Literature DB >> 26508641

Fundamental insights into ontogenetic growth from theory and fish.

Richard M Sibly1, Joanna Baker2, John M Grady3, Susan M Luna4, Astrid Kodric-Brown3, Chris Venditti2, James H Brown5.   

Abstract

The fundamental features of growth may be universal, because growth trajectories of most animals are very similar, but a unified mechanistic theory of growth remains elusive. Still needed is a synthetic explanation for how and why growth rates vary as body size changes, both within individuals over their ontogeny and between populations and species over their evolution. Here, we use Bertalanffy growth equations to characterize growth of ray-finned fishes in terms of two parameters, the growth rate coefficient, K, and final body mass, m∞. We derive two alternative empirically testable hypotheses and test them by analyzing data from FishBase. Across 576 species, which vary in size at maturity by almost nine orders of magnitude, K scaled as [Formula: see text]. This supports our first hypothesis that growth rate scales as [Formula: see text] as predicted by metabolic scaling theory; it implies that species that grow to larger mature sizes grow faster as juveniles. Within fish species, however, K scaled as [Formula: see text]. This supports our second hypothesis, which predicts that growth rate scales as [Formula: see text] when all juveniles grow at the same rate. The unexpected disparity between across- and within-species scaling challenges existing theoretical interpretations. We suggest that the similar ontogenetic programs of closely related populations constrain growth to [Formula: see text] scaling, but as species diverge over evolutionary time they evolve the near-optimal [Formula: see text] scaling predicted by metabolic scaling theory. Our findings have important practical implications because fish supply essential protein in human diets, and sustainable yields from wild harvests and aquaculture depend on growth rates.

Entities:  

Keywords:  Bertalanffy growth; metabolic theory of ecology; scaling theory

Mesh:

Year:  2015        PMID: 26508641      PMCID: PMC4653220          DOI: 10.1073/pnas.1518823112

Source DB:  PubMed          Journal:  Proc Natl Acad Sci U S A        ISSN: 0027-8424            Impact factor:   11.205


  21 in total

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Authors:  R H Devlin; C A Biagi; T Y Yesaki; D E Smailus; J C Byatt
Journal:  Nature       Date:  2001-02-15       Impact factor: 49.962

2.  Effects of size and temperature on metabolic rate.

Authors:  J F Gillooly; J H Brown; G B West; V M Savage; E L Charnov
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3.  Improving marginal likelihood estimation for Bayesian phylogenetic model selection.

Authors:  Wangang Xie; Paul O Lewis; Yu Fan; Lynn Kuo; Ming-Hui Chen
Journal:  Syst Biol       Date:  2010-12-27       Impact factor: 15.683

4.  Inferring the historical patterns of biological evolution.

Authors:  M Pagel
Journal:  Nature       Date:  1999-10-28       Impact factor: 49.962

5.  Rates of speciation and morphological evolution are correlated across the largest vertebrate radiation.

Authors:  Daniel L Rabosky; Francesco Santini; Jonathan Eastman; Stephen A Smith; Brian Sidlauskas; Jonathan Chang; Michael E Alfaro
Journal:  Nat Commun       Date:  2013       Impact factor: 14.919

Review 6.  Extent and scale of local adaptation in salmonid fishes: review and meta-analysis.

Authors:  D J Fraser; L K Weir; L Bernatchez; M M Hansen; E B Taylor
Journal:  Heredity (Edinb)       Date:  2011-01-12       Impact factor: 3.821

7.  How body mass and lifestyle affect juvenile biomass production in placental mammals.

Authors:  Richard M Sibly; John M Grady; Chris Venditti; James H Brown
Journal:  Proc Biol Sci       Date:  2014-01-08       Impact factor: 5.349

Review 8.  Relationship between growth and standard metabolic rate: measurement artefacts and implications for habitat use and life-history adaptation in salmonids.

Authors:  Jordan Rosenfeld; Travis Van Leeuwen; Jeffrey Richards; David Allen
Journal:  J Anim Ecol       Date:  2014-08-04       Impact factor: 5.091

9.  Physiological mechanisms underlying a trade-off between growth rate and tolerance of feed deprivation in the European sea bass (Dicentrarchus labrax).

Authors:  A Dupont-Prinet; B Chatain; L Grima; M Vandeputte; G Claireaux; D J McKenzie
Journal:  J Exp Biol       Date:  2010-04       Impact factor: 3.312

10.  Genetic signature of adaptive peak shift in threespine stickleback.

Authors:  Sean M Rogers; Patrick Tamkee; Brian Summers; Sarita Balabahadra; Melissa Marks; David M Kingsley; Dolph Schluter
Journal:  Evolution       Date:  2012-04-09       Impact factor: 3.694

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  6 in total

1.  Body size and mortality rates in coral reef fishes: a three-phase relationship.

Authors:  Christopher Harry Robert Goatley; David Roy Bellwood
Journal:  Proc Biol Sci       Date:  2016-10-26       Impact factor: 5.349

2.  Effects of genetics and early-life mild hypoxia on size variation in farmed gilthead sea bream (Sparus aurata).

Authors:  Erick Perera; Enrique Rosell-Moll; Fernando Naya-Català; Paula Simó-Mirabet; Josep Calduch-Giner; Jaume Pérez-Sánchez
Journal:  Fish Physiol Biochem       Date:  2020-11-13       Impact factor: 2.794

3.  Unique maternal and environmental effects on the body morphology of the Least Killifish, Heterandria formosa.

Authors:  J Alex Landy; Joseph Travis
Journal:  Ecol Evol       Date:  2018-05-24       Impact factor: 2.912

4.  Mixed interactions among life history stages of two harvested related species.

Authors:  Edwige Bellier
Journal:  Ecol Evol       Date:  2022-03-07       Impact factor: 2.912

5.  Community size structure varies with predator-prey size relationships and temperature across Australian reefs.

Authors:  Amy Rose Coghlan; Julia L Blanchard; Freddie J Heather; Rick D Stuart-Smith; Graham J Edgar; Asta Audzijonyte
Journal:  Ecol Evol       Date:  2022-04-07       Impact factor: 2.912

6.  Divergence in digestive and metabolic strategies matches habitat differentiation in juvenile salmonids.

Authors:  Gauthier Monnet; Jordan S Rosenfeld; Jeffrey G Richards
Journal:  Ecol Evol       Date:  2022-09-11       Impact factor: 3.167

  6 in total

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