Coarse Scale Research Articles

Differential responses of woodland caribou to fire and forestry across boreal and montane ecosystems—a literature review

Abstract Ecosystem-based management (EBM) is a landscape-level management and planning process that is common across North America. A primary tenet of EBM is that the area and intensity of anthropogenic disturbance should mimic the historical natural disturbance of the focal ecosystem. Biodiversity should persist, at least at a coarse scale, where anthropogenic disturbance, such as forest harvesting, matches natural disturbance. However, EBM is failing some species, particularly those that are dependent on old forest. Across many areas of Canada, woodland caribou (Rangifer tarandus caribou) are declining because of the direct and indirect effects of habitat loss and fragmentation. This is even though forest management often follows the principles of EBM. We conducted a qualitative comparison of the responses of woodland caribou to wildfire and forest harvesting, considering a broad range of responses, including habitat selection and distribution, forage, movement patterns, and population dynamics. We found that while wildfire and forest harvesting both influence caribou, the negative effects are generally greater following forest harvesting. For example, wildfire and forest harvesting result in the loss of habitat, but caribou are more likely to shift, abandon or contract their range in response to harvest. The literature also suggested a stronger negative population response of caribou to forest harvest when compared to wildfire. This difference could be the result of greater residual forest structure associated with wildfire as well as the extensive resource roads that are necessary for forestry operations. Although there is sound theoretical support for EBM, the practice, as implemented, may not be effective for maintaining the habitat and ultimately populations of woodland caribou.

A Pleistocene Biogeography in Miniature: The Small‐Scale Evolutionary History of Podarcis lusitanicus (Squamata, Lacertidae)

ABSTRACTAimRecent literature is unveiling more complex responses of biodiversity to Pleistocenic climatic oscillations than those derived from initial models based solely on southern glacial macrorefugia. Here, we zoom in to delve into the genetic diversity and population structure of Podarcis lusitanicus, a lizard species native to the north‐western Iberian Peninsula.LocationNorth‐western Iberian Peninsula.TaxonPodarcis lusitanicus.MethodsUtilising a comprehensive multilocus approach, we explored the species' phylogeography and demographic history at a very fine spatial scale and timeframe. Concurrently, we evaluated both current and historical habitat suitability for the species, linking it to the observed demographic trends. We examined DNA sequences from two mitochondrial (cytb and ND4) and three nuclear genes (ACM4, MC1R and PDC) in 230 specimens collected from 21 populations spanning most of the species' distribution range.ResultsOur findings corroborate cytonuclear discordances in Podarcis, with nuclear genes failing to identify the mitochondrial clades in P. lusitanicus. Despite its separation from other Podarcis species approximately 4 million years ago, its diversification occurred much more recently, around 0.4 million years ago, resulting in three monophyletic lineages that are geographically coherent. This pattern suggests that some lineages of P. lusitanicus might have gone extinct after the split from the common ancestor. Pleistocene climatic oscillations profoundly influenced the demographic history of the species. During warmer periods, such as the Last Interglacial, populations expanded and came into secondary contact, whereas during the Last Glacial Maximum, populations contracted and became isolated in multiple micro‐refugia. The subsequent Mid‐Holocene warming facilitated population recovery, range expansion and lineage admixture.Main ConclusionsThis study underscores the intricate interplay between isolation and dispersal at a microscale, driven by Pleistocene climatic fluctuations, which have profoundly shaped the genetic structure and diversity of Iberian terrestrial biodiversity and may remain overlooked by biogeographic studies at coarser scales.

Towards the construction of regional marine radiocarbon calibration curves: an unsupervised machine learning approach

Abstract. Radiocarbon may serve as a powerful dating tool in palaeoceanography, but its accuracy is limited by the need to calibrate radiocarbon dates to calendar ages. A key problem is that marine radiocarbon dates must be corrected for past offsets from either the contemporary atmosphere (i.e. “reservoir age” offsets) or a modelled estimate of the global average surface ocean (i.e. delta-R offsets). This presents a challenge because the spatial distribution of reservoir ages and delta-R offsets can vary significantly, particularly over periods of major marine hydrographic and/or carbon cycle change such as the last deglaciation. Modern reservoir age and delta-R estimates therefore have limited applicability. While forward modelling of past R-age variability has been proposed as a means of resolving this problem, this requires accurate a priori knowledge of past global radiocarbon budget closure (i.e. production, and cycling), which we currently lack. In this context, the construction of empirical regional marine calibration curves could provide a way forward. However, the spatial reach of such calibrations and their robustness subject to (uncertain) temporal changes in climate and ocean circulation would need to be tested. Here, we use unsupervised machine learning techniques to define distinct regions of the surface ocean that exhibit coherent behaviour in terms of their radiocarbon age offsets from the contemporary atmosphere (R ages), regardless of the causes of R-age variability. We apply multiple algorithms (k-means, k-medoids, and hierarchical clustering) to outputs from two different numerical models spanning a range of climate states, forcings, and timescales of adjustment. Comparisons between the cluster assignments across model runs confirm some robust regional patterns that likely stem from constraints imposed by large-scale ocean and atmospheric physics. At the coarsest scale, regions of coherent R-age variability correspond to the major ocean basins. By further dividing basin-scale shape-based clusters into amplitude-based subclusters, we recover regional associations, such as increased high-latitude R ages, or the propagation of R-age anomalies from regions of deep mixing in the Southern Ocean to upwelling sites in the eastern equatorial Pacific, which cohere with known modern oceanographic processes. We show that the medoids (i.e. the most representative locations) for these regional sub-clusters provide significantly better approximations of simulated local R-age variability than constant offsets from the global surface average. This remains true when cluster assignments obtained from one model simulation are applied to simulated R-age time series from another. Further, model-based clusters are found to be broadly consistent with existing reservoir age reconstructions that span the last ∼30 kyr. We therefore propose that machine learning provides a promising approach to the problem of defining regions for which empirical marine radiocarbon calibration curves may eventually be generated.

The affinity of vascular plants and bryophytes to forest microclimate buffering

Abstract With recent advances in technology and modelling, ecologists are increasingly advised to use microclimate, not the usual coarse scale macroclimate based on weather stations, to better reflect the proximal conditions that species experience. This is especially relevant in forest ecosystems, where natural disturbances and management create substantial heterogeneity in microclimates. Under dense canopies, species may experience buffered (less extreme) microclimate temperatures relative to macroclimate, as well as increased relative humidity, reduced light and wind. Focusing on understorey plants, we investigated species response curves to the buffering capacity of the canopy layer, measured as the log‐transformed slope parameter of the microclimate to macroclimate linear relationship. If lower or higher than zero, microclimate temperatures are buffered or amplified, respectively, relative to macroclimate. During leaf‐on conditions (July–September 2021), we measured hourly microclimate temperatures in 157 plots across three temperate deciduous forests with contrasted macroclimates. We used paired hourly macroclimate measurements from nearby weather stations to derive the slope parameter, quantifying microclimate buffering. We surveyed vascular plant and bryophyte communities in 400 m2 plots centred on our microclimate sensors. Species were classified into three groups of forest affinity: core specialists; edge specialists; and generalists. We fitted generalized linear mixed‐effects models, by forest affinity group and by species, to obtain logistic response curves of the probability of occurrence against microclimate buffering. The species' optimum was computed as the microclimate effect that maximizes the species' probability of presence. We found contrasted microclimate preferences: Most bryophytes as well as the vascular plants classified as forest core specialists had an optimum in microclimate buffering, while forest edge specialists and generalists among vascular plants had an optimum in microclimate amplification. As canopies undergo increased disturbance frequency and intensity, more generalists and less forest core specialists might thus be expected in understorey communities, especially for bryophytes. Synthesis. Understorey plants have a species‐specific affinity to the forest microclimate, which we quantify for the first time. The investigation of species response curves to microclimate processes—buffering or amplification—can improve our understanding of the ecology of understorey plants, and help us anticipate their redistribution under climate change.

Application of a finite element method variant in nonconvex domains to parabolic problems

In this paper we address one of the major difficulties which is the nonconvex behavior of the domains while finding the solution of the problems. The part of the domain where the nonsmoothness appears is where the challenge arises and the way that area is handled using different numerical methods reveals the effectiveness of these techniques. Here in this article, we study the semilinear parabolic problem in nonconvex polygonal domain. For the approximation of the solution we use the Composite Finite Element (CFE) method, which is a classification of the Finite Element Method. CFE discusses the two-scale discretization — the larger mesh also known as the coarse mesh with the size H and the smaller mesh, also known as the fine mesh with the size h. It helps in reducing the dimension of the domain space of consideration. The fine scale grid is used to resolve the nonconvexity of the boundary whereas the coarse scale grid is comprised of larger grids at an appropriate distance from the boundary. The degrees of freedom depends on the coarse grid. This is the precedence of CFE over other methods, i.e., it eases the task of reducing the domain complexity. In this article, we consider two approaches — the semi discrete analysis where only space discretization is carried out, and the fully discrete analysis where both the time and space discretization is done using both backward Euler and Crank–Nicolson method. We study the error analysis in the L∞(L2)-norm and in the L∞(H1)-norm for the semidiscrete case whereas for the fully discrete case, we study the error analysis in the L∞(L2)-norm. Also, we check for the optimal results. For the CFE technique in the L∞(L2)-norm, we derive the convergence having optimal order in time and almost optimal order in space even if the domain is nonconvex. We consider a T-shaped domain and another star shaped domain to carry out the theoretical findings. Thereafter, numerical computations are implemented to validate the theoretical results.

A geometric-tension-dynamics model of epithelial convergent extension

Convergent extension of epithelial tissue is a key motif of animal morphogenesis. On a coarse scale, cell motion resembles laminar fluid flow; yet in contrast to a fluid, epithelial cells adhere to each other and maintain the tissue layer under actively generated internal tension. To resolve this apparent paradox, we formulate a model in which tissue flow in the tension-dominated regime occurs through adiabatic remodeling of force balance in the network of adherens junctions. We propose that the slow dynamics within the manifold of force-balanced configurations is driven by positive feedback on myosin-generated cytoskeletal tension. Shifting force balance within a tension network causes active cell rearrangements (T1 transitions) resulting in net tissue deformation oriented by initial tension anisotropy. Strikingly, we find that the total extent of tissue deformation depends on the initial cellular packing order. T1s degrade this order so that tissue flow is self-limiting. We explain these findings by showing that coordination of T1s depends on coherence in local tension configurations, quantified by a geometric order parameter in tension space. Our model reproduces the salient tissue- and cell-scale features of germ band elongation during Drosophila gastrulation, in particular the slowdown of tissue flow after approximately twofold elongation concomitant with a loss of order in tension configurations. This suggests local cell geometry contains morphogenetic information and yields experimentally testable predictions. Defining biologically controlled active tension dynamics on the manifold of force-balanced states may provide a general approach to the description of morphogenetic flow.

Resource selection of den sites for bobcats (Lynx rufus) in the Northern Great Plains, United States

Abstract Dens play a crucial role in the survival of young and can ultimately affect population dynamics for felid carnivores. However, little is known about Bobcat (Lynx rufus) den site selection. Therefore, this study aimed to identify environmental correlates of Bobcat den sites in the Black Hills, South Dakota, United States. Our objectives for this study were to compare environmental characteristics of Bobcat dens to random sites at coarse (3.14 ha) and local (0.05 ha) spatial scales. Female bobcats were radio-collared (n = 35) and dens (n = 27) were located using ground telemetry. Bobcats selected for high horizontal cover and terrain ruggedness at the coarse scale, as well as high horizontal cover at the local scale, which could provide concealment from predators. In less rugged areas land management practices that promote increased tree and shrub habitat as horizontal cover may provide habitat for Bobcat dens and ensure the survival of this ecologically and culturally important species.

GAPANet: group alternate perceived attention network for optical imaging infrared thermal radiation effect correction

To address the problem of insufficient information interaction between different dimensions of image features, and the inability of real-time simultaneously in the current thermal radiation effect correction methods, in this paper, we construct a group alternate perceived attention network. At coarser scales, the group alternate perceived attention module is constructed, which requires only fewer parameters and computational complexity. The group alternate perceived attention module mainly consists of two alternating parts: a global attention block combined with the local feature of three-branch dilated convolution, and a channel attention block for local information interaction. At the finest scale, a dimensional information interaction attention module is constructed. The correction network fully realizes attention perception at the inter-dimensional level globally and channel level locally. The experimental results of a large number of simulated and actual degraded images show that our model corrects better and realizes real-time.

Generalized Multiscale Finite Element Method for discrete network (graph) models

In this paper, we consider a time-dependent discrete network model with highly varying connectivity. The approximation by time is performed using an implicit scheme. We propose the coarse scale approximation construction of network models based on the Generalized Multiscale Finite Element Method. An accurate coarse-scale approximation is generated by solving local spectral problems in sub-networks. Convergence analysis of the proposed method is presented for semi-discrete and discrete network models. We establish the stability of the multiscale discrete network. Numerical results are presented for structured and random heterogeneous networks.

Homogenization of word relationships in schizophrenia: Topological analysis of cortical semantic representations.

Patients with schizophrenia typically exhibit symptoms of disorganized thought and display concreteness and over-inclusion in verbal reports, depending on the level of abstraction. While concreteness and over-inclusion may appear contradictory, the underlying psychopathology that explains these symptoms remains unclear. In the current study, we used functional magnetic resonance imaging with an encoding modeling approach to examine how concepts of various words, represented as brain activity, are anomalously connected at different levels of abstraction in patients with schizophrenia. Fourteen individuals diagnosed with schizophrenia and 17 healthy controls underwent functional magnetic resonance imaging to measure brain activity representing concepts of various words. We used a persistent homology (PH) method to analyze the topological structures of word representations in schizophrenia patients, healthy controls, and random data, across different levels of abstraction by varying dissimilarity scales in the representation space. The results revealed that patients with schizophrenia exhibited more homogeneous word relationships across different levels of abstraction compared with healthy controls. Additionally, topological structures exhibited a shift toward a random network structure in patients with schizophrenia compared with controls. The PH method successfully distinguished semantic representations of patients with schizophrenia from those of controls. The current results provide an explanation for the mechanisms underlying the deficits in abstraction ability observed in schizophrenia. The isotopic connection of individual concepts reflects both the reduction of contextual connections at a semantically fine-grained scale and the absence of clear boundaries between related concepts at a coarse scale, which lead to concreteness and over-inclusion, respectively.

A Common Climate–Yield Relationship for Wheat and Barley in Japan and the United Kingdom

Wheat and barley yields in Japan are considerably lower than those in the UK, even where similar Climate Zones (CZs) of relatively cold and humid nature are shared. In order to understand this difference, it is first necessary to find out if any common climate–yield relationship exists between the two countries. The Climate Zonation Scheme (CZS) developed in the Global Yield Gap Atlas (GYGA) was used to analyse actual yield (Ya) with three climatic factors of the GYGA-CZS, i.e., growing degree days (GDD), aridity index (AI) and temperature seasonality (TS). A significant relationship was found between AI scores and Ya values across the two countries. Ya values decreased with an increase in AI scores; in other words, lower yields are associated with higher AI scores. In addition, the degree of yield reduction with the rise in AI scores was greater in Japan than in the UK. The present study also proposed a novel method to link CZs of the GYGA-CZS to regional classification units, especially for countries where statistical crop yield data are available only at a coarse scale.

A Scale-space Approach for Surface Normal Vector Estimation from Depth Maps

Surface normal vectors provide cues about the local geometric features of the scene which are utilized in many computer vision and computer graphics applications. Thus, the estimation of surface normals by utilizing structured range sensor data is an important research field. Thereupon, we propose a learning-free algorithm to estimate the surface normal vectors from depth maps. Our simple yet effective method relies on computations carried out in scale-space. Our main idea is to estimate the surface normals which cannot be properly computed in the finest scale from the coarser scales. Our method can estimate the surface normals even for images included in datasets that have challenging characteristics such as noisy real-world data or significantly large planar regions that either have a small or no gradient change. We analyze our algorithm’s performance by utilizing five benchmarks, namely, the MIT-Berkeley Intrinsic Images dataset, the New Tsukuba Dataset, the SceneNet RGB-D dataset, the IID-NORD dataset, and the NYU Depth Dataset V2, and by using two different evaluation strategies. According to the experimental results, our method can estimate surface normals efficiently without requiring neither complex computations nor huge amounts of data.

The effect of soil parameters and earthworm abundance on the fine-scale nocturnal habitat use of the Eurasian woodcock (Scolopax rusticola).

The Eurasian woodcock prefers habitats where its main prey, earthworms, can be found in higher densities. Although they are forest-dwelling birds, they regularly visit pastures and natural grasslands at night, where earthworm abundance is generally higher. However, there is little information on fine-scale habitat use in relation to variation in habitat characteristics and prey availability, particularly beyond the breeding season. In our study, we investigated if the nocturnal occurrence of woodcocks during migratory stopover periods differed between two neighbouring fields, or management units, with similar vegetation structure, and if within-field variation in the spatial patterns of woodcock sightings were associated with fine-scale earthworm densities and soil parameters. Specifically, we used GPS tracking data of two tagged woodcocks and direct observation data to study patterns of occurrence of birds in a mixed forest-pasture landscape in Hungary during pre- and post-breeding periods. We compared these patterns with fine-scale soil characteristics and earthworm abundance, acquired by field sampling. We found that the field with higher earthworm abundance was visited by woodcocks more frequently, and this correlation was similarly observed at the intra-field level. Our results demonstrate that woodcocks select foraging sites with higher earthworm densities at multiple spatial scales, both between fields (coarse scale), and within fields (fine-scale). Considering that woodcocks tended to return to the same field to forage at night, the strong associations between occupancy and resources provide a basis for developing habitat management strategies at the field level for conservation. As earthworm densities and soil parameters are good indicators of woodcock foraging habitat, measuring those variables, at least at a coarse scale, could aid in predicting important habitats for the species across the landscape.

Using multiple scales of movement to highlight risk–reward strategies of coyotes (Canis latrans) in mixed‐use landscapes

AbstractMany wildlife species vary habitat selection across space, time, and behavior to maximize rewards and minimize risk. Multi‐scale research approaches that identify variation in wildlife habitat selection can highlight not only habitat preferences and risk tolerance but also movement strategies that afford coexistence or cause conflict with humans. Here, we examined how anthropogenic and natural features influenced coyote (Canis latrans) habitat selection in a mixed‐use, agricultural landscape in Mendocino County, California, USA. We used resource selection functions and hidden Markov models to test whether coyote selection for anthropogenic and natural features varied by time of day or by behavioral state (resting, foraging, and traveling). We found that coyotes avoided development, but, contrary to our expectations, coyotes selected for roads, agriculture, and areas with risk of human encounter and rifle use regardless of diel period or behavioral state. While traveling, coyotes increased selection for roads and avoided ruggedness, indicating that unpaved roads may enhance connectivity for coyotes in mixed‐use landscapes. Finally, we found that coyotes selected for mountain lion habitat when resting and at night, signifying that risk from natural predators was not a factor in habitat selection at coarse scales. Coyote habitat selection for places and times associated with human activity, without variation across scales, signals a potential for conflict if coyotes are perceived by people as a nuisance.

Future Climate Projections for South Florida: Improving the Accuracy of Air Temperature and Precipitation Extremes With a Hybrid Statistical Bias Correction Technique

AbstractProjecting future climate variables is essential for comprehending the potential impacts on hydroclimatic hazards like floods and droughts. Evaluating these impacts is challenging due to the coarse spatial resolution of global climate models (GCMs); therefore, bias correction is widely used. Here, we applied two statistical methods—standard empirical quantile mapping (EQM) and a hybrid approach, EQM with linear correction (EQM‐LIN)—to bias correct precipitation and air temperature simulated by nine GCMs. We used historical observations from 20 weather stations across South Florida to project future climate under three shared socioeconomic pathways (SSPs). Compared to the EQM, the hybrid EQM‐LIN method improved R2 of daily quantiles by up to 30% over the historical period and improved MAE up to 70% in months that contain most extreme values. Projected extreme precipitation at the weather stations showed that, compared to the EQM‐LIN, the EQM method underestimates the high quantiles by up to 26% in SSP585. The projected changes in annual maximum precipitation from historical period (1985–2014) to near future (2040–2069) and far future (2070–2100) were between 2% and 16% across the study area. Projected future precipitation suggested a slight decrease during summer but an increase in fall. This, along with rising summer temperatures, suggested that South Florida can experience rapid oscillations from warmer summers and increased flooding in fall under future climate. Additionally, our comparative analyses with globally and nationally downscaled studies showed that such coarse scale studies do not represent the climatic extremes well, particularly for high quantile precipitation.

Effects of Different Zinc Content on Solidification, Microstructure, and Mechanical Properties in Tin–Bismuth Alloy

The present aims to evaluate the effect of adding Zn (0.5% and 9.0% in wt%) on phase transformation temperatures, microstructure coarsening, solidification parameters (cooling rate‐L and growth rate‐VL), macrosegregation, and mechanical properties of directionally solidified Sn‐34wt%Bi‐xZn alloys. The samples have been characterized by optical microscopy, scanning electron microscopy, X‐ray fluorescence, and X‐ray diffraction, in addition to Vickers microhardness and tensile tests. The CALPHAD method has been used for thermodynamic computations via Thermo‐calc software, in order to obtain thermodynamic data. The microstructure of Sn–Bi–Zn alloys is mainly dendritic, composed of a Sn‐rich matrix (β‐Sn) with Bi precipitates inside and surrounded by a Sn+Bi eutectic mixture of phases, predominantly observed at the coarse scale. Coarse Zn needles are also observed in the Sn‐34wt%Bi‐9wt%Zn alloy due to the high Zn content. On the whole, Zn provoked a coarsening of the dendritic arrangement. Moreover, Zn additions cause inverse segregation of Bi, as compared to the rather constant macrosegregation profile observed in the binary Sn–Bi alloy. On the whole, both additions of Zn (0.5 and 9.0) promoted increase in Vickers microhardness, yield strength (σY), and ultimate tensile strength (σu), however, causing an overall reduction in elongation‐to‐fracture (δ).

Realistic Facial Age Transformation with 3D Uplifting

AbstractWhile current facial re‐ageing methods can produce realistic results, they purely focus on the 2D age transformation. In this work, we present an approach to transform the age of a person in both facial appearance and shape across different ages while preserving their identity. We employ an α‐(de)blending diffusion network with an age‐to‐α transformation to generate coarse structure changes, such as wrinkles. Additionally, we edit biophysical skin properties, including melanin and hemoglobin, to simulate skin color changes, producing realistic re‐ageing results from ages 10 to 80 years. We also propose a geometric neural network that alters the coarse scale facial geometry according to age, followed by a lightweight and efficient network that adds appropriate skin displacement on top of the coarse geometry. Both qualitative and quantitative comparisons show that our method outperforms current state‐of‐the‐art approaches.

Asian Bittersweet (Celastrus orbiculatus) Occurrence at Coarse and Fine Scales in North Carolina and Virginia

Asian Bittersweet (Celastrus orbiculatus) Occurrence at Coarse and Fine Scales in North Carolina and Virginia

Downscaling spatial interaction with socioeconomic attributes

A variety of complex socioeconomic phenomena, for example, migration, commuting, and trade can be abstracted by spatial interaction networks, where nodes represent geographic locations and weighted edges convey the interaction and its strength. However, obtaining fine-grained spatial interaction data is very challenging in practice due to limitations in collection methods and costs, so spatial interaction data such as transportation data and trade data are often only available at a coarse scale. Here, we propose a gravity downscaling (GD) method based on readily accessible socioeconomic data and the gravity law to infer fine-grained interactions from coarse-grained data. GD assumes that interactions of different spatial scales are governed by the similar gravity law and thus can transfer the parameters estimated from coarse-grained regions to fine-grained regions. Results show that GD has an average improvement of 24.6% in Mean Absolute Percentage Error over alternative downscaling methods (i.e., the areal-weighted method and machine learning models) across datasets with different spatial scales and in various regions. Using simple assumptions, GD enables accurate downscaling of spatial interactions, making it applicable to a wide range of fields, including human mobility, transportation, and trade.

On the benefits of a multiscale domain decomposition method to model-order reduction for frictional contact problems

In this paper, the efficiency of a multiscale strategy based on a domain decomposition method (DDM) for model-order reduction of time-dependent frictional contact problems is presented. The proposed strategy relies on the LArge Time INcrement (LATIN) nonlinear solver combined with model reduction based on the Proper Generalized Decomposition (PGD). The LATIN presents a robust treatment of contact conditions, sharing similarities with augmented Lagrangian approaches, and naturally leads to a mixed DDM. In addition, the global space–time formulation of the method allows PGD-based model reduction to be used during computations, creating and enriching on-the-fly reduced bases per substructure to better track sliding fronts and propagative phenomena. The introduction of a multiscale strategy in the LATIN framework is consistent with the physics of contact problems, in which phenomena with different wavelengths interact: local solutions at contact interfaces presents high gradient effects with a short wavelength compared to the characteristic length of the structure. By taking advantage of this, the coarse scale problem of the strategy enables to capture efficiently the behavior of the problem at the structural level, focusing then on capturing the local contact variations at the contact interfaces. The most important features of the approach are shown comprehensively on a simple one-dimensional frictional contact problem. Then, its robustness and effectiveness are tested on a two-dimensional multibody frictional contact problem with more complex loadings. Guidelines are also given for choosing the parameters of the strategy, in particular those driving the construction of the reduced basis.