Forest dynamics arise from the interplay of environmental drivers and disturbances with the demographic processes of recruitment, growth, and mortality, subsequently driving biomass and species composition. However, forest disturbances and subsequent recovery are shifting with global changes in climate and land use, altering these dynamics. Changes in environmental drivers, land use, and disturbance regimes are forcing forests toward younger, shorter stands. Rising carbon dioxide, acclimation, adaptation, and migration can influence these impacts. Recent developments in Earth system models support increasingly realistic simulations of vegetation dynamics. In parallel, emerging remote sensing datasets promise qualitatively new and more abundant data on the underlying processes and consequences for vegetation structure. When combined, these advances hold promise for improving the scientific understanding of changes in vegetation demographics and disturbances.
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http://dx.doi.org/10.1126/science.aaz9463 | DOI Listing |
Mol Biol Rep
January 2025
Zoological Survey of India, Kolkata, 700053, India.
Background: The endangered Kashmir musk deer (Moschus cupreus), native to high-altitude Himalayas, is an ecological significant and endangered ungulate, threatened by habitat loss and poaching for musk pod distributed in western Himalayan ranges of India, Nepal and Afghanistan. Despite its critical conservation status and ecological importance in regulating vegetation dynamics, knowledge gaps persist regarding its population structure and genetic diversity, hindering effective management strategies.
Methods And Results: We aimed to understand the population genetics of Kashmir musk deer in north-western Himalayas using two mitochondrial DNA (mtDNA) regions and 11 microsatellite loci.
Glob Chang Biol
January 2025
Department of Renewable Resources, University of Alberta, Edmonton, Canada.
Soil microorganisms transform plant-derived C (carbon) into particulate organic C (POC) and mineral-associated C (MAOC) pools. While microbial carbon use efficiency (CUE) is widely recognized in current biogeochemical models as a key predictor of soil organic carbon (SOC) storage, large-scale empirical evidence is limited. In this study, we proposed and experimentally tested two predictors of POC and MAOC pool formation: microbial necromass (using amino sugars as a proxy) and CUE (by O-HO approach).
View Article and Find Full Text PDFBackground: Kyasanur forest disease virus (KFDV) is a tick-borne flavivirus causing debilitating and potentially fatal disease in people in the Western Ghats region of India. The transmission cycle is complex, involving multiple vector and host species, but there are significant gaps in ecological knowledge. Empirical data on pathogen-vector-host interactions and incrimination have not been updated since the last century, despite significant local changes in land use and the expansion of KFD to new areas.
View Article and Find Full Text PDFBMC Microbiol
January 2025
Center for Nuclear Energy in Agriculture, University of São Paulo, Piracicaba, SP, Brazil.
Amazonian Dark Earths (ADEs) are fertile soils from the Amazon rainforest that harbor microorganisms with biotechnological potential. This study aimed to investigate the individual and potential synergistic effects of a 2% portion of ADEs and Urochloa brizantha cv. Marandu roots (Brazil's most common grass species used for pastures) on soil prokaryotic communities and overall soil attributes in degraded soil.
View Article and Find Full Text PDFJ Clin Neurosci
January 2025
Department of Orthopedics, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China; Department of Spine Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China. Electronic address:
Background: Cervical spondylotic myelopathy (CSM) is a debilitating condition that affects the cervical spine, leading to neurological impairments. While the neural mechanisms underlying CSM remain poorly understood, changes in brain network connectivity, particularly within the context of static and dynamic functional network connectivity (sFNC and dFNC), may provide valuable insights into disease pathophysiology. This study investigates brain-wide connectivity alterations in CSM patients using both sFNC and dFNC, combined with machine learning approaches, to explore their potential as biomarkers for disease classification and progression.
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