The Mass-Longevity Triangle: Pareto Optimality and the Geometry of Life-History Trait Space

• Published in: PLoS computational biology 2015-10, Vol.11 (10), p.e1004524-e1004524
• Main Authors: Szekely, Pablo, Korem, Yael, Moran, Uri, Mayo, Avi, Alon, Uri
• Format: Article
• Language: English
• Subjects: Algorithms; Analysis; Animal behavior; Animals; Bats; Body Weight - physiology; Computer Simulation; Confidence intervals; Game Theory; Gene expression; Genotype & phenotype; Health aspects; Longevity; Longevity - physiology; Metabolism; Models, Biological; Models, Statistical; Pareto efficiency; Phylogenetics; Physiological aspects; Studies; Taxonomy
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1004524

When organisms need to perform multiple tasks they face a fundamental tradeoff: no phenotype can be optimal at all tasks. This situation was recently analyzed using Pareto optimality, showing that tradeoffs between tasks lead to phenotypes distributed on low dimensional polygons in trait space. The vertices of these polygons are archetypes--phenotypes optimal at a single task. This theory was applied to examples from animal morphology and gene expression. Here we ask whether Pareto optimality theory can apply to life history traits, which include longevity, fecundity and mass. To comprehensively explore the geometry of life history trait space, we analyze a dataset of life history traits of 2105 endothermic species. We find that, to a first approximation, life history traits fall on a triangle in log-mass log-longevity space. The vertices of the triangle suggest three archetypal strategies, exemplified by bats, shrews and whales, with specialists near the vertices and generalists in the middle of the triangle. To a second approximation, the data lies in a tetrahedron, whose extra vertex above the mass-longevity triangle suggests a fourth strategy related to carnivory. Each animal species can thus be placed in a coordinate system according to its distance from the archetypes, which may be useful for genome-scale comparative studies of mammalian aging and other biological aspects. We further demonstrate that Pareto optimality can explain a range of previous studies which found animal and plant phenotypes which lie in triangles in trait space. This study demonstrates the applicability of multi-objective optimization principles to understand life history traits and to infer archetypal strategies that suggest why some mammalian species live much longer than others of similar mass.