Ignoring temperature variation leads to underestimation of the temperature sensitivity of plant litter decomposition

Abstract

The majority of terrestrial net primary production decomposes, fueling detrital food webs and converting dead plant carbon to atmospheric CO2. There is considerable interest in determining the sensitivity of this process to climate warming. A common approach has been to use spatial gradients in temperature (i.e., latitude or elevation) to estimate temperature sensitivity. However, these studies typically relate decomposition rates to average temperatures at each site along such gradients, ignoring within‐site temperature variation. To evaluate the potential effects of temperature variation on estimates of temperature sensitivity, we simulated plant litter decomposition using both randomly generated and real time series of temperature. This simulation approach illustrated how temperature variation leads to higher decomposition rates at a given mean temperature than is predicted from simulations in which temperature is held constant. Increases in decomposition rate were most evident at cooler sites, where temporal variation in temperature tends to be greater than at warmer sites. This unbalanced effect of temperature variation shifted the slope of the relationships between average temperature and decomposition rate, resulting in lower estimated temperature sensitivities than were used to simulate decomposition. For example, estimates of activation energy (Ea) were as much as 0.15 eV lower than the true Ea when decomposition was simulated with the true Ea set to the canonical respiration value of 0.65 eV. We found that the estimated Ea was lower than the true Ea for surface, soil, and air temperatures, but not for stream temperatures, for which there was only a weak relationship between temperature variation and mean temperature. Our results suggest that commonly used methods may underestimate the temperature dependence of litter decomposition, particularly in terrestrial environments. We encourage publication of temperature data that include variation estimates and suggest an alternative method for calculating temperature sensitivity that accounts for variation in temperature.

Decomposing decomposition: isolating direct effects of temperature from other drivers of detrital processing

“…Annual mean temperature showed no relationship to temperature variance (standard deviation of the annual mean) across the nine sites, so we ignored any effect of variance on estimating temperature dependence of litter breakdown (Tomczyk et al. 2020).…”

“…Hendrick Mill and Little Schultz showed the smallest annual temperature variation, while Raccoon, Choccolocco, and Lick showed the greatest annual variation (Appendix S1: Table S1). Annual mean temperature showed no relationship to temperature variance (standard deviation of the annual mean) across the nine sites, so we ignored any effect of variance on estimating temperature dependence of litter breakdown (Tomczyk et al 2020).…”

Contrasting activation energies of litter-associated respiration and P uptake drive lower cumulative P uptake at higher temperatures

“…Under natural conditions, microbial fragmentation, physical abrasion, and consumption by macro-invertebrates can all drive meaningful amounts of leaf breakdown (Marks, 2019;Wilmot et al, 2021). Furthermore, our simulations were conducted at a constant temperature, which would lead to depressed rates of breakdown relative to simulations that include temperature variability (Tomczyk et al, 2020). Not including these processes in our model likely explains the high residence time of leaves in our simulations; at the low nutrient concentration and temperature our simulations had leaf residence times over 1000 d (Fig.…”

Contrasting activation energies of respiration and nutrient uptake drive lower ecosystem-level uptake at higher temperatures

“…Under natural conditions, microbial fragmentation, physical abrasion, and consumption by macroinvertebrates can all drive meaningful amounts of leaf breakdown (Marks, 2019;Wilmot et al, 2021). Furthermore, our simulations were conducted at a constant temperature, which would lead to depressed rates of breakdown relative to simulations that include temperature variability (Tomczyk et al, 2020). Not including these processes in our model likely explains the high residence time of leaves in our simulations; at the low nutrient concentration and temperature our simulations had leaf residence times over 1000 days (Figure 4b), while field studies have found residence times of around two years for Rhododendron leaves in minimally impacted streams (Manning et al, 2015).…”