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Key to Diptera families (adults)

Authors:
  • Natural History Museum of Denmark
Citation:
Buck, M., Woodley, N.E., Borkent, A., Wood, D.M., Pape, T., Vockeroth, J.R., Michelsen, V., & Marshall, S.A.
2009. Key to Diptera families - adults. Chapter 6, pp. 95-144. In Brown, B.V., Borkent, A., Cumming,
J.M., Wood, D.M., Woodley, N.E., & Zumbado, M.A. (eds.). Manual of Central American Diptera.
Volume 1. NRC Research Press, Ottawa. i-xii + 1-714.
See last page for book title page.
... The hymenopterans were morphologically characterized through the literatures of Goulet and Huber (1993) and Seltmann (2004) by pictorial, interactive and dichotomous keys. Further, the identification of adult dipterans was done through the pictorial, linear and dichotomous taxonomic keys designed by Buck et al. (2009) and Marshall et al. (2017). To estimate the pollinator density, four plots of 1 m 2 area were demarcated in the field and a total number of insect species visiting the eggplant flowers in a span of one minute during four time frames of the day (8.00 h, 11.00 h, 13.00 h and 15.00 h) in demarcated plots were recorded for five consecutive days and the ANOVA was assessed at p < 0.05 significance level and Tukey's-B test was conducted through SPSS software. ...
... by pictorial, interactive and dichotomous keys b For identification of Lepidopterans, the technical bulletin on butterflies of Almora prepared byStanley et al.(2018) was referred c The identification of adult Dipterans was done through the pictorial, linear and dichotomous taxonomic keys designed byBuck et al.(2009) andMarshall et al.(2017) Order of insect floral visitors Insect floral visitors classified up to species level ...
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Although eggplant is self-fertile and self-compatible crop, it needs assistance of insects for buzz pollination. In the present study, floral biology of “Shyamili” variety was examined and the flowers were classified into three types, wherein, 62.63 ± 5.53% of the flowers were true flowers with long style, followed by rudimentary flowers with short style (24.74 ± 2.32%) and flowers with medium style (12.26 ± 0.97%). The optimum time period for stigma receptivity and pollen germination was recorded between 8.30 and 10.30 h, during which the activity of insect pollinators was at peak. A total of 29 insect species visiting eggplant flowers and the pollination behaviour of three insect pollinators (Bombus haemorrhodalis, Apis cerana indica and Apis mellifera) actively foraging on eggplant flowers was analysed. B. haemorrhodalis spent significantly higher amount of time per flower (34.33 ± 4.54 s), whereas, A. mellifera was swift flyer and visited significantly higher number of flowers per unit time (6.67 ± 1.03). In case of yield-related parameters, the flowers pollinated by interaction of two pollinators (A. c. indica + B. haemorrhodalis) recorded the best quality fruit (fruit length, girth and weight), yield per unit area and per cent yield increase over closed control. Moreover, artificial introduction of insect pollinators shortened the crop cultivation period by reducing the time required for fruit set and final harvest. Further studies are required to utilise both domestic ATED and wild bees simultaneously for commercial eggplant production and ecological engineering of the fields to create a suitable microclimate for survival and reproduction of wild bees.
... They are also of medical and veterinary significance since they may result in a variety of different forms of myiasis in people and animals. W. magnifica, a species implicated in myiasis, is one of the members of this family (Sarcophagidae) (Pape 1996;Buck et al. 2009;Papp and Darvas 1997;Chandler 1998). W. magnifica is widespread around the globe, including reported populations in Africa, Asia, and Europe (Hall 2004;Pezzi et al. 2015). ...
Article
Wohlfahrtia magnifica (Schiner) (Diptera: Sarcophagidae) is a common cause of traumatic myiasis in livestock in Mediterranean countries as well as in Central and Eastern Europe. The current study investigated the utility of external body genitalia and mitochondrial and ribosomal DNA characteristics for identifying this species. W. magnifica was successfully identified morphologically and molecularly based on partial sequences of three DNA fragments, mitochondrial cytochrome oxidase I (mtCOI), 12S ribosomal DNA (rDNA), and 16S rDNA. A BLAST search for samples containing 12S and 16S rDNA sequences yielded 100 hits, with just one from W. magnifica (accession number: KU578263.1). More W. magnifica sequences were discovered for the mtCOI, including Egyptian isolates of W. magnifica cytochrome oxidase subunit I sequence (accession numbers: MN241041 and MN241043). As a result, we believe that mtCOI performed the best in terms of species identification. Phylogenetic analysis of mtCOI, 12S rDNA and 16S rDNA showed that W. magnifica identified in the current study was most closely related to W. magnifica retrieved from GenBank.
... The samples were brought to the Entomology Laboratory of Sylhet Agricultural University and preserved in 70% (v/v) ethanol for further identification and grouping into functional groups (predator, parasitoid, and pests). The preserved samples were placed in Petri dishes and identified to the family level using a microscope, colored photographs in books and published papers for the families Araneae (Levi 2002;Coddington 2005;Hamasaki et al. 2008;Nentwig et al. 2018), Blattodea (Baur et al. 2004;Hristov and Chobanov 2016), Coleoptera (Kurosawa et al. 1998;Lawrence et al. 1999;Choate 2003), Dermaptera (Kočárek 2014;Miles 2015), Diptera (Buck et al. 2009;Sawaby et al. 2018), Hemiptera (Weirauch et al. 2014), Hymenoptera (Shaw and Huddleston 1991;Goulet and Huber 1993), Isoptera (Scheffrahn et al. 2003(Scheffrahn et al. , 2006, Lepidoptera (Dombroskie 2011;Smetacek 2015;Parveen and Khan 2017;Kunte et al. 2020;Sondhi et al. 2020), Mantodea (Rivera 2010), Odonata (Bouchard 2004;Wright and Peterson 1994) and Orthoptera (Smith et al. 2004). Besides, taxonomic features of textbooks were also used for identification (Schell and Latchininsky 2007;Chapman 2013;Zettler et al. 2016). ...
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Naturally or artificially created diverse vegetation habitats often promote natural enemies of crop pests. The abundance of natural enemies increases with response to increasing habitat complexity at various spatial scales. Against this backdrop, we assessed the contribution of shade trees to conserve beneficial predatory and parasitoid arthropods at local scale in a subtropical tea agroecosystem in Bangladesh. Arthropods viz., predators and parasitoids were captured using Malaise traps in plots with shade trees and in those without shade trees having three different densities of tea bushes due to naturally created gaps (large-gap, small-gap and control). The number of arthropods captured in each plot corresponded to shade-tree and tea-bush densities, such that arthropod abundance in the shade-tree plot was twice that of the control plot, which had similar tea-bush density but no shade trees. Predators were least abundant in the large-gap plot, which had the lowest tea-bush density and no shade-trees, while 2.4–3.8 folds predators were captured in the control and shade-tree plots. There was a negative correlation between mean light intensity and number of predators in the four plots. Similar trends were observed for parasitoids. Relative abundance of predatory Staphylinidae was more than twice in the shade-tree and small-gap plots compared to control and large-gap plots. Relative abundance of parasitoid Ichneumonidae was ranked as small-gap > shade-tree > control > large-gap plots. Our results suggested that shade trees may help conserve and promote beneficial arthropods such as predators and parasitoids in tea agroecosystems.
... Insects are one of the largest and most diverse animal groups on the planet with an estimated 5.5 million species, yet only 20% are described (Stork, 2018) and many are disappearing faster than they can be identified (Costello et al., 2013), making it difficult to assess biodiversity. Once an insect is collected, a taxonomist will identify the insect to its lowest taxonomic level possible based on existing morphological character keys (Buck et al., 2009). Traditionally, taxonomists use identification keys describing physical characters to identify a given specimen. ...
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Classifying insect species involves a tedious process of identifying distinctive morphological insect characters by taxonomic experts. Machine learning can harness the power of computers to potentially create an accurate and efficient method for performing this task at scale, given that its analytical processing can be more sensitive to subtle physical differences in insects, which experts may not perceive. However, existing machine learning methods are designed to only classify insect samples into described species, thus failing to identify samples from undescribed species. We propose a novel deep hierarchical Bayesian model for insect classification, given the taxonomic hierarchy inherent in insects. This model can classify samples of both described and undescribed species; described samples are assigned a species while undescribed samples are assigned a genus, which is a pivotal advancement over just identifying them as outliers. We demonstrated this proof of concept on a new database containing paired insect image and DNA barcode data from four insect orders, including 1040 species, which far exceeds the number of species used in existing work. A quarter of the species were excluded from the training set to simulate undescribed species. With the proposed classification framework using combined image and DNA data in the model, species classification accuracy for described species was 96.66% and genus classification accuracy for undescribed species was 81.39%. Including both data sources in the model resulted in significant improvement over including image data only (39.11% accuracy for described species and 35.88% genus accuracy for undescribed species), and modest improvement over including DNA data only (73.39% genus accuracy for undescribed species). Unlike current machine learning methods, the proposed deep hierarchical Bayesian learning approach can simultaneously classify samples of both described and undescribed species, a functionality that could become instrumental in biodiversity monitoring across the globe. This framework can be customized for any taxonomic classification problem for which image and DNA data can be obtained, thus making it relevant for use across all biological kingdoms.
... Specimens were brought back to the taxonomy lab at the Faculty of Applied Sciences and Technology, Universiti Tun Hussein Onn Malaysia for identification using stereo microscopes. Specimens were identified to species level using keys by Romoser and Stoffolano (1998), Borror et al. (2005), Buck et al. (2009), Choate (2011a), Choate et al. (2011), Choate (2011b) and Orr (2014). At the time of publication, identification of doubtful species without comparison with type specimens and museum collections were denoted as 'unknown sp.' in Table 2. ...
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Species assembly of insects in Pulau Tinggi was extensively studied to obtain the first checklist of insect fauna on the island. Samples were collected from forested areas in Pulau Tinggi on three occasions (20-30 April, 1-10 May and 20-30 June 2019). Various sampling methods were used, including pitfall trap, light trap and active collection using sweep net and forceps. The specimens were dried and curated for identification. A total of 41 families from ten orders of insects representing 90 species and morphospecies were identified. Coleoptera was the most dominant order with 14 families, followed by seven families from the order Hemiptera, three from Hymenoptera, four from Orthoptera, three from Odonata, three from Diptera, two from Blattodea, one from Mantodea and one family from order Phasmatodea. This first inventory study has shown that this relatively unexploited island possesses many insect species. Hopefully, this data will assist policymakers and governing bodies, particularly the private landowners and Johor State Government in making conservation management decisions in the future.
... Later, every sample was preserved in 70% ethanol and deposited in the facilities of the Multitrophic Interaction Network (INECOL) for further studies. Each sample was identified to family level using taxonomical keys (Buck et al. 2009), and the trophic guilds of the larval stages were also documented Marshall 2012;Courtney et al. 2017) to explore the functional structure of the community. ...
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... Based on their taxonomic identity, all adult and immature samples (excluding mites) were assigned to one of the following trophic guilds: scavengers, coprophagous, hematophagous, parasitoids, mycetophagous, herbivores, saprophagous, omnivorous, and predators (Arnett, 1973;White, 1983;Cibrián et al., 1995;Fernández and Sharkey, 2006;Buck et al., 2009). This critical task was performed by a specialist taxonomist from the INECOL, who possesses a deep knowledge of the natural history of the soil arthropod. ...
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166); frons with fronto-orbital bristles small and hairlike (Fig. 181); face flat to concave
  • ........................ Chloropidae
Propleuron with sharp vertical ridge (similar to Fig. 166); frons with fronto-orbital bristles small and hairlike (Fig. 181); face flat to concave..........................Chloropidae, in part* *Note: Brachypterous species known from South America but not yet recorded from Central America.
Revisión parcial de las Pyrgotidae neotropicales y antárticas, con sinópsis de los géneros y especies (Diptera, Acalyptratae)
  • M L Aczél
Aczél, M.L. 1956. Revisión parcial de las Pyrgotidae neotropicales y antárticas, con sinópsis de los géneros y especies (Diptera, Acalyptratae). Revista Brasileira de Entomologia 6: 1–38.
A review of the Rhinophoridae (Diptera) and a revision of the Afrotropical species
  • R W Crosskey
Crosskey, R.W. 1977. A review of the Rhinophoridae (Diptera) and a revision of the Afrotropical species. Bulletin of the British Museum (Natural History), Entomology 36: 1–66.