Intensive ground vegetation growth mitigates the carbon loss after forest disturbance View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2017-11

AUTHORS

Bernhard Zehetgruber, Johannes Kobler, Thomas Dirnböck, Robert Jandl, Rupert Seidl, Andreas Schindlbacher

ABSTRACT

Aims: Slow or failed tree regeneration after forest disturbance is increasingly observed in the central European Alps, potentially amplifying the carbon (C) loss from disturbance. We aimed at quantifying C dynamics of a poorly regenerating disturbance site with a special focus on the role of non-woody ground vegetation. Methods: Soil CO2 efflux, fine root biomass, ground vegetation biomass, tree increment and litter input were assessed in (i) an undisturbed section of a ~ 110 years old Norway spruce stand, (ii) in a disturbed section which was clear-cut six years ago (no tree regeneration), and (iii) in a disturbed section which was clear-cut three years ago (no tree regeneration). Results: Total soil CO2 efflux was similar across all stand sections (8.5 ± 0.2 to 8.9 ± 0.3 t C ha-1 yr.-1). The undisturbed forest served as atmospheric C sink (2.1 t C ha-1 yr.-1), whereas both clearings were C sources to the atmosphere. The source strength three years after disturbance (-5.5 t C ha-1 yr.-1) was almost twice as high as six years after disturbance (-2.9 t C ha-1 yr.-1), with declining heterotrophic soil respiration and the high productivity of dense graminoid ground vegetation mitigating C loss. Conclusions: C loss after disturbance decreases with time and ground vegetation growth. Dense non-woody ground vegetation cover can hamper tree regeneration but simultaneously decrease the ecosystem C loss. The role of ground vegetation should be more explicitly taken into account in forest C budgets assessing disturbance effects. More... »

PAGES

239-252

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  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s11104-017-3384-9

    DOI

    http://dx.doi.org/10.1007/s11104-017-3384-9

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1091328320

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/29225378


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