Findings from eDNA metabarcoding are strongly influenced by experimental approach, yet the effect of pre‐PCR sample processing on taxon detection and estimates of biodiversity across different water types is still poorly resolved. To fill this data gap, we investigated the impact of sampling effort, extraction method, and filter pore size on DNA yield, PCR inhibition, and 16S rDNA metabarcoding results for fishes in water samples collected from inshore turbid‐ and offshore clear‐water environments. The turbid‐water samples had high concentrations of suspended organic and/or inorganic material and yielded ~3.2× more DNA and exhibited high levels of PCR inhibition compared with the low‐turbidity, clear‐water samples. Importantly, there were no striking differences in the results of our metabarcoding experiments based on extraction method or filter pore size. While a small number of unique species of relatively low read count were detected in all turbid‐water treatments, most species were consistently detected across samples. Results for the clear‐water samples were strikingly different, with low DNA yield, high levels of variation across replicates, and a high number of non‐overlapping species across treatments. These findings indicate a patchy distribution of eDNA in offshore environments, which means higher volumes of water (≥ 2 L per replicate) must be filtered in habitats where target DNA is likely to be sparse. In semi‐closed systems such as estuaries, higher concentrations of target DNA are expected, and we found that either a 1.0 or 3.0 µm filter pore size was sufficient to capture standing diversity, while decreasing the risk of clogging. For economical DNA extraction and inhibitor removal, we recommend a combination of Omega Bio‐tek E.Z.N.A Tissue DNA kit followed by a PCR inhibitor removal step using the Zymo Kit. Finally, we emphasize that pilot studies should be undertaken whenever sampling in a new environment to identify which protocol is most appropriate.

Water samples were filtered on different filter pore sizes and then DNA was extracted using DNA extraction kits. The DNA was then sequenced using Illumina MiSeq. Raw fastq files were processed bioinformatically to generate unique ASVs.

Each folder contains four following four files:

1. ASV_counts
2. ASVs
3. blasted_ASVs
4. lineage_with_header

ASV_counts = number of ASV generated for each of the replicates along with read number. Each replicate has been uniquely named.

ASVs = This file contains sequences for each of the ASVs in FASTA format.

blasted_ASVs = NCBI blast results for each of the ASVs.

Lineage_with_header = This file contains list of all the taxa obtained after NCBI blast with their taxonomic designations.