Detecting climate changes of concern in highly variable environments: Quantile regressions reveal that droughts worsen in Hwange National Park, Zimbabwe

Abstract

Rapid climate change is happening worldwide and is affecting ecosystems processes as well as plant and animal abundances and distribution. However, the large climate variability observed in arid and semi-arid regions often impairs the statistical detection of long-term trends using standard statistical methods, especially if one is primarily interested in specific components of the climate changes. Here we highlight how quantile regression overcomes some of the confounding effects of large climate variability in long-term rainfall data. For instance, we show how quantile regressions revealed that droughts worsened in Hwange National Park (Zimbabwe) during the course of the 20th century, a change that would not have been detected using simple linear regression. We briefly discuss the implications of our findings for the management of the park.