DOI: 10.5068/d1bq25
title: Different approaches to processing environmental DNA samples in turbid waters have distinct effects for fish, bacterial and archaea communities

description: 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.
description: 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).
description: 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).