Potential bias of daily soil CO2 efflux estimates due to sampling time

Abstract

Soil respiration (Rs) has been usually measured during daylight hours using manual chambers. This approach assumes that measurements made during a typical time interval (e.g., 9 to 11 am) represent the mean daily value; locally, this may not always be correct and could result in systematic bias of daily and annual Rs budgets. We propose a simple method, based on the temporal stability concept, to determine the most appropriate time of the day for manual measurements to capture a representative mean daily Rs value. We introduce a correction factor to adjust for biases due to nonoptimally timed sampling. This approach was tested in a semiarid shrubland using 24 hr campaigns using two treatments: trenched plots and plots with shrubs. In general, we found optimum times were at night and potential biases ranged from −29 to + 40% in relation to the 24 hr mean of Rs, especially in trenched plots. The degree of bias varied between treatments and seasons, having a greater influence during the wet season when efflux was high than during the dry season when efflux was low. This study proposes a framework for improving local Rs estimates that informs how to decrease temporal uncertainties in upscaling to the annual total.Soil respiration (Rs) represents the second largest flux within the terrestrial carbon cycle, being surpassed only by gross primary productivity 1 . This flux is estimated to be an order of magnitude greater than the CO 2 input to the atmosphere from anthropogenic fossil fuel combustion 2 . Rs represents a net loss of carbon derived from root respiration and from microbial metabolism of soil carbon 3,4 , the largest carbon pool globally 5 . Rs has complex spatio-temporal biophysical controls that vary on different scales 6 as a consequence of changes in biotic (e.g., photosynthesis 7-10 , microbial community 11 ) and abiotic (e.g., soil temperature 12,13 , soil moisture 14,15 , soil texture 16 ) factors. It is important to recognize that a small change within this pool could represent a significant feedback to the earth system 17 . Thus, sampling schemes and measurement strategies should be discussed to improve reports of Rs at the site level and across the world.Rs is a composite of two main sources, heterotrophic (e.g., microbial metabolism) and autotrophic (root and mycorrhizae respiration) 4 . Partitioning of those sources is commonly done using trenching experiments 18 , where roots are excised and excluded from small plots so that microbial metabolism can be assumed to be the only source of Rs. Understanding the contributions of autotrophic and heterotrophic respiration is important because they may respond differently to temperature, with different temporal correlations on a variety of time scales 19 .Rs has been measured for almost 90 years 20 and commonly has been measured using non-steady-state, manually-initiated portable chambers. Manual measurements have been popular around the world because of their portability, low implementation costs, and fewer power and security issues. Measurements using...

Estimating Soil Respiration in a Subalpine Landscape Using Point, Terrain, Climate, and Greenness Data

“…Even considering that out‐of‐bag r 2 can underestimate model performance, our model fit was not as high as those reported in similar studies (Bond‐Lamberty et al, ; Wu et al, ). This could have been due to a number of reasons: a more heterogeneous landscape that is not as fully represented by sampled plot locations, higher spatial resolution of our predictions, a smaller range of variability in respiration (due to both seasonal averaging and restricting sampling to a single forest type) compared to past efforts, lack of high‐resolution climate data, or timing of soil respiration sampling (Cueva et al, ). Soil respiration is highly variable over time, both seasonally and diurnally.…”

“…Mismatches between tower‐ and chamber‐based estimates of respiration are common (Bolstad et al, ; Speckman et al, ; Wang et al, ). Soil respiration sampling bias as discussed above could have contributed to this (Cueva et al, ). Another cause of mismatch could be errors in the estimated EC footprint shape and size that are not taken into account by our predicted estimates.…”

Soil Respiration Variability and Correlation Across a Wide Range of Temporal Scales

“…Similar dynamics have been observed in other forest ecosystems when root respiration is diminished, with some time lag, following a limitation in tree transpiration (Adachi et al, ; Bekku et al, ). Ecosystems with such (possibly transient) water limitations may have quite different dynamics; for example, at a dry shrub site Cueva et al () found the optimal measurement time to be at night, with potential biases ranging from −29% to +40%.…”

“…For example, Davidson et al () found that diel variation was ~25% of the daily mean, and that midmorning was the best time to measure a representative daily flux. This time‐of‐day question was shown to have a large effect in shrubland (Cueva et al, ) and agricultural (Parkin & Kaspar, ) ecosystems. In general it will be more important in systems with larger diel flux variability (Jian et al, ), driven by strong day‐to‐night contrasts; conversely, it might be expected to be weaker in the long summer daylight at high latitudes, for example.…”

Tree Stress and Mortality from Emerald Ash Borer Does Not Systematically Alter Short-Term Soil Carbon Flux in a Mixed Northeastern U.S. Forest

“…Measurements were collected across all collars on each sampling date between approximately 09:00-12:00. Previous studies have demonstrated that instantaneous soil carbon flux in the late-morning is a good approximation of the daily mean soil CO 2 flux at a northeastern U.S. temperate forest site [42], thus avoiding potential biases in the effect of measurement time on soil flux [43]. During each chamber measurement, soil temperature was measured with a digital thermometer inserted 10 cm below the soil surface adjacent to the chamber.…”