The fitness landscape of a community of Darwin’s finches

Author:

Beausoleil Marc-Olivier1ORCID,Carrión Paola Lorena1ORCID,Podos Jeffrey2ORCID,Camacho Carlos3ORCID,Rabadán-González Julio4ORCID,Richard Roxanne1ORCID,Lalla Kristen5ORCID,Raeymaekers Joost A M6ORCID,Knutie Sarah A7ORCID,De León Luis F8ORCID,Chaves Jaime A910ORCID,Clayton Dale H11ORCID,Koop Jennifer A H12ORCID,Sharpe Diana M T13ORCID,Gotanda Kiyoko M1141516ORCID,Huber Sarah K17ORCID,Barrett Rowan D H1ORCID,Hendry Andrew P1ORCID

Affiliation:

1. Redpath Museum and Department of Biology, McGill University , Montréal, Québec , Canada

2. Department of Biology, University of Massachusetts Amherst , Amherst, MA , United States

3. Department of Ecology and Evolution, Estación Biológica de Doñana—CSIC , Sevilla , Spain

4. Observation.org Spain , Almensilla, Seville , Spain

5. Department of Natural Resource Sciences, McGill University , Sainte-Anne-de-Bellevue, Québec , Canada

6. Faculty of Biosciences and Aquaculture, Nord University , Bodø , Norway

7. Department of Ecology and Evolutionary Biology, Institute for Systems Genomics, University of Connecticut , Storrs, CT , United States

8. Department of Biology, University of Massachusetts Boston , Boston, MA , United States

9. Department of Biology, San Francisco State University , San Francisco, CA , United States

10. Colegio de Ciencias Biológicas y Ambientales, Universidad San Francisco de Quito , Quito , Ecuador

11. School of Biological Sciences, University of Utah , Salt Lake City, UT , United States

12. Department of Biological Sciences, Northern Illinois University , DeKalb, IL , United States

13. Department of Organismic and Evolutionary Biology, Harvard University , Cambridge, MA , United States

14. Department of Biological Sciences, Brock University , St. Catharines, Ontario , Canada

15. Department of Zoology, University of Cambridge , United Kingdom

16. Département de biologie, Université de Sherbrooke , Québec , Canada

17. Virginia Institute of Marine Science, William and Mary , Gloucester Point, VA , United States

Abstract

Abstract Divergent natural selection should lead to adaptive radiation—that is, the rapid evolution of phenotypic and ecological diversity originating from a single clade. The drivers of adaptive radiation have often been conceptualized through the concept of “adaptive landscapes,” yet formal empirical estimates of adaptive landscapes for natural adaptive radiations have proven elusive. Here, we use a 17-year dataset of Darwin’s ground finches (Geospiza spp.) at an intensively studied site on Santa Cruz (Galápagos) to estimate individual apparent lifespan in relation to beak traits. We use these estimates to model a multi-species fitness landscape, which we also convert to a formal adaptive landscape. We then assess the correspondence between estimated fitness peaks and observed phenotypes for each of five phenotypic modes (G. fuliginosa, G. fortis [small and large morphotypes], G. magnirostris, and G. scandens). The fitness and adaptive landscapes show 5 and 4 peaks, respectively, and, as expected, the adaptive landscape was smoother than the fitness landscape. Each of the five phenotypic modes appeared reasonably close to the corresponding fitness peak, yet interesting deviations were also documented and examined. By estimating adaptive landscapes in an ongoing adaptive radiation, our study demonstrates their utility as a quantitative tool for exploring and predicting adaptive radiation.

Publisher

Oxford University Press (OUP)

Subject

General Agricultural and Biological Sciences,Genetics,Ecology, Evolution, Behavior and Systematics

Reference94 articles.

1. Genetics of ecological divergence during speciation;Arnegard,2014

2. The adaptive landscape as a conceptual bridge between micro- and macroevolution;Arnold;Genetica,2001

3. Data and code for the fitness landscape of a community of Darwin’s finches;Beausoleil,2023

4. Temporally varying disruptive selection in the medium ground finch (Geospiza fortis);Beausoleil,2019

5. mixtools: An R package for analyzing finite mixture models;Benaglia;Journal of Statistical Software,2009

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