Animals regulate their diet in order to maximise the expression of fitness traits that often have different nutritional needs. These nutritional trade-offs have been experimentally uncovered using the Geometric framework for nutrition (GF). However, current analytical methods to measure such responses rely on either visual inspection or complex models applied to multidimensional performance landscapes, making these approaches subjective, or conceptually difficult, computationally expensive, and in some cases inaccurate. This limits our ability to understand how animal nutrition evolved to support life-histories within and between species. Here, we introduce a simple trigonometric model to measure nutritional trade-offs in multidimensional landscapes (‘Nutrigonometry’). Nutrigonometry is both conceptually and computationally easier than current approaches, as it harnesses the trigonometric relationships of right-angle triangles instead of vector calculations. Using landmark GF datasets, we first show how polynomial (Bayesian) regressions can be used for precise and accurate predictions of peaks and valleys in performance landscapes, irrespective of the underlying structure of the data (i.e., individual food intakes vs fixed diet ratios). Using trigonometric relationships, we then identified the known nutritional trade-off between lifespan and reproductive rate both in terms of nutrient balance and concentration. Nutrigonometry enables a fast, reliable and reproducible quantification of nutritional trade-offs in multidimensional performance landscapes, thereby broadening the potential for future developments in comparative research on the evolution of animal nutrition
Bibliographical noteML receives support from the CNRS, the French Research Agency (ANR 3DNaviBee: ANR-19-CE37-0024), the European Regional Development Fund (FEDER ECONECT: MP0021763), and the European Research Council (ERC-CoG BEE-MOVE: GA101002644).
JM is supported by the BBSRC (BB/V015249/1). PC is supported by the EPSRC (EP/P025072/) and from École Polytechnique Fédérale de Lausanne via a collaboration agreement with the University of Aberdeen. ML receives support from the CNRS, the French Research Agency (ANR 3DNaviBee: ANR-19-CE37-0024), the French Environment and Energy Management Agency
(ADEME LOTAPIS), the European Regional Development Fund (FEDER ECONECT:
MP0021763), and the European Research Council (ERC-CoG BEE-MOVE: GA101002644). The authors would like to thank Prof Kwang Lee, Dr Teresa Kutz, and Prof Carla Sgrò for kindly sharing the data that was used to demonstrate the use of our model. We would like to thank Gordon M. Hay for the translation of our abstract into Doric.
- Nutritional Geometry
- lifespan-reproduction trade-of
- fitness maps