The effect of tides on nearshore environmental DNA

Abstract

Organisms of all kinds leave genetic traces in their environments, and in recent years, sequencing this environmental DNA (eDNA) has become a tractable means of surveying many species using water, air, or soil samples. The technique is beginning to become a core tool for ecologists, environmental scientists, and biologists of many kinds, but the temporal resolution of eDNA sampling is often unclear, limiting the ecological interpretations of the resulting datasets. Here, in a temporally and spatially replicated field study using ca. 330bp of eukaryotic COI mtDNA as a marker, we find that nearshore organismal communities are largely consistent across tides. Our findings suggest that nearshore eDNA tends to be endogenous to the site and water mass sampled, rather changing systematically as waters change over during the tidal cycle. However, where water-mass characteristics change, we find that the eDNA communities change in concert, again suggesting a close association between the habitat sampled and the eDNA community recovered.

Rapid assessment of coral cover from environmental DNA in Hawai'i

“…In principle, individual taxa should be represented in the eDNA pool in proportion to their biomass if DNA sloughing and degradation rates are similar across taxa. Because eDNA supply, transport, and degradation dynamics are poorly understood (Andruszkiewicz, Sassoubre, & Boehm, ; De Souza, Godwin, Renshaw, & Larson, ; Deiner & Altermatt, ; Dell'Anno & Corinaldesi, ; Jane et al, ; Jerde et al, ; Jo et al, ; Kelly, Gallego, & Jacobs‐Palmer, ; Pilliod, Goldberg, Arkle, & Waits, ; Shogren et al, ; Turner, Uy, & Everhart, ), researchers have focused on the development and critical assessment of a variety of eDNA methods that have different strengths and weaknesses. At one end of the analytical spectrum is quantitative PCR (qPCR), a highly precise and sensitive way to monitor template eDNA abundance in real‐time PCR reactions (Murray, Coghlan, & Bunce, ; Taberlet et al, ).…”

Biodiversity assessment of tropical shelf eukaryotic communities via pelagic eDNA metabarcoding

“…Studies targeting marine eukaryotic community diversity have, as of yet, been scarce (Cowart, Murphy, & Cheng, ; Deagle et al, ; Djurhuus et al, ; Drummond et al, ; Günther, Knebelsberger, Neumann, Laakmann, & Martínez Arbizu, ; Kelly, Gallego, & Jacobs‐Palmer, ; Villarino et al, ); the majority of these have relied on ribosomal markers, such as 18S (de Vargas et al, ; Guardiola et al, ; Lindeque et al, ), 16S (Kelly et al, ), and 12S (Miya et al, ), but due to their relatively conserved sequences, it is often impossible to distinguish taxa at the species, genus, or even family level (Tang et al, ), which represents a significant limitation to the evaluation of community changes and the biological and/or environmental mechanisms responsible for these changes (Mackas & Beaugrand, ).…”

Peer Review #1 of "A halo of reduced dinoflagellate abundances in and around eelgrass beds (v0.1)"

“…We followed updated versions of previously published procedures for bioinformatics, quality control, and decontamination (Kelly et al, 2018). This protocol uses a custom Unix-based script (Gallego, 2019) calling third-party programs to perform initial quality control on sequence reads from all four runs combined, demultiplexing sequences to their sample of origin and clustering unique variants into amplicon sequence variants (ASVs) (Martin, 2011;Callahan et al, 2016).…”

“…To address possible cross-sample contamination (see Schnell et al, 2015;Kelly et al, 2018), we subtracted the maximum proportional representation of each environmental ASV across all positive control samples (sequenced from extraction of kangaroo or ostrich tissue) from the respective ASV in field samples; 27 million reads from 19320 ASVs passed this step. After removing the two PCR replicates with an extremely low number of reads, we estimated the probability of ASV occurrence by performing site-occupancy modeling (Royle and Link, 2006;Lahoz-Monfort et al, 2016).…”

“…To assign taxonomy to each ASV sequence, we followed the protocol detailed in Kelly et al (2018). Briefly, this protocol uses 'blastn' (Camacho et al, 2009) on a local version of the full NCBI nucleotide database (current as of February 13, 2019), recovering up to 100 hits per query sequence with at least 85% similarity and maximum e-values of 10 −30 (culling limit = 5), and reconciling conflicts among matches using the last common ancestor approach implemented in MEGAN 6.4 (Huson et al, 2016).…”