Scaling between macro‐ to microscale climatic data reveals strong phylogenetic inertia in niche evolution in plethodontid salamanders. (1st May 2020)

• Record Type: Journal Article
• Authors: Farallo, Vincent R.; Muñoz, Martha M.; Uyeda, Josef C.; Miles, Donald B.
• Is Part Of: Evolution. Volume 74:Number 5(2020)
• Page Start: 979
• Page End: 991
• Publication Date: 2020-05-01
• Subjects: Behavior -- climate -- ecology -- evolution -- microclimate -- niche evolution; Evolution -- Periodicals; Heredity -- Periodicals; Évolution (Biologie) -- Périodiques; Hérédité -- Périodiques; 338.47004094
• DOI: 10.1111/evo.13959
• Languages: English
• ISSNs: 0014-3820

Abstract

Abstract: Macroclimatic niches are indirect and potentially inadequate predictors of the realized environmental conditions that many species experience. Consequently, analyses of niche evolution based on macroclimatic data alone may incompletely represent the evolutionary dynamics of species niches. Yet, understanding how an organisms' climatic (Grinnellian) niche responds to changing macroclimatic conditions is of vital importance for predicting their potential response to global change. In this study, we integrate microclimatic and macroclimatic data across 26 species of plethodontid salamanders to portray the relationship between microclimatic niche evolution in response to changing macroclimate. We demonstrate stronger phylogenetic signal in microclimatic niche variables than at the macroclimatic scale. Even so, we find that the microclimatic niche tracks climatic changes at the macroscale, but with a phylogenetic lag at million‐year timescales. We hypothesize that behavioral tracking of the microclimatic niche over space and phenology generates the lag: salamanders preferentially select microclimates similar to their ancestral conditions rather than adapting with changes in physiology. We demonstrate that macroclimatic variables are weak predictors of niche evolution and that incorporating spatial scale into analyses of niche evolution is critical for predicting responses to climate change.