DOI: 10.5061/dryad.69p8cz93p
title: Environmental DNA reveals fine-scale habitat associations for sedentary and resident marine species across a coastal mosaic of soft and hard-bottom habitats

description: Accurate knowledge on spatiotemporal distributions of marine species and
 their association with surrounding habitats is crucial to inform adaptive
 management actions responding to coastal degradation across the globe.
 Here, we investigate the potential use of environmental DNA (eDNA) to
 detect species-habitat associations in a patchy coastal area of the Baltic
 Sea. We directly compare species-specific qPCR analysis of eDNA with
 baited remote underwater video systems (BRUVS), two non-invasive methods
 widely used to monitor marine habitats. Four focal species (cod Gadus
 morhua, flounder Platichthys flesus, plaice Pleuronectes platessa and
 goldsinny wrasse Ctenolabrus rupestris) were selected based on contrasting
 habitat associations (reef- vs. sand-associated species), as well as
 differential levels of mobility and residency, to investigate whether
 these factors affected the detection of species-habitat associations from
 eDNA. To this end, a species-specific qPCR assay for goldsinny wrasse is
 developed and made available herein. In addition, potential correlations
 between eDNA signals and abundance counts (MaxN) from videos were
 assessed. Results from Bayesian multi-level models revealed strong
 evidence for a sand association for sedentary flounder (98% posterior
 probability) and a reef association for highly resident wrasse (99%
 posterior probability) using eDNA, in agreement with BRUVS. However,
 contrary to BRUVS, eDNA sampling did not detect habitat associations for
 cod or plaice. We found a positive correlation between eDNA detection and
 MaxN for wrasse (posterior probability 95%), but not for the remaining
 species and explanatory power of all relationships was generally limited.
 Our results indicate that eDNA sampling can detect species-habitat
 associations on a fine spatial scale, yet this ability likely depends on
 the mobility and residency of the target organism, with associations for
 sedentary or resident species most likely to be detected. Combined
 sampling with conventional non-invasive methods is advised to improve
 detection of habitat associations for mobile and transient species, or for
 species with low eDNA concentrations. 
description: Water samples were collected directly from the surface water and
 extracted using a disposable 60 mL sterile syringe and injected into an
 enclosed Sterivex-GP capsule filter (0.22 µm pore size, SVGPL10RC,
 Millipore, CA, USA). A total of 1000 mL seawater was filtered for each
 sample, however on one occasion, due to clogging, only 850 mL could be
 filtered. After filtration, the sterivex filters were closed using sterile
 luer lock caps (Cole Palmer, Vernon Hills, IL, USA), put in individual
 zip-locked bags and stored in a cooling box on ice before subsequently
 transferring them to a -20˚C freezer upon return from the field. A maximum
 of two water samples were taken on a given sampling day, for which site
 selection was based on accessibility of the site (depending on weather
 conditions) or otherwise balanced among sites as much as possible. One
 field blank was taken on 18 June 2018 at 12:50h by filtering 900 mL
 nuclease free water at site to control for potential DNA contamination
 related to the field sampling procedure. Further details on the sampling
 scheme are provided in the Supporting Information (Table S1).
 Immediately following the eDNA water sampling, we deployed two
 BRUVS at the same field site. BRUVS consisted of GoPro’s Hero 3 or 4
 (GoPro, USA), attached to a steel pole (1 m height; 3 cm diameter) at 20
 cm above the seabed and positioned firmly on a concrete tile with the
 camera field of view parallel to the seabed. BRUVS deployments were
 separated by at least 100 m to minimize the risk of double counting
 individuals between the two BRUVS while staying within the designated
 habitat type. We used chopped Atlantic herring (<em>Clupea
 harengus</em>) as bait, packed tightly in a mesh bait bag. The bait
 bag was positioned 15 cm above the seabed and attached via an 80 cm bait
 arm in the lower center of the field of view. Soak time of the BRUVS
 varied between 1h – 2h 40min depending on the camera’s battery life, with
 a small number of BRUVS (&lt; 5) marked unsuccessful due to battery
 failure or loss of the bait bag. Accordingly, a total of 41 seawater
 samples were taken for eDNA extraction and coupled with 78 BRUVS
 deployments across the four different field sites (Fig. 1; Table S1).
   We analyzed the video recordings using VLC media
 player (https://www.videolan.org/vlc/). The four focal species were
 identified and counted by trained video observers making use of reference
 images and conspicuous morphological features for identification.
 Individuals that were challenging to identify to species level (e.g. due
 to distance from the camera or poor visibility) were instead labelled on a
 genus or family level and omitted from the analysis in the present study.
 We expressed the abundances of focal species as the maximum number of
 individuals per species in a single video frame (MaxN), a metric widely
 used to avoid duplicate counts of individuals that are visible in multiple
 frames throughout the video (Cappo, Speare, & De’Ath, 2004). In
 addition, video observers estimated the functional visibility in each
 video clip from taped markers on the bait pole and by comparing the field
 of view to reference images.  All DNA extractions and setup of quantitative
 PCR (qPCR) reactions were conducted in a dedicated clean laboratory
 facility. DNA extraction
 was based on a modified version of a previously published eDNA extraction
 protocol (Spens et al.,
 2017). This protocol uses
 the DNeasy blood and tissue kit (Qiagen, Hilden, Germany) to extract eDNA
 directly from the Sterivex filters (Supporting information, Note S1). Each
 extraction batch included a negative control (extraction blank) to test
 for exogenous DNA contamination through the used reagents or from the
 laboratory. Final DNA concentration was measured for every sample using a
 QubitTM flourometer and the QubitTM dsDNA high sensitivity kit
 (ThermoFisher Scientific, Waltham, MA, USA).
 Four different qPCR assays
 targeting the mitochondrial cytochrome b gene (cytb) were used to analyze
 eDNA from the collected filters (Table 1). Assays for cod, flounder and
 plaice were based on published species-specific primers and probes
 (Knudsen et al.,
 2019) while the assay for
 goldsinny wrasse was designed for the study. This assay was developed
 based on thorough in silico and in vitro tests to ensure specificity. In
 order to ensure a broader use of the assay, the in silico analysis
 included all closely related species of wrasse with an available genetic
 cytb sequence in Genbank (https://www.ncbi.nlm.nih.gov/genbank/). After
 database establishment, all primers and probes were aligned to the
 reference sequences using Geneious Prime® 2019.0.4
 (https://www.geneious.com/) to verify specificity (Supporting Information,
 Table S2). Specificity was further tested in vitro via qPCR on 1 ng of
 extracted DNA from the target species, as well as Ballan wrasse
 (<em>Labrus bergylta</em>) and Corkwing wrasse
 (<em>Symphodus melops</em>), which are the only other wrasse
 species potentially occurring in the sampling area (Muus, Nielsen, Dahlstrom, & Nystrom,
 1999; Peter Rask Møller,
 Natural history Museum of Denmark, personal communication). Finally, the
 assay targeting goldsinny wrasse was optimized for sensitivity by testing
 all possible combinations of different concentrations of primers (200,
 400, 600 and 800 nM) and probes (200, 300 and 400 nM). The combination
 showing the lowest Ct-value (cycle-threshold) was chosen for final qPCR
 analysis. Quantitative PCR reactions were performed in 20
 µL reaction volumes containing the optimal concentration of primers and
 probe (Table 1) with 8 µL of TaqMan Environmental Master Mix 2.0
 (ThermoFisher Scientific, Waltham, MA, USA) and 4 µL DNA template. All
 qPCR reactions were analyzed on a StepOne Plus Real-time PCR instrument
 (Life Technologies, Carlsbad, CA, USA) using the following thermal cycling
 profile: 5 min at 50 °C and 10 min initial denaturation at 95°C, followed
 by 50 cycles of 95°C for 15 seconds and 60°C for 1 min. Initially, all
 samples were analyzed using a positive control (IPC, Applied Biosystems)
 to ensure that potential PCR inhibitors did not interfere with the PCR
 reaction. Subsequently, all samples were run in triplicates. These
 included one field blank, two extraction blanks, and six NTCs per analyzed
 species to test for potential exogenous DNA contamination from the field
 or laboratory. The number of DNA copies per reaction was estimated using a
 dilution series. DNA Amplicons were generated by conventional PCR using
 the qPCR primers and were purified using the Nucleospin Gel and PCR
 clean-up kit (Macherey-nagel, Düren, Germany). Amplicon concentrations
 were measured using a QubitTM flourometer and the QubitTM dsDNA High
 Sensitivity Kit to enable estimation of copy
 numbers. All qPCR results were compared to a standard
 dilution series and categorized in terms of limit of detection (LOD) and
 limit of quantification (LOQ). We defined the LOD as the lowest copy
 number detected in at least one of the triplicates in the standard
 dilution series, and LOQ as the lowest copy number detected in all
 triplicates in the standard dilution series (Ellison, English, Burns, & Keer,
 2006). The sensitivity of
 all assays was evaluated before analyzing the extracted samples based on
 analysis of a standard dilution series of 1-1⨯105 copies of DNA targets per reaction.
 Estimation of eDNA copy numbers in the collected samples was based on a
 slightly higher dilution series of 4-4⨯105 copies of DNA targets. Evaluation of LOD/LOQ
 was based on the lowest values observed from these tests. However, most
 qPCR estimates of copy numbers were below the LOQ due to low DNA
 concentrations of the four target species in the collected samples (see
 Results), which precluded a quantitative data treatment
 (Klymus et al.,
 2020). Thus, as a proxy
 for DNA concentration, we used the proportions of positive amplifications
 of the individual samples (similar to Biggs et al., 2015), as it is reasonable to expect that
 amplification rates increase with the relative concentration of target DNA
 molecules (Furlan,
 Gleeson, Hardy, & Duncan, 2016).
description: Explanation of the different variables used in the two datasets
 are provided in the ReadMe file.