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Environmental DNA analysis shows high potential as a tool for estimating intraspecific genetic diversity in a wild fish population

Environmental DNA (eDNA) analysis has recently been used as a new tool for estimating intraspecific diversity. However, whether known haplotypes contained in a sample can be detected correctly using eDNA-based methods has been examined only by an aquarium experiment. Here, we tested whether the haplotypes of Ayu fish (Plecoglossus altivelis altivelis) detected in a capture survey could also be detected from an eDNA sample derived from the field that contained various haplotypes with low concentrations and foreign substances. A water sample and Ayu specimens collected from a river on the same day were analysed by eDNA analysis and Sanger sequencing, respectively. The 10 L water sample was divided into 20 filters for each of which 15 PCR replications were performed. After high-throughput sequencing, denoising was performed using two of the most widely used denoising packages, UNOISE3 and DADA2. Of the 42 haplotypes obtained from the Sanger sequencing of 96 specimens, 38 (UNOISE3) and 41 (DADA2) haplotypes were detected by eDNA analysis. When DADA2 was used, except for one haplotype, haplotypes owned by at least two specimens were detected from all the filter replications. This study showed that the eDNA analysis for evaluating intraspecific genetic diversity provides comparable results for large-scale capture-based conventional methods, suggesting that it could become a more efficient survey method for investigating intraspecific genetic diversity in the field.


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The field of environmental DNA (eDNA) analysis has rapidly developed over the past decade 59 and has become a useful approach for investigating the species distribution of aquatic  Regarding the second issue, to apply eDNA analysis for the evaluation of intraspecific genetic 104 diversity on routine biodiversity surveys, we need to examine how efficiently the eDNA-based 105 methods can evaluate intraspecific genetic diversity in the population at the sampling site. Tsuji et al.

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(2019) reported that we could detect all multiple known haplotypes derived from eDNA of tank 107 water; however, field water usually contains more varying haplotypes with low concentrations, PCR  This study aimed to examine whether we could accurately and effectively evaluate 122 intraspecific genetic diversity in wild fish populations using an eDNA-based method by comparing 123 the results with those of the capture-based conventional method. We performed water sampling and 124 capture survey at a river on the same day and evaluated the intraspecific genetic diversity on the 125 basis of eDNA analysis and Sanger sequencing of tissue DNA, respectively. In eDNA analysis, to 126 examine the relationship between the detection probability and the frequency of each haplotype in 127 the specimens, we prepared multiple replications for filtration and PCR. Also, we performed 128 denoising using UNOISE3 and DADA2 and compared the results to examine a more suitable 129 denoising method for eDNA-based evaluation of intraspecific genetic diversity. We explored the 130 potential for detecting intraspecific genetic diversity with less labour and cost by comparing   Table S1 and deposited to the DNA database of Japan (Accession    Table S2. For the PCR negative controls of the 2nd PCR, the PCR negative 223 controls in the 1st PCR were added as a template. The thermal cycle profile was 3 min at 95ºC, 12 224 cycles of 20 s at 98ºC and 15 s at 65ºC, with the final extension for 5 min at 72ºC.

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All indexed 2nd PCR products were pooled in equal volumes (1 µL each), and the target size should be performed using field water samples. Thus, in this study, we preliminarily explored 261 optimal values for alpha in UNOISE3 and OMEGA_A in DADA2, respectively. We used default 262 9 values for OMEGA_C of DADA2 because we previously found that the result was mostly unaffected 263 by these changes. In UNOISE3, we found that an alpha = 3 exhibited better performance than did the 264 default value (alpha = 2) (see Discussion and Table S3). Conversely, in DADA2, we found that the 265 default value of OMEGA_A showed better performance than when other values were used (see Table   266 S2). Thus, in this study, UNOISE3 and DADA2 were performed with alpha = 3 and default settings 267 with the other parameters, respectively.  For DADA2, fastq files containing raw reads were processed using the DADA2 package ver 1.    Additionally, for reference haplotypes, the total sequence reads of each haplotype and the number of 312 owner specimens were compared using linear regressions.

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Haplotypes detected by the conventional method 317 In the Sanger sequencing of tissue-derived DNA, a total of 42 reference haplotypes were 318 detected from 96 Ayu specimens. The number of detected reference haplotypes increased with the 319 number of analysed specimens, but the accumulation curve of detected reference haplotypes did not 320 reach a plateau even after all 96 specimens were analysed (Fig. 2A). The number of owner 321 specimens of each haplotype ranged from 1 (reference haplotype ID; 12-42) to 28 (reference 322 haplotype ID: 01) (Fig. S1). Sample-based rarefaction and extrapolation sampling curves in a 323 conventional method suggested that haplotype diversity at the sampling site was about 250 types 324 (Fig. 2B).

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After denoising using UNOISE3 with alpha = 3, a total of 11,187,431 reads remained and were 331 successfully assigned to 972 haplotypes. Of 42 reference haplotypes, 38 haplotypes were detected 332 using the 20 filters (Fig. S1A). The multiple specimen-owned reference haplotypes were detected filtration negative controls and PCR negative controls, 2,398 reads were detected and finally 351 assigned to five haplotypes. All sequence reads detected from negative controls were omitted in the 352 following analysis because read abundance was negligible (<0.02% total reads).

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Of the 42 reference haplotypes, 38 reference haplotypes were detected by both UNOISE3 and 354 DADA2, 3 types were detected by DADA2 only, and 1 reference haplotype was undetectable by 355 both methods. Each of the reference haplotypes showed a significant positive relationship in total 356 read abundance between the two denoising software packages (lm, p < 0.001, R 2 = 0.85, Estimate 357 0.34; Fig. 3A). A total of 1,428 haplotypes did not correspond to any of the reference haplotypes, and 358 their total read abundance showed a significant positive relationship between the two denoising 359 software packages (lm, p < 0.001, R 2 = 0.10, Estimate 0.36; Fig. 3B). The 430 haplotypes shared by 360 UNOISE3 and DADA2 also exhibited a significant positive relationship in total read abundance 361 between the two denoising software packages (lm, p < 0.001, R 2 = 0.85, Estimate 1.02; Fig. 3B).

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The haplotypes detected by DADA2 364 According to the GLMs, the detection probability of each haplotype among 20 filters or 15 365 PCR replications showed significant positive relationships with the number of owner specimens (p < 366 0.001, p < 0.001, respectively; Fig. 4). The increase in the number of detected reference haplotypes 367 was gentle in both accumulation curves which were estimated using two data set including 368 haplotypes obtained from 5 or 15 PCR replicates per filter (Fig. 5AB). Besides, the 95% confidence 369 intervals of two accumulation curves overlapped considerably (Fig. 5AB). There was a significant   . Although it is highly likely that there was an underestimate because of the lack of 396 specimens, we estimated that at least an additional 1,900 specimens are needed to evaluate the whole 397 haplotype diversity at the sampling site (Fig. 2B). Therefore, it is likely that the results of the 398 conventional method in this study (n = 96) did not cover the whole intraspecific genetic diversity of 399 the Ayu population at the sampling site. Thus, eDNA analysis and the conventional method showed a 400 tendency to overestimate and underestimate intraspecific genetic diversity, respectively. The DADA2 successfully detected more reference haplotypes than did UNOISE3 (Fig. 3A,   404 Fig. S1), which suggests that the denoising of HTS data using DADA2 enables us to better estimate 405 the presented haplotype at the sampling site. This difference in detection accuracy between 406 UNOISE3 and DADA2 in this study is probably caused by the difference in the strategy of each 407 denoising software. The UNOISE3 algorithm emphasized more on the sequence read abundance to 408 denoise. eDNA of the rare haplotypes (i.e. where few individuals have the haplotypes) might exist at 409 a low concentration in the water sample and they are more difficult to amplify by PCR. Thus, the 410 numbers of sequence reads of the rare haplotypes were small and more likely to be eliminated.

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Although the sensitivity to the small difference is improved by increasing the alpha value, we do not  (Table S2).

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There was a significant positive relationship in the total reads of the detected unknown 415 haplotypes between UNOISE3 and DADA2. Also, when only shared haplotypes of two denoising 416 software packages were used, R 2 values drastically improved (Fig. 3AB). When all of the reference 417 haplotypes detected by UNOISE were also detected by DADA2, the haplotypes detected by both of 418 these denoising software packages are more likely to represent haplotypes that are present in a water 419 sample than unknown haplotypes detected by either of the two methods. with the number of owner specimens (Fig. 4A, B). Additionally, all but one reference haplotype  To investigate the causes of the false-negative results in this study, all fastq files including raw 455 reads were reanalysed without denoising. The base-calling errors were eliminated on the basis of the 456 quality filtering, and the pre-processing and dereplicating of data was performed using a custom 457 pipeline described by Sato et al. (2018). As a result, all reference haplotypes were obtained from the 458 20 filter replications; however, the detection probability of each reference haplotype among 15 PCR 459 replications was varied from 33% to 100% (Fig. S1C) 476 We found that the number of analysed filters and the number of PCR replications per filter 477 have little effect on the number of detected reference haplotypes (Fig. 5AB). This finding suggests 478 that many filters and PCR replications are not required in estimating intraspecific genetic diversity 479 when using eDNA analysis. For example, when three filters that have five PCR replications are 480 analysed, the detection efficiency of haplotypes will be equivalent to the situation where we analyse 481 about 70 to 90 specimens by the capture-based conventional method ( Fig. 2A, 5B). Although some 482 false-negatives might occur in the eDNA-based method, those tend to be the rare haplotypes in the 483 population. It is considered that the risk of false negatives in the eDNA-based method is lower than 484 that in capture-based methods because 70 or more specimens are not generally analysed at one 485 sampling site in capture-based methods. Therefore, we recommend the use of the eDNA-based 486 method as an alternative or screening method for capture-based methods in large-scale surveys (e.g. 487 in a wide area and/or in many sampling points) of intraspecific genetic diversity. An eDNA-based 488 method would efficiently provide valuable information for estimating and evaluating intraspecific 489 genetic diversity in a population. 490 We found a significant positive relationship between the total reads of each haplotype and the   Doi, H., Katano, I., Sakata, Y., Souma, R., Kosuge, T., Nagano, M., Ikeda, K., Yano, K., Tojo, K.