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1,271 results for “tropical forest”
FIG. 11. — Russula shoreae D.Chakr., A.Ghosh, K in Three new species of genus Russula Pers. from Sal dominated forests of tropical India based on morphotaxonomy and multigene phylogenetic analysis
FIG. 11. — Russula shoreae D.Chakr., A.Ghosh, K.Das & Buyck, sp. nov. (from holotype): A-C, fresh and dissected basidiomata in the field and basecamp; D, E, transverse section through pileipellis showing elements; F, transverse section through lamellae showing basidia; G, H, transverse section through lamellae showing hymenial cystidia near the lamellae edges; I-M, transverse section through lamellae showing hymenial cystidia near the lamellae sides. Scale bars: A, B, 20 mm; D, 20 μm; E-M, 10 μm.
FIG. 9. — Russula pseudoflavida A in Three new species of genus Russula Pers. from Sal dominated forests of tropical India based on morphotaxonomy and multigene phylogenetic analysis
FIG. 9. — Russula pseudoflavida A.Ghosh, Hembrom, I.Bera & Buyck, sp. nov. (from holotype): A, basidiospore; B, basidia; C, hymenial gloeocystidia near the lamellae edges; D, hymenial gloeocystidia near the lamellae sides; E, marginal cells; F, elements of the pileipellis near the pileus margin: hyphal terminations; G, elements of the pileipellis near the pileus centre: hyphal terminations; H, doubtfull primordial hyphae. Scale bars: 10 µm.
FIG. 4 in Three new species of genus Russula Pers. from Sal dominated forests of tropical India based on morphotaxonomy and multigene phylogenetic analysis
FIG. 4. — Phylogram generated by Maximum Likelihood analysis based on nrITS sequence data of Russula boddingii Hembrom, D.Chakr., A.Ghosh & K.Das, sp. nov. and allied species. Maximum Likelihood bootstrap support values (MLbs) ≥ 70% are shown on the left of "/" and Bayesian Posterior Probabilities (BPP) ≥ 0.95 are shown on the right above or below the branches at nodes. Russula boddingii Hembrom, D.Chakr., A.Ghosh & K.Das, sp. nov. is placed in red font to highlight its phylogenetic position in the tree.
FIG. 2 in Three new species of genus Russula Pers. from Sal dominated forests of tropical India based on morphotaxonomy and multigene phylogenetic analysis
FIG. 2. — SEM micrographs of basidiospores: A, B, Russula boddingii Hembrom, D.Chakr., A.Ghosh & K.Das, sp. nov.; C, D, R. pseudoflavida A.Ghosh, Hembrom, I.Bera & Buyck, sp. nov.; E, F, R. shoreae D.Chakr., A.Ghosh, K.Das & Buyck, sp. nov. Scale bars: A-C, E, F, 2 μm; D, 1 μm.
FIG. 6 in Three new species of genus Russula Pers. from Sal dominated forests of tropical India based on morphotaxonomy and multigene phylogenetic analysis
FIG. 6. — Russula boddingii Hembrom, D.Chakr., A.Ghosh & K.Das, sp. nov. (from holotype): A, basidiospore; B, hymenial gloeocystidia near the lamellae edges; C, elements of the pileipellis near the pileus centre: hyphal terminations; D, basidia; E, hymenial gloeocystidia near the lamellae sides; F, elements of the pileipellis near the pileus margin: hyphal terminations. Scale bars: 10 µm.
FIG. 8. — Russula pseudoflavida A in Three new species of genus Russula Pers. from Sal dominated forests of tropical India based on morphotaxonomy and multigene phylogenetic analysis
FIG. 8. — Russula pseudoflavida A.Ghosh, Hembrom, I.Bera & Buyck, sp. nov. (from holotype): A-C, fresh and dissected basidiomata in the field and basecamp; D-F, transverse section through pileipellis showing elements; G, primordial hyphae in carbolfuchsine; H-J, transverse section through lamellae showing hymenial gloeocystidia near the lamellae sides; K, transverse section through lamellae showing hymenial gloeocystidia near the lamellae edges. Scale bars: A, B, 20 mm; D, E, G, 20 μm; F, H-K, 10 μm.
FIG. 10 in Three new species of genus Russula Pers. from Sal dominated forests of tropical India based on morphotaxonomy and multigene phylogenetic analysis
FIG. 10. — Phylogram generated by Maximum Likelihood analysis based on nrITS sequence data of Russula shorae D.Chakr., A.Ghosh, K.Das & Buyck, sp. nov. and their allied species. Maximum Likelihood bootstrap support values (MLbs) ≥ 70% are shown on the left of "/" and Bayesian Posterior Probabilities (BPP) ≥ 0.95 are shown on the right above or below the branches at nodes. Russula shorae D.Chakr., A.Ghosh, K.Das & Buyck, sp. nov. is placed in red font to highlight their phylogenetic positions in the tree.
FIG. 1 in Three new species of genus Russula Pers. from Sal dominated forests of tropical India based on morphotaxonomy and multigene phylogenetic analysis
FIG. 1. — Distributional map and habitat of Russula boddingii Hembrom, D.Chakr., A.Ghosh & K.Das, sp. nov., R. pseudoflavida A.Ghosh, Hembrom, I.Bera & Buyck, sp. nov. and R. shoreae D.Chakr., A.Ghosh, K.Das & Buyck, sp. nov. in India.
FIG. 7 in Three new species of genus Russula Pers. from Sal dominated forests of tropical India based on morphotaxonomy and multigene phylogenetic analysis
FIG. 7. — Phylogram generated by Maximum Likelihood analysis based on nrITS sequence data of Russula pseudoflavida A.Ghosh, Hembrom, I.Bera & Buyck, sp. nov. and allied species. Maximum Likelihood bootstrap support values (MLbs) ≥ 70% are shown on the left of "/" and Bayesian Posterior Probabilities (BPP) ≥ 0.95 are shown on the right above or below the branches at nodes. Russula pseudoflavida A.Ghosh, Hembrom, I.Bera & Buyck, sp. nov. is placed in red font to highlight its phylogenetic position in the tree.
FIG. 12. — Russula shoreae D.Chakr., A.Ghosh, K in Three new species of genus Russula Pers. from Sal dominated forests of tropical India based on morphotaxonomy and multigene phylogenetic analysis
FIG. 12. — Russula shoreae D.Chakr., A.Ghosh, K.Das & Buyck, sp. nov. (from holotype): A, basidiospore; B, elements of the pileipellis near the pileus margin: hyphal terminations and pileocystidia; C, hymenial cystidia near the lamellae sides; D, basidia; E, hymenial cystidia near the lamellae edges; F, elements of the pileipellis near the pileus centre: hyphal terminations and pileocystidia. Scale bars: 10 µm.
FIG. 3 in Three new species of genus Russula Pers. from Sal dominated forests of tropical India based on morphotaxonomy and multigene phylogenetic analysis
FIG. 3. — Phylogram generated by Maximum Likelihood analysis based on combined sequence data of nrLSU, mtSSU and rpb2 for Russula boddingii Hembrom, D.Chakr., A.Ghosh & K.Das, sp. nov., R. pseudoflavida A.Ghosh, Hembrom, I.Bera & Buyck, sp. nov. and R. shoreae D.Chakr., A.Ghosh, K.Das & Buyck, sp. nov. and their allied species. Maximum Likelihood bootstrap support values (MLbs) ≥ 70% are shown on the left of "/" and Bayesian Posterior Probabilities (BPP) ≥ 0.95 are shown on the right above or below the branches at nodes. The new species are placed in red font to highlight their phylogenetic positions in the tree.
FIG. 5 in Three new species of genus Russula Pers. from Sal dominated forests of tropical India based on morphotaxonomy and multigene phylogenetic analysis
FIG. 5. — Russula boddingii Hembrom, D.Chakr., A.Ghosh & K.Das, sp. nov. (from holotype): A-D, fresh and dissected basidiomata in the field and basecamp; E, F, transverse section through pileipellis showing elements; G, transverse section through lamellae showing hymenial gloeocystidia near the lamellae edges; H, I, transverse section through lamellae showing hymenial gloeocystidia near the lamellae sides; J, transverse section through lamellae showing basidia. Scale bars: A, 40 mm; E, 100 μm; F-J, 10 μm.
Spreadsheets to model counterfactual tropical forest losses (1990-2019) for Brazil, Democratic Republic of Congo and Indonesia
<p>18 spreadsheets used to simulate the counterfactual forest losses underlying the publication:<br> "Trends in tropical forest loss and the social value of emission reductions"<br> by Thomas Knoke, Nick Hanley, Rosa Maria Roman-Cuesta, Ben Groom, Frank Venmans and Carola Paul published in Nature Sustainability (DOI: 10.1038/s41893-023-01175-9)</p> <p>Each spreadsheet covers a period of five years. The dynamic robust multifunctional land-use allocation model is based on</p> <p>Knoke, T. et al. Accounting for multiple ecosystem services in a simulation of land-use<br> decisions: Does it reduce tropical deforestation? Glob. Chang. Biol. 26, 2403–2420; 10.1111/gcb.15003 (2020).</p> <p>For details see linked publication and README file</p> <p> </p>
Seed dispersal syndrome predicts ethanol concentration of fruits in a tropical dry forest
<p><span>Studying fruit traits and their interactions with seed dispersers can improve how we interpret patterns of biodiversity, ecosystem function, and evolution. Mounting evidence suggests that fruit ethanol is common, variable, and may exert selective pressures on seed dispersers. To test this, we comprehensively assess fruit ethanol content in a wild ecosystem and explore sources of variation. We hypothesise that both phylogeny and seed dispersal syndrome explain variation in ethanol levels, and we predict that fruits with mammalian dispersal traits will contain higher levels of ethanol than those with bird dispersal traits. We measured ripe fruit ethanol content in species with mammal- (n = 16), bird- (n = 14), or mixed-dispersal (n = 7) syndromes in a Costa Rican tropical dry forest. Seventy-eight percent of fruit species yielded measurable ethanol concentrations. We detected a phylogenetic signal in maximum ethanol levels (Pagel's λ = 0.82). Controlling for phylogeny, we observed greater ethanol concentrations in mammal-dispersed fruits, indicating that dispersal syndrome helps explain variation in ethanol content and that mammals may be more exposed to ethanol in their diets than birds. Our findings further our understanding of wild fruit ethanol and its potential role as a selective pressure on frugivore sensory systems and metabolism.</span></p>
Figure 2 in Revisiting the ideas of trees as templates and the competition paradigm in pairwise analyses of ground-dwelling ant species occurrences in a tropical forest
Figure 2 Presence probabilities of ant species in each tree species only for those one involved in nonrandom pairs for the two sampling techniques and seasons. PR: pitfall,rainy season; PD: pitfall, dry season; WR: Winkler, rainy season; WD: Winkler, dry season. GG= Guarea guidonia; IE= Inga edulis; NM= Nectandra membranacea; PG= Piptadenia gonoachanta.
A new Approach to derive Productivity of Tropical Forests using Radar Remote
<p>Supplemental material to the publication " A new Approach to derive<br>Productivity of Tropical Forests using Radar Remote Sensing Measurements" in Royal Society Open Science</p><p>Abstract:</p><p>Deriving gross & net primary productivity (GPP & NPP) andcarbon turnover time of forests from remote sensing remainschallenging. This study presents a novel approach to estimateforest productivity by combining radar remote sensingmeasurements, machine learning and an individual-basedforest model. In this study, we analyse the role of differentspatial resolutions on predictions in the context of the RadarBIOMASS mission (by ESA). In our analysis, we use the forestgap model FORMIND in combination with a boostedregression tree (BRT) to explore how spatial biomassdistributions can be used to predict GPP, NPP and carbonturnover time (τ) at different resolutions. We simulatedifferent spatial biomass resolutions (4 ha, 1 ha and 0.04 ha) incombination with different vertical resolutions (20, 10 and 2m). Additionally, we analysed the robustness of this approachand applied it to disturbed and mature forests. Disturbedforests have a strong influence on the predictions which leadsto high correlations (R2> 0.8) at the spatial scale of 4 ha and 1ha. Increased vertical resolution leads generally to betterpredictions for productivity (GPP & NPP). Increasing spatialresolution leads to better predictions for mature forests andlower correlations for disturbed forests. Our results emphasizethe value of the forthcoming BIOMASS satellite missionand highlight the potential of deriving estimates for forestproductivity from information on forest structure. If appliedto more and larger areas, the approach might ultimatelycontribute to a better understanding of forest ecosystems.</p>
FIG. 3 in New species of lichen for Colombia tropical dry forest
FIG. 3. — Pyrenula gigaspora Soto-Medina, Aptroot & Lücking, sp. nov.: A, habitus; B, ascospores. Scale bars: A, 10 mm; B, 20 μm.
FIG. 2 in New species of lichen for Colombia tropical dry forest
FIG. 2. — Ocellularia vallensis Soto-Medina & Lücking, sp. nov.: A, habitus; B, C, ascospores. Scale bars: A, 10 mm; B, C, 10 μm.
FIG. 1 in New species of lichen for Colombia tropical dry forest
FIG. 1. — Astrothelium caucavallense Soto-Medina & Aptroot, sp. nov.: A, habitus; B, ascospores. Scale bars: A, 10 mm; B, 10 μm.
Fig. 1 Insect alpha-diversity across tropical forest habitats. a in Mangroves are an overlooked hotspot of insect diversity despite low plant diversity
Fig. 1 Insect alpha-diversity across tropical forest habitats. a Mangroves treated as one habitat; b Comparison of mangrove sites: Pulau Ubin (PU), Sungei Buloh (SB), Pulau Semakau old-growth (SMO), Pulau Semakau new-growth (SMN), other smaller mangrove fragments (see Additional File 1: Table S13); solid lines = rarefaction; dotted = extrapolations. The arrow on the x-axis indicates the point of rarefaction where species richness comparisons were made (see bar charts for absolute numbers with 95% confidence intervals)
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.