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In the last decade, the documentation of historical buildings has made tremendous progress in generalising the use of high-precision laser scanning and drone photogrammetry. Yet the potential of digital surveying is not fully exploited due to difficulties in manually analysing large amounts of collected data. Machine learning offers immense potential as a game-changer in building archaeology, especially for the documentation of structures composed of millions of units. This paper presents the first segmentation of large-scale surveys of historic masonry using machine learning, using the thirteenth-century Basilica of St Anthony (Padua, Italy) as a case study. Based on a drone survey of the north façade of the building (110 × 70 m), a state-of-the-art non-learning segmentation approach is described and its limitations for historical structures are illustrated. Then, a new workflow based on convolutional neural networks (CNN) is presented. The result is a precise mapping of about 300,000 individual bricks showing a large variety of formats and bonds. The automatic surveys are analysed using visual programming language (VPL), enabling a rapid and feature-based identification of building phases and repair interventions. The outcome demonstrates the validity of machine learning for the analysis of historical structures and its potential in the field of heritage. [ABSTRACT FROM AUTHOR]

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Sterility mosaic disease (SMD) is an emerging biotic risk to the cultivation of pigeonpea throughout the Indian subcontinent. The prevalence and distribution of SMD documented from diverse agro-climatic zones are still poorly known. In this investigation, we determined the spatial distribution of SMD covering major pigeonpea-growing states of Southern India namely, Karnataka, Telangana, Tamil Nadu, Maharashtra, and Andhra Pradesh by considering 2 years of exploratory survey data. Geospatial approaches were employed to determine the SMD incidence levels at unvisited locations using neighboring observations. The results indicated enormous variability in disease incidence ranging from 0 to 82.64% and 0 to 36.84%, respectively, in Kharif 2021-22 and 2022-23 in pigeonpea-growing states. Spatial autocorrelation analysis and Ripley's K function established the presence of average clustering over sampling locations. Two main clusters were identified via agglomerative hierarchical cluster analysis. From the Local Moran's I spatial autocorrelation (LISA) analysis, most of the districts were clustered together at (I<0). From the p-values, Bidar and Krishnagiri districts recorded the highest spatial dependence, while the remaining districts recorded medium spatial dependence (p < 0.05). Based on kriging, the Chittoor district of Andhra Pradesh, Medak and Ranga Reddy districts of Telangana, Bijapur district of Karnataka, and Latur district in Maharashtra states were found to be vulnerable to SMD occurrence in the future. The Matern and Spherical models were shown to be the best of the several semivariogram experimental models tested for spatial patterns of SMD incidence, from which OK and IK maps were created. Based on the interpolation results, the potential SMD hotspots/risk areas were most prominent in Telangana, Andhra Pradesh, Karnataka, and Tamil Nadu states. These identified hotspots for SMD revealed high levels of disease probability rates (>25%) above the threshold level and must be closely monitored to restrict and minimize further disease spread across the studied areas in Southern India. In pigeonpea, this is the first study in India that attempted to understand the spatial pattern of SMD using geostatistical methodologies. The results of the study will aid in the design and dissemination of specific management practices and curb the further spread of SMD. [ABSTRACT FROM AUTHOR]

The European Roller Coracias garrulus has suffered greatly from breeding habitat loss due to the renovation of old farmhouses and rural buildings and changing agricultural practices that took place extensively across Europe in the last decades. As a consequence, this species experienced a significant decline, and local extinctions of breeding populations were recorded in several European countries. We investigated nest sites and nesting area selection by the Italian Roller population during the breeding period (May–August) between 2016 and 2018. We collected 711 points from field surveys and used four types of point pattern analysis to detect space-time patterns of nest site and nesting area selection. We found that: (a) the spatial distribution of selected (i.e., occupied) nest sites was significantly nonrandom (p < 0.01) for all years and months; (b) only 2.6% of the selected nest sites was located within parks or reserves; (c) there were significant (p < 0.01) latitudinal, longitudinal, and altitudinal shifts of selected nest sites between May and August; (d) the geographical barycentres of selected nest sites shifted northward by about 80 km per month from May (southernmost barycentre) to August (northernmost barycentre); (e) four main nesting areas (7886 km2 in total) occurred in central and southern Italy, whose utilization by the European Rollers differed between months but not between years; (f) the detected nesting areas corresponded mainly to non-irrigated arable lands (41.22% of their extent) and natural grasslands (12.80%). Our results are useful to support conservation strategies for the breeding sites of this farmland species, which is not a regular visitor to protected areas in Italy. [ABSTRACT FROM AUTHOR]

Studying geothermal areas can require significant resources, especially in areas densely covered by vegetation. This study integrates remote sensing techniques, including hyperspectral, thermal infrared and LiDAR with petrology and biogeochemistry of rock, soil and plant samples to develop a new shallow hydrogeological conceptual model of Waiotapu Geothermal Field, New Zealand. Previous studies present in detail each technique converging in this comprehensive research work. This geothermal area is densely covered by kanuka, an endemic shrub species to geothermal areas of New Zealand. Kanuka served as a key component in generating foliar element zonation maps for antimony and barium, utilising random forest classification validated by leave-one-out cross-validation. Thermal infrared data were employed to assess the behaviour of thermal anomalies through point pattern analysis. Results identified two intermingling processes within the single system: one in the north characterised by acid-sulphate alteration, bioavailability of barium to kanuka, and clustered surface thermal anomalies; another in the south where elements like silver, arsenic, and antimony are bioavailable to kanuka, accompanied by chloride-rich waters and denser yet non-clustered surface thermal anomalies. These cohesive methodology illustrates the efficacy of remote sensing techniques, showcasing the effectiveness of remote sensing in evaluating vegetated areas for geothermal exploration potential. [ABSTRACT FROM AUTHOR]

Analyzing the spatial patterns and associations among tree species may help to elucidate the importance of the ecological processes behind population formation and the mechanisms of species coexistence. To explore this mechanism, we mapped and studied eight dominant tree species in Korean pine broad-leaved mixed forests in a temperate forest region in Jilin Province, Northeast China. The spatial distribution patterns and spatial associations of the eight dominant tree species at different life history stages and spatial scales were analyzed using the second-order spatial point pattern method based on pair correlation functions. The results indicated the following: (1) The diameter class structure of all individuals in the plots showed an obvious "L" shape, indicating that the community was well regenerated and belonged to a growing stand. (2) The distribution of trees was affected by scale, size, and habitat heterogeneity. The degree of aggregation decreased as the diameter class increased. (3) Out of the 56 pairs of individuals, a small number showed a significant correlation, while most were negatively correlated. It is concluded that seed dispersal limitations, competitive ability, and topography and light requirements may influence the spatial distribution and association of species to maintain species coexistence and diversity in Korean pine broad-leaved mixed forests. The results can provide insights into the ecological processes of population assembly, the mechanisms of species coexistence, and the relationship between forest management and restoration. [ABSTRACT FROM AUTHOR]

Copyright of Ibis is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Sterility mosaic disease (SMD) is an emerging biotic risk to the cultivation of pigeonpea throughout the Indian subcontinent. The prevalence and distribution of SMD documented from diverse agro-climatic zones are still poorly known. In this investigation, we determined the spatial distribution of SMD covering major pigeonpea-growing states of Southern India namely, Karnataka, Telangana, Tamil Nadu, Maharashtra, and Andhra Pradesh by considering 2 years of exploratory survey data. Geospatial approaches were employed to determine the SMD incidence levels at unvisited locations using neighboring observations. The results indicated enormous variability in disease incidence ranging from 0 to 82.64% and 0 to 36.84%, respectively, in Kharif 2021–22 and 2022–23 in pigeonpea-growing states. Spatial autocorrelation analysis and Ripley’s K function established the presence of average clustering over sampling locations. Two main clusters were identified via agglomerative hierarchical cluster analysis. From the Local Moran’s I spatial autocorrelation (LISA) analysis, most of the districts were clustered together at (I25%) above the threshold level and must be closely monitored to restrict and minimize further disease spread across the studied areas in Southern India. In pigeonpea, this is the first study in India that attempted to understand the spatial pattern of SMD using geostatistical methodologies. The results of the study will aid in the design and dissemination of specific management practices and curb the further spread of SMD.

The southern Rocky Mountains in Colorado and northern New Mexico hosted intracontinental magmatism that developed during a tectonic transition from shortening (Laramide orogeny, ca. 75 to 40 Ma) through extension and rifting. We present a novel approach that uses stochastic weighted bootstrap simulations of a large set of new and historical geochronology data to better understand how regional anisotropies responsible for focusing magma emplacement evolved through time. This technique can detect subtle trends in directional distributions, including multi‐modal orientations, and can be filtered from regional to local scales. Our results indicate that magmatism followed first the northeast trend of the Colorado mineral belt between 75 and 40 Ma and deviated afterward. These deviations vary depending on the scale of the analysis. At the smallest scale we evaluated (<75 km), the orientation of magmatism from 45 to 30 Ma rotated counter‐clockwise before aligning with the north‐south trend of the modern Rio Grande rift. Larger, regional‐scale analyses indicate magma centers between 40 to 35 Ma and 25 to 20 Ma were dominantly oriented southwest‐northeast, whereas magmatism between 35 and 25 Ma had north‐south orientation. The large areal footprint of magmatism and shifting regional patterns suggest that ancient zones of weakness in the North American lithosphere accommodated magma flow at different moments in time, rather than controlled by a retreating interface of the Farallon and North American plates. Plain Language Summary: Continents can be deformed by compression or extension, and this deformation is commonly accompanied by magma ascension and crystallization in the upper crust. In many cases, magmas generated during a given time period are emplaced and crystallize aligned to their dominant stress fields, such as compression or extension. It is also possible for magmas to solidify aligned along ancient faults (and associated weaknesses), and therefore their spatial orientations may reflect tectonic events much older than magma crystallization. However, these "magma alignments" are not always obvious in maps, because there are many factors that go into dating magma systems, and magma flow is not always limited to one or a few obvious faults or related features. This study introduces a technique that treats sample locations for dated rocks as a point pattern and applies previously developed point pattern analyses to detect subtle alignments in magmatism. We apply this tool to a well‐known example in the western United States, where magmatism was continuous from tectonic compression through extension, to evaluate how magma alignments shifted through time. Key Points: Fry analyses reveal shifting regional orientations in Eocene‐Oligocene magmatism in the southern Rocky MountainsRegional‐scale magmatism followed ancient continental structures at various time periodsSmaller‐scale analyses indicate magma orientations rotated counter‐clockwise between 45 and 30 Ma [ABSTRACT FROM AUTHOR]

The purpose of this research on the spatial distribution pattern and correlation of populations is to predict the succession trend of populations and vegetation reconstruction which has a great significance meaning for further research investigation. Based on the geographic location and growth stage data of sand-fixing shrubs on fixed sand dunes in the Mu Us Sandy Land, the spatial distribution pattern and correlation of three dominant populations of Artemisia ordosica (AO), Hedysarum mongolicum (HM) and Caragana korshinskii (CK) were analyzed by using univariate and bivariate paired correlation functions and Monte Carlo fitting test. The results are as follows. (1) The three populations were aggregated at small scale and randomly or uniformly distributed at large scale; the populations of HM and CK showed aggregation distribution within the spatial scale of the study. (2) The interspecies relationships showed that AO and HM were negatively correlated at the scale less than 5 meters and basically uncorrelated at other scales. (3) The intraspecific correlation among different growth and development stages of the populations showed that the intraspecific relationships of the 3 populations were basically positively correlated at small scales. At large scales, there was a negative correlation between the intraspecifc relationships of AO, while there was no correlation between the intraspecifc relationships of HM and CK. [ABSTRACT FROM AUTHOR]

Copyright of Estudios Demográficos y Urbanos is the property of El Colegio de Mexico AC and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Xinjiang University (Natural Science Edition) is the property of Xinjiang University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract Plants interact locally in many ways and the processes involved (e.g. competition for resources, natural regeneration, mortality or subsequent succession) are complex. These processes give rise to characteristic spatial patterns that vary over time. The corresponding spatial data, that is the locations of individuals and their specific characteristics (e.g. trees of a certain species and their diameters), are known as point patterns, and their statistical analysis can be used to study the underlying processes and their changes due to environmental scenarios. A special application of point pattern analysis is their numerical reconstruction, which is classically used (a) to generate null models that can be contrasted with observed patterns and (b) to evaluate the information contained in observed data using various summary statistics. Sometimes, the reconstructed datasets are also used to initialise individual‐based or agent‐based plant models with realistic but artificially generated data in order to analyse or forecast the development of plant systems. Previous reconstruction methods of point patterns consider only one mark, or they consider several marks but neglect their correlations. We introduce a method that considers individual locations and two marks simultaneously (in our example information on tree species, and diameter at breast height). The method uses different summary statistics of the second‐order point pattern analysis, such as the pair correlation function and the mark correlation function. By successively modifying the reconstructed spatial pattern, the distance (also called energy), measured in terms of differences in the summary statistics between the generated pattern and the observed pattern, is minimised and a high statistical similarity is achieved. After testing the method on different datasets, the suitability of our method for reconstructing complex spatial forest stands, including the spatial relationships of all considered marks, is shown. The presented method is a powerful tool for generating point pattern data. With minor changes, it even enables the reconstruction of forest stands and plant systems larger than those used to collect the inventory data, and although we used two marks only to demonstrate the power of the method, it is easy to include more marks.

Producing mineral potential model using GIS software has been increased over the past years. In this study, predictive map consisted of argillic alteration, philic alteration, iron oxide alteration, reduction to pole of aeromagnetic data, lineaments, cu geochemistry anomaly, and principal component analysis (component 3) were prepared from Shahre Babak area. For training model, 37 mineralized points were used. Point pattern analysis was used as well for making non-deposit points and for training model, percepteron artificial neural network with two layers was applied. The training model was used to prepare the final mineral potential model. Based on the mentioned model, the main promising areas were identified to be in the northwest and eastern part of the studied area. Moreover, two areas in the northern and southwestern parts of this area were identified for additional studies. For evaluating the model, ROC curve was used. ROC curve shows high precision of the produced model. For more evaluating, sensitivity, specificity, positive predict value, negative predict value, accuracy, and kappa were computed. The coefficients confirm the high accuracy of the mineral potential model. Introduction Mineral prospectivity mapping (MPM) is a multicriteria decision-making task that aims to outline and prioritize prospective areas for exploring undiscovered mineral deposits of the type sought (Carranza and Laborte, 2015; Yousefi and Carranza, 2015; Sun et al., 2019). In the early stages of exploration, if there are enough known indices in an area, data-driven modeling is proper for mineral potential prospectivity. In this method, at first, all the characteristics of the known indices, of the type of mineralization sought, are collected and the relationship of these characteristics with evidence and spatial patterns is quantified. Then, points with similar characteristics are searched in those areas. Shahre Babak as the studied area is a part of Urumieh-Dokhtar zone. Urumieh-Dokhtar zone is proper for porphyry copper deposits. In this study, at the first stage, conceptual model was defined for porphyry copper modelling. Then, based on the model, some predictive layers were made ready and the data were imported to the trained model of artificial neural network in MATLAB 2021. At the next stage, final model was presented. Material and methods For constructing mineral potential model, a conceptual model was defined. Based on this model, some predictive layers consisted of argillic alteration, iron oxide alteration, phillic alteration, reduction to pole of airborne magnetic map, cu geochemistry anomaly, principal component geochemistry anomaly, intrusive units, lineaments structures, and digital elevation models were made in ARCGIS in raster formats. The pixel size of the raster files is 100m*100m. After fuzzification of raster files, these features were extracted to ASCII formats. Geology data (Intrusive body, faults, and dykes) Shahre Babak geology map in 1:250000 scale was used for extracting geological information. The intrusive bodies, faults, and dykes were extracted from Shahre Babak geology map. After extracting geological information, based on the Euclidean distance, the distance maps were made in ARCGIS. Then these maps became fuzzy. Airborne magnetic data The airborne magnetic data were surveyed by Atomic Energy Organization of Iran (AEOI) during 1977 and 1978. The flight lines distance and the sensor altitude were about 500 and 120 m, respectively. The reduction to pole filter was applied on total magnetic intensity map. Geochemistry data Geochemistry data in 1:250000 scale was used for geochemical interpretations. The cu geochemistry anomaly was drawn from the data. Principal component analysis method was applied on geochemical data. Component 3 was extracted from the data. Aster data Band ratio method was used for extracting the alterations. Iron oxide alteration, philic alteration, and argillic alteration were drawn in ENVI software in raster format. The iron oxide, argillic, and philic alteration files were imported to ARCGIS software and transformed to shapefile format. The distance maps were drawn based on the Euclidean distance. Then these maps became fuzzy. Digital Elevation Model Digital Elevation Model (DEM) was extracted from Aster data. The data became fuzzy. Training dataset For training model, 37 deposit points were selected. Point pattern analysis was used for non-deposit points. Based on this method, 37 non-deposit points were extracted of the Shahre Babak (the studied area). Each of the labels was located in a unique pixel. The features of these points were extracted from the predictive maps. Then these points were imported to artificial neural network (perceptron neural network with two layers). 70% of data were used for training model and 30% were used for testing model. Then the trained model was applied on the ASCII format. The resulting model was drawn using ARCGIS. Artificial neural network ANN is a modelling approach that simulates human brain system inspired by biological neural networks (Celik and Basarir, 2017). ANN can be effectively applied for pattern recognition in a wide variety of geoscience investigations. In this network, the neurons of different layers are interconnected to exchange information in a unidirectional way starting from the input layer through hidden layers to the output layer (Rodriguez-Galiano et al., 2015; Celik and Basarir, 2017). The flow of information is performed by assigning weights to the connections of different neurons (Rodriguez-Galiano et al., 2015). The back-propagation algorithm is employed to ensure the learning capability of ANN. This algorithm computes the error between the outputted value and real target value, then feeds back it to ANN in order to adjust the weights and biases (Celik and Basarir, 2017). Results Mineral potential map of studied area was produced by artificial neural network. Based on resulting model, the first-class promising areas were detected in north western and eastern parts of the studied area. Moreover, two areas in north and south western parts of studied area were identified. For evaluating the model, ROC curve was used. This curve shows model accuracy with high precision. For further evaluation, sensitivity, specificity, positive predictive value, negative predictive value, accuracy, and kappa were calculated with 94.7%, 91.8%, 92.3%, 94.4%, 93.3%, and 89%, respectively. These coefficients also confirm the high accuracy of the mineral potential model.

The European Roller Coracias garrulus has suffered greatly from breeding habitat loss due to the renovation of old farmhouses and rural buildings and changing agricultural practices that took place extensively across Europe in the last decades. As a consequence, this species experienced a significant decline, and local extinctions of breeding populations were recorded in several European countries. We investigated nest sites and nesting area selection by the Italian Roller population during the breeding period (May–August) between 2016 and 2018. We collected 711 points from field surveys and used four types of point pattern analysis to detect space-time patterns of nest site and nesting area selection. We found that: (a) the spatial distribution of selected (i.e., occupied) nest sites was significantly nonrandom (p < 0.01) for all years and months; (b) only 2.6% of the selected nest sites was located within parks or reserves; (c) there were significant (p < 0.01) latitudinal, longitudinal, and altitudinal shifts of selected nest sites between May and August; (d) the geographical barycentres of selected nest sites shifted northward by about 80 km per month from May (southernmost barycentre) to August (northernmost barycentre); (e) four main nesting areas (7886 km2 in total) occurred in central and southern Italy, whose utilization by the European Rollers differed between months but not between years; (f) the detected nesting areas corresponded mainly to non-irrigated arable lands (41.22% of their extent) and natural grasslands (12.80%). Our results are useful to support conservation strategies for the breeding sites of this farmland species, which is not a regular visitor to protected areas in Italy.

Plants interact locally in many ways and the processes involved (e.g. competition for resources, natural regeneration, mortality or subsequent succession) are complex. These processes give rise to characteristic spatial patterns that vary over time. The corresponding spatial data, that is the locations of individuals and their specific characteristics (e.g. trees of a certain species and their diameters), are known as point patterns, and their statistical analysis can be used to study the underlying processes and their changes due to environmental scenarios. A special application of point pattern analysis is their numerical reconstruction, which is classically used (a) to generate null models that can be contrasted with observed patterns and (b) to evaluate the information contained in observed data using various summary statistics. Sometimes, the reconstructed datasets are also used to initialise individual‐based or agent‐based plant models with realistic but artificially generated data in order to analyse or forecast the development of plant systems.Previous reconstruction methods of point patterns consider only one mark, or they consider several marks but neglect their correlations. We introduce a method that considers individual locations and two marks simultaneously (in our example information on tree species, and diameter at breast height). The method uses different summary statistics of the second‐order point pattern analysis, such as the pair correlation function and the mark correlation function. By successively modifying the reconstructed spatial pattern, the distance (also called energy), measured in terms of differences in the summary statistics between the generated pattern and the observed pattern, is minimised and a high statistical similarity is achieved.After testing the method on different datasets, the suitability of our method for reconstructing complex spatial forest stands, including the spatial relationships of all considered marks, is shown.The presented method is a powerful tool for generating point pattern data. With minor changes, it even enables the reconstruction of forest stands and plant systems larger than those used to collect the inventory data, and although we used two marks only to demonstrate the power of the method, it is easy to include more marks. [ABSTRACT FROM AUTHOR]

While the mitigating effects of trees on shallow landslide occurrence are well recognised, the impact of landslides on tree community structure and tree-tree interactions have received much less research attention. The structures of tree communities before and after landslides were compared in a 25-ha subtropical forest plot. Tree-tree interactions were examined by analysing the pre- and post-landslide spatial point patterns of large (DBH ≥ 20 cm) and small (1 cm ≤ DBH < 20 cm) tree cohorts. In landslide scarps, 35 (34%) of 104 large trees and 467 (13%) of 3.072 small trees survived. Large (L) and small (S) tree cohorts were paired together for spatial analyses, including pre-landslide (PL) (LPL-SPL), surviving (S) (LS-SS), and missing (M) large-small tree paired cohorts (LM-SM). We randomly selected trees from the pre-landslide tree cohorts to create two virtual paired cohorts, the L34%-S13% and L66%-S87% paired cohorts, whose population sizes were identical to the field-observed LS-SS and LM-SM paired cohorts respectively, but with random spatial patterns. Post-landslide survival rates of trees increased monotonically with DBH. Large trees dislodged by landslides scarcely reduced small-tree survival. Evidence for this included: (i) the distance from small trees to the nearest large trees of the LM-SM paired cohort did not differ significantly from that of the virtual L66%-S87% paired cohort; (ii) survival rates of small trees near LM individuals did not differ significantly from those without large trees nearby. Surviving large trees had positive effects on the survival of small trees, indicated by: (i) the distance from small trees to the nearest large trees of the LS-SS paired cohort was significantly lower than that of the virtual L34%-S13% paired cohort; (ii) SS individuals clumped around LS individuals, whereas the virtual L34%-S13% spatial relationship was random. Large trees prevent landslide dislodgement of adjacent small trees through the nurse-plant effect. Our study suggests that landslide damage in sloping forests may be reduced simply by constantly maintaining a critical density of large trees.

Analyzing the spatial patterns and associations among tree species may help to elucidate the importance of the ecological processes behind population formation and the mechanisms of species coexistence. To explore this mechanism, we mapped and studied eight dominant tree species in Korean pine broad-leaved mixed forests in a temperate forest region in Jilin Province, Northeast China. The spatial distribution patterns and spatial associations of the eight dominant tree species at different life history stages and spatial scales were analyzed using the second-order spatial point pattern method based on pair correlation functions. The results indicated the following: (1) The diameter class structure of all individuals in the plots showed an obvious “L” shape, indicating that the community was well regenerated and belonged to a growing stand. (2) The distribution of trees was affected by scale, size, and habitat heterogeneity. The degree of aggregation decreased as the diameter class increased. (3) Out of the 56 pairs of individuals, a small number showed a significant correlation, while most were negatively correlated. It is concluded that seed dispersal limitations, competitive ability, and topography and light requirements may influence the spatial distribution and association of species to maintain species coexistence and diversity in Korean pine broad-leaved mixed forests. The results can provide insights into the ecological processes of population assembly, the mechanisms of species coexistence, and the relationship between forest management and restoration.

The spatial pattern of coarse woody debris (CWD) within the surrounding forest changes continuously during the expansion of Moso bamboo (Phyllostachys edulis), which partly reflects the death process of trees within the community. In this paper, we sampled an area of 0.72 hm2 within the transition zone of Moso bamboo expansion, which included continuous secondary coniferous and broad-leaved mixed forest (SF), transitional forest (TF), and Moso bamboo forest (MF) in Wuxie National Forest Park, Zhejiang Province. The spatial pattern and spatial correlation of CWD with different diameter classes, different decay classes, and different types in the three forests were analyzed using Ripley's L function. The results showed that, with the expansion of Moso bamboo, the volume of CWD in TF was higher than those in SF and MF, and the total density of CWD in the three forests varied, with a decreasing order of MF > TF > SF. The spatial pattern of CWD in the three forests was aggregated on a relatively small scale, but the pattern changed from aggregated to random distribution with the increase in scale. Moreover, the CWD of Moso bamboo in TF showed aggregated distribution, and the aggregation intensity increased with the increase in scale. With the expansion of Moso bamboo, the spatial pattern and spatial correlation of CWD with different diameter classes, decay classes, and types have changed significantly in the three forests. Our study suggests that the expansion of Moso bamboo is increasingly strengthening the intraspecific and interspecific competition and accelerating the death of neighboring trees. [ABSTRACT FROM AUTHOR]

This paper introduces a new modeling framework for the statistical analysis of point patterns on a manifold M d , defined by a connected and compact two-point homogeneous space, including the special case of the sphere. The presented approach is based on temporal Cox processes driven by a L 2 (M d) -valued log-intensity. Different aggregation schemes on the manifold of the spatiotemporal point-referenced data are implemented in terms of the time-varying discrete Jacobi polynomial transform of the log-risk process. The n-dimensional microscale point pattern evolution in time at different manifold spatial scales is then characterized from such a transform. The simulation study undertaken illustrates the construction of spherical point process models displaying aggregation at low Legendre polynomial transform frequencies (large scale), while regularity is observed at high frequencies (small scale). K-function analysis supports these results under temporal short, intermediate and long range dependence of the log-risk process. [ABSTRACT FROM AUTHOR]

Ecological processes such as seed dispersal or plant–plant interactions and environmental constraints such as climate or soil heterogeneity are known to influence establishment, and thus the spatial patterns of plant communities and populations. In this study, we hypothesized that key functional traits such as the specific leaf area (SLA), reproductive ratio (reproductive/vegetative biomass), seed mass and maximum plant height would influence the spatial patterns of individual species in annual, gypsophilous plant communities, and that these effects would be modulated by both the soil surface structure (biocrust) and climate (precipitation) conditions.We mapped the spatial patterns of all plants found in six 1 × 1 m plots (more than 1000 individuals per plot) in both the seedling (autumn) and adult stages (spring) under two biocrust experimental conditions (intact vs. disturbed biocrust) during two consecutive years which were contrasted in term of precipitation (dry year and wet year). To assess the spatial patterns of seedlings and adults, we fitted four different spatial point pattern models (i.e. Poisson, inhomogeneous Poisson, Poisson cluster and inhomogeneous Poisson cluster processes) to each of the 242 populations of the 26 most abundant species that had more than 15 individuals per plot.Most seedling populations exhibited clustered spatial patterns that persisted in the adult stage, which suggests that short‐distance dispersal is an adaptive trait for soil specialists such as gypsophilous plants. One‐third of the populations fitted an inhomogeneous model best but the physical structure of the biocrust was not related to them. More importantly, we found a connection between the functional strategies of species and the spatial distribution of plants. In particular, during the dry year, irrespective of the biocrust conditions, species with a high SLA and high Rep/Veg mainly exhibited clustered spatial patterns, whereas low SLA and low Rep/Veg were associated with random distributions. Species with heavy and light seed masses had random and clustered patterns, respectively. In both the dry and wet years, species with lower maximum heights had clustered patterns, whereas taller species exhibited random patterns. In addition, species with heavier seeds and greater maximum heights had the largest cluster sizes.Our results confirm that the spatial patterns of seedlings and adult plants are significantly determined by the functional strategy of each species. Read the free Plain Language Summary for this article on the Journal blog. [ABSTRACT FROM AUTHOR]

While the mitigating effects of trees on shallow landslide occurrence are well recognised, the impact of landslides on tree community structure and treetree interactions have received much less research attention. The structures of tree communities before and after landslides were compared in a 25-ha subtropical forest plot. Tree-tree interactions were examined by analysing the pre- and post-landslide spatial point patterns of large (DBH = 20 cm) and small (1 cm = DBH < 20 cm) tree cohorts. In landslide scarps, 35 (34%) of 104 large trees and 467 (13%) of 3,072 small trees survived. Large (L) and small (S) tree cohorts were paired together for spatial analyses, including pre-landslide (PL) (LPL-SPL), surviving (S) (LS-SS), and miSSing (M) large-small tree paired cohorts (LMSM). We randomly selected trees from the pre-landslide tree cohorts to create two virtual paired cohorts, the L34%-S13% and L66%-S87% paired cohorts, whose population sizes were identical to the field-observed LS-SS and LM-SM paired cohorts respectively, but with random spatial patterns. Post-landslide survival rates of trees increased monotonically with DBH. Large trees dislodged by landslides scarcely reduced small-tree survival. Evidence for this included: (i) the distance from small trees to the nearest large trees of the LM-SM paired cohort did not differ significantly from that of the virtual L66%-S87% paired cohort; (ii) survival rates of small trees near LM individuaLS did not differ significantly from those without large trees nearby. Surviving large trees had positive effects on the survival of small trees, indicated by: (i) the distance from small trees to the nearest large trees of the LS-SS paired cohort was significantly lower than that of the virtual L34%-S13% paired cohort; (ii) SS individuaLS clumped around LS individuaLS, whereas the virtual L34%-S13% spatial relationship was random. Large trees prevent landslide dislodgement of adjacent small trees through the nurse-plant effect. Our study suggests that landslide damage in sloping forests may be reduced simply by constantly maintaining a critical density of large trees. [ABSTRACT FROM AUTHOR]

Producing mineral potential model using GIS software has been increased over the past years. In this study, predictive map consisted of argillic alteration, philic alteration, iron oxide alteration, reduction to pole of aeromagnetic data, lineaments, cu geochemistry anomaly, and principal component analysis (component 3) were prepared from Shahre Babak area. For training model, 37 mineralized points were used. Point pattern analysis was used as well for making non-deposit points and for training model, percepteron artificial neural network with two layers was applied. The training model was used to prepare the final mineral potential model. Based on the mentioned model, the main promising areas were identified to be in the northwest and eastern part of the studied area. Moreover, two areas in the northern and southwestern parts of this area were identified for additional studies. For evaluating the model, ROC curve was used. ROC curve shows high precision of the produced model. For more evaluating, sensitivity, specificity, positive predict value, negative predict value, accuracy, and kappa were computed. The coefficients confirm the high accuracy of the mineral potential model. [ABSTRACT FROM AUTHOR]

In the Northern Apennines, significant modifications to the characteristic historical features of landscapes have occurred since the 1950s as agriculture declined in importance and villages were progressively depopulated. Today, European policies are promoting the repopulation of these regions to help preserve the cultural identity of territories and reduce demographic pressure inurban areas. Such initiatives increase the need for cultural and natural landscape management to be better integrated using interdisciplinary approaches. Sustainable landscape management is a dynamic process involving the formulation of strategies to underpin the preservation of landscape heritage and foster local development based on the values and opportunities provided by landscapes themselves. This study uses landscape archaeology and spatial statistics to provide insights into which parts of the historic landscape retain the greatest time-depth and which parts reflect the more recent radical change, enabling an understanding which goes beyond the basic spatial relationships between landscape components.

The invasion of cancer cells into the surrounding tissues is one of the hallmarks of cancer. However, a precise quantitative understanding of the spatiotemporal patterns of cancer cell migration and invasion still remains elusive. A promising approach to investigate these patterns are 3D cell cultures, which provide more realistic models of cancer growth compared to conventional 2D monolayers. Quantifying the spatial distribution of cells in these 3D cultures yields great promise for understanding the spatiotemporal progression of cancer. In the present study, we present an image processing and segmentation pipeline for the detection of 3D GFP-fluorescent triple-negative breast cancer cell nuclei, and we perform quantitative analysis of the formed spatial patterns and their temporal evolution. The performance of the proposed pipeline was evaluated using experimental 3D cell culture data, and was found to be comparable to manual segmentation, outperforming four alternative automated methods. The spatiotemporal statistical analysis of the detected distributions of nuclei revealed transient, non-random spatial distributions that consisted of clustered patterns across a wide range of neighbourhood distances, as well as dispersion for larger distances. Overall, the implementation of the proposed framework revealed the spatial organization of cellular nuclei with improved accuracy, providing insights into the 3 dimensional inter-cellular organization and its progression through time. [ABSTRACT FROM AUTHOR]

The quantitative characterisation of the degree of randomness and aggregation of surface micro‐ and nanostructures is critical to evaluate their effects on targeted functionalities. To this end, the methods of point pattern analysis (PPA), largely used in ecology and medical imaging, seem to provide a powerful toolset. However, the application of these techniques requires the extraction of the point pattern of nanostructures from their microscope images. In this work, we address the issue of the impact that Scanning Electron Microscope (SEM) image processing may have on the fundamental metric of PPA, that is, the Nearest Neighbour Index (NNI). Using typical SEM images of polymer micro‐ and nanostructures taken from secondary and backscattered electrons, we report the effects of the (a) noise filtering and (b) binarisation threshold on the value of NNI as well as the impact of the image finite size effects. Based on these results, we draw conclusions for the safe choice of SEM settings to provide accurate measurement of nanostructure randomness through NNI estimation. [ABSTRACT FROM AUTHOR]

The spatial patterns and grave good associations at the cemetery of Vedrovice - Široká u Lesa (Czech Republic) are explored using computationally intensive formal methods. Resampling approaches to both intra-site spatial arrangement and the distribution of grave goods are explored in relation to layers of identity represented by different buried body attributes - sex, age, and geographical origin. Polished stone adzes, grinding tools and ceramic bowls are non-randomly distributed grave goods. The investigation of differences among the non-randomly distributed artifacts and attributes of burials indicates that the most significant are dichotomies between sexes, accentuated by both origin and age. Spatial analysis shows clear trends toward clustering between burials, and the emerging clusters are further explored for similarities in burial attributes. [ABSTRACT FROM AUTHOR]

The analysis of the spatial phylogenetic and phenotypic structure of plant communities can provide insight into the underlying processes and interactions governing their assembly, and how these may change during plant ontogeny. We used point pattern analysis to find out if saplings and adult plants are surrounded by phylogenetically and phenotypically more similar or more dissimilar neighbours than expected by chance, and whether these associations change from the sapling to the adult stage. To this end, we combined information on the phylogenetic structure and eight phenotypic traits of 15 woody plant species in two Mediterranean mixed forests of southeastern Spain. At the community level, we found that the sapling bank at both sites did not show phylogenetic or phenotypic spatial patterns, but adults showed phylogenetic clustering (i.e. heterospecific neighbours were more similar than expected). At the species level, we found frequently repulsive patterns in the sapling bank of less abundant species (i.e. heterospecific sapling or adult neighbours were more dissimilar than expected) in both, phylogenetic and phenotypic analyses. For the adult stage, we found phylogenetic attraction (i.e. more similar neighbours) in just one species and phenotypic clustering in four species. The processes driving the assembly of the communities of saplings and adults leave detectable signals in the spatial phylogenetic and phenotypic structure of our two forest communities. Our findings reinforce the existence of ontogenetic shifts in the mechanisms involved in plant community assembly. Facilitation between phylogenetically distant and phenotypically divergent species favours the recruitment of less abundant species. However, processes acting later in the ontogeny ameliorate the competition between close relatives and determine the spatial structure of adult plants. Nevertheless, the role of phenotype in shaping the interactions between adult plants was context‐ and trait‐dependent. The use of spatial point pattern analysis allowed a nuanced interpretation of the phylogenetic and phenotypic structures of plant communities. [ABSTRACT FROM AUTHOR]

Spatial analysis has revealed interesting patterns of woody plant encroachment that were not previously revealed by conventional analyses. We examined the spatial patterns of regeneration 23 years after a long-term tree removal experiment was performed near Magudu (northern KwaZulu-Natal, South Africa) by the Agricultural Research Council of South Africa. We performed spatial analyses of trees using nearest-neighbour analysis from 12 plots differing in degree of clearing as well as point-pattern analyses from a pair of complete-removal plots and a pair of complete controls (unmanipulated). After 23 years, the same dominant encroaching tree species (Dichrostachys cinerea) had re-invaded all the removal plots despite the fact that several additional encroaching species occurred in the vicinity. Nearest-neighbour analyses showed that there was a positive correlation between the sizes of the trees and their nearest-neighbour distances, indicating that competition was acting. The spatial distribution of subordinate to dominant species from point-pattern analysis ranged between random to aggregated. We also found that there appeared to be facilitation of young trees by adult trees in both the control plots and the completely cleared plots based on point-pattern analyses. This is likely an effect of the root-suckering habit of the predominant encroaching species, D. cinerea, and not the statistical definition of facilitation per se, borne out by the clustering of juvenile trees of this species. We conclude that facilitation of young trees is an artefact and that intraspecific competition is the over-riding effect on the distribution of trees. [ABSTRACT FROM AUTHOR]

The spatial patterns and grave good associations at the cemetery of Vedrovice – Široká u Lesa (Czech Republic) are explored using computationally intensive formal methods. Resampling approaches to both intra-site spatial arrangement and the distribution of grave goods are explored in relation to layers of identity represented by different buried body attributes – sex, age, and geographical origin. Polished stone adzes, grinding tools and ceramic bowls are non-randomly distributed grave goods. The investigation of differences among the non-randomly distributed artifacts and attributes of burials indicates that the most significant are dichotomies between sexes, accentuated by both origin and age. Spatial analysis shows clear trends toward clustering between burials, and the emerging clusters are further explored for similarities in burial attributes.

Aims: Patterns of spatial community dissimilarity have inspired a large body of theory in ecology and biogeography. Yet key gaps remain in our understanding of the local-scale ecological processes underlying species replacement and species nestedness, the two fundamental components of spatial community dissimilarity. Here, we examined the relative influence of dispersal limitation, habitat filtering and interspecific species interactions on local-scale patterns of the replacement and nestedness components in eight stem-mapped temperate forest mega-plots at different ontogenetic stages (large versus small trees). Location: Eight large (20–35 ha), fully mapped temperate forest plots in northern China and northern U.S.A. Time period: 2004–2016. Major taxa studied: Woody plants. Methods: We combined decomposition of community dissimilarity (based on the Ružička index) and spatial point-pattern analysis to compare the spatial (i.e., distance-dependent) replacement and nestedness components of each plot with that expected under five spatially explicit null models representing different hypotheses on community-assembly mechanisms. Results: Our analyses revealed complex results. In all eight forests, spatial community dissimilarity was best explained by species replacement among local tree assemblages and by a null model based on dispersal limitation. In contrast, spatial nestedness for large and small trees was best explained by random placement and habitat filtering, respectively, in addition to dispersal limitation. However, interspecific interactions did not contribute to local replacement and nestedness. Main conclusions: Species replacement is the predominant process accounting for spatial community dissimilarity in these temperate forests and caused largely by local-scale species clustering associated with dispersal limitation. Nestedness, in contrast, is less prevalent and primarily associated with larger variation in local species richness as caused by spatial richness gradients or ‘hotspots’ of local species richness. The novel use of replacement and nestedness measures in point pattern analysis is a promising approach to assess local-scale biodiversity patterns and to explore their causes.

Ectomycorrhizal fungi (EMF) form symbiotic relationship with the roots of host plants. EMF communities are composed of highly diverse species; however, how they are assembled has been a long-standing question. In this study, we investigated from a phylogenetic perspective how EMF communities assemble on Pinus densiflora seedlings at different spatial scales (i.e., seedling scale and root tip scale). P. densiflora seedlings were collected from different habitats (i.e., disturbed areas and mature forests), and their EMF communities were investigated by morphotype sequencing and next-generation sequencing (NGS). To infer assembly mechanisms, phylogenetic relatedness within the community (i.e., phylogenetic structure) was estimated and spatial distribution of EMF root tips was analyzed. The EMF communities on pine seedlings were largely different between the two habitats. Phylogenetically restricted lineages (Amphinema, /suillus–rhizopogon) were abundant in the disturbed areas, whereas species from diverse lineages were abundant in the mature forests (Russula, Sebacina, /tomentella–thelephora, etc.). In the disturbed areas, phylogenetically similar EMF species were aggregated at the seedling scale, suggesting that disturbance acts as a powerful abiotic filter. However, phylogenetically similar species were spatially segregated from each other at the root tip scale, indicating limiting similarity. In the mature forest seedlings, no distinct phylogenetic signals were detected at both seedling and root tip scale. Collectively, our results suggest that limiting similarity may be an important assembly mechanism at the root tip scale and that assembly mechanisms can vary across habitats and spatial scales. [ABSTRACT FROM AUTHOR]

Objectives: Police enforcement can effectively deter risky driving behaviors and reduce traffic fatalities, including speed-related fatalities. Unlike other areas of data-driven policing, spatial methods to improve road safety are not well-described. The objectives of this study were as follows: (1) determine if proximity to a prior roadway fatality increases the risk of a traffic citation being issued after adjusting for relevant roadway variables; (2) determine if this effect varies between rural and urban roads. Methods: The study region included a rural county and adjacent small city (City of Charlottesville, Albemarle County, Virginia). Fatality locations were obtained from the Fatality Analysis Reporting System (FARS) from 2008 to 2018. Police citation data were obtained from the State of Virginia for 2020. Data on fatalities and roadway features were used to create a model to predict traffic citation density. Traffic stop locations were analyzed as a point pattern on a linear network, assuming a Poisson process with varying intensity. The model adjusted for average traffic volume, distance to the nearest fatal crash along the road network, rural vs urban roadway, posted speed limit, and interstate vs non-interstate road. To account for over-dispersion, quasi-Poisson model was used. Results: There were 138 fatalities and 651 traffic citations during the time periods examined. After adjusting for other covariates, the expected number of citations/km was higher with increasing proximity to prior fatal crashes, RR = 1.34 (95% CI: 1.04, 1.72) per km. The effect of proximity did not vary significantly between urban and rural roads (p = 0.2707). However, citation intensity was significantly higher on urban roads vs. rural roads, RR = 2.65 (1.09, 6.45). Predicted citation intensity reflected anticipated enforcement clusters inside the city limits and on major county roads, suggesting satisfactory model fit. Conclusions: This study demonstrated a novel approach to quantify the impact of road fatalities on police activity, measured by traffic citations. Proximity to fatal crashes was found to affect police citation rates, and this effect is consistent between urban and rural areas. Future work will aim to identify areas of under enforcement based on proximity to fatal crashes and other roadway variables. [ABSTRACT FROM AUTHOR]

The spatial pattern of coarse woody debris (CWD) within the surrounding forest changes continuously during the expansion of Moso bamboo (Phyllostachys edulis), which partly reflects the death process of trees within the community. In this paper, we sampled an area of 0.72 hm2 within the transition zone of Moso bamboo expansion, which included continuous secondary coniferous and broad-leaved mixed forest (SF), transitional forest (TF), and Moso bamboo forest (MF) in Wuxie National Forest Park, Zhejiang Province. The spatial pattern and spatial correlation of CWD with different diameter classes, different decay classes, and different types in the three forests were analyzed using Ripley’s L function. The results showed that, with the expansion of Moso bamboo, the volume of CWD in TF was higher than those in SF and MF, and the total density of CWD in the three forests varied, with a decreasing order of MF > TF > SF. The spatial pattern of CWD in the three forests was aggregated on a relatively small scale, but the pattern changed from aggregated to random distribution with the increase in scale. Moreover, the CWD of Moso bamboo in TF showed aggregated distribution, and the aggregation intensity increased with the increase in scale. With the expansion of Moso bamboo, the spatial pattern and spatial correlation of CWD with different diameter classes, decay classes, and types have changed significantly in the three forests. Our study suggests that the expansion of Moso bamboo is increasingly strengthening the intraspecific and interspecific competition and accelerating the death of neighboring trees.

Context: Forest restoration plays an important role in global efforts to slow biodiversity loss and mitigate climate change. Vegetation in remnant forests can form striking patterns that relate to ecological processes, but restoration targets tend to overlook spatial pattern. While observations of intact reference ecosystems can help to inform restoration targets, field surveys are ill-equipped to map and quantify spatial pattern at a range of scales, and new approaches are needed. Objective: This review sought to explore practical options for creating landscape-scale forest restoration targets that embrace spatial pattern. Methods: We assessed how hierarchy theory, satellite remote sensing, landscape pattern analysis, drone-based remote sensing and spatial point pattern analysis could be applied to assess the spatial pattern of reference landscapes and inform forest restoration targets. Results: Hierarchy theory provides an intuitive framework for stratifying landscapes as nested hierarchies of sub-catchments, forest patches and stands of trees. Several publicly available tools can map patches within landscapes, and landscape pattern analysis can be applied to quantify the spatial pattern of these patches. Drones can collect point clouds and orthomosaics at the stand scale, a plethora of software can create maps of individual trees, and spatial point pattern analysis can be applied to quantify the spatial pattern of mapped trees. Conclusions: This review explored several practical options for producing landscape scale forest restoration targets that embrace spatial pattern. With the decade on ecosystem restoration underway, there is a pressing need to refine and operationalise these ideas. [ABSTRACT FROM AUTHOR]

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Landslides are some of the primary causes of forest disturbances, and their frequency is increasing globally due to climate changes. The recruitment and colonization processes of seedlings are pivotal in forest restoration after landslides. However, the effects of new biotic and abiotic environmental conditions generated by landslides on subsequent seedling performance remain poorly understood. Specifically, this study addresses the following questions: 1) how newly recruited seedlings form spatial distribution patterns, 2) how the spatial distance between conspecific and heterospecific seedlings affect the survival and growth of seedlings, 3) what roles rill erosion plays in post-landslide sites on the process of seedling colonization, and 4) whether the distance of seedlings from neighbors and from the rill boundary interactively shapes seedling performance. To address these questions, we conducted surveys at three artificial landslide sites in northern temperate forests. During the first growing season following landslides, the spatial positions of all newly recruited seedlings of two dominant species, as well as the boundaries of rill erosion, were marked. Then, the survival and growth rate of seedlings were monitored. In the post-landslide area, newly recruited seedlings primarily exhibited spatial clustering with conspecific neighbors and demonstrated significantly higher abundance around rills. At the scale of several centimeters, neighboring seedlings influenced the performance of focal seedlings. Proximity to conspecific and heterospecific neighbors significantly enhanced focal seedling survival and growth. The survival of seedlings near rills decreased, and this response varied by species. The proximity of a seedling to heterospecific neighbors attenuated the negative influences of being near rills. Our study indicates that there were conspecific and heterospecific facilitative effects on seedling survival at a post-landslide habitat, but the relative importance of the two effects could be dependent on the degree of rill erosion. We suggest that the effects of plant-plant facilitation, together with the fine-scale geomorphological heterogeneity, shape seedling performance, thereby influencing the forest restoration process. • This study was performed by novel artificial landslide experimental approaches. • Newly recruited seedlings after a landslide shown conspecific spatial clustering. • Seedlings performance improved when close to con- and heterospecific neighbors. • Seedlings survival near rills decreased, and this response varied by species. • Close to heterospecific neighbors attenuated disturbance of rill on survival. [ABSTRACT FROM AUTHOR]

The distribution of space among forest trees is linked to the availability of resources, among-tree competition, and hence forest dynamics. We studied partitioning of horizontal space among trees and related spatial structures in an old-growth Picea abies (L.) Karst -dominated forest in northeastern subarctic Fennoscandia, where Betula pubescens (Ehrh.) is an important co-dominant. Specifically, we asked (1) how does growing space occupied by trees vary by tree species and size in an old-growth forest with open canopy structure, and (2) at what scales does the variation in tree growing space occur? We mapped an 8.8 ha forest plot with 4,884 live trees. We used Voronoi polygons to quantify the horizontal space potentially available to each tree. We modeled the Voronoi polygon area as a function of tree size and species by using generalized additive models (GAM). We used i-to-any L-functions to study the scale-dependence of tree densities around focal trees, and mark correlation functions to study the relative sizes of trees close to each other. The GAM models showed that tree growing space increased non-linearly with tree size before saturating, and that overall growing space was larger for B. pubescens than for P. abies. Mean space occupied by trees roughly doubled from the smallest diameter class (0–5 cm) to the largest (>25 cm), from 13.7 to 26.7 m2. Depending on diameter class, shade-intolerant B. pubescens occupied on average 5–10 m2 more space than shade-tolerant P. abies. Trees close to each other were smaller than average. Size- and species-dependent differences in local tree densities accumulated mostly at the scale of a few meters but showed also broader-scale variation possibly related to edaphic variation within the study plot. The tree species- and size-related variation in the trees’ growing space suggests that among-tree competition, together with clustering of trees, shape the spatial assembly of the forest.

The academic debate on methodological approaches to the measurement of urban sprawl, particularly its most cited dimension, the density of residential settlement, is discussed in this article. The methodology of point pattern analysis, and its benefits in comparison to land-use data analysis, especially for researching the morphology of residential development, is examined. This empirical study was conducted in the hinterland of Prague and is based on point data from 2007, 2010 and 2016. The paper contributes to the scholarly discussion of suburbanisation in Central and Eastern European countries, including the morphology of suburban development. The role of scale is also emphasised, given our observation of two ambiguous means of development, namely spatial dispersion at the regional scale and increasing density at the local scale. The findings support claims regarding the crucial role of micro-scale research in understanding suburban form. The largest Czech suburb of Jesenice serves as a case study, where the morphology of built-up areas is analysed in the local context.

Question: Stand structural complexity is increasingly valued by forest managers to promote resilience, and quantitative spatial data from reference stands can help achieve these goals. We questioned how stand structural elements were spatially distributed across a Pinus palustris woodland. Specifically, we sought to quantify patterns of individual trees, tree clumps, openings, and canopy disturbance and examine spatial relationships between P. palustris saplings and P. palustris and Quercus spp. trees. Location: Fall Line Hills, Alabama, USA (32°56′12′′ N, 87°25′37′′ W). Methods: We recorded tree species, diameter, age, and location of all trees, and reconstructed canopy disturbance events across a 1-ha site in a P. palustris woodland. We used global point pattern analysis (g(r) function) and a local spatial classification method (individuals, clumps, and openings) to examine the distribution of stand structural elements and canopy disturbance. Results: Trees were generally clustered by taxa and diameter. Pinus palustris saplings exhibited no spatial relationship with P. palustris trees, but were clustered around Quercus trees at variable distances. Approximately half of the trees in the stand occurred in large clumps, and ca. 10% of trees occurred as individuals that did not touch crowns with neighbors. For three of the five disturbance periods, canopy disturbances were clustered in space at various distances. Conclusions: Spatial randomness and clustering of structural elements and canopy disturbance events were evident across the reference site. The spatial patterns of stand structure quantified here, in conjunction with data from other georeferenced studies, can be used to develop silvicultural guidelines to enhance structural complexity and promote resilience. [ABSTRACT FROM AUTHOR]

In many central districts in cities across the world, different types of stores form clusters resulting from the benefits of spatial agglomeration. To precisely analyze co-location relationships in a micro-scale space, this study develops a new statistical method by addressing the limitations of the ordinary cross K function method. The objectives of this paper are, first, to formulate an exact statistical method for analyzing co-location along streets in a central district constrained by a street network; second, to implement this statistical method in computational procedures. Third, this method is extended to the analysis of repulsive-location, i.e. phenomena of stores locating repulsively among different types of stores. Fourth, the paper shows a graph-theoretic diagram illustrating the spatial structure of stores in a central district consisting of bilateral, unilateral co-location and repulsive-location. Last, the proposed method is applied to eight different types of stores in a trendy district in Tokyo. The results show that the method is useful for revealing the spatial structure consisting of co-location and repulsive-location in the central district. [ABSTRACT FROM AUTHOR]

Abstract Tree spatial patterns in dry coniferous forests of the western United States, and analogous ecosystems globally, were historically aggregated, comprising a mixture of single trees and groups of trees. Modern forests, in contrast, are generally more homogeneous and overstocked than their historical counterparts. As these modern forests lack regular fire, pattern formation and maintenance is generally attributed to fire. Accordingly, fires in modern forests may not yield historically analogous patterns. However, direct observations on how selective tree mortality among pre‐existing forest structure shapes tree spatial patterns is limited. In this study, we (a) simulated fires in historical and contemporary counterpart plots in a Sierra Nevadan mixed‐conifer forest, (b) estimated tree mortality, and (c) examined tree spatial patterns of live trees before and after fire, and of fire‐killed trees. Tree mortality in the historical period was clustered and density‐dependent, because trees were aggregated and segregated by tree size before fire. Thus, fires maintained an aggregated distribution of tree groups. Tree mortality in the contemporary period was widespread, except for dispersed large trees, because most trees were a part of large, interconnected tree groups. Thus, postfire tree patterns were more uniform and devoid of moderately sized tree groups. Postfire tree patterns in the historical period, unlike the contemporary period, were within the historical range of variability identified for the western United States. This divergence suggests that decades of forest dynamics without significant disturbances have altered the historical means of pyric pattern formation. Our results suggest that ecological silvicultural treatments, such as forest restoration thinnings, which emulate qualities of historical forests may facilitate the reintroduction of fire as a means to reinforce forest structural heterogeneity.

The pervasive adoption of GPS-enabled sensors has lead to an explosion on the amount of geolocated data that captures a wide range of social interactions. Part of this data can be conceptualized as event data, characterized by a single point signal at a given location and time. Event data has been used for several purposes such as anomaly detection and land use extraction, among others. To unlock the potential offered by the granularity of this new sources of data it is necessary to develop new analytical tools stemming from the intersection of computational science and geographical analysis. Our approach is to link the geographical concept of hierarchical scale structures with density based clustering in databases with noise to establish a common framework for the detection of crowd activity hierarchical structures in geographic point data. Our contribution is threefold: first, we develop a tool to generate synthetic data according to a distribution commonly found on geographic event data sets; second, we propose an improvement of the available methods for automatic parameter selection in density-based spatial clustering of applications with noise (DBSCAN) algorithm that allows its iterative application to uncover hierarchical scale structures on event databases and, lastly, we propose a framework for the evaluation of different algorithms to extract hierarchical scale structures. Our results show that our approach is successful both as a general framework for the comparison of crowd activity detection algorithms and, in the case of our automatic DBSCAN parameter selection algorithm, as a novel approach to uncover hierarchical structures in geographic point data sets.

Using imagery available through Google Earth Pro and a point sampling methodology, changes in land cover for three U.S. cities were assessed, beginning during the Great Recession (2007) and extending through to 2018. The cities were Buffalo (New York), Denver (Colorado), and San Diego (California), and 11 land cover classes were used to characterize each. The novel contributions of this work, and the innovative contributions to science include an analysis of urban land cover change in the years since the Great Recession, and the use of point pattern analysis on sample points that changed from non-developed in 2007 to developed in 2018, to determine whether a spatial pattern of land cover class change was evident. An initial assumption was made that forest cover change in these three cities would be minimal since the Great Recession. In fact, forest cover decreased by less than 1% in all three cities with the greatest decrease in Buffalo. Over the post-recession study period, increases in the developed land classes were evident in all three cities at the expense of grasses, tree cover, and other land classes. Some clustering of new development activities was noticed at a relatively small scale in San Diego, while some dispersion of new developed activities was noticed at a larger scale in Denver. Among other factors, changes in population, economics, and land use are factors that influence land cover change with specific impacts on forest cover, and therefore in the provision of urban forest benefits to the environment and society.

Abstract:What factors are related to establishment dynamics following disturbance in late-successional versus second-growth tropical forests of the Pacific islands? Are those relationships robust to interannual fluctuations in establishment? In three sites juveniles were enumerated in 30 (5 × 5-m) subplots within 45 × 50-m tree plots in 2004 and 2005, 2.5 and 3.5 y following a Category-3 tropical cyclone (hurricane), in the Vava'u Island Group, Kingdom of Tonga. Recruitment was almost three-fold greater in the second sample period. Spatial pattern of focal species density was related to density of other juveniles, proximity of conspecific adults and canopy cover in the two years using Seemingly Unrelated Regression. Shade-tolerant species were the most abundant recruits in late-successional sites, establishing near-conspecific adults and other juveniles, while shade-establishing species were recruiting in gaps in second-growth forest where they also constitute the canopy trees. This pattern, observed in both years, reinforces divergent successional trajectories for second-growth and late-successional forest.

The species of Sinojackia xylocarpa in sample plot of 1 hm² in Laoshan Mountain of Nanjing was taken as the object of study. Using the pairing correlation function g(r) and three null models: complete spatial randomness(CSR), heterogeneous Poisson(HP)and antecedent condition(AC)to analyze the relation between the structure and spatial distribution of the population. The potential mechanism of this pattern formation could be deeply recognized from the view of spatial pattern. The results were as follows:(1)The number of individuals of small DBH class in natural S. xylocarpa population was dominant, and it was a growing population.(2)In the study of point pattern analysis, the S. xylocarpa population showed the aggregate distribution at scale 0-26 m, and uniformly distribution at scale 29-30 m based on the CSR; and the S. xylocarpa population showed the aggregate distribution at scale 0-23 m, and uniformly distribution at scale 27-30 m based on the HP. The spatial distribution pattern of S. xylocarpa showed a trend of changing from aggregate distribution to uniformly distribution.(3) In the study of interspecific correlation, the spatial relationship between S. xylocarpa and Celtis sinensis showed from the negative correlation to the positive correlation. The spatial relationship between Sinojackia xylocarpa and Pistacia chinensis and that between Sinojackia xylocarpa and Acer buergerianum were about the same. There were positive correlations between them on a large scale, and occasionally negative correlation and no correlation. The above results indicate that Sinojackia xylocarpa population is in a good regeneration state. The spatial distribution pattern of S. xylocarpa population is basically aggregate distribution, and is mainly affected by interspecific competition, diffusion restriction and density restriction. Based on population status, carrying out in situ protection and appropriately disturbing the community are a scientific and effective protection measure for the endangered species of S. xylocarpa. [ABSTRACT FROM AUTHOR]

In the Northern Apennines, significant modifications to the characteristic historical features of landscapes have occurred since the 1950s as agriculture declined in importance and villages were progressively depopulated. Today, European policies are promoting the repopulation of these regions to help preserve the cultural identity of territories and reduce demographic pressure inurban areas. Such initiatives increase the need for cultural and natural landscape management to be better integrated using interdisciplinary approaches. Sustainable landscape management is a dynamic process involving the formulation of strategies to underpin the preservation of landscape heritage and foster local development based on the values and opportunities provided by landscapes themselves. This study uses landscape archaeology and spatial statistics to provide insights into which parts of the historic landscape retain the greatest time-depth and which parts reflect the more recent radical change, enabling an understanding which goes beyond the basic spatial relationships between landscape components. [ABSTRACT FROM AUTHOR]