PubAg

Main content area

Climate change and the incidence of a forest pest in Mediterranean ecosystems: can the North Atlantic Oscillation be used as a predictor?

Author:
Hódar, José A., Zamora, Regino, Cayuela, Luis
Source:
Climatic change 2012 v.113 no.3-4 pp. 699-711
ISSN:
0165-0009
Subject:
Pinus sylvestris, Thaumetopoea pityocampa, altitude, climate change, databases, defoliation, ecosystems, forest pests, forests, linear models, population dynamics, temperature, time series analysis, winter, Mediterranean region, Spain
Abstract:
Many forest pest species strongly depend on temperature in their population dynamics, so that rising temperatures worldwide as a consequence of climatic change are leading to increased frequencies and intensities of insect-pest outbreaks. In the Mediterranean area, the climatic conditions are strongly linked to the effects of the North Atlantic Oscillation (NAO). The aim of this work is to analyze the dynamics of the pine processionary moth (Thaumetopoea pityocampa), a severe pest of Pinus species in the Circunmediterranean, throughout a region of southern Spain, in relation to NAO indices. We related the percentage of forest plots with high defoliation by pine processionary moth each year with NAO values for the present and the three previous winters, using generalized linear models with a binomial error distribution. The time series is 16-year long, and we performed analyses for the whole database and for the five main pine species separately. We found a consistent relationship between the response variable and the NAO index. The relationship is stronger with pine species living at medium-high altitudes, such as Aleppo (P. halepensis), black (P. nigra), and Scots (Pinus sylvestris) pine, which show the higher defoliation intensities up to 3 years after a negative NAO phase. The results highlight, for the first time, the usefulness of using global drivers in order to understand the dynamics of pest outbreaks at a regional scale, and they open the window to the development of NAO-based predictive models as an early-warning signal of severe pest outbreaks.
Agid:
537460