4.6 Article

Complex biotic interactions drive long-term vegetation dynamics in a subarctic ecosystem

出版社

ROYAL SOC
DOI: 10.1098/rstb.2012.0486

关键词

plant community composition; herbivory; voles; lemmings; reindeer; moth

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资金

  1. Nordic Centre of Excellence TUNDRA
  2. Norden Top-Level Research Initiative 'Effect Studies and Adaptation to Climate Change'
  3. European Commission [ENV4-CT97-0586]
  4. Swedish Research Council for Environment, Agricultural Science and Spatial Planning [2006-1539, 2012-230]
  5. Goran Gustafssons Stiftelse for Natur och Miljo i Lappland

向作者/读者索取更多资源

Predicting impacts of global warming requires understanding of the extent to which plant biomass and production are controlled by bottom-up and top-down drivers. By annually monitoring community composition in grazed control plots and herbivore-free exclosures at an Arctic location for 15 years, we detected multiple biotic interactions. Regular rodent cycles acted as pulses driving synchronous fluctuations in the biomass of field-layer vegetation; reindeer influenced the biomass of taller shrubs, and the abundance of plant pathogenic fungi increased when densities of their host plants increased in exclosures. Two outbreaks of geometrid moths occurred during the study period, with contrasting effects on the field layer: one in 2004 had marginal effects, while one in 2012 severely reduced biomass in the control plots and eliminated biomass that had accumulated over 15 years in the exclosures. The latter was followed by a dramatic decline of the dominant understory dwarf-shrub Empetrum hermaphroditum, driven by an interaction between moth herbivory on top buds and leaves, and increased disease severity of a pathogenic fungus. We show that the climate has important direct and indirect effects on all these biotic interactions. We conclude that long time series are essential to identify key biotic interactions in ecosystems, since their importance will be influenced by climatic conditions, and that manipulative treatments are needed in order to obtain the mechanistic understanding needed for robust predictions of future ecosystem changes and their feedback effects.

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