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1,342 results for “pest”
Figure 3 in Steneotarsonemus ananas (Acari: Tarsonemidae): a complementary description from Australian pineapples and a new pest on Neoregelia spp. (Bromeliaceae) in Costa Rica
Figure 3 Steneotarsonemus ananas (Tryon). Male, dorsal view.
Figure 7 in Steneotarsonemus ananas (Acari: Tarsonemidae): a complementary description from Australian pineapples and a new pest on Neoregelia spp. (Bromeliaceae) in Costa Rica
Figure 7 Steneotarsonemus ananas (Tryon). Female. A – tibiotarsus I; B – tarsus II.
Data for: Direct and indirect effects of management and landscape on biological pest control and crop pest infestation in apple orchards
<p>Biological pest control, relying on naturally occurring predator-prey dynamics, is considered a key element to achieve more sustainable farming systems. However, the combined effects of local management and landscape factors on communities of natural enemies as well as the cascading effects on pest infestations are rarely addressed, especially in perennial crops. Here, we used Piecewise Structural Equation Modelling (PSEM) to test direct and indirect effects of landscape composition, landscape configuration and local management practices on natural enemy communities, the pest control services they provide and ultimately on pest infestation and pest-related yield damage in apple crops. To this end, we surveyed 12 organic and 12 Integrated Pest Management (IPM) orchards during three consecutive years, and we also established a semi-natural benchmark to quantify the extent to which predator communities in the orchards were degraded. Natural enemies had a different community composition and were more abundant in organic orchards compared to IPM orchards. This had a small and positive effect on sentinel egg predation rates in organic orchards, but overall had very little impact on actual apple pest infestation. On the contrary, apple pest infestation levels were directly and positively affected by organic management practices and by increasing semi-natural habitat cover and landscape edge density. Compared to a semi-natural benchmark, both agricultural management systems showed degraded predator communities, which translated into an impaired delivery of biological control services. Synthesis and applications. Our results indicate that organic management and habitat conservation can enhance natural enemies and stimulate pest control, but also show that these factors can enhance pest infestations and can even lead to an overall increase in pest-related crop damage. Our study thus highlights the complex interplay of ecosystem services and disservices provided by biodiversity, which should be taken into account when advising farmers, policy makers and land managers on effective and sustainable strategies to control pest species and safeguard crop production.</p>
Dataset for Article - a role of epigenetic mechanisms in regulating female reproductive responses to temperature in a pest beetle
<p>This dataset contains data for analysis on a role of epigenetic mechanisms in regulating female reproductive responses to temperature in a pest beetle. Dataset contains raw gene expression data, methylation-ELISA data, MSRE data and life history data collected in laboratory conditions using the study system, <em>Callosobruchus maculatus</em>.</p>
Figure 9 in Ramie Moth, Arcte coerula (Lepidoptera: Noctuidae): A New Invasive Pest in Hawaii on Endemic Plants
Figure 9. Arcte coerula adult. The hindwing markings are distinctive of this species.
Figure 8 in Ramie Moth, Arcte coerula (Lepidoptera: Noctuidae): A New Invasive Pest in Hawaii on Endemic Plants
Figure 8. Arcte coerula pupa.
2-class Grapevine Pest Dataset of Scaphoideus titanus and Orientus ishidae on yellow Sticky traps for Insect Detection
<p>This dataset consists of 615 images of <em>Scaphoideus titanus</em> (ST) and <em>Orientus ishidae</em> (OI) from yellow sticky traps (YST). Among these, 150 photos, which lack target insects, have been repurposed as background images. Insect annotations comprise 1329 ST and 1506 for OI, ensuring an almost class-balanced dataset. The images were acquired through four distinct methods:</p> <ul> <li>Photos from the field;</li> <li>Images of stored YST (T = 5±1°C) and reared insects within a controlled greenhouse environment;</li> <li>Digital scans of YST collected during regular monitoring activities in the fields;</li> <li>Photos from a smart trap prototype installed in our experimental vineyard.</li> </ul> Structure of the dataset, showing the number of images from each data source and the corresponding class annotations. <table><tbody> <tr> <td>Image source</td> <td>Number of images</td> <td>ST annotations</td> <td>OI annotations</td> <td>Number of background images</td> </tr> <tr> <td> <p>Field</p> </td> <td>18</td> <td>3</td> <td>101</td> <td>8</td> </tr> <tr> <td>Laboratory</td> <td>157</td> <td>473</td> <td>863</td> <td>8</td> </tr> <tr> <td>scanned</td> <td>390</td> <td>853</td> <td>542</td> <td>84</td> </tr> <tr> <td>smart-trap</td> <td>50</td> <td>0</td> <td>0</td> <td>50</td> </tr> </tbody> </table> <p>We provide the yellow sticky trap images already cropped in the pre-processing stage, the corresponding enhanced datasets focusing on <em>brightness & contrast</em>, <em>sharpness</em>, and a combination of both. Finally the annotations exported in YOLO format.</p> <h3>Dataset structure</h3> <ol> <li><em>crop/</em></li> <li><em>bright/</em></li> <li><em>sharp/</em></li> <li><em>bright_and_sharp/</em> </li> <li><em>labels/</em></li> </ol> <p>At the time of publication, this dataset is the largest publicly available resource in the control of FD vectors. Detailed documentation, along with model benchmarking and performance results is given in an accompanying journal paper: (paper under submission).</p> <h3>Deployment</h3> <p>You can use this dataset as starting point to train your own insect detection models. Open source Python scripts to deploy the trained models can be found in our <a href="https://github.com/checolag/insect-detection-scripts/tree/main">Github</a> repository.</p>
Figure 1 in First record of the beekeeping pest Aethina tumida Murray (Coleoptera: Nitidulidae) for Honduras
Figure 1. Aethina tumida from El Zamorano, Honduras, dorsal and ventral views. (Scale = 2.0 mm.)
The role of structural variants in pest adaptation and genome evolution of the Colorado potato beetle, Leptinotarsa decemlineata (Say)
<p>Structural variation has been associated with genetic diversity and adaptation in diverse taxa. Despite these observations, it is not yet clear what their relative importance is for microevolution, especially with respect to known drivers of diversity, e.g., nucleotide substitutions, in rapidly adapting species. Here we examine the significance of structural variants (SVs) in pesticide resistance evolution of the agricultural super-pest, the Colorado potato beetle,<em> Leptinotarsa decemlineata</em>. By employing a parent offspring trio sequencing procedure, we develop highly contiguous reference genomes to characterize structural variation within this species. These updated assemblies represent >100-fold improvement of contiguity and include derived pest and ancestral non-pest individuals. We identify >200,000 SVs, which appear to be non-randomly distributed across the genome as they co-occur with transposable elements and genes. SVs intersect exons for a large proportion of gene annotations (~20%) and are associated with insecticide resistance, development, and transcription, most notably cytochrome P450 (CYP) genes. To understand the role that SVs might play in adaptation we measure allele frequencies of SVs for an additional 57 individuals, using whole genome resequencing data, representing pest and non-pest populations of North America. Incorporating multiple independent tests of significance using SNP data, we identify 14<strong> </strong>positively selected genes that include SVs and SNPs of elevated frequency within the sampled pest lineages. Among these, four are associated with insecticide resistance. One of these genes, glycosyltransferase-13, is a duplicated gene enclosed within a structural variant that resides inside the <em>CYP4g15</em> genic region. Both gene products have been observed to be co-induced during insecticide exposure. These results demonstrate the significance of structural variations as a genomic feature to describe species history, genetic diversity, and adaptation.</p>
Fig. 14 in Male And Female Morphology Of Some Central European Delia (Anthomyiidae) Pests
Fig. 14. Female terminalia of Delia antiqua: a = dorsal, b = ventral view
Fig. 5 in Male And Female Morphology Of Some Central European Delia (Anthomyiidae) Pests
Fig. 5. Pregenital (5th) sternite of Delia platura male
Fig. 13 in Male And Female Morphology Of Some Central European Delia (Anthomyiidae) Pests
Fig. 13. Female terminalia of Delia radicum: a = dorsal and b = ventral view
Fig. 6 in Male And Female Morphology Of Some Central European Delia (Anthomyiidae) Pests
Fig. 6. Surstyli and cercus of Delia platura male
Fig. 2 in Male And Female Morphology Of Some Central European Delia (Anthomyiidae) Pests
Fig. 2. Delia antiqua male
Fig. 9 in Male And Female Morphology Of Some Central European Delia (Anthomyiidae) Pests
Fig. 9. Surstyli and cercus of Delia radicum male
Fig. 12 in Male And Female Morphology Of Some Central European Delia (Anthomyiidae) Pests
Fig. 12. Surstyli and cercus of Delia floralis male
Fig. 8 in Male And Female Morphology Of Some Central European Delia (Anthomyiidae) Pests
Fig. 8. Pregenital (5th) sternite of Delia radicum male
Fig. 1 in Male And Female Morphology Of Some Central European Delia (Anthomyiidae) Pests
Fig. 1. Delia radicum female
Fig. 7 in Male And Female Morphology Of Some Central European Delia (Anthomyiidae) Pests
Fig. 7. Male terminalia of Delia platura in lateral view
Fig. 3 in Male And Female Morphology Of Some Central European Delia (Anthomyiidae) Pests
Fig. 3. Hind leg of Delia platura male
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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.