R soil is a fraction of R eco and theoretically must be lower than R eco R eco was not consistently higher than R soil from daily to annual scales We discuss issues with current practices influencing under or overestimation of R eco and R soil Flux networks need a better integration of spatial and temporal variability of R eco and R soil

We studied carbon and nitrogen acquisition and water-use efficiency across the ontogeny of a rare cycad in relation to environmental gradients. Increased water-use efficiency at lower drier elevations and nitrogen fixation at upper elevations with nutrient poor soils may help maintaining the lower and upper altitudinal species range limits

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...

An important component of the terrestrial carbon balance is the efflux of CO 2 from soils to the atmosphere, which is strongly influenced by changes in soil moisture and temperature. Continuous measurements of soil CO 2 efflux are available around the world, and there is a need to develop and improve analyses to better quantify the precision of the measurements. We focused on random errors in measurements, which are caused by unknown and unpredictable changes such as fluctuating environmental conditions. We used the CO 2 gradient flux method with two different algorithms to study the temporal variation of soil CO 2 efflux and associated random errors at four different ecosystems with wide ranges in mean annual temperature, soil moisture, and soil CO 2 efflux. Our results show that random errors were better explained by a double-exponential distribution, had a mean value close to zero, were nonheteroscedastic, and were independent of soil moisture conditions. Random errors increased with the magnitude of soil CO 2 efflux and scale isometrically (scaling exponent ≈ 1) within and across all sites, with a single relation common to all data. This isometric scaling is unaffected by ecosystem type, soil moisture conditions, and soil CO 2 efflux range (maximum and minimum values within an ecosystem). These results suggest larger uncertainty under extreme events that increase soil CO 2 efflux rates. The accumulated annual uncertainty due to random errors varied between ±0.38 and ±2.39%. These results provide insights on the scalability of the sensitivity of soil CO 2 efflux to changing weather conditions across ecosystems.

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