis

Evidence for the impacts of urban physical attributes on the spread of COVID-19 remains weak due to few investigations into the modifiable areal unit problem (MAUP) and spatial autocorrelation, as well as the lack of fine-grained COVID-19 case data. This study presents a spatial and multi-scale approach to examine the relationships between two-dimensional (2D) and three-dimensional (3D) urban parameters and accumulative confirmed infection cases. The spatial distributions of COVID-19 infections are mapped using anonymized neighborhood-level report data with nearly 50,000 confirmed cases at the early stages of the epidemic in Wuhan, China, and the 2D and 3D built environment factors at multiple spatial scales are characterized. Next, geographical detector model is used to examine the MAUP in the spread of COVID-19 and effects of urban parameters, and spatial regression models are then used to address spatial autocorrelation in the results at the identified appropriate spatial scales. Results reveal that the influences of most urban parameters are sensitive to the scale and areal unit used, and 3D urban parameters, such as building height, have a greater influence than 2D parameters. The estimates from both spatial and non-spatial models suggest the presence of spatial dependence and unobserved autocorrelated factors, and the overestimation of the effects of most urban parameters.