Accurate forest projections require long‐term wood decay experiments because plant trait effects change through time

Abstract

Whether global change will drive changing forests from net carbon (C) sinks to sources relates to how quickly deadwood decomposes. Because complete wood mineralization takes years, most experiments focus on how traits, environments and decomposer communities interact as wood decay begins. Few experiments last long enough to test whether drivers change with decay rates through time, with unknown consequences for scaling short‐term results up to long‐term forest ecosystem projections. Using a 7 year experiment that captured complete mineralization among 21 temperate tree species, we demonstrate that trait effects fade with advancing decay. However, wood density and vessel diameter, which may influence permeability, control how decay rates change through time. Denser wood loses mass more slowly at first but more quickly with advancing decay, which resolves ambiguity about the after‐life consequences of this key plant functional trait by demonstrating that its effect on decay depends on experiment duration and sampling frequency. Only long‐term data and a time‐varying model yielded accurate predictions of both mass loss in a concurrent experiment and naturally recruited deadwood structure in a 32‐year‐old forest plot. Given the importance of forests in the carbon cycle, and the pivotal role for wood decay, accurate ecosystem projections are critical and they require experiments that go beyond enumerating potential mechanisms by identifying the temporal scale for their effects.

Wood construction more strongly shapes deadwood microbial communities than spatial location over 5 years of decay

“…The relative importance of wood identity and spatial location in shaping deadwood microbial communities mirrored their influence on decay in this system after both 1 year (Zanne et al ., 2015) and across 7 years (Oberle et al ., 2020). As such, this finding fits the expectation that environmental predictors indirectly affect rates of wood decay by directly shaping microbial communities.…”

“…This pattern could reflect the progressive homogenization of woody substrates during decay (Witkamp, 1966). What was surprising, however, was that wood construction persisted as the best environmental predictor of fungal and bacterial communities, even after 5 years of decay when samples had lost 72.9% (±1.58 standard error) of their initial mass (Oberle et al ., 2020). Previous studies have also detected strong associations among wood species on microbial communities through time, but these studies typically included fewer wood species and did not evaluate the relative influence of spatial location (van der Wal et al ., 2015; Baber et al ., 2016; Baldrian et al ., 2016).…”

“…From the perspective of microbes, wood construction defines both habitat and resource. Recent work indicates that wood construction explains more variation in wood‐decay rates than spatial variation and climatic conditions (Weedon et al ., 2009; Zanne et al ., 2015; Hu et al ., 2018; Oberle et al ., 2020). To the extent that deadwood microbial community structure influences decay community function, we expect the relative importance of wood construction and spatial location on decay rates to mirror their relative importance in structuring deadwood microbial communities (Fig.…”

Traits mediate drought effects on wood carbon fluxes

“…In addition, we explored the effect of wood nitrogen and carbon quality on decomposition with the initial values, but wood nitrogen density and carbon quality could change with the decomposition and potentially affect our results. Due to the slow processes of wood decomposition, narrow change in nitrogen density and carbon quality may has neglectful impact in this 1 year study, but caution should be taken in the long‐term research (Hu et al, 2017; Oberle et al, 2020).…”

Exploring controls of timber stock residence times in storage after severe storm events

“…We thus utilize an approach for systems characterized by a single input pulse and continuous output signals originating from chemical sciences: residence time distributions of chemical reactors as described by Danckwerts (1953) and also Levenspiel (1972). The Gamma distribution is a commonly used distribution to quantify residence time dynamics in environmental systems such as wetlands (e.g., Kadlec 1994) and groundwater aquifers (e.g., Maloszewski and Zuber 1982;Gilmore et al 2016) or carbon storage in ecosystems (e.g., Belshe et al 2019;Oberle et al 2019).…”