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The deep sea is a hot spot of fish body shape evolution

Citation

Martinez, Christopher (2021), The deep sea is a hot spot of fish body shape evolution, Dryad, Dataset, https://doi.org/10.25338/B8K048

Abstract

Deep sea fishes have long captured our imagination with striking adaptations to life in the mysterious abyss, raising the possibility that this cold, dark ocean region may be a key hub for physiological and functional diversification. We explore this idea through an analysis of body shape evolution across ocean depth zones in over 3,000 species of marine teleost fishes. We find that the deep ocean contains twice the body shape disparity of shallow waters, driven by elevated rates of evolution in traits associated with locomotion. Deep sea fishes display more frequent adoption of forms suited to slow and periodic swimming, whereas shallow living species are concentrated around shapes conferring strong, sustained swimming capacity and maneuverability. Our results support long-standing impressions of the deep sea as an evolutionary hotspot for fish body shape evolution and highlight that factors like habitat complexity and ecological interactions are potential drivers of this adaptive diversification.

Methods

This study makes use of a large morphological dataset for teleost fishes, collected collaboratively by the authors and a team of undergraduate researchers (see Price et al. 2020 for details on student involvement) on museum specimens from the Smithsonian National Museum of Natural History. Here, we use a subset of these data consisting of marine species with documented depth of occurrence. In total, we included 8,362 specimens from 3,033 species across 263 families and 34 orders. Eight linear traits were measured on each specimen (Fig. 1B), capturing major dimensions of body shape, with an emphasis on functional importance. A full account of collection procedures can be found in Price et al. (2019). Prior to analyses, measurements were averaged by species and matched to a published molecular phylogeny of teleost fishes (Rabosky, Chang, Title al et Alfaro 2018), trimmed to our dataset.

            To account for the effect of size on morphological traits (allometry), we performed phylogenetic generalized least-squares (PGLS) regressions of the natural logarithm of linear measurements on the natural logarithm of body size. For this, we used the “procD.pgls” function in the R package, geomorph version 3.2.0 (Adams et al. 2019; R Core Team 2020), which implements a residual randomization permutation procedure (n=10,000 iterations) from the RRPP package, version 0.4.3 (Collyer & Adams 2018; Collyer & Adams 2019). Residuals from regressions were used as allometrically-adjusted morphological data. Body size was measured as the cube-root of the product of length, depth, and width (Price et al. 2019). This metric was chosen to provide a more comprehensive measure of fish size, accommodating the full range of body shapes found in the data set, including very deep (e.g., butterflyfishes, Chaetodontidae), wide (e.g., goosefishes, Lophiidae), and elongate body plans (e.g., snake eels, Ophichthidae).

References:

Adams, D., Collyer, M. & Kaliontzopoulou, A. (2019). Geomorph: Software for geometric morphometric analyses. R package version 3.2.0.

Collyer, M.L. & Adams, D.C. (2018). RRPP: An R package for fitting linear models to high‐dimensional data using residual randomization. Methods Ecol. Evol., 9, 1772-1779.

Collyer, M.L. & Adams, D.C. (2019). RRPP: Linear Model Evaluation with Randomized Residuals in a Permutation Procedure. R package version 0.4.3.

Price, S.A., Friedman, S.T., Corn, K.A., Martinez, C.M., Larouche, O. & Wainwright, P.C. (2019). Building a body shape morphospace of teleostean fishes. Integr. Comp. Biol., 59, 716-730.

Price, S.A., Larouche, O., Friedman, S.T., Corn, K.A., Wainwright, P.C. & Martinez, C.M. (2020). A CURE for a major challenge in phenomics: a practical guide to implementing a quantitative specimen-based undergraduate research experience. Integr. Org. Biol.2, obaa004.

R Core Team (2020). R: A language and environment for statistical computing, version 4.0.2. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.

Rabosky, D.L., Chang, J., Title, P.O., Cowman, P.F., Sallan, L., Friedman, M., et al. (2018). An inverse latitudinal gradient in speciation for marine fishes. Nature, 559, 392-395.

Usage Notes

Data are allometrically-adjusted linear measurements of species' mean trait values. These are residuals from allometric regresssion (see methods). Eight linear morpphological traits are provided, in addition to:

"tree_name": Genus and species matching the Robosky et al (2018) phylogeny used in this study

"valid_name": Valid genus and species designations, based on fishbase.org

"maxdepth_m": Maximum depth of occurrence (in meters) for the species, based on data from fishbase.org

"depth_group": Categorical depth grouping, derived from maximum depth values

"family": Taxonomic Family for species in question

"order": Taxonomic Order for species in question

 

Funding

National Science Foundation, Award: DEB-1556953