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17 results for “flight interception trap”
Raw data supporting Identifying invertebrates from pitfall, flight interception traps and hand collecting
<p>Raw data supporting identifying invertebrates from pitfall, flight interception traps and hand collecting: comparing metabarcoding with traditional methods.</p> <p>Two step PCRs were performed on each sample replicate using modified primers mICOIintF and jgHCO2198 followed by the Nextera XT index kit v2 Set A (Illumina).</p> <p>The pool was loaded onto an illumina MiSeq using a MiSeq Reagent Kit v2 500 cycle kit (Illumina), with 10% Phi-X to generate 250-bp paired-end reads.</p>
Fig. 1. V in Scaphisomatini of Arizona (Coleoptera, Staphylinidae, Scaphidiinae) collected by V-Flight Intercept Traps
Fig. 1. V-flight intercept trap, with metal frame.
Data from: The shape and material of the flight interception trap matter for beetle sampling
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Data from: Low-cost automated flight intercept trap for the temporal sub-sampling of flying insects attracted to artificial light at night
<p>Sampling methods are selected depending on the targeted species or the spatial and temporal requirements of the study. However, most methods for passive sampling of flying insects have poor temporal resolution because it is time consuming, costly and/or logistically difficult. Effective sampling of flying insects attracted to artificial light at night (ALAN) requires sampling at user-defined time points (nighttime only) across well-replicated sites resulting in major time and labor-intensive survey effort or expensive automated technologies. Described here is a low-cost automated intercept trap that requires no specialist equipment or skills to construct and operate, making it a viable option for studies that require temporal sub-sampling across multiple sites. The trap can be used to address a wide range of other ecological questions that require a greater temporal and spatial scale than is feasible with previous trap technology.</p>
Data from: Low-cost automated flight intercept trap for the temporal sub-sampling of flying insects attracted to artificial light at night
Open the record for dataset details and reuse information.
Fig. 6 in Diurnal/Nocturnal Activity of Rove Beetles (Coleoptera: Staphylinidae) on Barro Colorado Island, Panama Assayed by Flight Intercept Trap
Fig. 6. Total number of Aleocharinae collected per collection event.
Fig. 3 in Diurnal/Nocturnal Activity of Rove Beetles (Coleoptera: Staphylinidae) on Barro Colorado Island, Panama Assayed by Flight Intercept Trap
Fig. 3. Relative abundance of different subfamilies of Staphylinidae.
Fig. 2 in Diurnal/Nocturnal Activity of Rove Beetles (Coleoptera: Staphylinidae) on Barro Colorado Island, Panama Assayed by Flight Intercept Trap
Fig. 2. Daily rainfall (mm) on BCI for the period of study recorded at 900 h.
Fig. 1. A in Diurnal/Nocturnal Activity of Rove Beetles (Coleoptera: Staphylinidae) on Barro Colorado Island, Panama Assayed by Flight Intercept Trap
Fig. 1. A set up for the flight intercept trap. Photograph by R. S. Hanley.
Fig. 5 in Diurnal/Nocturnal Activity of Rove Beetles (Coleoptera: Staphylinidae) on Barro Colorado Island, Panama Assayed by Flight Intercept Trap
Fig. 5. Total number of Staphylininae collected per collection event.
Fig. 4 in Diurnal/Nocturnal Activity of Rove Beetles (Coleoptera: Staphylinidae) on Barro Colorado Island, Panama Assayed by Flight Intercept Trap
Fig. 4. Total number of Staphylinidae collected per collection event.
Figs 11-16 in Scaphisomatini of Arizona (Coleoptera, Staphylinidae, Scaphidiinae) collected by V-Flight Intercept Traps
Figs 11-16. Aedeagi in Scaphisoma. (11-12) S. declivum Löbl, sp. nov., in dorsal and lateral views, scale = 0.1 mm. (13-14) S. desertorum Casey, in dorsal and lateral views, scale = 0.2 mm. (15) S. rufulum LeConte, in dorsal view, scale = 0.2 mm. (16) S. rubens Casey, in lateral view, internal sac extruded, scale = 0.1 mm.
Figs 3-10 in Scaphisomatini of Arizona (Coleoptera, Staphylinidae, Scaphidiinae) collected by V-Flight Intercept Traps
Figs 3-10. Aedeagi in Baeocera. (3 - 4) B. arizonensis Löbl, sp. nov., in dorsal and lateral views, scale = 0.1 mm. (5) Ditto, paramere in ventral view, scale = 0.1 mm. (6-7) B. obscura Löbl, sp. nov., in dorsal and lateral views, scale = 0.1 mm (basal bulb deformed, tip of apical process broken). (8.) Ditto, paramere in ventral view, scale = 0.05 mm. (9-10) B. vintercepta Löbl, sp. nov., in dorsal and lateral views, scale = 0.1 mm.
Figure 3 from: Lamarre G, Fine P (2012) A comparison of two common flight interception traps to survey tropical arthropods. ZooKeys 216: 43-55. https://doi.org/10.3897/zookeys.216.3332
Figure 3 - Box plot representing the relative abundance of the seven focal insect orders collected in each of the two traps. An asterisk above the bars represents significant differences between traps based on analysis of variance.
Fig. 2 in Scaphisomatini of Arizona (Coleoptera, Staphylinidae, Scaphidiinae) collected by V-Flight Intercept Traps
Fig. 2. Travel version of V-flight intercept trap.
Figure 2 from: Lamarre G, Fine P (2012) A comparison of two common flight interception traps to survey tropical arthropods. ZooKeys 216: 43-55. https://doi.org/10.3897/zookeys.216.3332
Figure 2 - Picture of the modified windowpane trap described in this study (Lamarre G).
Figure 1 from: Lamarre G, Fine P (2012) A comparison of two common flight interception traps to survey tropical arthropods. ZooKeys 216: 43-55. https://doi.org/10.3897/zookeys.216.3332
Figure 1 - Picture of a malaise trap installed in a flooded forest of French Guiana (Lamarre G).
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Allen Brain Atlas
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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.