Expansion of elevational range in a forest pest: Can parasitoids track their hosts?


Gradients in elevation impose changes in environmental conditions, which in turn modulate species distribution and abundance as well as the interactions they maintain. Along the gradient, interacting species (e.g., predators, parasitoids) can respond to changes in different ways. This study aims to investigate how egg parasitism of a forest pest, the pine processionary moth (PPM), Thaumetopoea pityocampa, vary along an elevational gradient (190–2000 m.a.s.l.) in a mountain range of SE Spain, including areas of recent elevational expansion, for a seven years period (2008–2014). We used generalized linear mixed models to ascertain the effect of both elevation and the winter North Atlantic Oscillation (NAO) index (a proxy of interannual climatic conditions) on the rate of parasitism, and the occurrence probabilities of two parasitoid species: a PPM specialist and a generalist species. Since four pine species are stratified along the elevational gradient, we repeated all the analyses separately for lowlands (190–1300 m.a.s.l.) and uplands (1350–2000 m.a.s.l.). Results showed a decrease in both parasitism rate and probability of occurrence of the two main parasitoid species with elevation, although decline was more severe for the specialist species. The effect of elevation was more conspicuous and intense in uplands than in lowlands. Positive NAO winter values, associated with cold and dry winters, reduced the rate of parasitism and the probability of occurrence of the two main parasitoid species—but particularly for the generalist species—as elevation increases. In a context of climate warming, it is crucial to mitigate PPM elevational and latitudinal expansion. Increasing tree diversity at the PPM expansion areas may favor the establishment of parasitoids, which could contribute to synchronizing host– parasitoid interactions and minimize the risk of PPM outbreaks.

Antonio J. Pérez-Luque
Antonio J. Pérez-Luque

My research interests include Global Change Ecology, Data Science and Remote Sensing matter.