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- ter Steege, Hans, et al.
(författare)
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Mapping density, diversity and species-richness of the Amazon tree flora
- 2023
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Ingår i: COMMUNICATIONS BIOLOGY. - 2399-3642. ; 6:1
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Tidskriftsartikel (refereegranskat)abstract
- Using 2.046 botanically-inventoried tree plots across the largest tropical forest on Earth, we mapped tree species-diversity and tree species-richness at 0.1-degree resolution, and investigated drivers for diversity and richness. Using only location, stratified by forest type, as predictor, our spatial model, to the best of our knowledge, provides the most accurate map of tree diversity in Amazonia to date, explaining approximately 70% of the tree diversity and species-richness. Large soil-forest combinations determine a significant percentage of the variation in tree species-richness and tree alpha-diversity in Amazonian forest-plots. We suggest that the size and fragmentation of these systems drive their large-scale diversity patterns and hence local diversity. A model not using location but cumulative water deficit, tree density, and temperature seasonality explains 47% of the tree species-richness in the terra-firme forest in Amazonia. Over large areas across Amazonia, residuals of this relationship are small and poorly spatially structured, suggesting that much of the residual variation may be local. The Guyana Shield area has consistently negative residuals, showing that this area has lower tree species-richness than expected by our models. We provide extensive plot meta-data, including tree density, tree alpha-diversity and tree species-richness results and gridded maps at 0.1-degree resolution. A study mapping the tree species richness in Amazonian forests shows that soil type exerts a strong effect on species richness, probably caused by the areas of these forest types. Cumulative water deficit, tree density and temperature seasonality affect species richness at a regional scale.
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| 2. |
- Tavares, Julia, et al.
(författare)
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Basin-wide variation in tree hydraulic safety margins predicts the carbon balance of Amazon forests
- 2023
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Ingår i: Nature. - : Springer Nature. - 0028-0836 .- 1476-4687. ; 617:7959, s. 111-117
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Tidskriftsartikel (refereegranskat)abstract
- Tropical forests face increasing climate risk(1,2), yet our ability to predict their response to climate change is limited by poor understanding of their resistance to water stress. Although xylem embolism resistance thresholds (for example, ?(50)) and hydraulic safety margins (for example, HSM50) are important predictors of drought-induced mortality risk(3-5), little is known about how these vary across Earth's largest tropical forest. Here, we present a pan-Amazon, fully standardized hydraulic traits dataset and use it to assess regional variation in drought sensitivity and hydraulic trait ability to predict species distributions and long-term forest biomass accumulation. Parameters ?(50) and HSM50 vary markedly across the Amazon and are related to average long-term rainfall characteristics. Both ?(50) and HSM50 influence the biogeographical distribution of Amazon tree species. However, HSM50 was the only significant predictor of observed decadal-scale changes in forest biomass. Old-growth forests with wide HSM50 are gaining more biomass than are low HSM(50 )forests. We propose that this may be associated with a growth-mortality trade-off whereby trees in forests consisting of fast-growing species take greater hydraulic risks and face greater mortality risk. Moreover, in regions of more pronounced climatic change, we find evidence that forests are losing biomass, suggesting that species in these regions may be operating beyond their hydraulic limits. Continued climate change is likely to further reduce HSM50 in the Amazon(6,7), with strong implications for the Amazon carbon sink.
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| 3. |
- Gonzalez Dias Carvalho, Patrícia Conceição, et al.
(författare)
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Baseline gene signatures of reactogenicity to Ebola vaccination: a machine learning approach across multiple cohorts
- 2023
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Ingår i: Frontiers in Immunology. - 1664-3224. ; 14
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Tidskriftsartikel (refereegranskat)abstract
- Introduction: The rVSVDG-ZEBOV-GP (Ervebo®) vaccine is both immunogenic and protective against Ebola. However, the vaccine can cause a broad range of transient adverse reactions, from headache to arthritis. Identifying baseline reactogenicity signatures can advance personalized vaccinology and increase our understanding of the molecular factors associated with such adverse events. Methods: In this study, we developed a machine learning approach to integrate prevaccination gene expression data with adverse events that occurred within 14 days post-vaccination. Results and Discussion: We analyzed the expression of 144 genes across 343 blood samples collected from participants of 4 phase I clinical trial cohorts: Switzerland, USA, Gabon, and Kenya. Our machine learning approach revealed 22 key genes associated with adverse events such as local reactions, fatigue, headache, myalgia, fever, chills, arthralgia, nausea, and arthritis, providing insights into potential biological mechanisms linked to vaccine reactogenicity.
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