Environmental DNA reveals fine‐scale habitat associations for sedentary and resident marine species across a coastal mosaic of soft‐ and hard‐bottom habitats

Abstract

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 multilevel 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.

Environmental DNA captures diurnal fluctuations of surface eukaryotes on a tropical coral reef

“…Several studies have also highlighted the detection of stable communities over multiple tidal cycles (Kelly et al., 2018; Lafferty et al., 2021) suggesting that the transport of eDNA into a given location is unlikely to influence the overall community detected. Evidence for fast degradation and limited eDNA transport is growing, with a number of studies reporting the detection of fine‐scale habitat association and local differentiation (60 m to 5 km) of communities with eDNA metabarcoding (Jeunen et al., 2019; O'Donnell et al., 2017; Port et al., 2016; West et al., 2020; Wilms et al., 2022). However, it is still unknown as to whether single eDNA samples, as are often collected in biomonitoring campaigns, provide a complete picture of diversity and community structure, or whether such “snapshots” may be influenced by fine‐scale temporal changes in eDNA.…”

Capturing drifting species and molecules—Lessons learned from integrated approaches to assess marine metazoan diversity in highly dynamic waters

“…This remains challenging, specifically when the aim is to determine the overall marine metazoan pelagic and benthic diversity including organisms of different taxa, sizes, and life‐history traits. Established marine metazoan sampling methods are numerous but limited to a certain subset of diversity (Walters & Scholes, 2017), such as different net types and mesh sizes capturing zooplankton (Berry et al, 2019), pelagic (Bleijswijk et al, 2020) and demersal fish (Thomsen et al, 2016), dredges and grabs for benthos, epi‐ and infauna (Aylagas et al, 2016), visual censuses for larger animals (Wilms et al, 2022) or hydro acoustics to detect mammals or fish producing distinctive sounds (Berger et al, 2020). Moreover, once the material is collected, traditional biodiversity identification primarily relies on morphological characteristics which can be impacted by sampling (e.g.…”