Biomonitoring planktonic threats to salmon aquaculture: morphological and eDNA metabarcoding data
Data files
Sep 30, 2024 version files 903.50 KB
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all_non_interpolated_data_January_2024.csv
837.71 KB
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README.md
3.61 KB
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Salmon_Code_For_Submission_Anonymised.Rmd
62.19 KB
Abstract
Salmonid aquaculture, a major component of the Northern European, North American, and Chilean coastal economies, is under threat from challenges to gill health, many of which originate from plankton communities. A first step towards mitigating losses is to characterize the biological drivers of poor gill health. Numerous planktonic taxa have been implicated, including toxic and siliceous microalgae, hydrozoans, and scyphozoans; however, rigorous longitudinal surveys of plankton diversity and gill health have been lacking. In the current study, we present and assess an exhaustive identification approach combining both morphological and molecular methods together with robust statistical models to identify the planktonic drivers of proliferative gill disease (PGD) and fish mortality. We undertook longitudinal evaluation at two marine aquaculture facilities on the west coast of Scotland using daily data collected during the 2021 growing season (March-October). Examining these two different sites, one sheltered and one exposed to the open sea, we identified potentially new, important, and unexpected planktonic drivers of PGD and mortality (e.g., doliolids and appendicularians) and confirmed the significance of some established threats (e.g., hydrozoans and diatoms). We also explored delayed or ‘lagged’ effects of plankton abundances on gill health and undertook a comparison of environmental DNA (eDNA) metabarcoding and microscopy in their ability to identify and quantify planktonic species. Our data highlight the diversity of planktonic threats to salmonid aquaculture as well as the importance of using both molecular and morphological approaches to detect those. There is now an urgent need to expand systematic longitudinal molecular and morphological approaches across multiple sites and over multiple years. The resultant catalogue of main biological drivers will enable early warning systems, new treatments, and, ultimately, a sustainable platform for future salmonid aquaculture in the marine environment.
https://doi.org/10.5061/dryad.08kprr5bp
Description of the data and file structure
Between March and October 2021, we monitored two sites on the NW coast of Scotland, UK: a sheltered site with a large freshwater input was monitored for 223 sampling days, whilst an exposed open water site provided us with 191 sampling days. This daily monitoring included phytoplankton and zooplankton surveys, as well as eDNA sampling. Moreover, matching our sampling period, our collaborators in the farms provided mortality data (interpolated z-score data), PGD and AGD scores (scaled from one to five for increasing severance, see Noguera & Marcos Lopez, 2019 for details), environmental data related to temperature, oxygen levels, salinity, and visibility in the water adjacent to the cages, and treatments applied to fish during our study.
Files and variables
File: Salmon_Code_For_Submission_Anonymised.Rmd
Description: Best plankton predictors for diminished salmon health: data analysis, models, and figures.
File: all_non_interpolated_data_January_2024.csv
Description: Daily monitoring in two salmon aquaculture sites, including phytoplankton and zooplankton surveys, as well as eDNA sampling. Other salmon health (mortality data, PGD, and AGD scores) and environmental data (temperature, oxygen levels, salinity, and visibility in the water adjacent to the cages), and treatments applied to fish during our study were provided by our collaborators in the farms and also added to this dataset.
Variables
- Date: March to October 2021
- Exposure: sheltered/exposed site
- Temperature: °C
- Oxygen: mg/L
- Salinity: ppm
- Clarity: m
- AGD.Score: Amoebic Gill Disease, scaled from one to five for increasing severance
- PGD.Score: Proliferative Gill Disease, scaled from one to five for increasing severance
- Mortality: interpolated z-score data
- Phytoplankton abundance: cells per liter
- Zooplankton abundance: individuals per m^3
- OTUs: The eDNA metabarcoding approach assigned 9577 OTUs. Upon aggregation of OTUs at the genus level and exclusion of non-relevant taxa (e.g. mammal, fish, amphibian, and avian DNA), the total number of plankton genera was 447.
Missing values: N/A
Code/software
For the purposes of statistical modelling, we transformed our eDNA metabarcoding data to compositional data using the R package “compositions” v.2.0-8 and the function “acomp” (van den Boogaart & Tolosana-Delgado, 2008).
For multivariate analysis of microscopy and eDNA metabarcoding species data, we determined between-sample similarities using Jaccard’s distance and visualized these with multidimensional scaling ordination using the “vegan” R package v.2.6-4 (Oksanen et al., 2022).
For modelling purposes, data gaps along the timeline were filled, separately within each site, using linear interpolation with the function na.approx and the R package zoo v1.8-12 (Zeileis & Grothendieck, 2005).
To test the individual effect of each species on PGD or mortality, after accounting for temperature, oxygen, and the other species present, we used the *glmm.hp *R package v.0.1.2 (Lai et al., 2022).
The lagging of data was performed on the fish condition data using the package lubridate v.1.9.3 (Grolemund & Wickham, 2011) and subsequently, this lagged dataset was merged with the non-lagged species-abundance dataset. All analysis was carried out in R v.4.3.1. (R Core Team, 2022).