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

• Published in: Philosophical Transactions of the Royal Society B: Biological Sciences
• Main Authors: Olofsson, Johan, te Beest, Mariska, Ericson, Lars
• Format: Article in Journal/Newspaper
• Language: English
• Published: The Royal Society 2013
• Subjects: Arctic; Global warming; Subarctic
• Online Access: http://dx.doi.org/10.1098/rstb.2012.0486; https://royalsocietypublishing.org/doi/pdf/10.1098/rstb.2012.0486; https://royalsocietypublishing.org/doi/full-xml/10.1098/rstb.2012.0486

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.