LiDAR patch metrics for object-based clustering of forest types in a tropical rainforest

Tropical rainforests support a large proportion of the Earth’s plant and animal species within a restricted global distribution, and play an important role in regulating the Earth’s climate. However, the existing knowledge of forest types or habitats is relatively poor and there are large uncertainties in the quantification of carbon stock in these forests. Airborne Laser Scanning, using LiDAR, has advantages over other remote sensing techniques for describing the three-dimensional structure of forests. With respect to the habitat requirements of different species, forest structure can be defined by canopy height, canopy cover and vertical arrangement of biomass. In this study, forest patches were identified based on classification and hierarchical merging of a LiDAR-derived Canopy Height Model in a tropical rainforest in Sumatra, Indonesia. Attributes of the identified patches were used as inputs for k-medoids clustering. The clusters were then analysed by comparing them with identified forest types in the field. There was a significant association between the clusters and the forest types identified in the field, to which arang forests and mixed agro-forests contributed the most. The topographic attributes of the clusters were analysed to determine whether the structural classes, and potentially forest types, were related to topography. The tallest clusters occurred at significantly higher elevations (> 850 m) and steeper slopes (> 26°) than the other clusters. These are likely to be remnants of undisturbed primary forests and are important for conservation and habitat studies and for carbon stock estimation. This study showed that LiDAR data can be used to map tropical forest types based on structure, but that structural similarities between patches of different floristic composition or human use histories can limit habitat separability as determined in the field

Analisis Kemampuan Kognitif Siswa Kelas X SMA Negeri 3 Rambah Hilir Kabupaten Rokan Hulu Pada Mata Pelajaran Fisika Setelah Penerapan Model Pembelajaran Advance Organizer Berbasis Mind Map

This study aims to determine the ability and cognitive criteria for students in physics learning using model Advance Organizer base on Mind Map. This research method is a quasi-experimental research conducted at SMA Negeri 3 Rambah hilir class X MIA . The sampling technique that is done is saturated sampling. After learning model Advance base on Organizer Mind Map is applied, the cognitive abilities of students has increased. It is shown from the results of student learning is the lowest gain value is 0.667 (medium criterion) and the highest gain value of 0.8 (high criteria) . While the criteria of students\u27 cognitive abilities of the average value in the classical style in the entire series is 0.712 (high criteria

Us adolescent Rest-Activity Patterns: insights From Functional Principal Component analysis (Nhanes 2011-2014)

BACKGROUND: Suboptimal rest-activity patterns in adolescence are associated with worse health outcomes in adulthood. Understanding sociodemographic factors associated with rest-activity rhythms may help identify subgroups who may benefit from interventions. This study aimed to investigate the association of rest-activity rhythm with demographic and socioeconomic characteristics in adolescents. METHODS: Using cross-sectional data from the nationally representative National Health and Nutrition Examination Survey (NHANES) 2011-2014 adolescents (N = 1814), this study derived rest-activity profiles from 7-day 24-hour accelerometer data using functional principal component analysis. Multiple linear regression was used to assess the association between participant characteristics and rest-activity profiles. Weekday and weekend specific analyses were performed in addition to the overall analysis. RESULTS: Four rest-activity rhythm profiles were identified, which explained a total of 82.7% of variance in the study sample, including (1) High amplitude profile; (2) Early activity window profile; (3) Early activity peak profile; and (4) Prolonged activity/reduced rest window profile. The rest-activity profiles were associated with subgroups of age, sex, race/ethnicity, and household income. On average, older age was associated with a lower value for the high amplitude and early activity window profiles, but a higher value for the early activity peak and prolonged activity/reduced rest window profiles. Compared to boys, girls had a higher value for the prolonged activity/reduced rest window profiles. When compared to Non-Hispanic White adolescents, Asian showed a lower value for the high amplitude profile, Mexican American group showed a higher value for the early activity window profile, and the Non-Hispanic Black group showed a higher value for the prolonged activity/reduced rest window profiles. Adolescents reported the lowest household income had the lowest average value for the early activity window profile. CONCLUSIONS: This study characterized main rest-activity profiles among the US adolescents, and demonstrated that demographic and socioeconomic status factors may shape rest-activity behaviors in this population

Locating emergent trees in a tropical rainforest using data from an Unmanned Aerial Vehicle (UAV)

Emergent trees, which are taller than surrounding trees with exposed crowns, provide crucial services to several rainforest species especially to endangered primates such as gibbons and siamangs (Hylobatidae). Hylobatids show a preference for emergent trees as sleeping sites and for vocal displays, however, they are under threat from both habitat modifications and the impacts of climate change. Traditional plot-based ground surveys have limitations in detecting and mapping emergent trees across a landscape, especially in dense tropical forests. In this study, a method is developed to detect emergent trees in a tropical rainforest in Sumatra, Indonesia, using a photogrammetric point cloud derived from RGB images collected using an Unmanned Aerial Vehicle (UAV). If a treetop, identified as a local maximum in a Digital Surface Model generated from the point cloud, was higher than the surrounding treetops (Trees_EM), and its crown was exposed above its neighbours (Trees_SL; assessed using slope and circularity measures), it was identified as an emergent tree, which might therefore be selected preferentially as a sleeping tree by hylobatids. A total of 54 out of 63 trees were classified as emergent by the developed algorithm and in the field. The algorithm is based on relative height rather than canopy height (due to a lack of terrain data in photogrammetric point clouds in a rainforest environment), which makes it equally applicable to photogrammetric and airborne laser scanning point cloud data

Sleeping trees and sleep-related behaviours of the siamang (Symphalangus syndactylus) in a tropical lowland rainforest, Sumatra, Indonesia

Sleeping tree selection and related behaviours of a family group and a solitary female siamang (Symphalangus syndactylus) were investigated over a 5-month period in northern Sumatra, Indonesia. We performed all day follows, sleeping tree surveys and forest plot enumerations in the field. We tested whether: (1) physical characteristics of sleeping trees and the surrounding trees, together with siamang behaviours, supported selection based on predation risk and access requirements; (2) the preferences of a solitary siamang were similar to those of a family group; and (3) sleeping site locations within home ranges were indicative of home range defence, scramble competition with other groups or other species, or food requirements. Our data showed that (1) sleeping trees were tall, emergent trees with some, albeit low, connectivity to the neighbouring canopy, and that they were surrounded by other tall trees. Siamangs showed early entry into and departure from sleeping trees, and slept at the ends of branches. These results indicate that the siamangs’ choice of sleeping trees and related behaviours were strongly driven by predator avoidance. The observed regular reuse of sleeping sites, however, did not support anti-predation theory. (2) The solitary female displayed selection criteria for sleeping trees that were similar to those of the family group, but she slept more frequently in smaller trees than the latter. (3) Siamangs selected sleeping trees to avoid neighbouring groups, monopolise resources (competition), and to be near their last feeding tree. Our findings indicate selectivity in the siamangs’ use of sleeping trees, with only a few trees in the study site being used for this purpose. Any reduction in the availability of such trees might make otherwise suitable habitat unsuitable for these highly arboreal small apes

Identification of novel risk loci, causal insights, and heritable risk for Parkinson's disease: a meta-analysis of genome-wide association studies

Background Genome-wide association studies (GWAS) in Parkinson's disease have increased the scope of biological knowledge about the disease over the past decade. We aimed to use the largest aggregate of GWAS data to identify novel risk loci and gain further insight into the causes of Parkinson's disease. Methods We did a meta-analysis of 17 datasets from Parkinson's disease GWAS available from European ancestry samples to nominate novel loci for disease risk. These datasets incorporated all available data. We then used these data to estimate heritable risk and develop predictive models of this heritability. We also used large gene expression and methylation resources to examine possible functional consequences as well as tissue, cell type, and biological pathway enrichments for the identified risk factors. Additionally, we examined shared genetic risk between Parkinson's disease and other phenotypes of interest via genetic correlations followed by Mendelian randomisation. Findings Between Oct 1, 2017, and Aug 9, 2018, we analysed 7·8 million single nucleotide polymorphisms in 37 688 cases, 18 618 UK Biobank proxy-cases (ie, individuals who do not have Parkinson's disease but have a first degree relative that does), and 1·4 million controls. We identified 90 independent genome-wide significant risk signals across 78 genomic regions, including 38 novel independent risk signals in 37 loci. These 90 variants explained 16–36% of the heritable risk of Parkinson's disease depending on prevalence. Integrating methylation and expression data within a Mendelian randomisation framework identified putatively associated genes at 70 risk signals underlying GWAS loci for follow-up functional studies. Tissue-specific expression enrichment analyses suggested Parkinson's disease loci were heavily brain-enriched, with specific neuronal cell types being implicated from single cell data. We found significant genetic correlations with brain volumes (false discovery rate-adjusted p=0·0035 for intracranial volume, p=0·024 for putamen volume), smoking status (p=0·024), and educational attainment (p=0·038). Mendelian randomisation between cognitive performance and Parkinson's disease risk showed a robust association (p=8·00 × 10−7). Interpretation These data provide the most comprehensive survey of genetic risk within Parkinson's disease to date, to the best of our knowledge, by revealing many additional Parkinson's disease risk loci, providing a biological context for these risk factors, and showing that a considerable genetic component of this disease remains unidentified. These associations derived from European ancestry datasets will need to be followed-up with more diverse data. Funding The National Institute on Aging at the National Institutes of Health (USA), The Michael J Fox Foundation, and The Parkinson's Foundation (see appendix for full list of funding sources)