Marine fish traits follow fast-slow continuum across oceans
Cite this dataset
Beukhof, Esther et al. (2019). Marine fish traits follow fast-slow continuum across oceans [Dataset]. Dryad. https://doi.org/10.5061/dryad.ttdz08kt8
A fundamental challenge in ecology is to understand why species are found where they are and predict where they are likely to occur in the future. Trait-based approaches may provide such understanding, because it is the traits and adaptations of species that determine which environments they can inhabit. It is therefore important to identify key traits that determine species distributions and investigate how these traits relate to the environment. Based on scientific bottom-trawl surveys of marine fish abundances and traits of >1,200 species, we investigate trait-environment relationships and project the trait composition of marine fish communities across the continental shelf seas of the Northern hemisphere. We show that traits related to growth, maturation and lifespan respond most strongly to the environment. This is reflected by a pronounced “fast-slow continuum” of fish life-histories, revealing that traits vary with temperature at large spatial scales, but also with depth and seasonality at more local scales. Our findings provide insight into the structure of marine fish communities and suggest that global warming will favour an expansion of fast-living species. Knowledge of the global and local drivers of trait distributions can thus be used to predict future responses of fish communities to environmental change.
We collated data from 21 scientific bottom-trawl surveys in the North Atlantic and North-East Pacific. We selected the period 2005 to 2015 in order to have a similar temporal coverage and a consistent sampling period across surveys. Although gears and sampling protocols vary between surveys, they all use bottom trawls and identify catches at species level whenever possible. Abundance data were standardized according to the duration or swept area of the tow, depending on which information on the tows was provided with the survey. We verified and updated the taxonomy of reported taxa with the World Register of Marine Species and discarded all non-fish taxa by keeping only organisms from the following classes: Actinopterygii, Elasmobranchii, Holocephali, Myxini and Petromyzonti. The two largest classes are the bony fish (Actinopterygii) and elasmobranchs (Elasmobranchii). Finally, we only kept taxa that had been recorded at the family, genus or species level. Our dataset consisted of 77,824 samples, recording the abundance of 1,889 different taxa (1,583 taxa identified at species level, 203 at genus level and 103 at family level).
To broadly represent the life history and ecology of fish in terms of their feeding, growth, survival and reproduction we selected seven commonly used traits: maximum body length (cm), trophic level, fecundity (number of offspring produced by a female per year), offspring size (egg diameter, length of egg case or length of pup in mm), age at maturity (year), lifespan (year) and the Von Bertalanffy growth coefficient K (1/year) as a proxy for individual growth rate. Trait information was extracted from a publicly available dataset on marine fish traits (Beukhof et al. 2019, DOI : 10.1594/PANGAEA.900866), for which most trait values were sourced from FishBase and collected at the level of Large Marine Ecosystems (LMEs) and FAO fishing areas in order to account for intraspecific variation in species traits across areas.
We selected seven environmental variables representing hydrography, habitat, food availability and anthropogenic pressures, which are known to affect the distribution of fish species. Monthly sea bottom temperature (SBT in °C) and sea bottom salinity (SBS in psu) from 2004 to 2015 were obtained from the Global Ocean Physics Reanalysis (GLORYSs2v4) with a spatial resolution of 1/4°. Chlorophyll a concentration (Chl in mg/m3) served as a proxy for primary production and food availability. Data were downloaded from the GlobColour database with a spatial resolution of approximatively 4 km.
To reduce computation time, sampling sites were aggregated into grid cells of 0.25 by 0.25 degree. For each grid cell the average relative species abundance and environmental condition was calculated over all sampling sites falling into the grid cell.
The zip-archive contains five files:
1- Labu.csv: relative abundances of fish species per 0.25 degree rectangle
2- Qtrait.csv: traits database of fish species
3- Renv.csv: environmental variables per 0.25 degree rectangle
4- Coordinates.csv: Coordinates of the 0.25 degree rectangles
5- scriptRLQ.R: the script to run the RLQ and Random Forest analysis
The script has been simplified substantially compared to the analysis in the referred article.
For example, we removed the sensitivity tests and the complex implementation of random forests in order to keep the core analysis in a single R-script.
The dataset provided is an aggregation of 72,258 stations into a grid of 0.25 degree resolution. Even with such simplification and due to the large size of the datasets, the R-script takes around 5-15 min to run.
Horizon 2020, Award: 675997
The Velux Foundations, Award: 13159
European Commission, Award: 675997
The Velux Foundations, Award: 131159