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1,342 results for “pest”
Research4Life Landscape and Situation Analysis - Trends in Scholarly Communication PEST Analysis
<p>A PEST infographic summarising the key trends in scholarly communication, as identified in the report 'Research4Life Landscape and Situation Analysis' prepared by Research Consulting for the Research4Life partnership.</p>
Research4Life Landscape and Situation Analysis - Trends in the funding of research in and for LMICs PEST Analysis
<p>A PEST infographic summarising the key trends in the funding of research in and for low and middle-income countries, as identified in the report 'Research4Life Landscape and Situation Analysis' prepared by Research Consulting for the Research4Life partnership.</p>
Data from: DNA metabarcoding for biodiversity monitoring in a national park: screening for invasive and pest species
<ol> <li><span>DNA metabarcoding was utilized for a large-scale, multi-year assessment of biodiversity in Malaise trap collections from the Bavarian Forest National Park (Germany, Bavaria). </span></li> <li><span>Principal Component Analysis of read count-based biodiversities revealed clustering in concordance with whether collection sites were located inside or outside of the National Park.</span></li> <li><span>Jaccard distance matrices of the presences of BINs at collection sites in the two survey years (2016 and 2018) were significantly correlated.</span></li> <li><span>Overall similar patterns in the presence of total arthropod BINs, as well as BINs belonging to four major arthropod orders across the study area, were observed in both survey years, and are also comparable with results of a previous study based on DNA barcoding of Sanger-sequenced specimens.</span></li> <li><span>A custom reference sequence library was assembled from publicly available data to screen for pest or invasive arthropods among the specimens or from the preservative ethanol.</span></li> <li> <span>A single 98.6% match to the invasive bark beetle </span><span>Ips duplicatus</span><span> was detected in an ethanol sample. This species has not previously been detected in the National Park.</span> </li> </ol>
Figure 6 in INTEGRATED PEST MANAGEMENT IN CONILON COFFEE
Figure 6. Moth (A); coffee leaf damage (B and C); caterpillar at the beginning of pupal stage (D); and the characteristic X-shaped cocoon of coffee tree miner (E).
Figure 1 in INTEGRATED PEST MANAGEMENT IN CONILON COFFEE
Figure 1. Rosette with coffee berry borer attack symptom (A); and detail of the pest hole in the crown region of the fruit (B).
Figure 9 in INTEGRATED PEST MANAGEMENT IN CONILON COFFEE
Figure 9. Orthezia colony (Praelongorthezia praelonga) on coffee leaves (A); leaf covered with dark- colored fungus, commonly referred to as sooty mold (B); and coffee plant with high defoliation caused by the pest (C).
Figure 7 in INTEGRATED PEST MANAGEMENT IN CONILON COFFEE
Figure 7. Flower bud (A), branches (B) and rosette (C) of the coffee tree infested with citrus mealybug.
Figure 3 in INTEGRATED PEST MANAGEMENT IN CONILON COFFEE
Figure 3. Prorops nasuta - Uganda wasp (A); C. stephanoderis - Ivory Coast Wasp (B); and C. hyalinipennis (C).
Figure 12 in INTEGRATED PEST MANAGEMENT IN CONILON COFFEE
Figure 12. Young form and pupa of the honeydew moth caterpillar and damages the coffee tree rosette.
Figure 4 in INTEGRATED PEST MANAGEMENT IN CONILON COFFEE
Figure 4. Parasitism stages of the Ivory Coast Wasp, Cephalonomia stephanoderis from the coffee berry borer larva (top of the figure) and the coffee berry borer pupa (bottom). Source: Benassi (1996).
Figure 2 in Aculus taihangensis (Acari: Prostigmata: Eriophyidae), a potential biological control agent identified from the highly invasive pest plant, tree of heaven, in Türkiye
Figure 2. Aculus taihangensis. Prodorsal shield and part of dorsal opisthosoma: A. Protogyne, B. Deutogyne.
Figure 4 in Aculus taihangensis (Acari: Prostigmata: Eriophyidae), a potential biological control agent identified from the highly invasive pest plant, tree of heaven, in Türkiye
Figure 4. Aculus taihangensis – Male: A. Prodorsal shield and part of dorsal opisthosoma, B. Coxigenital region.
Figure 5 in Aculus taihangensis (Acari: Prostigmata: Eriophyidae), a potential biological control agent identified from the highly invasive pest plant, tree of heaven, in Türkiye
Figure 5. Dense aggregation of Aculus taihangensis along the midrib of a leaflet of the tree of heaven.
Figure 1 in Aculus taihangensis (Acari: Prostigmata: Eriophyidae), a potential biological control agent identified from the highly invasive pest plant, tree of heaven, in Türkiye
Figure 1. Map of Türkiye showing the provinces from which leaf samples were collected from the tree of heaven in 2022 and 2023 (* indicates the site in Çanakkale Province at which the eriophyid mite, Aculus taihangensis, was collected).
Data from: Leveraging satellite observations to reveal ecological drivers of pest densities across landscapes
<p>Landscape ecologists have long suggested that pest abundances increase in simplified, monoculture landscapes. However, tests of this theory often fail to predict pest population sizes in real-world agricultural fields. These failures may arise not only from variations in pest ecology but also from the widespread use of categorical land-use maps that do not adequately characterize habitat availability for pests. We used 1163 field-year observations of <em>Lygus hesperus</em> (Western Tarnished Plant Bug) densities in California cotton fields to determine whether integrating remotely sensed metrics of vegetation productivity and phenology into pest models could improve pest abundance analysis and prediction. Because <em>L. hesperus</em> often overwinters in non-crop vegetation, we predicted that pest abundances would peak on farms surrounded by more non-crop vegetation, especially when the non-crop vegetation is initially productive but then dries down early in the year, causing the pest to disperse into cotton fields. We found that the effect of non-crop habitat on pest densities varied across latitudes, with a positive relationship in the north and a negative one in the south. Aligning with our hypotheses, models predicted that <em>L. hesperus</em> densities were 35 times higher on farms surrounded by high versus low productivity non-crop vegetation (EVI area 350 vs. 50) and 2.8 times higher when dormancy occurred earlier versus later in the year (May 15 vs. June 30). Despite these strong and significant effects, we found that integrating these remote-sensing variables into land-use models only marginally improved pest density predictions in cotton compared to models with categorical land cover metrics alone. Together, our work suggests that the remote sensing variables analyzed here can advance our understanding of pest ecology, but not yet substantively increase the accuracy of pest abundance predictions.</p>
F I G U R E 6 in Current and future potential geographical distribution of Bactericera cockerelli: an invasive pest of increasing global importance
F I G U R E 6 Predicted future climatic suitability for tomato potato psyllid (TPP; Bactericera cockerelli) in (a) its native range in North America, and (b) its invasive regions in Australia under a future climate change scenario predicted to the year 2090 in CLIMEX using the general circular model (GCM) CSIRO Mark 3.0, run with the A1B emissions scenario the known global distributions denoted by green colour dots.
F I G U R E 5 in Current and future potential geographical distribution of Bactericera cockerelli: an invasive pest of increasing global importance
F I G U R E 5 Predicted global climatic suitability for tomato potato psyllid (TPP; Bactericera cockerelli) under a future climate change scenario predicted to the year 2090 in CLIMEX using the general circular model (GCM) CSIRO Mark 3.0, run with the A1B emissions scenario.
F I G U R E 2 in Current and future potential geographical distribution of Bactericera cockerelli: an invasive pest of increasing global importance
F I G U R E 2 Predicted global climatic suitability for tomato potato psyllid (TPP; Bactericera cockerelli) under current climatic conditions in its native region in North America. The known global distributions are denoted by green colour dots.
F I G U R E 4 in Current and future potential geographical distribution of Bactericera cockerelli: an invasive pest of increasing global importance
F I G U R E 4 Predicted climatic suitability for tomato potato psyllid (TPP; Bactericera cockerelli) under current climatic conditions in New Zealand under current climatic conditions. The known global distributions are denoted by green colour dots.
F I G U R E 1 in Current and future potential geographical distribution of Bactericera cockerelli: an invasive pest of increasing global importance
F I G U R E 1 Predicted global climatic suitability (ecoclimatic index) for tomato potato psyllid (TPP; Bactericera cockerelli under current climatic conditions using the adjusted parameters given in Table 1 under (a) natural rainfall and (b) as composite of natural rainfall and irrigation based on areas identified by Siebert et al. (2013). The known global distributions are denoted by green colour dots.
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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.