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298 results for “abundance distribution”
Fig. 5 in Distribution, abundance and morphometry of Atlantoraja cyclophora (Regan, 1903) (Elasmobranchii: Rajidae) in southern Brazil, Southwestern Atlantic
Fig. 5. Relation between eviscerated body weight (g) and total length (cm) in males (n=198) and females (n=226) of Atlantoraja cyclophora.
Fig. 3 in Distribution, abundance and morphometry of Atlantoraja cyclophora (Regan, 1903) (Elasmobranchii: Rajidae) in southern Brazil, Southwestern Atlantic
Fig. 3. Frequency of occurrence of males (black columns, n = 130) and females (white column, n = 111) of Atlantoraja cyclophora by total length size classes of 1 cm in the summer surveys.
Fig. 2 in Distribution, abundance and morphometry of Atlantoraja cyclophora (Regan, 1903) (Elasmobranchii: Rajidae) in southern Brazil, Southwestern Atlantic
Fig. 2. Frequency of occurrence of males (black columns, n = 103) and females (white columns, n = 115) of Atlantoraja cyclophora by total length size classes of 1 cm in the winter surveys.
Fig. 1 in Distribution, abundance and morphometry of Atlantoraja cyclophora (Regan, 1903) (Elasmobranchii: Rajidae) in southern Brazil, Southwestern Atlantic
Fig. 1. Maps with the distribution of Atlantoraja cyclophora in southern Brazil according to sex (males = a and c; females = b and d) and season. Black circles represent CPUE as kg/h; 40 to 50 kg/h; 30 to 39,9 kg/h; 20 e 29,9 kg/h; 10 to 19.9 kg/h;
Fig. 4 in Distribution, abundance and morphometry of Atlantoraja cyclophora (Regan, 1903) (Elasmobranchii: Rajidae) in southern Brazil, Southwestern Atlantic
Fig. 4. Relation between total body weight (g) and total length (cm) in males (n=203) and females (n=223) of Atlantoraja cyclophora.
Fig. 6 in Distribution, abundance and morphometry of Atlantoraja cyclophora (Regan, 1903) (Elasmobranchii: Rajidae) in southern Brazil, Southwestern Atlantic
Fig. 6. Relation between disk width (cm) and total length (cm) in males (n=208) and females (n=227) of Atlantoraja cyclophora.
Species distribution and abundance modelling with dynamicSDM: a case study analysis of the red-billed quelea (Quelea quelea).
<p><strong>GBIF_all_aves_2000_2020.csv</strong><br> A dataset containing e-Bird sampling events for all bird species across southern Africa between 2000-2020 (Fink et al., 2021, GBIF, 2021). <br> <br> Fink, D., T. Auer, A. Johnston, M. Strimas-Mackey, O. Robinson, S. Ligocki, W. Hochachka, L. Jaromczyk, C. Wood, I. Davies, M. Iliff, L. Seitz. 2021. eBird Status and Trends, Data Version: 2020; Released: 2021. Cornell Lab of Ornithology, Ithaca, New York. \doi{10.2173/ebirdst.2020}<br> GBIF.org (12 July 2021) GBIF Occurrence Download \doi{10.15468/dl.ppcu6q}</p> <p><strong>RBQ_full_analysis.R</strong></p> <p>An R script for the generation of dynamic species distribution and abundance models for nomadic bird, the red-billed quelea (<em>Quelea quelea</em>) using dynamicSDM package functions. </p> <p><strong>Unfiltered_quelea_occurrence.csv</strong><br> A dataset containing species occurrence and abundance records for the bird species, the red-billed quelea (<em>Quelea quelea</em>) between 1976-2021 (GBIF 2021 & GBIF 2022 & sources listed in Table 1). <br> <br> GBIF.org (12 July 2021) GBIF Occurrence Download \doi{10.15468/dl.ppcu6q}<br> <br> GBIF.org (25 July 2022) GBIF Occurrence Download \doi{10.15468/dl.k2kftv}<br> </p> <p><strong>Table S1. </strong>Red-billed quelea (<em>Quelea quelea</em>) occurrence and abundance data sources.</p> <table align="left"> <tbody> <tr> <td> <p><strong>Data type</strong></p> </td> <td> <p><strong>Sources</strong></p> </td> </tr> <tr> <td> <p><strong>Control operation </strong></p> </td> <td> <ul> <li>Information Core for Southern African Migrant Pests (ICOSAMP, 2001-2005).</li> <li>Centre for Overseas Pest Research (COPR), Natural History Museum, Tring.</li> <li>Botswana Ministries of Agriculture.</li> <li>Mozambique Ministry of Agriculture</li> </ul> </td> </tr> <tr> <td> <p><strong>Citizen science</strong></p> </td> <td> <ul> <li>Global Biodiversity Information Facility, including iNaturalist, eBird, South Africa Bird Atlas Project (SABAP) and South Africa Bird Ringing Unit (SAFRING) sources.</li> </ul> </td> </tr> <tr> <td> <p><strong>Independent research</strong></p> </td> <td> <ul> <li>EXCEL File "NfA-yearposRAC" (unpublished data set complied by R. A. Cheke, 2010).</li> </ul> </td> </tr> </tbody> </table>
Fig. 5 in Morphology, distribution and abundance of antennal sensilla of the oyster mushroom fly, Coboldia fuscipes (Meigen) (Diptera: Scatopsidae)
Fig. 5. SEM micrographs of Ba1, Ba2 and Ba3 of C. fuscipes. (a) Ba1and Ba2; (b) Ba3. Ba1, Basiconica sensilla1; Ba2, Basiconica sensilla2; Ba3, Basiconica sensilla3. Scale bar = 2 µm in (a) and 1 µm in (b).
Fig. 4 in Morphology, distribution and abundance of antennal sensilla of the oyster mushroom fly, Coboldia fuscipes (Meigen) (Diptera: Scatopsidae)
Fig. 4. SEM micrographs of Chaetie sensilla and Coeloconic sensilla of C. fuscipes. (a) Ch; (b) Co. Ch, Chaetie sensilla; Co, Coeloconic sensilla. Scale bar = 2 µm in (a) and 2 µm in (b).
Fig. 1 in Morphology, distribution and abundance of antennal sensilla of the oyster mushroom fly, Coboldia fuscipes (Meigen) (Diptera: Scatopsidae)
Fig. 1. SEM micrographs of C. fuscipes antennae. (a) The features of adult C. fuscipes antennae; (b) anterior surface of the whole antenna. Ce, compound eyes; An, antennae; Sc, scape; Pc, pedicel; Fl, flagellum. Scale bar = 100 µm in (a) and 50 µm in (b).
Fig. 2 in Morphology, distribution and abundance of antennal sensilla of the oyster mushroom fly, Coboldia fuscipes (Meigen) (Diptera: Scatopsidae)
Fig. 2. SEM micrographs of scape and pedicel. (a) Scape of C. fuscipes antennae; (b) pedicel of C. fuscipes antennae. Ch, Chaetie sensilla; Mt1, microtrichiae. Scale bar = 20 µm in (a), 10 µm in (b) and 2 µm in (c).
Fig. 3 in Unveiling global species abundance distributions
Fig. 3 | The temporal change in our statistical understanding of gSADs. a, The final 20-year rolling window gSAD for each of ten example classes with the best fit overlaid for the log-series, negative binomial and Poisson log-normal distributions.b, Yearly goodness of fit (correlation) of each distribution for each 20-year rolling window gSAD.Example classes from top to bottom: Actinopterygii, Amphibia,Arachnida, Aves, Bivalvia, Cephalopoda, Cycadopsida, Insecta, Liliopsida and Mammalia.
Fig. 4 in Unveiling global species abundance distributions
Fig. 4 | How the relative position of the veil corresponds to species richness and the number of individuals in a class. a–c, The proportion of the gSAD uncovered,assuming a Poisson log-normal distribution, and its relationship to observed species richness/number of observations (a), number of observations (b) and species richness (c). To aid in visualizing the patterns, the red dashed line represents a fit from geom_smooth() and the shaded grey area represents the 95% confidence interval around that fit.
Fig. 1 in Unveiling global species abundance distributions
Fig. 1 | Conceptual scheme illustrating the Poisson sampling of a community with species abundances described by a gamma or a log-normal distribution. Two types of gSAD—gamma (left) and log-normal distribution (right) are shown at the top.Each distribution represents the probability f of a species having a given abundance λ, with the gamma distribution having parameters k (shape) and θ (scale) and the log-normal distribution having parameters μ (mean) and σ (standard deviation), and Γ() representing the gamma function.In the middle, sampling of the gSAD with the probability of each species having a given number of individuals sampled described by a Poisson distribution is illustrated. The mean abundance of each species sampled is randomly taken from the SAD.We exemplify two samples of different sizes, where different symbols denote individuals of different species. The bottom graphs show that: if the global abundances have a log-normal distribution, the mixture distribution of abundances in the sample is a Poisson log-normal; if the global abundances follow a gamma distribution the resulting mixture distribution is a negative binomial but in the limit k→0, we obtain the Fisher log-series.
Resilin distribution and abundance in Apis mellifera across biological age classes and castes
<p>The presence of resilin, an elastomeric protein, in insect vein joints provides the flexible, passive deformations that are crucial to flapping flight. This study investigated the resilin gene expression and autofluorescence dynamics among <em>Apis</em> <em>mellifera</em> (honey bee) worker age classes and drone honey bees. Resilin gene expression was determined via ddPCR on whole honey bees and resilin autofluorescence was measured in the 1m-cu, 2m-cu, Cu-V, and Cu2-V joints on the forewing and the Cu-V joint of the hindwing. Resilin gene expression varied significantly with age, with resilin activity being highest in the pupae. Autofluorescence of the 1m-cu and the Cu-V joints on the ventral forewing and the Cu-V joint on the ventral hindwing varied significantly between age classes on the left and right sides of the wing, with the newly emerged honey bees having the highest level of resilin autofluorescence compared to all other groups. The results of this study suggest that resilin gene expression and deposition on the wing is age-dependent and may inform us more about the physiology of aging in honey bees.</p> <p> </p>
Distribution System Environmental and Sequencing Datasets for Assessing the Impacts of Lead Corrosion Control on the Microbial Ecology and Abundance of Drinking Water Associated Pathogens in a Full-Scale Drinking Water Distribution System
<p>The dataset of environmental parameters and sequence fastqs used to create figures and do analysis in the paper <strong>Assessing the Impacts of Lead Corrosion Control on the Microbial Ecology and Abundance of Drinking Water Associated Pathogens in a Full-Scale Drinking Water Distribution System </strong>submitted to Environmental Science & Technology</p>
Data from: Geographic distribution of terpenoid chemotypes in Tanacetum vulgare mediates tansy aphid occurrence but not abundance
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Integrating presence-only and detection/non-detection data to estimate distributions and expected abundance of difficult-to-monitor species on a landscape-scale
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Data from: Terrestrial spatial distribution and summer abundance of Antarctic fur seals (Arctocephalus gazella) near Palmer Station, Antarctica, from drone surveys
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Future abundance and distribution of key bird species for pathogen transmission in the Netherlands
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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)
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DANDI Archive for NWB datasets
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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.