Comparative Analysis of Molecular Identification and Wing Morphometrics of Forensically Important Blow Flies in China

doi:10.21203/rs.3.rs-33222/v1

Download PDF

Research

Comparative Analysis of Molecular Identification and Wing Morphometrics of Forensically Important Blow Flies in China

https://doi.org/10.21203/rs.3.rs-33222/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

Background: Correct species identification is the most crucial step in applying entomological evidence to estimate the postmortem interval (PMI) since death of decomposed corpses. Wing morphometrics have been proposed in species classification as an alternative method of traditional morphology and molecular approaches. However, so far, this method has not been applied to the identification of Chinese Calliphoridae and few studies compare the two identification methods.

Methods: We used landmark-based geometric morphometrics of wings to identify nine medically and forensically important blow fly species of China. 270 specimens representing nine species and eight genera were sampled, 18 landmarks on the right wing were measured and analyzed using canonical variates analysis and discriminant function analysis. Then, a cross-validation test was used to evaluate reliability of the method. Moreover, in order to further assess the validity of this method, molecular identification is used for comparative analysis. Eighty sequences of cytochrome c oxidase subunit I (COI) of Calliphoridae isolated from different countries were downloaded from Genbank, including the data previously submitted by our team.

Results: Different species and genera can be well separated through morphometric analysis with an overall classification accuracy of 80~100%, but discrimination between sexes was less effective. The results indicated that the discriminative efficiency of the two methods is almost identical.

Conclusions: Wing morphometrics can be used as a complementary method of molecular identification for the geographical location and gender identification of certain species as a simple and cheap method.

Parasitology

Forensic entomology

Blow flies

Species identification

Molecular identification

Wing morphometrics

China

As an important forensic tool, forensic entomology, the interpretation of insect evidence in legal investigations, has been applied prevalently and rapidly to many countries in the early 21st century [1]. Correct identification of insect specimens collected from corpses or a crime scene is a crucial step of forensic entomology, which can provide critical information for forensic cases, including estimation of the minimum postmortem interval (PMImin) to solve cases involving death [2, 3]. Blow fly (Diptera: Calliphoridae) is usually the first colonizers of corpses, and also the most common insect related to animal and human corpses, and therefore become the focus of forensic entomological research[4]. Many of them are widely distributed around the world while others live in certain geographic regions. In general, the flesh flies can be identified by morphological method [5, 6, 7]. However, due to the high complexity of the structure of male reproductive organs, it is difficult for non-taxonomists to recognize them.

As an alternative to traditional morphological recognition methods, DNA-based analysis identification has been developed rapidly and widely used in many counties all over the world as a reliable routine tool in forensic entomology, which only requires a small amount of sample at any life cycle stage [8]. Among commonly applied genetic markers, mitochondrial cytochrome c oxidase subunit I (COI) has been extensively used because of its high copy number, high mutation rate, and haploid maternal inheritance [9]. Many studies have proved the robustness of COI as a marker for fly species discrimination [10]. Nevertheless, there is no significant difference in the usage of short fragments or even the entire sequence of COI [11]. DNA barcoding is based on a standardized small segment fragment sequence of cytochrome oxidase subunit 1 (COI) called "DNA barcode" and is widely used in public databases, such as GenBank and Bold (life data barcode). Thus, DNA barcoding is also widely used to identify fly species [12]. In addition, many studies have proved the effectiveness of COI barcodes for the identification of many species of Calliphoridae [13, 14]. Meanwhile, in GenBank, there are abundant molecular data about Calliphora, sequences of forensic important Calliphora have been published and uploaded online from different regions of the world, such as South Africa, the Caribbean Region, Southern European, the United States and Korea [15, 16, 17, 18], making it possible to systematically study the sequence of Calliphora around the world. However, the molecular identification is a destructive approach for sample materials. Sequencing has relative higher cost and sequence analysis might be problematic in identification of closely related species [19, 20]. Therefore, other effective alternative methods were needed.

Besides DNA identification, geometric morphometrics is a method by using a series of reference points or landmarks that make the morphological comparison of any object structure possible, removing irrelevant information such as the position and orientation of the specimens, considering only their shape [21]. It is a quantitative study of the size, shape and shape changes of species and their covariation with other biotic or abiotic factors [22]. As an alternative approach, the use of landmark-based geometric morphometric analysis of insect wings has shown to be a valuable tool in many studies following early work by Brown [23]. Wing morphometrics has been increasingly popular in studies of Diptera, demonstrating the species identification value in Syrphidae [24] and forensically important families like Muscidae [25] and Sarcophagidae [26]. It has been proved that wing morphometrics can be used for the identification of necrophagous Calliphoridae from Thailand [27] and Europe [28], and distinguish different genus and species of Calliphoridae, Cochliomyia [29], Lucilia [23, 30] and Chrysomya [29, 31]. In addition, geometric morphometric analysis can be used to distinguish variability between geographical populations [29, 32], seasonal morphs [33] or sexes [26, 34] among the same species, and recently it is used to identify Piophilidae (Diptera) for forensic purpose [35]. All of these shows that the analysis of wing morphometric is a very useful identification method.

Up to now, morphometric analysis has not been widely used in the species of Chinese Calliphoridae. In this study, we apply wing morphometric for the identification of necrophagous flies in China at genus and species levels, evaluating the effect of allometric growth on species identification. As female flies are more common and difficult to identify in the forensic field, the differences between male and female wings of nine species of flies were also investigated. In addition, we compared gene sequences of Calliphora from different cities of China with those from different countries and evaluate the reliability of the two types of methods for the identification of forensically relevant species of Calliphoridae. Therefore, this study examined the potential of these tools for specimen identification and concluded that wing morphometrics could be a simple and user-friendly method for research and practical application of forensic entomology.

Specimen collection

For wing morphometrics analysis, materials for the present study were collected from different places of China. Adult flies, both males and females, were collected directly with a handheld fly net or lured to slightly decomposed pig cadavers placed in a field. All specimens were identified according to Gregor et al. and then preserved in ethanol. A total of 270 specimens representing 8 genera and 9 species were used in this study: Triceratopyga calliphoroides (Rohdendorf, 1931), Calliphora vomitoria (Linnaeus, 1758), Chrysomya megacephala (Fabricius, 1794), Chrysomya rufifacies (Macquart, 1843), Calliphora vicina (Robineau-Desvoidy, 1830), Aldrichina grahami (Aldrich,1930), Hemipyrellia ligurriens (Wiedemann, 1830), Lucilia sericata (Meigen, 1826), Protophormia terraenovae (Robineau-Desvoidy, 1830). Each species included 15 males and 15 females. Hence, 9 species included in this study involved most of the taxa that are of medical and forensic importance (Table 1).

Table 1

Samples List of Chinese blow fly used in this study
Subfamily	Species	Species code	Collection site	Province	No. of specimens
Subfamily	Species	Species code	Collection site	Province	Males	Females	Total
Calliphorinae	A.grahami	AG	Xishuangbannna	Yunnan	3	2	5
			Guiyang	Guizhou	4	5	9
			Shenzhen	Guangdong	6	5	11
			Chongqing	Chongqing	2	3	5
		total			15	15	30
	C.vicina	CVI	Lhasa	Tibet	5	5	10
			Urumchi	Xinjiang	8	7	15
			Guiyang	Guizhou	1	1	2
			Xinyang	Henan	1	2	3
		total			15	15	30
	C.vomitoria	CVO	Lhasa	Tibet	9	12	21
			Jining	Shandong	5	3	8
			Lundazi	Tibet	1	-	1
		total			15	15	30
	T.calliphoroides	TC	Qufu	Shandong	6	8	14
			Beijing	Beijing	3	4	7
			Jining	Shandong	4	3	7
			Gunzhou	Shandong	2	-	2
		total			15	15	30
Chrysomyinae	Ch.megacephala	CM	Changsha	Hunan	10	12	22
			Guilin	Guangxi	5	3	8
		total			15	15	30
	Ch.rufifacies	CR	Shenzhen	Guangdong	6	8	14
			Guiyang	Guizhou	3	5	8
			Chongqing	Chongqing	6	2	8
		total			15	15	30
	P.terraenovae	PT	Lhasa	Tibet	15	15	30
	P.terraenovae	total			15	15	30
Luciliinae	H.ligurriens	HL	Changsha	Hunan	3	5	8
			Xishuangbannna	Yunnan	6	8	14
			Guiyang	Guizhou	6	2	8
		total			15	15	30
	L.sericata	LS	Lhasa	Tibet	13	15	28
			Guiyang	Guizhou	2	-	2
		total			15	15	30

Molecular Analysis

COI gene sequences are obtained from Genbank, and the length was set more than 650 bp to contain the region of COI barcode. Then, alignment and phylogenetic were performed in MEGA 10.0.5 (Center for Evolutionary Medicine and Informatics) [36] yielding a total data set of 80 COI sequences (Table S1). Consensus sequences were aligned using ClustalW under default parameters. The neighbor-joining (NJ) tree construction and genetic divergences calculation were conducted using the uncorrected pairwise distance model in MEGA version 10.0.5 [36]. The constructed tree was generated with the Tamura 3-parameter model, and 500 bootstrap replicates were used to assess the reliability of the tree [37]. Genetic distances were calculated by the Kimura 2-parameter (K2P) model in MEGA version 10.0.5 [36].

Wing Morphometrics

Shape variation of genera and species

At the genera level, the CVA revealed seven canonical variates, and the first two canonical variates (Fig. S1) explained 75% of the total variation (CV1 = 52, CV2 = 23). The scatter diagram from CV1 and CV2 (Fig. S1) showed three clearly separated clusters. The first cluster were specimens of genera Hemipyrellia, which was form a distinct group separated from other genera. The second cluster was genera Achoetandrus and Chrysomya. The third cluster included specimens of genera Lucilia, Calliphora, Aldrichina, Triceratopyga and Protophormia. The Mahalanobis distances acquired from the pairwise comparisons of all eight genera indicated highly significant differences (permutation test with 10,000 rounds in MorphoJ: P < 0.0001), ranging from 4.0299 (Calliphora and Aldrichina) to 29.9547 (Achoetandrus and Protophormia) (Table S2). The percentages of correctly classified specimens obtained from the cross-validation test ranged from 80–100.0% (Table S3).

At the species level, the CVA revealed eight canonical variates, the first two canonical variates (Fig. 3a) accounted 80 of the total variation (CV1 = 59, CV2 = 21). The scatter diagram from CV1 and CV2 (Fig. 3a) showed that there are three clearly separated clusters. The first cluster were specimens of species H. igurriens; the second cluster included species Ch. egacephala and Ch. ufifacies; the third cluster consisted of Pr. erraenovae, T. alliphoroides, A.rahami, C. vicina, C. Vomitoria and L. sericata. The Mahalanobis distances acquired from the pairwise comparisons of all nine species indicated highly significant differences (permutation test with 10,000 rounds in MorphoJ: P < 0.0001), ranging from 4.9238 (A. grahami and C. vicina) to 31.6453 (T. calliphoroides and Ch. rufifacies) (Table 3). Visualized shape changes along CV1 axis were found with landmarks 2, 3, 4, 5, 6, 9, 10, 16, 17, while shape changes along CV2 axis were most clear using landmarks 2, 3, 4, 5, 6, 7, 10 (Fig. 3b). The percentages of correctly classified specimens acquired from the cross-validation test ranged from 80–100% (Table 4).

Table 3

Difference in wing shapes of nine Calliphoridae species with canonical variate analysis(CVA).
	AG	CVO	CVI	CM	CR	HL	LS	PT	TC
AG	-	0.0462	0.0181	0.0775	0.0949	0.0414	0.0310	0.0397	0.0218
CVO	9.5639	-	0.0409	0.0437	0.0587	0.0627	0.0469	0.0691	0.0555
CVI	4.9238	6.2706	-	0.0766	0.0931	0.0487	0.0407	0.0485	0.0229
CM	19.6195	10.2053	15.9017	-	0.0288	0.0820	0.0661	0.0896	0.0882
CR	26.8853	11.7193	14.9944	6.4564	-	0.0960	0.0812	0.1060	0.1058
HL	10.6829	10.3548	11.0167	10.1122	12.5507	-	0.0285	0.0665	0.0593
LS	11.8235	11.4345	12.5332	14.8977	16.0697	10.1582	-	0.0519	0.0494
PT	10.9679	13.8355	12.8309	21.9797	29.9547	20.8557	15.3188	-	0.0364
TC	6.6546	11.911	6.4682	25.7662	31.6453	18.9556	15.9919	10.9637	-
Mahalanobis distances (bold) and Procrustes distances (narrow).
Abbreviations: AG, A. grahami; CVO, C. Vomitoria; CVI, C. vicina; CM, Ch. megacephala; CR, Ch. rufifacies; HL, He. ligurriens; LS, L. sericata; PT, Pr. terraenovae; TC, T. calliphoroides

Table 4

Percentages of correct classification acquired from pairwise comparison of species with cross-validation test in MorphoJ.
		Group 2
		AG	CVO	CVI	CM	CR	HL	LS	PT	TC
Group 1	AG	-	96.7	80	100	100	100	100	96.7	90
	CVO	96.7	-	93.3	100	100	100	100	100	93.5
	CVI	83.3	93.3	-	100	100	100	100	100	80
	CM	100	100	100	-	90	100	100	100	100
	CR	100	100	100	90	-	96.7	100	100	100
	HL	90	93.3	96.7	96.7	96.7	-	96.7	100	100
	LS	100	100	100	100	100	100	-	100	100
	PT	100	100	100	100	100	100	100	-	100
	TC	90	100	96.7	100	100	100	100	100	-
The values in bold are the percentages of the correct classification of group 1 obtained from group 1 compared with group 2; the values in narrow are the percentages of the correct classification of group 2 obtained from group 2 compared with group 1. Abbreviations: AG, A. grahami; CVO, C. Vomitoria; CVI, C. vicina; CM, Ch. megacephala; CR, Ch. rufifacies; HL, He. ligurriens; LS, L. sericata; PT, Pr. terraenovae; TC, T. calliphoroides

Shape Variation

Canonical variate analysis (CVA) was used to analyze the most important shape characteristics between groups (genera or species) using MorphoJ software version 1.06 [40]. The CVA was used to magnify interspecific variation and minimize intraspecific variation. Mahalanobis distances were computed from DFA (discriminant function) as a statistical measure to evaluate the distance between groups, assessing the similarity between different populations. Permutation tests (10,000 replications) with Mahalanobis distances and Procrustes distances were used to test the statistical significance of pairwise differences between species and sexes. A cross-validated classification in discriminant function analysis (DFA) was used to evaluate the accuracy of species identification [40].

Allometry

Before the allometric analysis, a Procrustes ANOVA was used to test species and sex differences in allometry across the whole data set to evaluate the ability of allometries to identify species and sex (significant sex × species interaction). Then, sex-specific multivariate regressions of shape on size were calculated for every species separately. The sex-dependent effect and species-dependent effect were analyzed with a permutation test of 10,000 rounds in MorphoJ software [40]. Besides, we also examined the results after removing allometry on species and sex discrimination. A cross-validation test of correctly classified specimens at the species level is conducted to compare the effects of species discrimination before and after removal of allometric effect.

Sexual Shape Dimorphism (sshd)

The DFA revealed no significant differences in wing shape between males and females. The percentage of correct classification of the males ranged from 60% (He. ligurriens) to 86.7% (L. sericata) while the percentage of correct classification of the females ranged from 46.7% (Pr. terraenovae) to 100% (C. vomitoria) (Table 8).

Table 8

Percentages of correct classification between sexes of nine blow fly species.
Species	No. of correctly classified specimens/total specimen No.
Species	Males	Females
A. grahami	73.3(11/15)	80(12/15)
C. vomitoria	86.7(13/15)	100(15/15)
C. vicina	80(12/15)	80(12/15)
Ch. megacephala	73.3(11/15)	73.3(11/15)
Ch. rufifacies	66.7(10/15)	73.3((11/15)
He. ligurriens	60(9/15)	53.3(8/15)
L. sericata	86.7(13/15)	93.3((14/15)
Pr. terraenovae	80(12/15)	46.7(7/15)
T. calliphoroides	66.7(10/15)	73.3(11/15)
The percentages were obtained from cross-validation test using permutation test with 10,000 rounds in MorphoJ.

Phenetic Relationships Among Blow Flies

As shown in Fig. 4, the UPGMA dendrogram of nine species were divided into two distinct groups, with Ch. rufifacies and Ch. Megacephala always in a separate group, other species in the same subfamilies cluster into a cluster except Pr. terraenovae. The comparison of three pictures shows that the identification efficiency with male samples is better than that with female samples. In all three dendrogram, C. vomitoria was wrongly assigned to the branch of He. ligurriens and L. sericata.

Phylogenetic analysis

The sequence of final alignment contained 111 variable sites, revealed a strong AT bias, with the average nucleotide compositions of A (30.0%), T (37.9%), C (17.4%), and G (14.7%), respectively. At the species level, the nine flesh fly species constituted their own monophyletic clusters with very strong supportive values. The NJ tree analyzed with the Tamura 3-parameter model showed that phylogenetic analyses of COI sequences yielded a tree of two distinct clades. The first clade contains two branches. The first branch consists of C. vicina, Tr. calliphoroides, C. Vomitoria and A. grahami while the second branch contains He. ligurriens and L. sericata. The second clade also includes two branches, one branch is Pro. terraenovae, the other branch containing Ch. rufifacies and Ch. megacephala. Although the samples come from different places, they can still form a cluster (Fig. 2) .

Intraspecific And Interspecific Divergence

The mean intraspecific variation ranged from 0.1–0.6% while distance analysis of intraspecific variation between each of the nine Calliphorid species ranged from 4.3% (C. vicina/T. calliphoroides) to 13.6% (Ch. rufifacies/L. sericata) (Table 2). Analyses were conducted with the Kimura 2-parameter (K2P) model [36].

Table 2

Interspecific and intraspecific difference according to the analysis of their COI sequences.
	Centroid size (ANOVA)			Shape (ANOVA)
	df	F	P	df	F	P
Species	8	10.20	0.0018	256	25.75	< .0001
Sex	1	4.69	0.0621	32	16.77	< .0001
Sex × species	8	30.34	< .0001	256	3.71	< .0001
Species	Intraspecific	Interspecific
A. grahami	0.10	1	2	3	4	5	6	7	8
C. vicina	0.30	6.90
C. vomitoria	0.20	6.40	4.60
Ch. megacephala	0.40	9.00	8.80	9.00
Ch. rufifacies	0.60	11.10	11.30	11.10	7.00
He. ligurriens	0.20	7.50	9.10	7.30	8.70	11.70
L. sericata	0.20	8.30	7.50	6.70	9.50	13.60	5.70
Pr. terraenovae	0.10	10.80	10.10	11.00	6.00	9.60	9.70	10.30
T. calliphoroides	0.30	8.70	4.30	6.10	10.20	13.30	10.60	9.70	12.40
Abbreviations: Ach: Achoetandrus; Ald: Aldrichina; Cal: Calliphora; Chr: Chrysomya; Hem: Hemipyrellia; Luc: Lucilia; Pro: Protophormia; Tri: Triceratopyga

Allometric Effects In Species And Sexes

The result of Procrustes ANOVA showed that allometries differed in species and sexes (significant sex × species interaction in Table 5), and sex has no significant influence on centroid size. The results of allometric analysis show that allometry accounted for 3.02% of the total shape variation, 2.49% of the female wings; for the male wings among nine species, allometry explained 2.16% of the total shape variation. The analysis of wing shape and size of each species revealed allometry in most species except for C. vicina, L. sericata and Pr. terraenovae (permutation test with 10,000 rounds in MorphoJ: P > 0.05). The wing shape variation was found to be significantly between sexes in A. grahami, Ch. megacephala and Ch. rufifacies (permutation test with 10,000 rounds in MorphoJ: P < 0.0001, P < 0.01, and P < 0.05) (Table 6). The percentage of correctly classified specimens at the species level in cross-validation test after removing the allometric effect is shown in Table 7.

Table 6

The predicted percentage in each blow fly species and between sexes of each species.
Species	predicted percentage within species	P-value	predicted percentage between sexes	P-value
A.grahami	8.1	0.0306	13.2	0.0293
C.vomitoria	15.4	0.0005	4.7	0.1381
C.vicina	2.4	0.6085	4.5	0.2023
Ch.megacephala	19.2	< .0001	6.2	0.0441
Ch.rufifacies	28.8	< .0001	6,3	0.0442
He.ligurriens	11.9	0.0044	4.2	0.2383
L.sericata	7.9	0.0539	5.3	0.1043
Pr.terraenovae	2.0	0.8132	4.1	0.2644
T.calliphoroides	11.3	< .0001	4.0	0.2951

Table 7

Percentages of correct classification acquired from pairwise comparison between species with cross-validation test*.
	Group 2
Group 1	AG	CVO	CVI	CM	CR	HL	LS	PT	TC
AG	-	93.3	83.3	100	100	90	100	100	96.7
CVO	93.3	-	93.3	100	96.7	93.3	100	100	100
CVI	80	96.7	-	100	100	96.7	100	100	100
CM	100	100	100	-	93.3	96.7	100	100	100
CR	100	100	100	90	-	100	100	100	100
HL	100	100	100	100	96.7	-	96.7	100	100
LS	100	100	100	100	100	100	-	100	100
PT	96.7	100	100	100	100	100	100	-	100
TC	86.7	96.7	80	100	100	100	100	100	-
*The cross-validation test is conducted in MorphoJ after removal of allometric growth using permutation test 10,000 rounds.

In this study, we use only wing shape rather than size for analysis since previous studies have demonstrated that wing size cannot be used to distinguish species [27]. Compared with size, the shape of wings is less susceptible to environmental factors and has been proved to be a more stable feature [42, 43], which can provide very rich information for the phylogeny and evolution of organisms [44, 45]. The results suggested that wing morphometrics can be useful for identification genera and species of necrophagous Calliphoridae in China, which is consistent with previous studies in other countries [29, 38]. Except He. ligurriens, Ch. megacephala and Ch. rufifacies, overlapping classification was observed among some species, but the percentage of overall correct classification rate at the level of genus and species is high, ranging from 80–100% in genus and from 76.7–100% in species.

In morphological research, multivariate regression used to estimate allometry effects of different species is an important step in shape analysis since allometric growth can affect taxonomic research and sexual dimorphism [27]. Procrustes ANOVA has showed that allometries differed in species or sexes, and both species and sex have a significant effect on wing shape. The results in this study showed wing size explains part of the variation in wing shape among species, within a species, and between sexes in most species. This study confirmed that size correction by using residuals from the regression of shape on size was a necessary step in species identification based on shape[27]. Besides, the correct identification rate of species before and after the removal of allometric growth were compared in this study. We found that the identification accuracy after the removal of allometric growth was slightly lower than that keeping allometric growth. But allometry accounted for only 3.02% of the total shape variation, revealing that the allometry effects seem only a very small factor on the shape variation of species. However, to ensure the accuracy of identification, we suggest that, in the future laboratory research or practical application, the allometric growth should be removed first.

The differences between sexes were insignificant in all species (P > 0.05), which is slightly different from other studies. Significant difference was found between sexes of C. vomitoria, but not of Ch. Megacephala and Pr. Terraenovae collected in Europe [28]while differences between sexes existed in blow flies collected in Thailand, namely Ch. Megacephala and He. Ligurriens [27], indicating the morphology of Calliphoridae might differ in various locality of origin around the world. Therefore, the geographical populations can be identified according to morphometric analysis [38]. It is worth noting that to compare the effectiveness of morphometric analysis in male and female identification for certain species, the same number of males and females were used in each species to avoid the bias caused by different numbers. There were high accuracy rates of sex identification in some species like C. vomitoria, C. vicina and L. sericata, and the correct recognition rate of female C. vomitoria up to 100, indicating the wing shape can be used to identify sex for certain species, but more samples are needed to confirm this conclusion.

According to taxonomic criteria, these species belong to three subfamilies. The genus Lucilia and Hemipyrellia belong to Luciliinae; Calliphora, Triceratopyga and Aldrichina belong to Calliphorinae; Protophormia (phormiini), Achoetandrus and Chrysomya (Chrysomyini) belong to Chrysomyinae. The results of molecular analysis clearly divided into three clusters with Luciliinae and Calliphorinae in a large branch, Chrysomyinae in a small branch, and phormiini and Chrysomyini separated from others. In addition, phylogenetic trees show that the same species from different countries cluster in one branch. The interspecific distance of less than 1% indicated less difference between species collected from different countries or different places in China. Distance analysis of intraspecific variation between species showed that high percentage of intraspecific divergence between calliphorid species ranged from 4.3–13.6%, demonstrating that these species can be well distinguished by molecular methods. The interspecific gaps between C. vicina and C. vomitori as well as C. vicina and T. calliphoroides are relatively low as they belong to the same subfamily, which proved the effectiveness of molecular species identification. However, as seen in Fig. 4, no matter female or male species were used in wing morphometrics, C. vomitori and L. sericata belonging to different subfamilies always formed in one branch. In contrast, through molecular methods, all species can be accurately identified to corresponding subfamilies, suggesting that the accuracy of molecular identification is higher than that of wing morphometrics in these species. Besides, the integrity of the wing must be ensured in wing morphometrics analysis.

The comparison results show that both molecular analysis and wing morphometrics can be used as important methods to distinguish species, but for certain species, wing morphometrics may give a wrong result. Meanwhile, molecular identification cannot distinguish geographical populations for some species [32]. Therefore, we recommend classical molecular identification for species identification to achieve better accuracy, and wing morphometrics can be used as a supplementary tool to identify geographical origin and sex. In the future, more species would be included to.

Our study results show that molecular analysis is more efficient for species identification, and wing morphometrics can be used as a complementary method combined with molecular methods or other methods to ensure the accuracy of identification. However, wing morphometrics has some advantages in identifying geographic population and sex of certain species. As a simpler and cheaper method, it is quite convenient for non-taxonomists to perform the identification after basic training [31, 35]. In the future research, larger sample size and other identification methods could be used to further test this conclusion and to achieve an optimal identification method set.

PMImin

Minimum postmortem interval

COI

Cytochrome c oxidase subunit I

K2P

Kimura 2-parameter

Neighbor-joining

CVA

Canonical variate analysis

CV1

Canonical variate axis

CV2

Canonical variate axis

DFA

Discriminant function analysis

SSD

Sexual size dimorphism

UPGMA

Unweighted pair-group method with arithmetic averages

Ethics approval and consent to participate

The study is approved by Ethics Committee of Central South University School of Basic Medical Sciences.

Consent for publication

All authors read and approved the final manuscript and give consent for publication.

Availability of data and materials

The data supporting the conclusions of this article are included within the article.

Competing interests

The authors declare that they have no competing interests

Funding

This study is supported by the National Science Foundation of Hunan Province, China (Grant No. 2019JJ40392).

Authors' contributions

All authors contributed and participated in the research and/or article preparation.

LXR and CTJ: preparing manuscript; SYJ: providing experimental advice, manuscript preparation and manuscript editing; RLP: analysis and editing manuscript; PPL: guiding data analysis; GYD: study design and providing advice; WY: study funding, data analysis, and manuscript editing; ZCQ: study design and preparing manuscript.

Acknowledgements

We thank Prof. Lushi Chen (Department of forensic entomology, Guizhou Police Officer Vocational College) for identification of insects.

Campobasso CP, Introna F. The forensic entomologist in the context of the forensic pathologist's role. Forensic Sci Int. 2001;120(1–2):132–9.
Amendt J, Campobasso CP, Gaudry E, Reiter C, LeBlanc HN, Hall MJ. Best practice in forensic entomology–standards and guidelines. Int J Legal Med. 2007;121 2:90–104.
Vanin S, Bonizzoli M, Migliaccio ML, Buoninsegni LT, Bugelli V, Pinchi V, et al. A Case of Insect Colonization Before the Death. J Forensic Sci. 2017;62 6:1665–7.
Baqué M, Filmann N, Verhoff MA, Amendt J. Establishment of developmental charts for the larvae of the blow fly Calliphora vicina using quantile regression. Forensic Sci Int. 2015;248:1–9.
Lutz L, Williams KA, Villet MH, Ekanem M, Szpila K. Species identification of adult African blowflies (Diptera: Calliphoridae) of forensic importance. Int J Legal Med. 2018;132(3):831–42.
Sanit S, Limsopatham K, Klong-Klaew T, Samerjai C, Yasanga T, Sukontason K, et al. Morphology of immature blow fly Hypopygiopsis infumata (Bigot) (Diptera: Calliphoridae), a potential species of forensic importance. Acta Trop. 2018;188:168–79.
Sontigun N, Sanit S, Wannasan A, Sukontason K, Amendt J, Yasanga T, et al. Ultrastructure of male genitalia of blow flies (Diptera: Calliphoridae) of forensic importance. Acta Trop. 2018;179:61–80.
Boehme P, Amendt J, Zehner R. The use of COI barcodes for molecular identification of forensically important fly species in Germany. Parasitol Res. 2012;110 6:2325–32.
Sperling FA, Anderson GS, Hickey DA. A DNA-based approach to the identification of insect species used for postmortem interval estimation. J Forensic Sci. 1994;39(2):418–27.
Wells J, karo V. Application of DNA- Based Methods in Forensic Entomology. 2014.
Meng F, Ren L, Wang Z, Deng J, Guo Y, Chen C, et al. Identification of Forensically Important Blow Flies (Diptera: Calliphoridae) in China Based on COI. J Med Entomol. 2017;54(5):1193–200.
Nelson LA, Wallman JF, Dowton M. Using COI barcodes to identify forensically and medically important blowflies. Med Vet Entomol. 2007;21(1):44–52.
Borges LM, Hollatz C, Lobo J, Cunha AM, Vilela AP, Calado G, et al. With a little help from DNA barcoding: investigating the diversity of Gastropoda from the Portuguese coast. Sci Rep. 2016;6:20226.
Pohjoismäki JL, Kahanpää J, Mutanen M. DNA Barcodes for the Northern European Tachinid Flies (Diptera: Tachinidae). PLoS One. 2016;11 11:e0164933.
Cooke T, Kulenkampff K, Heyns M, Heathfield LJ. DNA barcoding of forensically important flies in the Western Cape, South Africa. Genome. 2018;61 12:823–8.
GilArriortua M, Saloña-Bordas MI, Cainé LM, Pinheiro F. M MdP. Technical Note: "Mitochondrial and nuclear DNA approaches for reliable identification of Lucilia (Diptera, Calliphoridae) species of forensic interest from Southern Europe". Forensic Sci Int. 2015;257:393–7.
Park JH, Kim YH, Ham CS, Shin SE, Lee HJ, Ko KS, et al. Molecular identification of forensically important calliphoridae and sarcophagidae species using ITS2 nucleotide sequences. Forensic Sci Int. 2018;284:1–4.
Yusseff-Vanegas SZ, Agnarsson I. DNA-barcoding of forensically important blow flies (Diptera: Calliphoridae) in the Caribbean Region. PeerJ. 2017;5:e3516.
Sonet G, Jordaens K, Braet Y, Desmyter S. Why is the molecular identification of the forensically important blowfly species Lucilia caesar and L. illustris (family Calliphoridae) so problematic? Forensic Sci Int. 2012;223(1–3):153–9.
Tourle R, Downie DA, Villet MH. Flies in the ointment: a morphological and molecular comparison of Lucilia cuprina and Lucilia sericata (Diptera: Calliphoridae) in South Africa. Med Vet Entomol. 2009;23(1):6–14.
Tatsuta H, Takahashi KH, Sakamaki Y. Geometric morphometrics in entomology: Basics and applications. Entomological Science. 2017.
Webster M, Sheets HD. A practical introduction to landmark-based geometric morphometrics. 2010.
Brown K, Shipp E. Wing Morphometrics of Australian Luciliini (Diptera: Calliphoridae). Australian Journal of Zoology. 1977;25 4:765.
Mielczarek ŁE, Tofilski A. Semiautomated identification of a large number of hoverfly (Diptera: Syrphidae) species based on wing measurements. Oriental Insects. 2018;52:3:245–58.
Grzywacz A, Ogiela J, Tofilski A. Identification of Muscidae (Diptera) of medico-legal importance by means of wing measurements. Parasitol Res. 2017;116 5:1495–504.
Sontigun N, Samerjai C, Sukontason K, Wannasan A, Amendt J, Tomberlin JK, et al. Wing morphometric analysis of forensically important flesh flies (Diptera: Sarcophagidae) in Thailand. Acta Trop. 2019;190:312–9.
Sontigun N, Sukontason KL, Zajac BK, Zehner R, Sukontason K, Wannasan A, et al. Wing morphometrics as a tool in species identification of forensically important blow flies of Thailand. Parasit Vectors. 2017;10 1:229.
Szpila K, Żmuda A, Akbarzadeh K, Tofilski A. Wing measurement can be used to identify European blow flies (Diptera: Calliphoridae) of forensic importance. Forensic Sci Int. 2019;296:1–8.
Lyra ML, Hatadani LM, de Azeredo-Espin AM, Klaczko LB. Wing morphometry as a tool for correct identification of primary and secondary New World screwworm fly. Bull Entomol Res. 2010;100(1):19–26.
Brown KR. Multivariate Assessment of Phenetic Relationships Within the Tribe Luciliini (Diptera: Calliphoridae). Australian Journal of Zoology. 1979;27:3:465–77.
Macleod N, Hall MJR, Wardhana AH. Towards the automated identification of Chrysomya blow flies from wing images. Med Vet Entomol. 2018;32:3:323–33.
Limsopatham K, Hall MJR, Zehner R, Zajac BK, Verhoff MA, Sontigun N, et al. A molecular, morphological, and physiological comparison of English and German populations of Calliphora vicina (Diptera: Calliphoridae). PLoS One. 2018;13 12:e0207188.
Mielczarek LE, Oleksa A, Meyza K, Tofilski A. Seasonal polyphenism in Eristalis pertinax (Diptera: Syrphidae). European Journal of Entomology. 2016;113:489–96.
Virginio F, Oliveira Vidal P, Suesdek L. Wing sexual dimorphism of pathogen-vector culicids. Parasit Vectors. 2015;8:159. doi:10.1186/s13071-015-0769-6.
López-García J, Angell C, Martín-Vega D. Wing morphometrics for the identification of Nearctic and Palaearctic Piophilidae (Diptera) of forensic relevance. Forensic Sci Int. 2020;309:110192.
Kumar S, Stecher G, Li M, Knyaz C, Tamura K. MEGA X: Molecular Evolutionary Genetics Analysis across Computing Platforms. Mol Biol Evol. 2018;35 6:1547–9.
Tamura K. Estimation of the number of nucleotide substitutions when there are strong transition-transversion and G + C-content biases. Mol Biol Evol. 1992;9:4:678–87.
Hall MJ, MacLeod N, Wardhana AH. Use of wing morphometrics to identify populations of the Old World screwworm fly, Chrysomya bezziana (Diptera: Calliphoridae): a preliminary study of the utility of museum specimens. Acta Trop. 2014;138(Suppl):49–55.
Arnqvist G, Mrtensson T. Measurement error in geometric morphometrics: Empirical strategies to assess and reduce its impact on measures of shape. Acta Zoologica Academiae Scientiarum Hungaricae. 1998;44 1:73–96.
Klingenberg CP. MorphoJ: an integrated software package for geometric morphometrics. Mol Ecol Resour. 2011;11 2:353–7.
Hammer y, Harper DA, Ryan PD. PAST: Paleontological Statistics Software Package for Education and Data Analysis. Palaeontologia Electronica. 2001;4(1):1–9.
Dujardin JP. Morphometrics applied to medical entomology. Infect Genet Evol. 2008;8:6:875–90.
Jaramillo ON, Dujardin JP, Calle-Londoño D, Fonseca-González I. Geometric morphometrics for the taxonomy of 11 species of Anopheles (Nyssorhynchus) mosquitoes. Med Vet Entomol. 2015;29(1):26–36.
Klingenberg CP. Evolution and development of shape: integrating quantitative approaches. Nat Rev Genet. 2010;11:9:623–35.
Perrard A, Baylac M, Carpenter JM, Villemant C. Evolution of wing shape in hornets: why is the wing venation efficient for species identification? J Evol Biol. 2014;27(12):2665–75.

supplement1.docx

Download PDF

Version 1

posted

You are reading this latest preprint version

Comparative Analysis of Molecular Identification and Wing Morphometrics of Forensically Important Blow Flies in China

Status:

Version 1

Abstract

Figures

Background

Methods

Specimen collection

Molecular Analysis

Wing Morphometrics

Shape variation of genera and species

Shape Variation

Allometry

Sexual Shape Dimorphism (sshd)

Phenetic Relationships Among Blow Flies

Results

Phylogenetic analysis

Intraspecific And Interspecific Divergence

Allometric Effects In Species And Sexes

Discussion

Conclusion

Abbreviations

Declarations

References

Supplementary Files

Status:

Version 1