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44 results for “malaise trap”
Malaise-trap metabarcoding dataset from temperate-zone forest Oregon, USA
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Light and malaise traps tell different stories about the spatial variations in arthropod biomass and method-specific insect abundance
<p><span>1. Conclusions reached in meta-analyses of changes in insect communities may be influenced by method-specific sampling biases, which may lead to inappropriate conservation measures.</span></p> <p><span>2. </span><span>We argue that the contradictory conclusions regarding terrestrial insect biomass, abundance and richness patterns are, at least partly, due to methodological limitations that reflect taxon-specific responses to environmental changes.</span></p> <p><span>3. </span><span>In this study, light and Malaise traps were simultaneously deployed to sample insects at 52 plots in a temperate forest in Germany along gradients of elevation (> 1000 m) and canopy openness (3 - 100 %). These gradients were used as predictors in models of total arthropod biomass according to the two trapping methods, and in models of abundance and richness of three commonly targeted groups: nocturnal moths, sampled using light traps, and hoverflies and bees, collected with Malaise traps.</span></p> <p><span>4. </span><span>A comparison of the total arthropod biomass obtained with the two methods revealed contrary results along the canopy openness gradient. Biomass in light traps showed a decreasing trend with increasing canopy openness while biomass in Malaise traps increased. The same opposing pattern was found for the abundance of selected taxa.</span></p> <p><span>5. </span><span>The different patterns describing spatial variation of arthropod communities obtained using light and Malaise traps can be explained by differences in the taxa predominantly collected. Regarding the ongoing debate on insect decline, our results demonstrate that comparing different taxa from different taxon-specific traps is inappropriate. Thus, we recommend that future meta-analyses take into account the sampling methods and taxon-specific responses to environmental changes.</span></p>
Uganda Malaise trapping 2014–2015 Rhyssinae ecology data
<p>This dataset contains the data and analyses of our <a href="https://doi.org/10.1098/rsos.190913">paper</a> on the ecology of Ugandan Rhyssinae. We collected rhyssines by Malaise trapping in tropical forest in Kibale National Park 2014–2015. The dataset contains background data such as weather and vegetation around the traps, data on the 447 rhyssines caught, the figures in the paper, and the script used to analyse the data.</p> <p><br> The script (2 Rhyssinae ecology.R) will usually be of the greatest interest. It contains the R code used to explore and analyse the data, and to create the figures in the paper.</p>
Light and malaise traps tell different stories about the spatial variations in arthropod biomass and method-specific insect abundance
Open the record for dataset details and reuse information.
FIGURE 13. Near the Malaise trap n in Additional data on the fauna of Psilidae (Diptera) of France, with description of three new species of Chamaepsila and updated keys
FIGURE 13. Near the Malaise trap n°55 in France, Ristolas-Mont Viso (Hautes-Alpes), where the holotype and paratypes of Chamaepsila ristolasiensis sp. nov. were captured (Photo: Alain Bloc)
FIGURE 4. Malaise trap n in Additional data on the fauna of Psilidae (Diptera) of France, with description of three new species of Chamaepsila and updated keys
FIGURE 4. Malaise trap n°117 in France, Natural Reserve of hauts de Chartreuse (Isère), where the holotype of Chamaepsila withersi sp. nov. was captured (Photo: Jocelyn Claude)
FIGURE 3. Malaise trap n in Checklist of the Pipunculidae (Diptera) of mainland France: further faunistic records and description of a new species
FIGURE 3. Malaise trap n°4 in France, RN de la Côte de Mancy (Jura), where the holotype of Tomosvaryella estebani sp. nov. was captured (Photo: Frédéric Ravenot.)
Supplementary material 3 from: Swenson SJ, Eichler L, Hörren T, Kolter A, Köthe S, Lehmann GUC, Meinel G, Mühlethaler R, Sorg M, Gemeinholzer B (2022) The potential of metabarcoding plant components of Malaise trap samples to enhance knowledge of plant-insect interactions. Metabarcoding and Metagenomics 6: e85213. https://doi.org/10.3897/mbmg.6.85213
Supplementary material 3 from: Swenson SJ, Eichler L, Hörren T, Kolter A, Köthe S, Lehmann GUC, Meinel G, Mühlethaler R, Sorg M, Gemeinholzer B (2022) The potential of metabarcoding plant components of Malaise trap samples to enhance knowledge of plant-insect interactions. Metabarcoding and Metagenomics 6: e85213. https://doi.org/10.3897/mbmg.6.85213
FIGURE 3. A Malaise trap installed during February–April 2007 in Review of Gonatocerus (Hymenoptera: Mymaridae) in the Neotropical region, with description of eleven new species
FIGURE 3. A Malaise trap installed during February–April 2007 in Pucará, Neuquén, the type locality of a number of mymarid taxa described by A.A. Ogloblin from Argentina (photograph taken in February 2007).
FIGURE 4. Malaise trap sites. A in Dryinidae of the Afrotropical region (Hymenoptera, Chrysidoidea)
FIGURE 4. Malaise trap sites. A: South Africa, Asante Sana Game Reserve, 32°15.841′S 24°57.091′E, Camdeboo Escarpment Thicket, site ASA09-BUS1. B: South Africa, Grootvadersbsoch Nature Reserve, 33°59.030′S 20°49.128′E, Afromontane For- est, site GVB12-FOR1. C: South Africa, Kelkiewyn Farm, 31°12.923′S 19°40.812′E, Hantam Succulent Karoo, site KEL09- SUC1. D: South Africa, Orangekloof, 34° 0.035'S 18° 23.492'E, Afromontane forest, site OGK13-FOR1. E: South Africa, Swaarweerberg, Vredehoek Farm, 32°26.387′S 20°34.501′E, Roggeveld Shale Renosterveld, site SWA09-SUC1. F: Uganda, Kibale National Park, 0º33.836'N 30º21.700'E, site UG08-KF8.
FIGURE 3. Malaise trap sites. A in Dryinidae of the Afrotropical region (Hymenoptera, Chrysidoidea)
FIGURE 3. Malaise trap sites. A: South Africa, Constantiaberg, 34°2.175'S 18°23.528'E, Mesic Mountain Fynbos with kloof forest elements. B: Central African Republic, Parc National de Dzanga-Ndoki, Mabéa Bai, 3º02.01'N 16º24.57'E, Lowland Rainforest, marsh clearing, site CAR01. C: Central African Republic, Réserve Spéciale de Forêt Dense de Dzanga-Sangha, 3º00.27'N 16º11.55'E, Lowland Rainforest, site CAR01. D: South Africa, Asante Sana Game Reserve, 32°14.990′S 24°55.962′E, Karoo Escarpment Grassland. E: Central African Republic, Parc National de Dzanga-Ndoki, 2º21.60'N 16º09.20'E, Lowland Rainforest, site CAR01.
FIGURE 3. Malaise trap n in Peregrinations of the caddisfly Limnephilus affinis Curtis 1834 in the summit areas of the French Jura Mountains and Northern Prealps (Trichoptera, Limnephilidae)
FIGURE 3. Malaise trap n° 125 at Mont Granier, 1756 m a.s.l. (Chartreuse Massif, France). Photo by Jocelyn Claude.
Peru Malaise trapping data 1998 2000 2008 2011
<p>Data on the Peruvian Malaise trapping carried out in 1998, 2000, 2008 and 2011 by the University of Turku. Flying insects were collected by Malaise traps in Peruvian Amazonia by Ilari Sääksjärvi and Isrrael Gómez.</p> <p>The dataset contains data on what samples were collected, on the trap sites, on the vegetation around the trap sites, and on the weather. More importantly, it contains information on how this data was compiled. There was no complete list of Peruvian Malaise samples before this, and the sample data had to be combined from multiple sources.</p> <p>This dataset is also described in the <a href="https://doi.org/10.1101/2023.08.23.554460">associated paper</a>.</p>
Supplementary material 2 from: Swenson SJ, Eichler L, Hörren T, Kolter A, Köthe S, Lehmann GUC, Meinel G, Mühlethaler R, Sorg M, Gemeinholzer B (2022) The potential of metabarcoding plant components of Malaise trap samples to enhance knowledge of plant-insect interactions. Metabarcoding and Metagenomics 6: e85213. https://doi.org/10.3897/mbmg.6.85213
Table S2
Supplementary material 1 from: Swenson SJ, Eichler L, Hörren T, Kolter A, Köthe S, Lehmann GUC, Meinel G, Mühlethaler R, Sorg M, Gemeinholzer B (2022) The potential of metabarcoding plant components of Malaise trap samples to enhance knowledge of plant-insect interactions. Metabarcoding and Metagenomics 6: e85213. https://doi.org/10.3897/mbmg.6.85213
Table S1
Supplementary material 1 from: Henry SC, McQuillan PB, Kirkpatrick JB (2018) An Alpine Malaise trap. Alpine Entomology 2: 51-58. https://doi.org/10.3897/alpento.2.24800
Figure S1 :
Figure 3 from: Henry SC, McQuillan PB, Kirkpatrick JB (2018) An Alpine Malaise trap. Alpine Entomology 2: 51-58. https://doi.org/10.3897/alpento.2.24800
Figure 3 Alpine Malaise Trap including additional stake for securing the trap and passive sticky CD trap.
Figure 2 from: Henry SC, McQuillan PB, Kirkpatrick JB (2018) An Alpine Malaise trap. Alpine Entomology 2: 51-58. https://doi.org/10.3897/alpento.2.24800
Figure 2 Basic Alpine Malaise Trap deployed on kunanyi/Mount Wellington, Hobart (Fig. 1.), Tasmania, 2017.
Figure 5 from: Henry SC, McQuillan PB, Kirkpatrick JB (2018) An Alpine Malaise trap. Alpine Entomology 2: 51-58. https://doi.org/10.3897/alpento.2.24800
Figure 5 Sticky acetate sample sheet from AMT deployed for 6 weeks in Mount Field National Park. The sheet is cut to fit a CD case for storage and transport.
Supplementary material 2 from: Henry SC, McQuillan PB, Kirkpatrick JB (2018) An Alpine Malaise trap. Alpine Entomology 2: 51-58. https://doi.org/10.3897/alpento.2.24800
Figure S2 :
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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.