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Abstract The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) has great potential for development due to its advantages of the use of multiple beams, low energy consumption, high repetition frequency, and high measurement sensitivity. However, the weak photon signal emitted by the photon counting lidar is susceptible to the background noise caused by the sun and the atmosphere, which can seriously affect the processing and application of laser data. This paper proposes an improved DBSCAN clustering algorithm for denoising single photon laser point clouds in mountainous areas. Firstly, a grouping method based on elevation and distance statistics is proposed to reduce the influence of terrain undulations on denoising accuracy. Finally, an automatic radius search method is put forward to determine clustering radius of each group, automatically find the optimal radius, and improve the existing DBSCAN clustering method. The method proposed in this paper is compared with the classical DBSCAN algorithm. The results show that the proposed algorithm significantly improves denoising accuracy in mountainous areas and effectively filters out most background noise. Graphical Abstract

The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) has great potential for development due to its advantages of the use of multiple beams, low energy consumption, high repetition frequency, and high measurement sensitivity. However, the weak photon signal emitted by the photon counting lidar is susceptible to the background noise caused by the sun and the atmosphere, which can seriously affect the processing and application of laser data. This paper proposes an improved DBSCAN clustering algorithm for denoising single photon laser point clouds in mountainous areas. Firstly, a grouping method based on elevation and distance statistics is proposed to reduce the influence of terrain undulations on denoising accuracy. Finally, an automatic radius search method is put forward to determine clustering radius of each group, automatically find the optimal radius, and improve the existing DBSCAN clustering method. The method proposed in this paper is compared with the classical DBSCAN algorithm. The results show that the proposed algorithm significantly improves denoising accuracy in mountainous areas and effectively filters out most background noise. [ABSTRACT FROM AUTHOR]

To better understand complex human phenotypes, large-scale studies have increasingly collected multiple data modalities across domains such as imaging, mobile health, and physical activity. The properties of each data type often differ substantially and require either separate analyses or extensive processing to obtain comparable features for a combined analysis. Multimodal data fusion enables certain analyses on matrix-valued and vector-valued data, but it generally cannot integrate modalities of different dimensions and data structures. For a single data modality, multivariate distance matrix regression provides a distance-based framework for regression accommodating a wide range of data types. However, no distance-based method exists to handle multiple complementary types of data. We propose a novel distance-based regression model, which we refer to as Similarity-based Multimodal Regression (SiMMR), that enables simultaneous regression of multiple modalities through their distance profiles. We demonstrate through simulation, imaging studies, and longitudinal mobile health analyses that our proposed method can detect associations between clinical variables and multimodal data of differing properties and dimensionalities, even with modest sample sizes. We perform experiments to evaluate several different test statistics and provide recommendations for applying our method across a broad range of scenarios. [ABSTRACT FROM AUTHOR]

The field of neuroimaging dedicated to mapping connections in the brain is increasingly being recognized as key for understanding neurodevelopment and pathology. Networks of these connections are quantitatively represented using complex structures, including matrices, functions, and graphs, which require specialized statistical techniques for estimation and inference about developmental and disorder‐related changes. Unfortunately, classical statistical testing procedures are not well suited to high‐dimensional testing problems. In the context of global or regional tests for differences in neuroimaging data, traditional analysis of variance (ANOVA) is not directly applicable without first summarizing the data into univariate or low‐dimensional features, a process that might mask the salient features of high‐dimensional distributions. In this work, we consider a general framework for two‐sample testing of complex structures by studying generalized within‐group and between‐group variances based on distances between complex and potentially high‐dimensional observations. We derive an asymptotic approximation to the null distribution of the ANOVA test statistic, and conduct simulation studies with scalar and graph outcomes to study finite sample properties of the test. Finally, we apply our test to our motivating study of structural connectivity in autism spectrum disorder. [ABSTRACT FROM AUTHOR]

We analyze patterns of genetic variability of populations in the presence of a large seedbank with the help of a new coalescent structure called the seedbank coalescent. This ancestral process appears naturally as a scaling limit of the genealogy of large populations that sustain seedbanks, if the seedbank size and individual dormancy times are of the same order as those of the active population. Mutations appear as Poisson processes on the active lineages and potentially at reduced rate also on the dormant lineages. The presence of "dormant" lineages leads to qualitatively altered times to the most recent common ancestor and nonclassical patterns of genetic diversity. To illustrate this we provide a Wright--Fisher model with a seedbank component and mutation, motivated from recent models of microbial dormancy, whose genealogy can be described by the seedbank coalescent. Based on our coalescent model, we derive recursions for the expectation and variance of the time to most recent common ancestor, number of segregating sites, pairwise differences, and singletons. Estimates (obtained by simulations) of the distributions of commonly employed distance statistics, in the presence and absence of a seedbank, are compared. The effect of a seedbank on the expected site-frequency spectrum is also investigated using simulations. Our results indicate that the presence of a large seedbank considerably alters the distribution of some distance statistics, as well as the site-frequency spectrum. Thus, one should be able to detect from genetic data the presence of a large seedbank in natural populations. [ABSTRACT FROM AUTHOR]

Abstract: Uneven use of grasslands and savannas by livestock has a significant impact on ecosystem productivity, biodiversity, and function. In studies of livestock distribution, global positioning systems (GPS) collars are frequently used and the rapid rate of technological improvement has brought new opportunities to collect extremely large amounts of very accurate spatial information. However, these advances also pose statistical challenges associated with the analysis of large, temporally correlated, datasets. Our main goal was to find the optimal sampling time intervals for GPS collar schedules when studying livestock distribution in semi-arid ecosystems. The schedule must provide maximum spatio-temporal information while avoiding problems of autocorrelation of sequential locations to provide a methodology that is both practical and statistically valid. We used GPS collar data collected in the Southwestern region of the United States. In each study cattle were tracked and data were recorded every 5min. Location information from the 5-min GPS fixes were subsampled into 10, 20, 30, 60, 90, 120, 150, 180, 240, 300, 360, and 420-min regular intervals. We calculated the Euclidean distance between pairs of successive locations then conducted correlation analyses to determine the degree of similarity between successive traveled distances. We then selected two correlated and two non-correlated time-interval datasets to compare estimates of kernel home range and minimum convex polygon areas. Successive Euclidean distances between GPS locations were significantly correlated when time intervals were <120min. The calculated distance traveled was significantly reduced as time intervals between successive locations increased. Kernel home range values were smaller in correlated than in non-correlated datasets yet minimum convex polygon values were greater in correlated data than in non-correlated data sets. Our study shows the importance of considering different livestock sampling time intervals using GPS to achieve accurate and meaningful results on animal distributions especially in semi-arid ecosystems. Circumstances in which researchers may elect to use short-time interval autocorrelated data sets are also discussed. [Copyright &y& Elsevier]

Summary: We applied a novel analysis based on distance statistics to investigate how patterns of habitat heterogeneity affected the distribution of representative ground and rove beetle species (Coleoptera: Carabidae, Staphylinidae), sampled at an upland site of varied landform, soil and vegetation structure. The structural heterogeneity of the Nardus stricta-dominated grassland was further modified by varying grazing intensity with sheep, or sheep and cattle. We collected pitfall trap data from 120 sample points across the study area. Ground and rove beetle species were selected to represent the major trends in the species-trap abundance data, determined by the extent of their correlation with the main components of a factor analysis (Principal Components Analysis). The novel statistical analytical method, calculation of the Getis and Ord distance statistic, G, was applied to the distribution data of each selected species of ground and rove beetle. The distance statistic was calculated for the smallest distance to ensure that each sample point had at least one neighbour (73 m) and this distance was used to detect local spatial association and to explore the location and spatial scale of aggregations of each beetle species over the hillside. Clusters of high and low G (z) values were mapped to indicate the species'' functional heterogeneity compared with habitat heterogeneity determined by landform, soils or grazing management. The small number of large aggregations indicated the sensitivity of certain species to patterns of landform (Calathus melanocephalus and Pterostichus adstrictus). More aggregations of smaller size, coinciding with the pattern of particular grazing regimes indicated species sensitive to grazing intensity and species of mammalian herbivore (Carabus problematicus and Olophrum piceum). The aggregations of Othius angustus and Philonthus decorus related to landform, and suggested these species may have been directly responding to soil moisture and patterns of trampling by grazers. The method distinguished between those species that are sensitive to land use change and those that may be affected more by climate change. Wir nutzten eine neue auf Entfernungsstatistik basierende Analysemethode zur Ermittlung wie diese Muster der Habitatheterogenität die Verteilung von vertretenen Laufkäfer- und Kurzflüglerarten (Coleoptera: Carabidae, Staphylinidae) beeinflussen, welche an Hochlandstandorten verschiedener Physiotope, Boden- und Vegetationsgefüge gefangen wurden. Die strukturelle Heterogenität des hauptsächlich Nardus stricta dominierten Graslandes wurde außerdem durch verschiedene Beweidungsintensitäten mit Schafen oder Schafen und Kühen verändert. Es wurden Barber-Fallendaten von insgesamt 120 Probepunkten im Untersuchungsgebiet gesammelt. Laufkäfer- und Kurzflüglerarten, ermittelt durch das Ausmaß ihrer Übereinstimmung mit den Hauptkomponenten einer Faktoranalyse (Principal Componends Analysis), wurden ausgewählt, um die Haupttrends der erhaltenen Artendichtedaten darzustellen. Die neue statistische Analysemethode, Berechnung der Getis- und Ord- Entfernungsstatistik, G, wurde auf die Verteilungsdaten jeder der ausgesuchten Laufkäfer- und Kurzflüglerarten angewandt. Die Entfernungsstatistik wurde für die geringste Entfernung berechnet, um sicher zu stellen, dass jeder Probepunkt wenigstens einen Nachbarn (73 m) hatte. Diese Entfernung wurde genutzt, um die lokale räumliche Vergesellschaftung zu ermitteln und Standorte und das räumliche Ausmaß der Ansammlungen jeder Käferart am Hang zu untersuchen. Cluster von hohen und niedrigen G (z) Werten wurden kartiert, um der Arten funktionelle Heterogenität, verglichen mit der durch den Physiotop, Boden oder der Weidewirtschaft bestimmten Habitatheterogenität, zu zeigen. Die geringe Anzahl von großen Ansammlungen wies auf die Empfindlichkeit einzelner Arten auf Muster im Physiotop (Calathus melanocephalus und Pterostichus adstrictus) hin. Häufigere Ansammlungen kleinerer Größe, welche mit dem Muster einzelner Beweidungsregime zusammenpassten, wiesen auf Arten hin, die empfindlich auf Beweidungsintensität und Art der Weidetiere reagierten (Carabus problematicus und Olophrum piceum). Othius angustus und Philonthus decorus zeigten Ansammlungen, welche eine Wechselwirkung zwischen dem Muster der Beweidungsintensität und den abiotischen Faktoren vermuten ließen; diese Käfer waren möglicherweise empfindlich gegenüber den Trittstörungen der Weidetiere und den Veränderungen des Bodenfeuchtigkeitsgehaltes. Die Methode unterschied zwischen empfindlich auf Veränderungen in der Landnutzung reagieren Arten und solchen, welche mehr durch Klimaeinflüsse beeinträchtigt werden. [Copyright &y& Elsevier]