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Note that a Preprint of this article also exists, first published June 9, 2017.

Introduction

Forensic entomology is the application of the study of insects in legal investigations. Although several groups of insects, mainly of the orders Diptera and Coleoptera, are associated with cadaveric decomposition, blow flies (Diptera: Calliphoridae) are among the most dominant and conspicuous insects in the decomposition process (Catts, 1992). They are useful to determine time of death and, in particular situations, cause of death (Goff, 2000) or relocation of a body (Matuszewski, Szafalowicz & Jarmusz, 2013). During the last five decades of intensive studies in forensic entomology (Byrd & Castner, 2010; Catts & Haskell, 1990; Goff, 2000; Smith, 1986; Tomberlin & Benbow, 2015), the acceptance of insects as evidence in legal investigations has increased gradually and they are now included as standard operating procedures in crime scene investigations in many countries (Tomberlin & Benbow, 2015). Determining the post mortem interval (PMI) is one of the most important tasks during an investigation, and the use of immature stages of Calliphoridae is essential whenever time of death is difficult to establish based on other means (Catts & Haskell, 1990). Although the accurate determination of PMI and period of insect activity (PIA) depend of several factors that are discussed in detail by Catts (1992), the first one, and most important to resolve, is the correct identification of the specimens found at the crime scene. As each species has a specific developmental rate and range of distribution, the accurate identification of insects, mainly the larval stages, is critical because the incorrect determination will invalidate the estimated post mortem interval and impact other interpretations of the evidence (Goff, 2000; Wells & LaMotte, 2001).

Morphology is most commonly used to identify adult insects involved in cadaveric decomposition and taxonomic keys are available for most of the Calliphoridae species. In general, these taxonomic keys include the detailed description of the male and female genitalia, which is examined when external characteristics are not sufficient to establish identity (Tantawi, Whitworth & Sinclair, 2017; Whitworth, 2010; Whitworth, 2014; Whitworth & Rognes, 2012). Identification of immature stages (eggs, larvae and pupae) is more challenging, but possible when detailed taxonomic descriptions exist (Greenberg & Szyska, 1984; Sukontason et al., 2005; Szpila et al., 2013a; Szpila et al., 2014; Szpila & Villet, 2011; Wells, Byrd & Tantawi, 1999). However, in places like the Caribbean, where forensic entomology has not yet been developed, this approach is limited due to the lack of detailed descriptions of immature stages. For instance, from the 18 forensically important calliphorid species currently recognized in the Caribbean, plus the most important livestock pest parasite in the Americas, C. hominivorax (Whitworth, 2010), only eight have been documented well enough to be identified based on larvae, mainly using morphology of the third instar (Florez & Wolff, 2009; Wells, Byrd & Tantawi, 1999). For the other species, the identification of immature specimens would need to be done by rearing them to adulthood (Goff, 2000), which is time consuming, may delay legal investigations, and relies on the survival of larvae in the laboratory. Given local endemism, the scarce studies on this group in the Caribbean, and the lack of knowledge of immature stages for at least 11 species, developing alternative tools for identification is important.

With the advances in molecular methods, DNA barcoding has become a widely used technique for species delimitation and identification. This approach allows the identification of specimens during any development stage, including incomplete or damaged specimens, does not require taxonomic expertise, and it is also useful to recognize cryptic species that morphological approaches may not detect (Hebert et al., 2003; Hebert et al., 2004a; Hebert et al., 2004b). Worldwide many authors have used this method to identify species of the family Calliphoridae and these studies showed the potential of the ‘standard barcoding gene’ cytochrome c oxidase subunit I (COI) to distinguish between forensically significant species (Aly & Wen, 2013; Chen et al., 2009; Harvey et al., 2003; Liu et al., 2011; Nelson, Wallman & Dowton, 2007; Wells & Williams, 2007). However, COI does not reliably distinguish among certain closely related calliphorid species, specifically Chrysomya saffranea and Ch. megacephala (Harvey et al., 2008; Nelson, Wallman & Dowton, 2007), Ch. semimetalica and Ch. latifrons (Nelson, Wallman & Dowton, 2007), Calliphora stygia and C. albifrontalis, C. dubia and C. augur (Harvey et al., 2008; Wallman & Donnellan, 2001), C. aldrichia and C. montana (Tantawi, Whitworth & Sinclair, 2017), Cochliomyia macellaria and Co. aldrichi (Yusseff-Vanegas & Agnarsson, 2016), Lucilia mexicana and L. coeruleiviridis (DeBry et al., 2013; Whitworth, 2014), L. bazini and L. hainanenesis (Chen et al., 2014), L. illustris and L. caesar (Reibe, Schmitz & Madea, 2009; Sonet et al., 2012), L. cuprina and L. sericata (Williams & Villet, 2013). Given the serious implications of misidentification of forensic insects, an improved protocol for accurate identification is necessary. We propose using the nuclear internal transcribed spacer ITS2 as a second barcoding locus for taxonomic species determinations in calliphorids as suggested by GilArriortua et al. (2014). Although evaluations of ITS2 as unique identification marker have limitations for some taxa (Agnarsson, 2010), several studies have shown the potential application of ITS2 for blowfly species identification (Jordaens et al., 2013a; Nelson, Wallman & Dowton, 2007; Nelson, Wallman & Dowton, 2008, Song, Wang & Liang, 2008; Yusseff-Vanegas & Agnarsson, 2016). We expect a combination of barcodes from the nuclear and mitochondrial genomes to offer a general, simple and reliable way of identifying forensically important insects, even problematic sister species, as successfully done in certain other arthropod groups (Anslan & Tedersoo, 2015; Cao et al., 2016).

The success of DNA barcoding directly links to the quality of the underlying database (Candek & Kuntner, 2015; Coddington et al., 2016; DeBry et al., 2013; Harvey et al., 2003) not only in terms of the quality of identifications but also in terms of taxon sampling (species, geographic localities, populations). Existing efforts in this respect are lacking for Calliphoridae in the Caribbean, limiting the reliability of this technique for delimitation of species. Hitherto, three studies have included molecular data of a few Calliphoridae from the Caribbean (McDonagh, Garcia & Stevens, 2009; Whitworth, 2014; Yusseff-Vanegas & Agnarsson, 2016); they lack the geographic variation necessary to estimate the ratio between intraspecific variation and interspecific divergence from which barcoding accuracy depends (Meyer & Paulay, 2005). Our study provides the first thorough molecular study of Caribbean Calliphoridae.

Our aims are: (1) to establish COI barcode libraries for all Caribbean species and to test if barcodes offer reliable means of their identification, (2) to assess the usefulness of ITS2 as a second barcoding locus in species delimitation and identification, and, (3) to improve online databases with sequences from the Caribbean, including specimens from multiple localities in each island covering the geographic range for each species. To achieve these goals, we sampled 468 specimens of Calliphoridae representing 19 species.

Table 1:
Specimen details, collection information and GenBank accession numbers.
* Estimated coordinate points. ∧ Accession numbers from BOLD systems. – blank.
Genus Species Voucher ID Country Latitude Longitude COI ITS2
Calliphora maestrica DR084 Hispaniola N18.82138 W70.67935 MF097182 MF097580
Calliphora maestrica DR085 Hispaniola N18.82138 W70.67935 MF097183
Calliphora maestrica DR086 Hispaniola N18.82138 W70.67935 MF097184
Calliphora maestrica DR087 Hispaniola N18.82138 W70.67935 MF097185
Calliphora maestrica DR088 Hispaniola N18.82138 W70.67935 MF097186 MF097581
Chloroprocta idioidea CU008 Cuba N20.054178 W76.917603 MF097187 MF097582
Chloroprocta idioidea CU047 Cuba N21.582414 W77.783464 MF097188 MF097583
Chloroprocta idioidea CU048 Cuba N21.582414 W77.783464 MF097189 MF097584
Chloroprocta idioidea CU049 Cuba N21.582414 W77.783464 MF097190
Chloroprocta idioidea DR031 Hispaniola N18.316572 W71.576447* MF097191
Chloroprocta idioidea DR044 Hispaniola N18.316572 W71.576447* MF097192 MF097585
Chloroprocta idioidea DR045 Hispaniola N18.316572 W71.576447* MF097193
Chloroprocta idioidea DR051 Hispaniola N19.06753 W69.46445 MF097194
Chloroprocta idioidea DR052 Hispaniola N19.06753 W69.46445 MF097195 MF097586
Chloroprocta idioidea ME001 Mexico N21.07645 W89.501083 MF097587
Chloroprocta idioidea ME002 Mexico N21.07645 W89.501083 MF097196 MF097588
Chrysomya albiceps CO003 Colombia N5.900544 W74.852897* MF097589
Chrysomya albiceps CO004 Colombia N5.900544 W74.852897* MF097590
Chrysomya albiceps CO005 Colombia N5.900544 W74.852897* MF097591
Chrysomya albiceps LA103 Martinique N14.47428 W60.81463 MF097199 MF097592
Chrysomya albiceps LA104 Martinique N14.47428 W60.81463 MF097200 MF097593
Chrysomya albiceps LA125 Saint Lucia N14.100031 W60.92654 MF097201 MF097594
Chrysomya albiceps LA135 Barbados N13.2051667 W59.5295556 MF097197
Chrysomya albiceps LA136 Barbados N13.2051667 W59.5295556 MF097198
Chrysomya megacephala CO006 Colombia N5.900544 W74.852897* MF097202 MF097595
Chrysomya megacephala CO007 Colombia N5.900544 W74.852897* MF097596
Chrysomya megacephala CO008 Colombia N6.266242 W77.374903* MF097203 MF097597
Chrysomya megacephala CO009 Colombia N5.900544 W74.852897* MF097598
Chrysomya megacephala DR017 Hispaniola N19.89155 W071.65806 MF097205
Chrysomya megacephala DR018 Hispaniola N19.89155 W071.65806 MF097206
Chrysomya megacephala DR068 Hispaniola N19.06710 W69.46004 MF097207
Chrysomya megacephala DR069 Hispaniola N19.06710 W69.46004 MF097208
Chrysomya megacephala DR101 Hispaniola N18.35698 W68.61609 MF097209
Chrysomya megacephala DR102 Hispaniola N18.35698 W68.61609 MF097210
Chrysomya megacephala DR103 Hispaniola N18.35698 W68.61609 MF097211
Chrysomya megacephala DR104 Hispaniola N18.35698 W68.61609 MF097212
Chrysomya megacephala DR116 Hispaniola N18.32902 W68.80995 MF097213 MF097599
Chrysomya megacephala DR117 Hispaniola N18.32902 W68.80995 MF097214 MF097611
Chrysomya megacephala DR118 Hispaniola N18.32902 W68.80995 MF097215
Chrysomya megacephala DR119 Hispaniola N18.32902 W68.80995 MF097216
Chrysomya megacephala FL003 Florida, USA N25.614383 W80.584467 KX529521 KX529561
Chrysomya megacephala FL004 Florida, USA N25.614383 W80.584467 MF097218
Chrysomya megacephala FL011 Florida, USA N25.086633 W80.452217 MF097219
Chrysomya megacephala JA004 Jamaica N18.0598056 W77.5311944 MF097600
Chrysomya megacephala LA062 Dominica N15.34066 W61.33351 MF097220
Chrysomya megacephala LA001 Saint Eustatius N17.47637 W62.97470 MF097225
Chrysomya megacephala LA003 Saint Eustatius N17.47637 W62.97470 MF097217
Chrysomya megacephala LA025 Saint-Martin N18.07779 W63.05772 MF097235
Chrysomya megacephala LA055 Saint Barthélemy N17.91924 W62.86366 MF097234
Chrysomya megacephala LA063 Dominica N15.34066 W61.33351 MF097204
Chrysomya megacephala LA088 Guadeloupe N16.37752 W61.47869 MF097221
Chrysomya megacephala LA089 Guadeloupe N16.37752 W61.47869 MF097222
Chrysomya megacephala LA093 Nevis N17.14145 W62.57784 MF097226
Chrysomya megacephala LA116 Saint Kitts N17.3404083 W62.7410389 MF097223
Chrysomya megacephala LA117 Saint Kitts N17.3404083 W62.7410389 MF097224
Chrysomya megacephala LA123 Saint Lucia N14.100031 W60.92654 MF097604
Chrysomya megacephala ME013 Mexico N25.598592 W103.441156 MF097601
Chrysomya megacephala ME014 Mexico N25.598592 W103.441156 MF097602
Chrysomya megacephala PR038 Puerto Rico N18.412972 W66.026619 MF097227
Chrysomya megacephala PR124 Puerto Rico N18.370953 W66.026619 MF097228
Chrysomya megacephala PR125 Puerto Rico N18.370953 W66.026619 MF097229 MF097603
Chrysomya megacephala PR1251 Puerto Rico N18.370953 W66.026619 MF097230
Chrysomya megacephala PR126 Puerto Rico N18.370953 W66.026619 MF097231
Chrysomya megacephala PR138 Puerto Rico N18.447911 W65.948617 MF097232
Chrysomya megacephala PR139 Puerto Rico N18.447911 W65.948617 MF097233
Chrysomya rufifacies LA056 Saint Barthélemy N17.91924 W62.86366 MF097236
Chrysomya rufifacies LA057 Saint Barthélemy N17.91924 W62.86366 MF097237
Chrysomya rufifacies CU001 Cuba N20.054178 W76.917603 MF097238
Chrysomya rufifacies CU003 Cuba N20.054178 W76.917603 MF097239
Chrysomya rufifacies CU004 Cuba N20.054178 W76.917603 KX529555 KX529562
Chrysomya rufifacies CU005 Cuba N20.054178 W76.917603 MF097240
Chrysomya rufifacies CU009 Cuba N20.054178 W76.917603 MF097241
Chrysomya rufifacies CU034 Cuba N22.621386 W83.725944 MF097242
Chrysomya rufifacies CU035 Cuba N22.621386 W83.725944 MF097243
Chrysomya rufifacies CU036 Cuba N22.621386 W83.725944 MF097244
Chrysomya rufifacies CU037 Cuba N22.621386 W83.725944 MF097245
Chrysomya rufifacies DR001 Hispaniola N19.89155 W71.65806 MF097248
Chrysomya rufifacies DR002 Hispaniola N19.89155 W71.65806 MF097249
Chrysomya rufifacies DR003 Hispaniola N19.89155 W71.65806 MF097250
Chrysomya rufifacies DR004 Hispaniola N19.89155 W71.65806 MF097251
Chrysomya rufifacies DR006 Hispaniola N19.89155 W71.65806 MF097252
Chrysomya rufifacies DR007 Hispaniola N19.89155 W71.65806 MF097253
Chrysomya rufifacies DR008 Hispaniola N19.89155 W71.65806 MF097254
Chrysomya rufifacies DR016 Hispaniola N19.89155 W71.65806 MF097255
Chrysomya rufifacies DR036 Hispaniola N18.316572 W71.576447* MF097256
Chrysomya rufifacies DR037 Hispaniola N18.316572 W71.576447* MF097257
Chrysomya rufifacies DR038 Hispaniola N18.316572 W71.576447* MF097258
Chrysomya rufifacies DR039 Hispaniola N18.316572 W71.576447* MF097259
Chrysomya rufifacies DR070 Hispaniola N19.06710 W69.46004 MF097260
Chrysomya rufifacies DR071 Hispaniola N19.06710 W69.46004 MF097261 MF097605
Chrysomya rufifacies DR0711 Hispaniola N19.06710 W69.46004 MF097606
Chrysomya rufifacies DR093 Hispaniola N18.35698 W68.61609 MF097262
Chrysomya rufifacies DR094 Hispaniola N18.35698 W68.61609 MF097263
Chrysomya rufifacies DR095 Hispaniola N18.35698 W68.61609 MF097264
Chrysomya rufifacies DR096 Hispaniola N18.35698 W68.61609 MF097265
Chrysomya rufifacies DR097 Hispaniola N18.35698 W68.61609 MF097266
Chrysomya rufifacies DR098 Hispaniola N18.35698 W68.61609 MF097267
Chrysomya rufifacies DR099 Hispaniola N18.35698 W68.61609 MF097268
Chrysomya rufifacies DR100 Hispaniola N18.35698 W68.61609 MF097269
Chrysomya rufifacies DR132 Hispaniola N18.32902 W68.80995 MF097270
Chrysomya rufifacies DR133 Hispaniola N18.32902 W68.80995 MF097271
Chrysomya rufifacies DR135 Hispaniola N19.741319 W70.654975* MF097272
Chrysomya rufifacies DR150 Hispaniola N19.34405 W70.14824 MF097273
Chrysomya rufifacies DR151 Hispaniola N19.34405 W70.14824 MF097274
Chrysomya rufifacies DR152 Hispaniola N19.34405 W70.14824 MF097275
Chrysomya rufifacies DR155 Hispaniola N19.34405 W70.14824 MF097276
Chrysomya rufifacies DR157 Hispaniola N18.32902 W68.80995 MF097277
Chrysomya rufifacies DR158 Hispaniola N18.32902 W68.80995 MF097278
Chrysomya rufifacies DR159 Hispaniola N18.32902 W68.80995 MF097279
Chrysomya rufifacies DR160 Hispaniola N18.32902 W68.80995 MF097280
Chrysomya rufifacies DR161 Hispaniola N18.32902 W68.80995 MF097281
Chrysomya rufifacies DR162 Hispaniola N18.32902 W68.80995 MF097282
Chrysomya rufifacies DR163 Hispaniola N18.32902 W68.80995 MF097283
Chrysomya rufifacies FL001 Florida, USA N25.614383 W80.584467 MF097288
Chrysomya rufifacies FL010 Florida, USA N25.086633 W80.452217 MF097289 MF097607
Chrysomya rufifacies JA003 Jamaica N18.0598056 W77.5311944 MF097293 MF097608
Chrysomya rufifacies LA002 Saint Eustatius N17.47637 W62.97470 MF097284
Chrysomya rufifacies LA004 Saint Eustatius N17.47637 W62.97470 MF097285
Chrysomya rufifacies LA005 Saint Eustatius N17.47637 W62.97470 MF097286
Chrysomya rufifacies LA006 Saint Eustatius N17.47637 W62.97470 MF097287
Chrysomya rufifacies LA041 Saint-Martin N18.11677 W63.03902 MF097316
Chrysomya rufifacies LA042 Saint-Martin N18.11677 W63.03902 MF097317
Chrysomya rufifacies LA043 Saint-Martin N18.11677 W63.03902 MF097318
Chrysomya rufifacies LA044 Saint-Martin N18.11677 W63.03902 MF097319
Chrysomya rufifacies LA069 Dominica N15.34066 W61.33351 MF097246
Chrysomya rufifacies LA072 Dominica N15.34066 W61.33351 MF097247
Chrysomya rufifacies LA090 Guadeloupe N16.37752 W61.47869 MF097290
Chrysomya rufifacies LA091 Guadeloupe N16.37752 W61.47869 MF097291
Chrysomya rufifacies LA092 Guadeloupe N16.37752 W61.47869 MF097292
Chrysomya rufifacies LA101 Martinique N14.47428 W60.81463 MF097310
Chrysomya rufifacies LA108 Montserrat N16.77608 W62.30904 MF097309
Chrysomya rufifacies LA110 Saint Kitts N17.3404083 W62.7410389 MF097294 MF097609
Chrysomya rufifacies M074 Mona, Puerto Rico N18.086239 W67.906339 MF097295
Chrysomya rufifacies M075 Mona, Puerto Rico N18.086239 W67.906339 MF097296
Chrysomya rufifacies M082 Mona, Puerto Rico N18.11125 W67.933447 MF097297
Chrysomya rufifacies M083 Mona, Puerto Rico N18.11125 W67.933447 MF097298
Chrysomya rufifacies M089 Mona, Puerto Rico N18.06301 W67.88728 MF097299
Chrysomya rufifacies M090 Mona, Puerto Rico N18.06301 W67.88728 MF097300
Chrysomya rufifacies M091 Mona, Puerto Rico N18.06301 W67.88728 MF097301
Chrysomya rufifacies M093 Mona, Puerto Rico N18.084222 W67.939417 MF097302
Chrysomya rufifacies M094 Mona, Puerto Rico N18.084222 W67.939417 MF097303
Chrysomya rufifacies M095 Mona, Puerto Rico N18.084222 W67.939417 MF097304
Chrysomya rufifacies M096 Mona, Puerto Rico N18.084222 W67.939417 MF097305
Chrysomya rufifacies M101 Mona, Puerto Rico N18.084222 W67.939417 MF097306
Chrysomya rufifacies M108 Mona, Puerto Rico N18.11125 W67.933447 MF097307
Chrysomya rufifacies M109 Mona, Puerto Rico N18.11125 W67.933447 MF097308
Chrysomya rufifacies PR117 Puerto Rico N18.370953 W66.026619 MF097311
Chrysomya rufifacies PR118 Puerto Rico N18.370953 W66.026619 MF097312
Chrysomya rufifacies PR119 Puerto Rico N18.370953 W66.026619 MF097313
Chrysomya rufifacies PR120 Puerto Rico N18.370953 W66.026619 MF097314
Chrysomya rufifacies PR130 Puerto Rico N18.093306 W65.556083 MF097315 MF097610
Cochliomyia aldrichi M080 Mona, Puerto Rico N18.084222 W65.939417 KX529529 KX529563
Cochliomyia aldrichi M084 Mona, Puerto Rico N18.11125 W67.933447 MF097320
Cochliomyia aldrichi M085 Mona, Puerto Rico N18.11125 W67.933447 KX529530 KX529564
Cochliomyia aldrichi M086 Mona, Puerto Rico N18.06301 W67.88728 KX529531 KX529565
Cochliomyia aldrichi M087 Mona, Puerto Rico N18.06301 W67.88728 MF097321
Cochliomyia aldrichi M088 Mona, Puerto Rico N18.06301 W67.88728 MF097322
Cochliomyia aldrichi M102 Mona, Puerto Rico N18.11125 W67.933447 MF097323
Cochliomyia aldrichi M103 Mona, Puerto Rico N18.11125 W67.933447 KX529532 KX529566
Cochliomyia aldrichi M104 Mona, Puerto Rico N18.085972 W67.933447 MF097324
Cochliomyia aldrichi M105 Mona, Puerto Rico N18.085972 W67.933447 KX529533 KX529567
Cochliomyia aldrichi M106 Mona, Puerto Rico N18.084222 W67.939417 MF097325
Cochliomyia aldrichi M107 Mona, Puerto Rico N18.084222 W67.939417 KX529534 KX529568
Cochliomyia hominivorax CO001 Colombia N5.900544 W74.852897* MF097612
Cochliomyia hominivorax CU020 Cuba N22.621386 W83.725944 MF097613
Cochliomyia hominivorax CU033 Cuba N22.621386 W83.725944 KX529556 KX529571
Cochliomyia hominivorax DR042 Hispaniola N18.316572 W71.576447* KX529557 KX529572
Cochliomyia hominivorax DR105 Hispaniola N18.35698 W68.61609 KX529558 KX529573
Cochliomyia macellaria LA137 Saint Barthélemy N17.910299 W62.847221 MF097326
Cochliomyia macellaria LA139 Saint Barthélemy N17.910299 W62.847221 MF097327
Cochliomyia macellaria CO002 Colombia N5.900544 W74.852897* KX529522 KX529574
Cochliomyia macellaria CO010 Colombia N6.266242 W77.374903* KX529545 KX529575
Cochliomyia macellaria CU012 Cuba N22.621386 W83.725944 MF097330
Cochliomyia macellaria CU013 Cuba N22.621386 W83.725944 MF097331
Cochliomyia macellaria CU014 Cuba N22.621386 W83.725944 KX529541 KX529577
Cochliomyia macellaria CU015 Cuba N22.621386 W83.725944 MF097332
Cochliomyia macellaria CU016 Cuba N22.621386 W83.725944 MF097333
Cochliomyia macellaria CU017 Cuba N22.621386 W83.725944 MF097334
Cochliomyia macellaria CU018 Cuba N22.621386 W83.725944 KX529526 KX529578
Cochliomyia macellaria CU019 Cuba N22.621386 W83.725944 MF097335 MF097614
Cochliomyia macellaria CU050 Cuba N21.582414 W77.750131 MF097336
Cochliomyia macellaria CU051 Cuba N21.582414 W77.750131 MF097337
Cochliomyia macellaria DR009 Hispaniola N19.89155 W71.65806 MF097341
Cochliomyia macellaria DR010 Hispaniola N19.89155 W71.65806 KX529536 KX529579
Cochliomyia macellaria DR011 Hispaniola N19.89155 W71.65806 MF097342
Cochliomyia macellaria DR012 Hispaniola N19.89155 W71.65806 MF097343
Cochliomyia macellaria DR013 Hispaniola N19.89155 W71.65806 MF097344
Cochliomyia macellaria DR014 Hispaniola N19.89155 W71.65806 MF097345
Cochliomyia macellaria DR015 Hispaniola N19.89155 W71.65806 MF097346
Cochliomyia macellaria DR043 Hispaniola N18.316572 W71.576447* MF097347
Cochliomyia macellaria DR062 Hispaniola N19.06710 W69.46004 MF097348
Cochliomyia macellaria DR063 Hispaniola N19.06710 W69.46004 MF097349
Cochliomyia macellaria DR064 Hispaniola N19.06710 W69.46004 MF097350
Cochliomyia macellaria DR065 Hispaniola N19.06710 W69.46004 MF097351
Cochliomyia macellaria DR066 Hispaniola N19.06710 W69.46004 MF097352
Cochliomyia macellaria DR106 Hispaniola N18.35698 W68.61609 MF097353
Cochliomyia macellaria DR107 Hispaniola N18.35698 W68.61609 MF097354
Cochliomyia macellaria DR108 Hispaniola N18.35698 W68.61609 MF097355
Cochliomyia macellaria DR109 Hispaniola N18.35698 W68.61609 MF097356
Cochliomyia macellaria DR1091 Hispaniola N18.35698 W68.61609 MF097357
Cochliomyia macellaria DR120 Hispaniola N18.32902 W68.80995 MF097358
Cochliomyia macellaria DR121 Hispaniola N18.32902 W68.80995 MF097359
Cochliomyia macellaria DR134 Hispaniola N19.741319 W70.654975* KX529527 KX529580
Cochliomyia macellaria DR154 Hispaniola N19.34405 W70.14824 MF097360
Cochliomyia macellaria FL006 Florida, USA N25.614383 W80.584467 MF097615
Cochliomyia macellaria FL009 Florida, USA N25.457514 W80.4863 MF097361
Cochliomyia macellaria JA002 Jamaica N18.0598056 W77.5311944 MF097616
Cochliomyia macellaria LA022 Saint-Martin N18.07779 W63.05772 MF097384
Cochliomyia macellaria LA023 Saint-Martin N18.07779 W63.05772 MF097385
Cochliomyia macellaria LA024 Saint-Martin N18.07779 W63.05772 MF097386
Cochliomyia macellaria LA032 Saint-Martin N18.11677 W63.03902 MF097387
Cochliomyia macellaria LA033 Saint-Martin N18.11677 W63.03902 MF097388
Cochliomyia macellaria LA034 Saint-Martin N18.11677 W63.03902 MF097389
Cochliomyia macellaria LA035 Saint-Martin N18.11677 W63.03902 MF097390
Cochliomyia macellaria LA036 Saint-Martin N18.11677 W63.03902 MF097391
Cochliomyia macellaria LA049 Saint Barthélemy N17.91924 W62.86366 MF097371
Cochliomyia macellaria LA0491 Saint Barthélemy N17.91924 W62.86366 MF097372
Cochliomyia macellaria LA050 Saint Barthélemy N17.91924 W62.86366 MF097373
Cochliomyia macellaria LA053 Saint Barthélemy N17.91924 W62.86366 MF097383
Cochliomyia macellaria LA054 Saint Barthélemy N17.91924 W62.86366 MF097374
Cochliomyia macellaria LA066 Dominica N15.34066 W61.33351 MF097338
Cochliomyia macellaria LA067 Dominica N15.34066 W61.33351 MF097339
Cochliomyia macellaria LA068 Dominica N15.34066 W61.33351 MF097340
Cochliomyia macellaria LA071 Dominica N15.34066 W61.33351 KX529525 KX529583
Cochliomyia macellaria LA079 Guadeloupe N16.37752 W61.47869 MF097362
Cochliomyia macellaria LA080 Guadeloupe N16.37752 W61.47869 MF097363
Cochliomyia macellaria LA081 Guadeloupe N16.37752 W61.47869 MF097364
Cochliomyia macellaria LA094 Nevis N17.14145 W62.57784 MF097368
Cochliomyia macellaria LA096 Martinique N14.47428 W60.81463 KX529524 KX529584
Cochliomyia macellaria LA097 Martinique N14.47428 W60.81463 MF097367
Cochliomyia macellaria LA115 Saint Kitts N17.3404083 W62.7410389 MF097365
Cochliomyia macellaria LA118 Saint Kitts N17.3404083 W62.7410389 MF097392
Cochliomyia macellaria LA131 Barbuda N17.6054722 W61.8005833 MF097328
Cochliomyia macellaria LA132 Barbuda N17.6054722 W61.8005833 MF097329
Cochliomyia macellaria LA138 Saint Barthélemy N17.897522 W62.849694 MF097375
Cochliomyia macellaria LA140 Saint Barthélemy N17.897522 W62.849694 MF097376
Cochliomyia macellaria LA141 Saint Barthélemy N17.897522 W62.849694 MF097377
Cochliomyia macellaria LA142 Saint Barthélemy N17.897522 W62.849694 KX529523 KX529592
Cochliomyia macellaria LA143 Saint Barthélemy N17.897522 W62.849694 MF097378
Cochliomyia macellaria LA144 Saint Barthélemy N17.897522 W62.849694 MF097379
Cochliomyia macellaria LA145 Saint Barthélemy N17.897522 W62.849694 MF097380
Cochliomyia macellaria LA146 Saint Barthélemy N17.897522 W62.849694 MF097381
Cochliomyia macellaria LA147 Saint Barthélemy N17.897522 W62.849694 MF097382
Cochliomyia macellaria ME015 Mexico N25.598592 W103.441156 MF097617
Cochliomyia macellaria M077 Mona, Puerto Rico N18.086239 W67.906339 KX529539 KX529585
Cochliomyia macellaria M081 Mona, Puerto Rico N18.11125 W67.933447 KX529537 KX529586
Cochliomyia macellaria M112 Mona, Puerto Rico N18.11125 W67.933447 KX529544 KX529589
Cochliomyia macellaria ME004 Mexico N21.07645 W89.501083 MF097366
Cochliomyia macellaria PR029 Puerto Rico N17.961111 W66.863806 MF097369
Cochliomyia macellaria PR047 Puerto Rico N18.178722 W66.488111 MF097370
Cochliomyia macellaria PR121 Puerto Rico N18.370953 W66.026619 KX529544 KX529589
Cochliomyia macellaria PR128 Puerto Rico N18.093306 W65.552111 KX529540 KX529590
Cochliomyia macellaria PR129 Puerto Rico N18.093306 W65.552111 KX529542 KX529591
Cochliomyia minima CU010 Cuba N20.054178 W76.917603 MF097393
Cochliomyia minima CU021 Cuba N22.621386 W83.725944 MF097394
Cochliomyia minima CU022 Cuba N22.621386 W83.725944 KX529549 KX529593
Cochliomyia minima CU023 Cuba N22.621386 W83.725944 KX529550 KX529594
Cochliomyia minima CU024 Cuba N22.621386 W83.725944 MF097395
Cochliomyia minima CU025 Cuba N22.621386 W83.725944 MF097396
Cochliomyia minima CU026 Cuba N22.621386 W83.725944 MF097397
Cochliomyia minima CU027 Cuba N22.621386 W83.725944 MF097398
Cochliomyia minima CU043 Cuba N20.517817 W74.65865 MF097399
Cochliomyia minima CU044 Cuba N20.517817 W74.65865 MF097400
Cochliomyia minima CU045 Cuba N20.517817 W74.65865 MF097401
Cochliomyia minima CU046 Cuba N20.517817 W74.65865 KX529547 KX529595
Cochliomyia minima DR026 Hispaniola N19.04995 W70.89046 MF097402
Cochliomyia minima DR027 Hispaniola N19.04995 W70.89046 MF097403
Cochliomyia minima DR028 Hispaniola N19.04995 W70.89046 MF097404
Cochliomyia minima DR029 Hispaniola N19.04995 W70.89046 MF097405
Cochliomyia minima PR013 Hispaniola N18.316572 W71.576447* MF097406
Cochliomyia minima DR032 Hispaniola N18.316572 W71.576447* MF097407
Cochliomyia minima DR033 Hispaniola N18.316572 W71.576447* MF097408
Cochliomyia minima DR034 Hispaniola N18.316572 W71.576447* MF097409
Cochliomyia minima DR035 Hispaniola N18.316572 W71.576447* MF097410
Cochliomyia minima DR053 Hispaniola N19.06753 W69.46445 MF097411
Cochliomyia minima DR054 Hispaniola N19.06753 W69.46445 MF097412
Cochliomyia minima DR055 Hispaniola N19.06753 W69.46445 KX529552 KX529596
Cochliomyia minima DR056 Hispaniola N19.06753 W69.46445 MF097413
Cochliomyia minima DR067 Hispaniola N19.06710 W69.46004 MF097414
Cochliomyia minima DR072 Hispaniola N19.34864 W70.14910 MF097415
Cochliomyia minima DR073 Hispaniola N19.34864 W70.14910 MF097416
Cochliomyia minima DR074 Hispaniola N19.34864 W70.14910 MF097417
Cochliomyia minima DR075 Hispaniola N19.34864 W70.14910 MF097418
Cochliomyia minima DR076 Hispaniola N19.34864 W70.14910 MF097419
Cochliomyia minima DR136 Hispaniola N19.741319 W70.654975 KX529548 KX529597
Cochliomyia minima DR137 Hispaniola N19.741319 W70.654975 MF097420
Cochliomyia minima DR138 Hispaniola N19.741319 W70.654975 MF097421
Cochliomyia minima DR139 Hispaniola N19.741319 W70.654975 MF097422
Cochliomyia minima DR153 Hispaniola N19.34405 W70.14824 MF097423
Cochliomyia minima DR164 Hispaniola N18.32902 W68.80995 MF097424
Cochliomyia minima PR006 Puerto Rico N18.412972 W66.727222 MF097425
Cochliomyia minima PR007 Puerto Rico N18.412972 W66.727222 MF097426
Cochliomyia minima PR016 Puerto Rico N18.321333 W65.818722 MF097427
Cochliomyia minima PR018 Puerto Rico N18.321333 W65.818722 MF097428
Cochliomyia minima PR019 Puerto Rico N18.321333 W65.818722 MF097429
Cochliomyia minima PR041 Puerto Rico N18.174722 W66.491861 MF097430
Cochliomyia minima PR131 Puerto Rico N18.093306 W65.552111 MF097431
Cochliomyia minima PR132 Puerto Rico N18.093306 W65.552111 KX529553 KX529598
Cochliomyia minima PR133 Puerto Rico N18.093306 W65.552111 KX529554 KX529599
Cochliomyia minima PR140 Puerto Rico N18.447911 W65.948617 MF097432 MF097618
Cochliomyia minima PR141 Puerto Rico N18.447911 W65.948617 KX529551 KX529600
Cochliomyia minima PR145 Puerto Rico N18.449889 W65.595333 MF097433
Cochliomyia minima PR146 Puerto Rico N18.449889 W65.595333 MF097434
Lucilia cluvia FL005 Florida, USA N25.614383 W80.584467 MF097619
Lucilia cluvia FL017 Florida, USA N25.136917 W80.94855 MF097436 MF097620
Lucilia cluvia FL018 Florida, USA N25.136917 W80.94855 MF097621
Lucilia cluvia FL019 Florida, USA N25.323331 W80.833094 MF097437
Lucilia cluvia FL020 Florida, USA N25.323331 W80.833094 MF097438 MF097622
Lucilia cluvia FL025 Florida, USA N25.423053 W80.679114 MF097439 MF097623
Lucilia cluvia FL026 Florida, USA N25.423053 W80.679114 MF097440 MF097624
Lucilia cluvia PR147 Puerto Rico N18.429222 W66.178022 MF097441 MF097625
Lucilia cluvia PR148 Puerto Rico N18.429222 W66.178022 MF097442 MF097626
Lucilia coeruleiviridis FL007 Florida, USA N25.457514 W80.4863 MF097627
Lucilia coeruleiviridis FL013 Florida, USA N25.136917 W80.94885 MF097443 MF097628
Lucilia coeruleiviridis FL014 Florida, USA N25.136917 W80.94855 MF097629
Lucilia coeruleiviridis FL015 Florida, USA N25.136917 W80.94885 MF097444 MF097630
Lucilia coeruleiviridis FL016 Florida, USA N25.136917 W80.94885 MF097445 MF097631
Lucilia coeruleiviridis FL023 Florida, USA N25.457514 W80.4863 MF097446 MF097632
Lucilia coeruleiviridis FL024 Florida, USA N25.457514 W80.4863 MF097447 MF097633
Lucilia cuprina FL027 Florida, USA N25.457514 W80.4863 MF097448 MF097634
Lucilia cuprina FL028 Florida, USA N25.457514 W80.4863 MF097449 MF097635
Lucilia cuprina FL029 Florida, USA N25.457514 W80.4863 MF097450 MF097636
Lucilia cuprina FL030 Florida, USA N25.457514 W80.4863 MF097451 MF097637
Lucilia cuprina PR070 Puerto Rico N18.370953 W66.026619 MF097452
Lucilia cuprina PR071 Puerto Rico N18.370953 W66.026619 MF097453
Lucilia cuprina PR072 Puerto Rico N18.370953 W66.026619 MF097454
Lucilia cuprina PR073 Puerto Rico N18.370953 W66.026619 KX529559 KX529602
Lucilia cuprina PR122 Puerto Rico N18.370953 W66.026619 MF097455 MF097638
Lucilia cuprina PR123 Puerto Rico N18.370953 W66.026619 MF097456
Lucilia cuprina PR153 Puerto Rico N18.461053 W66.729803 MF097457
Lucilia cuprina PR154 Puerto Rico N18.461053 W66.729803 MF097458 MF097639
Lucilia eximia CO011 Colombia N5.900544 W74.852897* MF097459
Lucilia eximia CO012 Colombia N5.900544 W74.852897* MF097460 MF097640
Lucilia eximia CO013 Colombia N5.900544 W74.852897* MF097461 MF097641
Lucilia eximia CO015 Colombia N5.900544 W74.852897* MF097462 MF097642
Lucilia eximia CO016 Colombia N5.900544 W74.852897* MF097643
Lucilia eximia CO022 Colombia N6.067217 W73.645411 MF097463 MF097644
Lucilia eximia CO023 Colombia N6.067217 W73.645411 MF097464 MF097645
Lucilia eximia CU002 Cuba N20.054178 W76.917603 MF097646
Lucilia eximia CU006 Cuba N20.054178 W76.917603 MF097647
Lucilia eximia DR019 Hispaniola N19.89155 W071.65806 MF097467 MF097650
Lucilia eximia DR049 Hispaniola N18.316572 W71.576447* MF097468
Lucilia eximia DR050 Hispaniola N18.316572 W71.576447 MF097651
Lucilia eximia DR129 Hispaniola N18.32902 W68.80995 MF097469
Lucilia eximia FL021 Florida, USA N25.086633 W80.452217 MF097470 MF097652
Lucilia eximia FL022 Florida, USA N25.086633 W80.452217 MF097471 MF097653
Lucilia eximia LA064 Dominica N15.34066 W61.33351 MF097465 MF097648
Lucilia eximia LA065 Dominica N15.34066 W61.33351 MF097466 MF097649
Lucilia eximia LA124 Saint Lucia N14.100031 W60.92654 MF097483 MF097665
Lucilia eximia LA126 Saint Lucia N14.100031 W60.92654 MF097666
Lucilia eximia LA127 Saint Lucia N14.100031 W60.92654 MF097484 MF097667
Lucilia eximia M076 Mona, Puerto Rico N18.086239 W67.906339 MF097472 MF097654
Lucilia eximia M099 Mona, Puerto Rico N18.084222 W67.939417 MF097473
Lucilia eximia M100 Mona, Puerto Rico N18.084222 W67.939417 MF097474
Lucilia eximia M110 Mona, Puerto Rico N18.11125 W67.933447 MF097475 MF097655
Lucilia eximia M111 Mona, Puerto Rico N18.11125 W67.933447 MF097476 MF097656
Lucilia eximia ME005 Mexico N21.07645 W89.501083 MF097477 MF097657
Lucilia eximia ME006 Mexico N21.07645 W89.501083 MF097658
Lucilia eximia ME007 Mexico N21.07645 W89.501083 MF097478 MF097659
Lucilia eximia PR050 Puerto Rico N18.449889 W66.595333 MF097479 MF097660
Lucilia eximia PR060 Puerto Rico N17.971611 W66.865361 MF097480 MF097661
Lucilia eximia PR111 Mona, Puerto Rico N18.11125 W67.933447 MF097662
Lucilia eximia PR114 Puerto Rico N18.370953 W66.026619 MF097481
Lucilia eximia PR134 Puerto Rico N18.093306 W65.552111 MF097663
Lucilia eximia PR135 Puerto Rico N18.093306 W65.552111 MF097664
Lucilia eximia PR150 Puerto Rico N18.084222 W67.939417 MF097482
Lucilia fayeae M079 Mona, Puerto Rico N18.084222 W67.939417 MF097485 MF097668
Lucilia fayeae PR008 Puerto Rico N18.412972 W67.727222 MF097486 MF097669
Lucilia fayeae PR012 Puerto Rico N18.412972 W67.727222 MF097487
Lucilia fayeae PR020 Puerto Rico N18.321333 W65.818722 MF097488
Lucilia fayeae PR022 Puerto Rico N18.321333 W65.818722 MF097489
Lucilia fayeae PR023 Puerto Rico N18.293444 W65.791917 MF097490 MF097670
Lucilia fayeae PR045 Puerto Rico N18.174722 W66.491861 MF097491 MF097671
Lucilia fayeae PR053 Puerto Rico N18.449889 W66.595333 MF097492 MF097672
Lucilia fayeae PR116 Puerto Rico N18.370953 W66.032175 MF097493
Lucilia lucigerens JA005 Jamaica N18.0598056 W77.5311944 MF097494 MF097673
Lucilia lucigerens JA006 Jamaica N18.0598056 W77.5311944 MF097495
Lucilia lucigerens JA007 Jamaica N18.0598056 W77.5311944 MF097496 MF097674
Lucilia mexicana ME016 Mexico N25.598592 W103.441156 MF097497 MF097675
Lucilia mexicana ME020 Mexico N25.598592 W103.441156 MF097498 MF097676
Lucilia mexicana ME021 Mexico N25.598592 W103.441156 MF097499 MF097677
Lucilia retroversa CU007 Cuba N20.054178 W76.917603 MF097500 MF097678
Lucilia retroversa CU028 Cuba N22.621386 W83.725944 MF097501
Lucilia retroversa CU029 Cuba N22.621386 W83.725944 MF097502
Lucilia retroversa CU030 Cuba N22.621386 W83.725944 MF097503 MF097679
Lucilia retroversa CU031 Cuba N22.621386 W83.725944 MF097504
Lucilia retroversa CU038 Cuba N20.517817 W20.517817 MF097505
Lucilia retroversa CU039 Cuba N20.517817 W20.517817 MF097506
Lucilia retroversa CU040 Cuba N20.517817 W20.517817 MF097507
Lucilia retroversa CU041 Cuba N20.517817 W20.517817 MF097508 MF097680
Lucilia retroversa CU042 Cuba N20.517817 W20.517817 MF097509
Lucilia retroversa DR020 Hispaniola N19.04871 W70.88084 MF097510
Lucilia retroversa DR021 Hispaniola N19.04871 W70.88084 MF097511
Lucilia retroversa DR022 Hispaniola N19.04871 W70.88084 MF097512
Lucilia retroversa DR023 Hispaniola N19.04871 W70.88084 MF097513
Lucilia retroversa DR024 Hispaniola N19.04871 W70.88084 MF097514 MF097681
Lucilia retroversa DR025 Hispaniola N19.04871 W70.88084 MF097515
Lucilia retroversa DR030 Hispaniola N19.04871 W70.88084 MF097516
Lucilia retroversa DR040 Hispaniola N18.316572 W71.576447 MF097517
Lucilia retroversa DR046 Hispaniola N18.316572 W71.576447 MF097518
Lucilia retroversa DR047 Hispaniola N18.316572 W71.576447 MF097519
Lucilia retroversa DR048 Hispaniola N18.316572 W71.576447 MF097520
Lucilia retroversa DR057 Hispaniola N19.06753 W69.46445 MF097521
Lucilia retroversa DR058 Hispaniola N19.06753 W69.46445 MF097522
Lucilia retroversa DR059 Hispaniola N19.06753 W69.46445 MF097523
Lucilia retroversa DR060 Hispaniola N19.06753 W69.46445 MF097524
Lucilia retroversa DR061 Hispaniola N19.06753 W69.46445 MF097525
Lucilia retroversa DR079 Hispaniola N19.34864 W70.14910 MF097526
Lucilia retroversa DR080 Hispaniola N19.34864 W70.14910 MF097527
Lucilia retroversa DR081 Hispaniola N19.34864 W70.14910 MF097528
Lucilia retroversa DR082 Hispaniola N19.34864 W70.14910 MF097529
Lucilia retroversa DR083 Hispaniola N19.34864 W70.14910 MF097530
Lucilia retroversa DR089 Hispaniola N19.34864 W70.14910 MF097531
Lucilia retroversa DR090 Hispaniola N19.34864 W70.14910 MF097532
Lucilia retroversa DR091 Hispaniola N19.34864 W70.14910 MF097533
Lucilia retroversa DR092 Hispaniola N19.34864 W70.14910 MF097534
Lucilia retroversa DR111 Hispaniola N18.35698 W68.61609 MF097535
Lucilia retroversa DR110 Hispaniola N18.35698 W68.61609 MF097536
Lucilia retroversa DR112 Hispaniola N18.35698 W68.61609 MF097537
Lucilia retroversa DR122 Hispaniola N18.32902 W68.80995 MF097538
Lucilia retroversa DR123 Hispaniola N18.32902 W68.80995 MF097539 MF097682
Lucilia retroversa DR124 Hispaniola N18.32902 W68.80995 MF097540 MF097683
Lucilia retroversa DR125 Hispaniola N18.32902 W68.80995 MF097541
Lucilia retroversa DR126 Hispaniola N18.32902 W68.80995 MF097542
Lucilia retroversa DR128 Hispaniola N18.32902 W68.80995 MF097543
Lucilia retroversa DR140 Hispaniola N19.741319 W70.654975* MF097544
Lucilia retroversa DR141 Hispaniola N19.741319 W70.654975* MF097545
Lucilia retroversa DR142 Hispaniola N18.09786 W71.18925 MF097546
Lucilia retroversa DR143 Hispaniola N18.09786 W71.18925 MF097547
Lucilia retroversa DR144 Hispaniola N18.09786 W71.18925 MF097548
Lucilia retroversa DR145 Hispaniola N18.09786 W71.18925 MF097549
Lucilia retroversa DR146 Hispaniola N18.09786 W71.18925 MF097550
Lucilia retroversa DR147 Hispaniola N18.09786 W71.18925 MF097551
Lucilia retroversa DR148 Hispaniola N18.09786 W71.18925 MF097552
Lucilia rica LA007 Saint Eustatius N17.47637 W62.97470 MF097558
Lucilia rica LA008 Saint Eustatius N17.47637 W62.97470 MF097559
Lucilia rica LA009 Saint Eustatius N17.47637 W62.97470 MF097684
Lucilia rica LA010 Saint Eustatius N17.47637 W62.97470 MF097560
Lucilia rica LA016 Saint-Martin N18.07779 W63.05772 MF097572
Lucilia rica LA017 Saint-Martin N18.07779 W63.05772 MF097573 MF097697
Lucilia rica LA026 Saba N17.63980 W63.23373 MF097435
Lucilia rica LA027 Saba N17.63980 W63.23373 MF097692
Lucilia rica LA028 Saba N18.07779 W63.05772 MF097569 MF097693
Lucilia rica LA037 Saint-Martin N18.11677 W63.03902 MF097574
Lucilia rica LA045 Saint Barthélemy N17.91924 W62.86366 MF097570 MF097694
Lucilia rica LA061 Saint Barthélemy N17.91924 W62.86366 MF097571 MF097696
Lucilia rica LA073 Nevis N17.14145 W62.57784 MF097567 MF097690
Lucilia rica LA074 Nevis N17.14145 W62.57784 MF097568 MF097691
Lucilia rica LA098 Martinique N14.47428 W60.81463 MF097565 MF097688
Lucilia rica LA099 Martinique N14.47428 W60.81463 MF097566 MF097689
Lucilia rica LA106 Montserrat N16.77608 W62.30904 MF097564 MF097687
Lucilia rica LA114 Saint Kitts N17.3404083 W62.7410389 MF097563
Lucilia rica LA128 Antigua N17.0358611 W61.8246389 MF097553
Lucilia rica LA129 Antigua N17.0358611 W61.8246389 MF097554
Lucilia rica LA130 Antigua N17.0358611 W61.8246389 MF097555
Lucilia rica LA133 Barbuda N17.6054722 W61.8005833 MF097556
Lucilia rica LA134 Barbuda N17.6054722 W61.8005833 MF097557
Lucilia rica LA083 Guadeloupe N16.37752 W61.47869 MF097561 MF097685
Lucilia rica LA087 Guadeloupe N16.37752 W61.47869 MF097562 MF097686
Lucilia rica TLW042 Antigua and Barbuda As publisheda BNNR042
Lucilia rica TLW043 Antigua and Barbuda As publisheda BNNR043
Lucilia rica TLW044 Antigua and Barbuda As publisheda BNNR044
Lucilia rica TLW046 Antigua and Barbuda As publisheda BNNR046
Lucilia sp. CO027 Colombia N6.067217 W73.645411 MF097575 MF097698
Lucilia vulgata CO019 Colombia N6.067217 W73.645411 MF097576 MF097699
Lucilia vulgata CO025 Colombia N6.067217 W73.645411 MF097577 MF097700
Lucilia vulgata CO026 Colombia N6.067217 W73.645411 MF097578 MF097701
Lucilia vulgata CO028 Colombia N6.067217 W73.645411 MF097579 MF097702
Outgroups
Neobellieria bullata BG64 As publishedb JQ807156.1
Ravinia stimulans AZ60 As publishedb JQ807112.1
Sarcophaga carnaria NICC0410 As publishedc JQ582094.1
Blaesoxipha alcedo AY09 As publishedb JQ806830.1
Blaesoxipha masculina AW36 As publishedb JQ806832.1
DOI: 10.7717/peerj.3516/table-1

Notes:

(Stamper et al., 2012, unpublished data).

Methods

Specimens and DNA extraction

A total of 473 specimens were included in this study. Of these, 468 represented ingroup taxa and five represented outgroup taxa from the family Sarcophagidae (Sarcophaga Carnaria Linnaeus, 1758; Neobellieria bullata Parker, 1916; Ravinia stimulans Walker, 1849; Blaesoxipha masculina Aldrich, 1916 and Blaesoxipha alcedo Aldrich, 1916). We used a total of 600 DNA sequences and we obtained 521 (COI  = 398, ITS2  = 123) while 79 (COI  = 44, ITS2  = 35) were previously published (Table 1). The specimens were collected throughout the Caribbean (Fig. 1) from between 2011 and 2013 (see Table 1 for details). All specimens were collected under appropriate permits: USA, Florida, Everglades, United States Department of the Interior National Park Service EVER-2013-SCI-0028; Puerto Rico, DRNA: 2011-IC-035 (O-VS-PVS15-SJ-00474-08042011); Jamaica, NEPA, reference number #18/27; USA, USDI National Park Service, EVER-2013-SCI-0028; Costa Rica, SINAC, pasaporte científico no. 05933, resolución no. 019-2013-SINAC; Cuba, Departamento de Recursos Naturales, PE 2012/05, 2012003 and 2012001; Dominican Republic, Ministerio de Medio Ambiente y Recursos Naturales, no 0577; Colombia, Authoridad Nacional de Licencias Ambientales, 18.497.666 issued to Alexander Gómez Mejía; Saba, The Executive Council of the Public Entity Saba, no 112/2013; Martinique, Ministère de L’Écologie, du Développement Durable, et de L‘Énergie; Nevis, Nevis Historical & Conservation Society, no F001; Barbados, Ministry of Environment and Drainage, no 8434∕56∕1 Vol. II. Although L. vulgata, L. mexicana and L. coeruleiviridis are not present in the Caribbean islands, they are included as outgroups to the Calliphoridae from the West Indies. James (1970) reported L. coeruleiviridis from Cuba, however, this is likely an error as no specimens have been seen in collections from the region (Whitworth, 2010) and no specimens were collected during this study. All specimens, except the ones from Mexico, were collected using a novel trap designed for this study. We modified a standard butterfly trap by adding a conic form on the top with a vessel attached to the highest point like in the Malaise trap. Flies entered the trap attracted by the bait (chicken) and funneled into the collecting vessel containing 95% ethanol. Traps were hung 1m off the ground and were used to collect flies for 2–3 days at each locality. These traps proved efficient in collecting specimens for our molecular purposes, given that caught specimens were preserved in ethanol while the trap remained in the field. Collected specimens were transferred to Whirl-paks with 95% ethanol and stored at −20 °C. Adults were identified using the Whitworth (2010) taxonomic keys and the specimens with uncertain identity were sent to Dr. Whitworth at Washington State University for detailed examination and species confirmation. DNA was isolated from thoracic muscle or two legs of each individual with the QIAGEN DNeasy Tissue Kit (Qiagen, Inc., Valencia, CA). The remainder of the specimen was retained as a voucher currently held by the Agnarsson Lab; they will be placed in the Zadock Thompson Zoological Collections at the UVM Natural History Museum following completion of other studies currently being conducted using the material.

Map of collecting localities of all specimens used for the molecular analysis.

Figure 1: Map of collecting localities of all specimens used for the molecular analysis.

(Image credit: https://commons.wikimedia.org/wiki/File:Caribbean_map_blank.png#filelinks).

PCR amplification and sequencing

A region of the mitochondrial genome encoding COI was amplified in a single fragment using the primers LCO1490 (Folmer et al., 1994), and C1-N-2776 (Hedin & Maddison, 2001). Those primers amplified successfully in all Calliphoridae except Lucilia Robineau-Desvoidy. From the eight Caribbean species of Lucilia, only Lucilia retroversa amplified successfully using these primers. For the remaining Lucilia species two different primer-pairs were used. The Primer 1 (Gibson et al., 2011) with C1-N-2191 (Simon et al., 1994) and the C1-J-1751 (Gibson et al., 2011) with C2-N-3014. For the second internal transcribed spacer ITS2 we used the primers ITS4 and ITS5.8 (White et al., 1990). The primer sequences and protocols are listed in Table 2. Amplified fragments were sequenced in both directions by University of Arizona Genetics Core. Sequences were interpreted from chromatograms using Phred and Phrap (Green, 1999; Green & Ewing, 2002) using the Chromaseq module (Maddison & Maddison, 2010a) in Mesquite 3.03 (Maddison & Maddison, 2010b) with default parameters. The sequences were then proofread by examining chromatograms by eye. Alignments were done using MAFFT (Katoh et al., 2002) through the online portal EMBL-EBI with default settings. The matrices were exported to Mesquite 3.03 (Maddison & Maddison, 2010b) and the translation of coding sequences to proteins for COI were checked for potential errors.

Table 2:
COI amplification primers and protocols.
Primer name Sequence (5′–3′) Protocol Source protocol
ID CY D AN E FE
LCO1490 F GGTCAACAAATCATAAAGATATTGG 95 °C 2 min 35 95 °C 30 s 44 °C 45 s 72 °C 45 s 72 °C 10 min Agnarsson, Maddison & Aviles (2007)
CI-N-2776 R GGATAATCAGAATATCGTCGAGG
Primer 1 F TACAATTTATCGCCTAAACTTCAGCC 95 °C 3 min 35 94 °C 15 s 51 °C 15 s 72 °C 30 s 72 °C 5 min DeBry et al. (2013)
C1-N-2191 R CCCGGTAAAATTAAAATATAAACTTC
C1-J-1751 F GGAGCTCCTGACATAGCATTCCC 94 °C 90 s 36 94 °C 22 s 48 °C 30 s 72 °C 80 s 72 °C 60 s Harvey et al. (2003)
C2-N-3014 R TCCATTGCACTAATCTGCCATATTA
ITS4 F TCCTCCGCTTATTGATATGC 94 °C 2 min 38 94 °C 30 s 44 °C 35 s 72 °C 30 s 72 °C 3 min Agnarsson (2010)
ITS5.8 R GGGACGATGAAGAACGCAGC
DOI: 10.7717/peerj.3516/table-2

Notes:

F

Forward

R

Reverse

ID

Initial denaturation

CY

cycles

D

Denaturation

AN

annealing

E

Extension

FE

Final extension

Phylogenetic analysis

The COI gene was partitioned by codon positions, each partition and ITS2 gene were exported from Mesquite for model choice. The appropriate models were chosen using jModeltest v2.1.4 (Posada & Crandall, 1998), and the AIC criterion (Posada & Buckley, 2004). The corresponding model of evolution was used for the Bayesian analysis: GTR + Γ + I for COI1st, F81+ I for COI2nd, GTR + Γ for COI3rd and HKY + Γ + I for ITS2. We ran the MC3(Metropolis Coupled Markov Chain Monte Carlo) chain in MrBayes v3.2.3 (Huelsenbeck & Ronquist, 2001) through the online portal Cipres Science Gateway v3.3 (Miller, Pfeiffer & Schwartz, 2010). The analysis was run for 20,000,000 generations, sampling every 1,000 generations, and the sample points of the first 5,000,000 generations were discarded as ‘burnin’, after which the chains had reached stationarity as determined by analysis in Tracer (Rambaut & Drummond, 2009). Maximum likelihood (ML) analysis of the concatenated matrix was done in Garli (Zwickl, 2006) using the same partitioning scheme and models. Sequences were submitted to GenBank and BOLD.

Species delimitation

We used MEGA6 to calculate genetic distances within and among species level clades suggested by the barcoding analysis of the COI data and by morphology. We used the species delimitation plugin in Geneious 8.1.5 (Kearse et al., 2012; Masters, Fan & Ross, 2011) to estimate species limits under Rosenberg’s reciprocal monophyly P(AB) (Rosenberg, 2007) and Rodrigo’s P(RD) method (Rodrigo et al., 2008). For this analysis we used a 317 taxa subset of our data, produced by reducing the most densely sampled species like Co. minima, Co. macellaria, Ch. rufifacies and L. retroversa to 38 exemplars since P(RD) probability cannot be computed when there are more than 40 exemplars per clade. We also estimated the probability of population identification of a hypothetical sample based on the groups being tested P ID (Strict) and P ID (Liberal). The genealogical sorting index (gsi) statistic (Cummings, Neel & Shaw, 2008) was calculated using the gsi webserver (http://genealogicalsorting.org) on the estimated tree. As genetic distances in MEGA6, gsi and species delimitation metrics from Geneious require a priory species designation, 26 putative species were assigned to the data based on combined analysis of phylogenetic topology from COI and morphological and geographic information. Finally, we used a single locus Bayesian implementation (bPTP) of the Poisson tree processes model (Zhang et al., 2013) to infer putative species boundaries on a given single locus phylogenetic input tree available on the webserver: http://species.h-its.org/ptp/. The analysis was run as a rooted tree from the MrBayes analysis, for 500,000 generations with 10% burnin removed. For gsi and bPTP analysis we reduced the data to 103 taxa representing the 26 putative species because of limitations of the server.

Summary of the Bayesian tree based on the COI dataset including 442 individuals, with the results of four different species delimitation approaches in addition to morphology, genetic distances of >2% mtDNA, ITS2 and the concatenated matrix.

Figure 2: Summary of the Bayesian tree based on the COI dataset including 442 individuals, with the results of four different species delimitation approaches in addition to morphology, genetic distances of >2% mtDNA, ITS2 and the concatenated matrix.

See Fig. S1 for bootstrap support values.

Results

We present by far the most extensive DNA barcoding dataset of Calliphoridae from the Caribbean. It includes a ∼1,200 bp fragment of the mitochondrial COI gene from 437 Calliphoridae specimens and ∼450 bp of the ITS2 gene from 158 specimens chosen to represent unique COI haplotypes of all putative species and all localities (20 different islands in the Caribbean plus Florida, Colombia and Mexico). Ninety nine of the sequences are from specimens collected in the mainland and the other 496 are from the Caribbean Islands. In total, we included 19 species of Calliphoridae identified morphologically (Whitworth, 2006; Whitworth, 2010), 16 of them reported from the Caribbean and three species, L. coeruleiviridis, L. mexicana and L. vulgata, from the mainland. The sequences from the Caribbean represent 16 of the 18 species of forensically important Calliphoridae that occur in the West Indies plus one of the most important livestock pest parasites in the Americas, C. hominivorax (Whitworth, 2010). The two species not included in this dataset are reported from Bahamas (Phormia regina) and Trinidad (Hemilucilia segmentaria), where we were not able to sample. For most species we included numerous exemplars, covering the geographic range of each species in the region.

Species delimitation using COI

Although based on traditional taxonomy we recognized 19 species of Calliphoridae in this study, COI gene analyses suggest that the diversity of Calliphoridae in the Caribbean is greater than morphology can detect. The phylogenetic analysis of COI recuperates 24 distinct clades (Fig. 2, Fig. S1), showing substantial geographic variation for L. eximia (four clades), C. idioidea (three clades), L. retroversa (two clades) and L. rica (two clades). However, COI did not distinguish between the pairs, Co. macellaria and Co. aldrichi from the Caribbean and L. coeruleiviridis and L. mexicana from the mainland. These four species are clearly identifiable based on morphological characteristics. Most putative species lineages showed genetic distances >2.7% (Table 3) and most of them are separated by a barcoding gap (Table 4). All species delimitation methods supported Ca. maestrica, C. idioidea-DR, Co. minima, Co. hominivorax, Ch. albiceps, Ch. rufifacies, Ch. megacephala, L. cluvia, L. cuprina, L. eximia-CO+ME, L. eximia-LA, Lucilia eximia-GA L. lucigerens, Lucilia retroversa-DR, and L. rica 1 and 2 (Fig. 2, Table 5); however, the other eight putative species were poorly supported in our analyses. Lower divergences, between 0.5 and 1.2% were found between clades, L. coeruleiviridis+L. mexicana, L. vulgata and L. eximia-FL, L. fayeae and L. retroversa CU, and between L. rica 1 and 2 (Table 3). All but bPTP methods of species determination supported L. eximia-FL clade, L. vulgata, L. fayeae, L. retroversa-CU (Table 5). Regarding C. idioidea, the Cuban and Mexico species-clades are only supported by bPTP and P ID (liberal). The bPTP analysis estimated between 21 and 29 species including the initial 26 putative species. Other species delimitation methods showed similar results, 22 putative species had P ID (liberal) higher of 89, 20 had significant Rosenberg values and 21 had GSI values of 100. All species determination methods fail in distinguishing between the pairs Co. macellaria and Co. aldrichi, and L. coeruleiviridis and L. mexicana as sequence divergences between species pairs are extremely low <0.08%. Given that no one method can distinguish between these species, the addition of ITS2 as a second barcoding locus was necessary to clarify the monophyly and validity of these species and increase the confidence of delimitation and identification of species with low genetic divergences.

Table 3:
Genetic distances expressed in percentage among the 26 putative species groups as determined by an analysis in MEGA6.
Putative species 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26
1 Ca. maestrica
2 C. idioidea-CU 16.3
3 C. idioidea-DR 15.3 2.8
4 C. idioidea-ME 15.6 2.1 2.1
5 Ch. albiceps 15.5 13.8 13.1 13.1
6 Ch. megacephala 14.4 9.5 10.6 10.2 5.7
7 Ch. rufifacies 15.5 14.5 14.1 14.1 2.8 6.7
8 Co. aldrichi 14.4 10.6 9.5 9.9 10.2 9.2 12.0
9 Co. hominivorax 13.7 8.7 9.1 8.0 11.5 9.8 12.6 8.4
10 Co. macellaria 14.4 10.6 9.5 9.9 10.3 9.2 12.0 0.1 8.4
11 Co. minima 15.7 10.5 10.5 10.2 9.8 8.8 11.0 4.2 9.7 4.2
12 L.. cluvia 11.1 11.4 12.1 12.1 14.9 11.4 14.5 12.4 11.6 12.4 13.7
13 L.. coeruleiviridis 12.1 11.3 13.4 13.4 15.9 12.7 15.2 11.7 12.6 11.6 11.4 4.6
14 L. cuprina 11.6 9.2 9.5 10.2 13.1 9.5 13.8 10.6 11.9 10.6 11.2 8.2 8.5
15 L. eximia-CO-ME 12.4 12.3 11.7 12.4 14.0 11.8 14.0 12.8 13.0 12.8 13.4 5.4 7.1 7.6
16 L. vulgata 11.4 11.3 12.7 12.7 15.9 12.7 15.9 11.7 12.6 11.7 12.0 3.9 0.7 8.5 6.4
17 L. eximia-FL 11.6 11.0 12.4 12.4 15.5 12.4 14.8 12.0 12.4 11.9 11.1 4.8 1.2 8.7 6.9 1.2
18 L. eximia-GA 13.5 12.0 13.4 14.1 14.5 12.7 15.2 12.7 13.7 12.7 13.0 7.1 4.9 9.5 7.4 4.9 5.5
19 L. eximia-LA 12.1 11.3 9.9 11.3 13.1 11.3 13.8 11.7 11.5 11.6 12.3 4.3 6.0 6.7 2.6 5.3 5.8 6.4
20 L. fayeae 13.2 11.2 12.6 12.6 13.5 11.7 13.9 12.6 10.9 12.6 13.3 4.7 4.9 8.4 5.7 4.9 5.4 5.6 4.5
21 L. lucigerens 11.9 11.7 12.4 12.4 14.5 11.7 14.1 12.7 11.9 12.7 12.7 3.2 4.9 7.8 3.7 4.2 4.8 6.0 3.2 4.2
22 L. mexicana 12.1 11.3 13.4 13.4 15.9 12.7 15.2 11.7 12.6 11.6 11.4 4.6 0.0 8.5 7.1 0.7 1.2 4.9 6.0 4.9 4.9
23 L. retroversa-CU 13.7 11.2 12.6 12.6 14.0 12.2 14.4 12.6 11.4 12.6 13.3 5.2 4.8 8.4 5.6 4.8 5.4 5.3 4.5 0.5 4.1 4.8
24 L. retroversa-DR 13.5 12.4 13.1 13.1 14.8 13.4 14.5 12.7 13.3 12.7 13.4 4.0 5.0 9.2 5.4 4.3 4.8 5.7 4.6 2.8 3.6 5.0 2.7
25 L. rica_1 13.9 12.1 12.1 11.6 14.8 11.9 14.7 13.0 11.1 13.0 13.6 6.1 6.8 8.2 6.6 6.1 6.6 7.5 5.4 5.0 5.4 6.8 4.7 5.0
26 L. rica_2 13.4 11.9 11.9 11.2 14.7 11.6 14.8 12.6 10.7 12.6 13.3 5.6 6.3 8.0 6.4 5.6 6.1 7.3 5.6 5.6 5.2 6.3 5.3 5.3 1.0
DOI: 10.7717/peerj.3516/table-3

Notes:

CO

Colombia

CU

Cuba

DR

Dominican Republic

FL

Florida

GA

Greater Antilles

LA

Lesser Antilles

ME

Mexico

Table 4:
Genetic distances within the 26 putative species groups, as determined by an analysis in MEGA6.
The values are expressed as a percentage.
Putative species % variation within species
Ca. maestrica 0.14
C. idioidea-CU 0.00
C. idioidea-DR 0.00
C. idioidea-ME n/a
Ch. albiceps 0.00
Ch. megacephala 0.00
Ch. rufifacies 0.01
Co. aldrichi 0.00
Co. hominivorax 0.24
Co. macellaria 0.15
Co. minima 0.29
L.. cluvia 0.10
L.. coeruleiviridis 0.00
L. cuprina 0.00
L. eximia-CO-ME 0.61
L. vulgata 0.00
L. eximia-FL 1.06
L. eximia-GA 0.00
L. eximia-LA 0.00
L. fayeae 0.14
L. lucigerens 0.00
L. mexicana 0.00
L. retroversa-CU 0.18
L. retroversa-DR 0.08
L. rica_1 0.40
L. rica_2 0.15
DOI: 10.7717/peerj.3516/table-4

Notes:

CO

Colombia

CU

Cuba

DR

Dominican Republic

FL

Florida

GA

Greater Antilles

LA

Lesser Antilles

ME

Mexico

Table 5:
Results of species delimitation analysis based on COI.
The various measures of distance, isolation and exclusivity metrics of these clades follow including: (D), the probability of population identification of a hypothetical sample based on the groups being tested (P ID (Strict) and P ID (Liberal)), Rosenberg’s reciprocal monophyly (P(AB)), the genealogical sorting index (gsi), and a single locus Bayesian implementation of the Poisson tree processes model (bPTP). Sp congru. refers to species hypothesis that are congruent with all methods, and Sp cons. is our conservative estimate of actual species richness based on agreement among all methods and >2% mtDNA sequence divergence. Morph refers to species richness based morphology and Concat. refers to species richness based on the concatenated tree.
Putative species Mono D Intra D Inter Dtra/ Dter P ID(Strict) P ID(Liberal) P(AB) GSI bPTP Sp congru Sp cons Morph Concat
1. C. maestrica yes 0.001 0.096 0.01 0.93 (0.80, 1.0) 0.98 (0.88, 1.0) NAN 1 Y 1 1 1 1
2. C. idioidea-CU yes 0.0009 0.012 0.07 0.74 (0.57, 0.92) 0.97 (0.82, 1.0) 0.17 1 Y 2 2 2 2
3. C. idioidea-ME yes n/a 0.012 n/a n/a 0.96 (0.83, 1.0) 0.17 NA Y
4. C. idioidea-DR yes 0.003 0.014 0.19 0.81 (0.68, 0.93) 0.95 (0.85, 1.0) 1.98E −03 1 Y 3 3 3
5. Co. aldrichi no 0.0008 0.002 0.46 0.82 (0.75, 0.89) 0.95 (0.91, 0.99) NA 0.39 N 4 4 3 4
6. Co. macellaria no 0.003 0.002 1.47 0.00 (0.00, 0.00) 0.31 (0.28, 0.34) NA 0.61 N 4 5
7. Co. minima yes 0.002 0.030 0.07 0.97 (0.92, 1.0) 0.99 (0.96, 1.0) 6.30E −27 1 Y 5 5 5 6
8. Co. hominivorax yes 0.004 0.066 0.07 0.75 (0.57, 0.92) 0.97 (0.83, 1.0) 1.90E −07 1 Y 6 6 6 7
9. Ch. albiceps yes 0.002 0.033 0.05 0.90 (0.77, 1.0) 0.97 (0.87, 1.0) 4.90E −08 1 Y 7 7 7 8
10. Ch. rufifacies yes 0.0009 0.033 0.03 0.99 (0.93, 1.0) 1.00 (0.97, 1.0) 4.90E −08 1 Y 8 8 8 9
11. Ch. megacephala yes 0.001 0.054 0.02 0.99 (0.94, 1.0) 1.00 (0.97, 1.0) 1.40E −24 1 Y 9 9 9 10
12. L. cluvia yes 0.002 0.033 0.07 0.91 (0.81, 1.0) 0.98 (0.92, 1.0) 7.10E −12 1 Y 10 10 10 11
13. L. coeruleiviridis no 0.0008 0.0008 1.12 0.18 (0.05, 0.31) 0.49 (0.38, 0.59) NA 0.59 N 11 11 11 12
14. L. mexicana no 0.0007 0.0008 0.88 0.20 (0.02, 0.39) 0.51 (0.36, 0.66) NA 0.49 N 12 13
15. L. eximia-FL yes 0.002 0.005 0.40 0.39 (0.24, 0.54) 0.74 (0.58, 0.89) 0.03 1 N 13 14
16. L. vulgata yes 0.002 0.007 0.32 0.65 (0.51, 0.79) 0.89 (0.78, 1.0) 0.03 1 N 14 15
17. L. eximia-ME-CO yes 0.004 0.016 0.27 0.82 (0.71, 0.92) 0.93 (0.87, 0.99) 3.60E −04 1 Y 12 12 16
18. L. eximia-LA yes 0.002 0.016 0.12 0.79 (0.64, 0.93) 0.95 (0.84, 1.0) 3.60E −04 1 Y 13 13
19. L. fayeae yes 0.002 0.008 0.31 0.82 (0.73, 0.91) 0.94 (0.89, 0.99) 2.40E −06 1 N 14 14 15 17
20. L. retroversa-CU yes 0.004 0.008 0.46 0.75 (0.67, 0.84) 0.92 (0.87, 0.97) 2.40E −06 1 N 16 18
21. L. retroversa-DR yes 0.002 0.024 0.09 0.96 (0.91, 1.0) 0.99 (0.96, 1.0) 2.60E −14 1 Y 15 15 19
22. L. lucigerens yes 0.002 0.035 0.05 0.76 (0.58, 0.94) 0.98 (0.84, 1.0) 9.90E −07 1 Y 16 16 17 20
23. L. eximia-GA yes 0.001 0.048 0.03 0.98 (0.91, 1.0) 1.00 (0.96, 1.0) 1.30E −11 1 Y 17 17 21
24. L. rica_1 yes 0.003 0.011 0.24 0.90 (0.83, 0.96) 0.97 (0.92, 1.0) 4.40E −09 1 Y 18 18 18 22
25. L. rica_2 yes 0.002 0.011 0.22 0.90 (0.83, 0.97) 0.97 (0.92, 1.0) 4.40E −09 1 Y 19 23
26. L. cuprina yes 0.002 0.076 0.03 0.98 (0.91, 1.0) 1.00 (0.96, 1.0) 4.30E −19 1 Y 20 19 19 24
DOI: 10.7717/peerj.3516/table-5

Notes:

CO

Colombia

CU

Cuba

DR

Dominican Republic

FL

Florida

GA

Greater Antilles

LA

Lesser Antilles

ME

Mexico

Phylogenetic inference

From the 26 putative species analyzed here, 25 were represented by multiple individuals and one by a single individual in the COI analysis. All phylogenetic analyses (COI, ITS2, COI+ITS2) yielded well resolved trees with strong posterior probability support for most of the branches and broadly agreed on species limits but with some differences in topology (Figs. 24, Figs. S1S3). The Bayesian analysis of the ITS2 supported the monophyly of 21 of 26 putative species. It recovered the monophyly of Co. aldrichi, Co. macellaria, L. mexicana and L. coeruleiviridis, which failed with all other analysis. However it did not recover the geographic variation of C. idioidea from Mexico and Dominican Republic, L. retroversa from Cuba and Dominican Republic or L. rica 1 and 2, and it only recovers three of the four L. eximia clades indicated by COI analyses (Fig. 3, Fig. S2). The concatenated tree supports 24 of the 26 putative species including two clades within L. retroversa, L. rica, and C. idioidea, and three clades within L. eximia. The concatenated matrix did not support the monophyly of C. idioidea-CU that is nested within C. idioidea-ME and L. eximia-CO+ME nested within L. eximia-LA (Fig. 4, Fig. S3).

Discussion

Accurate identification of insects is a crucial step to using them as reliable evidence in legal investigations. Although morphology has been successfully used to identify immature specimens involved in cadaveric decomposition (Cardoso et al., 2014; Florez & Wolff, 2009; Szpila et al., 2013a; Szpila et al., 2013b; Szpila et al., 2014; Szpila & Villet, 2011; Wells, Byrd & Tantawi, 1999), this approach depends on the availability of taxonomic keys of the species present in the region. In the Caribbean, the immature stages of 11 species are unknown and other approaches are needed in order to identify them. Besides this, morphology may overlook potentially cryptic species and cannot be used on incomplete or destroyed specimens found on a crime scene. Here, we show DNA barcoding to be useful in overcoming these problems and provide tools to accelerate the identification and discovery of species. This is particularly important in areas like the Caribbean, where studies of insects involved in cadaveric decomposition are scarce (Whitworth, 2010; Yusseff-Vanegas & Agnarsson, 2016; Yusseff-Vanegas, 2007; Yusseff-Vanegas, 2014). One of the first steps required for this approach is creating a reliable DNA barcode database that can be used with confidence in order to identify unknown specimens found in death scenes investigation (DeBry et al., 2013; Harvey et al., 2003).

Bayesian tree based on ITS2 dataset including 158 specimens.

Figure 3: Bayesian tree based on ITS2 dataset including 158 specimens.

Individual terminal taxa have been replaced with species names, while full taxon clade structure is retained. Colors represent different species based on morphology. See Fig. S2 for bootstrap support values.
Bayesian tree based on the concatenate dataset including 137 specimens.

Figure 4: Bayesian tree based on the concatenate dataset including 137 specimens.

Individual terminal taxa have been replaced with species names, while full taxon clade structure is retained. Colors represent different species based on morphology. See Fig. S3 for bootstrap support values.

The success of DNA barcoding relies on the quality of the underlying database used to compare DNA sequences of new samples. A good database should contain DNA barcodes of expertly identified individuals, and preferably taxon sampling covering the distribution range of each species. Our study complies with both requirements and is the first thorough molecular study of Calliphoridae from the Caribbean. It includes a representative collection from all but two forensically relevant Calliphoridae from the region, and covers the whole geographic range of most of the investigated species (Table 1). All specimens in this study were carefully identified using traditional morphological taxonomy (Whitworth, 2006; Whitworth, 2010; Whitworth, 2014) and each individual was successfully allocated to one of the currently recognized calliphorid species, except for specimen CO027 that could only be identified to the genus level. Although morphological identification of specimens collected in this study corresponded to 19 previously reported species (Whitworth, 2010), our results based on molecular data indicate higher diversity. In all, 26 putative species lineages were identified, and in particular our results indicate that Lucilia and Chloroprocta are more diverse than suggested by current taxonomy. COI recovered substantial geographic variation for C. idioidea, L. eximia, L. retroversa and L. rica such that molecular data indicate up to eleven putative species lineages that cannot be, or at least have not been, recognized by morphology.

Lucilia eximia is considered a widespread species found from the southern United States through Central America to southern South America (Whitworth, 2014). Nevertheless, our molecular results show four distinct genetic clusters with an average inter-cluster divergence from 2.5 to 7.4% (Table 3). The clusters are geographically structured and three of them are widely separated (Fig. 2, Fig. S1). The first one is the Greater Antilles cluster (GA) that includes specimens from Puerto Rico, Mona Island and Dominican Republic, the second is a small cluster that includes specimens from Florida (FL), the third one contains specimens from Colombia and Mexico (CO-MEX), and the fourth contains specimens from the Lesser Antilles islands of Dominica and Saint Lucia (LA). Similar results were reported by Solano, Wolff & Castrol (2013) and Whitworth (2014) where widely separate clades of L. eximia were found using DNA barcodes. All species delimitation methods supported the uniqueness and genetic isolation of the four clades, each showing low intra-clade divergence (<1%, Table 4), and thus likely representing four distinct species. Although we found some morphological variation between L. eximia from the mainland and islands and among islands as previously reported (James, 1967; Whitworth, 2010; Woodley & Hilburn, 1994), detailed revision of those specimens by Dr. Whitworth from Washington State University concluded that there is not enough evidence to separate them as different morphological species, suggesting they may be morphologically cryptic species. Further studies on these populations will be necessary to establish their taxonomic status.

Lucilia rica was collected throughout the Lesser Antilles and is very abundant in most of the islands (personal observation). Although James (1970) listed this species from Puerto Rico, we did not find any specimens after very extensive collections on the island. Thus, we believe that L. rica is restricted to the Lesser Antilles and has not dispersed beyond Anguilla. Whitworth (2010) reported this species from Antigua, Bermuda, Guadalupe and St. Lucia; however, we found it in eight more islands (Table 1) and our data showed two geographic clusters (Figs. 2, 4; Figs. S1, S3). The first cluster (L. rica-1) contains specimens from St Martin, Saba, St. Eustatius, St. Kitts, Nevis and Martinique and the second one (L. rica 2) from Barbuda, Antigua, Montserrat and Guadeloupe. Although the genetic distance between clades is low (1%), it is much greater than the intra-clade divergences (<0.3%). While all species delimitation methods support the possibility of two different species (Tables 5 and 6), we did not find morphological evidence to support it. Nevertheless, given that this is the most abundant Lucilia species of the Lesser Antilles, additional studies on these populations are important to determine if the genetic difference is due to intraspecific variation or if they are cryptic species.

For Lucilia retroversa we find two geographic clusters, one from Cuba and one from the Dominican Republic with an average mtDNA distance of 2.5% (Table 3) and with low intra-clade divergence (<0.2%, Table 4). Whitworth (2010) reported some morphological differences between specimens from Bahamas (which share morphology with Cuban specimens) and the Dominican Republic, but after examination of male and female genitalia he concluded that those differences were intraspecific variation. However, he noticed that our L. retroversa specimens have a brown basicosta instead of white or yellow basicosta which is an important character used to separate L. retroversa from other species (see taxonomic key in Whitworth, 2010). Given that all of our species delimitation results support two possible cryptic species, we recommend further detailed molecular and morphological studies of these populations to determine if they merit the description of a separate species.

Table 6:
Results of species delimitation analysis based on the concatenated tree.
(D), the probability of population identification of a hypothetical sample based on the groups being tested (P ID (Strict) and P ID (Liberal)), Rosenberg’s reciprocal monophyly (P(AB)).
Putative species Closest species Mono D Intra D Inter Dtra/ Dter P ID(Strict) P ID(Liberal) P(AB)
1. Ca. maestrica L. cuprina yes 0.005 0.19 0.03 0.58 (0.43, 0.73) 0.97 (0.82, 1.0) 1.00E−05
2. L. cluvia L. coeruleiviridis yes 0.005 0.05 0.10 0.87 (0.74, 0.99) 0.97 (0.87, 1.0) 5.50E−09
3 L. coeruleiviridis L. eximia-FL yes 0.001 0.01 0.14 0.84 (0.72, 0.97) 0.96 (0.86, 1.0) 0.01
4. L. mexicana L. coeruleiviridis yes 0.0009 0.02 0.06 0.75 (0.58, 0.93) 0.97 (0.83, 1.0) 0.01
5. L. eximia-FL L. coeruleiviridis yes 0.005 0.01 0.48 0.34 (0.19, 0.50) 0.69 (0.53, 0.84) 4.94E−03
6. L. vulgata L. coeruleiviridis yes 0.003 0.02 0.18 0.75 (0.60, 0.89) 0.94 (0.83, 1.0) 0.1
7. L. eximiaCO-ME L. eximia-LA yes 0.006 0.02 0.32 0.79 (0.69, 0.90) 0.92 (0.86, 0.99) 0.01
8. L. fayeae L. retroversa-CU yes 0.006 0.04 0.15 0.84 (0.71, 0.96) 0.96 (0.86, 1.0) 4.30E−04
9. L. retroversa-CU L. retroversa-DR yes 0.004 0.02 0.18 0.50 (0.35, 0.65) 0.87 (0.72, 1.0) 0.03
10. L. lucigerens L. eximia-LA yes 0.003 0.04 0.08 0.55 (0.40, 0.70) 0.93 (0.78, 1.0) 3.10E−04
11 L. eximia-GA L. rica 2 yes 0.002 0.06 0.04 0.91 (0.78, 1.0) 0.98 (0.87, 1.0) 2.70E−06
12. L. rica 1 L. rica 2 yes 0.005 0.02 0.33 0.81 (0.72, 0.90) 0.94 (0.88, 0.99) 4.20E−04
13. L. rica 2 L. rica 1 yes 0.004 0.02 0.30 0.59 (0.42, 0.77) 0.84 (0.69, 0.98) 4.20E−04
14. L. cuprina L. cluvia yes 0.003 0.15 0.02 0.94 (0.83, 1.0) 1.00 (0.94, 1.0) 1.90E−11
15. Ch. albiceps Ch. rufifacies yes 0.003 0.04 0.06 0.75 (0.57, 0.93) 0.97 (0.83, 1.0) 2.98E−03
16. Ch. rufifacies Ch. albiceps yes 0.002 0.04 0.05 0.90 (0.78, 1.0) 0.97 (0.87, 1.0) 2.98E−03
17. Ch. megacephala Ch. albiceps yes 0.003 0.11 0.02 0.92 (0.79, 1.0) 0.98 (0.87, 1.0) 2.80E−05
18. Co. aldrichi Co. macellaria yes 0.002 0.01 0.13 0.85 (0.72, 0.97) 0.96 (0.86, 1.0) 4.70E−07
19. Co. macellaria Co. aldrichi yes 0.007 0.01 0.52 0.84 (0.78, 0.89) 0.96 (0.93, 0.99) 4.70E−07
20. Co. minima Co. aldrichi yes 0.007 0.05 0.14 0.88 (0.77, 0.99) 0.96 (0.90, 1.0) 4.50E−09
21. Co. hominivorax Co. aldrichi yes 0.007 0.09 0.08 0.88 (0.76, 1.0) 0.97 (0.87, 1.0) 1.00E−07
22. C. idioidea-DR C. idioidea-ME yes 0.003 0.02 0.19 0.74 (0.60, 0.88) 0.94 (0.83, 1.0) 4.08E−03
23 C. idioidea-CU C. idioidea-ME yes 0.001 0.01 0.06 0.56 (0.41, 0.71) 0.94 (0.79, 1.0) 0.33
24: L. retroversa-DR L. retroversa-CU yes 0.004 0.02 0.16 0.76 (0.62, 0.90) 0.94 (0.83, 1.0) 0.03
25: C. idioidea-ME C. idioidea-CU no 0.005 0.01 0.38 0.40 (0.24, 0.55) 0.75 (0.59, 0.90) NA
26. L. eximia-LA L. eximiaCO-ME no 0.007 0.02 0.36 0.63 (0.48, 0.77) 0.88 (0.77, 0.99) NA
DOI: 10.7717/peerj.3516/table-6

Notes:

CO

Colombia

CU

Cuba

DR

Dominican Republic

FL

Florida

GA

Greater Antilles

LA

Lesser Antilles

ME

Mexico

Chloroprocta idioidea, the only species of Chloroprocta, is a widespread species found from southern North America to southern South America (Dear, 1985; Whitworth, 2010). Our results show that C. idioidea is also geographically structured into three clades: one from Dominican Republic, one from Cuba and one from Mexico (Fig. 2, Fig. S1). Analysis of the genetic divergence between clades show more than 2% divergence between the Cuba and Dominican Republic clades but less than 2% divergence between the Mexico and Cuba clades (Table 3). Some authors (Hall, 1948; Shannon, 1926) believed there were two species of the genus in the Americas, however (Dear, 1985) concluded that there was only one single widespread species that exhibits some color variations which is dependent upon geographic distribution. Our molecular results indicate at least two, and perhaps three, separate species of Chloroprocta. All species delimitation methods (Table 5) and the concatenated matrix (Fig. 4, Fig. S3) suggest that the Dominican Republic versus the Cuba and Mexico clade are separate species, but were ambiguous about the status of C. idioidea-CU that is nested within C. idioidea-ME. Cuban and Mexican specimens are morphologically similar, dark-bluish in color with brownish to orange legs, however, as reported by Dear (1985) the Cuban females have brownish, instead of yellow-white calypters. Our specimens from Dominican Republic are similar to the southern USA specimens described by Dear (1985) but have darker post spiracles and clear wings with only the costa faintly tinted. Although we could see morphological differences between populations, those differences were based on a limited number of specimens (e.g., five specimens from Dominican Republic and three from Mexico). Further studies with larger number of specimens of C. idioidea, including detailed morphological descriptions and expanded molecular analysis, are necessary to further test species limits within this genus.

Our focus here is not to fully resolve calliphorid taxonomy. However, it is important to highlight the consequences of our findings for forensic entomology studies. Currently L. eximia is one of the most widespread and abundant Lucilia in the Neotropics (Whitworth, 2014). However, our results suggest that, in fact, this is not one widely distributed species, but potentially several species that differ in geographic range and possibly in biological traits (rates of development, diapause, habitat preference, feeding habits etc.). The same is true for L. retroversa and C. idioidea, both have genetically distinct clades in the Dominican Republic and in Cuba (Figs. 2 and 4). This finding will have direct consequences for the use of these species in legal investigations, if that variation reflects differences in behavior and biology, that can affect post mortem interval estimations (Tarone, Singh & Picard, 2015). Previous studies of Phormia regina (Byrd & Allen, 2001), C. macellaria and C. rufifacies (Yusseff-Vanegas, 2007) have shown that their developmental rate differ from different populations. Picard & Wells (2009) suggested that that variation is in part due to differences in population genetic structure, and for that reason, ecological data obtained from one population should not be generalized or extrapolated to other populations (Byrne et al., 1995). This is important at least for specimens collected in Cuba where both populations are present, probably as the result of recent dispersal of L. retroversa and C. idoidea from the Dominican Republic to Cuba. Our results (S1) show that two of the southeast Cuban specimens, CU007 (L. retroversa) and CU008 (C. idioidea), collected in Turquino National Park in Cuba (Table 1), cluster tightly with Dominican Republic specimens (S1). To confirm the genetic affinity of these specimens we added three more nuclear genes for a limited number of individuals from both populations and re-ran the analysis. The multi-gene analysis again strongly clustered CU007 and CU008 with the Dominican Republic specimens for each species. Thus, both the Dominican Republic and Cuban populations are clearly present in Cuba.

COI recuperated substantial geographic variation with high COI sequences divergence between populations of Lucilia eximia, L. retroversa, L. rica and C. idioidea (Fig. 2, Fig. S1), suggesting the possibility of different species (Hebert et al., 2003; Hebert, Ratnasingham & deWaard, 2003). However, genetic variation is not always indicative of species differentiation. For instance, studies including Phormia regina have found that the genetic distance between N American and W European populations is higher than 4% (Boehme, Amendt & Zehner, 2012; Desmyter & Gosselin, 2009). But after detailed molecular and morphological analysis of both populations, Jordaens et al. (2013a) concluded that the high differentiation at COI, COII and cytb, but low (16S, nDNA) and lack of morphological differentiation, was indicative of substantial intraspecific mtDNA sequence divergence, rather than a species level differentiation. In light of those results, definite conclusions cannot yet be drawn regarding the taxonomy of these species. Further population level studies of the four species in question are therefore necessary. A comprehensive molecular analysis including several mitochondrial and nuclear genes in combination with morphological examination and detailed description of the genitalia, are required to determine if they are in fact different species, or if the genetic difference between populations is the product of intraspecific variation. Meanwhile the use of these species for forensic purposes should be evaluated carefully and with reference to genetic and behavioral differences among its populations.

Regarding the other Calliphoridae species, Ca. maestrica, Co. minima, Co hominivorax, Ch. albiceps, Ch. rufifacies, Ch. megacephala, L. cluvia, L. cuprina and L. lucigerens, all showed reciprocal monophyly with strong posterior probability support and all can be successfully identified using the DNA barcoding approach. All species delimitation methods, phylogenetic analysis of ITS2, and the concatenated tree support their monophyly and species status, and the results are congruent with morphology. Calliphora maestrica is the only Calliphora species reported for the Caribbean and is endemic from the region. This species was originally described from Sierra Maestra region in Cuba (Peris et al., 1998) and later reported also from Jamaica and Dominican Republic (Whitworth, 2010). Although we collected on all three islands, we only found C. maestrica in Villa Pajon, Dominican Republic, a cold region at altitudes >2,140 m. We did not find it in Cuba or Jamaica, likely due to lack of sampling at altitudes above 1,200 m on both islands.

The three species of Chrysomya were recently introduced to the New World (Baumgartner & Greenberg, 1984). Although Whitworth (2010) reported Ch. megacephala and Ch. rufifacies from Dominica, Dominican Republic, Jamaica and Puerto Rico, they are abundantly present in most of the islands being found from Cuba to Martinique (Table 1). In contrast, Chrysomya albiceps has more restricted distribution being found in islands closer to South America (Table 1, Whitworth, 2010). Although Dear (1985) reported this species from Puerto Rico, we did not find it after extensive collections on the island. That report was based on a single larva found in a goat, probably of Ch. albiceps but the species was not confirmed (Gagne, 1981). We believe that Ch. albiceps has not dispersed beyond Dominica and that the species reported by Dear (1985) was in fact Ch. rufifacies. Given the high dispersal abilities of the species of this genus (Baumgartner & Greenberg, 1984) and their invasive behavior (Aguiar-Coelho & Milward-De-Azevedo, 1998; De Andrade et al., 2002; Faria et al., 1999; Wells & Greenberg, 1992), it is not surprising to find them widely distributed and very well established throughout the Caribbean. They do not show any geographic structure, suggesting their recent colonization from the mainland and the constant gene flow among populations.

Lucilia cluvia and L. cuprina, are widely distributed flies found in different parts of the world (Byrd & Castner, 2010). Lucilia cluvia is considered rare (Whitworth, 2010). Although it has been reported from several locations in Puerto Rico, Cuba, and Martinique, we have only found two specimens in a suburban area in Toa Baja, Puerto Rico. Lucilia cuprina is reported from several islands in the Caribbean, but we only found it in urban areas of Puerto Rico as our focus on other islands was in non-urban areas. Finally L. lucigerens is an endemic species from Jamaica and was collected abundantly throughout the island.

DNA barcoding in animals typically employs a single mitochondrial marker for identification and delimitation of species (Hebert et al., 2003; Hebert, Ratnasingham & DeWaard, 2003), and this approach has shown to be useful in Calliphoridae species identification. However it does not reliably distinguish among some recently diverged species (Harvey et al., 2003; Nelson, Wallman & Dowton, 2007), leading to doubt that COI alone is sufficient for identification of species (Nelson, Wallman & Dowton, 2007; Wells, Wall & Stevens, 2007). Rather, the use of multiple markers has been suggested as a means to increase the accuracy of species identification. Indeed, our results show that COI barcoding successfully identified most species, but did not distinguish between the pairs L. mexicana and L. coeruleiviridis as previously reported (DeBry et al., 2013; Whitworth, 2014; Williams, Lamb & Villet, 2016) and between Co. aldrichi and Co. macellaria (Tables 3 and 5, Fig. S1 ). The latter species is considered one of the most important Calliphoridae for forensic studies in the Americas (see discussion in Yusseff-Vanegas & Agnarsson, 2016). Additionally, COI showed very low genetic divergences (<0.7%, Table 3) between the putative species L. vulgata and L. coeruleiviridis, and L. fayeae and L. retroversa-CU; species that are clearly distinguished based on morphological characteristics. This low genetic divergence may reflect short histories of reproductive isolation (Hebert, Ratnasingham & DeWaard, 2003), or mitochondrial introgression. In either case the addition of the nuclear gene ITS2 resolved the monophyly of the four species that COI alone did not support, and added resolution for uncertain groups with mtDNA genetic distances lower than 2%. These findings agreed with previous studies where the analysis of ITS2 resolved complex species delimitation (GilArriortua et al., 2014; Song, Wang & Liang, 2008), however, not always addition of more genes resolved the monophyly of the sister species like the case of L. illustris and L. caesar, where, after analysis including six genes, the monophyly remain unresolved (Sonet et al., 2012).

In sum, our study demonstrates the importance employing a second nuclear marker for barcoding analyses and species delimitation of calliphorids and the power of molecular data in combination with a complete reference database to enable identification of taxonomically and geographically diverse insects of forensic importance. The combination of the two markers supported the higher diversity of Calliphoridae in the Caribbean recovering the monophyly of nine of the eleven possible cryptic species. However, definite conclusion about the taxonomy of these species will depend on further studies combining molecular and morphological approaches.

Conclusion

From almost a decade many studies have applied DNA-based methods for the identification of insects of forensic importance to enable identification of unknown insect specimens found in death scene investigations. However, this technique is not being implemented and the traditional time consuming methods of raising immature stages to adulthood is still in practice. The use of this approach has been unsuccessful because of lack of confidence due to sequence gaps and errors, unauthenticated reference DNA sequences in the database, and incomplete reference data set with partial taxon sampling. Thus, the base science foundation for application of DNA sequences analysis is unsolid for identification of evidentiary samples. Despite all studies of DNA based identification for insects involved in forensics, only a few of them include a complete reference data set. But even with a complete reference database, COI has failed in demonstrating reciprocal monophyly for several recently diverged species creating uncertainty about its use for identification. The addition of ITS2 as a second marker may be the key to increase certainty in identification and make this technique useful for forensic purposes. A great effort to build complete reference databases including extensive collections, accurate identification, geographical genetic variation for each targeted insect group and the addition of ITS2 as a second marker is needed. In general, COI barcodes are highly useful for species identification of the Caribbean calliphorids. ITS2 appears to be a good second marker that allows higher resolution and accurate identification of specimens that cannot be separated by COI alone. Our study provides, for the first time, a reliable dataset to accurately identify species of the family Calliphoridae from the Caribbean, and opens the door for future studies on biodiversity, biogeography, distribution and ecology of these forensically important flies.

Supplemental Information

Phylogenetic relationship within Calliphoridae based on a Bayesian analysis of nucleotide data from COI

Numbers indicate posterior probability support values. Specimen voucher codes referred to in Table 1 are shown following species names. For specimens from Lesser Antilles (LA), the three capital letters before the voucher code refers to the name of the islands abbreviated a follows: SBA, St. Barthelemy; SAB, Saba; BAR, Barbuda; NEV, Nevis; KIT, St. Kitts; MTQ, Martinique; ANT, Antigua; GUA, Guadeloupe; MON, Montserrat; EUS, St. Eustatius; SMA, St. Martin, SLU, St. Lucia; BBD Barbados.

DOI: 10.7717/peerj.3516/supp-1

Phylogenetic relationship within Calliphoridae based on a Bayesian analysis of nucleotide data from ITS

Numbers indicate posterior probability support values. Specimen voucher codes referred to in Table 1 are shown following species names. For specimens from Lesser Antilles (LA), the three capital letters before the voucher code refers to the name of the islands abbreviated a follows: SBA, St. Barthelemy; SAB, Saba; BAR, Barbuda; NEV, Nevis; KIT, St. Kitts; MTQ, Martinique; ANT, Antigua; GUA, Guadeloupe; MON, Montserrat; EUS, St. Eustatius; SMA, St. Martin, SLU, St. Lucia; BBD Barbados.

DOI: 10.7717/peerj.3516/supp-2

Phylogenetic relationship within Calliphoridae based on based on partitioned Bayesian analysis of the combined gene (COI and ITS2) data set

Numbers indicate posterior probability support values. Specimen voucher codes referred to in Table 1 are shown following species names. For specimens from Lesser Antilles (LA), the three capital letters before the voucher code refers to the name of the islands abbreviated a follows: SBA, St. Barthelemy; SAB, Saba; BAR, Barbuda; NEV, Nevis; KIT, St. Kitts; MTQ, Martinique; ANT, Antigua; GUA, Guadeloupe; MON, Montserrat; EUS, St. Eustatius; SMA, St. Martin, SLU, St. Lucia; BBD Barbados.

DOI: 10.7717/peerj.3516/supp-3

Data Matrix COI

DOI: 10.7717/peerj.3516/supp-4

Data Matrix ITS2

DOI: 10.7717/peerj.3516/supp-5