Size matters for linking traits to ecosystem multifunctionality

Guo, Chao; Yan, En‐Rong; Cornelissen, Johannes H. C.

doi:10.1016/j.tree.2022.06.003

Cited by 10 publications

(1 citation statement)

References 68 publications

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“…Wood decomposition rates depend on climatic conditions 2 and intra- and interspecific differences in wood traits (e.g. wood density, lignin, nitrogen or phosphorous content) 4 – 6 , but also on the communities of decomposers 2 , 7 , 8 . Microbes, particularly fungi, and arthropods, particularly termites and beetles, are the main biotic agents of wood decomposition 7 , 9 , 10 .…”

Section: Introductionmentioning

confidence: 99%

Quantifying wood decomposition by insects and fungi using computed tomography scanning and machine learning

Seibold

Müller

Allner³

et al. 2022

Sci Rep

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Wood decomposition is a central process contributing to global carbon and nutrient cycling. Quantifying the role of the major biotic agents of wood decomposition, i.e. insects and fungi, is thus important for a better understanding of this process. Methods to quantify wood decomposition, such as dry mass loss, suffer from several shortcomings, such as destructive sampling or subsampling. We developed and tested a new approach based on computed tomography (CT) scanning and semi-automatic image analysis of logs from a field experiment with manipulated beetle communities. We quantified the volume of beetle tunnels in wood and bark and the relative wood volume showing signs of fungal decay and compared both measures to classic approaches. The volume of beetle tunnels was correlated with dry mass loss and clearly reflected the differences between beetle functional groups. Fungal decay was identified with high accuracy and strongly correlated with ergosterol content. Our data show that this is a powerful approach to quantify wood decomposition by insects and fungi. In contrast to other methods, it is non-destructive, covers entire deadwood objects and provides spatially explicit information opening a wide range of research options. For the development of general models, we urge researchers to publish training data.

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