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40 results for “insect sampling”
Data from: Out-of-sample predictions from plant–insect food webs: robustness to missing and erroneous trophic interaction records
With increasing biotic introductions, there is a great need for predictive tools to anticipate which new trophic interactions will develop and which will not. Phylogenetic constraint of interactions in both native and novel food webs can make some novel interactions predictable. However, many food webs are sparsely sampled, or may include inaccurate interactions. In such cases, it is unclear whether modeling methods are still useful to anticipate novel interactions. We ran bootstrap simulations of host-use models on a Lepidoptera–plant data set to remove native trophic records or add erroneous records in order to observe the effect of missing or erroneous data on the prediction of interactions with novel plants. We found that the model was robust to a large amount of missing interaction records, but lost predictive power with the addition of relatively few erroneous interaction records. The loss of predictive power with missing records was due to inaccuracy in estimating phylogenetic distance between native and novel hosts. Removal of interaction records proportionally to their encounter frequency in the field had little effect on the loss of predictive power. Host-use models may have immediate value for predicting novel interactions from large, but sparsely sampled databases of trophic interactions.
Data from: Optimising sampling of flying insects using a modified window trap
Insect populations are globally declining but standardized long‐term data to evaluate trends and consequences are largely missing. One difficulty among many is the rather narrow taxonomic cover of most conventional trap types, which makes the use of several complementary collection methods necessary to achieve comprehensive coverage. To avoid the effort associated with operating multiple traps, we demonstrate how to modify window traps in a simple and standardizable way to capture a wider range of flying insect taxa. While a typical window trap only has a collection unit below the windows, we added an additional collection unit on top of the windows. We tested this modified trap design in 135 study plots in a temperate forest over 5 months and compared trap catches between top and bottom collection units. The top collection unit captured considerably more individuals of Hymenoptera, Diptera, Lepidoptera, Neuroptera, Auchenorrhyncha and Thysanoptera than the bottom collection unit. In contrast, there were more individuals of Coleoptera, Heteroptera, Sternorrhyncha and Psocoptera in the bottom collection unit. Both collection units captured a highly distinct insect community and patterns were consistent throughout the season. These modified traps are suitable for collecting a broader range of flying insects compared to conventional window traps. The additional top unit is fast and easy to build and the traps require little maintenance while operating in the field. These characteristics make modified window traps with top and bottom collection units a promising tool for standardized and replicable biodiversity studies covering a broad range of insect taxa.
Purification of full-length huntingtin (HTT) constructs Q23 and Q46 from insect sf9 cells and preparation of samples for electron microscopy (2017/03/30)
<p>Huntingtin structure-function open lab notebook project</p>
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>
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
Fig. 1 Sampling strategy for Streptomyces from insect microbiomes. Streptomyces were isolated from a in The antimicrobial potential from insect microbiomes of Streptomyces
Fig. 1 Sampling strategy for Streptomyces from insect microbiomes. Streptomyces were isolated from a wide range of insects and geographies (1445 insects; 10,178 strains; dot size, insects sampled). Streptomyces production of the antifungal mycangimycin (1) in the Southern Pine Beetle system is shown at right. Cyphomycin (2) is a new antifungal described herein. Photo credits: southern pine beetle - Erich G. Vallery; fungus-growing ant – Alexander Wild
Data from: Out-of-sample predictions from plant–insect food webs: robustness to missing and erroneous trophic interaction records
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Data from: Nondestructive sampling of insect DNA from defensive secretion
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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
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Data from: Optimising sampling of flying insects using a modified window trap
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Utilizing field collected insects for next generation sequencing: effects of sampling, storage, and DNA extraction methods
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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
Fig 2 in Is repeated cypermethrin fumigation dangerous for the mitochondrial DNA in dry insect samples?
Fig 2. Habitus of organisms used in this study. Left – Oxythyrea funesta (Poda von Neuhaus, 1761), right – Supella longipalpa (Fabricius, 1798). Scale bar = 5 mm.
Figure 1 from: Kaydan MB, Konczné Benedicty Z, Kiss B, Szita É (2016) A survey of scale insects in soil samples from Europe (Hemiptera, Coccomorpha). ZooKeys 565: 1-28. https://doi.org/10.3897/zookeys.565.6877
Figure 1 - Distribution of waxplates in Ortheziola genus. Figure based on Ortheziola britannica Kozár & Miller, female; after Kozár 2004.
Figure 5 from: Kaydan MB, Konczné Benedicty Z, Kiss B, Szita É (2016) A survey of scale insects in soil samples from Europe (Hemiptera, Coccomorpha). ZooKeys 565: 1-28. https://doi.org/10.3897/zookeys.565.6877
Figure 5 - Rhizoecus pseudocacticans, Hambleton, original.
Figure 3 from: Kaydan MB, Konczné Benedicty Z, Kiss B, Szita É (2016) A survey of scale insects in soil samples from Europe (Hemiptera, Coccomorpha). ZooKeys 565: 1-28. https://doi.org/10.3897/zookeys.565.6877
Figure 3 - Brevennia larvalis Kaydan, sp. n., adult female, holotype.
Figure 2 from: Kaydan MB, Konczné Benedicty Z, Kiss B, Szita É (2016) A survey of scale insects in soil samples from Europe (Hemiptera, Coccomorpha). ZooKeys 565: 1-28. https://doi.org/10.3897/zookeys.565.6877
Figure 2 - Ortheziola editae Szita & Konczné Benedicty, sp. n., adult female, holotype.
Figure 4 from: Kaydan MB, Konczné Benedicty Z, Kiss B, Szita É (2016) A survey of scale insects in soil samples from Europe (Hemiptera, Coccomorpha). ZooKeys 565: 1-28. https://doi.org/10.3897/zookeys.565.6877
Figure 4 - Brevennia larvalis Kaydan, sp. n., first instar nymph.
Raw data for: Identifying archived insect bulk samples using DNA metabarcoding: A case study using the long-term Rothamsted Insect Survey
<p>These are the raw files from the Illumina MiSeq for the 12 libraries sequenced.</p>
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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