Abstract. Polyphagous insect herbivores could be expected to perform relatively well in new areas because of their ability to exploit alternative resources. We investigated relative abundance patterns of the polyphagous thrips species Frankliniella schultzei, which is characteristically found on plants from many different families, to establish the role of different host plant species in a single locality where the species is not indigenous (Brisbane, south‐eastern Queensland, Australia). F. schultzei females and larvae were always present in flowers (where oviposition takes place) and never on leaves of the eight plant species that we surveyed regularly over one year. They were present in flowers of Malvaviscus arboreus in much higher densities than for any other host. F. schultzei females were more fecund and larvae developed faster on floral tissue diets of M. arboreus than on those of other hosts. M. arboreus is therefore regarded as the ‘primary’ host plant of F. schultzei in the locality that we investigated. The other species are regarded as ‘minor’ hosts. Available evidence indicates a common geographical origin of F. schultzei and M. arboreus. F. schultzei may therefore be primarily adapted to M. arboreus. The flowers of the minor species on which F. schultzei is also found may coincidentally share some features of the primary host. Adult thrips may therefore accumulate on minor hosts and breed there, but to a lesser extent than on the primary host. The general implications for investigating polyphagous host relationships and interpreting the ecology of these species as generalist invaders are spelt out.
1. Patterns of mite egg consumption by the phytophagous thrips Frankliniella schultzei Trybom were investigated. Although F. schultzei predation is somewhat similar to that of F. occidentalis (Pergande), the understanding of predation by these two phytophagous thrips was extended, allowing the functional significance of flower thrips’ predatory behaviour to be reinterpreted.2. Second‐instar larvae consumed significantly more eggs than any other life‐stage, and the daily intake of eggs by second‐instar larvae declined significantly with each successive day of the 4‐day duration of instar two.3. Mite eggs that had had their silken webbing removed were consumed at a significantly greater rate than those with their webbing intact.4. Frankliniella schultzei immatures developed successfully both on diets containing cotton (Gossypium hirsutum L.) leaf tissue plus mite eggs and on cotton leaf tissue alone. Supplementing a leaf tissue larval diet with mite eggs lowered the developmental time from egg to adult significantly, as well as lowering the percentage mortality. Continuation of the mite egg supplement beyond adult eclosion increased fecundity significantly and extended life span over that achieved on a leaf diet alone.5. In laboratory choice tests, mite eggs and pollen of Wax Mallow (Malvaviscus arboreus Cav.), the usual host of F. schultzei in Brisbane, were encountered with similar frequencies. Furthermore, the mean proportion of encounters with pollen grains that resulted in consumption of pollen did not differ significantly from the encounter : consumption rate for mite eggs.6. Frankliniella schultzei, like F. occidentalis, does not seem to be specifically adapted for preying on mite eggs, even though such predation enhances performance and reproductive output of F. schultzei when constrained on cotton leaves. Comparison of performance results with those published for F. schultzei when reared on the floral parts of one of its primary hosts (M. arboreus) (Milne et al., 1996), indicates that mite egg predation does not make up completely for a deficient adult or larval diet.
We determined the pattern of host plant use of Thrips tabaci by surveying wheat and ®ve weed species growing near cotton crops in the area centred on the Namoi Valley, northern New South Wales. Highest densities of T. tabaci females and larvae were found on in¯orescences and vegetative parts of turnip weed, Rapistrum rugosum, and lowest on wheat, Triticum aestivum. The other plant species (Paterson's curse, Echium plantagineum, curled dock, Rumex crispus, Mayne's pest, Verbena tenuisecta and sowthistle, Sonchus oleraceus) hosted intermediate densities of T. tabaci. The presence of high densities of reproductive T. tabaci on¯owering and vegetative parts of turnip weed suggests that this species is a primary host of T. tabaci in the study area.
ObjectivesArtificial intelligence (AI) algorithms have been developed to detect imaging features on chest X-ray (CXR) with a comprehensive AI model capable of detecting 124 CXR findings being recently developed. The aim of this study was to evaluate the real-world usefulness of the model as a diagnostic assistance device for radiologists.DesignThis prospective real-world multicentre study involved a group of radiologists using the model in their daily reporting workflow to report consecutive CXRs and recording their feedback on level of agreement with the model findings and whether this significantly affected their reporting.SettingThe study took place at radiology clinics and hospitals within a large radiology network in Australia between November and December 2020.ParticipantsEleven consultant diagnostic radiologists of varying levels of experience participated in this study.Primary and secondary outcome measuresProportion of CXR cases where use of the AI model led to significant material changes to the radiologist report, to patient management, or to imaging recommendations. Additionally, level of agreement between radiologists and the model findings, and radiologist attitudes towards the model were assessed.ResultsOf 2972 cases reviewed with the model, 92 cases (3.1%) had significant report changes, 43 cases (1.4%) had changed patient management and 29 cases (1.0%) had further imaging recommendations. In terms of agreement with the model, 2569 cases showed complete agreement (86.5%). 390 (13%) cases had one or more findings rejected by the radiologist. There were 16 findings across 13 cases (0.5%) deemed to be missed by the model. Nine out of 10 radiologists felt their accuracy was improved with the model and were more positive towards AI poststudy.ConclusionsUse of an AI model in a real-world reporting environment significantly improved radiologist reporting and showed good agreement with radiologists, highlighting the potential for AI diagnostic support to improve clinical practice.
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