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FIGURE 5 in Tracking biases in the regular echinoid fossil record: The case of Paracentrotus lividus in recent and fossil shallow-water, high-energy environments
FIGURE 5. Scanning electron micrographs of Paracentrotus remains from Is Mesas. A, Details of an ambulacral column affected by abrasion. B, Two ambulacral plates still sutured together. C, Interambulacral plate showing a cavity possibly related to bioerosion. D, Spine fragment showing fine surface details such as the crenulated milled ring and shaft striation. Scale bars equal 1 mm.
FIGURE 13 in Bone histology reveals the first record of titanosaur (Dinosauria: Sauropoda) from the Late Cretaceous of Bulgaria
FIGURE 13. Average bone apatite crystallite size (in Å) for the fossil bones from Vrabchov dol and Labirinta cave.
FIGURE 12. X in Bone histology reveals the first record of titanosaur (Dinosauria: Sauropoda) from the Late Cretaceous of Bulgaria
FIGURE 12. X-ray diffraction patterns of fossil bones from Vrabchov dol and Labirinta cave. The Miller indices of different Bragg reflections of fluorapatite are given on the top diffractogram. Abbreviations: Cal–Calcite; Mrc–Marcasite; Qz–Quartz.
FIGURE 8 in Bone histology reveals the first record of titanosaur (Dinosauria: Sauropoda) from the Late Cretaceous of Bulgaria
FIGURE 8. Osteohistology of the Bulgarian ornithomimosaur, specimen NMNHS F-31436–a partial left humerus, in transmitted cross-polarized light. Arrow in the lower right corner of each image indicates the direction of the periosteum. 1, Endosteal bone lining the medullary cavity and dense Haversian bone in the inner cortex; 2, Dense Haversian tissue extending up to the outermost cortex and sub-periosteally deposited lamellar primary bone, which probably represents incipient external fundamental system; 3, Interstices of laminar primary bone tissue with relatively organized bone scaffolding and longitudinal primary osteons preserved between the numerous densely packed secondary osteons. Up to 4 generations of secondary osteons can be recognized on the image; 4, Interstices of preserved primary bone tissues. Abbreviations: eb–endosteal bone; efs–external fundamental system; lb–lamellar bone; po–primary osteon; so–secondary osteon; wpc–woven-parallel complex. Scale bar equals 1–500 µm; 2-4–200 µm.
FIGURE 1. General geological settings. 1 in Bone histology reveals the first record of titanosaur (Dinosauria: Sauropoda) from the Late Cretaceous of Bulgaria
FIGURE 1. General geological settings. 1, Tectonic map of Bulgaria (after Ivanov, 2017); 2, Geological scheme for part of the Western Srednogorie zone and position of the dinosaur bone locality (after Sinnyovsky et al., 2012; simplified).
FIGURE 5 in Bone histology reveals the first record of titanosaur (Dinosauria: Sauropoda) from the Late Cretaceous of Bulgaria
FIGURE 5. Scanned images of the processed cross-section surface of Vrabchov dol's fossil bone fragments and osteohistology of specimen U.S., K21586 in reflected light. The dashed line in Figures 5.2-3 illustrates missing parts of the cortex. Blue arrow-heads mark a line of arrested growth (LAG). 1, The original whole bone fragment polished section of specimen U.S., K21586 after sectioning for thin-section U.S., K21586-1; 2, The whole bone section of U.S., K21586 after the further sectioning; 3, Partial bone fragment polished section of specimen NMNHS FR-16; 4-6, Different sections of specimen U.S., K21586 bone wall periphery following a single LAG formed sub-periosteally. Some large primary osteons with wide lumina are easily observed above the LAG. Abbreviations: c–cortex/compact bone tissue; cb–cancellous bone tissue; mr–medullary region; po–primary osteon; so–secondary osteon. Scale bar equals 2 cm (Figures 5.1-3); 1 mm (Figures 5.4-6).
FIGURE 4 in Bone histology reveals the first record of titanosaur (Dinosauria: Sauropoda) from the Late Cretaceous of Bulgaria
FIGURE 4. Scanned images of studied thin-sections prepared from the Vrabchov dol's fossil fragments, illustrating the degree of cortical loss during preparation, the size of medullary region, and the extent and spatial distribution of cancellous bone tissue. Solid black line illustrates the original cross-sectional form of the fossils. 1, Thin-section U.S., K21586-1; 2, Thin-section U.S., K21586-2; 3, Thin-section U.S., K21586-3; 4, A single thin-section from specimen NMNHS FR-16. Abbreviations: c–cortex/compact bone tissue; cb–cancellous bone tissue; mr–medullary region. Scale bar equals 2 cm.
FIGURE 7 in Bone histology reveals the first record of titanosaur (Dinosauria: Sauropoda) from the Late Cretaceous of Bulgaria
FIGURE 7. Osteohistology of specimen NMNHS FR-16, a putative titanosaurian long bone fragment, in transmitted cross-polarized light. Arrow in the lower right corner of each image indicates the direction of the periosteum. 1, Heavily remodeled outer cortex with anisotropic primary bone tissues preserved as interstices between secondary osteons; 2, Same area with preserved primary cortex as 1, but at higher magnification. Primary osteons appear to be longitudinal; 3, Dense Haversian bone with 4 generations of secondary osteons in the outer third of the cortex. Abbreviations: po–primary osteon; so–secondary osteon; wpc–woven-parallel complex. Scale bar equals 1–200 µm; 2-3 –100 µm.
Figura 1. Gymnopilus pholiotoides. A in Gymnopilus pholiotoides (Agaricales, Basidiomycota), an unexpected new record from an urban area in Brazil
Figura 1. Gymnopilus pholiotoides. A. Basidiomes observed in side-view. B. Basidiomes showing pileus surface. Bars = 20 mm.
Figs. 4A-B in New record and expansion of the geographic distribution of Parodia mueller-melchersii (Frič ex BAckeb.) N. P. TAylor (CActAceAe) in the Southern region of Rio GrAnde do Sul, BrAZil
Figs. 4A-B. Parodia mueller-melchersii habitat in Jaguarão, RS. A. Rocky outcrop; B. Surrounding vegetation.
Figs. 2A-D. Parodia mueller-melchersii.A in New record and expansion of the geographic distribution of Parodia mueller-melchersii (Frič ex BAckeb.) N. P. TAylor (CActAceAe) in the Southern region of Rio GrAnde do Sul, BrAZil
Figs. 2A-D. Parodia mueller-melchersii.A. General appearance (frontal view); B. General appearance (lateral view); C. Habitus; D. Juvenile specimens.
Figs. 4A–C. A in Tradescantia crassula (Commelinaceae) naturalised outside the AmericAs: first records of the species iN TAiwAN
Figs. 4A–C. A. Location of the Yangmingshan National Park in Taiwan; B. Distribution and coverage of Tradescantia crassula in the Park; C. Distribution and coverage of T. fluminensis in the Park. In B and C, lines inside the Park indicate the main trails.
Fig. 3 in Tradescantia crassula (Commelinaceae) naturalised outside the AmericAs: first records of the species iN TAiwAN
Fig. 3. Distribution map of Tradescantia crassula (triangle) and T. fluminensis (circles) in Taiwan.
Figs. 1A–D in Tradescantia crassula (Commelinaceae) naturalised outside the AmericAs: first records of the species iN TAiwAN
Figs. 1A–D. Tradescantia crassula in Taiwan. A. Overview of a population; B. Flowering specimens; C. Inflorescence; D. Detail of flower. Photographs by Mong-Huai Su. Scale bars: C = 1 cm; D =2 mm.
Data from: Performance of unmarked abundance models with data from machine-learning classification of passive acoustic recordings
<p>The ability to conduct cost-effective wildlife monitoring at scale is rapidly increasing due to availability of inexpensive autonomous recording units (ARUs) and automated species recognition, presenting a variety of advantages over human-based surveys. However, estimating abundance with such data collection techniques remains challenging because most abundance models require data that are difficult for low-cost monoaural ARUs to gather (e.g., counts of individuals, distance to individuals), especially when using the output of automated species recognition. Statistical models that do not require counting or measuring distances to target individuals in combination with low-cost ARUs provide a promising way of obtaining abundance estimates for large-scale wildlife monitoring projects but remain untested. We present a case study using avian field data collected in forests of Pennsylvania during the Spring of 2020 and 2021 using both traditional point counts and passive acoustic monitoring at the same locations. We tested the ability of the Royle-Nichols and time-to-detection models to estimate abundance of two species from detection histories generated by applying a machine-learning classifier to ARU-gathered data. We compared abundance estimates from these models to estimates from the same models fit using point-count data and to two additional models appropriate for point counts, the N-mixture model and distance models. We found that the Royle-Nichols and time-to-detection models can be used with ARU data to produce abundance estimates similar to those generated by a point-count based study but with greater precision. ARU-based models produced confidence or credible intervals that were on average 31.9% ( 11.9 SE) smaller than their point-count counterpart. Our findings were consistent across two species with differing relative abundance and habitat use patterns. The higher precision of models fit using ARU data is likely due to higher cumulative detection probability, which itself may be the result of greater survey effort using ARUs and machine-learning classifiers to sample significantly more time for focal species at any given point. Our results provide preliminary support the use of ARUs in abundance-based study applications, and thus may afford researchers a better understanding of habitat quality and population trends, while allowing them to make more informed conservation actions and recommendations.</p>
Fig.3 in Biodiversity survey, ecology and new distribution records of Marchantiophyta in a remnant of Brazilian Atlantic Forest
Fig.3. Graphical representation of substrates colonized by liverwort species in the fragment of dense montane ombrophilous forest studied in the National Park of Boa Nova, Bahia, Brazil.
Figs. 1 A-J. A-C. Myrcia huallagae. A in Taxonomic studies of Myrcia (Myrciinae, Myrtaceae) in Brazil: morphological novelties, circumscriptions, and new records for the Amazon
Figs. 1 A-J. A-C. Myrcia huallagae. A. Flowering branch; B. Flower, front view; C. Bract, showing adaxial side. D-F. Myrcia laruotteana. D. Flowering branch; E. Flower bud, showing calyx with valvate pre-flowering; F. Ovary 2-locular, cross-section. G, H. Myrcia mansoniana. G. Fruiting branch; H. Fruit, lateral view. I, J. Myrcia ovata. I. Flowering branch; J. Flower, showing valvate pre-flowering. (A-C. D. Mitja & D.C. Carvalho 10123; D-F. F. Lima s.n. (MG 10752); G, H. G.T. Prance et al. 24882; I, J. A.S. Rosário 01 & L.C.B. Lobato).
Figure 5 in The first record of Diacrisia metelkana (Lederer, 1861) for Kazakhstan with notes on its bionomics and distribution (Lepidoptera, Erebidae, Arctiinae, Arctiini)
Figure 5. The habitat of Diacrisia metelkana: NE Kazakhstan, Pavlodar Region, Zhelezinka District, Zolotaya griva, Ala-Akuly Lake 54°12'29.06''N 76°5'49.51''E, 15.VII.2018 (photo by T. Aylybayev).
Figure 4 in The first record of Diacrisia metelkana (Lederer, 1861) for Kazakhstan with notes on its bionomics and distribution (Lepidoptera, Erebidae, Arctiinae, Arctiini)
Figure 4. The habitat of Diacrisia metelkana: NE Kazakhstan, Pavlodar Region, Uspenka District, NE shore of Osolodochnoe Lake, ruins of uninhabited village Vesely Klin, 53°16'39.92''N 77°53'20.91''E, 18.VII.2016 (photo by S.V. Titov).
Figure 7 in The first record of Diacrisia metelkana (Lederer, 1861) for Kazakhstan with notes on its bionomics and distribution (Lepidoptera, Erebidae, Arctiinae, Arctiini)
Figure 7. The distribution map of Diacrisia metelkana. Green spots: previously known localities; red spots: new localities in Kazakhstan.
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.