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102 results for “pest insect”
Interactions between bats and agricultural insect pests worlwide
<p>This database illustrates the interactions between bats and agricultural insect pests detected conducting a systematic review in October 2022, entitled "<strong>Pest suppression by bats and management strategies to favour it: a global review</strong>", to be published in the journal Biological Reviews.</p> <p>Methodology applied:</p> <p>We compiled a comprehensive list of agricultural insect pests occurring in temperate and tropical regions. Since no more recent public documents or published lists were available, we extracted the main agricultural insect pests cited in Hill (1983, 1987). Note that species might be considered pests in certain regions while not in others, meaning that this comprehensive list will need careful review by entomologists and local or regional experts for use in agricultural management.</p> <p>We assembled a first list of 1,237 insect pest species or genera extracted from Hill (1987, 1983). We then conducted a literature search in the ISI Web of Science using the R package wosr. We searched for any indexed document containing the following terms in the topic field: "pest species name" AND "bat*", where ‘pest species name’ refers to each of the 1237 species. After the first check of the articles found, we added 562 new pest species to the first list, which were not included in Hill (1987, 1983), but were mentioned in the papers found. Thus, the updated list consisting of 1799 insect pest species was used again to perform the same literature search with the R package wosr. In addition, we also performed three literature searches including the following terms: (i) "bat" or "bats", "diet*", and "insect*"; (ii) "bat" or "bats", "predat*", and "insect*"; (iii) "bat" or "bats", "diet*", and "arthropod*". We identified a total of 1125 articles, of which we retained only those that identified bat prey at the genus or species level (N = 95).</p> <p>Predator - prey interactions were extracted from the articles reviewed and added in this data set, showing each bat species with the insect pest species it consumed, as well as the method used to confirm predation.</p>
Dataset: Relation of pest insect-killing and soilborne pathogen-inhibition abilities to species diversification in environmental Pseudomonas protegens
<p>This dataset is related to "<em>Relation of pest insect-killing and soilborne pathogen-inhibition abilities to species diversification in environmental Pseudomonas protegens</em>" and contains all the data obtained from insect experiments and plant-pathogen inhibition assays, as well as the code used for phylogenetic and Biolog anaylsis. </p>
Can the botanical azadirachtin replace phased-out soil insecticides in suppressing the soil insect pest Diabrotica virgifera virgifera ?
<p><strong>Can the botanical <em>azadirachtin</em> replace phased-out soil insecticides in suppressing the soil insect pest <em>Diabrotica virgifera virgifera </em>?</strong></p> <p><strong>Background</strong></p> <p>Due to recent bans on the use of several soil insecticides and insecticidal seed coatings, soil-dwelling insect pests are increasingly difficult to manage. One example is the western corn rootworm (<em>Diabrotica virgifera virgifera</em>, Coleoptera: Chrysomelidae), a serious root-feeder of maize (<em>Zea mays</em>). We investigated whether the less problematic botanical <em>azadirachtin</em>, widely used against above-ground insects, could become an option for the control of this soil insect pest.</p> <p><strong>Methods</strong></p> <p>Artificial diet-based bioassays were implemented under standard laboratory conditions to establish lethal dose curves for the pest larvae. Then, potted-plant experiments were implemented in greenhouse to assess feasibility and efficacy of a novel granular formulation of <em>azadirachtin </em>under more natural conditions and in relation to standard insecticides.</p> <p><strong>Results</strong></p> <p>Bioassays in three repetitions revealed a 3-day LD<sub>50</sub> of 22.3 µg <em>azadirachtin</em> per ml which corresponded to 0.45 µg per neonate of <em>D. v. virgifera </em>and a 5-day LD<sub>50</sub> of 19.3 µg per ml or 0.39 µg per first to second instar larva. No sublethal effects were observed. The three greenhouse experiments revealed that the currently proposed standard dose of a granular formulation of 38 g<em> azadirachtin </em>per hectare for in-furrow application at sowing is not enough to control <em>D. v. virgifera </em>or to prevent root damage. At 10x standard-dose total pest control was achieved as well as the prevention of most root damage. This was better than the efficacy achieved by <em>cypermethrin</em>-based granules and comparable to <em>tefluthrin</em>- granules, or <em>thiamethoxam</em> seed coatings. The ED<sub>50</sub> for suppressing larval populations were estimated at 92 g <em>azadirachtin</em> per ha, for preventing heavy root damage 52 g /ha and for preventing general root damage 220 g /ha.</p> <p><strong>Conclusions</strong></p> <p>There seems clear potential for the development of neem-based botanical soil insecticides for arable crops such as maize. They might become, if doses are increased and more soil insecticides phased out, a promising, safer solution as part of the integrated pest management toolkit against soil insects.</p>
Dataset Natural plant disease suppressiveness in soils extends to insect pest control
<p>This dataset is related to the study "<strong>Natural plant disease suppressiveness in soils extends to insect pest control</strong>" (Harmsen et al., 2024) and contains the raw data described therein. </p> <p>Sequencing data used in this study has been deposited in the NCBI Sequence Read Archive under the BioProject number <a href="https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1075215/">PRJNA1075215</a>.</p> <p>The scripts used to analyze the data generated in the study are available at <a href="https://github.com/nhrmsn/SuppressSoil-Data">GitHub</a>. </p>
Figure 2 in Susceptibility of the sweet pepper (Capsicum annuum L.) to the infestation of Tetranychus urticae (Acari: Tetranychidae) and the different insect pests under greenhouse conditions in Ismailia, Egypt
Figure 2. The interaction effects of seasons and cultivars on the Chl., Car., total protein and phenol contents (A) and the activity of the antioxidant enzymes (B) of the two sweet pepper cultivars during the two growing seasons 2021–22.
Figure 1 in Susceptibility of the sweet pepper (Capsicum annuum L.) to the infestation of Tetranychus urticae (Acari: Tetranychidae) and the different insect pests under greenhouse conditions in Ismailia, Egypt
Figure 1. Monthly abundance of total TSSM (A), associated insect pest (B), and predator (C) numbers on the two sweet pepper cultivars during the two growing seasons 2021–22.
Fig. 2 in Crop diversification for sustainable insect pest management in eggplant (Solanales: Solanaceae)
Fig. 2. Total ion current (TIC) mode chromatographic plot of marigold leaf volatiles sampled using the thermal desorption (TD) technique.
Fig. 3 in Crop diversification for sustainable insect pest management in eggplant (Solanales: Solanaceae)
Fig. 3. Total ion current (TIC) mode chromatographic plot of mint leaf volatiles sampled using the thermal desorption (TD) technique.
Fig. 5 in Towards incorporating insect isotope analysis using cavity ring-down spectroscopy into area-wide insect pest management programs
Fig. 5. Cumulative standard deviation of the mean carbon isotope signature of individual moths, field-caught LBAM (circles), mass-reared pink bollworm (squares) and mass-reared LBAM (triangles), analysed using the CM-CRDS module.
Fig. 3 in Towards incorporating insect isotope analysis using cavity ring-down spectroscopy into area-wide insect pest management programs
Fig. 3. Carbon isotope signature of common cutworm leg samples from different moths reared on the artificial laboratory diet or caught in the wild (circles, n = 5, Bars +/- 3 SD). The spermatophore data point (triangle) is the carbon isotope signature of spermatophores dissected from laboratory-reared females mated with field-caught males (n = 5, Bars +/- 3 SD). All samples measured using CM-CRDS.
Fig. 1 in Towards incorporating insect isotope analysis using cavity ring-down spectroscopy into area-wide insect pest management programs
Fig. 1. Carbon isotope ratios of 16 different common dietary components measured using either elemental analysis isotope ratio mass spectrometry (EAIRMS) or combustion module cavity ring down spectrometry (CM-CDRS).
Fig. 2 in Towards incorporating insect isotope analysis using cavity ring-down spectroscopy into area-wide insect pest management programs
Fig. 2. Carbon isotope ratios of 3 populations of the common cutworm measured using either elemental analysis isotope ratio mass spectrometry (EA-IRMS) or combustion module cavity ring down spectrometry (CM-CDRS): Field-caught moths: squares; synthetic diet-reared moths: circles and laboratory-reared on castor diet moths: triangles.
Fig. 1 in Conotelus sp. (Coleoptera: Nitidulidae), a new insect pest of passion fruit in the Amazon Biome
Fig. 1. Adults (a, b) of Conotelus sp. (Coleoptera: Nitidulidae) and damage (c, d) caused by this species in passion fruit flowers (Passiflora edulis f. flavicarpa).
Fig. 2 in Conotelus sp. (Coleoptera: Nitidulidae), a new insect pest of passion fruit in the Amazon Biome
Fig. 2. Population fluctuations of Conotelus sp. (Coleoptera: Nitidulidae) adults in passion fruit (Passiflora edulis f. flavicarpa) plantations. Right Y-axis denotes temperature and relative humidity.
Fig. 2 in Effects of Farming Systems on Insect Communities in the Paddy Fields of a Simplified Landscape During a Pest-control Intervention.
Fig. 2. Two-dimensional NMDS ordination of 40 insect communities sampled under different farming systems in northern Taiwan (stress = 0.18).
Figure 4 in Temporal variation and spatial distribution of the pest insect Edessa meditabunda in cotton (Gossypium hirsutum) as an alternative host plant
Figure 4. Surface maps constructed based on Inverse Distance Weight (IDW) interpolation showing spatial distribution of nymphs + adults in cotton between 55 (A) 70 (B), 77 (C), 84 (D), 91 (E) days after emergence (DAE) and Sum of all Evaluations (F). Low density is represented in green while red indicates high density of E. meditabunda.
Figure 3 in Temporal variation and spatial distribution of the pest insect Edessa meditabunda in cotton (Gossypium hirsutum) as an alternative host plant
Figure 3. Surface maps constructed based on Inverse Distance Weight (IDW) interpolation showing spatial distribution of adults in cotton between 55 (A) 70 (B), 77 (C), 84 (D), 91 (E) days after emergence (DAE) and Sum of all Evaluations (F). Low density is represented in green while red indicates high density of E. meditabunda.
Figure 1 in Temporal variation and spatial distribution of the pest insect Edessa meditabunda in cotton (Gossypium hirsutum) as an alternative host plant
Figure 1 Temporal variation of Edessa meditabunda population in the alternative host plant Gossypium hirsutum (cotton) in experimental Field of Dourados, Brazil.
Fig. 3 in Effect of BAM-FX in developing a management program to control major insect pests of tomato: sweetpotato whitefly (Hemiptera: Aleyrodidae), thrips (Thysanoptera: Thripidae), and their transmitted viruses
Fig. 3. Mean number of western flower thrips (Frankliniella occidentalis) per 5 leaf sample of tomato treated with various treatments of BAM‑FX and N‑P‑K granular fertilizer in 2016. Bars represent standard error of the means. T1 = BAM‑FX applied on soil, no pesticide, no N‑P‑K fertilizer; T2 = BAM‑FX applied on foliage, no pesticide, no N‑P‑K fertilizer; T3 = BAM‑FX applied on soil, pesticide, no N‑P‑K fertilizer; T4 = BAM‑FX applied on foliage, pesticide, no N‑P‑K fertilizer; T5 = BAM‑FX applied on foliage, pesticide, N‑P‑K fertilizer; T6 = no BAM‑FX, pesticide, N‑P‑K fertilizer; T7 = no BAM‑FX, no pesticide, N‑P‑K fertilizer; D1 = first sampling date (14 Dec); D2 = second sampling date (21 Dec); D3 = third sampling date (21 Dec); D4 = fourth sampling date (28 Dec); D5 = fifth sampling date (4 Jan); D6 = sixth sampling date (11 Jan).
Fig. 2 in Effect of BAM-FX in developing a management program to control major insect pests of tomato: sweetpotato whitefly (Hemiptera: Aleyrodidae), thrips (Thysanoptera: Thripidae), and their transmitted viruses
Fig. 2. Mean number of common blossom thrips (Frankliniella schultzei) per 5 leaf sample of tomato treated with various treatments of BAM‑FX and N‑P‑K granular fertilizer in 2016. Bars represent standard error of the means. T1 = BAM‑FX applied on soil, no pesticide, no N‑P‑K fertilizer; T2 = BAM‑FX applied on foliage, no pesticide, no N‑P‑K fertilizer; T3 = BAM‑FX applied on soil, pesticide, no N‑P‑K fertilizer; T4 = BAM‑FX applied on foliage, pesticide, no N‑P‑K fertilizer; T5 = BAM‑FX applied on foliage, pesticide, N‑P‑K fertilizer; T6 = no BAM‑FX, pesticide, N‑P‑K fertilizer; T7 = no BAM‑FX, no pesticide, N‑P‑K fertilizer; D1 = first sampling date (14 Dec); D2 = second sampling date (21 Dec); D3 = third sampling date (21 Dec); D4 = fourth sampling date (28 Dec); D5 = fifh sampling date (4 Jan); D6 = 6th sampling date (11 Jan).
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.