Change in trade wind inversion frequency implicated in the decline of an alpine plant

Abstract

Background Detailed assessments of species responses to climate change are uncommon, owing to the limited nature of most ecological and local climate data sets. Exceptions, such as the case of the Haleakalā silversword, can provide important insights into the complexity of biological responses to changing climate conditions. We present a time series of decadal population censuses, combined with a pair of early population projections, which together span the past 80 years of demographic history for this alpine plant. Results The time series suggests a strong population recovery from the 1930s through the 1980s, likely owing at least in part to management actions taken on its behalf. In contrast, the population is estimated to have suffered a decline of approximately 60 % since the early 1990s. Fine-scale estimates of rainfall within silversword habitat are strongly correlated with these decadal-scale population changes over the past 50 years, with rainfall estimated to be substantially lower on average during the two most recent inter-census periods (after 1991). The reversal in the silversword population trajectory, and declines in rainfall in silversword habitat, coincide with an abrupt increase in the frequency of occurrence of the trade wind inversion (TWI) in Hawaiʻi around 1990. Conclusions The shift in TWI incidence, which is linked to stronger subsidence in the Hadley circulation, has led to drier conditions in high elevation ecosystems in Hawaiʻi and appears to be eliciting ecological responses. Other regions influenced by the TWI could be similarly affected. The silversword case study reveals additional unexpected outcomes, such as the likely initial retraction from wetter, rather than drier, portions of the range in response to drying conditions. This pattern may stem in part from variation in drought tolerance across the range, highlighting the importance of detailed ecological and climatic information for making accurate predictions about climate change responses.