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The landscape in which people live is made up of many features, which are named and have importance for cultural reasons. Prominent among these are the naming of upland features such as mountains, but mountains are an enigmatic phenomenon which do not bear precise and repeatable definition. They have a vague spatial extent, and recent research has modelled such classes as spatial fuzzy sets. We take a specifically multiresolution approach to the definition of the fuzzy set membership of morphometric classes of landscape. We explore this idea with respect to the identification of culturally recognized landscape features of the English Lake District. Discussion focuses on peaks and passes, and the results show that the landscape elements identified in the analysis correspond to well-known landmarks included in a place name database for the area, although many more are found in the analysis than are named in the available database. Further analysis shows that a richer interrogation of the landscape can be achieved with Geographical Information Systems when using this method than using standard approaches.

Emerging diseases of wildlife origin are increasingly spilling over into humans and domestic animals. Surveillance and risk assessments for transmission between these populations are informed by a mechanistic understanding of the pathogens in wildlife reservoirs. For avian influenza viruses (AIV), much observational and experimental work in wildlife has been conducted at local scales, yet fully understanding their spread and distribution requires assessing the mechanisms acting at both local, (e.g., intrinsic epidemic dynamics), and continental scales, (e.g., long-distance migration). Here, we combined a large, continental-scale data set on low pathogenic, Type A AIV in the United States with a novel network-based application of bird banding/recovery data to investigate the migration-based drivers of AIV and their relative importance compared to well-characterized local drivers (e.g., demography, environmental persistence). We compared among regression models reflecting hypothesized ecological processes and evaluated their ability to predict AIV in space and time using within and out-of-sample validation. We found that predictors of AIV were associated with multiple mechanisms at local and continental scales. Hypotheses characterizing local epidemic dynamics were strongly supported, with age, the age-specific aggregation of migratory birds in an area and temperature being the best predictors of infection. Hypotheses defining larger, network-based features of the migration processes, such as clustering or between-cluster mixing explained less variation but were also supported. Therefore, our results support a role for local processes in driving the continental distribution of AIV.

Species distributions in forest‐dominated landscapes are closely tied to vegetation structure and heterogeneity, which can vary across spatial scales. As Fennoscandian wolverines recolonize their historical range in boreal forests, specific structural features linked to better resources, such as prey availability, cover, and suitable denning habitats, may promote occupancy in these areas. We studied wolverine (Gulo gulo) occurrence in mainland Finland between 2009–2010 and 2018–2022. We conducted a multi‐scale analysis using wildlife and field triangle data and Multi‐Source National Forest Inventory (MS‐NFI) remote sensing products. We applied generalized linear mixed models (GLMMs) to assess the influence of forest and landscape variables on the probability of occurrence at two spatial scales: local (3.13 km radius) and landscape (20 km radius). Occupied and unoccupied sites were distinguished by landscape fragmentation, tree volume, tree species composition, and distance to clearcuts. Sites were more likely to become occupied when forests were less fragmented and had broadleaved trees, while the probability of occurrence decreased if the total volume of trees was high or fresh clearcuts were in close proximity. Landscape scale seems to be more relevant than local scale when studying the overall forest structure's impact on wolverine occurrence. Our findings provide new insights into the occurrence of wolverines in Finnish boreal forests and could be used to aid species conservation and forest management planning. SUOMENKIELINEN TIIVISTELMÄ Lajien levinneisyys kytkeytyy metsävaltaisilla alueilla kasvillisuuden rakenteeseen ja koostumukseen, jotka voivat vaihdella spatiaalisen skaalan mukaan. Fennoskandian ahmat tekevät paluuta historiallisille elinalueilleen boreaalisiin metsiin. Monipuolisia resursseja tukevat metsien ja maiseman rakenneominaisuudet voivat kannustaa tietyn alueen asuttamiseen. Tutkimme ahman (Gulo gulo) esiintymisen muutoksia Manner‐Suomessa vuosien 2009–2010 ja 2018–2022 välillä. Aineistona käytimme riista‐ ja peltokolmioaineistoa sekä monilähteisen valtakunnan metsien inventoinnin (MVMI) kaukokartoitustuotteita. Yleistettyjen lineaaristen sekamallien (generalized linear mixed models) avulla analysoimme metsän ja maiseman rakenteiden vaikutuksia esiintymistodennäköisyyteen kahdella eri spatiaalisella skaalalla: paikallisella tasolla (3.12 km säde) ja maisematasolla (20 km säde). Asutut ja asuttamattomat alueet erosivat maiseman pirstoutuneisuuden, puuston kokonaistilavuuden, puulajikoostumuksen sekä avohakkuiden etäisyyden osalta. Ahmojen esiintymistodennäköisyys kasvoi, kun metsät olivat vähemmän pirstoutuneita ja lehtipuuvaltaisempia. Suurempi puuston kokonaistilavuus ja läheinen etäisyys avohakkuisiin alensivat esiintymistodennäköisyyttä. Tulosten perusteella maisemataso on paikallista merkityksellisempi, kun tutkitaan metsien yleisen rakenteen vaikutusta ahmojen esiintymisen todennäköisyyteen. Tutkimustulokset lisäävät ymmärrystä ahmojen esiintymisestä boreaalisissa metsissä. Using remote sensing products and wolverine snow‐track observations, we found that less fragmented forests with broadleaved trees promoted wolverine occurrence, while high total volume of trees and proximity to clearcuts hindered it. The landscape scale was more significant than the local scale in explaining the impact of forest structure on occurrence. Our results offer new insights on the occurrence of wolverines in the boreal forests.

In the topographically and climatically diverse region of Yunnan, clarifying the driving mechanisms and threshold effects of factors influencing forest net primary productivity (NPP) is crucial for managing forest carbon sinks. In this study, we established a comparative analysis framework for the stable forest (SF) and the changing forest (CF). Yunnan was then divided into five subregions based on topography and climate. Using a random forest model and the SHAP method, we systematically analyzed drivers of spatiotemporal NPP variations. The results show that CF exhibits greater NPP variation and spatial heterogeneity than SF and maintains stronger trend persistence. At the provincial scale, elevation and precipitation are the main drivers of NPP in SF and CF, respectively, while at the subregional scale, dominant factors differ and include solar radiation, temperature, and forest age, indicating clear scale‐dependent effects. The explanatory power of multiple‐factor interactions for NPP variations is generally higher than that of single factors, with precipitation showing particularly strong synergistic effects. Human activities also have a pronounced impact on NPP in CF. Key driving factors exhibit nonlinear threshold responses, with NPP in SF most suitable under temperature (14°C–20°C), precipitation (100–125 mm), elevation (~2000 m), and forest age (50–70 years), whereas CF shows broader response ranges, reflecting greater adaptability. This study highlights the nonlinear responses and threshold characteristics of forest NPP to multiple driving factors in complex mountainous environments. It also emphasizes the importance of interactions and scale effects in ecological modeling. We first classified the forests in Yunnan Province into stable forest and change forest regions, and then further divided the province into five sub‐regions based on topography and climate. Based on this classification, we systematically analyzed the spatiotemporal variation of forest net primary productivity (NPP) across these regions. Moreover, by employing the Random Forest model and SHAP analysis, we identified the key driving factors behind the NPP changes in different sub‐regions.

Coal seams in China have poor permeability and a complex pore structure, leading to a low gas extraction efficiency. Studying the pore structure of different metamorphic degrees of coal by using modern technological methods and employing appropriate pumping measures for different pore structures can improve the gas extraction efficiency. In this study, the pore sizes of three coal samples with different metamorphic grades were measured at different pore scales through CO2 adsorption, nitrogen adsorption, small‐angle X‐ray scattering, and mercury pressure methods. The pore structure was three‐dimensionally reconstructed using the lattice Boltzmann method‐based quartet structure generation set algorithm. The optimum pore size measurements performed using the CO2 adsorption, nitrogen adsorption, small‐angle X‐ray scattering, and mercury pressure methods were in the ranges of 0.3‐2, 1.7‐300, 1‐50, and 7.5‐11 000 nm, respectively; this demonstrates that the structure of a full‐scale coal model cannot be accurately tested using a single technique. The pore structure varies depending on the metamorphic degree of the coal; therefore, coal metamorphism should be considered for effective gas extraction. Based on carbon dioxide adsorption, nitrogen adsorption, small‐angle X‐ray scattering, and mercury pressure method, the 3D reconstruction of coal pore structure was made by LBM‐based QSGS algorithm, and finding the best measurements of pore size of coal by CO2 adsorption, nitrogen adsorption, small‐angle X‐ray scattering, and mercury pressure method is 0.3‐2, 1.7‐300, 1‐50, and 7.5‐11 000 nm.

Bat populations are declining globally. Maintaining high‐quality habitat for bats can help mitigate extinction risk. Natural and semi‐natural linear vegetation features have been shown to provide shelter and foraging habitat for bats in temperate agroecosystems in Europe, yet their value for bats in North America has received little attention. Using automated ultrasonic recorders, we assessed bat species richness and activity across agricultural drainage ditches  that varied in mean vegetation height, variability in vegetation height, and mean width in agroecosystems in eastern Ontario, Canada. Landscapes surrounding recording sites also varied in forest amount and mean field size, and recording sites were located at different distances from the nearest forest patch. We found that in general, bat activity at the community level and at the individual species level was positively associated with mean vegetation height and mean vegetation width; however, species appeared to vary in their response to variation in vegetation height. We also found a general positive relationship within and across species for bat activity with forest amount at the landscape scale. Overall, our results suggest maintaining or increasing vegetation height along drainage ditches and field margins as well as maintaining or increasing forest amount at the landscape scale will best support bats in temperate North American agroecosystems. Using automated ultrasonic recorders, we assessed bat species richness and activity across agricultural drainage ditches that varied in mean vegetation height, variability in vegetation height, and mean width, as well as forest amount, distance to forest, and mean field size at the landscape scale in agroecosystems in eastern Ontario, Canada. Overall, our results suggest maintaining or increasing vegetation height along drainage ditches and field margins, as well as increasing or maintaining forest amount at the landscape scale, will best support bats in temperate North American agroecosystems.

The Advanced Technology Microwave Sounder (ATMS) mounted on the Suomi National Polar-Orbiting Partnership (NPP) satellite can provide both temperature and humidity information for a weather prediction model. Based on the rapid-refresh multi-scale analysis and prediction system—short-term (RMAPS-ST), we investigated the impact of ATMS radiance data assimilation on strong rainfall forecasts. Two groups of experiments were conducted to forecast heavy precipitation over North China between 18 July and 20 July 2016. The initial conditions and forecast results from the two groups of experiments have been compared and evaluated against observations. In comparison with the first group of experiments that only assimilated conventional observations, some added value can be obtained for the initial conditions of temperature, humidity, and wind fields after assimilating ATMS radiance observations in the system. For the forecast results with the assimilation of ATMS radiances, the score skills of quantitative forecast rainfall have been improved when verified against the observed rainfall. The Heidke skill score (HSS) skills of 6-h accumulated precipitation in the 24-h forecasts were overall increased, more prominently so for the heavy rainfall above 25 mm in the 0–6 h of forecasts. Assimilating ATMS radiance data reduced the false alarm ratio of quantitative precipitation forecasting in the 0–12 h of the forecast range and thus improved the threat scores for the heavy rainfall storm. Furthermore, the assimilation of ATMS radiances improved the spatial distribution of hourly rainfall forecast with observations compared with that of the first group of experiments, and the mean absolute error was reduced in the 10-h lead time of forecasts. The inclusion of ATMS radiances provided more information for the vertical structure of features in the temperature and moisture profiles, which had an indirect positive impact on the forecasts of the heavy rainfall in the RMAPS-ST system. However, the deviation in the location of the heavy rainfall center requires future work.

Direct modeling of time-dependent transport and reactions in realistic heterogeneous systems, in a manner that considers the evolution of the quantities of interest in both, the macro-scale (suspending fluid) and the micro-scale (suspended particles), is currently well beyond the capabilities of modern supercomputing. This is understandable, since even a simple system such as this can easily contain over 107 particles, whose length and time scales differ from those of the macro-scale by several orders of magnitude. While much can be gained by applying direct numerical solution to representative model systems, the direct approach is impractical when the performance of large, realistic systems is to be modeled. In this study we derive and analyze a “hybrid” model that is suitable for fibrous reactors. The model considers convection/diffusion in the bulk liquid, as well as intra-fiber diffusion and reaction. The essence of our approach is that diffusion and (first-order) reaction in the intra-fiber space are handled semi-analytically, based on well-established theory. As a result, the problem of intra-fiber transport and reaction is reduced to an easily solvable set of n 0 ODEs, where n 0 is the number of terms in the Bessel expansion evaluated without recourse to approximation; this set is coupled, point-wise, with a numerical model of the macro-scale. When the latter is discretized using N nodes, the total “hybrid” model for the system consists of a system of N ( 2 + n 0 ) ODEs, which is easily solvable on a modest workstation. Parametric analyses are presented and discussed.

SUMMARY This paper investigates the optimal location of sensors in a timber structure when parametric uncertainty is considered in the microstructure of the material. In order to describe the proposed methodology, a timber beam is chosen for our numerical simulations. A classical sensor location methodology based on the Fisher information matrix is employed. Within a finite element framework, a homogenization‐based multi‐scale approach is adopted to model the constitutive response of timber, along with stochastic properties in the definition of the material. Nevertheless, by considering uncertainty in the micromechanical properties of wood, a significant computational cost is added to the solution of a large set of realizations represented by expensive multi‐scale analyses. In order to tackle this high cost, we build a statistical approximation to the output of the computer model, known as a Bayesian emulator. Following this strategy, three micromechanical parameters are chosen to study their influence on the selection of different configurations of sensor placement. The optimal location of sensors is assessed with three different criteria: the Fisher matrix determinant, the modal assurance criterion error and the condition number. Furthermore, the robustness of this configuration is investigated in the presence of noise. Copyright © 2014 John Wiley & Sons, Ltd.