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

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

Abstract

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.

Background

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.

Methods

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

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

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.

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.

Results

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 5

Results of Procrustes ANOVA on centroid size and shape.

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.

Discussion

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.

Conclusion

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.

Abbreviations

PMImin

Minimum postmortem interval

COI

Cytochrome c oxidase subunit I

K2P

Kimura 2-parameter

NJ

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

Declarations

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.

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