Phylogenetics to help predict active metabolism
This paper shows how to build predictive models involving phylogenetic information to estimate metabolic traits such as active metabolic costs. Fish swimming cost is often estimated from body mass and swimming speed. The parameters of the relationships between these variables and swimming cost vary...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2015-04-01
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| Series: | Ecosphere |
| Subjects: | |
| Online Access: | https://doi.org/10.1890/ES14-00479.1 |
| _version_ | 1828941080574296064 |
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| author | G. Guénard D. Boisclair P. Legendre |
| author_facet | G. Guénard D. Boisclair P. Legendre |
| author_sort | G. Guénard |
| collection | DOAJ |
| description | This paper shows how to build predictive models involving phylogenetic information to estimate metabolic traits such as active metabolic costs. Fish swimming cost is often estimated from body mass and swimming speed. The parameters of the relationships between these variables and swimming cost vary among species because each species has its own morphology and physiology. It is now widely recognized that traits are phylogenetically structured. Using new statistical approaches, it is possible to both correct swimming cost models for statistical phylogenetic non‐independence and use the inherent phylogenetic signal to improve models. With these models one can extend, to a larger set of species, empirical knowledge about traits that are difficult to obtain; swimming cost is one such trait. Swimming cost accounts for a large and variable component of a fish energy budget, yet models have only been developed from observations performed on a few species, thereby constraining the scope of bioenergetic models. Here, we propose a method where body mass and swimming speed are used together with phylogeny to predict swimming cost. The resulting model explained a large proportion of the variation (90%) in the forced swimming cost of 16 fish species submitted to forced swimming experiments. We also compared phylogenetically‐explicit predictions for forced swimming experiments with experimental results of routine swimming for five species, among which one was not used to build the model. Results confirmed that forced swimming underestimates the cost of unsteady swimming. The phylogenetic modeling could be used to estimate other variables of interest in bioenergetic studies. |
| first_indexed | 2024-12-14T03:29:45Z |
| format | Article |
| id | doaj.art-e1dae66ac87847bd95f9dd33c8ec9805 |
| institution | Directory Open Access Journal |
| issn | 2150-8925 |
| language | English |
| last_indexed | 2024-12-14T03:29:45Z |
| publishDate | 2015-04-01 |
| publisher | Wiley |
| record_format | Article |
| series | Ecosphere |
| spelling | doaj.art-e1dae66ac87847bd95f9dd33c8ec98052022-12-21T23:18:47ZengWileyEcosphere2150-89252015-04-016411110.1890/ES14-00479.1Phylogenetics to help predict active metabolismG. Guénard0D. Boisclair1P. Legendre2Département de sciences biologiques, Université de Montréal, C.P. 6128, succ. Centre-Ville, Montréal, Quebec, Canada H3C 3J7Département de sciences biologiques, Université de Montréal, C.P. 6128, succ. Centre-Ville, Montréal, Quebec, Canada H3C 3J7Département de sciences biologiques, Université de Montréal, C.P. 6128, succ. Centre-Ville, Montréal, Quebec, Canada H3C 3J7This paper shows how to build predictive models involving phylogenetic information to estimate metabolic traits such as active metabolic costs. Fish swimming cost is often estimated from body mass and swimming speed. The parameters of the relationships between these variables and swimming cost vary among species because each species has its own morphology and physiology. It is now widely recognized that traits are phylogenetically structured. Using new statistical approaches, it is possible to both correct swimming cost models for statistical phylogenetic non‐independence and use the inherent phylogenetic signal to improve models. With these models one can extend, to a larger set of species, empirical knowledge about traits that are difficult to obtain; swimming cost is one such trait. Swimming cost accounts for a large and variable component of a fish energy budget, yet models have only been developed from observations performed on a few species, thereby constraining the scope of bioenergetic models. Here, we propose a method where body mass and swimming speed are used together with phylogeny to predict swimming cost. The resulting model explained a large proportion of the variation (90%) in the forced swimming cost of 16 fish species submitted to forced swimming experiments. We also compared phylogenetically‐explicit predictions for forced swimming experiments with experimental results of routine swimming for five species, among which one was not used to build the model. Results confirmed that forced swimming underestimates the cost of unsteady swimming. The phylogenetic modeling could be used to estimate other variables of interest in bioenergetic studies.https://doi.org/10.1890/ES14-00479.1activityforced swimmingmultiple-species modelingphylogenetic modelingroutine swimmingswimming cost |
| spellingShingle | G. Guénard D. Boisclair P. Legendre Phylogenetics to help predict active metabolism Ecosphere activity forced swimming multiple-species modeling phylogenetic modeling routine swimming swimming cost |
| title | Phylogenetics to help predict active metabolism |
| title_full | Phylogenetics to help predict active metabolism |
| title_fullStr | Phylogenetics to help predict active metabolism |
| title_full_unstemmed | Phylogenetics to help predict active metabolism |
| title_short | Phylogenetics to help predict active metabolism |
| title_sort | phylogenetics to help predict active metabolism |
| topic | activity forced swimming multiple-species modeling phylogenetic modeling routine swimming swimming cost |
| url | https://doi.org/10.1890/ES14-00479.1 |
| work_keys_str_mv | AT gguenard phylogeneticstohelppredictactivemetabolism AT dboisclair phylogeneticstohelppredictactivemetabolism AT plegendre phylogeneticstohelppredictactivemetabolism |