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Abiotic conditions shape spatial and temporal morphological variation in North American birds.

Casey YoungfleshJames F SaraccoRodney B SiegelMorgan W Tingley
Published in: Nature ecology & evolution (2022)
Quantifying environment-morphology relationships is important not only for understanding the fundamental processes driving phenotypic diversity within and among species but also for predicting how species will respond to ongoing global change. Despite a clear set of expectations motivated by ecological theory, broad evidence in support of generalizable effects of abiotic conditions on spatial and temporal intraspecific morphological variation has been limited. Using standardized data from >250,000 captures of 105 landbird species, we assessed intraspecific shifts in the morphology of adult male birds since 1989 while simultaneously measuring spatial morphological gradients across the North American continent. We found strong spatial and temporal trends in average body size, with warmer temperatures associated with smaller body sizes both at more equatorial latitudes and in more recent years. The magnitude of these thermal effects varied both across and within species, with results suggesting it is the warmest, rather than the coldest, temperatures that drive both spatial and temporal trends. Stronger responses to spatial-rather than temporal-variation in temperature suggest that morphological change may not be keeping up with the pace of climate change. Additionally, as elevation increases, we found that body size declines as relative wing length increases, probably due to the benefits that longer wings confer for flight in thin air environments. Our results provide support for both existing and new large-scale ecomorphological 'rules' and highlight how the response of functional trade-offs to abiotic variation drives morphological change.
Keyphrases
  • climate change
  • genetic diversity
  • machine learning
  • electronic health record
  • arabidopsis thaliana
  • genome wide identification
  • artificial intelligence
  • big data
  • deep learning