Thermal compensation reduces DNA damage from UV radiation

Cold-induced skin darkening does not protect amphibian larvae from UV-associated DNA damage

Amphibian declines are sometimes correlated with increasing levels of ultraviolet radiation (UVR). While disease is often implicated in declines, environmental factors such as temperature and UVR play an important role in disease epidemiology. The mutagenic effects of UVR exposure on amphibians are worse at low temperatures. Amphibians from cold environments may be more susceptible to increasing UVR. However, larvae of some species demonstrate cold acclimation, reducing UV-induced DNA damage at low temperatures. Understanding of the mechanisms underpinning this response is lacking. We reared Limnodynastes peronii larvae in cool (15°C) or warm (25°C) waters before acutely exposing them to 1.5 h of high intensity (80 µW cm-2 ) UVBR. We measured the color of larvae and mRNA levels of a DNA repair enzyme. We reared larvae at 25°C in black or white containers to elicit a skin color response, and then measured DNA damage levels in the skin and remaining carcass following UVBR exposure. Cold-acclimated larvae were darker and displayed lower levels of DNA damage than warm-acclimated larvae. There was no difference in CPD-photolyase mRNA levels between cold- and warm-acclimated larvae. Skin darkening in larvae did not reduce their accumulation of DNA damage following UVR exposure. Our results showed that skin darkening does not explain cold-induced reductions in UV-associated DNA damage in L. peronii larvae. Beneficial cold-acclimation is more likely underpinned by increased CPD-photolyase abundance and/or increased photolyase activity at low temperatures.

Looking beyond the mean: quantile regression for comparative physiologists

Statistical analyses that physiologists use to test hypotheses predominantly centre on means, but the tail ends of the response distribution can behave quite differently and underpin important scientific phenomena. We demonstrate that quantile regression (QR) offers a way to bypass some limitations of least squares regression (LSR) by building a picture of independent variable effects across the whole distribution of a dependent variable. We used LSR and QR with simulated and real datasets. With simulated data, LSR showed no change in the mean response but missed significant effects in the tails of the distribution found using QR. With real data, LSR showed a significant change in the mean response but missed a lack of response in the upper quantiles which was biologically revealing. Together, this highlights that QR can help to ask and answer more questions about variation in nature.