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Demography and Localized Reservoirs of Diversity Underlie Global Divergence in the Giant Kelp Macrocystis pyriferaNARA Subscribed
Accurate assessments of genetic diversity are crucial for effective management of natural resources. Numerous evolutionary mechanisms and genetic constraints can shape lineage divergence, challenging our quantification of biodiversity. Here, using whole genome resequencing of globally distributed giant kelp, Macrocystis pyrifera (Order: Laminariales), we show clear separation of genetic groups across the Pacific based on 99 ge...
Unprecedented Burning in Tropical Peatlands During the 20th Century Compared to the Previous Two MillenniaNARA Subscribed
Tropical peatland wildfire incidence has risen in recent decades, driven by drainage for land use and intensified by severe droughts with global climate change. These disturbances have altered vegetation structure, disrupted ecosystem functioning, and increased carbon emissions, particularly in Southeast Asia. However, the long‐term history and characteristics of wildfires in tropical peatlands remain largely unknown. Here, we...
Pan‐Arctic Peatlands Have Expanded During Recent WarmingNARA Subscribed
The fate of carbon stored in Arctic peatlands remains uncertain because of the complex nature of the effects of climate change on permafrost and peatland carbon cycling. Expansion and/or shrinkage of Arctic peatlands under climate change also remain unknown due to lack of ground data and difficulties detecting changes in the extent of these ecosystems, meaning that land surface model predictions currently inadequately quantify...
Abdominal aortic calcification (AAC), a subclinical measure of cardiovascular disease (CVD) that can be assessed on vertebral fracture assessment (VFA) images during osteoporosis screening, is reported to be a falls risk factor. A limitation to incorporating AAC clinically is that its scoring requires trained experts and is time-consuming. We examined if our machine learning (ML) algorithm for AAC (ML-AAC24) is associated with...
The emerging field of prospective life cycle assessment (pLCA) offers opportunities for evaluating the environmental impacts of possible future consumption shifts. One such shift involves a transition from meat‐based to plant‐forward diets, acknowledged to mitigate environmental impacts of the food system under present day conditions. Current diets are often meat intensive (“meat‐based”), whilst “plant‐forward” diets include m...
Land use diversification may mitigate on‐site land use impacts on mammal populations and assemblagesNARA Subscribed
Land use is a major cause of biodiversity decline worldwide. Agricultural and forestry diversification measures, such as the inclusion of natural elements or diversified crop types, may reduce impacts on biodiversity. However, the extent to which such measures may compensate for the negative impacts of land use remains unknown. To fill that gap, we synthesised data from 99 studies that recorded mammal populations or assemblage...
Flooding represents around 32% of total disasters in Indonesia and disproportionately affects the poorest of communities. The objective of this study was to determine significant statistical differences, in terms of river catchment characteristics, between regions in West Java that reported suffering from flood disasters and those that did not. Catchment characteristics considered included various statistical measures of topog...
The Citizen Observation of Local Litter in coastal ECosysTems (COLLECT) project (2021-2022) is a citizen science initiative, supported by the Partnership for Observation of the Global Ocean (POGO), which aimed to acquire distribution and abundance data of coastal plastic litter in seven countries: in Africa (Benin, Cabo Verde, Côte d’Ivoire, Ghana, Morocco, Nigeria) and Asia (Malaysia). In this paper, we describe the workflow...
Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severityNARA Subscribed
The combined impact of common and rare exonic variants in COVID-19 host genetics is currently insufficiently understood. Here, common and rare variants from whole-exome sequencing data of about 4000 SARS-CoV-2-positive individuals were used to define an interpretable machine-learning model for predicting COVID-19 severity. First, variants were converted into separate sets of Boolean features, depending on the absence or the pr...