Soil heterotrophic respiration: Measuring and modeling seasonal variation and silvicultural impacts

Soil heterotrophic respiration: Measuring and modeling seasonal variation and silvicultural impacts

https://ift.tt/2MPrklU

Publication date: 15 December 2018

Source: Forest Ecology and Management, Volume 430

Author(s): Robert Brown, Daniel Markewitz

Abstract

To determine the effectiveness of forests in sequestering atmospheric carbon (C), we must know the amount of fixed carbon dioxide (CO2) that is subsequently lost due to heterotrophic microbial activity in the soil. Furthermore, the heterotrophic proportion of total soil respiration (Rs) must be quantified as it changes between different physiographic regions, seasons, and silvicultural treatments. This research quantified heterotrophic contributions to Rs in loblolly pine (Pinus taeda) plantations in the Piedmont (n = 3) and Upper Coastal Plain (n = 3) of the Southeastern USA under control, fertilized, and herbicide treatments over an annual cycle. Heterotrophic respiration (Rh) was separated in the field from autotrophic root respiration (Ra) using metal root-excluding collars. The Rh proportion of Rs was not significantly different between regions or treatments, but demonstrated some seasonal variance. The average Rh proportion across the study was found to be ∼73 ± 2% but ranged from ∼70% in winter, spring, and summer to 82% in the fall. Statistical models using microbial biomass, temperature, moisture, and other soil characteristics explained 82% and 75% of Rs and Rh variability, respectively. In contrast, the process based DAYCENT model, parameterized for each site to model Rs, Rh, and Rh proportion compared poorly to field measurements. Model predicted mean seasonal Rh proportions also extended beyond the range of those measured (65–88%) from 61 ± 1.3% to 94 ± 0.4%. DAYCENT performed slightly better (i.e., lower root mean square error) for Piedmont than Coastal Plain sites. DAYCENT does not simulate CO2 fluxes below 20 cm and may be missing substantial fluxes from deeper roots and microbial activity. The results from this study suggest that statistical models such as multiple regression may provide more accurate estimates of Rh proportion for regional extrapolation than the current formulation of the process based DAYCENT model. It is unclear, however, if either approach captures seasonal variation in Rh or how strongly Rh varies with season. Finally, the empirical field data suggest the use of fertilizer and herbicides in these ecosystems increases ecosystem productivity without increasing Rh, which results in an increase in net ecosystem productivity that may lead to greater rates of C sequestration.

Graphical abstract

Graphical abstract for this article

Superforest

via ScienceDirect Publication: Forest Ecology and Management https://ift.tt/2zaqiu8

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