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7 results for “Aromia”
Figure 5 in Establishment of an expansion-predicting model for invasive alien cerambycid beetle Aromia bungii based on a virtual ecology approach
Figure 5. Map of predicted occurrence units for the whole of Saitama Prefecture using both the river density model and river single model. The degree of shading reflects the theoretical invasion number predicted by each simulation model.
Figure 4 in Establishment of an expansion-predicting model for invasive alien cerambycid beetle Aromia bungii based on a virtual ecology approach
Figure 4. (a) Map of occurrence records for A. bungii through 2019. (b–g) Predicted occurrence units based on our models for each habitat variable. The degree of shading reflects the theoretical invasion number predicted by each model.
Figure 2 in Establishment of an expansion-predicting model for invasive alien cerambycid beetle Aromia bungii based on a virtual ecology approach
Figure 2. Basic structure of the cellular automata model. (A) Two values are associated with each cell: 1) the cell ID "x," a unique ID for each cell, and 2) the expansion probability "ex" indicating four directional vectors into adjacent cells (described below). (B) Values e1, e2, e3, and e4 indicate the probability of dispersion using the path to the top, left, bottom, and right cells, respectively. If the dispersion path value is 1, the insect population in this cell can expand to the adjacent cell.
Behavioral response of Aromia bungii adults to volatile compounds emitted by Prunus persica at different physiological status
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Figure 3 in Establishment of an expansion-predicting model for invasive alien cerambycid beetle Aromia bungii based on a virtual ecology approach
Figure 3. Flow chart of simulation procedure the study.
Figure 1 in Establishment of an expansion-predicting model for invasive alien cerambycid beetle Aromia bungii based on a virtual ecology approach
Figure 1. Study area and simulation unit
Aromia bungii ̶ Pest Report and Datasheet to support ranking of EU candidate priority pests
<p>These two files are part of the outputs produced under the mandate <a href="http://registerofquestions.efsa.europa.eu/roqFrontend/wicket/page?1-1.ILinkListener-contentPane-listContainer-pageable-21-mandateNumberLnk">M-2017-0056</a> of the European Commission requesting EFSA for technical assistance in the field of quarantine pests qualifying as priority pests as by Article 6(2) of the Regulation (EU) 2016/2031 <em>on protective measures against pests of plants</em>.</p> <p>Under the mandate EFSA produced: i) 1 methodology report (DOI available at the field "Related/alternate identifiers"), ii) 28 datasheets, one for each of the 28 candidate pests, and iii) 28 pest reports supporting the information provided in the datasheets.</p> <p>EFSA wishes to acknowledge the contribution of Mariangela Ciampitti and Bjorn Hoppe to the EKE and the review conducted by Christian MacQuarrie.</p>
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Allen Brain Atlas
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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.