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138 results for “pest control”
Dataset for "Best organic farming deployment scenarios for pest control: a modeling approach" V3
<p>Organic Farming (OF) has been expanding recently in response to growing consumer demand and as a response to environmental concerns. The area under OF is expected to further increase in the future. The effect of OF expansion on pest densities in organic and conventional crops remains difficult to predict because OF expansion impacts Conservation Biological Control (CBC), which depends on the surrounding landscape context. In order to understand and forecast how pests and their biological control may vary during OF expansion, we modeled the effect of spatial changes in farming practices on population dynamics of a pest and its natural enemy. We investigated the impact on pest density and on predator to pest ratio of three contrasted scenarios aiming at 50% organic fields through the progressive conversion of conventional fields. Scenarios were 1) conversion of Isolated conventional fields first (IP), 2) conversion of conventional fields within Groups of conventional fields first (GP), and 3) Random conversion of conventional field (RD). We coupled a neutral spatially explicit landscape model to a predator-prey model to simulate pest dynamics in interaction with natural enemy predators. The three OF expansion scenarios were applied to nine landscape types differing in their proportion and fragmentation of semi-natural habitat. We further investigated if the ranking of scenarios was robust to pest control methods in OF fields and pest and predator dispersal abilities.</p> <p>We found that organic farming expansion affected more predator densities than pest densities for most landscape types. The impact of OF expansion on final pest and predator densities was also stronger in organic than conventional fields and in landscapes with large proportions of highly fragmented semi-natural habitats. Based on pest densities and the predator to pest ratio, our results suggest that a progressive organic conversion with a focus on isolated conventional fields (scenario IP) could help promote CBC. Careful landscape planning of OF expansion appeared most necessary when pest management was substantially less efficient in organic than in conventional crops, and in landscapes with low proportion of semi-natural habitats.</p> <p><strong>This dataset contains simulation outputs and the R script that was used to describe, display and analyse data. The model itself can be found at <a href="https://doi.org/10.17605/OSF.IO/Z2QCX">https://doi.org/10.17605/OSF.IO/Z2QCX</a></strong></p> <p><strong>Please note that this is the third version of this dataset, following recommendations from the PCI Ecology reviewers and editor.</strong></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>
Video Series: Integrated Pest Management focusing on disease control in cereals
<p>Welcome to this video series on IPM, focusing on disease control in cereals. </p> <p>The challenge of IPM is to make the control methods we use appropriate to the circumstances, and to balance between the productivity of the crop and minimising the impact on the environment. The control decisions we make on one field or in one season may not be appropriate in another set of circumstances – there is no ‘blue print’. In these videos we delve into the physiology of the crop, the epidemiology of the diseases and how different control methods work. By understanding the biology of the systems we're trying to control, we're better equipped to make appropriate decisions. Going into depth means we can’t cover all aspects of IPM. In practice, decisions about disease control are being made alongside decisions about invertebrate pests and weeds, and in the wider context of integrated crop management. In the UK, information on those topics is available from organisations such as LEAF, AHDB and the Voluntary Initiative. Nevertheless, disease control is still a big topic, so we have broken it down into bite size chunks - although each video is still a pretty substantial bite and will need some digesting.</p> <p>The videos can be viewed in any order that interests you, but they'll make most logical sense viewed in the order in the menu.</p> <p>Links to the videos can be found in the summary document. </p> <p>PDF versions of the video presentations are provided. </p>
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).
Gene drives for vertebrate pest control: realistic spatial modelling of eradication probabilities and times for island mouse populations
<p>Invasive alien species continue to threaten global biodiversity. CRISPR-based gene drives, which can theoretically spread through populations despite imparting a fitness cost, could be used to suppress or eradicate pest populations. We develop an individual-based, spatially explicit, stochastic model to simulate the ability of CRISPR-based homing and X-chromosome shredding drives to eradicate populations of invasive mice (Mus muculus) from islands. Using the model, we explore the interactive effect of the efficiency of the drive constructs and the spatial ecology of the target population on the outcome of a gene-drive release. We also consider the impact of polyandrous mating and sperm competition, which could compromise the efficacy of some gene-drive strategies. Our results show that both drive strategies could be used to eradicate large populations of mice. Whereas parameters related to drive efficiency and demography strongly influence drive performance, we find that sperm competition following polyandrous mating is unlikely to impact the outcome of an eradication effort substantially. Assumptions regarding the spatial ecology of mice influenced the probability of and time required for eradication, with short-range dispersal capabilities and limited mate-search areas producing `chase' dynamics across the island characterised by cycles of local extinction and recolonization by mice. We also show that highly efficient drives are not always optimal, when dispersal capabilities are low, rapid local population supression around the introduction sites can cause loss of the gene drive before it can spread to the entire island. We conclude that, although the design of efficient gene drives is undoubtedly critical, accurate data on the spatial ecology of target species is critical for predicting the result of a gene-drive release.</p>
Figure 7 in Within-tree distribution and seasonal dynamics of Eutetranychus banksi and Euseius stipulatus (Acari: Tetranychidae, Phytoseiidae) on citrus: Implications for the biological control of the pest
Figure 7 Mean proportion of phytoseiids per leaf outside or inside the canopy (a), on the adaxial or abaxial side of the leaves (b), white or red coloured (c), and collected on fruits (d), whenE. banksi occurred or was absent. Capped bars represent ± standard error (SE). Significant differences are denoted with asterisks. Chi square contingency test:P <0.001.
Figure 1 in Within-tree distribution and seasonal dynamics of Eutetranychus banksi and Euseius stipulatus (Acari: Tetranychidae, Phytoseiidae) on citrus: Implications for the biological control of the pest
Figure 1 Mean number ofE. banksi(a–d) and phytoseiid mites (e–h) per leaf or per cm2 of leaves and fruits. Capped bars represent ± standard error (SE). Bars with different letters are significantly different (Wilcoxon rank-sum test).
Figure 5 in Within-tree distribution and seasonal dynamics of Eutetranychus banksi and Euseius stipulatus (Acari: Tetranychidae, Phytoseiidae) on citrus: Implications for the biological control of the pest
Figure 5 (a–d) Representation of the binomial (logit-link) generalized linear models (GLMs) showing the relationship between the proportion
Figure 3 in Within-tree distribution and seasonal dynamics of Eutetranychus banksi and Euseius stipulatus (Acari: Tetranychidae, Phytoseiidae) on citrus: Implications for the biological control of the pest
Figure 3 Seasonal relative abundance of motile forms of phytoseiid species in four (2018) and six (2019) citrus orchards. Percentage of each species per sampling is represented. The summer decline
Figure 4 in Within-tree distribution and seasonal dynamics of Eutetranychus banksi and Euseius stipulatus (Acari: Tetranychidae, Phytoseiidae) on citrus: Implications for the biological control of the pest
Figure 4 Variation in the spatial distribution and body coloration of phytoseiid in relation to the abundanceE. of banksi in four (2018) and six (2019) citrus orchards. Grey bars indicate the percentage of phytoseiids collected outside the canopy, on the leaf adaxial sides, fruits occupied by phytoseiids, and red phytoseiids (primary y-axis), in relation with the mean numberE of. banksi per leaf or fruit represented as a solid line (secondary, y-axis). Capped bars represent ± standard error (SE).
Figure 2 in Within-tree distribution and seasonal dynamics of Eutetranychus banksi and Euseius stipulatus (Acari: Tetranychidae, Phytoseiidae) on citrus: Implications for the biological control of the pest
Figure 2 Seasonal trends ofE. banksi and phytoseiid mites on leaves (solid line, first y-axis) and fruits (broken line, second y-axis) in four and six citrus orchards in 2018 and 2019 respectively. Mean number of mites collected per sampling unit (all the stages were pooled together). Note that first and second y-axis scales are different. Mean (solid line), maximum and minimum daily temperatures in °C (broken lines) and mean daily relative humidity (RH) were represented.
Fig. 2.- Monthly D in Dryocosmus kuriphilus Yasumatsu, 1951 (Hymenoptera: Cynipidae) in Galicia (NW Spain): pest dispersion, associated parasitoids and first biological control attempts.
Fig. 2.- Monthly D. kuriphilus phenology (E: egg; L1: first-instar larvae; L2: intermediate instar larvae; L3: terminal-instar larvae; Pp: pre pupae stage; P: pupae stage; A: adult). *: punctual presence; +: sterile eggs. Sweet chestnut fruit phenology is given for orientation (§: fructification and burr development).
Fig. 1 in Sugarcane stem borers of the Colombian Cauca River Valley: current pest status, biology, and control
Fig. 1. Male adults of 4 Diatraea species present in Colombia. A. D. saccharalis; B. D. indigenella; C. D. tabernella; D. D. busckella. In general, moths are difficult to distinguish, and clear species identification requires the dissection of male genitalia (photos L. A. Lastra).
Fig. 3. A in Sugarcane stem borers of the Colombian Cauca River Valley: current pest status, biology, and control
Fig. 3. A. "Dead heart" in sugarcane caused by Diatraea sp. (photo M. Rodríguez), and B. bored internode by Diatraea sp. can disrupt apical dominance and promote growth of multiple lateral shoots, diverting resources from sucrose synthesis to vegetative growth (photo AE Bustillo).
Fig. 2 in Sugarcane stem borers of the Colombian Cauca River Valley: current pest status, biology, and control
Fig. 2. Larvae of 4 Diatraea species present in Colombia. A. D. saccharalis; B. D. indigenella; C. D. tabernella; D. D. busckella. In general, larvae of D. saccharalis exhibit a well-sclerotized set of setal plates along their length, whereas the setal plates are ofen less distinguishable in D. indigenella due to dark, longitudinal dorsal stripes. Larvae of D. tabernella possess a distinctive set of blackish setal plates and adjacent purple spots that resemble transverse lines, which are absent in D. busckella (photos L. A. Lastra).
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).
Fig. 2 in Evaluation of abamectin as a potential chemical control for the lychee erinose mite (Acari: Eriophyidae), a new invasive pest in Florida
Fig. 2. Proportion of lychee plants that did not develop erinea on the new flush afer being sprayed with the treatment that was previously applied to the received leaflet. Lychee plants were sprayed with abamectin (red, N = 7), organosilicone surfactant (black, N = 9), combination of abamectin and organosilicone surfactant (grey, N = 8), water (blue, N = 10) or non-sprayed (green, N = 10). Shown are average proportions of lychee plants through time.
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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)
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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.