Single-scale Models Research Articles

Next-Generation Remote Sensing Data at Multiple Spatial Scales Improves Understanding of Habitat Selection by a Small Mammal

Recent advances in optical remote sensing (RS) technology in combination with lightweight Global Positioning System (GPS) tracking devices now make analyzing the multi-scale habitat selection (HS) of small mammals < 2 kg possible. However, there have been relatively few multi-scale HS studies integrating fine-scale RS data with data-rich, GPS-derived movement data from small mammals. This is critical because small mammals commonly select habitat features across multiple scales. To address this gap, we investigated the HS of a small mammal, fox squirrels (Sciurus niger), which are known to cover relatively large areas and select fine-scale environmental features. We specifically asked the following questions: (1) Do next-generation RS variables improve HS models at single spatial scales? (2) Do multi-scale HS models improve upon those at single spatial scales? Using data from 45 individuals, we constructed HS models at three spatial scales: 4 ha (210 m × 210 m), 0.09 ha (30 m × 30 m), and 0.01 ha (10 m × 10 m) using traditional and next-generation RS data. The 4-ha model, using traditional and next-generation RS data, produced the best single-scale model, explaining 58% of the variations in HS. However, the multi-scale model provided the most informative model, explaining 68% of the variations in HS. Our models provide evidence for the value of next-generation RS data when quantifying HS and additional support for the idea of studying HS at multiple spatial scales.

A multi-scale modeling approach for predicting and mitigating thermal runaway in electric vehicle batteries

This study presents a unique multi-scale modelling technique for electric vehicle (EV) battery thermal runaway prediction models using electrochemical, thermal, and machine learning approaches. This modelling framework has illuminated the thermal runaway’s physical process and the race between heat production and dissipation at the cell module, and pack levels.Experimental evaluation has demonstrated solid predictive performance for each component. The modified pseudo-two-dimensional (P2D) electrochemical model has achieved high voltage prediction accuracy (mean absolute error: 8.5 mV), and the 3D thermal model has captured battery module temperatures with an average error of ± 1.5 °C.The machine learning model has exhibited 96.8% accuracy, excellent classification precision, and an 18.3-minute early warning time. It was also demonstrated that the integrated multi-scale model outperformed standalone single-scale models with 15% higher prediction accuracy and 32% higher average early warning time.Based on these findings, adaptive cooling techniques, unique charge/discharge procedures, and early isolation methods were created. Simulations showed that these mitigation techniques decreased thermal runaway occurrence by 78% and severity by 93%. Despite its high computing cost, this technique might improve EV battery safety, guide battery pack design, and accelerate EV adoption, which would cut carbon emissions.

Numerical validation of scaling laws for stratified turbulence

Recent theoretical progress using multiscale asymptotic analysis has revealed various possible regimes of stratified turbulence. Notably, buoyancy transport can either be dominated by advection or diffusion, depending on the effective Péclet number of the flow. Two types of asymptotic models have been proposed, which yield measurably different predictions for the characteristic vertical velocity and length scale of the turbulent eddies in both diffusive and non-diffusive regimes. The first, termed a ‘single-scale model’, is designed to describe flow structures having large horizontal and small vertical scales, while the second, termed a ‘multiscale model’, additionally incorporates flow features with small horizontal scales, and reduces to the single-scale model in their absence. By comparing predicted vertical velocity scaling laws with direct numerical simulation data, we show that the multiscale model correctly captures the properties of strongly stratified turbulence within regions dominated by small-scale isotropic motions, whose volume fraction decreases as the stratification increases. Meanwhile its single-scale reduction accurately describes the more orderly, layer-like, quiescent flow outside those regions.

Rigorous development and comparison of multi-dimensional reactor models encompassing the catalyst domains for steam methane reforming

Hydrogen is being considered as a possible large-scale carbon-free industrial combustion and transportation fuel. Steam methane reforming (SMR) reaction is the most dominant industrial hydrogen production method. This paper develops comprehensive models and delves into the influences of the multi-dimensional reactor and catalyst domains in SMR. Several single-scale or multi-scale SMR packed-bed reactor models are developed with the Aspen Custom Modeler and solved by the built-in finite difference method. These models are categorized into pseudo-homogeneous and heterogeneous models. This study thoroughly compares and discusses the effects of axial and/or radial mixing due to mass/heat dispersion as well as interfacial and intraparticle mass/heat transport phenomena across various types of reactor-catalyst configurations. Furthermore, insights into the prediction accuracy of each model under various operating conditions are provided. This research fills a gap in the existing literature by developing rigorous dynamic models for SMR reactors to investigate their profiles and performances, offering a clearer understanding of the relative importance of different modeling approaches in predicting the behavior of SMR reactors. The findings provide valuable guidelines for researchers and industry professionals in selecting the appropriate model for their design, analysis, optimization, and control tasks.

Construction and simulation of a joint scale model for power electronic converters based on wavelet decomposition and reconstruction algorithms.

In power electronics systems, system design and operation often involve multiple time and space scales, ranging from nanosecond switching dynamics to hour-level system operation behavior. Due to the complexity of these systems and the rise of wide-gap semiconductor technology, a series of multi-scale phenomena have emerged that are difficult to ignore. The high frequency of switching operations makes multi-scale effects particularly significant, including the fast dynamic response of the power loop, EMI, and heat conduction problems. They are key factors that must be considered in the design to ensure the efficient and reliable operation of power electronic devices. This study proposes the construction and simulation of a joint scale model for power electronic converters based on wavelet decomposition and reconstruction algorithms to address the multi-scale phenomenon and limitations of single-scale power electronic converters. Firstly, a joint scale model for power electronic converters at both macro and micro-scales was established, targeting both single-scale models and simple combinations of multiple scale models for power electronic converters. The traditional single-scale model is sufficient to describe the average behavior of the converter, but it has serious limitations in capturing fast transient processes and high-frequency switching behavior in power electronic systems. These limitations often manifest themselves when there is a need to capture fine timescales of detail. By transforming between the time domain and the frequency domain, wavelet decomposition enables the model to capture both macroscopic average characteristics and microscopic transient dynamics. The wavelet reconstruction algorithm can simulate all kinds of fast changes in the actual working process more accurately and compress irrelevant information while retaining key signal features, so as to optimize the simulation performance of the model. Secondly, this algorithm is used to analyze BC in short time scale. Finally, the short time scale characteristics of power electronic converters are analyzed. Experimental results show that the fusion of wavelet decomposition and reconstruction algorithm enhances the accuracy of the power electronic converter model and improves the performance of the system. The model achieves an error reduction of nearly 3% in the calculation step size of 10-7s, which has a significant impact on the high precision requirements of high-frequency operations. In addition, the optimal calculation step size of 8×10-8s achieves an error reduction of more than 14%, making an important contribution to the transient analysis and fine structure simulation. The wavelet algorithm can improve the accuracy of multi-scale modeling in power electronic system and reduce the simulation time. The reduction of error not only shows the improvement of the accuracy of the model, but also shows its practical significance in the design and test of the actual power electronic system. The reduction in error reveals the ability to more accurately predict and mitigate potential performance problems in matching tests with actual hardware, as well as its ability to adapt to emerging wide bandgap semiconductor materials and structures.

Femtosecond laser shock peening Cu-Zn welds: Experiments and multi-scale modeling investigation

Laser shock peening of the weld surface is a cutting-edge treatment technology following welding. However, the conventional nanosecond pulsed lasers used for this process can unintentionally damage the weld surface quality due to secondary melting, which can reduce the comprehensive mechanical properties of the material. Here, we applied femtosecond laser shock peening (FLSP) to Cu-Zn welds as a paradigm. Experimental results show that FLSP cause the finer grain size on the surface of welds, with the maximum increases of approximately 17.6 % in surface hardness of the welds. The residual stress on the surface was transformed from tensile residual stress to compressive residual stress. Crucially, to provide a more precise depiction of the FLSP process, we developed a comprehensive multi-scale modeling approach that involves integrating the inputs and outputs among models with different scales. This multi-scale modeling approach provides an enhanced understanding of the FLSP process compared to traditional single scale models. The high-density electron pressure evolution process, the shock compression fragmentation process of the weld dislocation network during FLSP and the depth of compressive residual stress could be investigated by this model. These findings provide electronic-level and atomic-level insights and imply promising applications of FLSP on various welds surface.

Liquid-filled pipeline leak detection and localization based on multi-scale residual networks

Effective ways to improve the accuracy of liquid-filled pipeline leak detection are one of the key issues that need to be addressed urgently in a conservation-oriented society. Recently, pipeline leak detection methods based on deep learning have developed rapidly. To improve the learning ability of convolutional neural network for pipeline leak signal features and leak detection accuracy, a multi-scale residual networks (MSRNs) model is proposed in this paper for liquid-filled pipeline leak detection and localization. The model uses convolutional kernels of different scales to extract multiscale features of pipeline leakage signals based on deep residual networks (DRNs) and uses fully connected layers to fuse the features, thus improving the accuracy of pipeline leakage detection and localization. Among them, the large convolution kernel can acquire the low-frequency information of the signal due to its sizable perceptual field, the medium convolution kernel can capture the local and global features of the signal, and the small convolution kernel is more sensitive to the high-frequency information of the signal. Meanwhile, a pipeline leakage test platform is built to evaluate the proposed model. The test results show that the accuracy of leak detection and localization of MSRN model is 98.3%, which is better than that of single-scale DRN model. In addition, the proposed MSRN model is verified to have good generalization and noise immunity through testing and analyzing the leakage signals under different pressures and background noises.

ILLUMINATION ENHANCEMENT OF NIGHTTIME IMAGES USING A REGULATED SINGLE SCALE RETINEX ALGORITHM

People are active during the nighttime and capture many photos. Due to the low-light nature of the environment, captured images tend to appear dimmed with imbalanced illumination, low contrast, noise, and a lack of vibrant colors. For this purpose, this paper presents a direct and practical approach to improving the lighting of night images based on the single-scale retinex model and using image processing methods and other statistical approaches. The proposed algorithm begins by converting the image from the RGB to the HSI model. Then, it enhances only the I channel while preserving the H and S channels. Thus, processing on the I channel begins with estimating the illumination version of the image and calculating the logarithms of both the illuminated and original image, like the SSR model. Then, it subtracts these two images using a subtraction of logarithmic image processing. Subsequently, the cumulative distribution function of the Gumble probability distribution is applied, and the resulting output is further treated using a logarithmic transformation method. That produces the processed I channel, which combines with the conserved H and S channels to give the HSI image. Ultimately, it converts the image to RGB format. The proposed algorithm is applied to two datasets of nighttime images, compares their performance to seven different contemporary algorithms, and evaluates the results of the compares using three specific metrics of quality evaluation based on the results generated, deducing that the suggested algorithm enhanced the brightness of night images and surpassed other algorithms in terms of execution time, and image assessment methods. Additionally, it exceeded them in all metrics used.

Pore-Scale Modeling of Methane Hydrate Dissociation Using a Multiphase Micro-Continuum Framework

The development of methane hydrate extraction technology remains constrained due to the limited physical understanding of hydrate dissociation dynamics. While recent breakthroughs in pore-scale visualization techniques offer intuitive insights into the dissociation process, obtaining a profound grasp of the underlying mechanisms necessitates more than mere experimental observations. In this research, we introduce a two-phase micro-continuum model that facilitates the numerical simulation of methane hydrate dissociation at both single- and multiscale levels. We employed this numerical model to simulate microfluidic experiments and determined the kinetic parameters of methane hydrate dissociation based on experimental data under various dissociation scenarios. The simulations, once calibrated, correspond closely to experimental results. By comprehensively comparing the simulated results with experimental data, the rate constant and the effective diffusion coefficient were reliably determined to be kd = 1.5 × 108 kmol2/(J·s·m2) and Dl = 0.8 × 10−7 m2/s, respectively. Notably, the multiscale model not only matches the precision of the single-scale model but also presents considerable promise for streamlining the simulation of hydrate dissociation across multiscale porous media. Moreover, we contrast hydrate dissociation under isothermal versus adiabatic conditions, wherein the dissociation rate is significantly reduced under adiabatic conditions due to the shifted thermodynamic condition. This comparison highlights the disparities between microfluidic experiments and real-world extraction environments.

Research on the service condition monitoring method of rolling bearings based on adaptive deconvolution and transformer

Aiming at the complexity of the rolling bearing operation state in actual working conditions and the limitation of the feature extraction capability of single-scale model, this paper proposes a fault diagnosis method based on two-channel adaptive deconvolution and transformer module (TADAT). First, a frequency domain dataset is built and multiscale feature information is extracted using two-channel adaptive deconvolution at the beginning stage of the model; the global features of the signal are further extracted by combining them in series with the transformer (multi-head attention). Next, the loss function is optimized by introducing a regularized loss function with label smoothing and L2 regularization penalty term. Finally, the model is validated against an open-source dataset and operating condition data collected from an unbalanced bearing load test bed with an accuracy of over 98.5%. The model also maintains high accuracy under noisy samples. The test shows that the rolling bearing service condition monitoring method based on TADAT feature extraction model proposed by this method is characterized by simple model structure, high diagnostic accuracy and strong generalization performance, which provides a useful reference for the research of rolling bearing service condition monitoring.

A two-way coupled regional urban–street network air quality model system for Beijing, China

Abstract. Owing to the substantial traffic emissions in urban areas, especially near road areas, the concentrations of pollutants, such as ozone (O3) and its precursors, have a large difference compared to regional averages, and their distributions cannot be captured accurately by traditional single-scale air quality models. In this study, a new version of a regional urban–street network model (an Integrated Air Quality Modeling System coupling regional urban–street: IAQMS-street v2.0) is presented. An upscaling module is implemented in IAQMS-street v2.0 to calculate the impact of mass transfer to regional scale from street network. The influence of pollutants in the street network is considered in the concentration calculation on the regional scale, which is not considered in a previous version (IAQMS-street v1.0). In this study, the simulated results in Beijing during August 2021, using IAQMS-street v2.0, IAQMS-street v1.0, and the regional model (Nested Air Quality Prediction Modeling System, NAQPMS), are compared. On-road traffic emissions in Beijing, as the key model input data, were established using intelligent image-recognition technology and real-time traffic big data from navigation applications. The simulated results showed that the O3 and nitrogen oxide (NOx) concentrations in Beijing were reproduced by using IAQMS-street v2.0 on both the regional scale and street scale. The prediction fractions within a factor of 2 (FAC2s) between simulations and observations of NO and NO2 increased from 0.11 and 0.34 in NAQPMS to 0.78 and 1.00 in IAQMS-street v2.0, respectively. The normalized mean biases (NMBs) of NO and NO2 decreased from 2.67 and 1.33 to −0.25 and 0.08. In the coupled model, the concentration of NOx at the street scale is higher than that at the regional scale, and the simulated distribution of pollutants on a regional scale was improved in IAQMS-street v2.0 when compared with that in IAQMS-street v1.0. We further used IAQMS-street v2.0 to quantify the contribution of local on-road traffic emissions to the O3 and NOx emissions and analyze the effect of traffic regulation policies in Beijing. Results showed that heavy-duty trucks are the major source of on-road traffic emissions of NOx. The relative contributions of local traffic emissions to NO2, NO, and O3 concentrations were 53.41 %, 57.45 %, and 8.49 %, respectively. We found that traffic regulation policies in Beijing largely decreased the concentrations of NOx and hydrocarbons (HC); however, the O3 concentration near the road increased due to the decrease consumption of O3 by NO. To decrease the O3 concentration in urban areas, controlling the local emissions of HC and NOx from other sources requires consideration.

Development of Monthly Scale Precipitation-Forecasting Model for Indian Subcontinent using Wavelet-Based Deep Learning Approach

In the present work, a wavelet-based multiscale deep learning approach is developed to forecast precipitation using the lagged monthly rainfall, local climate variables, and global teleconnections such as IOD, PDO, NAO, and Nino 3.4 as predictors. The conventional methods are limited by their inability to capture the high precipitation variability in time and space. The proposed multiscale method was tested and validated over the Krishna River basin in India. The results from the proposed methods were compared with contemporary models based on Multiple Linear Regression and Neural Networks. Overall, the forecasting accuracy was higher using the wavelet-based hybrid models than the single-scale models. The wavelet-based methods yielded results with 13–34% reduced error when compared with the best single-scale models. The proposed multi-scale model was then applied to the different climatic regions of the country, and it was shown that the model could forecast rainfall with reasonable accuracy for different climate zones of the country.

Review on Multiscale Transport Phenomena in the Metal-Based Additive Manufacturing Process.

Metal-based additive manufacturing (MAM) method with high freedom and special fabricate technology has presented great universality in the aerospace and biomedicine field. However, a wide range of process parameters in the MAM method challenge the experimental study on the formation and evolution of defects. The numerical simulation presents its excellent accuracy and economy in predicting the evolution of multiphysics phenomena and was hence widely applied. In the current review, the available MAM methods with the fundamental phenomena were reviewed. Based on scales, numerical approaches divided into three categories were discussed and focused on their main prediction objectives and strengths or weaknesses of all the scales. To display the prediction results closer to real physical phenomena, advanced multiscale models coupled with various single-scale models are provided. The high prediction accuracy and computational efficiency enable better parameter control and defect avoidance. As a supplement and development to the physical-driven model, the data-driven model provides a new perspective on MAM methods. Based on the database generated by the physical-driven model and experiment, the data-driven models without calibration of input parameters are shown. In addition, this review discussed the development direction of numerical simulation, aiming to provide a reference for technical research in this field.

Effects of landscape structure and patch characteristics on the density of central populations of the eastern green lizard Lacerta viridis.

A better understanding of the impact of habitat loss on population density can be achieved by evaluating effects of both parameters within remnant habitat patches and parameters of the landscape surrounding those patches. The integration of predictors at the patch and landscape level is scarce in animal ecological studies, especially for reptiles. In this study, a patch-landscape approach was applied to evaluate the combined effects of within-patch habitat quality, patch geometry and landscape configuration and composition on the density of remnant populations of the eastern green lizard, Lacerta viridis, in a highly modified landscape in Bulgaria. Landscape composition variables (proportion of different land covers) were measured at different spatial scales surrounding patches. Single-scale models were built to evaluate combined effects of all predictors on density, when including all landscape composition variables at a specific spatial scale. Multi-scale models were applied to analyze combined effects when including landscape composition variables at the scale of their strongest effect (scale of effect, SoE). Results showed that the SoE of proportion of cropland and urban areas was small (50 m), while for proportion of habitat was large (1.5 km). The overall effect of habitat loss was better explained by the multi-scale model. Population density increased with patch area and decreased with patch shape irregularity and with the proportion of three land cover types surrounding patches-cropland, urban areas, and habitat. Combining patch and landscape parameters is important to identify ecological processes that occur simultaneously at different spatial levels and landscape scales, which would imply the application of multi-scale approaches for the protection of wild animal populations. Results are contrasted with what is known about occupancy patterns of the species in the same region and approaches to integrate both occupancy and density, in the field design of animal ecological studies are suggested.

Atomistic and continuum length scale coupling in materials using quasicontinuum method

We propose coupling atomistic and continuum length scale models in materials to develop a multiscale modeling framework. High fidelity models like Molecular Dynamics and Density Functional Theory exist at atomic scales capable of explaining various small-scale occurrences. However, these models cannot be scaled up efficiently to explain a range of observations occurring at multiple length and time scales. Since continuum properties can be considered emergent from numerous smaller-scale phenomena, models capable of effectively relating multiple length and time scales are needed. Therefore, we propose using existing models at differing yet individual length scales and suitably coupling them for tackling such multiscale problems. This coupling method should facilitate information exchange in real-time and demonstrate rapid convergence without adding significant computational overhead. Together with single-scale models, this coupling could help us relate continuum properties like stiffness, damping, and thermal conductivity to a material's electronic structure and atomic arrangement. As an illustration, we choose a simple one-dimensional problem with a limited region of interest which we wish to study at an atomic scale. We rely upon the Quasicontinuum method to model this limited atomic region, which significantly reduces the degrees of freedom involved for the atomistic subdomain. Preliminary results for this static problem indicate that the displacement error is relevant on the atomic scale. Importance is put on deriving a method capable of effectively coupling atomic and continuum subdomains for solving general static or dynamic structural problems.

Regret theory-based multivariate fusion prediction system and its application to interest rate estimation in multi-scale information systems

Estimating interest rates is a typical multivariate prediction problem that has garnered considerable attention in the finance industry. However, the rising complexity of the prediction environment has presented significant obstacles to the methodology and application of traditional single-scale rational prediction models. Considering this challenge, the paper presents a new multivariate fusion prediction system that aims to tackle the following issues: (1) How to mine prediction information from multi-scale information systems (MSISs)? (2) How to describe the irrational behavior of decision-makers (DMs) in prediction processes? (3) How to improve the prediction performance and generalization capability of the models? Specifically, the score function of each feature under MSISs is primarily established by applying scale rules, regret theory (RT), and dominance relations to thoroughly explore prediction information across varying scales. Second, an adaptive S3WD model is developed for feature selection, which leverages the feature score distribution and the sequential three-way decision (S3WD) concept to avoid the problem of dimensional disasters, thereby improving the accuracy of selected features and reducing the learning cost. Furthermore, to enhance the effectiveness and rationality of the kernel extreme learning machine (KELM), an improved version known as IKELM is developed, which combines the regret-based comprehensive score of each sample. Finally, a multivariate fusion prediction system is constructed and abbreviated as an S3WD-IKELM-PSO system, where the particle swarm optimization (PSO) algorithm is employed to determine optimal parameters. It is worth noting that actual interest rate estimation datasets are utilized to verify the feasibility and validity of the proposed prediction system. According to the experimental results, the S3WD-IKELM-PSO system exhibits superior prediction performance and generalization ability compared to existing machine learning models.

Fractal two-scale method for calculating the laser scattering distribution of asphalt pavement.

The laser scattering characteristic of pavement is one of the important factors that affect the detection performance of optical sensors such as lidars. Because the wavelength of laser does not match the roughness of the asphalt pavement, the common analytical approximation model of electromagnetic scattering is not applicable in this case, so it is difficult to calculate the laser scattering distribution of the pavement accurately and effectively. According to the self-similarity of the asphalt pavement profile, a fractal two-scale method (FTSM) based on fractal structure is proposed in this paper. We used the Monte Carlo method to obtain the bidirectional scattering intensity distribution (SID) and the back SID of the laser on the asphalt pavement with different roughness. Then we designed a laser scattering measurement system to verify the simulation results. We calculated and measured the SIDs of s-light and p-light of three asphalt pavements with different roughness (σ=0.34 mm; 1.74 mm; 3.08 mm). The results show that, compared with the traditional analytical approximation methods, the results of FTSM are closer to the experimental results. Compared with the single-scale model based on the Kirchhoff approximation, FTSM has a significant improvement in computational accuracy and speed.

Lightweight multi-scale classification of chest radiographs via size-specific batch normalization

Background and Objective:Convolutional neural networks are widely used to detect radiological findings in chest radiographs. Standard architectures are optimized for images of relatively small size (for example, 224 × 224 pixels), which suffices for most application domains. However, in medical imaging, larger inputs are often necessary to analyze disease patterns. A single scan can display multiple types of radiological findings varying greatly in size, and most models do not explicitly account for this. For a given network, whose layers have fixed-size receptive fields, smaller input images result in coarser features, which better characterize larger objects in an image. In contrast, larger inputs result in finer grained features, beneficial for the analysis of smaller objects. By compromising to a single resolution, existing frameworks fail to acknowledge that the ideal input size will not necessarily be the same for classifying every pathology of a scan. The goal of our work is to address this shortcoming by proposing a lightweight framework for multi-scale classification of chest radiographs, where finer and coarser features are combined in a parameter-efficient fashion.Methods:We experiment on CheXpert, a large chest X-ray database. A lightweight multi-resolution (224 × 224, 448 × 448 and 896 × 896 pixels) network is developed based on a Densenet-121 model where batch normalization layers are replaced with the proposed size-specific batch normalization. Each input size undergoes batch normalization with dedicated scale and shift parameters, while the remaining parameters are shared across sizes. Additional external validation of the proposed approach is performed on the VinDr-CXR data set.Results:The proposed approach (AUC 83.27±0.17, 7.1M parameters) outperforms standard single-scale models (AUC 81.76±0.18, 82.62±0.11 and 82.39±0.13 for input sizes 224 × 224, 448 × 448 and 896 × 896, respectively, 6.9M parameters). It also achieves a performance similar to an ensemble of one individual model per scale (AUC 83.27±0.11, 20.9M parameters), while relying on significantly fewer parameters. The model leverages features of different granularities, resulting in a more accurate classification of all findings, regardless of their size, highlighting the advantages of this approach.Conclusions:Different chest X-ray findings are better classified at different scales. Our study shows that multi-scale features can be obtained with nearly no additional parameters, boosting performance.

Scale mismatches between predictor and response variables in species distribution modelling: A review of practices for appropriate grain selection

There is a lack of guidance on the choice of the spatial grain of predictor and response variables in species distribution models (SDM). This review summarizes the current state of the art with regard to the following points: (i) the effects of changing the resolution of predictor and response variables on model performance; (ii) the effect of conducting multi-grain versus single-grain analysis on model performance; and (iii) the role of land cover type and spatial autocorrelation in selecting the appropriate grain size. In the reviewed literature, we found that coarsening the resolution of the response variable typically leads to declining model performance. Therefore, we recommend aiming for finer resolutions unless there is a reason to do otherwise (e.g. expert knowledge of the ecological scale). We also found that so far, the improvements in model performance reported for multi-grain models have been relatively low and that useful predictions can be generated even from single-scale models. In addition, the use of high-resolution predictors improves model performance; however, there is only limited evidence on whether this applies to models with coarser-resolution response variables (e.g. 100 km2 and coarser). Low-resolution predictors are usually sufficient for species associated with fairly common environmental conditions but not for species associated with less common ones (e.g. common vs rare land cover category). This is because coarsening the resolution reduces variability within heterogeneous predictors and leads to underrepresentation of rare environments, which can lead to a decrease in model performance. Thus, assessing the spatial autocorrelation of the predictors at multiple grains can provide insights into the impacts of coarsening their resolution on model performance. Overall, we observed a lack of studies examining the simultaneous manipulation of the resolution of predictor and response variables. We stress the need to explicitly report the resolution of all predictor and response variables.

Effects of multiple aspects of anthropogenic landscape change on mesopredator relative abundance

Context Anthropogenic landscape changes have substantial effects on biodiversity and animal populations worldwide. However, anthropogenic landscape change can take a variety of forms, and its effects on wildlife species can vary by landscape context and scale. It is therefore critical that studies of the effects of anthropogenic landscape change on wildlife consider landscape context and model effects of landscape change at multiple scales. Mesopredators serve as an excellent case study of scale-dependent and even contradictory effects of anthropogenic landscape change, because mesopredator populations can respond both positively and negatively to landscape change depending on its form (e.g. agricultural production, urbanisation) and scale. Aims The aim of this study was to model relationships between multiple aspects of anthropogenic landscape change, including agricultural production and the proliferation of non-native shrubs, and relative abundance of three mesopredator species: Virginia opossum (Didelphis virginiana), striped skunk (Mephitis mephitis), and raccoon (Procyon lotor). Methods We summarised landscape variables at two scales and built both single-scale and multi-scale models linking relative abundance of mesopredators to landscape variables. Key results We found that relative abundance of all three species was most related to either the transition from natural areas to agricultural areas (brown-to-green gradient) or the likelihood of presence of non-native shrubs. We also found that responses to anthropogenic landscape change varied by spatial scale; for instance, skunks and raccoons had higher relative abundance in parts of Illinois with more forest cover and agriculture, respectively, but avoided more highly forested and agricultural areas, respectively, at smaller scales. Conclusions Mesopredator responses to landscape change were highly variable and scale-dependent, but were generally related to transitions from forest to agriculture or the presence of non-native shrubs. Implications Our study demonstrates the need to model effects of anthropogenic landscape change at multiple scales, given the differing results that can be achieved when landscape variables are measured at multiple scales.