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Fig. 2 in Ecological fitting: Chemical profiles of plant hosts provide insights on selection cues and preferences for a major buprestid pest

Fig. 2. Ordination (nonmetric multidimensional scaling) plots of volatiles profiles of black ash (BA, Fraxinus nigra), blue ash (Blue, F. quadrangulata), Manchurian ash (MA, F. mandshurica), olive (OL, Olea europaea), and white fringetree (WF, Chionanthus virginicus), five plant hosts of emerald ash borer (Agrilus planipennis). a) Overall plant profiles, b) Green leaf volatile (GLV) profiles, c) monoterpene profiles, d) sesquiterpene profiles, and e) antennally active compounds. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

opencc-by-4.0Sep 2023View details →

The new association between the invasive pest Spodoptera frugiperda J.E. Smith (Lepidoptera: Noctuidae) and local parasitoids in Special Region Yogyakarta, Indonesia

<p>Database of parasitoid associated with FAW Spodoptera frugiperda in Yogyakarta, Indonesia</p>

FIGURE 8 in Morphological and molecular profiling of an entomopathogenic nematode Steinernema feltiae: Unlocking its biocontrol potential against vegetable insect pests

FIGURE 8. Median lethal time (LT50) of Steinernema feltiae in the larvae of different insect pests at different time intervals and at different nematode concentrations, respectively.

FIGURE 6 in Morphological and molecular profiling of an entomopathogenic nematode Steinernema feltiae: Unlocking its biocontrol potential against vegetable insect pests

FIGURE 6. Maximum-likelihood phylogenetic tree between Steinernema feltiae and other species of Steinernema in the Feltiae-group based on nucleotide sequences of the D2–D3 expansion segments of large subunit (28S) of rRNA flanked by primers D2F and 536. Numbers at nodes represent bootstrap values based on 100 replications. Bars represent average nucleotide substitutions per sequence position. NCBI accession numbers of the nucleotide sequences used for the analyses are shown next to the species names. The scale bar shows the number of substitutions per site.

FIGURE 4 in Morphological and molecular profiling of an entomopathogenic nematode Steinernema feltiae: Unlocking its biocontrol potential against vegetable insect pests

FIGURE 4. Steinernema feltiae (light microscopy). A–C: First-generation female; A: Neck region (arrow pointing excretory pore); B: Vulva region; C: Posterior end (arrow pointing mucron). D,E: First-generation male; D: Neck region (arrow pointing excretory pore); E: Posterior end showing spicules and gubernaculum (arrow pointing mucron). F,G: Second-generation male; F: Neck region (arrow pointing excretory pore); G: Posterior end showing spicules and gubernaculum (arrow pointing mucron). H–J: Second-generation female; H: Neck region (arrow pointing excretory pore); I: Vulva region; J: Posterior end (arrow pointing mucron).

FIGURE 2 in Morphological and molecular profiling of an entomopathogenic nematode Steinernema feltiae: Unlocking its biocontrol potential against vegetable insect pests

FIGURE 2. Steinernema feltiae (line). A–D: First-generation female; A: Neck region; B: Posterior end; C: Vulva region; D: Whole female. E,G–K: Second-generation female; E: Whole female; G: Neck region; H–J: Posterior region showing variation in tail region; K: Vulva region. F: Whole infective juvenile.

FIGURE 3 in Morphological and molecular profiling of an entomopathogenic nematode Steinernema feltiae: Unlocking its biocontrol potential against vegetable insect pests

FIGURE 3. Steinernema feltiae (line). A–C: First-generation male; A: Whole male; B–C: Posterior region showing variations in spicule morphology. D,E: Second-generation male; D: Whole male; E: Posterior region.

FIGURE 7 in Morphological and molecular profiling of an entomopathogenic nematode Steinernema feltiae: Unlocking its biocontrol potential against vegetable insect pests

FIGURE 7. Median lethal concentration (LC50) of Steinernema feltiae in the larvae of different insect pests at different time intervals and at different nematode concentrations, respectively.

FIGURE 4 in Priopoda macrophyae (Hymenoptera, Ichneumonidae, Ctenopelmatinae), a new species of parasitoid of Macrophya satoi (Tenthredinidae), a serious pest of Japanese ash tree (Oleaceae)

FIGURE 4. Larva of Macrophya satoi Shinohara & Li, 2015 attacked by Priopoda macrophyae sp. nov. (photo by M. Isono).

FIGURE 3 in Priopoda macrophyae (Hymenoptera, Ichneumonidae, Ctenopelmatinae), a new species of parasitoid of Macrophya satoi (Tenthredinidae), a serious pest of Japanese ash tree (Oleaceae)

FIGURE 3. Priopoda macrophyae sp. nov., female (holotype) and male (paratype)—A: head, lateral view; B: mandible and malar space, postero-lateral view; C: areolet of right fore wing; D: hind tarsal claw; E: propodeum, dorsal view; F: apex of metasoma, ventro-lateral view; G: posterior margin of subgenital plate; H: apex of metasoma, lateral view.

FIGURE 2 in Priopoda macrophyae (Hymenoptera, Ichneumonidae, Ctenopelmatinae), a new species of parasitoid of Macrophya satoi (Tenthredinidae), a serious pest of Japanese ash tree (Oleaceae)

FIGURE 2. Priopoda macrophyae sp. nov. and P. otaruensis (Uchida, 1930), females (A, B, D, E: holotype; F: paratype)—A: head and mesosoma, lateral view; B, C: mesopleuron, lateral view; D: hind femur, tibia, and tarsus; E: scutellum, postscutellum, and propodeum, dorsal view; F: T I to T III, dorsal view.

FIGURE 1 in Priopoda macrophyae (Hymenoptera, Ichneumonidae, Ctenopelmatinae), a new species of parasitoid of Macrophya satoi (Tenthredinidae), a serious pest of Japanese ash tree (Oleaceae)

FIGURE 1. Priopoda macrophyae sp. nov., female (holotype) and male (paratype)—A, D: lateral habitus; B, E: head, frontal view; C: head, mesosoma, and metasoma, dorsal view.

Fig. 17 in Empirical mode decomposition applied to acoustic detection of a cicadid pest

Fig. 17. The number of times each kernel returned the best result in the experiment performed for the BWC, RFC, OC and VDS criteria.

Fig. 10 in Empirical mode decomposition applied to acoustic detection of a cicadid pest

Fig. 10. Representation in PP for all analyzed series of the sets of vectors formed by the SD, SSC, TTC and EDT criteria.

Fig. 13 in Empirical mode decomposition applied to acoustic detection of a cicadid pest

Fig. 13. Box plots representing the accuracy of the vectors given by the SD, SSC, TTC and EDT criteria. The cross symbol represents the arithmetic mean.

Fig. 18 in Empirical mode decomposition applied to acoustic detection of a cicadid pest

Fig. 18. CM for the vectors given by the SD, SSC, TTC and EDT criteria. The parameters of the SVM were considered according to Table 5.

Fig. 7 in Empirical mode decomposition applied to acoustic detection of a cicadid pest

Fig. 7. Example of IMFs and the residue obtained by EMD with SD criterion applied to an audio signal of one second.

Fig. 6 in Empirical mode decomposition applied to acoustic detection of a cicadid pest

Fig. 6. SVM structure used in the proposed approach. Layer A is the input layer, with m passive elements; layer B is the hidden layer, with n(Xm) active sc elements; and layer C is the output layer, with one active linear element. In layer B, φ 2 = φn(Xm) 1, φ 1 = φn(Xm), w 2 = wn(Xm) 1 and w 1 = wn(Xm).

Fig. 15 in Empirical mode decomposition applied to acoustic detection of a cicadid pest

Fig. 15. Accuracy as a function of the number of training samples for the best series using minimum features and best overall series, respectively, both provided by the vectors generated by EMD with the EDT criterion.

Fig. 11 in Empirical mode decomposition applied to acoustic detection of a cicadid pest

Fig. 11. Representation in PP for all analyzed series of the sets of vectors formed by the BWC, RFC, OC and VDS criteria.