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Low winter precipitation, but not warm autumns and springs, threatens mountain butterflies in middle-high mountains.

Martin KonvickaTomas KurasJana LiparovaVit SlezakDita HoráznáJan KlečkaIrena Kleckova
Published in: PeerJ (2021)
Low-elevation mountains represent unique model systems to study species endangered by climate warming, such as subalpine and alpine species of butterflies. We aimed to test the effect of climate variables experienced by Erebia butterflies during their development on adult abundances and phenology, targeting the key climate factors determining the population dynamics of mountain insects. We analysed data from a long-term monitoring of adults of two subalpine and alpine butterfly species, Erebia epiphron and E. sudetica (Nymphalidae: Satyrinae) in the Jeseník Mts and Krkonoše Mts (Czech Republic). Our data revealed consistent patterns in their responses to climatic conditions. Lower precipitation (i.e., less snow cover) experienced by overwintering larvae decreases subsequent adult abundances. Conversely, warmer autumns and warmer and drier springs during the active larval phase increase adult abundances and lead to earlier onset and extended duration of the flight season. The population trends of these mountain butterflies are stable or even increasing. On the background of generally increasing temperatures within the mountain ranges, population stability indicates dynamic equilibrium of positive and detrimental consequences of climate warming among different life history stages. These contradictory effects warn against simplistic predictions of climate change consequences on mountain species based only on predicted increases in average temperature. Microclimate variability may facilitate the survival of mountain insect populations, however the availability of suitable habitats will strongly depend on the management of mountain grasslands.
Keyphrases
  • climate change
  • genetic diversity
  • aedes aegypti
  • electronic health record
  • human health
  • single cell
  • young adults
  • machine learning
  • drug delivery
  • zika virus
  • risk assessment