Skip to main content

Choice of primer pairs and PCR polymerase affect the detection of fish eDNA

Abstract

Efficient biomonitoring is essential for fish protection and management. Environmental DNA (eDNA) has become a promising tool for fish surveys, and its accuracy and robustness are closely related to the primer pairs and DNA polymerases, especially for different environmental samples. However, there is still a lack of sufficient efforts to assess the effects of both two factors on fish biomonitoring. Here, we selected ten primer pairs in the mitochondrial 12S rRNA gene region and three commercial DNA polymerases and analyzed their effects on fish eDNA monitoring in surface water and sediment samples of Dianchi Lake. We found that primer pairs and DNA polymerases significantly affected fish biomonitoring in surface water and sediments of Dianchi Lake. First, there were significant variations in annotated fish eDNA sequences in different groups of primer pairs and DNA polymerases, the percentage of fish sequences amplified by the groups related to primers Riaz-12S and 12S-V5 was more than 90% of the total sequences. Second, the composition of different classification levels of fish taxa varied considerably across groups of primer pairs and DNA polymerases, and the groups related to primers Riaz-12S (i.e., Taq Master‒Riaz-12S, Rapid Taq‒Riaz-12S) and 12S-V5 (i.e., Taq Master‒12S-V5, Rapid Taq‒12S-V5) identified more taxa than other groups. Third, primer pairs had greater impacts on the structure of fish communities than DNA polymerases, and the interactions between two factors had more significant effects than any single one. This study highlights that primer pairs and DNA polymerases play critical roles in fish biomonitoring, and this work aimed to provide methodological guidance for assisting the design of the fish eDNA survey scheme.

Introduction

Human activities and climate change drive a sharp decline in freshwater fish biodiversity [3, 55]. Rapid and efficient biomonitoring is a prerequisite for decision-making in fish protection. eDNA-based species detection has proven to be an efficient, cost-effective, non-invasive monitoring method [12, 45, 59], which is widely used for target species monitoring such as invasive and endangered species [10, 27, 44], and community surveys such as fish, zooplankton and macroinvertebrates [36, 61, 65]. Currently, eDNA studies mainly focus on sampling optimization, the ecology of eDNA (e.g., the origin, state, transport) and bioinformatics tool development [24, 33, 47]. As a critical step of eDNA technology, the efficiency of PCR assays is affected by many factors such as the choice of primer pairs and DNA polymerases [39, 54]. However, the extent to which both factors affect fish biomonitoring remains unclear.

The choice of primer pairs determines the accuracy and validity of fish biomonitoring [40, 66]. An important aspect to consider when selecting primer pairs is their ability to form stable double-stranded pairs with specific sites on the target DNA, and no duplex formation with other primer pairs or no hybridization at any other target site, as this would severely reduce the primer efficiency [46, 54, 62]. Ideally, universal primer pairs should have high specificity, coverage and species identification capability to ensure the complete and accurate species monitoring of environmental samples [42, 52]. For targeted species monitoring, the efficiency of specific primer pairs directly affects the false negative/false positive detection rate. Indeed, the presence of eDNA in the environment, decay and components affect the specific binding between the primer pairs and DNA templates. For instance, a large part of eDNA in sediment is extracellular DNA, with short fragments and severe damage, and these eDNA fractions in sediments are complex due to suspension redeposition [9, 17, 22]. In comparison, eDNA in surface water is directly discharged by organisms, with relatively long DNA fragments that mainly exist in a free state and degrade rapidly [28, 34]. The inherent differences in eDNA itself in environmental samples can affect the applicability of primer pairs, such as primer pairs that amplify long DNA fragments are not suitable for highly degraded eDNA samples. However, how the choice of primer pairs affects fish biomonitoring in different environment samples, remains poorly explored.

DNA polymerase is another critical point affecting the efficiency of PCR assays [20], because the thermal stability, fidelity and specificity of DNA polymerase determine the accuracy and amplified fragment length of PCR assays [4, 8]. The high fidelity of DNA polymerase can ensure a high yield of target products and provide accuracy in sequence replication, and high thermal stability helps overcome some difficulties in PCR assays, such as secondary structure, GC-rich sequences and long DNA amplification [7, 15]. Inhibitors in the sample are important factors leading to reduced efficiency or failure of the PCR assays [26, 53]. For example, humic acid has been identified as a major PCR inhibitor in sediments [1, 56, 58], the main inhibitors in the water samples were sodium dodecyl sulphate (SDS), metal ions and Immunoglobulin G (IgG) [21, 50]. Although inhibitors affect PCR efficiency by acting directly on DNA polymerases, the impact of DNA polymerases on fish biomonitoring in different environments still lacks a clear picture.

Here, we selected ten primer pairs widely used by scholars in the mitochondrial 12s rRNA gene region (Additional file 1: Table S1) and three commercial DNA polymerases, a total of 30 groups of PCR assays were set up (Additional file 1: Table S2). Surface water and sediment eDNA samples were collected from Dianchi Lake in China (24° 29ʹ–25° 28ʹ N, 102° 29′–103° 01′ E) and were analyzed with respect to the effects of primer pairs and DNA polymerases on fish biomonitoring. The purpose of this study is mainly achieved through the following three aspects: (1) the proportion of fish and non-fish eDNA sequences was calculated to assess the specificity with regard to fish in different groups of PCR assays; (2) the proportion of fish taxa at different classification levels was analyzed to compare the taxonomic specificity and richness; (3) the Jaccard dissimilarity matrix was calculated to reveal the structural differences of fish communities across different groups of PCR assays.

Materials and methods

Experimental design

To reveal the effects of primer pairs on fish eDNA biomonitoring, we retrieved 10 primer pairs located in the mitochondrial 12s rRNA gene region for fish eDNA biomonitoring retrieved from the literature (Additional file 1: Table S1). These primer pairs have been proven to have good amplification performance and have been widely used in fish surveys [25, 66]. Thirty groups generated by 10 primer pairs and 3 DNA polymerases (Additional file 1: Table S2), and 10 blanks (DEPC water as DNA template) were carried out PCR assays, the successful groups (i.e., the agarose gel electrophoresis have specific bands and correct amplification size) were performed to subsequent sequencing and data analysis. Each PCR assay was conducted in a 20 μl volume, including 1 μl forward primer, 1 μl reverse primer, 2 μl DNA template (collected from the Dianchi Lake), 10 μl 2 × DNA polymerases Master Mix (Vazyme Biotech) and 6 μl DEPC water. The target bands of PCR assays were checked by a 1.5–2% agarose gel electrophoresis. After dozens of attempts, the Mifish-U, AcMDB07, Elas02, Ac12S and Am12S failed to amplify specific bands in any reaction conditions (Additional file 1: Table S3). Finally, only five primer pairs (i.e., Mifish-E, Teleo, Tele02, Riaz-12S and 12S-V5, Table 1 were kept for subsequent high-throughput sequencing and statistical analysis. To analyze the effects of DNA polymerases on fish biomonitoring, we purchased three common commercial DNA polymerase mixes from the Vazyme Biotech Co., Ltd. (Nanjing, China, namely 2 × Taq Master Mix II (Dye Plus, 2 × Rapid Taq Master Mix and 2 × Phanta Flash Master Mix (Dye Plus. These three DNA polymerases are abbreviated as Taq Master, Rapid Taq and Phanta Flash, respectively. Among them, the Taq Master is easy to operate and has high stability; the Rapid Taq has a fast amplification speed (15 s/kb, simple operation and good stability; the Phanta Flash has a high amplification efficiency, fast amplification (4‒5 s/kb, high fidelity (up to 81 times of the common Taq Polymerase and high specificity (Table 2).

Table 1 Summary of five primer pairs in the mitochondrial 12s rRNA gene region used for fish eDNA sequencing in the current study, including primer name and sequences, annealing temperature (Ta), amplicon size (bp) and original references
Table 2 The amplification speed and advantages of three commercial DNA polymerases, and all DNA polymerases are premixes

Our experimental designs aim to answer the following three questions. First, to what extent do primer pairs affect fish biomonitoring, and are the results consistent across different samples (i.e., surface water and sediments)? To answer this question, we conducted PCR assays on surface water and sediment samples collected from Dianchi Lake in China, with three replicates and a blank control set for each primer pair. Second, how do DNA polymerases affect fish biomonitoring, and is the degree of influence different across different samples? To address this concern, three DNA polymerases were chosen to perform PCR assays of surface water and sediment samples, we set up three replicates and a blank control set for each DNA polymerase. Finally, based on the above experimental designs, we analyzed the dissimilarity of community structure and taxonomic richness across different groups of primer pairs and DNA polymerases, the interactions on fish biomonitoring were tested by the two-way ANOVA. All PCR assays with bright and specific bands were performed to high-throughput sequencing.

Sample collection and DNA extraction

Surface water and sediment eDNA samples were collected from 23 sites in Dianchi Lake in July 2022. At each site, three liters of surface water were sampled using sterile bottles (Thermo Fisher Scientific™, USA), and immediately transferred on cryogenic incubators with several ice packs (ca. 0 to 4 °C) until filtration treatment within 6 h. The sediment samples were collected by a gravity corer and were stored in sterile plastic bags at − 80 °C until DNA extraction. Surface water was vacuum-filtered through a 0.45 μm hydrophilic nylon membrane (Merck Millipore, USA). In addition, ca. 300 ml of autoclaved tap water was performed as blank controls to monitor possible contaminants across different sites. All replicates of the eDNA samples and blank controls were individually stored in 5.0 ml centrifugal tubes and then frozen and stored at − 20 °C until DNA extraction. eDNA from surface water and sediment samples (ca. 0.5 g dry weight) were extracted using a DNeasy Power Water Kit and DNeasy PowerSoil Kit (QIAGEN, Hilden, Germany) with the manufacturer’s protocol, respectively. Extracted eDNA was quantified using a Qubit Flex Fluorometer (Thermo Fisher Scientific™, USA) and Equalbit dsDNA HS Assay Kit (Vazyme Biotech, China). Finally, the surface water and sediment eDNA samples from 23 sites were individually pooled into one water eDNA and one sediment eDNA sample for subsequent PCR assays.

PCR and sequencing

A unique 12-nt nucleotide fragment (also known as barcode) was added to the 5ʹ ends of the forward or reverse primers (Shanghai Generay Biotech Co., Ltd.) to allow the identification of different eDNA samples during the split processing of sequencing data. Three PCR replicates were performed on pooled water and sediment eDNA samples to reduce potential PCR bias. PCR blank controls (i.e., nuclease-free water as DNA template) were used for all assays. The PCR was performed in a final volume of 20 μl, consisting of 1 μl of 10 μM forward and reverse primers, 2 μl of eDNA template (ca. 5–20 ng/μl), 10 μl of 2 × DNA polymerases Master Mix (Vazyme Biotech, China) and 6 μl of DEPC water. Details on the protocol of PCR amplification are shown in Additional file 1: Table S2. The target bands of PCR assays were checked by a 1.5–2% agarose gel electrophoresis. All PCR products were purified using the EasyPure PCR Purification Kit (TransGen Biotech, China), the purified products were quantified by a Qubit Flex Fluorometer (Thermo Fisher Scientific, USA) and pooled at equal DNA quantities of 200 ng/sample for library preparation. Two tagged PCR libraries were individually constructed using the VAHTS Universal DNA Library Prep Kit for Illumina (Vazyme Biotech, China). The library concentration was measured by a Qubit Flex Fluorometer. Before sequencing, each library was diluted to a final concentration of 100 pM. Finally, each library added with 10% of PhiX (control DNA) was sequenced on the Illumina MiSeq PE150 (Illumina, San Diego, California, USA).

Bioinformatic analysis

DNA sequencing data were pre-processed in the QIMME toolkit [6]. Raw sequence reads were filtered using a series of quality controls, first, the fastx-toolkit was used to assess the quality of sequence reads,second, the low-quality reads (e.g., series with average quality < 20, sequences contained ambiguous N, homopolymer and sequence length < 100 bp) were discarded using the split-libraries.py script with the parameter settings “-s 25 -w 50 -l 100 -L 500 -H 6” in the QIMME toolkit [6], and duplicate sequences were removed using the –derep_fulllength script in VSEARCH pipeline [43],then, the reads were sorted and distinguished by unique sample tag pairs, finally, sequence reads were clustered into operational taxonomic units (OTUs using UCLUST with 97% nucleotide similarity in UPARSE pipeline [16]. The OTUs were taxonomically assigned using BLAST against the mitochondrial genome database (i.e., Mitofish) [48] for fish detection. The relevant methods and criteria refer to previous studies, in brief, if the OTUs sequence matched one species with a max score and similarity > 97%, the species was assigned, and if the OTUs sequence matched one species with a max score but similarity < 97%, the genus was assigned [64, 66]. The assigned OTUs table was filtered referring to the following three criteria to exclude falsely positive and falsely negative OTU detections and establish a reliable dataset [31, 32]: (1) the OTUs with less than 50% detection frequency in all subsamples per site were discarded; (2) the OTUs with a relative abundance < 0.001% and a detection frequency < 10% were excluded; (3) the OTUs occurred in the blank controls were removed any taxa and. Finally, observed OTUs tables were obtained across the sampling sites (i.e., OTUs × sites) for diversity and composition analyses.

Effects of primer pairs and PCR polymerase

To assess the sequence specificity with regard to fish in different groups of PCR assays, the proportion of fish and non-fish eDNA sequences was calculated. The number of species at different taxonomic levels was calculated to identify the annotation resolutions for different groups. Non-metric multidimensional scaling (nMDS) analysis based on the Jaccard dissimilarity matrix was performed to reveal the structural differences of fish communities across different groups, the significance levels were tested by the Monte Carlo permutation tests with 999 permutations using the vegdist and metaMDS functions the R package vegan [14], and the figures were generated using the R package ggplot2 [23]. The two-way ANOVA was analyzed using the SPSS 22 software to test the interaction effects of primer pairs and DNA polymerases on fish richness.

Results

Analysis of fish eDNA sequence specificity

A total of 1212 OTUs and 3,116,330 high-quality reads were obtained in 45 surface water eDNA samples (5 primer pairs × 3 DNA polymerases × 3 replicates), among which fish accounted for 2,428,746 reads (77.94%) that were assigned to 13 orders, 23 families, 53 genera and 51 species (Fig. 1), and the details of sequence reads are shown in Additional file 1: Table S4. The proportion of sequence reads assigned to fish had a considerable variation range (i.e., the maximum is 82 times the minimum) in 15 groups of primer pairs and DNA polymerases (Additional file 1: Figure S1a). Specifically, the group Pr14 (Rapid Taq‒12S-V5) obtained 320,039 reads, of which 312,334 reads (97.59%) belong to fish, but only 1466 (1.19%) of the 123,402 reads of the group Pr12 (Phanta Flash‒Teleo) was assigned to fish. Our data showed that the percentage of fish reads amplified by all groups related to primers Riaz-12S (i.e., Taq Master‒Riaz-12S, Rapid Taq‒Riaz-12S and Phanta Flash‒Riaz-12S) and 12S-V5 (i.e., Taq Master‒12S-V5, Rapid Taq‒12S-V5 and Phanta Flash‒12S-V5) was more than 90%, followed by the percentage of fish reads amplified by three groups of Tele02 (i.e., Taq Master‒Tele02, Rapid Taq‒Tele02 and Phanta Flash‒Tele02) between 30.63% and 61.49%, and the percentage of fish reads successfully amplified by three groups of Mifish-E (i.e., Taq Master‒Mifish-E, Rapid Taq‒Mifish-E and Phanta Flash‒Mifish-E) between 2.01% and 15.52%, the groups related to primer Teleo (i.e., Taq Master‒Teleo, Rapid Taq‒Teleo and Phanta Flash‒Teleo) was the lowest, that is, the percentage of amplified fish reads was less than 4% (Additional file 1: Figure S1a). In addition, the proportion of sequence reads assigned to fish was considerably similar across three PCR replicates, such as the groups related to primers Riaz-12S and 12S-V5 (Additional file 1: Figure S2).

Fig. 1
figure 1

Fish taxonomic diversity monitored by the eDNA technology in surface water samples. Occurrence frequency (a) and relative sequence reads (b) of each species among all 15 groups of primer pairs and DNA polymerases. The shapes represent different DNA polymerases, and the colors refer to different primer pairs. The bubble size reflects the relative sequence reads of each group, that is, the sum of sequence reads of three replicates in each group divided by the total sequence reads in all groups

A total of 1826 OTUs and 3,707,983 high-quality reads were yielded in 45 sediment eDNA samples (5 primer pairs × 3 DNA polymerases × 3 replicates), among which fish accounted for 2,361,897 reads (63.7%) that were assigned to 12 orders, 24 families, 58 genera and 47 species (Fig. 2), and the details of sequence reads are shown in Additional file 1: Table S5. The highest percentage of fish sequences was the group Pr14 (Rapid Taq‒12S-V5), accounting for 94.61% (382,264) of the total reads (404,034, Additional file 1: Figure S1b); followed by the group Pr13 (Taq Master‒12S-V5, 92.51% of fish reads) and Pr15 (Phanta Flash‒12S-V5, 90.71% of fish reads), the lowest was the group Pr11 (Rapid Taq‒Teleo), which obtained 333,440 reads, while fish reads only accounted for 2.01%. In general, the data showed that the percentage of fish reads in all other groups was less than 50%, except for the groups related to primers Riaz-12S (i.e., Taq Master‒Riaz-12S, Rapid Taq‒Riaz-12S and Phanta Flash‒Riaz-12S) and 12S-V5 (i.e., Taq Master‒12S-V5, Rapid Taq‒12S-V5 and Phanta Flash‒12S-V5). In addition, the proportion of sequence reads assigned to fish in the groups related to primers Riaz-12S and 12S-V5 was considerably similar across three PCR replicates (Additional file 1: Figure S3).

Fig. 2
figure 2

Fish taxonomic diversity monitored by the eDNA technology in sediment samples. Occurrence frequency (a) and relative sequence reads (b) of each species among all 15 groups of primer pairs and DNA polymerases. The shapes represent different DNA polymerases, and the colors refer to different primer pairs. The bubble size reflects the relative sequence reads of each group, that is, the sum of sequence reads of three replicates in each group divided by the total sequence reads in all groups

Species composition and taxonomic richness

All groups of primer pairs and DNA polymerases in surface water samples showed significant differences in the composition of different classification levels of fish taxa (Fig. 3a), among which the group Pr08 (Rapid Taq‒Riaz-12S) detected the most fish species (44 species), followed by the group Pr07 (Taq Master‒Riaz-12S) and Pr13 (Taq Master‒12S-V5) monitoring 43 and 40 fish species, respectively, with the group Pr12 being the least (Phanta Flash‒Teleo, only 6 species). In addition, the groups related to primers Tele02 (i.e., Taq Master‒Tele02, Rapid Taq‒Tele02 and Phanta Flash‒Tele02), Teleo (i.e., Taq Master‒Teleo, Rapid Taq‒Teleo and Phanta Flash‒Teleo) and Mifish-E (i.e., Taq Master‒Mifish-E, Rapid Taq‒Mifish-E and Phanta Flash‒Mifish-E) detected fewer fish species than the other groups (only 6–15 species). Cypriniformes accounted for the largest proportion of fish taxa detected in all groups (50%‒88.89%) of fish taxa, followed by Cichliformes, Gobiiformes and Beloniformes (Fig. 3b). In terms of taxonomic richness, the groups related to primers Riaz-12S and 12S-V5 were significantly outperformed to other groups (Fig. 3c), for example, the group Pr07 (Taq Master‒Riaz-12S), Pr08 (Rapid Taq‒Riaz-12S), Pr13 (Taq Master‒12S-V5) and Pr14 (Rapid Taq‒12S-V5) showed significantly higher richness than other groups.

Fig. 3
figure 3

Distribution of taxonomic classification levels of fish (a), the composition of taxa at the order level (b), and fish taxonomic richness of all 15 groups of primer pairs and DNA polymerases in surface water samples (c). ‘Unknown’ indicates reads not assigned to known taxonomic taxa, and the “a”, “b” and “c” in panel C are the significance test by the one-way ANOVA, respectively

The results of sediment samples showed that the monitored fish in all groups of primer pairs and DNA polymerases were between 7 and 41 species (Fig. 4a). Specifically, the group Pr07 (Taq Master‒Riaz-12S), Pr13 (Taq Master‒12S-V5) and Pr14 (Rapid Taq‒12S-V5) had better monitoring performance, with 41, 40 and 40 fish species detection, respectively. The monitoring rate of the groups related to primers Mifish-E (i.e., Taq Master‒Mifish-E, Rapid Taq‒Mifish-E and Phanta Flash‒Mifish-E), Teleo (i.e., Taq Master‒Teleo, Rapid Taq‒Teleo and Phanta Flash‒Teleo) and Tele02 (i.e., Taq Master‒Tele02, Rapid Taq‒Tele02 and Phanta Flash‒Tele02) was low, with a maximum of 14 fish species amplified. Among the monitored fish taxa in all groups of primer pairs and DNA polymerases, Cypriniformes accounted for the largest proportion (53.3%‒77.7%) of fish taxa for all groups, followed by Gobiiformes and Cichliformes (Fig. 4b). Similar to surface water, the groups related to primers Riaz-12S and 12S-V5 were significantly outperformed to other groups in sediment samples (Fig. 4c), for example, the group Pr07 (Taq Master‒Riaz-12S), Pr13 (Taq Master‒12S-V5) and Pr14 (Rapid Taq‒12S-V5) showed significantly higher richness than other groups.

Fig. 4
figure 4

Distribution of taxonomic classification levels of fish (a), the composition of taxa at the order level (b), and fish taxonomic richness of all 15 groups of primer pairs and DNA polymerases in sediment samples (c). ‘Unknown’ indicates reads not assigned to known taxonomic taxa, and the “a”, “b” and “c” in panel C are the significance test by the one-way ANOVA, respectively

Community structure and interaction judgment

The nMDS ordination plots showed that the primer pairs generated different structures of fish communities in surface water, as shown by the presence/absence-based Jaccard matrix (Fig. 5a), and had higher effects than DNA polymerases. In particular, the structural dissimilarity of fish communities in the primers Teleo was significantly different from other primer sets, the sample dots of primers Riaz-12S and 12S-V5 were closer in the ordination plots, indicating that the monitored structure of fish communities was more similar between each other. In contrast, while the primer pairs still had greater effects on fish communities in sediment samples, the effect intensity of DNA polymerases increased (Fig. 5b). For example, the sample dots of primer Teleo were discrete from other primer pairs in the ordination plots, but the sample dots of different DNA polymerases also had obvious spatial dispersion when zooming in space, which was obviously different from that in surface water. For the primers Mifish-E and Tele02, it was not easy to summarize the structural dissimilarity of fish communities among samples through primer pairs, but samples can be more clearly distinguished in the nMDS ordination plots based on DNA polymerases.

Fig. 5
figure 5

Non-metric multidimensional scaling (NMDS) plots showing structural dissimilarities of fish communities across the groups of primer pairs and DNA polymerases in surface water (a) and sediment samples (b). The Jaccard dissimilarity matrix was performed to reveal the structural differences of fish communities across different groups, the significance levels were tested by the Monte Carlo permutation tests with 999 permutations

Our data showed that the primer pairs and polymerase had jointly significant effects on monitoring fish richness in both surface water and sediment (Table 3). Specifically, the effects of primer pairs on surface water (P < 0.0001) and sediment (P = 0.0015) were higher than those of DNA polymerases (P = 0.0079 and P = 0.0016). For the interactions, the effects of primer pairs and DNA polymerases on fish richness in sediment (P = 0.0062) were higher than that of surface water (P = 0.0272).

Table 3 Two-way ANOVA of the effects of primer pairs and DNA polymerases on fish richness detection in surface water and sediment samples

Discussion

The results showed that primer pairs and DNA polymerases significantly affected fish biomonitoring in both surface water and sediment samples of Dianchi Lake. We found that eDNA data and historical fish records had almost 70% overlap (at the genus level, Additional file 1: Table S6). The groups related to Riaz-12S and 12S-V5 had consistently higher taxonomic specificity, fish coverage and species resolution than others, and the effects of primer pairs on communities’ structure were higher than DNA polymerases. The critical role of primer pairs in fish biomonitoring has been highlighted in previous studies [63, 64], and the primer pairs Riaz-12S and 12S-V5 show relatively high fish sequence proportion and fish diversity [66]. Here we further explored another important factor in the PCR assays, and found that the role of DNA polymerases in sediment samples for fish biomonitoring would be slightly higher than that of surface water. We provide performance assessments of primer pairs and DNA polymerases across different environmental samples, and these findings could provide methodological guidance for assisting the design of the fish eDNA survey scheme in aquatic systems such as rivers and lakes.

Our data showed that Riaz-12S and 12S-V5 have a higher fish sequence percentage and a more specific classification resolution than other primer pairs in both water and sediment samples. Two reasons can explain this result, on the one hand, the degradation of DNA released by organisms into environmental media, primer pairs targeting relatively short fragments have a higher amplification success rate [2, 13]. A previous study has also shown that primer pairs that amplify longer DNA fragments do not necessarily produce more target sequence reads than shorter ones with the same GC content [38]. On the other hand, the primer pairs Riaz-12S and 12S-V5 have a higher annealing temperature (Ta), which makes them more stable and better combined with DNA templates [46]. Satterfield et al. suggested that a lower Ta value may cause non-amplification [49]. Given that eDNA is often stored in environmental media as shorter DNA fragments [5], we suggest that priority should be given to selecting primer sets with shorter target DNA fragments for fish biomonitoring, especially for highly degraded samples such as soils, sediments and samples from tropical regions [11, 18]. In addition, the Riaz-12S and 12S-V5 had similar results may also be due to their same reverse primer [42]. Although the primer pairs Riaz-12S and 12S-V5 showed high performance, primer pairs showed different classification ranges. For a comprehensive “health checkup” of the fish composition in ecosystems, we recommend multiple primer pairs to increase the probability and reliability of species detection [19, 51]. As we know, the biodiversity complexity of the study system and completeness of the reference databases can also complicate the effect of barcode size on taxonomic assignments [29, 66], the completeness and quality of reference databases are known to be geographically and taxonomically biased, so the construction of high-quality reference databases of local biological communities should be a priority in eDNA biodiversity biomonitoring.

We found that the DNA polymerase Taq Master and Rapid Taq showed high amplification performance. This is mainly because, as the optimized products of Taq DNA polymerase, these two have a strong 5’‒3’ DNA synthesis ability, high amplification performance and low mismatch rate, and they also add 3ʹ‒5ʹ exonuclease activity, hence their fidelity is six times higher than that of common Taq DNA polymerase. Previous studies have shown that Platinum HiFi (similar to Phanta Flash with High-fidelity polymerase) had a high bias in taxonomic coverage and mismatch rate [39]. In addition, we found that the effect of DNA polymerases on community structure was slightly higher in sediment samples than in surface water, which may be due to the high sensitivity of DNA polymerases to sediment characteristics such as high humus and humic acid. Previous studies have shown that sediment samples appear to contain more or higher concentrations of inhibitors, and DNA polymerases have poor resistance to the main inhibitor (e.g., humic acid) in sediment [1, 35], resulting in greater effects in sediment samples than in surface water. For the inhibitors in environmental samples, we suggest (1) extensive sample processing and purification; (2) reducing the amount of sample matrix, thereby removing or diluting matrix-derived inhibitors; (3) adding bovine serum albumin (BSA), T4 gene32 protein (GP32) Master Mix or using other DNA polymerases [1, 30], (4) model calibration in data analysis (e.g., PMMoV assay as a model system to study the effect of inhibitors of PCR in environment matrices) [41]. The applicability of DNA polymerases depends on different inhibitors and sample characteristics, and selecting DNA polymerases for different samples can effectively reduce PCR inhibition.

In summary, we compared the effects of different primer pairs and DNA polymerases on fish eDNA biomonitoring, and suggested that both two factors are essential to generate reliable and comprehensive fish eDNA datasets. We insist on emphasizing to managers and stakeholders that candidate primer pairs must be screened before conducting eDNA surveys, especially in unknown biodiversity regions (e.g., biodiversity hotspots or developing countries). If rudeness or copying others’ methods can cause significant errors in fish biomonitoring datasets, this is also the design intention and core purpose of our current study, rather than determining which one or two primer pairs are more suitable for fish eDNA biomonitoring, as no single or few primer pairs can be applied to all ecosystems. In addition, all primer pairs analyzed in this study have their own advantages and disadvantages, but they may also complement each other. Multiple primer pairs should be considered to increase species detection probability in an unknown or unexplored ecosystem. With the decrease in sequencing costs, optimized multi-primer methods should gradually become the standard for future eDNA research.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.

References

  1. Albers CN, Jensen A, Bælum J, Jacobsen CS (2013) Inhibition of DNA polymerases used in Q-PCR by structurally different soil-derived humic substances. Geomicrobiol J 30:675–681. https://doi.org/10.1080/01490451.2012.758193

    Article  CAS  Google Scholar 

  2. Andersen K, Bird KL, Rasmussen M, Haile J, Breuning-Madsen H, Kjaer KH, Orlando L, Gilbert MT, Willerslev E (2012) Meta-barcoding of “dirt” DNA from soil reflects vertebrate biodiversity. Mol Ecol 21:1966–1979. https://doi.org/10.1111/j.1365-294X.2011.05261.x

    Article  CAS  Google Scholar 

  3. Barbarossa V, Bosmans J, Wanders N, King H, Bierkens MFP, Huijbregts MAJ, Schipper AM (2021) Threats of global warming to the world’s freshwater fishes. Nat Commun 12:1701. https://doi.org/10.1038/s41467-021-21655-w

    Article  CAS  Google Scholar 

  4. Bessman MJ, Kornberg A, Lehman IR, Simms ES (1956) Enzymic synthesis of deoxyribonucleic acid. Biochem Biophys Acta 21:197–198. https://doi.org/10.1016/0006-3002(56)90127-5

    Article  CAS  Google Scholar 

  5. Bylemans J, Furlan EM, Gleeson DM, Hardy CM, Duncan RP (2018) Does size matter? An experimental evaluation of the relative abundance and decay rates of aquatic environmental DNA. Environ Sci Technol 52:6408–6416. https://doi.org/10.1021/acs.est.8b01071

    Article  CAS  Google Scholar 

  6. Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, Fierer N, Pena AG, Goodrich JK, Gordon JI, Huttley GA, Kelley ST, Knights D, Koenig JE, Ley RE, Lozupone CA, McDonald D, Muegge BD, Pirrung M, Reeder J, Sevinsky JR, Tumbaugh PJ, Walters WA, Widmann J, Yatsunenko T, Zaneveld J, Knight R (2010) QIIME allows analysis of high-throughput community sequencing data. Nat Methods 7:335–336. https://doi.org/10.1038/nmeth.f.303

    Article  CAS  Google Scholar 

  7. Chatterjee N, Walker GC (2017) Mechanisms of DNA damage, repair, and mutagenesis. Environ Mol Mutagen 58:235–263. https://doi.org/10.1002/em.22087

    Article  CAS  Google Scholar 

  8. Chen CY (2014) DNA polymerases drive DNA sequencing-by-synthesis technologies: both past and present. Front Microbiol 5:305. https://doi.org/10.3389/fmicb.2014.00305

    Article  Google Scholar 

  9. Corinaldesi C, Beolchini F, Dell’Anno A (2008) Damage and degradation rates of extracellular DNA in marine sediments: implications for the preservation of gene sequences. Mol Ecol 17:3939–3951. https://doi.org/10.1111/j.1365-294X.2008.03880.x

    Article  CAS  Google Scholar 

  10. Crane LC, Goldstein JS, Thomas DW, Rexroth KS, Watts AW (2021) Effects of life stage on eDNA detection of the invasive European green crab (Carcinus maenas) in estuarine systems. Ecol Indicat 124:107412. https://doi.org/10.1016/j.ecolind.2021.107412

    Article  Google Scholar 

  11. Deagle BE, Eveson JP, Jarman SN (2006) Quantification of damage in DNA recovered from highly degraded samples–a case study on DNA in faeces. Front Zool 3:11–11. https://doi.org/10.1186/1742-9994-3-11

    Article  CAS  Google Scholar 

  12. Deiner K, Bik HM, Machler E, Seymour M, Lacoursiere-Roussel A, Altermatt F, Creer S, Bista I, Lodge DM, de Vere N, Pfrender ME, Bernatchez L (2017) Environmental DNA metabarcoding: transforming how we survey animal and plant communities. Mol Ecol 26:5872–5895. https://doi.org/10.1111/mec.14350

    Article  Google Scholar 

  13. Deiner K, Renshaw MA, Li Y, Olds BP, Lodge DM, Pfrender ME (2017) Long-range PCR allows sequencing of mitochondrial genomes from environmental DNA. Methods Ecol Evol 8:1888–1898. https://doi.org/10.1111/2041-210X.12836

    Article  Google Scholar 

  14. Dixon P (2003) VEGAN, a package of R functions for community ecology. J Veg Sci 14:927–930. https://doi.org/10.1111/j.1654-1103.2003.tb02228.x

    Article  Google Scholar 

  15. Dodd T, Botto M, Paul F, Fernandez-Leiro R, Lamers MH, Ivanov I (2020) Polymerization and editing modes of a high-fidelity DNA polymerase are linked by a well-defined path. Nat Commun 11:5379. https://doi.org/10.1038/s41467-020-19165-2

    Article  CAS  Google Scholar 

  16. Edgar RC (2010) Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26:2460–2461. https://doi.org/10.1093/bioinformatics/btq461

    Article  CAS  Google Scholar 

  17. Ellegaard M, Clokie MRJ, Czypionka T, Frisch D, Godhe A, Kremp A, Letarov A, McGenity TJ, Ribeiro S, John Anderson N (2020) Dead or alive: sediment DNA archives as tools for tracking aquatic evolution and adaptation. Commun Biol 3:169. https://doi.org/10.1038/s42003-020-0899-z

    Article  CAS  Google Scholar 

  18. Epp LS, Boessenkool S, Bellemain EP, Haile J, Esposito A, Riaz T, Erseus C, Gusarov VI, Edwards ME, Johnsen A, Stenoien HK, Hassel K, Kauserud H, Yoccoz NG, Brathen K, Willerslev E, Taberlet P, Coissac E, Brochmann C (2012) New environmental metabarcodes for analysing soil DNA: potential for studying past and present ecosystems. Mol Ecol 21:1821–1833. https://doi.org/10.1111/j.1365-294X.2012.05537.x

    Article  CAS  Google Scholar 

  19. Evans NT, Olds BP, Renshaw MA, Turner CR, Li Y, Jerde CL, Mahon AR, Pfrender ME, Lamberti GA, Lodge DM (2015) Quantification of mesocosm fish and amphibian species diversity via environmental DNA metabarcoding. Mol Ecol Resour 16:29–41. https://doi.org/10.1111/1755-0998.12433

    Article  CAS  Google Scholar 

  20. Ganai RA, Johansson E (2016) DNA replication—a matter of fidelity. Mol Cell 62:745–755. https://doi.org/10.1016/j.molcel.2016.05.003

    Article  CAS  Google Scholar 

  21. Gibson KE, Schwab KJ, Spencer SK, Borchardt MA (2012) Measuring and mitigating inhibition during quantitative real time PCR analysis of viral nucleic acid extracts from large-volume environmental water samples. Water Res 46(13):4281–4291. https://doi.org/10.1016/j.watres.2012.04.030

    Article  CAS  Google Scholar 

  22. Giguet-Covex C, Ficetola GF, Walsh K, Poulenard J, Bajard M, Fouinat L, Sabatier P, Gielly L, Messager E, Develle AL, David F, Taberlet P, Brisset E, Guiter F, Sinet R, Arnaud F (2019) New insights on lake sediment DNA from the catchment: importance of taphonomic and analytical issues on the record quality. Sci Rep 9:14676. https://doi.org/10.1038/s41598-019-50339-1

    Article  CAS  Google Scholar 

  23. Ginestet C (2011) ggplot2: elegant graphics for data analysis. J R Stat Soc Ser A Stat Soc 174:245–245. https://doi.org/10.1111/j.1467-985X.2010.00676_9.x

    Article  Google Scholar 

  24. Goldberg CS, Turner CR, Deiner K, Klymus KE, Thomsen PF, Murphy MA, Spear SF, McKee A, Oyler-McCance SJ, Cornman RS, Laramie MB, Mahon AR, Lance RF, Pilliod DS, Strickler KM, Waits LP, Fremier AK, Takahara T, Herder JE, Taberlet P (2016) Critical considerations for the application of environmental DNA methods to detect aquatic species. Methods Ecol Evol 7:1299–1307. https://doi.org/10.1111/2041-210X.12595

    Article  Google Scholar 

  25. Hanfling B, Handley LL, Read DS, Hahn C, Li JL, Nichols P, Blackman RC, Oliver A, Winfield IJ (2016) Environmental DNA metabarcoding of lake fish communities reflects long-term data from established survey methods. Mol Ecol 25:3101–3119. https://doi.org/10.1111/mec.13660

    Article  CAS  Google Scholar 

  26. Hedman J, Lavander M, Salomonsson EN, Jinnerot T, Boiso L, Magnusson B, Rådström P (2018) Validation guidelines for PCR workflows in bioterrorism preparedness, food safety and forensics. Accred Qual Assur 23:133–144. https://doi.org/10.1007/s00769-018-1319-7

    Article  Google Scholar 

  27. Jerde CL, Mahon AR, Chadderton WL, Lodge DM (2011) “Sight-unseen” detection of rare aquatic species using environmental DNA. Conserv Lett 4:150–157. https://doi.org/10.1111/j.1755-263X.2010.00158.x

    Article  Google Scholar 

  28. Jo T, Murakami H, Masuda R, Sakata MK, Yamamoto S, Minamoto T (2017) Rapid degradation of longer DNA fragments enables the improved estimation of distribution and biomass using environmental DNA. Mol Ecol Resour 17:e25–e33. https://doi.org/10.1111/1755-0998.12685

    Article  CAS  Google Scholar 

  29. Keck F, Couton M, Altermatt F (2023) Navigating the seven challenges of taxonomic reference databases in metabarcoding analyses. Mol Ecol Resour 23:742–755. https://doi.org/10.1111/1755-0998.13746

    Article  Google Scholar 

  30. Kreader CA (1996) Relief of amplification inhibition in PCR with bovine serum albumin or T4 gene 32 protein. Appl Environ Microbiol 62:1102–1106. https://doi.org/10.1128/AEM.62.3.1102-1106.1996

    Article  CAS  Google Scholar 

  31. Li F, Guo F, Gao W, Cai Y, Zhang Y, Yang Z (2022) Environmental DNA biomonitoring reveals the interactive effects of dams and nutrient enrichment on aquatic multitrophic communities. Environ Sci Technol 56:16952–16963. https://doi.org/10.1021/acs.est.2c06919

    Article  CAS  Google Scholar 

  32. Li F, Qin S, Wang Z, Zhang Y, Yang Z (2023) Environmental DNA metabarcoding reveals the impact of different land use on multitrophic biodiversity in riverine systems. Sci Total Environ 855:158958. https://doi.org/10.1016/j.scitotenv.2022.158958

    Article  CAS  Google Scholar 

  33. Mathon L, Valentini A, Guérin P-E, Normandeau E, Noel C, Lionnet C, Boulanger E, Thuiller W, Bernatchez L, Mouillot D, Dejean T, Manel S (2021) Benchmarking bioinformatic tools for fast and accurate eDNA metabarcoding species identification. Mol Ecol Resour 21:2565–2579. https://doi.org/10.1111/1755-0998.13430

    Article  CAS  Google Scholar 

  34. Mauvisseau Q, Harper LR, Sander M, Hanner RH, Kleyer H, Deiner K (2022) The multiple states of environmental DNA and what is knownabout their persistence in aquatic environments. Environ Sci Technol 56:5322–5333. https://doi.org/10.1021/acs.est.1c07638

    Article  CAS  Google Scholar 

  35. McKee AM, Spear SF, Pierson TW (2015) The effect of dilution and the use of a post-extraction nucleic acid purification column on the accuracy, precision, and inhibition of environmental DNA samples. Biol Cons 183:70–76. https://doi.org/10.1016/j.biocon.2014.11.031

    Article  Google Scholar 

  36. Milner AM, Vega EML, Matthews TJ, Conn SC, Windsor FM (2023) Long-term changes in macroinvertebrate communities across high-latitude streams. Glob Change Biol 29:2466–2477. https://doi.org/10.1111/gcb.16648

    Article  CAS  Google Scholar 

  37. Miya M, Sato Y, Fukunaga T, Sado T, Poulsen JY, Sato K, Minamoto T, Yamamoto S, Yamanaka H, Araki H, Kondoh M, Iwasaki W (2015) MiFish, a set of universal PCR primers for metabarcoding environmental DNA from fishes: detection of more than 230 subtropical marine species. R Soc Open Sci. https://doi.org/10.1098/rsos.150088

    Article  Google Scholar 

  38. Nadeau JH, Bedigian HG, Bouchard G, Denial T, Kosowsky M, Norberg R, Pugh S, Sargeant E, TurnerPaigen RB (1992) Multilocus markers for mouse genome analysis: PCR amplification based on single primers of arbitrary nucleotide sequence. Mamm Genome 3:55–64. https://doi.org/10.1007/BF00431247

    Article  CAS  Google Scholar 

  39. Nichols RV, Vollmers C, Newsom LA, Wang Y, Heintzman PD, Leighton M, Green RE, Shapiro B (2018) Minimizing polymerase biases in metabarcoding. Mol Ecol Resour 18:927–939. https://doi.org/10.1111/1755-0998.12895

    Article  CAS  Google Scholar 

  40. Polanco FA, Richards E, Flück B, Valentini A, Altermatt F, Brosse S, Walser JC, Eme D, Marques V, Manel S, Albouy C, Dejean T, Pellissier L (2021) Comparing the performance of 12S mitochondrial primers for fish environmental DNA across ecosystems. Environ DNA 3:1113–1127. https://doi.org/10.1002/edn3.232

    Article  CAS  Google Scholar 

  41. Rački N, Dreo T, Gutierrez-Aguirre I, Blejec A, Ravnikar M (2014) Reverse transcriptase droplet digital PCR shows high resilience to PCR inhibitors from plant, soil and water samples. Plant Methods 10:42. https://doi.org/10.1186/s13007-014-0042-6

    Article  CAS  Google Scholar 

  42. Riaz T, Shehzad W, Viari A, Pompanon F, Taberlet P, Coissac E (2011) ecoPrimers: inference of new DNA barcode markers from whole genome sequence analysis. Nucleic Acids Res 39:e145. https://doi.org/10.1093/nar/gkr732

    Article  CAS  Google Scholar 

  43. Rognes T, Flouri T, Nichols B, Quince C, Mahé F (2016) VSEARCH: a versatile open source tool for metagenomics. PeerJ 4:e2584. https://doi.org/10.7717/peerj.2584

    Article  Google Scholar 

  44. Rojahn J, Pearce L, Gleeson DM, Duncan RP, Gilligan DM, Bylemans J (2021) The value of quantitative environmental DNA analyses for the management of invasive and endangered native fish. Freshw Biol 66:1619–1629. https://doi.org/10.1111/fwb.13779

    Article  CAS  Google Scholar 

  45. Ruppert KM, Kline RJ, Rahman MS (2019) Past, present, and future perspectives of environmental DNA (eDNA) metabarcoding: A systematic review in methods, monitoring, and applications of global eDNA. Glob Ecol Conserv 17:e00547. https://doi.org/10.1016/j.gecco.2019.e00547

    Article  Google Scholar 

  46. Rychlik W (1993) Selection of primers for polymerase chain reaction. Methods Mol Biol 15:31–40. https://doi.org/10.1385/0-89603-244-2:31

    Article  CAS  Google Scholar 

  47. Sansom BJ, Sassoubre LM (2017) Environmental DNA (eDNA) shedding and decay rates to model freshwater mussel eDNA transport in a river. Environ Sci Technol 51:14244–14253. https://doi.org/10.1021/acs.est.7b05199

    Article  CAS  Google Scholar 

  48. Sato Y, Miya M, Fukunaga T, Sado T, Iwasaki W (2018) MitoFish and MiFish pipeline: a mitochondrial genome database of fish with an analysis pipeline for environmental DNA metabarcoding. Mol Biol Evol 35:1553–1555. https://doi.org/10.1093/molbev/msy074

    Article  CAS  Google Scholar 

  49. Satterfield BC (2014) Cooperative primers: 2.5 million-fold improvement in the reduction of nonspecific amplification. J Mol Diagn 16:163–173. https://doi.org/10.1016/j.jmoldx.2013.10.004

    Article  CAS  Google Scholar 

  50. Schrader C, Schielke A, Ellerbroek L, Johne R (2012) PCR inhibitors—occurrence, properties and removal. J Appl Microbiol 113:1014–1026. https://doi.org/10.1111/j.1365-2672.2012.05384.x

    Article  CAS  Google Scholar 

  51. Shaw JLA, Clarke LJ, Wedderburn SD, Barnes TC, Weyrich LS, Cooper A (2016) Comparison of environmental DNA metabarcoding and conventional fish survey methods in a river system. Biol Conserv 197:131–138. https://doi.org/10.1016/j.biocon.2016.03.010

    Article  Google Scholar 

  52. Shinzato C, Narisoko H, Nishitsuji K, Nagata T, Satoh N, Inoue J (2021) Novel mitochondrial DNA markers for scleractinian corals and generic-level environmental DNA metabarcoding. Front Mar Sci. https://doi.org/10.3389/fmars.2021.758207

    Article  Google Scholar 

  53. Sidstedt M, Rådström P, Hedman J (2020) PCR inhibition in qPCR, dPCR and MPS—mechanisms and solutions. Anal Bioanal Chem 412:2009–2023. https://doi.org/10.1007/s00216-020-02490-2

    Article  CAS  Google Scholar 

  54. Stadhouders R, Pas SD, Anber J, Voermans J, Mes TH, Schutten M (2010) The effect of primer-template mismatches on the detection and quantification of nucleic acids using the 5’ nuclease assay. J Mol Diagn 12:109–117. https://doi.org/10.2353/jmoldx.2010.090035

    Article  CAS  Google Scholar 

  55. Su G, Logez M, Xu J, Tao S, Villéger S, Brosse S (2021) Human impacts on global freshwater fish biodiversity. Science 371:835–838. https://doi.org/10.1126/science.abd3369

    Article  CAS  Google Scholar 

  56. Sutlovic D, Gamulin S, Definis-Gojanovic M, Gugic D, Andjelinovic S (2008) Interaction of humic acids with human DNA: proposed mechanisms and kinetics. Electrophoresis 29:1467–1472. https://doi.org/10.1002/elps.200700699

    Article  CAS  Google Scholar 

  57. Taberlet P, Bonin A, Zinger L, Coissac E (2018) Environmental DNA: for biodiversity research and monitoring. Oxford University Press

    Book  Google Scholar 

  58. Tebbe C, Vahjen W (1993) Interference of humic acids and DNA extracted directly from soil in detection and transformation of recombinant DNA from bacteria and a yeast. Appl Environ Microbiol 59:2657–2665. https://doi.org/10.1128/AEM.59.8.2657-2665.1993

    Article  CAS  Google Scholar 

  59. Thomsen PF, Willerslev E (2015) Environmental DNA—an emerging tool in conservation for monitoring past and present biodiversity. Biol Cons 183:4–18. https://doi.org/10.1016/j.biocon.2014.11.019

    Article  Google Scholar 

  60. Valentini A, Taberlet P, Miaud C, Civade R, Herder J, Thomsen PF, Bellemain E, Besnard A, Coissac E, Boyer F, Gaboriaud C, Jean P, Poulet N, Roset N, Copp GH, Geniez P, Pont D, Argillier C, Baudoin JM, Peroux T, Crivelli AJ, Olivier A, Acqueberge M, Le Brun M, Moller PR, Willerslev E, Dejean T (2016) Next-generation monitoring of aquatic biodiversity using environmental DNA metabarcoding. Mol Ecol 25:929–942. https://doi.org/10.1111/mec.13428

    Article  CAS  Google Scholar 

  61. Wang S, Yan Z, Hänfling B, Zheng X, Wang P, Fan J, Li J (2021) Methodology of fish eDNA and its applications in ecology and environment. Sci Total Environ 755:142622. https://doi.org/10.1016/j.scitotenv.2020.142622

    Article  CAS  Google Scholar 

  62. Wu J-H, Hong P-Y, Liu W-T (2009) Quantitative effects of position and type of single mismatch on single base primer extension. J Microbiol Methods 77:267–275. https://doi.org/10.1016/j.mimet.2009.03.001

    Article  CAS  Google Scholar 

  63. Xiong F, Shu L, Zeng H, Gan X, He S, Peng Z (2022) Methodology for fish biodiversity monitoring with environmental DNA metabarcoding: The primers, databases and bioinformatic pipelines. Water Biol Secur 1:100007. https://doi.org/10.1016/j.watbs.2022.100007

    Article  Google Scholar 

  64. Yang J, Zhang L, Mu Y, Zhang X (2023) Small changes make big progress: a more efficient eDNA monitoring method for freshwater fish. Environ DNA 5:363–374. https://doi.org/10.1002/edn3.387

    Article  CAS  Google Scholar 

  65. Yang J, Zhang X (2020) eDNA metabarcoding in zooplankton improves the ecological status assessment of aquatic ecosystems. Environ Int 134:105230. https://doi.org/10.1016/j.envint.2019.105230

    Article  CAS  Google Scholar 

  66. Zhang S, Zhao J, Yao M (2020) A comprehensive and comparative evaluation of primers for metabarcoding eDNA from fish. Methods Ecol Evol 11:1609–1625. https://doi.org/10.1111/2041-210X.13485

    Article  Google Scholar 

Download references

Acknowledgements

We thank our group members for their assistance during sample collection and laboratory work.

Funding

This work was financially supported by the National Key Research and Development Program of China (2021YFC3201004), National Natural Science Foundation of China (52100216) and the Science and Technology Planning Project of Guangdong Province of China (2022A0505050075).

Author information

Authors and Affiliations

Authors

Contributions

XM and FL wrote the main manuscript text. FG, XZ and FZ reviewed the manuscript. FL and YZ supported the project.

Corresponding author

Correspondence to Feilong Li.

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1: Figure S1

. The proportion of eDNA sequences annotated (Success, blue box) and unassigned (Failure, grey box) into fish communities in 15 groups of three DNA polymerases and five primer pairs in surface water (a) and sediment samples (b). Prefixes T-, R- and P- in the abscissa are the Taq master polymerase, the Rapid Taq polymerase and the Phanta Flash polymerase, respectively. Figure S2. The proportional of fish eDNA sequences at the order level among three repeated samples in surface water samples. Prefixes T-, R- and P- in the abscissa are the Taq master polymerase, the Rapid Taq polymerase and the Phanta Flash polymerase, respectively. Figure S3. The proportional of fish eDNA sequences at the order level among three repeated samples in sediment samples. Prefixes T-, R- and P- in the abscissa are the Taq master polymerase, the Rapid Taq polymerase and the Phanta Flash polymerase, respectively. Table S1. Summary of 10 primer pairs in the mitochondrial 12 s rRNA gene region for fish eDNA biomonitoring retrieved from the literature and analyzed in this study, including primer name, target group, amplicon size, original references and primer sequences. Table S2. Summary of the reaction conditions for PCR assays on random groups of retrieved 10 primer pairs and 3 DNA polymerases, including the set time and temperature of the denaturation, annealing, and extension processes. All PCR assays run 35 cycles. Table S3. Results of successful PCR assays between primer pairs and DNA polymerase, namely, the agarose gel electrophoresis have specific bands and correct amplification size. The symbols “√” and “ × ” represent successful and failed PCR assays, respectively. Table S4. The number and proportion of eDNA sequences successfully annotated different taxonomic classifications of fish communities in surface water samples. Three replicates in each group are analyzed together, and percentages in parentheses are the After quality filtering/All sequences, Class seq/After quality filtering, Order-seq/After quality filtering and so on. Table S5. The number and proportion of eDNA sequences successfully annotated different taxonomic classifications of fish communities in sediment samples. Three replicates in each group are analyzed together, and percentages in parentheses are the After quality filtering/All sequences, Class seq/After quality filtering, Order-seq/After quality filtering and so on. Table S6. Comparison between historical records of common fish species in Dianchi Lake over the past decade and eDNA data in this current study. Blue shadows represent consistency between each other.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Min, X., Li, F., Zhang, X. et al. Choice of primer pairs and PCR polymerase affect the detection of fish eDNA. Environ Sci Eur 35, 103 (2023). https://doi.org/10.1186/s12302-023-00812-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12302-023-00812-6

Keywords