Coastal lagoons are an important habitat for endemic and threatened species in California that have suffered impacts from urbanization and increased drought. Environmental DNA has been promoted as a way to aid in the monitoring of biological communities, but much remains to be understood on the biases introduced by different protocols meant to overcome challenges presented by unique systems under study. Turbid water is one methodologic challenge to eDNA recovery in these systems as it quickly clogs filters, preventing timely processing of samples. We investigated biases in community composition produced by two solutions to overcome slow filtration due to turbidity: freezing of water prior to filtration (for storage purposes and long-term processing), and use of sediment (as opposed to water samples). Bias assessments of community composition in downstream eDNA analysis was conducted for two sets of primers, 12S (fish) and 16S (bacteria and archaea). Our results show that freezing water prior to filtration had different effects on community composition for each primer, especially for the 16S, when using a filter of larger pore size (3 μm). Nevertheless, pre-freezing water samples can still be a viable alternative for storage and processing of turbid water samples when focusing on fish communities (12S). The use of sediment samples as an alternative to processing water samples should be done with caution, and at minimum, the number of biological replicates and/or volume sampled should be increased.

A sterilized water jug was used to collect a single water sample in the lagoon, at a mid-point between the mouth margin and the road bridge (Fig. 1). The sample was then placed on ice and brought to the laboratory (~1 hr car ride). This method of “grab-and-hold” has proven to be similarly effective as on-site filtration in a previous study (Pilliod et al., 2013). Once in the laboratory, the total volume was divided for two separate protocols: centrifugation; and filtration.

The centrifugation protocol followed Doi et al. (2017) using five replicates of 50 mL tubes (with 27 mL water samples each). Besides extracting the pellet, we also included a filtration step of the supernatant using a 0.45 µm filter. For the filtration protocol, water was separated into ten 500 mL bottles (Fig. 2) and these were used in two separate protocols. Half (i.e. five bottles) were frozen in the -20°C for three days before thawing for filtration (hereafter referred to ‘pre-freezing (PF) protocol’), and half were not frozen and immediately filtered (hereafter referred to ‘no freezing (NF) protocol’). Filtration was done in two sequential steps for both treatments (pre- and no freezing) using an adapted vacuum pump in the pre-PCR room of the laboratory (Fig. S1). First, the water sample was filtered using a 3 µm filter, then the filtrate was passed through a 0.45 µm filter. All filters used in this work were cellulose nitrate. Here, however, we will focus only on the results from the first filtration step of the water filtration protocol (3 µm filters). More details on that are further explained in the supplemental material.

Surficial sediment (within the first 5 cm) was also collected at the same location where water was sampled (hereafter referred to ‘sediment (Sed) protocol’). We used five collection kits, and each kit consisted of three 2 mL tubes (15 tubes total). Subsamples of these were pooled in triplicates prior to DNA extraction following instructions as defined by the CALeDNA program (https://ucedna.com/methods-for-researchers). These were also kept on ice during field work and stored in a -80°C freezer upon arrival at the laboratory until DNA extractions.

Sequence data were bioinformatically processed in Hoffman2, the High Performance Computing cluster at UC Los Angeles, using the Anacapa Toolkit (Curd, Gomer, et al., 2018) with default settings. Taxonomic tables with a bootstrap confidence cutoff score of 0.6 were used for downstream analyses. All subsequent bioinformatic analyses were performed using R v.3.6.2 (R Core Team, 2018) in RStudio v.1.2.1335 (RStudio Team, 2020).

For processing reads into taxonomic tables, please refer to the Anacapa Toolkit (Curd, Gomer, et al., 2018). All other bioinformatic analyses can be performed using R v.3.6.2 (R Core Team, 2018) in RStudio v.1.2.1335 (RStudio Team, 2020).