Getting a clear definition of fitness for a particular evolutionary biology question is a complex challenge, fraught with pitfalls and misconceptions (Orr, 2009; Walsh & Lynch, 2018). In longitudinal surveys of wild populations, lifetime reproductive success (LRS) is generally considered the best measure of individual fitness (Bonnet, 2022). However, it is important to bear in mind that LRS is only a (noisy) measure of the realised success, relying on a substantial amount of assumptions (e.g. with regard to generation overlap, Walsh & Lynch, 2018), not a direct measure of fitness.

In a study on the clownfish, Marrot et al. (2024) studied the spatial and ecological drivers of lifetime reproductive success. To do so, they analysed a 10-year long survey on over 300 anemones harbouring clownfishes, and used a genetics-based pedigree to infer the LRS of each individual. Using a characterisation of the micro-habitat provided by each anemone, they used the anemone species, density and depth as ecological drivers and spatial-autocorrelated models to study more general (and undefined) spatial drivers.

The authors found that LRS was influenced by a significant amount by the spatial structure of the population, and, to some extent, by the anemone species harbouring the clownfish individuals. Together, they explain a substantial proportion of the individual variation in LRS.

While the actual determinants of spatial variation of LRS in this (and other) species remain understood, this study highlights an important aspect of measuring fitness in wild populations using LRS: it is particularly noisy and subject to environmental variation. This certainly does not mean that LRS is a bad proxy for fitness, it is still among the best measure of it we can have access to. However, it highlights how carefully we should thread when analysing it. Especially, spatial auto-correlation of LRS, combined with population structure within a population, would lead to genotype-environment correlation for fitness, which is likely to bias predictions of response to natural selection and would be extremely difficult to estimate (Falconer & Mackay, 1996).

References

Pascal Marrot, Cécile Fauvelot, Michael L. Berumen, Maya Srinivasan, Geoffrey P. Jones, Serge Planes, and Benoit Pujol (2024) Spatial autocorrelation and host anemone species drive variation in local components of fitness in a wild clownfish population. Zenodo, ver.3 peer-reviewed and recommended by PCI Evol Biol https://doi.org/10.5281/zenodo.13806778

Bonnet, T., Morrissey, M. B., de Villemereuil, P., Alberts, S. C., Arcese, P., Bailey, L. D., Boutin, S., Brekke, P., Brent, L. J. N., Camenisch, G., Charmantier, A., Clutton-Brock, T. H., Cockburn, A., Coltman, D. W., Courtiol, A., Davidian, E., Evans, S. R., Ewen, J. G., Festa-Bianchet, M., … Kruuk, L. E. B. (2022). Genetic variance in fitness indicates rapid contemporary adaptive evolution in wild animals. Science, 376(6596), 1012–1016. https://doi.org/10.1126/science.abk0853

Falconer, D. S. and Mackay, T. F. C. (1996). Introduction to quantitative genetics (4th ed.). Benjamin Cummings.

Orr, H. A. (2009). Fitness and its role in evolutionary genetics. Nature Reviews Genetics, 10(8), 531–539. https://doi.org/10.1038/nrg2603

Walsh, B. and Lynch, M. (2018). Evolution and selection of quantitative traits. Oxford University Press.