Rates and spatial patterns of tree mortality are predicted to change during forest structural development. In young forests, mortality should be primarily density dependent due to competition for light, leading to an increasingly spatially uniform pattern of surviving trees. In contrast, mortality in old-growth forests should be primarily caused by contagious and spatially autocorrelated agents (e.g., insects, wind), causing spatial aggregation of surviving trees to increase through time. We tested these predictions by contrasting a three-decade record of tree mortality from replicated mapped permanent plots located in young (< 60-year-old) and old-growth (> 300-year-old) Abies amabilis forests. Trees in young forests died at a rate of 4.42% per year, whereas trees in old-growth forests died at 0.60% per year. Tree mortality in young forests was significantly aggregated, strongly density dependent, and caused live tree patterns to become more uniform through time. Mortality in old-growth forests was spatially aggregated, but was density independent and did not change the spatial pattern of surviving trees. These results extend current theory by demonstrating that density-dependent competitive mortality leading to increasingly uniform tree spacing in young forests ultimately transitions late in succession to a more diverse tree mortality regime that maintains spatial heterogeneity through time.
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http://dx.doi.org/10.1890/15-0628.1 | DOI Listing |
Mil Med
January 2025
Division of Gynecologic Oncology, Department of Gynecologic Surgery & Obstetrics, Tripler Army Medical Center, Honolulu, HI 96859, USA.
Endometrial cancer is the most prevalent gynecologic cancer in the United States and has rising incidence and mortality. Endometrial intraepithelial neoplasia or atypical endometrial hyperplasia (EIN-AEH), a precancerous neoplasm, is surgically managed with hysterectomy in patients who have completed childbearing because of risk of progression to cancer. Concurrent endometrial carcinoma (EC) is also present on hysterectomy specimens in up to 50% of cases.
View Article and Find Full Text PDFBMC Endocr Disord
January 2025
Department of Public Health Studies, Elon University, Elon, NC, USA.
Background: The increasing prevalence of type 2 diabetes mellitus (T2DM) in lower and middle - income countries call for preventive public health interventions. Studies from Africa including those from Ghana, consistently reveal high T2DM-related mortality rates. While previous research in the Ho municipality has primarily examined risk factors, comorbidity, and quality of life of T2DM patients, this study specifically investigated mortality predictors among these patients.
View Article and Find Full Text PDFProc Natl Acad Sci U S A
January 2025
Department of Earth Sciences, Montana State University, Bozeman, MT 59717.
Climate-driven changes in high-elevation forest distribution and reductions in snow and ice cover have major implications for ecosystems and global water security. In the Greater Yellowstone Ecosystem of the Rocky Mountains (United States), recent melting of a high-elevation (3,091 m asl) ice patch exposed a mature stand of whitebark pine () trees, located ~180 m in elevation above modern treeline, that date to the mid-Holocene (c. 5,950 to 5,440 cal y BP).
View Article and Find Full Text PDFMicrobiol Spectr
January 2025
Laboratory of Microbiology and Immunology, School of Basic Medical Science, Inner Mongolia Medical University, Hohhot, China.
Colorectal cancer (CRC) is one of the malignant tumors globally, with high morbidity and mortality rates. The mainstay treatment of CRC includes surgery, radiotherapy, and chemotherapy. However, these treatments are associated with a high recurrence rate, poor prognosis, and highly toxic side effects.
View Article and Find Full Text PDFCrit Care
January 2025
Department of Pediatric, West China Second University Hospital, Sichuan University, Chengdu, China.
Background: Patients supported by extracorporeal membrane oxygenation (ECMO) are at a high risk of brain injury, contributing to significant morbidity and mortality. This study aimed to employ machine learning (ML) techniques to predict brain injury in pediatric patients ECMO and identify key variables for future research.
Methods: Data from pediatric patients undergoing ECMO were collected from the Chinese Society of Extracorporeal Life Support (CSECLS) registry database and local hospitals.
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