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Determining the drivers of plant rarity is a major challenge in ecology. Analysing spatial associations between different plant species can provide an exploratory avenue for understanding the ecological drivers of plant rarity. Here, we examined the different types of spatial associations between rare and common plants to determine if they influence the occurrence patterns of rare species. We completed vegetation surveys at 86 sites in woodland, forest, and heath communities in south-east Australia. We also examined two different rarity measures to quantify how categorisation criteria affected our results. Rare species were more likely to have positive associations with both rare and common species across all three vegetation communities. However, common species had positive or negative associations with rare and other common species, depending on the vegetation community in which they occurred. Rare species were positively associated with species diversity in forest communities. In woodland communities, rare species were associated negatively with species diversity but positively associated with species evenness. Rare species with high habitat specificity were more clustered spatially than expected by chance. Efforts to understand the drivers of plant rarity should use rarity definitions that consider habitat specificity. Our findings suggest that examining spatial associations between plants can help understand the drivers of plant rarity.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8687526 | PMC |
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0260215 | PLOS |
Mol Plant
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Division of Life Sciences and Medicine; Division of Molecular & Cell Biophysics, Hefei National Science Center for Interdisciplinary Sciences at the Microscale; MOE Key Laboratory for Membraneless Organelles and Cellular Dynamics; University of Science and Technology of China, The Innovation Academy
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July 2025
National Key Laboratory for Development and Utilization of Forest Food Resources, Zhejiang A&F University Hangzhou 311300, China Grand Health Research Institute of Senshan, Zhejiang A&F University Yiwu 322000, China.
Dendrobium officinale(DO) is a traditional Chinese medicinal and edible plant, while it is critically endangered worldwide. This article, primarily based on the original research findings of the author's team and available articles, provides a comprehensive overview of the factors contributing to the endangerment of DO and the key technologies for the conservation, efficient cultivation, and value-added utilization of this plant. The scarcity of wild populations, low seed-setting rates, lack of endosperm in seeds, and the need for symbiosis with endophytic fungi for seed germination under natural conditions are identified as the primary causes for the rarity and endangerment of DO.
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Instituto de Investigación de la Viña y el Vino, Escuela de Ingeniería Agraria, Universidad de León, 24009 León, Spain.
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Biocenter, Ludwig-Maximilians-Universität München (LMU Munich), Grosshaderner Str. 2, 82152 Planegg-Martinsried, Germany.
Representatives of Dermestidae (skin, larder, and carpet beetles) play a crucial role as decomposers in global ecosystems, facilitating the recycling of animal and plant biomass to sustain nutrient cycling. Despite their widespread ecological presence and functional importance, the fossil record of their larval stages has remained sparse, with previous documentation limited to occasional discoveries. This study significantly expands the larval fossil record by identifying 36 amber-preserved specimens from the Cretaceous, Eocene, and Miocene time slices, obtained from deposits distributed globally.
View Article and Find Full Text PDFNature
September 2025
Society for the Protection of Underground Networks (SPUN), Dover, DE, USA.
Mycorrhizal fungi are ecosystem engineers that sustain plant life and help regulate Earth's biogeochemical cycles. However, in contrast to plants and animals, the global distribution of mycorrhizal fungal biodiversity is largely unknown, which limits our ability to monitor and protect key underground ecosystems. Here we trained machine-learning algorithms on a global dataset of 25,000 geolocated soil samples comprising >2.
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