Systematic Review

published by Jacob Leung

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A Systematic Review of the Statistical Scope of Drosophila, Oviposition, and Behaviours
Jacob C.K. Leung
Oviposition (egg-laying) is common reproductive behavior in many organisms, such as insects, which serves as good indicators of the animal’s health, genetics, and predatory stress patterns [1–11]. There are many parameters that affect oviposition patterns. Specifically in insects, such as Drosophila melanogaster, the "common fruit fly", these oviposition factors includes biological interactions, physical and chemical properties of substrates, fly preconditions, genetics, and experimental conditions induced [12–17].
The study search was conducted via the Web of Science database for articles on Drosophila oviposition using the terms, "fruit fly", "oviposition", and "behavi*". 1.) From the 340 articles found, it was further refined by limiting the search to accessible and English publications. 2.) Articles were screened, and duplicates were removed. From this, 87 articles were remained. Two families of fruit flies were assayed in this set of articles: Drosophilidae and Tephritidae. 3.) Only those articles about the common fruit fly with statistical data were included  as in the PRISMA flow (Fig. 1). 4.) These 17 remaining articles were later sorted and tagged based on ovipositional factors, country, authorship, publication date,and statistical methods used in the assays via the Mendeley software. This information was then counted and inputted onto a spreadsheet. 5.)To compare Drosophila literature with all fruit fly literature, the 87 articles were all sorted and tagged as above.
Fig.1: PRISMA flow for literature search refinement in systematic review
Some of the common statistical tools used in these oviposition analysis are hypothesis testing, post-hoc analyses, correction tools, sampling, regression, and transformations [13,17,24–32]. Although many studies may not use all of the statistic tools above, most of the assays require hypothesis testing. And since oviposition data comes in groups due to the subjected factors being observed or the number of trials in different or the same strains, analysis of variance (ANOVA) tests are commonly used to observe the variation within [33–35]. Currently, only journal articles are available whereas systematic reviews and meta-analyses on the statistical scope on fruit fly oviposition are yet to be seen.Therefore, the purpose of this is to generate a meta-analysis to:
(I.)   to investigate and analyze how statistics were used in oviposition data by country (II.)  to determine the frequency distribution of articles for various different oviposition factors (III.) to investigate the differences between statistical approaches used in all fruit fly and the          common fruit fly literatures.
Results & Discussion
Figure 2 shows the Drosophila articles by country in a pie chart. From this, we can clearly see that France (29%), England (10%), and Canada (10%) dominated close to half (49%) of all the relevant literature. Also, out of these 17 Drosophila articles included, we can see that Europe had twice the amount of Drosophila oviposition publications (n=8) than the runner up regions, North America (n=4) and Asia (n=4). North America and European countries dominated this set of relevant literature because only English journals were sought and refined via the Web of Science database. This is demonstrated further in Figure 3, which shows the order of authorship of the included articles distributed internationally. Figure 3 also shows the order of authorship by country for the all the 87 articles on the general fruit fly. Most of the corresponding and primary authors were from the United States in contrast to just 9% in the Drosophila literature. Moreover, the other countries that were also leading in studying fruit fly oviposition were France (8%) and Mexico (10%). We can see that Mexico had a significant increase from 0% distribution; this means that a lot of studies were done on other species of fruit flies (such as the Caribbean fruit fly and the Mexican Anastrephe fraterculus) [51–55]. In Mexico, these species are considered as agricultural pest; hence, many studies were conducted to control their population [51–55]. Likewise, in countries near the Mediterranean, the Ceratitis capitata, Tephritid fruit fly prominent in Southern Europe and the Middle East, were also studied since they were considered pest near or in that region [26,38,56,57]. From this, we conclude that one of the main factors for fruit fly oviposition to be studied is mostly based on the agricultural impact each species have in and near a specific region.
Fig.2: Refined Drosophila Articles by Country
Fig.3:  Articles by Authorship and Country
To better understand the statistical scope encompassing Drosophila oviposition, Figure 5 was created via counting Mendeley tags to show the statistical analysis models chosen by each country.  Again, ANOVA test is the most popular test that is used commonly by most places. Some tests (such as the G-test and the Kruskal-Wallis test) were seen to be specifically used by articles from Japan and the United Kingdom.
With the aid of the Mendeley software tags, all the statistical models used in each of the included articles were attained per year. Via this, Figure 4 was obtained to show the statistical methods used throughout the years.The 6 tests that were used are ANOVA, χ2 test, G-test, Kruskal-Wallis test, Mann-Whitney, and the T-test. We see that throughout the years, the top statistical methods that were most frequently used were ANOVA, T-test, and Mann-Whitney test, which occupied 47.4%, 21.1%, and 15.8% of the articles respectively. Per figure 4, T-test, which was used in the beginning of 1991 as ANOVA, seemed to have been faded out by the year 2006. The contrast may be due to the fact that as the studies progressed, more than two oviposition al factors and more trials were performed and done at the same time; hence, multiple analysis tool, such as ANOVA, were more preferable in comparing groups of variables.
Moreover, we determined the frequency distribution of articles for various different oviposition factors. Several classifications were found and plotted as in Figure 6: Biological Interactions, Chemical Substrates, Fly Density, Fly Pre-conditions, Genetics, Physical Substrates, and Experimental Conditions.   As in Figure 6 , we can see that most of the oviposition factors assayed were regarding the chemical properties of the substrate (n=10). Biological interaction, in which oviposition responses were tested in the presence of other animals, was rarely studied (n=1).
The significance of the articles being published was also exhibited via finding the journal distribution and the corresponding journal’s impact factor as in Figure 7. In Figure 7 , we can see that most of the articles were from different journals except the Journal of Insect Physiology, with a journal distribution percentage of 12%. Furthermore, all the articles published seemed to have a low impact factor of less than 4 except one article that was published in the Journal of Science.
Comparing the included Drosophila articles with all the fruit fly literature, the distribution trend of articles per country was changed as in figure 2 and 8. Figure 8 shows the percentage of fruit fly articles (n=87) divided among 25 countries. In all fruit fly studies, USA, Mexico, and France dominated more than half of the articles on fruit flies (52%). To recall, USA, as in Figure 2, exhibited a distribution percentage of 10% in all Drosophila articles; however, in the general fruit fly literature, the percentage increased more than three folds (34%). Moreover, Mexico, which was not seen in the distribution of Drosophila articles, partook in 10% of all the fruit fly literature, which was the second highest country. This corroborate with the corresponding authorship map in Figure 3 and the agricultural reasoning as above.
To compare if the statistical scope in Drosophila conforms to the trends with all 87 fruit fly articles, Figure 9 was generated to show the statistical models used per year from 1991. From this, ANOVA was still very prevalent through the years and was used the most frequent yielding a frequency percentage of 44.5%. Other tests that were more frequently used such as the T-test, Mann-Whitney, and the χ2 test, which produced a usage frequencies of 17.2% 13.6%, and 11.2% respectively. In comparison to Figure 4, we have found that after 2006, more variety of statistical models (except the MANOVA) were used. Also, T-test seemed to have decreased in usage by 2006 in Drosophila articles; however, T-test was still performed past 2006 for the 87 fruit fly studies.
In Figure 10, the same classifications were found and plotted as in Figure 6. Likewise, as in the refined articles, we can see the same trend that most of the oviposition factors assayed in the 87 fruit fly articles were regarding the chemical properties of the substrate (n=38) due the objective of controlling the infestation of agricultural pests to generate and test the effectiveness of insecticides. In both figures, fly density and physical substrate properties were shown to be the least focused substrate parameter in fruit fly oviposition. In comparison of the two set of articles, the occurrence of biological interaction and fly pre-conditions assayed were higher in the general fruit fly literature due to more parasitoid studies in Tephritid flies and more studies concerning the diet, age, and stress induced prior to the assay [46,61–63].  
In Figure 11, we see that most of the articles were from the ENTOMOLOGIA EXPERIMENTALIS ET APPLICATA, with a journal distribution percentage of 13%. Similarly, all the articles published in both sets (Figure 7 and 11) showed only one article from Journal of Science, which held the highest impact factor. All other articles were all published in journals with an impact factor of less than 3.6. This shows that most of the journals were published in journals with low average citations and research influence. Also, there was a trend in which journals with more than one article published in both distributions showed an impact factor of less than 2.7.  
1. Q. Qin, A. Sun, R. Guo, M. Lei, S. Ying, and Z. Shi, Reprod. Biol. Endocrinol., 2013, 11, 65. 2. J. Dvořák and L. Gvoždík, Ethology, 2009, 115, 533–539. 3. M. Takahashi, Environ. Toxicol. Chem., 2007, 26, 1476. 4. B. a Sandkam and R. C. Fuller, J. Fish Biol., 2011, 78, 967–72. 5. E. Lansing, I. Natural, and H. Survey, 2009, 92, 415–420. 6. I. Higa and Y. Fuyama, 1993, 129–136. 7. T. Takamura and Y. Fuyama, Behav. Genet., 1980, 10, 105–20. 8. a K. Tiwari, P. Pragya, K. Ravi Ram, and D. K. Chowdhuri, Theriogenology, 2011, 76, 197–216. 9. A. Pandey, D. Vimal, S. Chandra, S. Saini, G. Narayan, and D. Kar Chowdhuri, Age (Dordr)., 2014, 36, 9628. 10. R. C. Bautista, E. J. Harris, and P. O. Lawrence, Entomol. Exp. Appl., 1998, 89, 79–86. 11. B. Z. Kacsoh, Z. R. Lynch, N. T. Mortimer, and T. a Schlenke, Science, 2013, 339, 947–50. 12. B. Wertheim, M. Dicke, and L. E. M. Vet, Entomol. Exp. Appl., 2002, 103, 61–71. 13. S. Bahadorani and A. J. Hilliker, J. Insect Behav., 2009, 22, 399–411. 14. M. I. L. Betti, E. M. Soto, and E. Hasson, Ann. Entomol. Soc. Am., 2014, 107, 944–953. 15. B. Wertheim, R. Allemand, L. E. M. Vet, and M. Dicke, Ecol. Entomol., 2006, 31, 216–226. 16. R. Snook and Y. So, J. Insect Physiol., 2000, 46, 1489–1496. 17. N. N. Kannan, R. Reveendran, S. Hari Dass, T. Manjunatha, and V. K. Sharma, J. Insect Physiol., 2012, 58, 245–55. 18. W. Atkinson, Aust. J. Zool., 1983, 31, 925. 19. E. Harada, D. Haba, T. Aigaki, and T. Matsuo, Genes Genet. Syst., 2008, 83, 257–64. 20. Z. Durisko and R. Dukas, 2013. 21. A. Menon, V. Varma, and V. K. Sharma, Chronobiol. Int., 2014, 31, 433–41. 22. T. Srivastava and B. N. Singh, Rev. Bras. Biol., 1998, 58, 491–5. 23. E. M. Schnebel and J. Grossfield, 2014, 116, 25–35. 24. L. Xu, C. Zhou, Y. Xiao, P. Zhang, Y. Tang, and Y. Xu, Ecol. Entomol., 2012, 37, 446–452. 25. W. L. Yee, 2008. 26. Y. Argov, A. Blanchet, and Y. Gazit, Biol. Control, 2011, 59, 209–214. 27. Y. Hidayat, N. Heather, and E. Hassan, Entomol. Exp. Appl., 2014, 152, 175–181. 28. N. Jaeger and L. Despre, 1999, 12, 822–831. 29. L. Medeiros-Santana and F. S. Zucoloto, Ann. Entomol. Soc. Am., 2009, 102, 819–825. 30. F. Dı, 2003, 1, 125–133. 31. K. Lam, D. Babor, B. Duthie, E. Babor, M. Moore, and G. Gries, Anim. Behav., 2007, 74, 81–92. 32. E. F. Boller, B. I. Katsoyannos, and C. Hippe, 1998, 122, 231–237. 33. C. Stuhl, J. Sivinski, P. Teal, and M. Aluja, Florida Entomol., 2012, 95, 1031–1039. 34. G. P. Fitt, 2006, 37–46. 35. a. Manrakhan and S. a. Lux, Bull. Entomol. Res., 2007, 96, 259–268.
36. S. Muthuthantri and A. R. Clarke, Aust. J. Entomol., 2012, 51, 289–298. 37. T. E. Shelly, 1999. 38. C. S. Ioannou, N. T. Papadopoulos, N. a. Kouloussis, C. I. Tananaki, and B. I. Katsoyannos, Physiol. Entomol., 2012, 37, 330–339. 39. D. R. Papaj, Behav. Ecol. Sociobiol., 1994, 34, 187–195. 40. T. Matsuo, Appl. Entomol. Zool., 2012, 47, 137–142. 41. T. S. Hoffmeister and M. Rohlfs, 2001, 37–50. 42. M. Amlou, B. Moreteau, and J. R. David, Behav. Genet., 1998, 28, 455–64. 43. T. Matsuo, S. Sugaya, J. Yasukawa, T. Aigaki, and Y. Fuyama, PLoS Biol., 2007, 5, e118. 44. F. Mery and T. J. Kawecki, Proc. Natl. Acad. Sci. U. S. A., 2002, 99, 14274–9. 45. S. Singh, J. Appl. Entomol., 2003, 127, 540–547. 46. F. S. Zucou and H. Preto, 1992, 38, 485–491. 47. H. Levinson, A. Levinson, and E. Osterried, J. Appl. Entomol., 2003, 127, 269–275. 48. R. J. Prokopy and J. J. Duan, 1998, 1, 117–122. 49. H. Genç and J. L. Nation, J. Pest Sci. (2004)., 2008, 81, 167–174. 50. A. R. Liczner and C. J. Lortie, PeerJ, 2014, 2, e265. 51. M. Aluja, M. Ordano, P. E. a Teal, J. Sivinski, D. García-Medel, and A. Anzures-Dadda, J. Insect Physiol., 2009, 55, 231–42. 52. M. Aluja, D. Pérez-Staples, R. Macías-Ordóñez, J. Piñero, B. McPheron, and V. Hernández-Ortiz, J. Econ. Entomol., 2003, 96, 1693–703. 53. C. Quality, 2002, 481–487. 54. P. Greany, A. Birke, and D. Pe, 2011, 57, 321–328. 55. M. Aluja, F. Díaz-Fleischer, and J. Arredondo, J. Econ. Entomol., 2004, 97, 293–309. 56. N. a Kouloussis, N. T. Papadopoulos, B. I. Katsoyannos, H.-G. Müller, J.-L. Wang, Y.-R. Su, F. Molleman, and J. R. Carey, Entomol. Exp. Appl., 2011, 140, 181–188. 57. S. O. Rtu, A. C. Occo, and R. D. Au, 2009, 62, 245–252. 58. J. Arredondo and F. Díaz-Fleischer, Bull. Entomol. Res., 2007, 96, 35–42. 59. D. Francisco, 2003, 16, 537–554. 60. H. K. M. Dweck, S. a M. Ebrahim, S. Kromann, D. Bown, Y. Hillbur, S. Sachse, B. S. Hansson, and M. C. Stensmyr, Curr. Biol., 2013, 23, 2472–80. 61. G. Benelli, G. Gennari, and A. Canale, J. Pest Sci. (2004)., 2012, 86, 245–251. 62. P. Sciences, A. Entomology, and E. T. H. Zentrum, 1994, 279–287. 63. M. Aluja, A. Birke, L. Guillén, F. Díaz-Fleischer, and D. Nestel, J. Insect Physiol., 2011, 57, 1592–601.
Fig. 4: Statistical Models used in the Drosophila Articles by Year
Fig. 5: Statistical Models used in the Drosophila Articles Country
Fig. 6: Ovipositional Factors assayed in Refined Articles
Fig. 7: Journal distribution and Impact factor of Drosophila Articles
Fig. 8: All Fruit Fly Articles by Country
Fig. 9: Statistical Models used in the Fruit Fly Articles by Year
Fig. 10: Ovipositional Factors assayed in Fruit Fly Articles
Fig. 11: Journal distribution and Impact factor of Fruit Fly Articles
In essences, we have conducted the first systematic review on Drosophila oviposition, and have compared it to the trends of all types of fruit flies.We have found that France and USA dominated both Drosophila and all fruit fly literature; whereas places like Mexico was only prevalent in all fruit fly (Tephritidae) articles due to its agricultural management of local pests. Also, in both set of literature, ANOVA tests were more commonly used. Lastly, the ovipositional factor that was found to be the most studied was chemical substrate; the area that were rarely studied in both sets were physical substrate conditions and fly density. By understanding the statistical scope and the distribution of articles on fruit fly oviposition, we can choose better statistical methods in analyzing our data and also to recognize the areas of oviposition which were overlooked and rarely studied.