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Thermal physiology of Amazonian lizards (Reptilia: Squamata).

Luisa Maria Diele-ViegasLaurie J VittBarry SinervoGuarino R ColliFernanda P WerneckDonald B MilesWilliam E MagnussonJuan C SantosCarla M SetteGabriel H O CaetanoEmerson PontesTeresa C S Ávila-Pires
Published in: PloS one (2018)
We summarize thermal-biology data of 69 species of Amazonian lizards, including mode of thermoregulation and field-active body temperatures (Tb). We also provide new data on preferred temperatures (Tpref), voluntary and thermal-tolerance ranges, and thermal-performance curves (TPC's) for 27 species from nine sites in the Brazilian Amazonia. We tested for phylogenetic signal and pairwise correlations among thermal traits. We found that species generally categorized as thermoregulators have the highest mean values for all thermal traits, and broader ranges for Tb, critical thermal maximum (CTmax) and optimal (Topt) temperatures. Species generally categorized as thermoconformers have large ranges for Tpref, critical thermal minimum (CTmin), and minimum voluntary (VTmin) temperatures for performance. Despite these differences, our results show that all thermal characteristics overlap between both groups and suggest that Amazonian lizards do not fit into discrete thermoregulatory categories. The traits are all correlated, with the exceptions of (1) Topt, which does not correlate with CTmax, and (2) CTmin, and correlates only with Topt. Weak phylogenetic signals for Tb, Tpref and VTmin indicate that these characters may be shaped by local environmental conditions and influenced by phylogeny. We found that open-habitat species perform well under present environmental conditions, without experiencing detectable thermal stress from high environmental temperatures induced in lab experiments. For forest-dwelling lizards, we expect warming trends in Amazonia to induce thermal stress, as temperatures surpass the thermal tolerances for these species.
Keyphrases
  • climate change
  • genome wide
  • machine learning
  • deep learning
  • high glucose
  • artificial intelligence
  • heat stress