Spatially Complex Research Articles

Model development of dynamic receptive field for remote sensing imageries

The object of research is the integration of a dynamic receptive field attention module (DReAM) into Swin Transformers to enhance scene localization and semantic segmentation for high-resolution remote sensing imagery. The study focuses on developing a model that dynamically adjusts its receptive field and integrates attention mechanisms to enhance multi-scale feature extraction in high-resolution remote sensing data. Traditional approaches, particularly convolutional neural networks (CNNs), suffer from fixed receptive fields, which hinder their ability to capture both fine details and long-range dependencies in large-scale remote sensing images. This limitation reduces the effectiveness of conventional models in handling spatially complex and multi-scale objects, leading to inaccuracies in object segmentation and scene interpretation. The DReAM-CAN model incorporates a dynamic receptive field scaling mechanism and a composite attention framework that combines CNN-based feature extraction with Swin Transformer self-attention. This approach enables the model to dynamically adjust its receptive field, efficiently process objects of various sizes, and better capture both local textures and global scene context. As a result, the model significantly improves segmentation accuracy and spatial adaptability in remote sensing imagery. These results are explained by the model’s ability to dynamically modify receptive fields based on scene complexity and object distribution. The self-attention mechanism further optimizes feature extraction by selectively enhancing relevant spatial dependencies, mitigating noise, and refining segmentation boundaries. The hybrid CNN-Transformer architecture ensures an optimal balance between computational efficiency and accuracy. The DReAM-CAN model is particularly applicable in high-resolution satellite and aerial imagery analysis, making it useful for environmental monitoring, land-use classification, forestry assessment, precision agriculture, and disaster impact analysis. Its ability to adapt to different scales and spatial complexities makes it ideal for real-time and large-scale remote sensing tasks that require precise scene localization and segmentation.

Limnological data derived from high frequency monitoring buoys are asynchronous in a large lake.

Autonomous data collection is rapidly becoming an integral part of water quality monitoring, particularly for agencies looking to manage and protect aquatic ecosystems. While beneficial, it is unclear how the collection of these data can be applied in spatially complex large lakes (e.g., Laurentian Great Lakes) given the spatial heterogeneity of the ecosystem. To address this potential shortcoming in large lakes, we assessed the synchrony of sensor variables between 10 pairs of static buoys in the western basin of Lake Erie (western basin surface area = 3,282 km2). Within western Lake Erie, water temperature was highly synchronous whereas dissolved oxygen, turbidity, chlorophyll and phycocyanin were asynchronous. The extent of this asynchrony was higher with increasing spatial distance between buoys. We found that between pairs of static buoys, temperature, dissolved oxygen, and turbidity all experienced decreasing correlations with increasing distance. Our results show that if researchers intend to leverage these data to answer important questions and provide real-time applications related to environmental issues like harmful algal/cyanobacterial blooms, monitoring networks need to be designed carefully with spatial complexity in mind. While autonomous data collection has many benefits, the reliance on a single or limited network of anchored monitoring buoys in large lake ecosystems has a high probability of missing important spatial features of these systems.

Geospatial environmental complexity, spatial brain volume, and spatial behavior across the Alzheimer's disease spectrum.

Understanding impact of environmental properties on Alzheimer's disease (AD) is paramount. Spatial complexity of one's routinely navigated environment is an important but understudied factor. A total of 660 older adults from National Alzheimer's Coordinating Center (NACC) dataset were geolocated and environmental complexity index derived from geospatial network landmarks and points-of-interest. Latent models tested mediation of spatial navigation-relevant brain volumes and diagnosis (cognitively-healthy, mild cognitive impairment [MCI], AD) on effect of environmental complexity on spatial behavior. Greater environmental complexity was selectively associated with larger allocentric (but not egocentric) navigation-related brain volumes, lesser diagnosis of MCI and AD, and better spatial behavioral performance, through indirect hierarchical mediation. Findings support hypothesis that spatially complex environments positively impact navigation neural circuitry and spatial behavior function. Given the vulnerability of these very circuits to AD pathology, residing in spatially complex environments may be one factor to help stave off the brain atrophy that accompanies spatial navigation deficits across the AD spectrum.

Local fractal dimension of collagen detects increased spatial complexity in fibrosis

Increase of collagen content and reorganization characterizes fibrosis but quantifying the latter remains challenging. Spatially complex structures are often analyzed via the fractal dimension; however, established methods for calculating this quantity either provide a single dimension for an entire object or a spatially distributed dimension that only considers binary images. These neglect valuable information related to collagen density in images of fibrotic tissue. We sought to develop a fractal analysis that can be applied to 3-dimensional (3D) images of fibrotic tissue. A fractal dimension map for each image was calculated by determining a single fractal dimension for a small area surrounding each image pixel, using fiber thickness as the third dimension. We found that this local fractal dimension increased with age and with progression of fibrosis regardless of collagen content. Our new method of distributed 3D fractal analysis can thus distinguish between changes in collagen content and organization induced by fibrosis.

Experimental Investigation of Two-Phase Flow Properties of Heterogeneous Rocks Based on X-Ray Microfocus Radiography

Summary An uncommon facet of formation evaluation is the assessment of flow-related in-situ properties of rocks. Most of the models used to describe two-phase flow properties of porous rocks assume homogeneous and/or isotropic media, which is hardly the case with actual reservoir rocks, regardless of scale; carbonates and grain-laminated sandstones are but two common examples of this situation. The degree of spatial complexity of rocks and its effect on the mobility of hydrocarbons are of paramount importance for the description of multiphase fluid flow in most contemporary reservoirs. There is thus a need for experimental and numerical methods that integrate all salient details about fluid-fluid and rock-fluid interactions. Such hybrid, laboratory-simulation projects are necessary to develop realistic models of fractional flow in complex rocks, i.e., saturation-dependent capillary pressure and relative permeability. Furthermore, these two crucial properties are usually measured independently. Capillary pressure is typically assessed using static measurements and unrealistic pressure conditions, whereas relative permeability is evaluated dynamically. Consequently, the disparity between the nature of the two experimental procedures often results in a potentially significant loss of information. We document a new high-resolution visualization technique that provides experimental insight to quantify fluid saturation patterns in heterogeneous rocks which allow for the simultaneous and dynamic evaluation of two-phase flow properties. The experimental apparatus consists of an X-ray microfocus scanner and an automated syringe pump. Rather than using traditional cylindrical cores, thin rectangular rock samples are examined, their thickness being one order of magnitude smaller than the remaining two dimensions. During the experiment, the core is scanned quasicontinuously while the fluids are being injected, allowing for time-lapse visualization of the flood front. Numerical simulations are then conducted to match the experimental data and quantify effective saturation-dependent relative permeability and capillary pressure. The experimental results indicate that flow patterns and in-situ saturations are highly dependent on the nature of the heterogeneity and bedding-plane orientation during both imbibition and drainage cycles. In homogeneous rocks, fluid displacement approaches piston-like behavior. The assessment of capillary pressure and relative permeability is performed by examining the time-lapse water saturation profiles resulting from fluid displacement. In spatially complex rocks, high-resolution time-lapse images reveal preferential flow paths along high-permeability sections and a lowered sweep efficiency. Our experimental procedure emphasizes that capillary pressure and transmissibility differences play an important role in fluid-saturation distribution and sweep efficiency at late times. The method is fast and reliable to assess mixing laws for fluid-transport properties of rocks in spatially complex formations.

Complex supply chain structures and multi-scope GHG emissions: the moderation effect of reducing equivocality

PurposeClimate change requires the reduction of direct and indirect greenhouse gas (GHG) emissions, a task that seems to clash with increasing supply chain complexity. This study aims to analyse the upstream supply chain complexity dimensions suggesting the importance of understanding the information processing that these may entail. Reducing equivocality can be an issue in some dimensions, requiring the introduction of written guidelines to moderate the effects of supply chain complexity dimensions on GHG emissions at the firm and supply chain level.Design/methodology/approachA three-year panel data was built with information obtained from Bloomberg, Trucost and Compustat. Hypotheses were tested using random effect regressions with robust standard errors on a sample of 394 SP500 companies, addressing endogeneity through the control function approach.FindingsHorizontal complexity reduces GHG emissions at the firm level, whereas vertical and spatial complexity dimensions increase GHG emissions at the firm and supply chain level. Although the introduction of written guidelines neutralises the negative effects of vertical complexity on firm and supply chain GHG emissions, it is not sufficient in the presence of spatial complexity.Originality/valueThis paper offers novel insights by suggesting that managers need to reconcile the potential trade-off effects on GHG emissions that horizontally complex supply chain structures can present. Their priority in vertically and spatially complex supply chain structures should be to reduce equivocality.

Time‐Domain Modeling of Three‐Dimensional Earth's and Planetary Electromagnetic Induction Effect in Ground and Satellite Observations

AbstractElectric currents induced in conductive planetary interiors by time‐varying magnetospheric and ionospheric current systems have a significant effect on electromagnetic (EM) field observations. Complete characterization of EM induction effects is difficult owing to nonlinear interactions between the three‐dimensional electrical structure of a planet and spatial complexity of inducing current systems. We present, a general framework for time‐domain modeling of three‐dimensional EM induction effects in heterogeneous conducting planets. Our approach does not assume that the magnetic field is potential, allows for an arbitrary distribution of electrical conductivity within a planet, and can deal with spatially complex time‐varying current systems. The method is applicable to both data measured at stationary observation sites and satellite platforms, and enables the calculation of three‐dimensional EM induction effects in near real‐time settings.

The impact of transition type on chromatic adaptation under dual lighting conditions

Over the years, many CATs (chromatic adaptation transform), typically based on the von Kries coefficient rule, have been developed to predict the corresponding colors under different illuminants. However, these CATs were derived for uniform stimuli surrounded by a uniform adapting field. To investigate the adaptation state under spatially complex illumination, an achromatic matching experiment was conducted under dual lighting conditions with three color pairs and two transition types. It has been found that the transition type has an impact on both the equivalent chromaticity and degree of adaptation. These results can help build a comprehensive von Kries based CAT model, with considering the spatial complexity of illumination.

Simulating spatial complexity in dry conifer forest restoration: implications for conservation prioritization and scenario evaluation

ContextSeveral initiatives seek to increase the pace and scale of dry forest restoration and fuels reduction to enhance forest resilience to wildfire and other stressors while improving the quality and reliability of key ecosystem services. Ecological effects models are increasingly used to prioritize these efforts at the landscape-scale based on simulated treatment outcomes.ObjectivesTreatments are often simulated using uniform post-treatment target conditions or proportional changes to baseline forest structure variables, but do not account for the common objective of restoration to mimic the complex forest structure that was present historically which is thought to provide an example of structural conditions that contributed to ecosystem diversity and resilience.MethodsWe simulate spatially homogenous fire hazard reduction treatments along with heterogeneous restoration treatments in dry conifer forests to investigate how spatial complexity affects ecological indicators of (1) forest structural heterogeneity, (2) forest and watershed vulnerability to high-severity fire, and (3) feasibility of future prescribed fire use.ResultsOur results suggest that spatially explicit restoration treatments should produce similar wildfire and prescribed fire outcomes as homogeneous fuels reduction treatments, but with greater forest structural heterogeneity. The lack of strong tradeoffs between ecological objectives suggests the primary benefit of spatially complex treatments is to increase forest structural heterogeneity which may promote biodiversity.ConclusionsWe show that landscape-scale prioritization to maximize ecological benefits can change when spatially complex restoration treatments are modeled. Coupling landscape-scale management simulations and ecological effects models offers flexible decision support for conservation assessment, prioritization, and planning.

Perspectives on Synthetic Materials to Guide Tissue Regeneration for Osteochondral Defect Repair.

Regenerative engineering holds the potential to treat clinically pervasive osteochondral defects (OCDs). In a synthetic materials-guided approach, the scaffold's chemical and physical properties alone instruct cellular behavior in order to effect regeneration, referred to herein as "instructive" properties. While this alleviates the costs and off-target risks associated with exogenous growth factors, the scaffold must be potently instructive to achieve tissue growth. Moreover, toward achieving functionality, such a scaffold should also recapitulate the spatial complexity of the osteochondral tissues. Thus, in addition to the regeneration of the articular cartilage and underlying cancellous bone, the complex osteochondral interface, composed of calcified cartilage and subchondral bone, should also be restored. In this Perspective, we highlight recent synthetic-based, instructive osteochondral scaffolds that have leveraged new material chemistries as well as innovative fabrication strategies. In particular, scaffolds with spatially complex chemical and morphological features have been prepared with electrospinning, solvent-casting-particulate-leaching, freeze-drying, and additive manufacturing. While few synthetic scaffolds have advanced to clinical studies to treat OCDs, these recent efforts point to the promising use of the chemical and physical properties of synthetic materials for regeneration of osteochondral tissues.

3D Virtual Models to Visualize Pelvic Floor & Perineum in Medical Gross Anatomy

IntroductionThe pelvic floor and perineum are difficult regions to visualize with most educational tools. Cadaveric dissection of this region is cumbersome due to limited accessibility and ambiguity of structures, and presents challenges with teaching spatial orientation. Supplemental learning material such as two‐dimensional anatomical illustrations often lack the ability to demonstrate spatial depth and complex relationships of the pelvis and perineum. We developed 3D virtual models to teach this spatially complex region in a didactic session on pelvic and perineal anatomy.AimThis study explored the efficacy of 3D virtual models as a supplementary teaching tool for pelvic floor and perineum.MethodsTwo review sessions were provided to a total of 37 first year medical students. The content of the lesson was initially presented in a traditional lecture format alongside 2D illustrations and schematics. Immediately following the lecture portion, 3D models of each region of interest were presented to students using 3D4Medical Complete Anatomy application.Prior to the session, students (n=31) completed a modified Likert scale (1–5) survey on their familiarity with pelvic musculature, perineal fasciae and spaces, and the course of the pudendal nerve. After the session, students (n=37) completed a similar survey reviewing their confidence in pelvic musculature, perineal fasciae and spaces, and the course of the pudendal nerve. Relative frequencies for each question have been reported as a percentage, with a score of 3 considered neutral.ResultsOf the students who responded to the pre‐session survey (n=31), 6.5% reported being familiar (a score of 4 or 5) with the muscles of the pelvic floor, 9.7% reported being familiar with the perineal fasciae and spaces, and 12.9% reported being familiar with the course of the pudendal nerve.Responses to the post‐session survey (n=37), revealed that 91.9% of students reported improved confidence (a score of 4 or 5) in their understanding of pelvic floor musculature and perineal fasciae and spaces. While, 83.8% reported improved confidence in their understanding of the pudendal nerve course.ConclusionThe use of 3D models, as a supplement to existing course content, is an effective method for improving student confidence in pelvic floor and perineum anatomy. Our results suggest that the combination of 3D virtual models and didactic knowledge transmission, facilitates the learning of pelvic floor and perineal regions.

Substrate Spatial Complexity Analysis for the Prediction of Ventricular Arrhythmias in Patients With Ischemic Cardiomyopathy.

Transition zones between healthy myocardium and scar form a spatially complex substrate that may give rise to reentrant ventricular arrhythmias (VAs). We sought to assess the utility of a novel machine learning approach for quantifying 3-dimensional spatial complexity of grayscale patterns on late gadolinium enhanced cardiac magnetic resonance images to predict VAs in patients with ischemic cardiomyopathy. One hundred twenty-two consecutive ischemic cardiomyopathy patients with left ventricular ejection fraction ≤35% without prior history of VAs underwent late gadolinium enhanced cardiac magnetic resonance images. From raw grayscale data, we generated graphs encoding the 3-dimensional geometry of the left ventricle. A novel technique, adapted to these graphs, assessed global regularity of signal intensity patterns using Fourier-like analysis and generated a substrate spatial complexity profile for each patient. A machine learning statistical algorithm was employed to discern which substrate spatial complexity profiles correlated with VA events (appropriate implantable cardioverter-defibrillator firings and arrhythmic sudden cardiac death) at 5 years of follow-up. From the statistical machine learning results, a complexity score ranging from 0 to 1 was calculated for each patient and tested using multivariable Cox regression models. At 5 years of follow-up, 40 patients had VA events. The machine learning algorithm classified with 81% overall accuracy and correctly classified 86% of those without VAs. Overall negative predictive value was 91%. Average complexity score was significantly higher in patients with VA events versus those without (0.5±0.5 versus 0.1±0.2; P<0.0001) and was independently associated with VA events in a multivariable model (hazard ratio, 1.5 [1.2-2.0]; P=0.002). Substrate spatial complexity analysis of late gadolinium enhanced cardiac magnetic resonance images may be helpful in refining VA risk in patients with ischemic cardiomyopathy, particularly to identify low-risk patients who may not benefit from prophylactic implantable cardioverter-defibrillator therapy. Visual Overview: A visual overview is available for this article.

Spatial variation in exploited metapopulations obscures risk of collapse.

Unanticipated declines among exploited species have commonly occurred despite harvests that appeared sustainable prior to collapse. This is particularly true in the oceans where spatial scales of management are often mismatched with spatially complex metapopulations. We explore causes, consequences, and potential solutions for spatial mismatches in harvested metapopulations in three ways. First, we generate novel theory illustrating when and how harvesting metapopulations increases spatial variability and in turn masks local-scale volatility. Second, we illustrate why spatial variability in harvested metapopulations leads to negative consequences using an empirical example of a Pacific herring metapopulation. Finally, we construct a numerical management strategy evaluation model to identify and highlight potential solutions for mismatches in spatial scale and spatial variability. Our results highlight that spatial complexity can promote stability at large scales, however, ignoring spatial complexity produces cryptic and negative consequences for people and animals that interact with resources at small scales. Harvesting metapopulations magnifies spatial variability, which creates discrepancies between regional and local trends while increasing risk of local population collapses. Such effects asymmetrically impact locally constrained fishers and predators, which are more exposed to risks of localized collapses. Importantly, we show that dynamically optimizing harvest can minimize local risk without sacrificing yield. Thus, multiple nested scales of management may be necessary to avoid cryptic collapses in metapopulations and the ensuing ecological, social, and economic consequences.

Evaluating the Variability of Urban Land Surface Temperatures Using Drone Observations

Urbanization and climate change are driving increases in urban land surface temperatures that pose a threat to human and environmental health. To address this challenge, we must be able to observe land surface temperatures within spatially complex urban environments. However, many existing remote sensing studies are based upon satellite or aerial imagery that capture temperature at coarse resolutions that fail to capture the spatial complexities of urban land surfaces that can change at a sub-meter resolution. This study seeks to fill this gap by evaluating the spatial variability of land surface temperatures through drone thermal imagery captured at high-resolutions (13 cm). In this study, flights were conducted using a quadcopter drone and thermal camera at two case study locations in Milwaukee, Wisconsin and El Paso, Texas. Results indicate that land use types exhibit significant variability in their surface temperatures (3.9–15.8 °C) and that this variability is influenced by surface material properties, traffic, weather and urban geometry. Air temperature and solar radiation were statistically significant predictors of land surface temperature (R2 0.37–0.84) but the predictive power of the models was lower for land use types that were heavily impacted by pedestrian or vehicular traffic. The findings from this study ultimately elucidate factors that contribute to land surface temperature variability in the urban environment, which can be applied to develop better temperature mitigation practices to protect human and environmental health.

Interaction Dimensionality Scales Up to Generate Bimodal Consumer-Resource Size-Ratio Distributions in Ecological Communities

Understanding constraints on consumer-resource body size-ratios is fundamentally important from both ecological and evolutionary perspectives. By analyzing data on 4685 consumer-resource interactions from nine ecological communities, we show that in spatially complex environments—where consumers can forage in both two (2D, e.g., benthic zones) and three (3D, e.g., pelagic zones) spatial dimensions—the resource-to-consumer body size-ratio distribution tends towards bimodality, with different median 2D and 3D peaks. Specifically, we find that median size-ratio in 3D is consistently smaller than in 2D both within and across communities. Furthermore, 2D and 3D size (not size-ratio) distributions within any community are generally indistinguishable statistically, indicating that the bimodality in size-ratios is not driven simply by a priori size-segregation of species (and therefore, interactions) by dimensionality, due to other factors. We develop theory that correctly predicts the direction and magnitude of these differences between 2D and 3D size-ratio distributions. Our theory suggests that community-level size-ratio bimodality emerges from the stronger scaling of consumption rate with size in 3D interactions than in 2D, which both, maximizes consumer fitness, and allows coexistence, across a larger range of size-ratios in 3D. We also find that consumer gape-limitation can amplify differences between 2D and 3D size-ratios, and that for either dimensionality, higher carrying capacity allows coexistence of a wider range of size-ratios. Our results reveal new and general insights into the size structure of ecological communities, and show that spatial complexity of the environment can have far reaching effects on community structure and dynamics across scales of organization.

Large‐scale ecosystem engineering by flamingos and fiddler crabs on West‐African intertidal flats promote joint food availability

Although the ecosystem engineering concept is well established in ecology, cases of joint engineering by multiple species at large scales remain rare. Here, we combine observational studies and exclosure experiments to investigate how co‐occurring greater flamingos Phoenicopterus roseus and fiddler crabs Uca tangeri promote their own and each other's food availability by creating a spatially complex mosaic of depressions (bowls, gullies) and hummocks (plateaus, mounds) in the intertidal zone. This results in a mosaic of microhabitats with different tidal inundation regimes. These microhabitats are spatially organized with labyrinth‐like patterns in the high intertidal zone and spotted patterns in the lower intertidal, both of which likely arise from biophysical interactions between these organisms and hydrodynamic forces. We show that the resulting spatial complexity is vital for biofilm production. The depression microhabitats were wetter and richer in organic matter and biofilms compared with hummocks. Excluding flamingos and crabs resulted in an increase in biofilm biomass over the shorter term (six months), but a decrease over the longer term (after one year). Moreover, our results strongly suggest that these biogeomorphological microhabitats in the mosaics were maintained by the feeding activities of flamingos and to a lesser extent crabs. During a period of flamingo exclusion, all the spotted patterns filled up with sediment, while the exclusion of crabs led to gradual sediment accumulation in the labyrinth‐like patterns. Collectively, these findings provide empirical evidence for large‐scale joint promotion of food availability by multiple taxa in a marine ecosystem.

Effect of head motion on MRI B0 field distribution.

To identify and characterize the sources of B0 field changes due to head motion, to reduce motion sensitivity in human brain MRI. B0 fields were measured in 5 healthy human volunteers at various head poses. After measurement of the total field, the field originating from the subject was calculated by subtracting the external field generated by the magnet and shims. A subject-specific susceptibility model was created to quantify the contribution of the head and torso. The spatial complexity of the field changes was analyzed using spherical harmonic expansion. Minor head pose changes can cause substantial and spatially complex field changes in the brain. For rotations and translations of approximately 5 º and 5 mm, respectively, at 7 T, the field change that is associated with the subject's magnetization generates a standard deviation (SD) of about 10 Hz over the brain. The stationary torso contributes to this subject-associated field change significantly with a SD of about 5 Hz. The subject-associated change leads to image-corrupting phase errors in multi-shot -weighted acquisitions. The B0 field changes arising from head motion are problematic for multishot -weighted imaging. Characterization of the underlying sources provides new insights into mitigation strategies, which may benefit from individualized predictive field models in addition to real-time field monitoring and correction strategies.

Simulated juvenile salmon growth and phenology respond to altered thermal regimes and stream network shape.

It is generally accepted that climate change will stress coldwater species like Pacific salmon. However, it is unclear what aspect of altered thermal regimes (e.g., warmer winters, springs, summers, or increased variability) will have the greatest effect, and what role the spatial properties of river networks play. Thermally diverse habitats may afford protection from climate change by providing opportunities for aquatic organisms to find and use habitats with optimal conditions for growth. We hypothesized that climate-altered thermal regimes will change growth and timing of life history events such as emergence or migration but that changes will be moderated in topologically complex stream networks where opportunities to thermoregulate are more readily available to mobile animals. Because climate change effects on populations are spatially variable and contingent upon physiological optima, assessments of risk must take a spatially explicit approach. We developed a spatially-structured individual based model for Chinook Salmon (Oncorhynchus tshawytscha) in which movement decisions and growth were governed by water temperature and conspecific density. We evaluated growth and phenology (timing of egg emergence and smolting) under a variety of thermal regimes (each having a different minimum, rate of warming, maximum, and variability) and in three network shapes of increasing spatial complexity. Across networks, fish generally grew faster and were capable of smolting earlier in warmer scenarios where water temperatures experienced by fish were closer to optimal; however, growth decreased for some fish. We found that salmon size and smolt date responded more strongly to warmer springs and summers than to warmer winters or increased variability. Fish in the least complex network grew faster and were ready to smolt earlier than fish in the more spatially complex network shapes in the contemporary thermal regime; patterns were similar but less clear in warmer thermal regimes. Our results demonstrate that network topology may influence how fish respond to thermal landscapes, and this information will be useful for incorporating a spatiotemporal context into conservation decisions that promote long-term viability of salmon in a changing climate.

Spatial complexity of character-based writing systems and arithmetic in primary school: a longitudinal study.

Previous research has consistently found an association between spatial and mathematical abilities. We hypothesized that this link may partially explain the consistently observed advantage in mathematics demonstrated by East Asian children. Spatial complexity of the character-based writing systems may reflect or lead to a cognitive advantage relevant to mathematics. Seven hundered and twenty one 6–9-year old children from the UK and Russia were assessed on a battery of cognitive skills and arithmetic. The Russian children were recruited from specialist linguistic schools and divided into four different language groups, based on the second language they were learning (i.e., English, Spanish, Chinese, and Japanese). The UK children attended regular schools and were not learning any second language. The testing took place twice across the school year, once at the beginning, before the start of the second language acquisition, and once at the end of the year. The study had two aims: (1) to test whether spatial ability predicts mathematical ability in 7–9 year-old children across the samples; (2) to test whether acquisition and usage of a character-based writing system leads to an advantage in performance in arithmetic and related cognitive tasks. The longitudinal link from spatial ability to mathematics was found only in the Russian sample. The effect of second language acquisition on mathematics or other cognitive skills was negligible, although some effect of Chinese language on mathematical reasoning was suggested. Overall, the findings suggest that although spatial ability is related to mathematics at this age, one academic year of exposure to spatially complex writing systems is not enough to provide a mathematical advantage. Other educational and socio-cultural factors might play a greater role in explaining individual and cross-cultural differences in arithmetic at this age.

Evaluating the conservation risks of aggregate harvest management in a spatially-structured herring fishery

Evaluating the conservation risks of aggregate harvest management in a spatially-structured herring fishery