10.5061/DRYAD.TTDZ08KT8
Beukhof, Esther
0000-0002-0350-9596
Technical University of Denmark
Frelat, Romain
0000-0002-8631-4398
University of Hamburg
Pecuchet, Laurene
0000-0002-8930-8786
The Arctic University of Norway
Maureaud, Aurore
0000-0003-4778-9443
Technical University of Denmark
Dencker, Tim Spaanheden
0000-0001-7804-0828
Technical University of Denmark
Sólmundsson, Jón
0000-0002-2685-777X
,
Punzón, Antonio
0000-0001-6703-7690
Spanish Institute of Oceanography
Primicerio, Raul
The Arctic University of Norway
Hidalgo, Manuel
0000-0002-3494-9658
Spanish Institute of Oceanography
Möllmann, Christian
0000-0001-9161-6342
University of Hamburg
Lindegren, Martin
Technical University of Denmark
Marine fish traits follow fast-slow continuum across oceans
Dryad
dataset
2019
bottom trawl survey
Adaptations
Horizon 2020
675997
The Velux Foundations
https://ror.org/007ww2d15
13159
European Commission
https://ror.org/00k4n6c32
675997
The Velux Foundations
https://ror.org/007ww2d15
131159
2019-11-29T00:00:00Z
2019-11-29T00:00:00Z
en
https://doi.org/10.1594/PANGAEA.900866
https://doi.org/10.1038/s41598-019-53998-2
3375769 bytes
5
CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
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.