Effects of initial microbial biomass abundance on respiration during pine litter decomposition

Abstract

25Microbial biomass is increasingly used to predict respiration in soil organic carbon (SOC) 26 models. Its increased use combined with the difficulty of accurately measuring this variable 27 points a need to directly assess the importance of microbial biomass abundance for carbon (C) 28 cycling. To test the hypothesis that the initial microbial biomass abundance (i.e. biomass 29 abundance on new plant litter) is a strong driver of plant litter C cycling, we manipulated 30 biomass abundance by 10 and 100-fold dilution and composition using 12 source communities 31 on sterile pine litter and measured respiration in microcosms for 30 days. In the first two days of 32 microbial growth on fresh litter, a 100-fold difference in initial biomass abundance caused an 33 average difference in respiration of nearly 300%, but the effect rapidly declined to less than 30% 34 in 10 days and to 14% in 30 days. Parallel simulations with a soil carbon model, SOMIC 1.0, 35 also predicted a 14% difference over 30 days, consistent with the experimental results. Model 36 simulations predicted convergence of cumulative CO 2 to within 10% in three months and within 37 4% in three years. Rapid microbial growth likely attenuates the effects of large initial differences 38 in biomass abundance. In contrast, the persistence of source community as an explanatory factor 39 in driving differences in respiration across microcosms supports the importance of microbial 40 composition in C cycling. Overall, the results suggest that the initial abundance of microbial 41 biomass on litter is a weak driver of C flux from litter decomposition over long timescales 42 (months to years) when litter communities have equal nutrient availability. By extension, slight 43 variation in the timing of microbial dispersal to fresh litter is likely to be a minor factor in long-44 term C flux. 45 46 Importance 3 47Microbial biomass is one of the most common microbial parameters used in land carbon 48 (C) cycle models, however, it is notoriously difficult to measure accurately. To understand the 49 consequences of mismeasurement, as well as the broader importance of microbial biomass 50 abundance as a direct driver of ecological phenomena, greater quantitative understanding of the 51 role of microbial biomass abundance in environmental processes is needed. Using microcosms, 52 we manipulated the initial biomass of numerous microbial communities across a 100-fold range 53 and measured effects on CO 2 production during plant litter decomposition. We found that the 54 effects of initial biomass abundance on CO 2 production was largely attenuated within a week, 55 while the effects of community type remained significant over the course of the experiment.56 Overall, our results suggest that initial microbial biomass abundance in litter decomposition 57 within an ecosystem is a weak driver of long-term C cycling dynamics. 58 59 Introduction 60 Microbial decomposition of plant litter is a key process in terrestrial carbon (C) cycling 61 [1]. Although the dynamics of plan...

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“…Litter-derived fraction of SOC ranging from days in simple laboratory microcosms [7] to the decades at the global scale [6]. We used SOMic to assess if differences in the quantity of microbial biomass added to laboratory microcosms could be a substantial factor in the large variation in carbon flow observed among communities decomposing plant litter over a short (6-week) timescale [7].…”

“…Litter-derived fraction of SOC ranging from days in simple laboratory microcosms [7] to the decades at the global scale [6]. We used SOMic to assess if differences in the quantity of microbial biomass added to laboratory microcosms could be a substantial factor in the large variation in carbon flow observed among communities decomposing plant litter over a short (6-week) timescale [7]. The simulations from SOMic showed respiration (CO2 efflux) dynamics that matched experimental measurements ( Figure 9) and provided substantial information such as probable microbial biomass dynamics that was not possible to measure in the experiment.…”