4.7 Article

Context dependent fungal and bacterial soil community shifts in response to recent wildfires in the Southern Appalachian Mountains

Journal

FOREST ECOLOGY AND MANAGEMENT
Volume 451, Issue -, Pages -

Publisher

ELSEVIER
DOI: 10.1016/j.foreco.2019.117520

Keywords

Phoenicoid fungi; Forests; Burn severity; Composite bum index; Great Smoky Mountain National Park; Nantahala National Forest

Categories

Funding

  1. Highlands Biological Station grant
  2. Appalachian Highlands Science Learning Center Research Program
  3. North Carolina Biotechnology Grant [2015-BRG-1206]
  4. University of Memphis

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Decades of fire suppression coupled with changing climatic conditions have increased the frequency and intensity of wildfires. The Southern Appalachia region of the United States is predicted to be particularly susceptible to climatic changes, with predicted increases in fire severity and occurrence. Following the record breaking fire season in 2016 in Southern Appalachia, we examined wildfire impacts on soil chemistry and below ground communities (fungi and bacteria - Illumina MiSeq) within two substrates (duff and soil) at two adjacent locations with similar plant communities (Great Smoky Mountains National Park - 'Chimney Top 2' Fire (GRSM) and Nantahala National Forest - 'Cliffside' Fire (NNF)) from replicate plots representing a range of fire severities (unburned, low severity, moderate severity, severe). Differing fire severities changed community composition, and fire severity played a stronger role in structuring bacterial communities than in structuring fungal communities. Further, fire impacts on soil communities and functional guilds responses were location- and substrate specific with NNF responding more strongly to fire than GRSM. Additionally, using a novel analysis tool (Axis Weighted Ordination Distance - AWOrD), domain and location specific responses to wildfire severity are demonstrated. Taken together, our results suggest context-dependency in microbial responses to fire that must be accounted for to generate ecosystem-wide recovery predictions.

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