The unprecedented threats to coral reef ecosystems from global climate change (GCC) require an 24 urgent response from the aquarium community, which is becoming an increasingly vital coral 25 conservation resource. Unfortunately, many hermatypic corals in aquaria are not identified to 26 species level, which hinders assessment of their conservation significance. Traditional methods of 27 species identification using morphology can be challenging, especially to non-taxonomists. DNA 28 barcoding is an option for species identification of Scleractinian corals, especially when used in 29 concert with morphology-based assessment. This study uses DNA barcodes to try to identify 30 aquarium specimens of the diverse reef-forming genus Acropora from 127 samples. We identified 31 to our best current knowledge, to species name 44% of the analysed samples and provided 32 provisional identification for 80% of them (101/127, in the form of a list of species names with 33 associate confidence values). We highlighted a sampling bias in public nucleotide sequences 34 repertories (e.g.: GenBank) towards more charismatic and more studied species, even inside a well-35 studied genus like Acropora. In addition, we showed a potential "single observer" effect with over a 36 quarter of the reference sequences used for these identifications coming from the same study. We 37 propose the use of barcoding and query matching as an additional tool for taxonomic experts and 38 general aquarists, as an additional tool to increase their chances of making high confidence species-39 level identifications. We produce a standardised and easily repeatable methodology to increase the 40 Introduction 45 It is widely acknowledged that coral reef ecosystems are facing unprecedented threats from global 46 climate change (GCC) e.g. (Pratchett et Reference sequences for the two targeted regions were collated. FASTA files were created with the 221 records matching the following query on GenBank: mtCR=""Acroporidae"[Organism] AND 222 mitochondrial "complete genome" OR "control region" OR "putative control region" OR 223 mitochondrial control region"; PaxC=""Acroporidae" Successively the frequency distribution of intra/inter-specific distances was plotted. The quantile 229 function (R package "stats") was used to define the 99; 50; 1% quantile confidence interval used in 230 the blast match analysis. Note: the two alignments were constructed and used only for the specific 231 purposes of examining the barcoding gap for the targeted region and determining potential 232 confidence interval for the blast match analysis. 233
Separate unrooted Maximum-likelihood (ML) phylogenetic trees were created, one for each 235 targeted region. The references obtained from the previously mentioned query were cleaned for 236 duplicated sequences and subsequently aligned. The two phylogenetic analyses were performed 237 using the program IQ-TREE, with default settings and automatic model selection (Minh et performed. To filter for species match with identical scores a decision tree (Fig. 2) was designed 245 relying on Bit score, Percentage identity, and number of mismatches. Matches shorter than 2/3 of 246 the original sample sequence query were removed. The decision tree prioritised 100% pairwise 247 identity matches where possible, then to the highest bit score, then lowest number of base 248 mismatches. 100% pairwise identity matches to a species group rather than a single species name 249 were prioritised over single species name matches with lower pairwise identity with higher bit 250 score, both possible IDs were retained. We confronted the match from the two targeted regions to 251 obtain a single most probable ID. To account for conflicting matches between the two region we 252 relied on the 99; 50; 1% quantile distribution of the intraspecific distances obtained from the 253 barcoding gap analysis. Using pairwise identity threshold as confidence intervals to discern which 254 region offers the better match for the ID (R-scripts as Online Resource 3 and 4 (Fig. 3) . The PaxC alignment of 352 sequences resulted in a matrix 272 of 123 904 genetic distances, grouped by 55 species names. 3% of these (3 726) are intraspecific 273 distances, while 97% (120 178) are interspecific distances (Fig. 3) . Between the two groups there is 274 an overlap of 49 species names. Table 3 The quantile within species distribution produce the following distance threshold for our confidence 282 interval: mtCR 0.027 (99%); 0.0068 (50%); 0 (1%) and PaxC 0.17 (99%); 0.014 (50%); 0 (1%). of polyphyly. Looking at our best matches (i.e.: 100% match to a reference), most of our sample 291 sequences match to single species -63% of mtCR 100% matches and all of the PaxC 100% 292 matches. Focusing on just these sequences with 100% matches to a reference, we see that 0/19 293 mtCR sequences and 1/4 PaxC match to single-species clades (a monophyletic group composed of 294 more than one sample of a single taxon name). However, some of these match to species that a 295 polyphyletic within the tree (All of 100% mtCR matches tie to polyphyletic taxa and 75% for 296 PaxC). 297 298 Blast match 299 The local database for blast matching contained 510826 sequences. In Table 2 we see that 23 of our 300 76 samples (with both regions sequenced) were uniquely identified to a single species, while 13 301 samples matched to conflicting names. 20 of our samples had a unique species name match for the 302 PaxC region but mtCR region matched to 2 or more names with all but 6 of these including the 303 matching PaxC name. Very few samples matched to multiple species for both regions (4). 304
We have 47 % of samples matching to a single species name with both regions, 5% with the same 306 identical match. While we have 42% of the matches to a single species name that are in conflict, 307 25% of these conflicts are resolvable (i.e.: one of the two region with a match better fitting than the 308 other one, based on the defined confidence thresholds of each region, rather than directly on the 309 pairwise identity). Similar results for the other sample matching for two or more in at least one of 310 the two regions. Out of the 76 samples, in 15 cases we observe the second region adding 311 information to the first ones (Table 5 as Online Resource 2). Mainly from a mtCR multi name ID to 312 a single PaxC name (i.e.: sample P269 has 7 mtCR identical matches to 7 different species names, 313 and the PaxC matches at a lower rate to only one of them). In addition, the second region functions 314 as backup option whenever one of the two falls too short (5/76 sample have too low-quality match 315 to be trusted independently). 316
The complete list of our current best species name match, with species group information and 318 detailed genetic info is provided as Online Resource (Table 5 56% of the total sequences on GenBank (Benson 2000)). The genus focus of our study, Acropora, is 331 one of the more commonly studied and widespread hard corals in the ocean (Wallace 1999; Fukami 332 et al. 2000) making it one of the most covered coral genera in GenBank. The reality is that even for 333 a widely studied group such as Acropora we can observe a disparity in the number of sequences 334 available for each species name (Fig. 4) . The best example of this is the delta between two species 335 group: group humilis being the most sampled (251 mtCR and 61 PaxC references sequences from 336 our query) and group robusta one of the least sampled (11 mtCR and 13 PaxC references sequences 337 from our query). Note that both species group are formed by of the same number of species (8 338 species each ). However, while counting the number of sequences available for 339 a group may be an indication of the level of research that group receives, it does not mean that 340 groups with the most sequences have the best reference collection for a DNA barcoding project. 341
The most important quality for a reference sequence collection is it's representation of the 342 underlying species diversity, ideally covering a wide geographical and morphological range 343 Another potential issue with the use of these methods of identification is the source of the reference 364 sequences. With databases like GenBank, with little or no review on the uploaded information, the 365 question is the confidence that the initial identification of the now available reference sequences is 366 indeed correct (James Harris 2003). In our specific case after the processing we performed for the 367 mtCR reference alignment, out of the total 579 included 42% (242 sequences) come from a single 368 doctoral thesis project, while 5.4% (33 sequences) are from unpublished papers (Table 3) . For the 369 PaxC 16% (58 sequences) are from unpublished papers, and another 39% (138 sequences) from a 370 single paper (Richards et al. 2008) (Table 4 ). Highlighting clearly the need for more study on the 371 subject to increase the number of potential sources and alleviate the possible bias of a "single 372 observer". We observed high levels of polyphyly across all of our highest confidence matches, with only 1 of 380 evident case in which the 100% match was to a monophyletic species (Acropora humilis PaxC 381 region). Our preference is to employ a standardised, quickly repeatable, objective process, while 382 keeping in mind that the results are imperfect and designed to inform but not dictate identification 383 efforts. 384 385 Blast match 386 The use of query match with blast is far from being a complete solution, with 26 out of 76 samples 387 without a single species or species group resolved ID (Table 5) , we have proposed a theoretical 388 decision tree that standardise the choice when there is a need of sorting through the output of a 389 multi-species name blast match (Fig. 2 ). In addition we used two different regions, a mitochondrial 390 one and a nuclear one to increase the confidence in our results (Suzuki et al. 2008 ). Our proposed 391 species ID are provisional and subject to revision when more species and sequences are available, 392 hence the value of a repeatable process for matching and re-matching. 393 394 All methods of identification that rely on DNA barcodes (e.g.: Blast matching, Barcoding Gap 395 analysis, etc.) use genetic divergence thresholds to assign individuals to correct species. This based 396 on two fundamental assumptions 1) monophyly of species with respect to the molecular marker 397 used, and 2) intraspecific genetic divergence is much smaller than interspecific genetic differences 398 (Toffoli et al. 2008 ). The first assumption is more dependent on the experimental choices made (i.e. Conflict of interest 449 On behalf of all authors, the corresponding author states that there is no conflict of interest. 450 Chen I-P, Tang C-Y, Chiou C-Y, Hsu J-H, Wei Ewers N  s  a  m  p  l  e  s  N  s  a  m  p  l  e  s  o  v  e  r  l  a  p  p  i  n  g  G  e  n  e  N  S  e  q  u  e  n  c  e  s  M  i  n  i  m  u  m  s  e  q  u  e  n  c  e  l  e  n  g  t  h   M  a  x  i  m  u  m  s  e  q  u  e  n  c  e  l  e  n  g  t  h   M  e  d  i  a  n  s  e  q  u  e  n  c  e  l  e  n  g  t  h   m  t  C  R   8  4  5  6  3  9  0  4  7  3  4 P a x C 9 0 4 2 9 8 6 9 6 4 9 1 2 7 7 6 J  o  u  r  n  a  l  A  u  t  h  o  r  s  T  i  t  l  e  N  %   P  L  o  S  O  N  E  3  (  9  )  ,  E  3  2  4  0  (  2  0  0  8  )  Z  .  T  .  R  i  c  h  a  r  d  s  ,  M  .  J  .  H  .  v  a  n  O  p  p  e  n  ,  C  .  C  .  W  a  l  l  a  c  e  ,  B  .  L  .  W  i  l  l  i  s  ,  a  n  d  D  .  J  .  M  i  l  l  e  r  S  o  m  e  r  a  r  e  I  n  d  o  -P  a  c  i  f  i  c  c  o  r  a  l  s  p  e  c  i  e  s  a  r  e  p  r  o  b  a  b  l  e  h  y  b  r  i  d  s  1  3  8  3  9 . 