Marine fish traits follow fast-slow continuum across oceans
Authors/Creators
- 1. Technical University of Denmark
- 2. University of Hamburg
- 3. The Arctic University of Norway
- 4. Przedsiębiorstwo Badań i Doradztwa
- 5. Spanish Institute of Oceanography
Description
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.
Notes
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.
Funding provided by: Horizon 2020
Crossref Funder Registry ID:
Award Number: 675997
Funding provided by: Villum Fonden
Crossref Funder Registry ID: http://dx.doi.org/10.13039/100008398
Award Number: 13159
Funding provided by: Horizon 2020 Framework Programme
Crossref Funder Registry ID: http://dx.doi.org/10.13039/100010661
Award Number: 675997
Funding provided by: Villum Fonden
Crossref Funder Registry ID: http://dx.doi.org/10.13039/100008398
Award Number: 131159
Files
Beukhof2019_FishRLQ_Datascript.zip
Files
(3.4 MB)
| Name | Size | Download all |
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md5:e49908482deacc0e3db14ee40f9aedb0
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3.4 MB | Preview Download |
Additional details
Related works
- Is cited by
- 10.1038/s41598-019-53998-2 (DOI)
- Is supplemented by
- 10.1594/PANGAEA.900866 (DOI)