Climate and landscape patterns of pine forest decline after invasion by the pinewood nematode

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26 Citations (Scopus)


Context: The pinewood nematode, Bursaphelenchus xylophilus, has been identified as being responsible for the decline of pine forests in Portugal, since its detection in the territory about 20 years ago. Forest decline due to disease and/or pests is a complex phenomenon, and a knowledge of the spatial patterns of tree mortality is important for providing an understanding of forest susceptibility and pest dynamics. Objectives: The aim of the present study was to investigate the relationship between the spatial structure of pine mortality and that of different climatic, edaphic and landscape variables in an area of Portugal where B. xylophilus is known to have spread during the last decade. Methods: Pine mortality was assessed in the field, in a large, complex area of Central-North Portugal. An ArcGis database with a range of environmental variables was created for the study area. Spatial autocorrelation of the different variables was investigated by means of Moran's I and Mantel r analysis. The relationship between pine decline and the different variables was analyzed using bivariate Moran's I. Results: Environmental descriptors exhibit a clear spatial pattern, influenced by an undulating landscape. Pine mortality is spatially aggregated; warm, dry locations with higher evapotranspiration present high values, and in areas of extensive pine occupancy in a diverse landscape mortality decreases. Conclusions: Forest mortality attributed to infestation by a non-native forest pathogen cannot be dissociated from the effects of climate and landscape diversity associated with pine forests. The analysis of landscape patterns appears to be crucial for gaining an understanding of forest pathology and decline.

Original languageEnglish
Pages (from-to)43-51
Number of pages9
JournalForest Ecology And Management
Publication statusPublished - 15 Feb 2019


  • Bursaphelenchus xylophilus
  • Moran's I
  • Pine wilt disease
  • Pinus pinaster
  • Spatial auto-correlation


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