Greater carbon allocation to mycorrhizal fungi reduces tree nitrogen uptake in a boreal forest
ECOLOGY, 97 (4):1012-1022
Hasselquist NJ, Metcalfe DB, Inselsbacher E, Stangl Z, Oren,R, Nasholm T, Hogberg P
The central role that ectomycorrhizal (EM) symbioses play in the structure and function of boreal forests pivots around the common assumption that carbon (C) and nitrogen (N) are exchanged at rates favorable for plant growth. However, this may not always be the case. It has been hypothesized that the benefits mycorrhizal fungi convey to their host plants strongly depends upon the availability of C and N, both of which are rapidly changing as a result of intensified human land use and climate change. Using large-scale shading and N addition treatments, we assessed the independent and interactive effects of changes in C and N supply on the transfer of N in intact EM associations with similar to 15 yr. old Scots pine trees. To assess the dynamics of N transfer in EM symbioses, we added trace amounts of highly enriched (NO3-)-N-15 label to the EM-dominated mor-layer and followed the fate of the N-15 label in tree foliage, fungal chitin on EM root tips, and EM sporocarps. Despite no change in leaf biomass, shading resulted in reduced tree C uptake, ca. 40% lower fungal biomass on EM root tips, and greater N-15 label in tree foliage compared to unshaded control plots, where more N-15 label was found in fungal biomass on EM colonized root tips. Short-term addition of N shifted the incorporation of N-15 label from EM fungi to tree foliage, despite no significant changes in below-ground tree C allocation to EM fungi. Contrary to the common assumption that C and N are exchanged at rates favorable for plant growth, our results show for the first time that under N-limited conditions greater C allocation to EM fungi in the field results in reduced, not increased, N transfer to host trees. Moreover, given the ubiquitous nature of mycorrhizal symbioses, our results stress the need to incorporate mycorrhizal dynamics into process-based ecosystem models to better predict forest C and N cycles in light of global climate change.
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