Back

Coevolution between life-history and metabolic rate depends on ontogenetic stage

Sowersby, W.; Morozov, S.; Eckerstrom-Liedholm, S.; Lehmann, P.; Rowinski, P.; Naslund, J.; Gonzalez-Voyer, A.; Rogell, B.

2019-07-18 evolutionary biology
10.1101/705707 bioRxiv
Show abstract

Metabolic rate is considered to determine the energetic investment placed into life-history traits, regulating the speed of an organisms life-cycle and forming the so called \"pace-of-life\". However, how metabolic rate and life-history traits co-evolve remains unclear. For instance, the energetic demands of life-history traits, including the number of energy allocation trade-offs, is unlikely to remain constant over ontogeny. Therefore, the predicted coevolution between metabolic rate and life-history could be specific to particular ontogenetic stages, rather than a stable property of an organism. Here, we test the ontogenetic dependency of the coevolution between metabolic rate and the pace of life-history, under strictly standardized conditions using 30 species of killifish, which are either annual species adapted to ephemeral pools or non-annual species inhabiting more permanent waterbodies. Standard metabolic rates were estimated at three ontogenetic stages, together with relevant life-history traits, i.e. growth (juveniles), maturity (young adults), and reproductive rate (reproductive adults). Life-history traits largely followed predicted pace-of-life patterns, with overall faster/higher rates in annual species. The divergences in life-history traits across species tended to increase over ontogeny, being smallest during juvenile growth and largest in reproductive adults. However, associations between life-history strategy and metabolic rate followed a reversed pattern, being strongest in juveniles, but lowest in reproductive adults. Our results are concordant with the number of energetic trade-offs increasing over ontogeny, which results in a stronger covariation between physiology and life-history traits earlier in ontogeny.

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.