Fitness surfaces and local thermal adaptation in Drosophila along a latitudinal gradient
Metadatos
Mostrar el registro completo del ítemAutor
Alruiz, José M.; Peralta Maraver, Ignacio Fernando; Cavieres, Grisel; Bozinovic, Francisco; Rezende, Enrico L.Editorial
Wiley
Materia
Darwinian fitness geographical gradient thermal death time curves
Fecha
2024-04-16Referencia bibliográfica
Alruiz, J.M., Peralta-Maraver, I., Cavieres, G., Bozinovic, F. & Rezende, E.L. (2024) Fitness surfaces and local thermal adaptation in Drosophila along a latitudinal gradient. Ecology Letters, 27, e14405. Available from: https://doi.org/10.1111/ele.14405
Patrocinador
Agencia Nacional de Investigación y Desarrollo, Gobierno de Chile, Grant/ Award Number: ANID PIA/BASAL FB0002; Fondo Nacional de Desarrollo Científico y Tecnológico, Gobierno de Chile, Grant/Award Number: FONDECYT 1211113; HORIZON EUROPE Marie Sklodowska-Curie Actions, Grant/ Award Number: Marie Skłodowska-Curie postdoctoral fellowship 2022 (project number 101110111)Resumen
Local adaptation is commonly cited to explain species distribution, but how fitness
varies along continuous geographical gradients is not well understood. Here, we
combine thermal biology and life-history
theory to demonstrate that Drosophila
populations along a 2500 km latitudinal cline are adapted to local conditions. We
measured how heat tolerance and viability rate across eight populations varied
with temperature in the laboratory and then simulated their expected cumulative
Darwinian fitness employing high-resolution
temperature data from their eight
collection sites. Simulations indicate a trade-off
between annual survival and
cumulative viability, as both mortality and the recruitment of new flies are
predicted to increase in warmer regions. Importantly, populations are locally
adapted and exhibit the optimal combination of both traits to maximize fitness
where they live. In conclusion, our method is able to reconstruct fitness surfaces
employing empirical life-history
estimates and reconstructs peaks representing
locally adapted populations, allowing us to study geographic adaptation in silico.





